A Non-Monetary Multidimensional Poverty Analysis of Tunisia Using Generalized Sen-Shorrocks-Thon Measures

  • Naouel ChtiouiEmail author
  • Mohamed Ayadi
Part of the Economic Studies in Inequality, Social Exclusion and Well-Being book series (EIAP, volume 9)


A monetary approach cannot represent the complex and multidimensional phenomena of poverty, as it only takes economic aspects into account. Many attempts have been made to propose multidimensional approaches to poverty using basic needs or capabilities approaches. For our study we adopted a non-monetary approach, using multivariate correspondence analysis to construct a composite welfare indicator as an aggregation index of the various well-being attributes. In order to measure poverty within a composite indicator distribution we first developed new classes of poverty measures. Indeed, we developed classes of ethical generalized Sen-Shorrocks-Thon (SST) poverty measures. These are a generalization of the Shorrocks (Econometrica 63:1225–1230, 1995) poverty measure which itself is a modified version of the Sen (Econometrica 44:219–231, 1976) poverty measure. We analyzed the non-monetary poverty trend in Tunisia between 1994 and 2006 using the composite welfare indicator and the generalized SST poverty measures. In order to achieve this, we constructed confidence intervals and tested hypotheses based on the bootstrap method. We found that poverty decreased between 1994 and 2006, but that there were inequalities within the poorer population. We also found that poverty was essentially rural poverty and that it was concentrated in the North West, Center West, and South East of Tunisia.

