A distributional framework for matched employer employee data

Stéphane Bonhomme, Thibaut Lamadon and Elena Manresa ()
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Elena Manresa: New York University, Postal: New York, USA

No 2017:24, Working Paper Series from IFAU - Institute for Evaluation of Labour Market and Education Policy

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. 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 establish identi cation in short panels, and develop tractable two-step estimators where firms are classified into heterogeneous classes in a first step. Applying our method to Swedish administrative data, we find that log-earnings are approximately additive in worker and firm heterogeneity, with a strong association between workers and firms, and a small relative contribution of firm heterogeneity 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 past firm.

Keywords: two-sided heterogeneity; bipartite networks; matched employer employee data; sorting; job mobility (search for similar items in EconPapers)
JEL-codes: C23 J31 J62 (search for similar items in EconPapers)
Pages: 111 pages
Date: 2017-12-10
New Economics Papers: this item is included in nep-bec
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Citations: View citations in EconPapers (14)

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Related works:
Journal Article: A Distributional Framework for Matched Employer Employee Data (2019)
Working Paper: A Distributional Framework for Matched Employer Employee Data (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:hhs:ifauwp:2017_024

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