the actual financial losses that would be experienced by households under different scenarios, conditional on households adopting all other risk management strategies at their disposal (which may have already reduced their welfare in advance of the shock).

The challenges noted above are evident in existing research to elaborate quantitatively the relationships between hazard occurrence and household‐level poverty outcomes, with backward looking data. A few examples for further reading are listed below:

- The derivation of quantitative relationships between typhoon wind speed and infant mortality from historical typhoon data in the Philippines (Anttila‐Hughes and Hsiang 2013);

- Quantitative examination of the impact of rainfall variability on household consumption in Ethiopia (Hill and Tsehaye 2014, Porter 2012);

- Quantitative examination of the impact of both climate variability and disaster occurrence on the annual probability of permanent household migration in Indonesia (Bohra‐Mishra et al. 2014);

- Use of panel datasets to examine the impact of climate variability on allocation time among child labour activities as well as participation in education and labour activities  (Colmer 2013).

# **a. Selection of hazard measure**

The measure of drought chosen for this study is an index of crop yield shortfall, taken from the World Food Programme’s LEAP (Livelihoods Early Assessment and Protection) software. The index has been developed for the two main cropping seasons in Ethiopia, the _Meher_ (main) and _Belg_ (minor) seasons.

The yield shortfall calculation uses time‐variable meteorological recordings combined with data tables on soil and crop characteristics to calculate yield reductions relative to the expected production under non‐limiting water conditions. The yield shortfall calculation is a seasonal value and can be calculated for both _Belg_ and _Meher_ cropping seasons. It is available at the Woreda<sup>7</sup> level through creation of a composite index for the relevant crop basket.

The methodology for the calculation is described in Box 2.1 below. If we consider the methodology in the context of a probabilistic catastrophe risk modelling framework, the key ‘hazard’ input into the process is the rainfall data used in the calculation of Actual Evapotranspiration. It is the high levels of variability around rainfall that are determining the occurrence or otherwise of meteorological drought (translated into crop yield loss), and this is where the large covariate shocks arise that probabilistic catastrophe models seek to capture.

Recorded climate data on temperature, humidity, windspeed and solar radiation are also used in the methodology to determine the evaporating power of the atmosphere at a specific location and time of the year. However, whilst simulating trends in these parameters over the long term could be an interesting exercise on the impact of climate change, the low variability of these parameters over time compared to rainfall make them less interesting candidates for probabilistic simulation in the context of drought.

The LEAP software makes available a combination of satellite and weather station datasets from which historical precipitation values can be taken or estimated. The primary precipitation dataset comes from the National Meteorological Agency of the Republic of Ethiopia (NMA). NMA provides rainfall (and other climate data) on a daily and dekadal<sup>8</sup> basis for its network of weather stations.

> 7 _Woreda_ is an administrative unit in Ethiopia, equivalent to a county in the United States.

> 8 Ten days accumulated

8