A Test of Asymmetric Learning in Competitive Insurance With Partial Information Sharing

INTRODUCTION In economics and contract theory, information asymmetry refers to the situation where one party possesses information that is not available to another party in a transaction. Asymmetric learning, a particular form of information asymmetry, arises when a "seller" could gain advantageous information on its repeat "buyers" over its rivals through repeated contracting. Asymmetric learning could result in market power, and thus lead to higher profits from repeat customers (see Kunreuther and Pauly, 1985). In this study, we aim to examine asymmetric learning in repeated contracting when the market lacks a complete information-pooling mechanism among various participants. Since the seminal work of Akerlof (1970), Spence (1973), and Rothschild and Stiglitz (1976), the pervasive effects of information asymmetry in markets have attracted much attention and have been extensively studied in numerous contexts. Historically, the insurance industry provides an interesting environment to investigate information asymmetry theoretically and empirically (see Dionne, Doherty, and Fombaron, 2000, for a comprehensive review). This empirical study differs from most of the existing literature in its concerns with the dynamics of repeated contracting. We use a unique panel data set to test whether repeat customers provide an insurer with information-based advantages over its rival companies in the Singapore automobile insurance market. The concept of asymmetric learning evolves from a dynamic context where private information develops through time. Since the initial efforts of Rothschild and Stiglitz (1976) and Wilson (1977) to remedy adverse selection in a competitive insurance market, insurance models with information asymmetry have been well developed for multiperiod contracting. In particular, different predictions on the evolution of insurer profits over time are obtained under various commitment assumptions between the contractual parties. Early works focus on the multiperiod models with full commitment on the supply side. One example is Cooper and Hayes (1987), where the contracts are offered in a way that high-risk agents select short-term contracts and low-risk agents receive long-term contracts. Thus, with experience rating, this arrangement results in a highballing profit pattern; that is, the long-term contracts are initially profitable to the insurer but become unprofitable in later periods. Recent studies look more into the limited commitment relation between an insurer and its insurees. The repeated insurance model in a competitive market without commitment was first proposed by Kunreuther and Pauly (1985), in which, through offering pure price contracts, an insurer will take a lowballing pricing strategy in that new business is unprofitable but economic rent will be extracted from repeat business. The same lowballing prediction could also be derived using the traditional screening device of price-quantity contracts. This is shown and extended in later studies such as Prendergast (1992), Nilssen (2000), and de Garidel-Thoron (2005). In an intermediate case between full commitment and no commitment, known as commitment with renegotiation, insurers have the option to recontract with their insurees though the proposition could be rejected by the insurees. With renegotiation, the observed pricing strategy of insurers could be one of highballing (Dionne and Doherty, 1994) or lowballing when outside options are endogenous (Dionne, Doherty, and Fombaron, 2000). Asymmetric learning is consistent with the lowballing prediction in that the contracting insurer possesses an informational advantage over competitors in repeatedly monitoring its policyholders, and thus has the potential to gain ex post market power and make higher profits from repeated contracting. The above competing models provide testable predictions on the temporal pattern of profits and consumer lock-in that allow us to empirically examine the significance of information asymmetry. …