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Misreporting of Government Transfers: How Important Are Survey Design and Geography?

Author

Listed:
  • Meyer, Bruce D.

    (University of Chicago)

  • Mittag, Nikolas

    (CERGE-EI)

Abstract

Recent studies linking household surveys to administrative records reveal high rates of misreporting of program receipt. We use the FoodAPS survey to examine whether the findings of these studies of general household surveys using one or two states generalize to a survey with a narrow focus and across many states. First, we study how reporting errors differ from other surveys. We find a lower rate of false negatives (failures to report true receipt) in FoodAPS, likely partly due to the shorter recall period of FoodAPS. Misreporting varies with household characteristics and between interviewers. Second, we examine geographic heterogeneity in survey error to assess whether we can extrapolate from linked data from a few states. We find systematic differences between states in unconditional error rates but no evidence of substantial differences conditional on common covariates. Thus, extrapolating error rates across states may yield more accurate receipt estimates than uncorrected survey estimates.

Suggested Citation

  • Meyer, Bruce D. & Mittag, Nikolas, 2018. "Misreporting of Government Transfers: How Important Are Survey Design and Geography?," IZA Discussion Papers 12038, IZA Network @ LISER.
  • Handle: RePEc:iza:izadps:dp12038
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    References listed on IDEAS

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    1. J. B. Copas & F. J. Hilton, 1990. "Record Linkage: Statistical Models for Matching Computer Records," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 153(3), pages 287-312, May.
    2. Bruce D. Meyer & Nikolas Mittag, 2019. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness, and Holes in the Safety Net," American Economic Journal: Applied Economics, American Economic Association, vol. 11(2), pages 176-204, April.
    3. Charles Courtemanche & Augustine Denteh & Rusty Tchernis, 2019. "Estimating the Associations between SNAP and Food Insecurity, Obesity, and Food Purchases with Imperfect Administrative Measures of Participation," Southern Economic Journal, John Wiley & Sons, vol. 86(1), pages 202-228, July.
    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. Nikolas Mittag, 2019. "Correcting for Misreporting of Government Benefits," American Economic Journal: Economic Policy, American Economic Association, vol. 11(2), pages 142-164, May.
    6. Bruce Meyer & Robert Goerge, 2011. "Errors in Survey Reporting and Imputation and Their Effects on Estimates of Food Stamp Program Participation," Working Papers 11-14, Center for Economic Studies, U.S. Census Bureau.
    7. Meyer, Bruce D. & Mittag, Nikolas & Goerge, Robert M., 2018. "Errors in Survey Reporting and Imputation and Their Effects on Estimates of Food Stamp Program Participation," IZA Discussion Papers 11776, IZA Network @ LISER.
    8. 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.
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    Cited by:

    1. Charles Courtemanche & Augustine Denteh & Rusty Tchernis, 2019. "Estimating the Associations between SNAP and Food Insecurity, Obesity, and Food Purchases with Imperfect Administrative Measures of Participation," Southern Economic Journal, John Wiley & Sons, vol. 86(1), pages 202-228, July.
    2. Helen H. Jensen & Brent Kreider & Oleksandr Zhylyevskyy, 2019. "Investigating Treatment Effects of Participating Jointly in SNAP and WIC when the Treatment Is Validated Only for SNAP," Southern Economic Journal, John Wiley & Sons, vol. 86(1), pages 124-155, July.

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    Keywords

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    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
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • 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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