Azevedo, João Pedro

Global Practice on Poverty, The World Bank
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Inequality and Shared Prosperity, Social Protection and Labor, Education
Global Practice on Poverty, The World Bank
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Last updated July 19, 2023
João Pedro Azevedo is a Lead Economist at the World Bank in Washington. He currently works for the Poverty and Equity Global Practice in the European and Central Asia region, focusing on Central Asia and Turkey and leading the region's Statistics Team. João Pedro also leads the Global Solution Group on Welfare Measurement and Statistical Capacity for Results from the Poverty and Equity Global Practice. João Pedro has focused much of his work on helping developing countries improve their systems for evidence-based decision making. He worked in Colombia, Brazil and the Dominican Republic for five years, and led important regional public efforts such as the Latin American & Caribbean Stats Team and the LAC Monitoring and Evaluation Network. João Pedro brings solid and varied experience in applied econometrics to the fields of poverty and inequality. Before joining the Bank, João Pedro served as the superintendent of monitoring and evaluation at the Secretary of Finance for the State of Rio de Janeiro, as well as a research fellow at the Institute of Applied Economic Research from the Brazilian Ministry of Planning. He is a former chairman of the Latin American & Caribbean Network on Inequality and Poverty and holds a PhD in Economics.
Citations 125 Scopus

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Now showing 1 - 2 of 2
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    Who Are the Poor in the Developing World?
    (World Bank, Washington, DC, 2016-10) Castaneda, Andes ; Doan, Dung ; Newhouse, David ; Nguyen, Minh Cong ; Uematsu, Hiroki ; Azevedo, Joao Pedro
    This paper presents a new demographic profile of extreme and moderate poverty, defined as those living on less than $1.90 and between $1.90 and $3.10 per day in 2013, based on household survey data from 89 developing countries. The face of poverty is primarily rural and young; 80 percent of the extreme poor and 75 percent of the moderate poor live in rural areas. Over 45 percent of the extreme poor are children younger than 15 years old, and nearly 60 percent of the extreme poor live in households with three or more children. Gender differences in poverty rates are muted, and there is scant evidence of gender inequality in poor children's educational attainment. A sizable share of the extreme and moderate poor, 40 and 50 percent, respectively, have completed primary school. Compared with the extreme poor, the moderate poor are significantly more likely to have completed primary school and are less likely to work in agriculture. After conditioning on other individual and household characteristics, having fewer than three children, having greater educational attainment, and living in an urban area are strongly and positively associated with economic well-being. The results reinforce the central importance of households in rural areas and those containing large numbers of children in efforts to reduce extreme poverty, and are consistent with increased educational attainment and urbanization hastening poverty reduction.
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    SAE - A Stata Package for Unit Level Small Area Estimation
    (World Bank, Washington, DC, 2018-10) Nguyen, Minh Cong ; Corral, Paul ; Azevedo, Joao Pedro ; Zhao, Qinghua
    This paper presents a new family of Stata functions devoted to small area estimation. Small area methods attempt to solve low representativeness of surveys within areas, or the lack of data for specific areas/sub-populations. This is accomplished by incorporating information from outside sources. Such target data sets are becoming increasingly available and can take the form of a traditional population census, but also large scale administrative records from tax administrations, or geospatial information produced using remote sensing. The strength of these target data sets is their granularity on the subpopulations of interest, however, in many cases they lack the ability to collect analytically relevant variables such as welfare or caloric intake. The family of functions introduced follow a modular design to have the flexibility with which these can be expanded in the future. This can be accomplished by the authors and/or other collaborators from the Stata community. Thus far, a major limitation of such analysis in Stata has been the large size of target data sets. The package introduces new mata functions and a plugin used to circumvent memory limitations that inevitably arise when working with big data. From an estimation perspective, the paper starts by implementing a methodology that has been widely used for the production of several poverty maps.