_GLOBAL FOOD INSECURITY OUTLOOK_ _5_ work and lending is geared toward investments that contribute to set development goals that are grounded in country economic analyses. Food insecurity in turn poses wide economic risks that may lead to serious setbacks in progress toward the development goals of affected countries.<sup>3</sup> Economic developments and food insecurity trends should therefore not be analyzed in isolation of one another.<sup>4</sup> The focus on prevention has motivated researchers to develop forward-looking capabilities. This paper specifically contributes to understanding the ability of statistical models to anticipate future food insecurity conditions in the particular context of assessing development financing needs and builds on several earlier efforts. Mellor (1986) discussed prevention strategies with an emphasis on economic weakness, crop failure, and subsequent price signals as leading indicators of famine, providing a modeling context that remains relevant to this day. Inflation signals in particular had also been discussed for instance by Seaman and Holt (1980), who theorized that in anticipation of extreme food shortage, market prices should increase as populations begin hoarding food items. Cutler (1984) evidenced this using data from the Ethiopian famine in 1972–1974, and Khan (1994) during the famine of 1984-1985 in Niger. Andr ́ee (2021a) provides a longer list of severe food crises that occurred at the backdrop of record inflation levels.

More recently, researchers have begun to implement these ideas using machine learning techniques that draw from a wider variety of indicators for prediction. The majority of these efforts focus on granular predictions derived from novel data sources. For example, Mwebaze et al. (2010); Okori and Obua (2011) predict household famine in Uganda between 2004 and 2005, Vu et al. (2022) predict changes in household level food insecurity in Vietnam, and Lentz et al. (2019) predict food insecurity for village clusters in Malawi. Martini et al. (2021); Foini et al. (2022) develop high-frequency now-casting capabilities at a global scale focused on the food consumption score and Balashankar et al. (2021) extract leading signals from news streams. Andr ́ee et al. (2020) predict the outbreaks of new food crises up to a full year ahead at the administrative level in 21 highrisk countries using the price data from Andr ́ee (2021a,b) combined with conflict data and remote sensing data. Using the data set from Andr ́ee et al. (2020), Wang et al. (2020, 2022) also provide a stochastic simulation model that projects

> 3Beyond immediate impacts, food crises also have long-lasting development consequences for countries, including inter-generational health and education effects (Galler and Barrett, 2001; Veenendaal et al., 2013; Galler and Rabinowitz, 2014; Asfaw, 2016; Li et al., 2017), and so they may rapidly alter the long-term development prospects of a country.

> 4This interplay between economic outcomes, food insecurity outcomes, and policy decisions was particularly highlighted by the events that unfolded during and after the Arab Spring in 2011. Food insecurity following the 2008 and 2011 global food price spikes exacerbated existing frustrations and disrupted the social and political order of several countries, culminating in the Arab Spring in 2011 (Barrett, 2014). This event had long-lasting regional impacts which triggered financial stress dynamics from which some countries arguably never recovered (Elsayed and Yarovaya, 2019). The food security policies that governments adopted during the Arab Spring played an important role in shaping the nature and severity of subsequent protest (Soffiantini, 2020). More recently, rising food prices have put several countries in major financial distress and at risk of default as governments first sought to maintain price controls on staple foods, and then faced the consequences once prices eventually surged after government funds were insufficient to meet the surging subsidy requirements.