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

Author

Listed:
  • 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
    Note: CH LS PE
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    Cited by:

    1. Akanksha Negi & Digvijay S. Negi, 2025. "Difference‐in‐Differences With a Misclassified Treatment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(4), pages 411-423, June.
    2. Kakimoto, Shunkei & Mieno, Taro, 2025. "Size and the Nature of Measurement Error in Gridded Weather Datasets and its Consequential Estimation Bias in Regression Model: An Application to PRISM Datasets for the US Midwest Regions," 2025 AAEA & WAEA Joint Annual Meeting, July 27-29, 2025, Denver, CO 360727, Agricultural and Applied Economics Association.
    3. Jonathan Eggleston & Linden McBride & Mark Klee, 2025. "The Design of Sampling Strata for the National Household Food Acquisition and Purchase Survey," Working Papers 25-13, Center for Economic Studies, U.S. Census Bureau.
    4. Nicolas Frémeaux, 2023. "The More, the Better? Individual and Joint Interviewing in Surveys," Annals of Economics and Statistics, GENES, issue 149, pages 63-96.
    5. 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.
    6. Heng Chen & Joy Wu, 2025. "Low Response Rate from Merchants? Sample and Ask Consumers! An Application of Indirect Sampling Under a Consumer-Merchant Bipartite Network," Technical Reports 126, Bank of Canada.
    7. Wang, Guangyi & Bitler, Marianne & Schillinger, Dean & Halla, Martin & Stillman, Steven & Hamad, Rita, 2025. "Impact of the 2009 WIC revision on infant and maternal health: A quasi-experimental multi-state study," Social Science & Medicine, Elsevier, vol. 373(C).
    8. Nguyen, Ha Trong & Mitrou, Francis, 2025. "Inconsistencies in self-reported weather-related home damage among household members," GLO Discussion Paper Series 1624, Global Labor Organization (GLO).
    9. Jordan C. Stanley & Evan S. Totty, 2026. "Synthetic Data and Social Science Research: Accuracy Assessments and Practical Considerations from the SIPP Synthetic Beta," NBER Chapters, in: Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and New Findings, National Bureau of Economic Research, Inc.
    10. Dhingra, Aarushi & Fiorentini, Gianluca & Connelly, Luke, 2025. "The impact of individuals’ preventive behaviours on health and healthcare utilisation," Economics & Human Biology, Elsevier, vol. 57(C).
    11. Adam Bee & Irena Dushi & Joshua Mitchell & Brad Trenkamp, 2024. "Measuring Income of the Aged in Household Surveys: Evidence from Linked Administrative Records," Working Papers 24-32, Center for Economic Studies, U.S. Census Bureau.
    12. Li, Haizheng & Liu, Qinyi & Xu, Yiting, 2024. "Noncognitive Human Capital and Misreporting Behavior in Online Surveys," IZA Discussion Papers 17332, IZA Network @ LISER.
    13. Bertola, Giuseppe & Lo Prete, Anna, 2025. "Who prefers guessing to admitting They Don't Know? Measurement error in financial literacy surveys," Journal of Economic Behavior & Organization, Elsevier, vol. 233(C).
    14. 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.
    15. Tom Akana & Amber Lee, 2025. "How Well Do Survey Self-Reports Align with Administrative Data? The Case of U.S. Consumer Credit Records," Consumer Finance Institute discussion papers 101848, Federal Reserve Bank of Philadelphia.
    16. Richiardi, Matteo & van de Ven, Justin & Vella, Melchior, 2024. "Mind vs matter: economic and psychologic determinants of take-up rates of social benefits in the UK," Centre for Microsimulation and Policy Analysis Working Paper Series CEMPA6/24, Centre for Microsimulation and Policy Analysis at the Institute for Social and Economic Research.
    17. Jonathan Eggleston & Julia Yang, 2024. "Incorporating Administrative Data in Survey Weights for the 2018-2022 Survey of Income and Program Participation," Working Papers 24-58, Center for Economic Studies, U.S. Census Bureau.
    18. Luis Laguinge & Leonardo Gasparini & Guido Neidhöfer, 2024. "The Long-Run Effects of Conditional Cash Transfers: the Case of Bolsa Familia in Brazil," CEDLAS, Working Papers 0328, CEDLAS, Universidad Nacional de La Plata.
    19. Pablo A. Celhay & Bruce D. Meyer & Nikolas Mittag, 2022. "Stigma in Welfare Programs," NBER Working Papers 30307, National Bureau of Economic Research, Inc.
    20. Krista Ruffini, 2023. "Does Unconditional Cash during Pregnancy Affect Infant Health?," Opportunity and Inclusive Growth Institute Working Papers 072, Federal Reserve Bank of Minneapolis.
    21. Akanksha Negi & Digvijay S. Negi, 2024. "Difference-in-Differences with a Misclassified Treatment," Working Papers 121, Ashoka University, Department of Economics.

    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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