GTAP Resources: Resource Display
| GTAP Resource #7825 |
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"Modeling the Impact of Generative AI on Global Wages and Trade: A GTAP Framework with Occupational Details" Authors: Yuan, Wen Jin, Michael Ferrantino and Marinos Tsigas Abstract This paper examines the impacts of AI-driven productivity increases on global patterns of trade, income, and wages, using an implementation of CGE modeling that disaggregates the U.S. labor force into 22 occupations and leveraging recent research into the adoption of AI by workers and its labor market impacts. AI exposure and its associated productivity impacts tend to be concentrated in high-skill, high-wage occupations, most notably computer science and mathematics, business and finance, and architecture and engineering. These occupations are used most intensively in the production of services and certain knowledge-intensive goods like computer, electronic, and optical products. While AI increases real GDP in all regions of the world, gains are highest in the United States and other high-income countries that have high exposure to AI. AI reshapes the U.S. pattern of comparative advantage in favor of business services. Increased import demand in high-income countries induces an increase in China’s manufacturing output and exports. Skill premiums decrease universally across all countries and regions. The results for GDP, output, and exports are highly robust to whether capital-skill (K-S) complementarity is included in the model. However, wages are sensitive to the model specifications. While all wages increase in the traditional GTAP model, wages for skilled labor often decrease in the model with K-S complementarity, with declines concentrated in the most AI-exposed occupations. |
| Resource Details () | GTAP Keywords | ||||||||||||||||
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- GTAP Data Base and extensions - Domestic policy analysis - Labor market issues - Technological change - Baseline development - Model extension/development - Asia (East) - European Union - Global - North America |
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Public Access  Paper (985.9 KB) Replicated: 0 time(s) Presentation (503.7 KB) Replicated: 0 time(s)Restricted Access No documents have been attached. Special Instructions No instructions have been specified. |
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Last Modified: 8/24/2026 8:40:13 AM
