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Partially Identifying Treatment Effects with an Application to Covering the Uninsured

Partially Identifying Treatment Effects with an Application to Covering the Uninsured 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Human Resources University of Wisconsin Press

Partially Identifying Treatment Effects with an Application to Covering the Uninsured

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Publisher
University of Wisconsin Press
ISSN
1548-8004

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.

Journal

Journal of Human ResourcesUniversity of Wisconsin Press

Published: Apr 4, 2012

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