A Computational Approach to Economic Inequality, Happiness and Human Development

In this paper, we study the connections of categorical levels of Human Development Index (HDI), GDP per capita, World Happiness Index, the Gini indexes of Income and Wealth inequalities together with poverty rate for 98 world countries. By clustering analysis we identify four groups of countries with similar features. K-means clustering algorithm is applied to obtain four clusters of sizes 21-26 countries by explaining 68.3% of the total variation in data. The analysis reveals significant differences between the clusters, while also factors with largest differences within the clusters. Secondly, multinomial logistic regression (MLR) is applied in predicting the HDI categories of the full sample of 98 world countries for year 2018. The MLR model can capture also nonlinear relationship. The logistic regression model achieved 91.8% overall accuracy. The results of our research together from earlier literature is followed by suggestions for the future research.