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 speedversus-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

No abstract