non-agricultural labors in rural areas remain poor, and more broadly, what determines inequality within rural areas. Further research is therefore needed to better understand, for example, how industry of work relates to economic welfare, and which types of jobs have led to larger reductions in rural extreme poverty.

Third, poverty and educational attainment are strongly and negatively correlated, among both children and adults. Most notably, although only 15 percent of adults have no formal education, nearly 25 percent of them live in extreme or moderate poverty, and another 33 percent live in moderate poverty. At the same time, a sizable proportion of extremely and moderately poor adults, 27 percent and 38 percent, respectively, have at least some secondary education. Not surprisingly, those with tertiary education are almost exclusive non-poor. The different education profiles of extremely poor, moderately poor, and non-poor adults highlight the role of education in driving poverty reduction. Nonetheless, graduating from primary school, while important, far from ensures an escape from poverty.

Fourth, despite similarities in terms of age, household composition, and residential sector, there are two noticeable differences between the extreme poor and the moderate poor. First, the moderate poor are much less likely to make their living from agriculture. Second, they are significantly more likely to have graduated from primary school, but not secondary school. If one considers moderate poverty as a transition stage between extreme poverty and the absence of deprivation, these differences point to non-farm employment and basic education as potential pathways to improve living standards of the extreme poor.

Finally, many of the most striking differences in the demographic profiles of the poor remain when controlling for differences in various characteristics as well as country fixed effects. Conditional on other characteristics, living in an urban area, having fewer than three children, and having greater educational attainment have a particularly strong and positive association with economic welfare within countries.

The main findings on the nature of poverty are generally robust to a variety of methods. The share that is rural seems to vary little regardless of the lineup method or sample used. Furthermore, calculations based on South Asian data suggest that the share of the poor in rural areas would remain high even if welfare aggregates were spatially deflated in all countries. On the other hand, the shares of the poor that are young and less educated, and working in agriculture are mildly sensitive to how the welfare aggregates for each country are lined up to 2013, with differences of one to two percentage points. Therefore, further research could usefully document in detail the exact procedure used to line up the estimates and explore whether lining up improves the accuracy of profiles. As with all household survey data, there are missing values because information is occasionally not reported or processed. Because patterns of missing values vary across different variables, restricting the analysis to a common subsample would distort the key findings.

This study is a first step towards exploiting the World Bank’s unique inventory of household survey data to better understand the poor’s living conditions, earning capacity, and economic constraints at a large scale. The database can also help fill in other important knowledge gaps on global poverty. These include a more detailed look at the relationship between labor market 31