specific woredas will necessarily result in many vulnerable Ethiopians being left without protection. The analysis finds that 37 percent of households living in non-PSNP woredas are vulnerable to absolute poverty.

# **6 Conclusions and policy implications**

We use a nationally representative dataset merged with climate and price data to estimate the impact of climate and price shocks on household consumption. We find that many Ethiopians are unable to protect their consumption against lack of rainfall and sudden increases in food prices. There is considerable heterogeneity in the impact of all types of shocks. Rural households in drought prone areas that are not currently recipients of the safety net, and households with little education in urban areas are most at risk to suffer drops in their consumption. We estimate that a moderate drought causes a 9 percent reduction in consumption for households that are not covered by the PSNP in drought-prone areas. This implies a substantial increase in poverty when rains are poor. As an example, were the drought of 2002 to be repeated, we estimate that the headcount poverty rate would increase by 21 percentage points from 30 to 51 percent. Sudden food price increases can be similarly devastating for urban households. We estimate that the high inflation experienced in the first half of 2011 resulted in a reduction of 14 percent in the consumption of uneducated households in urban areas. We take actual distributions of climate and price shocks in each district to simulate the probable ex-ante distribution of consumption and use this to quantify vulnerability to poverty in Ethiopia. Our numbers can be compared directly to official poverty numbers, to show how sensitive current poverty estimates are to shocks that are prevalent in the context. We find that 42 percent of rural households are vulnerable to poverty, but only 21 percent of urban households. Vulnerability to poverty is much higher than poverty in rural areas but this is not the case in urban areas, reflecting the fact that the household survey was conducted during a year of good conditions for rural households and very difficult conditions for urban households. Within rural areas, vulnerability is higher in areas prone to drought. However, much vulnerability is not geographically determined, but instead determined by other factors such related to gender and education. We show that the ranking of vulnerability across groups (e.g. rural female, rural male) are fairly stable across the choice of measure, though measures that are more sensitive to depth of potential deprivation should also be carefully checked.

The methodology used avoids a number of the limitations of cross-section data where possible but caveats to the analysis remain with regard to unobserved household characteristics. For this type of analysis, the data requirements are indeed high, and future challenges include generalizing estimates of consumption more reliably out-of-sample in order to improve the vulnerability estimates (Anttila-Hughes and Sharma, 2014).

In terms of policy, the results highlight the need for caution in using poverty data collected once every five years to target programs, as underlying rates of vulnerability can be quite different from the poverty rate captured at one point in time. More frequent collection of poverty data, and vulnerability analysis such as performed in this paper can 18