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.
|Post Program Survey Status||First Quarter UI Status||Total|
|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.
|Reason for Mismatch||Number of Cases||Percent|
|Employed out of state||517||15.3|
|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|
|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.
|Treatment Earnings ($)||Control Earnings ($)||Treatment Employment Rate||Control Employment Rate|
|Adult women (4,943; 18,275; 8,916)|
|Ratio (survey UI)||1.23||1.24||1.03||1.01|
|Adult men (3,651; 13,329; 6,482)|
|Female youth (2,113; 9,452; 4,316)|
|Male youths without a prior arrest (1,225; 5,009; 2,442)|
|Male youths with a prior arrest (386; 1,646; 705)|
|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)
- Mean UI
|Adult Women||Adult Men||Female Youth||Male Youth No Arrest||Male Youth With Arrest|
|-$1 - -$200||16.2||13.2||17.3||10.8||11.1|
|-401 - -600||3.3||4.2||3.3||3.4||1.6|
|-601 - -1,000||3.4||4.6||2.0||4.5||3.4|
|-1,001 - -2,000||2.3||4.1||1.1||1.7||1.6|
|Mean diff ($)||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.
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