# **4.2 Comparison of Different Measures and Policy Analysis**

We begin by comparing the three data sources visually and exploring the correlations between them. We then demonstrate how the data sources compare when used to measure the impact of a particular policy. The focus is on the set of policies that were implemented in 2020 as a response to the COVID19 pandemic. As in many other countries, the Government of Senegal instituted a number of policies starting on March 14, 2020, with the goal of limiting mobility and thus curbing the spread of COVID19.<sup>15</sup> Literature from other contexts has documented important externalities from these types of policies, including large decreases in pollution (Venter _et al._ , 2020). Therefore, it is possible to use the timing of these policies in Senegal to study how the different sources of data measure this externality in Dakar. Additionally, this is a time for which we have data from all three data sources, which makes it possible to compare measured impact across all three.

To study the policy impact, a difference-in-differences style design is used. Air pollution data right before and right after the policy is compared to air pollution data during those same pre- and post-policy periods in a different year. This strategy makes it possible to control for seasonality, which plays an important role in PM2.5 levels. The specification estimated is: where _PMyt_ is PM2.5 at hour _t_ of year _y_ . _I_ 2020 _y_ is an indicator equal to one if the year is 2020 and zero otherwise. _Postt_ is an indicator equal to one for all hours after the policies were put in place (March 14 to May 12) and zero before the COVID-19 policies (January 23 to March 13). _Xyt_<sup>_′_isavectorofcontrols</sup> that includes weather variables (temperature and humidity) and indicator variables for hour of the day, the day of the week and week of the year (i.e., an indicator of the week within a calendar year, starting from week 1), and _εyt_ is the error term.<sup>16</sup> We conduct the analysis comparing 2020, when we expect reduced mobility and levels of PM2.5, to 2023, when we expect mobility to have normalized in the city.<sup>17</sup> There was consistent data (more than 80 days during the analysis period) available from two regulatory-grade monitors in 2020, Guediawaye and Republique. We include data from both monitors and control for monitor fixed effects. For 2023, there is only data from a third regulatory monitor that was recently installed in Pikine. We use the hourly MERRA-2 data for 2020 and 2023 for the city. There is only one Purple Air monitor for which data is available for several months before the policy in 2020 and also after the policy, but this monitor does not have data beyond 2020. Instead, for 2023 we include data from all monitors, which includes data on at least 80 different days within the period of 110 days of the analysis. We control for monitor fixed effects. In addition to using the unadjusted Purple Air data, we also conduct the analysis using two off-the-shelf calibrations of the data from Barkjohn and Jaffe, as well as our local and satellite calibrations.

We conduct several robustness checks. We conduct the analysis at the daily level, using both the

> 15These included banning public and private gatherings, a curfew from 20:00 to 6:00, and closing schools.

> 16Precipitation is not included as a weather control because the period of analysis includes only the dry period in Dakar and precipitation is consistently zero.

> 17There were still some COVID-19 policies in place in 2021 that could affect mobility, and therefore pollution, and we do not have data from any regulatory monitors in 2022 to allow for comparison of the policy results across data sources.

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