IDEAS home Printed from https://ideas.repec.org/a/aea/jecper/v29y2015i4p199-226.html
   My bibliography  Save this article

Household Surveys in Crisis

Author

Listed:
  • Bruce D. Meyer
  • Wallace K. C. Mok
  • James X. Sullivan

Abstract

Household surveys, one of the main innovations in social science research of the last century, are threatened by declining accuracy due to reduced cooperation of respondents. While many indicators of survey quality have steadily declined in recent decades, the literature has largely emphasized rising nonresponse rates rather than other potentially more important dimensions to the problem. We divide the problem into rising rates of nonresponse, imputation, and measurement error, documenting the rise in each of these threats to survey quality over the past three decades. A fundamental problem in assessing biases due to these problems in surveys is the lack of a benchmark or measure of truth, leading us to focus on the accuracy of the reporting of government transfers. We provide evidence from aggregate measures of transfer reporting as well as linked microdata. We discuss the relative importance of misreporting of program receipt and conditional amounts of benefits received, as well as some of the conjectured reasons for declining cooperation and for survey errors. We end by discussing ways to reduce the impact of the problem including the increased use of administrative data and the possibilities for combining administrative and survey data.

Suggested Citation

  • Bruce D. Meyer & Wallace K. C. Mok & James X. Sullivan, 2015. "Household Surveys in Crisis," Journal of Economic Perspectives, American Economic Association, vol. 29(4), pages 199-226, Fall.
  • Handle: RePEc:aea:jecper:v:29:y:2015:i:4:p:199-226
    Note: DOI: 10.1257/jep.29.4.199
    as

    Download full text from publisher

    File URL: http://www.aeaweb.org/articles.php?doi=10.1257/jep.29.4.199
    Download Restriction: no

    File URL: http://www.aeaweb.org/jep/data/2904/29040199_data.zip
    Download Restriction: no

    File URL: http://www.aeaweb.org/jep/ds/2904/29040199_ds.zip
    Download Restriction: no

