Exposure of the Portuguese Workforce to Computerization and Artificial Intelligence: A regional analysis

Details

Supervisors

Thewissen, James

Faculty

Louvain School of Management

Degree label

Master [120] : ingénieur de gestion, à finalité spécialisée

Abstract

This dissertation investigates the influence of agglomeration externalities from industrial and occupational variety, as well as educational specialization, on regional workforce exposure to labor-saving and labor-augmenting digital technologies. Utilizing panel data covering Portuguese municipalities from 2011 to 2021, we employ occupational-level exposure measures developed by Frey and Osborne (2017) and Felten et al. (2021) to assess the likelihood of labor replacement by computerization and exposure to the transformative effects of Artificial Intelligence (AI), respectively. Using a dynamic model, we find that both related and unrelated industrial variety significantly increase the share of workers at high risk of job replacement by technology, as well as those with high exposure to the complementary effects of AI. However, related occupational variety does not significantly impact the share of workers at high risk of job replacement, while unrelated occupational variety demonstrates a positive, but marginally significant, effect. Both measures of occupational variety lead to an increase in regional exposure to the job-complementing effects of AI. Additionally, our results suggest that regions characterized by a higher specialization of education are more susceptible to the adverse effects of digital technologies. These findings highlight the importance of considering not only a region’s industrial makeup but also its occupational and educational composition when assessing workforce exposure to new technologies. Our research offers valuable insights for policymakers, particularly in smaller economies, indicating the necessity of tailoring policies to regional characteristics. By doing so, policymakers can effectively mitigate risks associated with emerging technologies while maximizing their potential benefits.