Comparison of Classification Success of Human Development Index by using Ordered Logistic Regression Analysis and Artificial Neural Network Methods

Keywords

Human Development Index, Ordered Logistic Regression, Artificial Neural Network

Abstract

Economic development and growth are among the most important objectives for many countries. Not only economic development but also human development, which means enhancing and improving people’s quality of life, plays an important role for reaching this objective. In this way, it’s possible to take a more human oriented perspective by taking education, health and welfare dimensions of development into consideration by widening the perspective, which is focused narrowly on economic growth only. For this reason, human development index has become a widely preferred and recognized numerical indicator for comparison and classification of countries. Human Development Index (HDI) calculated by Human Development Report Office of United Nations Development Program (UNDP) measure people’s level of welfare every year. The purpose of this research is to compare the classification success of Human Development Index by using ordered logistic regression and artificial neural network. The data of 81 countries, which has United Nations Development Program’s Human Development Index, between the years of 2010-2012 were used in this study. Countries are classified for having very high, high and moderate levels of human development. The results of the ordered logistic regression model indicate that determinants including infant mortality rate, health expenses, number of internet users, import and export were observed as statistically significant. As a result of the analysis, Ordered Logistic Regression Analysis proved 88% success in classification while Elman’s back propagation learning algorithm showed 92% success.

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