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Correcting for Misreporting of Government Benefits

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  • Nikolas Mittag

Abstract

Data linkage studies often document, but do not remedy, severe survey errors. To improve survey estimates despite restricted linked data access, this paper develops a convenient and general estimation method that combines public use data with conditional distribution parameters estimated from linked data. Analyses using linked SNAP data show that this method sharply improves estimates and consistently outperforms corrections that mainly rely on survey data. Yet, some univariate corrections perform well when linked data do not exist. For SNAP, extrapolating from linked data across time and geography still improves upon estimates using survey data only, even after survey-based corrections.

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  • Nikolas Mittag, 2019. "Correcting for Misreporting of Government Benefits," American Economic Journal: Economic Policy, American Economic Association, vol. 11(2), pages 142-164, May.
    • Handle: RePEc:aea:aejpol:v:11:y:2019:i:2:p:142-64
    Note: DOI: 10.1257/pol.20160618
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    References listed on IDEAS

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    5. 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," Upjohn Working Papers 15-242, W.E. Upjohn Institute for Employment Research.
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    Citations

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    Cited by:

    1. Celhay, Pablo & Meyer, Bruce D. & Mittag, Nikolas, 2024. "What leads to measurement errors? Evidence from reports of program participation in three surveys," Journal of Econometrics, Elsevier, vol. 238(2).
    2. Bruce D. Meyer & Nikolas Mittag & Derek Wu, 2024. "Race, Ethnicity, and Measurement Error," NBER Chapters, in: Race, Ethnicity, and Economic Statistics for the 21st Century, National Bureau of Economic Research, Inc.
    3. 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.
    4. Celhay, Pablo & Meyer, Bruce D. & Mittag, Nikolas, 2022. "Stigma in Welfare Programs," IZA Discussion Papers 15431, Institute of Labor Economics (IZA).
    5. McKernan, Signe-Mary & Ratcliffe, Caroline & Braga, Breno, 2021. "The effect of the US safety net on material hardship over two decades," Journal of Public Economics, Elsevier, vol. 197(C).
    6. Meyer, Bruce D. & Mittag, Nikolas & Wu, Derek, 2024. "Race, Ethnicity, and Measurement Error," IZA Discussion Papers 17349, Institute of Labor Economics (IZA).
    7. Madeira, Carlos & Margaretic, Paula, 2022. "The impact of financial literacy on the quality of self-reported financial information," Journal of Behavioral and Experimental Finance, Elsevier, vol. 34(C).
    8. Adam I. Biener & Chad Meyerhoefer & John Cawley, 2024. "Non‐classical measurement error in instrumental variables estimation: An application to the medical care costs of obesity," Health Economics, John Wiley & Sons, Ltd., vol. 33(11), pages 2558-2574, November.
    9. Warwick, Ross & Harris, Tom & Phillips, David & Goldman, Maya & Jellema, Jon & Inchauste, Gabriela & Goraus-Tańska, Karolina, 2022. "The redistributive power of cash transfers vs VAT exemptions: A multi-country study," World Development, Elsevier, vol. 151(C).
    10. Yixia Cai & Timothy Smeeding, 2019. "Deep and Extreme Child Poverty in Rich and Poor Nations: Lessons from Atkinson for the Fight Against Child Poverty," LIS Working papers 780, LIS Cross-National Data Center in Luxembourg.
    11. repec:ags:aaea22:335818 is not listed on IDEAS
    12. 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.
    13. Elwell, James & Corinth, Kevin & Burkhauser, Richard V., 2019. "Income Growth and its Distribution from Eisenhower to Obama: The Growing Importance of In-Kind Transfers (1959-2016)," IZA Discussion Papers 12757, Institute of Labor Economics (IZA).
    14. 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.
    15. James X. Sullivan, 2020. "A Cautionary Tale of Using Data From the Tail," Demography, Springer;Population Association of America (PAA), vol. 57(6), pages 2361-2368, December.
    16. Celhay, Pablo & Meyer, Bruce D. & Mittag, Nikolas, 2022. "What Leads to Measurement Errors? Evidence from Reports of Program Participation in Three Surveys," IZA Discussion Papers 14995, Institute of Labor Economics (IZA).
    17. Meyer, Bruce D. & Mittag, Nikolas, 2018. "Misreporting of Government Transfers: How Important Are Survey Design and Geography?," IZA Discussion Papers 12038, Institute of Labor Economics (IZA).
    18. Bruce D. Meyer & Nikolas Mittag, 2019. "Misreporting of Government Transfers: How Important Are Survey Design and Geography?," Southern Economic Journal, John Wiley & Sons, vol. 86(1), pages 230-253, July.
    19. 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.
    20. Yixia Cai & Timothy Smeeding, 2020. "Deep and Extreme Child Poverty in Rich and Poor Nations: Lessons from Atkinson for the Fight Against Child Poverty," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 6(1), pages 109-128, March.
    21. Lehner, Lukas & Parolin, Zachary & Wilmers, Nathan, 2024. "Declining Earnings Inequality, Rising Income Inequality: What Explains Discordant Inequality Trends in the United States?," IZA Discussion Papers 16874, Institute of Labor Economics (IZA).
    22. James X. Sullivan, 2020. "Another Plea for Caution When Using Survey Income Data From the Far-Left Tail," Demography, Springer;Population Association of America (PAA), vol. 57(6), pages 2377-2381, December.

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    More about this item

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • H75 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Government: Health, Education, and Welfare
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs

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