A New Approach to Unidimensional Poverty Analysis: Application to the Tunisian Case

Abstract

Fuzzy conceptualization of privation has been a step closer to more realistic handling of poverty. However, fuzzy approaches to poverty are still grounded on parametric axioms. Moreover, construction of poverty lines within these approaches still relies on ad-hoc methods. In this paper, we advance instead a fuzzy procedure based on the non-parametric bootstrap method, allowing us to depict fuzzy unidimensional privation states with boundaries drawn spontaneously from data. Fuzzy nonparametric measures of privation within each state as well as a collective fuzzy non-parametric index of poverty are derived, along with their corresponding confidence intervals. The new approach is applied to the analysis of poverty in Tunisia in 2005. JEL Code: I32

An analysis of multidimensional poverty and its determinants in rural Nigeria

“…Viewing from literacy or tangible assets could be more reasonable methods for the assessment of poverty which could also capture longterm poverty. The identification of "poor" is the main focus of both the unidimensional and multidimensional poverty approaches which serve as a leap towards the accuracy of poverty measurement and analyses (Zedini and Belhadj, 2015). Poverty in sub-Sahara Africa (SSA) is endemic and thus causes low level of infrastructural development in the region.…”

New membership function for poverty measure

“…Different poverty lines falling under these two approaches have been criticized of being ad hoc expert judgement dependent. An alternative procedure devised by Zedini and Belhadj (2015) allows detecting fuzzy non‐parametric boundaries by resorting directly to the data, that is, calculate poverty lines without abstraction of the reality considered.…”

“…In this research, we use this procedure that uses a divisive algorithm based on two steps (see Zedini & Belhadj, 2015 for further details) to estimate the position of the fuzzy sets. We start, in the first step, by considering 100 fuzzy sets of poverty for , where each one is defined as the set of couples for all the universe Y , where Y is the income distribution and where is the m.f associating a real number in the interval [0, 1] to each point of Y .…”

“…Different methods have been used for constructing m.fs adapted to poverty measurement and analysis (see e.g., Cerioli & Zani, 1990). Zedini and Belhadj (2015) use a triangular membership function. In this paper, we make the choice for a form of m.f more adapted to the real features of poverty phenomenon (Equation , p. 9).…”

“…In the second step of the procedure of Zedini and Belhadj (2015), we plot the values of the matrix of membership grades. Then, we consider the analysis of this fuzzy structure via the scalar cardinality approach.…”