Abstract

This study explores the Global Happiness Index using data compiled from the OECD and Our World in Data to identify key factors contributing to societal well-being. Six primary predictors were analyzed: GDP per capita, social support, healthy life expectancy, freedom to make life choices, generosity, and perceptions of corruption. Regression and clustering techniques were employed to uncover patterns among countries. By expanding the analytical scope beyond conventional economic and social indicators, this study helps identify new pathways for improving well-being across diverse cultural and economic landscapes. Additional variables such as perceived safety, political engagement, and values related to family and leisure were integrated to enrich the analysis. Environmental quality and income inequality were also considered to provide a more comprehensive view of life satisfaction determinants. Our findings highlight the most statistically significant predictors of happiness and reveal two primary country clusters: one characterized by traditional values and lower satisfaction, and another marked by modern, leisure-oriented societies with higher well-being. These insights offer a data-driven framework for comparing nations and understanding the multidimensional nature of happiness. Ultimately, this analysis provides actionable recommendations for policymakers aiming to implement more targeted, effective, and sustainable interventions to improve life satisfaction globally.

Recommended Citation

Hernandez, Victoria and Wachira, Christy W. (2025) "Analyzing the Global Happiness Index," SMU Data Science Review: Vol. 9: No. 2, Article 1.
Available at: https://scholar.smu.edu/datasciencereview/vol9/iss2/1