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What Leads to Measurement Errors? Evidence from Reports of Program Participation in Three Surveys

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

Abstract

Measurement errors are often a large source of bias in survey data. Lack of knowledge of the determinants of such errors makes it difficult for data producers to reduce the extent of errors and for data users to assess the validity of analyses using the data. We study the determinants of reporting error using high quality administrative data on government transfers linked to three major U.S. surveys. Our results support several theories of misreporting: Errors are related to event recall, forward and backward telescoping, salience of receipt, the stigma of reporting participation in welfare programs and respondent’s degree of cooperation with the survey overall. We provide evidence on how survey design choices affect reporting errors. Our findings help survey users to gauge the reliability of their data and to devise estimation strategies that can correct for systematic errors, such as instrumental variable approaches. Understanding survey errors allows survey producers to reduce them by improving survey design. Our results indicate that survey producers should take into account that higher response rates as well as collecting more detailed information may have negative effects on survey accuracy.

Suggested Citation

  • Pablo A. Celhay & Bruce D. Meyer & Nikolas Mittag, 2022. "What Leads to Measurement Errors? Evidence from Reports of Program Participation in Three Surveys," NBER Working Papers 29652, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:29652
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    3. 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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    Cited by:

    1. Nicolas Frémeaux, 2023. "The More, the Better? Individual and Joint Interviewing in Surveys," Annals of Economics and Statistics, GENES, issue 149, pages 63-96.
    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. Celhay, Pablo & Meyer, Bruce D. & Mittag, Nikolas, 2022. "Stigma in Welfare Programs," IZA Discussion Papers 15431, Institute of Labor Economics (IZA).
    4. Cameron Deal & Shea Greenberg & Gilbert Gonzales, 2024. "Sexual identity, poverty, and utilization of government services," Journal of Population Economics, Springer;European Society for Population Economics, vol. 37(2), pages 1-31, June.
    5. Krista Ruffini, 2023. "Does Unconditional Cash during Pregnancy Affect Infant Health?," Opportunity and Inclusive Growth Institute Working Papers 072, Federal Reserve Bank of Minneapolis.

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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
    • 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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