Large Language Models and the Labour Market: Spatial Evidence from Job Ads

This is the database for the following article: Baranyai, E., Granát, M., Szepesi, M. (2026) Large Language Models and the Labour
Market: Spatial Evidence from Job Ads.

Despite the rapid rise of large language models (LLMs) and their implications for productivity and employment, little is known about how exposure to LLMs varies within and across countries. Understanding these patterns matters because technology spillovers are often geographically localised, and regional disparities can affect domestic and international labour market flows, long-term growth, social cohesion, and political stability. Using geolocated, task-level data from all job advertisements on Hungarys largest online job portal, we apply an LLM-based mapping approach to estimate job exposure. Extrapolating to the national labour market, we find Hungarys average LLM exposure to be 8%, substantially lower than estimates for the United States. This gap is partly explained by Hungarys higher share of physically intensive occupations and lower prevalence of office-based roles. Job-level exposure rarely exceeds 30%, suggesting that LLMs primarily complement rather than replace tasks. Spatial variation in exposure is driven mainly by industry composition, with higher exposure in urban areas and associations with current demographic characteristics and historical economic development. These findings highlight regions and sectors where productivity gains from LLM adoption may arise and where targeted education and employment policies could support workforce adjustment.