used by Google Maps to predict real-time congestion. The Google Community Mobility Reports show daily changes in the amount of time spent at different places within a given geographic area. These data capture daily percent changes from February 15 through July 15 relative to a baseline representing the median value for the corresponding day of the week during the five-week period from January 3 through February 6, 2020.<sup>6</sup> Figure 2 displays the Google Community Mobility data for both the entire country of Mali and for Bamako specifically. Google aggregates locations into six types of places: Grocery and Pharmacy, Retail and Recreation, Parks, Transportation Stations, Workplaces, and Residential. Overall, for the entire country of Mali, we observe a decrease in time spent at all of these locations, except for places of residence, where we see a relatively small increase. This suggests that either the government containment policies or the risk of contracting the virus disrupted movement and economic activities in Mali.

Relative to the entire country, Bamako itself had a larger percent change in absolute value in the time spent at each of these places. Specifically, as reported in the Google Community Mobility Reports, for the period between June 13 and July 25, people in Mali spent 2 percent less time at grocery stores and pharmacies relative to the baseline period whereas people in Bamako specifically spent 9 percent less time at grocery stores and pharmacies. A similar finding holds for other types of places. People in Mali spent 5 percent less time in parks, 25 percent less time at transportation stations, and 4 percent more time in residential spaces, relative to the baseline period. In comparison, people in Bamako specifically spent 12 percent less time in parks, 32 percent less time at transportation stations, and 6 percent more time in residential spaces relative to the baseline period. This highlights that the reduced mobility induced by the risk of contracting COVID-19 and government response to the pandemic was concentrated mostly in Bamako, and perhaps, in other urban areas. In rural areas, by contrast, such pandemic-related disruptions were not as noticeable. This supports the validity of our approach for investigating the effect of the coronavirus pandemic and Mali’s containment policies on food security in Mali.

Finally, we use information from the COVID-19 phone panel survey, our third source of information used to investigate the intensity of pandemic-related disruptions within Mali. Figure 3 summarizes information about coronavirus-related awareness, beliefs, and behavior.<sup>7</sup> We see that approximately everyone in the sample, with no discernible difference between urban and rural areas, had previously heard of the coronavirus and received some information about social distancing. Although most respondents are, reportedly, satisfied with the Malian government’s response to the coronavirus, those who live in rural areas are more satisfied on average than those who live in urban areas. Again, this supports our claim that the pandemic-related disruptions were more intense in urban areas compared to rural areas. Moreover, people living in urban areas are more likely to take extra health precautions due to the pandemic (e.g.,

> 6Additional details about these data are available at `https://www.google.com/covid19/mobility/` . A few notes about these data are important. First, these data are not representative of the whole country. Google only reports on regions if they have a sufficient number of observations over time to make credible estimates of mobility. Thus, these data exist for Mali and for Bamako specifically. No other regions within Mali are reported in Google’s Community Mobility Reports. Second, Google data only rely on individuals who use Google applications and have turned on the location history setting in their Google account.

> 7Table A1 in the Supplemental Appendix provides additional details on the statistics reported in Figure 3.

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