FROM AI VIBRANCY TO LABOUR MARKET OUTCOMES: TESTING DISPLACEMENT ACROSS EDUCATION GROUPS
FROM AI VIBRANCY TO LABOUR MARKET OUTCOMES: TESTING DISPLACEMENT ACROSS EDUCATION GROUPS

Summary/Abstract: Artificial intelligence is expanding rapidly, intensifying policy concerns that more vibrant AI ecosystems may displace workers and increase unemployment. This study aims to test whether national AI vibrancy is associated with higher unemployment across education groups (advanced, intermediate and basic). Using an unbalanced panel of 34-35 countries from 2017 to 2023, the analysis combines Stanford's AI Vibrancy Score with World Bank indicators and estimates two-way fixed- and random-effects models, employing Box-Cox/log transformations and dependence-robust inference (including country/time clustering and Driscoll-Kraay standard errors). The results provide little support for the displacement hypothesis. For advanced-education unemployment, AI vibrancy is statistically insignificant in the two-way FE model. It remains insignificant under all robust corrections (ln(AI vibrancy): β = −0.099, country-clustered p = 0.494, time-clustered p = 0.544, Driscoll-Kraay p = 0.468). For basic-education unemployment, AI vibrancy is likewise insignificant in the two-way FE model (p = 0.782). It remains insignificant under country clustering (p = 0.830), time clustering (p = 0.813) and Driscoll-Kraay inference (p = 0.819). For intermediate-education unemployment, the AI coefficient remains insignificant under country clustering (p = 0.273), time clustering (p = 0.310), and Driscoll-Kraay corrections (p = 0.226), indicating no robust unemployment-increasing effect across education groups during the observed period.

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