A Distributional Framework for Matched Employer Employee Data

• 成果类型: Article
• 署名作者: Bonhomme, Stephane; Lamadon, Thibaut; Manresa, Elena
• 署名单位: University of Chicago; New York University
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA15722
• 发表日期: 2019
• 页码: 699-739
• 关键词: high wage workers; FIRMS; identification; models; Heterogeneity; differentials; likelihood; variables

摘要

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.