# 4. Conclusion

This paper discusses the potential of artificial intelligence for the next generation of work. While most previous economic research has focused on high-income countries, this is one of the first papers to look specifically at middle- and low-income countries. The analysis applies information from the AI occupation exposure index developed by Felten et al. (2021) to a harmonized set of representative labor force surveys for 25 countries across income groups. The total sample contains about 3 million observations and represents a population of 3.5 billion people. The results show that substantial disparities persist across income groups: High-income countries have the highest exposure to AI, with more than one in two workers exposed, 62 percent. This is followed by uppermiddle-income countries with an exposure level of 49, lower-middle-income countries with 44, and low-income countries with lower exposure levels of 37. The persistent gap underscores the uneven global distribution of the potential benefits and challenges of AI. The result suggests heterogeneity in the impact of AI on labor markets across national contexts, which may have important policy implications.

The multivariate analysis of worker characteristics on occupational exposure to AI underlines the need for a more nuanced perspective across income levels: Education has a pronounced effect in lower- and upper-middle-income countries. This suggests that better educated individuals in these regions may be able to adopt or adapt more quickly to AI-induced changes in the labor market. Gender differences in AI exposure exist and are particularly pronounced in middle-income countries. Urban workers are generally more exposed to AI than rural workers, emphasizing the role of urbanization and occupational choice for a possible effect of AI technologies on employment, productivity, and earnings. We find further support for the idea that advances in AI will disproportionately affect professionals and technicians compared to those in elementary occupations. This notion is also supported by a sectoral analysis, which shows that white-collar industries are more exposed to AI, especially in low- to upper-middle-income countries, while blue-collar industries are less exposed.

Often, analyses build on AI approaches without considering the needed infrastructure. We find that workers in low-income countries, and particularly in rural areas, do not have access to electricity, a prerequisite to any AI development. This needs to be considered in any future AI policy response. Households in low-income countries and in rural areas are often at the very last end of development and instead of bridging the gap, AI has the potential to even further exacerbate the gap between rural and urban areas, but also between low income and other income countries.

Overall, these findings underscore the importance of tailored policy responses to manage the impact of AI on the workforce. The labor market structure in low- and middle-income countries calls for a different policy response to the recent AI advancements than in high-income countries. The observed within-group differences further suggest more nuanced, country-specific strategies to effectively harness the productive potential of AI while mitigating its risks. The results in this paper can act as an early baseline to start a discussion on AI investments and opportunities in lowand middle-income countries.

24