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Errors in Survey Reporting and Imputation and Their Effects on Estimates of Food Stamp Program Participation

Author

Listed:
  • Bruce D. Meyer
  • Nikolas Mittag
  • Robert M. Goerge

Abstract

Accurately measuring government benefit receipt in household surveys is necessary when studying disadvantaged populations and welfare programs. The Food Stamp Program is especially important given its size and recent growth. To validate survey reports, we link administrative data on participation in two states to three key household surveys. We find that between 23 and 50 percent of true food stamp recipient households do not report receipt. A substantial number of true nonrecipients are also recorded as recipients. We examine reasons for these errors, including imputation, an important source of error. Error rates vary with household characteristics, implying complicated biases in multivariate analyses, such as regressions. We directly examine biases in common survey-based estimates of program receipt by comparing them to estimates from our linked data. We find that the survey estimates understate participation among single parents, nonwhites, and low-income households and also lead to errors in multiple program receipt and time and age patterns of receipt.

Suggested Citation

  • Bruce D. Meyer & Nikolas Mittag & Robert M. Goerge, 2022. "Errors in Survey Reporting and Imputation and Their Effects on Estimates of Food Stamp Program Participation," Journal of Human Resources, University of Wisconsin Press, vol. 57(5), pages 1605-1644.
    • Handle: RePEc:uwp:jhriss:v:57:y:2022:i:5:p:1605-1644
    Note: DOI: 10.3368/jhr.58.1.0818-9704R2
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    File URL: http://jhr.uwpress.org/cgi/reprint/57/5/1605
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    Citations

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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. 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.
    3. Pourya Valizadeh & Bart L. Fischer & Henry L. Bryant, 2024. "SNAP enrollment cycles: New insights from heterogeneous panel models with cross‐sectional dependence," American Journal of Agricultural Economics, John Wiley & Sons, vol. 106(1), pages 354-381, January.
    4. Flores-Lagunes, Alfonso & Jales, Hugo B. & Liu, Judith & Wilson, Norbert L., 2023. "Moving Policies Toward Racial and Ethnic Equality: The Case of the Supplemental Nutrition Assistance Program," GLO Discussion Paper Series 1272, Global Labor Organization (GLO).
    5. Liu, Xueyue & Zuo, Sharon Xuejing, 2023. "From equality to polarization: Changes in urban China’s gender earnings gap from 1988 to 2016," Journal of Economic Behavior & Organization, Elsevier, vol. 205(C), pages 303-337.
    6. Alfonso Flores‐Lagunes & Hugo B. Jales & Judith Liu & Norbert L. Wilson, 2024. "Moving policies toward racial and ethnic equality: The case of the supplemental nutrition assistance program," American Journal of Agricultural Economics, John Wiley & Sons, vol. 106(2), pages 573-594, March.

    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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