The Impact of Migration and Inflation on Youth Unemployment in Indonesia: An ARDL Approach

Misdawita; Cahya Puspita Rahmadhini; Yulia Anggun Sari Br Sembiring; Tuty Susanty L Toruan

doi:10.61255/jeemba.v4i5.991

Authors

Misdawita Universitas Riau
Cahya Puspita Rahmadhini Universitas Riau
Yulia Anggun Sari Br Sembiring Universitas Riau
Tuty Susanty L Toruan Universitas Riau

DOI:

https://doi.org/10.61255/jeemba.v4i5.991

Keywords:

ARDL, Indonesia, Inflation, Migration, Unemployment

Abstract

Purpose - This study examines migration and inflation in relation to youth unemployment in Indonesia. It covers the period 2000-2024. The outcome concerns people aged 15-24. The focus is the transition from school to work. The analysis distinguishes long-run associations from short-run dynamics.

Design/methodology/approach – Annual BPS-based series are analysed using an autoregressive distributed lag (ARDL) model. Youth unemployment covers ages 15-24. Migration uses the five-year retrospective risen-migration concept. Its annual provenance remains unverified, making migration estimates provisional. ARDL accommodates I(0) and I(1) variables and distinguishes long-run relationships from short-run changes.

Finding/Results – The results provide mixed evidence of a long-run equilibrium relationship. The finite-sample bounds test points to cointegration, but the asymptotic test is inconclusive and the insignificant error-correction term indicates that adjustment toward equilibrium is not statistically reliable. In the long run, migration is negatively associated with youth unemployment, whereas inflation is positively associated with it. The short-run effects are not statistically robust and therefore require cautious interpretation.

Originality/Value – This study examines migration, inflation, and youth unemployment jointly in Indonesia. Its focus is youth labour-market dynamics. It considers mobility alongside macroeconomic price pressure. The results inform discussion of labour matching and price stability. The contribution remains conditional on the model and data limitations.

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