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New membership function for poverty measure

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  • Besma Belhadj
  • Firas Kaabi

Abstract

Fuzziness in a fuzzy set is determined by its membership function (m.f) which translates the reality of a problem. Accordingly, the shapes of membership functions (m.fs) are important for a particular problem such as poverty since they effect on a fuzzy inference system. Some authors have used to visualize the behaviour of poverty, different shapes like triangular, trapezoidal. In this paper, a specific (m.f), named modified logistic membership function better illustrating the complicated reality, is proposed to measure poverty. The modified logistic membership function is first formulated for several states of poverty and its flexibility in taking up vagueness in poverty is established by an analytical approach using aggregate operators in order to infer a logical conclusion measuring poverty. An application based on individual well‐being data from Tunisian households in 2010 is presented to illustrate use of proposed concepts.

Suggested Citation

  • Besma Belhadj & Firas Kaabi, 2020. "New membership function for poverty measure," Metroeconomica, Wiley Blackwell, vol. 71(4), pages 676-688, November.
    • Handle: RePEc:bla:metroe:v:71:y:2020:i:4:p:676-688
    DOI: 10.1111/meca.12297
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    References listed on IDEAS

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    1. Belhadj, Besma, 2024. "Fuzzy multiple regressions for Cross-Section and Panel data," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    2. Belhadj, Besma, 2023. "New fuzzy multiple regressions for the instantaneous and panel data “The determinants of Poverty in the Countries MENA”," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).

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