11 Estimation of �� yields an estimate of the average effect of natural disaster k on household consumption. Yet different socio‐economic groups are likely to be affected differently either because they are less/more exposed to the disaster ex ante or because they are better/worse able to cope with it ex post. For example, farm households with irrigated fields are less likely affected by droughts than those without, and the more educated may be better able to cope with natural disasters ex post. Similarly, the reception of disaster relief or food aid may lead one to (erroneously) conclude that there were no welfare losses associated with the disaster, even though they induced substantial budgetary outlays elsewhere (by the local or national government) (Yamano, Alderman and Christiaensen, 2005). To explore such heterogeneity in damage or the existence of indirect/hidden welfare losses, natural disasters are interacted with different household and community characteristics and assets ����������� ).

Much of the empirical literature has so far assumed natural disaster measures to be exogenous. Not only does this depend on how they have been measured (objective versus subjective, as discussed above), even when objective measures are used, they may still yield biased estimates. The occurrence of a natural disaster in a locality is likely correlated with the likelihood of it occurring in the first place, which may in turn affect the level of consumption, e.g. through accumulated asset loss at the household and/or community level. Moreover, households in localities that are frequently plagued by natural disasters have likely adapted to these circumstances (for example by adopting more disaster resistant, but less remunerative portfolios (Dercon, 1996), or at times even by building successful livelihood systems around it—“living with floods”). The use of panel data provides the cleanest way to protect welfare estimates of natural disasters from (time invariant) unobserved heterogeneity across communities. However, sufficiently long panels of communities are usually not available for large geographical areas, let alone countries. Within community estimators are usually also less efficient given the lower signal‐to‐noise ratio, especially when panels are short. This in turn may lead to the disproportionate acceptance of the null‐ hypothesis of no welfare effect.

Here, repeated cross sections are used instead. To mitigate potential bias from unobserved community heterogeneity, a comprehensive set of socio‐economic and agro‐ecological characteristics of the locality is included. Most importantly the likelihood of the occurrence of different disaster types is controlled for, omission of which is identified as the major cause for biased estimation of the coefficients on natural disasters. Explicit inclusion of disaster exposure further permits exploration of the occurrence of adaptation. In particular, disaster incidence is hypothesized to mitigate the immediate effect of a particular disaster if households have reverted to low‐risk, low‐return activities. They are likely to suffer less when disasters hit, and benefit less if there is a rebound, for example due to accelerated replacement of the capital base with more productive equipment.

Empirical analysis of the effects of shocks on household welfare is often predicated on the assumption that the observations are independent from each other, thereby also abstracting from spatial correlation. But in many cases, it seems more likely that observations that are "near" in a geographic sense either influence each other more so than an observation that is far, or that they are more influenced by the same unmeasured force than an observation that is far. This seems especially relevant in a study of natural disasters, where their effects on welfare may permeate across locations especially when product (e.g. food) and/or factor (e.g. labor) markets are