IDEAS home Printed from https://ideas.repec.org/p/hka/wpaper/2017-075.html
   My bibliography  Save this paper

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
    Note: MIP
    as

    Download full text from publisher

    File URL: http://humcap.uchicago.edu/RePEc/hka/wpaper/Meyer-Mittag_2017_linked-survey-data_better-measure-income.pdf
    File Function: First version, July 30, 2017
    Download Restriction: no
    ---><---

    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Wooldridge, Jeffrey M., 2007. "Inverse probability weighted estimation for general missing data problems," Journal of Econometrics, Elsevier, vol. 141(2), pages 1281-1301, December.
    14. 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.
    15. Bruce Meyer & Nikolas Mittag, 2013. "Misclassification In Binary Choice Models," Working Papers 13-27, Center for Economic Studies, U.S. Census Bureau.
    16. 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.
    17. Ravallion, Martin, 1996. "Issues in Measuring and Modelling Poverty," Economic Journal, Royal Economic Society, vol. 106(438), pages 1328-1343, September.
    18. 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.
    19. 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.
    20. 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.
    21. Mittag, Nikolas, 2016. "Correcting for Misreporting of Government Benefits," IZA Discussion Papers 10266, Institute of Labor Economics (IZA).
    22. 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.
    23. repec:taf:jnlbes:v:30:y:2012:i:2:p:191-201 is not listed on IDEAS
    24. Meyer, Bruce D. & Mittag, Nikolas, 2017. "Misclassification in binary choice models," Journal of Econometrics, Elsevier, vol. 200(2), pages 295-311.
    25. 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.
    26. 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.
    27. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Meyer, Bruce D. & Mittag, Nikolas, 2017. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," IZA Discussion Papers 10943, Institute of Labor Economics (IZA).
    2. Bruce D. Meyer & Nikolas Mittag, 2015. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," NBER Working Papers 21676, National Bureau of Economic Research, Inc.
    3. Mittag, Nikolas, 2016. "Correcting for Misreporting of Government Benefits," IZA Discussion Papers 10266, Institute of Labor Economics (IZA).
    4. Meyer, Bruce D. & Mittag, Nikolas, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," IZA Discussion Papers 12266, Institute of Labor Economics (IZA).
    5. Bruce D. Meyer & Nikolas Mittag, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," NBER Working Papers 25738, National Bureau of Economic Research, Inc.
    6. 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," AEI Economics Working Papers 1018925, American Enterprise Institute.
    7. Adam Bee & Joshua Mitchell & Nikolas Mittag & Jonathan Rothbaum & Carl Sanders & Lawrence Schmidt & Matthew Unrath, 2023. "National Experimental Wellbeing Statistics - Version 1," Working Papers 23-04, Center for Economic Studies, U.S. Census Bureau.
    8. Meyer, Bruce D. & Mittag, Nikolas, 2021. "An empirical total survey error decomposition using data combination," Journal of Econometrics, Elsevier, vol. 224(2), pages 286-305.
    9. 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.
    10. Bradley Hardy & Timothy Smeeding & James P. Ziliak, 2018. "The Changing Safety Net for Low-Income Parents and Their Children: Structural or Cyclical Changes in Income Support Policy?," Demography, Springer;Population Association of America (PAA), vol. 55(1), pages 189-221, February.
    11. Michele Lalla & Patrizio Frederic & Daniela Mantovani, 2022. "The inextricable association of measurement errors and tax evasion as examined through a microanalysis of survey data matched with fiscal data: a case study," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(5), pages 1375-1401, December.
    12. Michele Lalla & Maddalena Cavicchioli, 2020. "Nonresponse and measurement errors in income: matching individual survey data with administrative tax data," Department of Economics 0170, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    13. Stüber, Heiko & Grabka, Markus M. & Schnitzlein, Daniel D., 2023. "A tale of two data sets: comparing German administrative and survey data using wage inequality as an example," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 57, pages 1-8.
    14. Ha Trong Nguyen & Huong Thu Le & Luke Connelly & Francis Mitrou, 2023. "Accuracy of self‐reported private health insurance coverage," Health Economics, John Wiley & Sons, Ltd., vol. 32(12), pages 2709-2729, December.
    15. Nayoung Lee, 2022. "Measurement error and its impact on estimates of income dynamics," Empirical Economics, Springer, vol. 63(5), pages 2539-2550, November.
    16. 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).
    17. James P. Ziliak & Charles Hokayem & Christopher R. Bollinger, 2022. "Trends in Earnings Volatility Using Linked Administrative and Survey Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 12-19, December.
    18. Christian Imboden & John Voorheis & Caroline Weber, 2023. "Self-Employment Income Reporting on Surveys," Working Papers 23-19, Center for Economic Studies, U.S. Census Bureau.
    19. Bruckmeier, Kerstin & Riphahn, Regina T. & Wiemers, Jürgen, 2019. "Benefit underreporting in survey data and its consequences for measuring non-take-up: new evidence from linked administrative and survey data," IAB-Discussion Paper 201906, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    20. Fisher, Jonathan D. & Houseworth, Christina A., 2013. "Occupation inflation in the Current Population Survey," Journal of Economic and Social Measurement, IOS Press, issue 3, pages 243-261.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hka:wpaper:2017-075. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Jennifer Pachon (email available below). General contact details of provider: https://edirc.repec.org/data/mfichus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.