Mputu Losala Lomo, Denis-Robert (2022): Application de la Classification Ascendante Hiérarchique à la Répartition des Ressources Budgétaires dans la Ville-Province de Kinshasa.
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Abstract
More specifically, we want to resolve the problem of the injustice observed in the distribution of national resources allocated to Decentralized Territorial Entities (DTEs), the case of the 24 municipalities of the city-province of Kinshasa. Indeed, this distribution is done in practice by using a single variable (or criterion) which is “Population” instead of three (Production capacity, area and population), proposed by the legislator, although all are heterogeneous. Also, there is no mechanism for sharing common resources using several heterogeneous variables and taking into account the relationships between individuals in the sense of reducing the inequalities existing between them and leading to the calculation of their respective shares.
Extending the problem to other cases which arise both in Mathematics and in practical life, we have identified four different causes of injustice when sharing, in particular, a sum of money. These are: (1) Using a single variable instead of several. (2) The direct use of the starting data from homogeneous variables for which the same unit of measurement is expressed differently for each variable. (3) Direct use of initial data from heterogeneous variables. (4) The lack of reduction in inequalities between individuals.
To solve these problems, we have proposed two methods which use several homogeneous or heterogeneous variables allowing the calculation of the shares of individuals: the first is called "Process of Distribution of Ressource Without reduction of inequalities (PDRW)". It deals with the case where individuals have contributed to the creation of the resource and does not allow the reduction of inequalities. The second, called "Process of Distribution of Resource from the results of Clustering (more specifically, the Agglomerative Hierarchical Clustering) (PDRC)" which deals with the case where individuals have not contributed to the creation of resource and which leads to reducing inequalities between them. These methods use several interesting formulas that we have proposed. That concerns especially the "inequalities level index" to measure the degree of inequalities between individuals and "the function of corrected values" for the reduction of inequalities between individuals...
Item Type: | MPRA Paper |
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Original Title: | Application de la Classification Ascendante Hiérarchique à la Répartition des Ressources Budgétaires dans la Ville-Province de Kinshasa |
English Title: | Application of Hierarchical Ascendant Clustering to the Distribution of Budgetary Ressources in the City-Province of Kinshasa |
Language: | French |
Keywords: | Agglomerative Hierarchical Clustering, Decentralized Territorial Entities, Sharing, Reduction of inequalities, Distribution of ressource. |
Subjects: | C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C55 - Large Data Sets: Modeling and Analysis |
Item ID: | 113774 |
Depositing User: | Denis-Robert Mputu Losala lomo |
Date Deposited: | 09 Aug 2022 13:38 |
Last Modified: | 05 Jul 2024 07:42 |
References: | 1. Auray J. P. , Duru G.et Zighed A. (1990) , Analyse des données multidimensionnelles, 4 tomes, édition Alexandre, Lacassagne 2. Baccini A (2010), Statistique Descriptive Multidimensionnelle, Institut de Math de Toulouse, Toulouse 3. Béatrice de Tilière (2009) , Analyse Statistiques Multivariée, (http://proba.jussien.fr/ detiline/cours/polycop-Bio.pdf (Consulté le 20/02/2016)) 4. Benzecri F (1985), la classification ascendante hiérarchique d’après un exemple de données économiques, Les cahiers de l’analyse des données, tome 3, n°3, Dunod 5. Benzecri J. P. et cie (1980) et Cie, l’Analyse des données : 1 La taxinomie }, 3è édition, Collection Bordas, Dunod, Paris 6. Benzecri J. P. et cie (1980) et Cie, l’Analyse des données : 2 L’Analyse des correspondances }, 3è édition, Collection Bordas, Dunod, Paris 7. Bertrand F. et Maumy-Bertrand M. (2013), Notions fondamentales en statistique, Université de Strasbourg, (2013) 8. Billaudot B. (2011), Justice distributive et justice commutative dans la société moderne, (https://halshs.archives-ouvertes.fr/halshs-00644799 (Submitted on 25 Nov 2011)) 9. Bouchier A. (2011), Programmer avec R, Montpellier 10. Bouroche J. M. et Bertier P. (1977), Analyse des données Multidimensionnelles, 2è édition, PUF |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/113774 |