TY - JOUR AU - Remler,Dahlia K. AU - Zivin,Joshua Graff AU - Glied,Sherry A. TI - Modeling Health Insurance Expansions: Effects of Alternate Approaches JF - National Bureau of Economic Research Working Paper Series VL - No. 9130 PY - 2002 Y2 - August 2002 UR - http://www.nber.org/papers/w9130 L1 - http://www.nber.org/papers/w9130.pdf N1 - Author contact info: dremler Joshua S. Graff Zivin University of California, San Diego 9500 Gilman Drive, MC 0519 La Jolla, CA 92093-0519 Tel: 858/822-6438 E-Mail: jgraffzivin@ucsd.edu Sherry A. Glied Mailman School of Public Health Columbia University Department of Health Policy and Management 600 West 168th Street, Room 610 New York, NY 10032 Tel: 212/305-0299 Fax: 212/305-3405 E-Mail: sag1@columbia.edu AB - Estimates of the costs and consequences of many types of public policy proposals play an important role in the development and adoption of particular policy programs. Estimates of the same, or similar, policies that employ different modeling approaches can yield widely divergent results. Such divergence often undermines effective policy-making. These problems are particularly prominent for health insurance expansion programs. Concern focuses on predictions of the numbers of individuals that will be insured and the costs of the proposals. Several different simulation modeling approaches are used to predict these effects, making the predictions difficult to compare. In this paper, we do the following: (1) We categorize and describe the different approaches used; (2) we explain the conceptual and theoretical relationships between the methods; (3) we demonstrate empirically an example of the (quite restrictive) conditions under which all approaches can yield quantitatively identical predictions; and (4) we empirically demonstrate conditions under which the approaches diverge and the quantitative extent of that divergence. All modeling approaches implicitly make assumptions about functional form that impose restrictions on unobservable heterogeneity. Those assumptions can dramatically affect the quantitative predictions made. ER -