JEL Classification

D63 I32 


  1. Asselin, L. M. (2002). Multidimensional poverty: composite indicator of multidimensional poverty. Lévis: Institut de Mathématique Gauss.Google Scholar
  2. Atkinson, A. B. (1970). On the measurement of inequality. Journal of Economic Theory, 2, 244–263.CrossRefGoogle Scholar
  3. Ayadi, M., El Lahga, A., Chtioui, N. (2007). Poverty and inequality in Tunisia: a non-monetary approach. PMMA Working Paper No. 2007-05.Google Scholar
  4. Bibi, S. (2004). Comparing multidimensional poverty between Egypt and Tunisia. CIRPEE Working Paper No. 04-16.Google Scholar
  5. Booth, C. (1902). A case of town life. London: Macmillan.Google Scholar
  6. Chakravarty, S. R. (1983). Ethically flexible measures of poverty. The Canadian Journal of Economics, 16, 74–85.CrossRefGoogle Scholar
  7. Chakravarty, S. R. (1990). Ethical social index numbers. New York: Springer-Verlag.CrossRefGoogle Scholar
  8. Chtioui, N. (2004). Les Mesures de la Pauvreté Multidimensionnelle. Mémoire de Mastère de Modélisation non publiée. Tunis: Institut Supérieur de Gestion.Google Scholar
  9. Dalton, H. (1920). The measurement of the inequality incomes. Economic Journal, 30, 348–362.CrossRefGoogle Scholar
  10. Donaldson, D., & Weymark, J. A. (1980). A single-parameter generalization of the Gini indices of inequality. Journal of Economic Theory, 22, 67–86.CrossRefGoogle Scholar
  11. Duclos, J.-Y., & Gregoire, P. (2002), Absolute and Relative Deprivation and the Measurement of Poverty, Review of Income and Wealth, series 48(4).Google Scholar
  12. Duclos, J.-Y., Sahn, D., & Younger, S.D. (2006). Robust multidimensional poverty comparisons. Economic Journal, 116(514), 943–968.Google Scholar
  13. Efron, B. (1979). Bootstrap methods: another look at the jackknife. Annals of Statistics, 7, 1–26.CrossRefGoogle Scholar
  14. Efron, B. (1981). Nonparametric estimates of standard error: The jackknife, the bootstrap and other methods. Biometrika, 68, 589–599.CrossRefGoogle Scholar
  15. Efron, B. (1982). The Jackknife, the Bootstrap, and other resampling plans. Philadelphia: Society for Industrial and Applied Mathematics.CrossRefGoogle Scholar
  16. Efron, B., & Tibshirani, R. J. (1993). An introduction to the Bootstrap. New York: Chapman and Hall.Google Scholar
  17. Filmer, D., & Pritchett, L. (1998). Estimating wealth effects without expenditure data or tears: an application of educational enrollment in states of India, Mimeo. Washington: The World Bank.Google Scholar
  18. Foster, J. E. (1984). On economic poverty: a survey a aggregate measures. In R. L. Basmann, G. F. Rhodes (Eds.), Advances in Econometrics, 3.Connecticut (pp. 215–251). Bingley: JAI Press.Google Scholar
  19. Foster, J. E. & Sen, A. (1997). Economic inequality after a quarter century. In On Economic Inequality (expanded edition). Oxford: Clarendon Press.Google Scholar
  20. Hall, P. (1992). The Bootstrap and Edgeworth expansion. New York: Springer.Google Scholar
  21. Hammer, J. (1998). Health outcomes across wealth groups in Brazil and India. Mimeo. DECRG. Washington: The World Bank.Google Scholar
  22. Jenkins, S. P., & Lambert, P. J. (1997). Three is of poverty curves, with an analysis of UK poverty trends. Oxford Economic Papers, 49, 317–327Google Scholar
  23. Kolm, S. C. (1969). The optimal production of social justice. In J. Magolis & H. Guittor (Eds.), Public economics (pp. 145–200). London: Macmillan.Google Scholar
  24. Moran, T. (2005). Bootstrapping the LIS: statistical inference and patterns of inequality in the Global North. LIS Working Paper No. 378.Google Scholar
  25. Osberg, L. (2000). Poverty in Canada and the United States: measurement, trends, and implications. The Canadian Journal of Economics/Revue Canadienne d’Economique, 33(4), 847–877.Google Scholar
  26. Osberg, L. & Xu, K. (1999). Poverty intensity-How well do Canadian provinces compare? Canadian Public Policy, 25(2), 179–195.Google Scholar
  27. Osberg, L. & Xu, K. (2000). International comparison of poverty intensity: Index decomposition and bootstrap inference. Journal of Human Resources, 35(1), 51–81.Google Scholar
  28. Pollak, R. A. (1971). Additive utility functions and linear Engel curves. Review of Economic Studies, 38, 401–414.CrossRefGoogle Scholar
  29. Ravallion, M. (1992). On hunger and public action: a review article on the book by Jean Dreze and Amartya Sen. The World Bank Research Observer, 7, 1–16.CrossRefGoogle Scholar
  30. R Development Core Team (2008). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. ISBN 3-900051-07-0, URL
  31. Robeyns, I. (2005). The capability approach: a theoretical survey. Journal of Human Development, 6(1), 93–117.CrossRefGoogle Scholar
  32. Rowntree, B. S. (1901). Poverty: a study of town life. London: Macmillan.Google Scholar
  33. Seidl, C. (1988). Poverty measurement: a survey. In D. Bös, M. Rose & C. Seidl (Eds.), Welfare and efficiency in public economics (pp. 219–231). Heidelberg: Springer-Verlag.Google Scholar
  34. Sen, A. (1973). On Economic Inequality. Oxford: Clarendon Press.Google Scholar
  35. Sen, A. (1976). Poverty: an ordinal approach to measurement. Econometrica, 44, 219–231.CrossRefGoogle Scholar
  36. Sen, A. (1985). Commodities and capabilities (p. 89). New Delhi: Oxford India paper backs.Google Scholar
  37. Shorrocks, A. F. (1995). Revisiting the Sen Poverty index. Econometrica, 63, 1225–1230.CrossRefGoogle Scholar
  38. Stifel, D., Sahn, D. & Younger, S. (1999). Inter-temporal changes in welfare: preliminary results from nine African countries. CFNPP WP, 6–94.Google Scholar
  39. Streeten, P. (1981). First things first: meeting basic human needs in developing countries. New York: Oxford University Press.Google Scholar
  40. Thon, D. (1979). On measuring poverty. Review of Income and Wealth, 25, 429–439.CrossRefGoogle Scholar
  41. Thon, D. (1983). A note on a troublesome axiom for poverty indices. Economic Journal, 93, 199–200.CrossRefGoogle Scholar
  42. Weymark, J. A. (1981). Generalized Gini inequality indices. Mathematical Social Sciences, 1, 409–430.CrossRefGoogle Scholar
  43. Xu, K. (1998). The statistical inference for the Sen-Shorrocks-Thon index of poverty intensity. Journal of Income Distribution, 8(1), 143–152.Google Scholar
  44. Xu, K. (2000). Inference for generalized Gini indices using the iterated bootstrap method. Journal of Business and Economics Statistics, 18(2), 223–227.Google Scholar
  45. Xu, K., & Osberg, L. (2002). On Sen’s approach to poverty measures and recent developments, paper presented at the Sixth International Meeting of the Society for Social Choice and Welfare (2002). Printed in Chinese in China Economic Quarterly, 1, 151–170.Google Scholar
  46. Zhen G, B. (1993). Poverty measurement, statistical inference, and an application to the United States. Ph.D Dissertation, West Virginia University, Virginia.Google Scholar
  47. Zheng, B. (1997). Aggregate poverty measures. Journal of Economic Surveys, 11, 123–163.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Département d’Economie, Méthodes Quantitatives et InformatiqueInstitut Supérieur d’Administration des Entreprises de Gafsa, et UAQUAP, Institut Supérieur de Gestion, TunisLe BardoTunisie
  2. 2.Département d’Economie et MéthodesInstitut Supérieur de Gestion et UAQUAP, Université de TunisLe BardoTunisie

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