Industrial Reorganization: Learning About Patient Substitution Patterns from Natural Experiments

Federal Trade Commission Working Paper No. 329

82 Pages Posted: 26 May 2016

See all articles by Devesh Raval

Devesh Raval

Federal Trade Commission Bureau of Economics

Ted Rosenbaum

Federal Trade Commission Bureau of Economics

Nathan Wilson

Government of the United States of America - Federal Trade Commission, Bureau of Economics

Date Written: May 25, 2016

Abstract

Despite their widespread usage, little is known about the predictive accuracy of different discrete choice demand models. To evaluate their performance, we use a series of natural disasters that unexpectedly removed hospitals from consumers' choice sets. We compare the model predictions of post-disaster behavior to the benchmark of actual post-disaster consumer behavior. Across our different settings, we find that models that allow for flexible interactions between patient characteristics and unobserved hospital quality perform the best and that it is important to use different classes of models. Further, the use of less accurate models could lead to more lax merger enforcement.

Keywords: hospitals, natural experiment, patient choice, forecasting, antitrust

JEL Classification: C18, I11, L1, L41

Suggested Citation

Raval, Devesh and Rosenbaum, Ted and Wilson, Nathan, Industrial Reorganization: Learning About Patient Substitution Patterns from Natural Experiments (May 25, 2016). Federal Trade Commission Working Paper No. 329. Available at SSRN: https://ssrn.com/abstract=2784772 or http://dx.doi.org/10.2139/ssrn.2784772

Devesh Raval (Contact Author)

Federal Trade Commission Bureau of Economics ( email )

600 Pennsylvania Ave NW
Washington, DC 20580
United States

Ted Rosenbaum

Federal Trade Commission Bureau of Economics ( email )

600 Pennsylvania Ave NW
Washington, DC 20580
United States

Nathan Wilson

Government of the United States of America - Federal Trade Commission, Bureau of Economics ( email )

600 Pennsylvania Avenue, NW
Washington, DC 20580
United States
202 326 3485 (Phone)

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