_ANDR EE_<sup>_ ́_</sup> _SEPTEMBER 28, 2022_ _4_ model. Section II then introduces the data and develops the modeling framework. Section III presents validation and key prediction results, section IV continues the discussion and section V concludes. Additional results are found in the appendix.

# **I. Background**

Food crises have devastating effects on populations. Black et al. (2013) conclude that almost half of global child deaths are associated with undernutrition and Devereux (2000) estimates that famines in the 20th century were responsible for an equal amount of deaths as those from World Wars I and II combined. The international community has long responded to critical food insecurity situations with humanitarian aid after the severity of a situation has been declared, a definition that may involve mortality itself. By this time, irreversible harm has already been done. Delay in unlocking financing resources until after a rise in mortality has been confirmed played a critical role in the severity of the Somali famine of 2010-2011 (Lautze et al., 2012; Seal and Bailey, 2013) which left almost 300,000 dead despite more than a billion dollars in aid spent on response (Checchi and Courtland, 2013). In recent years, that realization has resulted in a growing emphasis on investment in prevention and resilience (Haan et al., 2012).<sup>2</sup> In many cases, planning responses ahead has been shown to save more lives and be more cost effective than responding after situations have deteriorated (Meerkatt et al., 2015; Mechler, 2016). A pro-active approach to emergency response financing requires a capacity to anticipate when, where and how many people will be at risk of severe food insecurity conditions under baseline economic scenarios.

To contribute to this capacity, this paper explores a statistical model to estimate and project global food insecurity conditions using macro-economic outlooks. Extreme food insecurity situations are multifaceted in nature and frequently develop as part of multiple interlocking challenges (Maxwell et al., 2020). The paper’s approach anchors the food insecurity outlooks to economic assumptions in an attempt to contribute to broader foresight that is internally consistent so that multiple policy actions can be informed jointly by a cohesive set of expectations. This choice of approach is motivated further by the fact that development finance involves large capital flows that are replenished following a slow multi-year replenishment cycle. The general trends in the magnitude of future at-risk populations should therefore ideally be projected years in advance so that existing financing mechanisms can be restructured, or new ones designed, according to the projected changes in needs. Country eligibility for development finance operations is typically also conditional on the existence of a developed macro-economic frame-

> 2Recent financial innovations in this direction are for example the World Bank’s Early Response Financing (ERF) tools under the Crisis Response Window (CRW) that may provide rapid disbursements to respond to slower-onset events which are identified as having the potential to escalate into major crises. Eligibility is conditional on having or developing a preparedness plan for food insecurity. Another example is the Crisis and Emergency Response Component (CERC), which can be used to rapidly channel pre-allocated emergency funds to respond to emergencies, such as droughts or floods triggered by an El Nino / La Nina event whose risks can be pre-identified.