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Studies of Welfare Populations: Data Collection and Research Issues

Publication Date

Panel on Data and Methods for Measuring the Effects of Changes in Social Welfare Programs

Edited by:
Michele Ver Ploeg, Robert A. Moffitt, and Constance F. Citro
Committee on National Statistics
Division of Behavioral and Social Sciences and Education
National Research Council


How to obtain a printed copy of this report

A printed copy of this report may be obtained by contacting the National Academy Press:

National Academy Press
2101 Constitution Ave, NW, Lockbox 285
Washington, DC 20055
(800) 624-6242
(202) 334-3313

Members of the Panel on Data and Methods for Measuring the Effects of Changes in Social Welfare Programs


  • Robert A. Moffitt (Chair), Department of Economics, Johns Hopkins University
  • John L. Adams, RAND, Santa Monica, California
  • Thomas Corbett, Institute for Research on Poverty, University of Wisconsin, Madison
  • John L. Czajka, Mathematica Policy Research, Inc., Washington, DC
  • Kathryn Edin, Institute for Policy Research, Northwestern University
  • Irwin Garfinkel, School of Social Work, Columbia University
  • Robert M. Goerge, Chapin Hall Center for Children, University of Chicago
  • Eric A. Hanushek, Hoover Institution, Stanford University
  • V. Joseph Hotz, Departments of Economics and Policy Studies, University of California, Los Angeles
  • Richarc A. Kulka, Statistics, Health, and Social Policy, Research Triangle Institute, Research Triangle Park, NC
  • Rebecca A Maynard, Graduate School of Education, University of Pennsylvania
  • Suzanne M. Randolph, Department of Family Studies, University of Maryland
  • Werner Schink, California Department of Social Services, Sacramento


  • Michele Ver Ploeg, Study Director
  • Constance F. Citro, Senior Program Officer
  • Michael Siri, Project Assistant


This volume is the product of the hard work of many individuals to whom we are grateful. We would first like to thank the authors of papers for their contributions and for presenting the papers at the workshop or at meetings of the panel. Each of these papers was reviewed by members of the panel and outside reviewers. Most of these reviewers were also discussants at the workshop on Data Collection for Low-Income and Welfare Populations held December 16-17, 1999. We are indebted to all reviewers for their constructive comments tot he authors: Sandra Berry, Rand; Harold Bloom, Manpower Demonstration Research Corporation; Mike Brick, Westat; Sheldon Danziger, University of Michigan; Betsy Martin, U.S. Census Bureau; Daniel McCaffrey, RAND; Charles Metcalf, Mathematica Policy Research; Jeffrey Moore, U.S. Census Bureau; John Karl Scholz, University of Wisconsin-Madison; Matthew Stagner, Urban Institute; William Winkler, U.S. Census Bureau; and Laura Zayatz, U.S. Census Bureau. I would also like to thank my fellow members of the Panel on Data and Methods for Measuring the Effects of Changes in Social Welfare Programs for helping to shape the topics of the papers, developing the workshop, and reviewing the papers. Graham Kalton, Westat, and Robert Groves, University of Michigan, should also be thanked for helping the Panel identify authors and discussants for these papers.

I would also like to thank the staff of the Committee on National Statistics for their work on this volume. I would also like to thank my coeditors, Michele Ver Ploeg and Constance F. Citro, for their diligence in polishing the papers and guiding them through the review process. The production of this volume could not be possible without the efforts of Michael Siri, project assistant for the Committee on National Statistics. Michael did an excellent job on the difficult task of getting 14 papers with 14 difference formats into a common format for the volume. All of these papers were professionally edited by Laura Penney, to which we are also grateful. Yvonne Wise of the Division of Behavioral and Social Sciences of the National Research COuncil is to be thanked for her assistance in shepherding the report through the phases of production.

Finally, we are also grateful to the Office of the Assistant Secretary for Planning and Evaluation in the Department of Health and Human Services for its sponsorship of the panel that made this volume possible.

Robert A. Moffitt, Chair
Panel on Data and Methods for Measuring the Effects of Changes in Social Welfare Programs


Academic and policy interest in the U.S. welfare system has increased dramatically over the past 15 years, an interest that has accelerated and is currently at an all-time high. Beginning in the late 1980s with welfare reform initiatives in a few states around the country and continuing in the first half of the 1990s as more states made changes in their income support programs, welfare reform culminated at the federal level with the passage of the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) in 1996. PRWORA replaced the long-standing federal entitlement program for low-income families and children (Aid to Families with Dependent Children, AFDC) with a program financed by state-administered block grants, the Temporary Assistance for Needy Families (TANF) program. The legislation imposed several new requirements on state TANF programs, including lifetime limits on receipt of benefits, minimum work requirements, and requirements for unmarried teenage parents to reside with an adult and continue their education in order to receive benefits. Otherwise, it allowed states to configure their programs as they see fit, continuing a trend of devolving the design and control of familial assistance programs from the federal government to state governments that began earlier in the 1990s.

The enactment of PRWORA provided the impetus for a large volume of research studies aimed at studying its impact and that of changes in other federal income support programs, such as the Food Stamp Program. These studies are now yielding results and reporting new findings on an almost-daily basis. PRWORA is slated to come up for reauthorization in 2002, and it is already clear that research findings will play a significant role in the debate over the directions that welfare reform should take from here.

The Panel on Data and Methods for Measuring the Effects of Changes in Social Welfare Programs of the National Research Council was formed in 1998 to review the evaluation methods and data that are needed to study the effects of welfare reform. Sponsored by the Office of the Assistant Secretary for Planning and Evaluation (ASPE) of the U.S. Department of Health and Human Services through a congressional appropriation, the panel has issued interim and final reports (National Research Council, 1999, 2001).

Early in its deliberations, particularly after reviewing the large number of so-called welfare leaver studies  studies of how families who left the TANF rolls were faring off welfare  the panel realized that the database for conducting studies of welfare reform had many deficiencies and required attention by policy makers and research analysts. In its final report, the panel concluded that welfare reform evaluation imposes significant demands on the data infrastructure for welfare and low-income populations and that . . . inadequacies in the nations data infrastructure for social welfare program study constitutes the major barrier to good monitoring and evaluation of the effects of reform (NRC 2001:146). The panel concluded that national-level surveys were being put under great strain for PRWORA research given their small sample sizes, limited welfare policy-related content, and, often, high rates of nonresponse (see also National Research Council, 1998). State-level administrative data sets, the panel concluded, are of much more importance with the devolution of welfare policy but are difficult to use for research because they were designed for management purposes. In addition, although they have large sample sizes, their content is limited. Surveys for specific states with more detailed content have been only recently attempted  usually telephone surveys of leavers  and the panel expressed concern about the capacity and technical expertise of state governments to conduct such surveys of adequate quality. To date, for example, many surveys of welfare leavers have unacceptably high rates of nonresponse. Overall, the panel concluded that major new investments are needed in the data infrastructure for analysis of welfare and low-income populations.

This concern led the panel to plan a workshop on data collection on welfare and low-income populations for which experts would be asked to write papers addressing in detail not only what the data collection issues are for this population, but also how the quality and quantity of data can be improved. A workshop was held on December 16-17, 1999, in Washington, DC. The agenda for the workshop is listed as an Appendix to this volume. Approximately half the papers presented at the workshop concerned survey data and the other half concerned administrative data; one paper addressed qualitative data. Altogether, the papers provide a comprehensive review of relevant types of data. The volume also contains four additional papers that were commissioned to complement the conference papers. One of them discusses methods for adjusting survey data for nonresponse. The other three papers focus on welfare leavers, a subpopulation of particular interest to Congress that a number of states have studied with grants from ASPE, as well as the importance of understanding the dynamics of the welfare caseload when interpreting findings from these studies.

After the conference, the papers were revised, following National Research Council procedures, to reflect the comments of discussants at the workshop, panel members, and outside reviewers. The additional commissioned papers also were revised in response to comments from panel members and outside reviewers. This volume contains the final versions of the papers.

In this introduction, we summarize each of the 14 papers in the volume. Together, they are intended as a guide and reference tool for researchers and program administrators seeking to improve the availability and quality of data on welfare and low-income populations for state-level, as well as national-level, analysis.

Survey Data

The volume contains six papers on survey issues. They address:

  1. Methods for designing surveys taking into account nonresponse in advance;
  2. Methods for obtaining high response rates in telephone surveys;
  3. Methods for obtaining high response rates in in-person surveys;
  4. The effects of incentive payments;
  5. Methods for adjusting for missing data in surveys of low-income populations; and
  6. Measurement error issues in surveys, with a special focus on recall error.

In their paper on Designing Surveys Acknowledging Nonresponse, Groves and Couper first review the basic issues involved in nonresponse, illustrating the problem of bias in means and other statistics, such as differences in means and regression coefficients, and how that bias is related to the magnitude of nonresponse and the size of the difference in outcomes between respondents and nonrespondents. They also briefly review methods of weighting and imputation to adjust for nonresponse after the fact. The authors then discuss the details of the survey process, including the exact process of contacting a respondent and how barriers to that contact arise, noting that welfare reform may generate additional barriers (e.g., because welfare recipients are more likely to be working and hence not at home). They also provide an in-depth discussion of the respondents decision to participate in a survey, noting the importance of the environment, the respondent, and the survey design itself, and how the initial interaction between survey taker and respondent is a key element affecting the participation decision. They propose a fairly ambitious process of interviewer questioning, which involves contingent reactions to different statements by the respondent, a process that would require expert interviewers. They conclude with a list of 10 principles for surveys of the low-income population for improvement in light of nonresponse.

Cantor and Cunningham discuss methods for obtaining high response rates in telephone surveys of welfare and low-income populations in their paper, first identifying best practices and then comparing those to practices used in some welfare leaver telephone surveys. The authors note the overriding importance of recognizing language and cultural diversity among respondents and the need to take such diversity into account in designing content and deploying interviewers. They then discuss specific issues in increasing response rates, including obtaining contact information in the presurvey process (e.g., from administrative records); obtaining informed consent to gather information needed for subsequent tracking; address-related problems with mail surveys; methods for tracing hard-to-locate respondents; dealing with answering machines; the importance of highly trained interviewers, echoing the emphasis of Groves and Couper; considerations in questionnaire design, including the critical nature of the introduction; and refusal conversion. Cantor and Cunningham then review a set of telephone surveys of welfare recipients and welfare leavers. They find that response rates often are quite low and that use of the telephone alone only rarely will obtain response rates greater than 50 percent, which is a very low number by the traditional standards of survey research. They suggest that higher, acceptable response rates will almost surely require substantial in-person followup, which can move the response rate up above 70 percent. The authors note that nonresponse is mainly an issue of inability to locate respondents rather than outright refusals, which makes tracing and locating respondents of great importance. They find that many welfare records are of poor quality to assist in tracing, containing inaccurate and out-of-date locator information, and they emphasize that expertise in tracing is needed in light of the difficulties involved. Refusal conversion is also discussed, with an emphasis again on the need for trained interviewers in using this method. Finally, the authors discuss random-digit dialing telephone surveys of this population (as opposed to surveys based on list samples such as those from welfare records) and explore the additional difficulties that arise with this methodology.

The paper by Weiss and Bailar discusses methods for obtaining high response rates from in-person surveys of the low-income population. The principles are illustrated with five in-person surveys of this population conducted by the National Opinion Research Certer (NORC). All the surveys drew their samples from administrative lists, provided monetary incentives for survey participation, and applied extensive locating methods. Among the issues discussed are the importance of the advance letter, community contacts, and an extensive tracing and locating operation, including field-based tracing on top of office-based tracing. The authors also provide an in-depth discussion of the importance of experienced interviewers for this population, including experience not only in administering an interview, but also in securing cooperation with the survey. The use of traveling interviewers and the importance of good field supervisory staff and site management are then addressed.

In their paper, Singer and Kulka review what is known about the effects of paying respondents for survey participation (incentives). Reviewing both mail and telephone surveys, the authors report that incentives are, overall, effective in increasing response rates; that prepaid incentives are usually more effective than promised incentives; that money is more important than a gift; and that incentives have a greater effect when respondent burden is high and the initial response rate is low. They also note that incentives appear to be effective in panel surveys, even when incentives are not as high in subsequent waves of interviews as they are in the initial wave. After discussing the evidence on whether incentives affect item nonresponse or the distribution of given responses  the evidence on the issue is mixed  the authors review what little is known about the use of incentives in low-income populations. The little available evidence suggests, again, that incentives are effective in this population as well. The authors conclude with a number of recommendations on the use of incentives, including a recommendation that payments to convert initial refusals to interviews be made sparingly.

Mohadjer and Choudhry provide an exposition of methods for adjusting for missing data after the fact  that is, after the data have been collected. Their paper focuses on traditional weighting methods for such adjustment and includes methods for adjustment for noncoverage of the population as well as nonresponse to the survey. The authors present basic weighting methods and give examples of how variables are used to construct weights. They also discuss the effect of using weights derived from the survey sample versus weights obtained from outside data sets on the population as a whole. For population-based weights, they discuss issues of poststratification and raking that arise. Finally, they provide a brief discussion of the bias-variance tradeoff in designing weights, which is intrinsic to the nature of weights.

Measurement error is discussed in the paper by Mathiowetz, Brown, and Bound. The paper first lists the sources of measurement error in the survey process, which include the questionnaire itself; the respondent; the interviewer; and the conditions of the survey (interviewer training, mode, frequency of measurement, etc.). The authors then review issues relating to the cognitive aspects of measurement error and provide an extended discussion of the problem of questions requiring autobiographical memory. Other topics discussed in the paper include the issue of social desirability of a particular response; errors in response to sensitive questions; and errors in survey reports of earnings and income. A number of existing studies of measurement error are reviewed, but none are focused on welfare or low-income populations per se or on populations with unstable income and employment streams. The authors point out how earnings reports need to be based on salient events and give examples in which such salience is absent. A detailed review is then provided of what is known about measurement error in reports of transfer program income, child support income, hours of work, and unemployment histories. Finally, the authors list a number of issues that should be addressed that can help reduce measurement error, including proper attention by cognitive experts to comprehension of the question by respondents, care for the process of retrieval when writing questions, the use of calendars and landmark events, and a number of other questionnaire design topics. Methods for asking socially sensitive questions also are discussed.

Administrative Data

Administrative records can be a valuable source of information about the characteristics and experiences of welfare program beneficiaries and past beneficiaries. To comply with federally mandated time limits on receipt of TANF benefits, states will need to develop the capability to track recipients over time, something not usually done in the old AFDC system. Such longitudinal tracking capability should make program records more useful for analysis; however, differences in programs across states will likely make it harder to conduct cross-state analyses. Research use of administrative records, whether TANF records or records from other systems (e.g., Unemployment Insurance) that can be used to track selected outcomes for welfare and low-income populations, poses many challenges.

Four papers on administrative data covering a wide range of different topics are included in the volume. The four address:

  1. Issues in the matching and cleaning of administrative data;
  2. Issues of access and confidentiality;
  3. Problems in measuring employment and income with administrative data compared to survey data; and
  4. The availability of administrative data on children.

Issues in the matching and cleaning of administrative data are discussed by Goerge and Lee. The authors begin by noting the importance of cleaning administrative data in a comprehensive sense, namely, converting what are management files into analytic files suitable for research use. They also note the importance of matching records across multiple administrative data sets (i.e., record linkage), which provides more information on respondents. A number of issues are involved in the cleaning process, many of which involve methods for assessing data quality and other aspects of the variables available in the administrative data. A number of important issues in record linkage also are discussed, perhaps the most important being the availability and accuracy of matching variables. The authors discuss deterministic and probabilistic record linkage as well as data quality issues in such linkage. The paper concludes with a number of recommendations on the cleaning and linking of administrative data.

Brady, Grand, Powell, and Schink discuss access and confidentiality issues with administrative data in their paper and propose ways for increasing researcher access to administrative data. The authors begin by noting that the legal barriers to obtaining access to administrative data by researchers often are formidable. Although laws in this area generally are intended to apply to private individuals interested in identifying specific persons, researcher access often is denied even though the researcher has no interest in identities and often intends to use the research results to help improve administration of the program. The authors provide a brief overview of the legal framework surrounding administrative data, confidentiality, and privacy, making a number of important distinctions between different types of issues and clarifying the content of several pieces of legislation  federal and state  governing access and confidentiality. They then turn to a review of how 14 ASPE-funded state welfare leaver studies have dealt with these issues and whether general lessons can be learned. The authors conclude that while success in dealing with access and confidentiality problems has been achieved in many cases, the methods for doing so are ad hoc, based on longstanding relationships of trust between state agencies and outside researchers, and not buttressed and supported by an adequate legal framework. Twelve key principles are laid out for governing data access and confidentiality. Finally, the authors recommend more use of masking methods as well as institutional mechanisms such as secure data centers to facilitate responsible researcher access to and use of confidential administrative data.

Hotz and Scholz review the measurement of employment and income from administrative data and discuss why and whether measures taken from administrative data differ from those obtained from survey data. Employment and income are, of course, two of the key outcome variables for welfare reform evaluation and hence assume special importance in data collection. They find that there often are differences in administrative and survey data reports of employment and income and that the differences are traceable to differences in population coverage, in reporting units, in sources of income, in measurement error, and in incentives built into the data-gathering mechanism. The authors provide a detailed review of the quality of employment and income data from, first, the major national survey data sets; then from state-level administrative data taken from Unemployment Insurance records; and, finally, from Internal Revenue Service records. They review what is known about differences in reports across the three as well. The authors conclude with several recommendations on reconciling potentially different results from these data sources.

Administrative data on children are discussed in the paper by Barth, Locklin-Brown, Cuccaro-Alamin, and Needell. The authors first discuss the policy issues surrounding the effects of welfare reform on children and what the mechanisms for those effects might be. They identify several domains of child well-being that conceivably can be measured with administrative data, including health, safety (child abuse and neglect), education, and juvenile justice. In each area, they find that a number of different administrative data sets could be matched, in principle, with welfare records. They identify the exact variables measured in each data set as well. The authors find that good health measures often are present in various data sets, but they are often inaccessible to researchers, while child abuse and neglect data are more often available but have many data quality issues that require careful attention. Education and juvenile justice data are the least accessible to researchers and also contain variables that would only indirectly measure the true outcomes of interest. The authors find that privacy and confidentiality barriers impose significant limitations on access to administrative data on children, similar to the finding in the paper by Brady et al.

Qualitative Data

Qualitative data increasingly have been used in welfare program evaluations and studies. Although there is a fairly long history of the use of process analysis in formal evaluations, there is less history in using direct observation of study respondents or even using focus groups. Yet in attempting to learn how current or former welfare recipients are faring, qualitative data can provide information that neither survey nor administrative data offer.

The paper by Newman discusses the use of qualitative data for investigating welfare and low-income populations. Newman notes that qualitative data can assist in helping to understand the subjective points of view of families in these populations, provide information on how recipients understand the rules of the welfare system, uncover unexpected factors that are driving families situations, explore any unintended consequences of a policy change, and focus attention on the dynamic and constantly changing character of most families in the low-income population. The author reviews a range of methods, from open-ended questions in survey questionnaires to focus groups to detailed participant observation in the field, in each case listing the advantages and disadvantages of the method. Newman then discusses the use of qualitative data in several recent welfare reform projects to illustrate how the methods can be used. The author concludes with a recommendation that additional expertise in qualitative data be brought into state governments and that the use of these methods increase.

Welfare Leavers and Welfare Dynamics

An initial focus of concern of policy makers has been the effects of PRWORA on people who left AFDC and successor TANF programs  welfare leavers. In response to a congressional mandate, ASPE provided grant funds to states and counties to analyze administrative records and conduct surveys of two cohorts of welfare leavers. In fiscal year 1998, ASPE provided grant funds to 14 jurisdictions (10 states, the District of Columbia, and 3 counties or groups of counties) to study welfare leavers. In fiscal year 1999 it provided funds to one state to also follow welfare leavers, and to six jurisdictions (five states and one county group) to study those who were either formally or informally diverted from enrolling for TANF  divertees.

In its interim and final report (National Research Council, 1999, 2001), the panel commented on some problems with leaver studies. These problems include differences in welfare caseload trends across states, such as faster declines in welfare rolls in some states than others and earlier program changes in states that sought AFDC waiver provisions, both of which could affect the comparability of data for cohorts of welfare leavers defined at a point in time. Also, states do not define leavers in the same way; for example, some states count child-only cases as leavers and others do not. (In such cases, adult members of a family are not eligible for benefits but the children are.) The panel also emphasized the need for leaver studies to categorize sample cases by their previous welfare behavior, distinguishing between people who had been on welfare for a long period or only a short period or whether they had been cyclers (i.e., alternating periods of welfare receipt with periods of nonreceipt). To illustrate the problems in welfare leaver studies and best practice in such analyses, the panel commissioned three papers.

The first paper on this topic, Studies of Welfare Leavers: Data, Methods, and Contributions to the Policy Process by Acs and Loprest, reviews existing welfare leaver studies, including those funded by ASPE and others. It describes the definitions, methods, and procedures used in each study and identifies their strengths and weaknesses. The paper also compares some findings of leaver studies across studies that use different methodologies to illustrate points about comparability.

The second paper, Preexit Benefit Receipt and Employment Histories and Postexit Outcomes of Welfare Leavers by Ver Ploeg, uses data from the state of Wisconsin to analyze welfare leavers. The analysis breaks the sample members into long-termers, short-termers, and cyclers and shows that this categorization is important for understanding outcomes for these groups. The paper also stratifies the sample by work experience prior to leaving welfare and finds that there are sizable differences in employment outcomes across groups with more work experience compared to those with less work experience and that such categorizations also can be useful in understanding outcomes of leavers.

The last paper in this section and the final paper in the collection, Experience-Based Measures of Heterogeneity in the Welfare Caseload by Moffitt, uses data from the National Longitudinal Survey of Youth to construct measures of heterogeneity in the welfare population based on the recipients own welfare experience. A number of classifications of women in the U.S. population are used to characterize the amount of time they have spent on welfare, the number of welfare spells they have experienced, and the average length of their welfare spells. The same long-termer, short-termer, and cycler distinctions are used in the paper as well. The analysis of the characteristics of these groups reveals that short-termers have the strongest labor market capabilities but, surprisingly, that cyclers and long-termers are approximately the same in terms of labor market potential. More generally, the only significant indicator of labor market capability is the total amount of time a recipient has been on welfare, not the degree of turnover or lengths of spells she experiences. The analysis suggests that welfare cycling is not a very useful indicator of a recipients labor market capability and that the nature of welfare cyclers and reasons that cycling occur are not well understood.


National Research Council

1998 Providing National Statistics on Health and Social Welfare Programs in an Era of Change, Summary of a Workshop. Committee on National Statistics. Constance F. Citro, Charles F. Manski, and John Pepper, eds. Washington, DC: National Academy Press.

1999 Evaluating Welfare Reform: A Framework and Review of Current Work. Panel on Data and Methods for Measuring the Effects of Changes in Social Welfare Programs. Robert A. Moffitt and Michele Ver Ploeg, eds. Washington, DC: National Academy Press.

2001 Evaluating Welfare Reform in an Era of Transition. Panel on Data and Methods for Measuring the Effects of Changes in Social Welfare Programs. Robert A. Moffitt and Michele Ver Ploeg, eds. Washington, DC: National Academy Press.

Part I: Survey Data

Designing Surveys Acknowledging Nonresponse

The Nature of Nonresponse Error in Survey Statistics

Sample surveys used to describe low-income populations are effective only when several things go right. The target population must be defined well, having the geographical and temporal extents that fit the goals of the survey. The sampling frame, the materials used to identify the population, must include the full target population. The measurement instrument must be constructed in a way that communicates the intent of the research question to the respondents, ideally in their nomenclature and within their conceptual framework. The sample design must give known, nonzero chances of selection to each low-income family/person in the sampling frame. All sample persons must be contacted and measured, eliminating nonresponse error. Finally, the administration of the measurement instrument must be conducted in a manner that fulfills the design.

Rarely does everything go exactly right. Because surveys are endeavors that are (1) customized to each problem, and (2) constructed from thousands of detailed decisions, the odds of imperfections in survey statistics are indeed large. As survey methodology, the study of how alternative survey designs affect the quality of statistics, matures, it is increasingly obvious that errors are only partially avoidable in surveys of human populations. Instead of having the goal of eliminating errors, survey researchers must learn how to reduce them within reason and budget and then attempt to gain insight into their impacts on key statistics in the survey.

This paper is a review of a large set of classic and recent findings in the study of survey nonresponse, a growing concern about survey quality. It begins with a review of what nonresponse means and how it affects the quality of survey statistics. It notes that nonresponse is relevant to simple descriptive statistics as well as measures of the relationship between two attributes (e.g., length of time receiving benefits and likelihood of later job retention). It then reviews briefly what survey statisticians can do to reduce the impact of nonresponse after the survey is complete, through various changes in the analysis approach of the data.

After this brief overview of the basic approaches to reducing the impacts of nonresponse on statistical conclusions from the data concludes, the paper turns to reducing the problem of nonresponse. It reviews current theoretical viewpoints on what causes nonresponse as well as survey design features that have been found to be effective in reducing nonresponse rates.

Nonresponse Rates and Their Relationship to Error Properties

Sample surveys often are designed to draw inferences about finite populations by measuring a subset of the population. The classical inferential capabilities of the survey rest on probability sampling from a frame covering all members of the population. A probability sample assigns known, nonzero chances of selection to every member of the population. Typically, large amounts of data from each member of the population are collected in the survey. From these variables, hundreds or thousands of different statistics might be computed, each of which is of interest to the researcher only if it describes well the corresponding population attribute. Some of these statistics describe the population from which the sample was drawn; others stem from using the data to test causal hypotheses about processes measured by the survey variables (e.g., how length of time receiving welfare payments affects salary levels of subsequent employment).

One example statistic is the sample mean as an estimator of the population mean. This is best described by using some statistical notation in order to be exact in our meaning. Let one question in the survey be called the question, Y, and the answer to that question for a sample member, say the ith member of the population, be designated by Yi. Then we can describe the population, mean by

Y = summation of Y/N, for all Y from 1 to N          (1)

where N is the number of units in the target population. The estimator of the population mean is often

01-(2).gif          (2)

where r is the number of respondents in the sample and wi is the reciprocal of the probability of selection of the ith respondent. (For readers accustomed to equal probability samples, as in a simple random sample, the wi is the same for all cases in the sample and the computation above is equivalent to 01-(2a).gif.)

One problem with the sample mean as calculated here is that is does not contain any information from the nonrespondents in the sample. However, all the desirable inferential properties of probability sample statistics apply to the statistics computed on the entire sample. Lets assume that in addition to the r respondents to the survey, there are m (for missing) nonrespondents. Then the total sample size is n = r + m. In the computation mentioned we miss information on the m missing cases.

How does this affect our estimation of the population mean? Lets make first a simplifying assumption. Assume that everyone in the target population is either, permanently and forevermore, a respondent or a nonrespondent. Let the entire target population, thereby, be defined as N = R + M, where the capital letters denote numbers in the total population.

Assume that we are unaware at the time of sample selection about which stratum each person occupies. Then in drawing our sample of size n, we will likely select some respondents and some nonrespondents. They total n in all cases, but the actual number of respondents and nonrespondents in any one sample will vary. We know that in expectation that the fraction of sample cases that are respondents should be equal to the fraction of population cases that lie in the respondent stratum, but there will be sampling variability about that number. That is, E(r) = fR, where f is the sampling fraction used to draw the sample from the population. Similarly, E(m) = fM.

For each possible sample we could draw, given the sample design, we could express a difference between the full sample mean, n, and the respondent mean, in the following way:

01-(3).gif          (3)

which, with a little manipulation, becomes



This shows that the deviation of the respondent mean from the full sample mean is a function of the nonresponse rate (m/n) and the difference between the respondent and nonrespondent means.

Under this simple expression, what is the expected value of the respondent mean over all samples that could be drawn given the same sample design? The answer to this question determines the nature of the bias in the respondent mean, where bias is taken to mean the difference between the expected value (over all possible samples given a specific design) of a statistic and the statistic computed on the target population. That is, in cases of equal probability samples of fixed size, the bias of the respondent mean is approximately



where the capital letters denote the population equivalents to the sample values. This shows that the larger the stratum of nonrespondents, the higher the bias of the respondent mean, other things being equal. Similarly, the more distinctive the nonrespondents are from the respondents, the larger the bias of the respondent mean.

These two quantities, the nonresponse rate and the differences between respondents and nonrespondents on the variables of interest, are key issues to surveys of the welfare population.

Figures 1-1a through 1-1d show four alternative frequency distributions for respondents and nonrespondents on a hypothetical variable, y, measured on all cases in some target population. The area under the curves is proportional to the size of the two groups, respondents and nonrespondents. These four figures correspond to the four rows in Table 1-1 that show response rates, means of respondents and nonrespondents, bias, and percentage bias for each of the four cases.

TABLE 1-1:
Bias and Percentage Bias in Respondent Mean Relative to Total Sample Mean for Four Situations in Figures 1-1a-1-1d and
Sample Size of Nonrespondents Needed to Detect the Nonresponse Bias
Response Rate Difference Response Rate Percentage Respondent Mean Nonrespondent Mean Total Sample Mean Bias Bias Percentage Required Sample Size of Nonrespondents
High Small 95 $201 $228 $202 $1.35 -0.7 20,408
High Large 95 $201 $501 $216 $15.00 -6.9 210
Low Small 60 $201 $228 $212 $10.80 -5.1 304
Low Large 60 $201 $501 $321 $120.00 -37.4 7

The first case reflects a high response rate survey and one in which the nonrespondents have a distribution of y values quite similar to that of the respondents. This is the lowest bias case; both factors in the nonresponse bias are small. For example, assume the response rate is 95 percent, the respondent mean for reported expenditures on clothing for a quarter is $201.00, and the mean for nonrespondents is $228.00. Then the nonresponse error is .05($201.00 - $228.00) = -$1.35.

FIGURE 1-1a.  High response rate, nonrespondents similar to respondents.

FIGURE 1-1a.  High response rate, nonrespondents similar to respondents.
SOURCE: Groves and Couper (1998).
NOTE: y = outcome variable of interest.

The second case, like the first, is a low nonresponse survey, but now the nonrespondents tend to have much higher y values than the respondents. This means that the difference term, (mean of y r minus mean of y m.), is a large negative number, meaning the respondent mean underestimates the full population mean. However, the size of the bias is small because of the low nonresponse rate. Using the same example as above, with a nonrespondent mean now of $501.00, the bias is .05($201.00 - $501.00) = -$15.00.

FIGURE 1-1b.  High response rate, nonrespondents different from respondents.

FIGURE 1-1b.  High response rate, nonrespondents different from respondents.
SOURCE: Groves and Couper (1998).
NOTE: y = outcome variable of interest.

The third case shows a very high nonresponse rate (the area under the respondent distribution is about 50 percent greater than that under the nonrespondent  a nonresponse rate of 40 percent). However, as in the first graph, the values on y of the nonrespondents are similar to those of the respondents. Hence, the respondent mean again has low bias due to nonresponse. With the same example as mentioned earlier, the bias is .40($201.00 - $228.00) = [-$10.80].

FIGURE 1-1c.  Low response rate, nonrespondents similar to respondents.

FIGURE 1-1c.  Low response rate, nonrespondents similar to respondents.
SOURCE: Groves and Couper (1998).
NOTE: y = outcome variable of interest.

The fourth case is the most perverse, exhibiting a large group of nonrespondents who have much higher values in general on y than the respondents. In this case, both m/n is large (judging by the area under the nonrespondent curve) and (mean of y r minus mean of y m.) is large in absolute terms. This is the case of large nonresponse bias. Using the previous example, the bias is .40($201.00 - $501.00) = -$120.00, a relative bias of 37 percent compared to the total sample mean!

FIGURE 1-1d.  Low response rate, nonrespondents different from respondents.

FIGURE 1-1d.  Low response rate, nonrespondents different from respondents.
SOURCE: Groves and Couper (1998).
NOTE: y = outcome variable of interest.

These four very different situations also have implications for studies of nonrespondents. Lets imagine we wish to mount a special study of nonrespondents in order to test whether the respondent mean is biased. The last column of Table 1-1 shows the sample size of nonrespondents required to obtain the same stability for a bias ratio estimate (assuming simple random sampling and the desire to estimate a binomial mean statistic with a population value of .50). The table shows that such a nonresponse study can be quite small (n = 7) and still be useful to detect the presence of nonresponse bias in a low-response-rate survey with large differences between respondents and nonrespondents (the fourth row of the table). However, the required sample size to obtain the same precision for such a nonresponse bias test in the high-response-rate case is very large (n= 20,408, in the first row). Unfortunately, prior to a study being fielded, it is not possible to have much information on the size of the likely nonresponse bias.

Nonresponse Error on Different Types of Statistics

The discussion in the previous section focused on the effect of nonresponse on estimates of the population mean, using the sample mean. This section briefly reviews effects of nonresponse on other popular statistics. We examine the case of an estimate of a population total, the difference of two subclass means, and a regression coefficient.

The Population Total

Estimating the total number of some entity is common in federal, state, and local government surveys. For example, most countries use surveys to estimate the total number of unemployed persons, the total number of new jobs created in a month, the total retail sales, and the total number of criminal victimizations. Using similar notation as previously, the population total is 01-(5c).gif which is estimated by a simple expansion estimator, 01-(5d).gif or by a ratio expansion estimator, 01-(5e).gif where X is some auxiliary variable, correlated with Y, for which target population totals are known. For example, if y were a measure of the length of first employment spell of a welfare leaver, and x were a count of sample welfare leavers, X would be a count of the total number of welfare leavers.

For variables that have nonnegative values (like count variables), simple expansion estimators of totals based only on respondents always underestimate the total. This is because the full sample estimator is



Hence, the bias in the respondent-based estimator is

The bias in the respondent-based estimator.          (7)

It is easy to see, thereby, that the respondent-based total (for variables that have nonnegative values) always will underestimate the full sample total, and thus, in expectation, the full population total.

The Difference of Two Subclass Means

Many statistics of interest from sample surveys estimate the difference between the means of two subpopulations. For example, the Current Population Survey often estimates the difference in the unemployment rate for black and nonblack men. The National Health Interview Survey estimates the difference in the mean number of doctor visits in the past 12 months between males and females.

Using the expressions above, and using subscripts 1 and 2 for the two subclasses, we can describe the two respondent means as

01-(8).gif          (8)

01-(9).gif          (9)

These expressions show that each respondent subclass mean is subject to an error that is a function of a nonresponse rate for the subclass and a deviation between respondents and nonrespondents in the subclass. The reader should note that the nonresponse rates for individual subclasses could be higher or lower than the nonresponse rates for the total sample. For example, it is common that nonresponse rates in large urban areas are higher than nonresponse rates in rural areas. If these were the two subclasses, the two nonresponse rates would be quite different.

If we were interested in 01-(9a).gif as a statistic of interest, the bias in the difference of the two means would be approximately

01-(10).gif          (10)

Many survey analysts are hopeful that the two terms in the bias expression cancel. That is, the bias in the two subclass means is equal. If one were dealing with two subclasses with equal nonresponse rates that hope is equivalent to a hope that the difference terms are equal to one another. This hope is based on an assumption that nonrespondents will differ from respondents in the same way for both subclasses. That is, if nonrespondents tend to be unemployed versus respondents, on average, this will be true for all subclasses in the sample.

If the nonresponse rates were not equal for the two subclasses, then the assumptions of canceling biases is even more complex. For example, lets continue to assume that the difference between respondent and nonrespondent means is the same for the two subclasses. That is, assume 01-(10a).gif Under this restrictive assumption, there can still be large nonresponse biases.

For example, Figure 1-2 examines differences of two subclass means where the statistics are proportions (e.g., the proportion currently employed). The figure treats the case in which the proportion employed among respondents in the first subclass (say, women on welfare a long time) is 01-(10b).gif = 0.5 and the proportion employed among respondents in the second subclass (say, women on welfare a short time) is 01-(10c).gif = 0.3. This is fixed for all cases in the figure. We examine the nonresponse bias for the entire set of differences between respondents and nonrespondents. That is, we examine situations where the differences between respondents and nonrespondents lie between -0.5 and 0.3. (This difference applies to both subclasses.) The first case of a difference of 0.3 would correspond to


FIGURE 1-2.  Illustration of nonresponse bias for difference between proportion currently employed.

FIGURE 1-2.  Illustration of nonresponse bias for difference between proportion currently employed (0.5 employed among respondents on welfare a short time versus 0.3 employed among respondents on welfare a long time), given comparable differences in each subclass between respondents and nonrespondents.
SOURCE:  Groves and Couper (1998).

The figure shows that when the two nonresponse rates are equal to one another, there is no bias in the difference of the two subclass means. However, when the response rates of the two subclasses are different, large biases can result. Larger biases in the difference of subclass means arise with larger differences in nonresponse rates in the two subclasses (note the higher absolute value of the bias for any given 01-(10e).gif value for the case with a .05 nonresponse rate in subclass [1 and a 0.5, in subclass 2] than for the other cases).

A Regression Coefficient

Many survey data sets are used by analysts to estimate a wide variety of statistics measuring the relationship between two variables. Linear models testing causal assertions often are estimated on survey data. Imagine, for example, that the analysts were interested in the model

01-(11).gif          (11)

which using the respondent cases to the survey, would be estimated by

01-(12).gif          (12)

The ordinary least squares estimator of Br1 is

01-(13).gif          (13)

Both the numerator and denominator of this expression are subject to potential nonresponse bias. For example, the bias in the covariance term in the numerator is approximately

01-(14).gif          (14)

where srxy is the respondent-based estimate of the covariance between x and y based on the sample (Srxy is the population equivalent) and Smxy is a similar quantity for nonrespondents.

This bias expression can be either positive or negative in value. The first term in the expression has a form similar to that of the bias of the respondent mean. It reflects a difference in covariances for the respondents (Srxy) and nonrespondents (Smxy). It is large in absolute value when the nonresponse rate is large. If the two variables are more strongly related in the respondent set than in the nonrespondent, the term has a positive value (that is the regression coefficient tends to be overestimated). The second term has no analogue in the case of the sample mean; it is a function of cross-products of difference terms. It can be either positive or negative depending on these deviations.

As Figure 1-3 illustrates, if the nonrespondent units have distinctive combinations of values on the x and y variables in the estimated equation, then the slope of the regression line can be misestimated. The figure illustrates the case when the pattern of nonrespondent cases (designated by 01-(14a).gif) differ from that of respondent cases (designated by 01-(14b).gif). The result is the fitted line on respondents only has a larger slope than that for the full sample. In this case, normally the analyst would find more support for a hypothesized relationship than would be true for the full sample.

We can use equation (14) to illustrate notions of ignorable and nonignorable nonresponse. Even in the presence of nonresponse, the nonresponse bias of regression coefficients may be negligible if the model has a specification that reflects all the causes of nonresponse related to the dependent variable. Consider a survey in which respondents differ from nonrespondents in their employment status because there are systematic differences in the representation of different education and race groups among respondents and nonrespondents. Said differently, within education and race groups, the employment rates of respondents and nonrespondents are equivalent. In this case, ignoring this information will produce a biased estimate of unemployment rates. Using an employment rate estimation scheme that accounts for differences in education and race group response rate can eliminate the bias. In equation (12), letting x be education and race can reduce the nonresponse bias in estimating a y, employment propensity.

Considering Survey Participation a Stochastic Phenomenon

The previous discussion made the assumption that each person (or household) in a target population either is a respondent or a nonrespondent for all possible surveys. That is, it assumes a fixed property for each sample unit regarding the survey request. They always will be a nonrespondent or they always will be a respondent, in all realizations of the survey design.

An alternative view of nonresponse asserts that every sample unit has a probability of being a respondent and a probability of being a nonrespondent. It takes the perspective that each sample survey is but one realization of a survey design. In this case, the survey design contains all the specifications of the research data collection. The design includes the definition of the sampling frame; the sample design; the questionnaire design; choice of mode; hiring, selection, and training regimen for interviewers; data collection period, protocol for contacting sample units; callback rules; refusal conversion rules; and so on. Conditional on all these fixed properties of the sample survey, sample units can make different decisions regarding their participation.

In this view, the notion of a nonresponse rate takes on new properties. Instead of the nonresponse rate merely being a manifestation of how many nonrespondents were sampled from the sampling frame, we must acknowledge that in each realization of a survey different individuals will be respondents and nonrespondents. In this perspective the nonresponse rate given earlier (m/n) is the result of a set of Bernoulli trials; each sample unit is subject to a coin flip to determine whether it is a respondent or nonrespondent on a particular trial. The coins of various sample units may be weighted differently; some will have higher probabilities of participation than others. However, all are involved in a stochastic process of determining their participation in a particular sample survey.

The implications of this perspective on the biases of respondent means, respondent totals, respondent differences of means, and respondent regression coefficients are minor. The more important implication is on the variance properties of unadjusted and adjusted estimates based on respondents.

Postsurvey Compensation for Nonresponse

Two principal techniques are used to account for unit nonresponse in the analysis of survey data: weighting and imputation. In computing final statistics, weighting attempts to increase the importance of data from respondents who are in classes with large nonresponse rates and decrease their importance when they are members of classes with high response rates. Imputation creates data records for nonrespondents by examining patterns of attributes that appear to co-occur among respondents, and then estimating the attributes of the nonrespondents based on information common to respondents and nonrespondents.

All adjustments to the analysis of data in the presence of nonresponse can affect survey conclusions: both the value of a statistic and the precision of the statistic can be affected.

Weighting to Adjust Statistics for Nonresponse

Two kinds of weighting are common to survey estimation in the presence of nonresponse: population-based weighting (sometimes called poststratification) and sample-based weighting. Population weighting applies known population totals on attributes from the sampling frame to create a respondent pool that resembles the population on those attributes. For example, if the Temporary Assistance for Needy Families (TANF) leavers frame were used to draw a sample and auxiliary information were available on food stamp, general assistance, Supplemental Security Income (SSI), Medicaid, and foster care payment receipt, it would be possible to use those variables as adjustment factors. The ideal adjustment factors are those that display variation in response rates and variation on key survey statistics. To illustrate, Table 1-2 shows a survey estimating percentage of TANF leavers employed, in different categories of prior receipt status. In this hypothetical case, we are given the number of months unemployed of sample persons (both employed and unemployed). We can see that the mean number of months unemployed is 3.2 for respondents but 6.5 for nonrespondents. In this case we have available an attribute known on the entire population (the type of transfer payments received), and this permits an adjustment of the overall mean. The adjusted mean merely assures that the sample statistic will be based on the population distribution of the sampling frame, on the adjustment variable. In this case, the adjusted respondent mean equals 0.05*0.2 + 0.3*0.5 + 0.3*3.2 + 0.35*8.1 = 3.955. (The true mean is 3.966.)

TABLE 1-2:
Illustration of Proportion of TANF Leavers Currently Employed, by Type of Assistance Received,
For Population, Sample, Respondents, and Nonrespondents.
Category Population
Sample Respondents Nonrespondents
n Response Rate n Months Unemployed n Months Unemployed
General assistance only 5,000 50 .95 47 0.2 3 0.1
Gen. asst. and food stamps 30,000 300 .90 270 0.5 30 0.4
Gen. asst. and SSI 30,000 300 .90 270 3.2 30 3.1
Gen. asst. and other 35,000 300 .50 175 8.1 175 8.2
Total 100,000 1,000 .76 762 3.2 238 6.5

Why does this seem to work? The adjustment variable is both correlated to the response rate and correlated to the dependent variable. In other words, most of the problem of nonresponse arises because the respondent pool differs from the population on the distribution of type of transfer payment. Restoring that balance reduces the nonresponse error. This is not always so. If the adjustment variables were related to response rates but not to the survey variable, then adjustment would do nothing to change the value of the survey statistic.

What cannot be seen from the illustration is the effects on the precision of the statistic of the adjustment. When population weights are used, the effect is usually to increase the precision of the estimate, a side benefit (Cochran, 1977). For that reason, attempting to use sampling frames rich in auxiliary data is a wise design choice in general. Whenever there are possibilities of linking to the entire sampling frame information that is correlated with the likely survey outcomes, then these variables are available for population-based weighting. They can both reduce nonresponse bias and variance of estimates.

What can be done when there are no correlates of nonresponse or the outcome variables available on all sample frame elements? The next best treatment is to collect data on all sample elements, both respondent and nonrespondent, that would have similar relationships to nonresponse likelihood and survey outcomes. For example, it is sometimes too expensive to merge administrative data sets for all sample frame elements but still possible for the sample. In this case, a similar weighting scheme is constructed, but using information available only on the sample. Each respondent case is weighted by the reciprocal of the response rate of the group to which it belongs. This procedure clearly relies on the assumption that nonresepondents and respondents are distributed identically given group membership (i.e., that nonrespondents are missing at random). Sometimes this weighting is done in discrete classes, as with the example in Table 1-2; other times response propensity models that predict the likelihood that each respondent was actually measured, given a set of attributes known for respondents and nonrespondents are constructed (Ekholm and Laaksonen, 1991).

Whatever is done with sample-based weights, it is generally the case that the precision of weighted sample estimates is lower than that of estimates with no weights. A good approximate of the sampling variance (square of standard error) of the adjusted mean in a simple random sample is

01-(15).gif          (15)

where the wh is the proportion of sample cases in a weight group with rh respondents, yrh is the mean of the respondents in that group, and ys is the overall sample mean based on all n cases. The first term is what the sampling variance would be for the mean if the sample had come from a sample stratified by the weight classes. The second term reflects the lack of control of the allocation of the sample across the weight classes; this is the term that creates the loss of precision (as well as the fact that the total sample size is reduced from n to 01-(15a).gif where (01-(15b).gif) is the response rate.)

One good question is why weights based on the full population tend to improve the precision of estimates and why weights based on the sample reduce the precision. This rule of thumb is useful because, other things being equal, sample-based nonresponse weights are themselves based on a single sample of the population. Their values would vary over replications of the sample; hence, they tend not to add stability to the estimates but further compound the instability of estimates. Although this greater instability is unfortunate, most uses of such sample-based weights are justified by the decrease in the biasing effects of nonresponse. Thus, although the estimates may have higher variability over replications, they will tend to have averages closer to the population parameter.

Imputation to Improve Estimates in the Face of Missing Data

The second approach to improving survey estimation when nonresponse is present is imputation. Imputation uses information auxiliary to the survey to create values for individual missing items in sample data records. Imputation is generally preferred over weighting for item-missing data (e.g., missing information on current wages for a respondent) than for unit nonresponse (e.g., missing an entire interview). Weighting is more often used for unit nonresponse.

One technique for imputation in unit nonresponse is hot deck imputation, which uses data records from respondents in the survey as substitutes for those missing for nonrespondents (Ford, 1983). The technique chooses donor respondent records for nonrespondents who share the same classification on some set of attributes known on all cases (e.g., geography, structure type). Ideally, respondents and nonrespondents would have identical distributions on all survey variables within a class (similar logic as applies to weighting classes). In other words, nonrespondents are missing at random (MAR). The rule for choosing the donor, the size of the classes, and the degree of homogeneity within classes determine the bias and variance properties of the imputation.

More frequently imputation involves models, specifying the relationship between a set of predictors known on respondents and nonrespondents and the survey variables (Little and Rubin, 1987). These models are fit on those cases for which the survey variable values are known. The coefficients of the model are used to create expected values, given the model, for all nonrespondent cases. The expected values may be altered by the addition of an error term from a specified distribution; the imputation may be performed multiple times (Rubin, 1987) in order to provide estimates of the variance due to imputation.

Common Burdens of Adjustment Procedures

We can now see that all practical tools of adjustment for nonresponse require information auxiliary to the survey to be effective. This information must pertain both to respondents and nonrespondents to be useful. To offer the chance of reducing the bias of nonresponse, the variables available should be correlated both with the likelihood of being a nonrespondent and the survey statistic of interest itself. When the dependent variable itself is missing, strong models positing the relationship between the likelihood of nonresponse and the dependent variable are required. Often the assumptions of these models remain untestable with the survey data themselves.

Researchers can imagine more useful adjustment variables than are actually available. Hence, the quality of postsurvey adjustments are limited more often by lack of data than by lack of creativity on the part of the analysts.

Decomposing the Survey Participation Phenomenon

The phenomenon of survey participation is sequential and nested. First, the location of sample persons must be determined. Second, sample persons must be contacted. Third, they are given a request for survey information. Those not contacted make no decision regarding their participation that is known by the survey organization. Those contacted and given a survey request can cooperate, they can refuse, or they can provide information that communicates that they cannot physically or cognitively perform the respondent role. Because these are four separate processes, it is important to keep them as separate nonresponse phenomena: failure to locate, noncontact, refusals, and other noninterview is a common category-labeling scheme.

Locating Sample Persons

The first step in gaining contact with a sample person, when selected from a list of persons, is locating that person.(1) If the sample person has not changed address or telephone number from the time the list was prepared, this is a trivial issue. The difficulty arises when persons or households change addresses. The propensity of locating units is driven by factors related to whether or not the unit moves and the quality of contact information provided at the time of initial data collection.

A number of survey design features may affect the likelihood of locating sample units. For example, the quality of the contact information decays as time between the initial data collection (or creation of the list) and the followup survey increases. Similarly, tracking rules affect location propensity. For cost reasons, a survey organization may track people only within a limited geographic area, such as a county or within a country. The amount and quality of information collected by the survey organization specifically for tracking movers also is driven by cost considerations. The more reliable and valid data available for tracking purposes can reduce tracking effort, and make more resources available for those units that are proving to be particularly difficult to locate.

Household characteristics also affect the likelihood of moving, and thus the propensity to locate the household or household members. Geographic mobility is related to the household or individual life stage, as well as cohort effects. For example, younger people are typically much more mobile than older persons. The number of years that a household or individual has lived at a residence, the nature of household tenure (i.e., whether the household members own or rent the dwelling), and community attachments through family and friends also determine the likelihood of moving.

Household income is strongly related to residential mobility. Using data from the Current Population Survey, we find that 19.6 percent of those with household incomes under $10,000 had moved between March 1996 and March 1997, compared to 10 percent of those with incomes above $75,000. Similarly, 25.9 percent unemployed persons age 16 or older had moved in this period, compared to 16.8 percent of those employed, and 11.1 percent not in the labor force.

Life events also are known to be related to moving likelihood. A birth in a household, a death of a significant individual, marriage, job change, crime victimization, and other events are associated with increased likelihood of moving. Furthermore, these life events may increase the difficulty of locating individuals. For example, a name change in marriage or following divorce can make it more difficult to track and locate someone who has moved. This is particularly relevant for welfare leaver studies, as this population is likely to be undergoing these very types of changes.

An important factor that can reduce the likelihood of moving, or provide more data on units that do move, is the social aspect of community attachment or connectedness. Individuals who are engaged in the civic aspects of their community or participate socially are posited to be more stable and less likely to move. Furthermore, those linked into their current community life are likely to leave many traces to their new address, and likely to be politically, socially, and economically engaged in their new community. Their lives are more public and accessible through multiple databases such as telephone directories, credit records, voter registration, library registration, membership in churches or religious organizations, or children in schools. Again, we expect that sample units in welfare leaver studies are not particularly rich in these sources of tracking information.

To the extent that the survey variables of interest are related to mobility, lifestyle changes, social isolation, or willingness to be found, nonresponse through nonlocation can lead to bias. Because these studies are primarily about changes in individual lives, failure to obtain complete data on the more mobile or those subject to lifestyle changes will underrepresent individuals with these particular characteristics in such surveys. Furthermore, the effects of disproportionate representation in the sample due to mobility or lifestyle changes may not be simply additive. For example, we expect that those who do not have a telephone and those who refuse to provide a telephone number both would be difficult to locate in subsequent waves of a survey, but for different reasons.

The Process of Contacting Sample Persons

Theoretically the process of contacting a sample household, once located, is rather straightforward. As Figure 1-4 shows, the success at contacting a household should be a simple function of the times at which at least one member of the household is at home, the times at which interviewers call, and any impediments the interviewers encounter in gaining access to the housing unit. In face-to-face surveys the latter can include locked apartment buildings, gated housing complexes, no-trespassing enforcement, as well as intercoms or any devices that limit contact with the household. In telephone surveys, the impediments include caller ID, call blocking, or answering machines that filter or restrict direct contact with the household.

FIGURE 1-4. Influences on the likelihood of contact with a sample household.

FIGURE 1-4. Influences on the likelihood of contact with a sample household.
SOURCE: Groves and Couper (1998)

In most surveys the interviewer has no prior knowledge about the at-home behavior of a given sample household. In face-to-face surveys interviewers report that they often make an initial visit to a sample segment (i.e., a cluster of neighboring housing units sampled in the survey) during the day in order to gain initial intelligence about likely at-home behaviors. During this visit the interviewer looks for bicycles left outside (as evidence of children), signs of difficulty of accessing the unit (e.g., locked apartment buildings), small apartments in multi- unit structures (likely to be single-person units), absence of automobiles, or other signs. Sometimes when neighbors of the sample household are available, interviewers seek their advice on a good time to call on the sample unit. This process is the practical method of gaining proxy information about what call times might successfully encounter the household members at home. In telephone surveys, no such intelligence gathering is possible. The only information about at-home practices of a sample household is obtained by calling the number. (This imbalance leads to the larger number of calls required to make first contact with a household in telephone surveys; see Groves and Kahn, 1979.)

Information from time-use surveys, which ask persons to report on their activities hour by hour, has shown common patterns of at-home behavior by weekday mornings and afternoons, weekday evenings, and weekends. Those in the employed labor force are commonly out of the house, with the lowest rates of occupancy between 10 a.m. and 4:30 p.m. (Hill, 1978). Interviewers make repeated calls on households they do not contact on the first call. Their choice of time for those callbacks can be viewed as repeated samples from a day-of-week, time-of-day frame. They base their timing of successive calls on information they obtain on prior unsuccessful visits and on some sense of consistency. For example, interviewers often are trained to make a callback on a unit not contacted at the last visit on Tuesday afternoon, by visiting during an evening or weekend.

Physical impediments are sometimes so strong that they literally prevent all contact with a sample unit. For example, some higher priced multiunit structures have doormen that are ordered to prevent entrance of all persons not previously screened by a resident. Such buildings may be fully nonrespondent to face-to- face surveys. Similarly, although there is evidence that the majority of owners of telephone answering machines use them to monitor calls to their unit when they are absent, some apparently use them to screen out calls when they are at home (see Tuckel and Feinberg, 1991; Tuckel and ONeill, 1995), thus preventing telephone survey interviewers from contacting the household.

Other impediments to contacting households may offer merely temporary barriers, forcing the interviewer to make more than the usual number of calls before first contacting the households. For example, apartment buildings whose entrance is controlled by a resident manager may require negotiations with the manager before access to sample households is given.

Is there empirical evidence regarding the model in Figure 1-4? First, lets look at the distribution of the number of calls required to make first contact with a sample household. Figure 1-5 shows the proportion of sample households contacted by calls to first contacts. This figure displays the result for several surveys at once, some telephone and some face to face. The pattern is relatively stable across the surveys, with the modal category being the first call  immediate contact with someone in the household. The proportion contacted on later calls is uniformly decreasing in subsequent calls. Rather uniformly, if the first call attempt is unsuccessful, the likelihood of contact declines with each successive call.

FIGURE 1-5. Percentage of eligible households contacted by calls to first contact.

FIGURE 1-5. Percentage of eligible households contacted by calls to first contact.

Does the character of sample households vary by calls to first contact? Figure 1-6 shows an increasing percentage of the households are single-person households as the number of calls to first contact increases. Single-person households tend to be more difficult to contact. Other analysis shows that the exception to this tendency is single-person households with elderly persons, which tend to be home more often than other households.

FIGURE 1-6. Percentage of contacted households with one person, by calls to first contact.

FIGURE 1-6. Percentage of contacted households with one person, by calls to first contact (National Survey of Health and Stress).

Figure 1-7 shows a similar result for an access impediment in telephone surveys, the answering machine, which now is present in more than 50 percent of homes nationwide (Tuckel and ONeil, 1995). The percentage of contacted households with answering machines increases with each succeeding category of number of calls to first contact. Households with answering machines slow down contact with household members, requiring more calls to first contact.

FIGURE 1-7. Percentage of contacted households with ananswering machine by calls to first contact.

FIGURE 1-7. Percentage of contacted households with ananswering machine by calls to first contact.

Other empirical results are similar to these could be presented. Households with access impediments slow down contact of interviewers with sample units. More calls are required to even deliver the survey request. Furthermore, households that are home less often require more calls; these include households where all adult members work out of the home during the day, urban versus rural households, and in telephone surveys, unlisted households.

The Decision to Participate in a Survey

Once the interviewer contacts a sample household we believe that the influences on the householders decision to participate arise from relatively stable features of their environments and backgrounds, fixed features of the survey design, as well as quite transient, unstable features of the interaction between the interviewer and the householder. This conceptual scheme is portrayed in Figure 1-8, which lists influences of the social environment, householder, survey design features, interviewer attributes and behavior, and the contact-level interaction of interviewers and householders.

FIGURE 1-8. A conceptual framework for survey cooperation.

FIGURE 1-8. A conceptual framework for survey cooperation.
SOURCE: Groves and Couper (1998).

The influences on the left of the figure (social environment and sample household) are features of the population under study, out of control of the researcher. The influences on the right are the result of design choices by the researcher, affecting the nature of the survey requests and the attributes of the actors (the interviewers) who deliver them. The bottom of the figure, describing the interaction between the interviewer and the householder, is the occasion when these influences come to bear. Which of the various influences are made most salient during that interaction determines the decision outcome of the householder.

Social Environmental Influences on Survey Participation

Because surveys are inherently social events, we would expect that societal and group-level influences might affect their participation rates. There is a set of global characteristics in any society that affect survey participation. These factors serve to determine the context within which the request for participation takes place, and constrain the actions of both householder and interviewer. For example, the degree of social responsibility felt by a sample person may be affected by factors such as the legitimacy of societal institutions, the degree of social cohesion, and so on. Such factors influence not only the expectations that both interviewer and respondent bring to the interaction, but also determine the particular persuasion strategies (on the part of the interviewer) and decision-making strategies (on the part of the respondent) that are used. More specific to the survey-taking climate are such factors as the number of surveys conducted in a society (the oversurveying effect) and the perceived legitimacy of surveys.

We would expect, therefore, to the extent that societies differ on these attributes to observe different levels of cooperation for similar surveys conducted in different countries. There is evidence for this (see De Heer and Israëls, 1992), but the evidence is clouded by different design features used across countries, especially intensity of effort to reduce nonresponse. These include different protocols for advance contact with sample households, for repeated callbacks on noncontacted cases, and for dealing with initial refusals.

There are also environmental influences on survey cooperation below the societal level. For example, urbanicity is one of the most universal correlates of cooperation across the world. Urban dwellers tend to have lower response rates than rural dwellers. This contrast has been commonly observed in part because the urbanicity variable is often available from the sampling frame. The nature of urbanicity effects on response rates has been found to be related to crime rates (House and Wolf, 1978), but also may be related to population density, the type of housing structures, and household composition in urban areas. The effect also may be a function of inherent features of urban life  the faster pace, the frequency of fleeting single-purpose contacts with strangers, and the looser ties of community in such areas.

Characteristics of the Sample Householder

The factors affecting nonresponse that are most widely discussed in the survey literature are sociodemographic characteristics of the householder or sample person. These include age, gender, marital status, education, and income. Response rates have been shown to vary with each of these, as well as other, characteristics.

Other factors associated with these also have been studied for their relationship to response rates. These include household structure and characteristics, such as the number and ages of the household members and the quality and upkeep of housing, and the experience of the respondent, such as exposure to situations similar to the interview interaction or a background that provided information or training relevant to the survey topic.

We do not believe these factors are causal to the participation decision. Instead, they tend to produce a set of psychological predispositions that affect the decision. Some of them are indicators of the likely salience of the topic to the respondent (e.g., socioeconomic indicators on income-related surveys); others are indicators of reactions to strangers (e.g., single-person households).

The sociodemographic factors and household characteristics all may influence the householders psychological predispositions. Feelings of efficacy, embarrassment, or helpfulness and moods of depression, elation, or anger all will be affected by these factors. All of these characteristics will then influence the cognitive process that will occur during the interaction with the interviewer.

Few householders appear to have strongly preformed decisions about survey requests. Rather, these decisions are made largely at the time of the request for participation. Much social and cognitive psychological research on decision making (e.g., Eagly and Chaiken, 1984; Petty and Caccioppo, 1986) has contrasted two types of processes. The first is deep, thorough consideration of the pertinent arguments and counterarguments of the costs and benefits of options. The second is shallower, quicker, more heuristic decision making based on peripheral aspects of the options. We have a very specific meaning of heuristic in this context  use of general rules of behavior (e.g., strange men at the telephone are to be avoided) to guide the survey decision rather than judgments based on the specific information provided about the survey.

We believe the survey request situation most often favors a heuristic approach because the potential respondent typically does not have a large personal interest in survey participation and, consequently, is not inclined to devote large amounts of time or cognitive energy to the decision of whether or not to participate. Furthermore, little of the information typically provided to the householder pertains to the details of the requested task. Instead, interviewers describe the purpose of the survey, the nature of the incentive, or the legitimacy of the sponsoring organization. All of these in some sense are peripheral to the respondents task of listening to the interviewers questions, seriously considering alternative answers, and honestly reporting ones judgment.

Cialdini (1984) has identified several compliance principles that guide some heuristic decision making on requests and appear to be activated in surveys. These include reciprocation, authority, consistency, scarcity, social validation, and liking. We review these briefly there (see also Groves et al., 1992) and link them to other concepts used in the literature.

Reciprocation. This heuristic suggests that a householder should be more willing to comply with a request to the extent that compliance constitutes the repayment of a perceived gift, favor, or concession. Thus, one may choose to participate in a survey based on a perceived sense of obligation to the organization making the request, or to the broader society it represents. On a narrower level, more peripheral features of the request (e.g., incentives, interviewer compliments) may be sufficient to invoke the reciprocity heuristic.

Reciprocation, as a concept, is closely related to sociological notions of social exchange. Social exchange theories tend to focus on long-run relationships between individuals and groups, but contain the same influence of past favors given by another influencing similar actions by a focal person or group.

Authority. People are more likely to comply with a request if it comes from a properly constituted authority, someone who is sanctioned by the society to make such requests and to expect compliance. In the survey interview context, the immediate requester is typically not the authority figure but is seen as representing some sponsoring organization that can be judged to have varying degrees of authority status. Survey organizations with greater legitimacy (e.g., those representing federal government agencies) are more likely to trigger the authority heuristic in influencing the householders decision to participate.

Notions of social isolation, the perception by people that they are not part of the larger society or bound by its norms, may be useful here. Socially isolated groups include both those believing they have suffered historical inequities at the hands of major institutions or groups and those identifying quite strongly with a distinct subculture. These types of groups may be guided by the same norms of reciprocation or influences of authority during interactions involving institutions of the majority culture, but in such cases the effect on cooperation may be negative.

We have found concepts of reciprocation and authority very important to understanding the behavior of sample persons. In addition, however, four other compliance heuristics described by Cialdini (1984) are relevant to surveys: consistency, scarcity, social validation, and liking.

Consistency. The consistency heuristic suggests that, after committing oneself to a position, one should be more willing to comply with requests for behaviors that are consistent with that position. This is the likely explanation for the foot-in-the-door effect in surveys (e.g., Freedman and Fraser, 1966), where compliance with a small initial request leads to greater willingness to accede to a larger request.

Scarcity. This heuristic notes that one should be more willing to comply with requests to secure opportunities that are scarce. To the extent that the survey request is perceived as a rare opportunity to participate in an interesting and/or important activity, the scarcity principle may lead to greater likelihood of acceptance of the request.

Social validation. Using this heuristic, one would be more willing to comply with a request to the degree that one believes similar others are likely to do so. If householders believe that most people like themselves agree to participate in surveys, they may be more inclined to do so themselves.

Liking. Put simply, one should be more willing to comply with the requests of liked others. A variety of factors (e.g., similarity of attitude, background, or dress; praise) have been shown to increase liking of strangers, and these cues may be used to guide the householders decision in evaluating the interviewers request.

Although we believe these heuristics often come to the fore when a householder is confronted with a request to participate in a survey, other factors more closely associated with a rational choice perspective also may influence their decision.

For example, a common finding in research on attitude change (see, for example, Petty and Caccioppo, 1986) is that when the topic of discussion is highly salient to laboratory subjects, they tend to give careful consideration to the arguments pro and con concerning the topic. Similarly, we think that saliency, relevance, and interest in the survey topic are relevant to the householders decision process. That is, when the survey topic is highly relevant to the well-being or for other reasons of interest to the householders, they might perform a more thorough analysis of the merits of cooperating with the survey request.

However, in contrast to the laboratory experiments in the attitude change literature, largely based on willing and motivated subjects, the survey setting probably limits cost-benefit examination of a survey request. Calls by interviewers to sample households generally are unscheduled events. The amount of discretionary time perceived to be possessed by the householders at the time of contact also will affect their tendency to engage in deliberate, careful consideration of the arguments to participate in the survey. Householders who see themselves as burdened by other obligations overwhelmingly may choose heuristic shortcuts to evaluate the survey request.

Attributes of the Survey Design

Much survey research practice is focused on reducing nonresponse by choosing features of the survey design that generate higher participation rates. These by and large are fixed attributes of the request for an interview that are applied to all cases. This section discusses those features in an indirect manner, by identifying and elaborating the concepts that underlie their effectiveness.

Many of the survey design features aimed at gaining cooperation use one or more of the compliance heuristics reviewed earlier. For example, the reciprocation heuristic probably underlies the large literature on the effects of incentives on survey participation rates. Consistent with the concept of reciprocation, there appear to be larger effects of incentives provided prior to the request for the survey, compared to those promised contingent on the completion of the interview (Berk et al., 1987; Singer et al., 1996).

The concept also underlies the common training guideline in some surveys for interviewers to emphasize the potential benefits of the survey to the individual respondent. For example, in the Consumer Expenditure Survey, used as part of the Consumer Price Index of the United States, interviewers often tell elderly householders that their government Social Security payments are affected by the survey.

One implication of the consistency principle for survey design is that an interviewer who can draw a connection between the merits of particular (or general) survey participation and the respondents committed beliefs, attitudes, and values (e.g., efficiency in government, advancement of knowledge) is likely to be more successful in gaining compliance.

Evoking authority is a common tool in advance mailings in household surveys and in the introductory script of interviewers. Advance letters often are crafted to use stationery that evokes legitimate authority for the information collection; the letters are signed, whenever possible, by persons with titles conveying power and prestige. Some social surveys (e.g., studies of community issues) seek the endorsement of associations or organizations that would aid the communication of legitimate authority to collect the data. Furthermore, interviewers often are trained to emphasize the sponsor of their survey when the sponsor generally is seen as having legitimate authority to collect the information (e.g., government or educational institutions), but rarely to do so when that is less likely (e.g., certain commercial organizations).

The scarcity principle may underlie the interviewer tactics of emphasizing the value to a respondent of making your voice heard or having your opinion count while noting that such an opportunity is rare (e.g., We only contact one person in every 30,000). This principle may also help explain the decline of survey participation in Western society that has coincided with the proliferation of surveys. People may no longer consider the chance to have their opinions counted as an especially rare, and therefore valuable, event. Consequently, at the end of the interviewing period, some interviewers are known to say that There are only a few days left. Im not sure Ill be able to interview you if we dont do it now  a clear attempt to make the scarcity principle apply.

Similarly, survey organizations and interviewers may attempt to invoke social validation by suggesting that Most people enjoy being interviewed, or Most people choose to participate, or by evincing surprise at the expression of reluctance by a householder.

The use of race or gender matching by survey organizations may be an attempt to invoke liking through similarity, as well as reducing the potential threat to the householder.

Other survey design features do not fit nicely into the compliance heuristics conceptualized by Cialdini. Indeed, these are much more closely aligned with rational choice, cost versus benefit tradeoff decisions. For example, there is some evidence that longer questionnaires require the interviewer to work harder to gain cooperation. In interviewer-assisted surveys some of the disadvantages can be overcome by interviewer action, but more work is required. Thus, other things being equal, choosing a short survey interview may yield easier attainment of high participation.

Related to burden as measured by time is burden produced by psychological threat or low saliency. Survey topics that ask respondents to reveal embarrassing facts about themselves or that cover topics that are avoided in day-to-day conversations between strangers may be perceived as quite burdensome. For example, surveys about sexual behaviors or income and assets tend to achieve lower cooperation rates, other things being equal, than surveys of health or employment. On the other hand, when the topic is salient to the householders, when they have prior interest in the topic, then the perceived burden of answering questions on the topic is lower. This probably underlies the finding of Couper (1997) that householders who express more interest in politics are interviewed more easily than those with no such interests.

Attributes of the Interviewer

Observable attributes of the interviewer affect participation because they are used as cues by the householder to judge the intent of the visit. For example, consider the sociodemographic characteristics of race, age, gender, and socioeconomic status. At the first contact with the interviewer, the householder is making judgments about the purposes of the visit. Is this a sales call? Is there any risk of physical danger in this encounter? Can I trust that this person is sincere? Assessments of alternative intentions of the caller are made by matching the pattern of visual and audio cues with evoked alternatives. All attributes of the interviewer that help the householder discriminate the different scripts will be used to make the decision about the intent of the call. Once the householder chooses an interpretation of the intent of the call  a cognitive script in Abelsons (1981) terms  then the householder can use the script to guide his or her reactions to the interviewer.

The second set of influences from the interviewer is a function of the householders experience. To select an approach to use, the interviewer must judge the fit of the respondent to other respondent types experienced in the past (either through descriptions in training or actual interaction with them). We believe that experienced interviewers tend to achieve higher levels of cooperation because they carry with them a larger number of combinations of behaviors proven to be effective for one or more types of householders. A corollary of this is that interviewers experiencing diverse subpopulations are even more resourceful and are valuable for refusal conversion work. We can also deduce that the initial months and years of interviewing offer the largest gains to interviewers by providing them with new persuasion tools.

The third set of attributes might be viewed as causally derivative of the first two, interviewer expectations regarding the likelihood of gaining cooperation of the householder. Research shows that interviewers who believe survey questions are sensitive tend to achieve higher missing-data rates on them (Singer and Kohnke-Aguirre, 1979). Interviewers report that their emotional state at the time of contact is crucial to their success: I do not have much trouble talking people into cooperating. I love this work and I believe this helps 'sell' the survey. When I knock on a door, I feel Im gonna get that interview! We believe these expectations are a function of interviewer sociodemographic attributes (and their match to those of the householder), their personal reactions to the survey topic, and their experience as an interviewer.

Respondent-Interviewer Interaction

When interviewers encounter householders, the factors discussed come to bear on the decision to participate. The strategies the interviewer employs to persuade the sample person are determined not only by the interviewers own ability, expectations, and other variables, but also by features of the survey design and by characteristics of the immediate environment and broader society. Similarly, the responses that the sample person makes to the request are affected by a variety of factors, both internal and external to the respondent, and both intrinsic and extrinsic to the survey request.

We have posited that most decisions to participate in a survey are heuristically based. The evidence for this lies in the tendency for refusals to come quickly in the interaction; for interviewers to use short, generally nonoffensive descriptors in initial phases of the contact; and for respondents to only rarely seek more information about the survey. This occurs most clearly when participation (or lack thereof) has little personal consequence. With Brehm (1993) we believe that the verbal reasons for refusals  Im too busy, Im not interested  partially reflect these heuristics, mirroring current states of the householder but, in contrast to Brehm, we believe they are not stable under alternative cues presented to the householder. We believe there are two constructs regarding interviewer behavior during the interaction with a householder that underlie which heuristics will dominate in the householders decision to participate. These are labeled tailoring and maintaining interaction.

Tailoring. Experienced interviewers often report that they adapt their approach to the sample unit. Interviewers engage in a continuous search for cues about the attributes of the sample household or the person who answers the door, focusing on those attributes that may be related to one of the basic psychological principles reviewed previously. For example, in poor areas, some interviewers choose to drive the familys older car and to dress in a manner more consistent with the neighborhood, thereby attempting to engage the liking principle. In rich neighborhoods, interviewers may dress up. In both cases, the same compliance principle  similarity leads to liking  is engaged, but in different ways.

In some sense, expert interviewers have access to a large repertoire of cues, phrases, or descriptors corresponding to the survey request. Which statement they use to begin the conversation is the result of observations about the housing unit, the neighborhood, and immediate reactions upon first contact with the person who answers the door. The reaction of the householder to the first statement dictates the choice of the second statement to use. With this perspective, all features of the communication are relevant  not only the words used by the interviewer, but the inflection, volume, pacing (see Oksenberg et al., 1986), as well as physical movements of the interviewer.

From focus groups with interviewers, we found that some interviewers are aware of their tailoring behavior: I give the introduction and listen to what they say. I then respond to them on an individual basis, according to their response. Almost all responses are a little different, and you need an ability to intuitively understand what they are saying. Or I use different techniques depending on the age of the respondent, my initial impression of him or her, the neighborhood, etc. Or From all past interviewing experience, I have found that sizing up a respondent immediately and being able to adjust just as quickly to the situation never fails to get their cooperation, in short being able to put yourself at their level be it intellectual or street wise is a must in this businessÉ.

Tailoring need not occur only within a single contact. Many times contacts are very brief and give the interviewer little opportunity to respond to cues obtained from the potential respondent. Tailoring may take place over a number of contacts with that household, with the interviewer using the knowledge he or she has gained in each successive visit to that household. Tailoring also may occur across sample households. The more an interviewer learns about what is effective and what is not with various types of potential respondents encountered, the more effectively requests for participation can be directed at similar others. This implies that interviewer tailoring evolves with experience. Not only have experienced interviewers acquired a wider repertoire of persuasion techniques, but they are also better able to select the most appropriate approach for each situation.

Maintaining interaction. The introductory contact of the interviewer and householder is a small conversation. It begins with the self-identification of the interviewer, contains some descriptive matter about the survey request, and ends with the initiation of the questioning, a delay decision, or the denial of permission to continue. There are two radically different optimization targets in developing an introductory strategy  maximizing the number of acceptances per time unit (assuming an ongoing supply of contacts), and maximizing the probability of each sample unit accepting.

The first goal is common to some quota sample interviewing (and to sales approaches). There, the supply of sample cases is far beyond that needed for the desired number of interviews. The interviewer behavior should be focused on gaining speedy resolution of each case. An acceptance of the survey request is preferred to a denial, but a lengthy, multicontact preliminary to an acceptance can be as damaging to productivity as a denial. The system is driven by number of interviews per time unit.

The second goal, maximizing the probability of obtaining an interview from each sample unit, is the implicit aim of probability sample interviewing. The amount of time required to obtain cooperation on each case is of secondary concern. Given this, interviewers are free to apply the tailoring over several turns in the contact conversation. How to tailor the appeal to the householder is increasingly revealed as the conversation continues. Hence, the odds of success are increased as the conversation continues. Thus, the interviewer does not maximize the likelihood of obtaining a yes answer in any given contact, but minimizes the likelihood of a no answer over repeated turntaking in the contact.

We believe the techniques of tailoring and maintaining interaction are used in combination. Maintaining interaction is the means to achieve maximum benefits from tailoring, for the longer the conversation is in progress, the more cues the interviewer will be able to obtain from the householder. However, maintaining interaction is also a compliance-promoting technique in itself, invoking the commitment principle as well as more general norms of social interaction. That is, as the length of the interaction grows, it becomes more difficult for one actor to summarily dismiss the other.

Figure 1-9 is an illustration of these two interviewer strategies at work. We distinguish between the use of a general compliance-gaining strategy (e.g., utilizing the principle of authority) and a number of different (verbal and nonverbal) arguments or tactics within each strategy (e.g., displaying the ID badge prominently, emphasizing the sponsor of the survey). The successful application of tailoring depends on the ability of the interview to evaluate the reaction of the householder to his or her presence, and the effectiveness of the arguments presented. Note that the interviewers initial goal is to maintain interaction (avoiding pushing for the interview) as long as the potential respondents reaction remains neutral or noncommittal. An interviewer will continue to present different arguments until the householder is clearly receptive to an interview request, or there are no more arguments to present. For inexperienced interviewers the latter may occur before the former, forcing the interviewer to (prematurely in some cases) initiate the interview request.

