The Empirical Measure of Information Problems with Emphasis on Insurance Fraud and Dynamic Data

Abstract

The author would like to thank Marie-Gloriose Ingabire for her help with bibliographical research, and FQRSCQuebec and SSHRC-Canada for their financial support. He would also like to acknowledge the researchers who helped him develop several of the ideas on the subject over the years: J. Abstract:We discuss the difficult question of measuring the effects of asymmetric information problems on resource allocation. Three problems are examined: moral hazard, adverse selection, and asymmetric learning. One theoretical conclusion, drawn by many authors, is that information problems may introduce significant distortions into the economy. However, we verify, in different markets, that efficient mechanisms have been introduced in order to reduce these distortions and even eliminate, at the margin some residual information problems. This conclusion is stronger for pure adverse selection. One explanation is that adverse selection is related to exogenous characteristics, while asymmetric learning and moral hazard are due to endogenous actions that may change at any point in time. Dynamic data help to identify the three information problems by permitting causality tests.

WHAT HAPPENS WHEN COMPENSATIONFOR WHIPLASH CLAIMS Is MADE MORE GENEROUS?

“…Compensation for permanent loss of earnings capacity is only awarded after extensive examinations by health professionals, whereas compensation for temporary loss of earnings is not. Our results are consistent with existing theoretical and empirical knowledge about situations with scope for ex post moral hazard (Dionne, ; Picard, ). The use of insurance increases with the generosity of compensation when there is no state verification, but does not when there is state verification.…”

“…Butler, Kleinman, and Gardner () and Dionne () provide recent and more complete reviews of this literature. However, apart from Bolduc et al () and Dionne and St. Michel (), these studies often do not distinguish between ex ante and ex post moral hazard.…”

Selection Behavior in the Market for Private Complementary Long-Term Care Insurance in Germany

“…It should be noted that finding a positive correlation is not a sufficient condition for the existence of adverse selection, as such a correlation may also arise from the presence of moral hazard, i.e., reverse causality may be present (e.g., Chiappori et al 2006;Dionne 2013). In the latter case, individuals with a high level of insurance coverage have fewer incentives to put effort into prevention, leading to higher risks compared to individuals with lower insurance coverage (ex ante moral hazard); they may also use more benefits due to decreased marginal costs (ex post moral hazard).…”

“…In the latter case, individuals with a high level of insurance coverage have fewer incentives to put effort into prevention, leading to higher risks compared to individuals with lower insurance coverage (ex ante moral hazard); they may also use more benefits due to decreased marginal costs (ex post moral hazard). Cohen and Siegelman (2010) and Dionne (2013) discuss several approaches to disentangling different information problems, such as the causality test of Dionne, Michaud, and Dahchour (2013), which uses dynamic data to separate moral hazard from adverse selection.…”

Two Tests for Ex Ante Moral Hazard in a Market for Automobile Insurance

“…Hence, if estimated, the coefficient˛1 would provide a biased estimate of ex ante moral hazard. Second, if RT * (Arrow, 1985) and PE * (Winter, 1992) were perfectly observable, Dionne (2013) argues that when the task is to estimate an empirical model of insurance that contains a potentially endogenous variable, it is often better to instrument this variable (see Dionne, St-Amour, and Désiré, 2009;Dionne, La Haye, and Bergéres, 2010;Rowell 2011, for more details). We consider two methods, previously described by Rowell 2011, to obtain a consistent estimate of˛1 from Equation (1).…”