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National University of Science and Technology (NUST) 2022

Modelling poverty in Namibia using beta distribution.

Mafale, Ndubano

Titre : Modelling poverty in Namibia using beta distribution.

Auteur : Mafale, Ndubano

Université de soutenance : Namibia University of Science and Technology

Grade : Master of Science in Applied Statistics 2022

Résumé partiel
Modelling poverty is important as it helps to pinpoint human development areas that are most affected by poverty. Also, modelling poverty helps in understanding the patterns and levels of poverty, which helps policy makers to plan and make targeted interventions to reduce poverty. The traditional methods of estimating poverty such as the cost of basic needs approach or the poverty line approach are surrounded by a lot of controversies as they are said to underestimate or overestimate poverty. These methods are uni-dimensional as they only estimate poverty in one dimension (e.g consumption, income and expenditure) which neglects the humanistic needs side of poverty such as access to health or education. On the other hand, methods that include the Alkire and Santos (2011) method measure poverty in more than one dimension (e.g living standards, health, and education) but are faced with prejudice as the weighting method used is based on experts’ opinion or the consensus of different stakeholders. Thus, this type of weighting method may result in biased weights and consequently result in inaccurate estimates of Multidimensional Poverty Index (MPI) values. This study focused on developing a multidimensional poverty model using beta distribution capable of estimating poverty for Namibia on regional and national levels. In addition, the study aimed at assessing the impact of weighting methods on MPI. The first specific objective was to develop a multidimensional poverty model using beta distribution that could be used to model poverty for Namibia. The developed model showed that the MPI is equivalent to the expected value of the left-truncated beta distribution. The uncertainty surrounding the MPI was measured through the specification of the variance. The second specific objective was to assess the impact of weighting methods on MPI.

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Page publiée le 27 novembre 2022