How AI-Driven Skill Reallocation Shapes Wage Inequality and Productivity in Emerging Economies

Abstract

This study examines how artificial intelligence–driven skill reallocation relates to productivity and wage inequality in emerging economies. Using a synthetic panel of 400 observations across ten fictional economies, five sectors, and eight periods, it illustrates a quantitative framework for analysing technological adoption and labour market adjustment. Fixed effects models assess relationships among AI adoption, occupational reallocation, training access, digital readiness, productivity, and wage dispersion. This findings show positive associations between AI adoption, occupational reallocation, and productivity. Training access moderates the adoption–inequality relationship, with broader access associated with a smaller increase in wage dispersion. The interaction between digital readiness and adoption is statistically inconclusive. These findings reflect relationships embedded in the data-generating process rather than evidence about actual economies. The study demonstrates the importance of distinguishing adoption from exposure, occupational redistribution from complementary skill upgrading, and productivity growth from equitable wage outcomes. Future research should validate these potential relationships using observed worker and sector data and credible identification strategies.

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