By: Caroline Ratcliffe and Henry Chen The Urban Institute and Trina R. Williams Shanks, Yunju Nam, Mark Schreiner, Min Zhan, and Michael Sherraden Center for Social Development Washington University in St. Louis
Project Home Page: Poor Finances: Assets and Low-Income Households
The report has benefited from comments of the entire project teams at the Urban Institute, the Center for Social Development, and the New America Foundation as well as helpful comments and suggestions from Jeremías Alvarez, Laura Chadwick, Susan Hauan, Gretchen Lehman, Linda Mellgren, Annette Rogers, Reuben Snipper, Kendall Swenson, and Joan Turek of ASPE and Jim Gatz, Leonard Sternbach, and John Tambornino of the Administration for Children and Families (ACF)/HHS. Individuals affiliated with the organizations responsible for the three primary data sets featured in this report also reviewed this report. We thank Robert Avery and Arthur Kennickell of the Federal Reserve Board, Alfred Gottschalck of the U.S. Census Bureau, and Robert Schoeni and Frank Stafford of the University of Michigan Institute for Social Research for their helpful comments and suggestions.
This report is part of a series entitled Poor Finances: Assets and Low-Income Households, produced in a partnership between the Urban Institute, Center for Social Development, and New American Foundation.
Questions about the Project or any of the Papers?
Please contact: Gretchen Lehman Social Science Analyst Office of Human Services Policy Office of the Assistant Secretary for Planning and Evaluation U.S. Dept. of Health and Human Services 200 Independence Ave SW, Room 426F.6 Washington, DC 20201 202-401-6614 (phone) 202-690-6562 (fax)Gretchen.Lehman@hhs.gov
This report identifies the most informative and reliable data sources for understanding low-income households' assets and liabilities. We evaluate 12 data sets using four criteria: relevancy, representativeness, recurrence, and richness of correlates. Based on this evaluation, we identify three data sets as having the greatest potential for future asset research — the Survey of Consumer Finances (SCF), the Survey of Income and Program Participation (SIPP), and the Panel Study of Income Dynamics (PSID). Each of these three data sets provides important information about asset holdings and liabilities, and the report details each data set's structure, strengths, and limitations.
Given the strengths and weaknesses of these three data sets, researchers can select the most appropriate one for their particular study. The most useful data set for understanding the asset and liabilities of the overall low-income population is the SCF. The SCF's very detailed questions allow researchers to study the types of assets low-income households own and the types of debt they hold, along with the value of their asset and debt holdings. For examining subgroups of the low-income population, the SIPP data are most valuable. The large sample size and detailed questions of the SIPP permits a wide variety of analyses of asset and liability information.
Researchers studying the determinants of financial asset building and life course patterns of asset accumulation would be best served using longitudinal data sets with rich correlates, such as the PSID or SIPP. The PSID's long panel is especially useful for studying life course patterns. Understanding the benefits and consequences of asset building (such as economic, social, and child well-being) requires data that provide these outcome measures. Again, both the SIPP and PSID are potentially strong for these purposes because of the richness of data collected and their longitudinal nature. Further, longitudinal data and a rich set of correlates can be used to control for unobserved and observed differences, thus identifying causal benefits and consequences rather than just associations between asset holdings and outcomes.
Finally, this report presents 15 options for improving data on assets. We identify general options for improving asset and liability data across multiple surveys, as well as specific options for improving each of the three primary data sets. Some options include collecting information not currently available in surveys (e.g. dynamics of asset accumulation), while other options are designed to improve the quality of currently collected asset and liability data. These options could be implemented individually or jointly, with the decision to implement a particular option (and not another) based on goals for the survey.