Publication: Guidelines to Small Area Estimation for Poverty Mapping
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2022-06-16
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2022-07-20
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The eradication of poverty, which was the first of the millennium development goals (MDG) established by the United Nations and followed by the sustainable development goals (SDG), requires knowing where the poor are located. Traditionally, household surveys are considered the best source of information on the living standards of a country’s population. Data from these surveys typically provide a sufficiently accurate direct estimate of household expenditures or income and thus estimates of poverty at the national level and larger international regions. However, when one starts to disaggregate data by local areas or population subgroups, the quality of these direct estimates diminishes. Consequently, national statistical offices (NSOs) cannot provide reliable wellbeing statistical figures at a local level. For example, the module of socioeconomic conditions of the Mexican national survey of household income and expenditure (ENIGH) is designed to produce estimates of poverty and inequality at the national level and for the 32 federate entities (31 states and Mexico City) with disaggregation by rural and urban zones, every two years, but there is a mandate to produce estimates by municipality every five years, and the ENIGH alone cannot provide estimates for all municipalities with adequate precision. This makes monitoring progress toward the sustainable development goals more difficult.
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“Corral, Paul; Cojocaru, Alexandru; Segovia, Sandra; Molina, Isabel. 2022. Guidelines to Small Area Estimation for Poverty Mapping. © World Bank. http://hdl.handle.net/10986/37728 License: CC BY 3.0 IGO.”
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Publication Pull Your Small Area Estimates Up by the Bootstraps(World Bank, Washington, DC, 2020-05)After almost two decades of poverty maps produced by the World Bank and multiple advances in the literature, this paper presents a methodological update to the World Bank's toolkit for small area estimation. The paper reviews the computational procedures of the current methods used by the World Bank: the traditional approach by Elbers, Lanjouw and Lanjouw (2003) and the Empirical Best/Bayes (EB) addition introduced by Van der Weide (2014). The addition extends the EB procedure of Molina and Rao (2010) by considering heteroscedasticity and includes survey weights, but uses a different bootstrap approach, here referred to as clustered bootstrap. Simulation experiments comparing these methods to the original EB approach of Molina and Rao (2010) provide empirical evidence of the shortcomings of the clustered bootstrap approach, which yields biased point estimates. 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