FIGURE 1-9. Interviewer behavior during interaction with householders.

FIGURE 1-9. Interviewer behavior during interaction with householders.
SOURCE: Groves and Couper (1998).

There is some support from training procedures that the maintaining interaction model operates as theorized. First, interviewers typically are warned against unintentionally leading the householder into a quick refusal. If the person appears rushed or preoccupied by some activity in the household (e.g., fighting among children), the interviewer should seek another time to contact the unit. A common complaint concerning inexperienced interviewers is that they create many soft refusals (i.e., cases easily converted by an experienced interviewer) by pressing the householder into a decision prematurely. Unfortunately, only rarely do interviewer recruits receive training in the multiturn repartee inherent in maximizing the odds of a yes over all contacts. Instead, they are trained in stock descriptors of the survey leading to the first question of the interview.

We note how similar the goals of a quota sample interviewer are to those of any salesperson, but how different are those of the probability sample interviewer. Given this, it is not surprising that many attempts to use sales techniques in probability sample surveys have not led to large gains in cooperation. The focus of the salesperson is on identifying and serving buyers. The browser must be ignored when a known buyer approaches. In contrast, the probability sample interviewer must seek cooperation from both the interested and uninterested.

At the same time that the interviewer is exercising skills regarding tailoring and maintaining interaction, the householder is engaged in a very active process of determining whether there has been prior contact with the interviewer, what is the intent of the interviewers call, whether a quick negative decision is warranted, or whether continued attention to the interviewers speech is the right decision. Figure 1-10 describes this process.

FIGURE 1-10. Householder behavior during interactions with the interviewer.

FIGURE 1-10. Householder behavior during interactions with the interviewer.
SOURCE: Groves and Couper (1998)

The process has various decision points at which the householder can make positive or negative decisions regarding participation in the survey. These arise because the householder misinterprets the visit as involving some unpleasant nonsurvey request; that is, the householder chooses the wrong script. They arise if there are very high opportunity costs for the householder to continue the interaction with the interviewer. They arise if any of the heuristics point to the wisdom of a negative or positive decision.

Designing Surveys Acknowledging Nonresponse

The previous discussions review various theoretical perspectives on nonresponse. These theoretical perspectives have two implications for survey design: (1) contact and interviewer protocols should be chosen to be consistent with the diverse influences, and (2) no single survey design will achieve 100- percent response rates and defenses to nonresponse error should be built into the chosen survey design.

The Value of Rich Sampling Frames

The list of the target population (or the materials used to construct a list) is a tool to assure that a probability sample will offer a useful description of the full population. When the designer acknowledges that nonresponse inevitably will occur in the survey, the frame takes on new value. When the designer has a choice of frames (e.g., a list frame from a social welfare agency containing data about the persons prior income and employment experience, an area frame, a random digit dial frame), evaluation of the frame must include both coverage and nonresponse issues. Coverage, the extent to which the frame includes all target population elements and nothing else, is an important attribute.

Sampling frames that contain information beyond simple identifiers can help reduce nonresponse error. If the frames include data on prior addresses, then those with a history of moves might be identified as likely movers, with higher than expected locating effort. If frames contain data on use of agency services in the past, the data might be used to customize approaches to sample persons in an effort to address potential interests and concerns about survey participation (e.g., having interviewers explain the importance of the survey to measuring the well-being of former food stamp recipients). Sometimes data exists that are correlates of key survey variables (e.g., participation in types of programs [higher correlated with current statuses like work training programs]). Such data might be useful in assessing nonresponse errors and building weighting adjustment or imputation models.

Collecting Additional Information to Enhance Data Collection

Sometimes interviewers can observe that sample persons have certain attributes that are related to certain concerns about survey participation. Followup efforts to persuade the sample person to be interviewed can use this information creatively to improve response rates. This has included treating sample numbers generating answering machine responses as candidates for calling at different times of the day, attempting to avoid times when the machine is activated. It includes interviewer observation about any concerns regarding the legitimacy of the survey request, followed by special mailings or communications demonstrating the sponsorship and purpose of the survey. It includes, in face-to-face surveys, observations of housing units for entrance impediments (e.g., locked apartment buildings, locked gates, security guards), leading to more intensive calling patterns on those sample units versus others.

Collecting Information Valuable in Postsurvey Adjustment

Several studies (Purdon et al, 1999; Groves and Couper, 1998; Brehm, 1993) now demonstrate that the utterances of sample persons during their interactions with interviewers contain some information regarding motivations for their reaction to the survey request and the likelihood of eventual cooperation with the survey request. The evidence comes more from face-to-face surveys than from telephone surveys, although Couper and Groves (1995) find some support for links between the utterances and the final outcome in telephone surveys as well. These become useful predictors in response-propensity models sometimes used in postsurvey adjustment.

Another set of variables involves fixed attributes of the housing unit, best observed in face-to-face surveys. For example, the Consumer Expenditure Survey uses observations from the interviewer about whether the unit is owned or rented in postsurvey adjustments based on the belief that the consumption patterns are relatively homogeneous in the two groups. Similarly, observations of multiple-person households (through records on who answered the telephone) the presence of children, etc. are possible in some designs. These too can be useful in forming postsurvey adjustment weighting classes.

Two-Phase Sampling to Acquire Information About Nonrespondents

When survey data are used in legal or policy settings, the credibility of results is sometimes enhanced by mounting separate studies concerning nonresponse. There are two possible foci: experimental comparisons of different protocols and two-phase sample surveys of nonrespondents. An example of the first study is a mixed-mode design based on a list frame sample of prior recipients, one mode using telephone matching and telephone survey requests; and the other uses address locating and face-to-face interviews. For cost reasons the face-to-face mode might use a smaller sample size than the telephone mode. The telephone mode is likely to have lower response rates than the face-to-face mode. The sample sizes might be fixed to determine the magnitude of mode differences at some prior specified standard error. The total cost of the survey per unit measured lies between the telephone and face-to-face modes, but the additional information purchased with the mixed-mode design is protection against large-mode effects on key survey conclusions.

A two-phase sample design for nonresponse studies begins after the main survey has completed its work. The intent under perfect conditions is that a probability subsample of nonrespondents to the first phase of the survey can yield evidence regarding the likelihood of large nonresponse errors in the first- phase estimates. The perfect conditions yield 100 percent response rates on the second-phase cases, thus providing unbiased estimates of the characteristics of the nonrespondent pool. Although such designs have a long history (Deming, 1953; Hansen and Hurwitz, 1958), they never inevitably achieve the perfect conditions in practice. They are used, however, when some information on the nonrespondents is judged to be of crucial importance. For example, a second-phase sample of nonrespondents was taken on the National Survey of American Families, using a radically reduced telephone interview, relaxed respondent rules, and an incentive offer. Among the nonrespondent cases to the first-phase effort (spanning many months and repeated refusal conversion efforts), 36 percent of screener nonrespondents and 58 percent of full interview nonrespondents complied with the second-phase request (Groves et al., 1999). Those responding were found not to have large socioeconomic status differences from the respondent group (what differences did exist suggested higher income households were more likely to be nonrespondents).

Judgments Regarding Desirable Design Features for Surveys of the U.S. Low-Income Population

As survey methodology matures, it is increasingly finding that the process of survey participation is subject to diverse causes across different subgroups. In short, what works for some groups does not for others. Furthermore, in free societies 100 percent compliance is not to be expected; survey designers should incorporate nonresponse concerns into every aspect of their designs.

What follows is a listing of the top 10 lessons from the survey methodology literature regarding nonresponse in studies of the low-income population. These are current judgments of the authors of this paper based on experience and study of the field.

1. No record system is totally accurate or complete.

Using a record system as a sampling frame generally asks more of the record than it was designed to provide. Surveys demand accurate, up-to-date, personal identifiers. They demand that the person sampled can be located.

2. Choose sample sizes that permit adequate locating, contacting, and recruitment efforts.

Sample surveys suffer from the tyranny of the measurable, with sampling errors dominating design decisions because they can be measured more easily than nonresponse errors. It is tempting to assume the absence of nonresponse error and to maximize sample size to achieve low reported sampling errors. It is important to note that the larger the sample size, the greater the proportion of total error likely to come from nonresponse bias, other things being equal. (Sampling errors can be driven down to a trivial amount, but nonresponse biases may remain the same.)

3. Assume nonresponse will occur; prepare for it.

In practice no sample survey avoids nonresponse completely. Assuming at the design stage that it will not occur leaves the researcher unprepared to deal with it at the estimation stage. Whenever possible use interviewers to collect information that can be used either to reduce nonresponse (e.g., utterances of the sample person suggesting reasons for nonresponse, useful later in tailoring refusal conversion protocol) or to adjust for nonresponse (e.g., observations about respondents and nonrespondents related to propensities to respond).

4. Consider relationships with the sponsoring agency as sources of nonresponse error.

Sample persons with prior experiences or relationships with the sponsoring agency for the survey make decisions based partially on how they evaluate those relationships. This may underlie the tendency for those persons dependent on programs to respond at higher levels. It also underlies the findings of those with relatively low trust in government to respond at lower rates to some government surveys. Mixed-mode designs and alternative sponsoring organizations may act to reduce these sources of differential nonresponse.

5. Do not script interviewers; use flexible interviewer behaviors.

The research literature is increasingly strong on the conclusion that effective interviewers need to be trained to deliver information relevant to a wide variety of concerns that different sample persons may have. Stock phrases and fixed approaches defeat the need to address these diverse concerns. Once interviewers can classify the sample persons utterances into a class of concerns, identify a relevant piece of information to convey to the person, and deliver it in the native language of the sample person, cooperation rates can be higher.

6. Consider incentives, especially for the reluctant.

Incentives have been shown to have disproportionately large effects on those who have no other positive influence to respond. Although not completely clear from the literature, the value of a given incentive may be dependent on relative income/assets of the sample person. If greater effects pertain to low- income populations, then incentives might be more attractive to studies of that population.

7. Give separate attention to location, noncontact, refusal; each has different causes and impacts on error.

Sample persons not interviewed because of failure to locate are disproportionately movers. All the correlates of residential mobility (rental status, small households, relative youth, few extended family ties), if relevant to the survey measures, make nonlocation nonresponse a source of error. Noncontacts and refusals may have very different patterns of correlates. Treating nonresponse rates as an undifferentiated source of nonresponse error is thus naive. Separate tracking of these nonresponse rates is needed.

8. Mount special studies of nonrespondents.

The higher the nonresponse rate, the higher the risk of nonresponse error, other things being equal. With higher than desired nonresponse rates, the investigators have an obligation to assure themselves that major nonresponse errors are not present, damaging their ability to draw conclusions from the respondent- based statistics. Special studies of nonrespondents are appropriate in these cases, using auxiliary data from records, followback attempts at samples of respondents, and other strategies.

9. Perform sensitivity analyses on alternative postsurvey adjustments.

Postsurvey adjustments (weighting and imputation) entail explicit or implicit assumptions about the relationships between propensity to respond to the survey and survey variables. Insight is sometimes gained into the dependence on nonresponse adjustments of substantive conclusions by varying the assumptions, using different postsurvey adjustments, and comparing their impact on conclusions.

10. Involve the target population.

Using focus groups and other intensive qualitative investigations can offer insights into how the target population might receive the survey request. Such insights are rarely native to research investigators who are members of different subcultures.


1.  Gaining contact may not necessarily be the first step if the sample is not generated from a list. For example, screening households in sampled areas may be necessary to obtain sample members needed for the study.


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Methods for Obtaining High Response Rates in Telephone Surveys

The purpose of this paper is to review methods used to conduct telephone surveys of low-income populations. The motivation for this review is to provide information on best practices applicable to studies currently being conducted to evaluate the Personal Responsibility and Work Opportunity Reconciliation Act of 1996 (PRWORAВ  hereafter referred to as Welfare Reform). The National Academy of Sciences panel observed that many of the states are conducting telephone surveys for this purpose and that it would be useful to provide them with information on the best methods for maximizing response rates. The information provided in this paper is intended to assist these individuals, as well as others, to either conduct these studies themselves or to evaluate and monitor contractors conducting the studies.

We have divided the telephone surveys into two types. The first, primary, method is to sample welfare recipients or welfare leavers from agency lists. This can take the form of a randomized experiment, where recipients are randomly assigned to different groups at intake, with a longitudinal survey following these individuals over an extended period of time. More commonly, it takes the form of a survey of those leaving welfare during a particular period (e.g., first quarter of the year). These individuals are then followed up after X months to assess how they are coping with being off welfare.

The second type of telephone survey is one completed using a sample generated by random digit dialing methods (RDD). In this type of study, telephone numbers are generated randomly. The numbers then are called and interviews are completed with those numbers that represent residential households and that agree to participate in the interview. To effectively evaluate welfare reform, this type of survey would attempt to oversample persons who are eligible and/or who are participating in welfare programs.

The issues related to these two types of telephone surveys, one from a list of welfare clients and one using RDD, overlap to a large degree. The following discussion reviews the common issues as well as the unique aspects related to each type of survey. In the next section, we discuss methods to increase response rates on telephone surveys, placing somewhat more emphasis on issues related to conducting surveys from lists of welfare clients. We chose this emphasis because this is the predominant method being used by states to evaluate welfare reform. The third section reviews a number of welfare studies that have been implemented recently. In this section we discuss how the methods that are being used match up with the best practices and how this may relate to response rates. The fourth section provides an overview of issues that are unique to RDD surveys when conducting a survey of low-income populations. To summarize the discussion, the final section highlights practices that can be implemented for a relatively low cost but that could have relatively large impacts.

Methods to Increase Response Rates

In this section we discuss the methods needed to obtain high response rates in a telephone survey. These methods include locating, contacting, and obtaining the cooperation of survey subjects. The review applies to all types of telephone surveys, but we have highlighted those methods that seem particularly important for conducting surveys from lists of welfare clients. A later section provides issues unique to RDD.

The Importance of Language

The methods discussed in the following sections should be considered in terms of the language and cultural diversity of the state being studied. The percentage of non-English speakers ranges from as high as a third in California to a quarter in New York and Texas, down to a low of 4 to 5 percent in South Carolina, Missouri, and Georgia (1990 Census). Spanish is the most common language spoken by non-English speakers. Again these percentages vary a great deal by state, with 20 percent of the population in California and Texas speaking Spanish at home and only 2 percent in South Carolina. These variations imply that surveys may have to be prepared to locate and interview respondents in languages other than English and Spanish. Moreover, language barriers are greater among low-income households, and low-income households are more likely to be isolated linguistically, where no one in the household speaks English.

The need for bilingual staff as well as Spanish (and perhaps other languages) versions of all questionnaires and materials is crucial, particularly in some states. It is important to keep in mind that many people who do not speak English also may not be literate in their native language, so they may not be able to read materials or an advance letter even if it is translated into a language they speak. In some situations it might be useful to partner with social service agencies and community groups that serve and have special ties with different language and culture communities. Such groups may be able to vouch for the legitimacy of the survey, provide interviewers or translators with special language skills, and assist in other ways. Representatives of respected and trusted organizations can be invaluable in communicating the purpose of the study and explaining to prospective respondents that it is in the communitys best interest to cooperate.

Locating Respondents

Locating survey subjects begins with having sufficient information to find those that moved from their latest residence. Low-income households move at higher rates than the general population, and it seems reasonable to assume that within this group, welfare leavers will be the most mobile. Therefore, if surveys are going to become a routine part of the evaluation process, agencies should consider future evaluation needs in all their procedures. This includes changing intake procedures to obtain additional information to help locate subjects in the future and designing systems to allow access to other state administrative records. In the sections that follow, these presurvey procedures are discussed in more detail. This is followed by a description of the initial mail contact, which provides the first indication of whether a subject has moved. The section ends with a discussion of some tracing procedures that might be implemented if the subject is lost to the study.

Presurvey Preparations

As part of the intake process, or shortly thereafter (but perhaps separately from the eligibility process), detailed contact information should be obtained for at least two other people who are likely to know the subjects whereabouts and who do not live in the same household as the subject. In addition to name, address, and telephone number, the relationship of the contact to the subject should be determined along with his or her place of employment. We believe this step is crucial to obtaining acceptable response rates. This step also may be difficult to achieve because, in some situations, it may require a change in the intake system.

It is also useful to consider obtaining the subjects informed consent as needed to access databases that require consent at the same time as contact information is obtained. It is hard to state when and how consent might be used given the differences in state laws, but we assume that, at a minimum, state income tax records fall into this category (if they are assessable at all, even with consent). This is a common procedure on studies that track and interview drug users and criminal populations (Anglin et al., 1996).

Data to Be Provided to the Contractor with the Sample

In addition to the subjects name, address, telephone number, Social Security number, and all contact information, consideration should be given to running the subject through other state administrative databases (Medicaid, food stamps, etc.) prior to the survey. This may be particularly useful if the information in the sample file from which the sample is drawn is old or if the information in the files is different. Initial contacts should always start with the most recent address and telephone number. The older information is useful if a subject needs to be traced. The advantage of using the older information is that it might help to avoid unnecessary calls and tracing.

If the field period extends for a long period of time, it might be necessary to update this information for some subjects during the course of the survey.

Contacting Survey Subjects by Mail

Sending letters to prenotify the subject is accepted practice when conducting surveys (Dillman, 1978). It serves the dual purpose of preparing the subject for the telephone interview and identifying those subjects whose address is no longer valid. It is always iterative in a survey of this type. That is, each time a new address is located for a subject (through tracing as discussed later), an advance letter is sent prior to telephone contact.

If an envelope is stamped return service requested, for a small fee, the U.S. Postal Service will not forward the letter, but instead will affix the new address to the envelope and return it to the sender. This only works if (1) the subject has left a forwarding address and (2) the file is still active, which is usually just 6 months. If the letter is returned marked undeliverable, unknown, insufficient address, etc., additional tracing steps must be initiated.

Because the post office updating procedure is only active for 6 months, it is important to continue mail contacts with survey subjects if they are to be interviewed at different points in time. These mail contacts can be simple and include thoughtful touches such as a birthday card or perhaps a newsletter with interesting survey results.

Mailings should include multiple ways for the subject to contact the survey organization, such as an 800 number and a business reply post card with space to update name, address, and telephone numbers. Some small percentage will call and/or return the post card, negating the need for further tracing.

One of the problems with first-class letters is that the letters often do not reach the subject. The address may be out of date and not delivered to the correct household (e.g., Traugott et al., 1997), the letter may be thrown out before anyone actually looks at it, or the subject may open but not read the letter. To increase the chances that the subject does read the letter, consideration should be given to using express delivery rather than first-class mail. This idea is based on the logic that express delivery will increase the likelihood that the package will be opened by potential respondents and the contents perceived to be important. Express delivery may also provide more assurance that the letter has actually reached the household and the subject, particularly if a signature is required. However, requiring a signature may not produce the desired result if it becomes burdensome for the subject, for example, if the subject is not home during the initial delivery and needs to make special arrangements to pick it up. The annoyance may be lessened if, in addition to the letter, an incentive is enclosed.

Because express delivery is costly (but less than in-person contacts), it should be saved for those prenotification situations in which other means of contact have not been fruitful. For example, if first-class letters appear to be delivered, yet telephone contact has not been established, and tracing seems to indicate the address is correct, an express letter might be sent. It also might be used if the telephone number is unlisted or if the subject does not have a telephone. In these situations an express letter with a prepaid incentive might induce the subject to call an 800 number to complete the interview by telephone.


Tracing is costly. Tracing costs also vary quite a bit by method. As a general rule, it is best to use the least costly methods first when the number of missing subjects is greatest, saving the costlier methods for later when fewer subjects are missing. Database searches are generally the least costly at a few pennies a hit, while telephone and in-person tracing can cost hundreds of dollars a hit.

Two key components of a tracing operation are: (1) a comprehensive plan that summarizes the steps to be taken in advance, and (2) a case management system to track progress. The case management system should maintain the date and result of each contact or attempt to contact each subject (and each lead). The system should provide reports by subject and by tracing source. The subject reports provide tracers with a history and allow the tracer to look for leads in the steps taken to date. The reports should provide cost and hit data for each method to help manage the data collection effort. In the end it helps to determine those methods that were the most and least cost effective for searching for the population of interest, and this knowledge can be used for planning future surveys. Each of the tracing sources is discussed briefly in the following paragraphs.

Directory assistance (DA). Several DA services are now available. Accuracy of information from these services is inconsistent. DA is useful and quick, however, when just one or two numbers are needed. If the first DA attempt is not successful it may be appropriate to try again a few minutes later (with a different operator) or to try a different service. These calls are not free and the rates vary widely. Costs also include the labor charges of the interviewer/tracer making the calls.

Telephone lookup databases. There are several large telephone lookup services that maintain telephone directory and information from other sources in a database. These data are available by name, by address, or by telephone number. The search is based on a parameter determined by the submitter, such as, match on full name and address, match on last name and address, match on address only, or match on last name in zip code. Early in the tracing process the criteria should be strict, with matches on address only and/or address with the last name preferred. Later in the process broader definitions may be incorporated. Charges for database lookups are generally based on the number of matches found, rather than the total number of submissions. The cost is usually a few cents. These lookups are quick, generally requiring less than 48 hours, with many claiming 24-hour turnaround. However, the match rate is likely to be low. In a general population survey the match rate might be as high as 60 percent, and of those, some proportion will not be accurate. For a highly mobile, low-income population, where only those whose numbers are known to have changed are submitted, the hit rate is likely to be quite low. However, given the relatively low cost, even a very low match rate makes this method attractive.

Several companies provide this information, so one might succeed where another might fail. There also may be regional differences, with data in one area being more complete than in others. In California, for example, telephone numbers are often listed with a name and city, but no address. This limits the datas usefulness, especially for persons with common last names.

Specialized databases. These include credit bureaus and department of motor vehicles (DMV) checks where permitted. Checks with one or more of the credit bureaus require the subjects Social Security number, and they are more costly than other database searches. Charges are based on each request not the outcome of the request. More up-to-date information will be returned if the subject has applied for credit recently, which is less likely with a low-income population than the general population. DMV checks in many states, such as California, require advance planning to obtain the necessary clearances to search records.

Other databases. Proprietary databases available on the Internet and elsewhere contain detailed information on large numbers of people. Access to the databases is often restricted. However, these restrictions are often negotiable for limited searches for legitimate research purposes. Like credit bureaus, these files often are compiled for marketing purposes and low-income populations may not make the purchases necessary to create a record. Records on people often are initiated by simple acts such as ordering a pizza or a taxi.

Telephone tracers. For the purpose of this discussion, it is assumed that each telephone number and address that has been obtained for the subject has led to a dead end. This includes all original contact information and results from all database searches. At this point tracing becomes expensive. Tracers receive special training on how to mine the files for leads. People who have done similar work in the past, such as skip tracing for collection agencies, tend to be adept at this task. Tracers need investigative instincts, curiosity, and bullheadedness that not all interviewers possess. Tracers usually are paid more than regular interviewers.

The tracers task is to review the subjects tracing record, looking for leads, and to begin making telephone calls in an attempt to locate the subject. For example, online criss-cross directories and mapping programs might be used to locate and contact former neighbors; if children were in the household, neighborhood schools might be called; and employers, if known, might be contacted. Of course, all contact must be carried out discreetly. Some of these techniques are more productive in areas where community members have some familiarity with one another, generally places other than the inner cities of New York, Chicago, and Los Angeles. Nonetheless, even in urban areas, these techniques sometimes work.

Cost control is crucial in this process because much of the work is limited only by the imagination of the tracer (and tracers sometimes follow the wrong trail). Perhaps a time limit of 15 or 20 minutes might be imposed. At that time limit, the tracers work could be reviewed by a supervisor to determine if further effort seems fruitful, if another approach might be tried, or the case seems to have hit a dead end.

In-person tracing. This is the most expensive method of tracing, and it is most cost effective if it is carried out in conjunction with interviewing. Like telephone tracing, in-person tracing requires special skills that an interviewer may not possess and vice versa. For this reason it might be prudent to equip tracers with cellular telephones so that the subject, when located, can be interviewed by telephone interviewers. The tracer can thus concentrate on tracing.

Tracing in the field is similar to telephone tracing except that the tracer actually visits the former residence(s) of the subject and interviews neighbors, neighborhood businesses, and other sources. Cost control is more of a problem because supervisory review and consultation is more difficult but just as important.

Contacting Subjects

When a telephone number is available for either a subject or a lead, the process of establishing contact becomes important. An ill-defined calling protocol can lead to significant nonresponse. In this section we discuss some of the issues related to contact procedures.

Documenting Call Histories and Call Scheduling

Telephone calls need to be spread over different days of the week and different times of the day in order to establish contact with the household (not necessarily the subject). If contact with the household is established, it is possible to learn if the subject can be contacted through that telephone number, and if so, the best time to attempt to call. If the subject is no longer at the number, questions can be asked to determine if anyone in the household knows the subjects location.

If the telephone is not answered on repeated attempts, an assessment must be made of the utility of further attempts against the possibility that the number is no longer appropriate for the subject. In other words, how many times should a nonanswered telephone be dialed before checking to make sure it is the correct number for the respondent? It is important to remember that this is an iterative process applicable to the initial number on the subjects record as well as to each number discovered through tracing, some of which will be better than others. The issue is assessing the tradeoffs between time and cost.

Many survey firms suggest making seven calls over a period of 2 weeks  on different days (two), evenings (three), and weekends (two)-before doing any further checking (e.g., checking with DA; calling one or more of the contacts; or searching one of the databases). Other firms suggest doubling the number of calls, theorizing that the cost of the additional calls is less than the cost of the searches. Unfortunately, there is no definitive answer because much depends on the original source of the number being dialed, the time of year, the age of the number, and other factors. Very old numbers are less likely to be good, and perhaps fewer calls (perhaps seven) should be made before moving to a tracing mode. If contact information is available, checking with the contact may be cost effective earlier in the process. In the summer or around holidays, more calls (perhaps 10 to 12) might be prudent.

Call histories, by telephone number, for the subject (and lead) should be documented thoroughly. This includes the date, time, outcome, as well as any comments that might prove useful as a lead should tracing be necessary.

Message Machines

Message machines are now present in an estimated 60 to 70 percent of U.S. households (STORES, 1995; Baumgartner et al., 1997). As more households obtain machines, there has been a growing concern that subjects will use them to screen calls and thereby become more difficult to contact. However, empirical evidence to date has not shown message machines to be a major impediment to contacting respondents. Oldendick and Link (1994) estimate that a maximum of 2 to 3 percent of respondents may be using the machine in this way.(1)

A related issue has been the proper procedure to use when an interviewer reaches an answering machine. Should a message be left? If so, when should it be left? Survey organizations differ on how they handle this situation. Some organizations leave a message only after repeated contacts fail to reach a respondent on the phone (as reported by one of the experts interviewed). Other organizations leave a message at the first contact and do not leave one thereafter. The latter procedure has been found to be effective in RDD studies relative to not leaving any message at all (Tuckel et al., 1996; Xu et al., 1993). The authors favor leaving messages more often (perhaps with every other call with a maximum of four or five) than either of these approaches. We believe, but cannot substantiate empirically, that if the goal is to locate and interview a particular person, then the number of messages left might signal the importance of the call to the person hearing the message and might induce that person to call the 800 number. Even if the caller says the subject does not live there, that is useful information. However, leaving too many messages may have a negative effect.

Obtaining Cooperation

In this section we highlight some of the standard survey procedures for obtaining high cooperation rates once contact with the subject has been established. These can be divided into issues of interviewer training, the questionnaire, and the treatment of refusals.

Interviewer Materials and Training

Interviewer experience has been found to be related to obtaining high respondent cooperation (Groves and Fultz, 1985; Dillman et al., 1976). The theory is that experience makes interviewers familiar with many questions reluctant respondents may have about cooperating (Collins et al., 1988), and allows them to respond in a quick and confident manner. Showing any type of hesitation or lack of confidence is correlated with high refusal rates.

This finding suggests that intense training of interviewers on how to handle reluctant respondents may provide them with increased confidence, as well as the necessary skills, to handle difficult situations. Groves and Couper (1998) present results from an experiment on an establishment survey that shows significant improvement in cooperation rates once interviewers are provided with detailed training on how to handle reluctant respondents. This training consisted of drilling interviewers, through a series of role plays, on providing quick responses to respondent concerns about participating in the study. Because this study was done in an establishment survey, the applicability to a survey of low-income respondents is not clear. Respondents to establishment surveys are more willing to converse with the interviewer, which allows for more time to present arguments on why the respondent should participate in the study.

Nevertheless, this suggests that interviewers must have the skills to answer the subjects questions, to overcome objections, and to establish the necessary rapport to conduct the interview. Training in these areas is crucial if refusals are to be avoided. Answers to Frequently Asked Questions (FAQs) must be prepared and practiced so that the answers sound like the interviewers own words rather than a script that is being read. Interviewers also must be trained to know when to accept a refusal, leaving the door open for future conversion by a different interviewer who might have more success. This type of training is more difficult than training that is centered on the content of questions, but it is also vital if refusals are to be avoided.

Questionnaire Design

Several areas related to the design of the questionnaire could impact response rates. These include: (1) the length of the questionnaire, (2) the introduction used, and (3) the type and placement of the questions. Each of these has been hypothesized to affect the ability of the interviewer to obtain a high response rate. Interestingly, for each of these characteristics, there is some belief that the effects are primarily on the interviewers perception of the task, rather than concerns the respondent may have with the procedure. If interviewers perceive the task to be particularly difficult to complete, their confidence levels may go down and their performance might be affected.

Pretests of the questionnaire should be conducted as part of any research design. Pretests, and accompanying debriefings of the interviewers often uncover problems that are easily corrected prior to interviewing the sample subjects. More elaborate pretesting methods also should be considered. These include, for example, cognitive interviews, as well as review of the questionnaire by a survey research professional who has experience in conducting structured interviews.

Questionnaire length. Although it is commonly believed that the length of the questionnaire is related to response rates, very little empirical evidence shows that this, in fact, is true. Much of the evidence that does show a relationship between length and response rates concerns mail surveys, where respondents get visual cues on how long the interview may be (Bogen, 1996). The length of a telephone interview may not be mentioned unless the respondent asks, so the respondent may not know how long it will take. This fact further confuses the relationship between interview length and response rates.

Two exceptions to this are studies by Collins et al. (1988) and Sobal (1982). Both found a relationship between how long the interviewer told the respondent the interview would take and the response rate. Collins et al. (1988) found a modest effect of approximately 2 percent, while Sobal (1982) found a much larger reduction of 16 percent when comparing a 5-minute interview to a 20-minute interview. These studies, however, are difficult to generalize to other studies because they do not compare the effects of different descriptions of the length of the interview to one that does not state the length at all. This makes it unclear what the overall effect of interview length might be in the context of another survey, which does not state the length of the interview (unless asked).

This research does suggest, however, that significantly shortening the interview to 5 minutes may increase response rates to some degree. If the interview were shortened to this length, then it might be advantageous to state the length of the interview in the introduction to the survey. One would assume that cutting the interview to just 5 minutes is not an efficient way to increase the response rate. The loss of information needed for analyses will be much larger than anticipated gains in the response rate. For this reason, it might be useful to consider shortening the interview only for a special study of refusers. If this strategy significantly increases the number of persons who are converted after an initial refusal, more information might be obtained on how respondents differ from nonrespondents.

Survey introduction. A natural place to start redesigning the questionnaire to improve response rates is the introduction. Many respondents refuse at this point in the interview. This is especially the case for an RDD survey, where interviewers do not have the name of the respondent and the respondent does not recognize the voice on the other end of the call. For this reason, it is important to mention anything that is seen as an advantage to keeping the respondent on the line. Advantages generally are believed to be: (1) the sponsor of the study, (2) the organization conducting the interviews, (3) the topic of the survey, and (4) why the study is important.

Research in an RDD survey context has not found any general design parameters for the introduction that are particularly effective in increasing response rates. Dillman et al. (1976), for example, find no effects of offering respondents results from the survey or statements about the social utility of the survey. Similarly, Groves et al. (1979) find variations in the introduction do not change response rates. Exceptions to this are a few selected findings that: (1) government sponsorship seems to increase response rates (Goyder, 1987), (2) university sponsorship may be better than private sponsorship, and (3) making a nonsolicitation statement (e.g., I am not asking for money) can help if the survey is not sponsored by the government (Everett and Everett, 1989).

The most widely agreed-on rule about introductions is that they need to be as short as possible. Evidence that shorter is better is found in Dillman et al. (1976), as well as our own experience. Because the interviewer may not have the full attention of the respondent at the initial outset of the call, it is better to simply state the best points of the survey and get the respondent to react to the first question. Interviewers also generally prefer short introductions, because they provide a greater opportunity to involve the respondent in the conversation (less opportunity to hang up). By increasing interviewer confidence, the response rate should be affected positively. It is important to balance the informational requirements with the need to be brief and simple. Long explanations, going into great detail about the survey, may turn respondents off more than motivate them to participate. The best approach is to provide the respondent with a broad set of statements to capture their attention at this point in the interview. Once rapport and trust have built up a bit, more details about the study can be presented.

Type/placement of questions. Sensitive questions have higher rates of nonresponse and should be placed later in the questionnaire but still positioned logically so that the flow from one topic to the next is smooth. Sensitive information includes topics such as income, detailed household composition (e.g., naming everyone in the household), participation in social programs, and child care. Careful placement allows these questions to be asked after rapport has been established. This is especially true with initial contacts into the household. Asking sensitive questions within the first few minutes of the initial contact may turn respondents off unnecessarily.

Refusal Conversion

If a respondent refuses to participate, it is important for the interviewer to indicate the level of hostility, if any. It may not be desirable (nor cost effective) to try to convert subjects who are extremely hostile (e.g., one in which the respondent is abusive). Other subjects might be recontacted in an attempt to have them reconsider their decision. This recontact should take place several days (7 to 21) after the initial contact to allow the respondent time to reconsider.

Prior to refusal conversion, a letter should be sent to try to convince the respondent to participate. This letter has been shown to be particularly effective if: (1) an incentive is enclosed, and (2) express delivery is used for mailing (Cantor et al., 1999). Comparisons between the use of express delivery to a first class refusal conversion letter show a difference of 10 percentage points in conversion rates on an RDD study and a difference of 15 to 20 percentage points if an incentive is enclosed. These results are not likely to be as dramatic for a survey of welfare leavers. However, this strategy has been applied in this context and is believed to be effective.

Based on work related to personal interviews (Couper et al., 1992), it is possible to create specialized letters for refusal conversion based on what the respondent said at the time of the refusal. A procedure adopted for the National Survey of Americas Families (NSAF) was to have the interviewer provide a recommendation on the type of letter that would be sent to the respondent after the refusal occurred. Because most refusals fall into two or three categories (e.g., no time, not interested), special letters could be developed that emphasized particular arguments why the respondent should cooperate (e.g., no time  emphasize the length of the interview; can do the interview in several calls). The problem with this procedure is that for most refusals, the interviewer has little information on which to base a good decision on the reason for refusal. A large number of respondents hang up before providing detailed feedback to the interviewer. As a result, a large majority of the mailouts for the refusal conversion are done using the general letter, which does not emphasize anything in particular.

However, in a survey of welfare leavers, where the interviewer may have more information about the reason for refusal, tailoring the letters to the respondents concerns may be useful. This would depend on the amount of information the interviewer is able to collect on the reason for the nonresponse.

Refusal conversion calls are best handled by a select group of handpicked interviewers who are trained to carry out this type of work. They must be trained to analyze the reason for the refusal and be able to prepare answers for different situations.

Studies of Welfare Leavers

Table 2-1 summarizes the procedures discussed previously. It is organized around the three primary activities required to conduct a study: (1) locating the subject, (2) contacting the subject, and (3) obtaining cooperation.

In this section we discuss how these best practices have been applied in a number of surveys that have been conducted to evaluate welfare reform in different states. The purpose of this review is to provide a picture of the range of practices that have been used and how these practices relate to results.

TABLE 2-1:
Summary of Best Practices for Conducting Telephone Surveys of Welfare Leavers
Task Method Comment
Locate Respondent
  • Accurate address and telephone number
  • Contact for persons not living with subject
Collect at intake and update regularly Try to collect consent to search other databases
  • Use other sources to locate subject
Use available administrative databases (e.g., food stamps, Medicaid, drivers licenses); use commercially available sources (reverse directories, credit bureaus) Start with the least expensive methods
  • Telephone tracing; in-person tracing
  • In-person tracing
Review tracing record and follow leads Very expensive and requires specialized skills
Contacting Subjects
  • Pre-notification
  • Incentives and continued contact
Send letter prior to making contact
Repeated mailings to subjects
Use express delivery if possible
  • Call scheduling
Spread out calls over day/night; weekdays/weekends  
Obtaining Cooperation
  • Interviewer training and experience
Provide interviewers with answers to common questions Try to use experienced interviewers with good records
  • Questionnaire design
Minimize redundant questions
Keep length as short as possible
Pretest questions and allow for time to revise after the pretest
  • Survey introduction
Keep initial introduction as short as possible  
  • Refusal conversion
Prenotify with express mail and incentives  

Description of Methods Used in Recent Studies

To better understand the methods that have been implemented in recent studies of welfare reform, we collected information on a small sample of state surveys. The largest portion of our sample of studies is from the group of Assistant Secretary for Planning and Evaluation (ASPE) grantees funded in FY99 (9 of the 13 studies). The remaining studies were chosen by networking or referral by colleagues. Information was collected through interviews with the director of the research team and any reports that were available. These studies are meant to represent what the current practice is for welfare-leaver studies.

A summary of key characteristics for these 13 surveys is shown in Table 2-2. In 12 of the 13 surveys for which we collected information, a mixed-mode, two-step, approach was used. First, as many telephone interviews as possible were conducted using information accessible to home office staff. Respondents were contacted initially using information available from the administrative records from the sample frame. Advance letters were sent out. For those persons who do not have a phone number, the letter asked for the subject to call an 800 number to do the interview or set up an appointment.

TABLE 2-2:
Results of Interviews With 14 Selected Telephone Surveys of Welfare or Ex-Welfare Recipients
Advance Letter/
Telephone Tracking Sources Field Tracking Refusal
Rate %
Field Period
1 Yes/Yes Directory assistance No No 30 3 months
    Reverse directory        
2 Yes/Yes Directory assistance Yes Yes 51 9 months+
    Reverse directory Exp. staff      
    Specialized tracking Firm        
    Motor vehicle/ID records        
3 Yes/Yes Directory assistance Yes Yes Total: >70 5 months
    Credit databases Exp. staff   Tel: 50  
    Other welfare offices        
4 Yes/Yes Directory assistance Yes Yes Total: >70 5 months
    Credit Databases Exp. staff   Tel: 50  
    Other welfare offices        
5 Yes/Yes DK Yes DK 52 DK
      Unknown Exp.      
6 No/No Directory assistance Yes No Total: 78 4 months
    Other agency database Exp. staff   Tel: 66  
7 Yes/Yes Directory assistance Yes Yes Total: 72 60 months
    Credit databases Exp. staff   Tel: 25 . 30  
    Motor vehicle No exp.      
    Other agency databases        
8 Yes/Yes Reverse directory Yes no Total: 46 DK
    Other agencies No exp.   Tel: 40  
9 DK Directory assistance Yes DK Total: 80 DK
    Tracing contact Exp. staff   Tel: 50  
    Credit databases        
    Other agencies        
10 Yes/Yes Directory assistance Yes DK Total: 75 DK
    Credit databases Exp. staff      
    Other agencies        
11 Yes/Yes Directory assistance Yes Yes Total: 81%* DK
    Credit databases Exp. staff      
    Other Agencies        
12 Yes/Yes Directory Assistance No DK Total: 40 4 months
    Credit Databases        
    Other Agencies        
13 Yes/Yes Directory Assistance Yes No Total: 72 DK
    Other Agencies Exp. staff      
14++ Yes/Yes** Directory Assistance Yes No Total: 72 2 months
    Other Agencies No Exp   Tel: >65  
DK = Dont Know
* Response rate after 5 years.
** Used non-monetary incentive.
+ Interviewing period was 3 months. Used 6 months before interviewing period to establish contact information and find respondents.
++ Did extensive tracing over the telephone with highly experienced personnel.

If the telephone number did not lead to the subject, tracing was done from the home office. This typically included using directory assistance, reverse directories to find other addresses and free services on the Internet. Other methods implemented by most of the studies included:

  • Searches of credit databases: These include databases such as Transunion, CBI/Equifax and TRW. Stapulonis et al. (1999) report the use of an unnamed database that seemed to add information above and beyond these.
  • Searches of other databases across agencies: These included food stamps, unemployment insurance, child support enforcement, motor vehicles, Medicaid, employment training, Social Security, vital records, and state ID cards.

The ability to search the other databases was possible because in all cases the research organizations had the Social Security number of the respondent.

In discussions with different organizations, we got a clear sense that the original contact information was not of high quality. One study reported, for example, that 78 percent of the original phone numbers did not lead directly to subjects. This may be, in part, because there is very little need for agency representatives to maintain contact with recipients over the telephone. In one state, for example, recipients are paid using a debit card that is continually re-valued at the beginning of a payment period. Thus, the address and telephone number information is not used on a frequent basis. In a study conducted by Westat several years ago, a similar result was found when trying to locate convicted felons (Cantor, 1995). Contact information provided by probation officers was found to be accurate about 50 percent of the time.(2)

If the subject cannot be located with available contact information, the case is sent out into the field. In some instances, the field interviewer is expected to both locate and interview the subject. In other instances the interviewer asked the subject to call a central interviewing facility. If the subject does not have a telephone, the interviewer provides them with a cellular telephone to call the facility. Several organizations reported that having the respondent call into the central office allowed for more specialization in the field tracing task. Interviewers would not be required to administer the interview. When hiring field personnel, therefore, the agency should be able to recruit individuals who are especially adept at tracking and tracing.

Empirical Results and Relation to Best Practice

These 14 studies provide some data on the possibilities and limitations related to conducting welfare-leaver studies. Many of these studies are implementing the best practices summarized in Table 2-1. These include, for example, advance letters, incentives, tracking/tracing, and refusal conversion. Resulting response rates ranged from a low of 30 percent to a high of 80 percent. Many studies are in the 40 to 50 percent range.

It is clear from these data, as well as from the authors collective experience, that no single design feature guarantees a high response rate. The effectiveness of particular methods varies by situation and a number of methods are needed to maximize response rates. A useful illustration is a survey that was completed in Iowa of current and former Temporary Assistance for Needy Families recipients (Stapulonis et al, 1999). This was a mixed-mode survey that implemented all of the methods discussed earlier, including: (1) repeated mailings to respondents, (2) use of telephone interviewers experienced in tracking respondents over the phone, (3) incentive payments, (4) specialized database searches, and (5) use of field staff to trace and interview respondents. As reported by Stapulonis et al. (1999), no single method produced a high response. A response rate of approximately 25 to 30 percent was achieved through the use of the telephone. At the end of 16 weeks, a 48-percent response rate was achieved by offering an incentive of $10 and sending cases into the field. The remainder of the 60-week field period was used to increase the rate to 72 percent. During this interim period, numerous methods were instituted, such as increasing incentive payments, remailings (using express mail) to households, field tracing, and using more specialized tracing sources and methods. The latter included using highly experienced trackers in the telephone center and the field.

The data in Table 2-2 seem to indicate that the mixed-mode approach, at least as currently implemented by most of these states, is necessary to achieve response rates of at least 50 percent. The data also indicate that for many studies, use of only the telephone yields a response rate of approximately 30 to 40 percent. The clearest example of this is study #1 and #2. These two studies were completed in the same state by the same organizations. In study #1, where a 30- percent response rate was obtained, only telephone and limited tracking was done from a central office. Study #2 instituted a number of additional tracing steps, but also added a field component. Similarly, study #7 reported a 25 to 30-percent response rate before going into the field and study #8 reported a 40-percent response rate before releasing cases to the field. The major exceptions to these patterns are the few studies that report final response rates of at least 70 percent. In these instances, the response rate obtained over the telephone is at least 50 percent and, in one case, 66 percent. Study #14 had a response rate of 72 percent and reported very poor experiences with their field tracers. Effectively, most of their cases were completed using the telephone. A few of these higher rates were achieved as part of planned experiments, where contact was initiated while the recipient was in the program. Other successes may be attributed more to the quality of the information available at the start of the study. Overall, we believe that if response rates of at least 50 percent are desired, it would seem important to use both telephone and field personnel to trace and locate respondents.

This pattern is consistent with our general experience in working with low-income populations. Although it is possible, using proper procedures and preparation, to complete a significant number of interviews via mail and telephone, a proportion of this group simply does not respond to anything but in-person contacts. This may be related to this groups mobility rate, the intermittent availability of the telephone, or simply busy work schedules. Whatever is the case, it is unlikely that achieving extremely high response rates (e.g., 70 percent or above) for welfare leavers can be achieved by simply the use of mail or telephone interviews.

Tracking Respondents

As one might expect, the primary source of nonresponse in these studies is noncontact, rather than refusals. For example, of the 25-percent nonresponse in the study #6, approximately 17 percent is from not being able to locate respondents and 8 percent is from refusals. For surveys that have lower response rates (e.g., around 50 percent), the percent of nonlocatables is even higher. This suggests that improving response rates has to do most with improving tracking.

Given this, an important component to pushing response rates above 50 percent is to improve the ability to find subjects. This relates to both the type of staff and the type of information available for finding the subjects. Study #6, with a 78-percent response rate, is a good illustration of the importance of experienced staff. This study did not implement many of the procedures discussed previously, including prenotification letters, refusal conversion, or incentives. The staff doing the interviewing and tracing, however, were program quality assurance personnel. Because part of their job is to find and interview welfare recipients to conduct audits, they were highly experienced in finding this population. In addition, the supervisor seemed to have very strong oversight of the interviewers progress. Similarly, study #14 completed all interviews over the telephone and achieved a 72-percent response rate. The study did not offer a monetary incentive and did not conduct refusal conversion. The success of this survey was attributed to the interviewers, who were also part of the quality assurance program.

Alternatively, a number of survey directors reported that the major barriers they encountered were related to inexperienced staff, either in the phone center or in the field, in tracking and tracing subjects. Stapulonis et al. (1999) report failure of a field effort because of inexperienced field trackers, as did the survey director for study #8. In the latter case, the telephone interviewers were asked to conduct field interviewing.

Our experience has been very similar to this profile and it applies to in-person interviewing as well as tracking from a central telephone facility. The ability to look over case records, find leads, and followup on those leads requires the ingenuity of a detective, as well as a personality that gains trust when calling neighbors or other community members.

Having solid information from which to trace subjects is also essential to finding them eventually. As noted previously, most survey directors commented on the poor quality of the information contained in the original sample records. In many cases, the information is quite old (e.g., up to 6-9 months) and, in many cases, of questionable accuracy. Because this is a highly mobile population, the age of the records limits the utility of the information quickly. Study #2 attempted to minimize this problem by beginning the tracking process as soon as subjects came off the welfare records. Although this may lead to tracking too many people,(3) it provided a way to maintain contact with subjects until the field interviewing started 6 months later. The success of this process is yet to be evaluated, but this method may provide a way to keep the information about sampled persons as up to date as possible.

All the studies had Social Security numbers for subjects, as well as access to databases in other parts of the government (e.g., motor vehicle registrations, food stamps, child support enforcement, Medicaid, unemployment insurance). These provide a powerful set of tools to find respondents. However, only two of the studies have tracing contact information, containing the names and phone numbers of at least one person, preferably someone who the subject does not live with, who is likely to know where the person is at any point in the future. These two studies both achieved response rates above 75 percent. Both studies were experiments, set up in advance to sample clients at intake and collect this information at that time.

The availability of tracing contacts would not only improve the tracking rates for these studies, but it would reduce the amount of time devoted to tracing. In fact, our experience, has shown that with good tracing contacts, as well as occasional interim contacts with subjects (e.g., every 6 months), little in-person tracking has to be done. Respondents can be located by interviewers making update phone calls. This is what many longitudinal surveys do as part of their routine activities for staying in touch with respondents. As a point of illustration, Westat recently completed a feasibility study that located 85 percent of subjects 3 years after their last contact with the study. These subjects were high-risk youth who had been diverted into a family counseling program in 1993 and were last contacted in 1996. At that time, tracing contact information had been collected. This population lived in highly urbanized, poor neighborhoods and could be considered comparable to those being traced in the welfare-leaver studies discussed previously. Approximately 67 percent of the population was found through the use of mail and telephone contacts. The remaining 18 percent were found by field tracing.

Increasing Cooperation

Pushing response rates higher also can be done through selective adoption of other methods related to making the response task easier. Some percentage of the persons classified as nonlocatable are really tacit refusers. That is, some of those individuals that cant be located are explicitly avoiding contact with the interviewer or field tracer because of reluctance to participate in the survey. This reluctance may be because the person does not want to take the time to do the survey or it may be more deep-seated and related to a general fear of being found by anyone who happens to be looking for them.

Several of the studies found that repeated mailings to the same addresses over time did result in completed interviews. This approach seemed to be especially effective when these mailings were tied to increased incentives. This approach would tend to support the idea that at least some portion of the noncontacts are actually persons who are tacitly refusing to do the interview, at least the first few times around. Express mail was also used for selected followup mailings, although it is unclear whether this method of delivery was particularly effective.

As noted in Table 2-2, a number of the studies do not implement any type of refusal conversion. The reluctance stems from fear that this would be viewed as coercive, because the agency conducting the research is the same agency responsible for providing benefits on a number of support programs. Other survey groups, however, reported confidence in conducting refusal conversion activities, as long as they were convinced the interviewers were well trained and understood the line between trying to directly address respondents concerns and coercion. In fact, many initial refusals are highly situational. The interviewer may call when the kids are giving the parent an especially difficult time or at a time when the subject just came home from an exhausting day at work. In another situation, the respondent may not have understood the nature of the survey request. In all of these cases, calling back at another time, with an elaborated explanation of the survey, is useful. In fact, one study director reported that about 50 percent of the initial refusers in the study were eventually converted to final completed interviews. This is not out of line with refusal conversion rates found on other studies, either of the general or low-income populations.

Special Issues for RDD Surveys of Low-Income Populations

In many ways, RDD surveys pose a much different set of challenges than those for list-based samples, especially on issues related to nonresponse. For surveys of welfare clients, the target population is identified clearly and quality issues have to do with finding sample members to conduct the interview. For RDD surveys, the primary issues have to do with efficiently identifying low-income subjects and, once identified, convincing them to participate in a survey.

Response Rates on RDD Surveys

To provide some perspective on the level of response achieved on RDD surveys, Massey et al. (1998) presented results of a study that reviewed the response rates of a large number of RDD surveys conducted for government agencies or as part of a large survey effort. Their results found a median response rate 60 to 64 percent with about 20 percent of the surveys exceeding 70 percent. The overall perception among survey analysts is that the trend is for this rate to decrease over time. That is, achieving high response rates for RDD surveys is becoming more difficult.

An RDD survey of low-income populations faces several hurdles relative to achieving a high response rate. The first is the need to screen all households on the basis of income. This leads to two types of problems. The first is that it adds an opportunity for someone to refuse to do the survey. A screener written to find low-income households has to include a number of questions that respondents are sensitive to, including information on who lives in the households, and some type of income measure. Much of the nonresponse on RDD surveys occur at this point in the process. For example, on the NSAF, a national RDD survey that over-samples low-income groups, the screener response rate was in the high 70s. Once a respondent within the household was selected, the response rate to do the extended interview was in the 80s. Nonetheless, the combination of the two rates, which form the final response rate, resulted in a rate in the mid-60s (Brick et al., 1999).

Low response rates on RDD surveys are partly an issue of credibility. Relative to a survey of welfare leavers, the issue of credibility places more emphasis on design features that motivate respondents to participate in the survey (vis-à-vis trying to locate respondents). For example, research on methods to increase RDD response rates has shown that prenotification prior to the call, methods of delivery of prenote letters, and use of incentives can provide important boosts above those normally achieved when implementing many of the other important design features reviewed earlier. All three of these increase response rates in the context of an RDD survey (Camburn et al., 1995; Brick et al., 1997; Cantor et al, 1999).

In addition, refusal conversion is particularly important for an RDD survey, because such a large proportion of the nonresponse is from refusals. Refusal to the screener could be from almost any member of the household, because most screeners accept responses from any adult who answers the phone. Calling the household a second time provides an opportunity to obtain another person in the household (who may be more willing to participate) or reach the same respondent who may not be as difficult to convince to participate in a short screening instrument. Refusal to the extended interview may be more difficult to turn around. Refusal conversion strategies at this level are amenable to more traditional tailoring methods (e.g., Groves and Couper, this volume: Chapter 1), because respondents at this stage of the process may be more willing to listen to the interviewer.

Efficiently Sampling Low-Income Populations

A second issue related to conducting RDD surveys of low-income populations is the ability to actually find and oversample this group. Screening for persons of low-income has been found to have considerable error. This has been assessed when comparing the poverty status reported on the initial screener and the income reported when using more extensive questions in the longer, extended interview. For example, on the NSAF approximately 10 to 15 percent of those who report being below 200 percent of poverty on the longer interview initially tell the screener they are above this mark. Alternatively, 20 to 30 percent of those reporting themselves as above 200 percent of poverty on the extended interview initially screen in as above this mark (Cantor and Wang, 1998). Similar patterns have been observed for in-person surveys, although the rates do not seem to be as extreme. This reduces the overall efficiency of the sample design. This, in turn, requires increasing sample sizes to achieve the desired level of precision.

To date, the problem has not had a clear solution. In-person surveys have developed more extensive screening interviews to allow predicting income status at the point of the screener (Moeller and Mathiowetz, 1994). This approach also might be taken for RDD screeners, although there is less opportunity to ask the types of questions that are needed to predict income. For example, asking detailed household rosters, or collecting information on jobs or material possessions likely would reduce the screener response rate.

A second issue related to sample design on an RDD survey is the coverage of low-income households. Although only 6 percent of the national population is estimated to be without a telephone (Thornberry and Massey, 1988), about 30 percent of those under poverty are estimated to be in this state. For an RDD survey of a low-income populations, therefore, it is important to decide how coverage issues will be approached. One very expensive approach would be to introduce an area frame into the design. This would include screening for nontelephone households in person and then conducting the extended interviews either in person or over the telephone.(4)

Over the past few years, a new method, based on an imputation method, has been tested that does not require doing in-person interviews (Keeter, 1995). The premise of the method is based on the idea that for a certain segment of the population, having telephone service is a dynamic, rather than stable, characteristic. Consequently, many of the people who do not have service at one point in time may have service shortly thereafter. This implies that one might be able to use persons that have a telephone, but report interrupted service, as proxies for those who do not have telephones at the time the survey is being conducted. Based on this idea, telephone surveys increasingly are including a question that asks respondents if they have had any interruptions in their telephone service over an extended period of time (e.g., past 12 months). If there was an interruption, they are asked how long they did not have service. This information is used in the development of the survey weights. Those reporting significant interruptions of service are used as proxies for persons without a telephone.

Recent evaluations of this method as a complete substitute for actually conducting in-person interviews has shown some promise (Flores-Cervantes et al., 1999). Initial analysis has shown that the use of these questions significantly reduces the bias for key income and other well-being measures when compared to estimates that use in-person interviewing. This is not always the case, however. For certain statistics and certain low income subgroups, the properties of the estimator are unstable. This may be due, in part, to developing better weighting strategies than currently employed. Nonetheless, the use of these questions seems to offer a solution that, given the huge expense involved with doing in-person interviews, may offer significant advantages.

The use of this method also may be of interest to those conducting telephone surveys with persons from a list of welfare clients. Rather than being viewed as a way to reduce coverage error, however, they could be used when trying to impute missing data for high nonresponse rates.

Highlighting Low-Cost Actions

This paper has attempted to provide information on methods to achieve high response rates on telephone surveys of low-income populations. We have concentrated much of the review on studies that start with a list of welfare recipients, but we also have provided information for persons conducting RDD interviews. The second section of this paper provided a list of best practices that should be considered when conducting telephone surveys. The third section provided examples of what is currently being practiced in recently completed welfare-leaver studies and how these practices relate to results. The fourth section provided special issues related to RDD surveys. In this section we concentrate on highlighting suggestions that seem practical and relatively low cost.

Improve Tracking and Tracing

Clearly one primary theme taken from our review is the need to improve the ability of studies to find subjects. Most agencies face similar situations  the information used to track respondents is both relatively old (6-9 months) and limited. The age of the information could be addressed partly through methods such as those mentioned earlier  start contacting respondents immediately after they leave the program. Maintain this contact until the time to conduct the interview (e.g., 6 months after leaving). This approach, however, is relatively expensive to implement. Following subjects over extended periods of time can be labor intensive. Furthermore, the information provided when exiting the program may not have been updated for quite some time. This constraint is difficult to get around.

A more effective and cost-efficient method to improve contact information is to collect tracing contacts when the subjects initially enter the program. This type of information does not go out of date nearly as fast as a single name and address. Even if they go out of date, the names and addresses can provide additional leads that can be followed up by trackers. When collecting this information, it is important that the names are of persons who do not live with the subject. This decreases the possibility that if the subject moves, the contact person would have moved as well.

Another potentially rich source of information is case history documentation. Many of the studies reviewed above reported using information from other government databases, such as motor vehicles or other recipiency programs, to collect updated addresses and phone numbers. Examination of hardcopy case folders, if they exist, would be one way to supplement this information. One study reported doing this and found it was a good source for tracing contact information. Subjects, at some point, could have provided information on references, employers and friends as part of the application process. This information, if confidentiality issues can be addressed, can be examined to locate further leads to find and track those people who cannot be found.

To provide some perspective on the impact that tracing might have on the cost of a survey, we developed estimates of cost under two scenarios, one in which contact information is available and another in which it is not available. Costs for surveys of this type are difficult to estimate because so much depends on the ability of the data collector to monitor the process; the availability of skilled staff to carry out the tracing; and the nature and quality of information that is available at the start. The first two factors rest with the data collector while the latter depends on information obtained about each subject (and his or her accessibility) by the agency. If the data are not current or not complete, tracing is difficult and costly regardless of the controls the data collector has in place.

Under the assumptions described in Table 2-3, we estimate that approximately 600 fewer hours are required to trace 1,000 subjects if tracing contact information is available. Contact information, for this example, would have been obtained during the intake process and delivered to the data collector with the sample. The table may be somewhat deceptive because, for purposes of illustration, we have forced the two samples to have approximately the same location rate in order to compare the level of effort. In reality, the location rate (and consequently the response rate) for those with contact data would be higher than for those without.

In creating Table 2-3, we assumed the following times for each level of tracing. In practice, most of the welfare leaver studies have used both telephone and in-person surveys:

  • 20 minutes for calling through the contacts
  • 20 minutes for calls to the hits of database searches
  • 1 hour for intense telephone tracing
  • 7 hours for in-person tracing

TABLE 2-3:
Comparison of Tracing Hours, by Availability of Contact Information.
No Tracing
Calling Contacts
Database Search
Intense Telephone Follow-up
In-person Tracing
Tracing Time Per Sample Unit (minutes) 0 20 20 60 420  
With Contact Information
Sample size 1,000 700 490 343 240 1,000
Percent of cases located 0.30 0.30 0.30 0.30 0.15 0.80
Number located 300 210 147 103 36 796
Estimated number of hours 0 233 163 343 1,681 2,420
Without Contact Information
Sample size 1,000 N/A 700 469 314.23 1,000
Found rate 0.30   0.33 0.33 0.33 0.79
Number found 300   231 154.77 104 789
Estimated number of hours 0   233 469 2,200 2,902

Although these estimated times are reasonable, they also can be misleading. For example, if several databases are used (e.g., agency, credit bureau, DMV, commercial) each can produce a hit and require followup, so it is likely that more than one followup call might be carried out for some sample members, and none for others. In organizing a hit, care must be taken to make sure it is genuine and not a duplicate of an earlier hit that already has been invalidated. This adds time, though the process can be aided by a case management system.

The level of interviewer/tracer effort is only one dimension of cost. Supervisory hours will range between 20 to 40 percent of interviewer hours, depending on the activity, with the highest percentage needed for the intense tracing. Other variable labor costs include all clerical functions related to mailing and maintaining the case management system, and all direct nonlabor costs. These include, but are not limited to charges from database management companies to run files, directory assistance charges, telephone line charges, field travel expenses, and postage/express delivery charges.

Fixed costs include the management costs to coordinate the activities and the programming functions to develop a case management system; preparing files for data searches; updating files with results of data searches; and preparing labels for mailing.

A second important point to remember for agencies operating on a limited budget is to hire supervisory and interviewing staff who are experienced at locating subjects. Prudent screening of personnel, whether internal employees or external contractors, potentially have a big payoff with respect to maximizing the response rate. Strong supervisors are especially important because they can teach new interviewers methods of finding particular cases. They also can provide guidance and new ideas for experienced interviewers. The supervision has to be done on an interviewer-by-interviewer basis. Supervisors should review each case with interviewers on a frequent basis (e.g., every week) and provide feedback/advice on how to proceed with each one. This includes making sure the interviewer is following up with the leads that are in hand, as well as discussing ideas on how to generate more leads.

Effective locating and management of a survey cannot be learned on the job. Therefore, sponsoring agencies should gather evidence that the personnel involved have the appropriate experience and successful track record to complete the work successfully. This advice applies if using personnel within the sponsoring agency or through a contractor. When considering a contractor, the sponsoring agency should ask for hard evidence that a study like this has been conducted, and check references and evaluate success rates. Questions should be asked about the availability of experienced staff to complete the work. If the work is to be done by telephone, then some information on the track record of telephone tracers should be requested. For in-person contacts, information on the experience of personnel who reside in the local area where the study is to be conducted should be collected.

Improving Methods to Contact and Obtain Cooperation

First and foremost in any survey operation is the need to develop an infrastructure that maintains control over cases as they move from the initial prenotification letter to call scheduling and case documentation. Understanding what stage each case is in and what has been tried already is critical to making sure each case goes through all possibilities. These basics are not particularly expensive to implement and can yield a large payoff in terms of completed interviews. For example, supervisory staff should be reviewing telephone cases as they move to different dispositions, such as ring, no answer, initial refusal, or subject not at this number. As with tracing, supervisors should review cases and make case-by-case determinations on the most logical next step.

Monitoring of the call scheduling also should ensure that different times of the day and different days are used when trying to contact respondents. This is one big advantage of a centralized computer-assisted telephone interview (CATI). The computer deals cases at the appropriate times and pretty much ensures that the desired calling algorithms are followed. However, if the study is being done with paper and pencil, then a system to document and monitor call history should be in place to ensure that this occurs.

Prenotification is being used extensively for the studies reviewed earlier. Low-income populations are particularly difficult to reach by mail. For this reason, some attention to the form and content of this correspondence is likely worth a small investment of professional time. This includes, for example, the way the letters are addressed (e.g., labels, computer generated, hand written), the method of delivery (express delivery versus first-class mail) and the clarity of the message. The contents of the letter should be structured to be as clear and as simple as possible. One study reviewed earlier noted an improvement (although not experimentally tested) when formatting letters with large subheadings and minimal text. The details surrounding the study were relegated to a question-and-answer sheet. We also have found this to be an improvement over the standard letter-type format. Similarly, use of express delivery, at least when there is some confidence in the validity of the respondents address, may also be a cost-effective way to provide respondents with information about the survey that would eventually increase their motivation to participate.

Incentives are also being used in the studies mentioned previously. We have not elaborated on this much, partly because another paper will be presented on just this topic. One pattern we did notice for the studies reviewed earlier was that all incentives are promised for completion of the interview. Amounts generally ranged from $15 to $50, with the largest amounts being paid at the end of field periods to motivate the most reluctant respondents. Research has found that prepaid incentives are more effective than promised incentives. Research we have done in an RDD context has shown, in fact, that not only is more effective, but the amount of money needed to convince people to participate is much smaller. It may be worth experimenting with prepayments that are considerably smaller than the current promised incentives (e.g., $5) to see if there is a significant improvement in the ability to locate and interview respondents.

In conclusion, conducting a telephone survey of low-income populations is a task that requires careful preparation and monitoring. The surveys implemented by states to this point have been discovering this as they attempt to locate and interview respondents. Improving response rates will require attention to increasing the information used to locate respondents, as well as making it as easy as possible for respondents to participate. This paper has provided a thumbnail sketch of some important procedures to consider to achieve this goal. It will be interesting to see how future surveys adapt or innovate on these procedures to overcome the barriers they are currently encountering.


1. A related concern is whether respondents are using caller ID in a similar way.

2. This rate is surprisingly low, given that probation officers should be in regular contact with probationers.

3. Most studies had, as an eligibility criteria, that leavers had to stay off the welfare program for at least 2 months. Sampling within a month of leaving the program, therefore, eventually results in having to drop subjects because they return to the program within 2 months.

4.  Telephone interviews would be conducted by having the respondent call into a central facility using a cellular telephone.


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High Response Rates for Low-Income Population In-Person Surveys

In this paper we will look at the context of interviewing low-income populations and the unique challenges presented to survey practitioners. Within that context, we will explore what data collection approaches can increase the likelihood of success in the pursuit of high response rates while staying within the limits of a projects budget and schedule. Finally, we will make some recommendations for future efforts in this arena.

The Context

In the Best Practices booklet published by the American Association of Public Opinion Research (1997a) 1 of the 12 named best practices is to maximize cooperation or response rates within the limits of ethical treatment of human subjects (p. 5). In surveys concentrated on low-income populations, high response rates are especially important. In the past few years, there has been a great deal of interest in finding out what is happening to people after they leave the welfare rolls. Outside of the usual concern about nonrespondents causing a potential bias, there is often the need to stratify populations by their relationship to welfare systems. For example, though those that leave welfare are of great interest, so are the stayers, as are potential applicants diverted from programs or those who do not apply. If samples are to be large enough to make meaningful comparisons among groups, then nonresponse must be kept to a minimum.

Low-income populations are of special interest to survey practitioners. Whether one is doing a survey of employment, crime victimizations, health conditions, or health insurance status, the low-income population has an abundance of people who are having difficulty. In its most recent report on poverty, the U.S. Census Bureau reported that people who worked at any time during 1998 had a lower poverty rate than nonworkers (6.3 percent compared with 21.1 percent). The Census Bureau also recently reported that 16.3 percent of all people in the United States were without health insurance for the entire year of 1998, but that 32.3 percent of poor people were in that category (Campbell, 1999).

Of interest to the survey community are the statistics cited by Federal Communications Commission Chairman Reed Hundt about access to communication services in the United States. Of households on food stamps, roughly 30 percent have telephone service. In 1993, 27 percent of households with children and below the poverty line did not have phone service. About 12 percent of unemployed adults did not have phone service.

This lack of telephone service shows the importance of expanding the mode of data collection for low-income persons beyond telephone surveys. Nonresponse rates by income type show that refusals are lowest for low-income populations (Groves and Couper, 1998). However, those who are not contacted in surveys are clustered among those who are in the low-income groups. Groves and Couper show that in areas of high population density, more than 6 percent of the population were not contacted. In central cities, 7.2 percent were not contacted. When homeownership was below 48.5 percent, 4.9 percent were not contacted. In areas where minorities made up more than 8 percent of the population, the noncontact rate was 3.6 percent or higher. Therefore, when looking at income distributions, the high end would be underrepresented primarily because of refusals and the low income would be underrepresented because of noncontacts. If the low-income population is approached only by telephone, the nonresponse rates would be even higher because of the lower incidence of telephones among this population.

In-person efforts will be critical to achieving high response rates for people who have no usual residence, those who move frequently, those who have no telephones, and those who need some immediate gratification before they agree to be interviewed. Often, concepts and ideas can be explained easier when face to face.

The low-income populations of interest in surveys present some special challenges. They are often hard to find. Though they may have lived at a fixed address at one time, low-income people move often, mostly within the same neighborhood, but not always. Sometimes they live in regular housing until their money runs out, then live on the streets until the next influx of money. A survey organization must be prepared to spend resources locating respondents. Low-income respondents are often suspicious of strangers and the government. Often they do not want to be found. Names are not always given freely, nor are responses to where people can be found. In National Opinion Research Center (NORC) surveys, a common problem is that it is hard to make and keep appointments with potential respondents.

In addition, because of high immigration in the past 15 years, many people in the population do not speak English. In many surveys, people who do not speak English or Spanish are excluded. However, in surveys of low-income populations, these people with language barriers may be extremely important. Thus, a survey organization must be ready to find interviewers who speak the needed languages, and have a facility for translating questionnaires. Using a questionnaire translated into other languages brings additional problems. The translated version needs pretesting to make sure that the correct meaning is used and that the basic concepts have not been lost. To make these situations work, it is important to collaborate with the ethnic communities and enlist their help. This collaboration also can be helpful in gaining access to the communities so that respondents will cooperate. Some interesting work at the Census Bureau in a series of ethnographic studies (de la Puente, 1995) shows how a difference in meaning that affects responses can occur when there is not collaboration.

These special issues that arise in interviewing low-income populations all have appropriate solutions. Which of these solutions can be applied for a given survey will be dependent on budget, schedule, and Institutional Review Board (IRB) and Office of Management and Budget (OMB) constraints. NORC has conducted several studies of low-income populations and has been successful in interviewing them. This paper reviews the methods leading to success.

All the surveys referenced for this paper are list samples. (Note that the D.C. Networks Study used targeted chain referral sampling to build its list sample.) Five NORC surveys will be referenced to illustrate methods for finding and interviewing these populations. Response rates for the five surveys were all 75 percent or above. Indeed, in follow-up surveys of the same populations, rates higher than 90 percent were achieved in most instances.

To be most relevant for State grantees who are conducting or planning to conduct surveys of low-income and welfare populations, studies with the following characteristics are discussed: respondents are primarily from low-income and/or welfare populations; the sample is clustered within one area rather than being national; paper and pencil interviewing (PAPI) is the mode for all but one of the studies, which is computer-assisted personal interviewing (CAPI); extensive locating is required; and respondents are offered an incentive for participation. Note that the issues related to survey materials being available in multiple languages will not be addressed in this paper; only one of the studies referenced here offered Spanish-language materials, New York Minority Youth.

Each of the five studies used to illustrate NORCs approach to obtaining high response rates with low-income populations is based on a list sample and involves follow-up interviews. These seem most appropriate for people who wish to survey low-income and welfare populations. The lists came from a variety of sources, one of them compiled in the mid-1960s (Woodlawn Studies). List samples illustrate the importance of good methods of locating respondents, many of whom have moved. Each of the studies is confined to a specific area. Though PAPI was used for four of the five studies, CAPI was used for one (D.C. Networks Study). The rationale behind the use of PAPI was either cost or speed. Some people fear that carrying laptops into areas where low-income people live is too dangerous, but NORC has not experienced problems. Laptop surveys in big cities are routinely conducted year-round. (Table 3-1 provides some basic information about the studies we will reference in the paper as: the Seattle Study, the Woodlawn Studies, the New York Minority Youth Study, and the D.C. Networks Study.)

TABLE 3-1:
Listing of Representative Surveys of Low-Income Population
Study Name and Dates of Data Collection Activities Location Sample Questionnaire Length Percentage of Response Rates Respondent Incentives Mode
Seattle Study
King County, Washington 571 for baseline

list sample

45-60 minutes 75 baseline
90-97 on each of four follow-ups

$5-urine specimen

Woodlawn Study
Lived in Woodlawn neighborhood of Chicago in 1966 and 1967 1,242 African-American respondents

list sample

90 minutes (avg.) 85 $25 PAPI, some telephone interviews
Woodlawn Mothers
Subsample of mothers of original 1966-67 sample 1,026 African-American mothers

list sample

90 minutes (avg.) 79 $25 PAPI, some telephone interviews
New York Minority Youth
East Harlem area of New York City; respondents were 7th-10th graders in 1990 1,330 youth, 666 mothers.; African-American and Puerto Rican

list sample

75 minutes-youth

75 minutes-mother

92 $25-Youth


D.C. Networks Study
Washington, D.C., area 500 cases;

targeted chain referral sampling for baseline, now list sample

90-120 minutes

30-180 minutes

86 baseline
82 and 62 on followups one and two 

$20-ethnographic interview

CAPI and ethnographic interviews

NORC has adopted the following protocol outline for obtaining high response rates. It includes measures we have developed to: (1) locate and contact the sample; (2) staff and train interviewers; (3) optimize field support and communications; and (4) control budget and quality.

The following is a compilation of input regarding this topic from NORCs top field management team members who were actively involved in carrying out these studies successfully.

The Sample


Ideally, the sample list will be up to date, comprehensive, and accurate. However, most often it contains aged information provided by the client based on administrative records. The standard information  including full name, most recent address and phone number, and date of birth  can be enhanced by researching other ancillary information. This includes maiden name for women, drivers license or state identification number, employers, schools or training programs attended, military service, prison records, and persons likely to know where the sample member can be found (a parent, grandparent, close friend, or neighbor). Once obtained, it is essential that this augmenting information and its source be documented accurately for future reference.

Advance Letter

The initial correspondence to the respondent is a critical step toward gaining cooperation. It sets the tone of the survey and must compel participation. The advance letter should be straightforward and brief. Proprietary terms and legal jargon should be avoided. The letter explains the study and certifies that the interview: (1) will be strictly confidential; (2) is voluntary; and (3) will be conducted by a properly identified and trained interviewer. If a respondent fee will be provided it should be mentioned, and if such a fee can be exempt from income reporting by virtue of the client obtaining a waiver, that should be mentioned too. (A respondent fee is strongly recommended as a method of assuring maximum response rates.) A toll-free telephone number is supplied in the letter to permit the respondent to ask questions and/or set up an interview.

The advance letter serves another valuable purpose: to update preliminary locating information. The envelope is marked Address Service Requested. This statement will result in the post office providing information about the address/person; if the post office forwards the mail to another address, it will provide notification of that new address. For all other mail that does not go directly to the addressee, the mail is sent back with the reason for return, such as a missing apartment number, transposed street numbers, or lack of forwarding address. If one prefers that the letter not be forwarded, the envelope can be marked Do Not Forward and it will be returned, allowing it to be remailed to the correct address. Names and addresses from returned letters can be submitted in batch mode through the National Change of Address if time allows. Recent experience shows that this latter approach is more useful when the sample is quite outdated, namely 5 years or more. When time and budget allow, it also helps to work the unlocatables through centralized database searches. The sample file should be updated with any leads obtained through this prefield stage; releasing the sample to interviewers without having made the updates will result in extra costs caused by duplicated efforts.

Community Authority Contacts

Informing and/or gaining the support of influential community leaders can be pivotal to the success of the survey. Letters to the local police, Better Business Bureau, ethnic leaders in the National Association for the Advancement of Colored People (NAACP) or Hispanic Council, housing authorities, and others serve two important purposes. They provide a sense of security to the interviewer, who then knows that appropriate officials have been notified. Respondents can be shown the letter as a reenforcement measure. The leaders, in turn, often supply essential strategic information regarding places to avoid, whether an escort is justified, and safest times to interview. The letter to community authorities should explain the survey, in addition to how and by whom the interviews will be conducted. It assures them that interviewers will wear photo identification badges.


NORC has established and maintains a locating protocol that documents, in order of cost, the basic steps involved in locating people. The locating effort, critical to any projects success, is influenced by budget, schedule, IRB and/or OMB constraints, and the locating skills of the projects assigned staff. Therefore, emphasis is placed on centralizing the process before employing the more costly means of in-field locating. Depending on available resources, the centralization of locating can be in a central/home office or in the field (if locating experts equipped with computers that can access the relevant databases and the Internet are available). Centralizing this locating effort allows efficient access to the resources to do the preliminary work of checking phone directories, mailing addresses, contact names, employers, and other information. Field staff are then called on to personally visit the last known address and talk to neighbors, the mail carrier, and others. Interviewers document the results of each locating step on a Record of Calls. Many projects provide the field interviewers with a job aid, referred to as a Locating Checklist. It identifies the steps to be taken by the field in locating a respondent, listing the steps in order of cost. This greatly reduces duplication of effort.

