Swiss Journal of Economics and Statistics

, Volume 146, Issue 2, pp 431–450 | Cite as

Poverty in Tunisia: A Fuzzy measurement approach

  • Besma Belhadj
  • Mohamed Salah Matoussi
Open Access


Although poverty is widely recognised as a multidimensional phenomenon, we still believe that monetary aspect has a fundamental role and therefore deserves a special treatment. For this reason we propose an individual unidimensional measure according to a fuzzy approach that, unlike conventional methods, is consistent with the vague nature of poverty and preserves all the available statistical information. Referred to the overall population, we use an Information Theory approach to design unidimensional fuzzy collective index. The methodology proposed here is illustrated by means of the Tunisia case.


P46 I32 D81 


fuzzy sets poverty poverty line membership function information function 


  1. Berenger, V., and A. Verdier-Chouchane (2007), “Multidimensional Measures of Well-Being: Standard of Living and Quality of Life Across Countries”, World Development, 35 (7), pp. 1259–1276.CrossRefGoogle Scholar
  2. Betti, G., G. Cheli and R. Cambini (2004), “A Statistical Model for The Dynamics Between Two Fuzzy States: Theory and Application to Poverty Analysis”, Metron, 62 (3), pp. 391–411.Google Scholar
  3. Bourguignon, F., and S. R. Chakravarty (2003), “The Measurement of Multidimensional Poverty”, Journal of Economic Inequality, 1 (1), pp. 25–49.CrossRefGoogle Scholar
  4. Cerioli, A., and S. Zani (1990), “A Fuzzy Approach to the Measurement of Poverty”, in: C. Dagum and M. Zenga, (eds), Income and Wealth Distribution, Inequality and Poverty, Studies in Contemporary, Economics, Springer Verlag, Berlin, pp. 272–284.CrossRefGoogle Scholar
  5. Cheli, B. and A. Lemmi. (1995), “Totally Fuzzy and Relative Approach to the Multidimensional Analysis of Poverty”, Economic Notes, 24, pp. 115–134.Google Scholar
  6. Chiappero Martinetti, E. (2000), “A Multidimensional Assessment of Well-Being Based on Sen’s Functioning Approach”, Rivista Internazionale di Scienze Sociali, 108, pp. 207–239.Google Scholar
  7. Chiappero Martinetti, E. (2006), “Capability Approach and Fuzzy Set Theory: Description, Aggregation and Inference Issues”, in A. Lemmi and G. Betti (eds), Fuzzy Set Approach to Multidimensional Poverty Measurement, Springer + Business Media, LLC, New-York, pp. 139–153.Google Scholar
  8. Dagum, C., R. Gambassi and A. Lemmi (1992), “New Approaches to the Measurement of Poverty. In Poverty Measurement of Economics in Transition”, Polish Statistical Association & Central Statistical Office, Warsaw.Google Scholar
  9. Foster, J., J. Greer and E. Thorbecke (1984), “A Class of Decomposable Poverty Measures”, Econometrica, 52, pp. 761–765.CrossRefGoogle Scholar
  10. INS (1990), Enquête sur le budget et la consommation des ménages en Tunisie, Tunisian Institute of Statistics, Ministère du plan, Tunis.Google Scholar
  11. Kakwani, N., and J. Silber (2008), Quantitative Approaches to Multidimensional Poverty Measurement, Palgrave Macmillan.Google Scholar
  12. Kaufmann, A., and M. M. Gupta (1991), Introduction to Fuzzy Arithmetic, International Thomson Computer Press.Google Scholar
  13. Massoumi, E. (1993), “A Compendium to Information Theory in Economics and Econometrics”, Econometric Reviews, 12 (2), pp. 137–181.CrossRefGoogle Scholar
  14. Ragin, C. C. (2000), Fuzzy Set Social Science, The University of Chicago Press, Chicago.Google Scholar
  15. Ravallion, M. (1994), Poverty Comparisons, Fundamentals of Pure and Applied Economics Series, Harwood Academic Press, New York.Google Scholar
  16. Ravallion, M. and B. Bidani (1994), “How Robust is a Poverty Profile?”, The Word Bank Economic Review.Google Scholar
  17. Schaich, E., and R. (1996), “Der Fuzzy-Set-Ansatz in der Armutsmessung”, Jahrbücher für Nationalökonomie and Statistik, 215, pp. 444–469.CrossRefGoogle Scholar
  18. Sen, A. K. (1976), “Poverty: An Ordinal Approach to Measurement”, Econometrica, 44, pp. 219–231.CrossRefGoogle Scholar
  19. Shorroks, A. F. and S. Subramanian (1994), “Fuzzy Poverty Indices”, mimeo, University of Essex.Google Scholar
  20. Theil, H. (1967), Economics and Information Theory, Rand McNally & Company, Chicago.Google Scholar
  21. Watts, H. W. (1967), “The Iso-Prop Index: An Approach to the Determination of Differential Poverty Income Thresholds”, The Journal of Human Resources, 2, pp. 3–18.CrossRefGoogle Scholar
  22. Zadeh, L. (1965), “Probability Theory and Fuzzy Logic are Complementary rather than Competitive”, Technometrics, 37, pp. 271–276.CrossRefGoogle Scholar
  23. Zheng, B. (1997), “Aggregate Poverty Measures”, Journal of Economic Surveys, 11, pp. 123–162.CrossRefGoogle Scholar

Copyright information

© Swiss Society of Economics and Statistics 2010

Authors and Affiliations

  • Besma Belhadj
    • 1
  • Mohamed Salah Matoussi
    • 2
  1. 1.Higher Institute of ManagementUniversity of TunisiaUSA
  2. 2.University of EconomicsUniversity of TunisiaUSA

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