New technologies may allow principals or firms to learn more quickly about the characteristics of their agents or clients—at some cost of employing that technology. We study the implications of this speed-versus-cost tradeoff for equilibrium pricing and purchasing decisions in insurance markets featuring adverse selection. In particular, we study dynamic competitive equilibrium in a theoretical model featuring individuals who differ in their privately known risk types and featuring two types of insurers: conventional insurers who employ a legacy learning technology and tech insurers who employ a new technology. Equilibrium in our model features sorting of low-risk types into tech firms and high-risk types into conventional firms. We show, intuitively, that lowering the technology cost raises this cutoff and thus increases the equilibrium market share of tech firms. Perhaps counter-intuitively, however, we show that adverse selection effects within the conventional market can cause an attempt by conventional insurers to catch up with tech firms—by increasing the speed at which they learn about the risk types of their insureds—to backfire and lead to an increase in the market share of tech firms. Our results are readily adapted to bank lending and labor market settings.
Bibliographical noteFunding Information:
Ruo Jia and Jieyu Lin thank the financial support from the Natural Science Foundation of China (Grant Nos. 72173005 and 71703003). Open Access Funding provided by Universitat St Gallen.
© 2021 The Authors. Journal of Risk and Insurance published by Wiley Periodicals LLC on behalf of American Risk and Insurance Association.
- adverse selection
- asymmetric learning
- dynamic equilibrium
- market structure
- technology progress