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Forecasting Youth Unemployment Through Educational and Demographic Indicators: A Panel Time-Series Approach

Author

Listed:
  • Arsen Tleppayev

    (Faculty of Economics and Entrepreneurship, Kazakh-German University, Almaty 050010, Kazakhstan)

  • Saule Zeinolla

    (Faculty of Economics and Entrepreneurship, Kazakh-German University, Almaty 050010, Kazakhstan)

Abstract

Youth unemployment remains a pressing issue in many emerging economies, where educational disparities and demographic pressures interact in complex ways. This study investigates the links between higher-education enrolment, demographic structure and youth unemployment in eight developing countries from 2009 to 2023. Panel cointegration techniques—Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS)—are applied to estimate the long-run effects of gross tertiary-school enrolment on youth unemployment while controlling for GDP growth and youth-cohort size. Robustness is confirmed through complementary estimations with pooled-mean-group ARDL and system-GMM panels, which deliver consistent coefficient signs and significance levels. Results show a significant negative elasticity between enrolment and youth unemployment, indicating that wider access to higher education helps lower joblessness among young people. Youth-population growth exerts an opposite, positive effect, while GDP growth reduces unemployment but less uniformly across regions. The evidence points to an integrated policy mix—expanding tertiary (especially vocational and technical) education, managing demographic pressure and maintaining macro-economic stability—to improve youth-employment outcomes in emerging economies.

Suggested Citation

  • Arsen Tleppayev & Saule Zeinolla, 2025. "Forecasting Youth Unemployment Through Educational and Demographic Indicators: A Panel Time-Series Approach," Forecasting, MDPI, vol. 7(3), pages 1-20, July.
    • Handle: RePEc:gam:jforec:v:7:y:2025:i:3:p:37-:d:1703239
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    References listed on IDEAS

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