The Seattle Study Experience

The respondents in the Seattle Study were first interviewed in their final month of eligibility for drug-addicted or alcoholic Supplemental Security Income (SSI). The baseline sample information included the identity of a payee to whom the prospective respondents SSI check was sent. Because many of the payees were agency staff, the interviewers often were able to work directly with the payee to determine when the respondent would be coming in to pick up the check. The agency often let the interviewer have space to interview the respondent at the time of that visit.

However, because of the nature of the sample, there were large numbers of respondents who were homeless. The field manager obtained a list of all the agencies that serviced the homeless and went in person to each place with a list of names. Interviewers made daily visits to many of these locations and eventually found many respondents. The field staff worked diligently to identify the extensive homeless network in the area; they asked homeless people questions such as where they slept, where they got their meals, and where they kept their belongings. This effort proved beneficial during the baseline interview as well as during the follow-ups, which were done at 6-month intervals to examine the effects of the programs termination on former recipients. During this process, the field staff found it is important to learn a respondents street name, because many of them do not go by their legal, given names out in the community. Field staff on this study believed it would be helpful, if possible, to obtain IRB/OMB approval for the interviewer to take a snapshot of the respondent that could be used during subsequent locating efforts.

Also, because all the respondents were in the study because their alcohol- and/or drug-related SSI benefits had been discontinued, another potential locating source was expected to be area taverns. The field manager in charge organized night-time locating trips into the areas of Seattle where the homeless gather. Two or three field interviewers would travel with the field manager into the core area of the city searching for respondents among those waiting in line for entrance into a shelter for the night, or among those patrons in the taverns and bars frequented by street people. These pub crawls, as the field interviewers called them, were very helpful in locating homeless respondents.

Prisons and jails were another valuable source for locating respondents. On the Seattle Study, a census of all the jails was available. Interviewers checked the list regularly looking for names that matched the pending sample list. Some interviewers were able to obtain special IDs after agreeing to a background check done by the jail. These IDs allowed the interviewers to come and go just as lawyers do, and their visits did not impact on the respondents allowed number of visitations. To access prisons, in some cases, the client for the Seattle Study had to complete the requisite paperwork before the interviewers could approach incarcerated respondents. On the D.C. Networks Study, a significant effort was made to gain access to the prison system by working closely with the D.C. Department of Corrections. One experienced field person on that study who was particularly effective was a private investigator before joining the interviewer and field management staff at NORC. Protocols related to working in jails and prisons vary considerably by state, so it is important to determine the kinds of access that interviewers will be allowed at the outset of the data collection period. Many states now have a Web site and/or telephone number for locating inmates.

On the Woodlawn Studies in which the original respondents were first graders enrolled in elementary school in an inner-city, predominantly African American urban neighborhood in 1966 and 1967, the locating challenges were enormous. The client had made interim contacts with some respondents, but much of the sample information was very old, so the field staff relied on intensive locating efforts in the neighborhood. They went to the neighborhood and tried to locate the oldest residents on the block, visited neighborhood churches to talk with long-time members, called people with the same last name living in the place of birth to look for relatives of the respondent, and mailed letters to every old address and every new address they found. With regard to the last step, they mailed again and again if not returned by the post office; their persistence often paid off as many respondents moved back to their hometown during the course of the fieldwork.

On the New York Minority Youth Study, a useful locating resource was the schools that respondents had attended. Because the baseline data were collected in the school setting, the client contacted the schools to obtain permission to contact them for locating information. The follow-up interviews were with a sample of inner-city African American and Puerto Rican adolescents and their mothers. Prison contacting was also helpful for this population.

On the D.C. Networks Study, where 62 percent of the respondents have a monthly income of $500 or less, 63 percent have been drug injectors for more than 21 years, and only 50 percent have lived in an apartment or house during the past 6 months  the locating challenges for follow-up have been intense. This is a study in which two outreach workers who are street wise and know a lot about the locations where drugs are sold and used, identify respondents in the streets and bring them into the site office to be interviewed. The experienced field staff on the study (four interviewers, a locating expert, and a field site manager) also work on the case, locating by phone or in the field, but they leave the locating in drug areas to the outreach workers.

Table 3-2 indicates some of the specific locating resources that were used during these representative studies.

TABLE 3-2:
Locating Methods Used
Locating Effort/Source Seattle
Networks Study
Probation/parole officers x     x
Doormen/guards at building complexes       x
Known contacts, such as family members, case workers x x x x
Last known address x x x x
Jails/detention centers/prisons x x x x
Halfway houses x     x
Clinics x     x
Hospitals, regular and rehabilitation x     x
Drug treatment centers x     x
Known geographical areas for drug purchase/use x     x
Homeless shelters x     x
Schools     x x
Churches x x    
Food banks x      
Old neighborhood x x x x
Food banks x      
Needle exchanges x      

Staffing and Training

Data Collection Plan

Optimum results are more likely to be achieved when the data collection plan reflects both the theoretical underpinnings of the clients research goals and data needs and the best practices of the data collection contractor. Such a plan should be preapproved by the client and precisely match the resources available. This avoids any misunderstanding of what can reasonably be provided by the contractor within the time and budget allowed. Also, as the work proceeds it is important to be in close contact with the client, to share successes and obstacles encountered. Contingency planning within the constraints of the research goals must be addressed in a timely manner.

For example, the Seattle Study was tasked to begin on short notice, with no flexibility on the start date. It had to be started before the respondents SSI benefits ended, then completed as quickly as possible. A data collection plan was rapidly developed and approved by all parties, thus avoiding any ensuing disagreements regarding production results.

On the Woodlawn Studies, the client was very supportive and even helped with gaining access to some records for locating purposes. She met with the field staff whenever she was in Chicago if her schedule permitted. When production was low, she remained optimistic and reminded the staff how important their efforts were to her research. The sense of team camaraderie on these projects has been unrivaled on other studies and contributed to an outcome that was satisfactory to the client, even though more time than originally projected was needed to reach the final completion rates of 85 percent on the Woodlawn Study and 79 percent on the Woodlawn Mothers.


Key to assigning interviewers who are appropriate to low-income and welfare populations is the recognition that unique attributes are needed. Not all interviewers, even experienced ones, are equally effective in this environment. Screening prospective interviewers begins in the help wanted ad. It must specifically state that the job entails interviewing low income persons in their residences or elsewhere out in the field. The fact that the work will require some evenings and weekends must be understood. Supplying this information beforehand will avoid any misconceptions that may occur later.

During the job interview, it is important that applicants be evaluated on their ability to be nonjudgmental in the situations to which they may be exposed. If the content of the questionnaire is sensitive, it is useful to show candidates a sample of the questions. Some candidates will eliminate themselves, knowing they would be uncomfortable asking these kinds of questions. Successful candidates, both experienced and new to interviewing, will be comfortable with the gaining cooperation aspect of the job. When conducting exit interviews with interviewers who have left a project, one of the frequently mentioned reasons for leaving relates to the door-to-door sales aspect of interviewing; they often did not realize how difficult that preinterview step could be and were not up for the challenge or the rejection that can be associated with slammed doors or hung-up phones.

NORC experience with studies involving hard-to-reach populations and/or sensitive topics supports the findings by Groves and Couper that experienced interviewers are more adept at gaining cooperation than inexperienced interviewers. Those who thrive in the interviewing environment see these situations as personal challenges to which they apply their skills gained from earlier experiences.

To select an approach to use, the interviewer must judge the fit of the respondent to other respondent types experienced in the past (either through descriptions in training or actual interaction with them). We believe that experienced interviewers tend to achieve higher levels of cooperation because they carry with them a larger number of combinations of behaviors proven to be effective for one or more types of householders. (Groves and Couper, 1988:36)

On the D.C. Networks Study, all the interviewers have had experience working in difficult neighborhoods or previous studies in the D.C. area. The experienced locating specialist has been helping them to gain access to prisons and has been doing a great deal of street locating.


Interviewers must be well versed in basic interviewing techniques, including reading questions as worded, neutral probing, training the respondent, and confidentiality. At NORC, these basic topics are covered in an eight hour general interviewing techniques training session, which is required of all interviewers new to NORC. In the recent literature on obtaining high response rates, Sullivan et al. (1966) put forth a retention protocol for conducting longitudinal studies with mobile populations that includes three phases, the first of which is relevant to training. In Phase I of their retention protocol (which relates to setting the stage for future contacts with the respondents) Sullivan et al. refer to the importance of establishing trust between the researcher and the respondent (1996:266). To accomplish this, interviewers need to be able to convey to respondents why the survey is needed and how it might impact others in similar circumstances, stress confidentiality of data, and so on. Ensuring that interviewers understand these basics is important to the quality of the data being collected.

Project-specific training then focuses on the purpose of the study, the questionnaire, the informed consent procedure, gaining cooperation, sensitivity, safety, production goals, and other areas. When a project has unique protocols for locating, such as in a study of battered women conducted by Sullivan and colleagues, this is the forum where such procedures would be covered. They had the respondent sign a Release of Information form indicating that she gave her permission to the alternate contact to give us her address and phone number. Each participant receiving governmental assistance was also asked to sign a release form for the government agency handling her case. This is a protocol that has been used successfully at NORC, primarily on drug study follow-up interviews. Contacts are more comfortable knowing (by actually seeing the respondents signature on the form) that the respondent has given permission to help locate them.

Training on gaining respondent cooperation is essential on all types of studies, and is best provided when woven throughout the training session, rather than just being covered directly in a module of its own. The ultimate goal in this type of training is to enhance the interviewers abilities to tailor his or her reaction to the respondent and to maintain interaction with the respondent long enough to gain cooperation. (See Groves and Couper, 1998, Chapter 9, for elaboration on the concepts of tailoring and maintaining interaction.) During training, interviewers practice their approach to gaining cooperation through role playing. They are encouraged to rely on all tools provided by the study. For example, each of the five NORC studies referenced in this paper offered an important tool for gaining cooperation, namely, respondent incentives (see Table 3-1). Interviewers report that when a survey involves a long questionnaire that focuses on sensitive topics, as each of these surveys did, incentives make their task of gaining cooperation/averting refusals significantly easier.

Sensitivity training often is appropriate to prepare interviewers for the situations they may encounter. It is designed to help them respond respectfully to the respondents with whom they will interact and to make them unshockable. Sensitivity training typically covers some background information about the kinds of situations likely to be encountered. The presentation of this information can be done by the principal investigator, an outside expert, or an experienced senior-level field manager. On a study of the terminally ill, for example, the principal investigators talked with the interviewers at training; the interviewers saw a videotape about terminal illness and its effect on the respondent and his or her family; and grief counseling was available to field staff during the course of data collection. In addition to providing interviewers with substantive background, the training often provides opportunities to help the trainees to deal with the emotional responses they are likely to experience themselves and to handle those reactions in the interview situation. On some studies, the field staff are invited to attend special conference sessions prior to the studys implementation. For example, field staff working on the D.C. Networks Study, attended an HIV conference to make them more aware of the types of situations facing potential respondents.

Traveling Interviewers

Supplementing local interviewers with a team of highly experienced traveling interviewers is a strategy that has been successful and cost effective on these studies. This is especially true when the sample is clustered and therefore requires a large number of newly hired interviewers. It is also particularly valuable if the data collection period is very short. On the Seattle Study, several experienced travelers came in at the start of the data collection period. When some of them had to leave, others came in to assume their assignments. Throughout the data collection period, the local field staff worked together with the travelers. NORC experience shows that seasoned travelers can focus on weak data collection areas and apply their proven skills in locating, refusal conversion, and strong production. They also help to motivate and train local interviewers, providing role models of success for new interviewers to emulate. This modeling is especially important when the number of refusals from respondents grows during the field period. Experienced interviewers can describe and/or demonstrate (in the field and/or in role plays) how they prepare for and approach respondents who have refused at least once. They help the less experienced interviewers to move beyond experiencing refusals as personal attacks and turn the focus back onto the importance of the study, the production goals, and how to use the information obtained and recorded in the call notes for each case. Successful interviewers see each problem case as a personal challenge and help convey that state of mind to less experienced interviewers.

Support and Communications


Supporting and motivating field staff on low-income studies can differ markedly from the traditional methods used on a national study with a mixed sample. Assigning strong, experienced supervisors with field interviewing expertise is key to achieving high response rates. Interviewers need continual support, brainstorming opportunities, motivation, locating assistance, and morale boosting from involved and caring supervisors. Supervisors must:

  1. Communicate by phone with interviewers no less than twice a week, or more often if indicated.
  2. Discuss numbers, projections, costs, and disposition codes for cases during one call, and have a completely separate call for problem solving. The second call is for question-and-answer periods and problem solving, uninterrupted by the administrative process.
  3. Offer to do locating through central office or Internet sources or to help convert refusals. Managers sometimes can do phone interviews for interviewers on projects that allow it.
  4. Pair up new interviewers or ones hesitant to interview during late hours with experienced interviewers or escorts. (For example, traveling interpreters worked with interviewers who needed to interview Chinese, Vietnamese, and other ethnic groups on the recent Media Use Study.)
  5. Readily transfer cases around once the interviewers have established a work pattern. Supervisors must be quick to recognize procrastinators and replace them with more effective interviewers. This also helps to motivate less productive persons to improve and increase their efforts. Some interviewers prove to be more effective on the telephone than in person, so flexibility is key.

Supervisors also should be adept at refusal aversion, refusal conversion, and locating in order to help interviewers strategize effectively.

Site Office

A centrally located site office, whether for the duration of the study or just during the startup and the final crunch phase of the data collection effort, has proven beneficial. On the Woodlawn Studies, the field management staff were based at an office at NORCs University of Chicago location. This office was set up with multiple telephone lines to allow for centralized locating and some telephone interviewing by the field staff. On the New York Minority Youth Study, the office was set up in client-provided space at Columbia University. On the D.C. Networks Study, a permanent office is set up in a storefront centrally located to the sample members. On the Seattle Study, the site office was set up at the hotel where training was held and the travelers stayed; for the baseline interviewing it was maintained and staffed for the entire data collection period, whereas for the other rounds of interviewing it was set up for training and maintained for the first couple of weeks of data collection. After that the interviewers were supervised remotely, although the supervisor visited at least a few times to meet with field interviewers. There were travelers (experienced interviewers) in for the entire data collection period, although they were not the same individuals during the entire time.

In many studies the site office served to make interviewers more responsible and provided supervisors with greater flexibility to transfer cases and assignments when necessary. Interviewers were required to submit their Time & Expense Reports in person together with their completed cases. This closely tied pay to production and receipt control. Site offices also permitted supervisors to review Records of Calls and do the strategy planning face to face with interviewers.

On the New York Minority Youth Study, the front-line field manager believed that having a site office for the field interviewers helped in many ways. The respondent population was very transient, presenting multiple locating, refusal aversion, and conversion problems. Having a site itself lent a helping hand to interviewers who were not strong in these areas. The site office also provided a physical opportunity to brainstorm and share successful approaches with peers. Where one interviewer may have been unsuccessful with a certain case, the field manager could have another interviewer share his or her experience with similar cases or transfer that case for another approach. The field manager believed another benefit of the site office was in the team pressure it created. Interviewers had the opportunity to shine in person when they had a great week, and those who were not as successful felt pressured to perform better the following week.


Field managers on all projects know they are expected to be available to their interviewers 7 days a week. However, on some of these studies that expectation was intensified. On the Seattle Study, for example, a communication link between the field manager and the interviewers was needed 7 days a week and 24 hours a day. Respondents were given a toll-free number that was staffed by the senior field manager in charge who could page any of the interviewers if a respondent called and wanted an appointment. On this study, all interviewers had pagers, and the toll-free number was set up with three-way calling, caller ID, call waiting, and other features. This allowed the supervisor to contact an interviewer while she had a respondent on the phone and set up an appointment on the spot. Cellular phones would have been even more efficient, but at the time they were too costly to rent.


Support also comes in the form of working together in teams, during either the interviewing or the locating phases. The team could include a field supervisor or experienced traveler who can model an effective approach at the door and gain cooperation when new interviewers are unsure of themselves. It also can involve sending both a male and a female interviewer to an area where the female interviewer alone might be uncomfortable. The team effort also can be invoked for a blitz when all of the interviewers and supervisors work together to finish up specific cases.

Budget Control/Quality Control

Successful containment of costs requires strict measures and frequent monitoring. Senior field staff are involved in developing the proposal and the associated budget. During this process, alternative options and tradeoffs are discussed until all are in agreement on priorities and budget caps. Contingency plans, in keeping with the clients objectives, must be in place. Field staff are then provided with a budget they helped formulate and are given the responsibility to manage it.

During the Woodlawn Studies, when the locating became more time consuming than expected, the client extended the field period to give the field staff more time. When extending the data collection period may not be feasible, as was the case during the baseline interviewing for the Seattle Study, other contingencies were adopted, such as keeping the travelers on site longer than anticipated originally. Others included the need for attrition training if interviewers dropped out for one reason or another, lowering targeted response goals, and so on.

The pressures imposed on the interviewers in a study characterized by a short field period, low budget, and difficult-to-locate respondents increase the importance of quality control efforts. It is essential to conduct validation interviews for at least 10 percent of each interviewers cases, sampling from completed cases as well as noninterviews. If possible, especially if there is a site office, plan to have supervisors observe some of the interviewing. This step displays their interest in quality control.


The protocol described in this paper for obtaining high response rates in in-person surveys of low-income and welfare populations (summarized in Box 3-1) includes, but goes beyond, the factors identified by Gordon et al. as being important in follow-up surveys of low-income populations: initial contact information; updating of contact information; sophisticated tracking methods; mixed-mode interviewing; and respondent payments (Gordon et al., 1992). To those factors, the NORC approach adds effective field staffing; training with appropriate emphasis placed on the gaining cooperation tasks; and strong field support. Without identifying and deploying the resources to collect the data in the most supportive manner, even the best sample information will not result in a completed interview. The people involved in the actual data collection tasks are key, from the field interviewers to the field supervisors to the support staff in the home office. Groves and Coupers (1998) concepts of tailoring and maintaining interaction support our recommendations. In terms of the staffing approach, the most effective field staff are expert at tailoring their approach to respondents; staffing as many experienced field interviewers as possible and/or supplementing a staff of less experienced interviewers with experienced travelers is important. On the training front, it is important to cover issues related to training the respondent and gaining cooperation, along with examples and opportunities for practice, throughout the course of training. On the field support front, having a site office where interviewers and field managers can interact in person and brainstorm and allow early intervention if a problem is developing further supports the opportunities for interviewers to learn how important tailoring and maintaining interaction can be.

Finally, because of cost constraints, we recognize that face-to-face interviewing is not going to be affordable in many cases. Therefore, we strongly recommend that more focus be given to planned mixed-mode studies, acknowledging that high response rates by mail or telephone are very difficult and potentially miss key parts of this population, such as the homeless and other respondents who move frequently or those who lack phones. Part of a successful mixed-mode model would include approaches such as collaborative locating efforts with agency staff to help cut locating costs; adaptation of a Release of Information form for use with locating contacts (Sullivan et al., 1996:267); use of respondent incentives; and perhaps even piggybacking of some data collection that could offer a more cost-effective way to obtain additional data.

Box 3-1:

Key Elements in Obtaining High Response Rates in In-Person Studies.

Locating and Contacting the Sample
  • Quality of the list sample:  Prior to fielding the sample, make any effort possible to update the list. Collaboration with the client often can be very beneficial.
  • Use of advance letter:  Interviewers report that an advance letter sent to the respondent helps to emphasize the legitimacy and importance of the survey, thus becoming a tool in their gaining cooperation kit.
  • Community authority contacts:  Interviewers feel supported and safer when a project alerts community authorities of the study and their presence in the community.
  • Locating:  Resources devoted to locating efforts, both centralized and in the field, are essential for obtaining high completion rates with low-income populations. Putting together a cost-effective locating protocol is key because it is easy to spend a great deal on these efforts.
Staffing and Training Interviewers
  • Data collection plan:  It is important that the researchers and data collection staff consult about the feasibility of any proposed data collection strategies.
  • Recruiting field interviewers:  Careful screening and selection criteria applied by experienced field recruiters are critical. Not all interviewers, even those who are experienced, are effective working with low-income populations.
  • Training:  Training for interviewers should cover basic interviewing techniques, project-specific topics, and sensitivity training. It should be ongoing throughout data collection and focus on the needs that emerge, such as dealing with refusals.
  • Use of experienced, traveling interviewers:  Although this may seem counterintuitive on a survey with limited data collection funds, NORCs experience has shown that such a strategy can be cost effective if planned from the outset and managed carefully.
Optimizing Field Support and Communication
  • Field supervision:  Use experienced field supervisors who have experience working successfully with low-income populations. Make sure the budget allows for close supervision, not just taking reports.
  • Site office:  When the sample is clustered, setting up a site office can be very effective for motivating interviewers to stay on task. Even when the site is set up only temporarily, such as at the beginning and end of data collection, it can be a positive impact on production.
  • Communications:  Be available to interviewers beyond regular business hours. Depending on the schedule and sample, consider use of beepers, cell phones, and other communications methods.
  • Teamwork:  Interviewers are more likely to be successful if they feel they are part of a team and have contact with that team during the data collection period, even if just via conference calls.
Controlling Budget and Quality
  • Budget:  Review the budget on a regular basis.
  • Contingency plans:  Have contingency plans ready for implementation in case original budget assumptions dont hold.
  • Quality control:  Dont skimp on quality control; be sure to validate a percentage of each interviewers cases, both completes and noninterviews.


American Association for Public Opinion Research
1997a Best Practices for Survey and Public Opinion Research. Available:
1997b Survey Practices That AAPOR Condemns. Available: and end of data collection, it can be a positive impact on production.
Breakwell, Glynis M., Sean Hammond, and Chris Fife-Schaw, eds.
1995 Research Methods in Psychology. London: Sage Publications.
Campbell, J.
1999 Health insurance coverage. Current Population Reports, P-60(208):1-8.
Day, Neil Atherton, David Dunt, and Susan Day
1995 Maximizing response to surveys in health program evaluation at minimum cost using multiple methods. Evaluation Review 19(4):436-450.
de la Puente, Manuel
1995 Using ethnography to explain why people are missed or erroneously included by the census: Evidence from small-area ethnographic studies. In Proceedings of the Section on Survey Research Methods. Alexandria, VA: American Statistical Association.
Gordon, Anne, Jonathan Jacobson, and Thomas Fraker
1996 Approaches to Evaluating Welfare Reform:  Lessons from Five State Demonstrations. Unpublished report submitted to Office of the Assistant Secretary for Planning and Evaluation at the U.S. Department of Health and Human Services by Mathematica Policy Research, Inc. Contract #06587-005.
Groves, Robert M., and Mick P. Couper
1998 Non-Response in Household Interview Surveys. New York: John Wiley and Sons.
Lannon, Larry
1995 Telephony 229(22):76.
National Governors Association, National Conference of State Legislatures, and the American Public Human Services Association
1998 Tracking Welfare Reform: Designing Follow-Up Studies of Recipients Who Leave Welfare. Issue brief based on Conference on Tracking and Follow-up Under Welfare Reform, Falls Church, VA, February. Prepared by the Center for Best Practices. Available: [Accessed August 2, 1999].
Oueulette, T.
1999 Conducting Surveys with Welfare Leavers. Unpublished paper provided as background to the Fall 1999 Welfare Outcomes Grantee Meeting of the U.S. Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation, Washington, DC, October 25-26.
Rennison, Callie
1999 Bureau of Justice Statistics, National Crime Victimization Survey, Criminal Victimization 1998, Changes 1997-98 with Trends 1993-98. Washington, DC: U.S. Department of Justice.
Sullivan, Cris, Maureen Rumptz, Rebecca Campbell, Kimberly Eby, and William Davidson, II
1996 Retaining participants in longitudinal community research: A comprehensive protocol. Journal of Applied Behavioral Science, 32(3):262-276.
Wisconsin Department of Workforce Development
1999 Tracing and Representativeness of Responses. Unpublished paper prepared for the Fall 1999 Welfare Outcomes Grantee Meeting of the U.S. Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation, Washington, DC, October 25-26.

Paying Respondents for Survey Participation

The Problem: Surveying Low-Income Populations

To evaluate the effects of recent changes in welfare policy on the lives of people living at or below the poverty level, it is often necessary to survey a representative sample. As the chapter in this volume by Groves and Couper makes clear, achieving such a representative sample can be problematic both because members of low-income groups are hard to locate  they are more mobile, more likely to live in multifamily households, and less likely than the more affluent to have telephones  and because they may not be highly motivated to participate in surveys. Incentives  especially monetary incentives  are particularly useful in countering the second difficulty, as a supplement or complement to other efforts at persuasion. In this paper, we briefly consider why people participate in surveys (or fail to do so) and then review the use of incentives in counteracting certain kinds of nonresponses. We also review separately those findings that appear to be particularly relevant for low-income populations. Finally, we consider two special issues: The potential consequences of refusal conversion payments for respondents and interviewers, and the cost effectiveness of prepaid incentives.

Why Do People Participate in Surveys?

Porst and von Briel (1995) point out that although a great deal is known about survey respondents  their demographic characteristics, as well as their answers to thousands of different survey questions  little is known about why they choose to participate. Based on a content analysis of open-ended responses, their study of 140 participants in 5 waves of a German Methods Panel identifies 3 pure types of participants: (1) those who respond for altruistic reasons (e.g., the survey is useful for some purpose important to the respondent, or the respondent is fulfilling a social obligation  31 percent of respondents); (2) those who respond for survey-related reasons (e.g., they are interested in the survey topic, or find the interviewer appealing  38 percent); and (3) those who cite what the authors call personal reasons (e.g., they promised to do it  30 percent). In reality, of course, most people participate for a variety of reasons.

More recently, Groves et al. (2000) outlined a theory describing the decision to participate in a survey as resulting from a series of factors  some survey specific, such as topic and sponsorship, others person specific, such as concerns about privacy, still others specific to the respondents social and physical environment  each of which may move a particular person toward or away from cooperation with a specific survey request. Furthermore, these factors assume different weights for different persons, and they become salient for a specific individual  the potential respondent  when an interviewer calls to introduce the survey and request participation.

From this perspective, monetary as well as nonmonetary incentives are an inducement offered by the survey designer to compensate for the relative absence of factors that might otherwise stimulate cooperation  for example, interest in the survey topic or a sense of civic obligation. Although other theoretical frameworks such as social exchange theory (cf. Dillman, 1978), the norm of reciprocity (Gouldner, 1960), and economic exchange (e.g., Biner and Kidd, 1994) also can be used to explain the effectiveness of incentives, the present perspective is able to account for the differential effects of incentives under different conditions (e.g., for respondents with differing interest in the survey topic or with different degrees of community activism) in a way that other theories cannot easily do.

Incentives and Hard-to-Reach Populations

As indicated above, members of a group may be hard to interview both because they are difficult to locate or to find at home and because they have little motivation to participate in a survey. There is no empirical evidence that incentives are helpful in overcoming the first problem in a random digit dial (RDD) survey, nor any theoretical justification for believing that they would or should be. Thus, if the primary problem is one of finding people at home for such a survey, incentives may not be very useful. However, an experiment by Kerachsky and Mallar (1981) with a sample of economically disadvantaged youth suggests that prepayment may be helpful in locating members of a list sample, especially in later waves of a longitudinal survey. One reason, apparently, is that prepayment (and perhaps promised incentives from a trusted source) may be useful in persuading friends or relatives to forward the survey organizations advance letter or to provide interviewers with a current telephone number for the designated respondent.

The remainder of this chapter is devoted to reviewing the evidence pertaining to the second reason for survey nonresponse  namely, the situation in which the respondent has little intrinsic motivation to respond to the survey request. This situation is likely to characterize many low-income respondents, especially those who no longer receive welfare payments because of changes in federal and state legislation. Hence, the findings reported in this chapter about the effectiveness of prepaid monetary incentives are especially likely to apply to this population.

What Do We Know About the Effects of Incentives?

In this section we review what is known about the intended effects of incentives on response rates in mail as well as interviewer-mediated surveys, drawing on two existing meta-analyses (Church, 1993; Singer et al., 1999a) as well as subsequent work by the same and other authors. We specifically consider the usefulness of lotteries as an incentive and the use of incentives in panel studies. We also review what is known about unintended consequences of incentives such as effects on item nonresponse and response bias.

Effects on Response Rates

In an effort to counter increasing tendencies toward noncooperation, survey organizations are offering incentives to respondents with increasing frequency, some at the outset of the survey, as has been done traditionally in mail surveys, and some only after the person has refused, in an attempt to convert the refusal.

The use of incentives has a long history in mail surveys (for reviews, see Armstrong, 1975; Church, 1999; Cox, 1976; Fox et al.,1988; Heberlein and Baumgartner, 1978; Kanuk and Berenson, 1975; Levine and Gordon, 1958; Linsky, 1975; Yu and Cooper, 1983). In such surveys, incentives are one of two factors, the other being number of contacts, that have been found to increase response rates consistently.

A meta-analysis of the experimental literature on the effects of incentives in mail surveys by Church (1999) classifies incentives along two dimensions: whether the incentive is a monetary or nonmonetary reward, and whether it is offered with the initial mailing or made contingent on the return of the questionnaire. Analyzing 38 mail surveys, Church concluded that:

  • Prepaid incentives yield higher response rates than promised incentives;
  • The offer of contingent (promised) money and gifts does not significantly increase response rates;
  • Prepaid monetary incentives yield higher response rates than gifts offered with the initial mailing; and
  • Response rates increase with increasing amounts of money.

Studies using prepaid monetary incentives yielded an average increase in response rates of 19.1 percentage points, representing a 65-percent average increase in response. Gifts, on the other hand, yielded an average increase of only 7.9 percentage points. The average value of the monetary incentive in the mail surveys analyzed by Church was $1.38 (in 1989 dollars); the average value of the gift could not be computed, given the great diversity of gifts offered and the absence of information on their cost. Reports similar to those of Church are reported by Hopkins and Gullikson (1992).

Incentives are also used increasingly in telephone and face-to-face surveys, and the question arises as to whether their effects differ from those found consistently in mail surveys. A meta-analysis of 39 experiments by Singer et al. (1999a) indicates that they do not, although the percentage point gains per dollar expended are much smaller, on average (and the levels of incentives paid significantly higher), than those reported by Church. Their main findings are as follows:

  • Incentives improve response rates in telephone and face-to-face surveys, and their effect does not differ by mode of interviewing. Each dollar of incentive paid results in about a third of a percentage point difference between the incentive and the zero-incentive condition. As in the analyses by Church (1999) and Yu and Cooper (1983), the effects of incentives are linear: within the range of incentives used, the greater the incentive, the greater the difference in response rates between the lowest and the higher incentive conditions.
  • Prepaid incentives result in higher response rates than promised incentives, but the difference is not statistically significant. However, prepaid monetary incentives resulted in significantly higher response rates in the four studies in which it was possible to compare prepaid and promised incentives within the same study.
  • Money is more effective than a gift, even controlling for the value of the incentive.
  • Increasing the burden of the interview increases the difference in response rates between an incentive and a zero-incentive condition. However, incentives have a significant effect even in low-burden studies.
  • Incentives have significantly greater effects in surveys where the response rate without an incentive is low. That is, they are especially useful in compensating for the absence of other motives to participate. They are also most effective in the absence of other persuasion efforts. A number of studies have found that the difference in response rates between the group that received the incentive and the group that did not receive an incentive diminished after repeated follow-up attempts.

Lotteries as Incentives

Some researchers, convinced of the value of incentives but reluctant to use prepaid incentives for all respondents, have advocated the use of lotteries as an incentive for stimulating response. This might be thought desirable, for example, in surveys of women on welfare in those states where incentives are counted against the value of the benefits they receive. The studies reported in the literature  all mail surveys or self-administered questionnaires distributed in person  have yielded inconsistent findings (e.g., positive effects by Balakrishnan et al., 1992; Hubbard and Little, 1988; Kim et al., 1995; and McCool, 1991; no effects in four studies reviewed by Hubbard and Little, 1988, or in the experiment by Warriner et al., 1996). A reasonable hypothesis would seem to be that lotteries function as cash incentives with an expected value per respondent (e.g., a $500 prize divided by 10,000 respondents would amount to an incentive of 5 cents per respondent), and that their effect on response rates would be predicted by this value. Thus, the effect of lotteries would generally be small, both because the expected value per respondent is small, and because they are essentially promised, rather than prepaid, incentives.

Incentives in Panel Studies

Many studies of welfare leavers are panel studies  that is, they reinterview the same household, or the same respondent, more than once over a period of time. Assuring participation is especially important for panel studies because participation at baseline usually sets a ceiling for the retention rate over the life of the panel.(1)  For this reason, investigators often advocate using sizable incentives at the first wave of a panel study. An incentive experiment was carried out at Wave 1 of the 1996 Survey of Income and Program Participation (SIPP), a longitudinal survey carried out by the U.S. Census Bureau to provide national estimates of sources, amounts, and determinants of income for households, families, and persons. SIPP primary sample units were divided into three groups to receive $0, $10, and $20. James (1997) found that the $20 incentive significantly lowered nonresponse rates in Waves 1 to 3 compared with both the $10 and the $0 conditions, but the $10 incentive showed no effect relative to the zero-incentive group. Mack et al. (1998) reported on the results through Wave 6 using cumulative response rates, including an analysis of the effects of incentives on households differing by race, poverty status, and education in Wave 1. They found that an incentive of $20 reduced household, person, and item (gross wages) nonresponse rates in the initial interview and that household nonresponse rates remained significantly lower, with a cumulative 27.6 percent nonresponse rate in the $0 incentive group, 26.7 percent in the $10 group, and 24.8 percent in the $20 group at Wave 6, even though no further incentive payments were made. (The SIPP does not attempt to reinterview households that do not respond in Wave 1 or that have two consecutive noninterviews.) Differences between the $10 incentive and the no-incentive group were not statistically significant. A subsequent experiment with paying incentives in Waves 8 and 9 of the 1996 SIPP to all Wave 7 and 8 nonrespondents (Martin et al., 2001) found that both a $20 and a $40 prepayment significantly increased the response rate above that in the $0 group; there was no significant difference between the two incentive groups. (Differential responsiveness to incentives by respondents differing in economic status is discussed in the later section on Findings for Low-Income Populations.)

Research on the Health and Retirement Survey (HRS) suggests that respondents who are paid a refusal conversion incentive during one wave do not refuse at a higher rate than other converted refusers when reinterviewed during the next wave (Lengacher et al., 1995). Unlike the SIPP, all respondents to the HRS receive an incentive at each wave, but these are much lower than the refusal conversion payments.

In sum, although the evidence currently available is still quite limited, that which is available suggests that the use of incentives in panel studies to increase initial response rates, convert refusals, and reduce subsequent attrition can be quite effective. Moreover, although in this context it is often assumed that once incentives are paid one must continue to offer them in all subsequent waves of data collection, these studies suggest that the effects of incentives on nonresponse and attrition in panel surveys can be sustained, even when incentives are not paid in subsequent waves of the study.

Effects on Respondents or Effects on Interviewers?

Are the consistent effects of incentives in telephone and face-to-face interviews attributable to their effect on respondents, or are they, perhaps, mediated by their effect on interviewers? Clearly this question does not arise with respect to mail surveys, where incentives also have been consistently effective, but it seems important to try to answer it with respect to interviewer-mediated surveys. It is possible, for example, that interviewers expect respondents who have received an incentive to be more cooperative, and that they behave in such a way as to fulfill their expectations.(2)  Or they may feel more confident about approaching a household that has received an incentive in the mail, and therefore be more effective in their interaction with the potential respondent.

To separate the effects of incentives on interviewers from their effects on respondents, Singer et al. (2000) randomly divided all sample numbers in an RDD survey that could be linked to addresses into three groups. One third of the group was sent an advance letter and $5; interviewers were kept blind to this condition. Another third also received a letter plus $5, and still another third received the letter only. Interviewers were made aware of these last two conditions by information presented on their Computer-Assisted Telephone Interview (CATI) screens.

The results of this experiment are shown in Table 4-1. Large differences were observed between the letter-only and the letter-plus-incentive conditions, but there is no evidence that this is due to the effect of incentives on interviewers. Only one of the differences between the conditions in which interviewers were aware of the incentive and those in which they were not aware reaches statistical significance, and here the results are in a direction opposite of that hypothesized. Thus prepayment of a $5 incentive substantially increases cooperation with an RDD survey, and the incentive appears to exert its effect directly on the respondent rather than being mediated through interviewer expectations. This conclusion is in accordance with research by Stanley Presser and Johnny Blair, at the University of Maryland, who also found substantial increases in response rates as a result of small prepayments to respondents to which interviewers were blind (personal communication, n.d.).

TABLE 4-1:
Response and Cooperation Rates by Advance Letters and Letters Plus Prepaid Incentive,
Controlling for Interviewer Expectations
  Response Rate (a,b) Cooperation Rate (b,c)
Not Interviewed
% (n)
Not Interviewed

% (n)

May 1998
  • Letter only
62.9 37.1 (62) 68.4 31.6 (57)
  • Letter + $5, interviewers blind
75.4 24.6 (69) 86.7 13.3 (60)
  • Letter + $5, interviewers not blind
78.7 21.3 (61) 82.8 17.2 (58)
Ltr only vs. ltr + $5 X2=4.13, df=1, p<.05 X2=6.27, df=1, p<.05
Blind vs. not blind n.s. n.s.
June 1998
  • Letter only
58.2 41.8 (55) 62.8 37.2 (51)
  • Letter + $5, interviewers blind
73.8 26.2 (61) 86.5 13.5 (52)
  • Letter + $5, interviewers not blind
74.6 25.4 (59) 83.0 17.0 (53)
Ltr only vs. ltr + $5 X2=4.52, df=1, p<.05 X2=9.56, df=1, p<.01
Blind vs. not blind n.s. n.s.
July 1998
  • Letter only
61.8 38.2 (55) 72.3 27.7 (47)
  • Letter + $5, interviewers blind
81.3 18.6 (59) 87.3 12.7 (55)
  • Letter + $5, interviewers not blind
69.6 30.4 (56) 72.2 27.8 (54)
Ltr only vs. ltr + $5 X2=3.47, df=1, p=.06 n.s.
Blind vs. not blind n.s. X2=5.83, df=1, p<.10
August 1998
  • Letter only
63.8 36.2 (58) 69.8 30.2 (53)
  • Letter + $5, interviewers blind
75.0 25.0 (68) 81.0 19.0 (63)
  • Letter + $5, interviewers not blind
76.7 23.3 (60) 85.2 14.8 (54)
Ltr only vs. ltr + $5 X2=2.85, df=1, p=.09 X2=3.75, df=1, p=.05
Blind vs. not blind n.s. n.s.
SOURCE: Singer et al. (2000).

a  Includes noncontacts in denominator.
b  After refusal conversion.
c  Excludes noncontacts from denominator.

Unintended Consequences of Incentives

Effects on Item Nonresponse

One question often raised about the use of incentives in surveys is whether they bring about an increase in the response rate at the expense of response quality. This does not appear to be the case. On the contrary, what evidence there is suggests that the quality of responses given by respondents who receive a prepaid or a refusal conversion incentive does not differ from responses given by those who do not receive an incentive. They may, in fact, give better quality answers, in the sense that they have less item-missing data and provide longer open-ended responses (Baumgartner et al., 1998; Singer et al., 2000; Shettle and Mooney, 1999; but cf. Wiese, 1998). Experiments reported by Singer et al. (2000) indicate that promised and prepaid incentives reduce the tendency of older people and nonwhites to have more item-missing data, resulting in a net reduction in item nonresponse.

Findings reported by Mason and Traugott (1999) suggest that persistent efforts to persuade reluctant respondents to participate may produce more respondents at the price of more missing data. But these authors did not use incentives, and motivational theory suggests that people who are rewarded for their participation would continue to give good information, whereas those who feel harassed into participation may well retaliate by not putting much effort into their answers. However, there is no evidence about the effect of incentives on validity or reliability, and this is an important research question.

Effects on Response Distributions

Even more troubling, potentially, than an effect on item missing data is the effect of incentives on the distribution of responses. Does offering or paying incentives to people who might otherwise refuse affect their answers to the survey questions?

It is useful to think about the reasons why effects on response distributions might occur. One is that the use of incentives brings into the sample people whose characteristics differ from those who otherwise would be included, and their answers differ because of those differing characteristics. If that is the case, the apparent effect on response distributions is really due to a change in the composition of the sample, and should disappear once the appropriate characteristics are controlled. An example of the first process is presented by Berlin et al. (1992), who demonstrate that the apparent effect of a monetary incentive on literacy scores can be accounted for by the disproportionate recruitment of respondents with higher educational levels into the zero-incentive group. There was no significant relationship between incentive level and the proportion of items attempted, indicating that the incentive influenced the decision to participate, but not performance on the test. Another example is presented by Merkle et al. (1998) in their report of an experimental effort to increase the response rate to exit polls by having interviewers in a random sample of precincts carry clipboards and folders clearly identifying them as associated with the major media and handing out pens with the same logo. Although the response rate was increased by these methods (not necessarily by the incentive alone), the responses were actually distorted because a greater number of Democratic voters were brought into the sample  apparently as a result of the clearer identification of the poll with the media. Effects of incentives on sample composition are discussed further in the following section.

A second reason incentives might influence responses is if they influence peoples opinions directly, or at any rate the expression of those opinions. A striking example of such influence (not, however, involving an incentive) is reported by Bischoping and Schuman (1992) in their analysis of discrepancies among Nicaraguan preelection polls in the 1990 election and the failure of many to predict the outcome of the election accurately. Bischoping and Schuman speculate that suspicions that preelection polls had partisan aims may have prevented many Nicaraguans from candidly expressing their voting intentions to interviewers. They tested this hypothesis by having interviewers alternate the use of three different pens to record responses: one carried the slogan of the Sandinista party; another, that of the opposition party; the third pen was neutral. The expected distortions of responses were observed in the two conditions that clearly identified the interviewers as partisan. Even in the third, neutral, condition, distortion occurred. The authors claim that polls apparently were not perceived as neutral by many respondents. In the Nicaraguan setting, after a decade of Sandinista rule, a poll lacking partisan identification was evidently regarded as likely to have an FSLN (Sandinista) connection (p. 346); the result was to bias the reporting of vote intentions, and therefore the results of the preelection polls, which predicted an overwhelming Sandinista victory when in fact the opposition candidate won by a large majority.

Still a third way in which incentives might affect responses is suggested by theory and experimental findings about the effects of mood (Schwarz and Clore, 1996). If incentives put respondents in a more optimistic mood, then some of their responses may be influenced as a result. Using 17 key variables included in the Survey of Consumer Attitudes, Singer et al. (2000) looked at whether the response distributions varied significantly by (1) the initial incentive or (2) refusal conversion payments, controlling for demographic characteristics.(3)

The offer of an initial incentive was associated with significantly different response distributions (at the .05 level) on 4 of the 17 variables; a refusal conversion payment also was associated with significantly different response distributions on 4 of them. One variable was affected significantly by both types of incentives.(4)  In five of these cases, the responses given with an incentive were more optimistic than those given without an incentive; in two cases, they were more pessimistic. In the remaining case, respondents who received an incentive were somewhat more likely to respond good and bad, and somewhat less likely to give an equivocal reply. Thus, there is a suggestion that respondents to the Survey of Consumer Attitudes who receive an incentive may give somewhat more optimistic responses than those who do not. Similar findings have been reported by Brehm (1994) and James and Bolstein (1990). However, such effects were not observed by Shettle and Mooney (1999) in their experimental investigation of incentives in a survey of college graduates, which found only 8 significant differences (at the .05 level) in response distributions to 148 questions  a number that does not differ from that expected on the basis of chance.

Effects on Surveys of Low-Income Populations

The question of particular interest to this audience is how effective monetary and other incentives are in recruiting and retaining members of low-income populations. In a 1995 paper presented to a Council of Professional Associations on Federal Statistics (COPAFS) workshop, Kulka reported some evidence suggesting that monetary incentives might be especially effective in recruiting into the sample low-income and minority respondents, groups that ordinarily would be underrepresented in a probability sample. Reviewing a number of experimental studies that provided evidence on the issue of sample composition, including the studies discussed by Kulka, Singer et al. (1999a) found that in three studies, there was an indication that paying an incentive might be useful in obtaining higher numbers of respondents in demographic categories that otherwise tend to be underrepresented in sample surveys (e.g., low-income or nonwhite race).(5)  Five other studies reported no significant effects of incentives on sample composition, and in one study the results were mixed.

Since then, additional evidence has accumulated suggesting that monetary incentives can be effective in recruiting and retaining minority respondents. Mack et al. (1998) found that the use of a $20 incentive in the first wave of a SIPP panel was much more effective in recruiting and retaining black households and households in poverty than it was in recruiting and retaining nonblack and nonpoverty households.(6)  Martin et al. (2001) found that $20 was more effective in converting black and other race nonrespondents than in converting white respondents. These results agree with findings reported by Juster and Suzman (1995). They report that a special Nonresponse Study, in which a sample of people who refused normal refusal conversion efforts on the Health and Retirement Survey were offered $100 per individual or $200 per couple to participate,(7) brought into the sample a group of people distinctly different from other participants: they were more likely to be married, in better health, and, particularly, they had about 25 percent more net worth and a 16 percent higher income than other refusal conversion households or those who never refused. Finally, analyses by Singer et al. (2000) indicate that a $5 incentive paid in advance to a random half of RDD households for which an address could be located brought a disproportionate number of low-education respondents into the sample; there were no significant differences on other demographic characteristics.

In other words, these studies suggest that, while monetary incentives are effective with all respondents, less money is required to recruit and retain low-income (and minority) groups than those whose income is higher, and for whom the tradeoff between the time required for the survey and the incentive offered may be less attractive when the incentive is small. It should be noted that few, if any, of these studies (Mack et al., 1998, is a notable exception) have explicitly manipulated both the size of the incentive and the income level of the population; the findings reported here are based on ex post facto analyses for different subgroups, or on analyses of the composition of the sample following the use of incentives.

A number of other studies also have reported on the effects of incentives on sample composition. In some of these, it appears that incentives can be used to compensate for lack of salience of, or interest in, the survey by some groups in the sample. For example, the survey reported on by Shettle and Mooney (1999), the National Survey of College Graduates, is believed to be much more salient to scientists and engineers than to other college graduates, and in the 1980s the latter had a much lower response rate. Although this was also true in the 1992 pretest for the 1993 survey, the bias was less in the incentive than in the nonincentive group (7.1 percentage-point underreporting, compared with 9.8 percentage points), though not significantly so.(8)  Similar findings are reported by Baumgartner and Rathbun (1997), who found a significant impact of incentives on response rate in the group for which the survey topic had little salience, but virtually no impact in the high-salience group, and by Martinez-Ebers (1997), whose findings suggest that a $5 incentive, enclosed with a mail questionnaire, was successful in motivating less satisfied parents to continue their participation in a school-sponsored panel survey. Berlin et al. (1992) found that people with higher scores on an assessment of adult literacy, as well as people with higher educational levels, were overrepresented in their zero-incentive group. Groves et al. (2000) reported a similar result; in their study, the impact of incentives on response rates was significantly greater for people low on a measure of community involvement than for those high on community involvement, who tend to participate at a higher rate even without monetary incentives. In these studies, incentives function by raising the response rate of those with little interest, or low civic involvement; they do not reduce the level of participation of the highly interested or more altruistic groups.

In these studies, certain kinds of dependent variables would be seriously mismeasured if incentives had not been used. In the case of Groves et al. (2000), for example, the conclusions one would reach about the distribution of community involvement would be in error if drawn from a survey that did not use incentives. Nevertheless, questions remain about how representative of their group as a whole those brought into the sample by incentives are, and this is true for low-income and minority respondents, as well. In other words, low-income respondents brought into the sample by the lure of an incentive may well differ from those who participate for other reasons. But even if prepaid incentives simply add more respondents to the total number interviewed, without reducing the nonresponse bias of the survey, they still may prove to be cost effective if they reduce the effort required to achieve a desired sample size. The theory of survey participation outlined at the beginning of this paper (Groves et al. 2000) suggests that the representativeness of the sample will be increased by using a variety of motivational techniques, rather than relying on a single one.

Issues in the Use of Differential Incentives

Some of the research reported in the previous section suggests that it may make economic sense to offer lower incentives to people with lower incomes and higher incentives to those who are economically better off. Another instance of differential incentives is the use of refusal conversion payments, in which respondents who have expressed reluctance, or who have actually refused, are offered payment for their participation whereas cooperative respondents are not. In both of these situations, the question arises how respondents who received lower, or no, rewards would feel if they learned of this practice, and how this might affect their future participation in this or another survey.

Effects of Disclosure of Differential Incentives on Perceptions of Fairness

From an economic perspective, the fact that some people refuse to be interviewed may be an indication that the survey is more burdensome for them and that therefore the payment of incentives to such respondents (but not others) is justified. Nevertheless, some researchers are concerned that using incentives in this way will be perceived as inequitable by cooperative respondents, and that if they learn of the practice, this will adversely affect their willingness to cooperate in future surveys (Kulka, 1995).

These unintended consequences were the focus of two studies (Singer et al., 1999b; Groves et al., 1999). The first was conducted as part of the Detroit Area Study (DAS), using face-to-face interviews, and the second was done in the laboratory with community volunteers, using self-administered responses to videotaped vignettes.

In the first study, respondents were asked a series of questions concerning their beliefs about survey organization practices with respect to incentives. Three-quarters believed that such organizations offer monetary incentives to respondents to encourage participation (8.9 percent said they did not know). Those who received a prepaid $5 incentive (a random two-thirds of the survey sample) were significantly more likely than those who received no such payment to say that at least some survey organizations use incentives. Indeed, beliefs about this practice appeared to increase with the total amount ($0, $5, $25, or $30) of the incentive the respondent received or was offered, with 94 percent of those who received $30 expressing the belief that at least some survey organizations use incentives.(9)

All respondents also were asked the following question:  Some people do not want to be interviewed. However, to get accurate results, everyone chosen for the survey needs to be interviewed. Otherwise, the data may mislead people in the government who use the conclusions to plan important programs that affect everyone. Do you think its fair or unfair for people who refuse to be interviewed to receive money if other people dont? Despite the extensive justification for differential payment included here, 74 percent said they considered the practice unfair.

Near the end of the survey, in a more stringent test of whether the payment of differential incentives was perceived as fair or unfair, a random half of the respondents were informed that because of the importance of including everyone in the sample, some of those who had expressed reluctance to participate had been offered $25, while others had received nothing; they were asked whether they considered this practice fair or unfair. Again, almost three-quarters (72.4 percent) said they considered the practice unfair.

Effects of Disclosure of Differential Incentives on Willingness to Participate

Singer et al. (1999b) hypothesized that those to whom the payment of differential incentives was disclosed would be less willing to participate in a future survey.

In the laboratory study described in the previous section, subjects were significantly more likely to say they would not be willing to participate in a survey where some respondents received a payment for participating but others did not. However, the difference was reduced to insignificance when an explanation for the payment was offered by the interviewer.

In the field study, there were no differences in expressed willingness to participate between those to whom differential payments had been disclosed and those to whom they had not. About a quarter of each group said they definitely would be willing to participate in another survey by the same organization. Even those to whom differential incentive payments were disclosed and who perceived these payments as unfair did not differ significantly in their expressed willingness to participate in a subsequent survey by the same organization, although the trend in responses was as predicted: 25.8 percent versus 32.8 percent expressed such willingness.(10)  The investigators speculated that rapport with the interviewer might have mitigated the deleterious effects of disclosing differential incentives that previously had been observed in the laboratory experiment (Groves et al. 1999).

A little more than a year later, all the original DAS respondents for whom an address could be located were sent a mail questionnaire on the topic of assisted suicide, ostensibly from a different survey organization. There were no significant differences in participation between those to whom differential payments had been disclosed a year earlier and those to whom they had not.

Thus, the data indicate that most respondents believe survey organizations are currently using incentives to encourage survey participation; that these beliefs are affected by personal experience; that only half of those who are aware of the use of incentives believe that payments are distributed equally to all respondents; and that a large majority of respondents perceive the practice of paying differential incentives as unfair. However, disclosure of differential payments had no significant effect on expressed willingness to participate in a future survey, nor were respondents to whom differential incentives had been disclosed significantly less likely to respond to a new survey request, from an ostensibly different organization a year later, although again the differences were in the hypothesized direction.

Are Prepaid Incentives Cost Effective?

For a variety of reasons, including those discussed in the previous section, prepaid incentives to everyone in the sample may be preferable to refusal conversion or other differential payments.

One reason is that interviewers like them. Knowing the household is in receipt of an advance payment, modest though it may be, interviewers feel entitled to ask the respondent to reciprocate with an interview. Furthermore, prepaid incentives are equitable. They reward equally everyone who happens to fall into the sample, and they reward them for the right behavior  that is, for cooperation, rather than refusal. Both of these advantages are likely to make modest prepaid incentives an attractive alternative to refusal conversion payments in many types of surveys. There is also indirect evidence that the use of refusal conversion payments to persuade reluctant respondents leads to increasing reliance on such payments within an organization, in all likelihood because of their effects on interviewer expectations.

Still, the question arises whether such incentives are cost effective. It would appear that paying a small number of refusal conversion payments to reluctant respondents would be cheaper than paying everyone, even if those initial payments are smaller.

Several studies have concluded that prepaid incentives are cost effective in mail surveys. For such surveys, the comparison ordinarily has been among incentives varying in amount or in kind, or in comparison with no incentive at all, rather than with refusal conversion payments. Two recent investigations of cost effectiveness, by James and Bolstein (1992) and by Warriner et al. (1996), have included information on the relative effectiveness of various incentives. James and Bolstein (1992) found that a prepaid incentive of $1 was the most cost effective, yielding nearly as high a return as larger amounts for about one-quarter of the cost. Warriner et al. (1996:9) conclude that for their study, a $5 prepaid incentive was the optimal amount, resulting in a saving of 40 cents per case (because the same response rate could be achieved as in a no-incentive, two-follow-up condition). The $2 incentive resulted in costs per case only a dollar less than the $5 incentive, while yielding a response rate 10 percentage points lower. Similar findings have been reported by Asch et al. (1998) in a mail survey of physicians.

For interviewer-mediated studies, as noted earlier, the comparison is much more likely to be with refusal conversion payments. The answer is likely to depend on the nature of the study and the importance of a high response rate, on how interesting the study is to respondents (i.e., how many of them are willing to participate even without a prepaid incentive), on whether prepaid incentives reduce the effort required, and on a variety of other factors.

Several face-to-face surveys have reported that promised monetary incentives are cost effective. Berlin et al. (1992), for example, reported that use of a $20 promised incentive in a field-test experiment with the National Adult Literacy Survey, which entails completion of a test booklet by the respondent, resulted in cost savings on a per interview basis when all field costs were taken into account. Similarly, Chromy and Horvitz (1978) reported (in a study of the use of monetary incentives among young adults in the National Assessment of Educational Progress) that when the cost of screening for eligible respondents is high, the use of incentives to increase response rates actually may reduce the cost per unit of data collected.

Singer, Van Hoewyk, and Couper(11) investigated this problem in the Survey of Consumer Attitudes (SCA). They found that a $5 incentive included with an advance letter significantly reduced the number of calls required to close out a case (8.75 calls when an incentive was sent, compared with 10.22 when it was not; p=.05), and significantly reduced the number of interim refusals (.282 refusals when an incentive was sent, compared with .459 when it was not). As expected, there was no significant difference between the incentive and the no-incentive condition in calls to first contact. The outcome of the first call indicates that compared with the letter only, the addition of a $5 incentive results in more interviews, more appointments, and fewer contacts in which resistance is encountered.

Given the size of the incentive and the average cost per call aside from the incentive, sending a prepaid incentive to respondents for whom an address could be obtained was cost effective for the SCA. However, as we have tried to indicate, this conclusion depends on the size of the incentive as well as the structure of other costs associated with a study for a given organization, and should not be assumed to be invariant across organizations and incentives.

An argument that can be raised against the use of prepaid incentives is that they may undermine more altruistic motives for participating in surveys. Indeed, we have found that prepaid incentives have smaller effects on survey participation for people who score high on a measure of community activism (Groves et al., 2000) than on people who score low on this characteristic. But this is because groups high in community activism already respond at a high rate. There is no evidence (because we did not test this hypothesis) that people high on community activism who are offered a prepaid incentive respond at a lower rate than they would have had they not been offered the incentive, nor do we know whether such an effect would appear on a later survey. Although anecdotal evidence shows that some people are offended by the offer of an incentive, going so far as to return the incentive to the survey organization, by all accounts such negative reactions are few.

Prepaid incentives have been common in mail surveys for many years, although the amounts used are ordinarily quite modest (see Church, 1999). We suspect that the use of such incentives will increase in interviewer-mediated surveys as well. Such incentives are likely to be especially appropriate when other reasons that might move potential respondents to participate are weak or lacking, and when the names and addresses (or telephone numbers) of such potential respondents are known.

Recommendations and Conclusions

The workshop for which this chapter was prepared is focused specifically on collecting better data from low-income and welfare populations, and one of the clear challenges associated with surveying such populations is how to achieve high enough levels of participation to minimize bias due to nonresponse. Increasingly, respondent incentives have been proposed as a valuable tool in achieving this goal. Thus, the basic question addressed in this chapter is whether the payment of respondent incentives is indeed an effective means of reducing non-response, both for surveys in general and, especially, in surveys conducted with low-income and welfare populations.

As noted in the paper, a substantial research literature consistently has demonstrated the value of incentive payments to survey respondents for increasing cooperation and improving speed and quality of response in a broad range of data collection efforts, most notably in mail surveys. Because mail surveys are of limited utility in studies of welfare reform or low-income populations, experiments involving the use of incentives in face-to-face or telephone interviews are of greatest relevance to answering this basic question. These experiments are more recent in vintage, sparser in coverage, and not entirely consistent in their findings.(12)

Thus, although it is tempting to generalize from the findings presented here, it is important to note that many of the results are based on only a few studies and may not apply to other populations or situations, including especially those of particular interest here (i.e., surveys of low-income and welfare populations on questions related to welfare reform). Thus, if at all possible, we urge pretesting of the particular incentive plan proposed with the population targeted by ones survey and the instrumentation and other survey methods to be employed, rather than relying exclusively on this research literature.

Nevertheless, with these cautions, a few basic conclusions, guidelines, and recommendations can be gleaned from the evidence accumulated to date:

  1. Consistent with an extensive literature on the use of incentives with mail surveys, prepaid monetary incentives seem to be useful in recruiting low-income and minority respondents into interviewer-mediated surveys, even when the burden imposed on participants is relatively low. The use of incentives probably should be part of the design and strategy for all such surveys. However, they should not be used as substitutes for other best-practice persuasion strategies designed to increase participation, such as explanatory advance letters, endorsements by people or organizations important to the population being surveyed, assurances of confidentiality, and so on.
  2. How much money to offer respondents in these circumstances is not at all clear from the evidence currently available. Less money appears to be needed to recruit lower income respondents into a survey than those with higher incomes, but the optimal amount likely will depend on factors such as the length of the interview and the salience of the topic, and may also change over time. To determine the appropriate incentive amount for a given study, we reiterate our prior admonition that there is no real substitute for a careful pretest of various incentive amounts within the specific population and design context proposed for a given survey.
  3. Although it is tempting to speculate on this issue, and we often have been asked to venture an educated guess on what an appropriate range might be for incentives in studies of welfare and low-income populations, we believe that doing so would not be prudent for a number of reasons. In particular, as we have noted, the experimental literature that provides evidence directly relevant to this question is relatively sparse, idiosyncratic, and inconsistent, and the dynamics associated with providing incentives to these populations quite likely are both fluid and in large part specific to location, economy, and even cultural factors.

    As a general guideline, the Office of Management and Budget (OMB) has most recently approved the use of respondent incentives in the $20-$30 range based on empirical experimental tests conducted with specific target populations similar to those of interest here, but incentive amounts both higher and lower than these also have been approved and successfully implemented.

  4. Prepaid respondent incentives are especially important in panel surveys (a design favored by many studies of low-income populations and studies of welfare reform because of the questions of particular interest in such studies) because of the critical need to recruit a high proportion of the eligible population into the initial round of measurement. When it is possible to send payment in advance to at least a portion of the sample, the amount of cash interviewers must carry with them is reduced. Although such concerns have not been systematically validated either empirically or by anecdotal evidence from survey practitioners (see Kulka, 1995), the potential for putting either respondents or interviewers at increased risk of crime through the use of incentives is at least partially offset by this approach, along with accruing the well-established benefits of prepayment.
  5. For a number of practical reasons, including restrictions on the use of state and federal monies to compensate survey participants (especially those receiving state aid), the use of lotteries as an incentive strategy has considerable appeal. However, lotteries rather consistently appear to be less effective than individual prepaid incentives in stimulating survey response.
  6. It is possible that the use of prepaid incentives will change responses to at least some questions by affecting a respondents mood (i.e., making the respondent more optimistic about the surveys content). Although evidence of this phenomenon is mixed, it is worth evaluating this possibility empirically through an experiment whenever it is feasible to do so.
  7. Although the use of incentives strictly or primarily for refusal conversion is fairly widespread in current survey practice, incentives should be used sparingly as a refusal conversion technique. Respondents regard this practice as unfair or inequitable, although there is no evidence that such differential payments reduce future willingness to participate in surveys, including termination of payments in subsequent waves of a panel survey in which an incentive was previously provided. However, there are suggestions that the routine use of refusal conversion payments may condition interviewers to expect (and depend on) them, and that this may have a negative impact on overall interviewer performance.
  8. Finally, several issues broadly related to the protection of human subjects are sometimes raised in connection with using respondent incentives. First, specific to welfare populations is the issue of whether incentives count against the value of benefits received. Although the legislative and regulatory bases for such restrictions vary by state, and there is at least anecdotal evidence that some states have been reluctant to authorize the use of incentives in their surveys for this reason, such restrictions do not yet appear to be widespread, and researchers and officials in some states have indicated that such restrictions can be waived by the state in any case.

Second, it is well known that the OMB has had a longstanding policy that has strongly discouraged the use of incentives in federal statistical surveys. Although these policies are currently in review, recent drafts of OMBs Implementing Guidance prepared to support the Paperwork Reduction Act of 1995 provide more specific guidelines to federal agencies on the use of incentives, when incentives might be justified, and the types of documentation or evidence required to support a request for incentives. Specifically, these guidelines make clear that: (1) incentives are not intended to pay respondents for their time; (2) noncash or monetary incentives of modest size ($20-$30) are preferred; and (3) one must demonstrate empirically that such payments will significantly increase response rates (and the resulting reliability and validity of the study), although the potential need for and efficacy of incentives for certain purposes and circumstances is clearly acknowledged.

Third, some welfare reform researchers have noted a recent and potentially growing problem with Institutional Review Boards (IRBs), some of which have argued that the use of incentives (especially large incentives) may be regarded as coercive, especially among low-income respondents, thereby posing a credible threat to truly informed consent. That is, having been offered (or paid) an incentive to participate in a study, potential respondents feel they cannot really refuse, even if they are reluctant to do so for other reasons. Although assessing this potential human subject threat is clearly within the purview of IRB review, most incentive payments used to date have in fact been fairly modest in size. These are often characterized as tokens of appreciation rather than compensation for time spent. Most IRBs to date have determined that these token incentives are not so large as to constitute coercion, provided that such incentives are not cited as part of informed consent or as one of the benefits of participation in the study.


1.  Some investigators (see, e.g., Presser, 1989) recommend attempting to interview in later waves the nonrespondents to an earlier wave, but often this is not done. Even when it is, cooperation on a subsequent wave is generally predicted by prior cooperation.

2.  For evidence concerning interviewer expectation effects, see Hyman (1954); Sudman et al. (1977); Singer and Kohnke-Aguirre (1979); Singer et al. (1983); and Hox (1999). Lynn (1999) reports an experiment in which interviewers believed respondents who had received an incentive responded at a lower rate, whereas their response rate was in fact significantly higher than those who received no incentive. However, these interviewer beliefs were measured after, rather than before, the survey.

3.  They used the multinomial logit specification in CATMOD, which allows researchers to perform modeling of data that can be represented by a contingency table. CATMOD fits linear models to functions of response frequencies and can use linear modeling, log-linear modeling, logistic regression, and repeated measurement analysis. A more complete description can be found in: SAS Institute Inc., 1989, SAS/STAT Users Guide, Version 6, Fourth Edition, Volume 1, Cary, NC: SAS Institute Inc.

4.  These counts are based on the bivariate distributions, without controls for demographic characteristics. The effects do not disappear with such controls; indeed, three additional variables show such effects with demographic controls.

5.  To our knowledge, however, no high-quality studies are available yet that explore potential differences in the effectiveness of incentives by ethnicity or language per se.

6.  However, Sundukchi (1999) reports that an incentive paid in Wave 7 to all low-income households that had received an incentive in Wave 1 reduced the nonresponse rate among nonblack low-income households, but not among black low-income households.

7.  In that study, all nonrespondents were sent the incentive offer by FedEx mail; hence, it was not possible to separate the effect of the monetary incentive from the special mailing. In a subsequent small-scale experiment, money had a significant effect on converting refusals, whereas a FedEx mailing did not (Daniel Hill, personal communication n.d.).

8.  Shettle and Mooney (1999) conclude that the incentive does not reduce nonresponse bias in their study. It is true that after extensive followups, there is no difference at all between the incentive and the no-incentive groups. Nevertheless, the trends prior to phone followup are in the expected direction.

9.  The finding that respondent beliefs about survey organization practices are affected by their own experience parallels findings reported elsewhere (Singer et al. 1998c). In that Singer et al. study, 31 percent of respondents to the Survey of Consumer Attitudes who had not been offered any incentives 6 months earlier said, in 1997, that respondents should get paid for participating in that type of survey; 51 percent of those offered $5 said, 6 months later, that they thought respondents should get paid; and 77 percent of respondents who received $20 or $25 as a refusal conversion payment said respondents should get paid.

10.  However, as we would expect, the perception of fairness is directly and significantly related to whether or not respondents had themselves received a refusal conversion payment. Among those who did not receive such a payment, 74.5 percent (of 200) considered this practice unfair. Among those who did receive a refusal conversion payment, only 55 percent (of 20) considered the practice unfair; this difference is significant at the .06 level.

11.  This discussion is based on unpoublished analyses by Van Hoewyk, Singer, and Couper of data from the Survey of Consumer Attitudes during 8 months in 1998.

12.  Such inconsistencies are not largely due to differences in sample sizes, that is an inability to detect significant differences between incentive and nonincentive groups (or other relevant comparisons) because the sample sizes in these studies were too low. Sample sizes were provided for each of the studies cited in their original reports. Although we have not repeated them here, they were, with very few exceptions, adequate to detect reasonable expected differences between experimental groups.


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Adjusting for Missing Data in Low-Income Surveys

The authors are grateful to Graham Kalton, Joseph Waksberg, Robert Moffitt, and the referees for their valuable comments and suggestions that led to improvements in this paper.

Partly as a consequence of the recent significant changes in welfare programs and policies, many states are conducting or sponsoring surveys to investigate the effect of changes in welfare policy on the well-being of people living at or below the poverty level. Under ideal circumstances, every low-income person (or family) in the state would have a chance of selection for such a survey, would be located and agree to participate in the survey, and would provide correct answers to all questions asked. In practice, these circumstances are not realized in any population survey. This paper focuses on the problems of missing data in surveys arising from a failure to give all members of the target population a chance of selection for the survey and a failure to obtain the survey data from some of those sampled. The following sections indicate how missing data can lead to biased survey estimates and describe some widely used methods to reduce this effect.

Missing data in surveys can be divided usefully into three classes:

Noncoverage. Noncoverage occurs when persons (or families) in the target population of interest are not included in the sampling frame from which the sample is selected. In the case of a survey of a state's low-income population, noncoverage could, for instance, occur if the list from which the sample was drawn was out of date, and hence failed to include those most recently enrolled.

Unit nonresponse. Unit nonresponse occurs when a sampled unit (person or family) fails to participate in the survey. Unit nonresponse can occur, for example, because the sampled person cannot be located, refuses to participate, is too ill to participate, cannot participate because of language or hearing problems, or is away from the area for the period of the survey fieldwork.

Item nonresponse. Item nonresponse occurs when a sampled unit participates in the survey but fails to provide responses to one or more of the survey questions. This failure may occur because the respondent refuses to answer a question on the grounds that it is too sensitive or personal, or because he or she does not know the answer to the question. Item nonresponse also occurs when an interviewer fails to ask a question or record the answer and when recorded responses are deleted in editing a questionnaire because the response is inconsistent with the answers recorded for other questions.

There is a potential for bias whenever sampled persons who did not participate in the survey have different characteristics than those who did. For some important characteristics, the respondents may be substantially different from those with missing data. In fact, if such differences exist and no attempt is made to adjust for them in the analyses, estimates or inferences for the target population may be misleading. The potential for bias is particularly great when nonresponse rates are high. Thus, for example, if those recently enrolled are not included on the sampling frame for enrollees in a state's welfare program, the survey clearly will produce biased estimates of the distribution of length of time on the program, and any other associated estimates. Similarly, in a survey of welfare leavers, it may be that those who participate in the survey have had appreciably different experiences than those who do not, and thus, estimates based on the respondents will be biased. Suppose that families with positive outcomes (those who successfully made the transition from welfare) are easier to locate and more willing to respond than families with negative outcomes. In fact, policy makers are concerned that this situation does exist and that nonresponding and nonlocatable families and those whose current status is no longer reflected in administrative data are worse off and at greater risk than families for whom data are available (U.S. General Accounting Office, 1999). This situation can result in estimates with large nonresponse bias.

The standard method of attempting to reduce the potentially biasing effect of noncoverage and of unit nonresponse is a ''weighting adjustment." Weighting adjustments for these two sources of missing data are described in this paper. Because some state surveys have experienced high nonresponse rates, non-
response weighting adjustments are likely to be particularly important.The intent of this paper is to describe how they may be applied.

All methods for handling missing data aim to reduce their potential biasing effects, but these methods cannot be expected to eliminate the effects of missing data. The best protection against potential nonresponse bias is to plan and implement field procedures that maintain a high level of cooperation. A wide variety of tools and strategies are available to improve survey response rates. Some examples include an advance letter to sampled cases, effective callback or followup strategies, reductions in the length of the questionnaire or the interview, improved interviewer training, and payment of incentives. The literature includes an extensive discussion on methods for obtaining high response rates in surveys. Cantor and Cunningham (this volume), Weiss and Bailar (this volume), and Singer and Kulka (this volume) describe such methods for low-income surveys. However, even with the best strategies, some nonresponse occurs.

The standard method of attempting to reduce the potentially biasing effect of noncoverage and of unit nonresponse is a ''weighting adjustment." Weighting adjustments for these two sources of missing data are described in this paper. Because some state surveys have experienced high nonresponse rates, nonresponse weighting adjustments are likely to be particularly important.(1) The intent of this paper is to describe how they may be applied.

The usual method for handling item nonresponse is some form of imputation, that is, assigning a value for the missing response based on the responses given to other questions in the survey and usually conducted within classes of sample persons with similar characteristics. If done well, imputation usually can reduce bias in survey estimates. It is nearly always preferable to impute missing items rather than treating them as randomly missing data at the analysis stage because confining analyses to nonmissing responses to questionnaire items may lead to biased estimates. But bias reduction depends on the suitability of the assumptions made in the imputation. When imputations are performed separately on different variables, the bias may be reduced for univariate statistics, but multivariate relationships among variables could become distorted. Also, researchers may treat the resulting data set as if it were complete, thus affecting the variances of the estimates. An extensive body of literature currently exists for compensating for item nonresponse in surveys. Readers are referred to Kalton and Kasprzyk (1986) and Brick and Kalton (1996).

This paper focuses on the standard weighting adjustment methods used to compensate for noncoverage and unit nonresponse in survey research. These methods are general-purpose strategies that automatically adjust all analyses of the survey data, at a low cost. Other available procedures are more complex and may produce somewhat better results when analysts have a specific model they plan to estimate. Because these procedures have only limited applicability in multipurpose surveys, they have not been included here. Refer to Little and Rubin (1987) for information about these methods.

Studies of low-income populations involve various methods of data gathering. We begin with a brief description of two alternative types of low-income studies. We then provide a brief discussion of noncoverage and unit nonresponse in low-income surveys. Sample weighting is then described, followed by a review of the most common general-purpose nonresponse adjustment procedures. Finally, we include a brief summary of the paper. The procedures are illustrated using examples that we carry throughout the paper.

Low-Income Population Studies

We begin this discussion about nonresponse adjustments with a review of two different types of studies often conducted by state welfare agencies. Studies of the low-income population (such as studies of the current welfare population or studies of those who have left welfare rolls) mainly rely on two types of data collection: one collects data directly from administrative records and the other collects data directly from a sample of eligible persons. Some studies use a combination of administrative data and data from survey respondents.

States' welfare systems generally collect administrative data on the demographic characteristics of welfare recipients, the number of adults and children in the welfare case, and the receipt and value of welfare benefits. Many research studies use administrative records, and researchers frequently match the records to data from sources such as the Food Stamp Program and Medicaid. The state Unemployment Insurance files also are used to collect information about employment and earnings for families who have left welfare. Some studies rely on information available in administrative records, and thus do not require any contact with the subjects of the study.

Some states collect data through surveys. These are most often telephone interviews, although some states also conduct in-person interviews to ensure that families without telephones are included. Surveys usually collect information from respondents that is not available in administrative data.

Both types of studies of low-income populations usually suffer from some form of missing data. For example, in studies that include only administrative data collection, persons or families for whom no information is included in the administrative list (used as the sampling frame) have no chance of being included in the sample, and thus will not be represented in the results of the study. In addition, a number of sampled persons, or families, may not have the required data because they were not matched correctly or had no record in other administrative files used to collect outcome data (e.g., earnings data from Unemployment Insurance records). Similarly, surveys that collect data from sampled persons also are subject to underrepresentation due to sampling from incomplete or outdated lists, as well as missing information due to nonresponse. Later, we describe, in more detail, the sources of missing data in the two types of low-income studies.

As mentioned earlier, this paper describes the common procedures used to adjust for nonresponse and noncoverage. These procedures rely on the auxiliary data available for both respondents and nonrespondents. In general, the greater the amount of auxiliary data that can be used for adjustment, the better the adjustment is likely to be. To evaluate the availability and the amount of such data for low-income surveys, we contacted a number of states to inquire about the content and the quality of their administrative data. The main focus of this inquiry was the availability and quality of demographic, socioeconomic, and geographic variables. The results of this survey are provided in a later section. In general, we found that many states have high-quality data for demographic variables such as age, gender, race/ethnicity, and number of children. Welfare income and length of time on welfare seemed to be among the socioeconomic variables of high quality, and county name and zip code were the geographic variables with good-quality data for the states that responded to our survey. In a later section, we show how this information (or any other data source available to states) can be used to adjust for nonresponse in state surveys.

Noncoverage and Nonresponse in Surveys

A fundamental objective in the design of any state survey is to adequately represent the targeted population; for the state surveys under consideration here, this usually consists of low-income persons. However, the target population is not completely represented by the sample when either some persons or families are not included in the sampling frame (e.g., the administrative records if used for sampling) or information cannot be obtained for some eligible sampled persons. The term ''noncoverage" refers to situations where some units in the target population have no chance of selection into the sample. The following subsection provides more detail on reasons for survey noncoverage. The term ''nonresponse" in surveys refers to failure to obtain data for those eligible units that were selected into the sample. The subsection after that provides a summary of various sources of nonresponse in sample surveys.

Survey Noncoverage

Most population surveys are subject to some noncoverage. Surveys of low-income populations are no exception. One source of noncoverage is the use of incomplete or outdated administrative files as sampling frames, resulting in the omission of a part of the population of interest. Similarly, noncoverage occurs when telephone interviewing is the only vehicle for data collection, because those without telephones have no chance of being selected and thus will not be covered in the survey.

In many survey applications, the omitted part of the population differs in many ways from the part that is included in the sampling frame. For example, if the objective of the study is to obtain information about the postreform status of all low-income families, families eligible for Temporary Assistance for Needy Families (TANF) who did not become welfare recipients will not be included in the welfare records. These families are not covered in the administrative file used for sampling, and thus they will not be covered in the sample.

