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Proposal of a Semi Fuzzy Poverty Index

Author

Listed:
  • Majda Fikri

    (Mathematical Laboratory of Modelling & Scientific Computing)

  • Mohammed Elkhomssi

    (Laboratory MDA)

  • Sahar Saoud

    (Mathematical Laboratory of Modelling & Scientific Computing)

Abstract

Certainly poverty is a phenomenon easy to understand and to describe, but difficult to measure and to determine. In fact, to measure poverty, we are first in front of the problem of choice of the threshold which depends itself on the choice of adapted approach, then in the choice of the indicator which must be faithful, and reflects clearly the real state of the population in study, in aim to optimize the planning of socio-economic policies authorizing the reduction of the poverty intensity. This paper aims to avoid part of these weaknesses and difficulties. We present a new reading of the FGT (Foster, Greer and Thorbecke) index with a human dimension instead of the monetary, then we propose a combination between the fuzzy approach, and a classic measure of poverty, by defining a semi-fuzzy indicator which we generalize at the end of this paper.

Suggested Citation

  • Majda Fikri & Mohammed Elkhomssi & Sahar Saoud, 2011. "Proposal of a Semi Fuzzy Poverty Index," EuroEconomica, Danubius University of Galati, issue 28, pages 23-37, May.
    • Handle: RePEc:dug:journl:y:2011:i:28:p:23-37
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    References listed on IDEAS

    as
    1. Foster, James & Greer, Joel & Thorbecke, Erik, 1984. "A Class of Decomposable Poverty Measures," Econometrica, Econometric Society, vol. 52(3), pages 761-766, May.
    2. Sara Lelli, 2001. "Factor Analysis vs. Fuzzy Sets Theory: Assessing the Influence of Different Techniques on Sen's Functioning Approach," Public Economics Working Paper Series ces0121, Katholieke Universiteit Leuven, Centrum voor Economische Studiën, Working Group Public Economics.
    3. Kakwani, Nanak, 1980. "On a Class of Poverty Measures," Econometrica, Econometric Society, vol. 48(2), pages 437-446, March.
    4. Stephen Klasen, 2000. "Measuring Poverty And Deprivation In South Africa," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 46(1), pages 33-58, March.
    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. Paul Makdissi & Quentin Wodon, 2004. "Fuzzy Targeting Indices and Orderings," Bulletin of Economic Research, Wiley Blackwell, vol. 56(1), pages 41-51, January.
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    Cited by:

    1. Mohammed EL KHOMSSI & Ghizlane CHAIBI, 2014. "Tools for the selection of microeconomic from socioeconomic rentability," EuroEconomica, Danubius University of Galati, issue 1(33), pages 19-30, May.
    2. Majda Fikri & Mohammed El Khomssi, 2017. "Axiomatic Analysis of the Semi-Fuzzy Poverty Indices MIf and PGf," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 13(4), pages 209-226, AUGUST.

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