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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Paper provided by Iowa State University, Department of Economics in its series Staff General Research Papers with number
12296.
Length: Date of creation: 17 Apr 2005 Date of revision: Publication status: Published in Journal of Human Resources, April 2009, Vol. 44, No. 2, pp. 409-49. Handle: RePEc:isu:genres:12296
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Find related papers by JEL classification: C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General I1 - Health, Education, and Welfare - - Health
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References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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.
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