The Use of Fuzzy Logic to Measure Multidimensional Poverty in Cameroon
Gabriel Rodrigue BILOA
Economist, Faculty of Economics and Applied Management (FSEGA), Department of Quantitative Techniques, University of Douala, Cameroon.
https://doi.org/10.47191/jefms/v5-i8-01ABSTRACT:
The protean nature of poverty does not make it easy to understand, as it is a concept that can be seen from several angles (sociological, anthropological and economic). However, the aim of this paper is to provide a measure of multidimensional poverty in Cameroon. In other words, to make a multidimensional analysis of poverty. To achieve this, the fuzzy logic approach of Lotfi Zadeh (1965) would be ideal, as it appears to be the appropriate tool for specifying such vague concepts as poverty.
Using the third Cameroonian household survey (ECAM3), we were able to identify the different poverty groups. This enabled us to construct a multidimensional poverty index in three stages. First, the non-monetary dimensions were selected, then the deprivation indicators were extracted and finally the results were aggregated.
Because of the calculations, the fuzzy poverty index in Cameroon is 0.6010. This indicates that 60.10% of Cameroonian households are structurally poor. Disaggregating this index by region, stratum and gender of the head of household, shows that the Far North region has the highest fuzzy proportion (P=0.7367), while the two major metropolises of Yaoundé and Douala have better scores. For the most part, rural areas are the poorest with a fuzzy proportion of (0.7463), while female-headed households are the most indigent (P=0.6264).
Analysis of the deprivation indicators shows, however, that the supply of drinking water (0.7657), the mode of disposal of wastewater (0.9501), the level of education of heads of households (0.7430) and household income (0.9051) are those that accentuate the poverty of Cameroonian households. Of the ten regions of Cameroon, the Far North (0.1585), the North West (0.1452) and the West (0.1161) are those that contribute most to poverty.
KEYWORDS:
Fuzzy subsets/Fuzzy logic, Membership functions, Fuzzy measures of poverty, Poverty decomposition, Deprivation indicators, Totally fuzzy and relative approach.
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