Multidimensional poverty decomposition: a fuzzy set approach
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This article extends the paper of Dagum C. and Costa M. (2004). We further develop the study of
multidimensional poverty using fuzzy sets by introducing a mixture of decomposition analysis. The
model yields the most relevant dimensions of poverty (health, education, etc.) and the most relevant
sub-groups (areas, gender, etc.) in order to identify the main forces that contribute to the overall
amount of the state of poverty. These results are useful for decision makers that contemplate socioeconomic
policies in favour of poverty reduction. Finally, we apply this decomposition to study the
level of poverty of Argentina in 1998.
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