The following example(2) illustrates the potential effect of the choice of sampling frame on survey noncoverage. Assume that a survey is designed to evaluate, in two states, the impact of the loss of Supplemental Security Income (SSI) benefits by individuals whose low-income status was caused by substance abuse. SSI benefit termination for this population was mandated by federal legislation (Public Law 104-121) in 1996. After SSI benefits were terminated, some of the past SSI recipients applied to Referral and Monitoring Agencies (RMA), funded by the Center for Substance Abuse Treatment (CSAT). Refer to Tonkin et al. (in press) for more details on the methodology for the CSAT study.

One of the first steps in designing a survey is to define the study population of interest. Assume that the target population consists of all individuals between the ages of 21 and 59 who were receiving SSI as a result of substance abuse and who had an active case on or before July 1, 1996. Although the population of interest is all individuals receiving benefits because of substance abuse, assume that the two states (State A and State B) used different frames for sample selection; State A used the RMA client rosters, which covered only 66 percent of the target population, and State B used the Social Security Administration client roster, which was a complete frame.

In the State A sample, individuals not included in the RMA lists of active cases (i.e., 34 percent of individuals) had no chance of being selected into the sample. This is a potential source of bias if the characteristics of interest (e.g., drug abuse) are different for individuals not covered by the RMA frame compared to those on the RMA frame. The potential for noncoverage bias increases as the frame coverage rate decreases.

Survey Nonresponse

Unit nonresponse in surveys occurs for various reasons, including the failure to locate sampled persons and the refusal of sampled persons to be interviewed. In some cases, collected data may be lost during data transmission stages. In welfare studies that collect outcome data from administrative files, nonresponse can occur because of inability to match the sampled case to the administrative file that includes the outcome data. Statistics derived from survey data may be biased if the missed persons are different, with respect to the variable of interest to the survey, from those who participated in the survey.

In the SSI benefit example, assume that the response rate was 67 percent for the State A sample and 92 percent for the State B sample. The lower response rate for State A is another source of potential bias. If those who did not participate differ in important ways from those who did, the bias due to nonresponse could be substantial.

The marginal population distributions of demographic variables  age, gender, and race  usually are available, and these can be used to examine the potential for noncoverage and nonresponse biases, as shown in the following text. Tables 5-1 and 5-2 provide the marginal distributions of the demographic variables age, gender, and race for the assumed target population (SSI only); the enrollment sample; (State A and State B selected samples), and the respondents for the two states.(3)

Hypothetical Population and Sample Distribution for State A
  SSI Population (%) State A Sample (%) State A Respondents (%)
Factor 1: Age
Less than 40 30 26 31
40-49 44 44 47
50 or over 26 30 22
Factor 2: Gender
Male 65 66 58
Female 35 34 42
Factor 3: Race
White 30 38 40
Black 34 12 10
Others 36 50 50
Note: SSI = Supplemental Security Income

Hypothetical Population and Sample Distribution for State B
  SSI Population (%) State B Sample (%) State B Respondents (%)
Factor 1: Age
Less than 40 37 39 40
40-49 40 38 40
50 or over 23 23 20
Factor 2: Gender      
Male 71 72 70
Female 29 28 30
Factor 3: Race
White 7 7 6
Black 88 89 90
Others 5 4 4
Note: SSI = Supplemental Security Income

We observe that for State A, in spite of 34-percent noncoverage, the sample distribution is not distorted for the variable gender. However, blacks are underrepresented and the race category ''others" is overrepresented in the State A sample. For State B, where a complete frame was available for sampling, the sample distributions are similar to the SSI population distributions for all three variables.

The State A sample had a moderately low response rate, resulting in a distortion in the distributions for respondents by gender and age. This obviously would bias the results if the outcome characteristics of males and females are different, or if the age distribution affects the outcome. The response rate for State B was high, and the sample distributions and the respondent distributions of all three demographic variables are very similar to the population distributions. This suggests a lower potential for substantial nonresponse and noncoverage bias.

The hypothetical samples for States A and B are examples of the impact of noncoverage and nonresponse in survey outcome. In the following subsection, we provide a general formula that attempts to quantify the bias associated with noncoverage and nonresponse.

Nonresponse Bias

The size of the nonresponse bias depends on the amount of nonresponse and the difference between the respondent and nonrespondent mean values of the study variables. For example, in an equal probability sample (a description of an unequal probability sample is provided in the section on base weights) of size n selected from a population of N families, let n1stand for the number of respondents and let n2 stand for the nonrespondents where n2(= n - n1 ). Let y be the study variable (e.g., family income) with fig1.gifas the respondent mean and fig2.gif as the mean for nonrespondents (wherefig2.gifis unknown). The sample mean fig3.giffor the total sample can be expressed as (see, for example, Groves, 1989)


Becausefig2.gifis unknown, analysts usefig1.gifto estimate fig3.giffor the target population. When no nonresponse adjustments are made, the bias can be estimated as


Therefore, the extent of bias is a function of both the nonresponse rate (n2 /n ) and the difference between the respondent and nonrespondent means fig6.gif.

For example, assume that a survey of 820 low-income families has been carried out and that the variable of interest is the average monthly family income. Table 5-3 provides examples of the amount of bias in the estimate of variable of interest (i.e., average monthly family income) for various levels of nonresponse and average incomes for respondents and nonrespondents.

The level of bias in Table 5-3 is a function of both the variable response rates and the difference in the mean income for respondents and nonrespondents.

Level of Bias by Nonresponse Rate and Differences in Average Income of Respondents and Nonrespondents
  Example 1 Example 2 Example 3
Sample Size Average Income Sample Size Average Income Sample Size Average Income
Respondents 600 $1,500 410 $1,500 600 $2,000
Nonrespondents 220 $1,100 410 $1,100 220 $1,100
Survey estimate with no nonresponse adjustment $1,500 $1,500 $2,000
Estimated population value $1,393 $1,300 $1,759
Bias $ 107 $ 200 $ 241
NOTES: The data used for the Family Income Survey(FIS) example is hypothetical.

Some part of differences in average income between respondents and nonrespondents is usually due to differences in their demographic composition (e.g., race, age, as in the States A and B examples) and the fact that income tends to vary among these demographic groups. The bias resulting from this distortion of the respondent sample can be reduced considerably by devising adjustment factors and applying them to the responding units data. Adjustment factors typically vary among demographic groups, and their purpose is to establish a data set whose sample representation has been adjusted to compensate for the missing nonrespondent data. (We used the term ''demographic groups" because race, age, gender, and other factors, are most frequently known for the population of interest. However, sometimes additional information such as income in a recent time period or employment status also is available for both respondents and nonrespondents. Adjustment factors can, of course, be developed for these variables as well as the demographic variables.) The underlying assumption for these adjustments is that respondents are similar to nonrespondents within the adjustment subgroups (or classes); that is, the data are missing at random (MAR)(4) and nonresponse is ignorable within the nonresponse adjustment group (Little and Rubin, 1987). Because respondents are not fully representative of nonrespondents (the MAR assumption does not hold perfectly), some unknown bias remains, even after conducting weighting adjustments.

The adjustments for nonresponse described in this report are designed to eliminate the part of the bias arising from the distortion in the respondent sample, but they have little effect on other causes of bias, which are usually independent of the sample composition. (Among possible reasons are that many persons who cannot be located have obtained jobs outside the area and have moved and that nonrespondents are in some ways psychologically different from the general population and the differences affect their ability to find employment.) Unfortunately, the extent to which these causes affect the result of a particular survey are, in most cases, not known, and consequently there is the possibility of significant bias when high nonresponse rates exist. Although we strongly recommend the adjustment procedures, they should not be considered replacements for a vigorous effort to achieve the highest response rate possible. They are an adjunct to such an effort.

A later section provides a summary of the general-purpose nonresponse adjustment methods currently used in many social surveys. The nonresponse adjustment factors are incorporated into the survey weights. The next section reviews the properties of sampling weights in preparation for the discussion of nonresponse adjustment procedures.

Weighting Survey Data

Sample weighting is carried out to accomplish a number of objectives, including adjustments for nonresponse. The purpose of weighting the survey data is to permit analysts to produce estimates of statistics for the total target population. For example, state surveys usually involve the selection of a random sample of low-income persons from an existing administrative data file. Sampling weights produced for such surveys can be considered as estimated measures of the number of persons in the target population that the particular sampled low-income individual represents. Weighting takes into account several features of the survey: the specific probabilities of selection of individuals in the sample (as described in the following subsection), as well as nonresponse and differences between the sample of low-income persons and the total low-income population. Differences between the sample and the population may arise because of sampling variability, differential noncoverage in the survey among population subgroups, and possibly other types of response errors, such as differential response rates or misclassification errors.

In summary, sample weighting in surveys is carried out to accomplish the following objectives:

  • To enable the production of tabulations that provide estimates of the number of persons (or families) in the population for the various categories tabulated;
  • To compensate for disproportionate sampling of various subgroups in the sample;
  • To reduce biases arising from the fact that nonrespondents may be different from those who participate;
  • To compensate, to the extent possible, for noncoverage in the sample due to inadequacies in the sampling frame or other reasons for noncoverage; and
  • To reduce variances in the estimation procedure by using auxiliary information that is known with a high degree of accuracy if the auxiliary variables are highly correlated with the study variables.

We start with a description of base weights because the adjustments are applied to these weights.

Base Weights

The base weight for a sample unit (e.g., a sampled low-income family) is defined as the reciprocal of the probability of including the unit in the sample. The base weight for the i-th unit in the sample is given by


wherefig8.gif is the known probability of including unit i in the sample. If the sample units are selected with equal probability, the probability of selection is

fig9.gif for all sample units, where n is the sample size and N is the number of units in the sampling frame. The base weight, therefore, is fig10.gif for all sampled units. In this case, fig11.gifIn the family income survey (FIS) example given earlier, assume that a sample of n = 820 families was selected with equal probabilities of selection from a population of N = 41,000 families. Then the probability of selection for each unit in the sample is equal to n/N = 820/41,000, and the base weight would be equal to N/n = 50 for each sampled family. Thus, each family selected into the sample represents 50 families in the administrative file used for sampling.

State surveys may be designed to provide an equal probability sample (similar to the previous example) or a disproportionate sample of low-income persons with respect to a selected set of characteristics (e.g., demographic characteristics). In an equal-probability sample, the distribution of the sample is expected to be similar to the administrative frame. For example, if the administrative frame in state S includes 10 percent Hispanics and 90 percent non-Hispanics, an equal probability sample is expected to include about 10 percent Hispanics and 90 percent non-Hispanics. However, if state S is interested in analyzing the well-being of the low-income Hispanic population, the survey is likely to include an oversampling of low-income Hispanic persons. The oversampling can be accomplished by stratifying the frame into two strata, Hispanics and non-Hispanics, and applying a larger sampling rate to Hispanics. In this case, the sample will contain a disproportionate representation of Hispanics. When disproportionate sampling is applied in stratified sampling, different weights (referred to as base weights) are used to compensate for the unequal representation in the sample. Otherwise, estimates will be biased. Returning to the FIS example, assume that Hispanic families are sampled at a rate of 1 in 30 and that non-Hispanics are sampled at a rate of 1 in 60. Then the base weight for the Hispanics is equal to 30, and the base weight for non-Hispanics is equal to 60. Thus, each sampled Hispanic family represents 30 Hispanic families in the population, and each non-Hispanic family in the sample represents 60 non-Hispanic families in the population. For more information on disproportionate sampling, refer to Kish (1992).

Although the base weights are theoretically unbiased weights that ''inflate" the sample observations to population levels, in practice, most survey practitioners find it useful to modify the base weights. Nonresponse in the survey, for example, results in losses in the sample data that can be partially compensated for by adjusting the weights of the respondents. If the sampling frame is deficient because it is outdated or its coverage of certain population subgroups is inadequate, further adjustment of the weights may be desirable to compensate for these deficiencies. The following section provides brief descriptions of various weight adjustment procedures commonly used in large-scale surveys.

Common Nonresponse Adjustment Methods in Surveys

In spite of the best strategies for collecting data from sampled units, nonresponse nearly always occurs in population surveys, including those of low-income families. A ''nonrespondent" is any sampled unit that is eligible for the study but for which data are not obtained for any reason. Failure to match the sample cases with the administrative files used to gather outcome data, refusal to participate in the survey, or situations such as ''not-at-home after multiple calls," ''language problems," and ''knowledgeable person not available" are some of the reasons why an eligible sampled unit may not participate in a survey. On the other hand, sampled units that are ineligible for the survey are not considered nonrespondents, even though they do not provide survey data. As discussed later in this section, nonrespondents and ineligibles are treated differently in the nonresponse adjustment process.

When nonresponse is present, a weight adjustment can partially compensate for the loss of data. This weight adjustment increases the weights of the sampled cases for which data were collected. The first step in adjusting for nonresponse is the construction of weighting classes. As discussed in the following text, within each weighting class, the base weights are inflated by the inverse of the response rate so that the sum of the adjusted base weights for respondents is equal to the sum of the base weights for the total eligible sample selected in the weighting class. Returning to the FIS example, assume that 160 families were selected (with equal probability) within a weighting class and that 77 families responded to the survey. Because the weight for each family is equal to 50 (as shown earlier), the nonresponse-adjusted weight is about 104 (i.e., 50 multiplied by 160/77). Thus, after nonresponse adjustment each responding family in the sample represents about 104 families in the population within the weighting class.

The effectiveness of nonresponse adjustment procedures in reducing nonresponse bias is directly related to the ability to construct appropriate nonresponse adjustment classes. The following subsection provides a brief summary of two procedures commonly used to construct adjustment classes. The next subsection discusses sample-based adjustment procedures that are commonly used to compensate for nonresponse. Then we describe population-based adjustment procedures (poststratification and raking) that are widely used for noncoverage adjustment, or sometimes used to correct simultaneously for both nonresponse and noncoverage. Additional benefits of population-based adjustments include reduction in the sampling errors of the sample estimates as well as achieving consistency with the known population counts. In poststratification and raking, respondents are categorized according to one or more variables (e.g., age, gender, race, or income level) at a recent point in time, and the survey estimates are benchmarked to the known population totals. For a general review of weighting for nonresponse, refer to Elliot (1991). Finally, we provide a discussion of the importance of balancing bias and variance when adjusting survey data.

Construction of Nonresponse Adjustment Classes

Implementing nonresponse adjustment procedures requires the specification of appropriate weighting classes or cells. Survey responses generally are correlated with certain characteristics of the sample units, and it would be desirable to form classes based on these characteristics. Often, little is known about the nonrespondents. Relevant information about each sampled unit sometimes can be obtained through data retrieval efforts to collect limited data about the nonrespondents or by interviewer observation (if applicable). The availability of this information would enhance the effectiveness of the nonresponse adjustment.

Data used to form classes for nonresponse adjustments must be available for both respondents and nonrespondents. In state low-income surveys, the administrative files used to select the sample are good sources of information for forming weighting classes. In a recent survey, we contacted a number of states to inquire about the availability and the quality of their administrative data, including the following variables:

  • Demographic
    • Age
    • Gender
    • Race/ethnicity
    • Marital status
    • Number of children
  • Socioeconomic
    • Education
    • Employment
    • Earned income
    • Welfare income
    • Housing subsidy
    • Length of time on welfare
  • Geographic
    • Urban/rural
    • Metropolitan/nonmetropolitan status
    • County code
    • Zip code

Thirteen states completed our questionnaire. All states reported having data on age, gender, race/ethnicity, number of children, and length of time on welfare. Most states also have data on earned income, welfare income, employment, county code, zip code, and marital status. About 50 to 60 percent of states reported having data on education, housing subsidies, metropolitan/nonmetropolitan status, and urbanicity. The 13 states that responded to the questionnaire on auxiliary data also indicated their assessments of the quality of the administrative data that their state maintains. We observed that the quality of data on demographic variables was quite high, with less than 1 percent missing values. For the socioeconomic variables, the only two variables with high-quality data are ''welfare income" and ''length of time on welfare," where length of time on welfare is measured for the most recent episode. Data on employment and earned income, if applicable, were obtained by matching with quarterly wage records. The only geographic variables of high quality are county and zip codes. We encourage state welfare program administrators to look for other potential data sources that could be used as auxiliary variables for nonresponse and/or noncoverage adjustments, such as wages and employment data sources. The above variables are usually good candidates for use in nonresponse adjustment. However, missing data on items used for nonresponse adjustment can present problems for postsurvey adjustments. If a substantial amount of data are missing for an item on the sampling frame, this variable is probably not appropriate for the purpose of nonresponse adjustments.

The variables used to form weighting classes should be effective in distinguishing between subgroups with different response rates. They are most useful when survey responses are roughly similar for respondents and nonrespondents within a class. If this implicit assumption holds, the estimates are effectively unbiased. In establishing the nonresponse adjustment classes, the following should be kept in mind:

  • The variables used in nonresponse adjustment should be available for both respondents and nonrespondents;
  • Response rates should be different among the nonresponse adjustment classes;
  • Survey responses are expected to be different among the classes; and
  • The adjustment classes should respect a balance between bias and variance (refer to the section entitled ''Balancing Bias and Variance When Adjusting for Nonresponse" for a discussion of balancing bias and variance when creating adjusted sampling weights).

As mentioned earlier, knowledge of the likely behavior of persons in various demographic and socioeconomic classes can be used to construct weighting classes. A preliminary analysis of response rates in these classes can refine the classification further.

Returning to the FIS example provided earlier, assume that nonresponse evaluation research has identified the gender and race (white/nonwhite) of the head of family as the best predictors of nonresponse. Then, the sample is divided into four classes, as shown in Table 5-4. Note that mean income and the nonresponse rate are both quite variable across the four classes. This suggests that the adjustments have the potential to reduce the nonresponse bias.

Nonresponse Adjustment Classes for the FIS Example
Adjustment Class Head of Family's
Gender and Race
Sample Size Respondent
Mean Income ($)
Respondents Nonresponse
Rate (%)
1 White 160 1,712 77 52
2 Non White 51 1,509 35 31
3 White 251 982 209 17
4 Non White 358 911 327 9
  Total 820 1,061 648 21
NOTES: *Family Income Survey(FIS)

More sophisticated methods also are available. We discuss two commonly used procedures (referred to as modeling response propensity) for defining weighting classes using data on auxiliary variables. The first method involves classification or segmentation based on a categorical search algorithm. The second method is based on logistic regression modeling. Software is available to perform the computations required for both procedures.

The first class of methods divides a population into two or more distinct groups based on categories of the ''best" predictor of a dependent variable. The dependent variable is a categorical variable with two categories: respondents and nonrespondents. The predictor variable with the highest significance level is used to split the sample into groups. It then splits each of these groups into smaller subgroups based on other predictor variables. This splitting process continues until no more statistically significant predictors can be found, or until some other stopping rule is met (e.g., there are too few observations for further splitting). The result is a tree-like structure that suggests which predictor variable may be important.(5) It is a highly efficient statistical technique for segregation, or tree growing, with many different versions currently available, as described in Breiman et al., (1993).

The second approach models the response status of the sampled units using predictor variables that are known for both respondents and nonrespondents from the sampling frame. Most commonly, the prediction approach is based on a logistic or probit regression model effectively using auxiliary variables, such as demographic, socioeconomic, and geographic variables, to predict the probability of response. For more information on logistic response propensity modeling, refer to Little and Rubin (1987), Brick and Kalton (1996), and Iannacchione et al. (1991).

Sample-Based Adjustment Procedures

Sample-based nonresponse adjustments make use of information that is available for the sample, and thus do not require any external population counts. In effect, sample-based nonresponse adjustments distribute the base weights of the nonresponding units to the responding sampled units so that the sum of the adjusted weights over the responding units equals the sum of the base weights for the entire sample.

The basic form of the sample-based nonresponse adjustments is a ratio of sums of base weights where the sums extend over specified subsets of the sample defined by response status. The particular form of the adjustment depends on whether the eligibility status of the nonresponding units can be ascertained. First, we describe the nonresponse adjustment under the assumption that every sampled unit can be assigned to one of the following three response status groups:

Group 1: Respondents. This group consists of all eligible sample units that participated in the survey (i.e., provided usable survey data).

Group 2: Nonrespondents. This group consists of all eligible sample units that did not provide usable survey data.

Group 3: Ineligible or out of scope. This group consists of all sample units that were ineligible or out of scope for the survey.

In this particular case, it is assumed that all of the nonrespondents (Group 2) in the sample have been determined to be eligible for the survey and that all of those in Group 3 have been determined to be ineligible for the survey. If eligibility is unknown for some of the selected cases, the usual approach is to distribute proportionally the weights of those with unknown eligibility to those for which eligibility was determined. In the FIS example, let's assume that 850 families originally were selected from an administrative file. However, it was determined later that 30 families were ineligible because the administrative frame was outdated, for example. The total number of eligible families is 820, and 648 responded to the survey. In this case, Group 1 = 648, Group 2 = 172, and Group 3 = 30. The corresponding sample-based nonresponse adjustment factorfig13.gifis defined to be the ratio of sums:


where Wiis the base weight for the sampled unit i, R represents survey respondents (Group 1), and N represents nonrespondents (Group 2). The adjustment factor fig13.gifis applied only to the base weights of the respondents (Group 1) in the sample; that is, the nonresponse-adjusted weight fig14.gifis computed as

fig14.gif= 0, if unit i is nonrespondent (Group 2).

fig14.gif = Wi, if unit i is out of scope (Group 3).

fig15.gifif unit i is an eligible respondent (Group 1).

In practice, the nonresponse adjustment,fig13.gif, is calculated within specified weighting or adjustment classes. The procedures for forming appropriate weighting classes for this purpose were discussed earlier.

Table 5-5 shows the nonresponse adjustment factors and adjusted weights for the FIS example. Because the base weights are equal to N/n (=50) for each sampled family (as shown in an earlier section on base weights), the nonresponse adjustment factors in column 4 are simply equal to the ratio of column 2 to column 3. The base weights would be adjusted by multiplying the base weights by the nonresponse adjustment factors i.e., column 1 multiplied by column 4. That is, the adjusted weight for each of the respondents in the four cells created by gender and race is equal to the weight given in column (5).

Nonresponse Adjustment Factors and the Adjusted Weights for the FIS* Example
Head of Family's
Gender and Race
Base Weight
Sample Size
Nonresponse Adjustment Factor
Adjusted Weight**
White 50 160 77 2.08 104
Non white 50 51 35 1.46 73
White 50 251 209 1.20 60
Non white 50 358 327 1.10 55
Total   820 648    
*  Family Income Survey
** For presentation purposes, we have rounded up the adjustment factors (to two decimals) and the adjusted weights (to whole numbers). The calculations, however, carry all the decimals.

Population-Based Adjustments

In applications where external control counts are available for weighting, the usual practice is to first calculate sample-based nonresponse-adjusted weights and then to further adjust these weights through population-based adjustments. Population-based adjustment tends to reduce the effects of noncoverage (e.g., incomplete frames) and improve the representation of the sample. Sometimes, it is convenient or necessary to bypass the intermediate step of calculating the sample-based nonresponse-adjusted weights. In this case, the base weights would be ratio adjusted directly to known control totals in a single step. For example, if the classes used for nonresponse adjustment also are used for population-based adjustments, the two-step procedure of first adjusting for nonresponse and then adjusting to known control totals is equivalent to the single population-based adjustment procedure discussed in this section. Separate nonresponse adjustments are necessary when the nonresponse weighting classes are different from those planned for the population-based adjustments. This is usually, although not always, the case because different sources of data are available for each adjustment. In the following sections, we briefly describe the two most commonly used population-based adjustment procedures.

The term ''calibration" is used in the literature to cover a variety of techniques used in benchmarking the weights to known external totals. In this paper, we focus our attention on the two procedures most commonly used in general surveys: poststratification and raking.


Poststratification is a popular estimation procedure in which the weights of the respondents are adjusted further so that the sums of the adjusted weights are equal to known population totals for certain subgroups of the population. For example, take the case where the population totals of subgroups (referred to as poststrata) defined by age, gender, and race/ethnicity are known from the sampling frame (or other external sources), and they also can be estimated from the survey. Poststratification adjusts the survey weights so that the distribution by subgroups (when weighted by the poststratified weights) is the same as the population distribution from the survey frame or external sources.

Letfig16.gifdenote the population count in the poststratum denoted by g as obtained from the sampling frame or an external source, and let fig18.gifbe the corresponding survey estimate obtained by using the nonresponse-adjusted weights. Then the ratiofig19.gifis the poststratification adjustment factor for subgroup g .

The main advantage of poststratification is that the procedure reduces the bias from some types of noncoverage and nonresponse. An additional advantage of poststratification is the improvement in the reliability of the survey estimates for variables that are highly correlated with the variables used for poststratification. Generally, the poststratified weights are the final survey weights, and these would be used to tabulate the survey results. Occasionally, an additional weighting factor, called a ''trimming factor," is used to protect against extremely high variances. A brief description of trimming procedures used in practice is provided in a later section. If a trimming factor is calculated for a survey data file, it should be incorporated into the final weight as another multiplication factor.

Earlier, we illustrated the nonresponse adjustment procedure by assuming that the number of families in the population was 41,000 and that there was no noncoverage. We continue the FIS example, assuming that the number of families in the population was actually 46,000 and that the sampling frame contained only 41,000 families because information necessary for locating respondents was missing for 5,000 families. However, some limited demographic and other socioeconomic information was available in the data files for all 46,000 families. Suppose further that the noncoverage rate varies within the four cells defined by the cross-classification of employment status (employed/not employed) and education (high school diploma/no high school diploma) of the head of the family. Poststratification adjustment can be applied to reduce the bias arising from noncoverage.

The poststratification adjustment factor for a poststratification cell is the ratio of the known family count within the poststratification cell to the corresponding estimate of the family count from the survey. The estimate of the family count within a poststratification cell is obtained by summing the nonresponse-adjusted weights of the families (as shown in Table 5-5) in the poststratification cell. Because the base weights were adjusted to account for the nonresponse (as given in Table 5-5), these adjusted weights would vary by poststratified adjustment classes. Therefore, Table 5-6 gives the count and the adjusted weight for the 16 cells defined by the cross-classification of nonresponse adjustment classes (4 classes) and poststrata (4 cells).

Column 2 is the nonresponse adjusted weight for each family in the gender/race/ employment/education class. The initial estimate of total number of families in each class (taking nonresponse into account) is the product of columns 1 and 2 and is given in column 3. The total of the nonresponse-adjusted weights (column 3) can be used to estimate the number of families by poststrata defined by employment status and education of the head of the family. Table 5-7 provides the estimates of the family count and the corresponding known family count from external sources by poststrata. The table also gives the poststratification adjustment factors, defined as the ratio of the known family count and the survey estimate.

Distribution of Nonresponse-Adjusted Weights by Gender, Race, Employment, and Education for the FIS* Example
Head of family Respondent Count
Nonresponse-Adjusted Weight**
Initial Estimated No. of Families
Gender and Race Employment Education
White Employed HS *** 38 104 3,948
Non White  Employed HS  15 73 1,093
White Employed No HS 11 104 1,143
Non White Employed   No HS 6 73 437
White No HS diploma HS 12 104 1,247
Non White  No HS diploma HS  5 73 364
White Unemployed No HS 16 104 1,662
Non White  Unemployed  No HS 9 73 656
White Employed HS 101 60 6,065
Non White Employed  HS  158 55 8,649
White Employed No HS 30 60 1,801
Non White  Employed  No HS 47 55 2,573
White Unemployed HS 33 60 1,982
Non White  Unemployed  HS 51 55 2,792
White Unemployed No HS 45 60 2,702
Non White  Unemployed No HS  71 55 3,887
Total 648   41,000
* Family Income Study
** For presentation purposes, adjusted weights are rounded to whole numbers. The calculations, however, carry all the decimals.
*** HS =High school diploma 

Poststratification Adjustment Factors for the FIS* Example
Poststratum Initial Survey Estimate* Known Auxiliary Total Adjustment Factor**
HS *** 19,757 22,125 1.12
No HS 5,955 6,313 1.06
HS 6,385 6,966 1.09
No HS 8,908 10,596 1.19
* Family Income Study
** For presentation purposes, we have rounded up the adjustment factors (to two decimals) and the adjusted weights (to whole numbers). The calculations, however, carry all the decimals.
*** HS =High school diploma 

The final survey weights are defined as the product of the base weight and the adjustment factors for nonresponse and poststratification. Table 5-8 includes the final weights for the FIS example. The final weight in column 5 is equal to the product of the base weight in column 1 and the nonresponse adjustment in column 3 and the poststratification factor in column 4.

It is not always possible to use poststratification because it requires data on the cross-classification of categorical variables that are used to define poststrata. Either the cell-level population counts may not be available or the sample sizes for some of the cells in the poststrata may not be adequate (for a discussion of adequate cell sample sizes, refer to the following section entitled ''Balancing Bias and Variance When Adjusting for Nonresponse"). In such situations, survey practitioners frequently use a more complex poststratification method, referred to as a raking procedure, which adjusts the survey estimates to the known marginal totals of several categorical variables.

Raking Procedure

This methodology is referred to as raking ratio estimation because an iterative procedure is used to produce adjustment factors that provide consistency with known marginal population totals. Typically, raking is used in situations where the interior cell counts of a cross-tabulation are unknown or the sample sizes in some cells are too small for efficient estimation (refer to the following section for more information about sufficient cell sample size).

Raking ratio estimation is based on an iterative proportional fitting procedure developed by Deming and Stephan (1940). It involves simultaneous ratio adjustments of sample data to two or more marginal distributions of the population counts. With this approach, the weights are calculated such that the marginal distribution of the weighted totals conforms to the marginal distribution of the targeted population; some, or all, of the interior cells may differ.

The raking procedure is carried out in a sequence of adjustments. The base weights (or nonresponse-adjusted weights) are first adjusted to produce one marginal distribution, the adjusted weights are used to produce a second marginal distribution, and so on, up to the number of raking dimensions. One sequence of adjustments to the marginal distributions is known as a cycle or iteration. The sequence of adjustments is repeated until convergence is achieved, meaning that the weights no longer change with each iteration. In practice, the raking procedure usually converges, but the number of iterations may be large when there are many marginal distributions involved in raking.

Final Poststratified Weights for the FIS* Example
Poststratum Base
Post- Strat.
Adjust- Ment(4)**
Final Weight
Final Estimate of
No. of Families (6)
Head of Family's
Gender and Race
Employment Education
White Employed HS *** 50 38 2.08 1.12 116 4,422
Non white Employed  HS  50 15 1.46 1.12 82 1,224
White Employed No HS 50 11 2.08 1.06 110 1,212
Nonwhite Employed  No HS  50 6 1.46 1.06 77 463
White Unemployed HS 50 12 2.08 1.09 113 1,360
Non white Unemployed   HS 50 5 1.46 1.09 80 397
White Unemployed No HS 50 16 2.08 1.19 124 1,978
Non white Unemployed  No HS  50 9 1.46 1.19 87 780
White Employed HS 50 101 1.20 1.12 67 6,793
Non white Employed  HS  50 158 1.10 1.12 62 9,687
White Employed No HS 50 30 1.20 1.06 64 1,910
Non white Employed  No HS  50 47 1.10 1.06 58 2,728
White Unemployed HS 50 33 1.20 1.09 65 2,162
Non white  Unemployed HS  50 51 1.10 1.09 60 3,046
White Unemployed No HS 50 45 1.20 1.19 71 3,215
Non white  Unemployed No HS  50 71 1.10 1.19 65 4,624
Total       648       46,000
* Family Income Survey
** For presentation purposes, we have rounded up the adjustment factors (to two decimals) and the adjusted weights (to whole numbers). The calculations, however, carry all the decimals.
*** HS =High school diploma 

The final weights are produced automatically by the software that implements raking. The raking procedure only benchmarks the sample to known marginal distributions of the population; it should not be assumed that the resulting solution is ''closer to truth" at the cross-classification cell level as well. The final solution from a raking procedure may not reflect the correlation structure among different variables. For a more complete discussion of raking, refer to Kalton and Kasprzyk (1986).

As noted earlier, raking is one of a range of related methods known as calibration methods. One specific calibration method is GREG (Generalized REGression). GREG is not as commonly used as poststratification and raking because of its rather complex application and some of its limitations. Refer to Särndal et al. (1992) and Valliant et al. (2000) for a description of GREG.(6) For information about calibration techniques, refer to Deville and Särndal (1992) and Theberge (2000).

The weighting system is implemented by assigning weights to each person (or family) in the sample, inserting the weight into the computer record for each person, and incorporating the weights in the estimation process using software created for survey data analysis.

Balancing Bias and Variance When Adjusting for Nonresponse

The fundamental objective of the design of any survey sample is to produce a survey data set, that, for a given cost of data collection, will produce statistics that are nearly unbiased and sufficiently precise to satisfy the goals of the expected analyses of the data. In general, the goal is to keep the mean square error (MSE) of the primary statistics of interest as low as possible. The MSE of a survey estimate is

MSE = Variance + (Bias)2 . [5]

The purpose of the weighting adjustments discussed in this paper is to reduce the bias associated with noncoverage and nonresponse in surveys. Thus, the application of weighting adjustments usually results in lower bias in the associated survey statistics, but at the same time adjustments may result in some increases in variances of the survey estimates.

The increases in variance result from the added variability in the sampling weights due to nonresponse and noncoverage adjustments. Thus, the analysts who create the weighting adjustment factors need to pay careful attention to the variability in the sampling weights caused by these adjustments. The variability in weights will reduce the precision of the estimates. Thus, a tradeoff should be made between variance and bias to keep the MSE as low as possible. However, there is no exact rule for this tradeoff because the amount of bias is unknown.

In general, weighting class adjustments frequently result in increases in the variance of survey estimates when (1) many weighting classes are created with a few respondents in each class, and (2) some weighting classes have very large adjustment factors (possibly due to much higher nonresponse or noncoverage rates in these classes). To avoid such situations, survey statisticians commonly limit the number of weighting classes created during the adjustment process. In general, although exact rules do not exist for minimum sample sizes or adjustment factors for adjustment cells, statisticians usually avoid cells with fewer than 20 or 30 sample cases or adjustment factors larger than 1.5 to 2. Refer to Kalton and Kasprzyk (1986) for more information on this topic.

Occasionally, the procedures used to create the weights may result in a few cases with extremely large weights. Extreme weights can seriously inflate the variance of survey estimates. ''Weight trimming" procedures are commonly used to reduce the impact of such large weights on the estimates produced from the sample.

Weight trimming refers to the process of adjusting a few extreme weights to reduce their impact on the weighted estimates (i.e., increase in the variances of the estimates). Trimming introduces a bias in the estimates; however, most statisticians believe that the resulting reduction in variance decreases the MSE. The inspection method, described in Potter (1988, 1990), is a common trimming method used in many surveys. This method involves the inspection of the distribution of weights in the sample. Based on this inspection, outlier weights are truncated at an acceptable level (the acceptable level is derived based on a tradeoff between bias and variance). The truncated weights then are redistributed so that the total weighted counts still match the weighted total before weight trimming.

Analysts should pay attention to the variability of the weights when working with survey data, even though all measures (such as limits on adjustment cell sizes, and weight trimming) may have been taken to keep the variability of weights in moderation. Analysts should keep in mind that large variable values in conjunction with large weights may result in extremely influential observations, that is, observations that dominate the analysis.

Analyzing Weighted Survey Data

Because estimates will be based on sample data, they will differ from figures that would have been obtained from complete enumeration of the universe. Results are subject to both sampling and nonsampling errors. Nonsampling errors include biases because of inaccurate reporting, measurement and processing errors, as well as errors because of nonresponse and incomplete sampling frames. Inaccurate or incomplete responses can occur due to misunderstanding or the misinterpretation of questions. Errors can also occur when responses are coded, edited, and entered into the database. Generally, the nonsampling errors cannot be measured readily but a number of quality assurance techniques are employed to reduce the frequency of such errors.

For the computation of sampling errors, most standard techniques used in statistical packages (e.g., SAS, SPSS, and others) assume that observations are independent and drawn using simple random sampling (SRS) selection methods and that all sampled cases participated in the survey. The estimates of variances for complex survey designs computed using standard statistical software packages that assume simple random sampling and a 100-percent response rate are biased. Once a sample departs from SRS and in the presence of nonresponse (especially in cases where nonresponse is rather high), new computational procedures are required in order to take into account the impact of survey design and nonresponse on statistical estimation. Two common approaches available for estimation of variances for complex survey data are Taylor linearization and replication. Using these procedures, factors such as stratification and the use of differential sampling rates to oversample a targeted subpopulation, and adjustments for nonresponse, can be reflected appropriately in estimates of sampling error. Wolter (1985) is a useful reference on the theory underlying variance estimation using replication and Taylor linearization methods. For information about relevant survey analysis software, visit


The occurrence of missing dataÑwhether for a unit or an item and whether due to nonresponse or noncoverage--creates the potential for bias. The potential for bias is particularly great in the presence of high nonresponse rates. In this paper, we provided brief descriptions of the methods most commonly used to adjust for unit nonresponse and noncoverage in general-purpose surveys. However, it is also very important to pay attention to nonresponse rates for each item in the questionnaire, and data analysts should consider using imputation procedures to compensate for missing items in the state surveys.

As discussed earlier, studies of the low-income population usually suffer from missing data. In studies that include only administrative data, noncoverage bias can result from using an incomplete administrative frame of eligible persons, and nonresponse occurs because of an inability to match the sample with the administrative file that includes the outcome data. Surveys are also subject to both underrepresentation due to nonresponse and frame noncoverage. Descriptions of nonresponse and frame noncoverage also are provided.

We also summarize the most commonly used procedures for nonresponse adjustments in multipurpose surveys. There are basically two types of adjustments, sample-based and population-based adjustments. The first group is based on procedures that use only sample information to reduce the nonresponse bias. The second approach uses external data to reduce the effects of both nonresponse and noncoverage. These adjustments are applied to respondents' records after the sample has been divided into a number of subgroups, called nonresponse adjustment classes. Adjustment methods for unit nonresponse involve deriving adjustment factors to be incorporated into sampling weights. A brief description of sample weighting is given in a previous section. When data are collected as part of a survey and sample weights are created, special procedures are needed to analyze the survey data. The previous section provides a brief review of the current procedures used to analyze weighted survey data.

Nonresponse adjustment methods can serve to reduce nonresponse bias. However, the total elimination of such bias generally is not possible, because within any weighting class the respondents ordinarily will not be fully representative of the nonrespondents. The impact of nonresponse bias is usually small in surveys with low nonresponse rates when nonresponse-adjusted weights are used along with the survey data. Although sample weighting cannot take all differences between respondents and nonrespondents into account, the weighting cells that are usually used appear, in general, to reduce the effect of any potential differences between respondents and nonrespondents.

The potential for bias is particularly great in the presence of high nonresponse rates. Thus, analysts are advised to take survey nonresponse rates and effects on the reliability of data into account when analyzing and reporting survey data. Analysis based on data from surveys with low nonresponse rates can be reported with a much higher level of confidence than those coming from surveys with high nonresponse rates.


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1.  Nonresponse adjustments are usually conducted by creating a factor or a "nonresponse adjustment weight" for each respondent in the sample. In the final survey analysis, the nonresponse weight may be used with additional adjustment factors that serve other purposes, including sometimes compensating for noncoverage.

2.  The example is hypothetical but is based on actual surveys conducted in a number of counties across the United States.

3.  Note that in the SSI example, samples were selected from the associated frames with equal probability. For cases where sampling involves unequal probabilities of selection, "weighted" sample distributions should be compared to the associated frame distributions. Refer to the section that discusses weighting for unequal probabilities of selection.

4.  A more relaxed assumption is where data are missing completely at random (MCAR). The MCAR model assumes that nonresponse occurs completely at random and does not depend on the characteristics of nonrespondents (Little and Rubin, 1987). In most surveys, however, the MCAR assumption is not realistic, as is shown in many nonresponse bias analyses conducted for state and national surveys (for example, see U.S. General Accounting Office (1999).

5.  The above cell creation can be carried out using APSS AnswerTree. For more information about APSS AswerTree, visit

6.  GREG and some similar procedures are available in GES (Generalized Estimation Systems), developed by Statistics Canada. For more information about GES, refer to Estevao et al. (1995).

Measurement Error in Surveys of the Low-Income Population

The measurement of the characteristics and behavioral experience among members of the low-income and welfare populations offers particular challenges with respect to reducing various sources of response error. For many of the substantive areas of interest, the behavioral experience of the welfare populations is complex, unstable, and highly variable over time. As the behavioral experience of respondents increases in complexity, so do the cognitive demands of a survey interview. Contrast the task of reporting employment and earnings for an individual continuously employed during the past calendar year with the response task of someone who has held three to four part-time jobs. Other questionnaire topics may request that the respondent report sensitive, threatening, socially undesirable, or perhaps illegal behavior. From both a cognitive and social psychological perspective, there is ample opportunity for the introduction of error in the reporting of the events and behaviors of primary interest in understanding the impacts of welfare reform.

This paper provides an introduction to these sources of measurement error and examines two theoretical frameworks for understanding the various sources of error. The empirical literature concerning the quality of responses for reports of earnings, transfer income, employment and unemployment, and sensitive behaviors is examined, to identify those items most likely to be subjected to response error among the welfare population. The paper concludes with suggestions for attempting to reduce the various sources of error through alternative questionnaire and survey design.

Sources of Error in the Survey Process

The various disciplines that embrace the survey method, including statistics, psychology, sociology, and economics, share a common concern with the weakness of the measurement process, the degree to which survey results deviate from "those that are the true reflections of the population" (Groves, 1989). The disciplines vary in the terminology used to describe error as well as their emphasis on understanding the impact of measurement error on analyses or the reduction of the various sources of error. The existence of these terminological differences and our desire to limit the focus of this research to measurement error suggests that a brief commentary on the various conceptual frameworks may aid in defining our interests unambiguously.

One common conceptual framework is that of mean squared error, the sum of the variance and the square of the bias. Variance is the measure of the variable error associated with a particular implementation of a survey; inherent in the notion of variable error is the fundamental requirement of replication, whether over units of observation (sample units), questions, or interviewers. Bias, as used here, is defined as the type of error that affects all implementations of a survey design, a constant error, within a defined set of essential survey conditions (Hansen et al., 1961). For example, the use of a single question to obtain total family income in the Current Population Survey (CPS) has been shown to underestimate annual income by approximately 20 percent (U.S. Bureau of the Census, 1979); this consistent underestimate would be considered the extent of the bias related to a particular question for a given survey design.

Another conceptual framework focuses on errors of observation as compared to errors of nonobservation (Kish, 1965). Errors of observation refer to the degree to which individual responses deviate from the true value for the measure of interest; as defined, they are the errors of interest for this research, to be referred to as measurement errors. Observational errors can arise from any of the elements directly engaged in the measurement process, including the questionnaire, the respondent, and the interviewer, as well as the characteristics that define the measurement process (e.g., the mode and method of data collection). Errors of nonobservation refer to errors related to the lack of measurement for some portion of the sample and can be classified as arising from three sources, coverage: nonresponse (both unit and item nonresponse), and sampling. Errors of nonobservation are the focus of other papers presented in this volume (see, for example, Groves and Couper, this volume).

Questionnaire as Source of Measurement Error

Ideally a question will convey to the respondent the meaning of interest to the researcher. However, several linguistic, structural, and environmental factors affect the interpretation of the question by the respondent. These factors include the specific question wording, the structure of each question (open versus closed), and the order in which the questions are presented. Question wording is often seen as one of the major problems in survey research; although one can standardize the language read by the respondent or the interviewer, standardizing the language does not imply standardization of the meaning. In addition, a respondent's perception of the intent or meaning of a question can be shaped by the sponsorship of the survey, the overall topic of the questionnaire, or the environment more immediate to the question of interest, such as the context of the previous question or set of questions or the specific response options associated with the question.

Respondent as Source of Measurement Error

Once the respondent comprehends the question, he or she must retrieve the relevant information from memory, make a judgment as to whether the retrieved information matches the requested information, and communicate a response. The retrieval process is potentially fraught with error, including errors of omission and commission. As part of the communication of the response, the respondent must determine whether he or she wishes to reveal the information. Survey instruments often ask questions about socially and personally sensitive topics. It is widely believed, and well documented, that such questions elicit patterns of underreporting (for socially undesirable behaviors and attitudes) as well as overreporting (for socially desirable behaviors and attitudes).

Interviewers as Sources of Measurement Error

For interviewer-administered questionnaires, interviewers may affect the measurement processes in one of several ways, including:

  • Failure to read the question as written;
  • Variation in interviewers' ability to perform the other tasks associated with interviewing, for example, probing insufficient responses, selecting appropriate respondents, or recording information provided by the respondent; and
  • Demographic and socioeconomic characteristics as well as voice characteristics that influence the behavior and responses provided by the respondent.

The first two factors contribute to measurement error from a cognitive or psycholinguistic perspective in that different respondents are exposed to different stimuli; thus variation in responses is, in part, a function of the variation in stimuli. All three factors suggest that interviewer effects contribute via an increase in variable error across interviewers. If all interviewers erred in the same direction (or their characteristics resulted in errors of the same direction and magnitude), interviewer bias would result. For the most part, the literature indicates that among well-trained interviewing staff, interviewer error contributes to the overall variance of estimates as opposed to resulting in biased estimates (Lyberg and Kasprzyk, 1991).

Other Essential Survey Conditions as Sources of Measurement Error

Any data collection effort involves decisions concerning the features that define the overall design of the survey, here referred to as the essential survey conditions. In addition to the sample design and the wording of individual questions and response options, these decisions include:

  • Whether to use interviewers or to collect information via some form of self-administered questionnaire;
  • The means for selecting and training interviewers (if applicable);
  • The mode of data collection for interviewer administration (telephone versus face to face);
  • The choice of respondent rule, including the extent to which the design permits the reporting of information by proxy respondents;
  • The method of data collection (paper and pencil, computer assisted);
  • The extent to which respondents are encouraged to reference records to respond to factual questions;
  • Whether to contact respondents for a single interview (cross-sectional design) or follow respondents over time (longitudinal or panel design);
  • For longitudinal designs, the frequency and periodicity of measurement;
  • The identification of the organization for whom the data are collected; and
  • The identification of the data collection organization.

No one design or set of design features is clearly superior with respect to overall data quality. For example, as noted, interviewer variance is one source of variability that obviously can be eliminated through the use of a self-administered questionnaire. However, the use of an interviewer may aid in the measurement process by providing the respondent with clarifying information or by probing insufficient responses.

Measurement Error Associated with Autobiographical Information: Theoretical Framework

Three distinct literatures provide the basis for the theoretical framework underlying investigations of measurement error in surveys. These theoretical foundations come from the fields of cognitive psychology, social psychology, and to a lesser extent, social linguistics.(1) Although research concerning the existence, direction, magnitude as well as correlates of response error have provided insight into the factors associated with measurement error, there are few fundamental principles that inform either designers of data collection efforts or analysts of survey data as to the circumstances, either individual or design based, under which measurement error is most likely to be significant or not. Those tenets that appear to be robust across substantive areas are outlined in the following sections.

Cognitive Theory

Tourangeau (1984) as well as others (see Sudman et al., 1996, for a review) have categorized the survey question-and-answer process as a four-step process involving comprehension of the question, retrieval of information from memory, assessment of the correspondence between the retrieved information and the requested information, and communication. In addition, the encoding of information, a process outside the control of the survey interview, determines a priori whether the information of interest is available for the respondent to retrieve from long-term memory.

Comprehension of the interview question is the "point of entry" to the response process. Does the question convey the concept(s) of interest? Is there a shared meaning among the researcher, the interviewer, and the respondent with respect to each of the words as well as the question as a whole? The comprehension of the question involves not only knowledge of the particular words and phrases used in the questionnaire, but also the respondent's impression of the purpose of the interview, the context of the particular question, and the interviewer's behavior in the delivery of the question.

The use of simple, easily understood language is not sufficient for guaranteeing shared meaning among all respondents. Belson (1981) found that even simple terms were subject to misunderstanding. For example, Belson examined respondents' interpretation of the following question: "For how many hours do you usually watch television on a weekday? This includes evening viewing." He found that respondents varied in their interpretation of various terms such as "how many hours" (sometimes interpreted as requesting starting and stopping times of viewing), "you" (interpreted to include other family members), "usually," and "watch television" (interpreted to mean being in the room in which the television is on).

Much of the measurement error literature has focused on the retrieval stage of the question-answering process, classifying the lack of reporting of an event as retrieval failure on the part of the respondent, comparing the characteristics of events that are reported to those that are not reported. One of the general tenets from this literature concerns the length of the recall period; the greater the length of the recall period, the greater the expected bias due to respondent retrieval and reporting error. This relationship has been supported by empirical data investigating the reporting of consumer expenditures and earnings (Neter and Waksberg, 1964); the reporting of hospitalizations, visits to physicians, and health conditions (e.g. Cannell et al., 1965); and reports of motor vehicle accidents (Cash and Moss, 1969), crime (Murphy and Cowan, 1976); and recreational activities (Gems et al., 1982). However, even within these studies, the findings with respect to the impact of the length of recall period on the quality of survey estimates are inconsistent. For example, Dodge (1970) found that length of recall was significant in the reporting of robberies but had no effect on the reporting of various other crimes, such as assaults, burglaries, and larcenies. Contrary to theoretically justified expectations, the literature also offers several examples in which the length of the recall period had no effect on the magnitude of response errors (see, for example, Mathiowetz and Duncan, 1988; Schaeffer, 1994). These more recent investigations point to the importance of the complexity of the behavioral experience over time, as opposed to simply the passage of time, as the factor most indicative of measurement error. This finding harkens back to theoretical discussions of the impact of interference on memory (Crowder, 1976).

Response errors associated with the length of the recall period typically are classified as either telescoping error, that is the tendency of the respondent to report events as occurring earlier (backward telescoping) or more recently (forward telescoping) than they actually occurred, or recall decay, the inability of the respondent to recall the relevant events occurring in the past (errors of omission). Forward telescoping is believed to dominate recall errors when the reference period for the questions is of short duration, while recall decay is more likely to have a major effect when the reference period is of long duration. In addition to the length of the recall period, the relative salience of the event affects the likelihood of either telescoping or memory decay. For example, events that are unique or that have a major impact on the respondent's life are less likely to be forgotten (error of omission) than less important events; however, the vividness of the event may lead respondents to recall the event as occurring more recently than is true (forward telescoping).

Another tenet rising from the collaborative efforts of cognitive psychologists and survey methodologists concerns the relationship between true behavioral experience and retrieval strategies undertaken by a respondent. Recent investigations suggest that the retrieval strategy undertaken by the respondent to provide a "count" of a behavior is a function of the true behavioral frequency. Research by Burton and Blair (1991) indicate that respondents choose to count events or items (episodic enumeration) if the frequency of the event/item is low and they rely on estimation for more frequently occurring events. The point at which respondents switch from episodic counting to estimation varies by both the characteristics of the respondent and the characteristics of the event. As Sudman et al. (1996) note, "no studies have attempted to relate individual characteristics such as intelligence, education, or preference for cognitive complexity to the choice of counting or estimation, controlling for the number of events" (p. 201). Work by Menon (1993, 1994) suggests that it is not simply the true behavioral frequency that determines retrieval strategies, but also the degree of regularity and similarity among events. According to her hypotheses, those events that are both regular and similar (brushing teeth) require the least amount of cognitive effort to report, with respondents relying on retrieval of a rate to produce a response. Those events occurring irregularly require more cognitive effort on the part of the respondent.

The impact of different retrieval strategies with respect to the magnitude and direction of measurement error is not well understood; the limited evidence suggests that errors of estimation are often unbiased, although the variance about an estimate (e.g., mean value for the population) may be large. Episodic enumeration, however, appears to lead to biased estimates of the event or item of interest, with a tendency to be biased upward for short recall periods and downward for long recall periods.

A third tenet springing from this same literature concerns the salience or importance of the behavior to be retrieved. Sudman and Bradburn (1973) identify salient events as those that are unique or have continuing economic or social consequences for the respondent. Salience is hypothesized to affect the strength of the memory trace and subsequently, the effort involved in retrieving the information from long-term memory. The stronger the trace, the lower the effort needed to locate and retrieve the information. Cannell et al. (1965) report that those events judged to be important to the individual were reported more completely and accurately than other events. Mathiowetz (1986) found that short spells of unemployment were less likely to be reported than longer (i.e., more salient) spells.

The last maxim concerns the impact of interference related to the occurrence of similar events over the respondent's life or during the reference period of interest. Classical interference and information-processing theories suggest that as the number of similar or related events occurring to an individual increases, the probability of recalling any one of those events declines. An individual may lose the ability to distinguish between related events, resulting in an increase in the rate of errors or omission. Inaccuracy concerning the details of any one event also may increase as the respondent makes use of general knowledge or impressions concerning a class of events for reconstructing the specifics of a particular occurrence. Interference theory suggests that "forgetting" is a function of both the number and temporal pattern of related events in long-term memory. In addition, we would speculate that interference also contributes to the misreporting of information, for example, the reporting of the receipt of Medicare benefits rather than Medicaid benefits.

Social Psychology: The Issue of Social Desirability

In addition to asking respondents to perform the difficult task of retrieving complex information from long-term memory, survey instruments often ask questions about socially and personally sensitive topics. Some topics are deemed, by social consensus, to be too sensitive to discuss in "polite" society. This was a much shorter list in the 1990s than in the 1950s, but most would agree that topics such as sexual practices, impotence, and bodily functions fall within this classification. Some (e.g., Tourangeau et al., 2000) hypothesize that questions concerning income also fall within this category. Other questions may concern topics that have strong positive or negative normative responses (e.g., voting, the use of pugnacious terms with respect to racial or ethnic groups) or for which there may be criminal retribution (e.g., use of illicit drugs, child abuse).

The sensitivity of the behavior or attitude of interest may affect both the encoding of the information as well as the retrieval and reporting of the material; little of the survey methodological research has addressed the point at which the distortion occurs with respect to the reporting of sensitive material. Even if the respondent is able to retrieve accurate information concerning the behavior of interest, he or she may choose to edit this information at the response formation stage as a means to reduce the costs, ranging from embarrassment to potential negative consequences beyond the interview situation, associated with revealing the information.

Applicability of Findings to the Measurement of Economic Phenomena

One of the problems in drawing inferences from other substantive fields to that of economic phenomena is the difference in the nature of the measures of interest. Much of the assessment of the quality of household-based survey reports concerns the reporting of discrete behaviors; many of the economic measures that are the subject of inquiry with respect to the measurement of the welfare population are not necessarily discrete behaviors or even phenomena that can be linked to a discrete memory. Some of the phenomena of interest could be considered trait phenomena. Let's consider the reporting of occupation. We speculate that the cognitive process by which one formulates a response to a query concerning current occupation is different from the process related to reporting the number of doctor visits during the past year.

For other economic phenomena, we speculate that individual differences in the approach to formulating a response impact the magnitude and direction of error associated with the measurement process. Consider the reporting of current earnings related to employment. For some respondents, the request to report current earnings requires little cognitive effort--it may be almost an automatic response. For these individuals, wages may be considered a characteristic of their self-identity, a trait related to how they define themselves. For other individuals, the request for information concerning current wages may require the retrieval of information from a discrete episode (the last paycheck), the retrieval of a recent report of the information (the reporting of wages in an application for a credit card), or the construction of an estimate at the time of the query based on the retrieval of information relevant to the request.

Given both the theoretical and empirical research conducted within multiple branches of psychology and survey methodology, what would we anticipate are the patterns of measurement error for various economic measures? The response to that question is a function of how the respondent's task is formulated and the very nature of the phenomena of interest. For example, asking a respondent to provide an estimate of the number of weeks of unemployment during the past year is quite different from the task of asking the respondent to report the starting and stopping dates of each unemployment spell for the past year. For individuals in a steady state (constant employment or unemployment), neither task could be considered a difficult cognitive process. For these individuals, employment or unemployment is not a discrete event but rather may become encoded in memory as a trait that defines the respondent. However, for the individual with sporadic spells of unemployment throughout the year, the response formulation process most likely would differ for the two questions. Although the response formulation process for the former task permits an estimation strategy on the part of the respondent, the latter requires the retrieval of discrete periods of unemployment. For the reporting of these discrete events, we would hypothesize that patterns of response error evident in the reporting of events in other substantive fields would be observed. With respect to social desirability, we would anticipate patterns similar to those evident in other types of behaviors: overreporting of socially desirable behaviors and underreporting of socially undesirable behaviors.

Measurement Error in Household Reports of Income

As noted by Moore et al. (1999), the reporting of income by household respondents in many surveys can be characterized as a two-step process: the first involving the correct enumeration of sources of household income and the second, the accurate reporting of the amount of the income for the specific source. They find that response error in the reporting of various sources and amounts of income may be due to a large extent to cognitive factors, such as "definitional issues, recall and salience problems, confusion, and sensitivity" (p. 155). We return to these cognitive factors when considering alternative means for reducing measurement error in surveys of the low-income population.


Empirical evaluations of household-reported earnings information include the assessment of annual earnings, usual earnings (with respect to a specific pay period), most recent earnings, and hourly wage rates. These studies rely on various sources of validation data, including the use of employers' records, administrative records, and respondents' reports for the same reference period reported at two different times.

With respect to reports of annual earnings, mean estimates appear to be subject to relatively small levels of response error, although absolute differences indicate significant overreporting and underreporting at the individual level. For example, Borus (1970) focused on survey responses of residents in low-income census tracts in Fort Wayne, Indiana. The study examined two alternative approaches to questions concerning annual earnings: (1) the use of two relatively broad questions concerning earnings, and (2) a detailed set of questions concerning work histories. Responses to survey questions were compared to data obtained from the Indiana Employment Security Division for employment earnings covered by the Indiana Unemployment Insurance Act. Borus found that the mean error in reports of annual earnings was small and insignificant for both sets of questions; however, more than 10 percent of the respondents misreported annual earnings by $1,000 (based on a mean of $2,500). Among poor persons with no college education, Borus found that the broad questions resulted in more accurate data than the work history questions.

Smith (1997) examined the reports of earnings data among individuals eligible to participate in federal training programs. Similar to the work by Borus (1970), Smith compared the reports based on direct questions concerning annual earnings to those responses based on summing the report of earnings for individual jobs. The decomposition approach, that is, the reporting of earnings associated with individual jobs, led to higher reports of annual earnings, attributed to both an increase in the reporting of number of hours worked as well as an increase in the reporting of irregular earnings (overtime, tips, and commissions). Comparisons with administrative data for these individuals led Smith to conclude that the estimates based on adding up earnings across jobs led to overreporting, rather than more complete reporting.(2)

Duncan and Hill (1985) sampled employees from a single establishment and compared reports of annual earnings with information obtained from the employer's records. The nature of the sample, employed persons, limits our ability to draw inferences from their work to the low-income population. Respondents were interviewed in 1983 and requested to report earnings and employment-related measures for calendar years 1981 and 1982. For neither year was the mean of the sample difference between household-based reports and company records statistically significant (8.5 percent and 7 percent of the mean, respectively), although the absolute differences for each year indicate significant underreporting and overreporting. Comparison of measures of change in annual earnings based on the household report and the employer records indicate no difference; interview reports of absolute change averaged $2,992 (or 13 percent) compared to the employer-based estimate of $3,399 (or 17 percent).

Although the findings noted are based on small samples drawn from either a single geographic area (Borus) or a single firm (Duncan and Hill), the results parallel the findings from empirical research comprised of nationally representative samples. Bound and Krueger (1991) examined error in annual earnings as reported in the March, 1978 CPS. Although the error was distributed around approximately a zero mean for both men and women, the magnitude of the error was substantial.

In addition to examining bias in mean estimates, the studies by Duncan and Hill and Bound and Krueger examined the relationship between measurement error and true earnings. Both studies indicate a significant negative relationship between error in reports of annual earnings and the true value of annual earnings. Similar to Duncan and Hill (1985), Bound and Krueger (1991) report positive autocorrelation (.4 for men and .1 for women) between errors in CPS-reported earnings for the 2 years of interest, 1976 and 1977.

Both Duncan and Hill (1985) and Bound and Krueger (1991) explore the implications of measurement error for earnings models. Duncan and Hill's model relates the natural logarithm of annual earnings to three measures of human capital investment: education, work experience prior to current employer, and tenure with current employer, using both the error-ridden self-reported measure of annual earnings and the record-based measure as the left-hand-side variable. A comparison of the ordinary least squares parameter estimates based on the two dependent variables suggests that measurement error in the dependent variable has a sizable impact on the parameter estimates. For example, estimates of the effects of tenure on earnings based on interview data were 25 percent lower than the effects based on record earnings data. Although the correlation between error in reports of earnings and error in reports of tenure was small (.05) and insignificant, the correlation between error in reports of earnings and actual tenure was quite strong (Ð.23) and highly significant, leading to attenuation in the estimated effects of tenure on earnings based on interview information.

Bound and Krueger (1991) also explore the ramifications of an error-ridden left-hand-side variable by regressing error in reports of earnings with a number of human capital and demographic factors, including education, age, race, marital status, region, and standard metropolitan statistical area (SMSA). Similar to Duncan and Hill, the model attempts to quantify the extent to which the correlation between measurement error in the dependent variable and right-hand-side variables biases the estimates of the parameters. However, in contrast to Duncan and Hill, Bound and Krueger conclude that mismeasurement of earnings leads to little bias when CPS-reported earnings are on the left-hand side of the equation.

The reporting of annual earnings within the context of a survey is most likely aided by the number of times the respondent has retrieved and reported the information. For some members of the population, we contend that the memory for one's annual earnings is reinforced throughout the calendar year, for example, in the preparation of federal and state taxes or the completion of applications for credit cards and loans. To the extent that these requests have motivated the respondent to determine and report an accurate figure, such information should be encoded in the respondent's memory. Subsequent survey requests therefore should be "routine" in contrast to many of the types of questions posed to a survey respondent. Hence we would hypothesize that response error in such situations would result from retrieval of the wrong information (e.g., annual earnings for calendar year 1996 rather than 1997; net rather than gross earnings), social desirability issues (e.g., overreporting among persons with low earnings related to presentation of self to the interviewer), or privacy concerns, which may lead to either misreporting or item nonresponse.

Although the limited literature on the reporting of earnings among the low-income population indicates a high correlation between record and reported earnings (Halsey, 1978), we hypothesize that for some members of the population--such as low-income individuals for whom there are fewer opportunities to retrieve and report annual earnings information--a survey request would not be routine and may require very different response strategies than for respondents who have regular opportunities to report their annual earnings. Only two studies cited here, Borus (1970) and Smith (1997), compared alternative approaches to the request for earnings information among the low-income population. Borus found that the broad-based question approach led to lower levels of response error than a work history approach and Smith concluded that a decomposition approach led to an overestimation of annual earnings. The empirical results of Borus and Smith suggest, in contrast to theoretical expectations, that among the lower income populations, the use of broad questions may result in more accurate reports of income than detailed questions related to each job. Despite these findings, we speculate that for the low income population, those with loose ties to the labor force, or those for whom the retrieval of earnings information requires separate estimates for multiple jobs, the use of a decomposition approach or some type of estimation approach may be beneficial and warrants additional research.

In contrast to the task of reporting annual earnings, the survey request to report weekly earnings, most recent earnings, or usual earnings is most likely a relatively unique request and one that may involve the attempted retrieval of information that may not have been encoded by the respondent, the retrieval of information that has not been accessed by the respondent before, or the calculation of an estimate "on the spot." To the extent that the survey request matches the usual reference period for earnings (e.g., weekly pay), we would anticipate that requests for the most recent period may be well reported. In contrast, we would anticipate that requests for earnings in any metric apart from a well-rehearsed metric would lead to significant differences between household reports and validation data.

A small set of studies examined the correlation between weekly or monthly earnings as reported by workers and their employer's reports (Keating et al., 1950; Hardin and Hershey, 1960; Borus, 1966; Dreher, 1977). Two of these studies focus on the population of particular interest, unemployed workers (Keating et al., 1950) and training program participants (Borus, 1966). All four studies report correlations between the employee's report and the employer's records of .90 or higher. Mean reports by workers are close to record values, with modest overreporting in some studies and underreporting in others. For example, Borus (1966) reports a high correlation (.95) between household and employer's records of weekly earnings, small mean absolute deviations between the two sources, and equal amounts of overreporting and underreporting.

Carstensen and Woltman (1979), in a study among the general population, compared worker and employer reports, based on a supplement to the January, 1977 CPS. Their survey instruments allowed both workers and employers to report earnings in whatever time unit they preferred (e.g., annually, monthly, weekly, hourly). Comparisons were limited to those reports for which the respondent and the employer reported earnings using the same metric. When earnings were reported by both worker and employer on a weekly basis, workers underreported their earnings by 6 percent; but when both reported on a monthly basis, workers overreported by 10 percent.

Rodgers et al. (1993)(3) report correlations of .60 and .46 between household reports and company records for the most recent and usual pay, respectively, in contrast to a correlation of .79 for reports of annual earnings. In addition, they calculated an hourly wage rate from the respondents' reports of annual, most recent, and usual earnings and hours and compared that hourly rate to the rate as reported by the employer; error in the reported hours for each respective time period therefore contributes to noise in the hourly wage rate. Similar to the findings for earnings, correlation between the employer's records and self-reports were highest when based on annual earnings and hours (.61) and significantly lower when based on most recent earnings and hours and usual earnings and hours (.38 and .24, respectively).

Hourly wages calculated from the CPS-reported earnings and hours compared to employers' records indicate a small but significant rate of underreporting, which may be due to an overreporting of hours worked, an underreporting of annual earnings, or a combination of the two (Mellow and Sider, 1983). Similar to Duncan and Hill (1985), Mellow and Sider examined the impact of measurement error in wage equations; they concluded that the structure of the wage determination process model was unaffected by the use of respondent- or employer-based information, although the overall fit of the model was somewhat higher with employer-reported wage information.

As noted earlier, one of the shortfalls with the empirical investigations concerning the reporting of earnings is the lack of studies targeted at those for whom the reporting task is most difficult--those with multiple jobs or sporadic employment. Although the empirical findings suggest that annual earnings are reported more accurately than earnings for other periods of time, the opposite may be true among those for whom annual earnings are highly variable and the result of complex employment patterns.

One of the major concerns with respect to earnings questions in surveys of Temporary Assistance for Needy Families (TANF) leavers is the reference period of interest. Many of the surveys request that respondents report earnings for reference periods that may be of little salience to the respondent or for which the determination of the earnings is quite complex. For example, questions often focus on the month in which the respondent left welfare (which may have been several months prior to the interview) or the 6 month period prior to exiting welfare. The movement off welfare support would probably be regarded as a significant and salient event and therefore be well reported. However, asking the respondent to reconstruct a reference period prior to the month of exiting welfare is most likely a cognitively difficult task. For example, consider the following question:

  • During the six months you were on welfare before you got off in MONTH, did you ever have a job which paid you money?

For this question, the reference period of interest is ambiguous. For example, if the respondent exited welfare support in November 1999, is the 6-month period of interest defined as May 1, 1999, through October 31, 1999, or is the respondent to include the month in which he or she exited welfare as part of the reference period, in this case, June 1999-November 1999? If analytic interest lies in understanding a definitive period prior to exiting welfare, then the questionnaire should explicitly state this period to the respondent (e.g., "In the 6 months prior to going off welfare, that is, between May 1 and October 31, 1999") as well as encourage the respondent to use a calendar or other records to aid recall. The use of a calendar may be of particular importance when the reference period spans 2 calendar years. If the analytic interest lies in a more diffuse measure of employment in some period prior to exiting welfare, a rewording of the question so as to not imply precision about a particular 6 months may be more appropriate.

Transfer Program Income and Child Support

For most surveys, the reporting of transfer program income is a two-stage process in which respondents first report recipiency (or not) of a particular form of income and then, among those who report recipiency, the amount of the income. One shortcoming of many studies that assess response error associated with transfer program income is the design of the study, in which the sample for the study is drawn from those known to be participants in the program. Responses elicited from respondents then are verified with administrative data. Retrospective or reverse record check studies limit the assessment of response error, with respect to recipiency, to determining the rate of underreporting; prospective or forward record check studies that only verify positive recipiency responses are similarly flawed because by design they limit the assessment of response error only to overreports. In contrast, a "full" design permits the verification of both positive and negative recipiency responses and includes in the sample a full array of respondents. Validation studies that sample from the general population and link all respondents, regardless of response, to the administrative record of interest represent full study designs.

We focus our attention first on reporting of receipt of a particular transfer program. Among full design studies, there does appear to be a tendency for respondents to underreport receipt, although there are also examples of overreporting recipiency status. For example, Oberheu and Ono (1975) report a low correspondence between administrative records and household report for receipt of Aid to Families with Dependent Children (AFDC)--monthly and annual--and food stamps (disagreement rates exceeding 20 percent), but relatively low net rates of underreporting and overreporting. Underreporting of the receipt of general assistance as reported in two studies is less than 10 percent (e.g., David, 1962). In a study reported by Marquis and Moore (1990), respondents were asked to report recipiency status for 8 months (in two successive waves of Survey of Income and Program Participation [SIPP] interviews). Although Marquis and Moore report a low error rate of approximately 1 percent to 2 percent, the error rate among true recipients is significant, in the direction of underreporting. For example, among those receiving AFDC, respondents failed to report receipt in 49 percent of the person-months. Underreporting rates were lowest among Old-Age and Survivors Insurance and Disability Insurance (OASDI) beneficiaries, for which approximately 5 percent of the person-months of recipiency were not reported by the household respondents. The mean rates of participation based on the two sources differed by less than 1 percentage point for all income types. However, because some of these programs are so rare, small absolute biases mask high rates of relative underreporting among true participants, ranging from +1 percent for OASDI recipiency to nearly 40 percent for AFDC recipiency. In a followup study, Moore et al. (1996) compared underreporting rates of known recipients to overreporting rates for known nonrecipients and found underreporting rates to be much higher than the rate of false positives by nonrecipients. They also note that underreporting on the part of known recipients tends to be due to failure to ever report receipt of a particular type of income rather than failure to report specific months of receipt.

In contrast, Yen and Nelson (1996) found a slight tendency among AFDC recipients to overreport receipt in any given month, such that estimates based on survey reports exceeded estimates based on records by approximately 1 percentage point. Oberheu and Ono (1975) also note a net overreporting for AFDC (annual) and food stamp recipiency (annual), of 8 percent and 6 percent, respectively. Although not investigated by these researchers, one possible explanation for apparent overreporting on the part of the respondent is confusion concerning the source of recipiency, resulting in an apparent overreporting of one program coupled with an underreporting of another program. Because many of the validity studies that use administrative records to confirm survey reports are limited to verification of one or two particular programs, most response error investigations have not addressed this problem.

Errors in the reporting of recipiency for any given month may be attributable to misdating the beginning and end points of a spell, as opposed to an error of omission or confusion concerning the source of support. The "seam effect" refers to a particular type of response error resulting from the misdating of episodic information in panel data collection efforts (Hill, 1987). A seam effect is evident when a change in status (e.g., from receipt of AFDC to nonreceipt of AFDC) corresponds to the end of a reference period for Wave x and the beginning of a reference period for Wave x+1. For example, a respondent may report receipt of AFDC at the end of the first wave of interviewing; at the time of the second wave of interviewing, he or she reports that no one in the family has received such benefits for the entire reference period. Hence it appears (in the data) as if the change in status occurred on the day of the interview.

With respect to the direction and magnitude of estimates concerning the amount of the transfer, empirical investigations vary in their conclusions. Several studies report a significant underreporting of assistance amount (e.g., David, 1962; Livingston, 1969; Oberheu and Ono, 1975; Halsey, 1978) or significant differences between the survey and record reports (Grondin and Michaud, 1994). Other studies report little to no difference in the amount based on the survey and record reports. Hoaglin (1978) found no difference in median response error for welfare amounts and only small negative differences in the median estimates for monthly Social Security income. Goodreau et al. (1984) found that 65 percent of the respondents accurately report the amount of AFDC support; the survey report accounted for 96 percent of the actual amount of support. Although Halsey (1978) reported a net bias in the reporting of unemployment insurance amount of -50 percent, Dibbs et al. (1995) conclude that the average household report of unemployment benefits differed from the average true value by approximately 5 percent ($300 on a base of $5,600).

Schaeffer (1994) compared custodial parents' reports of support owed and support paid to court records among a sample of residents in the state of Wisconsin. The distribution of response errors indicated significant underreporting and overreporting of both the amount owed and the amount paid. The study also examined the factors contributing to the absolute level of errors in the reports of amounts owed and paid; the findings indicate that the complexity of the respondent's support experience had a substantial impact on the accuracy of the reports. Characteristics of the events (payments) were more important in predicting response error than characteristics of the respondent or factors related to memory decay. The analysis suggests two areas of research directed toward improving the reporting of child support payments: research related to improving the comprehension of the question (specifically clarifying and distinguishing child support from other transfer payments) and identifying respondents for whom the reporting process is difficult (e.g., use of a filter question) with follow-up questions specific to the behavioral experience.

Hours Worked

The number of empirical investigations concerning the quality of household reports of hours worked are few in number but consistent with respect to the findings. Regardless of whether the measure of interest is hours worked last week, annual work hours, usual hours worked, or hours associated with the previous or usual pay period, comparisons between company records and respondents' reports indicate an overestimate of the number of hours worked. We note that none of the empirical studies examined in the following text focuses specifically on the low-income or welfare populations.

Carstensen and Woltman (1979) assessed reports of "usual" hours worked per week. They found that compared to company reports, estimates of the mean usual hours worked were significantly overreported by household respondents: 37.1 hours versus 38.4 hours, respectively, a difference on average of 1.33 hours, or 3.6 percent of the usual hours worked. Similarly, Mellow and Sider (1983) report that the mean difference between the natural log of worker-reported hours and the natural log of employer-reported hours is positive (.039). Self-reports exceeded employer records by nearly 4 percent on average; however, for approximately 15 percent of the sample, the employer records exceeded the estimate provided by the respondent. A regression explaining the difference between the two sources indicates that professional and managerial workers were more likely to overestimate their hours, as were respondents with higher levels of education and nonwhite respondents. In contrast, female respondents tended to underreport usual hours worked.

Similar to their findings concerning the reporting of earnings, Rodgers et al. (1993) report that the correlation between self-reports and company records is higher for annual number of hours worked (.72) than for either reports of hours associated with the previous pay period (.61) or usual pay period (.61). Barron et al. (1997) report a high correlation between employers' records and respondents' reports of hours last week, .769. Measurement error in hours worked is not independent of the true value; as reported by Rodgers et al. (1993), the correlation between error in reports of hours worked and true values (company records) ranged from -.307 for annual hours worked in the calendar year immediately prior to the date of the interview to -.357 for hours associated with the previous pay period and -.368 for hours associated with usual pay period.

Examination of a standard econometric model with earnings as the left-hand-side variable and hours worked as one of the predictor variables indicates that the high correlation between the errors in reports of earnings and hours (ranging from .36 for annual measures to .54 for last pay period) seriously biases parameter estimates. For example, regressions of reported and company record annual earnings (log) on record or reported hours, age, education, and tenure with the company provide a useful illustration of the consequences of measurement error. Based on respondent reports of earnings and hours, the coefficient for hours (log hours) is less than 60 percent of the coefficient based on company records (.41 versus 1.016) while the coefficient for age is 50 percent larger in the model based on respondent reports. In addition, the fit of the model based on respondent reports is less than half that of the fit based on company records (R2 of .352 versus .780).

Duncan and Hill (1985) compare the quality of reports of annual hours worked for two different reference periods, the prior calendar year and the calendar year ending 18 months prior to the interview. The quality of the household reports declines as a function of the length of the recall period, although the authors report significant overreporting for each of the two calendar years of interest. The average absolute error in reports of hours worked (157 hours) was nearly 10 percent of the mean annual hours worked for 1982 (=1,603) and nearly 12 percent (211 hours) of the mean for 1981 (=1,771). Comparisons of changes in hours worked reveal that although the simple differences calculated from two sources have similar averages, the absolute amount of change reported in the interview significantly exceeds that based on the record report.

