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

IMF Working Papers, June 7, 2024

Source details

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

Authors: Mauro Cazzaniga, Carlo Pizzinelli, Emma J Rockall, Marina Mendes Tavares
Published: June 7, 2024
Series: IMF Working Papers
DOI: https://doi.org/10.5089/9798400278631.001

College-educated workers in Brazil and the UK frequently move from high-exposure, low-complementarity occupations (those more likely to be negatively affected by AI) to high-exposure, high-complementarity occupations (those more likely to be positively affected by AI).
This transition from high-exposure/low-complementarity to high-exposure/high-complementarity occupations is especially common for young college-educated workers.
The observed transition for young college-educated workers is associated with an increase in average salaries.
Young highly educated workers are identified as the demographic group for which AI-driven structural change could most expand opportunities for career progression but could also be highly disrupted by the removal of stepping-stone jobs that facilitate labor-market entry.
Patterns of “upward” labor market transitions for college-educated workers are broadly similar 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.
Non-college workers in Brazil face markedly higher chances of moving from better-paid high-exposure and low-complementarity occupations to low-exposure occupations, indicating a higher risk of income loss if AI reduces labor demand for those better-paid, high-exposure/low-complementarity jobs.

For college-educated workers:
- AI-related structural change appears to enable moves toward occupations that are both high-exposure and high-complementarity, with correlated wage gains.
- Young college graduates are both the main beneficiaries of upward occupational mobility and the group most exposed to disruption in entry-stage or stepping-stone jobs.
For non-college workers, especially in Brazil:
- Historical mobility patterns imply greater vulnerability to income losses if AI adoption reduces demand for better-paid, high-exposure/low-complementarity occupations.
Cross-country comparison:
- Similarities between the UK and Brazil in college-educated upward transitions suggest some commonality in how AI adoption could reshape opportunities for the highly educated across different income settings.