Kreider, Brent, and Steven C. Hill. 2009. “Partially Identifying Treatment Effects with an Application to Covering the Uninsured.” Journal of Human Resources 44(2): 409–449.
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.
Brent Kreider is an associate professor of economics at Iowa State University. Steven C. Hill is a senior economist in the Center for Financing, Access and Cost Trends (CFACT) of the Agency for Healthcare Research and Quality (AHRQ). Dr. Kreider acknowledges generous financial support by a grant from the Robert Wood Johnson Foundation through the Economic Research Initiative on the Uninsured (ERIU). We received many helpful comments on earlier versions of this paper from participants at the 2004 ERUI conference at the University of Michigan, the 2004 Southern Economic Association Meeting, the CFACT seminar series, Georgetown University, Iowa State University, the University of Virginia, the Inaugural Conference of the American Society of Health Economists, and other readers. Discussant comments from John Bound and Shakeeb Khan were especially useful, as were comments from anonymous referees. Kathleen McMillan provided expert programming to construct key health insurance variables, and Dzmitry Asinski provided valuable assistance with the data. The views expressed in this paper are those of the authors, and no official endorsement by AHRQ or the U.S. Department of Health and Human Services is intended or should be inferred. All errors remain our own. The data used in this article can be used in the AHRQ Data Center and the facilities of the U.S. Census Research Data Center (RDC) network beginning October 2009 through September 2012. Contact Steven C. Hill, CFACT, AHRQ, 540 Gaither Rd, Rockville, MD 20850, shill@ahrq.gov.