In contrast to the findings with respect to annual earnings, we see both a bias in the population estimates as well as a bias in the individual reports of hours worked in the direction of overreporting. This finding persists across different approaches to measuring hours worked, regardless of whether the respondent is asked to report on hours worked last week (CPS) or account for the weeks worked last year, which then are converted to total hours worked during the year (Panel Study of Income Dynamics [PSID]). Whether this is a function of social desirability or whether it is related to the cognitive processes associated with formulating a response to the questions measuring hours worked is something that can only be speculated on at this point. One means by which to attempt to repair the overreporting of hours worked is through the use of time-use diaries, where respondents are asked to account for the previous 24-hour period. Employing time-use diaries has been found to be an effective means for reducing response error associated with retrospective recall bias as well as bias associated with the overreporting of socially desirable behavior (Presser and Stinson, 1998).


In contrast to the small number of studies that assess the quality of household reports of hours worked, there are a number of studies that have examined the quality of unemployment reports. These studies encompass a variety of unemployment measures, including annual number of person-years of unemployment, weekly unemployment rate, occurrence and duration of specific unemployment spells, and total annual unemployment hours. Only one study reported in the literature, the PSID validation study (Duncan and Hill, 1985; Mathiowetz, 1986; Mathiowetz and Duncan, 1988), compares respondents' reports with validation data; the majority of the studies rely on comparisons of estimates based on alternative study designs or examine the consistency in reports of unemployment duration across rounds of data collection. In general, the findings suggest that retrospective reports of unemployment by household respondents underestimate unemployment, regardless of the unemployment measure of interest. Once again, however, these studies focus on the general population; hence our ability to draw inferences to the low income or welfare populations is limited.

The studies by Morganstern and Bartlett (1974), Horvath (1982), and Levine (1993) compare the contemporaneous rate of unemployment as produced by the monthly CPS to the rate resulting from retrospective reporting of unemployment during the previous calendar year.(4) The measures of interest vary from study to study; Morganstern and Bartlett focus on annual number of person-years of unemployment as compared to average estimates of weekly unemployment (Horvath) or an unemployment rate, as discussed by Levine. Regardless of the measure of interest, the empirical findings from the three studies indicate that when compared to the contemporaneous measure, retrospective reports of labor force status result in an underestimate of the unemployment rate.

Across the three studies, the underreporting rate is significant and appears to be related to demographic characteristics of the individual. For example, Morganstern and Bartlett (1974) report discrepancy rates in the range of around 3 percent to 24 percent with the highest discrepancy rates among women (22 percent for black women; 24 percent for white women). Levine compared the contemporaneous and retrospective reports by age, race, and gender. He found the contemporaneous rates to be substantially higher relative to the retrospective reports for teenagers, regardless of race or sex, and for women. Across all of the years of the study, 1970-1988, the retrospective reports for white males, ages 20 to 59, were nearly identical to the contemporaneous reports.

Duncan and Hill (1985) found that the overall estimate of mean number of hours unemployed in years t and t-1 based on employer reports and company records did not differ significantly. However, microlevel comparisons, reported as the average absolute difference between the two sources, were large relative to the average amount of unemployment in each year, but significant only for reports of unemployment occurring in 1982.

In addition to studies examining rates of unemployment, person-years of unemployment, or annual hours of unemployment, several empirical investigations have focused on spell-level information, examining reports of the specific spell and duration of the spell. Using the same data as presented in Duncan and Hill (1985), Mathiowetz and Duncan (1988) found that at the spell level, respondents failed to report more than 60 percent of the individual spells. Levine (1993) found that 35 percent to 60 percent of persons failed to report an unemployment spell one year after the event. In both studies, failure to report a spell of unemployment was related, in part, to the length of the unemployment spell; short spells of unemployment were subject to higher rates of underreporting.

The findings suggest (Poterba and Summers, 1984) that, similar to other types of discrete behaviors and events, the reporting of unemployment is subject to deterioration over time. However, the passage of time may not be the fundamental factor affecting the quality of the reports; rather the complexity of the behavioral experience over longer recall periods appears to be the source of increased response error. Both the microlevel comparisons as well as the comparisons of population estimates suggest that behavioral complexity interferes with the respondent's ability to accurately report unemployment for distant recall periods. Hence we see greater underreporting among population subgroups who traditionally have looser ties to the labor force (teenagers, women). Although longer spells of unemployment appear to be subject to lower levels of errors of omission, a finding that supports other empirical research with respect to the effects of salience, at least one study found that errors in reports of duration were associated negatively with the length of the spell. Whether this is indicative of an error in cognition or an indication of reluctance to report extremely long spells of unemployment (social desirability) is unresolved.

Sensitive Questions: Drug Use, Abortions

A large body of methodological evidence indicates that embarrassing or socially undesirable behaviors are misreported in surveys (e.g., Bradburn, 1983). For example, comparisons between estimates of the number of abortions based on survey data from the National Survey of Family Growth (NSFG) and estimates based on data collected from abortion clinics suggest that fewer than half of all abortions are reported in the NSFG (Jones and Forrest, 1992). Similarly, comparisons of survey reports of cigarette smoking with sales figures indicates significant underreporting on the part of household respondents, with the rate of underreporting increasing over time, a finding attributed by the authors as a function of increasing social undesirability (Warner, 1978).

Although validation studies of reports of sensitive behaviors are rare, there is a growing body of empirical literature that examines reports of sensitive behaviors as a function of mode of data collection, method of data collection, question wording, and context (e.g., Tourangeau and Smith, 1996). These studies have examined the reporting of abortions, AIDS risk behaviors, use of illegal drugs, and alcohol consumption. The hypothesis for these studies is that, given the tendency to underreport sensitive or undesirable behavior, the method or combination of essential survey design features that yields the highest estimate is the "better" measurement approach.

Studies comparing self-administration to interviewer-administered questions (either face to face or telephone) indicate that self-administration of sensitive questions increases levels of reporting relative to administration of the same question by an interviewer. Increases in the level of behavior have been reported in self-administered surveys (using paper and pencil questionnaires) concerning abortions (London and Williams, 1990), alcohol consumption (Aquilino and LoSciuto, 1990), and drug use (Aquilino, 1994). Similar increases in the level of reporting sensitive behaviors have been reported when the comparisons focus on the difference between interviewer-administered questionnaires and computer-assisted self administration (CASI) questionnaires.

One of the major concerns with moving from an interviewer-administered questionnaire to self-administration is the problem of limiting participation to the literate population. Even among the literate population, the use of self-administered questionnaires presents problems with respect to following directions (e.g., skip patterns). The use of audio computer-assisted self-interviewing (ACASI) techniques circumvents both problems. The presentation of the questions in both written and auditory form (through headphones) preserves the privacy of a self-administered questionnaire without the restriction imposed by respondent literacy. The use of computers for the administration of the questionnaire eliminates two problems often seen in self-administered paper and pencil questionnaires--missing data and incorrectly followed skip patterns. A small but growing body of literature (e.g., O'Reilly et al., 1994; Tourangeau and Smith, 1996) finds that ACASI methods are acceptable to respondents and appear to improve the reporting of sensitive behaviors. Cynamon and Camburn (1992) found that using portable cassette players to administer questions (with the respondent recording answers on a paper form) also was effective in increasing reports of sensitive behaviors.

Methods for Reducing Measurement Error

As we consider means for reducing measurement error in surveys of the low-income population, we return to the theoretical frameworks that address the potential sources of error: those errors associated with problems of cognition and those resulting from issues associated with social desirability.

Repairs Focusing on Problem of Cognition


Of primary importance in constructing question items is to assure comprehension on the part of the respondent. Although the use of clear and easily understood language is a necessary step toward achieving that goal, simple language alone does not guarantee that the question is understood in the same manner by all respondents.

The literature examining comprehension problems in the design of income questions indicates that defining income constructs in a language easily understood by survey respondents is not easy (Moore et al., 1999). Terms that most researchers would consider to be well understood by respondents may suffer from differential comprehension. For example, Stinson (1997) found significant diversity with respect to respondents' interpretations of the term "total family income." Similarly, Bogen (1995) reported that respondents tend to omit sporadic self-employment and earnings from odd jobs or third or fourth jobs in their reports of income due to the respondents' interpretations of the term "income." These findings suggest the need for thorough testing of items among the population of interest to assess comprehension.

Comprehension of survey questions is affected by several factors, including the length of the question, the syntactical complexity, the degree to which the question includes instructions such as inclusion and exclusion clauses, and as the use of ambiguous terms. Consider, for example, the complexity of the following questions:

Example 1: Since your welfare benefits ended in (FINAL BENEFIT MONTH), did you take part for at least one month in any Adult Basic Education (ABE)   classes for improving your basic reading and math skills, or General Education Development (GED) classes to help you prepare for the GED test, or classes to prepare for a regular high school diploma?
Example 2: In (PRIOR MONTH), did you have any children of your own living in the household? Please include any foster or adopted children. Also include any grandchildren living with you.
Example 3: Since (FINAL BENEFIT MONTH), have you worked for pay at a regular job at all? Please don't count unpaid work experience, but do include any paid jobs, including paid community service jobs or paid on-the-job training.

Each of these items is cognitively complex. The first question requires the respondent to process three separate categories of education, determine whether the conditional phrase "at least one month" applies only to the adult basic education classes or also to the GED and regular high school classes, and also attribute a reason for attending ABE ("improving reading and math skills") or GED classes. Separating example 1 into three simple items, prefaced by an introductory statement concerning types of education, would make the task more manageable for the respondent. Examples 2 and 3 suffer from the problem of providing an exclusion or inclusion (or in the case of example 3, both) clause after the question. Both would be improved by defining for the respondent what the question concerns and then asking the question, so that the last thing the respondent hears is the question. Example 2 may be improved by simply asking separate questions concerning own children, foster children, and grandchildren. Although questionnaire designers may be reluctant to add questions to an instrument for fear of longer administration times, we speculate that the administration of several well-designed short questions actually may be shorter than confusing compound questions that may require repeating or clarification.

With respect to question length, short questions are not always better. Cannell and colleagues (Cannell et al., 1977; Cannell et al., 1981) demonstrated that longer questions providing redundant information can lead to increased comprehension, in part because the longer question provides additional context for responding as well as longer time for the respondent to think about the question and formulate a response. On the other hand, longer questions that introduce new terms or become syntactically complex will result in lower levels of comprehension.

Comprehension can suffer from both lexical and structural ambiguities. For example, the sentence "John went to the bank" could be interpreted as John going to a financial institution or the side of a river. Lexical problems are inherent in a language in which words can have different interpretations. Although difficult to fix, interpretation can be aided through context and the respondent's usual use of the word (in this case, most likely the financial institution interpretation). Note that when constructing a question, one must consider regional and cultural differences in language and avoid terms that lack a clearly defined lexical meaning (e.g., "welfare reform"). Structural ambiguities arise when the same word can be used as different parts of speech--for example, as both a verb or an adjective in the sentence "Flying planes can be dangerous." Structural ambiguities most often can be repaired through careful wording of the question.

Questionnaire designers often attempt to improve comprehension by grouping questions so as to provide a context for a set of items, writing explicit questions, and, if possible, writing closed-ended items in which the response categories may aid in the interpretation of the question by the respondent. In addition, tailoring questions to accommodate the language of specific population subgroups is feasible with computer-assisted interviewing systems.

Comprehension difficulties are best identified and repaired through the use of selected pretesting techniques such as cognitive interviewing or expert panel review (e.g., Presser and Blair, 1994; Forsyth and Lessler, 1991). Requesting respondents to paraphrase the question in their own words often provides insight into different interpretations of a question; similarly, the use of other cognitive interviewing techniques such as think-aloud interviews or the use of vignettes can be useful in identifying comprehension problems as well as offer possible alternative wording options for the questionnaire designer.


Many of the questions of interest in surveying the welfare population request that the respondent report on retrospective behavior, often for periods covering several years or more (e.g., year of first receipt of AFDC benefits). Some of these questions require that the respondent date events of interest, thus requiring episodic retrieval of a specific event. Other questions request that respondents provide a numeric estimate (e.g., earnings from work last month); in these cases the respondent may rely on episodic retrieval (e.g., the more recent pay-check), reconstruction, an estimation strategy, or a combination of retrieval strategies to provide a response. As noted earlier, response strategies are often a function of the behavioral complexity experienced by the respondent; however, the strategy used by the respondent can be affected by the wording of the question.

Although both responses based on episodic enumeration and estimation are subject to measurement error, the literature suggests that questions which direct the respondent toward episodic enumeration tend to suffer from errors of omissions (underreports) due to incomplete memory searches on the part of the respondent, whereas responses based on estimation strategies result in both inclusion and exclusion errors, resulting in greater variance but unbiased population estimates (Sudman et al., 1996). The findings from Mathiowetz and Duncan (1986) illustrate the difference in reports based on estimation strategies as compared to episodic enumeration. In their study, population estimates of annual hours of unemployment for a 2-year reference period based on respondents' reports of unemployment hours were reasonably accurate. In contrast, when respondents had to report the months and years of individual spells of unemployment (requiring episodic enumeration) more than 60 percent of the individual spells of unemployment were not reported.

Several empirical investigations have identified means by which to improve the reporting of retrospective information for both episodic enumeration and estimation-based reports. These questionnaire design approaches include:

Event History Calendar. Work in the field of cognitive psychology has provided insight into the structure of autobiographic information in memory. The research indicates that "certain types of autobiographical memories are thematically and temporally structured within an hierarchical ordering" (Belli, 1998). Event history calendars have been found to be effective in reducing response error related to the reporting of what, when, and how often events occurred (Freedman et al., 1988). Whereas traditional survey instruments ask for retrospective reports through a set of discrete questions (e.g., "In what month and year did you last receive welfare payments?"), thereby emphasizing the discrete nature of events, event history calendars emphasize the relationship between events within broad thematic areas or life domains (work, living arrangements, marital status, child bearing and rearing). Major transitions within these domains such as getting married or divorced, giving birth to a child, moving into a new house, or starting a job, are identified by the respondent and recorded in such ways as to facilitate "an extensive use of autobiographical memory networks and multiple paths of memory associated with top-down, sequential, and parallel retrieval strategies" (Belli, 1998). If the question items of interest require the dating of several types of events, the literature suggests that the use of event history calendars will lead to improved reporting. For example, event history calendars could prove to be beneficial in eliciting accurate responses to questions such as "What was the year and month that you first received welfare cash assistance as an adult?"

Landmark Events. The use of an event history calendar is most beneficial if the questionnaire focuses on the dating and sequencing of events and behaviors across several life domains. In some cases, the questionnaire contains a limited number of questions for which the respondent must provide a date or a correct sequence of events. In these cases, studies have indicated that the use of landmark dates can improve the quality of reporting by respondents (Loftus and Marburger, 1983). Landmark events are defined as either public or personal landmarks; for some of these, the respondent can provide an accurate date (personal landmark such as birthday, anniversary) whereas public landmarks can be dated accurately by the researcher. Landmarks are effective for three reasons: (1) landmark dates make effective use of the cluster organization of memory; (2) landmark dates may convert a difficult absolute judgment of recency to an easier relative judgment; and (3) landmark dates may suggest to the respondent the need to pay attention to exact dates and not simply imprecise dates. One way to operationalize landmark dates is to begin the interview with the respondent noting personal and/or public landmark dates on a calendar that can be used for reference throughout the interview.

Use of Records. If the information has not been encoded in memory, the response quality will be poor no matter how well the questions have been constructed. For some information, the most efficient and effective means by which to improve the quality of the reported data is to have respondents access records. Several studies report an improvement in the quality of asset and income information when respondents used records (e.g., Maynes, 1968; Grondin and Michaud, 1994; Moore et al., 1996). Two factors often hinder questionnaire designers from requesting that respondents use records: interviewers' reluctance and mode of data collection. Although in some cases interviewers have been observed discouraging record use (Marquis and Moore, 1990), studies that request detailed income and expenditure information such as the SIPP and the National Medical Expenditure Survey, have both reported success in encouraging respondents to use records (Moore et al., 1996). Record use by respondents is directly related to the extent to which interviewers have been trained to encourage their use by respondents. For telephone interviews, the fear is that encouraging record use may encourage nonresponse; a small body of empirical literature does not support this notion (Grondin and Michaud, 1994). One form of record to consider is the prospective creation of a diary that is referenced by the respondent during a retrospective interview.

Recall versus Recognition. Any free-recall task, such as the enumeration of all sources of income, is a cognitively more difficult task than the task of recognition, such as, asking the respondent to indicate which of a list of income sources is applicable to his or her situation. Consider the two approaches taken in examples 1 and 2:

Example 1: In (PRIOR MONTH), did you receive any money or income from any other source? This might include (READ SLOWLY) unemployment insurance, workers' compensation, alimony, rent from a tenant or boarder, an income tax refund, foster child payments, stipends from training programs, grandparents' Social Security income, and so on.

Example 2: Next, I will read a list of benefit programs and types of support and I'd like you to tell me whether you or someone in your home gets this.

  • Food stamps
  • Medicaid
  • Child-care assistance
  • Child support from a child's parent
  • Social Security

In the first example, the respondent must process all of the items together; most likely after the first or second item on the list was read, the respondent failed to hear or process the remaining items on the list. Hence the list does not provide an effective recognition mechanism. In the second example, the respondent is given time to process each item on the list individually (the entire list consists of 20 items).

Complex Behavioral Experience. Simple behavioral experiences are relatively easy to report even over long reference periods whereas complex behavioral experiences can be quite difficult to reconstruct. For example, the experience of receiving welfare benefits continuously over a 12-month period is quite different from the experience of receiving benefits for 8 of the 12 months. The use of filter questions to identify those for whom the behavioral experience is complex would permit the questionnaire designer to concentrate design efforts on those respondents for whom the task is most difficult. Those with complex behavioral experiences could be questioned using an event history calendar whereas those for whom the recent past represents a steady state could be asked a limited number of discrete questions.

Recall Strategies. When respondents are asked to report a frequency or number of times an event or a behavior occurred, they draw on different response strategies to formulate a response. The choice of response strategy is determined, in part, by the actual number or frequency as well as the regularity of the behavior. Rare or infrequent events often are retrieved through episodic enumeration in which the respondent attempts to retrieve each occurrence of the event. Such strategies are subject to errors of omission as well as misdating of the event by the respondent. When the event or behavior of interest occurs frequently, respondents often will use some form of estimation strategy to formulate a response. These strategies include rule-based estimation (recall a rate and apply to time- frame of interest), automatic estimation (drawn from a sense of relative or absolute frequency), decomposition (estimate the parts and sum), normative expectations, or some form of heuristic, such as availability heuristic (based on the speed of retrieval). All estimation approaches are subject to error, but a well-designed questionnaire can both suggest the strategy for the respondent to use and attempt to correct for the expected biases. For example, if the behavior or event of interest is expected to occur on a regular basis, a question that directs the respondent to retrieve the rule, and apply the rule to the time frame of interest, and then probes to elicit exceptions to the rule may be a good strategy for eliciting a numeric response.

Current versus Retrospective Reports. Current status most often is easier to report, with respect to cognitive difficulty, than retrospective status, so it is often useful to consider beginning questions concerning current status. Information retrieved as part of the reporting of current status also will facilitate retrieval of retrospective information.

Repairs Focusing on Problems Related to Social Desirability

Questions for which the source of the measurement error is related to perceived sensitivity of the items or the socially undesirable nature of the response often call for the use of question items or questionnaire modes that provide the respondent within greater sense of confidentiality or even anonymity as a means for improving response quality. The questionnaire designer must gauge the level of sensitivity or threat (or elicit information on sensitivity or threat through developmental interviews or focus groups) and respond with the appropriate level of questionnaire modifications. The discussion that follows attempts to provide approaches for questions of varying degrees of sensitivity, moving from slightly sensitive to extremely sensitive or illegal behaviors.

Reducing Threat Through Question Wording

Sudman and Bradburn (1982) provide a checklist of question approaches to minimize threat from sensitive questions. Among the suggestions made by the authors are the use of open questions as opposed to closed questions (so as to not reveal extreme response categories), the use of longer questions so as to provide context and indicate that the subject is not taboo, the use of alternative terminology (e.g., street language for illicit drugs), and embedding the topic in a list of more threatening topics to reduce perceived threat, because threat or sensitivity is determined in part by the context.

Alternative Modes of Data Collection

For sensitive questions, one of the most consistent findings from the experimental literature indicates that the use of self-administered questionnaires results in higher reports of threatening behavior. For example, in studies of illicit drug use, the increase in reports of use was directly related to the perceived level of sensitivity, greatest for the reporting of recent cocaine use, less profound but still significant with respect to marijuana and alcohol use. Alternative modes could involve the administration of the questions by an interviewer, with the respondent completing the response categories using paper and pencil, or administration of the questionnaire through a portable cassette and self-recording of responses. More recently, face-to-face data collection efforts have experimented with CASID in which the respondent reads the questions from the computer screen and directly enters the responses and ACASI, in which the questions can be heard over headphones as well as read by the respondent. The latter has the benefit of not requiring the respondent to be literate; furthermore, it can be programmed to permit efficient multilingual administration without requiring multilingual survey interviewers. In addition, both computer-assisted approaches offer the advantage that complicated skip patterns, not possible with paper and pencil self-administered questionnaires, can be incorporated into the questionnaire. Similar methods are possible in telephone surveys, with the use of push-button or voice recognition technology for the self-administered portion of the questionnaire.

Randomized Response and Item Count Techniques

Two techniques described in the literature provide researchers with a means of obtaining a population estimate of an event or a behavior but not information that can be associated with the individual. Both were designed initially for use in face-to-face surveys; it is feasible to administer an item count approach in a telephone or self-administered questionnaire. The randomized response technique is one in which two questions are presented to the respondent, each with the same response categories, usually yes and no. One question is the question of interest; the other is a question for which the distribution of the responses for the population is known. Each question is associated with a different color. A randomized device, such as a box containing beads of different colors, indicates to the respondent which of the questions to answer, for which he or she simply states to the interviewer either "yes" or "no." The probability of selecting the red bead as opposed to the blue bead is known to the researcher. An example is as follows: A box contains 100 beads, 70 percent of which are red, 30 percent of which are blue. When shaken, the box will present to the respondent one bead (only seen by the respondent). Depending on the color, the respondent will answer one of the following questions: (Red question) Have you ever had an abortion? and (Blue question) Is your birthday in June? In a survey of 1,000 individuals, the expected number of persons answering "yes" to the question about the month of the birthday is approximately 1,000(.30)/12 or 25 persons (assuming birthdays are equally distributed over the 12 months of the year). If 200 persons said "yes" in response to answering either the red or blue questions, then 175 answered yes in response to the abortion item, yielding a population estimate of the percent of women having had an abortion as 175/(1000*.70) or 25 percent.

The item count method is somewhat easier to administer than the randomized response technique. In the item count method, two nearly identical lists of behaviors are developed; in one list k behaviors are listed and in the other list, k +1 items are listed, where the additional item is the behavior of interest. Half of the respondents are administered the list with k items and the other half are offered the list with the k +1 behaviors. Respondents are asked to simply provide the number of behaviors in which they have engaged (without indicating the specific behaviors). The difference in the number of behaviors between the two lists provides the estimate of the behavior of interest.

The major disadvantage of either the randomized response technique or item count method is that one cannot relate individual characteristics of the respondents with the behavior of interest; rather one is limited to a population estimate.


The empirical literature addressing response errors specifically among the low-income or welfare population is limited. However, if we couple those limited findings with results based on studies of the general population, some principles of questionnaire design to minimize response error emerge. At the risk of appearing to provide simple solutions to complex problems, we speculate on some guidelines to assist in the construction of questionnaires targeted at the low-income or welfare populations.

  • Complex versus simple behavioral experience. One finding that is consistent throughout the literature indicates that complex behavioral experiences are more difficult to retrieve and report accurately than simple behavioral experiences. Despite this, questionnaire designers tend to treat all potential respondents the same, opting for a single set of questions for many questions, such as a single question or set of questions concerning annual earnings or amount of program support. One means by which to attempt to improve the reporting for those persons for whom the task is most difficult is to adopt, as suggested by Schaeffer (1994), the use of filter questions to determine the complexity of the experience, offering different follow-up questions for those with simple and complex behavior. For example, the person who has been employed continuously at a single job or unemployed continuously during a particular reference period easily can be identified and directed toward a different set of questions concerning earnings than the individual who has held several jobs, either concurrently or sequentially. Similarly, one can ask the respondent whether the amount of income from a particular income support program varies from month to month, with follow-up questions based on the response. Although this approach to questionnaire design deviates from the desire to "standardized" the measurement process, it acknowledges the need to be flexible within a standardized measurement process so as to maximize the quality of the final productr
  • Simple, single-focus items often are more effective than complex, compound items. Whenever possible, a question should attempt to address a single concept. Questions that include the use of "and" or "or" or that end with exclusion or inclusion clauses often can be confusing to respondents. Although these questions often are constructed so as to minimize the number of questions read to the respondent (and therefore minimize administration time), we speculate that the use of several shorter questions is more effective, both from the perspective of administration time as well as the quality of the data. As an example, let's return to an earlier example:

Since your welfare benefits ended in (FINAL BENEFIT MONTH), did you take part for at least one month in any Adult Basic Education (ABE) classes for improving your basic reading and math skills, or GED classes to help you prepare for the GED test, or classes to prepare for a regular high school diploma?

One means to improve this item would be as follows:

Since (FINAL BENEFIT MONTH) have you taken any of the following classes?
  1. An Adult Basic Education class for improving basic reading and math skills? YES/NO
  2. A GED class to prepare for the GED test? YES/NO
  3. A class or classes to prepare for a regular high school diploma? YES/NO

If the "one month" qualifier offered in the original question was important analytically, each "yes" response could be followed up with a probe directed at the length of the class.

  • Reduce cognitive burden whenever possible. Regardless of the population of interest, we know that, from a cognitive perspective, some tasks are easier to perform than others. Several means by which this can be accomplished include:
  • Phrase tasks in the form of recognition rather than free recall. For example, asking the respondent to answer the question "Did you receive income from any of the following sources?" followed by a list of income sources is easier than asking the respondent to identify all income sources for the reference period of interest. Note that in asking a recognition question such as the one described, the ideal format would be to have the respondent respond "yes/no" to each income source, so only one item needs to be processed.
  • Request information that requires estimation rather than episodic recall. For example, asking for the total number of jobs held during the reference period of interest requires less cognitive effort than asking for the starting and ending date of each job. If the latter information is needed to address analytic needs, preceding the request with an estimation question may aid the respondent's retrieval of individual episodes.
  • Request information in the format or metric used by the respondent. For example, earning information may be best reported when the most salient or most rehearsed metric is used by the respondent. For example, the findings by Borus (1970) and Smith (1997) that indicated a single broad-based question yielded a more accurate reporting by low-income respondents than a series of questions that required event-history type reconstruction of earnings simply may indicate that annual earnings are well rehearsed and more easily accessible to respondents than earnings related to any one job. One means by which to determine whether to ask the respondent about annual earnings, monthly earnings, or hourly earnings is to ask the respondent how he or she is best able to respond. Once again, this implies that tailoring the questionnaire to the respondent's circumstances may result in higher quality data.
  • Focus on reference periods that are salient to the respondent. The 6-month period prior to exiting welfare may not necessarily be a particularly salient reference period, even though the date of termination of benefits may be quite salient. For reference periods that may not be salient to the respondent, the use of calendars or other records coupled with the identification of landmark events within the reference period may aid retrieval of information and the dating of events and behaviors.
  • Provide the respondent with assistance in how to perform the task. For the most part, respondents rarely perform the task we are asking them to tackle. Instructions and feedback throughout the process can clarify the task for the respondent as well as provide feedback for appropriate respondent behavior. Instructions indicating that the questionnaire designer is interested in all spells of unemployment, including short spells lasting less than a week, provides an instruction to the respondent as well as additional time for the respondent to search his or her memory. Should the respondent provide such information, appropriate feedback would indicate that such detailed information is important to the study. Other forms of instruction could focus the respondent on the use of a calendar or other types of records.

    In addition, we know from the literature that use of additional probes or cues stimulates the reporting of additional information. When there is interest in eliciting information from the respondent concerning short spells of employment or unemployment or odd or sporadic sources of income, repeated retrieval attempts by the respondent in response to repeated questions may be the most effective approach.

    In some cases, the provision of some information may be preferable to no information from the respondent. Consider the case in which the respondent reports "don't know" in response to a question concerning earnings. One approach that has been effective is the use of broad-based followup questions in response to "don't know" items, for example, asking the respondent if his or her earnings were more than or less than a specific amount, with subsequent followup items until the respondent can no longer make a distinction (see Hurd and Rodgers, 1998).

  • Comprehension. The concepts of interest for many surveys of the low-income and welfare populations are fairly complex, for example, distinguishing among the various income support programs or determining whether sporadic odd jobs count as being employed. As indicated in several of the studies reviewed, research directed toward improving the comprehension of survey questions is greatly needed. For those developing questionnaires, this implies the need for iterative testing and pretesting, focusing on the interpretation of questions among members of the population of interest.

The empirical literature provides evidence of both reasonably accurate reporting of earnings, other sources of income, and employment as well as extremely poor reporting of these characteristics on the part of household respondents. The magnitude of measurement error in these reports is in part a function of the task as framed by the question. Careful questionnaire construction and thorough testing of questions and questionnaires can effectively identify question problems and reduce sources of error.


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1.  Note that although statistical and economic theories provide the foundation for analysis of error-prone data, these disciplines provide little theoretical foundation for understanding the source of the measurement error nor the means for reducing measurement error. The discussion presented here will be limited to a review of cognitive and social psychological theories applicable to the measures of interest in understanding the welfare population.

2.  An alternative interpretation of the findings might suggest that the decomposition approach was more accurate and that the apparent overestimation, when compared to administrative records, is because of underreporting of income in the administrative records rather than overreporting of earnings using the decomposition method.

3.  Based on the Panel Study of Income Dynamics validation study, a survey conducted among a sample of employees at a single establishment and comparing their responses to those obtained from company records. The data from the first wave of this two-wave study were the basis for the study reported by Duncan and Hill (1985).

4  The CPS is collected each month from a probability sample of approximately 50,000 households; interviews are conducted during the week containing the 19th day of the month: respondents are questioned about labor force status for the previous week, Sunday through Saturday, which includes the 12th of the month. In this way, the data are considered the respondent's current employment status, with a fixed reference period for all respondents, regardless of which day of the week they are interviewed. In addition to the core set of questions concerning labor force participation and demographic characteristics, respondents interviewed in March of any year are asked a supplemental set of questions (hence the name March supplement) concerning income recipiency and amounts, weeks employed, unemployed and not in the labor force, and health insurance coverage for the previous calendar year.

Part II: Administrative Data

This paper addresses the cleaning and linking of individual-level administrative data for the purposes of social program research and evaluation. We will define administrative data as that collected in the course of programmatic activities for the purposes of client-level tracking, service provision, or decision making--essentially, nonresearch activities. Although some data sets are collected with both programmatic and research activities in mind--birth certificates are a good example--researchers usually think of administrative data as a secondary data source in contrast to surveys that are conducted solely for research purposes.

When we refer to administrative data to be used for research and evaluation of social programs, we are referring primarily to data from management information systems designed to assist in the administration of participant benefits, including, income maintenance, food stamps, Medicaid, nutritional programs, child support, child protective services, childcare subsidies, Social Security programs, and an array of social services and public health programs. Because the focus of the research is often on individual well-being, most government social programs aimed at individuals could be included.

Before these administrative data can be used for research purposes, cleaning the data is a major activity as it is in conducting and using any large social surveys. Cleaning is necessary because there are numerous sources of potential error in the data and because the data are not formatted in a way that is easily analyzed by social scientists. We take a broad view of cleaning. It is not just correcting "dirty" data; it is producing a clean data set from a messy assortment of data sets. In this paper, cleaning refers to the entire process of transforming the data as it exists in the information system into an analytic data set.

Record linkage is a major activity in the use of administrative data, especially if the research is longitudinal--and, by definition, evaluation nearly always is. Record linkage is the process of determining that two data records belong to the same individual. Being able to track an individual from one time to the next or across numerous data sets is nearly always necessary when using administrative data, especially because, in most cases, one does not have access to the independent sources of data that can assure that a time 1 measurement and time 2 measurement are of the same person or that an agency 1 record and agency 2 record are for the same individual.(1)

Data cleaning and record linkage are closely related activities. At its most simple level, record linkage is necessary to determine if any duplicate records exist for a single individual or case in a particular data set. Record linkage is used to produce clean, comprehensive data sets from single program data sets. Without accurate record linkage, it is likely that the data on an individual will be incomplete or contain data that do not belong to that individual. And, to do accurate record linkage, the data fields necessary to perform the linkage, typically individual identifiers, must be accurate and in a standardized format across all the data sets to be linked.

Matching and Cleaning Administrative Data

Advantages and Disadvantages of Administrative Data

The advantages and disadvantages of administrative data can be identified most easily when they are compared with survey data. However, the comparison of these two types of data collection is a straw man. The research questions that are appropriately addressed are qualitatively different from those appropriately addressed by surveying a subset of the general population. The type of data that one should use for answering a particular research question should be determined by the question. If a comprehensive study of a particular issue requires a survey of a population not covered by administrative data or if important variables are unavailable, other data collection is necessary. However, it is almost always the case that a rich study of a particular issue that identifies or rules out multiple potential causes or correlates of a particular phenomena requires data from multiple sources.

Administrative data, in most cases, are superior to other data sources for identifying program participation--what benefits were provided to whom, when, and in what amount. (The exact reason why people are participating is often missing.) Administrative data are collected on an entire population of individuals or families participating in a given program. This is advantageous for two reasons. First, it is possible to study low-incidence phenomena that may be expensive to uncover in a survey of the general population. Second, and related to the first, it is possible to study the spread of events over a geographical area; this is even easier if extensive geographical identifiers are available on the data record. Given that information about events is usually collected when the events happen, there is much less opportunity for errors because of faulty recall.

Using administrative data is also advantageous for uncovering information that a survey respondent is unlikely to provide in an interview. In our work, we were relatively certain that families would underreport their incidence of abuse or neglect. The same issue exists for mental health or substance abuse treatment. Although survey methods have progressed significantly in addressing sensitive issues, administrative data can prove to be an accurate source of indicators for phenomena that are not easily reported by individuals--if one can satisfactorily address issues of accessing sensitive or confidential data.

Because the data record for an individual or case is likely viewed often by the program staff, opportunities exist for correcting and updating the data fields. The value of this is even greater when the old information is maintained in addition to the updates. A major problem with administrative data archiving and storing is that when data are updated, the old information is lost when it is overwritten.

As noted, the disadvantages of administrative data are often listed as a contrast to the characteristics of survey data. Although this may be a straw man argument, other legitimate concerns should be addressed when using administrative data. The concerns are related to the choice-, event-, or participation-based nature of the data; the reliability of administrative data for research purposes; the lack of adequate control variables; and the facts that all outcomes of interest are not measured (e.g., some types of indicators of well-being) that data are available only for the periods that the client is in the program, and that the level of reliability of administrative data is uncertain. Also, the data are difficult to access because of confidentiality issues (as far as getting informed consent) and because of bureaucratic issues in obtaining approval. When the data will be available, therefore, is often unpredictable.

Finally, there is often a lack of documentation and information about quality. One must do ethnographic research to uncover "qualitative" information about the condition of the data. There is no shortcut for understanding the process behind the collection, processing, and storage of the administrative data.

Assessing the Quality of the Data and Cleaning the Data for Research Purposes

In this section, we present strategies for determining if a particular administrative data set can be used to answer a particular question. Researchers seldom go directly to the online information system itself to assess its quality--although this may be one step in the process. Typically, government agencies give researchers both inside and outside the agency an extract of the information system of interest. This file may be called a "pull" file. It is a selection of data fields, never all of them, typically on all individuals in the information system during a specified period of time created for a particular purpose, usually not specified each time a request for data is made. Any one actual pull refers to a time period that corresponds to some administrative time period--for example, month or fiscal year. These cross-sectional pulls are very useful for agency purposes because they describe the point-in-time caseload for which an agency is responsible. As we will explain, this approach is not ideal for social research or evaluation.

The programming for a pull file is often a time-consuming task that is done as part of the system design based on the analytic needs at the time of the design. Even a small modification to the pull file may be costly or impossible given the capacity of the state or county agency information systems division. The advantage of this practice is that multiple individuals usually have some knowledge of the quality of the pull file--they may know how some of the fields are collected and how accurate they are. The disadvantage is that it probably requires additional cleaning to answer a particular set of research questions.

We cannot stress enough the importance of assessing data sets individually for each new research project undertaken. A particular data set may be ideal for one question and a disaster for another. Some fields in a database that may be perfectly reliable because of how the agencies collect or audit these fields, while other fields may almost seem to contain values entered in a random manner. Also, a particular programmatic database may have certain fields that are reliable at one point in time and not at other points. Needless to say, one field may be entered reliably in one jurisdiction and not in another.

For example, income maintenance program data are ideal for knowing the months in which families received Aid to Families with Dependent Children (AFDC) or Temporary Assistance for Needy Families (TANF) grants. However, because they rely on the reporting of grantees for employment information and there are often incentives for providing inaccurate information, addressing questions about the employment of TANF recipients using income maintenance program data is not ideal. Furthermore, information about the grantee, such as marital status or education, may only be collected at case opening and therefore is more likely to be inaccurate the longer the time since the case opening. Undertaking these tasks of assessing data quality is quite time consuming and resource intensive. The resource requirements are similar to those of cleaning large survey data sets, however, where to go to get information to do the cleaning is often unclear. Often documentation is unavailable and the original system architects have moved to other projects. Therefore, cleaning administrative data is often a task that goes on for many years as more is learned about the source and maintenance of the particular database.

In the following paragraphs, we provide some of the strategies and methods that we use to assess and address issues of data quality in the use of administrative data. The most basic, and perhaps best, of these is to compare the data with another source on the same event or individual. We will end with a discussion of that strategy.

Assessing Data Quality

Initially, the researcher would want to assess if the data entry were reliable, which would include knowing whether the individual collecting the data had the skill or opportunity to collect reliable information. The questions that should be asked are as follows:

  • What is the motivation for collecting the data? Often a financial or contractual motivation produces the most reliable data. When reimbursement is tied to a particular data field, both the payer and the payee have incentives to ensure that neither party is provided with an additional benefit. The state agency does not want to pay more TANF that it needs to pay, and a grantee (or his or her advocate) wants to ensure that the family gets all to which they are entitled. Also, an agency may have a legal requirement to track individuals and their information. Properly tracking the jail time of incarcerated individuals would seem to be one such activity for which one could be fairly certain of the data accuracy--although not blindly so.
  • Is there a system for auditing the accuracy of the data? Is there a group of individuals who sample the data and cross-check the accuracy of the data with another source of the information? In some agencies, the computer records will be compared to the paper files.
  • Are the data entered directly by the frontline worker? Adding a step to the process of entering the data--having a worker filling out a paper form and then passing it on to a data entry function--allows another opportunity for error and typically also excludes the opportunity for the worker to see the computerized record in order to correct it.
  • Do "edit checks" exist in the information system? If there is no direct audit of the data or the data are not entered or checked by a frontline worker, having edit checks built into the data entry system may address some errors. These checks are programmed to prevent the entry of invalid values or not entering anything into a field. (This is similar to the practice of programming skip patterns or acceptable values for data entry of survey instruments.) For example, an edit check can require that a nonzero dollar amount is entered into a current earnings field for those individuals who are labeled as employed.
  • What analyses have been done with these data in the past? There is no substitute for analyzing the data--even attempting to address some of the research questions--in the process of assessing the quality, especially when the administrative data have not been used extensively. A good starting point for such analysis is examining the frequencies of certain fields to determine if there are any anomalies, such as values that are out of range; or examining inexplicable variation by region, suggesting variation in data entry practices; or seeking missing periods of the time series. Substantive consistency of the data is an important starting point as well. One example of this with which we have been wrestling is why 100 percent of the AFDC caseload were not eligible for Medicaid. We were certain that we had made some error in our record linkage. When we conferred with the welfare agency staff, they also were stymied at first. We eventually discovered that some AFDC recipients are actually covered by private health insurance through their employers. With this information, we are at least able to explain an apparent error.
  • Finally, are the items in the data fields critical to the mission of the program? This issue is related to the first noted issue above. Cutting checks is critical for welfare agencies. If certain types of data are required to cut checks, the data may be considered to be accurate. For example, if a payment cannot be made to an individual until a status that results in a sanction is addressed, one typically expects that the sanction code will be changed so payment can be made. On the other hand, if a particular assessment is not required for a worker to do his or her job or if an assessment is outside the skill set of the typical worker doing the assessment, one should have concerns about the accuracy (Goerge et al., 1992). For example, foster care workers have been asked to provide the disability status of the child on his or her computerized record. This status in the vast majority of the cases has no impact on the decision making of the worker. Therefore, even if there is an edit check that requires a particular set of codes, one would not expect the coding to be accurate.

We will continue to give examples of data quality issues as we discuss ways to address some of them. The following examples center on the linking of an administrative data set with another one in order to address inadequacies in one set for addressing a particular question.

The choice-based nature of administrative data can be addressed in part by linking the data to a population-based administrative data set. Such linkages allows one to better understand who is participating in a program and perhaps how they were selected or selected themselves into the program. There are some obvious examples of choice-based linking data to population-based data. In analyzing young children, it is possible to use birth certificate data to better understand what children might be selected into programs such as Women, Infants and Children (WIC), Early, Periodic, Screening, Diagnosis And Treatment Program (EPSDT), and foster care. If geographic identifiers are available, administrative data can be linked to census tract information to provide additional information on the context as well as the selection process. For example, knowing how many poor children live in a particular census tract and how many children participate in a welfare program can address whether the welfare population is representative of the entire population of those living at some fraction of the poverty level.

If one is interested in school-age children, computerized school data provide a base population for understanding the selection issues. One example is to link the 6- to 12-year-old population and their School Lunch Program (SLP) information to Food Stamp administrative data to understand who uses Food Stamps and what population the administrative data actually represent. Because SLP eligibility is very similar to Food Stamps (without the asset test), such data could provide a very good idea of Food Stamp participation. The criticism that administrative data only tracks individuals while they are in the program is true. Extending this a bit, administrative data, in general, only track individuals while they are in some administrative data set. Good recent examples of addressing this issue are the TANF leaver studies being conducted by a number of states. They are linking records of individuals leaving TANF with UI and other administrative data, as well as survey data, to fill in the data that welfare agencies typically have on these individuals--data from the states' FAMIS or MMIS systems. Especially when we are studying welfare or former welfare recipients, it is likely that these individuals appear in another administrative data set--Medicaid, Food Stamps, child support, WIC, or child care, to name a few. Although participation in some of these is closely linked to income maintenance, as we have learned in the recent past, there is also enough independence from income maintenance programs to provide useful post-participation information. Finally, if they are not in any of these social programs databases, they are likely to be in the income tax return databases or in credit bureau databases, both now becoming data sets used more commonly for social research (Hotz et al., 1999).

A more thorny problem may be situations in which an individual or a family leaves the jurisdiction where administrative data were collected. We may be "looking" for them in other databases when they may have moved out of the county or state (or country) in which the data were collected. The creation of national-level data sets may help to address this problem simply through a better understanding of mobility issues, if not actually linking data from multiple states to better track individuals or families.

It is certainly possible that two administrative databases will label an individual as participating in two programs that should be mutually exclusive. For example, in our work in examining the overlap of AFDC or TANF and foster care, we find that children are identified as living with their parents in an income maintenance case when they are actually living with foster parents. Although these records eventually may be reconciled for accounting purposes (on the income maintenance side), we do need to accurately capture the date that living in an AFDC grant ended and living in foster care began. Foster care administrative data typically track accurately where children live on a day-to-day basis. Therefore, in studying these two programs, it is straightforward to truncate the AFDC record when foster care begins. However, one would want to "overwrite" the AFDC end date so that one would not use the wrong date if one were to analyze the overlap between AFDC and another program, such as WIC, where the participation date may be less accurate than in the foster care program.

Basic reliability issues also arise. For example, some administrative databases do a less than acceptable job of identifying the demographic characteristics of an individual. At a minimum, data entry errors may occur in entering gender or birth dates (3/11/99, instead of 11/3/99). Also, data on workers' determination of race/ethnicity might not be self-reported, or race/ethnicity might not be critical to the business of the agency, although this is often a concern of external parties. In some cases, when one links two administrative data files, the race/ethnicity codes for an individual do not agree. This discrepancy may be a particular problem when the data files cover time periods that are far apart, because some individuals do change how they label themselves and the labels used by agencies may change (Scott, 2000). Linking administrative data with birth certificate data--often computerized for decades in many states--or having another source of data can help address these problems. We will discuss this issue below when we discuss record linkage in detail (Goerge, 1997).

Creating Longitudinal Files

As mentioned earlier, the pull files provided by government agencies are often not cumulative files and most often only span a limited time period. For most social research, longitudinal data are required, and continuous-time data--as opposed to repeated, cross-sectional data--are preferred, again depending on the question. Although these pull files may contain some historical information, this is often kept to a minimum to limit the file size. The historical information is typically maintained for the program's unit of administration. For TANF, this is the family case. For Food Stamps, it is the household case. In either program, the historical data for the individual member of the household or family are not kept in these pull files. The current status typically is recorded in order to accurately calculate the size of the caseload. Therefore, to create a "clean" longitudinal file at the individual level, one must read each monthly pull file in order to recreate the individual's status history. Using a case history for an individual would be inaccurate. An example is the overlap between AFDC and foster care discussed earlier. The case history for the family--often that of the head of the household, and which may continue after the child enters foster care--would not accurately track the child's income maintenance grant participation. More on this topic is discussed in the following sections.

Linking Administrative Data and Survey Data

The state of the art in addressing the most pressing policy issues of the day is to use administrative data and survey methods to obtain the richest, most accurate data to answer questions about the impact and implementation of social programs. The TANF leaver studies mentioned earlier, which use income maintenance administrative data to select and weight samples and TANF and other programmatic databases to locate former TANF participants, provide certain outcome measures (e.g., employment and readmission) and characteristics of the grantees and members of the family. Survey data are used to obtain perceptions about employment and fill in where the administrative data lack certain information. Administrative lists have also been used to generate samples for surveys that intend to collect data not available in the administrative data.

Such studies can be helpful in understanding data quality issues when the two sources of data overlap. For example, we worked with other colleagues to compare reports of welfare receipt with administrative data and were able to gauge the accuracy of participant recall. We have some evidence for situations in which it is quite defensible to use surveys when administrative data are too difficult or time consuming to obtain. For example, although childcare utilization data may be available in many states, the data often are so decentralized that bringing them together into a single database may take many more resources than a survey. Of course, this depends on the sample size needed. However, much more needs to be done in this vein to understand when it is worthwhile to take on the obstacles that are more the rule than the exception in using administrative data.

Administrative Data Record Linkage

A characteristic of administrative data that offers unique opportunities for researchers is the ability to link data sets in order to address research questions that have otherwise been difficult to pursue because of lack of suitable data.(2) For example, studying the incidence of foster care placement, or any low-incidence event, among children who are receiving cash assistance requires a large sample of children receiving cash assistance given that foster care placement is a rare event. The resources and time required to gather such data using survey methods can be prohibitive. However, linking cash assistance administrative data and foster care data solves the problem of adequate sample size in a cost-effective way. Linking administrative data sets is also advantageous when the research interest is focused in one particular service area. For example, if one is interested in studying the multiple recurrences of some event, such as multiple reentries to cash assistance, recurring patterns of violent crime, or reentries to foster care, the size of the initial baseline sample must be large enough to observe an adequate number of recurrences in a reasonable time period. Linking administrative data over time at the population level for each area of concern is an excellent resource for pursuing such research questions without large investments of time and financial resources.

When the linked administrative data sets are considered as an ongoing research resource, it is preferable to have data from the entire population from each source database that are linked to each other and maintained. Given the large number of cases needed to be processed during record linkage, the idea of working with data from the entire population could overwhelm the researcher. However, because most data processing now is done using computers, the sheer size of the data files needed to be linked is typically not a major factor in the time and resources needed. On the other hand, the importance of having good programmers with necessary analytic and programming skills cannot be overemphasized for achieving successful record-linking results. Because the amount of skilled programming for a sample file may be equal to the amount needed for an entire large file, the additional cost involved in linking the entire files rather than samples is justifiable in computerized record-linking situations. The advantages that arise from having population data (as opposed to samples from each system or some systems) far exceed the costs involved. When tracking certain outcomes of a base population using linked data, one needs at least the population-level data from the data source that contains information about the outcome of interest.

For example, suppose one is interested in studying the incidence of receiving service X among a 10 percent random sample of a population in data set A. The receipt of service X is recorded in data set B. Because the researcher must identify all service X receipt for the 10 percent sample in data set A, the sample data must be linked to the entire population in data set B. Suppose the researcher only has a 10 percent random sample of data set B. Linking the two data samples would provide, at best, only 10 percent of the outcomes of interest identified in the 10 percent sample of the base population A. Furthermore, the "unlinked" individuals in the sample would be a combination of those who did not receive service X and those who received service X but were not sampled from data set B. Because one cannot distinguish the two groups among the "unlinked" individuals, any individual-level analysis becomes impossible (see Deming and Glasser, 1959, for a discussion of the issue of linking samples and the difficulty associated with it).

Research Applications of Data Linking

There are four different research applications of linked data sets. Each represents a different set of issues and challenges. The four types of linking applications can be broadly defined as: (1) linking an individual's records within a service system over time, (2) linking different information system data sets across service areas, (3) linking survey data to administrative data sets when the survey sample is drawn from an administrative data set, and 4) linking sample data to administrative data sets when the sample is drawn independent of administrative data.

The first type of linking application is the most common. Typically researchers take advantage of administrative data's historical information for various longitudinal analyses of service outcomes. Often this type of research requires linking data on individuals across several cross-sectional extracts from an agency's information system. Many agency information systems only contain information on the most recent service activities or service populations. Some information systems were designed that way because the agency's activity is defined as delivering services to a caseload at a given point in time or at some intervals. A good example would be a school information system in which each school year is defined as the fixed service duration, and each school year population is viewed as a distinct population. In this case, there is typically no unique individual ID in the information system across years because every individual gets a new ID each year--one that is associated with the particular school year. Even in a typical state information system on cash assistance, case status information is updated (in other words, overwritten) in any month when the status changes. To "reconstruct" the service histories, as discussed in the earlier section on cleaning, one must link each monthly extract to track service status changes.

At times, the information system itself is longitudinal, and no data are purged or overwritten. Even when the database is supposedly longitudinal, a family or an individual can be given multiple IDs over time. For example, many information systems employ a case ID system, which includes a geographic identifier (such as county code or service district code) as part of a unique individual ID. In this instance, problems arise when a family or an individual moves and receives a different ID. Our experience suggests that individuals are often associated with several case IDs over time in a single agency information system. Sometimes individuals may have several agency IDs assigned to them either because of a data entry error or a lack of concerted effort to track individuals in information systems. In any situation outlined here, careful examination of an explicit linking strategy is necessary.

The second type of linking application most often involves situations in which different agency information systems do not share a common ID. Where the funding stream and the service delivery system are separate and categorical in nature, information systems developed to support the functions of each agency are not linked to other service information systems. In some instances, information systems even in a single agency do not share a common ID. For example, many child welfare agencies maintain two separate legacy information systems; one tracks foster care placement and payments and the other records child maltreatment reports. Although following the experiences of children from a report of abuse or neglect to a subsequent foster care event is critical for child welfare agencies, the two systems were not designed to support such a function. Obviously, where there is no common ID, linking data records reliably and accurately across different data sources is an important issue. Also, as in the case of linking individual records over time in a single information system, there is always a possibility of incorrect IDs, even when such a common ID exists. In fact, a reliable record linking between the two information systems that contain a common ID on a regular basis could provide a means to "correct" such incorrect IDs. For example, when the data files from the two systems are properly linked by using data fields other than the common ID, such as names and birth dates, the results of such a link could be compared to the common IDs in the information systems to identify incorrectly entered IDs.

The third type of linking application is when a sample of individuals recorded in administrative data is used as the study population. In such a study, researchers employ survey methods to try to collect information not typically available in administrative data. Items such as unreported income, attitude, and psychological functioning are good examples of information that is unavailable in administrative data. Most often, this type of application is not readily perceived as a linking application. However, when researchers use administrative data to collect information about the service receipt history of the sample, either retrospectively or prospectively, they face the same issues as one faces in linking administrative data in a single information system or across multiple systems. Also, if researchers rely on the agency ID system to identify the list of "unique" individuals when the sampling frame is developed, the quality of the agency ID has important implications for the representativeness of the sample. The degree of multiple IDs for the same individuals should be ascertained and the records unduplicated at the individual level for the sampling frame.

The fourth type of linking application involves cases in which researchers supplement the information collected through survey methods with detailed service information; they do this by linking survey data to service system administrative data after the survey is completed. Because the sample is drawn independent of the administrative data, no common ID is designated between the sample and the administrative data. Here the major concern is the kinds of identifying information that are available for linking purposes from both data sources. In particular, whether and how much identifying information--such as full names, birth dates, and Social Security numbers (SSNs)--is available from the survey data is a critical issue. When the identifying information is collected, data confidentiality issues might prohibit researchers from making information available for linking purposes.

Technical Issues in Linking

Two Methods of Linking: Probabilistic and Deterministic Record-Linkage Methods

Linking data records reliably and accurately across different data sources is key to the success in the four applications outlined. In this section, we focus on the data linkage methods. Our main purpose is to provide basic concepts for practitioners rather than to present a rigorous theoretical method. Our discussion focuses on two methods of record linkage that are possible in automated computer systems: deterministic and probabilistic record linking.

Deterministic Record Linkage

Deterministic linkage compares an identifier or a group of identifiers across databases; a link is made if they all agree. For example, relying solely on an agency's common ID when available for linking purposes is a type of deterministic linking. When a common ID is unavailable, standard practice is to use alternative identifiers--such as SSNs, birth dates, and first and last names of individuals--that are available in two sets of data. Researchers also use combinations of different pieces of identifying information in an effort to increase the validity of the links made. For example, one might use SSN and the first two letters of the first and last names. In situations where an identifier with a high degree of discriminating power (such as SSN) is unavailable, a combination of the different pieces of identifying information must be used because many people have the same first and last names or birth dates. What distinguishes deterministic record linkage is that when two records agree on a particular field, there is no information on whether that agreement increases or decreases the likelihood that the two records are from the same individual. For example, the two situations in which, on last name, Goerge matches Goerge, and where Smith matches Smith, would be treated with similar matching power, even though it is clear that because there are few Goerges and many Smiths, these two matches mean different things.

Probabilistic Record Linkage

Because of the problems associated with deterministic linking, and especially when there is no single identifier distinguishing between truly linked records (records of the same individual) in the data sets, researchers have developed a set of methods known as probabilistic record linkage.(3) Probabilistic record linking is based on the assumption that no single match between variables common to the source databases will identify a client with complete reliability. Instead, the probabilistic record-linking method calculates the probability that two records belong to the same client by using multiple pieces of identifying information. Such identifying data may include last and first name, SSN, birth date, gender, race and ethnicity, and county of residence.

The process of record linkage can be conceptualized as identifying matched pairs among all possible pairs of observations from two data files. For example, when a data file A with A observations and a data file B with B observations are compared, the record-linkage process attempts to classify each record pair from the A by B pairs into the set of true matches (M set) and the set of true nonmatches (U set). First introduced by Newcombe et al. (1959) and further developed by Fellegi and Sunter (1969), the two probabilities for each field that are needed to determine if a pair belongs to M or U are m and u probabilities. Each field that is being compared in the record-linking process has m and u probabilities. The m probability is the probability that a field agrees given that the record pair being examined is a matched pair. The m probability is a measure of validity of the data field used in the record-linkage process because it is essentially one minus the error rate of the field. Thus, one can see that a more reliable data field will provide greater m probability. The u probability is the probability that a field agrees given that the record pair being examined is not a matched pair. This is a chance probability that a field agrees at random. For example, if the population has the same number of males and females, the u probability will be .5 because there is a 50 percent chance the gender field will match when the pair being examined is not a matched pair. Accordingly, a variable such as SSN will have a very low u probability because it is very unlikely that different individuals have the same SSN. Although there are many methods to calculate M and U probabilities, recent studies show that maximum-likelihood-based methods such as the Expectation-Maximization (EM) algorithm is the most effective of those developed and tested (Winkler, 1988; Jaro, 1989).

Using m and u probabilities, Fellegi and Sunter (1969) define weights that measure the contribution of each field to the probability of making an accurate classification of each pair into M or U sets. The "agreement" weight when a field agrees between the two records being examined is calculated as log2(m/u) . The "disagreement" weight when a field does not agree is calculated as log2((1-m)/(1-u)) . These weights indicate how powerful a particular variable is in determining whether two records are from the same individual. These weights will vary based on the distribution of values of the identifiers. For example, a common last name match will provide a lower agreement weight than a match with a very uncommon name because u probability for such a common name will be greater than the uncommon name.

Fellegi and Sunter (1969) further showed that a composite weight could be calculated by summing the individual data field's weights. Using the composite weights, one can classify each pair of records into three groups: a link when the composite weight is above a threshold value (U), a non link when the composite weight is below another threshold value (L), and a possible link for clerical review when the composite weight is between U and L. Furthermore, the threshold values can be calculated given the accepted probability of false matches and the probability of false nonmatches (Fellegi and Sunter, 1969; Jaro, 1989). This contrasts favorably with the link or non link dichotomy in deterministic linkage.

Since the seminal work by Fellegi and Sunter (1969), the main focus of record linkage research has been how to determine the threshold values of U and L to improve the accuracy of determining what the threshold weight is for a certain link, as well as the threshold value for a certain non link. Recent development in improving record linkage allows us to take advantage of the speed and cost that computerized and automated linkage confer, such as deterministic matching, while allowing a researcher to identify at which "level" a match would be considered to be a true one (see for example; Jaro, 1989; Winkler, 1993, 1994, 1999).

Standardization and Data-Cleaning Issues in Record Linking

Regardless of which method of deterministic linking is used, entry errors, typographical errors, aliases, and other data transmission errors can cause problems. For example, one incorrectly entered digit of a Social Security number will produce a nonmatch between two records for which all other identifying information is the same. Names that are spelled differently across different systems also cause a problem. A first name of James that is recorded in one system as Jim and in the other as James will produce a nonmatch when the two records, in fact, belong to the same individual. The data cleaning in the record linkage process often involves (1) using consistent value-states for the data fields used for linking, (2) parsing variables into components that need to be compared, (3) dealing with typographical errors, and (4) unduplicating each source file for linkage.

Because record linking typically involves data sets from different sources, the importance of standardizing the format and values of each variable is used for linking purposes cannot be overemphasized. The exact coding schemes of all the variables from different source files used in the matching process should be examined to make sure all the data fields have consistent values. For example, males coded as "M" in one file and "1" in another file should be standardized into a same value. In the process, missing and invalid data entries also should be identified and coded accordingly. For example, a birth year 9999 should be recognized as a missing value before the data set is put into the record-linking process. Otherwise, records with a birth year 9999 from the two data sets can be linked because they have the "same" birth year. We also find that standardization of names in the matching process is important because names are often spelled differently or misspelled altogether across agency information systems. For example, a first name of Bob, Rob, and Robert should be standardized into a same first name such as Robert to achieve better record-linking results.

The data cleaning and standardization in matching process often requires parsing variables into a common set of components that can be compared. Names may have to be split or parsed into first name, middle initial, and last name and suffix (e.g., Junior). In using geographic information, street names and the form of the addresses must be standardized. This may mean parsing the address into number (100), street prefix (West), street name (Oak), and street suffix (Boulevard).

Because of typographical errors, an exact character-by-character comparison for certain fields used in a record-linking process may miss many "true" matches. A good example is variant spellings of names. For example, character-by-character comparison of a last name spelled as "Goerge" in one data file to a misspelled name "George" in another file would cause disagreement in the last name comparison even though "George" in the second file was a misspelling. In some situations, these types of typological errors can be a serious problem in record linkage. Winkler and Thibaudeau (1991) and Jaro (1989) describe how researchers at the U.S. Bureau of the Census reported that about 20 percent of last names and 25 percent of first names disagreed character by character among true matches in the Post Enumeration Survey. In recent years researchers in the field of record linkage have made substantial progress in developing algorithms to deal with such problems in character-by-character comparisons. As a result, some complex string comparator algorithms also have been developed to determine how close two strings of letters or numbers are to each other that account for insertions, deletions, and transpositions (Jaro, 1985, 1989; Winkler, 1990; Winkler and Thibaudeau, 1991).

In the record linkage process, one critical data cleaning process is to "unduplicate" each source data set before any two data sets are linked. As discussed earlier, often individuals are associated with several IDs because of data entry errors or a lack of concerted effort to track individuals in agency information systems. Obviously, multiple records for the same individual in each data set being linked produce uncertain links because the process must deal with N to N link situations. Unduplication of the records in a single data set can be thought as "self-match" of the data set. Once a match has been determined, a unique number is assigned to the matched records so that each individual can be uniquely identified. The end result of the unduplication process is a "person file," which contains the unique number assigned during unduplication and the individual's identifying data (name, birth date, race/ethnicity, gender, and county of residence) with a "link file" that links the unique individual ID to all the IDs assigned by an agency. Once each data set is unduplicated in such a way, the unduplicated person files can be used for cross-system record links.

Accuracy of Record Linking

Regardless of which method is used, the ultimate concern is in the degree of validity and accuracy of the links made. Whether it is a deterministic or probabilistic record-linkage technique that is used, the linking process essentially involves making an educated guess about whether two records belong to the same individual. Because the decision is a guess, it might be wrong. These errors in record linkage can be viewed as making false-positive and false-negative errors. A false-positive error occurs when the match is made between the two records when the two records, in fact, do not belong to the same individual. This type of error is comparable to a Type I error in statistical hypothesis testing. A false-negative error occurs when the match is not made between the two records when they, in fact, belong to the same individual. The type of error is comparable to a Type II error in statistical hypothesis testing.

As with Type I and Type II errors, although the probability of making a false-positive error can be easily ascertained in the linking process, determining the probability of a false negative error is more complex. Because the "weights" calculated in the probabilistic record-linkage method are essentially relative measures of the probability of a match, the weights can be converted to an explicit probability that a record pair is a true match (i.e., 1-false positive error rate). Belin and Rubin (1995) introduced a method for estimating error rates for cutoff weight values in the probabilistic record-linkage process. Many developments also have been made in dealing with linkage errors in post-linkage analysis stages (such as a regression analysis using linked files) (see Scheuren and Winkler, 1993). In the case of deterministic record linkage, an audit check on the matched pairs could provide an estimate of false-positive errors. Estimating the false-negative error rate is much more complex because it conceptually requires knowing the true matches prior to the linking and comparing the linking results to the true matches.

Adding to the complexity, as one tries to reduce one type of error, the other type of error increases. For example, in an effort to reduce false-positive errors, one might use a stringent rule of labeling the compared matches as matched pairs only when they are "perfect" matches. In the process, a slight difference in identifying information (such as one character mismatch in the names) might cause a non link when, in fact, the two records belong to the same individual. Hence, false-negative error rates increase. In the opposite scenario, one might accept as many possible matches as true matches, thereby relaxing the comparison rule by reducing false-negative errors. In this case, false-positive errors increase.

An Example

In practice, it would be useful to consider false-positive and false-negative error rates as a means to compare different methods of record linkage. One practical issue researchers face is determining which linkage method to use, especially when an ID variable such as SSN is available in the two data sets to be linked. Although most experts agree that probabilistic record linkage is a more reliable method than deterministic linking, it requires extensive programming or the purchase of software, which can be quite expensive. If one does not have ready access to suitable commercial record-linkage software, it may be sufficient for a good programmer to write a quick deterministic linkage program that matches a good deal of the records. There are other situations where there is no apparent common ID and the quality of identifying information in the data is questionable (such as many typographical errors in certain data fields), so that only using probabilistic record-linkage methods will yield acceptable linking results.

We present some empirical data comparing the two methods in the following paragraphs and corresponding tables. The methods compared are a deterministic record link using SSN and a probabilistic link using SSN, full name, birth date, race/ethnicity, and county of residence. We use data from the Client Database and the Cornerstone Database from the Illinois Department of Human Services. The Client Database records receipt of AFDC/TANF and Food Stamps and documents all those who are registered as eligible for Medicaid from 1989 to the present. The Cornerstone database contains WIC and case management service receipt at the individual level. There is no common ID between the two systems, while SSN and other identifying information are available in both systems.

Because both systems serve mainly low-income populations and contain data for a long period of time, we expected a high degree of overlap between the two populations. When the existence of SSN in both systems is examined, we find that about 38 percent of the Cornerstone records have missing SSNs while the Client Database identifies nearly 100 percent of the SSNs. In our first analysis, we excluded the records with missing SSNs from the Cornerstone data. Table 7-1 compares the number of matched and unmatched Cornerstone data records to the Client Database records comparing the deterministic match using SSN and the probabilistic match using all other identifying information, including SSN. As shown in Table 7-1, the probabilistic match identified about 86 percent of non-SSN-missing Cornerstone record links to the Client Database. The SSN deterministic method identified about 84 percent of the matches.

Comparison of SSN Match (Deterministic) Versus Probabilistic Match (Without Missing SSN)
    Probabilistic Matching Number Probabilistic Matching Percent
Non-Match Match Total Non-Match Match Total
SSN Non-match 74,496 45,987 120,483 61.8% 38.2% 100.0%
Matching Match 5,849 438,959 444,808 1.3% 98.7% 100.0%
  Total 80,345 484,946 565,291 14.2% 85.8% 100.0%

Although the percentage of overall matches is similar, the distribution of error types is quite different, as shown in Table 7-1. The false-negative error rate of using the SSN deterministic record-linking method when compared to the results from the probabilistic match is about 38 percent. On the other hand, the false positive error rate is about 1 percent. We checked the results of the probabilistic link from random samples of the disagreement cells (i.e., probabilistic match/SSN no match and probabilistic nonmatch/SSN match) to verify the validity of the probabilistic match. We found that the probabilistic match results are very reliable. For example, we found that most of the pairs in the probabilistic match/SSN no match cell involve typographical errors in SSN with the same full name and birth date. Also, we found that most of the pairs in the probabilistic nonmatch/SSN match involve entirely different names or birth dates. Although the findings might be somewhat different when applied to different data systems, our finding suggests that employing a probabilistic record-linkage method helps to reduce both false-negative and false-positive errors. The findings also show that the benefit of employing probabilistic record linkage is greater in reducing false-negative errors (Type II errors) than in reducing false-positive errors (Type I errors) when compared with a deterministic record-linkage method using SSN.

Next, we included the Cornerstone records with missing SSN in the analysis. The findings are presented in Table 7-2. As one might expect, the probabilistic record-linkage method significantly enhances the results of the match by linking many more records. Compared with the results presented in Table 7-1, the number of matches from the probabilistic match increases by about 210,000 records, representing about 62 percent of matches made among the records with missing SSNs. Again, most of the benefit of using the probabilistic linkage method is in reducing false-negative errors. With about 30 percent of the records showing missing SSNs, the false-negative error rate of the SSN deterministic link method is about 57 percent. From the above results, one can conclude that when SSN information is nearly complete in the two data sets, the added benefit of using probabilistic linking is relatively smaller (although quite significant) and the benefit comes largely from identifying false-negative errors. As the number of records with missing SSN increases, the benefit of employing a probabilistic record-linkage method increases.

Comparison of SSN Match Versus Probabilistic Match (With Missing SSN)
    Probabilistic Matching Number Probabilistic Matching Percent
Non-Match Match Total Non-Match Match Total
SSN Non-Match 199,442 260,720 460,162 43.3% 56.7% 100.0%
Matching Match 5,782 438,758 475,537 1.3% 98.7% 100.0%
  Total 205,224 699,478 904,702 22.7% 77.3% 100.0%

Very often in practice, being able to link different data sources involves many other issues than that of the linking method. A key issue is data confidentiality, especially when full names are needed for linking purposes in the absence of a common ID. One possible solution to the confidentiality issue is the use of Soundex codes. Even though Soundex is not a complete method to preserve confidentiality, it provides added protection compared to using actual full names. The Soundex system is a method of indexing names by eliminating some letters and substituting numbers for other letters based on a code. Although experts disagree on what should be the authoritative Soundex system, the most familiar use of Soundex is by the U.S. Bureau of the Census, which uses it to create an index for individuals listed in the Census. Because it is impossible to derive an exact name from a Soundex name, the system can be used to conceal the identity of an individual to some extent. (For example, similar sounding but different names are coded to a same Soundex name.)

The issue in probabilistic linking, however, is how valid a Soundex name is alone compared to using full names. We examine this issue by comparing the two methods involving the same data sets with the other identifying information fixed. The other identifying information variables are SSN, birth date, race/ethnicity, and county of residence. Table 7-3 presents the results of such an exercise. The agreement rate between the Soundex-only method and the full-name method is very high--close to 100 percent. The results suggest that Soundex coded names work equally as well as full names in a probabilistic match. In situations in which full names cannot be accessed for linking purposes, Soundex names might be a good alternative while providing a better means of protecting individual identities.(4)

Comparison of Full Name Match Versus Soundex Code Match
    Full Name Matching Number   Full Name Matching Percent  
Non-Match Match Total Non-Match Match Total
Soundex Non-Match 256,628 221 256,849 99.9% 0.1% 100.0%
Matching Match 40 43,111 43,151 0.1% 99.9% 100.0%
  Total 256,668 43,332 300,000 85.6% 14.4% 100.0%



We recommend a number of activities in the cleaning of administrative data for research use. These include:

  • Examining the internal consistency of the data;
  • Examining how the data were collected, processed, and maintained before delivery to the researcher;
  • Taking every opportunity to compare with other data sets, either survey or administrative, through record linkage; and,
  • Most important, getting to know the operations of the program, not just the collection of administrative data, but also how services are provided so that inconsistencies in the data might be understood better.

We also recommend using probabilistic record linkage and not relying on any one identifier for linking records. We believe our analysis above makes this case. The golden rule of record linkage is that there is no such thing as a unique identifier, because individuals can match on many identifiers. In many cases the same SSN has been provided to two or more individuals.

Developments in Information Technology That May Improve Administrative Data

Much of what is discussed previously is required because public policy organizations are still, for the most part, in their first generation of information systems. These "legacy" systems are typically a decade or older mainframe installations that do not take advantage of much of today's technology. Data entry in the legacy systems, for example, is often quite cumbersome and requires a specialized data entry function. Frontline workers are typically not trained to do this or do not have the time or resources to take on the data entry task. An exception is in entitlement programs in some jurisdictions, where the primary activity for eligibility workers is collecting information from individuals and entering it into a computerized eligibility determination tool. The development of new graphical user interfaces that are more worker friendly--in that the screens flow in a way that is logical to a worker--is likely to have a positive effect on data entry both because of the ease of entry and because the worker may be able to retrieve information more easily. If this is the case, the worker will have a greater stake in the quality of the data.

The development of integrated online information systems, where a worker can obtain information on a client's use of multiple programs, also may have a positive effect on the quality of the data. First, the actual job of linking across the programs will likely be an improvement over the after-the-fact linking of records. For example, if an integrated system already exists, when a mental health case is opened for an individual with Medicaid eligibility, his or her records should be linked immediately. This, of course, requires an online record-linkage process for the one case or individual. Even though a researcher would still want to check whether an individual has multiple IDs, the process at the front end will greatly improve the quality of the analytic database.

Many states are now creating data warehouses in order to analyze many of the issues of multiple-program use and caseload overlap. These data warehouses "store" data extracts from multiple systems and link records from individuals across programs. If states are successful in creating comprehensive, well-implemented data warehouses, researchers may not have to undertake many of the cleaning or linking activities discussed in this paper. Government will have already done the data manipulations. The researchers, just as is typically done with survey data, will have to verify that the warehouse was well built. Although this may require some confidential information, it should make it easier to access administrative data.


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1997 Potential and problems in developing indicators on child well-being from administrative data. In Indicators of Children's Well-Being, R.M. Hauser et al., eds. New York: Russell Sage Foundation.
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1992 Special education experiences of foster children: An empirical study. Child Welfare 71:5.
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1999 EITC Eligibility, Participation and Compliance Rates for AFDC Households: Evidence from the California Caseload. Working Paper 102. Joint Center for Poverty Research, University of Maryland and University of Michigan.
Jaro, M.A.
1985 Current record linkage research. In Proceedings of the Section on Statistical Computing. Alexandria, VA: American Statistical Association.
1989 Advances in record-linkage methodology as applied to matching the 1985 census of Tampa, Florida. Journal of the American Statistical Association 84(June):414-420.
Kalil, A., P.L. Chase-Lansdale, R. Coley, R. Goerge, and B.J. Lee
1998 Correspondence between Individual and Administrative Reports of AFDC Receipt. Unpublished paper presented at the Annual Workshop of the National Association for Welfare Research and Statistics, Chicago, August 2-5.
Pandiani, J., S. Banks, and L. Schacht
1998 Personal privacy versus public accountability: A technical solution to an ethical dilemma. Journal of Behavioral Health Services and Research 25:456-463.
Scheuren, F., and W.E. Winkler
1993 Regression analysis of data files that are computer matched. Survey Methodology 19:39-58.
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2000 Identifying the race/ethnicity of SSI recipients. In Turning Administrative Systems into Administrative Systems. Washington, DC: U.S. Department of the Treasury, Internal Revenue Service.
Winkler, W. E.
1988 Using the EM algorithm for weight computation in the Fellegi-Sunter model of record linkage. In Proceedings of the Section on Survey Research Methods. Alexandria, VA: American Statistical Association.
1990 String comparator metrics and enhanced decision rules in the Felligi-Sunter model of record linkage. In Proceedings of the Section on Survey Research Methods. Alexandria, VA: American Statistical Association.
1993 Matching and record linkage. In Statistical Research Division Report 93/08. Washington, DC: U.S. Bureau of the Census.
1994 Advanced methods for record linkage. In Statistical Research Division Report 94/05. Washington, DC: U.S. Bureau of the Census.
1999 The state of record linkage and current research problems. In Statistical Research Division Report 99/04. Washington, DC: U.S. Bureau of the Census.
Winkler, W.E., and Y. Thibaudeau
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1.  Because of problems with recall, often the individual cannot confirm his or her participation at a particular point in time (see Kalil et al., 1998).

2.  Our discussion of record linkage focuses on its application at the individual level, where research interests require individual-level linkage as opposed to aggregate population overlap statistics. In other words, we address the need to follow the outcome of interest at the individual level, focusing on research questions dealing with temporal data on timing and sequence. Utility of statistical techniques that are developed to estimate aggregate population overlap among different data sets without doing individual-level record linkage, such as the probabilistic population estimation method, is beyond the scope of our discussion in this paper. (For further information on such a technique, refer to Pandiani et al., 1998.)

3.  Presentation of the detailed mathematical process of probabilistic record linkage method is beyond the scope of this paper. Readers interested in the theory should refer to references cited in this section of the paper.

4.  Popular software programs such as SAS provide a simple method of converting names to Soundex codes.

Access and Confidentiality Issues with Administrative Data

Administrative Data, Confidentiality, and Privacy: Definitions and Legal Framework

The passage of welfare reform in 1996 marked a significant shift in public policy for low-income families and children. The previous program, Aid to Families with Dependent Children (AFDC), provided open-ended cash assistance entitlements. The new program, Temporary Assistance for Needy Families (TANF), ended entitlements and provided a mandate to move adult recipients from welfare to work within strict time limits. This shift poses new challenges for both monitoring and evaluating TANF program strategies. Evaluating the full impact of welfare reform requires information about how TANF recipients use TANF, how they use other programs--such as child support enforcement, the Food Stamp Program, employment assistance, Medicaid, and child protective services--and how they fare once they enter the job market covered by the Unemployment Insurance (UI) system.

Administrative data gathered by these programs in the normal course of their operations can be used by researchers, policy analysts, and managers to measure and understand the overall results of the new service arrangements occasioned by welfare reform. Often these data are aggregated and made available as caseload statistics, average payments, and reports on services provided by geographic unit. These aggregate data are useful, but information at the individual and case levels from TANF and other programs is even more useful, especially if it is linked with several different sets of data so that the histories and experiences of people and families can be tracked across programs and over time. Making the best use of this individual level information will require major innovations in the techniques of data matching and linking for research and evaluation.

