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arXiv · 2412.19024

Nonparametric Estimation of Matching Efficiency and Elasticity in a Spot Gig Work Platform: 2019-2023

Hayato Kanayama·Suguru Otani
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Abstract

This paper provides new evidence on spot gig work platforms for individuals seeking flexible, short-term jobs with minimal educational or experience requirements in Japan. Using proprietary data from Timee, a private matching platform, the study analyzes trends in active users, vacancies, hires, and labor market tightness, compared to part-time data from Hello Work, a public employment service. Applying a nonparametric approach, it finds that the private platform exhibits substantially higher matching efficiency, especially after 2022. Elasticities also differ across platforms: for Hello Work, the user elasticity fluctuates around 0.3--0.5, while the vacancy elasticity ranges roughly from 0.4 to slightly above 1.0; for the private platform, the user elasticity remains around 0.2--0.3, while the vacancy elasticity ranges from 0.7 to 1.1. At the prefecture level, the three prefectures exhibit broadly similar movements early in the sample, followed by divergence and partial re-convergence later on, while elasticities remain stable and similar across regions. These results reveal how digital platforms reshape job matching dynamics relative to traditional systems.

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Hayato Kanayama, Suguru Otani. 2024-12-26. Nonparametric Estimation of Matching Efficiency and Elasticity in a Spot Gig Work Platform: 2019-2023. https://arxiv.org/abs/2412.19024

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