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Ethnolinguistic diversity and urban agglomeration
Edited by Eric Sheppard, University of California, Los Angeles, CA and accepted by Editorial Board Member Susan Hanson May 19, 2020 (received for review February 5, 2020)

Significance
Urbanization and agglomeration of economic activity are key drivers of economic development. Many factors underlying city sizes and locations continue to be well studied. However, a key factor has so far been generally ignored: the role of the ethnolinguistic composition of local populations. We address this gap, drawing on a very detailed dataset on local urban agglomeration and ethnolinguistic diversity. We find that, in multiethnic areas, social tensions arise more easily, discouraging the move to bigger cities. Ethnolinguistically diverse regions feature less urbanization and agglomeration, with potentially profound economic consequences.
Abstract
This article shows that higher ethnolinguistic diversity is associated with a greater risk of social tensions and conflict, which, in turn, is a dispersion force lowering urbanization and the incentives to move to big cities. We construct a worldwide dataset at a fine-grained level on urban settlement patterns and ethnolinguistic population composition. For 3,540 provinces of 170 countries, we find that increased ethnolinguistic fractionalization and polarization are associated with lower urbanization and an increased role for secondary cities relative to the primate city of a province. These striking associations are quantitatively important and robust to various changes in variables and specifications. We find that democratic institutions affect the impact of ethnolinguistic diversity on urbanization patterns.
Footnotes
↵1U.J.E., J.V.H, D.R., and K.S. contributed equally to this work.
- ↵2To whom correspondence may be addressed. Email: j.v.henderson{at}lse.ac.uk, kurt.schmidheiny{at}unibas.ch, dominic.rohner{at}unil.ch, or ulrich.eberle{at}unil.ch.
Author contributions: U.J.E., J.V.H., D.R., and K.S. designed research, performed research, analyzed data, and wrote the paper.
The authors declare no competing interest.
This article is a PNAS Direct Submission. E.S. is a guest editor invited by the Editorial Board.
Data deposition: Replication code and generated data have been deposited in the Harvard Dataverse (https://doi.org/10.7910/DVN/PLDXPD).
This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2002148117/-/DCSupplemental.
Published under the PNAS license.
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