continued through the COVID-19 outbreak, they find evidence of severe disruptions in market activity which led to large declines in income among market vendors. However they find no effect on food security for the rural households in their sample.

We also validate this claim, in the context of Mali, with data on recorded COVID-19 infections, Google mobility data, and the COVID-19 phone survey. Therefore, in our core regression specification, we compare the difference in food security measures between urban and rural areas before the peak of the coronavirus pandemic to the same difference after the peak. We estimate the following linear regression: In equation (1), _ycht_ is an indicator of food security measured with the FIES within sampling cluster _c_ , in household _h_ , in time _t_ . This outcome variable takes several forms throughout our analysis. First, we report the raw FIES score which is simply the sum of responses to the eight FIES questions each capturing a different component of experienced food insecurity at the household level. We standardize this variable so that this scale has a mean of zero and standard deviation of one in each survey wave. We take this approach because the baseline EHCVM food security indicator questions use a 12-month reference period and the COVID-19 phone survey use a 30-day reference period. By standardizing the outcome within each survey wave the data represent deviations from the within-wave mean and therefore mitigate complications with inconsistent reference periods. Additionally, this standardization process allows for easier interpretation of our estimated coefficients in terms of standard deviations instead of a unitless score.<sup>12</sup> Second, we report dichotomous indicators of different levels of food insecurity severity following the methods of Smith _et al._ (2017) and discussed above. These dichotomous variables represent “mild food insecurity,” “moderate food insecurity,” and “severe food insecurity” based on the raw FIES score.

On the right-hand-side of equation (1), _αt_ is survey wave or time fixed effect, and _γch_ is a household fixed effect. The coefficient _β_ represents the difference-in-differences estimate on the interaction between a dichotomous variable _{ch_ = _Urban}_ indicating the household resides in an urban area (or in Bamako, in alternative specifications) and a dichotomous variable _{t_ = _Post}_ indicating a post-pandemic outbreak survey wave. Finally, _εcht_ is the error term.

This empirical strategy relies on the claim that disruptions due to the coronavirus pandemic have been much more severe in urban areas than in rural areas in Mali. We validate this claim with observations from three different sources of information. First, according to the United Nations’ Office for the Coordination of Humanitarian Affairs (OCHA) and shown in Figure 1, despite the fact that Bamako is home to roughly 12 percent of Mali’s population, over 60 percent of Mali’s COVID-19 infections were located in Bamako, as of the end of July 2020 (HDE, 2020). Although this statistic likely represents easier access to COVID-19 testing in Bamako relative to other parts of Mali and positive cases of COVID-19 likely do exist in rural

> 12We show results without this standardization procedure in the Supplemental Appendix. The estimates are qualitatively consistent.

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