Open Access

Poverty in Tunisia: A Fuzzy measurement approach

  • Besma Belhadj1 and
  • Mohamed Salah Matoussi2
Swiss Journal of Economics and Statistics2010146:BF03399322

Published: 11 January 2010


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.




fuzzy setspovertypoverty linemembership functioninformation function


Authors’ Affiliations

Higher Institute of Management, University of Tunisia, USA
University of Economics, University of Tunisia, USA


  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.View ArticleGoogle 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.View ArticleGoogle 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.View ArticleGoogle 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.View ArticleGoogle 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.View ArticleGoogle 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.View ArticleGoogle Scholar
  18. Sen, A. K. (1976), “Poverty: An Ordinal Approach to Measurement”, Econometrica, 44, pp. 219–231.View ArticleGoogle 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.View ArticleGoogle Scholar
  22. Zadeh, L. (1965), “Probability Theory and Fuzzy Logic are Complementary rather than Competitive”, Technometrics, 37, pp. 271–276.View ArticleGoogle Scholar
  23. Zheng, B. (1997), “Aggregate Poverty Measures”, Journal of Economic Surveys, 11, pp. 123–162.View ArticleGoogle Scholar


© Swiss Society of Economics and Statistics 2010