Data and code for: The economics of urban density
Principal Investigator(s): View help for Principal Investigator(s) Gilles Duranton, University of Pennsylvania. The Wharton School; Diego Puga, CEMFI
Version: View help for Version V1
Version Title: View help for Version Title Accepted version
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Project Description
Summary:
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Urban density boosts productivity and innovation, improves access to goods and services, reduces typical travel distances, encourages energy-efficient construction and transport, and facilitates sharing scarce amenities. However, density is also synonymous with crowding, makes living and moving in cities more costly, and concentrates exposure to pollution and disease. In this article, we explore the appropriate measurement of density and describe how it is both a cause and a consequence of the evolution of cities. We then discuss whether and how policy should target density and why the trade-off between its pros and cons is unhappily resolved by market and political forces.
This repository distributes and documents computer programs and data to replicate the results obtained by Gilles Duranton and Diego Puga in their article 'The economics of urban density', to be published in Journal of Economic Perspectives.
Urban density boosts productivity and innovation, improves access to goods and services, reduces typical travel distances, encourages energy-efficient construction and transport, and facilitates sharing scarce amenities. However, density is also synonymous with crowding, makes living and moving in cities more costly, and concentrates exposure to pollution and disease. In this article, we explore the appropriate measurement of density and describe how it is both a cause and a consequence of the evolution of cities. We then discuss whether and how policy should target density and why the trade-off between its pros and cons is unhappily resolved by market and political forces.
This replication package calculates two measures of density: "naive density" (population per square kilometre) and "experienced density" (population within ten kilometres of the average resident) for US metropolitan areas and uses these data to produce the two panels in figure 1 in the article. It also calculates three elasticities for US metropolitan areas reported in the text of the article: the elasticity of experienced density with respect to city population, the elasticity of naive density with respect to city population, and the elasticity of average distance to the centre with respect to city population. Finally, it calculates experienced density for the entire Canada and for the entire United States.
Scope of Project
Subject Terms:
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density;
agglomeration;
urban costs
JEL Classification:
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R12 Size and Spatial Distributions of Regional Economic Activity
R31 Housing Supply and Markets
R32 Other Spatial Production and Pricing Analysis
R12 Size and Spatial Distributions of Regional Economic Activity
R31 Housing Supply and Markets
R32 Other Spatial Production and Pricing Analysis
Geographic Coverage:
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Canada,
United States of America
Data Type(s):
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aggregate data;
census/enumeration data;
geographic information system (GIS) data;
program source code
Related Publications
Published Versions
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