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Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net

Author

Listed:
  • Bruce Meyer

    (The University of Chicago)

  • Nikolas Mittag

    (Center for Economic Research and Graduate Education – Economics Institute)

Abstract

We examine the consequences of underreporting of transfer programs in household survey data for several prototypical analyses of low-income populations. We focus on the Current Population Survey (CPS), the source of official poverty and inequality statistics. We link administrative data for food stamps, TANF, General Assistance, and subsidized housing from New York State to the CPS at the household level. Program receipt in the CPS is missed for over one-third of housing assistance recipients, over 40 percent of food stamp recipients and over 60 percent of TANF and General Assistance recipients. Dollars of benefits are also undercounted for reporting recipients, particularly for TANF, General Assistance and housing benefits. We find that the survey sharply understates the income of poor households. Underreporting in the survey data also greatly understates the effects of anti-poverty programs and changes our understanding of program targeting, often making it seem that welfare programs are less targeted to both the very poorest and middle-income households than they are. Using the combined data rather than survey data alone, the poverty reducing effect of all programs together is nearly doubled while the effect of housing assistance is tripled. We also re-examine the coverage of the safety net, specifically the share of people without work or program receipt. Using the administrative measures of program receipt rather than the survey ones often reduces the share of single mothers falling through the safety net by one-half or more.

Suggested Citation

  • Bruce Meyer & Nikolas Mittag, 2017. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," Working Papers 2017-075, Human Capital and Economic Opportunity Working Group.
  • Handle: RePEc:hka:wpaper:2017-075
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    File URL: http://humcap.uchicago.edu/RePEc/hka/wpaper/Meyer-Mittag_2017_linked-survey-data_better-measure-income.pdf
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    References listed on IDEAS

