Multivariate analysis of post-transition OECD countries in the context of inequality measures

Abstract

The aim of this paper is to classify the post-transition OECD countries according to the Gini coefficient for income inequality, the S80/S20 ratio, the income share of the bottom 40% of the population, educational attainment – tertiary education, and labor force participation rate using factor and cluster analyses. Factor analysis resulted in two extracted factors, and factor scores were calculated. Hierarchical and non-hierarchical cluster analysis was performed on factor scores to classify eight posttransition OECD countries and three candidate countries. The research question of this paper is to investigate whether there are similarities/differences between existing members and candidates for membership in the OECD, among the selected post-transition countries of Europe, in the context of income inequality. Based on the dendrogram obtained by the hierarchical Ward’s method, a three cluster solution was selected. The non-hierarchical k-means method for the three-cluster solution clustered Croatia with Bulgaria and Romania. These three countries are OECD candidate countries. Our findings confirm that the three candidate countries remain behind because of historical reasons and the non-implementation of structural reforms.

Tiered Data Envelopment as method for clustering suppliers

“…Cluster analysis as a multivariate statistical technique is well known, so will not be presented. A thorough overview of cluster analysis can be found in the paper [19] and textbook [28]. Data envelopment analysis may be suitable for ranking DMUs, although doing this involves making a set of assumptions.…”