Task-Specific Technical Change and Comparative Advantage

Artificial intelligence (AI) reshapes workers’ comparative advantage by altering the tasks they perform and the skills those tasks require. We develop a dynamic task-based model to quantify the general-equilibrium effects of task-specific technical change. Workers have multidimensional skills, choose occupations, and accumulate skills on the job; occupations combine tasks, and productivity depends on how workers’ skills match task requirements. We develop a computationally efficient procedure to estimate the model using panel data and a new database of task-level skill requirements. We apply the model to AI, allowing it to augment, automate, and simplify tasks. We find that AI narrows wage inequality and raises average wages across scenarios ranging from slow to rapid AI progress. The key equalizing force is simplification: by lowering tasks’ skill requirements, AI lets lower-skill workers compete for previously inaccessible jobs. Adoption costs, highest for lower-skill workers, dampen but do not eliminate the decline in inequality.