    as
    1. Richard Blundell & Monica Costa Dias & Costas Meghir & Jonathan Shaw, 2016. "Female Labor Supply, Human Capital, and Welfare Reform," Econometrica, Econometric Society, vol. 84, pages 1705-1753, September.
    2. Robert Moffitt & John Karl Scholz, 2010. "Trends in the Level and Distribution of Income Support," NBER Chapters, in: Tax Policy and the Economy, Volume 24, pages 111-152, National Bureau of Economic Research, Inc.
    3. Bruce D. Meyer & Wallace K. C. Mok & James X. Sullivan, 2015. "Household Surveys in Crisis," Journal of Economic Perspectives, American Economic Association, vol. 29(4), pages 199-226, Fall.
    4. John M. Abowd & Martha H. Stinson, 2013. "Estimating Measurement Error in Annual Job Earnings: A Comparison of Survey and Administrative Data," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1451-1467, December.
    5. Mittag, Nikolas, 2016. "Correcting for Misreporting of Government Benefits," IZA Discussion Papers 10266, Institute of Labor Economics (IZA).
    6. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    7. 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.
    8. repec:taf:jnlbes:v:30:y:2012:i:2:p:191-201 is not listed on IDEAS
    9. C. Adam Bee & Joshua Mitchell, 2017. "The Hidden Resources of Women Working Longer: Evidence from Linked Survey-Administrative Data," NBER Chapters, in: Women Working Longer: Increased Employment at Older Ages, pages 269-296, National Bureau of Economic Research, Inc.
    10. Ravallion, Martin, 1996. "Issues in Measuring and Modelling Poverty," Economic Journal, Royal Economic Society, vol. 106(438), pages 1328-1343, September.
    11. Meyer, Bruce D. & Mittag, Nikolas, 2017. "Misclassification in binary choice models," Journal of Econometrics, Elsevier, vol. 200(2), pages 295-311.
    12. Benjamin Cerf Harris, 2014. "Within and Across County Variation in SNAP Misreporting: Evidence from Linked ACS and Administrative Records," CARRA Working Papers 2014-05, Center for Economic Studies, U.S. Census Bureau.
    13. Marianne P. Bitler & Hilary W. Hoynes, 2010. "The State of Social Safety Net in the Post-Welfare Reform Era," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 41(2 (Fall)), pages 71-147.
    14. Hilary W. Hoynes & Marianne E. Page & Ann Huff Stevens, 2006. "Poverty in America: Trends and Explanations," Journal of Economic Perspectives, American Economic Association, vol. 20(1), pages 47-68, Winter.
    15. Christopher R. Bollinger & Barry T. Hirsch, 2006. "Match Bias from Earnings Imputation in the Current Population Survey: The Case of Imperfect Matching," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 483-520, July.
    16. Manasi Deshpande, 2016. "Does Welfare Inhibit Success? The Long-Term Effects of Removing Low-Income Youth from the Disability Rolls," American Economic Review, American Economic Association, vol. 106(11), pages 3300-3330, November.
    17. Philip Armour & Richard V. Burkhauser & Jeff Larrimore, 2013. "Deconstructing Income and Income Inequality Measures: A Crosswalk from Market Income to Comprehensive Income," American Economic Review, American Economic Association, vol. 103(3), pages 173-177, May.
    18. Rebecca M. Blank & Robert F. Schoeni, 2003. "Changes in the Distribution of Children's Family Income over the 1990's," American Economic Review, American Economic Association, vol. 93(2), pages 304-308, May.
    19. Charles Hokayem & Christopher Bollinger & James P. Ziliak, 2015. "The Role of CPS Nonresponse in the Measurement of Poverty," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 935-945, September.
    20. Bruce Meyer & Nikolas Mittag, 2013. "Misclassification In Binary Choice Models," Working Papers 13-27, Center for Economic Studies, U.S. Census Bureau.
    21. Yonatan Ben-Shalom & Robert A. Moffitt & John Karl Scholz, "undated". "An Assessment of the Effectiveness of Anti-Poverty Programs in the United States," Mathematica Policy Research Reports cfc848ed6ab647bcb38ab47bb, Mathematica Policy Research.
    22. Bollinger, Christopher R, 1998. "Measurement Error in the Current Population Survey: A Nonparametric Look," Journal of Labor Economics, University of Chicago Press, vol. 16(3), pages 576-594, July.
    23. Claudia Goldin & Lawrence F. Katz, 2017. "Introduction to "Women Working Longer: Increased Employment at Older Ages"," NBER Chapters, in: Women Working Longer: Increased Employment at Older Ages, pages 1-8, National Bureau of Economic Research, Inc.
    24. Wooldridge, Jeffrey M., 2007. "Inverse probability weighted estimation for general missing data problems," Journal of Econometrics, Elsevier, vol. 141(2), pages 1281-1301, December.
    25. Lesley J. Turner & Sheldon Danziger & Kristin S. Seefeldt, 2006. "Failing the Transition from Welfare to Work: Women Chronically Disconnected from Employment and Cash Welfare," Social Science Quarterly, Southwestern Social Science Association, vol. 87(2), pages 227-249, June.
    26. Arie Kapteyn & Jelmer Y. Ypma, 2007. "Measurement Error and Misclassification: A Comparison of Survey and Administrative Data," Journal of Labor Economics, University of Chicago Press, vol. 25(3), pages 513-551.
    27. Black, Dan & Sanders, Seth & Taylor, Lowell, 2003. "Measurement of Higher Education in the Census and Current Population Survey," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 545-554, January.
    28. D. L. Oberski & A. Kirchner & S. Eckman & F. Kreuter, 2017. "Evaluating the Quality of Survey and Administrative Data with Generalized Multitrait-Multimethod Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1477-1489, October.
    29. Bollinger, Christopher R. & Hirsch, Barry & Hokayem, Charles M. & Ziliak, James P., 2018. "Trouble in the Tails? What We Know about Earnings Nonresponse Thirty Years after Lillard, Smith, and Welch," IZA Discussion Papers 11710, Institute of Labor Economics (IZA).
    30. Molly Dahl & Thomas DeLeire & Jonathan A. Schwabish, 2011. "Estimates of Year-to-Year Volatility in Earnings and in Household Incomes from Administrative, Survey, and Matched Data," Journal of Human Resources, University of Wisconsin Press, vol. 46(4), pages 750-774.
    31. Bruce D. Meyer & James X. Sullivan, 2012. "Identifying the Disadvantaged: Official Poverty, Consumption Poverty, and the New Supplemental Poverty Measure," Journal of Economic Perspectives, American Economic Association, vol. 26(3), pages 111-136, Summer.
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    Cited by:

    1. Achille Lemmi & Donatella Grassi & Alessandra Masi & Nicoletta Pannuzi & Andrea Regoli, 2019. "Methodological Choices and Data Quality Issues for Official Poverty Measures: Evidences from Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 299-330, January.
    2. Bruce D. Meyer & Derek Wu, 2018. "The Poverty Reduction of Social Security and Means-Tested Transfers," NBER Working Papers 24567, National Bureau of Economic Research, Inc.
    3. Bruce D. Meyer & Derek Wu & Victoria R. Mooers & Carla Medalia, 2019. "The Use and Misuse of Income Data and Extreme Poverty in the United States," NBER Working Papers 25907, National Bureau of Economic Research, Inc.
    4. Kerstin Bruckmeier & Regina T. Riphahn & Jürgen Wiemers, 2021. "Misreporting of program take-up in survey data and its consequences for measuring non-take-up: new evidence from linked administrative and survey data," Empirical Economics, Springer, vol. 61(3), pages 1567-1616, September.

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    More about this item

    Keywords

    poverty; Inequality; measurement error; administrative data; survey misreporting; linked data;
    All these keywords.

    JEL classification:

    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs

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