Reproducibility package for Disruption Without Dividend? How The Digital Divide And Task Differences Split GenAI’s Global Impact

This paper examines how Generative Artificial Intelligence (GenAI) could affect labor markets globally, with particular attention to the uneven distribution of risks and opportunities between advanced and developing economies. Cross-country differences in occupational structure suggest that developing economies face lower aggregate automation exposure than developed countries but comparable potential for task augmentation. However, disparities in digital infrastructure create an asymmetry: workers in positions vulnerable to automation typically maintain sufficient internet connectivity to experience displacement effects even in low-income settings, while those who could benefit from GenAI augmentation face substantial digital infrastructure gaps that may prevent them from realizing productivity gains. This finding suggests that developing countries may experience the disruptive effects of GenAI faster than its productivity benefits. On the other hand, conventional occupational exposure measures systematically overestimate GenAI's impact in developing countries by assuming uniform task content across economies. Using PIAAC and STEP survey data, we demonstrate that workers in developing countries perform substantially fewer non-routine analytical tasks—GenAI's primary targets—even within occupations classified as highly exposed. These findings highlight the importance of adapting GenAI exposure measures to developing countries' distance from the technology frontier.