The role of immigrants, emigrants and locals in the historical formation of European knowledge agglomerations

ABSTRACT

Did migrants make Paris a mecca for the arts and Vienna a beacon of classical music? Or was their rise a pure consequence of local actors? We use data on more than 22,000 historical individuals born between the years 1000 and 2000 to estimate the contribution of famous immigrants, emigrants and locals to the knowledge specialisations of European regions. We find that the probability that a region develops or keeps specialisation in an activity (based on the birth of famous physicists, painters, etc.) grows with both the presence of immigrants with knowledge about that activity and immigrants with knowledge in related activities. In contrast, we do not find robust evidence that the presence of locals with related knowledge explains entries and/or exits. We address some endogeneity concerns using fixed-effects models considering any location–period–activity-specific factors (e.g., the presence of a new university attracting scientists).

KEYWORDS:

• migration
• knowledge spillovers
• relatedness
• economic history
• economic complexity

JEL:

• N90
• O15
• R11

ACKNOWLEDGEMENTS

We thank two anonymous referees, Andrea Morrison, Eva Coll, Jesús Crespo Cuaresma, Ron Boschma, Andrea Belmartino, the attendees of the 2022 Economic Geography PhD school in Utrecht, the attendees of the WICK#10 PhD Workshop in Economics of Innovation, Complexity and Knowledge in Turin, the attendees of the 13th Geoffrey J. D. Hewings Regional Economics Workshop in Vienna, members of the Complexity Science Hub and EcoAustria in Vienna, and the members of the Center for Collective Learning for valuable feedback. Working paper versions of this article have been published in the Papers in Evolutionary Economic Geography series of Utrecht University (PEEG #22.31) and on arXiv (arXiv:2210.15914).

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Additional information

Funding

This project was supported by the Agence Nationale de la Recherche [grant number ANR-19-P3IA-0004], the 101086712-LearnData-HORIZON-WIDERA-2022-TALENTS-01 financed by the European Research Executive Agency (REA), and the European Lighthouse of AI for Sustainability [grant number 101120237-HORIZON-CL4-2022-HUMAN-02].