# **3 Quantifying the Impact of a Drought on Stunting**

## **3.1 Relationship between Droughts and Stunting**

In what follows, we document two important characteristics of the relationship between drought shocks and child stunting. First, we show that our measures of drought shocks are causally related to stunting and other child malnourishment outcomes. Second, we show that the average magnitudes of these impacts are comparable to the impacts associated with differences in wealth status, as well as with differences in a mother’s literacy level.<sup>20</sup> It is possible that droughts occur more frequently in areas that are less developed and that are characterized by a greater prevalence of child malnourishment. The most disaggregate spatial unit in the Demographic and Health Surveys (DHS) is the cluster. This is a village or a small collection of villages. By employing cluster x month fixed effects, we control for time-invariant influences at the cluster level (such as variation in remoteness, local governance and social structures, and in agro-ecological factors such as altitude and soil quality) interacted with the factors related to the month (harvest cycles, festivals, and school calendars). The drought coefficients are identified by variation in the incidence of shocks across years.

When we discuss controlling for month effects, we refer to the month that a child was born. This is measured with error (Larsen et al. (2019)). In the appendix (see Tables A1 and A2), we show that our results are robust to removing children born at the start of the year (January and February), the end of the year (November and December), children with birth months corresponding to whole years, and including _age_ and _age_<sup>2</sup> as explanatory variables.

Table 2 describes results when child outcomes are anthropometric regressed against droughts, with cluster x month fixed effects. The sample includes rural populations of five Southern African countries (Mozambique, Malawi, Tanzania, Zimbabwe and Zambia). For the stunting regression, we control for 2,934 cluster x month fixed effects. As a first-pass estimate of the magnitude of the impact of drought shocks on the likelihood of stunting, it is useful to start with this basic set of regressions that uses wealth quintiles as the only household-level controls (Equation 1). _Sict_ measures whether a child _i_ born in cluster _c_ in year _t_ is stunted at the time of measurement. _Dct_ is a dummy variable that

> 20These are likely to be under-estimates. Responses to droughts — both systematic and ad-hoc — from national governments, international organizations, and even large non-profits, may have mitigated the impacts of previous shocks in all of the countries that we examine. In addition, there are other confounding factors that we have not adjusted for — including those related to migration, and infant and child mortality. As a robustness check, we exclude children whose families moved in the preceding 5 years before they were surveyed and show that our results are robust to this exclusion (Table A5). Further, wealth is measured at the time of the survey and not at the time of the occurrence of a drought.

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