[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp11776.html
   My bibliography  Save this paper

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

Author

Listed:
  • Meyer, Bruce D.

    (University of Chicago)

  • Mittag, Nikolas

    (CERGE-EI)

  • Goerge, Robert M.

    (Chapin Hall)

Abstract
Accurately measuring government benefit receipt in household surveys is necessary when studying disadvantaged populations and the programs that serve them. The Food Stamp Program is especially important given its size and recent growth. To validate survey reports, we use administrative data on participation in two states linked to the American Community Survey (ACS), the Current Population Survey (CPS), and the Survey of Income and Program Participation (SIPP). We find that 23 percent of true food stamp recipient households do not report receipt in the SIPP, 35 percent in the ACS, and fully 50 percent in the CPS. A substantial number of true non-recipients are also recorded as recipients, especially in the SIPP. We examine reasons for these errors including imputation, an important source of error. Both false negative and false positive reports vary with household characteristics, implying complicated biases in multivariate analyses, such as regressions. We then directly examine biases in common survey-based estimates of program receipt by comparing them to estimates from our combined administrative and survey data. We find that the survey estimates understate participation among single parents, non-whites, and low-income households, and also lead to errors in multiple program receipt, and time and age patterns of receipt.

Suggested Citation

  • Meyer, Bruce D. & Mittag, Nikolas & Goerge, Robert M., 2018. "Errors in Survey Reporting and Imputation and Their Effects on Estimates of Food Stamp Program Participation," IZA Discussion Papers 11776, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp11776
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp11776.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lorenzo Almada & Ian McCarthy & Rusty Tchernis, 2016. "What Can We Learn about the Effects of Food Stamps on Obesity in the Presence of Misreporting?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 98(4), pages 997-1017.
    2. 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.
    3. 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.
    4. Douglas Almond & Hilary W. Hoynes & Diane Whitmore Schanzenbach, 2011. "Inside the War on Poverty: The Impact of Food Stamps on Birth Outcomes," The Review of Economics and Statistics, MIT Press, vol. 93(2), pages 387-403, May.
    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. Bruce Meyer & Robert Goerge, 2011. "Errors in Survey Reporting and Imputation and Their Effects on Estimates of Food Stamp Program Participation," Working Papers 11-14, Center for Economic Studies, U.S. Census Bureau.
    2. Charles Courtemanche & Augustine Denteh & Rusty Tchernis, 2019. "Estimating the Associations between SNAP and Food Insecurity, Obesity, and Food Purchases with Imperfect Administrative Measures of Participation," Southern Economic Journal, John Wiley & Sons, vol. 86(1), pages 202-228, July.
    3. Karen S Hamrick & Margaret Andrews, 2016. "SNAP Participants’ Eating Patterns over the Benefit Month: A Time Use Perspective," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-18, July.
    4. 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.
    5. 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.
    6. 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.
    7. Bruce D. Meyer & James X. Sullivan, 2003. "Measuring the Well-Being of the Poor Using Income and Consumption," NBER Working Papers 9760, National Bureau of Economic Research, Inc.
    8. Bharadwaj, Prashant & Pai, Mallesh M. & Suziedelyte, Agne, 2017. "Mental health stigma," Economics Letters, Elsevier, vol. 159(C), pages 57-60.
    9. 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.
    10. Susan Chen & Le Wang, 2021. "SNAP participation, diet quality, and obesity: robust evidence with estimation techniques without external instrumental variables," Empirical Economics, Springer, vol. 61(3), pages 1641-1667, September.
    11. 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].
    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. Jorge González Chapela, 2022. "A Binary Choice Model with Sample Selection and Covariate-Related Misclassification," Econometrics, MDPI, vol. 10(2), pages 1-20, March.
    14. Charles Baum, 2012. "The effects of food stamp receipt on weight gained by expectant mothers," Journal of Population Economics, Springer;European Society for Population Economics, vol. 25(4), pages 1307-1340, October.
    15. Meyer, Bruce D. & Mittag, Nikolas, 2017. "Misclassification in binary choice models," Journal of Econometrics, Elsevier, vol. 200(2), pages 295-311.
    16. John Abowd & Martha Stinson, 2011. "Estimating Measurement Error in SIPP Annual Job Earnings: A Comparison of Census Bureau Survey and SSA Administrative Data," Working Papers 11-20, Center for Economic Studies, U.S. Census Bureau.
    17. Kaspar W thrich, 2013. "Set Identification of Generalized Linear Predictors in the Presence of Non-Classical Measurement Errors," Diskussionsschriften dp1304, Universitaet Bern, Departement Volkswirtschaft.
    18. Fabian T C Schmidt & Clemens M Lechner & Daniel Danner, 2020. "New wine in an old bottle? A facet-level perspective on the added value of Grit over BFI–2 Conscientiousness," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-25, February.
    19. Nicholas Moellman, 2020. "Healthcare and Hunger: Effects of the ACA Medicaid Expansions on Food Insecurity in America," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(2), pages 168-186, June.
    20. Verónica Amarante & Marco Manacorda & Edward Miguel & Andrea Vigorito, 2016. "Do Cash Transfers Improve Birth Outcomes? Evidence from Matched Vital Statistics, Program, and Social Security Data," American Economic Journal: Economic Policy, American Economic Association, vol. 8(2), pages 1-43, May.

    More about this item

    Keywords

    imputation; measurement error; survey errors; program takeup; food stamps; under-reporting; poverty;
    All these keywords.

    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

    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:iza:izadps:dp11776. 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: Holger Hinte (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.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.