Assessing Households Vulnerability to Poverty in Ethiopia: Estimates from Sedentary Areas of Afar Region: Ethiopia

L.D Sudhakara Babu, Nega Afera


During analysis of vulnerability to poverty across time there is a chance that a household that is not poor becomes poor, one that is poor remains poor. This leads to vulnerability assessment in terms of ‘vulnerability to poverty’. ‘Vulnerability to poverty’ is the probability that a household will be poor next period. So it is an important concept to deepen the understanding of poverty, since it reveals information on what measures should be taken to prevent poverty while poverty dynamics largely imply the past and contemporary poverty situation and is helpful on how to alleviate the existing situation

Using four round HCIS survey panel data for the years 2004, 2008, 2011 and 2014, collected by central statistics authority of Ethiopia, this thesis examined vulnerability to poverty of rural households in Afar region: Ethiopia. It also analyzed correlates of vulnerability to poverty. Using Stratified random sample of 360 households was drawn from sedentary zones of Afar region.

To estimate the vulnerability to poverty of a household the researcher adopted the vulnerability measure in Chaudhuri (2003). In order to estimate this measure the researcher followed a three step Feasible Generalized Least Squares (FGLS) to estimate the expected log per adult equivalent consumption expenditure. Using these estimates and assuming that per adult equivalent consumption expenditure is log normally distributed the researcher estimated the vulnerability measure as the probability that the standard normal variate will fall below standardized poverty line. Thus, using the GLS estimates determinants of vulnerability was analyzed using OLS method and later on logit model for comparison.

The finding shows that there is high vulnerability to poverty, i.e. high probability of becoming poor in a period ahead, in the region and it is significantly and negatively correlated with household head age, agricultural extension services, land fertility use of improved seeds, irrigation, off farm income, farm size, TLU but  positively correlated with household age squared, dependency ratio and family size.

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