**Table 2.2: Household characteristics and proxy variables**

|Household characteristics|Interactingvariables|
|---|---|
|Abilityof head to access copingstrategies|Head education level,sector of occupation, gender|
|Household composition that allows labour response|Dependencyratio,ratio of able‐bodied|
|Household assets that mitigate shock|Cattle,other livestock, jewelry|
|Other shocks that compromise ability to mitigate<br>shock|Illness, livestock disease, crop damage from pests|
|Access to institutional coping strategies|Distance to market, access to financial products<br>(insurance,credit), public safetynet access.|

Finally, agro‐climactic zone is discussed in a separate section below.

# **c. Internal validity**

We keep with the notation of Antilla‐Hughes and Sharma (2014, AHS) who outline succinctly the issues in establishing internal validity when estimating hazard relationships: in our case the internal validity is concerned with the establishment of a causal (or, at least, externally replicable) relationship between crop loss and consumption. In the absence of a randomised experiment assigning some households to better community‐level crop yields than others, we must assume implicitly that (in some specific sense) the crop loss in a given year is exogenous to (uncorrelated with) unobservable household and community characteristics that affect household consumption. We acknowledge that there are some issues around this. For example, if crop yields are highly correlated over time this may lead to lower average consumption in the community, and therefore a bias towards households who are less intrinsically productive living in high crop loss communities. Without a panel dataset we are not able to deal with this and to the extent that other unobservable household and community characteristics that impact consumption are not included in the regression, and _are correlated with the level of the drought shock in a given year_ , we may be capturing biased estimates of the relationship between drought and consumption. We can include average rainfall at the community level to help control for this, or alternatively, a fixed‐effect at the zonal level (there are 68 zones in Ethiopia). The datasets used in the analysis do allow a rich set of controls in which we aim to capture as many characteristics as is feasible, without over‐fitting the model to the dataset at hand. With this in mind, we also use the statistical learning procedures to validate the model.

The question of internal validity may be more weakly stated as being a “stable” observable relationship between drought‐crop‐loss and consumption over several specifications (and external validity over several datasets), and answerable by looking at whether the point estimates and associated p‐values established through the regression work, and statistical learning through the bootstrapping method described later. By giving a level of significance around the relationship between consumption and crop loss (defined by the coefficients output from the regression, and their associated functional form), the derived p‐values give us information on the strength of fit of the modelled relationship. The bootstrapping method generates a mean estimate of the relationship and a related standard error (see below for more detailed methodology) which allows us to examine the stability of the coefficients that have been derived.

However, it must be noted that from an econometric perspective, the absence of random application of the shock to households (e.g. if at all there may be a bias for households more susceptible to the impacts of shocks locating themselves in hazard‐prone areas) limits the extent to which internal robustness/validity can be examined using cross‐sectional data. The purpose of the research sidesteps this issue somewhat, as the question of interest is not primarily about causality _per se_ but rather, an externally replicable association. That is, whether it is possible to capture a 12