Even more challenging, however, are the complex questions about privacy and confidentiality that arise in using individual-level data. The underlying concern motivating these questions is the possibility of inappropriate disclosures of personal information that could adversely affect an individual or a family. Such fear is greatest with respect to disclosure of conditions that may lead to social stigma, such as unemployment, mental illness, or HIV infection.

In this paper we consider ways to facilitate researchers' access to administrative data collected about individuals and their families in the course of providing public benefits. In most cases, applicants to social welfare programs are required to disclose private information deemed essential to determining eligibility for those programs. Individuals who are otherwise eligible for services but who refuse to provide information may be denied those services. Most people forgo privacy in these circumstances; that is, they decide to provide personal information in order to obtain public benefits. They believe that they have little choice but to provide the requested information. Consequently, it is widely agreed that the uses of this information should be limited through confidentiality restrictions to avoid unwanted disclosures about the lives of those who receive government services.

Yet this information is crucial for evaluating the impacts of programs and for finding ways to improve them. Making the 1996 welfare reforms work, for example, requires that we know what happens to families as they use TANF, food stamps, the child support enforcement system, Medicaid, child protective services, and employment benefits such as the UI system. In this fiscally conservative political environment, many program administrators feel using administrative data from these programs is the only way to economically carry out the required program monitoring. Program administrators believe that they are being "asked to do more with less" and that administrative data are an inexpensive and reliable substitute for expensive survey and other primary data collection projects.

How, then, should we use administrative data? Guidance in thinking about the proper way to use them comes from other circumstances in which individuals are required to forgo a certain degree of privacy in order to collect important information. These situations include the decennial census, public health efforts to control the spread of communicable diseases, as well as the information collected on birth certificates. Underlying each of these situations is a determination that the need for obtaining, recording, and using the information outweighs the individual's privacy rights. At the same time, substantial efforts go into developing elaborate safeguards to prevent improper disclosures.

Administrators of public programs must, therefore, weigh the public benefits of collecting and using information versus the private harms that may occur from its disclosure. The crucial questions are the following: What data should be collected? Who should have access to it? Under what conditions should someone have access? Answering these questions always has been difficult, but the need for answers was less urgent in the days of paper forms and files. Paper files made it difficult and costly to access information and to summarize it in a useful form. Inappropriate disclosure was difficult because of the inaccessibility of the forms. It was also unlikely because the forms were controlled directly by public servants with an interest in the protection of their clients.

Computer technology has both increased the demand for data by making it easier to get and increased the dangers of inappropriate disclosure because of the ease of transmitting digital information. Continued advances in computer technology are providing researchers and others with the capabilities to manipulate multiple data sets with hundreds of thousands (in some cases, millions) of individual records. These data sets allow for sophisticated and increasingly reliable evaluations of the outcomes of public programs, and nearly all evaluations of welfare reform involve the extensive use of administrative data. The benefits in terms of better programs and better program management could be substantial. At the same time, the linking of data sets necessitates access to individual-level data with personal identifiers or other characteristics, which leads to an increased risk of disclosure. Thus, the weighing of benefits versus harms must now contend with the possibilities of great benefits versus substantial harms.

The regulatory and legal framework for dealing with privacy and confidentiality has evolved enormously over the past 30 years to meet some of the challenges posed by computerization, but it has not dealt directly with the issues facing researchers and evaluators. There is a good deal of literature on the laws and regulations governing data sharing for program administration, much of which presupposes limiting access to these data for just program administration in order to avoid or at least limit unwanted disclosures. Unfortunately, little has been said in the literature regarding the use of such data for research and evaluation, particularly in circumstances where these analyses are carried out by researchers and others from "outside" organizations that have limited access to administrative data. Because research and evaluation capabilities generally are limited by tight staffing at all levels of government, researchers and evaluators from universities and private nonprofit research organizations are important resources for undertaking evaluations and research on social programs. Through their efforts, these organizations contribute to improving the administration of social welfare programs, but they are not directly involved in program administration. Therefore, these organizations may be prevented from obtaining
administrative data by laws that only allow the data to be used for program administration.

The problem is even more complex when evaluations require the use of administrative data from other public programs (e.g., Medicaid, Food Stamp Program, UI) whose program managers are unable or unwilling to share data with social welfare program administrators, much less outside researchers. To undertake evaluations of social welfare programs, researchers often need to link individual-level information from multiple administrative data sets to understand how people move from one situation, such as welfare, to another, such as work. But unlike program administrators, credit card companies, investigative agencies, or marketing firms, these researchers have no ultimate interest in the details of individual lives. They do, however, need to link data to provide the best possible evaluations of programs. Once this linking is complete, they typically expunge any information that can lead to direct identification of individuals, and their reports are concerned with aggregate relationships in which individuals are not identifiable. Moreover, these researchers have strong professional norms against revealing individual identities.

Problems arise, however, because the laws developed to protect confidentiality and to prevent disclosure do so by limiting access to administrative data to only those involved in program administration. Even though researchers can contribute to better program administration through their evaluations, they may be unable to obtain access to the data they need to evaluate a program.

Ironically, evaluations have become harder to undertake just as new policy initiatives--such as those embodied in federal welfare reform--require better and more extensive research to identify successful strategies for public programs. Evaluations have become more difficult because disclosures of individual information--fears driven by considerations having virtually nothing to do with research uses of the data--have led to legislation making it difficult to provide the kinds of evaluations that would be most useful to policy makers.

Against this background, this paper considers how researchers can meet the requirements for confidentiality while gaining greater access to administrative data. In the next section of the paper, we define administrative data, provide an overview of the concepts of privacy and confidentiality, and review current federal laws regarding privacy and confidentiality. We show that these laws have developed absent an understanding of the research uses of administrative data. Instead, the laws have focused on the uses of data for program administration where individual identities are essential, with lawmakers limiting the use of these data so that information about individuals is not used inappropriately. The result is a legal framework restricting the use of individual level information that fails to recognize that for some purposes, such as research, identities only have to be used at one step of the process for matching data and then can be removed from the data file.

After a relatively brief overview of the state regulatory framework for privacy and confidentiality in which we find a melange of laws that generally mimic federal regulations, the paper turns to an extended discussion, based on information from a survey of 14 Assistant Secretary for Planning and Evaluation (ASPE)-funded welfare leavers studies, of how states have facilitated data matching and linkage for research despite the many obstacles they encountered. Based on our interviews with those performing studies that involve data matching, we identify and describe 12 principles that facilitate it. We show that states have found ways to make administrative data available to researchers, but these methods often are ad hoc and depend heavily on the development of a trusting and long-term relationship between state agencies and outside researchers. We end by arguing that these fragile relationships need to be buttressed by a better legal framework and the development of technical methods such as data masking and institutional mechanisms such as research data centers that will facilitate responsible use of administrative data.

Administrative Data, Matched Data, and Data Linkage

Before defining privacy and confidentiality, it is useful to define what we mean by administrative data, matched data, and data sharing. Our primary concern is with administrative data for operating welfare programs--"all the information collected in the course of operating government programs that involve the poor and those at risk of needing public assistance" (Hotz et al., 1998:81). Although not all such information is computerized, more and more of it is, and our interest is with computerized data sets that typically consist of individual-level records with data elements recorded on them.

Records can be thought of as "forms" or "file folders" for each person, assistance unit, or action. For example, each record in Medicaid and UI benefit files is typically about one individual because eligibility and benefit provisions typically are decided at the individual level. Each record in TANF and Food Stamp Program files usually deals with an assistance unit or case that includes a number of individuals. Medicaid utilization and child protective services records typically deal with encounters in which the unit is a medical procedure, a doctor's visit, or the report of child abuse.

Records have information organized into data elements or fields. For individuals, the fields might be the name of the person, his or her programmatic status, income last month, age, sex, and amount of grant. For encounters, the information might be the diagnosis of an illness, the type and extent of child abuse, and the steps taken to solve the problem, which might include medical procedures or legal actions.

It is important to distinguish between statistical and administrative data. Statistical data are information collected or used for statistical purposes only. Data gathered by agencies such as the U.S. Census Bureau, Bureau of Labor Statistics, Bureau of Justice Statistics, and the National Center for Health Statistics is statistical data. Administrative data are information gathered in the course of screening and serving eligible individuals and groups. The data gathered by, for example, state and local welfare departments are an example of administrative data. Administrative data can be used for statistical purposes when they are employed to describe or infer patterns, trends, and relationships for groups of respondents and not for directing or managing the delivery of services.

Administrative data, however, are used primarily for the day-to-day operation of a program, and they typically only include information necessary for current transactions. Consequently, they often lack historical information such as past program participation and facts about individuals, such as educational achievement that would be useful for statistical analysis. In the past, when welfare programs were concerned primarily with current eligibility determination, historical data were often purged and data from other programs were not linked to welfare records. Researchers who used these data to study welfare found that they had to link records at the individual or case level over time to develop histories of welfare receipt for people. In addition, to make these data even more useful, they found it was worthwhile to perform data matches with information from other programs such as UI wage data; vital statistics on births, deaths, and marriages: and program participation in Medicaid, the Food Stamp Program, and other public programs. Once this matching was completed, researchers expunged individual identities, and they analyzed the data to produce information about overall trends and tendencies. Matched files are powerful research tools because they allow researchers to determine how participation in welfare varies with the characteristics of recipients and over time. They also provide information on outcomes such as child maltreatment, employment, and health.

Matched administrative data are becoming more and more widely used in the evaluation and management of social programs. In February 1999, UC Berkeley's Data Archive and Technical Assistance completed a report to the Northwestern/University of Chicago Joint Center for Poverty Research that provided an inventory of social service program administrative databases in 26 states (1) and an analysis of the efforts in these states to use administrative data for monitoring, evaluation, and research. Unlike other studies that have dealt with data sharing in general, this study was concerned primarily with the use of administrative data for research and policy analysis.

The UC study found that the use of administrative data for policy research was substantial and growing around the country. More than 100 administrative data-linking projects were identified in the study sample. Linkages were most common within public assistance programs (AFDC/TANF, Food Stamp Program, and Medicaid), but a majority of states also had projects linking public assistance data to Job Opportunities and Basic Skills, UI earnings, or child support data.

Approximately a third of the states had projects linking public assistance data to child care, foster care, or child protective services. Four-fifths of the states used outside researchers to conduct these studies, and about half of all the projects identified were performed outside of state agencies. The vast majority of projects were one time, but there is a small, and growing, trend toward ongoing efforts that link a number of programs.

Figure 8-1 indicates the likelihood of finding projects that linked data across eight programs. Programs that are closer on this diagram are more likely to have been linked. Arrows with percentages of linkage efforts are included between every pair of programs for which 35 percent or more of the states had linkage projects. Percentages inside the circles indicate the percentage of states with projects linking data within the program over time. AFDC/TANF, Food Stamp Program, and Medicaid eligibility are combined at the center of this diagram because they were the major focus of the study and because they are often combined into one system. The diagram clearly shows that there are many linkage projects across data sets from many different programs, frequently involving sensitive information.

Figure 8-1:
Percent of states with projects linking data from social service programs

Figure 8-1 Percent odf states with projects linking data from social service programs

Source: U.C. Data Archive and Technical Assistance(1999)

Data Sharing

Matched data and data linkage should be distinguished from data sharing(2), which implies a more dynamic and active process of data interchange. Data sharing among agencies refers to methods whereby agencies can obtain access to one another's data about individuals, sometimes immediately but nearly always in a timely fashion. Data sharing offers a number of benefits. If different agencies collect similar data about the same person, the collection process is duplicative for both the agencies and the person. Data sharing therefore can increase efficiencies by reducing the paperwork burden for the government and the individual because basic information about clients only needs to be obtained once. Improved responsiveness is also possible. Data sharing enables agencies and researchers to go beyond individual program-specific interventions to design approaches that reflect the interactive nature of most human needs and problems, reaching beyond the jurisdiction of one program or agency. For example, providing adequate programs for children on welfare requires data about the children from educational, juvenile justice, and child welfare agencies. Data sharing is one way to ensure better delivery of public services and a "one-stop" approach for users of these services. Preis (1999) concluded, in his analysis of California efforts to establish integrated children's mental health programs, that data sharing is essential to good decision making and a prerequisite for service coordination. In fact, "if data cannot be exchanged freely among team members an optimal service and support plan cannot be created" (Preis, 1999:5).

Although data sharing has many benefits, it raises issues regarding privacy and confidentiality. Should data collected for one program be available to another? What are the dangers associated with having online information about participants in multiple programs? Who should have access to these data? How can confidentiality and privacy rights be protected while gaining the benefits of linking program data?

When agencies engage in data sharing, the technical problems of getting matched data for research and policy analysis are easily surmounted because information from a variety of programs is already linked. But matched and linked data sets for research and policy analysis can be created without data sharing, and data matching poses far fewer disclosure risks than data sharing because identifiers only need to be used at the time when data are merged. As soon as records are matched, the identifiers are no longer needed and can be removed. The merged data can be restricted to a small group of researchers, and procedures can be developed to prohibit any decisions from being made about individuals based on the data. Nevertheless, even data matching can lead to concerns about invasions of privacy and breaches of confidentiality.

Both data sharing and data matching require the careful consideration of privacy issues and techniques for safeguarding the confidentiality of individual level data. The starting place for understanding how to attend to these considerations is to review the body of law about privacy and confidentiality and the definitions of key concepts that have developed in the past few decades. After defining the concepts of privacy, disclosure, confidentiality, and informed consent, we then briefly review existing federal privacy and confidentiality laws.


The right to privacy is the broadest framework for protecting personal information. Based on individual autonomy and the right to self-determination, privacy embodies the right to have beliefs, make decisions, and engage in behaviors limited only by the constraint that doing so does not interfere unreasonably with the rights of others. Privacy is also the right to be left alone and the right not to share personal information with others. Privacy, therefore, has to do with the control that individuals have over their lives and information about their lives.

Data collection can intrude on privacy by asking people to provide personal information about their lives. This intrusion itself can be considered a problem if it upsets people by asking highly personal questions that cause them anxiety or anguish. However, we are not concerned with that problem in this paper because we only deal with information that has already been collected for other purposes. The collection of this information may have been considered intrusive at the time, but our concern begins after the information has already been collected. We are concerned with the threat to privacy that comes from improper disclosure.


Disclosure varies according to the amount of personal information that is released about a person and to whom it is released. Personal information includes a broad range of things, but it is useful to distinguish among three kinds of information. Unique identifiers include name, Social Security number, telephone number, and address. This information is usually enough to identify a single individual or family. Identifying attributes include sex, birth date, age, ethnicity, race, residential address, occupation, education, and other data. Probabilistic matching techniques use these characteristics to match people across datasets when unique identifiers are not available or are insufficient for identification. Birth date, sex, race, and location are often enough to match individual records from different databases with a high degree of certainty. Finally, there is information about other attributes that might include program participation status, disease status, income, opinions, and so on. In most, but not all cases, this information is not useful for identification or matching across data sets. But there are some instances, as with rare diseases, that this other information might identify a person. These three categories are not mutually exclusive, but they provide a useful starting place for thinking about information.

Identity disclosure occurs when someone is readily identifiable on a file, typically through unique identifiers. It can also occur if there are enough identifying characteristics. Attribute disclosure occurs when sensitive information about a person is released through a data file. Inferential disclosure occurs when "released data make it possible to infer the value of an attribute of a data subject more accurately than otherwise would have been possible" (National Research Council and Social Science Research Council, 1993:144). Almost any release of data leads to some inferential disclosure because some of the general facts about people are better known once the data are published. For example, when states publish their welfare caseloads, it immediately becomes possible to say something precise about the likelihood that a random person in the state will be on welfare. Consequently, it would be unrealistic to require "zero disclosure." "At best, the extent of disclosure can be controlled so that it is below some acceptable level" (Duncan and Lambert, 1986:10).

One fallback position might be to say that the publication of data should not lead to absolute certainty regarding some fact about a person. This would rule out the combination of identity and attribute disclosure to an unauthorized individual.(3) This approach, however, may allow for too much disclosure because data could be published indicating a high probability that a person has some characteristic. If this characteristic is a very personal matter, such as sexual orientation or income, then disclosure should be limited further.

Disclosure, then, is not all or nothing. At best it can be limited by making sure that the amount of information about any particular person never exceeds some threshold that is adjusted upward as the sensitivity of the information increases. In the past 20 years, statisticians have begun to develop ways to measure the amount of information that is disclosed by the publication of data (Fellegi, 1972; Cox, 1980; Duncan and Lambert, 1986). Many complexities have been identified. One is the issue of the proper baseline. If everyone knows some sensitive facts from other sources, should researchers be allowed to use a set of data that contains these facts? For example, if firms in some industry regularly publish their income, market share, and profit, should data files that contain this information be considered confidential? Another problem is the audience and its interest in the information. Disclosure of someone's past history to an investigative agency is far different from disclosure to a researcher with no interest in the individual. Finally, there is the issue of incremental risks. In many instances, hundreds and even tens of thousands of individuals are authorized to access administrative data. As such, access by researchers represents an incremental risk for which appropriate safeguards are available and practical.

Because disclosure is not all or nothing, we use the phrase "improper disclosure" throughout this paper.(4) Through this usage we mean to imply that disclosure is inevitable when data are used, and the proper goal of those concerned with confidentiality is not zero disclosure unless they intend to end all data collection and use. Rather, the proper goal is a balance between the harm from some disclosure and the benefits from making data available for improving people's lives.


Confidentiality is strongly associated with the fundamental societal values of autonomy and privacy. One definition of confidentiality is that it is "a quality or condition accorded to information as an obligation not to transmit that information to an unauthorized party" (National Research Council and Social Science Research Council, 1993:22). This definition leaves unanswered the question of who defines an authorized party. Another definition of confidentiality is more explicit about who determines authorization. Confidentiality is the agreement, explicit or implicit, made between the data subject and the data collector regarding the extent to which access by others to personal information is allowed (National Research Council and Social Science Research Council, 1993:22). This definition suggests that the data subject and the data collector decide the rules of disclosure.

Confidentiality rules ensure that people's preferences are considered when deciding with whom data will be shared. They also serve a pragmatic function, encouraging participation in activities that involve the collection of sensitive information (e.g., medical information gathered as a part of receiving health care). Guarantees of confidentiality are also considered essential in encouraging participation in potentially stigmatizing programs, such as mental health and substance abuse treatment services, and HIV screening programs.

Confidentiality limits with whom personal information can be shared, and confidentiality rules are generally found in program statutes and regulations. Varying levels of sensitivity are associated with different data. Accordingly, variations in privacy and confidentiality protections can be expected.

Confidentiality requires the development of some method whereby the limits on data disclosure can be determined. In most situations, the data collection organization (which may be a governmental agency) and the source of the information should be involved in determining this method. In addition, as the government, as the representative of the general public, has an obvious interest in regulating the use of confidential information. There are several ways that these parties can ensure confidentiality, including anonymity, informed consent, and notification.


Anonymity is an implicit agreement between an individual and a data collector based on the fact that no one can identify the individual. Privacy can be protected by not collecting identifying information so that respondents are anonymous. Anonymity is a strong guarantor of protection, but it is sometimes hard to achieve. As noted earlier, even without names, Social Security numbers, and other identifying information, individuals sometimes can be identified when enough of their characteristics are collected.

Informed Consent and Notification

The strongest form of explicit agreement between the data subject and the data collector regarding access to the personal information collected is informed consent. An underlying principle of informed consent is that it should be both informed and voluntary. In order for consent to be informed, the data subject must understand fully what information will be shared, with whom, how it will be used, and for how long the consent remains in effect. Consent requires that the subject indicate in some way that he or she agrees with the use of the information.

Consent can be written, verbal, or passive. Written consent occurs when a data subject reads and signs a statement written by the data collector that explains the ways information will be used. Verbal consent occurs when a data subject verbally agrees to either a written or verbal explanation of how information will be used. Verbal consent is often used when data subjects are contacted over the telephone, when they are illiterate, or when written consent might create a paper trail that might be harmful to the subject.

Passive informed consent is similar to, but distinct from, notification. Passive consent occurs when people have been notified about the intent to collect or use data and told that their silence will be construed as consent. They can, however, object and prevent the collection or use of the data. With notification the elements of choice and agreement are absent. People are simply informed that data will be used for specified purposes. Notification may be more appropriate than informed consent when data provided for stated purposes are mandatory (such as information required for participation in a public program).

Some privacy advocates believe that conditioning program participation on the completion of blanket information release consent forms is not voluntary (Preis, 1999). Without choice, it is argued that the integrity of the client-provider relationship is compromised. As a result, many confidentiality statutes and regulations provide a notification mechanism so that the subjects of data being released can be informed of the release (e.g., Privacy Act), or they provide a mechanism for data subjects to decide who will be allowed access to their personal information (e.g., Chapter 509, California Statutes of 1998).

One of the difficulties facing data users in attempting to gain informed consent is that it is often very hard to describe the ultimate uses to which information will be put, and blanket descriptions such as "statistical purposes" are often considered too vague by those who regulate the use of data. It is also possible that data users may want to use the data for reasons not previously anticipated when the data were originally collected and, hence, not described when informed consent was initially granted from data providers. In such cases, data users may need to recontact data providers to see if providers are willing to waive confidentiality or data access provisions covering their data for the new uses of the data. However, the legality of these waivers is still being sorted out. See NRC (1993) for an example of a case where such waivers were not considered sufficient to cover the public release of collected data.

Confidentiality and Administrative Data

Administrative data are often collected with either no notification or some blanket notification about the uses to which the information will be put. As a result, legislatures and administrative agencies are left with the problem of determining the circumstances under which program participation records, drivers' license data, or school performance data should be considered private information and treated confidentially. One solution is to release only anonymous versions of these data through aggregation of the data or removal of identifying information. Anonymity, however, is not always feasible, especially when researchers want to link individual-level data across programs. In this case, should the collecting agency regulate the use of the information to ensure confidentiality when the individual has not been notified or has not provided informed consent? Can the government or some other regulatory body regulate the use of information and substitute for informed consent? What constitutes notification or informed consent? In the next section, we provide a quick overview of how the federal government has dealt with some of these issues.

Federal Privacy and Confidentiality Laws

Fair Information Practices

Several important bodies of federal law and regulation protect privacy and confidentiality of individuals served by one or more government programs, and about which government collects information. These laws reflect the Fair Information Practice Principles that were voluntarily developed and adopted by several government groups and privacy sector organizations in the 1970s. In 1973, the U.S. Department of Health, Education, and Welfare's (HEW's) Advisory Committee on Automated Personal Data Systems, Records, Computers and the Rights of Citizens published these principles in the report, Records, Computers, and the Rights of Citizens. These principles have served as the basis for formulation of the federal Privacy Act of 1974, the Freedom of Information Act, and subsequent federal laws and regulations. The Committee recommended five basic information principles for governing the use of personal information:

  1. There must be no personal data record-keeping systems whose very existence is secret.
  2. There must be a way for a person to find out what information about the person is in a record and how it is used.
  3. There must be a way for a person to prevent information about the person that was obtained for one purpose from being used or made available for other purposes without the person's consent.
  4. There must be a way for a person to correct or amend a record of identifiable information about the person.
  5. Any organization creating, maintaining, using, or disseminating records of identifiable personal data must assure the reliability of the data for their intended use and must take precautions to prevent misuses of the data.

These principles were clearly developed to regulate situations where data would be used to learn about individuals or to make decisions about them.(5) Rules 1, 2, and 4 require that individuals know about databases and can correct faulty information. These are important principles for agencies that collect information, but they have little relevance for researchers who want to use these data. Rules 3 and 5, however, propose strict ground rules for researchers' use of data. Under the strictest construction, they might require researchers to get prior consent from subjects for the use of administrative data. In reality, federal law has been somewhat less restrictive than this construction might imply.

Numerous federal privacy and confidentiality laws have been enacted in recent decades that elaborate on the Fair Information Practices. These include the Privacy Act of 1974 and the Data Matching and Privacy Protection Act of 1988.(6)

Privacy Act of 1974

The Privacy Act of 1974 was born out of the Watergate scandal in response to public outcry against the many invasions of privacy that occurred in that case. The concern was focused on the government's collection and disclosure of personal information. The Privacy Act places information disclosure limitations on the federal government, providing that certain records cannot be disclosed without the permission of the individual who is the subject of the record.

The act establishes certain responsibilities and conditions for information collection, maintenance, use, and dissemination. The information gathered must be relevant and necessary to the agency's mission. It should be collected directly from the individual to the greatest extent possible. The individual subjects of the data have to be informed of (1) the purpose of data collection, (2) whether participation in the collection of data is voluntary or mandatory, (3) the planned uses for the data, and (4) the consequences to an individual who does not provide the information.(7)

Third-party disclosure by a federal agency is also regulated by the Privacy Act. Data may be disclosed only when (1) the data subject has provided written consent authorizing the disclosure and (2) the disclosure in question is altogether exempted by the Act or it falls within an exception that allows for certain types of disclosures without consent.

The Fair Information Practices and the requirements of the Privacy Act of 1974 would seem to make research use impossible in the typical case where data are used by researchers in unanticipated ways after they have been collected and where contacting individuals at that point is nearly always prohibitively expensive. Research has, however, proceeded by using the "routine use" exemptions of the Privacy Act and similar legislation that serve as the legal basis for disclosing information to a state agency that operates a parallel benefits program.(8) This exemption requires that (1) the use is compatible with the purposes for which the information was collected, and (2) the agency places notices about its information disclosure plans in the Federal Register and provides a 30-day opportunity for interested persons to comment on any new or intended use of the agency's data. The act also provides that consent is not required when the recipient of data provides the agency with written assurance that the data will be used solely as a statistical record and will be transferred in a form that is not identified individually.

The Privacy Act establishes limitations on what can be done with personal information collected by federal agencies, but the act itself is not the primary source of protection at the agency level. Separate federal laws and regulations have been promulgated that govern federally funded programs, and the provisions of the Privacy Act frequently have been included in them, thus extending its protections down to the state and local governments and other nongovernmental entities that administer and deliver these federally funded services. Thus, the Privacy Act provides a good starting place for understanding the legal issues associated with data sharing, but a thorough understanding requires examining informational privacy, confidentiality, and consent provisions for each specific federal program and agency.

Data Matching and Privacy Protection Act of 1998

In response to concerns about computer matching and perceived attempts by government agencies to circumvent the Privacy Act, Congress passed the Computer Matching and Privacy Protection Act of 1988 (and amendments to this new Act in 1990). Although no new standard is established by this Act, it creates procedural requirements for agencies that engage in computer matching. Matching agreement contracts are required between source and recipient agencies in a data-sharing program. The agreement must specify the purpose, justification, and procedures for the intended matching program. Although there are no criteria for determining when matching is appropriate, these agreements do provide notice and regulate the behavior of each party to the agreement. Matching agreements must describe the procedure by which applicants and recipients of federal assistance will be notified that information they provide may be subject to verification via a matching program. In addition, there must be procedures for verification and the opportunity of data subjects to contest findings.

The Computer Matching and Privacy Protection Act also adds new oversight provisions to the Privacy Act. Specifically, Data Integrity Boards are required for federal agencies that are involved in computer matching activities. These boards, composed of senior agency officials, have responsibility for reviewing matching agreements and programs for compliance with federal privacy laws. They also serve a clearinghouse and reporting function.

These acts and practices create a regulatory framework for the collection and use of data. For researchers, there are exemptions from requiring informed consent in which recipients did not give their consent when the data were collected initially. Agencies, for example, can forego informed consent when the use of the data is compatible with the purposes for which the information was collected and when the agency provides notice of its intentions in the Federal Register. They can also use data when the data will be used solely as a statistical record and will be transferred in a form that is not individually identifiable. In most cases, these procedures were not designed specifically to facilitate research, but they have been used for that purpose.

Common Rule--Institutional Review Boards

Concerns about the conduct of research have led to the development of Institutional Review Boards (IRBs) at universities, at government agencies, and at private organizations that conduct federally sponsored research involving human subjects. IRBs play an increasingly important role in the regulation of organizations that undertake social policy research using administrative data.

The federal "Common Rule," adopted in 1991, governs nearly all research involving human subjects that is conducted or supported by any federal department or agency.(9) Researchers and their institutions must comply with safeguards that ensure that individuals freely consent to participate in such research. Researchers also must ensure that the research employs procedures that are consistent with sound research design and that do not pose unnecessary risk to the research subjects. Finally, there must be adequate provisions to protect the privacy of research subjects and to maintain the confidentiality of individually identifiable private information.

The review of all federally funded research by IRBs is the principal mechanism by which these safeguards are implemented, and informed consent is the primary way that IRBs ensure that human subjects are protected. However, an IRB may waive some or all elements of informed consent under a number of circumstances.(10) Research involving the use of educational testing, surveys, and interviews is entirely exempt from review if individual identities cannot be established from the information so obtained. Research involving analysis of existing data is exempt if the information is either publicly available or recorded in a manner such that individuals cannot be identified either directly or through identifiers linked to individuals. Also exempt from the rule is research that is designed to evaluate public benefit or service programs and that is conducted by or subject to the approval of federal department or agency heads. Finally, a waiver of informed consent may be given if the research involves no more than minimal risk to the subjects, the waiver will not adversely affect the rights and welfare of the subjects, and the research could not practicably be carried out without the waiver.

As with the Privacy Act, IRBs place a great emphasis on informed consent, although there are some provisions for waiving consent when anonymity can be assured, when risk is minimal, or when public benefit programs are being evaluated. The emphasis on informed consent is not surprising because IRBs were established initially to oversee medical research which often involves medical procedures. The need for informed consent regarding the procedure to be performed is obvious in this case because of the great potential for harm. Moreover, there may be no other way to protect subjects except through informed consent.

The role of informed consent is somewhat different in the conduct of most social science research, which involves acquiring information about subjects. It is possible, of course, to do harm through the collection of social science data by asking questions that provoke great anxiety or consternation, but the major danger is undoubtedly the possibility that private information will be revealed. In this case, confidentiality may be the primary concern, and some method for controlling the use of the data may be much more important than informed consent regarding its collection . Informed consent is one way to control the use of data, but it is not the only way. Anonymity potentially provides even better protection than informed consent. Other methods for protecting confidentiality also might provide the protections that are needed. For example, the confidentiality of administrative data might be protected without informed consent through the development of procedures such as the Data Integrity Boards and other mechanisms created by the Privacy Act and the Computer Matching and Privacy Protection Act. At the moment, however, IRBs rely heavily on informed consent, and they typically have only a limited understanding of the intricacies of matching administrative data and the laws regarding confidentiality.

Summary of Federal Legislation

Federal legislation has been built on a concern about disclosure of information about individuals. It has been done without much thought about the needs of researchers who only care about individual identities when they match data sets. At the moment, the federal regulatory environment for data is characterized by a multiplicity of laws, cross-cutting jurisdictions (e.g., Data Integrity Boards and IRBs), and some incoherence. The emphasis on informed consent in many laws would appear to limit severely the use of administrative data, but agencies have used the provisions for statistical analysis and for "routine use" to allow researchers to use administrative data. All in all, the legal situation is highly ambiguous for researchers, and no one has come to grips with what should be done with data when informed consent is not possible and when researchers need identities solely for the interim stage of data matching.(11)

State Privacy and Confidentiality Considerations

It would be useful to conduct a state-by-state analysis of how privacy, confidentiality, and consent laws affect research and to compare the results with the impacts of federal laws and regulations. This analysis would contribute significantly to achieving a more complete and substantial understanding of how state and federal requirements interact with one another. However, this task is far beyond what we can do here. Instead, we make some comments based on the secondary literature.

State constitutional privacy protections are very diverse. For example, in California, privacy protections are expressly mentioned in the constitution, while Washington state's constitution requires that certain information--such as who receives welfare--be publicly available. In addition to state constitutional provisions regarding privacy and confidentiality, every state has enacted numerous privacy protection laws principally drafted in response to a specific perceived problem. The result is many narrow prescriptions, rather than a coherent statement of what information is private, when it can be collected, and how it can be used. Consequently, it is hard to know exactly what information is protected, and how it is protected. In addition, many privacy laws have exceptions and exemptions that make them hard to understand, hard to apply, and subject to divergent interpretations (Stevens, 1996). The resulting laws have been described as "reactive, ad-hoc, and confused" (Reidenberg and Gamet-Pol, 1995).

There are two broad classes of laws, those dealing with privacy in general and those that mention privacy and confidentiality in the process of establishing programs. The general privacy laws deal with computer crime, medical records, the use of Social Security numbers, access to arrest records, and other issues. Table 8-1 indicates the presence of general state privacy protections for the states in which there are ASPE welfare leavers studies (Smith, 1999).(12) It shows that state privacy laws cover a broad range of issues from arrest records to wiretaps, and that some topics, such as arrest records, computer crime, medical records, and wiretaps, have led to more legislative activity by states than other topics such as the uses of Social Security numbers, credit information, or tax records. Moreover, some states, such as California, Florida, Maryland, Massachusetts, Ohio, and Washington, have laws that cover many more areas of concern than other states such as Missouri, South Carolina, or Texas. These laws affect researchers when they seek to utilize Social Security numbers for matching or to obtain school, arrest, or tax records.

Privacy Laws in States with Welfare Leavers Studies
Arrest records x x x x x x x x x x x   x x x   x  
Computer crime x x   x x x x x x x x x x x x x x x
Credit x x   x x   x x   x x   x     x x x
Criminal justice x x   x x x x x   x   x x   x   x x
Employment   x x x x x x x   x x x x x     x x
Government data banks x x x x   x x x x x x x x x x   x x
Insurance x x x x x x x x x x   x x   x     x
Medical x x x x x x x x x x x   x x   x x x
Polygraphing x x x   x x x x     x     x x x x x
Privacy statutes x x   x x x   x   x x x   x x x x x
Privileges         x   x x x x x x x     x x  
School records x x   x   x x x   x x   x     x x x
Social Security numbers   x   x x             x x       x x
Tax records x       x   x x     x x x   x   x x
Testing       x     x x       x x x     x  
Wiretaps x x x x x x x x   x x x x x   x x x
Miscellaneous   x   x   x   x     x             x
x: the state law covers the subject (but not necessarily that the law affords a great deal of privacy protection)
Blank: the state does not have a law covering the topic

Programmatic laws regulate the collection and uses of information as part of the social program's legislation at the federal and state levels. Harmon and Cogar (1998) found that federal program statutes and regulations provide substantial privacy protections similar to that in the federal Privacy Act. Explicit limits on disclosure within the statutes authorizing federal programs and agencies are common, as is the imposition of informational privacy protections on states via federal program regulations. Harmon and Cogar (1998) also found that--as with the provisions of the Privacy Act--federal regulations do not clearly specify penalties or the consequences of violating the regulations by state or local personnel or contractors. Their study of five states found state information privacy laws to be similar to federal protections.

Most of the state and federal laws regarding the collection and use of data for programs are quite restrictive, but they typically have a clause, similar to the "routine use" provisions in the federal Privacy Act, that allows agencies to use data to achieve the "program's purpose." Researchers and others who want access to the data use this clause in the same way as the "routine use" clause of the Privacy Act. Harmon and Cogar (1998) suggest that federal agencies often label their data uses as "routine" without determining if the use is consistent with the purpose for which the information was collected. Some state agencies follow a similar practice, although standards vary dramatically from state to state and agency to agency.

In their report about experiences in five states, "The Protection of Personal Information in Intergovernmental Data-Sharing Programs," Harmon and Cogar (1998) describe the complexity of the information protection provisions that apply to individuals under the U.S. Department of Agriculture (USDA) Food Stamp Program's Electronic Benefit Transfer (EBT) project and the HHS Child Support Enforcement Program's Federal Parent Locator Service/National Directory of New Hires project. None of the states reported major violations of privacy in the operation of the Child Support Enforcement and EBT programs, but the significant variation in regulation of information across the states could prove a significant barrier to the overall data-sharing responsibilities of the systems and for researchers who want to use the data. Moreover, most of the states, with the exception of Maryland, paid little heed to researchers' needs. Maryland's statutes specifically authorize public agencies to grant researchers access to personal information under specified conditions. This statute appears as Appendix 8-A as an example of model legislation that authorizes researcher access to data.(13)

UC Berkeley's Data Archive and Technical Assistance also explored confidentiality issues in its inventory (UC Data Archive and Technical Assistance, 1999) of social service administrative databases in 26 states. This study found that researchers and administrators from other programs who seek access to social service data must negotiate with the owners of the data, and they must demonstrate that they meet the legal criteria for access. Legislation and regulations were characterized as generally requiring the party petitioning for access to the data to identify: (1) the benefits associated with release of the data, (2) how the research will benefit administration of the programs, and (3) how confidentiality of the data will be protected from unauthorized disclosure.

In most cases, a formal contract or interagency agreement was required, and often these agreements are required because of legislative mandates. Apart from the legal issues of gaining access to confidential data, there are often coordination issues that affect the transfer of information from one agency to another. Only about half of the states surveyed for this report had specific, well-outlined policies and procedures for sharing confidential administrative data.

The use of administrative data for research purposes has not been considered in the development of most federal and state legislation. The major purpose of most federal and state confidentiality and privacy legislation has been to regulate the use and disclosure of information about individuals.(14) As a result, a strict interpretation of most laws might preclude research uses that require data matching even though identifiers are removed before data analysis and researchers have no interest in individual information. This outcome would be mostly inadvertent. In their desire to protect individuals, lawmakers typically have written legislation that makes no distinction between research uses and disclosure of information about individuals. State and federal agencies sometimes have overcome restrictions on research by accommodating researchers through the use of the routine use and program purpose clauses. This accommodation is fitful and uncertain because it depends on each agency's interpretation of these clauses and its overall interest in allowing researcher access to administrative data.

Access to Confidential Data in Practice: Interviews with Researchers Conducting Welfare Leavers Studies

The legal basis for the use of social program administrative data by nongovernmental researchers is ambiguous. Consequently, governmental agencies that are inclined to provide data to researchers usually can find a legal way to do so through a broad interpretation of the statutory "routine use" or "program purposes" clauses, while agencies that are inclined to block researcher uses can also do so by interpreting these clauses narrowly. From the research perspective, the best solution to this problem would be that privacy and confidentiality legislation take into account the significantly fewer risks posed by research uses of data and develop clearcut regulatory mechanisms tailored to the needs of researchers. We discuss this possibility later (Guiding Principle 12), but it is worth knowing that in the absence of a favorable regulatory environment, many researchers and program administrators have found ways to undertake research with administrative data. Because it may be difficult to get better legislation, the methods used by these program administrators and researchers deserve careful consideration.

To identify these methods, we interviewed researchers and state administrators working in federally funded welfare leavers projects. Because of the complexity of the lives of individuals leaving welfare, these studies require diverse types of data, including multiple sources of confidential administrative data. In this section, we discuss information from 14 welfare leavers studies.(15) These include projects that received fiscal year 1998 ASPE grants to study the outcomes of individuals and families who left the TANF program, and Texas.(16) (We refer to this group of projects as "Welfare Leavers Studies".)

This research began by reviewing the findings from the inventory of research uses of social services administrative data in 26 states that UC DATA completed in 1999. A series of questions then was developed as the basis for telephone interviews with the state officials and researchers conducting ASPE-funded Welfare Leavers Studies. Officials and researchers working on these studies were queried about their experiences with confidentiality and data access. More than 20 individuals in the 14 locations listed in Box 8-1 were interviewed in winter 1999/2000.

BOX 8-1
Welfare Leavers Studies:  States/Localities Interviewed

  • Arizona
  • California (San Francisco Bay Area Counties; Los Angeles County)
  • Florida
  • Georgia
  • Illinois
  • Missouri
  • New York
  • Ohio (Cuyahoga County)
  • Massachusetts
  • South Carolina
  • Texas
  • Washington State
  • Washington, D.C.
  • Wisconsin

In the course of our interviews with Welfare Leavers Studies representatives, we identified 12 guiding principles or practices we believe to be at the heart of successfully overcoming issues of data confidentiality and privacy. We found repeated examples of these principles or practices being put into action across the country in varying ways. They are listed in Box 8-2. The principles, the keys to data collaboration, fell naturally into four categories that are discussed in more detail later: the characteristics of the requesting organization, the characteristics of the organization providing the data, the characteristics of the requesting organization, the "contract" process itself, and the legal framework.

BOX 8-2
Twelve Guiding Principles of Data Access and confidentiality

The Characteristics of the Organization With the Data

1. Strong Political or Administrative Leadership
2. Designation of a "Data Steward" in the Department and structuring staffing levels and responsibilities to cover data access requests
3. Develop a written confidentiality and security procedure--keep a catalog of written documents: contracts, memorandums of understanding (MOU's), personal security agreements
4. The Agency architecture encompasses all "providing" agencies as in "Super Agencies"
5. A Central Clearinghouse Negotiates or Assists in Legal and/or Technical Issues
6. Plan for Data Access in the Development of Information Systems

The Characteristics of the Requesting Organization

7. The Reputation and Integrity of the Requesting Organization Engenders Trust
8. Trust Between Organizations, a History of Working Together, and Strong Personal Relationships.
9. Develop a Confidentiality/Security Procedure and Keep a Catalog of Exemplary Written Contracts, MOUs, and Personal Security Agreements.

The "Contract" Process

10. Put in Writing Mechanisms for Monitoring Confidentiality and Security and for Sanctioning Breaches
11. Congruence of Research Agency Goals: Demonstrated Benefits to Participating Organizations

The Legal or Statutory Authority

12. Statutory Language Authorizes or Is Broadly Interpretable to Authorize Data Access for Researchers

The specific principles range from the obvious--"Put Procedures and Contracts in Writing"--to the sublime--Find Strong Leadership." We discuss each of the principles in detail and give illustrative examples of these principles. See Table 8-2 for a complete listing of examples of the principles in the Welfare Leavers Study sites.

Twelve Guiding Principles of Data Access and Confidentiality
Examples from Interviews with Welfare Leavers Study Researchers (Fall 1999)

The Characteristics of Donor Organization

1. Strong Leadership
  • California: California Department of Social Services (CDSS), Employment Development Department (EDD)
  • Illinois
  • Missouri: Governor Mel Carnahan, Missouri Training & Employment Commission
  • New York: Federal Department of Labor
  • Texas: Federal Department of Labor
2. Staff Levels or Responsibilities
  • California: Labor Market Information Division
  • Illinois: Bureau of Program Design & Evaluation
  • Missouri: "Administrative Data Guardian"
  • Washington State: Office of Planning & Research
  • Wisconsin: Data Stewardship
3. Written Confidentiality/Security Procedure
  • California
  • Illinois: Dept. of Human Services
  • Wisconsin: Data Stewardship
4. Agency Architecture
  • Arizona: Arizona Department of Economic Security (ADES)
  • Illinois: Dept. of Human Services
5. Central Clearinghouse
  • Arizona: ADES Data Mart
  • Florida: Florida Education & Training Program Placement Information Program
  • Illinois: Chapin Hall, University of Chicago
  • South Carolina: Budget & Contracts Board
  • Texas: State Occupational Information Coordinating Committee
  • Washington State: Internal Review Board
6. Plan for Data Sharing in Development of Information Systems
  • California: Family Health Outcomes Project

The Characteristics of Requesting Organization

7. Reputation and/or Integrity
  • California: RAND
  • Illinois: Chapin Hall, University of Illinois
  • Massachusetts: Center for Survey Research at University of Massachusetts-Boston
  • Ohio: Manpower Demonstration Research Program (MDRC)
8. History of Working Together, Personal Relationships
  • California: UC DATA & CDSS
  • Georgia: Georgia State University & Department of Children and Family Services (DFCS)
  • Illinois: Chapin Hall at University of Chicago, Illinois & Department of Children and Family Services, Department of Employment Security & Illinois Department of Human Services
  • Missouri: University of Missouri & state agencies
  • New York: Office of Transitional and Disability Assistance (OTDA) and Department of Labor (DOL)
  • Ohio: Case Western University (CWRU) and Bureau of Employment Services (BES), CWRU and DSS, and CWRU and MDRC
  • Washington, DC: Urban Institute & Department of Human Services
9. Written Confidentiality/Security Procedure
  • California: UC DATA, RAND
  • Ohio: CWRU

The "Contract" Process

10. Put in Place Mechanisms for Monitoring Confidentiality and Security and/or Sanctioning Breaches. Contracts In Writing
  • California
  • Georgia
  • Illinois
  • Missouri
  • New York
  • Ohio
  • South Carolina
  • Washington State
  • Washington, DC
  • Wisconsin
11. Congruence of Research to Agency Goals-Demonstrated Benefits to Participating Organizations
  • Arizona
  • California CalWORKs
  • California Leavers Studies
  • Florida
  • Georgia
  • Illinois
  • Massachusetts
  • Missouri
  • New York
  • Ohio
  • South Carolina
  • Washington State
  • Washington, DC
  • Wisconsin

The Legal or Statutory Authority

12. Statutory Language Authorizes or Is Broadly Interpreted to Authorize Data Access
  • California
  • Georgia
  • Illinois
  • Missouri
  • New York
  • Ohio
  • South Carolina
  • Washington State


ADES Arizona Department of Economic Security
AFDC Aid to Families with Dependent Children
ASPE Assistant Secretary for Planning and Evaluation (of the U.S. Department of Health and Human Services
BES Bureau of Employment Services
CDSS California Department of Social Services
CWRU Case Western Reserve University
DCFS Department of Children and Family Services
DFACS Department of Family and Children's Services
DOL Department of Labor
DSHS Department of Social and Human Services
DTF Department of Taxation and Finance
EDD Employment Development Department
FETPIP Florida Education and Training Placement Information Program
GSU Georgia State University
IDB Integrated Database
JTPA Job Training Partnership Act
LMID Labor Market Information Division
MDRC Manpower Demonstration Research Corporation
MEDS Medi-Cal Eligibility Data System
MOU Memorandum of Understanding
MTEC Missouri Training and Employment Council
OCSE Office of Child Support Enforcement
OTDA Office of Transitional and Disability Assistance
SOICC State Occupational Information Coordinating Committee
TANF Temporary Assistance for Needy Families
UI Unemployment Insurance
Data Access Principles Regarding the Organization with the Data

Principle 1: Strong Political or Administrative Leadership

We found that many new and established data-matching projects were successful because they had the interest or patronage of well-connected or inspiring leaders. This, in and of itself, comes as no surprise. However, the sources of this leadership are diverse.

In some cases, this leadership was political in nature. For example, the University of Missouri at Columbia Department of Economics began its long collaboration with the Missouri Department of Social Services at the request of Governor Mel Carnahan. In January 1997, the university was asked to begin an analysis of the workforce development system for the Governor's Training and Employment Council. Because of the high-profile support for this project, the agencies providing data were forthcoming so as not to appear to be hindering the effort. A governor's directive can be powerful.

Another example of political leadership can be found in the moving force behind the Texas State Occupational Information Coordinating Committee (SOICC). The SOICC was mandated by the U.S. Congress via the federal Job Training Partnership Act (JTPA) and the Carl D. Perkins Vocational Education Act of 1976. The Texas SOICC receives no state general revenue funding and is supported by the U.S. Department of Labor through the national network organization National Occupational Information Coordinating Committee.

Data linking is facilitated when those at the top make it clear that they want to know about the impacts governmental programs are having on clients. Governors can provide this kind of leadership. More commonly, and perhaps most effectively, this leadership can be found among program administrators, bureau chiefs, and agency heads. For example, California found valuable leadership in the California Department of Social Services (CDSS) Research Branch. Staff in the Research Branch made use of many years of experience in service to the state to forge data-sharing coalitions between CDSS and the California Employment Development Department. In Illinois, the decisions to link data were made by Department of Human Services administrators who were supporting the Welfare Leavers Study.

Principle 2: Designation of a "Data Steward" in the Department and Structuring Staffing Levels and Responsibilities to Cover Data Access Requests

Adequate staffing is essential for ironing out the issues of data access. Data-linking requests require extensive administrative and analytic effort. In fact, as the rapid growth of information technology makes privacy and security policies de rigueur, information security officers in many states are requiring the completion of more and more complicated data security and confidentiality procedures for data linking.

Information security offices are not solely responsible for the time and effort it takes to get a data-linking project approved. Each state department often requires approval by a contracts office, a legal office, and the program with the data. In addition, many projects are required to submit their project for review by the state's human subjects committee. Each of these approvals can take from a few days to a few weeks, or even months in some cases.

Success in data-linking projects requires staff dedicated to shepherding data requests through the complexities of confidentiality requirements and data access issues. Although lawyers are often assigned these tasks because of their knowledge of statutorily defined notions of confidentiality, experienced government staff with a research bent must be involved as well in order to explain the technical aspects of data linking. In fact, agency staff with a strong investment in data linking and a belief in the benefits of research can overcome exaggerated fears about data linking and overly narrow interpretations of the law.

A delicate balance must be reached here. The law regarding the use of administrative data is typically sufficiently ambiguous that beliefs about the usefulness of a research project, about the risks from data matching, and about the trustworthiness of researchers can determine the outcome of a data request. It is easy for lawyers to assume that research is not very useful, that the risks of data matching are great, and that researchers cannot be trusted with the data. Yet we found in our interviews that research staff believe data matching provides extraordinary opportunities for high-quality and relatively inexpensive evaluations. Moreover, researchers can make the case that the risks from data matching for research purposes typically are quite low--certainly much lower than the risks from many other kinds of data matching projects. What is needed is a balance of agency staff committed to both the appropriate protection of data and the appropriate sharing of data for research and evaluation. We were told in our interviews that there are plenty of staff people, legal and otherwise, who are zealously "protecting" data in the name of confidentiality, but there are not enough with strong investments in data linking and a belief in the benefits of research to their department to make the case for data matching.

Our interviews provide examples. One respondent in Missouri referred to himself as the administrative data "guardian." He saw himself as the data shepherd, the person who saw that the data got to where it needed to go and got there safely. He facilitated data access, safeguarded data confidentiality, and educated researchers about the complexities of the data. Other Missouri respondents reported this administrator to be knowledgeable and helpful. In the Washington State Department of Social and Health Services, staff in the Office of Planning and Research blazed new trails of data access through state divisions that were unfamiliar with, if not uncomfortable with, providing data to researchers. One respondent from Wisconsin reported an environment of data "stewardship" coming about in the state, an environment of making data available in a responsible manner. The California Employment Development Department, Labor Market Information Division has designated a Confidential Data Coordinator. In Illinois, the Bureau of Program Design and Evaluation in the Department of Human Services frequently negotiates data access arrangements.

Principle 3: Develop a Written Confidentiality and Security Procedure--Keep a Catalog of Written Documents: Contracts, Memorandums of Understanding (MOU's), Personal Security Agreements.

A written policy of confidentiality and security is a must. This document should make explicit the data security procedures required of the data requesting organizations by the agencies with the data. This written policy should include detailed standards to maintain the privacy of individual data subjects. Another necessary document is a written guideline to obtaining data. This document can be provided to data requesters to assist them in applying for access to confidential data. The confidentiality and security manual and the guideline to obtaining data can provide assurance to data-providing agencies that proper consideration will be given to maintaining the confidentiality of their data in advance of the data being requested of them. They will also reassure data-providing organizations that their staff will not waste precious staff time fielding fly-by-night data requests.

In addition to these documents, there should be an archive of exemplary memorandums of understanding, letters of understanding, contracts for goods and services, data access agreements, and confidentiality agreements for use among state agencies or between state agencies and nongovernmental organizations. These documents should have explicit sections on the maintenance of data security and confidentiality, similar to the protocol described. The archive should also contain statements regulating individual behaviors, commonly known as "personal security agreements" or "statements of confidentiality". These documents require each individual staff person on the project to acknowledge procedures required for maintaining confidentiality and penalties for a breach of these procedures. An archive promotes quick and thorough contract negotiations, and it avoids the nuisance of having to start from scratch with every data request.

The California Department of Social Services Research Branch has prepared two such model documents: "The CDSS Confidentiality and Security Policy" and "The Guidelines for the Preparation of A Protocol." Also, in the new environment of "Data Stewardship," Wisconsin is developing templates and exemplar agreements.

Principle 10, "Put in Writing Mechanisms for Monitoring Confidentiality and Security and for Sanctioning Breaches," discusses briefly which confidentiality and security procedures one might want to include in a contract and therefore in the archive of documents.

Principle 4: The Agency Architecture Encompasses All "Providing" Agencies as in "Super Agencies"

In some cases, a "super agency" organization can facilitate sharing of data among departments within the agency. For example, in response to the latest welfare reforms, some states combined state agencies under an umbrella organization. In most cases, administrative data are considered to be owned by this overarching agency. Although this does not eliminate the need for appropriate bureaucratic negotiation on data access, in most cases it makes the process easier.

One respondent referred to the Illinois Department of Human Services as a "super agency." The department handles data for AFDC/TANF, the Food Stamps Program, Substance Abuse, Mental Health, Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) (family case management), Medicaid, and Child Care programs (and their data). Gaining access to some of these data was reported to be easier because of the "super agency" structure. It was reported that gaining access to data from Substance Abuse and WIC (family case management), although by no means easy, would have been even harder had not the agencies been part of this "super agency."

The Arizona Department of Economic Security (ADES) also can be considered a super agency. ADES covers a broad range of programs, including AFDC/TANF, Food Stamp Program, Medicaid, Child Welfare, Child Care, and Child Support Enforcement and Unemployment Insurance. A respondent reported that no interagency data access agreements were necessary with any of these programs because of this all-encompassing administrative structure.

Principle 5: A Central Clearinghouse Negotiates or Assists in Legal and/or Technical Issues

A centrally located institution or center can help facilitate data access. This center can be placed in the state government or outside, and it can serve a number of purposes.

First, a central organization can serve as a data archive or data warehouse that actually stores data from multiple state agencies, departments, and divisions. In some cases, data archives match the data and provide data requesters with match-merged files. In other cases, data archives provide a place where data from multiple agencies are stored so that data requesters can obtain the data from one source and match the data themselves.

Second, a central organization can serve as a data broker. This organization does not actually store data from other agencies but "brokers" or "electronically mines" data from other agencies on an ad hoc or regular basis. This organization then performs analyses on these data and reports results back to the requesting agency. The data are stored only temporarily at the location of the data broker, before they are returned to the providing agency or destroyed.

Third, a central organization can serve another very important purpose, as a clearinghouse for legal issues around confidentiality. Organizations like this are sometimes called internal review boards. They maintain exemplar or template agreements, contracts, documents, as described earlier.

For example, the South Carolina Budget and Control Board (SC BCB) serves all three functions--data archive, data broker, and internal review board. The SC BCB plays a key role in the general management of state government. This institution is unique to South Carolina and oversees a broad array of central administrative and regulatory functions. In our interview with staff from the Welfare Leavers Study grantee in South Carolina, we learned of the office of Research and Statistics in the SC BCB. The office gathers, analyzes, and publishes data vital to the social, and economic well-being and health of residents of South Carolina. These data are used by other state agencies and by local governments to guide planning, management, and development decisions. The office also works with other agencies to prevent overlap and duplication of data-gathering activities. The Welfare Leavers Study grantee (South Carolina Department of Social Services) negotiated data access through the SC BCB and conducted their analysis inhouse. However, one South Carolina respondent noted that despite the central location of this clearinghouse, it was still necessary to obtain legal authorization to data access on an agency-by-agency basis.

The Arizona Department of Economic Security is in the process of building a data warehouse, referred to as the "data mart." The data mart will automatically receive and link data from all the programs covered by ADES. The Welfare Leavers Study researchers used this resource to access data. At this point, the data mart provides only data-archiving and data-matching functions. However, eventually the data mart will include front-end data analysis functions.

The Texas State Occupational Information Coordinating Committee (SOICC) serves as a data broker. SOICC does not archive or store data at all. Our respondent reported that SOICC "mines data electronically" from relevant agencies, conducts analysis, and provides requesters with results of these analyses.

In Florida, the Florida Education and Training Placement Information Program (FETPIP) serves a data brokerage role by archiving data and providing analysis. However, our respondent reported that FETPIP did not archive data or provide analyses for the Florida Welfare Leavers Study grantee.

The Chapin Hall Center for Children at the University of Chicago has developed an extensive archive of child welfare and family welfare data. The center uses these data to assess the impacts of welfare reform and other programs on child well-being. Chapin Hall's archive of data on children's welfare is called the Integrated Database on Children's Services in Illinois (IDB). Built from administrative data collected over two decades by Illinois human services programs, the IDB allows researchers to create a comprehensive picture of the interactions children and their families have with social programs offered by the state. One respondent cited this database as an absolutely invaluable resource.

The University of Missouri at Columbia Department of Economics is another example of a center that archives and analyzes data. Here data from multiple state agencies are matched, merged, and analyzed. The archive contains data from five state agencies: the Department of Economic Development, the Department of Social Services, the Department of Labor and Industrial Relations, the Department of Elementary and Secondary Education, and the Department of Higher Education. The staff provide research and analysis for many of the separate agencies on an ad hoc and a contractual basis.

In Washington State, the Institutional Review Board serves a role as a central place to resolve legal issues of data access. The IRB assisted the Welfare Leavers Study grantee in ironing out legal issues. The IRB serves as a human subjects review board and maintains exemplar documents.

Principle 6: Plan for Data Access in the Development of Information Systems

It would be difficult to include all the requirements for the development of information systems in a single principle. The development of information systems requires a set of its own guiding principles, including, but not limited to, adoption of common identifiers and establishment of standardized data definitions.

Rather than try to list all of the relevant principles, we cite the following example from California: The Family Health Outcomes Project (FHOP). It is a joint project of the Department of Family and Community Medicine and the Institute for Health Policy Studies (both at the University of California at San Francisco). Initiated in 1992, FHOP is a planning and training effort to streamline and standardize the administrative aspects of state child and family health programs in California.

FHOP has developed an information structure for an extremely fragmented and difficult-to-access system--health care and health-related services for women and children in California. California has many categorical health and social service programs serving women, children, and families. Each has a separate application and eligibility process, although all require similar application information. Clients must complete an application for each service they wish to receive, often at different times and in different locations. To bring these programs "together," FHOP has developed CATS, a "Common Application Transaction System." CATS addresses the need for a uniform, accessible application and eligibility determination process and provides aggregate data for state and local planning and management.

CATS is a methodology for integrating registration and eligibility determination across numerous state-funded family health programs. CATS establishes unique client identification through the use of core data elements (birth name, birth date, birth place, mother's first name, and gender) and confirmatory data elements (social security number, other client number, father's name, mother's maiden name, current name/client alias/nickname, county of client's residence, and zip code of client's residence). Utilizing probabilistic matching and relative weighting of the core data elements, CATS can uniquely identify clients and find duplicate records for the same client.

Health care providers can link local automated registration systems to the state CATS hub, which can then return eligibility and demographic information. The CATS goal is to simplify the eligibility process so that the necessary demographic and self-declared financial information need only be collected and entered once.

In summary, CATS includes a standardized approach to collecting demographic, race, ethnic, and financial eligibility information; standardized confidentiality procedures and informed consent for sharing information; information on client eligibility status for Medi-Cal, Family Planning, Healthy Families Children's Health Insurance Program (CHIP), and Children's Medical Services; methods for the discovery of duplicate client records for tracking and case management; and a secure Internet connection option for community clinics and private providers. By providing a common method for collecting information on participation in state child and family health programs, CATS makes it possible to identify clients across programs, track them over time, and monitor outcomes. From a researcher's perspective, systems such as CATS make matching data across data systems much simpler.

Data Access Principles That Have to Do With the Characteristics of Requesting Organization

Principle 7: The Reputation and Integrity of the Requesting Organization Engenders Trust

In many cases, we found that the reputation of the requesting agency was a major factor in successfully obtaining approval for the use of administrative data. This reputation can be technical, academic, or professional. We found that some of our respondents were reassured by the sheer prominence of the requesting organization.

However, in most cases, feelings of confidence were firmly based on the earned substantive reputation of the requesting organization. Most of the examples we found were organizations that had established a reputation through extensive experience with similar types of research and therefore provided key expertise. For example, Chapin Hall has a well-deserved reputation for its extensive technical expertise in the complex issue of matching administrative data from child and family welfare systems. In fact, Chapin Hall's reputation is so great that the Illinois Department of Human Services believes that it could do no better than subcontract with Chapin Hall when doing any matching of children's and families' services data.

Another example comes from Massachusetts, where the Department of Transitional Assistance contracted with the Center for Survey Research at the University of Massachusetts, Boston, to field the survey of former TAFDC (Transitional Aid to Families with Dependent Children) households. The Department of Transitional Assistance provided the Center for Survey Research with confidential data necessary for developing a sample of welfare leavers. It was reported that the department chose the center in large part because of the center's local reputation for expertise and competence.

Principle 8: Trust Between Organizations, a History of Working Together, and Strong Personal Relationships

Of all the guiding principles, trust between organizations appears to make the most wide-ranging contribution to successful data access. In our interviews with Welfare Leavers Study grantees, and in discussions with other researchers and state and nongovernmental staff, we learned of countless longstanding relationships between departments, between organizations, and between individuals. These relationships played a large and very important role in establishing the trust and confidence necessary for smooth contract negotiation and productive collaboration in the Welfare Leavers Studies.

The separation of Principle 7, "Reputation," and Principle 8, "Trust," does not mean these two are mutually exclusive, but it is meant to imply they are somewhat different. Past projects may have been established because of the requesting organization's reputation, but future projects depend heavily on the development of trust. In many cases, the established association was continued because the projects went well. In some cases, however, it was reported that the past project was not entirely successful, but that the association was continued, it seems, merely based on personal friendships or the force of one or more personalities (not necessarily friendships). Whatever the case, these prior relationships were a major factor in the success of the majority of the data access efforts we examined, including projects in California (San Francisco Bay area counties), Washington, DC, Georgia, Illinois, Missouri, New York, Ohio, and South Carolina.

Obviously, this phenomenon is not limited to welfare leavers projects. California, New York, Missouri, Arizona, and Texas respondents all reported knowledge of data access projects that were facilitated because of personal relationships. Indeed, it should be noted here that many of these longstanding relationships were the result of Principle 1, Strong Leadership.

Principle 9: Develop a Confidentiality/Security Procedure and Keep a Catalog of Exemplary Written Contracts, MOU's, and Personal Security Agreements.

This principle is parallel to Principle 3 except that it applies to the requesting organization. Every data-requesting organization should maintain a file of data access and confidentiality documents. Such a resource provides reassurance to the providing agency that the requester has given appropriate consideration to the issues of data access. In fact, one state administrator said they do not take seriously organizations that do not have a written procedure. Furthermore it allows the requesting agency to respond quickly to data access opportunities without having to reinvent the wheel. UC Data Archive and Technical Assistance at the University of California, at Berkeley has a Manual on Confidentiality and Security, which includes exemplar contracts, personal security agreements, and description of extensive data security procedures.

Principle 10 discusses briefly what confidentiality and security procedures one might want to include in a contract and therefore in the archive of documents.

Data Access Principles That Have to Do with the "Contract" Process

Principle 10: Put in Writing Mechanisms for Monitoring Confidentiality and Security and for Sanctioning Breaches

A contract between the requesting organization and the department providing the data makes accessing administrative data much easier. In a contract, confidentiality and security measures or requirements are clarified and put in writing. Written provisions to uphold confidentiality and security provide a vehicle for action if a breach of confidentiality occurs. Nearly all our respondents reported that their collaboration was governed by a written contract.

Contracts should include clauses that contractually provide for data security and maintenance of confidentiality. The following list provides examples of provisions that should be specified in any written contract governing access to confidential data. This list, although not intended to be exhaustive, illustrates most of the procedures requested by state agencies for protecting the confidentiality of individuals in research projects using administrative microdata files:

  • Prohibition on redisclosure or rerelease.
  • Specification of electronic data transmission (e.g., encryption methods for network access).
  • Description of storage and/or handling of paper copies of confidential data.
  • Description of storage and/or handling of electronic media such as tapes or cartridges.
  • Description of network security.
  • Requirement for notification of security incidents.
  • Description of methods of statistical disclosure limitation.
  • Description of the disposition of data upon termination of contract.
  • Penalties for breaches.

Furthermore, contracts should include references to statutes that provide for explicit sanctions of breaches of confidentiality. For example, California State Penal Code, Section 502, included in contracts, states that:

(c) Except as provided in subdivision (h), any person who commits any of the following acts is guilty of a public offense:

(1) Knowingly accesses and without permission alters, damages, deletes, destroys, or otherwise uses any data, computer, computer system, or computer network in order to either (A) devise or execute any scheme or artifice to defraud, deceive, or extort, or (B) wrongfully control or obtain money, property, or data.

(2) Knowingly accesses and without permission takes, copies, or makes use of any data from a computer, computer system, or computer network, or takes or copies any supporting documentation, whether existing or residing internal or external to a computer, computer system, or computer network.

(4) Knowingly accesses and without permission adds, alters, damages, deletes, or destroys any data, computer software, or computer programs which reside or exist internal or external to a computer, computer system, or computer network.

(5) Knowingly and without permission disrupts or causes the disruption of computer services or denies or causes the denial of computer services to an authorized user of a computer, computer system, or computer network.


(d) (1) Any person who violates any of the provisions of paragraph (1), (2), (4), or (5) of subdivision (c) is punishable by a fine not exceeding ten thousand dollars ($10,000), or by imprisonment in the state prison for 16 months, or two or three years, or by both that fine and imprisonment, or by a fine not exceeding five thousand dollars ($5,000), or by imprisonment in a county jail not exceeding one year, or by both that fine and imprisonment.

All staff members who have access to the confidential data should sign a document agreeing to uphold the required confidentiality measures. This is sometimes called a "personal security agreement," a "confidentiality agreement," or a "disclosure penalty document." This agreement should notify the employee of the penalties for disclosure of the personal identities of the individuals of the data and requires that the employee acknowledge and understand the penalties. This task can be time consuming, but it is worth the effort. It is simplified if files of exemplar documents are maintained (Principles 3 and 9).

If money cannot flow between the requesting organization and the providing organization, then a no-cost contract can be put in place, which puts the requesting agency under the confidentiality constraints.

Principle 11: Congruence of Research Agency Goals: Demonstrated Benefits to Participating Organizations

Successful collaborations occur when all the parties perceive benefits for themselves. Requesting organizations should make sure that the goals of their research contribute to the goals of the organization providing the data. All our respondents reported that this was an important factor is easing the data access process. The importance of studying welfare leavers and the federal funding of the studies helped to facilitate data access. More generally, researchers find that they have greater access to data when there is obvious congruence between their research goals and the agency's need to comply with federal or state requirements, e.g., waiver demonstrations, reporting of performance measures, or completing of specified grant-related evaluations.

But the benefits are not always obvious and can come in many forms. For example, researchers can provide briefings, presentations, or technical assistance on special analyses to state administrators and staff on research completed with the administrative data. Researchers who have successfully obtained administrative data with confidential identifiers can return merged, cleaned, and enhanced databases to their state colleagues. As part of completing their research with the administrative data, researchers often clean and enhance the data. They may eliminate questionable outliers, identify likely biases, develop ways of dealing with the biases, and enhance the data by geocoding addresses. Researchers often develop high levels of expertise with certain types of administrative data. Sometimes researchers develop software applications to do their own analyses of the data which, if provided to the agency, would allow the agency to conduct their own analyses more efficiently. When this expertise comes back to the agency, in the form of briefing, technical assistance, software applications, or other format, the agency sees the benefits to them of sharing these data.

Also, researchers from the academic and nonprofit research fields can serve on and can often provide great benefit to the agencies' ad hoc or standing expert panels. These panels give guidance to the agencies on research methodologies, data analysis, software development, reports or products produced by contractors, development of information systems, public policies, technical administrative procedures, and legislative solutions.

Requesting organizations must seriously consider including services like these to the data-providing agencies in their contracts and requests to state departments. They not only provide state officials with something for the trouble of making data available, but they also provide proof to legislators and the general public that data access provides substantial public benefits.(17)

Data Access Principles That Have to Do with the Legal or Statutory Authority

Principle 12: Statutory Language Authorizes or Is Broadly Interpretable to Authorize Data Access for Researchers

As discussed earlier, lawmakers have written legislation that protects the privacy of individuals but makes no distinction between research uses and disclosure of personal information. State agencies sometimes have overcome the legislative restrictions by accommodating researchers through a broad interpretation of the statutory "routine use" and/or "program purposes" clauses.

In our interviews, we learned that many state agencies interpret evaluation and research to be an integral part of the performance of their duty. Respondents reported knowledge of statutory language that was being broadly interpreted to allow diverse data-linking projects, such as "administration of programs under this title," "eligibility determination," "performance of the agency's duty," "implementation of state policy," "routine use," "direct benefit to the public," and "research into employment and training programs." For example, one contract stated that data were "being shared pursuant to Section 1704 of the Unemployment Insurance Act which states in pertinent part that ÔThe Director shall take all appropriate steps to assist in the reduction and prevention of unemploymentÉand to promote the reemployment of unemployed workers throughout the State in every feasible wayÉ" (820 ILCS 405/1704). It was reported that the New York Department of Labor has had access to welfare data and employment-related data for 5-6 years under statutorily approved language to "monitor employment and training programs."


In a very ambiguous and unclear legal environment, states nevertheless have found ways to provide researchers with data, but it is a difficult process requiring strong leadership, adequate staff, extensive negotiations over confidentiality and security, and trust between the data-requesting and data-providing organizations. It also requires that data-providing organizations believe that they are obtaining substantial benefits from providing their data to researchers. In some cases, the benefits follow because the state has contracted with the researchers, but in other cases researchers must find ways to convince agencies that their research will be helpful to the agency itself.

All in all, the situation for research uses of administrative data is precarious. The laws are unclear about whether data can be used for research. Agencies are only sometimes convinced that research is in their best interests. Coordinating and convincing many different agencies is a difficult task. An obvious solution would be to develop a better legal framework that would recognize the smaller risks of data disclosure from datalinking for research, but before this can be done, researchers have to develop a menu of technical and institutional solutions to the problems of data confidentiality.

Technical and Institutional Solutions

There are two basic ways to limit disclosure, data alteration, and restricted access to data. The recent National Research Council (2000) report on "Improving Access to and Confidentiality of Research Data" notes the strengths and weaknesses of each method:

Data alteration allows for broader dissemination, but may affect researchers' confidence in their modeling output and even the types of models that can be constructed. Restricting access may create inconveniences and limit the pool of researchers that can use the data, but generally permits access to greater data detail (29).

"Anonymizing" data by removing identifying information is one method of data alteration, but this procedure may not limit disclosure enough. Data alteration can be thought of as a more versatile and thorough collection of methods for reducing the risk of disclosure.

Requiring informed consent for the use of data can be thought of as an institutional method for restricting access, but it may be impractical or it may be inadequate in many cases. Once data have been collected in an administrative system, it is nearly impossible to go back and obtain informed consent, but perhaps more importantly, informed consent might not really serve the purposes of individuals who cannot easily judge the costs and benefits of the various ways data might be used. We discuss some institutional methods such as Confidential Research Data Centers that can protect individual privacy and ensure confidentiality while making data available to researchers.

Data Alteration

Cross tabulations. One way to avoid unwanted disclosure is to present only aggregate data in the form of tables. In many cases, this amply limits disclosure, although at the cost of losing the analytical power that comes from being able to analyze individual-level data. Moreover, in some cases, the identification of individuals, families, firms, or other specific units can still be inferred from the tables themselves. One way to guard against this is to require a minimum number of reporting units, for example, five individuals in each cell of the table. This goal can be achieved starting with tables developed from unadjusted microdata through aggregation, suppression, random rounding, controlled rounding, and confidentiality edits (see Cox, 1980; Duncan and Pearson, 1991; Office of Management and Budget, 1994, 1999; Jabine, 1999; Kim and Winkler, no date).

Aggregation involves reducing the dimensionality of tables such that no individual cells violate the rules for minimum reporting. For example, data for small geography such as census block groups might be aggregated to census tracks for sparsely represented areas.

Suppression is the technique of not providing any estimate where cells are below a certain prespecified size. As row and column totals generally are provided in tabular data, there is a further requirement when suppressing cells to identify complementary cells that are also suppressed to ensure that suppressed data cannot be imputed. The identification of complementary cells and ensuring that suppressed cells cannot be imputed generally requires judgments of which potential complementary cells are least important from the vantage of data users. It also requires statistical analyses to ensure that suppressed cells cannot be estimated.

Random rounding is a technique whereby all cells are rounded to a certain level, such as to multiples of 5. The specific procedure provides that the probability for rounding up or down is established on the initial cells value. For example, the number 2 would not automatically be rounded to 0 but instead would be assigned a 60-percent probability of rounding down and a 40- percent probability of rounding up, and the final rounded value would be based on these random probabilities. Similarly, 14 would have an 80-percent probability of rounding to 15 and a 20-percent probability of rounding to 10. A problem with random rounding is that row and column cell totals will not necessarily equal reported actual totals.

Controlled rounding is a process using linear programming or other statistical techniques to adjust the value of rounded cells so that they equal published (actual) totals. Potential problems with this approach include (1) the need for more sophisticated tools, (2) for some situations there may not be any solution, and (3) for large tables the process may be computationally intensive.

Confidentiality edit is a process whereby the original microdata are modified. One confidentiality edit procedure called "swapping" is to identify households in different communities that have a certain set of identical characteristics and swap their records. The Census Bureau used this procedure in developing some detailed tabulations of the 100-percent file. Another edit procedure called "blank and impute" involves selecting a small sample of records and blanking them out and refilling with imputed values.

Tables of magnitude data. An additional problem arises with magnitude data such as total employees or revenue for a firm. For example, where a single firm is dominant, the publication of data on the industry may allow a fairly accurate estimate of the firm's data. In this case rules need to be established, for instance that no single firm can account for more than 80 percent of the cell total, to provide protection. This rule can be generalized in the form of "no fewer than n (a small number) of firms can contribute more than k percent of the cell total." These rules are used to identify "sensitive cells" that require suppression. The process of suppression requires complementary suppression, as discussed.

Unfortunately, all of these methods lead to a loss of significant amounts of information. Published tables, because they generally only provide cross-tabulations of two or three data elements, often do not provide the precise analysis that a researcher needs, and they are usually not useful for multivariate analysis. In these cases, researchers need to obtain microdata.

Masking public use microdata(18) Although microdata provide extraordinary analytical advantages over aggregated data, they also pose substantial disclosure problems for two reasons. Microdata sets, by definition, include records containing information about individual people or organizations, and micro-datasets often include many data elements that could be used to identify individuals. Although it is very unlikely that an individual could be identified on a data set by age group, size category, the combination of these three items might be enough to identify at least some people (Bethlehem et al, 1990:40). In fact:

In every microdata set containing 10 or more key variables, many persons can be identified by matching this file with another file containing the key and names and addresses (disclosure matching). Furthermore, response knowledge (i.e., knowing that the person is on the file) nearly always leads to identification (disclosure by response knowledge), even on a low-resolution key. Finally, analysis showed that on a key consisting of only two or three identifiers, a considerable number of persons are already unique in the sample, some of them "rare persons" and therefore also unique in the population" (p. 44).

A variety of methods can be used to mask the identity of individuals or households in microdata, although it is harder to mask the identities of firms because of the small number of firms and the high skew of establishment size in most business sectors. Units can be masked by providing only sample data, not including obvious identifiers, limiting geographical detail, and limiting the number of data elements in the file. High-visibility elements can be masked by using top or bottom coding, recoding into intervals or rounding, adding noise, and swapping records.

  • Sampling provides a means of creating uncertainty about the uniqueness of individuals or households.
  • Eliminating obvious identifiers involves removing items such as name, address, and Social Security number or other variables that would allow for identification of individuals or households.
  • Limiting geographical detail creates a greater pool and reduces the chance of identification of records with unique characteristics. For example, the Census Bureau restricted the geography for the Public Use Microdata Sample for the 1990 Census to areas with populations of at least 100,000.
  • Limiting the number of data elements in a file reduces the probability that an individual can be uniquely identified.
  • Top and bottom coding provide a means of eliminating disclosure risk. Top coding establishes an upper bound on continuous data, for example, 85 years and older would be coded as 85. Bottom coding is similar and might be used for old housing units.
  • Recoding into intervals and rounding are a means of taking continuous data and grouping the data. In each case unique information can be modified to mask identity. For example, data of birth might be transformed into age groups.
  • Random noise can be added to microdata by adding or multiplying values by a randomly determined factor. This process can be useful in preventing individuals from attempting to match the public use database with other databases where identity is known.
  • Swapping, blanking and imputing, and blurring are techniques used to modify the original data but not significantly change the statistical properties of the database. Swapping is identifying matching records based on key fields and swapping the detailed data. Blanking and imputing is to blank out certain data on selected records and statistically impute new values. Blurring is to replace exact values with mean values of all records meeting certain profiles.

