Asymmetric information and insurance: An experimental approach

Abstract Separate identification of moral hazard and adverse selection in insurance markets is empirically difficult. To overcome this limitation, this paper develops a series of controlled laboratory experiments to examine how adverse selection and moral hazard separately affect agent performance in a real-effort task. We explore how agent performance changes across a baseline with no insurance option, a treatment where individuals can choose to purchase insurance, and a third treatment where individuals must purchase insurance. We believe our experimental design can be used as a wind-tunnel that is flexible enough to incorporate alternative price changes or contract designs while permitting researchers to separately identify moral hazard and adverse selection under those conditions.

[1]  Rajeev Dehejia,et al.  The Effect of Automobile Insurance and Accident Liability Laws on Traffic Fatalities* , 2003, The Journal of Law and Economics.

[2]  Charles A. Holt,et al.  Risk Aversion and Incentive Effects , 2002 .

[3]  Alma Cohen,et al.  Testing for Adverse Selection in Insurance Markets , 2009 .

[4]  P. Chiappori,et al.  Moral Hazard and the Demand for Physician Services: First Lessons from a French Natural Experiment , 1998 .

[5]  Joseph E. Stiglitz,et al.  Equilibrium in Competitive Insurance Markets: An Essay on the Economics of Imperfect Information , 1976 .

[6]  D. Goldman,et al.  Disentangling Moral Hazard and Adverse Selection in Private Health Insurance , 2016 .

[7]  Jörg Oechssler,et al.  Mandatory Sick Pay Provision: A Labor Market Experiment , 2010 .

[8]  Arthur Snow,et al.  Evidence on Adverse Selection: Equilibrium Signaling and Cross-Subsidization in the Insurance Market , 1994, Journal of Political Economy.

[9]  J. Heckman,et al.  Adverse selection and moral hazard in insurance: Can dynamic data help to distinguish? , 2003 .

[10]  G. Dionne,et al.  Separating Moral Hazard from Adverse Selection and Learning in Automobile Insurance: Longitudinal Evidence from France , 2004 .

[11]  Dean S. Karlan,et al.  Observing Unobservables: Identifying Information Asymmetries with a Consumer Credit Field Experiment , 2005 .

[12]  Thomas Stratmann,et al.  Diabetes Treatments and Moral Hazard , 2007, The Journal of Law and Economics.

[13]  U. Fischbacher z-Tree: Zurich toolbox for ready-made economic experiments , 1999 .

[14]  Amanda E. Kowalski,et al.  Adverse Selection and an Individual Mandate: When Theory Meets Practice. , 2015, The American economic review.

[15]  Jörg Oechssler,et al.  Sick Pay Provision in Experimental Labor Markets , 2008 .

[16]  James M. Poterba,et al.  Adverse Selection in Insurance Markets: Policyholder Evidence from the U.K. Annuity Market , 2000, Journal of Political Economy.

[17]  I Hendel,et al.  Asymmetric information in health insurance: evidence from the National Medical Expenditure Survey. , 2001, The Rand journal of economics.

[18]  Daifeng He The life insurance market: Asymmetric information revisited , 2009 .

[19]  Bruno Jullien,et al.  Asymmetric information in insurance: general testable implications , 2006 .

[20]  R. Kaestner,et al.  New Estimates of the Labor Market Effects of Workers' Compensation Insurance , 1997 .

[21]  J. Stiglitz,et al.  The Basic Analytics of Moral Hazard , 1988 .

[22]  B. Corgnet,et al.  Why real leisure really matters: incentive effects on real effort in the laboratory , 2014 .

[23]  Ben Greiner,et al.  Subject pool recruitment procedures: organizing experiments with ORSEE , 2015, Journal of the Economic Science Association.

[24]  D. Bolduc,et al.  Workers' Compensation, Moral Hazard, and the Composition of Workplace Injuries , 2002 .

[25]  Christian Gourieroux,et al.  Testing for Evidence of Adverse Selection in the Automobile Insurance Market: A Comment , 2001, Journal of Political Economy.

[26]  Pierre-André Chiappori,et al.  Testing for Asymmetric Information in Insurance Markets , 2000, Journal of Political Economy.

[27]  Separating Moral Hazard from Adverse Selection and Learning in Automobile Insurance: Longitudinal Evidence from France , 2010 .