Jochmans, Koen (2025) Identification in Models for Matched Worker-Firm Data with Two-Sided Random Effects. TSE Working Paper, n. 25-1649, Toulouse

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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.