Econometrica · 2020 · Christopher R. Taber、Rune Vejlin
中文摘要
本文构建了一个模型，涵盖Roy模型、搜寻模型、补偿性工资差异以及在职人力资本积累的关键组成部分。我们明确了模型中哪些组成部分可以进行非参数识别，哪些不能。我们估计了该模型，并用它评估不同因素对总体工资不平等的相对贡献。我们发现，进入劳动力市场之前的技能差异（Roy模型的关键特征）是最重要的组成部分，能够解释工资变异中的大部分。我们还表明，其他组成部分之间存在较强的相互作用，尤其是，在搜寻摩擦下获得的岗位匹配的重要性随其他组成部分的处理方式而变化，介于约4%与约29%之间。对工作的非货币性方面的偏好所引起的不平等（此类偏好会导致补偿性工资差异），以及搜寻，对于解释数据的其他特征都非常重要。搜寻对于人员流动很重要，对工作的非货币性方面的偏好也同样重要，因为在所有涉及两份工作的选择中，如果劳动者只关心工资，三分之一的选择就会产生不同的结果。
Abstract
In this paper, we develop a model that captures key components of the Roy model, a search model, compensating differentials, and human capital accumulation on‐the‐job. We establish which components of the model can be non‐parametrically identified and which ones cannot. We estimate the model and use it to assess the relative contribution of the different factors for overall wage inequality. We find that variation in premarket skills (the key feature of the Roy model) is the most important component to account for the majority of wage variation. We also demonstrate that there is substantial interaction between the other components, most notably, that the importance of the job match obtained by search frictions varies from around 4% to around 29%, depending on how we account for other components. Inequality due to preferences for non‐pecuniary aspects of the job (which leads to compensating differentials) and search are both very important for explaining other features of the data. Search is important for turnover, but so are preferences for non‐pecuniary aspects of jobs as one‐third of all choices between two jobs would have resulted in a different outcome if the worker only cared about wages.