# **Figure 2: Hypothetical scenarios leading to IPC4**

These two scenarios illustrate how two entirely different situations of food insecurity lead to the same classification under the 20% rule, where a region or country is classified based on which IPC classification the 80th-percentile of its population finds itself in. Our framework addresses this shortcoming by using a compositional transformation and leverages the full distributional information in the data, thereby accurately reflecting the more detrimental situation in Scenario 2.

<!-- Start of picture text --> Scenario 1 Scenario 2<br>1.00<br>Threshold (80%)<br>0.75 Population share<br>(cumulated)<br>0.50 Population share<br>0.25<br>0.00<br>IPC1 IPC2 IPC3 IPC4 IPC5 IPC1 IPC2 IPC3 IPC4 IPC5<br><!-- End of picture text --> in the respective IPC phases over all districts.2 The reported IPC values are net of humanitarian assistance impacts as the model is intended to estimate outcomes if urgent action is not taken.

As previously outlined, food insecurity is driven by complex interactions between conflict, poverty, extreme weather, climate change and food price shocks (D’Souza & Jolliffe, 2013; Headey, 2011; Misselhorn, 2005; Singh, 2012). This means that a large number of possible covariates may explain dynamics in the food insecurity distribution.

Agronomic and weather-related variables reflect the agricultural conditions. Historically, crop failures and fluctuations in crop yields have been a key reason for worsening food conditions. We use satellite data to calculate stress indicators and anomalies in vegetation, humidity and soil moisture. To highlight anomalies from historical norms, we also calculate differences with respect to long-term averages for a particular month.

Closely related are prices of staple food items, whose composition may differ between countries. We constructed a staple food price index to capture the market conditions for food commodities. Volatility in food prices are seen as major drivers for food insecurity (IPC Global Partners, 2019). The treatment of the raw data is discussed by (Andrée et al., 2020). We obtain food price data from the FAO and the WFP. We additionally include price data that are seasonally adjusted.

Political instability and conflicts have been other key drivers of famines in recent history, even with ample food supply. For instance, Devereux (2000) argues that famines in Sub-Saharan Africa were the result of the combination of natural disasters, such as droughts, and political triggers, such as civil wars. We gather data on conflict events and the number of associated fatalities from Armed Conflict Location & Event Data Project (ACLED). Due to the sparsity of conflict events, and their possible wider regional impact, we use inverse-distance weighting to proxy the effect of violent outbreaks close to a district of interest.

2To illustrate, the formula below shows how the share of people in IPC2 is calculated for a given period.

> _퐼푃퐶푐,푑_ is the IPC phase of district _푑_ in country _푐_ . _푝표푝푐,푑_ is the population in district _푑_ of country _푐_ and _푝표푝푐_ =<sup>�</sup> _푑_<sup>_푝표푝_</sup> _푐,푑_<sup>.</sup> I( _푐표푛푑_ ) is an indicator function, which takes on the value 1 if the condition _푐표푛푑_ is satisfied and 0 otherwise.

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