Estimation of Poverty in Somalia Using Innovative Methodologies

Somalia is highly data-deprived, leaving policy makers to operate in a statistical vacuum. To overcome this challenge, the World Bank implemented wave 2 of the Somali High Frequency Survey to better understand livelihoods and vulnerabilities and, especially, to estimate national poverty indicators. The specific context of insecurity and lack of statistical infrastructure in Somalia posed several challenges for implementing a household survey and measuring poverty. This paper outlines how these challenges were overcome in wave 2 of the Somali High Frequency Survey through methodological and technological adaptations in four areas. First, in the absence of a recent census, no exhaustive lists of census enumeration areas along with population estimates existed, creating challenges to derive a probability-based representative sample. Therefore, geospatial techniques and high-resolution imagery were used to model the spatial population distribution, build a probability-based population sampling frame, and generate enumeration areas to overcome the lack of a recent population census. Second, although some areas remained completely inaccessible due to insecurity, even most accessible areas held potential risks to the safety of field staff and survey respondents, so that time spent in these areas had to be minimized. To address security concerns, the survey adapted logistical arrangements, sampling strategy using micro-listing, and questionnaire design to limit time on the ground based on the Rapid Consumption Methodology. Third, poverty in completely inaccessible areas had to be estimated by other means. Therefore, the Somali High Frequency Survey relies on correlates derived from satellite imagery and other geo-spatial data to estimate poverty in such areas. Finally, the nonstationary nature of the nomadic population required special sampling strategies