Digitalization, change in skill distance between occupations and worker mobility – a gravity model approach
Speaker: Sara Signorelli, Ecole Polytechnique, Paris, FR
DATE: Wednesday, 8 November 2023
TIME: 13.00 – 14.00
6, Rue Richard Coudenhove-Kalergi
– Free seminar
– Invitation link
Tel: +352 46 66 44 6283
Joint work with Arnaud Dupuy, Morgan Raux
Technological shocks such as the rapid diffusion of digital technologies during the decade of 2010s are known to be skill biased and thus to increase the demand for high-skill workers relative to low skill ones. However, little is known about the effect of such shocks on the skill distance between occupations, and on the consequences of this channel for workers’ welfare. In this project we use Burning Glass data covering the near-universe of online job postings in the United States between 2010 and 2019 to document the rapid diffusion of digital skill requirements across many occupations, and the resulting change in skill distance between jobs. In particular, occupations that were intensive in digital skills at the beginning of the period increased their proximity to many other (skilled) occupations over the decade. In a second step we adopt a micro-funded gravity model and administrative employer-employee data from France to estimate how these changes in distance driven by new digital technologies affect the patterns of mobility across occupations.
About Sara Signorelli:
Sara Signorelli has recently joined the economics department of CREST – Ecole Polytechnique in Paris as an assistant professor, after two years spent at the University of Amsterdam. She holds a PhD from the Paris School of Economics and she remains affiliated to the PSE Labor Chair and International Migration Economics Chair. Her work mainly focuses on labor economics with a particular focus on the impact of skilled migration and technological change on employment, wages, and firm performance. Her second research focus revolves around the economics of innovation, and on how inventor mobility across countries is a key determinant of knowledge transfers.
Supported by the Luxembourg National Research Fund (FNR) 17931929