review above, the scope and severity of shocks faced by households, together with the available resources at home and in communities, determine the extent of each household’s success in responding to such shocks.

Table 2 also includes descriptive statistics for the main explanatory variables employed in the analyses. Both the IHS3 and IHPS rounds contain information on household socio‐demographic characteristics that can be used to proxy the consumption smoothing capacity of households, including the size and life‐cycle of the family according to the age and gender composition of its members; as well as the age, gender, civil status and ethnicity of the household head. We have also created two indices of wealth, as detailed in footnotes a and b, Table 2. The first index includes measures of household wealth and education levels, and the second index includes measures of agricultural specific assets and landholdings. Higher levels of wealth, education, agricultural assets and landholdings are all expected to be associated with higher welfare outcomes per capita. The impact on variance measures, however, is ambiguous. Greater wealth levels may enable households to pursue production and income strategies that are more lucrative but also more risky. On the other hand, wealthier households may be more able to finance adoption of risk‐ reducing activities, such as pursuing income diversification strategies or adopting relatively costly sustainable land management activities (Arslan et al., 2015; Asfaw et al., 2015).

We also control for the availability of socioeconomic infrastructure that exists in the community and the degree of accessibility and slope as proxies for the structures of opportunity that rural households face at the village level. These variables are included to capture a measure of the community conditions relevant to households’ economic and social decisions. Higher infrastructure and accessibility scores are expected to correlate with greater ease of conducting economic and social activities, and thus lead to higher, and potentially more stable, welfare outcomes. Higher slope levels are included to capture agricultural land that is more susceptible to erosion and flood damage, and which may lead lower and more variable harvests, and potentially, lower and more variable incomes and consumption levels. We also gather information on economic heterogeneity by looking at the difference in landholding size between the poorest and the wealthiest. On the one hand, heterogeneity can make it more difficult to provide public goods and cooperation that can shield farmers from poor outcomes (McCarthy and Kilic, 2015; Asfaw et al., 2014). On the other hand, relatively large landholders are also more likely to be in a position to offer informal insurance, credit or wage labor in the event of idiosyncratic risk realized by poorer households. We also include the number of AEDOs per farming family as a measure of the degree of government presence in the district beyond the infrastructure and accessibility characteristics captured elsewhere. Finally, we obtained information on the number of donor‐led projects operating within each district, from the EPIC team based at the FAO.<sup>6</sup> Such projects may increase welfare outcomes as well as increase stability of those outcomes.

# **4. Empirical Strategy**

In conventional poverty analysis, the first thing to do is choose a welfare indicator. Then one computes a poverty threshold to distinguish the poor from the non‐poor. Classifying a person or household as vulnerable under the expected poverty approach requires extra steps that are not usually taken in poverty analysis. The first relates to the estimation of the ex‐ante probability distribution of ex‐post welfare outcomes of interest. Once the distribution of future welfare has been established, one then needs to establish the vulnerability threshold.

> 6 More information on the EPIC project can be found at: http://www.fao.org/climatechange/epic/home/en/

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