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From Mincer to AKM: Decomposing School Effects on Early-Career Wages

Author

Listed:
  • István Boza

    (ELTE Centre for Economic and Regional Studies)

  • Dániel Horn

    (Corvinus University Budapest; ELTE Centre for Economic and Regional Studies)

Abstract

This paper introduces a framework that combines a traditional Mincer wage equation with an Abowd–Kramarz–Margolis (AKM) decomposition in a unified linear framework. The approach allows pre-labor market entry group-level factors to be mapped transparently onto the underlying channels of wage determination, including individual earning capacity, firm sorting, and occupational allocation. Applying the method to linked employer–employee administrative data from Hungary, we study how secondary schools are related to early-career wage inequality. Secondary school affiliation explains about 15% of wage variation among young workers, with a substantial share operating through sorting into firms and occupations. Controlling for completed educational attainment reduces school effects. However, these effects do not disappear completely and persist even after controlling for pre-existing differences in student pools measured around the age of 14-15. More broadly, the framework provides a general tool for studying how institutions shape labor-market outcomes through multiple economic channels.

Suggested Citation

  • István Boza & Dániel Horn, 2026. "From Mincer to AKM: Decomposing School Effects on Early-Career Wages," CERS-IE WORKING PAPERS 2604, Institute of Economics, Centre for Economic and Regional Studies.
    • Handle: RePEc:has:discpr:2604
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    Keywords

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    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • I26 - Health, Education, and Welfare - - Education - - - Returns to Education
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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