Death in the household has a large impact on consumption in urban areas (significant for households headed by uneducated females and by those with education) but no significant impact on consumption in rural areas. Job loss does not appear to have an impact on consumption, this may be due to the fact that relatively few people are in employment, and few report such a shock, resulting in low power to detect effects. We also re-estimated the model omitting the self-reported shocks of death and job loss given their potential bias. The results on the impact of rainfall and inflation were unchanged.

We performed several robustness checks on the estimated impact of rainfall-induced crop-loss (including a squared term on the shock and a nonlinear impact for shocks above 60% crop losses) and conclude that the results are fairly robust to our choice of specification. As an additional robustness check, we use propensity score matching methods<sup>15</sup> to calculate the “treatment effect” of a higher than average drought. For this we created a dummy variable for the ‘treatment’ equal to one if the woreda experienced crop loss greater than the mean for the sample (13.6% or higher), and matched households based on their characteristics and risk exposure. The ATT estimate was -.155 (s.e. =.0001). Given the distribution of the crop losses in the sample (32% of households have experienced a drought shock defined in this way), the result is consistent with the marginal effect estimated in the regression model. We also estimated a within-zone pseudo panel model (i.e., collapsing to the average for each zone) and found similar, though less precise results for the price shock and the drought shock.

# **5 Vulnerability estimates**

Using the methodology described in section 3 above, we calculated total vulnerability to poverty, as shown in table 4. In 2011, vulnerability to poverty was 38.2%, as measured by the proportion of the population that had a higher than 50% chance of falling below the poverty line (column 2). This shows that vulnerability is higher than poverty in Ethiopia (with a headcount rate of just under 30% as shown in column 1), as is often the case. Columns 3-5 outline some further measures of vulnerability that are more sensitive to depth of future poverty episodes (rather than simply the probability of falling below the line) as discussed in section 3. These are the expected poverty gap, expected squared poverty gap (both as outlined in equation 3 above). The final column shows the Calvo and Dercon (2013) “no compensation” measure (equation 5).

Although poverty is 4 percentage points higher in rural areas than urban areas, vulnerability according to all measures is substantially higher in rural areas. In particular, the measures that are sensitive to depth are more than twice as high in rural areas compared to urban areas, showing that it is rural households that are more vulnerable. Crop conditions were relatively good throughout Ethiopia in 2011. This may seem surprising given this was the onset of the Horn of Africa drought, but the crisis largely affected the pastoral regions of the country, which are not included in our sample. The relatively good crop conditions in non-pastoral regions resulted in lower levels of poverty for many

> were not impacted by the shock so defined (see Table 7 in the annex).

> 15teffects psmatch in stata 13.

15