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Partially Identifying Treatment Effects with an Application to Covering the Uninsured

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  • Kreider, Brent
  • Hill, Steven C.

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% and 16%, respectively, across the nonelderly population.

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Bibliographic Info

Paper provided by Iowa State University, Department of Economics in its series Staff General Research Papers with number 12296.

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Date of creation: 16 Apr 2005
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Publication status: Published in Journal of Human Resources, April 2009, vol. 44 no. 2, pp. 409-449
Handle: RePEc:isu:genres:12296

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Postal: Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070
Phone: +1 515.294.6741
Fax: +1 515.294.0221
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Web page: http://www.econ.iastate.edu
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References

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  1. Jonathan Gruber & Brigitte C. Madrian, 2002. "Health Insurance, Labor Supply, and Job Mobility: A Critical Review of the Literature," NBER Working Papers 8817, National Bureau of Economic Research, Inc.
  2. 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.
  3. repec:att:wimass:9217 is not listed on IDEAS
  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, 07.
  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. Molinari, Francesca, 2005. "Missing Treatments," Working Papers 05-11, Cornell University, Center for Analytic Economics.
  7. 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.
  8. Hugo Benitez-Silva & Moshe Buchinsky & Hiu Man Chan & Sofia Cheidvasser & John Rust, 2000. "How Large is the Bias in Self-Reported Disability?," Working Papers 2000-01, Brown University, Department of Economics.
  9. Charles F. Manski & John V. Pepper, 1998. "Monotone Instrumental Variables with an Application to the Returns to Schooling," NBER Technical Working Papers 0224, National Bureau of Economic Research, Inc.
  10. 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.
  11. 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.
  12. Bollinger, Christopher R., 1996. "Bounding mean regressions when a binary regressor is mismeasured," Journal of Econometrics, Elsevier, vol. 73(2), pages 387-399, August.
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Cited by:
  1. Gundersen, Craig & Kreider, Brent, 2006. "Food Stamps and Food Insecurity: What Can Be Learned in the Presence of Non-Classical Measurement Error?," Staff General Research Papers 12690, Iowa State University, Department of Economics.
  2. 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, American Statistical Association, vol. 107(499), pages 958-975, September.
  3. Stefan Boes, 2009. "Bounds on Counterfactual Distributions Under Semi-Monotonicity Constraints," Working Papers 0920, University of Zurich, Socioeconomic Institute.
  4. 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.
  5. 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.
  6. Huber, Martin & Mellace, Giovanni, 2011. "Sharp bounds on causal effects under sample selection," Economics Working Paper Series 1134, University of St. Gallen, School of Economics and Political Science.
  7. 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.
  8. Kreider, Brent, 2006. "Partially Identifying the Prevalence of Health Insurance Given Contaminated Sampling Response Error," Staff General Research Papers 12588, Iowa State University, Department of Economics.

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