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

Exposure to Artificial Intelligence and Occupational Mobility: A Cross-Country Analysis
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 patterns of “upward” labor market transitions for college-educated workers look broadly alike in the UK and Brazil, suggesting 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.

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A recent cross-country analysis conducted by researchers Mauro Cazzaniga, Carlo Pizzinelli, Emma J Rockall, and Marina Mendes Tavares explores the impact of Artificial Intelligence (AI) on workers' transitions across occupations and over the life-cycle in Brazil and the UK. The study reveals that college-educated workers frequently move from occupations more likely to be negatively affected by AI to those more likely to be positively affected, especially young college-educated workers, leading to an increase in average salaries. This pattern of "upward" labor market transitions for college-educated workers is similar in both countries, suggesting that the impact of AI adoption on the highly educated labor force could be similar across advanced economies and emerging markets. However, non-college workers in Brazil face higher chances of moving from better-paid occupations to lower-paid ones, indicating a higher risk of income loss if AI were to reduce labor demand for their jobs. The full report is available for free download on the IMF website.

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