Many of these methods are now commonly used when microdata are released to the public. Researchers, however, worry that the loss of information from data alteration may make it difficult or even impossible to do many kinds of analysis, and some statisticians have suggested that these methods do not provide sufficient disclosure protection (Bethlehem et al., 1990). These worries have led some to propose even more radical alterations of the data that would amount to creating "simulated data."

Simulated data can be created from the original microdata by using variants of imputation methods (see Rubin, 1987, 1993; Little and Rubin, 1987, Kennickell, 1997, 1998) to impute entirely new values of every variable for every case. The resulting data set is composed entirely of "made-up" people, and it may be possible to do analysis that is almost as good with these data as with the original information. Developing these methods is an active research area.

Some researchers, however, are wary of these methods, and in a recent seminar run by the Committee on National Statistics, Richard Suzman of the National Institute on Aging (NIA) reported that "all leading researchers currently supported by NIA are opposed to the imposition of synthetic data" (National Research Council, 2000:32). The solution may be to turn to institutional solutions, as suggested by Bethlehem et al. (1990:45):

Therefore, if microdata are released under the conditions that the data may be used for statistical purposes only and that no matching procedures may be carried out at the individual level, any huge effort to identify and disclose clearly shows malicious intent. In view of the duty of a statistical office to disseminate statistical information, we think disclosure protection for this kind of malpractice could and should be taken care of by legal arrangements, and not by restrictions on the data to be released.
Institutional Methods for Restricted Access

If data alteration is not the final answer (and there is substantial disagreement about this given some of the technical possibilities), then some new institutional forms need to be created to protect confidentiality. Many approaches are possible, but we shall discuss two especially useful ones, research data centers and licensing combined with substantial penalties for misuse.

Research data centers. The U.S. Census Bureau has been working with other federal agencies for the past few years to create Census Research Data Centers (CRDCs) in several locations around the country (Boston, California, Chicago, Pittsburgh, and North Carolina) where researchers can go to work with nonpublic census data under strict supervision and after a stringent application process. The goal of the CRDCs is to improve the quality of census data by getting researchers to use the data in new ways that push the data to their limits. The centers are locked and are secure facilities where researchers can come to work on microdata, but only after they have developed a proposal indicating how their work will help to improve the data and signed a contract promising to meet all the obligations to protect it required of Census Bureau employees. Once they have passed these hurdles, they can work with the data in the CRDC facility, but they can only remove output once it has undergone disclosure analysis from an on-site Census Bureau employee.

The CRDC model has worked well for some innovative projects, but it has its drawbacks. It is costly, requiring several hundred thousand dollars a year to cover space, equipment, the Census Bureau employee salary, and other needs, and it is not clear how these costs can be covered in the long run even though fees have been charged to researchers. Although the CRDCs have improved access for some researchers, others still must travel some distance to the nearest site. The approval process takes time, and the outcome is uncertain. Data availability often depends on the ability of Census Bureau employees to devote time to projects that may not be their first priority. There is some concern on the part of the Census Bureau about having microdata located away from the Census Bureau itself. Universities have concerns about storing confidential data on-site.

Despite these problems, something like these centers seems to be an inevitable result of researchers' desires for data and the confidentiality concerns of the governmental agencies that own the data. In our discussion of principle 5, "A central clearinghouse negotiates or assists in legal and technical issues," we noted that organizations such as the University of Chicago's Chapin Hall, the South Carolina Budget and Control Board, and the University of Missouri at Columbia's Department of Economics are developing variants of these centers. We can imagine many different approaches to these centers depending on where they are located (state governments or universities), how they are funded, how they determine access to data, and what types of responsibilities and limitations are placed on researchers.

Licensing and increased penalties for misuse--The great drawbacks of the RDC model are the costs and the need to travel to specific locations to do research. For some data sets, another approach might make more sense. Since 1991, the National Center for Educational Statistics (NCES) has issued nearly 500 licenses for researchers to use data from NCES surveys (National Research Council, 2000:44). As part of the licensing process, researchers must describe their research and justify the need for restricted data, identify those who will have access to the data, submit affidavits of nondisclosure signed by those with this access, prepare and execute a computer security plan, and sign a license agreement binding the institution to these requirements. Criminal penalties can be invoked for confidentiality violations. This model easily could be extended to other data, and it would work especially well for discouraging disclosure matching in cases where unique identifiers, but not all key identifiers, have been removed from the data.

Summary of Alternatives for Ensuring Confidentiality

Both data alteration and institutional restrictions hold promise for making data accessible while protecting confidentiality. Both approaches are still in their infancy, and much needs to be learned. It is possible that combinations of the two will work best. Simulated data sets might be released to the public to allow researchers to learn about the data and to test preliminary hypotheses. When the researcher feels ready, he or she could go to a research data center for a relatively short period of time to finish the analysis.

Summary and Recommendations

Matching and linking administrative data can be a great boon to researchers and evaluators trying to understand the impacts of welfare reform, but researchers sometimes find that they cannot access administrative data because of concerns about individual privacy, the ambiguity of statutory authority, and agency fears about public scrutiny.

Concerns about individual privacy and the desire to protect confidential data have grown dramatically in the past decade. Data matching often raises the Orwellian threat of a big brother government that knows all about its citizens' lives. The result has been a welter of laws that have often reacted to the worst possibilities that can be imagined rather than to realistic threats. Researchers, we have argued, do not pose the worst threats to data confidentiality, but they have had to cope with laws that assume data users will try to identify individuals and use sensitive information in inappropriate ways. In fact, researchers have only a passing interest in individual identifiers and microlevel data. They want to be able to do analysis that employs the full power of individual level data and to link data using identifiers to create even more powerful data sets. But as researchers they have no interest in information about individuals.(19) At worst, researchers pose only a moderate risk of disclosure.

Nevertheless, agencies with data must deal with an ambiguous legal environment that makes it hard to know whether and under what circumstances information can be shared with another agency or with researchers. Many agencies are hesitant to share information because of the lack of clear-cut statutory authority about who can access and use data. Others prefer the current situation, viewing ambiguous laws as providing greater flexibility and latitude. The downside of this ambiguity is that much is left to the individual judgments of agency managers who must deal with fears of legislative and public scrutiny. Although providing greater access to information potentially increases public knowledge and understanding about the agency, this information may cause others to second-guess the agency. The result is a skeptical and suspicious posture toward researchers' requests for data.

Overcoming these obstacles requires experience, leadership, the development of trust, and the availability of resources.(20)

Most data requesters and potential data providers are just beginning to gain experience with the rules governing research uses of administrative data. Most requesters are unfamiliar with the relevant laws and with agencies' concerns about confidentiality. Many agencies with administrative data have not had much experience with researchers, and they lack the relatively long time horizon required to wait for research to pay off. This is especially true of those parts of the agency that control administrative data. As a result, data requestors are impatient with procedures and find it hard to proceed. Agencies, faced with the unknown, delay providing data because they prefer to attend to their day-to-day problems. Leadership is essential for overcoming these problems.

Trust is also important. Trust may be hard to establish because of fears about how the data will be used and worries about whether the data will be protected against inappropriate disclosure. The "providing" agency must trust that the "receiver" will both protect confidentiality and not use the information in a way that compromises the basis on which the providing agency collected the information. The data provider also must believe it will receive some payoff for it from providing the data.

Even with experience, leadership, and trust, enough resources may not be available to overcome the many obstacles to providing data. Requesters may run out of steam as they encounter complicated requirements and seemingly endless meetings and negotiations. Providers may balk at the requester's requests for documentation and technical assistance in using the data. Adequate resources, also are essential for successful projects. There must be staff members who can help prepare data requests and the data themselves. There must be resources to fund the facilities (such as data archives or research data centers) that facilitate data access.

We found many instances where administrative data were used successfully, but the legal, technical, and institutional situation is parlous. Laws and regulations continue to be enacted with virtually no consideration of the needs of researchers. Technical advances offer some hope of making data available while protecting confidentiality, but technical advances such as the Internet and powerful computers also threaten data security. Institutional arrangements are precarious, often perched on nothing more than the leadership and trust developed by a few individuals.


Against this backdrop, our recommendations fall naturally into three categories: legal, technical, and institutional. Interestingly, in our interviews and in those reported in another study(21) we found differences of opinion about the proper set of prescriptions. One perspective is that the only way that data access will work is if there is a specific legislative mandate requiring it. Otherwise, it is argued, agencies will have no incentives to solve the many problems posed by efforts to make data more accessible. The other perspective suggests that just requiring public agencies to engage in making data available does not mean they will have the capacity or the ability to actually implement it. Rather, the priority should be on providing the tools and resources necessary to support research access to administrative data, with sparing use of statutory mandates. There seems to be some truth in both perspectives, and we make recommendations on both sides.

Legal Issues

Two sets of legal issues seem most pressing to us:

  1. Develop model state legislation allowing researchers to use administrative data. Although we have some models for legislation that would help researchers gain access to data, we do not have a thoroughgoing legal analysis of what it would take to facilitate access while protecting confidentiality. We strongly suspect, for example, that such legislation must carefully distinguish research from other uses by developing a suitable definition of what is meant by research. In addition, it must describe how researchers could request data, who would decide whether they can have access, how data would be delivered to them, and how the data would be safeguarded. At the federal level, H.R. 2885, "The Statistical Efficiency Act of 1999" appears to provide an important means for improving researcher access to confidential data.
  2. Clarify the legal basis for research and matching with administrative data, with special attention to the role of informed consent and Institutional Review Boards--Most of the projects using administrative data have relied on "routine use" and "program purposes" clauses to obtain access to the data, but IRBs prefer to base permissions to use data on informed consent, which is typically not obtained for administrative data. These approaches are somewhat at odds, and they have already started to collide in some circumstances where IRBs have been leery of allowing researchers access to data because of the lack of informed consent. Yet informed consent may not be the best way to protect administrative data because of the difficulty of ensuring that subjects are fully informed about the benefits and risks of using these data for research. At the same time, "routine use" and "program purpose" clauses may not be the best vehicle either. Some innovative legal thinking about these issues would be useful. This thinking might provide the basis for implementing our first recommendation.

Technical Issues

New techniques may make it easier to protect data making the data accessible to researchers:

  1. Develop better methods for data alteration, especially "simulated" data. Although there are differences of opinion about the usefulness of simulated data, there is general agreement that simulated data would at least help researchers get a "feel" for a data set before they go to the time and trouble of gaining access to a confidential version. It would be very useful to develop a simulated dataset for some state administrative data, then see how useful the data are for researchers and how successfully they protect confidentiality.
  2. Develop "thin-clients" that would allow researchers access to secure sites where research with confidential data could be conducted. Another model for protecting data is to provide access through terminals--called "thin-clients"--that are linked to special servers where confidential data reside. The linkages would provide strong password protection, and ongoing monitoring of data usage. All data would reside on the server, and the software would only allow certain kinds of analysis. As a result, agencies would have an ongoing record of who accessed what data, and they would be able to block some forms of sensitive analysis such as disclosure matching.

Institutional Issues

The primary lesson of our interviews with those doing Welfare Leavers Studies is that institutional factors can contribute enormously to the success or failure of an effort to use administrative data:

  1. Support agency staff who can make the case for research uses of administrative data. There is a large and growing infrastructure to protect data, but there is no corresponding effort to support staff who can make the case for research uses of administrative data. Without such staff, agencies may find it much easier to reject data requests, even when they are justified on legal and practical grounds.
  2. Support the creation of state data archives and data brokers who can facilitate access to administrative data. One way to get a critical mass of people who can help researchers is to develop data archives and data brokers whose job is to collect data and make the data available within the agency and to outside researchers. In our presentation of Data Access Principle 5, we described several models for what might be done to create central clearinghouses that negotiate and assist in legal and technical issues related to data access. A data archive or data warehouse stores data from multiple state agencies, departments, and divisions. In some cases, an archive matches the data and provides data requesters with match-merged files. In other cases, data archives provide a place where data from multiple agencies are stored so that data requesters can obtain the data from one source and match it for themselves. Data brokers do not actually store data from other agencies but "brokers" or "electronically mines" data from other agencies on an ad hoc or regular basis. These organizations then perform analyses on the data and report results back to the requesting agency. The data are stored only temporarily at the location of the data broker, before being returned to the providing agency or destroyed.
  3. Support the creation of university-based research data centers. Another model worth exploring is university-based research data centers modeled after the Census Bureau's Research Data Centers. These centers, located around the country, provide a site where researchers can use nonpublic Census data to improve the quality of census data by getting researchers to evaluate new ways to push the data to their limits. The centers are locked and secure facilities where researchers can come to work on microdata, but only after they have developed a proposal indicating how their work will help to improve the data and signed a contract promising to meet all the obligations to protect it required of Census Bureau employees. Once they have passed these hurdles, they can work with the data in the CRDC facility, but they can only remove output once it has undergone disclosure analysis from an on-site Census Bureau employee. A similar model could be developed for administrative data.
  4. Use contract law to provide licenses and criminal and civil law to provide penalties for misuse of data. Licensing arrangements would allow researchers to use data at their own workplace. Researchers would describe their research and justify the need for restricted data, identify those who will have access to the data, submit affidavits of nondisclosure signed by those with this access, prepare and execute a computer security plan, and sign a license agreement binding themselves to these requirements. Criminal penalties could be invoked for confidentiality violations. This model would work especially well for discouraging matching in cases where unique identifiers, but not all key identifiers, have been removed from the data.


Bethlehem, J.G., W.J. Keller, and J. Pannekoek

1990 Disclosure control of microdata. Journal of the American Statistical Association 85(March):38-45.

Cox, Lawrence H.

1980 Suppression methodology and statistical disclosure control. Journal of the American Statistical Association 75 (June):377-385.

Duncan, G.T., and D. Lambert

1986 Disclosure-limited data dissemination. Journal of the American Statistical Association 18 (March):10-18.

Duncan, G.T., and R.W. Pearson

1991 Enhancing access to microdata while protecting confidentiality: Prospects for the future. Statistical Science 6(August):219-232.

Fellegi, I.P.

1972 On the question of statistical confidentiality. Journal of the American Statistical Association 67(March):7-18.

Harmon, J.K., and R. N. Cogar

1998 The Protection of Personal Information in Intergovernmental Data-Sharing Programs: A Four-Part Report on Informational Privacy Issues in Intergovernmental Programs. Electronic Commerce, Law, and Information Policy Strategies, Ohio Supercomputer Center, Columbus, OH, June.

Hotz, V. Joseph, Robert George, Julie Balzekas, and Francis Margolin.

1998 Administrative Data for Policy-Relevant Research: Assessment of Current Utility and Recommendations for Development. Chicago: Joint Center for Poverty Research.

Jabine, Thomas B.

1999 Procedures for restricted data access. Journal of Official Statistics 9(2):537-589.

Kennickell, Arthur B.

1997 Multiple Imputation in the Survey of Consumer Finances. Washington, DC: Federal Reserve Bank.
1998 Multiple Imputation in the Survey of Consumer Finances. Unpublished paper Prepared for the Joint Statistical Meetings, Dallas, Texas.

Kim, Jay J., and W.E. Winkler

no date. Masking Microdata Files. Unpublished Bureau of the Census discussion paper.

Landsbergen, D., and G. Wolken

1998 Eliminating Legal and Policy Barriers to Interoperable Government Systems. Electronic Commerce, Law, and Information Policy Strategies, Ohio Supercomputer Center, Columbus, OH.

Little, Roderick, and Donald B. Rubin

1987 Statistical Analysis with Missing Data. New York. John Wiley and Sons.

National Research Council

2000 Improving Access to and Confidentiality of Research Data: Report of a Workshop, Christopher Mackie and Norman Bradburn, eds. Commission on Behavioral and Social Sciences and Education, Committee on National Statistics. Washington, DC: National Academy Press.

National Research Council and Social Science Research Council

1993 Private Lives and Public Policies: Confidentiality and Accessibility of Government Statistics. G.T. Duncan, T.B. Jabine, and V.A. de Wolf, eds. Commission on Behavioral and Social Sciences and Education, Committee on National Statistics. Washington, DC: National Academy Press.

Office of Management and Budget

1994 Report on Statistical Disclosure and Limitation Methodology. Statistical Policy Working Paper 22. Prepared by the Subcommittee on Disclosure Limitation Methodology, Federal Committee on Statistical Methodology, May.
1999 Checklist on Disclosure Potential of Proposed Data Releases. Prepared by the Interagency Confidentiality and Data Access Group: An Interest Group of the Federal Committee on Statistical Methodology, July.

Preis, James

1999 Confidentiality: A Manual for the Exchange of Information in a California Integrated Children's Services Program. Sacramento: California Institute for Mental Health.

Reamer, F.G.

1979 Protecting research subjects and unintended consequences: The effect of guarantees of confidentiality. Public Opinion Quarterly 43(4):497-506.

Reidenberg and Gamet-Poll

1995 The fundamental role of privacy and confidence in the network. Wake Forest Law Review 30(105).

Rubin, Donald B.

1987 Multiple Imputation for Nonresponse in Surveys. New York: John Wiley and Sons.
1993 Discussion of statistical disclosure limitation. Journal of Official Statistics 9(2):461-468.

Smith, R. E.

1999 Compilation of State and Federal Privacy Laws with 1999 Supplement. Providence: Privacy Journal.

Stevens, D.

1996 Toward an All Purpose Confidentiality Agreement: Issues and Proposed Language. Baltimore, MD: University of Baltimore.

UC Data Archive and Technical Assistance

1999 An Inventory of Research Uses of Administrative Data in Social Service Programs in the United States 1998. Chicago: Joint Center for Poverty Research.

U.S. Department of Health, Education, and Welfare, Advisory Committee on Automated Personal Data Systems, Records, Computers and the Rights of Citizens

1973 Records, Computers, and the Rights of Citizens. Washington, DC: U.S. Department of Health, Education, and Welfare.


State Statutes Providing Researcher Access to Data


This Maryland statute is a model for what might be done in other states.

Government Code. §10-624. Personal records

(c) Access for research.--The official custodian may permit inspection of personal records for which inspection otherwise is not authorized by a person who is engaged in a research project if:

(1) the researcher submits to the official custodian a written request that:

  • (i) describes the purpose of the research project;
  • (ii) describes the intent, if any, to publish the findings;
  • (iii) describes the nature of the requested personal records;
  • (iv) describes the safeguards that the researcher would take to protect the identity of the persons in interest; and
  • (v) states that persons in interest will not be contacted unless the official custodian approves and monitors the contact;

(2) the official custodian is satisfied that the proposed safeguards will prevent the disclosure of the identity of persons in interest; and

(3) the researcher makes an agreement with the unit or instrumentality that:

  • (i) defines the scope of the research project;
  • (ii) sets out the safeguards for protecting the identity of the persons in interest; and
  • (iii) states that a breach of any condition of the agreement is a breach of contract.


The following statute from Washington state also provides language for model legislation that authorizes researcher access to data.

Revised Code of Washington (RCW). Chapter 42.48. Release of Records for Research

RCW 42.48.010 Definitions.

For the purposes of this chapter, the following definitions apply:

(1) "Individually identifiable" means that a record contains information which reveals or can likely be associated with the identity of the person or persons to whom the record pertains.

(2) "Legally authorized representative" means a person legally authorized to give consent for the disclosure of personal records on behalf of a minor or a legally incompetent adult.

(3) "Personal record" means any information obtained or maintained by a state agency which refers to a person and which is declared exempt from public disclosure, confidential, or privileged under state or federal law.

(4) "Research" means a planned and systematic sociological, psychological, epidemiological, biomedical, or other scientific investigation carried out by a state agency, by a scientific research professional associated with a bona fide scientific research organization, or by a graduate student currently enrolled in an advanced academic degree curriculum, with an objective to contribute to scientific knowledge, the solution of social and health problems, or the evaluation of public benefit and service programs.

This definition excludes methods of record analysis and data collection that are subjective, do not permit replication, and are not designed to yield reliable and valid results.

(5) "Research record" means an item or grouping of information obtained for the purpose of research from or about a person or extracted for the purpose of research from a personal record.

(6) "State agency" means: (a) The department of social and health services; (b) the department of corrections; (c) an institution of higher education as defined in RCW 28B.10.016; or (d) the department of health.

[1989 1st ex.s. c 9 § 207; 1985 c 334 § 1.] NOTES: Effective date -- Severability -- 1989 1st ex.s. c 9: See RCW 43.70.910 and 43.70.920.

RCW 42.48.020 Access to personal records.

(1) A state agency may authorize or provide access to or provide copies of an individually identifiable personal record for research purposes if informed written consent for the disclosure has been given to the appropriate department secretary, or the president of the institution, as applicable, or his or her designee, by the person to whom the record pertains or, in the case of minors and legally incompetent adults, the person's legally authorized representative.

(2) A state agency may authorize or provide access to or provide copies of an individually identifiable personal record for research purposes without the informed consent of the person to whom the record pertains or the person's legally authorized representative, only if:

(a) The state agency adopts research review and approval rules including, but not limited to, the requirement that the appropriate department secretary, or the president of the institution, as applicable, appoint a standing human research review board competent to review research proposals as to ethical and scientific soundness; and the review board determines that the disclosure request has scientific merit and is of importance in terms of the agency's program concerns, that the research purposes cannot be reasonably accomplished without disclosure of the information in individually identifiable form and without waiver of the informed consent of the person to whom the record pertains or the person's legally authorized representative, that disclosure risks have been minimized, and that remaining risks are outweighed by anticipated health, safety, or scientific benefits; and

(b) The disclosure does not violate federal law or regulations; and

(c) The state agency negotiates with the research professional receiving the records or record information a written and legally binding confidentiality agreement prior to disclosure. The agreement shall:

(i) Establish specific safeguards to assure the continued confidentiality and security of individually identifiable records or record information;

(ii) Ensure that the research professional will report or publish research findings and conclusions in a manner that does not permit identification of the person whose record was used for the research. Final research reports or publications shall not include photographs or other visual representations contained in personal records;

(iii) Establish that the research professional will destroy the individual identifiers associated with the records or record information as soon as the purposes of the research project have been accomplished and notify the agency to this effect in writing;

(iv) Prohibit any subsequent disclosure of the records or record information in individually identifiable form except as provided in RCW 42.48.040; and

(v) Provide for the signature of the research professional, of any of the research professional's team members who require access to the information in identified form, and of the agency official authorized to approve disclosure of identifiable records or record information for research purposes.

[1985 c 334 § 2.]

RCW 42.48.030 Charge for costs of assistance.

In addition to the copying charges provided in RCW 42.17.300, a state agency may impose a reasonable charge for costs incurred in providing assistance in the following research activities involving personal records:

(1) Manual or computer screening of personal records for scientific sampling purposes according to specifications provided by the research professional;

(2) Manual or computer extraction of information from a universe or sample of personal records according to specifications provided by the research professional;

(3) Statistical manipulation or analysis of personal record information, whether manually or by computer, according to specifications provided by the research professional.

The charges imposed by the agency may not exceed the amount necessary to reimburse the agency for its actual costs in providing requested research assistance.

RCW 42.48.050 Unauthorized disclosure--Penalties.

Unauthorized disclosure, whether wilful [sic] or negligent, by a research professional who has obtained an individually identifiable personal record or record information from a state agency pursuant to RCW 42.48.020(2) is a gross misdemeanor. In addition, violation of any provision of this chapter by the research professional or the state agency may subject the research professional or the agency to a civil penalty of not more than ten thousand dollars for each such violation.

RCW 42.48.060 Exclusions from chapter.

Nothing in this chapter is applicable to, or in any way affects, the powers and duties of the state auditor or the joint legislative audit and review committee.

[1996 c 288 § 34; 1985 c 334 § 6.]

RCW 42.48.900 Severability -- 1985 c 334.

If any provision of this act or its application to any person or circumstance is held invalid, the remainder of the act or the application of the provision to other persons or circumstances is not affected.

[1985 c 334 § 8.]


1.  The 26 states inventoried in the report included the 10 states with the largest populations plus a random selection of at least four states from the northeast, south, west, and midwestern regions of the nation. These states comprise four-fifths of the U.S. population and more than five-sixths of the welfare population. This report can be viewed at

2.  Note that we are using the term "data sharing" in a fashion that is much narrower than its colloquial meaning.

3.  Bethlehem et al. (1990:38) define disclosure in this way when they say that "Identification is a prerequisite for disclosure. Identification of an individual takes place when a one-to-one relationship between a record in released statistical information and a specific individual can be established." It seems to us that this is a sufficient condition for improper disclosure to have occurred, but it is not clear that it is a necessary condition.

4.  Most of the literature on statistical data collection (e.g., National Research Council and Social Science Research Council, 1993) assumes that disclosure in and of itself is a bad thing. This presumption developed because most of this literature deals with a very specific situation where statistical agencies have collected data under the promise that they will not share it with anyone and where disclosure refers to information that can be readily attached to an individual. Because we deal with a much broader class of situations, we find it useful to distinguish between disclosure and improper disclosure where impropriety may vary with the circumstances of data collection and data use.

5.  Other commissions and organizations developed similar codes of fair information practice that appear to limit severely the availability of data. Hotz et al. (1998) summarizes the common themes as follows:

  • Promote openness.
  • Provide for individual participation.
  • Limit the collection of personal information.
  • Encourage accurate, complete, and current information.
  • Limit the use of information.
  • Limit the disclosure of information.
  • Ensure the information is secure.
  • Provide a mechanism for accountability.

6.  Other important laws include the Freedom of Information Act (enacted in 1966), the Family Education Rights and Privacy Act of 1974, the Confidentiality of Alcohol and Drug Abuse Patient Records Act, the Right to Financial Privacy Act of 1978, and the Drivers Privacy Protection Act of 1994.

7.  U.S.C.S. §552a(e).

8.  U.S.C.S. §552a(b)(3).

9.  45 CFR Part 46.

10.  In an effort to simplify the complex regulations governing IRBs, we conflate waiver of informed consent (which does not necessarily mean exemption from IRB review) with exemptions.

11.  The recent National Academy Press publication, Improving Access to and Confidentiality of Research Data (National Research Council, 2000) is directed to this exact set of concerns.

12.  Basic information about state privacy laws in all states is available in Compilation of State and Federal Privacy Laws (Smith, 1999). We have focused on states with ASPE leavers studies to complement the survey described later.

13.  We also include Washington state's statute, which provides for researchers having access to administrative data.

14.  Basic information about state privacy laws is available in a recent publication, Compilation of State and Federal Privacy Laws (Smith, 1999).

15.  Fall 1999.

16.  Texas was not an ASPE Fiscal Year 1998 welfare leavers study grantee.

17.  The U.S. Census Bureau "Research Data Centers Programs" is entirely based on the strong belief that researchers can help the Bureau improve the quality of its data, and researchers are required, by law and regulation, to develop strong rationales for why their work will improve census data.

18.  See "Report on Statistical Disclosure and Limitation Methodology" prepared by the Subcommittee on Disclosure Limitation Methodology and published by the Office of Management and Budget in 1994.

19.  The exception is when researchers want to contact individuals list in administrative file. The human subject risks are greater here, and they require greater scrutiny

20.  There are also technical obstacles to using administrative data, but we do not believe these are the major difficulties faced by most researchers. Theses obstacles include hardware and software incompatibility and lack of common standards. Fortunately, technological advances increasingly are addressing these issues, and they are less import compared to other difficulties.

21.  Landsbergen and Wolken (1998) interviewed officials in five states about barriers to establishing, maintaining and evaluating informational data sharing policies and practices. Although this study focused on data sharing and these five states' experiences with regard to environmental programs, the conclusions clearly extend to data access in other topical areas.

Measuring Employment and Income for Low-Income Populations with Administrative and Survey Data

With passage of the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) of 1996 and the expansions of the Earned Income Tax Credit (EITC) over the past decade, increasing attention has been paid to the employment experiences, labor market earnings, and transfer income received by disadvantaged individuals and households. This attention, prompted by explicit performance goals in PRWORA and implicit goals of the EITC expansions, focuses on whether low-income households can achieve self-sufficiency without resorting to Temporary Assistance for Needy Families (TANF) for other public assistance programs. Although income and employment levels are only partial indicators of the well-being of households, they continue to be ones most often used to assess the consequences, intended and unintended, of welfare reform.

More broadly, good measures of income and employment for low-income families are necessary to (1) assess the well-being and labor market attachment of low-income and welfare populations at the national, state, and local levels; (2) evaluate welfare reform and learn the effects of specific policies, such as time limits and sanctions; and (3) meet reporting requirements under TANF and aid in the administration of welfare programs.

There are two data sources for measuring employment and incomes of the disadvantaged: survey data and administrative. Surveys have been the mainstay of evaluating welfare programs and of monitoring changes in income and employment for decades. These include national surveys--such as the U.S. Censuses of Population, the Current Population Survey (CPS), the Survey of Income and Program Participation (SIPP), the National Longitudinal Surveys (NLS), and the Panel Study of Income Dynamics (PSID)--and more specialized surveys that gather data for targeted groups, such as current or former welfare recipients, and at the state or local level. (1) Although survey data continue to be important, the use of administrative data sources to measure income and employment has grown dramatically over the past 30 years. Data on wages and salaries from state Unemployment Insurance (UI) systems, for example, have been used to measure the earnings and employment of individuals that participated in state AFDC/TANF programs, manpower training, and other social programs. Data on earnings (and employment) from Social Security Administration (SSA) records have been linked with the records of welfare and social program participants.

What type of data one uses to measure income and employment among current and past welfare participants and welfare-eligible households may have important consequences for implementing and evaluating recent welfare reforms. Recent debates between the states and the federal government, for example, over employment targets and associated sanctions mandated under PRWORA hinged crucially on exactly how the fraction of a state's caseload that is employed would be measured. Furthermore, the conclusions of several recent assessments of the impacts of welfare reform and caseload decline appear to depend on how income and employment of welfare leavers and welfare-eligible populations are measured.(2)

In this paper we assess the strengths and weaknesses of using survey or administrative data to measure the employment and income of low-income populations. We review a number of studies, most of which have been conducted in the past 10-15 years,(3) that assess the comparability of income and employment measures derived from surveys and administrative records. Clearly the primary criterion for evaluating data sources is their accuracy or reliability. Ideally one would compare the income and employment measures derived from either surveys or administrative data sources with their true values in order to determine which source of data is the most accurate.

Unfortunately this ideal is rarely achieved. One seldom, if ever, has access to the true values for any outcome at the individual level. At best, one only can determine the relative differences in measures of a particular outcome across data sources. In this paper, we try to summarize the evidence on these relative differences and the state of knowledge as to why they differ. These studies point to several important dimensions along which surveys and administrative records differ and, as such, are likely to account for some, if not all, of the differences in the measures of income and employment derived from each. These include the following:

  • Population Coverage: Surveys generally sample the population while administrative data typically cover the population of individuals or households who are enrolled in some program. In each case issues arise about the sizes of samples at state or substate levels and sample designs that may limit the issues that can be examined.
  • Reporting Units: Different data sources focus on individuals, households, tax-filing units, or case units. Differences in reporting units hinder the ability to move across data sources to obtain measures of income and complicate efforts to evaluate the differential quality of income data across data sets. Furthermore, differences in reporting units may have important consequences for the comprehensiveness of income measures, an issue especially relevant when attempting to assess the well-being, and changes in the well-being, of disadvantaged populations.
  • Sources of Income: Data sources differ in the breadth of the sources of individual or household income they collect. Surveys such as the CPS and, especially, the SIPP, attempt to gather a comprehensive set of income elements, including labor earnings, cash benefits derived from social programs, and income from assets. In contrast, administrative data sources often contain only information on a single type of income (as in the case of UI earnings) or only those sources of income needed for the purposes of a particular record-keeping system.
  • Measurement Error: Different data sources may be subject to different sources of measurement problems, including item nonresponse, imputation error, and measurement error with respect to employment and income (by source). Furthermore, issues such as locating respondents, respondent refusals, and sample attrition are important in conducting surveys on low-income populations.
  • Incentives Associated with Data-Gathering Mechanisms: Data sources also may differ with respect to the incentives associated with the gathering of information. In the case of surveys, respondents' cooperation may depend on a comparison of the financial remuneration for a survey with the respondent "burden" associated with completing it. In the case of administrative data, the incentives relate to the administrative functions and purposes for which the information is obtained. What is important is attempting to anticipate the potential for and likelihood of biases in measures of income and employment that may result from such incentives.

The importance of various strengths and weaknesses of different data sources for measuring employment and income generally will depend on the purpose to which these measures are put. We note five considerations. First, when conducting an experimental evaluation of a program, the criteria for judging data sources is whether they yield different estimates of program impact, which generally depends on differences in income (employment) between treatment and control groups. In this case, errors in measuring the level of income between treatment and control groups could have little effect on the evaluation. Alternatively, suppose one's objective is to describe what happened to households who left welfare. In this case, researchers will be interested in the average levels of postwelfare earnings (or employment). We discuss results from Kornfeld and Bloom (1999) where UI data appear to understate the level of income and employment of treatments and controls in an evaluation of the Job Training Partnership Act (JTPA), but differences between the two groups appear to give accurate measures of program impacts. Depending on the question of interest, the UI data may be suitable or badly biased.

Second, surveys, and possibly tax return data, can provide information on family resources while UI data provide information on individual outcomes. When assessing the well-being of case units who leave welfare, we often are interested in knowing the resources available to the family. When thinking about the effects of a specific training program, we often are interested in the effects on the individual who received training.

Third, data sets differ in their usefulness in measuring outcomes over time versus at a point in time. UI data, for example, make it relatively straightforward to examine employment and earnings over time, while it is impossible to do this with surveys unless they have a longitudinal design.

Fourth, sample frames differ between administrative data and surveys. Researchers can not use administrative data from AFDC/TANF programs, for example, to examine program take-up decisions because the data only cover families who already receive benefits. Surveys, on the other hand, generally have representative rather than targeted or "choice-based" samples.

Fifth, data sources are likely to have different costs . These include the costs of producing the data and implicit costs associated with gaining access. The issue of access is often an important consideration for certain sources of administrative data, particularly data from tax returns.

The remainder of this paper is organized as follows: We characterize the strengths and weaknesses of income and employment measures derived from surveys, with particular emphasis on national surveys, from UI wage records, and from tax returns. For each data source, we summarize the findings of studies that directly compare the income and employment measures derived from that source with measures derived from at least one other data source. We conclude the paper by identifying the "gaps" in existing knowledge about the survey and administrative data sources for measuring income and employment for low-income and welfare-eligible populations. We offer several recommendations for future research that might help to close these gaps.

Using Survey Data to Measure Employment and Income

In this section, we discuss the strengths and weaknesses of measuring income and employment status for low-income populations using survey data. Most of our analysis focuses on the use of national surveys--CPS and SIPP in particular--because of the availability of several high-quality studies that compare their income and employment measures to other data sources. Where available, we also summarize studies that assess income and employment measurement with more targeted surveys.

The key features of the national surveys for the purposes of this paper are summarized in Table 9-1.

Key features of selected national surveys that report employment and income
status of ndividuals and households
Feature Current Population Survey (CPS) Survey of Income and Program Participation (SIPP) Panel Study of Income Dynamics (PSID) National Longitudinal Survey of Youth, 1979
Nationally representative sample? Yes Yes Only at sample Inception in 1968 No, but representative for cohorts covered at sample inception
Primary unit of analysis Household Household Household Individual
Longitudinal data? No Yes Yes Yes
Typical sample size 60,000 households 21,000 households 8,700 households 11,400 individuals
Capacity for state and local Analysis For all but small states For large states only Limited Limited
Coverage of income sources Broad Very broad Broad Very broad
Accuracy of earnings data(a) 97% 92% -- --
Accuracy of AFDC data(b)     -- --
Timeliness of data Several months 2+ years 2-year lag 1-2 year lag
a For 1990, See Table 9-3.
b AFDC = Aid to Families with Dependent Children, for 1990, See Tables 9-2 and 9-3.
Potential Strengths

The CPS and SIPP are vital data sets for understanding the functioning of low-wage labor markets and the effects of antipoverty programs. These data get high marks on many of the concerns mentioned in the introduction. They have a national sampling frame covering program participants and nonparticipants that make these data valuable for developing a broad perspective on developments in low-wage labor markets. An example of this type of study is Primus et al. (1999), which uses CPS data to show that AFDC/TANF and Food Stamp Program participation rates have declined considerably faster than poverty rates between 1993 and 1997. They further report that incomes of poor single mothers fell between 1995 and 1997 (after rising between 1993 and 1995), and that the safety net is lifting fewer children from poverty than in the past. Concerns arise with this study, some of which are mentioned in the text that follows. Nonetheless, the CPS and the SIPP are the only data sets that would allow analysts to address the important issues that Primus et al. examine on a national scale.

The other national data sets that have been used to analyze the employment and income status of low-income populations are the National Longitudinal Survey (particularly the National Longitudinal Survey of Youth 1979) and the PSID. Both of these data sets have the additional feature that they are longitudinal surveys so that one can obtain information on earnings and employment status over time for the same person (and household). (4) The PSID has surveyed, until very recently, its respondents and the "splitoffs" of initial respondent households on an annual basis since 1968. Similarly, until 1994 the NLSY79 conducted annual surveys of a random sample of individuals who were 14-21 years of age in 1979. Both of these surveys gathered detailed information on labor market earnings and employment status of respondents, earnings and some employment information on other adult household members, and some information on other sources of income, including income from various public assistance programs. One of the advantages of longitudinal data sets such as SIPP, PSID, and NLSY is that they allow one to monitor the entry into and exit from welfare or other social programs and the factors related to welfare dynamics, including changes in earnings and family structure.

The CPS, SIPP, and PSID, in addition to having nationally representative samples, focus on households as the unit of analysis, and include information on all adult household members.(5) Given the general presumption that families pool resources, data sets that focus on families or households (and include information on cohabiting partners) are valuable. A calculation in Meyer and Cancian (1998) illustrates the usefulness of having data on family, as well as individual, incomes. Their study examines the economic well-being of women in the 5 years after leaving AFDC. They show that in the first year upon exit from AFDC, 79 percent of the women have incomes below the poverty line, but when family income is considered, a smaller number, 55.5, have income below the (correspondingly larger) poverty line. After 5 years, 64.2 percent of the women still have incomes below the poverty line, while only 40.5 percent of the broader family unit had income below the poverty line.

The nationally representative surveys provide information on multiple sources of income, especially in the SIPP, either through separate questions or prompting of specific income sources. By asking specific questions about, for example, welfare receipt or food stamps, the data identify participants and (eligible) nonparticipants, so the data can be used to study program entry effects.

The national surveys also measure income and employment in a comparable fashion both over time and across geographical locations, though in January 1994 the way that earnings information was elicited in the CPS was changed (Polivka, 1997).(6)

Another strength of the nationally representative surveys is that questions can be modified to reflect changing circumstances. For example, the U.S. Census Bureau periodically conducts cognitive interviews of respondents to the CPS in order to assess how they responded to different CPS income- and welfare-related questions. Such studies are used to determine which of the CPS questions were confusing and how respondents interpreted questions. Results from these cognitive interviews are used to improve the way questions are asked, with the goal of improving the quality of the data on key variables such as income and program participation. 7 Typically, this sort of sophisticated assessment can only be done on large-scale, national surveys.

To summarize, there are several potential strengths of using survey data to measure income and employment. These include the following:

  • Surveys can provide representative samples for specific populations and generally include data for other family members.
  • Surveys typically provide demographic data and data on other characteristics of households (such as educational attainment). They also may gather detailed information on many distinct income sources.
  • National surveys provide consistent information across states and localities.
  • Surveys can be flexible, so their developers can control what information is collected about income and employment, and this information can be improved over time

Three general concerns arise with the nationally representative surveys that keep them from being the solution, or "core" data, for understanding the effects of welfare reform. The most important issue is that sample sizes and sampling frames are such that these data cannot be used to examine certain subpopulations of interest, such as welfare recipients in a particular state (perhaps with the exception of the largest states, such as California, New York, and Texas). A distinguishing feature of welfare reform is that program responsibility now largely rests with states and even counties within a state. The nationally representative data sets do not have sample designs and sample sizes that allow analysts to examine behavior at a level that corresponds to where program decisions are being made.

Second, there appear to be systematic changes in the coverage of low-income populations in the CPS. Studies have found that AFDC and Food Stamp Program benefits and the number of recipients in the CPS have declined over time relative to estimates of participants from administrative records. This issue of coverage is a serious concern for studies that use the CPS for measuring the income of welfare populations.(8) In Table 9-2, we reproduce comparisons of aggregate AFDC/TANF and Food Stamp Benefits Program between CPS and administrative data sources from the Primus et al. (1999) study. It shows there has been a sharp decline between 1990 and 1997 in the percentage of AFDC/TANF and Food Stamp Program benefits reported in the CPS compared to amounts reported in administrative data.(9) The reduction in coverage of AFDC/TANF (or family assistance) benefits also is consistent with Roemer's (2000: Table 3b) calculations from the CPS for 1990 through 1996. Interestingly, the apparent decline in AFDC/TANF coverage does not show up in the SIPP, though the SIPP appears to capture only about three-quarters of aggregate benefits.

AFDC/TANF and Food Stamp Aggregate Benefits Paid based on administrative data
compared to estimates from Current Population Survey (CPS) (Calendar Year), in billions of dollars
AFDC/TANF Benefits* Food Stamp Benefits
CPS Data Administrative Data Ratio (%) CPS Data Administrative Data Ratio (%)
1990 14.259 18.855 75.6 10.335 13.556 76.2
1991 15.554 20.804 74.8 12.373 16.551 74.8
1992 15.362 22.258 69.0 13.394 20.014 66.9
1993 17.540 22.307 78.6 15.010 22.253 67.5
1994 17.145 22.753 75.4 15.317 22.701 67.5
1995 15.725 21.524 73.1 14.542 22.712 64.0
1996 13.494 19.710 68.5 14.195 22.440 63.3
1997 10.004 15.893 62.9 12.274 19.570 62.7
Source: Primus et al. (1999:65) which in turn gives the sources, as HHS and USDA administrative records, CBPP tabulations of CPS data.
* Acronyms:
AFDC - Aid to Families with Dependent Children
TANF - Temporary Assistance for Needy Families
HHS - Health and Human Services
USDA - United States Department of Agriculture
CBPP - Center on Budget and Policy Priorities

Polivka (1998) compares the monthly average number of AFDC recipients in the March CPS to the monthly average reported to the Department of Health and Human Services (prior to quality control). She finds there has been a modest decrease in the proportion of total months on AFDC as measured in the CPS. The ratio of the CPS estimated to the administrative count (excluding Guam, the Virgin Islands, and Puerto Rico) is 83.0 (1989), 86.7 (1990), 86.0 (1991), 82.5 (1992), 84.2 (1993), 78.5 (1994), 75.5 (1995), and 79.6 (1996). The timing of the drop in the ratio corresponds to changes in the March CPS survey instrument. Taken together, the Primus et al. (1999) and Polivka (1998) results suggest that the decline in benefits reported in the CPS results from both a reduction in the coverage of families receiving AFDC and from an underrepresentation of benefits conditional on receipt, though the second factor seems quantitatively more important than the first.

The third potential weakness of national surveys is that there is little or no "cost" to respondents of misreporting of income, employment, or other circumstances.(10)

Some specific potential weaknesses associated with the PSID and NLSY79 are of potential relevance for obtaining information on the income and employment status of low-income populations. Most notable is the fact that they are not, by design, representative of the general population over time. Both data sets began with samples that were representative of their targeted groups--young adults in the case of the NLSY79 and the national population as of 1968 in the case of the PSID--but are not designed to be representative of the national population, or even of the age group covered in the NLSY79, in subsequent years. This feature can result in biased measures of summary statistics on income and employment vis-à-vis the nation as a whole in more recent years.

The other feature of the NLSY79 and PSID relevant for assessing the income and employment status of low-income populations is their respective sample sizes. The original sample for the NLSY79 was 12,686 young men and women, from which approximately 90 percent of the original sample remains today. The original sample in the PSID was 5,000 U.S. households in 1968 and, because of its growth through the accumulation of additional households through splitoffs from original households, it contained more than 8,700 in 1995. Although these are not small sample sizes, the sizes of low-income samples at a point in time are relatively small compared to both the CPS (which contains some 60,000 households at a point in time) and most waves of the SIPP (which, in its larger waves, contains data on 21,000 households). The sizes of the low-income or welfare subsamples in the NLSY79 and PSID for even the largest states are generally too small to derive reliable measures on income and employment, let alone other outcomes.

To summarize, there are two primary potential weaknesses with using national survey data to measure income and employment of low-income populations. They are the following:

  • Sample sizes in national surveys often are small for studies that focus on welfare or low-income populations, or that wish to examine specific targeted groups, such as current or former welfare recipients.
  • There appears to be falling coverage (of both recipients and benefits) in national surveys.
Direct Assessments of Income and Employment Measures from Survey Data

Moore et al. (1997) conducted a general survey of income reporting in the CPS and SIPP, and Roemer (2000) assesses trends in SIPP and CPS income reporting between 1990 and 1996.(11) A central finding in Moore et al. (1997) and Roemer (2000) is that there is underreporting of many types of income in surveys. The reasons for this and, hence, solutions in the design of effective surveys are complex. The magnitudes of CPS and SIPP underreporting for selected years are given in Tables 9-3a and 9-3b, taken from the two papers. (Note that differences may be the result of flawed benchmarks rather than flawed surveys.)

Surveys of Income Reporting in the SIPP and CPS

The understatement of certain types of income, such as interest and dividend receipts, is probably not critical for low-income populations because low-income families typically receive small amounts of income from these sources. Based on the evidence presented in Tables 9-3a and 9-3b, it appears that wages and salaries are fairly accurately reported in the CPS, although less accurately in the SIPP. But Moore et al. (1997) note that 26.2 percent (35,205,000 out of 134,135,000 total weighted cases) of the wage and salary "responses" in CPS surveys are imputed from cases where the respondent did not give an answer, replied "don't know," or refused to answer the question. They also report that 7 to 8 percent of households refuse to participate in the CPS, so imputations and imputation quality is clearly a critical element in survey quality.

The apparent accuracy of wage and salary reporting in Tables 9-3a and 9-3b does not fully resolve concerns that we have about data accuracy for low-income populations, because we do not know much about the characteristics of families that underreport their incomes. If, for example, most of the underreporting of income occurs among the disadvantaged, the findings of Moore et al. (1997) and Roemer (2000) on wage and salary reporting in the CPS and SIPP may be of little comfort. Roemer, for example, shows there are significantly more aggregate dollars reported below family income of $25,000 in the SIPP relative to the March CPS. He suggests that the SIPP does a better job than the CPS of capturing the incomes of low earners and a worse job of capturing the incomes of high earners. Learning more about the nature of underreporting would appear to be a high priority for future research.

Matching Studies of Wage and Salary Income

Roemer (2000) examines the accuracy of CPS wage and salary reports by matching CPS data to Internal Revenue Service (IRS) tax returns in selected years for the first half of the 1990s. The sample is limited to nonjoint returns and selected joint returns where each filer matches a March CPS person. The sample is restricted further to observations with no imputed wages in the CPS. He finds that in the middle of the income distribution (from $15,000 to $150,000), at least half the CPS and tax reports are within 10 percent of each other. Anywhere from 60 to 80 percent of the observations are within 15 percent of one another. Discrepancies appear much larger in the bottom and very top of the income distribution. Below $10,000 and above $150,000, at least half the observations have discrepancies exceeding 20 percent, and most are larger than that. Discrepancies are both positive and negative, though, as expected, CPS incomes tend to be larger than incomes reported on tax returns in the bottom of the income distribution, and CPS incomes tend to be smaller than incomes reported on tax returns in the top of the income distribution.

TABLE 9-3a
Ratio of SIPP and CPS March Income Supplement Aggregate Income Estimates to Independent
Aggregate Income Estimates for 1984 and 1990
  1984 1990
Source of income Indep. Estimate (billions $) SIPP (%) CPS (%) Indep. Estimate (billions $) SIPP (%) CPS (%)
Employment income:
Wages and salaries $1,820.1 91.4 97.3 $2,695.6 91.8 97.0
Self-employment 192.6 103.1 70.2 341.4 78.4 66.8
Asset income:            
Interest $244.8 48.3 56.7 $ 282.8 53.3 61.1
Dividends 59.3 65.9 51.8 126.3 46.1 31.3
Rents and royalties 19.4 211.3 95.4 44.1 102.9 87.8
Govt. transfer income:
Social Security $160.5 96.2 91.9 $ 225.5 98.3 93.0
Railroad Retirement 5.6 96.4 71.4 6.9 95.7 66.7
SSI 9.9 88.9 84.8 13.6 94.9 89.0
AFDC 13.9 83.5 78.4 19.7 70.1 71.6
Other cash welfare 2.0 135.0 120.0 2.9 86.2 80.2
Unemployment Ins. 16.3 76.1 74.8 17.7 84.2 80.2
Workers' Comp. 14.1 56.7 48.2 14.6 86.3 94.5
Vets' pens. And comp. 13.9 82.0 59.7 13.8 84.1 77.5
Retirement income:
Private pensions $65.2 63.8 57.2 $ 70.2 107.1 110.8
Federal employee pens. 20.3 98.0 84.7 30.4 73.4 82.6
Military retirement 15.6 105.1 98.1 20.4 92.2 89.2
S&L employee pens 21.9 88.1 71.7 36.1 75.1 80.1
Miscellaneous income:
Alimony $2.7 100.0 81.5 $ 2.5 116.0 124.0
Source: These figures are adapted from Coder and Scoon-Rogers (1996).

SIPP - Survey of Income and Program and Participation
CPS - Current Population Survey
AFDC - Aid to Families with Dependent Children
S&L - Savings and Loan

Beyond the cited studies, there appears to be little recent work on the accuracy of the wage and salary income in the SIPP, CPS, or related national surveys.(12) The dates of the citations for American work on this topic (there also is one Canadian study) are 1958, 1970, and 1980. In each case there seemed to be a small (on the order of 5 percent) incidence of non-reporting of wage and salary income.(13) Coder (1992) compares a restricted set of SIPP households with tax data (married couples with valid Social Security numbers who file joint returns and have positive wage and salary income in either the SIPP or on tax returns) and finds a roughly 5-percent discrepancy in the existence of wage and salary income. Moore et al. (1996) examine a sample of SIPP households working for specific employers and find that respondents sometimes drop months of wage and salary receipt over a 4-month interview cycle, though virtually all accurately reported the presence of a job during the wave.

TABLE 9-3b
Ratio of SIPP and CPS March Income Supplement Aggregate Income Estimates to Independent Aggregate
Income Estimates for 1990 and 1996
  1990 1996
Source of Income Indep. Estimate (billions $) SIPP (%) CPS (%) Indep.Estimate (billions $) SIPP (%) CPS (%)
Employment income:            
Wages and salaries $2,727.7 90.1 95.9 $3,592.3 91.0 101.9
Self-employment 333.5 85.1 68.5 475.9 69.1 52.6
Asset income:            
Interest $258.5 56.7 67.1 $ 187.1 50.2 83.8
Dividends 96.8 65.8 40.9 129.4 51.0 59.4
Rents and royalties 45.6 113.1 85.0 76.2 82.0 58.6
Govt. transfer income:
Social Security and Railroad Retirement $283.4 97.1 90.6 $ 332.2 87.9 91.7
SSI 15.3 83.1 78.9 26.5 101.4 84.2
Family assistance 18.9 75.6 74.4 19.8 76.3 67.7
Other cash welfare 2.9 81.9 85.6 3.4 114.0 80.5
Unemployment Ins. 17.9 77.5 79.9 21.6 69.4 81.6
Workers' Comp. 15.4 67.8 89.5 17.0 71.7 62.7
Vets' pens. and comp. 14.5 83.1 73.9 17.8 72.9 89.6
Retirement income:
Private pensions $68.5 91.8 98.3 $ 98.7 98.1 93.1
Federal employee pens. 30.5 75.9 82.7 38.8 75.6 80.8
Military retirement 21.4 87.4 85.6 28.3 101.6 58.2
S&L employee pens. 36.9 76.8 78.7 66.0 67.8 57.3
Source: These figures are from Roemer (2000). The independent estimates are the mean values of the implied independent estimates from the SIPP and the CPS (from Tables -2a, 2b, 3a, and 3b in Roemer, 2000).

Several other studies assess the quality of income and earnings measurement based on matching survey data with various types of administrative data. Bound and Krueger (1991) match CPS data from 1977 and 1978 with SSA earnings records and find essentially zero net bias in CPS income reports for those whose incomes did not exceed the SSA's earnings maximum cutoff. In fact, more than 10 percent of the CPS sample matched their Social Security reported earnings to the dollar, and 40 percent were within 2.5 percent. Second, Rodgers et al. (1993) examine wage records in the PSID for unionized men working fulltime at an hourly rate in one specific durable goods manufacturing firm in 1983 and 1987. These authors examine three common measures of earnings: earnings from the previous week, from the previous year, and "usual" earnings. They find annual earnings are reported fairly reliably, but this is less true for the other two measures. They also find for each measure that there is a tendency for workers with lower than average earnings to overreport and for workers with higher than average earnings to underreport.(14)

Studies of Program Participation and Transfer Income

The previous discussion focused on income reporting. There are also several studies of transfer program reporting in surveys, though the cited studies are old (dates for the citations are 1940, 1962, 1969, 1969, 1971, 1975, 1978, 1980, and 1984). These are not "complete" design studies, in that they typically focus on a sample of recipients and examine whether or not they report benefits. Complete designs also would look at nonrecipients and see if they falsely report receipt. More recent studies do the latter. Most, but not all, of these studies find fairly substantial underreporting of transfer program receipt.

Marquis and Moore (1990), using two waves of the 1994 SIPP panel, did a comprehensive study of the accuracy of reporting of transfer program participation. They discuss evidence of substantial underreporting of program participation among true program participants, on the order of 50 percent for Workers' Compensation and AFDC, 39 percent for UI and 23 percent for food stamps and Supplemental Security Income. Overall participation rates for transfer programs, however, were quite close to what would be expected from administrative controls.

Subsequent work by Moore et al. (1996) on a sample of households from Milwaukee found smaller underreporting among true recipients, and found that most error, when it exists, is due to participants' failures to report the sources of income, rather than a failure to report all months of participation.

Bollinger and David (2001) give a detailed examination to food stamp underreporting in the 1984 SIPP panel. They find that the high rate of underreporting for food stamps arises in part from failures to locate the person legally certified within the household. About half of the underreports within a household were offset by an overreport from another household member. The net effect was underreporting of food stamps receipt of 12 to 13 percent in the 1984 SIPP panel. Bollinger and David also (2001) document the important point that nonresponse and false answers are correlated across survey waves in the SIPP.

Finally, Yen and Nelson (1996) examine survey and administrative records from Washington state and find that 93 percent of the nearly 49,000 person-months are reported correctly, and net overreports roughly equal net underreports.

Assessment of Income and Transfer Program Reporting in National Surveys

Moore et al. (1997:12) conclude their survey of what is known about income measurement in surveys by stating that:

Wage and salary income response bias estimates from a wide variety of studies are generally small and without consistent sign, and indicators of unreliability (random error) are quite low. Bias estimates for transfer income amount reporting vary in magnitude but are generally negative, indicating underreporting, and random error also is an important problem.

They conclude, "in general we find that the additional data continue to support the conclusion of very little bias in survey reports of wage and salary income, and little random error as well." They conclude that studies that match administrative records of transfer programs and survey data "suggest a general tendency for transfer program income to be at least modestly--and in some instances substantially--under reported" (p. 16).

Based on our review of available assessments of income and employment measurement in national surveys, we think the above quotation is still correct. The CPS, SIPP, NLS, and PSID surveys provide:

  • Valuable information on the behavior of the low-income population (and many other issues). They have national samples, and broad and fairly accurate measures of income, and their focus on families as the unit of analysis and their ease of access greatly enhance their value.
  • The value of these data sets for evaluating welfare reform is severely limited, however. With the devolution of responsibility for TANF, the CPS and SIPP sampling frames and sample sizes mean that, at best, they can be only supplementary data sources for understanding the effects of welfare reform at the state and local levels. The apparent decline in program coverage in the CPS is also worrisome.(15)

Unemployment Insurance Wage Records Data to Measure Income and Employment for Low-Income Population

We now consider the evidence on using UI wage records to measure the income and employment status of low-income populations. UI wage records contain the earnings reported by employers (on a quarterly basis) to state UI agencies for each employee. As we noted above, UI data often are linked to information on targeted samples, such as participants in evaluations of specific welfare or training programs. Thus, the populations for which UI wage data are used to measure their income and employment varies with the particular investigation being conducted. We report on several of these studies, attempting to draw some general conclusions about the strengths and weaknesses of this data source.

Potential Strengths

Using UI wage records to measure income and employment has several potential advantages. The first is that wages reported to state UI programs are thought to include most of the wage earnings of individuals. By law, any employer paying $1,500 in wages during a calendar quarter to one or more employees is subject to a state UI tax and, hence, must report quarterly what is paid to each employee, including regular earnings, overtime, and tips and bonuses. Agricultural employers must report earnings if they have either a quarterly payroll of at least $20,000 or have hired 10 or more employees in each of 20 or more weeks during the preceding calendar year. Employers of paid household help must report wages if they pay at least $1,000 in cash wages during any quarter. In a study of the use of UI wage records to measure the post enrollment earnings of JTPA recipients, Baj et al. (1991) claim that, "Virtually all jobs that most observers would consider appropriate targets for JTPA terminee placement are covered by the UI reporting system." (More on this study follows.)

A second potential advantage of UI wage data is their presumed accuracy. Hill et al. (1999), for example, made the following, perhaps incorrect argument. "Employers are liable for taxes up to an earnings threshold. Because this threshold is quite low, there appears to be little incentive for employers to underreport earnings for most employees. Moreover, employers' reports are used to determine unemployment benefits. Discrepancies between employer and employee reports upon application of unemployment benefits can result in employer sanctions." Baj, Trott and Stevens (1991:10) write, "The accuracy of the reporting of money wages is unknown. However, relatively few corrections occur in the routine processing of individual unemployment insurance claims. In addition electronic payroll processing is increasing, electronic cross-matching capabilities are expanding, and new revenue quality control practices have been introduced. Thus, there is reason to think that the accuracy of UI data is higher than that of most self-reported sources of earnings information. Intentional underreporting of wages constitutes fraud, which is subject to sanctions. Unintentional misreporting is subject to penalty payments."

A third presumed advantage of using UI data to measure employment and wage income of individuals is its ready availability, at least for certain authorized studies, and the ability to link this data with information from other administrative or survey data sources. (Note that state UI authorities control access to UI wage records and the Social Security numbers necessary to link these data to other data sources for individuals, in order to safeguard the confidentiality of this information.) UI wage records are commonly used in state-level evaluations of welfare reform and other social programs. As Baj et al. (1991) conclude in their study of the feasibility of using UI wage data from different states to monitor the post training earnings outcomes of individuals who received training services in JTPA:

The findings from the first phase of this project indicate that JTPA and any other program [emphasis added] whose goal is to increase the employment and earnings of participants can use UI wage-record data with confidence. Obtaining post-program information from state UI systems is not only a viable option, it is far more cost-effective than the current practice of gathering this information through contact with participants. Furthermore, UI data are of higher quality than corresponding survey-based information. (p. 30)

They found, for example, that the response rate to the survey was 70.2 percent for those who were employed at termination compared to 49.6 percent for those who were not. Based on these results, they concluded that using UI wage data was preferred to obtaining data via surveys, especially given the cost of conducting surveys on this population.

To summarize, using UI wage records to measure income and employment has several potential strengths. These include the following:

  • UI data are available at the state level and can be matched to individuals in existing samples at relatively low cost (as long as Social Security numbers are available).
  • It is straightforward to do follow-up analyses on income and employment for workers that remain in the state.
  • Data are timely in that they become available with roughly a two-quarter lag.
  • For most workers the reporting of wage and salary income appears to be accurate; however, concerns are noted in the following section.

Relying on UI wage records to measure employment and income for low-income populations has two potentially serious weaknesses. The first arises because UI wage records do not cover all forms of employment. In particular, state UI systems typically do not cover the employment of self-employed persons, most independent contractors, military personnel, federal government workers, railroad employees, some part-time employees of nonprofit institutions, employees of religious orders, and some students employed by their schools. Therefore, wage earnings from these types of employment are not contained in state UI wage records.

The importance of these exemptions is unclear. In at least two places in the literature, an assertion is made that 90 percent of workers in the U.S. economy are in jobs covered by the UI system (Baj et al., 1991; Kornfeld and Bloom, 1999).(16) As noted in the following paragraphs, this statistic is challenged by the results of Blakemore et al. (1996) and Burgess et al. (1998), but even if true, it is not clear how comforting it should be if the topic of interest is low-wage labor markets. If, for example, 8 percent of all jobs are missing from UI wage records, but all 8 percent are low-income workers (which in turn is a much larger fraction of all low-income workers), the usefulness of UI data in monitoring the effects of welfare reform would be severely eroded.

Blakemore et al. (1996) and Burgess et al. (1998) report results of a fascinating study of 875 Illinois employers from 1987 that were subjected to detailed audits of their UI reports. As part of the data set, routine information such as the employment size of the firm, the statutory UI tax rate for each firm, one-digit Standard Industrial Classification codes, and UI reporting punctuality were compiled. They also have unique audit information on unreported workers, underreported total and taxable wages, and UI taxes due on these unreported wages. They also merged information on the total number of independent contractors that each firm reported to the IRS. The data set does not attempt to identify employers who are part of the underground economy.

If the results for Illinois are projected nationally,(17) employers failed to report the presence of 11.1 million UI-eligible workers and $70.6 billion in wages to state UI agencies in 1987. This is 13.6 percent of all workers. Some of the undercoverage arose from failure to report casual or part-time workers, and failure to report tips, bonuses, or other types of irregular compensation. By far the largest problem (accounting for roughly 50 percent of the discrepancy), however, was with independent contractors. Issues surrounding independent contractors are among the most vexing in tax administration and labor law. In brief (and at the risk of oversimplification), in tax law there is a somewhat subjective, 20-part test to define a worker as a regular employee or independent contractor. Elements of the test include (from IRS Publication 15A: Employer's Supplemental Tax Guide) whether the business has "behavioral control" of the worker (does the business give instructions and train the worker?); financial control (can the worker make a profit or loss, does the worker have unreimbursed business expenses, or does the worker make services available to a broad market?); and type of relationship (does the job have benefits, is it permanent, are the tasks a key aspect of the regular business of the company?). If a worker is treated as an independent contractor, an employer does not have to withhold income taxes, withhold and pay Social Security and Medicare taxes, or pay UI taxes.

It is not clear if the issues raised in the Illinois UI audits are associated strictly with independent contractors (in the technical sense) or more broadly with flexible staffing arrangements. Houseman (1999) provides a nice introduction to issues associated with flexible staffing arrangements. She reports data from the February 1997 CPS Supplement on Contingent and Alternative Work Arrangements showing that 6.7 percent of workers were "independent contractors," 1 percent were "agency temporaries," 1.6 percent were "on-call or day laborers," .6 percent were "contract company workers," and 2.6 percent were "other direct-hire temporaries." These categories compose 12.5 percent of the workforce. The use of flexible staffing arrangements appears to have been growing sharply over time, but detailed information on its growth is not available. Houseman (1999) reports that the IRS estimates it loses billions in tax revenues each year due to misclassification of employees.

Houseman (1999) also reports information on the incomes of "flexible workers" drawn from the February 1995 CPS Supplement on Contingent and Alternative Work Arrangements, matched to the March 1995 CPS. Of "regular" employees 7.5 percent had incomes below 125 percent of poverty. The corresponding figures for agency temporaries was 21.7 percent; 16.2 percent for on-call or day laborers; 10.8 percent for independent contractors; 11.5 percent for contract company workers; and 15.1 percent for other short-term direct hires. Consequently, a failure of UI data to fully capture workers in flexible staffing arrangements could be a major problem for studies that rely exclusively on UI data to measure the income and employment of low-income workers.

In many industries, employers have considerable flexibility in designating the status of workers. At least in the Illinois audit study, employers aggressively overused the independent contractor designation. In all, 45 percent of employers make some underreporting error. This includes nearly 500,000 cases in which workers were excluded erroneously, which resulted in $2.6 billion in wages being underreported. Smaller firms were estimated to underreport 14 percent of their taxable wages and 56 percent of their UI-covered workforce. In statistical models, the percentage of workers on the payroll who are independent contractors and the turnover of the firms' workers are two key explanatory variables. The effective tax rate, while related to turnover, also appears to be positively associated with compliance. The characteristics of firms that make errors on UI reports would appear to be positively correlated with the type of employers who disproportionately hire workers with low levels of human capital.

Hence, we view the Blakemore et al. (1996) and Burgess et al. (1998) studies as raising a serious concern about the coverage of UI data, and hence its suitability as the exclusive source of data with which to evaluate welfare reform. In our conclusions, we recommend that at least one additional study be conducted along the lines of the Illinois study to assess UI coverage. It is our impression, based on casual, anecdotal evidence, that the use of independent contractors has increased fairly substantially over time, and thus the work based on 1987 Illinois data may understate the problem.

The second potentially major weakness with using UI data for evaluating welfare reform is that they contain limited accompanying demographic information on individuals, and, more importantly, may not allow one to form an accurate measure of family income. In assessing the impacts of welfare reform, many argue that it is important to assess how these changes affect the well-being of children and the families in which they reside. As such, families constitute the natural "unit of analysis" for such assessments and family income often is used as an indicator of this unit's well-being.

The potential problem of relying on earnings data from UI wage records when the objective is to assess the level of family resources in studying the impact of welfare reform recently has been highlighted by Rolston (1999). Based on past research, Rolston notes that changes in individual income account for only 40 to 50 percent of exits from welfare. Thus, to have a complete picture of the effects of welfare reform, analysts need information on other economic and demographic changes occurring in the family. Given this, the problem is clear. Income as reported through UI records fails to include sources of nonemployment income and income of partners that is available to a family. Income sources that are not UI data may result in a family not receiving cash assistance or being ineligible.

The calculations from Meyer and Cancian (1998) suggest the concern raised by Rolston (1999) is economically important. Recall that Meyer and Cancian found, for example, that 5 years after leaving welfare, 64.2 percent of the women still have incomes below the poverty line, while, when considering the broader family unit, only 40.5 percent have income below the poverty line. In a related calculation, however, Primus et al. (1999) do an analysis that shows "for most single-mother families, including the income of unrelated male individuals does not materially change the picture drawn of a decline in overall disposable income between 1995 and 1997." More needs to be learned about the importance of the issue raised by Rolston in assessing the level and trend in family well-being following welfare reform.

To summarize, using UI wage records to measure income and employment has two potential weaknesses. These are as follows:

  • UI data do not cover all workers, including the self-employed, military, federal employees, independent contractors, and other employment arrangements. Some evidence shows that gaps in coverage may be significant.
  • UI data follow individuals, so one cannot get information on incomes of other family members, at least without Social Security numbers of other household members. UI data also provide limited demographic and background information on workers.
Direct Comparisons of UI Wage (and Employment) Data with Income and Employment Measures from Surveys

In this section, we review two sets of studies that make direct comparisons of income and employment measurements across several data sources for the same individual and/or family. We first consider the results of a comparison of measures of income and employment gathered from UI records in 11 states and from a survey for a sample of 42,564 adults who left JTPA programs during the 1986 program year. The findings from this study are described in Baj et al. (1991) and Baj et al. (1992).(18) Of those terminees, 27,721 responded to all three of the questions that were mandatory for "terminees" of JTPA-sponsored programs, giving an overall response rate of 65.1 percent. The investigators had access to separate data files containing UI wage records for the full sample of terminees, where the latter information was drawn from the UI systems for the 11 Midwestern states included in this study. Baj et al. (1991) drew the following conclusions about estimating the post enrollment incomes of these JTPA terminees with these two alternative data sources:

There are two major conclusions to be drawn from these analyses. First, there is ample evidence to suggest that the post-program survey data is substantially affected by the presence of non-response bias. While this conclusion is based largely on the examination of post-program employment experiences, it is suspected that the same conclusion would hold if the focus was on post-program earnings. The second conclusion is that the major source of this bias, i.e., the different post-program employment experiences of respondents and non-respondents who were employed at termination, is not addressed through current non-response adjustment procedures. The implication of these findings is that the estimates of post-program performance based on the information gathered through the post-program survey are not a true reflection of the actual post-program experiences of all JTPA terminees. (p. 35)

The survey they examined was not constructed in a way that allows comparisons of earnings reports. Instead, the presence of employment in a given quarter was compared across the survey and UI data. To do this, they sharply restrict the sample to people leaving Title II-A (JTPA) a week prior to the week containing the starting date of a fiscal quarter. For data reasons three states also were dropped from the sample.(19) This left 1,285 participants, of which 863 responded to the survey. Even with these sample restrictions, employment comparisons are not completely straightforward because UI earnings are reported for the quarter in which they are paid, not the quarter in which they are earned. With these issues in mind, Table 9-4 shows the result of the comparisons.

The diagonal elements in Table 9-4 show that 81.7 percent (72.8 percent + 8.9 percent) of the UI-survey observations are in agreement on employment status. The lower off diagonal element indicates that 5.1 percent of the matched sample report that they were unemployed during the quarter, yet they had UI earnings. One might think welfare recipients would be reluctant to report earnings, but they were only slightly (5.4 percent) more likely to not report earnings (when they had positive UI earnings) than nonrecipients (4.4 percent). This result has two potential explanations. First, respondents may have earned the UI wages reported for the quarter during the previous quarter and subsequently lost their jobs. Second, respondents may have provided inaccurate reports. Given that many of these 44 cases were employed at the time they left JTPA, Baj et al. (1991) suggest the second explanation is more likely than the first.

Comparison of the Employment Status Results for the 13-Week Program Survey and UI Wage Record Data.
Title II-A Adult Survey Population
Post Program Survey Status First Quarter UI Status Total
Employed Unemployed
Employed 628 (72.8%) 114 (13.2%) 742 (86%)
Unemployed 44 (5.1%) 77 (8.9%) 121 (14%)
Total 672 (77.9%) 191 (22.1%) 863 (100%)
Source: Baj et al. (199:39).

The upper diagonal element shows that 13.2 percent of the sample report being employed yet have no UI wages.(20) Again, it is possible that the timing of UI wage reports can partially account for this discrepancy, though most of these people were employed at the time they left JTPA, so this again is an unlikely explanation. Instead, it is likely that some of these people were employed out of state, and that others had jobs that were not covered by UI.(21)

Baj et al. (1992) update the Baj et al. (1991) calculations and provide more detail on the potential sources of discrepancy between UI data and the survey that was administered. In 1987, 11.3 percent of the sample report being unemployed for the quarter but have UI data (the corresponding figure from the earlier study was 5.1 percent), and 9.1 percent have no UI record but report they are employed (the corresponding figure from the earlier study was 13.2 percent). Baj et al. (1992) discuss three possible reasons to explain cases that claim to be employed but show no UI record.(22) The respondent may have been employed out-of-state or employed in the quarter but have wages that were not paid until the next quarter, or are employed in a job not covered by UI or where the employer fails to report UI wages.

Causes for Mismatches: Participants Employed at Termination With No UI Record for the Quarter of Termination,
Illinois Program Year 1987, Title II-A Adult Population
Reason for Mismatch Number of Cases Percent
Employed out of state 517 15.3
Self-employed 51 1.5
Federal employment 172 5.1
Within program UI record 81 2.4
1st-quarter UI record 608 18.0
2nd-quarter UI record 93 2.7
No related UI record 1,865 55.0
No UI record 1,325 39.1
Mismatched employers 540 15.9
Total 3,387 100.0
Source: Baj et al.(1992:142).

To look at these factors, the authors used data from the Illinois JTPA management information system, which gives detailed information on the employment status at termination of the program and compares that to UI status at termination. The analysis focuses on 3,387 cases (13.1 percent of the sample) that reported that JTPA participants were employed at termination, but there was no UI record for the termination quarter. Table 9-5 suggests some explanations for the mismatches (at the termination quarter). The table shows that out-of-state employment accounts for 15.3 percent of the discrepancies (line 1). Identifiable employment in uncovered (self-employed and federal appointments) sectors accounts for 6.6 percent of the discrepancy (lines 2 and 3). The next three rows of the table--the within, first-quarter, and second-quarter UI entries--are supposed to reflect timing differences in the data. Collectively these account for 23.1 percent of the discrepancy (lines 4, 5, and 6). Another 15.9 percent of the discrepancies seem to result from name mismatches between employers that could be reconciled fairly easily. This still leaves 39.1 percent of the remaining sample unexplained. Of this group of 1,325 participants, there were 1,108 different employers. The potential explanations for the discrepancy that Baj et al. (1992) offer include: errors in reporting the Social Security number on the JTPA or UI data systems, an employer's neglect of UI reporting requirements, and reporting errors by JTPA operators.

Baj et al. (1991) and Baj et al. (1992) examine the existence of employment in survey and UI data, but do not provide comparisons of earnings as their survey did not elicit information on earnings. Kornfeld and Bloom (1999) look at both employment and earnings. They describe their study as attempting "to determine whether wage records reported by employers to state unemployment insurance agencies provide a valid alternative to more costly retrospective sample surveys of individuals as the basis for measuring the impacts of employment and training programs for low-income persons" (p. 168). Kornfeld and Bloom (1999) is based on data covering 12,318 people from 12 sites around the country in which an experimental evaluation of JTPA training programs was conducted. For each site, they had access to data from both UI wage records and follow-up surveys of experimental (received JTPA services) and control (did not receive JTPA services) group members. In their analysis, they dropped observations with missing or imputed data, but included observations where earnings were recorded as zeros in the follow-up surveys.

Another, and slightly different, comparison of measurement of employment status and wage income across two different data sources for a sample of individuals who were provided access to JTPA services is found in Kornfeld and Bloom (1999). They assess how UI and survey data differ, where the latter was conducted as part of the National JTPA Study, in estimating the levels of earnings and the differences in mean earnings and employment rates between experimental and control group members, where control group members were denied access to JTPA services. Although the primary objective of the Kornfield and Bloom (1999) is how to assess how the estimated impacts of JTPA services on income and employment status vary by data source--they found virtually no difference in the estimates of impact by data source--we shall focus on what they found with respect to differences in levels of earnings across the two sources of income and employment data available to them.

Table 9-6, drawn from their study, shows that employment rates calculated from the two data sources are quite close. The discrepancies between employment data derived from their survey versus from UI records range anywhere from employment being 1 percent lower in surveys to being 11 percent more. At the same time, Kornfeld and Bloom find that the discrepancies in the level of earnings for JTPA participants are much greater. In particular, they consistently find that the level of earnings from survey data is higher than those found in UI data. The nature of this discrepancy in earnings measures is different from the one raised in Rolston (1999). Recall that Rolston is concerned that using UI wage data to measure the earnings of welfare leavers tends to be biased because such data do not include the income of other family members. Rolston argues that this lack of inclusion of the earnings of other family members is important given evidence that suggests that many exits from welfare are coincident with changes in family structure. The comparison in Table 9-6 from Kornfeld and Bloom (1999) focuses on only earnings reports for individuals. It documents systematic discrepancies of UI and survey data, where income reported by UI data is always substantially lower (in one case, by half) than that reported in survey data. Because the employment rates are comparable, Kornfeld and Bloom conclude that the earnings differences must reflect either differences in hours of work for JTPA participants who are recorded as being employed in a quarter, differences in the rate of pay recorded for this work, or both.

