中文摘要
我们提出了一个在匹配面板数据上识别和估计收入分布与工人构成的框架，允许在收入中存在双边工人-企业不可观测异质性与互补性。我们引入两个模型：一个允许工人与企业之间非线性交互的静态模型，以及一个在此基础上还允许马尔可夫收入动态和内生流动的动态模型。我们表明该框架嵌套了若干关于工资与工人流动性的结构模型。我们在短面板中确立了识别，并发展了易于处理的两步估计量，其中企业在第一步被分类。将我们的方法应用于瑞典行政数据，我们发现对数收入在工人与企业异质性上近似可加。我们的估计意味着工人与企业之间存在强排序模式，且企业对收入离散度的贡献——剔除工人构成后——很小。此外，我们记录了工资对流动性具有直接效应，以及除了依赖于当前企业之外，换工作后的收入也依赖于前雇主。
Abstract
We propose a framework to identify and estimate earnings distributions and worker composition on matched panel data, allowing for two‐sided worker‐firm unobserved heterogeneity and complementarities in earnings. We introduce two models: a static model that allows for nonlinear interactions between workers and firms, and a dynamic model that allows, in addition, for Markovian earnings dynamics and endogenous mobility. We show that this framework nests a number of structural models of wages and worker mobility. We establish identification in short panels, and develop tractable two‐step estimators where firms are classified in a first step. Applying our method to Swedish administrative data, we find that log‐earnings are approximately additive in worker and firm heterogeneity. Our estimates imply the presence of strong sorting patterns between workers and firms, and a small contribution of firms—net of worker composition—to earnings dispersion. In addition, we document that wages have a direct effect on mobility, and that, beyond their dependence on the current firm, earnings after a job move also depend on the previous employer.