# **1 Introduction**

Rural Sub-Saharan Africa has the largest concentration of poor and deprived households in the world (World Bank (2018), Skoufias et al. (2019)). These households are particularly vulnerable to anomalous weather events.<sup>1</sup> These events are projected to increase in frequency and become more severe in a warmer world. However, policy responses to these events are often slow and large funding gaps are the norm. A transparent and data-driven early warning and monitoring framework may engender the requisite budget flexibility and responsiveness.<sup>2</sup> Yet, survey data that would inform such a framework are collected infrequently and at a considerable cost (Beegle et al. (2016), Castañeda et al. (2018)). Consequently, existing surveys need to be complemented with current data and additional analysis. Our objective is to contribute to the design of a transparent and comparable early warning framework that identifies deprived children in areas that are both data-constrained and vulnerable to the impacts of climate change.

In what follows, we develop a drought-contingent framework, for five countries in Southern Africa, that identifies children at risk of being stunted. This paper makes four contributions that may inform policies designed to mitigate the impacts that climate change has on the world’s most vulnerable populations. First, we provide a prototype of a drought-contingent targeting system for data-scarce contexts.<sup>3</sup> While several countries (c.f. Devereux and Nzabamwita (2018)) and economists (e.g. Alderman (2009), Barrett (2010), Clarke and Dercon (2016), and Hill et al. (2019)) have advocated for a standard and data-driven approach, there are few studies that provide a detailed empirical analysis of a drought-contingent targeting strategy. In this paper we build prototype droughtframeworks that are across countries because em- contingent targeting comparable they ploy standard, objective and verifiable measures of child and maternal anthropometrics, in addition to standard measures of household deprivation such as those related to housing, sanitation and education.

Second, our design is intended to assist programs in reaching beneficiaries based on

> 1See, for example, Kudamatsu et al. (2012), Alfani et al. (2019), Damania et al. (2017, Ch. 3), Wineman et al. (2017), Lentz et al. (2019), Hill and Mejia-Mantilla (2017), Baez et al. (Forthcoming), and Cooper et al. (2019). For context, see the thoughtful analysis aimed at practitioners by Hallegatte et al. (2016), and the evocative reportage by Sengupta (2018).

> 2While a consensus exists on the need for a timely, transparent and objective framework — see, for example, Alderman (2009), Barrett (2010), Clarke and Dercon (2016), Hill et al. (2019), Maxwell and Gelsdorf (2019), and especially Lentz et al. (2019) — there is little agreement on its design or implementation. de Waal (2018) argues that famines are rare and caused by conflict, which is tenuously related to weather events. We examine episodes of food insecurity, rather than famines.

> 3Our focus here is on the empirical analysis, rather than the implementation details. The latter includes a host of critical issues, including the nature of the support provided, and is outside of the scope of this paper, but is arguably more important. Interested readers may choose to begin with Del Ninno and Mills (2015) or Devereux et al. (2017).

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