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Education-occupation mismatch and dispersion in returns to education: Evidence from India

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  • Grover, Shweta
  • Sharma, Ajay

Abstract

Using a national level sample survey on labour market in India, we analyze the role of education-occupation (mis-)match (EOM) in explaining within-group dispersion in returns to education. Applying a double sample selection bias correction and Mincerian quantile wage regression estimation, the analysis reveals interesting findings. First, on average, overeducated workers suffer a wage penalty of seven percent and undereducated workers do not receive a wage reward as compared to their adequately educated counterparts. Second, the inclusion of match status reduces within-education group dispersion in returns. The finding highlights that ignoring EOM and thus, adopting a restrictive view of similarity across workers may lead to overestimation of the within-education group dispersion in returns. This study argues for focusing on EOM to increase both pecuniary and social benefits of education in terms of productivity gains and wages as well as to reduce wage dispersion.

Suggested Citation

  • Grover, Shweta & Sharma, Ajay, 2020. "Education-occupation mismatch and dispersion in returns to education: Evidence from India," GLO Discussion Paper Series 651, Global Labor Organization (GLO).
    • Handle: RePEc:zbw:glodps:651
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    1. is not listed on IDEAS
    2. Aibanskhem Nongspung & Veronica Pala, 2025. "Self-Selection and Private Returns to Education in India: Evidence from NSSO Data," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 23(1), pages 253-283, March.

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    JEL classification:

    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • 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

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