Labour and technology at the time of COVID-19: can artificial intelligence mitigate the need for proximity?
Carbonero, Francesco Scicchitano, Sergio
Carbonero, Francesco
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Abstract
Social distancing has become the key public policy globally implemented during the COVID-19 pandemic and reducing the degree of proximity among workers turned out to be an important dimension of this. An emerging literature looks at the role of automation in supporting the work of humans but the potential of artificial intelligence (AI) to influence the need for physical proximity on the workplace has been left largely unexplored. Using a unique and innovative dataset that combines data on advancements in AI at the occupational level with information on the required proximity in the workplace and administrative employer-employee data on job flows, we show that AI and proximity have an inverse u-shaped relationship at the sectoral level, with high advancements in AI being negatively associated with proximity. We detect this pattern among sectors that were closed due to lockdown measures as well as among sectors that remained open. We argue that, apart from the expected gains in productivity and competitiveness, preserving jobs and economic activities in a situation of high contagion may be additional benefits of a policies favouring AI.
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2025
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Keywords
Artificial intelligence, Automation, COVID-19, Proximity
Citation
Carbonero, Francesco, and Sergio Scicchitano. “Labour and Technology at the Time of COVID-19: Can Artificial Intelligence Mitigate the Need for Proximity?” Eurasian Business Review 15 (4): 1167–203. 2025.