Small Area Estimation Methods for Poverty Mapping: A Selective Review

Isabel Molina, J.N.K. Rao, Maria Guadarrama

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Poverty mapping in small areas is currently having increasing interest, because those maps aid governments and international organizations to design, apply and monitor more effectively regional development polices, directing them to the actual places or population subgroups where they are more urgently needed. After a simulated census method used by the World Bank, several other procedures have been developed that proved to have better properties. We will review several methods that are applied for poverty mapping in small areas, including those based on area level modes and used by the U. S. Census Bureau for estimating poor school age children and methods based on unit level models such as the traditional method used by the World Bank and empirical best (EB) and hierarchical Bayes (HB) methods based on optimality criteria. We will also discuss some variations of the unit level model methods that can used to deal with certain situations such as informative sampling or two-stage sampling. We will discuss pros and cons of these methods from a practical point of view, but based on the theory that is currently known.
    Original languageEnglish
    Pages (from-to)11-22
    JournalStatistics and Applications
    Volume17
    Issue number1
    Publication statusPublished - 24 May 2019

    Keywords

    • area level model
    • empirical best estimation
    • hierarchical bayes estimation
    • local poverty indicators
    • unit level models

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