Exposure to Artificial Intelligence and Occupational Mobility: A Cross-Country Analysis

Abstract

We document historical patterns of workers’ transitions across occupations and over the life-cycle for different levels of exposure and complementarity to Artificial Intelligence (AI) in Brazil and the UK. In both countries, college-educated workers frequently move from high-exposure, low-complementarity occupations (those more likely to be negatively affected by AI) to high-exposure, high-complementarity ones (those more likely to be positively affected by AI). This transition is especially common for young college-educated workers and is associated with an increase in average salaries. Young, highly educated workers thus represent the demographic group for which AI-driven structural change could most expand opportunities for career progression, but also highly disrupt entry into the labor market by removing stepping-stone jobs. These similar patterns of “upward” labor market transitions for college-educated workers suggest that the impact of AI adoption on the highly educated labor force could be similar across advanced economies and emerging markets. Meanwhile, non-college workers in Brazil face markedly higher chances of moving from better-paid high-exposure and low-complementarity occupations to low-exposure ones, suggesting a higher risk of income loss if AI were to reduce labor demand for the former type of jobs.