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Does vocational education pay off in China? Evidence from city-level education supply shocks

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  • Dai, Li
  • Martins, Pedro S.

Abstract

China hosts the world’s largest secondary education sector: more than 14 million adolescents enrol in secondary academic and vocational schools every year. Despite the large literature on returns to education, little evidence exists as to how these two streams compare in the country. Using 2013 China Household Income Project data, we estimate the returns to secondary vocational education both at the mean and along the conditional wage distribution. We use instrumental variables based on the considerable variation in education provision across cities and years (and a 1995 policy reform). We find that vocational education generates a large wage premium (up to 54%), especially for those of lower earnings potential. Our findings indicate that vocational education can be a good option for those who do not wish to enter tertiary education, especially the less well-off.

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  • Dai, Li & Martins, Pedro S., 2024. "Does vocational education pay off in China? Evidence from city-level education supply shocks," Economic Modelling, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:ecmode:v:140:y:2024:i:c:s0264999324002207
    DOI: 10.1016/j.econmod.2024.106863
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    More about this item

    Keywords

    Returns to education; Vocational education; Heterogeneity; Instrumental variable quantile regression; China;
    All these keywords.

    JEL classification:

    • I26 - Health, Education, and Welfare - - Education - - - Returns to Education
    • I25 - Health, Education, and Welfare - - Education - - - Education and Economic Development
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

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