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Income Data for Policy Analysis: A Comparative Assessment of Eight Surveys

Author

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  • John L. Czajka
  • Gabrielle Denmead

Abstract

Income is a critical classification variable for policy-related analyses, and together with poverty status is often key in the development of public policy. Most federal household surveys collect some income data and provide measures of poverty status.

Suggested Citation

  • John L. Czajka & Gabrielle Denmead, "undated". "Income Data for Policy Analysis: A Comparative Assessment of Eight Surveys," Mathematica Policy Research Reports 19724257b78544bdbd55f15be, Mathematica Policy Research.
    • Handle: RePEc:mpr:mprres:19724257b78544bdbd55f15be6d42d0e
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    File URL: https://www.mathematica.org/-/media/publications/pdfs/incomedata.pdf
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    References listed on IDEAS

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    1. John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics," Journal of Human Resources, University of Wisconsin Press, vol. 33(2), pages 251-299.
    2. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
    3. Goudreau, Karen & Oberheu, Howard & Vaughan, Denton, 1984. "An Assessment of the Quality of Survey Reports of Income from the Aid to Families with Dependent Children (AFDC) Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(2), pages 179-186, April.
    4. Hurd, Michael D, 1999. "Anchoring and Acquiescence Bias in Measuring Assets in Household Surveys," Journal of Risk and Uncertainty, Springer, vol. 19(1-3), pages 111-136, December.
    5. Marc Roemer, 2002. "Using Administrative Earnings Records to Assess Wage Data Quality in the March Current Population Survey and the Survey of Income and Program Participation," Longitudinal Employer-Household Dynamics Technical Papers 2002-22, Center for Economic Studies, U.S. Census Bureau.
    6. Schenker, Nathaniel & Raghunathan, Trivellore E. & Chiu, Pei-Lu & Makuc, Diane M. & Zhang, Guangyu & Cohen, Alan J., 2006. "Multiple Imputation of Missing Income Data in the National Health Interview Survey," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 924-933, September.
    7. Hurd, Michael D, 1999. "Anchoring and Acquiescence Bias in Measuring Assets in Household Surveys," Journal of Risk and Uncertainty, Springer, vol. 19(1-3), pages 111-136, December.
    8. Arie Kapteyn & Pierre-Carl Michaud & James P. Smith & Arthur Van Soest, 2006. "Effects of Attrition and Non-Response in the Health and Retirement Study," Working Papers WR-407, RAND Corporation.
    9. Kapteyn, Arie & Michaud, Pierre-Carl & Smith, James P. & van Soest, Arthur, 2006. "Effects of Attrition and Non-Response in the Health and Retirement Study," IZA Discussion Papers 2246, Institute for the Study of Labor (IZA).
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    Cited by:

    1. Meijer, Erik & Karoly, Lynn A., 2017. "Representativeness of the low-income population in the Health and Retirement Study," The Journal of the Economics of Ageing, Elsevier, vol. 9(C), pages 90-99.
    2. Bruce D. Meyer & Wallace K. C. Mok & James X. Sullivan, 2009. "The Under-Reporting of Transfers in Household Surveys: Its Nature and Consequences," Working Papers 0903, Harris School of Public Policy Studies, University of Chicago.

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