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A Quantitative Analysis of Global Well-Being


Abstract: The study of well-being has become increasingly important in society during the past few decades, especially after the COVID-19 Pandemic. While exploration of the different features of individual well-being has grown substantially, research has lagged in comprehending trends of global-scale well-being. This paper aims to delve deeper into the properties that make up global well-being and to track the indicators that yield the greatest influence on a nation’s quality of life. Such findings could help guide government policies for the improvement of public life and provide a more abstract understanding of measuring general well-being. To conduct this analysis of 183 countries, I compiled seven socioeconomic indicators from the United Nations Human Development Programme and The World Bank, and I chose the Human Development Index (HDI) to represent the well-being of each country. I utilized the Python language to deploy multivariate Linear Regression models to analyze the extent to which each indicator influences the HDI of a country, and I validated the observed relationships using regression slope t-tests, Mean Test Error, and R-squared. This study found that the College Enrollment and Control of Corruption indicators demonstrated a universal positive correlation to HDI, regardless of developmental or regional differences in countries, while the other chosen indicators varied in influence from region to region. Governments of developing countries could use the detailed impacts of each indicator to properly allocate funds to improve the necessary socioeconomic aspects of their societies.


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