Profiling the Fuzzy Latent Structure of Multidimensional Poverty: Toward Valuable Insights for Poverty Policymakers

Several multidimensional poverty indices have been proposed, and have been extensively studied in the literature. On the other hand, the need for aggregation of poverty indicators into one multidimensional index has been questioned. It has been argued even so that this aggregation can be misleading for political targeting strategies. Subsequently, some researchers have advocated that the use of the latent class analysis would address these issues. However, this setting does not allow to take into account the fuzzy nature of the latent poverty concept. The contribution here is to use the Grade-of-Membership (GoM) model to profile the fuzzy latent structure of multidimensional poverty, for a more realistic handling of this phenomenon. The application of the GoM methodology to multivariate poverty data for the Tunisian case reveals four most prevalent multidimensional poverty profiles. The results emphasize the role played by contextual effects. Indeed, the rural cluster is suffering more intense deprivation and groups in the central and coastal regions have a more comfortable status in comparison with the group of households residing in inland regions. A thorough analysis of these patterns is put forward in this research, giving valuable insights to policy makers.