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Errors in Reporting and Imputation of Government Benefits and Their Implications

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  • Pablo A. Celhay
  • Bruce D. Meyer
  • Nikolas Mittag

Abstract

We document the extent, nature, and consequences of survey errors in cash welfare and SNAP receipt in three major U.S. household surveys. We find high rates of misreporting, particularly failure to report receipt. The surveys inaccurately capture patterns of multiple program participation, even though there is little evidence of program confusion. Error rates are higher among imputed observations, which account for a large share of false positive errors. Many household characteristics have significant effects on both false positives and false negative errors. Error rates sharply differ by race, ethnicity, income and other household characteristics. The errors greatly affect models of program receipt and estimated effects of income and race are noticeably biased. We examine error due to item non-response and imputation, as well as whether imputation improves estimates. Item non-respondents have higher receipt rates than the population conditional on covariates. The assumptions for consistent estimates in multivariate models fail both when excluding item non-respondents and when using the imputed values. In binary choice models of program receipt, linked data estimates favor excluding item non-respondents rather than using imputed values. Biases are well predicted by the error patterns we document, helping researchers make informed decisions on whether to use imputed values.

Suggested Citation

  • Pablo A. Celhay & Bruce D. Meyer & Nikolas Mittag, 2021. "Errors in Reporting and Imputation of Government Benefits and Their Implications," NBER Working Papers 29184, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:29184
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    Cited by:

    1. Meyer, Bruce D. & Mittag, Nikolas & Wu, Derek, 2024. "Race, Ethnicity, and Measurement Error," IZA Discussion Papers 17349, Institute of Labor Economics (IZA).
    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. Elira Kuka & Bryan A. Stuart, 2021. "Racial Inequality in Unemployment Insurance Receipt and Take-Up," NBER Working Papers 29595, National Bureau of Economic Research, Inc.
    4. Atamanov, Aziz & Tandon, Sharad & Lopez-Acevedo, Gladys & Vergara Bahena, Mexico Alberto, 2020. "Measuring Monetary Poverty in the Middle East and North Africa (MENA) Region: Data Gaps and Different Options to Address Them," IZA Discussion Papers 13363, Institute of Labor Economics (IZA).
    5. Celhay, Pablo & Meyer, Bruce D. & Mittag, Nikolas, 2022. "Stigma in Welfare Programs," IZA Discussion Papers 15431, Institute of Labor Economics (IZA).
    6. R. Bollinger, Christopher & Valentinova Tasseva, Iva, 2022. "Income source confusion using the SILC," ISER Working Paper Series 2022-04, Institute for Social and Economic Research.
    7. Borjas, George J. & Hamermesh, Daniel S., 2023. "The Mismeasurement of Work Time: Implications for Wage Discrimination and Inequality," IZA Discussion Papers 16699, Institute of Labor Economics (IZA).
    8. Ethan Krohn, 2024. "Earnings Through the Stages: Using Tax Data to Test for Sources of Error in CPS ASEC Earnings and Inequality Measures," Working Papers 24-52, Center for Economic Studies, U.S. Census Bureau.
    9. Colleen Heflin & Michah W. Rothbart & Mattie Mackenzie-Liu, 2022. "Below the Tip of the Iceberg: Examining Early Childhood Participation in SNAP and TANF from Birth to Age Six," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(2), pages 729-755, April.
    10. 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).

    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
    • 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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