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I didn't tell, and I won't tell: dynamic response error in the SIPP

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  • Martin H. David

    (Joint Program for Survey Methodology, University of Maryland, USA)

  • Christopher R. Bollinger

    (Department of Economics, University of Kentucky, USA)

Abstract

Using state administrative records matched to the 1984 Survey of Income and Program Participation, we examine intertemporal relationships in response errors. False negative errors in reporting food stamps are highly correlated across interviews for the same household. Hypotheses that the error process can be explained by learning behaviour are not supported. Bivariate probit of response error in two periods reveals that responses to covariates are stable over time and the latent error terms are positively correlated. These findings support the hypothesis that respondents have a latent tendency to cooperate (or not cooperate) with surveys. Copyright © 2005 John Wiley & Sons, Ltd.

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  • Martin H. David & Christopher R. Bollinger, 2005. "I didn't tell, and I won't tell: dynamic response error in the SIPP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 563-569.
    • Handle: RePEc:jae:japmet:v:20:y:2005:i:4:p:563-569
    DOI: 10.1002/jae.794
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    1. Griliches, Zvi & Hausman, Jerry A., 1986. "Errors in variables in panel data," Journal of Econometrics, Elsevier, vol. 31(1), pages 93-118, February.
    2. Greenberg, David & Halsey, Harlan, 1983. "Systematic Misreporting and Effects of Income Maintenance Experiments on Work Effort: Evidence from the Seattle-Denver Experiment," Journal of Labor Economics, University of Chicago Press, vol. 1(4), pages 380-407, October.
    3. Mathiowetz, Nancy A & Duncan, Greg J, 1988. "Out of Work, Out of Mind: Response Errors in Retrospective Reports of Unemployment," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(2), pages 221-229, April.
    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. Ferber, Robert, 1969. "Validation of a National Survey of Consumer Financial Characteristics: Savings Accounts," The Review of Economics and Statistics, MIT Press, vol. 51(4), pages 436-444, November.
    6. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
    7. Bollinger, Christopher R, 1998. "Measurement Error in the Current Population Survey: A Nonparametric Look," Journal of Labor Economics, University of Chicago Press, vol. 16(3), pages 576-594, July.
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    Cited by:

    1. Erik Scherpf & Benjamin Cerf, 2019. "Local Labor Demand and Program Participation Dynamics: Evidence from New York SNAP Administrative Records," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 38(2), pages 394-425, March.
    2. Brizmohun, Roshini & Duffy, Patricia A., 2016. "Do Personal Attitudes about Welfare Influence Food Stamp Participation?," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235698, Agricultural and Applied Economics Association.
    3. Charles L. Baum & Shin-Yi Chou, 2011. "The Socio-Economic Causes of Obesity," NBER Working Papers 17423, National Bureau of Economic Research, Inc.
    4. Craig Gundersen & Brent Kreider, 2008. "Food Stamps and Food Insecurity: What Can Be Learned in the Presence of Nonclassical Measurement Error?," Journal of Human Resources, University of Wisconsin Press, vol. 43(2), pages 352-382.
    5. Thomas F. Crossley & Joachim K. Winter, 2014. "Asking Households about Expenditures: What Have We Learned?," NBER Chapters, in: Improving the Measurement of Consumer Expenditures, pages 23-50, National Bureau of Economic Research, Inc.
    6. Sonja C. Kassenboehmer & Stefanie Schurer, 2018. "Survey item-response behavior as an imperfect proxy for unobserved ability: Theory and application," Working Papers 2018-035, Human Capital and Economic Opportunity Working Group.
    7. Charles L. Baum II, 2010. "The Effects of Food Stamps on Obesity," Working Papers 201003, Middle Tennessee State University, Department of Economics and Finance.
    8. Benjamin Cerf Harris, 2014. "Within and Across County Variation in SNAP Misreporting: Evidence from Linked ACS and Administrative Records," CARRA Working Papers 2014-05, Center for Economic Studies, U.S. Census Bureau.
    9. Andrea Neri & Roberta Zizza, 2010. "Income reporting behaviour in sample surveys," Temi di discussione (Economic working papers) 777, Bank of Italy, Economic Research and International Relations Area.
    10. 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.
    11. 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.
    12. Himelein,Kristen, 2015. "Interviewer effects in subjective survey questions: evidence from Timor-Leste," Policy Research Working Paper Series 7208, The World Bank.
    13. Jonathan A. Parker & Nicholas S. Souleles & Christopher D. Carroll, 2014. "The Benefits of Panel Data in Consumer Expenditure Surveys," NBER Chapters, in: Improving the Measurement of Consumer Expenditures, pages 75-99, National Bureau of Economic Research, Inc.
    14. Zheng, Yuqing & (Jason) Zhao, Jianqiang & Buck, Steven & Burney, Shaheer & Kaiser, Harry M. & Wilson, Norbert L., 2021. "Putting grocery food taxes on the table: Evidence for food security policy-makers," Food Policy, Elsevier, vol. 101(C).
    15. Nikolas Mittag, 2013. "A Method Of Correcting For Misreporting Applied To The Food Stamp Program," Working Papers 13-28, Center for Economic Studies, U.S. Census Bureau.
    16. Kruppe, Thomas & Matthes, Britta & Unger, Stefanie, 2014. "Effectiveness of data correction rules in process-produced data : the case of educational attainment," IAB-Discussion Paper 201415, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    17. Erik Scherpf & Benjamin Cerf, 2016. "Local Labor Demand and Program Participation Dynamics," CARRA Working Papers 2016-10, Center for Economic Studies, U.S. Census Bureau.
    18. Jordan W. Jones & Charles Courtemanche & Augustine Denteh & James Marton & Rusty Tchernis, 2022. "Do state Supplemental Nutrition Assistance Program policies influence program participation among seniors?," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 591-608, June.
    19. 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).

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