Working Paper: NBER ID: w25218
Authors: Prottoy A. Akbar; Victor Couture; Gilles Duranton; Adam Storeygard
Abstract: We develop a methodology to estimate robust city level vehicular mobility indices, and apply it to 154 Indian cities using 22 million counterfactual trips measured by a web mapping service. There is wide variation in mobility across cities. An exact decomposition shows this variation is driven more by differences in uncongested mobility than congestion. Under plausible assumptions, a one standard deviation improvement in uncongested speed creates much more mobility than optimal congestion pricing. Denser and more populated cities are slower, only in part because of congestion. Urban economic development is correlated with better (uncongested and overall) mobility despite worse congestion.
Keywords: urban mobility; congestion; India; transportation; economic development
JEL Codes: R41
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| uncongested speed (L91) | overall mobility (J62) |
| urban density (R11) | mobility (J62) |
| urban economic development (R58) | uncongested mobility (L91) |
| urban economic development (R58) | congestion (L91) |
| city characteristics (R12) | mobility indices (J60) |