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Errors in Reporting and Imputation of Government Benefits and Their Implications

Author

Listed:
  • Celhay, Pablo

    (Pontificia Universidad Catolica de Chile)

  • Meyer, Bruce D.

    (University of Chicago)

  • Mittag, Nikolas

    (CERGE-EI)

Abstract

We document the extent, nature, and consequences of survey errors for receipt of cash welfare and SNAP in three major U.S. household surveys linked to administrative program records. Our results confirm high rates of misreporting of program receipt, particularly failure to report receipt. The surveys inaccurately capture patterns of participation in multiple programs, even though there is little evidence of program confusion. Error rates are higher among imputed observations, which also account for a large share of false positive errors. Many household characteristics have significant effects on errors in reporting receipt, both false positives and false negatives. We find large differences in survey errors by race, ethnicity, income and other household characteristics. We provide evidence on the consequences of these errors for models of program receipt. Estimated effects of income and race are noticeably biased. We then 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, even conditional on many 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, estimates from the linked data favor excluding item non-respondents rather than using their imputed values. The biases in each case are well predicted by the error patterns we document, so such analyses can help researchers make more informed decisions on the use of imputed values.

Suggested Citation

  • Celhay, Pablo & Meyer, Bruce D. & Mittag, Nikolas, 2021. "Errors in Reporting and Imputation of Government Benefits and Their Implications," IZA Discussion Papers 14396, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp14396
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    Cited by:

    1. 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.
    2. 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.
    3. 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).
    4. Celhay, Pablo & Meyer, Bruce D. & Mittag, Nikolas, 2022. "Stigma in Welfare Programs," IZA Discussion Papers 15431, Institute of Labor Economics (IZA).
    5. Bollinger, Christopher R. & Tasseva, Iva, 2023. "Income source confusion using the SILC," LSE Research Online Documents on Economics 119351, London School of Economics and Political Science, LSE Library.
    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. 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.
    9. 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

    Keywords

    linked survey data; measurement error; item non-response; imputation; program receipt;
    All these keywords.

    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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