To ensure our measures of food insecurity are as similar as possible over time, we create a standardized FIES score developed by Adjognon et al. (2021) and implemented in Rudin-Rush et al. (2022) that is in addition to our mild, moderate, and severe indicators. The standardized measure counts the number of affirmative answers to FIES questions in the pre-outbreak data by country and uses survey weights to standardize the variable such that its mean is zero and standard deviation is one.<sup>7</sup> Following a similar process, the post-outbreak data are standardized by country across all data rounds. As such, the standardization process facilitates comparison between preand post-outbreak data and across country by ensuring our measure of food insecurity is as similar as possible over time and across countries. This allows for comparisons of deviations from the pre-pandemic mean and the mean of the variables after the onset of the pandemic within each country. Additionally, standardization allows us to interpret estimated coefficients in terms of standard deviations instead of a unitless score.

As seen in Figure 2, food insecurity in all three countries increased substantially between 2019 and the summer of 2020, following the onset of the pandemic. Recovery in food security throughout the subsequent year was slow in all three countries, with about 80 percent of households in Malawi and Nigeria and about 60 percent in Ethiopia experiencing mild food insecurity in almost every month following the outbreak. Prior to the onset of the pandemic, mild food insecurity affected less than 30 percent of households in Ethiopia and about 60 percent in Malawi and Nigeria. Similarly, moderate food insecurity spiked after the COVID-19 outbreak and slowly recovered in subsequent months. Severe food insecurity increased in Malawi and Nigeria in June 2020 and rose slightly in Ethiopia after the initial outbreak period. In all three countries, food security has not returned to pre-pandemic levels.

# **3 Method**

We use two econometric approaches to investigate the causal effect of livelihood diversification on mitigating the impact of COVID-19 on food insecurity. The first approach is a dynamic panel data estimator in which food insecurity for a particular round is explained by the diversity index from the previous round. This approach is designed to capture the potential _ex post_ effects of changing livelihood diversification strategies in response to the pandemic. The second approach is an ANCOVA estimator, in which we regress food insecurity for a particular round on the preCOVID-19 diversity index. This approach is designed to capture the potential _ex ante_ effects of livelihood diversification in anticipation of a shock. For each specification, we run regressions for each country separately. Survey weights are included in all specifications.

> 7The standardization process creates a _z_ -score by subtracting the mean and dividing by the standard deviation to get _FIES ∼N_ (0 _,_ 1). In calculating the mean and standard deviation, we use weights so that the mean is the weighted mean and the standard deviation is the weighted standard deviation. This preserves the representativeness in the standardized variable.

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