The Analysis of Human Development Index (HDI) for Categorizing the Member States of the United Nations (UN)

Abstract

To categorize the nations to reflect the development status, to date, there are many conceptual frameworks. The Human Development index (HDI) that is published by the United Nations Development Programme is widely accepted and practiced by many people such as academicians, politicians, and donor organizations. However, though the development of HDI has gone through many revisions since its formulation in 1990, even the current version of the index formulation published in 2016 needs research to better understand and to gap-fill the knowledge base that can enhance the index formulation to facilitate the direction of attention such as release of funds. Therefore, in this paper, based on principal component analysis and K-means clustering algorithm, the data that reflect the measures of life expectancy index (LEI), education index (EI), and income index (II) are analyzed to categorize and to rank the member states of the UN using R statistical software package, an open source extensible programming language for statistical computing and graphics. The outcome of the study shows that the proportion of total eigen value (i.e., proportion of total variance) explained by PCA-1 (i.e., first principal component) accounts for more than 85% of the total variation. Moreover, the proportion of total eigen value explained by PCA-1 increases with time (i.e., yearly) though the amount of increase with time is not significant. However, the proportions of total eigen value explained by PCA-2 and PCA-3 decrease with time. Therefore, the loss of information in choosing PCA-1 to represent the chosen explanatory variables (i.e., LEI, EI, and II) may diminish with time if the trend of increasing pattern of proportion of total eigen value explained by PCA-1 with time continues in the future as well. On the other hand, the correlation between EI and PCA-1 increases with time although the magnitude of increase is not that significant. This same trend is observed in II as well. However, in contrast to these observations, the correlation between PCA-1 and LEI decreases with time. These findings imply that the contribu- tions of EI and II to PCA-1 increase with time, but the contribution of LEI to PCA-1 decreases with time. On top of these, as per Hopkins statistic, the clusterability of the information conveyed by PCA-1 alone is far better than the clusterability of the information conveyed by PCA scores (i.e., PCA-1, PCA-2, and PCA-3) and the explanatory variables. Therefore, choosing PCA-1 to represent the chosen explanatory variables is becoming more concrete.

Financial Inclusion and Human Development in Indian States: Evidence from the Post-Liberalisation Periods

“…According to Santos and Alkire (2011), due to paucity of data on these indicators, this methodology is sometimes modified in case of developing countries. But studies like Lai (2003), Nguefack-Tsague et al (2011) and Mylevaganam (2017) have used the PCA to compute HDI. According to these studies, arbitrary equal weighting of the three components by treating human development as a latent concept imperfectly captured by its three component indices.…”

The Role of Economic and Political Features in Classification of Countries-in-Transition by Human Development Index

“…The high scores for some of the indexes that make up it are consistent with a high degree of government intervention [17] and, for example, the countries with high scores on the education index have the highest shares of GNI spent on education. It is obvious that the components (1)-(3) of HDI are its very good predictors, but national income level (3) has been found the most important one followed by education level (2) and life-expectancy, in this order [18]. Using the direct component of HDI, neural network modelling has shown over 95% accuracy in predicting the level of HDI, while classical linear regression has reached an accuracy level of 90% [19].…”

“…The experiment showed that ANNs accuracy was better, 95%. HDI has its limitations as an indicator of development, but it is widely used and factors affecting it are of major interest for policy makers [18]. Market-economy system based on free exchange requires rules and they are affected by political systems within which decision-makers govern.…”

Impact of Economic and Political Systems on Human Development Index: A Computational Evaluation

“…HDI focuses on human development as the ultimate goal regardless of the factors contributing to it [16], [17], [18]. It is obvious that the components of HDI are its very good predictors, and the national income level (3) has been found the most important one followed by education level (2) and life-expectancy (1), in this order [19]. Using the direct components of HDI, neural network modelling has shown over 95% accuracy in predicting the level of HDI, while classical linear regression has reached an accuracy level of 90% [20].…”

“…While for advanced market economies like OECD countries, a better well-being measure is argued to be OECD's Better Life Index (BLI) [24], for countries-in-transition of this study, HDI is appropriate as all these countries have still unachieved development to make it to the highest HDI levels. The factors affecting their HDI levels are of major interest for policy-makers and researchers alike [19], [25]. Market-economy system based on free exchange requires rules and they are affected by political system and level of democracy within which the policy-makers govern [26], [27].…”