Comparison of Quarterly Earnings and Employment Rates from UI and Survey Data
  Treatment Earnings ($) Control Earnings ($) Treatment Employment Rate Control Employment Rate
Adult women (4,943; 18,275; 8,916)
Survey data $1,294 $1,141 59.2% 54.5%
UI data $1,048 $922 57.6% 54.1%
Ratio (survey UI) 1.23 1.24 1.03 1.01
Adult men (3,651; 13,329; 6,482)
Survey data $1,917 $1,824 65.8% 63.5%
UI data $1,456 $1,398 61.7% 60.7%
Ratio (survey/UI) 1.32 1.30 1.07 1.05
Female youth (2,113; 9,452; 4,316)
Survey data $951 $949 51.3% 50.6%
UI data $701 $700 50.6% 51.2%
Ratio (survey/UI) 1.36 1.36 1.01 0.99
Male youths without a prior arrest (1,225; 5,009; 2,442)
Survey data $1,556 $1,655 65.5% 69.3%
UI data $1,015 $1,103 61.3% 63.2%
Ratio (survey/UI) 1.53 1.50 1.07 1.10
Male youths with a prior arrest (386; 1,646; 705)
Survey data $1,282 $1,531 58.0% 61.3%
UI data $759 $760 52.8% 55.0%
Ratio (survey/UI) 1.69 2.01 1.10 1.11
Source: Kornfeld and Bloom (1999), Tables 1 and 2. Numbers after each panel heading reflect the number of persons represented (4,943 adult women) with the number of person-quarters in the treatment and control groups.

Kornfeld and Bloom (1999) also condition on whether a JTPA participant was receiving AFDC benefits during a particular quarter and find that, while the level of earnings is lower, the discrepancy between survey and UI data is strikingly similar. Survey earnings reports for adult women and female youth are 24 to 34 percent higher than reported UI earnings levels. There was also wide variation across JTPA sites in the size of earnings discrepancies between survey and UI data, but the survey always yielded larger numbers than did the UI data. The "ratio range" was 1.15 to 1.40 for adult women, 1.16 to 1.72 for adult men, 1.16 to 1.76 for female youth and even larger for male youth. Whatever the mechanism is generating these discrepancies, it exists across all 12 geographically diverse JTPA sites.

The dispersion of earnings discrepancies is very large, so the means mask large variations, across earnings reports. We do not know, of course, which measure of earnings more closely resembles the truth. If survey data tend to be more accurate, however, the discrepancies shown in Table 9-7 would be reason for one to give pause in using UI data to assess the economic well-being of families following welfare reform. It shows that more than 10 percent of women and 20 percent of men have discrepancies that exceed $1,000 in a quarter.(23)

Distribution of Mean Individual-Level Differences Between Survey and UI-Reported Quarterly Earnings
Mean Survey
- Mean UI
Adult Women Adult Men Female Youth Male Youth No Arrest Male Youth With Arrest
$2,001 + 3.5 9.9 2.0 8.7 9.3
1,001-2,000 7.7 11.1 7.7 15.0 16.1
601-1,000 7.9 9.4 9.1 12.4 13.2
401-600 6.8 5.9 8.3 8.2 7.3
201-400 10.4 8.6 13.0 11.9 9.6
1-200 17.3 12.7 20.6 13.2 14.8
0 14.3 8.1 10.0 3.8 5.7
-$1 - -$200 16.2 13.2 17.3 10.8 11.1
-201- -400 6.2 6.2 5.3 5.6 6.0
-401 - -600 3.3 4.2 3.3 3.4 1.6
-601 - -1,000 3.4 4.6 2.0 4.5 3.4
-1,001 - -2,000 2.3 4.1 1.1 1.7 1.6
-2,001 - 0.9 2.0 0.2 0.8 0.5
Mean diff ($) 228 451 256 547 605
Source: Table 5 from Kornfeld and Bloom (1999).

Kornfeld and Bloom (1999) also examine those JTPA participants for whom they found positive earnings in one data source but not the other. "When only the survey reported employment (and UI data presumably missed it), mean earnings were more than twice what they were when only UI data reported employment (and the surveys presumably missed it). This suggests that surveys are more likely to miss 'low earnings' quarters, perhaps because respondents forget about minor, or short-term, jobs. In contrast, UI data appear more likely to miss 'average earnings' quarters--where mean earnings are similar to when both data sources report employment. This might be due to random errors in matching UI wage records, out-of-state jobs, jobs that are not covered by UI, and/or earnings that are 'off the books.' (p. 184)

The above-noted discrepancies could arise between the data sources because some jobs are uncovered, some jobs may be located out of state, some payments may go unreported because of unintentional or intentional noncompliance, or Social Security numbers may be misreported. To provide further insight, Kornfeld and Bloom compare the earnings reports that employers make about their employees to state UI systems with those they make to the IRS. Although employers have an incentive to underreport earnings to the UI system (and hence avoid paying UI taxes), they have no incentive to conceal earnings when reporting to the IRS, because wages are a business expense that will lower tax payments. The sample for doing this comparison is smaller than the previous samples because each observation needs to be there for 4 consecutive quarters, corresponding to the calendar year. The ratio of mean IRS earnings to mean UI earnings ranged from 1.14 for adult women to 1.25 for male youth, so UI wage records clearly are missing earnings from some jobs.

Based on their analysis, Kornfeld and Bloom draw the following conclusions from their investigation.(24) Approximately half of the survey-UI earnings difference reflects earnings that are missing from UI wage records (by making use of the IRS data). Out-of-state jobs do not explain why UI wage records reported lower earnings than sample surveys. Uncovered jobs account for only a small part of the survey/UI earnings difference. There is little evidence consistent with recall bias in the survey data. There is no evidence that large survey discrepancies result from survey reports of "unusually good" jobs or weird reports of industry of employment. Survey discrepancies also do not appear to be driven by overtime or odd pay periods.

From the direct comparisons of the data sources used to measure income and employment status found in the studies reviewed above, we draw the following tentative conclusions about the differences between using survey versus UI data:

  • Earnings in UI data generally appear be lower than earnings reported in survey data. The UI data may miss earnings from second or casual jobs. At the same time, surveys may overstate earnings. Smith (1997) provides a thorough comparison of a dataset of JTPA-eligible nonparticipants at 4 of the 16 JTPA training centers with data from the SIPP. He provides evidence that his JTPA survey may be biased upward, due to nonresponse bias (lower earners are not covered in the survey) and upward-biased measures of overtime and usual hours in the survey.
  • Employment rates derived from UI data are comparable to lower than those that result from survey data. We expect UI-based employment rates to be lower because of coverage problems with flexible workers and independent contractors. Surveys also suffer from nonresponse, however, so undercounts in both data sources may be comparable, making the UI-based rates similar to survey-based rates.
Overall Assessment of Using UI Administrative Data to Measure Income and Employment

Our review of the literature has pointed to three critical concerns that arise with using UI data to measure the earnings and employment of low-income and welfare-eligible populations. The concerns are as follows:

  • First, earnings are available only for individuals, while changes in family composition upon exit from welfare have been shown to have a large bearing on economic well-being. UI data do not allow us to track these changes.
  • Second, there appears to be a substantial problem with some workers being classified as independent contractors, and hence not entering the UI system. Overall gaps in coverage appear to be at least 13 percent and may be significantly higher.
  • Third, even when wages are reported, they appear to be understated by at least 11 to 14 percent (based on the Kornfeld and Bloom comparisons with IRS data) and perhaps more than twice that (based on their comparisons with survey data). Smith (1997) shows, however, that survey responses also can be biased upward.

Data from Federal (And State) Income Tax Returns

Although not widely used in past evaluations, wage and salary data from federal and state income tax returns represent an alternative to UI data for measuring the income and employment of low-income populations. Here we outline the potential strengths and weaknesses of these data sources and briefly summarize a recent comparison of UI wage and tax return data for a disadvantaged population drawn from the AFDC caseload in California.

Potential Strengths

Compared to using surveys or UI records, using tax return data for measuring the income and employment has at least two potential advantages. These are the following:

The data are accurate. Taxpayers provide information under the threat of audit and there is third-party information reporting, so employers as well as recipients are reporting wage and salary information.

The definition of income that is reported is broader than that provided by unemployment insurance data, including, most importantly, self-employment income and in cases where a person is married and they file a joint return, spousal income.(25)


Several potential weaknesses are associated with using tax returns data to measure income and employment. We summarize several: Note that some of these weaknesses apply to the general population, while others are more relevant for low-income populations. First, the access by researchers to tax returns data is extremely limited and constrained because of Section 6103 of the Internal Revenue Code. Section 6103 explicitly states that tax data cannot be released, except to organizations specifically designated in Section 6103(j). The exceptions are the Department of Commerce, but only as it relates to the Census and National Income Accounts, the Federal Trade Commission, the Department of the Treasury, and the Department of Agriculture (for conducting the Census of Agriculture). Penalties for unauthorized disclosure are severe, including jail terms of up to 5 years.

Second, tax return data also contain only limited information on demographic characteristics of taxpayers. For example, the tax system does not collect information on the race or education of tax filers.

Third, tax-filing units differ from both families and individuals. Married couples can file either a joint return or separate returns (as "married filing separate"). Cohabiting couples, even if fully sharing resources, will file separate returns as individuals or head of household (generally meaning the filer is a single parent with dependents). In general we believe families pool resources so families are the best unit of analysis for assessing economic well-being. Hence, case units probably are the most useful unit of analysis.

Fourth, there also are differences between tax return data and other data sources in the frequency of reporting. Unemployment insurance wages are reported quarterly. Transfer program information is reported monthly. Tax returns are filed annually. Because shorter periods can be aggregated into longer ones and there can be major changes in family composition over time, the annual frequency of tax reporting is less appealing than monthly or quarterly reporting in other data sets. To the extent that family structure changes over these intervals, problems may arise when trying to link different data sets to assess well-being.

A fifth concern relates to the incidence and accuracy of tax filing by individuals and households, especially among low-income populations. This concern takes two forms: (1) whether people file any tax return, and (2) if they file, whether they report all sources of income to the IRS (or state taxing authorities).(26) We consider each in turn.

If large fractions of low-income taxpayers do not file tax returns, then tax return data have very limited value. Unfortunately, there is not a lot of information on the filing propensities of people with low income. Information from the early 1990s (Scholz, 1994) suggests that 14 to 20 percent of those entitled to the earned income tax credit at the time failed to receive it, meaning that they failed to file tax returns.(27) Later, we discuss one recent study on the tax filing propensities of a low-income population that sheds some preliminary light on this issue.

Among filing units, it is also possible that their members do not report all of their sources of income on their tax returns. For example, individuals may fail to file income received as independent contractors. Although firms or individuals who use independent contractors are obligated to report payments to such contractors to the IRS, failures to do this generally are difficult to detect. Again, we know little about the incidence of underreporting of various income sources for low-income populations.

To summarize, using tax return data to measure income and employment has several potential weaknesses. These are the following:

  • Gaining access to tax returns is difficult.
  • The data provide limited information on demographic and other characteristics.
  • Some low-income workers may not file, despite being eligible for the earned income tax credit, or may not report all their income.
Comparison of Income Reporting from UI Wage and IRS Tax Filings Data for a Low-Income Population

In a recent study of the EITC for a sample of assistance units on the California caseload, Hill et al. (1999) compared UI wage data with linked data from the sample members' IRS tax returns. The study used data from the California Work Pays Demonstration Project (CWPDP), which was conducted in four counties (Alameda, Los Angeles, San Bernardino, and San Joaquin) starting in 1992. The data consisted of two sets of assistance units drawn from the caseloads in these counties. One set, which is used for the sample in Table 9-8, consisted of a random sample drawn from a caseload at a particular date in 1992. Although this sample is representative of the caseload at that time, recall that the study by Bane and Ellwood (1983) showed that random samples from the existing caseload of AFDC are disproportionately made up of assistance units that are "welfare dependent."

The second set of assistance units, which is the sample used for Table 9-9, is a random sample of new entrants to the caseload in 1993. Bane and Ellwood (1983) and others have found that a significant proportion of new entrants remain on welfare for only a relatively short period.(28) Furthermore, Gritz and MaCurdy (1991) find that most new entrants exit from AFDC to employment. We also break both samples up into female-headed households (Aid to Families with Dependent Children-Family Group AFDC-FG cases) and two-parent households (AFDC-U). We report on annual earnings information for the year after the samples were drawn, that is, 1993 for the random sample of the caseload and 1994 for the new entrants sample.(29)

The first two lines of each panel of each table give estimates of the employment rates of each sample of AFDC recipients. As expected, employment rates of the point-in-time caseload (Table 9-8) are lower than the sample of new entrants (Table 9-9). Employment rates of one-parent cases (AFDC-FG) are lower than the employment rates of two-parent cases (Aid to Families with Dependent Children-Unemployed Parent [AFDC-U]). What is striking and not necessarily expected, however, is that the implied employment rates using UI data and using tax return data are nearly identical. From Table 9-8, employment rates of the point-in-time AFDC-FG caseload were 26 percent using UI data and 22 percent using tax return data. The corresponding rates for AFDC-U cases were 31 percent for both data sources. Employment rates were 37 percent using UI data for the new entrant sample and 33 percent using tax returns. Employment rates were 48 percent using UI data for the AFDC-U new entrants and 49 percent using tax returns.

Random Sample from Caseload in 1992, Information for Tax Year 1993
  Those Filing Tax Returns Full Sample
AFDC-FG cases
% of households with UI earnings   26
% of households that filed tax returns   22
Average UI earnings of adults in household $4,514 $1,242
Average adjusted gross earnings on tax returns $10,589 $2,378
Average wage & salary earnings on tax returns (Line 7) $9,748 $2,189
Average income reported to AFDC $1,222 $360
% of households with No UI earnings, but filed tax return   5.89
% of households with UI earnings, but filed no tax return   11.41
% of households for which AGI < UI wages   12.61
% of households for which AGI = UI wages   78.59
% of households for which AGI > UI wages   8.80
% of households for which AGI < UI wages, for UI wages > 0   3.39
% of households for which AGI > UI wages, for AGI > 0   40.47
Self-employment income reported on tax returns    
Fraction of filers reporting any 0.06  
Average amount reported $357  
AFDC-U Cases    
% of households with UI earnings   31
% of households that filed tax returns   31
Average UI earnings of adults in household $5,223 $1,792
Average adjusted gross earnings on tax returns $8,482 $2,595
Average wage & salary earnings on tax returns (line 7) $7,554 $2,311
Average income reported to AFDC $2,513 $894
% of households with no UI earnings, but filed tax return   7.07
% of households with UI earnings, but filed no tax return   8.21
% of households for which AGI < UI wages   9.26
% of households for which AGI = UI wages   78.39
% of households for which AGI > UI wages   12.05
% of households for which AGI < UI Wages, for UI Wages > 0   3.97
% of households for which AGI > UI wages, for AGI > 0   40.53
Self-employment income reported on tax returns    
Fraction of filers reporting any 0.12  
Average amount reported $562  
Source: Hill et al. (1999)

Although tax return data and UI data would give similar perspectives about employment patterns of the 4-county California sample, it is clear that each sample covers workers that the other misses. For example, in the top panel of Table 9-8 (AFDC-FG cases from the point-in-time sample), roughly one-quarter of people (5.89/22) who filed tax returns had no corresponding UI record.(30) Over 40 percent (11.41/26) of those with positive UI earnings did not file taxes.(31) Of those with both UI and tax return earnings, more than 40 percent reported more earnings on tax returns than would be expected based on UI data. Similar figures apply to each other group, though for AFDC-U cases, only about 20 percent of the cases with UI earnings do not file tax returns.

Random Sample of New Entrants to AFDC Caseload in 1993, Information for Tax Year 1994
  Those Filing Tax Returns Full Sample
AFDC-FG cases
% of households with UI earnings   37
% of households that filed tax returns   33
Average UI earnings of adults in household $6,769 $2,868
Average adjusted gross earnings on tax returns $13,185 $4,342
Average wage & salary earnings on tax returns (Line 7) $12,575 $4,141
Average income reported to AFDC $1,625 $709
% of households with no UI earnings, but filed tax return   8.34
% of households with UI earnings, but filed no tax return   13.55
% of households for which AGI < UI wages   15.69
% of households for which AGI = UI wages   71.31
% of households for which AGI > UI wages   13.00
% of households for which AGI < UI wages, for UI wages > 0   4.21
% of households for which AGI > UI wages, for AGI > 0   39.88
Self-employment income reported on tax returns    
Fraction of filers reporting any 0.04  
Average amount reported $95  
AFDC-U cases    
% of households with UI earnings   48
% of households that filed tax returns   49
Average UI earnings of adults in household $8,516 $5,138
Average adjusted gross earnings on tax returns $12,970 $6,360
Average wage & salary earnings on tax returns (Line 7) $11,421 $5,601
Average income reported to AFDC $3,264 $1,831
% of households with no UI earnings, but filed tax return   10.45
% of households with UI earnings, but filed no tax return   7.94
% of households for which AGI < UI wages   11.77
% of households for which AGI = UI wages   64.71
% of households for which AGI > UI wages   23.51
% of households for which AGI < UI wages, for UI Wages > 0   6.12
% of households for which AGI > UI wages, for AGI > 0   46.83
Self-employment income reported on tax returns    
Fraction of filers reporting any 0.11  
Average amount reported $512  
Source: Hill et al. (1999)

The fact that across all four groups (two samples, and AFDC-FG and AFDC-U cases), tax return income exceeded UI income in at least 40 percent of the cases with positive earnings from both sources, is consistent with households from this welfare-based population having earnings that are not from covered employment. The fact does not seem to be explained by people leaving welfare (through changes in family structure). Among AFDC-FG cases, only 1 to 13 percent of these households had no months on AFDC during the tax reference year and between 56 and 83 percent were on welfare for 9 to 12 months during that year. There is also little evidence that self-employment income plays an important role in earnings differences between tax return and UI income.

Based on comparisons between UI and tax return data, we offer several tentative conclusions:

  • Tax return and UI data appear to give very similar information on employment rates of the four-county California caseload. There are good reasons, however, to think that both data sources will lead to underestimates. UI data will miss independent contractors and possibly other "flexible workers." Tax return data will miss families who do not file tax returns.
  • The two data sources appear highly complementary . Each appears to capture a significant number of families that the other misses. Using them together, therefore, should result in more accurate measures of the employment experiences of the caseload than using either separately.
  • Tax return data have a broader definition of income and, if the household unit is married, will cover both spouses and hence are likely to offer more accurate income information.
  • UI data are much easier to access than tax returns.


Taking into account all of the features of a data source, including not only its accuracy but also its cost and ease of access, it appears that no single source can be declared "preferred." The inability to find a preferred data source is inevitable given the differences in the desired uses of data, the constraints imposed by budgets for data collection, and the access limitations to data. The fact that UI wage data are inexpensive, timely to obtain, and available at the state level, for example, implies that they will continue to be a focal data set for state-level evaluations of welfare reform. But our review raises a number of serious questions about UI data. In the remainder of this paper, we highlight selected issues that we believe need further attention in the hopes of encouraging future research on at least some of them.

Certain questions related to welfare reform can only be answered with nationally representative data sets, such as the CPS or SIPP. While Moore et al. (1990) and Roemer (1999a) conclude that income, especially labor earnings, are measured well in the CPS and SIPP, there are, in our view, several important questions that remain with respect to income and employment measurements for low-income populations with national surveys. The questions are as follows:

  • First, none of these studies, to our knowledge, focus on the reporting of income by disadvantaged, welfare-eligible, and/or welfare-prone populations.
  • Second, as noted in Primus et al. (1999), participation in welfare programs is underreported in the CPS (and the SIPP). Moreover, this underreporting appears to have increased over time. This is a troubling problem, especially as one looks to the future when TANF programs become state specific, with different names.

Recommendation 1: We would like to see further work on the sources of antipoverty program underreporting and its origins in nationally representative survey data.

Plans are under way for some of the needed work. Professor Hotz is a principal investigator on a project recently approved by the U.S. Census Bureau to match data from UI wage records and administrative data on AFDC/TANF participation for the California subsamples of several waves of the SIPP.(32) The work of this project should yield some more recent information on both the welfare participation underreporting and income reporting issues. This study--or comparable ones done with matches of the SIPP with administrative data for the subsamples from other states--also may provide some insight into the impact of changes in family structure on income reporting for welfare leavers by exploiting the (limited) panel structure of the SIPP.

Further research also is needed on the use of UI wage records to measure the income of low-income and welfare-prone populations. While the Kornfeld and Bloom (1999) evaluation suggested that UI wage data and survey data produced similar estimates of the impact of a social program (i.e., JTPA-funded training programs) on earnings and employment, their study also found that average earnings of JTPA-eligible individuals were consistently lower than those based on survey data. Furthermore, the study by Hill et al. (1999) also found that UI wage data produced substantially lower estimates of earnings than did tax returns data for a welfare-based population drawn from the California AFDC caseload. Learning more about the quality of this data source for measuring income is extremely important because UI wage data presumably will continue to be a core resource in state and local evaluations of the effects of welfare reform.

Several issues related to UI wage data appear to need further scrutiny. First, the studies by Burgess and his coauthors raises important concerns about the "coverage" of UI and tax returns, particularly for the low-income population.

Recommendation 2: It would be extremely useful to follow the helpful lead of the various Burgess studies to closely examine the coverage and trends in coverage of low-income populations with UI data. Such an examination could be aided by using a match of UI data with respondents in a national survey, such as the SIPP, so that one could learn more about the demographic characteristics of individuals (and households) that report labor market earnings on a survey that are not recorded in UI wage records data.

  • States may be able to augment UI data used for evaluation of welfare reform by collecting supplemental information on the degree to which employers are designating workers as independent contractors. Additional work at the state level to assess the overall coverage of UI data also would be valuable.

Second, more work is needed to understand the extent to which UI wage data provide a misleading measure of the earnings available to low-income households .This problem arises in short- and long-term follow-up analyses of earnings for welfare samples drawn from state caseloads. One can use UI data to measure subsequent earnings for individuals who were in assistance units as long as they remain on welfare. However, as noted by Rolston (1999), one may not be able to accurately measure household income after assistance units leave the rolls because it is difficult to keep track of the identities of household members. The evidence provided in the Meyer and Cancian (1998) and Hill et al. (1999) studies suggest that this may be a serious problem.

Recommendation 3: To learn more about family well-being, it will be necessary to continue to rely on targeted follow-up surveys to monitor samples of welfare leavers. Unfortunately surveys are expensive. We recommend that a pilot study be undertaken to devise a survey that is designed just to obtain Social Security numbers of other adults in a household, which can then be used to obtain UI wage earnings for these family members.

  • It might be useful for state TANF agencies to analyze the methods that their JTPA agencies use to gather follow-up earnings data on terminees from their programs. Such follow-up assessments are required under JTPA, and many states have contracted with firms and/or universities to gather these follow-up data.
  • Tax returns data also may be useful to learn more about whether the discrepancies between UI wage data and income measures from tax returns noted in that study are the result of differences in family composition and the "composition" of income reported on tax returns.

A third issue relates to the possibility that wage earnings are missed because individuals move out of the state from which UI wage data are drawn or because workers earn part of their income in other states. Again, comparisons of UI wage data with data from federal tax returns may help us to assess the importance of this problem and, more importantly, the biases that it imparts on measures of individual and household income. To learn more, it may be useful to take a closer look at what is known about the interstate mobility of disadvantaged and welfare-prone populations, such as the work done on movements of welfare populations in response to "welfare magnets," as in Meyer (1999) and the citations therein, and the implications this mobility has for the coverage of low-income workers in UI data.


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2001 Estimation with response error and non-response: Food stamp participation in the SIPP. Journal of Business and Economic Statistics 19(a):129-141.
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1.  Often these samples are gathered in the context of evaluations of specific welfare or training programs.

2.  See, for example, studies by Primus et al. (1999), Cancian et al. (1999), and Rolston (1999) for a flavor of how this debate hinges on measurement issues.

3.  Several earlier studies compared employment measures for low-income populations across alternative data sources, most notably the study by Greenberg and Halsey (1983) with data from the SIME/DIME Experiments. Given changes over time in such things as Unemployment Insurance coverage and response rates in surveys, we focus on the most recent studies available to maximize the relevance of our findings for the measurement of these outcomes for current and future studies.

4.  Each wave of the SIPP is a longitudinal survey with between 2.5 and 4 years of data on the residents of a sample housing unit. Surveys to these respondents are asked every 4 months.

5.  The NLSY79 focuses on the original respondent, but it gathers a considerable amount of information on the respondent's spouse and/or cohabiting partner.

6.  Previously, earnings had to be reported in weekly amounts, and amounts over $2,000 per week were truncated. Now earnings can be reported over any interval and the data (to Bureau of Labor Statistics) are not truncated. Studies that use repeated cross-sections of the CPS that span 1994 risk misinterpreting results if they fail to account for the redesign. Polivka provides adjustment factors for earnings (at the 10th, median, and 90th percentiles) reported prior to 1994 to make the series comparable. She also shows that top-coded values that are imputed using a Pareto distribution do a good job of fitting the distribution of data that are not top coded.

7.  See Bogen et al. (1997) and Bogen (1998).

8.  Roemer (1999) suggests the reduction in coverage could be related to PRWORA--the March 1997 survey did not use state-specific labels for TANF benefits in 14 states that had abolished AFDC. Benefit estimates were 4.5 percentage points lower than the benchmark in states that had abolished AFDC than in states that had not. The delivery mechanism of benefits in some circumstances (for example, through employers), an enhanced sense of stigma, and caseload reductions that exacerbate recall errors may also contribute to underreporting.

9.  Primus et al. adjust the CPS data proportionately to account for the decline in benefits over time, but the value of this adjustment depends on the patterns of discrepancies in the data. Unfortunately, we know little about the factors associated with the underrepresentation of program participants in the CPS or the SIPP.

10.  Shroder and Martin (1996), for example, show subsidized housing (broadly defined) is badly reported on surveys, including the American Housing Survey (and presumably the SIPP). An underlying problem is that the phrase "public housing" means different things to different people, ranging from only projects to any kind of subsidized housing.

11.  There are no comprehensive assessments of the quality of income and employment measurements for either the NLSY79 or the PSID. Roemer (1999) and Nelson et al. (1998) update the CPS calculations to 1996. Roemer (2000) also provides a nice discussion of adjustments that need to be made to compare aggregate SIPP and CPS totals to National Income and Product Account data.

12.  Abraham et al. (1998) conclude, "There is some evidence that CPS hours worked are overreported, that this overreporting may have worsened over time. Given the paucity of data on hours worked, we view our conclusions on this subject as suggestive rather than definitive" (p. 319).

13.  Moore et al. (1997) also provide a brief discussion of income data collected as part of the Gary Negative Income Tax Experiment (from the late 1960s and early 1970s). They note that the income data in this experiment, gathered through surveys of respondents, was not very reliable. In the Seattle and Denver Income Maintenance experiments, there was evidence of statistically significant underreporting of wage and salary amounts. But the magnitude of underreporting was only 2 to 4 percent, leading Halsey (1978) to conclude that they were not large enough to be important economically. The correlation between administrator records and reported values was .9, also indicating high reliability.

14.  They also examine several measurement error assumptions that challenge standard practice in empirical economics.

15.  We were not able to find a comparable study of trends in program participation for the SIPP. U.S. Department of Commerce, Bureau of the Census (1998) compiles summaries of an extensive, long-running research program on SIPP quality. It starts with an overview of SIPP design, and then describes sample selection, data collection, nonresponse and measurement error, data preparation, weighting, sampling error, evaluation of estimates from the 1984 to 1993 panels, and the 1996 redesign of the SIPP.

16.  Despite our efforts, we have not found documentation for this particular statistic.

17.  Clearly strong assumptions are needed to make this projection, but the size and industrial composition of the Illinois sample is not sharply different from national statistics. The Illinois UI system is typical of what is observed nationally, and, if anything, Midwestern states tend to have lower rates of income and payroll tax noncompliance than other states.

18.  Stevens et al. (1994) is a similar study that focuses on Maryland.

19.  Title II-A is the nation's employment and training program for low-income adults and out-of-school youth with significant barriers to employment.

20.  Although this figure would appear to be comparable to Blakemore et al. (1996), it actually suggests a much smaller gap in coverage. This study found a 13.6-percent gap for the total workforce, while the Baj et al. (1991) study found that the corresponding gap is 13.2 percent for a JTPA sample.

21.  Tabulations from the Wisconsin Department of Workforce Development (1999) suggest that as many as 16 percent (60/375) of a small sample of recent welfare recipients have missing employment episodes in UI data.

22.  They do not discuss independent contractor issues that are the focus of Blakemore et al. (1996). Instead, these would be grouped into the last category.

23.  All zeros in the table correspond to people without earnings in both data sets. No observations with positive earnings agreed exactly.

24.  A third comparative study assessing differences in income and employment across data sources was conducted by the Rockefeller Institute of Government (Primus et al., 1999). This study summarizes (in its Table 2) six studies that compare UI data and survey data. The studies include five that we do not review in this paper and those from Kornfeld and Bloom (1999), which we do review. The results from the Rockefeller Institute study differ somewhat from the Kornfeld and Bloom results. One of two other studies finds UI earnings are lower than survey data (though one found them nearly identical), like Kornfeld and Bloom. Several other studies suggest that employment rates from surveys were significantly higher (on the order of 20 percent) than employment rates from UI data, unlike the Kornfeld and Bloom evidence. We have not assessed the quality of these other studies.

25.  It also will include interest and dividend income, farm income, capital gains and losses, and gambling winnings, and indicate recipients of government transfers and Social Security benefits.

26.  If one is just interested in enumerating the population (as opposed to knowing incomes associated with families and individuals within the population), IRS data appear to be comprehensive. Sailer and Weber (1999) report that the IRS population count is 95.4 percent of the Census population count. The consistency is fairly good across gender, age, and state. Unfortunately, for many of the people enumerated, the IRS does not know anything about them other than that they exist.

27.  Cilke (1998) uses a CPS-IRS exact match file to examine the characteristics of people who are not required to file tax returns and actually did not file tax returns. The entire paper is presented as proportions, however, so it does not provide information on the absolute number of low-income families with earnings who fail to file.

28.  For example, Bane and Ellwood (1983) estimate that 65 percent of new entrants leave the caseload in 2 years.

29.  Through an interagency agreement between the California Department of Social Services (CDSS) and the state's taxing authority, the Franchise Tax Board (FTB), UI wages and wages and adjusted gross income (AGI) from tax returns were merged by the FTB. The researchers were able to specify computer runs on these merged files. Assistance units in the study could, and did, leave AFDC after they were enrolled in this study. Nonetheless, wage and income data from UI records and tax returns were available for all of the original assistance units in the CWPDP study.

30.  We took great care in the analysis to make sure the comparison samples did not have changes in marital status and had a full four quarters of UI data (including zero quarters).

31.  Households with low earnings are not obligated to file tax returns. For example, a married couple, is not required to file if their income is below the standard deduction and two exemptions ($12,200 in 1997), regardless of how many children they have. Hill et al. (1999) also show that most of these non filers had very low levels of UI earnings ($2,500 or less in annual covered earnings).

32.  The other investigators on this project are in collaboration with David Card, Andrew Hildreth, and Michael Clune at University of California-Berkeley and Robert Schoeni at RAND.

Administrative Data on the Well-Being of Children On and Off Welfare

The Personal Responsibility and Work Opportunity Reconciliation Act of 1996 (PRWORA) significantly altered the way the United States provides assistance to its neediest citizens. The act eliminated the federal entitlement to services that existed under the Aid to Families with Dependent Children (AFDC) program and replaced it with the block grant program Temporary Assistance for

Needy Families (TANF). TANF provides temporary financial assistance while recipients make the mandatory transition from welfare to work. These efforts to move adult recipients toward self-sufficiency may have consequences for the well-being of their children (Collins and Aber, 1996; Zaslow et al., 1998). Potential implications include changes in children's health, safety, education, and social competence.

Whether these consequences are positive or negative depends on how reforms impact family income, parenting practices or parental stress, and access to services (Collins and Aber, 1996; Zaslow et al., 1998). For example, economic hardship related to loss of benefits or other supports may complicate families' efforts to provide supportive environments for their children (Knitzer and Bernard, 1997). Increased parental stress related to economic, employment, or child care difficulties also may lead to poor parent/child interactions or exacerbate existing mental health conditions such as depression or substance abuse, thereby increasing the risk of negative outcomes (Knitzer and Bernard, 1997; Zaslow et al., 1998). Child health and safety also might be compromised if TANF alters access to non-TANF services such as health and childcare.

In contrast, positive changes in these areas may be beneficial to children and families (Collins and Aber, 1996; Zaslow et al., 1998). Specifically, policy changes might lead to improved outcomes for children whose parents become employed successfully. Children might benefit from access to more comprehensive health care, opportunities to observe parents coping effectively with work demands, higher educational aspirations and achievement, and exposure to parental peers who are engaged in more prosocial activities.

PRWORA currently is being praised by some, and criticized by others, for moving nearly 1.7 million recipients from welfare to work. However, until the impact of these reforms on child well-being is known, such celebrations are premature. Even if reforms succeed in moving mothers from welfare to work, if this in turn has negative consequences for children, its effectiveness will need to be reevaluated in light of these costs.

Prior to passage of welfare-to-work legislation, more than 40 states received waivers to experiment with welfare-to-work programs. Experimental evaluations of these initiatives now under way will provide valuable information about possible effects of certain aspects of PRWORA. Most, however, focus primarily on adult outcomes such as changes in income, employment, family formation, and attitude, and cover only a limited number of child outcomes (Research Forum on Children, Families, and the New Federalism, 1999; Yaffe, 1998). Additionally, those child outcomes "typically lack depth and uniformity" (Yaffe, 1998). Several states (e.g., Connecticut, Florida, Iowa, and Minnesota) are looking at child outcomes resulting from parental participation in AFDC waiver conditions that approximate TANF and that eventually will yield child-level outcome data. The Administration for Children and Families (ACF), Office of the Assistant Secretary for Planning and Evaluation (ASPE) in the Department of Health and Human Services has implemented the Project on State-Level Child Outcomes to assist waiver states in using administrative data to expand these child outcome measures and make them comparable across states. Although this uniformity will allow for the assessment of different state models, their utility is limited by their small sample sizes. In particular, small sample sizes make subgroup comparisons difficult and prohibit evaluation of rare events such as foster care placement or child mortality. Current evaluations of state welfare-to-work initiatives under PRWORA suffer from similar limitations. Given these limitations, researchers increasingly are turning to administrative data to try to gauge the relationship between receiving income assistance services under TANF and child well-being.

The purpose of this paper is to assist researchers in addressing the following questions:

  • What are the key policy issues and related domains of child well-being associated with the impact of PRWORA?
  • What are the opportunities and challenges in using administrative data to measure the impact of PRWORA on children?

Key Policy Issues and Child Well-Being

Data about social welfare programs for children are fragmented and incomplete and lack a cohesive framework or policy ownership. Given this, it is important to identify the priority of policy issues and the specific research questions about PRWORA and child well-being when research is conducted. These early steps will assist in then defining the population of interest, the appropriate methodology, and the data sources available (National Research Council, 1999).

Key Policy Issues

Several key policy issues have been identified for states examining the impact of welfare reform (Child Trends, 2000a; National Conference of State Legislators, 1999). First is the need to understand the specific components of an individual state's welfare reform program. This is especially important given the diversity across states in the implementation of PRWORA and corresponding use of TANF funds. Specific components left up to the states include mandated work, time limits, and sanctions. The implementation of these components may impact the outcomes for families and children; for example, mandated work without childcare may lead to cases of child neglect. Also needed is an examination of the interaction between welfare reform--that is, the change from AFDC to TANF--and other social welfare programs such as Medicaid, Children's Health Insurance Program (CHIP), the Food Stamps Program, Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), Child Protective Services, and foster care, which also may be undergoing changes. Although efforts over the past two decades have sought to delink AFDC to other social welfare programs, in practice and for individual families these programs remain interwoven. The eligibility, availability, and accessibility of these supplemental services can impact family outcomes in conjunction with, or separate from, TANF enrollment, exit, and reentry. Understanding TANF's impact on children requires thoughtful specification of the child well-being outcomes TANF might influence so that investigators can fashion data collection efforts to maximize measurement of the predicted impact. Research about child outcomes can guide questions about current implementation and future programming using the flexibility of TANF funds.

Domains of Child Well-Being

Child Trends, a nonprofit, nonpartisan research center, along with colleagues at the federal and state levels participating in the Project on State-Level Child Outcomes, have offered a conceptual framework that organizes and clarifies the pathways through which welfare reform can impact children. This framework is displayed in Figure 10-1.

Figure 10-1 Child Trends, Inc. conceptual framework for child well-being--how welfare policies might affect children

In this conceptual framework, state policies regarding PRWORA will directly effect family income, employment, family formation outcomes such as marriage or out-of-wedlock births, and attitudes of the adults in the case, such as self-esteem or feelings of being in control of one's life. Changes in these four areas will further influence the family and child's psychological well-being, the stability of the child's home, involvement of the absent parent (through child support enforcement), use of health and human services, and consumption of goods and services spent on the child. As a single mother copes with job training, new employment, transportation problems, childcare dilemmas, and confusion about eligibility for supplemental services, the child's physical home environment, relationship to the parent, and time with the parent can be expected to change. Such changes in the capacity and day-to-day schedule of maternal caretaking very likely will, in turn, impact the child's educational experiences, health and safety, and social and emotional adjustment.

This conceptual framework evinces the multifaceted ways that welfare programs can influence a range of child outcomes. The framework offers a critical organizing logic about how welfare reform impacts children, one that can be used to shape research agendas and frame research questions. The framework was developed to guide survey researchers studying about welfare, but it also serves as a useful reference for the inclusion of administrative data in research initiatives. Administrative data are far more useful for estimating some components of this model than others. Specifically, administrative data rarely contain in-depth information about parental psychological well-being or home environment, parenting practices, or social and emotional adjustment of children. Survey research is much better suited to these components. Administrative data can, however, be used to estimate family stability and turbulence through the inclusion of information about household changes and movement in and out of foster care. Administrative data also can help explain the changes in the utilization of supplemental health and human services--especially Medicaid, CHIP, Food Stamps, or WIC--which could be an indirect result of welfare changes. Finally, administrative data have the potential to measure health and safety outcomes, including abuse and neglect, injury, and mortality.

The remaining sections of this paper are developed to assist researchers in defining indicators for domains of child well-being and to clarify substantive issues that must be considered in applying these indicators toward addressing key evaluation questions about the impact of welfare reform. The paper is divided into sections roughly corresponding to the constructs and domains of child well-being offered by Child Trends (1999): (1) health, (2) safety (child welfare), (3) education, and (4) social and emotional adjustment (juvenile justice). For each domain we will identify information that directly describes or reflects on child well-being. Each section also includes a description of key data that are available to inform us about children's outcomes. For each domain we discuss exemplary efforts to use these data for evaluation of welfare reform. Finally, we conclude by discussing some of the scientific sensibilities that should be respected in the use of such data during research on welfare reform, including a discussion of linkages between population surveys and administrative data.

hild Health

Access to health care services is a central consideration in the assessment of welfare reform, as these reforms change existing relationships among income, employment, and insurance of health care services for poor families and children (Child Trends, 2000a; Darnell and Rosenbaum, 1997; Moffitt and Slade, 1997; Schorr, 1997). Measures of child health typically emphasize access to care as an important measure, recognizing that health care is necessary but not sufficient for positive child health outcomes (Gortmaker and Walker, 1984; Margolis et al., 1997; Andrulis, 1998).

Parents access health care for their children through several paths. Many children receive health insurance provided by their parent's employer. However, some children of working parents may not have employer-sponsored health plans, and children of nonworking parents certainly do not have this benefit. These children of low-income or nonworking parents are eligible for services paid by publicly funded programs such as Medicaid or the new CHIP. The PRWORA legislation did not significantly alter Medicaid eligibility, and CHIP is designed to reach more of these uninsured children. Yet in July 1999 an estimated 4.7 million uninsured children were eligible for Medicaid but not enrolled (Families USA, 1999). Many states are beginning to track children's enrollment in Medicaid and CHIP, implement outreach efforts to increase CHIP enrollment, and expand Medicaid and CHIP income-level guidelines (Families USA, 1999; Children's Defense Fund, 1998).

The actual health services the child receives are also major determining factors in child health status. Examples of services may include (1) preventive care such as immunizations or dental care; (2) diagnostic screening such as vision and hearing screening, or weight for height measures; and (3) treatment for chronic conditions and disability, with corresponding risk of secondary disability. State policies about welfare reform have the potential to change, positively or negatively, the family environment where health behaviors and health decisions are carried out (Willis and Kleigman, 1997; O'Campo and Rojas-Smith, 1998; Brauner and Loprest, 1999). For example, even if a child is enrolled in Medicaid or CHIP, PRWORA work requirements may constrain a parent's ability to access health care. When access to health care services is limited, either through limited availability or limited utilization of services, children's health could suffer. Alternatively, the work requirements could encourage the parent to secure a job that includes health insurance (gaining access to health care), which may mean the family is able to utilize more services.

Access and utilization of services are interesting for evaluation purposes because they are believed to contribute to the actual health of the child. However, direct measures of child health outcomes are also needed to measure the effects of welfare reform on children. Direct measures of child health outcomes are scarce, however. Often researchers have to rely on indicators of health status. Recent discussions about welfare reform and health suggest some indicators to measure child health status. Children in poverty are more likely to be undernourished, iron deficient, or lead exposed (Geltman et al., 1996). Several measures such as infant mortality, injury, and the use of preventive medical services can be good indicators of child health status (Pappas, 1998). Starfield's Child Health and Illness Profile (Starfield et al., 1993) combines several of these indicators into a bio psycho social developmental assessment but is not found in administrative data sets. Even in survey research, questions about child health status may be limited to asking parents to rate their child's health from excellent to poor (Child Trends, 2000b). Thus, when using administrative data about child health status, it is often necessary to use measures of health services as markers for positive outcomes such as immunizations, enrollment in health plans, or preventive screening, along with indicators of actual outcomes such as infant mortality, low birthweight, blood lead levels, or adolescent substance abuse.

Our purpose here is to identify a reasonably comprehensive set of child health indicators available in at least some administrative data that are relevant to changes in welfare policy because they address health access or status of children. Healthy People 2000, an initiative begun in 1990 by the U.S. Department of Health and Human Services, set health objectives for the nation, including child health status objectives (National Center for Health Statistics, 1996). Over the years, the initiative has prompted state and local communities to develop their own similar objectives and indicators of progress toward achieving them. As a result, the Healthy People 2000 effort has created a set of fairly common measurements of child health across a range of public and private health programs. For example, one of the Healthy People objectives is to reduce infant mortality. This supports the inclusion of infant mortality reduction as part of most state health objectives, and as part of many state and local programs targeted toward women and children. At the federal level, the Maternal and Child Health Bureau (MCHB) identified 18 of the Healthy People 2000 objectives that specifically relate to women and children. Of these, 15 are child health status indicators that can be used to measure impact of welfare reform (Maternal and Child Health Bureau, 1996). Table 10-1 presents these indicators, along with several others, as recommendations for measuring utilization of health services as well as child health status. For each indicator, we describe whether data generally are available at the individual level or aggregated to some larger population. We also identify suggested data sources for these indicators. Many of these data sources are being used in current research about child health (Vermont Agency of Human Services, 2000; Child Trends, 1999).

Of the data sources identified in Table 10-1, the core indicators come from Medicaid and vital statistics. The following two sections discuss these two sources of data, how they can be used in studies of welfare reform outcomes on children, and some methodological issues in their use.

TABLE 10-1
Suggested Child Health Indicators
Indicator Level Data Sources
Medicaid eligibility/enrollment/services Individual Medicaid data files
CHIP eligibility/enrollment/services Individual CHIP data files
Number/percent uninsured Population State dept. of insurance
SSI benefits Individual SSA data
Infant mortality Individual Vital statistics
Low birth weight Individual Vital statistics
HIV infection among women with live births Individual Vital statistics
Prenatal care Individual Vital statistics
Newborn screening Individual Vital statistics
Birth defects registry
State data system for newborn screening
Early and Periodic Screening, Diagnosis, and Treatment (EPSDT) Population Medicaid services/payment data
Identification of hearing impairments Individual
State data system for newborn screening
Program evaluation data
Immunizations Individual
Medicaid, state immunization registry
Program evaluation data
Blood lead levels Individual
EPSDT, clinic record
Program evaluation data
Dental caries Individual Medicaid
Public health dept.
Unintentional injuries Individual Vital statistics, hospital discharge

School-Based health centers

Child homicide Individual Vital statistics
Adolescent suicides Individual Vital statistics
Adolescent substance use rates Individual
Vital statistics
Hospital discharge/health deptartment
School-based health centers
Program evaluation data
STD rates among youth Population Hospital discharge
Program evaluation data
School-based health centers
Adolescent pregnancy rates Individual Vital statistics

Medicaid Data

Data from Medicaid eligibility, enrollment, and claims and the new state CHIP can be linked to provide longitudinal tracking of a child or family's health care services or lack of services. For example, a state could track the Medicaid or CHIP enrollment of a child whose mother left AFDC. Since it is unlikely that many families leaving TANF will promptly go to jobs with sufficient health benefits or wages above the Medicaid and CHIP guidelines, that is, 200 percent of the federal poverty level in most states but 350 percent of the federal poverty level in some Medicaid expansion programs, lack of Medicaid coverage of a child in an AFDC/TANF leaver family may indicate that the child is at risk of having no health care coverage. If Early and Periodic Screening and Diagnostic Testing (EPSDT) services also are recorded in the Medicaid files, similar linkages with welfare data will allow tracking of the utilization of preventive services for these low-income children. Linked administrative data from AFDC/TANF and Medicaid have also been used as the sample frame for complementary survey research, which can gather indicators of health status or measures of health care utilization and provide more in-depth measures. For example, the Next Generation, a project conducted by Manpower Demonstration Research Corporation (2000), will use survey data from 10 studies to obtain a more comprehensive perspective about the effects of welfare reform on health outcomes. Variables about health will be measured through survey questions, but the project also will include the existing administrative data used in each of the 10 studies.

Using administrative data from Medicaid and CHIP (or other health-related supplemental services such as the Food Stamps Program or WIC) requires attention to a variety of considerations.

One must consider the populations in the data sets in relation to the population of interest for the study. Specifically:

  • Determining what cases are to be included in the population of study. Study populations that can be drawn from Medicaid or CHIP files include: applicants, eligible cases, open cases, closed cases, cases closed with high risk, time-limited or sanctioned cases, or reentry cases.
  • Within the group of eligible children are several subgroups that might be of interest. One group for Medicaid is those children actually enrolled. This subgroup of enrolled children includes a second subgroup of children receiving services. This group is not representative of all children enrolled, or all children eligible, or all low-income children in need of health care.
  • Medicaid data can be used to extend the analysis of the impact of welfare reform beyond the TANF population because the Medicaid eligibility pool is larger than the TANF eligibility pool. For example, California uses data files on Medicaid recipients as the core of its data sharing/data integration initiatives (National Conference of State Legislators, 1999). This strategy can allow evaluators to track service provided across time and programs to low-income children and families.
  • However, Medicaid administrative data can provide data on some of these populations, but not all (i.e. Medicaid administrative data do not represent the entire population of children eligible for Medicaid).
  • Public services data tend to overrepresent families at greatest risk. Findings must be interpreted with this in mind. If a family or child leaves TANF and does not appear on Medicaid enrollment files, this does not necessarily mean the child does not have access to health care as they could be covered by private insurance (Child Trends, 2000a).
  • Beyond data on eligibility and enrollment, the actual Medicaid or CHIP benefits within a state also should be considered part of the evaluation. State CHIP programs can vary by age, geographic area, disability status, or calculation of income.

A thorough understanding of the administrative data being used is necessary.

  • One consideration is whether historical AFDC or Medicaid data were defined the same way across the years.
  • In a cross-state context, one must consider possible differences in programs, definitions of data, caseload characteristics, and take-up rates in each state. Within state differences in each of these are also possible.
  • The dynamics of changing caseloads to determine whether changes are due to differences in entries to health services or differences in lengths of stay in those services need to be clarified (Greenberg, 1998).
  • Administrative data systems for Medicaid often are inadequately automated, even though provision of Medicaid benefits to needy families and children are highly dependent on automated systems. These systems may erroneously terminate a family from Medicaid. Also, eligibility systems typically are not part of the Medicaid division's information system, but reside elsewhere in state government. Because current technology dollars are being spent on TANF automated data systems, there may be some migration away from more archaic Medicaid data (Ellwood, 1999).
  • When designing research about children's access to health care services, it is important to remember that a family or adult parent can be dropped from Medicaid but the child can remain eligible.

Data linkage and confidentiality issues also arise:

  • How cases in the two files are linked requires the establishment of clear decision rules that are appropriate to specific research questions. There are inherent challenges to linking welfare data to Medicaid data because welfare data are case based (and can include a family or group of siblings) and Medicaid data are individual based (Ellwood, 1999).
  • It is useful to maximize the use of common health identifiers. In some states, such as North Carolina, a common health identifying number is used across a range of data sets, from vital statistics to disease registries (North Carolina State Center for Health Statistics, 1997). Where the health identifying number and social services number can be linked together, one can evaluate a child's experiences and outcomes with both health and social service programs.
  • Examining claims data under Medicaid or CHIP requires that issues of confidentiality are responsibly addressed. Many states, such as California, Maryland, Kentucky, and Tennessee, are already addressing these concerns through data sharing and data warehousing projects: (National Conference of State Legislators, 1999).

In addition to these concerns about administrative data, identification of the relevant research questions is critical in guiding the analysis plan and selection of relevant data sets. The question of whether regulations make health care services available to all children who need them could be answered with eligibility data. The question of whether children leaving TANF continue to get needed health services cannot be answered with eligibility or enrollment data. That question only can be answered with service utilization data. The question of whether children exiting TANF are continuing to get timely immunizations could be answered by Medicaid services data or by separate immunization registries within a state (Child Trends, 1999).

Another relevant research question to include would be whether the population of cases had changed since PRWORA was enacted. Will you study AFDC populations before PRWORA, or just those TANF cases after the legislation was implemented? This would require including AFDC and TANF cases in the research. Beyond analysis of the data about AFDC/TANF and Medicaid, the Food Stamps Program, or WIC, research should include questions about barriers to supplemental services for families exiting welfare. One possible barrier is the continued linking of welfare to these supplemental services, despite efforts over the past decade to delink regulations about the programs. In practice, and for individual families, these programs remain interwoven. Another barrier is the complicated eligibility rules for services to support families leaving TANF and the media about the program that might affect whether families think they are eligible or not (Ellwood, 1999). Finally, a research question of interest would be "Upon exiting TANF, do families drop supplemental services, add supplemental services, or maintain existing levels?"

Vital Statistics

As a second predominant type of administrative data, vital statistics systems also can be linked to welfare data sets to provide a range of child health data. Issues of interest in using birth and death certificate files are described as follows:

  • Although obtaining access to birth records varies in difficulty depending on many state characteristics, birth records carry information of at least three kinds: the timing and nature of the birth (e.g., family size, birth spacing); services and the payment source for the birth (e.g., prenatal care used and whether the birth was covered by private pay, Medicaid, or medically indigent funds); the family (e.g., marital status); and the well-being of the child at the time of birth (e.g., birthweight, length of hospital stay, 5- and 10-minute scores, and the presence of congenital abnormalities).
  • Birth and death records increasingly are being maintained in electronic form, with greatly improved systems for updating this information in a more timely manner and linking these data for research purposes.
  • Child death is another indicator that can show differences as a result of services received (Barth and Blackwell, 1998). Although preventing child death is not the primary or sole responsibility of public assistance programs alone, child death rates are sensitive to conditions affected by public assistance, including poverty and lack of supervision. For example, if TANF programs increase the likelihood of home visits by caseworkers who also look for dangerous conditions in the home, if they result in changes in parental substance abuse, if they change access to health care, if they lead to longer spells in which a child is not supervised, changes in death rates from accidents, illnesses, and overdoses could result. Thus, death rates and types of deaths that comprise that rate can change for program users, nonusers, and former users.
  • A variety of relevant outcomes can be captured using death records, including adolescent suicide, adolescent homicide, many kinds of accidental deaths, deaths caused by injuries, deaths from abuse and neglect, and deaths caused by substance abuse (if overdose is the cause of death).
  • Death records are in the public domain and are available at the state level as well as from the National Death Index (NDI)--a central computerized index of death record information on file in state vital statistics offices. Investigators also can obtain data at the state level and make arrangements with the appropriate state offices to obtain copies of death certificates or specific statistical information such as cause of death.
  • Several public welfare agencies have matched child deaths against their welfare caseloads to better understand the vulnerability of their populations. Children who participate in AFDC, Medicaid, or Food Stamp Programs may experience an overall death rate greater than or different than that for other children (Maine Department of Human Services, 1983). Parents on welfare in Maine did not have mortality that was significantly higher than other children in poverty, although some types of mortality were high among welfare recipients; for example, the risk ratio for children whose parents were on welfare had a five times greater risk of experiencing a death from nonmotor vehicle accidents than other children in poverty. In a more recent study (Philips et al., 1999), mortality related to homicide, suicide, and automobile accidents (when substance abuse was mentioned on the death certificates) was shown to be substantially higher in the first week of the month--probably related to the greater availability of discretionary income following the arrival of government assistance checks and pay checks.
  • Evaluations of the relationship between deaths and welfare changes need to assess the type and timing of the deaths. Because child mortality is relatively rare--even among high-risk populations--studies of welfare populations may need to combine these mortality data with injury data and incarceration data (discussed later in this paper) to obtain an overall assessment of significant threats to well-being.

Other data sets may not be as easily linked to welfare data sets, yet they should be considered. Twenty-one states have comprehensive databases on hospital discharges (Pappas, 1998). These data can provide information about a wide variety of health concerns, such as child injuries, acute illnesses, and emergency room visits. These data sets may include measures of income, payment authorization, or actual welfare status. Injury data can be linked to welfare participation for individual-level analyses if they can be obtained from local hospital organizations.

Data from school-based health centers are available in fewer places, but could be expected to become more useful as school-based health clinics expand their reach. Although not yet widely available, school-based health centers are growing in coverage and in some states now blanket the state. Some states, such as Massachusetts, have initiated statewide systems of maintaining school health data. School-based clinics often are under the umbrella of a local hospital, and can serve as Medicaid providers under managed care contracts. These data will be most useful when they cover a large proportion of all youth in the area under study and when they provide additional information not available in the Medicaid data. This is the case in Colorado and Connecticut, which have extensive school-based health center networks (Koppelman and Lear, 1998).

Another source of data is programs funded under Title V, the Maternal and Child Health Block Grant (MCHB), which requires performance measurement for contracting and evaluation. State welfare reform evaluators should collaborate with Title V program staff to explore data linkage, inclusion of common data elements of welfare status and health across data sets, and other ways to share data and evaluate child health in the era of reform. For example, several states, including Kansas and Arizona, are implementing performance measurement systems in their Title V maternal and child health programs (Gabor et al., 1997; Grason and Nachbar, 1997). In a pilot project involving seven states, sponsored by the MCHB in 1998, core performance measurements are monitored. These measurements include: needs assessments, percentage of Medicaid-eligible children enrolled, standards of care for women and children, health insurance coverage, and cooperative agreements among state Medicaid, WIC, and other human service agencies. An emphasis on information systems development is also part of these pilot programs and should be explored for linkage with welfare reform evaluation. In another example, the Institute for Child Health Policy at the University of Florida, Gainesville is currently evaluating enrollment in its Healthy Kids programs of outreach to uninsured children, as well as the quality of services in the program for children with special health care needs (Reiss, 1999; Shenkman, 1999).

Efforts to promote and monitor state health objectives should include indicators of children's health according to welfare, employment, and/or income status. As state and local communities plan for future Healthy People 2010 objectives, the impact of continuing welfare reform should be part of future health objectives. Where monitoring systems exist or are planned, they should include either linkage to state and local welfare data sets or common data elements that would provide for evaluation. For example, child health status measures could be monitored regularly according to the following categories: employed families with private health coverage, employed families with Medicaid or CHIP coverage, employed families with no coverage, unemployed families with Medicaid or CHIP, unemployed families with no coverage. These categories could be applied across a range of child health measures: prenatal care, infant mortality, low birth-weight, immunizations, hearing and vision screening, specialist care for children with special health care needs, injuries, or teen pregnancy.

The Aspen Roundtable on Comprehensive Community Based Initiatives has addressed the issue of using administrative data and identified several useful sources for conducting small-area analysis (Coulton and Hollister, 1999). These data include Head Start records, emergency medical service records, immunization registries, and hospital discharge records. Aggregate data at the neighborhood level, combined with comparable welfare data aggregated to the same level, can answer research questions about selected high-risk neighborhoods within a county, within major metropolitan areas, or across a state. Table 10-2 lists several Web sites of organizations conducting these types of neighborhood-level analyses using small-area analysis (Child Trends, 2000,b).

TABLE 10-2
Small Area Analysis Using Administrative Data: Web Sites
Web sites for Local Research in Welfare Reform
United Way of Chittenden County, Vermont Chapin Hall Center for Children at the University of Chicago
Social Assets and Vulnerabilities Indicators for Central Indiana (SAVI) Center for the Study of Social Policy
United Way of Central Indiana Community Building Resource Exchange of the Aspen Institute
National Governors' Association Aspen Institute Roundtable on Comprehensive Community Initiatives
Urban Strategies Council Oakland, CA Zero Population Growth
The Center on Urban Poverty and Social Change at Case Western Reserve University

Examples of current research using administrative data on Medicaid use, health care access, and other health outcomes to evaluate the impact of welfare reform on children's health are increasing. In spring 2000, a three-state study about children's movement among AFDC, Medicaid, and foster care was released by the Assistant Secretary for Planning and Evaluation (ASPE) of the U.S. Department of Health and Human Services. The study was conducted by Chapin Hall Center for Children at the University of Chicago, Center for Social Services Research at the School of Social Welfare of the University of California at Berkeley, the University of North Carolina School of Social Work at Chapel Hill, and the American Institutes for Research (2000). The study used administrative data from 1995 to 1996 from AFDC, Medicaid, and child welfare programs, obtained through close collaboration with state agencies responsible for these program areas. A baseline population was identified and entry cohorts for each program were used to track experiences of children over the period just prior to PRWORA. The study focused on research questions about transitions from AFDC, including:

  • Percentage of AFDC cohort that leave AFDC, by 1 year.
  • Percentage of AFDC cohort that after 1 year transition to Medicaid only.
  • Percentage of AFDC cohort that after 1 year exit the system (AFDC, Medicaid, and foster care)
  • Among AFDC exiters at 1 year, the percentage who use Medicaid.

Wide variation was found in these measures across Illinois, California, and North Carolina. Differences also were identified for the four measures across children's age groups. This study provides an example of (1) the need to collaborate with other state agencies when using administrative data; (2) the importance of defining the population of study, in this case entry cohorts for 1 year prior to PRWORA with no AFDC/TANF enrollment in the previous 2 years; and (3) the difficulty of generalizing across local areas when studying the characteristics and consequences of welfare programs.

South Carolina has also developed linking capacity of administrative data called CHILD LINK (South Carolina Department of Social Services 1999). This state system links the following data sets: AFDC/TANF, food stamps, Medicaid eligibility, Medicaid payments, work support program data, child protective services, foster care, juvenile justice, alcohol and substance abuse, and wage data. The purpose is to better understand the Medicaid utilization for children after a parent becomes employed and to determine whether, after a client leaves welfare, they use other services to help them through the transition period.

Finally, an inventory of administrative data sets was prepared by UC Data Archive and Technical Assistance of the University of California at Berkeley (1999). This inventory was the result of surveying 26 states about their use of administrative data sets and their capacity to link them. Ninety-five percent of the 26 states were linking AFDC/TANF, Medicaid eligibility, and Food Stamp Program data. Fifty percent were linking AFDC/TANF, Medicaid claims, Medicaid eligibility, and Food Stamp Program data.

Child Abuse and Neglect

The considerable overlap between welfare and child welfare service populations is well documented. Children from welfare families account for as much as 45 percent of those served by the child welfare system (American Humane Association, 1984). The strong association between welfare and child maltreatment may be due to a number of factors, including the stresses associated with poverty, the existence of concurrent risk factors such as mental illness and illicit drugs, and welfare recipients' more frequent contact with public authorities (Coulton et al., 1995; Gelles, 1992; Gil, 1971; Giovannoni and Billingsley, 1970; Wolock and Magura, 1996; Zuravin and DiBlasio, 1996).

Given the documented association between welfare and child maltreatment, a number of authors have reflected on the possible impacts of welfare reform on child welfare (Aber et al., 1995; Haskins, 1995; Meezan and Giovannoni, 1995; Wilson et al., 1995; Zaslow et al., 1995). Essentially all conclude that efforts to induce welfare mothers to self-sufficiency may impact rates of child maltreatment. Again, whether this impact is positive or negative depends in part on what effect reforms have on family income, parental stress, and access to services (Collins and Aber, 1996). For example, loss of benefits or other income supports such as Supplemental Security Income may strain a family's abilities to provide basic necessities such as food and shelter, causing increased neglect and homelessness, even abandonment (Collins, 1997; Knitzer and Bernard, 1997; Shook, 1998). Increased parental stress related to economic, employment, or childcare difficulties may also lead to increased rates of abuse (Knitzer and Bernard, 1997; Meezan and Giovannoni, 1995).

In contrast, positive changes in these areas may be favorable to children and families. For example, rates of abuse and neglect may decline if reforms reduce family's economic hardship. Additionally, gainful employment might improve the mental health of single mothers thereby decreasing the risk of child maltreatment (Garfinkel and McLanahan, 1986). Better access to mental health and drug services also might have similar effects. In addition to impacting the actual rates of maltreatment, the increased scrutiny by public authorities faced by TANF participants and their families might result in greater detection of previously unreported abuse and neglect. Whether positive or negative, these changes likely will be reflected in the number and types of maltreatment reports, the number of case investigations and substantiations, and the number of children placed in foster care.

Welfare reform also may affect the experiences of the children served by the child welfare system. With the passage of PRWORA, a family's economic circumstances become a critical component of the child welfare decision-making process. In particular, parental TANF status could influence the decision to remove a child from a sanctioned parent without any legitimate source of income, and if removed, the TANF status of potential kin caregivers might alter the subsequent placement decision (Zeller, 1998). For example, the proportion of kin placements might decline because kin caregivers might not be exempt from TANF requirements (Berrick et al., 1999; Geen and Waters, 1997; Boots and Green, 1999). Economic factors also might influence children's length of stay in foster care, placement stability, as well as their rates and types of exits from the system. Specifically, parental TANF status might facilitate or stall reunification efforts impacting the duration of children's out-of-home placements. Children placed with kin might experience placement disruptions if their TANF status changes. Although the impact of TANF noncompliance on reunification efforts is clear, compliance also might be problematic, with work making it difficult for parents to meet child welfare timelines such as visitation and court appearances (Knitzer and Bernard, 1997).

In addition to these potential impacts on exit rates, changes in a family's TANF status following reunification might lead to an increased likelihood of reabuse and child welfare system recidivism. Recent research on the child welfare experiences of families in Cleveland suggests that families that go on and off of welfare are more likely to fail in their attempts with reunification of their children than families that continuously receive welfare during the reunification period (Wells and Guo, in press). This, along with data from California (Needell et al., 1999) showing that AFDC families with breaks in AFDC receipt are more likely to become involved with child welfare services, suggests the substantial sensitivity of welfare families to changes in service circumstances.

Unlike the domain of child health, child welfare data traditionally have offered little uniform program participation data. Relevant data are collected only by state child protective service and foster care service departments. In some states (e.g., California) all child welfare administrative data are now entered into one data system. In most states, however, child abuse and neglect reporting and investigation data are gathered separately from data about foster care and adoption. The following section provides an overview of different configurations of these data sources that can be utilized to assess the impact of welfare reform on child maltreatment rates and children's experiences in and exits from the child welfare system. Access and confidentiality issues loom large when using such data to study vulnerable children. Readers should consult Brady and his colleagues (1999, this volume) for an in-depth review of these important topics.

Child Welfare Services Indicators

Most administrative data in the child welfare domain is composed of service event types and dates that can be configured to construct a variety of outcome indicators. The two most common configurations are descriptions of caseloads at a point in time (or several points in time) and longitudinal data analyses of individual service careers over time. In addition to program participation data, demographic data for the children and families under study (e.g., birthdate, ethnicity, home address or location) are also common elements found in these databases. When combined with these demographic data, caseload and longitudinal indicators can provide a source for estimating system performance and client status.

Caseload data provide a snapshot of welfare and child welfare at a specific point in time. They are usually used for program management purposes and can contribute to the assessment of system impacts by indicating covariation between subpopulations in welfare and child welfare. Broadly, for instance, caseload indicators of how many children of a certain age are leaving welfare and how many children of a certain age are entering foster care can provide some indication of whether the welfare exits might be contributing to increases in foster care. However, given the large size of the welfare caseload and the small numbers of children entering foster care, this relationship could not be adequately understood without individual-level data that linked welfare and child welfare histories.

Recently the ACF's Child Welfare Outcomes and Measures Project developed a set of outcome measures using point-in-time data from the Adoption and Foster Care Analysis and Reporting System (AFCARS) to assess state performance in operating child welfare programs. Outcomes include annual incidence of child maltreatment, types of exits from the child welfare system, timing of exits, and placement stability. Although point-in-time data also can be used to measure case status outcomes such as foster care length of stay; the resulting statistics are biased because they overrepresent children with longer stays in care and are not very sensitive to changes in entries to foster care because the newcomers to care are just a portion of the overall population. Thus, although point-in-time estimates are the easiest and least expensive configuration of administrative data, this inherent bias limits their usefulness until the individual records comprising these caseload data are reconfigured into longitudinal data.

Administrative data typically can be reconfigured into event-level files that record program participation histories. Depending on the scope of available data, these events may be restricted to foster care spells or placements, or may more broadly include child abuse reports, investigations, and services provided in the home. Working with entry cohorts provides the clearest evidence of changes in patterns of care that might be associated with changes in welfare programs because the interpretation of the outcomes does not have to disentangle the contributions of different service programs (e.g., AFDC and TANF). Using data that can be subset into entry cohorts captures the dynamics of both system entries and exits, and therefore provides a more accurate assessment of outcomes than caseload. Although free from the biases of point-in-time data, longitudinal data analyses often are preceded by considerable programming to reconfigure data into an even-level, longitudinal format, and to link welfare and child welfare files.

The Multistate Foster Care Data Archive provides an illustration of the complexity as well as promise that longitudinal data offer researchers trying to understand child welfare careers (and how they might be influenced by TANF). The archive is an initiative by the Children's Bureau of the U.S. Department of Health and Human Services that is designed to foster increased collaboration among states regarding administrative data collection in the child welfare services arena. Administered by the Chapin Hall Center for Children at the University of Chicago, the archive currently includes data from child welfare agencies in 11 states. The archive processes state data to make them comparable across state systems. To ensure data comparability, the project focuses on "a limited set of characteristics and events that have clear meaning in all jurisdictions" (Wulczyn et al., 1999:1). The core of the archive is two databases--one consisting of child records, including unique identifiers and demographic information, and a second event-level field that stores information on child welfare events of interest. This structure allows researchers to use the data in a longitudinal format to capture children's spells in child welfare as well as other experiences. Additionally, data can be configured to provide traditional point-in-time estimates of caseload flow over time.

A sufficiently comprehensive set of outcome indicators is shown in Box 10-1 (note that some indicators have a clearer theoretical relationship to welfare reform than others).

BOX 10-1
Minimum Child Welfare Services Indicators

  • Child maltreatment reports (with reason for report)
  • Case investigation (with reason for not investigating)
  • Case substantiations (with reasons for providing services or not)
  • In-home services (duration and frequency of provisions)
  • Foster care placements (with placement dates and type of placement)
  • Placement moves
  • Foster care exits (with type of exit)
  • Reentry to foster (with reason for reentry)

Depending on the purpose of the analysis, indicators can be derived from either point-in-time or longitudinal data. Indicators can be expressed as rates based on the number of people at risk in a state, county, and even zip code of the underlying populations, such as the foster care incidence (entry) rates and prevalence (caseload) rates by age and ethnicity. Benchmarks can be set for both caseload and longitudinal indicators, such as prevalence rate over time, or number and proportion of children who experience reabuse within a year of being reunified from foster care.

Measuring Impact

In anticipation of later analyses of the effects of welfare reform, researchers in several states have undertaken projects using linked longitudinal AFDC and child welfare data to better understand the overlap between these two programs. These projects serve as models of what will be possible with post-TANF data. In one such endeavor, the Child Welfare Research Center at the University of California at Berkeley undertook an analysis to identify the characteristics of poor families at risk of child maltreatment. Using data from the California Children's Services Archive, researchers constructed a longitudinal database of children entering AFDC between 1988 and 1995 using MediCal data in 10 counties. Probability-matching software was employed to link AFDC histories for these children with birth records, statewide foster care data, and child maltreatment reporting data. Results revealed substantial overlap between the welfare and child welfare populations, with approximately 27 percent of all 1990 child AFDC entrants having child welfare contact, within 5 years and 3 percent entering foster care. This indicates that the overlap between welfare and child protective services is large enough to allow modeling of changes over time and across program types, although analyses of transitions to foster care may be too few to allow powerful modeling. Both total time on aid as well as the number of spells on aid were associated with child welfare contact. Children who transitioned to the child welfare system were more likely to come from single-parent families, larger families, have low birthweight and late or no prenatal care (Needell et al., 1999).

A similar analysis was undertaken in Illinois at the University of Chicago's Chapin Hall Center for Children. Using linked longitudinal data from the state Department of Children and Family Services and the Division of Financial Support Services, Shook (1998) set out to identify baseline rates of maltreatment among children in the Illinois AFDC program between 1990 and 1995. She also identified risk factors for child welfare contact among this population. Risks were higher for children on nonparent cases, children from single-parent families, and white children. Of particular interest were the findings that transitions were more likely among children with sanctioned family grants, because child removals for neglect, lack of supervision, or risk of harm were more likely among sanctioned cases. In addition to helping to identify possible implications of TANF sanctions, the research highlights the use of linked administrative data in assessing the impact of welfare reform on child welfare.

Administrative data from child welfare records also have been combined with qualitative survey data to study the impact of welfare reform. For example, a study currently under way (a collaborative effort by The Urban Institute and The University of California at Berkeley's Center for Social Services Research and UC Data Archive and Technical Assistance, funded by the Stuart Foundation) will combine qualitative data of welfare recipients with data from their administrative welfare records and any available data on children in the home that exist in child welfare administrative data records. This "marriage" between administrative data and qualitative data holds great promise. Specifically, although administrative data can provide information to answer questions such as "How many? What proportion? How long?", other methods can shed some light as to "Why?"

The Center for Social Services Research at the University of California at Berkeley routinely has used child welfare administrative data to draw representative samples to study children using other research methods. For example, counties in the Bay Area Social Services Consortium have funded research to understand the educational needs of children in foster care. A random sample of caregivers drawn from administrative data records is being interviewed to gather detailed information about the children in their care. Similarly, case records of children have been reviewed to look at concurrent planning in child welfare. (Concurrent planning is the provision of an alternative permanent plan, such as adoption, simultaneously with efforts to return a child to his or her birthparent.) In both cases, the samples were drawn from administrative data. Such methods easily could be adapted to provide more in-depth analysis of critical welfare reform issues.

Despite the wide variety of outcome indicators that can be configured from child welfare administrative data, like all services data, child welfare data cover only those who receive services. Because child welfare data are available only for those abused and neglected children who come to the attention of public systems of care, changes to the undetected abuse rate that may result from welfare program changes cannot be assessed. Despite the hurdles associated with linking welfare and child welfare data, given the established association between poverty and maltreatment, child welfare advocates and policy makers must examine the impact of welfare reform on child welfare services. In particular, whether these changes increase the likelihood of maltreatment has important consequences for both the TANF families and children as well as the general social good. In addition to the immediate risk of physical harm and even death maltreated children face, longer term consequences include deficits in emotional and physical health, cognitive development, and socialization difficulties (Ammerman et al., 1986; Couch and Milner, 1993). Furthermore, observed relationships between childhood maltreatment and later criminal activity or abusive behavior also increase future consequences for both children and society (Gray, 1988; Jonson-Reid and Barth, 2000).


Educational success is a key indicator of a child's well-being and clearly is related to current and future economic and physical well-being (Barnett, 1998; Card and Krueger, 1998). Educational success can be affected by educational histories, parental work, and targeted efforts to address parents' educational needs. Indeed, some welfare programs (i.e., Job Opportunities and Basic Skills [JOBS]) have more provisions directed at parental education than others (i.e., TANF). Because of the strong relationship between education of parents and children, when welfare programs help recipients to improve their educational skills (Boudett and Friedlander, 1997), they can be expected to have an influence on the learning of their children.

Certainly, improved educational performance of children is one hope of TANF. Because TANF does not pay for substantial educational programs for parents, the benefits for the education of children would have to be by more indirect means. This process may take several forms. For example, if parents' employment efforts result in relocation to communities that have schools with higher achievement for low-income children, this could result in educational achievement. Or, by witnessing their parents' success at the worksite, children could be inspired to have higher standards for their own achievement.

A limited set of pre-TANF research studies indicates there may not be a simple, sizable effect of welfare participation on children's educational attainment. Hill and O'Neill (1994) found that parental AFDC participation has no effect on children's scores on a standardized test of vocabulary, given income. Currie and Duncan (1995) confirmed that their results hold up even when sibling comparisons are used to account for unobserved maternal background characteristics. Yet a recent analysis of National Longitudinal Study of Youth data that included access to other mother and child services found a relationship between program participation and children's learning (Yoshikawa, 1999). Although the evidence base for research on educational outcomes and welfare reform primarily comes from surveys, there is good reason to suggest the importance of using administrative records to study this relationship. This will be particularly fruitful as the availability and meaningfulness of educational records continue to improve.

Measures of educational success include data elements that describe the child's achievement as well as their receipt of services. Many of these data are now in electronic databases in the school districts, but the automation of educational records tends to begin with the high schools and trickle down to the elementary schools. Thus, elementary school grades are not as likely to be automated as middle school or high school grades. Standardized statewide test scores are now quite routinely required of all students, as are periodic achievement test scores during certain sentinel years. The variety and repetition of tests is becoming quite extensive. (As an illustration, Box 10-2 includes the testing schedule for students in North Carolina schools.)

BOX 10-2
Educational Tests Routinely Used In North Carolina

  • End-of-grade tests (grades 3 - 8)
  • Writing assessment (grades 4, 7, 10)
  • Norm-referenced testing (grades 5, 8; sampled)
  • Open-ended assessment (grades 4, 8)
  • Computer Skills Proficiency (grade 8)
  • Reading and Mathematics competency testing (screen in grade 8; must pass for diploma by grade 12)
  • End-of-course tests in Algebra, Biology, English, and U.S. History

BOX 10-3
Minimum Educational Indicators

  • Academic Achievement (T scores from standardized tests)
  • Absences and dates of absences (Full day and part day)
  • Suspensions and dates of suspensions (With reasons)

Most, but not all, students take these tests. Exemptions may be given to students in special education, as determined by their Individual Education Program teams. Exemptions also may be given to students who are not following a standard course of study, such as those in alternative education or adolescent parenting programs.

Grade retention histories usually are available (or can be inferred from birthdates and grade levels). Educational reform is making grade retention data more valuable. Although widespread adherence to the principles of social promotion have dominated the nation's public schools for many years, legislation in many states (e.g., California, New York, North Carolina) is now discouraging social promotion. In the future, grade retention may indicate a child's true performance, not just a school's educational strategy regarding social promotion.

School services data also are obtainable, although the lack of standardization makes it difficult to assess change when students also change schools during the period under study. School attendance data also is likely to be automated, although comparisons across schools and, especially, unified school districts must be done with care because of different ways of administering the statewide definitions of attendance. Schools also have data about student's disciplinary actions--nearly always including suspensions or expulsions, but also including a variety of other disciplinary actions that are less severe. But caution is also needed in making comparisons about disciplinary actions in school. This is particularly true of suspensions, as some schools use them routinely and some schools use them only after considerable effort to mediate the problematic situation. Further, different rules typically apply to children receiving special education services and the proportion of children receiving special education services is, in turn, quite variable across schools. To assess the effects of welfare reform on the educational outcomes of children, even a minimum data set that included measures of academic achievement, absences, and suspensions would be useful (see Box 10-3).

Access to School Records Data

A major imped