Identification of Insurance Models with Multidimensional Screening

55 Pages Posted: 30 Jun 2012 Last revised: 10 Mar 2016

See all articles by Gaurab Aryal

Gaurab Aryal

Washington University in St. Louis

Isabelle Perrigne

Pennsylvania State University - College of the Liberal Arts

Vuong Quang

New York University (NYU)

Date Written: Jan 16, 2016

Abstract

This paper addresses the identification of insurance models with multidimensional screening where insurees have private information about their risk and risk aversion. The model includes a random damage and the possibility of several claims. Screening of insurees relies on their certainty equivalence. The paper then investigates how data availability on the number of offered coverages and reported claims affects the identification of the model primitives under four different scenarios. We show that the model structure is identified despite bunching due to multidimensional screening and/or a finite number of offered coverages. The observed number of claims plays a key role in the identification of the joint distribution of risk and risk aversion. In addition, the paper derives all the restrictions imposed by the model on observables. Our results are constructive with explicit equations for estimation and model testing.

Keywords: Insurance, Identification, Adverse Selection, Multidimensional Screening.

Suggested Citation

Aryal, Gaurab and Perrigne, Isabelle and Quang, Vuong, Identification of Insurance Models with Multidimensional Screening (Jan 16, 2016). Available at SSRN: https://ssrn.com/abstract=2094995 or http://dx.doi.org/10.2139/ssrn.2094995

Gaurab Aryal (Contact Author)

Washington University in St. Louis ( email )

Seigle Hall 335
One Brookings Drive
St. Louis, MO 63130
United States

Isabelle Perrigne

Pennsylvania State University - College of the Liberal Arts ( email )

University Park, PA 16802-3306
United States

Vuong Quang

New York University (NYU) ( email )

Bobst Library, E-resource Acquisitions
20 Cooper Square 3rd Floor
New York, NY 10003-711
United States

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