Nonparametric Estimation of Matching Efficiency and Mismatch: An Application to Japanese Labor Markets

Suguru Otani

Papers from arXiv.org

Abstract: I identify significant biases in the traditional Cobb-Douglas function under misspecification of nonadditive, time-varying matching efficiency, and evaluate the finite-sample performance of the nonparametric estimation method of Lange and Papageorgiou (2020). Additionally, I extend the mismatch index by Sahin et al (2014) to a nonparametric framework and develop a computational methodology. Applying the method to Japanese labor market data from government surveys and Hello Work, I analyze changes in matching efficiency, elasticities, and mismatch. Both datasets show declining matching efficiency, aligned with decreasing job-finding and vacancy-filling rates. The unemployment elasticity is around 0.5-0.9 in the Hello Work data and lower in the nationally representative aggregate, while the vacancy elasticity is higher in the nationally representative aggregate. Furthermore, I demonstrate that occupational mismatch is more severe than geographic mismatch, with the Cobb-Douglas mismatch index significantly underestimating the true extent of mismatch.

Date: 2024-07, Revised 2026-09
New Economics Papers: this item is included in nep-lab
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