# Extended Abstract

This paper brings together two strands of research that have thus far been developed independently: catastrophe risk modelling, and economic analysis of vulnerability to poverty. The analysis focuses on a specific example to fix ideas: the impact of drought hazard on the welfare of rural households in Ethiopia. The aim is to determine the potential for applying a derived set of damage (vulnerability) functions based on realized shocks and household expenditure/consumption outcomes, onto a forward‐looking view of drought risk. The paper outlines the contribution that combining the two analyses can bring, shows preliminary results for the validation of a regression model estimating ex‐post drought impacts, and outlines future plans with regard to catastrophe risk modeling.

The catastrophe risk modeling framework (CAT risk modeling) and the role of the vulnerability module, which describes the response of an affected exposure (household, building, crop) to a given hazard intensity, is first outlined. CAT risk models have been mainly used for modelling financial losses, and the paper describes some of the challenges in applying the CAT risk modelling framework, and specifically the vulnerability module, to create a view of the impact of hazards on household poverty. The need to explicitly account for different household characteristics that determine vulnerability within our model, is considered, analogous to how a CAT risk model would differentiate damage functions by different classes of construction. The importance of this differentiation in external validity of any derived relationships is acknowledged in the development of the analysis methodology. In the applied micro‐economics literature, considerable progress has been made in estimating the ex‐post impact of realized shocks on welfare outcomes, notably household consumption and associated poverty. Hill and Porter (2014) extended such a backward looking model with simulations from the historical distribution of drought, to quantify vulnerability, defined as high probability of falling into poverty in future periods. The analysis herein then attempts to develop the model of drought and consumption further, in particular to extend the heterogeneous impacts on different household types. The validity/generalizability of the derived functions are then assessed, to infer applicability of the derived relationships within a CAT risk modelling framework.

For the applied regression analysis representative cross‐section data for Rural Ethiopia in 2005 and 2011 is used, as also used by Hill and Porter (2014) for vulnerability analysis (Household Income Consumption and Expenditure Survey, HICES, and Welfare Monitoring Survey, WMS). The data comprise household characteristics, consumption outcomes (our variable of interest for poverty analysis) and measures of realized shocks such as household member illness, food prices, crop damage, unemployment. The analysis also incorporates the drought data as compiled by the world food program (WFP, LEAP data). This drought measure shows the expected crop losses at community level based on water adequacy specific to the crops grown in that community. A regression model is fitted to the pooled data, and several different specifications are compared. The main goal is to assess the stability of the drought impact across years, and also across “types” of household (analogous to catastrophe risk modelling e.g. of hurricane impacts on different building structures). The results show significantly different impact of the drought for households with assets, access to the PSNP, and financial access: these characteristics are seen to be mitigating the impact of the drought. However, households that suffered other crop damage experienced a heavier impact of the drought.

An attempt is then undertaken to “stress‐test” the model by using statistical methods of resampling. This involves randomly splitting the data into “training” and “testing” datasets. The training data is used to fit the model, and then the goodness of fit is tested on the holdout sample or testing 2