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Partially Identifying Treatment Effects with an Application to Covering the Uninsured

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  • Brent Kreider
  • Steven C. Hill

Abstract

We extend the nonparametric literature on partially identified probability distributions and use our analytical results to provide sharp bounds on the impact of universal health insurance on provider visits and medical expenditures. Our approach accounts for uncertainty about the reliability of self-reported insurance status as well as uncertainty created by unknown counterfactuals. We construct health insurance validation data using detailed information from the Medical Expenditure Panel Survey. Imposing relatively weak nonparametric assumptions, we estimate that under universal coverage monthly per capita provider visits and expenditures would rise by less than 8 percent and 16 percent, respectively, across the nonelderly population.

Suggested Citation

  • Brent Kreider & Steven C. Hill, 2009. "Partially Identifying Treatment Effects with an Application to Covering the Uninsured," Journal of Human Resources, University of Wisconsin Press, vol. 44(2).
  • Handle: RePEc:uwp:jhriss:v:44:y:2009:i2:p409-449
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    1. Bound, John & Burkhauser, Richard V., 1999. "Economic analysis of transfer programs targeted on people with disabilities," Handbook of Labor Economics,in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 51, pages 3417-3528 Elsevier.
    2. Manski, C.F., 1992. "Identification Problems in the Social Sciences," Working papers 9217, Wisconsin Madison - Social Systems.
    3. Michael Baker & Mark Stabile & Catherine Deri, 2004. "What Do Self-Reported, Objective, Measures of Health Measure?," Journal of Human Resources, University of Wisconsin Press, vol. 39(4).
    4. Mark C. Berger & Dan A. Black & Frank A. Scott, 1998. "How Well Do We Measure Employer-Provided Health Insurance Coverage?," Contemporary Economic Policy, Western Economic Association International, vol. 16(3), pages 356-367, July.
    5. 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.
    6. Horowitz, Joel L & Manski, Charles F, 1995. "Identification and Robustness with Contaminated and Corrupted Data," Econometrica, Econometric Society, vol. 63(2), pages 281-302, March.
    7. Craig A. Olson, 1998. "A comparison of parametric and semiparametric estimates of the effect of spousal health insurance coverage on weekly hours worked by wives," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(5), pages 543-565.
    8. Kreider, Brent & Pepper, John V., 2007. "Disability and Employment: Reevaluating the Evidence in Light of Reporting Errors," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 432-441, June.
    9. Bollinger, Christopher R., 1996. "Bounding mean regressions when a binary regressor is mismeasured," Journal of Econometrics, Elsevier, vol. 73(2), pages 387-399, August.
    10. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
    11. Jonathan Gruber & Brigitte C. Madrian, 2002. "Health Insurance, Labor Supply, and Job Mobility: A Critical Review of the Literature," JCPR Working Papers 255, Northwestern University/University of Chicago Joint Center for Poverty Research.
    12. repec:aph:ajpbhl:2000:90:6:924-928_4 is not listed on IDEAS
    13. Molinari, Francesca, 2010. "Missing Treatments," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 82-95.
    14. Hugo Benítez-Silva & Moshe Buchinsky & Hiu Man Chan & Sofia Cheidvasser & John Rust, 2004. "How large is the bias in self-reported disability?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(6), pages 649-670.
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    Cited by:

    1. Kreider, Brent, 2006. "Partially Identifying the Prevalence of Health Insurance Given Contaminated Sampling Response Error," Staff General Research Papers Archive 12588, Iowa State University, Department of Economics.
    2. Daniel Millimet & Manan Roy, 2015. "Partial identification of the long-run causal effect of food security on child health," Empirical Economics, Springer, vol. 48(1), pages 83-141, February.
    3. Krauth Brian, 2016. "Bounding a Linear Causal Effect Using Relative Correlation Restrictions," Journal of Econometric Methods, De Gruyter, vol. 5(1), pages 117-141, January.
    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. Tsunao Okumura & Emiko Usui, 2014. "Concave‐monotone treatment response and monotone treatment selection: With an application to the returns to schooling," Quantitative Economics, Econometric Society, vol. 5, pages 175-194, March.
    6. Martin Huber & Giovanni Mellace, 2015. "Sharp Bounds on Causal Effects under Sample Selection," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 129-151, February.
    7. 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.
    8. Stefan Boes, 2009. "Bounds on Counterfactual Distributions Under Semi-Monotonicity Constraints," SOI - Working Papers 0920, Socioeconomic Institute - University of Zurich.
    9. Stoye, Jörg, 2012. "Minimax regret treatment choice with covariates or with limited validity of experiments," Journal of Econometrics, Elsevier, vol. 166(1), pages 138-156.
    10. Gundersen, Craig & Kreider, Brent, 2009. "Bounding the effects of food insecurity on children's health outcomes," Journal of Health Economics, Elsevier, vol. 28(5), pages 971-983, September.
    11. Gundersen, Craig & Kreider, Brent & Pepper, John, 2012. "The impact of the National School Lunch Program on child health: A nonparametric bounds analysis," Journal of Econometrics, Elsevier, vol. 166(1), pages 79-91.
    12. Wooyoung Kim & Koohyun Kwon & Soonwoo Kwon & Sokbae (Simon) Lee, 2014. "The identification power of smoothness assumptions in models with counterfactual outcomes," CeMMAP working papers CWP17/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Jian Huang & Henriëtte Maassen van den Brink & Wim Groot, 2012. "Does education promote social capital? Evidence from IV analysis and nonparametric-bound analysis," Empirical Economics, Springer, vol. 42(3), pages 1011-1034, June.
    14. Manan Roy, 2012. "Identifying the Effect of WIC on Infant Health When Participation is Endogenous and Misreported," Departmental Working Papers 1202, Southern Methodist University, Department of Economics.
    15. Monique de Haan, 2011. "The Effect of Parents' Schooling on Child's Schooling: A Nonparametric Bounds Analysis," Journal of Labor Economics, University of Chicago Press, vol. 29(4), pages 859-892.

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