A Distributional Framework for Matched Employer Employee Data

Stéphane Bonhomme, Thibaut Lamadon, Elena Manresa

    Research output: Contribution to journalArticlepeer-review


    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.

    Original languageEnglish (US)
    Pages (from-to)699-739
    Number of pages41
    Issue number3
    StatePublished - May 2019


    • Two-sided heterogeneity
    • bipartite networks
    • job mobility
    • matched employer employee data
    • sorting

    ASJC Scopus subject areas

    • Economics and Econometrics


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