We develop a model where risk-averse workers can costly invest in their skills before matching with heterogenous firms. At the investment stage, workers face multiple sources of risk. They are uncertain about how skilled they will turn out and also about their income shock realizations at the time of employment. We analyse the equilibria of two versions of the model that depend on when uncertainty resolves, which determines the available risk-sharing possibilities between workers and firms. We provide a thorough analysis of equilibrium comparative statics regarding changes in risk, worker and firm heterogeneity, and technology. We derive conditions on the match output function and risk attitudes under which these shifts lead to more investment and show how this affects matching and wages. To illustrate the applied relevance of our theory, we provide a stylized quantitative assessment of the model and analyse the sources (risk, heterogeneity, or technology) of rising U.S. wage inequality. We find that changes in risk were the most important driver behind the surge in inequality, followed by technological change. We show that these conclusions are significantly altered if one neglects the key feature of our model, which is that educational investment is endogenous.

中文翻译：

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风险匹配

我们开发了一个模型，在该模型中，规避风险的工人可以在与异质公司匹配之前对他们的技能进行昂贵的投资。在投资阶段，工人面临多种风险来源。他们不确定自己的技能水平，也不确定他们在就业时的收入冲击。我们分析了模型的两个版本的平衡，这取决于不确定性何时解决，这决定了工人和公司之间可用的风险分担可能​​性。我们对关于风险、工人和公司异质性以及技术变化的均衡比较静态数据进行了全面分析。我们推导出匹配输出函数和风险态度的条件，在这些条件下这些转变导致更多投资，并展示了这如何影响匹配和工资。为了说明我们理论的应用相关性，我们对该模型进行了程式化的定量评估，并分析了美国工资不平等加剧的来源（风险、异质性或技术）。我们发现，风险的变化是不平等加剧的最重要驱动因素，其次是技术变革。我们表明，如果忽略我们模型的关键特征，即教育投资是内生的，这些结论就会发生显着变化。