Prime Locations
(2020) In CEPR Discussion Paper- Abstract
- We harness big data to detect prime locations---large clusters of know-ledge-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... (More)
- We harness big data to detect prime locations---large clusters of know-ledge-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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/680e498d-41f7-4737-8014-b3ac2487a9d7
- author
- Ahlfeldt, Gabriel ; Albers, Thilo LU and Behrens, Kristian
- publishing date
- 2020
- type
- Working paper/Preprint
- publication status
- published
- subject
- in
- CEPR Discussion Paper
- issue
- 15470
- ISSN
- 0265-8003
- language
- English
- LU publication?
- no
- id
- 680e498d-41f7-4737-8014-b3ac2487a9d7
- alternative location
- http://cepr.org/active/publications/discussion_papers/dp.php?dpno=15470
- date added to LUP
- 2020-12-10 16:23:50
- date last changed
- 2020-12-10 16:46:10
@misc{680e498d-41f7-4737-8014-b3ac2487a9d7, abstract = {{We harness big data to detect prime locations---large clusters of know-ledge-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.}}, author = {{Ahlfeldt, Gabriel and Albers, Thilo and Behrens, Kristian}}, issn = {{0265-8003}}, language = {{eng}}, note = {{Working Paper}}, number = {{15470}}, series = {{CEPR Discussion Paper}}, title = {{Prime Locations}}, url = {{http://cepr.org/active/publications/discussion_papers/dp.php?dpno=15470}}, year = {{2020}}, }