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New fuzzy indices of poverty by distinguishing three levels of poverty

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  • Belhadj, Besma

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.

Suggested Citation

  • Belhadj, Besma, 2011. "New fuzzy indices of poverty by distinguishing three levels of poverty," Research in Economics, Elsevier, vol. 65(3), pages 221-231, September.
    • Handle: RePEc:eee:reecon:v:65:y:2011:i:3:p:221-231
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    References listed on IDEAS

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    1. 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.
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    6. 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.
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    1. Khaled Nasri & Besma Belhadj, 2018. "Measuring Vulnerability to Multidimensional Poverty in Tunisia: Dual cut-off method and Fuzzy Sets approach," Working Papers 1262, Economic Research Forum, revised 03 Dec 2018.
    2. Noor Hidayah Zakaria & Rohayanti Hassan & Muhamad Razib Othman & Zalmiyah Zakaria & Shahreen Kasim, 2017. "A Review on Classification of the Urban Poverty Using the Artificial Intelligence Method," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 7(11), pages 450-458, November.
    3. Belhadj Besma, 2016. "Inequality among the poor in poverty measure case of Tunisia (2005–2010)," OPSEARCH, Springer;Operational Research Society of India, vol. 53(2), pages 409-425, June.
    4. Ke-Mei Chen & Chao-Hsien Leu & Te-Mu Wang, 2019. "Measurement and Determinants of Multidimensional Poverty: Evidence from Taiwan," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 145(2), pages 459-478, September.
    5. Asma Zedini & Besma Belhadj, 2015. "A New Approach to Unidimensional Poverty Analysis: Application to the Tunisian Case," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 61(3), pages 465-476, September.
    6. 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).
    7. Bruno Cheli & Achille Lemmi & Nicoletta Pannuzi & Andrea Regoli, 2019. "From the TFR to the IFR approach for the multidimensional analysis of poverty and living conditions," Discussion Papers 2019/252, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.

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