Islam is the world's second largest religious group with nearly 2 billion Muslims. 1 Zakat is compulsory for Muslims adhering to Nisab. 2 In contemporary times, it is considered to be voluntary, and this, in part, explains its reduced amounts and its low contribution in the majority of the Muslim nations' socioeconomic systems due to their reluctance regarding the collection of Zakat. Indeed, around 35% of poor in the world belong to Muslim nations (World Bank, 2010). Pryor (2007) explains, in the analysis of Muslim economies, religion does not appear to be a useful explanatory variable, as it has relatively little influence on most economic or social performance indicators. 1 'The Global Religious Landscape' 18 December 2012. 2 Nisab is the minimum amount of a Muslim's net worth that is obligated to be given as Zakat and it is considered to determine the government's poverty threshold.
Zakat is one of the five pillars of Islam, which is compulsory to all the well-off Muslims, and it is given to the needy. It is used to fight poverty. This study examines Zakat's impact on poverty in Tunisia. Using simulated data of individual well-being from Tunisian household survey in 2010, the potential importance of the Zakat to struggle with poverty is highlighted. Fuzzy Poverty Measurement is computed which shows that Zakat does reduce this measurement. The simulation results display a significant decrease of the poverty index of Tunisia's seven regions, and poverty can be eradicated under cases West regions.
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
a Higher Institute of Management, University of Tunisia. The researchers wish to thank the anonymous reviewers for their comments and reviews including all the important points raised.
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