Integrated Insurance Design in the Presence of Multiple Medical Technologies

14 Pages Posted: 31 Jan 2007 Last revised: 2 Sep 2022

See all articles by Dana P. Goldman

Dana P. Goldman

National Bureau of Economic Research (NBER); University of Southern California

Tomas Philipson

University of Chicago; National Bureau of Economic Research (NBER)

Date Written: January 2007

Abstract

The classic theory of moral hazard concerns the insurance of a single good and predicts that co-insurance is larger when the elasticity of demand is higher and when small risks are insured. We extend this analysis to the insurance of multiple goods; for example, the simultaneous insurance of medical services and prescription drugs. We show that when multiple goods are either complements or substitutes--so that a change in co-insurance for one service affects the demand of others--the classic moral hazard results do not hold. For example, the single good model would predict high co-payments for prescription drugs since drug demand is elastic and of modest financial risk. However, a model of multi-good insurance suggests such drug coverage may optimally involve zero or even negative co-insurance when it is a substitute to other services insured such as hospital care or physician services. We summarize some of the empirical evidence in support of markets adopting optimal integrated pricing structures rather than individually optimal pricing structures.

Suggested Citation

Goldman, Dana P. and Philipson, Tomas J., Integrated Insurance Design in the Presence of Multiple Medical Technologies (January 2007). NBER Working Paper No. w12870, Available at SSRN: https://ssrn.com/abstract=960445

Dana P. Goldman (Contact Author)

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
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University of Southern California ( email )

2250 Alcazar Street
Los Angeles, CA 90089
United States

Tomas J. Philipson

University of Chicago ( email )

Graduate School of Business
1101 East 58th Street
60637

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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