congestion as measured by Waze to predict vehicles on the road during the months when we do not have data for some of the intersections.<sup>2</sup> In a context like Nairobi, where there is not a good measure of vehicles on the road or vehicle miles traveled, this high-frequency data provides one of the best measures for how the number of vehicles traveling throughout the day might be changing during this period.<sup>3</sup> The predicted vehicle counts after the curfew is implemented across the different intersections demonstrates very similar magnitudes of change. This makes us more confident that the changes we measure in these intersections are likely representative of vehicles on the road more broadly in the city. We generate an index for each intersection for vehicle counts using vehicle counts in the pre-curfew period of 2020 by day of week as the baseline. We use an average of this index across intersections as a proxy for the change in total vehicle counts for the city.

To measure how the curfew may have affected drivers’ behaviors taking into account the overall decrease in mobility, we create a proxy of crash rates (crashes per car density) estimated as total road traffic crashes divided by the index of vehicle counts in 6 principal intersections in Nairobi described previously. This allows us to assess if there were changes in the probability of a crash due to the curfew in a context of lower mobility.

To measure speed, we query real-time travel times from Google Maps. We query travel times along segments of major corridors in Nairobi, including Mombasa Road, Uhuru Highway and Waiyaki Way, that we selected due to their importance for travel within Nairobi. We obtain information on the time it takes to travel from the start to the end of the segment using a vehicle given current traffic conditions. The speed is calculated by dividing the distance of the segment by the estimated travel time. We query this information every 30 minutes. We verify these measures using speed information from Uber Movement data. Uber provides speed data aggregated from Uber trips at the road segment level across Nairobi. Average speed is available hourly, and data is made available through March 2020, after which time Uber stopped producing these statistics. Given data is not available beyond this time, the Uber data is only used for cross-validating speed data from Google and is not used for the analysis. An overview of data sources and variable construction is shown in Table B.2.

For all the analyses, across all outcomes and indicators, we analyze 3 time intervals: (1) Daytime, including 5:00 to 17:00 hours, (2) Pre-Curfew time, including 17:00 to 20:00 hours, and (3) Curfew time that covers 20:00 to before 5:00 hours. While the curfew started at 19:00, we include the 19:00-20:00 hour interval in the pre-curfew time given the combination of: (1) the likely behavior with people rushing and getting in crashed around and beyond the curfew threshold at 19:00, and (2) the delay in the reporting of crashes by the police while they arrive to the crash scene, particularly at the time they are enforcing the curfew starting time.

When working with data derived from smartphones, it is important to consider the potential for biased measures (Milusheva et al., 2021). The traffic and congestion data from Waze and Google Maps are likely to be minimally

> 2See Appendix B for a description of how these predictions were generated.

> 3For example, while there are data on vehicle registrations, data is not collected on vehicles that are no longer in commission, which hinders an accurate picture of the volume of active vehicles.

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