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New fuzzy indices of poverty by distinguishing three levels of poverty

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  • Belhadj, Besma
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    Abstract

    In order to avoid a rigid poor/non-poor dichotomy (see e.g. Hagenaars, 1986), the fuzzy sets approach to poverty measurement has been used. The aim of this paper is to propose fuzzy measures of unidimensional and multidimensional poverty by distinguishing three levels of poverty. A methodological research is proposed as follows: first, the poor are analyzed by partitioning the total population in three mutually exclusive groups around the poverty line, and three levels of poverty are distinguished. Second, a general rule for the construction of different fuzzy measure unions (Zadeh, 1975) is proposed, that is, rules for the construction of overall poverty starting from different levels of poverty. Finally, classes of fuzzy measures of poverty referring to the overall population are suggested. An application using individual well-being data from Tunisian households in 1990 is presented to illustrate use of one of the proposed concepts.

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    Bibliographic Info

    Article provided by Elsevier in its journal Research in Economics.

    Volume (Year): 65 (2011)
    Issue (Month): 3 (September)
    Pages: 221-231

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    Handle: RePEc:eee:reecon:v:65:y:2011:i:3:p:221-231

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    Web page: http://www.elsevier.com/locate/inca/622941

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    Keywords: Fuzzy sets Strong poverty Medium poverty Weak poverty Membership function Poverty measures;

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    1. Bourguignon, F. & Chakravarty, S.R., 1998. "The Measurement of Multidimensional Poverty," DELTA Working Papers 98-12, DELTA (Ecole normale supérieure).
    2. Ravallion, Martin & Bidani, Benu, 1994. "How Robust Is a Poverty Profile?," World Bank Economic Review, World Bank Group, vol. 8(1), pages 75-102, January.
    3. Ragin, Charles C., 2000. "Fuzzy-Set Social Science," University of Chicago Press Economics Books, University of Chicago Press, edition 1, number 9780226702766, October.
    4. Joseph Deutsch & Jacques Silber, 2005. "Measuring Multidimensional Poverty: An Empirical Comparison Of Various Approaches," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 51(1), pages 145-174, 03.
    5. Gianni Betti & Bruno Cheli & Riccardo Cambini, 2004. "A statistical model for the dynamics between two fuzzy states: theory and an application to poverty analysis," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 391-411.
    6. Zheng, Buhong, 1997. " Aggregate Poverty Measures," Journal of Economic Surveys, Wiley Blackwell, vol. 11(2), pages 123-62, June.
    7. Maria Ana Lugo & Esfandiar Maasoumi, 2008. "Multidimensional Poverty Measures from an Information Theory Perspective," Working Papers 85, ECINEQ, Society for the Study of Economic Inequality.
    8. A. E. Fernández Jilberto, 1991. "Introduction," International Journal of Political Economy, M.E. Sharpe, Inc., vol. 21(1), pages 3-9, April.
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    Cited by:
    1. Andos Juhász, 2012. "A Satisfaction-Driven Poverty Indicator: A Bustle around the Poverty Line," SOEPpapers on Multidisciplinary Panel Data Research 461, DIW Berlin, The German Socio-Economic Panel (SOEP).

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