Artificial Intelligence, Task Reallocation, and Wage Inequality in Emerging Economies



The rapid diffusion of Artificial Intelligence (AI) is transforming labor markets worldwide, yet its implications for emerging economies remain underexplored. Unlike advanced economies, where AI adoption often complements high-skilled labor, emerging economies face distinct structural challenges such as informality, skill mismatches, and limited social protection. This research examines how AI-driven task reallocation affects employment structures and wage inequality in emerging economies, with a particular focus on sectoral heterogeneity and skill polarization.




The study builds on task-based economic theory, which suggests that AI substitutes routine and codifiable tasks while complementing non-routine cognitive and socio-emotional tasks. In emerging economies, a large share of employment is concentrated in routine-intensive manufacturing, clerical services, and informal sectors, making these labor markets particularly vulnerable to AI-induced displacement. At the same time, AI adoption may create new opportunities by increasing productivity, generating demand for new occupations, and enabling firms to move up global value chains.

Using a mixed-methods approach, the research proposes to combine firm-level and worker-level panel data with occupational task indices to assess changes in employment composition, wage distribution, and skill demand following AI adoption. Econometric techniques such as difference-in-differences and dynamic panel modeling can be employed to identify causal effects. Additionally, qualitative case studies of AI adoption in selected industries—such as finance, logistics, and manufacturing—will provide contextual insights into firm strategies and worker adaptation.


#ArtificialIntelligence #LabourEconomics #WageInequality #EmergingEconomies #FutureOfWork #DigitalTransformation #SkillDevelopment #EconomicPolicy #InclusiveGrowth
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