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This article examines the characteristics of the victims of the October 1993 massacres in Burundi. Using information on parents of the respondents of a 2002 demographic household survey, the author finds that the probability of a parent being killed during the events is significantly affected by age, sex and wealth, in the sense that older, wealthier and male persons were more likely to be killed. Using the median level of education of a parent's offspring to proxy the parental investment in human capital, the author finds that people who invested more in the human capital of their children ran a higher risk of being killed. The analysis also shows important spatial disparities in the killings. In trying to explain these locational effects, the author focus on two key hypotheses set forth with respect to the October 1993 events in Burundi: the land crisis and the questionable role played by the Front Démocratique du Burundi (FRODEBU), the dominant political actor at that time. The author finds that communal land pressure significantly increases the probability of being killed and that communal popular support for FRODEBU increases, in a non-linear fashion, the risk of being affected by the killings. The results are interpreted in light of the prevailing political economy of 1993 Burundi.

We analyse the effect of civil war on household welfare. Using Burundian panel data for the 1998-2007 period in which we re-interviewed original as well as newly formed households (split-offs), we show that headcount poverty decreased by 3.5 % points when split-off households are taken into account and 1% when splits are left out. Poverty is persistent while prosperity is not, in particular in war-affected areas. We find that 25 war-related deaths or wounded at the village level reduce consumption growth by 13%. We also find that violence afflicted on household members decreases growth whereas membership of rebel groups increases it. Apart from such war-related effects - and controlling for initial levels of consumption - we find that temporarily famine-induced migration and illness decrease growth while good harvests, more split-offs and higher initial levels of education increase it. Good harvests are found to have persistent positive effects on growth. Our results are robust for different household and province fixed effects specifications. ; info:eu-repo/semantics/published

We combine household survey data with event data on the timing and location of armed conflicts to examine the impact of Burundi's civil war on children's health status. The identification strategy exploits exogenous variation in the war's timing across provinces and the exposure of children's birth cohorts to the fighting. After controlling for province of residence, birth cohort, individual and household characteristics, and province-specific time trends, we find that children exposed to the war have on average 0.515 standard deviations lower height-for-age z-scores than non-exposed children. This negative effect is robust to specifications exploiting alternative sources of exogenous variation.

The COVID-19 (coronavirus) pandemic and its negative economic effects create an urgent need for timely data and evidence to help monitor and mitigate the social and economic impacts of the crisis and protect the welfare of the least well-off in Ethiopia's society. To monitor the impacts of the COVID-19 (coronavirus) pandemic on Ethiopia's economy and people and inform interventions and policy responses, the World Bank Ethiopia team, in collaboration with the government, designed and implemented two high-frequency phone surveys, one with firms and one with households.

This paper uses the night lights (satellite imagery from outer space) approach to estimate growth in and levels of subnational 2013 gross domestic product for 47 counties in Kenya and 30 districts in Rwanda. Estimating subnational gross domestic product is consequential for three reasons. First, there is strong policy interest in how growth can occur in different parts of countries, so that communities can share in national prosperity and not get left behind. Second, subnational entities want to understand how they stack up against their neighbors and competitors, and how much they contribute to national gross domestic product. Third, such information could help private investors to assess where to undertake investments. Using night lights has the advantage of seeing a new and more accurate estimation of informal activity, and being independent of official data. However, the approach may underestimate economic activity in sectors that are largely unlit notably agriculture. For Kenya, the results of the analysis affirm that Nairobi County is the largest contributor to national gross domestic product. However, at 13 percent, this contribution is lower than commonly thought. For Rwanda, the three districts of Kigali account for 40 percent of national gross domestic product, underscoring the lower scale of economic activity in the rest of the country. To get a composite picture of subnational economic activity, especially in the context of rapidly improving official statistics in Kenya and Rwanda, it is important to estimate subnational gross domestic product using standard approaches (production, expenditure, income).

The authors use the night lights (satellite imagery from outer space) approach to estimate subnational 2013 GDP growth and levels for 47 counties in Kenya and 30 districts in Rwanda. Estimating subnational GDP is consequential for three reasons: First, there is strong policy interest in seeing how growth can occur in different parts of countries, so that communities can share in national prosperity and not get left behind. Second, sub-nationals themselves want to understand how they stack up against their neighbors and competitors, and how much they contribute to national GDP. Third, such information could help private investors to better assess where to undertake investments. Using night lights has the advantage of seeing a new (and more accurate) estimation of informal activity, and being independent of official data. However it may underestimate economic activity in sectors that are largely unlit (notably agriculture). Indeed, we find that the association between nightlights and GDP is stronger where unlit agriculture accounts for a smaller part of overall economic activity. With these caveats in mind, our analysis yields some interesting results. For Kenya, our results affirm that Nairobi County is the largest contributor to national GDP. However, at 13 percent, this contribution is lower (of 60 percent) as commonly thought. For Rwanda, the three Districts of Kigali account for 40 percent of national GDP, underscoring the lower scale of economic activity in the rest of the country. To get a composite picture of subnational economic activity, especially in the context of rapidly improving official statistics in Kenya and Rwanda, the authors note the importance of estimating subnational GDP using standard approaches (production, expenditure, income).