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        Empirical Analysis of Poverty Dynamics

        With Case Studies from Sub-Saharan Africa

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        Author(s)
        Günther, Isabel
        Language
        English
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        Abstract
        The empirical analysis of poverty over time is still severely constrained by the available survey data in developing countries. In the past, this has led to a neglect of certain aspects of poverty dynamics or even biased assessments of poverty dynamics. This book explicitly takes into account the present data limitations, proposing alternative methods for the empirical analysis of poverty dynamics. The work addresses both the problems related to limited data in the analysis of macro-level (or national) as well as micro-level (or household) poverty dynamics. The proposed methods are applied to survey data from various sub-Saharan African countries. As these countries do not only have the most limited economic survey data but also show the highest poverty rates in the world an accurate understanding of the underlying poverty dynamics seems to be most important for these countries.
        URI
        http://library.oapen.org/handle/20.500.12657/26891
        Keywords
        Africa; Analysis; Armut; Case; Dynamics; Empirical; from; Günther; Inflation Inequality; Poverty; Pro-Poor Growth; Saharan; Schwarzafrika; Studies; Vulnerability to Poverty; With
        DOI
        10.3726/b13881
        ISBN
        9783631753590
        OCN
        1082955771
        Publisher
        Peter Lang International Academic Publishers
        Publisher website
        https://www.peterlang.com/
        Publication date and place
        Bern, 2018
        Series
        Goettinger Studien zur Entwicklungsoekonomik / Goettingen Studies in Development Economics, 21
        Classification
        Political science and theory
        Political structure and processes
        Development economics and emerging economies
        Pages
        174
        Rights
        https://creativecommons.org/licenses/by/4.0/legalcode
        • Imported or submitted locally

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        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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