# **1. Introduction**

Probabilistic catastrophe risk models, used extensively in the international insurance and reinsurance markets, develop a view of risk beyond the historical occurrence of catastrophes. This is done through the generation of thousands of synthetic stochastic events whose characteristics, evolution and pathways are calibrated based on historical event occurrence and a physical knowledge of the potential of the system that generates them. This framework is powerful as it allows for changes in exposed population and assets over time, and considers an extensive range of possible event scenarios well beyond the historical record (see Appendix). This is of particular value when evaluating low recurrence frequency catastrophe events, which by nature have a sparse historical record.

Probabilistic catastrophe risk modelling frameworks have yet to be applied to poverty outcomes at the household level<sup>3</sup> . If these frameworks could provide perspectives on the relationship between the local severity of hazards arising from natural catastrophes and indicators of poverty/welfare, practitioners would have tools to assess the impacts of shocks under a forward‐looking view of potential catastrophe occurrence, with a view to providing assistance or insurance (Muir‐Wood, 2014, Anttila –Hughes and Sharma, 2014).

This paper takes the premise that the principal challenge in the application of probabilistic catastrophe risk modelling frameworks to estimate poverty outcomes at the household level is the development of general form relationships between hazard occurrence and indicators of welfare outcome (Anttila‐Hughes and Sharma 2014 for further discussion). These relationships would comprise the ‘vulnerability module’ in a catastrophe risk modelling framework as outlined in the Appendix: a model of the relationship between the modelled hazard occurrence and the impact on the exposure. We note here that much work in economics that has been done to show that the ex‐ post impact of shocks on consumption is a “reduced form” approach which evaluates the impact after households have used every strategy available to them to mitigate the shock (diversification, asset sales etc – see below for further discussion, as well as Porter, 2012). If such strategies include opting for low‐risk, low‐return activities (Dercon, 1996), then risk also carries an ex‐ante cost, which cannot be measured with this particular approach. However, this cost can be difficult to quantify and is very data intensive. It is also less compatible with a perspective of quantifying realized financial losses to be compensated ex‐post, if for example index insurance were offered to households.

Poverty, for the purpose of policy analysis, is most often conceptualized as a level of consumption or expenditure that is below some pre‐specified level that is considered the minimum for an acceptable standard of living (e.g. based on a basket of food to reach a calorific minimum plus other basic expenditures). Other outcomes such as ownership of household assets, or human capital have also been used in this context. Much poverty analysis up to the turn of the Millennium had been in a static context, but the World Development Report 2000/01 highlighted issues of risk and a hitherto neglected stochastic component of poverty, noting that poor people have low incomes, but also higher risks, and fewer resources to cope with shocks, should they hit.

> 3 The potential to use probabilistic catastrophe risk models outside of the context of insurance, has been recognized in recent years. This has resulted in developments such as the Pacific Risk Information System<sup>3</sup> and the CAPRA<sup>3</sup> Program which both apply the probabilistic catastrophe risk modelling framework developed by the insurance markets for disaster risk management. A similar framework has also been applied to estimate food security needs through the Africa RiskView platform. See http://pcrafi.sopac.org/about/;

> http://www.ecapra.org/;http://www.rockefellerfoundation.org/uploads/files/fa08d48b‐08ef‐4fc7‐8991‐4872f6e929b0‐ africa.pdf

4