<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The main purpose of this paper is to provide an objective analysis of the economic development level of countries. This is done by measuring it through a new index and by classifying the countries in an optimal number of clusters, each group characterizing different levels of economic development. The proposed methodology is based on three steps: creating a composite index (by applying the principal component analysis), establishing the optimal number of development groups (based on the number of principal components and on the hierarchical clustering) and clustering countries into them (with the help of k-means analysis). Therefore, this approach solves the difficulty of classifying the countries, complication that is mentioned in the specialized literature. Also, the paper creates a better understanding on the economic development level of countries, as, usually, the papers examine the economic growth level of countries. The analysis is conducted at the level of 60 countries for year 2015, using 12 indicators from categories that influence economic development (income, inequality, health, education and living conditions). The empirical results revealed that the countries can be grouped in two groups: economical developed countries (approximatively 2/3) and economic developing countries (approximatively 1/3). The countries that are most developed from an economic point of view are: Singapore, Luxemburg and Finland.</p></abstract>

The main purpose of this paper is to provide an objective analysis of the economic development level of countries. This is done by measuring it through a new index and by classifying the countries in an optimal number of clusters, each group characterizing different levels of economic development. The proposed methodology is based on three steps: creating a composite index (by applying the principal component analysis), establishing the optimal number of development groups (based on the number of principal components and on the hierarchical clustering) and clustering countries into them (with the help of k-means analysis). Therefore, this approach solves the difficulty of classifying the countries, complication that is mentioned in the specialized literature. Also, the paper creates a better understanding on the economic development level of countries, as, usually, the papers examine the economic growth level of countries. The analysis is conducted at the level of 60 countries for year 2015, using 12 indicators from categories that influence economic development (income, inequality, health, education and living conditions). The empirical results revealed that the countries can be grouped in two groups: economical developed countries (approximatively 2/3) and economic developing countries (approximatively 1/3). The countries that are most developed from an economic point of view are: Singapore, Luxemburg and Finland.

Characterizing the level of economic development of countries

The Bucharest University of Economic Studies

Proceedings of the International Conference on Applied Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Characterizing the level of economic development of countries"}

The main purpose of this paper is to provide an objective analysis of the economic development level of countries. This is done by measuring it through a new...

Characterizing the level of economic development of countries

Published Online: Feb 13, 2020

Page range: 343 - 354

DOI: https://doi.org/10.2478/icas-2019-0030

Keywords
economic development, composite index, principal component analysis, k-means, SPSS

© 2019 Alexandra Nastu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Characterizing the level of economic development of countries

Published Online: Feb 13, 2020

Page range: 343 - 354

DOI: https://doi.org/10.2478/icas-2019-0030

Keywordseconomic development, composite index, principal component analysis, k-means, SPSS

© 2019 Alexandra Nastu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
economic development, composite index, principal component analysis, k-means, SPSS