Identification in Models for Matched Worker-Firm Data with Two-Sided Random Effects

Koen Jochmans

No 25-1649, TSE Working Papers from Toulouse School of Economics (TSE)

Abstract: This paper is concerned with models for matched worker-firm data in the presence of both worker and firm heterogeneity. We show that models with complementarity and sorting can be nonparametrically identified from short panel data while treating both worker and firm heterogeneity as discrete random effects. This paradigm is different from the framework of Bonhomme, Lamadon and Manresa (2019), where identification results are derived under the assumption that worker effects are random but firm heterogeneity is observed. The latter assumption requires the ability to consistently assign firms to latent clusters, which may be challenging; at a minimum, it demands minimal firm size to grow without bound. Our setup is compatible with many theoretical specifications and our approach is constructive. Our identification results appear to be the first of its kind in the context of matched panel data problems.

Keywords: bipartite graph; nonlinearity; panel data; sorting; unobserved heterogeneity (search for similar items in EconPapers)
JEL-codes: C23 J31 J62 (search for similar items in EconPapers)
Date: 2025-06-26
New Economics Papers: this item is included in nep-ecm, nep-inv and nep-lma
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Persistent link: https://EconPapers.repec.org/RePEc:tse:wpaper:130607

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