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Difficulty of Reaching Respondents and Nonresponse Bias: Evidence from Large Government Surveys

Author

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  • Ori Heffetz

    (The Hebrew University of Jerusalem and Cornell University)

  • Daniel B. Reeves

    (Cornell University)

Abstract

How high is unemployment? How low is labor force participation? Is obesity more prevalent among men? How large are household expenditures? We study the sources of the relevant official statistics—the Current Population Survey, the Behavioral Risk Factor Surveillance System, and the Consumer Expenditure Survey—and find that the answers depend on whether we look at easy- or at difficult-to-reach respondents, measured by the number of call and visit attempts made by interviewers. A challenge to the (conditionally-)random-nonresponse assumption, these findings empirically substantiate the theoretical warning against making population-wide estimates from surveys with low response rates.

Suggested Citation

  • Ori Heffetz & Daniel B. Reeves, 2019. "Difficulty of Reaching Respondents and Nonresponse Bias: Evidence from Large Government Surveys," The Review of Economics and Statistics, MIT Press, vol. 101(1), pages 176-191, March.
    • Handle: RePEc:tpr:restat:v:101:y:2019:i:1:p:176-191
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    Citations

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    Cited by:

    1. Naomi Feldman & Ori Heffetz, 2022. "A Grant to Every Citizen: Survey Evidence of the Impact of a Direct Government Payment in Israel," National Tax Journal, University of Chicago Press, vol. 75(2), pages 229-263.
    2. Daniel J. Benjamin & Kristen Cooper & Ori Heffetz & Miles S. Kimball, 2023. "From Happiness Data to Economic Conclusions," NBER Working Papers 31727, National Bureau of Economic Research, Inc.
    3. Adrian Chadi, 2019. "Dissatisfied with life or with being interviewed? Happiness and the motivation to participate in a survey," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 53(3), pages 519-553, October.
    4. Borgschulte, Mark & Cho, Heepyung & Lubotsky, Darren, 2022. "Partisanship and survey refusal," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 332-357.
    5. 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).
    6. Li, Teng & Barwick, Panle Jia & Deng, Yongheng & Huang, Xinfei & Li, Shanjun, 2023. "The COVID-19 pandemic and unemployment: Evidence from mobile phone data from China," Journal of Urban Economics, Elsevier, vol. 135(C).
    7. Lovett, Nicholas & Xue, Yuhan, 2020. "Family first or the kindness of strangers? Foster care placements and adult outcomes," Labour Economics, Elsevier, vol. 65(C).
    8. Ori Heffetz & Daniel Reeves, 2020. "Measuring Unemployment in Crisis: Effects of COVID-19 on Potential Biases in the CPS," NBER Working Papers 28310, National Bureau of Economic Research, Inc.
    9. Robert Bernhardt & David Munro & Erin L. Wolcott, 2024. "How does the dramatic rise of nonresponse in the Current Population Survey impact labor market indicators?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 498-512, April.
    10. Bernhardt, Robert & Munro, David & Wolcott, Erin, 2021. "How Does the Dramatic Rise of CPS Non-Response Impact Labor Market Indicators?," GLO Discussion Paper Series 781, Global Labor Organization (GLO).
    11. Holly Matulewicz & Katharine Bradley & Sarah Wagner, "undated". "Beneficiary Survey Design and Administration for Eligibility and Coverage Demonstration Evaluations," Mathematica Policy Research Reports 88ef00cc37754b84a67e9f860, Mathematica Policy Research.

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