set of distinct damage functions. These functions may then be modified using empirical and analytical methods to account for the presence of secondary characteristics that increase or decrease the base vulnerability of the class. Such secondary characteristics include roof pitch for wind damage, foundation type for earthquake shaking hazard and minimum floor height for flood damage (see (World Bank, 2013) for a list of secondary characteristics modelled by AIR for PCRAFI).

**Table 2.1: Global Earthquake Model examples of parameter types characterizing building seismic response**

|**Type of Parameter**|**Examples**|
|---|---|
|**Mechanical**<br>**Characteristics**|Strength of the material of the lateral load‐resisting system|
|**Dimension**<br>**Characteristics**|Total height / Storey height<br>Number of storeys<br>Plan dimensions ‐ Bay length|
|**Structural Detailing**|Tie spacing at the column<br>Reinforcement ratio at the column<br>Hardening ratio of steel|
|**Geometric**<br>**Configuration**|Perimeter Frame Building ‐ Space Frame Building<br>Rigid Roof / Deformable Roof<br>Column orientation|

Source: Global Earthquake Model from D’Ayala et al, 2014.

The ability to differentiate the response of a building (or other asset) to hazard intensity according to vulnerability‐determining characteristics is an important facilitator of the ‘external validity’ of damage functions. For example, damage functions developed for buildings in one region can be modified for use in another, if sufficient detail around differences in construction types and quality is available. This out‐of‐context application is more challenging when considering the impact of hazards on poverty, rather than physical damage, outcomes.

The pathway of impact of disasters on household welfare outcomes is complex and involves many indirect mechanisms. In economics, much research has focused on the ex‐post welfare impacts of shocks, using either a “reduced‐form” or “structural approach” (Chetty, 2008). The reduced form approach is taken in this paper. Practically, this means that the shock impact on the final outcome is estimated without a structural model of behavioural change. The structural model approach places (as the name suggests) more structure on preferences and behavior of households. In particular, this approach allows the researcher to understand the potential ex‐ante effect of behavioural responses, that may cause lower returns (such as adopting low risk, low return crops or activities), and also represent a more complete picture of the welfare loss associated with risk and shocks (Dercon 1996). However, structural models require some quite restrictive assumptions. Further, data scarcity poses significant challenges here, as the complexity of causal mechanisms demands substantial data to be sure of the validity of the apparent relationships observed in the data (Chetty, 2008).

We further acknowledge in our examination of rainfall and poverty that extreme drought is not the only cause of famine for example, the work of Sen (1981) shows that famine is also a political process, and has its root causes in the failure of entitlements. Devereux (2009) shows that people are often pushed into hunger when rainfall may fail only moderately, but if asset prices fall considerably, or food prices increase rapidly, then coping strategies may break down.

With this discussion in mind, we pursue our more positivist approach and obtain the reduced form estimates, following directly from Hill and Porter (2014) though noting that by not estimating ex‐ ante risk impacts, we are potentially underestimating the full impact of the shock, rather, showing 7