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 128 Scopus

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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.