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Estimating the Impacts of Program Benefits: Using Instrumental Variables with Underreported and Imputed Data

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Listed:
  • Melvin Stephens, Jr.
  • Takashi Unayama

Abstract

Survey non-response has risen in recent years which has increased the share of imputed and underreported values found on commonly used datasets. While this trend has been well-documented for earnings, the growth in non-response to government transfers questions has received far less attention. We demonstrate analytically that the underreporting and imputation of transfer benefits can lead to program impact estimates that are substantially overstated when using instrumental variables methods to correct for endogeneity and/or measurement error in benefit amounts. We document the importance of failing to account for these issues using two empirical examples.

Suggested Citation

  • Melvin Stephens, Jr. & Takashi Unayama, 2015. "Estimating the Impacts of Program Benefits: Using Instrumental Variables with Underreported and Imputed Data," NBER Working Papers 21248, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:21248
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    References listed on IDEAS

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    1. Lillard, Lee & Smith, James P & Welch, Finis, 1986. "What Do We Really Know about Wages? The Importance of Nonreporting and Census Imputation," Journal of Political Economy, University of Chicago Press, vol. 94(3), pages 489-506, June.
    2. Patrick Kline & Andres Santos, 2013. "Sensitivity to missing data assumptions: Theory and an evaluation of the U.S. wage structure," Quantitative Economics, Econometric Society, vol. 4(2), pages 231-267, July.
    3. Giovanni Mastrobuoni & Matthew Weinberg, 2009. "Heterogeneity in Intra-monthly Consumption Patterns, Self-Control, and Savings at Retirement," American Economic Journal: Economic Policy, American Economic Association, vol. 1(2), pages 163-189, August.
    4. James J. Heckman & Paul A. LaFontaine, 2006. "Bias-Corrected Estimates of GED Returns," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 661-700, July.
    5. 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.
    6. Wim Van Lancker & Joris Ghysels & Bea Cantillon, 2012. "An international comparison of the impact of child benefits on poverty outcomes for single mothers," Working Papers 1203, Herman Deleeck Centre for Social Policy, University of Antwerp.
    7. Thomas J. Kane & Cecilia Elena Rouse & Douglas Staiger, 1999. "Estimating Returns to Schooling When Schooling is Misreported," NBER Working Papers 7235, National Bureau of Economic Research, Inc.
    8. 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.
    9. Melvin Stephens & Takashi Unayama, 2011. "The Consumption Response to Seasonal Income: Evidence from Japanese Public Pension Benefits," American Economic Journal: Applied Economics, American Economic Association, vol. 3(4), pages 86-118, October.
    10. Mogstad, M. & Wiswall, M., 2012. "Instrumental variables estimation with partially missing instruments," Economics Letters, Elsevier, vol. 114(2), pages 186-189.
    11. Brent Kreider & John V. Pepper & Craig Gundersen & Dean Jolliffe, 2012. "Identifying the Effects of SNAP (Food Stamps) on Child Health Outcomes When Participation Is Endogenous and Misreported," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 958-975, September.
    12. 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.
    13. Barry T. Hirsch & Edward J. Schumacher, 2004. "Match Bias in Wage Gap Estimates Due to Earnings Imputation," Journal of Labor Economics, University of Chicago Press, vol. 22(3), pages 689-722, July.
    14. Card, David, 1996. "The Effect of Unions on the Structure of Wages: A Longitudinal Analysis," Econometrica, Econometric Society, vol. 64(4), pages 957-979, July.
    15. Melvin Stephens Jr & Takashi Unayama, 2015. "Child Benefit Payments and Household Wealth Accumulation," The Japanese Economic Review, Japanese Economic Association, vol. 66(4), pages 447-465, December.
    16. Thomas J. Kane & Cecilia Rouse & Douglas Staiger, 1999. "Estimating Returns to Schooling When Schooling is Misreported," Working Papers 798, Princeton University, Department of Economics, Industrial Relations Section..
    17. Esmeralda A. Ramalho & Richard J. Smith, 2013. "Discrete Choice Non-Response," Review of Economic Studies, Oxford University Press, vol. 80(1), pages 343-364.
    18. Nicholas S. Souleles, 1999. "The Response of Household Consumption to Income Tax Refunds," American Economic Review, American Economic Association, vol. 89(4), pages 947-958, September.
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    Citations

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    Cited by:

    1. Melvin Stephens, Jr. & Desmond J. Toohey, 2018. "The Impact of Health on Labor Market Outcomes: Experimental Evidence from MRFIT," NBER Working Papers 24231, National Bureau of Economic Research, Inc.
    2. Pierre Nguimkeu & Augustine Denteh & Rusty Tchernis, 2017. "On the Estimation of Treatment Effects with Endogenous Misreporting," NBER Working Papers 24117, National Bureau of Economic Research, Inc.
    3. Padmaja Ayyagari & David Frisvold, 2016. "The Impact of Social Security Income on Cognitive Function at Older Ages Full Access," American Journal of Health Economics, MIT Press, vol. 2(4), pages 463-488, Fall.
    4. Martha Bailey & Connor Cole & Morgan Henderson & Catherine Massey, 2017. "How Well Do Automated Methods Perform in Historical Samples? Evidence from New Ground Truth," NBER Working Papers 24019, National Bureau of Economic Research, Inc.
    5. Melvin Stephens Jr & Takashi Unayama, 2015. "Child Benefit Payments and Household Wealth Accumulation," The Japanese Economic Review, Japanese Economic Association, vol. 66(4), pages 447-465, December.
    6. Padmaja Ayyagari & David Frisvold, 2015. "The Impact of Social Security Income on Cognitive Function at Older Ages," NBER Working Papers 21484, National Bureau of Economic Research, Inc.

    More about this item

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • H53 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Welfare Programs
    • H55 - Public Economics - - National Government Expenditures and Related Policies - - - Social Security and Public Pensions

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