Researchers have developed a machine learning framework to construct a Global Ease of Living Index that quantifies quality of life across major economies using a composite of socio-economic and infrastructural indicators. The study, authored by Selvaraj, Arun Kumar, Panat, Tanay, and Chandra, Rohitash, and published on arXiv, addresses the need for a transparent, longitudinal tool to assess living conditions amid global disruptions such as the COVID-19 pandemic.
A transparent and comprehensive living index must include multiple dimensions of living conditions.
A Machine Learning Approach to Quality of Life
The Global Ease of Living Index combines economic indicators that define living standards into a single composite score. According to the paper, the framework utilises Principal Component Analysis (PCA) and Factor Analysis for dimensionality reduction, enabling the creation of a consistent index dating back to 1970 for major economies. The approach also incorporates a machine learning technique to handle missing data for certain economic indicators in specific countries.
Methodology: Dimensionality Reduction and Missing Data
The researchers curated and updated data for multiple socio-economic and infrastructural factors. By applying PCA and Factor Analysis, they reduced the dimensionality of the data while retaining the most significant variance. The machine learning framework specifically addresses gaps in data, ensuring that the index remains robust even when some indicators are unavailable for particular countries or years. The authors emphasise that the use of open data and code makes the index reproducible and transparent for ongoing research and policy development.
Applicability for Policymakers and Investors
The index is designed as a practical tool for policymakers to identify areas needing targeted intervention, such as healthcare systems, employment opportunities, and public safety. For business executives and investors, the longitudinal nature of the index offers insights into long-term trends in quality of life across major economies, which can inform strategic decisions on market entry, investment, and risk assessment. The study notes that understanding the long-term implications of cost of living and quality of life is essential in the context of drastic changes in the global economy and geopolitical conditions.
Next Steps
The full paper, including the methodology, data sources, and code, is available on arXiv under a Creative Commons license. The authors invite further application and adaptation of the framework to various contexts, aiming to provide a transparent and accessible basis for quality-of-life assessment.