Dabalen, Andrew

Chief Economist, Africa, World Bank
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Poverty, Inequality, Economics of education, Development economics, Labor economics
Chief Economist, Africa, World Bank
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Last updated January 31, 2023
Andrew Dabalen is the World Bank’s Africa Region Chief Economist since July 1, 2022. The Chief Economist is responsible for providing guidance on strategic priorities and the technical quality of economic analysis in the region, as well as for developing major regional economic studies, among other roles. He has held various positions including Senior Economist in the World Bank’s Europe and Central Asia Region, Lead Economist and Practice Manager for Poverty and Equity in Africa and most recently, Practice Manager for Poverty and Equity in the South Asia Region. His research and scholarly publications focused on poverty and social impact analysis, inequality of opportunity, program evaluation, risk and vulnerability, labor markets, and conflict and welfare outcomes. He has co-authored regional reports on equality of opportunity for children in Africa, vulnerability and resilience in the Sahel, and poverty in a rising Africa. He holds a master’s degree in International Development from University of California - Davis, and a PhD in Agricultural and Resource Economics from University of California - Berkeley.
Citations 53 Scopus

Publication Search Results

Now showing 1 - 4 of 4
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    Can Agricultural Households Farm Their Way Out of Poverty?
    (World Bank Group, Washington, DC, 2014-11) Oseni, Gbemisola ; McGee, Kevin ; Dabalen, Andrew
    This paper examines the determinants of agricultural productivity and its link to poverty using nationally representative data from the Nigeria General Household Survey Panel, 2010/11. The findings indicate an elasticity of poverty reduction with respect to agricultural productivity of between 0.25 to 0.3 percent, implying that a 10 percent increase in agricultural productivity will decrease the likelihood of being poor by between 2.5 and 3 percent. To increase agricultural productivity, land, labor, fertilizer, agricultural advice, and diversification within agriculture are the most important factors. As commonly found in the literature, the results indicate the inverse-land size productivity relationship. More specifically, a 10 percent increase in harvested land size will decrease productivity by 6.6 percent, all else being equal. In a simulation exercise where land quality is assumed to be constant across small and large holdings, the results show that if farms in the top land quintile had half the median yield per hectare of farms in the lowest quintile, production of the top quintile would be 10 times higher. The higher overall values of harvests from larger land sizes are more likely because of cultivation of larger expanses of land, rather than from efficient production. It should be noted that having larger land sizes in itself is not positively correlated with a lower likelihood of being poor. This is not to say that having larger land sizes is not important for farming, but rather it indicates that increasing efficiency is the more important need that could lead to poverty reduction for agricultural households.
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    Can We Measure Resilience? A Proposed Method and Evidence from Countries in the Sahel
    (World Bank Group, Washington, DC, 2015-01) Alfani, Federica ; Dabalen, Andrew ; Fisker, Peter ; Molini, Vasco
    Although resilience has become a popular concept in studies of poverty and vulnerability, it has been difficult to obtain a credible measure of resilience. This difficulty is because the data required to measure resilience, which involves observing household outcomes over time after every exposure to a shock, are usually unavailable in many contexts. This paper proposes a new method for measuring household resilience using readily available cross section data. Intuitively, a household is considered resilient if there is very little difference between the pre- and post-shock welfare. By obtaining counterfactual welfare for households before and after a shock, households are classified as chronically poor, non-resilient, and resilient. This method is applied to four countries in the Sahel. It is found that Niger, Burkina Faso, and Northern Nigeria have high percentages of chronically poor: respectively, 48, 34, and 27 percent. In Senegal, only 4 percent of the population is chronically poor. The middle group, the non-resilient, accounts for about 70 percent of the households in Senegal, while in the other countries it ranges between 34 and 38 percent. Resilient households account for about 33 percent in all countries except Niger, where the share is around 18 percent.
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    The Short-Run Impact of Import Bans on Poverty: The Case of Nigeria (2008–2012)
    (Published by Oxford University Press on behalf of the World Bank, 2018-06) Dabalen, Andrew ; Nguyen, Nga Thi Viet
    The Nigerian government uses food import prohibition as part of policies that seeks to protect existing domestic producers and reduce the country's dependence on imports. This paper argues that such policies have negative effects on net consumers of such products due to higher prices. With 70 percent of poor households' budget spent on food, and about 13 percent of the total budget devoted to products subject to import bans, poor households are vulnerable to such trade policies. Prices of some import prohibited food products are found to be higher than what they would be in the absence of such bans. The elimination of import bans is estimated to reduce national poverty rates by as much as 2.6 percentage points.
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    Vulnerability to Stunting in the West African Sahel
    (Elsevier, 2019-02) Alfani, Federica ; Dabalen, Andrew ; Fisker, Peter ; Molini, Vasco
    This paper presents a simple simulation framework for understanding and analyzing vulnerability to stunting. We utilize Demographic and Health Surveys merged with satellite data on climatic shocks. Children aged 0–5 years are grouped into three categories: consistently stunted, vulnerable, and non-vulnerable. The first group constitutes those who are stunted and will also be stunted in any hypothetical period. Non-vulnerable are those whose likelihood to be stunted is zero. The vulnerable face a probability between 0 and 1 of being stunted. The probability is calculated as the share of years in which the child would be stunted, given the village level distribution of weather shocks over the period 2000–2013. We provide estimates of vulnerability to stunting in Burkina Faso, Northern Ghana, Mali, Northern Nigeria, and Senegal by aggregating over villages, districts and countries.