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The Effects of Training under the Employment Adjustment Subsidy during the COVID-19 Pandemic: Evidence from Japan

Author

Listed:
  • Yudai Higashi

    (Faculty of Economics, Kyoto Sangyo University and Research Institute for Economics & Business Administration, Kobe University, JAPAN)

  • Masaru Sasaki

    (Graduate School of Economics, Osaka University, JAPAN and Institute of Labor Economics (IZA), GERMANY)

Abstract

In Japan, many establishments adversely affected by the COVID-19 pandemic have received an Employment Adjustment Subsidy (EAS) to maintain employment. Beyond subsidizing temporary leave allowances, the EAS also includes a scheme that subsidizes employee training provided by establishments. Using establishment-level administrative data, this study examines the characteristics of establishments providing EAS-supported training to their employees and evaluates its effects on the probability of establishment closure, employment flows, and business activity levels during the pandemic. The results indicate that establishments with workforce compositions and industry affiliations linked with higher expected returns to human capital investment are more likely to provide EAS-supported training. Furthermore, the EAS-supported training provided early during the pandemic reduced the probability of establishment closures. Although this training temporarily increased the hiring rate during the EAS receipt period, this effect did not persist once receipt ended. There is no evidence that EAS-supported training reduced job separation rates or improved subjective business activity levels. Overall, the effects of EAS-supported training appear limited.

Suggested Citation

  • Yudai Higashi & Masaru Sasaki, 2026. "The Effects of Training under the Employment Adjustment Subsidy during the COVID-19 Pandemic: Evidence from Japan," Discussion Paper Series DP2026-11, Research Institute for Economics & Business Administration, Kobe University.
    • Handle: RePEc:kob:dpaper:dp2026-11
    as

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    File URL: https://www.rieb.kobe-u.ac.jp/academic/ra/dp/English/DP2026-11.pdf
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    References listed on IDEAS

    as
    1. Kapinos, Pavel, 2021. "Did the Paycheck Protection Program have negative side effects on small-business activity?," Economics Letters, Elsevier, vol. 208(C).
    2. Alberto Abadie & Guido W. Imbens, 2016. "Matching on the Estimated Propensity Score," Econometrica, Econometric Society, vol. 84, pages 781-807, March.
    3. Isamu YAMAMOTO & Sachiko KURODA, 2016. "The Effect of Labor Turnover on Firm Performance among Japanese Firms (Japanese)," Discussion Papers (Japanese) 16062, Research Institute of Economy, Trade and Industry (RIETI).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

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    JEL classification:

    • H25 - Public Economics - - Taxation, Subsidies, and Revenue - - - Business Taxes and Subsidies
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs
    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance

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