Factor Analysis of Socio-economic Indicators in Africa: A Case Study of African Development Bank, 2017
The main objective of this paper is to reduce 45 socioeconomic indicators of 54 African countries into few factors that summarize the characteristics contained in the original set of indicators. Factor analysis through principal component as a statistical technique is used as a model. The purpose is to explain the total variance of the indicators. Usually, the indicators are reduced into few major factors or components, each factor or component contributes in explaining the total variance with a certain percentage. The research questions are: To what extent can factor analysis reduces the 45 socioeconomic indicators to few groups (factors)? What are the contributions of these factors in explaining the total variation? What are the most important and effective factors? This methodology enables more useful characterization of the territory for policy-making purposes. The Statistical pocketbook of African Development Bank (AFDB) of 2017 is used in this paper. The 45 socioeconomic indicators were reduced to eleven factors. Each factor is a linear equation for a number of indicators. The first factor is the Multi- indicators factor, it contains fifteen indicators and explains 25% of the total variance. The second factor is the economic factor, there are six indicators in this factor and 18% of the total variance is due to it. The commercial factor represents the third factor; it consists of six indicators and explains 11% of the total variance. The fourth factor; is the development factor, it contains five indicators and explains 8% of the total variance. The overall deficit factor is the fifth factor; it consists of three indicators and 7% of the total variance is due to it. The six factor; is the grants factor, it explains 6% of the total variance. The expenditure factor explains 5% of the total variance. The monetary factor, revenue and grant factor, imports factor and external debt factor explain 5%, 4%, 4% and 3 of the total variance respectively. Although the eleven factors explain 93.890% of the total variance between the indicators, but the first five factors are the most important, because it explains separately 68.341% of the total variance.
African Countries, Principal Component Method, AFDB Pocketbook, Model
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