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Analysis of Determinants of Happiness of Many Countries by Using Support Vector Machine (in Japanese)

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  • Kazutoshi TANABE
  • Takahiro SUZUKI

Abstract

A large-scale empirical experiment to analyze determinants of national happiness of human well-beings has been carried out based on happiness data of nations in the world as dependent variable and numerous factors as explanatory variables. The correlation between the happiness data containing World Database of Happiness for 149 countries and the data of 56 factors of countries’ indices such as economical, political, social, health, resource, environmental, life-style, and cultural fields was statistically analyzed to evaluate the influence of each factor on happiness. The determinants of happiness across nations were investigated by training non-linear regression support vector machine (SVM) models using the data of happiness and the country factors, and by optimizing the explanatory variables by the sensitivity analysis method. The results indicate that 20 factors satisfactorily represent the happiness data of 130 countries with the root mean squared error of 0.48 and the coefficient of determination of 0.867. A nonlinear regression technique like SVM is crucial for constructing a happiness predicting model due to the high nonlinear relationship between happiness and the explanatory variables. It was also revealed that health is the most important among various factors which influence the happiness due to the large contribution of health factors (e.g. life expectancy and mortality rate) to happiness. It is suggested that the direct contribution of economical factors such as gross domestic product (GDP) to happiness is not significant, but their indirect effect is not negligible through in the health condition.

Suggested Citation

  • Kazutoshi TANABE & Takahiro SUZUKI, 2014. "Analysis of Determinants of Happiness of Many Countries by Using Support Vector Machine (in Japanese)," Economic Analysis, Economic and Social Research Institute (ESRI), vol. 188, pages 46-60, March.
  • Handle: RePEc:esj:esriea:188c
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    File URL: http://www.esri.go.jp/jp/archive/bun/bun188/bun188c.pdf
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