Mobility and Congestion in Urban India

Author:

Akbar Prottoy1,Couture Victor2,Duranton Gilles3,Storeygard Adam4

Affiliation:

1. Aalto University and Helsinki Graduate School of Economics (email: )

2. Vancouver School of Economics, University of British Columbia (email: )

3. Wharton School, University of Pennsylvania (email: )

4. Department of Economics, Tufts University (email: )

Abstract

We develop a methodology to estimate robust city-level vehicular speed indices, exactly decomposable into uncongested speed and congestion. We apply it to 180 Indian cities using 57 million simulated trips measured by a web mapping service. We verify the reliability of our simulated trips using a number of alternative data sources, including data on actual trips. We find wide variation in speed across cities that is driven more by differences in uncongested speed than congestion. Denser and more populated cities are slower, only in part because of congestion. Urban economic development is correlated with faster speed despite worse congestion. (JEL O15, O18, R23, R41)

Publisher

American Economic Association

Subject

Economics and Econometrics

Reference40 articles.

1. The Economics of Density: Evidence From the Berlin Wall

2. Akbar, Prottoy, Victor Couture, Gilles Duranton, and Adam Storeygard. 2023. "Replication Data for: Mobility and Congestion in Urban India." American Economic Association [publisher], Inter-university Consortium for Political and Social Research [distributor]. https://doi.org/10.3886/ E182681V1.

3. Akbar, Prottoy A., and Gilles Duranton. 2018. "Measuring Congestion in a Highly Congested City: Bogotá." Unpublished.

4. An arterial grid of dirt roads

5. Baugh, Kimberly, Christopher D. Elvidge, Tilottama Ghosh, and Daniel Ziskin. 2010. Development of a 2009 stable lights product using DMSP-OLS data. In Proceedings of the Asia-Paci c Advanced Network, Volume 30. https://www.ngdc.noaa.gov/eog/data/web_data/v4composites/F182013. v4.tar (accessed December 11, 2015).

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