case, Google's estimate of the duration and distance on the most recommended and fastest routes was recorded and each trip was queried at multiple times of the day and days of the year.<sup>9</sup> To construct comparable<sup>10</sup> city-specific speed indices from trips _i_ in city c( _i_ ), Akbar et al. (2021) regress their trip-level measure of speed outcomes on observable trip characteristics _X,_ including trip length, distance to the city center, trip type, time of day, day of week, and weather, to estimate city fixed effects that serve as city-level mobility indices, _Mobilityc(i)_ : ′ log S S o o 0 + α M . (1) ii ii cc(ii) ii SS_o SS = ββ + MMooM ooMM + SS Real-time speed outcomes ( _Speed_outcome_ i) on recommended route are used to estimate a baseline index of city speed, or an overall mobility index. Then, because Akbar et al. (2018; 2021) observe the same trips at different times of the day, they conduct analogous estimations of (1) using speed outcomes in the absence of traffic. From these estimations they obtain city-level uncongested mobility indexes. The difference between the city-level overall and uncongested mobility indexes is the city-level index of congestion delay caused by traffic. Accordingly, the overall mobility index decomposes exactly into the congestion index and the uncongested mobility index, and each index can be related to city characteristics for cross-city comparisons. We use the three indexes to distinguish the role of congestion from the role of uncongested mobility in the relationship between mobility, density, and agglomeration economies. Descriptive statistics of the obtained speed indices are provided in Appendix A2.

The mobility data of Akbar et al. (2021) offers several advantages over alternative sources, such as INRIX and TomTom data. First, the mobility indices are based on millions of trips on Google Maps, simulated over the same period of time using the same trip sampling strategy for cities all around the world. This was done precisely to facilitate cross-country analysis of the kind undertaken here. Second, Akbar et al. (2018) show that their mobility indices are robust to various alternative trip sampling strategies (such as with different weights on trip destinations, directions of travel or time of day). The indices also condition out the effect of trip characteristics that may vary systematically across cities and would bias any speed estimates based on actual trips. Third, Akbar et al. (2018) show for India that the predicted speeds on the simulated trips are still

> 9 Akbar et al. (2021) design the aggregate distribution of departure times (i.e., the time of query) across trip instances to mimic the distribution of departure times in the US National Household Travel Survey, except that they oversample sparse late night departure times in order to estimate travel times when there is no traffic. 10 The naive approach would be to divide the sum of trip lengths by the sum of trip durations to get an average speed for each city. However, this would ignore the fact that characteristics of trips can differ systematically across cities. Another attractive feature of these indices over raw means of travel times (such as from travel surveys) is that they are not confounded by trip characteristics that may be important determinants of speed but may also vary systematically across cities, such as trip length, departure times and weather conditions.

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