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Dynamic Matching and Evolving Reputations

机译:动态匹配和不断发展的声誉

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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 Becker (1973). 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.
机译:本文介绍了一种匹配的通用模型,其中包括不断发展的公共贝叶斯声誉和随机生产。尽管有生产性的互补性,但与高折扣率的因素相比,分类匹配仍然会失败,这与Becker(1973)不同。这种失败使“高技能”(“低技能”)技术的信誉最高(最低)。我们发现喜欢的匹配最终会消失。在另一个生命周期发现中,年轻工人的报酬低于其边际产品,而老年工人的报酬则高于其边际产品。而且,工资随着任期的增长而增加,但不必反映边际产品:信息租金产生非单调和不连续的工资状况。

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