Table 4 reports selected coefficients. Longer trips are faster: the elasticity of trip speed with respect to trip length is 0.24 in columns 1 and 4, and larger for longer trips in the other columns where we introduce a quadratic term. This is a prominent feature of urban transportation data in other contexts.<sup>26</sup> Regressing log trip speed on log trip length without any further control yields an R<sup>2</sup> of 0.40.

Unsurprisingly, trips further from the center are also faster. The elasticity of trip speed with respect to distance from the center of 0.15 is a quite large, implying that a trip at 10 kilometers from the center of a city is about 40% faster than one a kilometer away.

In column 1, we find fairly large differences of up to 10% in speed between different types of trips. These differences become mostly insignificant and economically small when controls for trip location are added in column 2. In the end, amenity trips are slightly faster while circumferential trips are slower but the speed difference between them is only about 1%. We also note that regressing log trip speed solely on trip type indicators yields an R<sup>2</sup> of only about 0.003. These two results are reassuring, and suggest that the design of our hypothetical trips is not driving our results. In Appendix C, we report versions of table 4 for each type of trip. While the non-linearities for the effect of trip length and distance to the center slightly differ, the results overall are similar to those in table 4, suggesting that the simple additive specification of table 4 is not obscuring deeper differences between trip types.

We now turn to the regression coefficients not reported in table 4. Starting with the weather, we find that characteristics associated with bad weather such as rain, high levels of humidity, high temperatures, and more windy conditions tend to be associated with slightly _higher_ travel speeds. For instance, in columns 3 and 6, trips in rain are 2–3% faster.

To explain this contrast, we conjecture that roads in many Indian cities are ‘multi-purpose’ public goods used by various classes of motorized and non-motorized vehicles to travel and park as well as a wide variety of other users such as street-sellers, animals, or children playing. Non-transportation uses of the roadway arguably slow down motorized vehicles. Worse weather may reduce these activities and thus make travel faster. We provide further indirect evidence for this conjecture below.<sup>27</sup>

> 26 Couture _et al._ (2018) estimate a larger elasticity close to 0.40 using self-reported us data where the measure of trip duration also includes a fixed cost of getting into one’s vehicle and getting into traffic. Using self-reported data, Akbar and Duranton (2018) find an even larger elasticity for Bogotá travelers, because their sample also includes transit trips, with even larger fixed costs. Using analogous Google Maps data for the same Bogotá trips, Akbar and Duranton (2018) find an elasticity of 0.21, very close to the elasticity estimated here.

> 27However, it is important to note that our data collection period did not include monsoon season. Extreme weather conditions may affect mobility negatively, including for a period of time after they end.

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