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Special Economic Zones (SEZ) Wage Premium Hypothesis: An Unconditional Quantile Regression and Decomposition Approach

Author

Listed:
  • Nana Yaw Agyeman Owusu
  • Robert Darko Osei
  • Charles Godfred Ackah

Abstract

This study examines the determinants of wages within special economic zones (SEZs) and the broader workforce. It explores how SEZ employment, unionisation, education and gender affect wage levels and compares wage differentials between SEZ and non-SEZ employees. Using ordinary least squares, unconditional quantile regression and decomposition techniques, the study analyses firm–employee dataset of 1,240 employees in 2022. The findings show SEZ employment and unionisation significantly increase wages, with SEZ workers enjoying a wage premium. Education consistently boosts wages across percentiles, while gender disparities are more pronounced at higher-wage levels, with a wage gap of up to 21.10% against females. Decomposition reveals that 16.9% of the SEZ and non-SEZ wage gap is explained by observable factors like skills, while the remainder points to institutional or cultural biases. The study highlights SEZs’ role in wage structures and emphasises the need for policies promoting wage equity, educational opportunities and gender equality in the labour market. JEL Codes: F16, F13, C21

Suggested Citation

  • Nana Yaw Agyeman Owusu & Robert Darko Osei & Charles Godfred Ackah, 2026. "Special Economic Zones (SEZ) Wage Premium Hypothesis: An Unconditional Quantile Regression and Decomposition Approach," Journal of Interdisciplinary Economics, , vol. 38(2), pages 158-181, July.
    • Handle: RePEc:sae:jinter:v:38:y:2026:i:2:p:158-181
    DOI: 10.1177/02601079251314229
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    References listed on IDEAS

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    Keywords

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    JEL classification:

    • F16 - International Economics - - Trade - - - Trade and Labor Market Interactions
    • F13 - International Economics - - Trade - - - Trade Policy; International Trade Organizations
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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