_GLOBAL FOOD INSECURITY OUTLOOK_ _9_ and are well maintained. Two additional fixed controls are added.<sup>9</sup> First, a fixed country size indicator is added to capture the nominal resource potential implied by the land cover shares. Second, the change from the 2006-07 minimum to 2008-12 maximum CPI inflation rates is added to capture overall domestic price (in)stability during the previous global food price crisis when global food prices spiked in 2008 and 2011. This also allows the model to relate the 3-year inflation rates to the extreme price changes that occurred in this period.

The covariates can be grouped roughly across five dimensions: macro-economic stability, food affordability, the ability to expand the short-term food supply, pressures on food resources, and pre-existing vulnerabilities. More specifically, the covariates inform the model about trends in productivity growth in relation to population growth and price growth, and whether growth in purchasing power occurs equitably. The 3-year inflation rate captures the rate at which prices increase and captures overall macro-economic stability. The percent of merchandise imports spent on food provides a measure of food import dependency, and the percent spent on fuel captures whether the domestic food production system, through diesel fuel, is dependent on imports. The availability of agricultural land, and the share of GDP produced by the agricultural system, capture the size and added value of the domestic food production system. Interactions between these two variables will be most informative to the model. A higher share of agricultural GDP for a given level of arable land suggests high-yield technologies are used. Conversely, low values in both suggest a low capacity of domestic production systems. The complement to agricultural land is roughly natural land since urban areas are usually just a fraction of total land cover. The presence of forest land cover thus captures whether natural land is barren or lush, which allows the model to roughly distinguish between different agro-climatic zones. Finally, a historical deep lag of child mortality is used to capture fragility in the recent past. In section IV, possible missing data dimensions and future avenues for data integration are discussed further.

The application builds on a 1999-2019 data set covering all 144 IDA plus IBRD countries. Kossovo, Somalia and Syria did not have sufficient data and are treated separately as described in the appendix, but 10 additional countries with complete data were added for model estimation purposes.<sup>10</sup> This means there are 3,171 observations (N=151, T=21). Some observations are missing and have to be interpolated. This is only needed for a small share of data points, primarily in the poverty data, and is discussed in detail in the appendix.

> 9As a third factor, income-level country classification dummies were also added to the model and a recursive elimination and re-evaluation of cross-validated prediction performance was applied to perform variable selection (following for instance, Andr ́ee et al. (2019a)). The final model performed best without any of the dummies, likely because the World Bank income classifications follow (somewhat) arbitrary cut-off points on GDP per capita, while the Cubist model can partition the data at any income level and grow new decision rules optimized for the prediction task.

> 10The criteria used to add countries for training purposes was that i) the countries’ GDP per capita was not more than 25% above the highest observed in the 144 IDA + IBRD countries in ppp terms, and ii) the lowest observed severe food insecurity prevalence rates were at least 1%.