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Partially Identifying Treatment Effects with an Application to Covering the Uninsured

  • Kreider, Brent
  • Hill, Steven C.

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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File URL: http://www.econ.iastate.edu/sites/default/files/publications/papers/p5543-2005-04-17.pdf
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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
Contact details of provider: Postal: Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070
Phone: +1 515.294.6741
Fax: +1 515.294.0221
Web page: http://www.econ.iastate.edu
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  1. 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.
  2. 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.
  3. 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.
  4. Bollinger, Christopher R., 1996. "Bounding mean regressions when a binary regressor is mismeasured," Journal of Econometrics, Elsevier, vol. 73(2), pages 387-399, August.
  5. Molinari, Francesca, 2010. "Missing Treatments," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 82-95.
  6. 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.
  7. Kreider, Brent & Pepper, John V., 2003. "Disability and Employment: Reevaluating the Evidence in Light of Reporting Errors," Staff General Research Papers 10229, Iowa State University, Department of Economics.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. Manski, C.F., 1992. "Identification Problems in the Social Sciences," Working papers 9217, Wisconsin Madison - Social Systems.
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