Dynamic Matching and Evolving Reputations
构建了一个包含演化贝叶斯声誉和随机产出的匹配模型，发现高折现因子下分类匹配失效，且同类匹配最终会解散；年轻工人工资低于边际产出，老年工人则相反，工资随任期上升但不反映边际产出。
This paper introduces a general model of matching that includes evolving public Bayesian reputations and stochastic production. Despite productive complementarity, assortative matching robustly fails for high discount factors, unlike in <cross-ref type="bib" refid="R3">Becker (1973)</cross-ref>. This failure holds around the highest (lowest) reputation agents for “high skill” (“low skill”) technologies. We find that matches of likes eventually dissolve. In another life-cycle finding, young workers are paid less than their marginal product, and old workers more. Also, wages rise with tenure but need not reflect marginal products: information rents produce non-monotone and discontinuous wage profiles.