AI and the Increase of Productivity and Labor Inequality in Latin America: Potential Impact of Large Language Models on Latin American Workforce

We assess the potential effect of large language models (LLMs) on the labor markets of Chile, Mexico, and Peru using the methodology of Eloundou et al. (2023). This approach involves detailed guidelines (rubrics) for each job to assess whether access to LMM software would reduce the time required for workers to complete their daily tasks. Adapting this methodology to the Latin American context necessitated developing a comprehensive crosswalk between the Occupational Information Network (O*NET) and regional occupational classifications, SINCO-2011 and ISCO-2008. When we use this adaptation, the theoretical average task exposure of occupations under these classifications is 32% and 31% for each classification. Recognizing the unique characteristics of each country's labor market, we refined these ratings to reflect better each nation's capacity to adopt and effectively implement new technologies. After these adjustments, the task exposure for SINCO-2011 drops to 27% and for ISCO-2008 to 23%. These adjusted exposure ratings provide a more accurate depiction of the real-world implications of LLM integration in the Latin American context. According to this methodology, the LLM-powered exposure using GPT-4 estimates suggests that the percentage of jobs with task exposure exceeding 10% is 74% in Mexico, 76% in Chile, and 76% in Peru. When we raise the exposure threshold to 40% or more, the proportion of affected occupations significantly decreases to 9% in Mexico, 20% in Chile, and 6% in Peru. The exposure is close to zero after this threshold. In other words, the exposure would only affect less than half of the total labor force in these countries. Further analysis of exposure by socioeconomic conditions indicates higher exposure among women, individuals with higher education, formal employees, and higher-income groups. This suggests a potential increase in labor inequality in the region due to adopting this technology. Our findings highlight the need for targeted policy interventions and adaptive strategies to ensure that the transition to an AI-enhanced labor market benefits all socio-economic groups and minimizes disruptions.