    File URL: http://www.aeaweb.org/jep/app/2904/29040199_app.pdf
    Download Restriction: Access to full text is restricted to AEA members and institutional subscribers.
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Christopher R. Bollinger & Barry T. Hirsch, 2006. "Match Bias from Earnings Imputation in the Current Population Survey: The Case of Imperfect Matching," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 483-520, July.
    2. Scherpf, Erik & Newman, Constance & Prell, Mark, 2014. "Targeting of Supplemental Nutrition Assistance Program Benefits: Evidence from the ACS and NY SNAP Administrative Records," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 174295, Agricultural and Applied Economics Association.
    3. Lisa Barrow & Jonathan Davis, 2012. "The upside of down: postsecondary enrollment in the Great Recession," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 36(Q IV), pages 117-129.
    4. Bollinger, Christopher R & David, Martin H, 2001. "Estimation with Response Error and Nonresponse: Food-Stamp Participation in the SIPP," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 129-141, April.
    5. John M. Abowd & Martha H. Stinson, 2013. "Estimating Measurement Error in Annual Job Earnings: A Comparison of Survey and Administrative Data," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1451-1467, December.
    6. 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.
    7. Christopher D. Carroll & Thomas F. Crossley & John Sabelhaus, 2015. "Improving the Measurement of Consumer Expenditures," NBER Books, National Bureau of Economic Research, Inc, number carr11-1.
    8. Mark Aguiar & Erik Hurst, 2007. "Measuring Trends in Leisure: The Allocation of Time Over Five Decades," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(3), pages 969-1006.
    9. Duncan, Greg J & Hill, Daniel H, 1989. "Assessing the Quality of Household Panel Data: The Case of the Panel Study of Income Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(4), pages 441-452, October.
    10. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    11. Adam Bee & Bruce D. Meyer & James X. Sullivan, 2013. "The Validity of Consumption Data: Are the Consumer Expenditure Interview and Diary Surveys Informative?," NBER Chapters, in: Improving the Measurement of Consumer Expenditures, pages 204-240, National Bureau of Economic Research, Inc.
    12. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mittag, Nikolas, 2016. "Correcting for Misreporting of Government Benefits," IZA Discussion Papers 10266, Institute of Labor Economics (IZA).
    2. 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," NBER Working Papers 21676, National Bureau of Economic Research, Inc.
    3. Meyer, Bruce D. & Mittag, Nikolas, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," IZA Discussion Papers 12266, Institute of Labor Economics (IZA).
    4. Bruckmeier, Kerstin & Riphahn, Regina T. & Wiemers, Jürgen, 2019. "Benefit underreporting in survey data and its consequences for measuring non-take-up: new evidence from linked administrative and survey data," IAB-Discussion Paper 201906, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    5. Michele Lalla & Maddalena Cavicchioli, 2020. "Nonresponse and measurement errors in income: matching individual survey data with administrative tax data," Department of Economics 0170, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    6. Bruce D. Meyer & Wallace K. C. Mok & James X. Sullivan, 2009. "The Under-Reporting of Transfers in Household Surveys: Its Nature and Consequences," NBER Working Papers 15181, National Bureau of Economic Research, Inc.
    7. Meyer, Bruce D. & Mittag, Nikolas, 2021. "An empirical total survey error decomposition using data combination," Journal of Econometrics, Elsevier, vol. 224(2), pages 286-305.
    8. Bruce D. Meyer & James X. Sullivan, 2017. "Consumption and Income Inequality in the U.S. Since the 1960s," NBER Working Papers 23655, National Bureau of Economic Research, Inc.
    9. Meyer, Bruce D. & Mittag, Nikolas, 2017. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," IZA Discussion Papers 10943, Institute of Labor Economics (IZA).
    10. 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.
    11. Bruce D. Meyer & Nikolas Mittag, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," NBER Working Papers 25738, National Bureau of Economic Research, Inc.
    12. Bollinger, Christopher R. & Hirsch, Barry & Hokayem, Charles M. & Ziliak, James P., 2018. "Trouble in the Tails? What We Know about Earnings Nonresponse Thirty Years after Lillard, Smith, and Welch," IZA Discussion Papers 11710, Institute of Labor Economics (IZA).
    13. Bruce Meyer & Nikolas Mittag, 2017. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," Working Papers 2017-075, Human Capital and Economic Opportunity Working Group.
    14. Kerstin Bruckmeier & Katrin Hohmeyer & Stefan Schwarz, 2018. "Welfare receipt misreporting in survey data and its consequences for state dependence estimates: new insights from linked administrative and survey data," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 52(1), pages 1-21, December.
    15. Olivier Coibion & Yuriy Gorodnichenko & Dmitri Koustas, 2021. "Consumption Inequality and the Frequency of Purchases," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(4), pages 449-482, October.
    16. Pottier, Antonin, 2022. "Expenditure elasticity and income elasticity of GHG emissions: A survey of literature on household carbon footprint," Ecological Economics, Elsevier, vol. 192(C).
    17. Kilic,Talip & Van den Broeck,Goedele & Koolwal,Gayatri B. & Moylan,Heather G., 2020. "Are You Being Asked ? Impacts of Respondent Selection on Measuring Employment," Policy Research Working Paper Series 9152, The World Bank.
    18. Fisher, Jonathan D. & Houseworth, Christina A., 2013. "Occupation inflation in the Current Population Survey," Journal of Economic and Social Measurement, IOS Press, issue 3, pages 243-261.
    19. Christopher R. Bollinger & Barry T. Hirsch, 2010. "GDP & Beyond – die europäische Perspektive," RatSWD Working Papers 165, German Data Forum (RatSWD).
    20. Zachary H. Seeskin, 2016. "Evaluating the Use of Commercial Data to Improve Survey Estimates of Property Taxes," CARRA Working Papers 2016-06, Center for Economic Studies, U.S. Census Bureau.

    More about this item

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aea:jecper:v:29:y:2015:i:4:p:199-226. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Michael P. Albert (email available below). General contact details of provider: https://edirc.repec.org/data/aeaaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.