Assortative Matching and Reputation

Abstract

Consider Becker's classic 1963 matching model, with unobserved fixed types and stochastic publicly observed output. If types are complementary, then matching is assortative in the known Bayesian posteriors (the 'reputations'). We discover a robust failure of Becker's result in the simplest dynamic two type version of this world. Assortative matching is generally neither efficient nor an equilibrium for high discount factors. In a labor theoretic rationale, we show that assortative matching fails around the highest (lowest) reputation agents for 'low-skill (high-skill) concealing' technologies. We then find that as the number of production outcomes grows, almost all technologies are of either form. Our theory implies the dynamic result that high-skill matches eventually break up. It also reveals that the induced information rents create discontinuities in the wage profile. This in turn produces life-cycle effects: young workers are paid less than their static marginal product, and old workers more.assortative matching, incomplete information, wages, Bayesian posterior, value function