Prime Locations

74 Pages Posted: 2 Dec 2020

See all articles by Gabriel M. Ahlfeldt

Gabriel M. Ahlfeldt

London School of Economics & Political Science (LSE) - Department of Geography and Environment

Thilo Albers

Humboldt University of Berlin

Kristian Behrens

University of Quebec at Montreal (UQAM) - Department of Economics

Multiple version iconThere are 2 versions of this paper

Date Written: November 2020

Abstract

We harness big data to detect prime locations---large clusters of knowledge-based tradable services---in 125 global cities and track changes in the within-city geography of prime service jobs over a century. Historically smaller cities that did not develop early public transit networks are less concentrated today and have prime locations farther away from their historic cores. We rationalize these findings in an agent-based model that features extreme agglomeration, multiple equilibria, and path dependence. Both city size and public transit networks anchor city structure. Exploiting major disasters and using a novel instrument---subway potential---we provide causal evidence for these mechanisms and disentangle size- from transport network effects.

JEL Classification: R38, R52, R58

Suggested Citation

Ahlfeldt, Gabriel M. and Albers, Thilo and Behrens, Kristian, Prime Locations (November 2020). CEPR Discussion Paper No. DP15470, Available at SSRN: https://ssrn.com/abstract=3737603

Gabriel M. Ahlfeldt (Contact Author)

London School of Economics & Political Science (LSE) - Department of Geography and Environment ( email )

Houghton Street
London, WC2A 2AE
United Kingdom

HOME PAGE: http://personal.lse.ac.uk/ahlfeldg/

Thilo Albers

Humboldt University of Berlin ( email )

Unter den Linden 6
Berlin, AK Berlin 10099
Germany

Kristian Behrens

University of Quebec at Montreal (UQAM) - Department of Economics ( email )

P.O. Box 8888, Downtown Station
Montreal, Quebec H3C 3P8
Canada

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