The Exposure of Workers to Artificial Intelligence in Low- and Middle-Income Countries

Research on the labor market implications of artificial intelligence has focused principally on high-income countries. This paper analyzes this issue using microdata from a large set of low- and middle-income countries, applying a measure of potential artificial intelligence occupational exposure to a harmonized set of labor force surveys for 25 countries, covering a population of 3.5 billion people. The approach advances work by using harmonized microdata at the level of individual workers, which allows for a multivariate analysis of factors associated with exposure. Additionally, unlike earlier papers, the paper uses highly detailed (4 digit) occupation codes, which provide a more reliable mapping of artificial intelligence exposure to occupation. Results within countries, show that artificial intelligence exposure is higher for women, urban workers, and those with higher education. Exposure decreases by country income level, with high exposure for just 12 percent of workers in low-income countries and 15 percent of workers in lower-middle-income countries. Furthermore, lack of access to electricity limits effective exposure in low-income countries. These results suggest that for developing countries, and in particular low-income countries, the labor market impacts of artificial intelligence will be more limited than in high-income countries. While greater exposure to artificial intelligence indicates larger potential for future changes in certain occupations, it does not equate to job loss, as it could result in augmentation of worker productivity, automation of some tasks, or both