Table 4: Vulnerability Estimates, 2011

||Headcount|V(prob)|E(Pov Gap)|E(Squared Gap)|Calvo-Dercon|
|---|---|---|---|---|---|
|All|0.294|0.382|0.082|0.050|0.142|
|(Standard error)||(0.003)|(0.001)|(0.001)|(0.001)|
|Urban Households|0.261|0.210|0.038|0.020|0.065|
|(Standard error)||(0.003)|(0.001)|(0.000)|(0.001)|
|Rural Households|0.302|0.416|0.082|0.050|0.142|
|(Standard error)||(0.005)|(0.001)|(0.001)|(0.002)|

Notes: Survey-weighted proportion of population that have the characteristics. Headcount is the Foster et al. (1984) headcount measure, E(pov gap) and E(squared gap) are the expected values of the FGT poverty measures respectively. The final column is Calvo and Dercon (2013) measure with parameter _θ_ set at one-third. Bootstrapped standard errors in brackets.

households as higher crop incomes allowed them to consume more. Although households were less poor in 2011 than in previous years, they were still quite vulnerable to poverty in the future. In urban areas more households were poor in 2011 (26 percent) than had a higher than 50 percent chance of being poor in the future (21 percent). This reflects the fact that urban household were experiencing a very bad year in 2011 as a result of high price inflation.

We explore next whether there are gender differences in the urban and rural vulnerability profiles. Klasen et al. (2015) examine the correlates of poverty and vulnerability in Thailand and Vietnam with respect to gender of the household head, and find that in both countries, female headed households are more vulnerable, though this is driven by different factors: higher exposure to shocks in Thailand, and higher poverty in Vietnam. The authors also call for more gender disaggregated poverty and vulnerability analysis as a guide to policy. In Ethiopia there are still significant gaps between male and female adult outcomes, especially with regard to education and literacy - in 2011, more than half of Ethiopian women had no education (compared with 38 percent of men). We therefore calculated our various vulnerability measures, disaggregated by gender, for both urban and rural areas. Table 7 in the annex shows some key descriptive statistics disaggregated by location and gender. The results show that in rural areas female-headed households have significantly lower levels of vulnerability (37 percent) than male-headed households (42 percent). This result is fairly consistent across all of our calculated vulnerability measures. In contrast, female-headed households show significantly higher vulnerability in urban areas, with 22 percent being likely to be poor, compared to 20 percent of male-headed households. Referring back to our regression results in table 3, we found that uneducated households suffered most from food price shocks. Higher urban vulnerability can in part be attributed to the fact that female headed households have lower levels of education. For the rural results, we see that female headed households have slightly lower poverty than male headed households. We also find that the impact of drought was lower for PSNP beneficiaries with low land holdings (which describes most female-headed households), and so PSNP targeting of female headed households could 16