Global Ease of Living Index: a machine learning framework for longitudinal analysis of major economies

Researchers have developed a machine learning framework called the Global Ease of Living Index that combines socio-economic and infrastructure indicators to measure quality of life across major economies since 1970. Using dimensionality reduction techniques and algorithms to handle missing data, the index provides policymakers with a transparent tool to identify areas requiring intervention such as healthcare, employment, and public safety.

This research addresses a critical gap in how governments and institutions quantify quality of life across diverse economies. Rather than relying on single metrics like GDP, the Global Ease of Living Index aggregates multiple socio-economic dimensions into a composite score, capturing the nuanced reality of living conditions that citizens experience. The framework's use of machine learning to address data gaps across countries and time periods enables longitudinal analysis spanning over five decades, revealing how geopolitical shifts, pandemics, and economic cycles have affected populations globally.

The methodology employs Principal Component Analysis and Factor Analysis to reduce dimensionality while preserving meaningful variance in the data. This technical approach ensures that the index reflects genuine patterns rather than noise, making it suitable for rigorous policy analysis. The open-source nature of the research—with publicly available data and reproducible code—establishes a new standard for transparency in quality-of-life measurement, allowing independent verification and adaptation across different regional contexts.

For policymakers and development institutions, this framework provides actionable intelligence about where targeted interventions yield the greatest impact. Rather than implementing broad economic policies, governments can identify specific deficiencies in healthcare systems, employment opportunities, or public safety and allocate resources accordingly. The index's historical depth enables governments to evaluate the effectiveness of past interventions and adjust future strategies.

Looking forward, this research demonstrates how machine learning can democratize access to sophisticated analytical tools for policy development. The framework's replicability suggests broader applications in emerging markets and developing economies where data collection remains fragmented, potentially reshaping how international organizations approach development metrics and poverty reduction.

• →Researchers created a machine learning framework that combines multiple socio-economic indicators into a single Global Ease of Living Index for major economies since 1970.
• →The index uses Principal Component Analysis and Factor Analysis to handle missing economic data and identify genuine patterns in quality of life metrics.
• →Open-source design and reproducible code enable policymakers to identify specific areas requiring intervention like healthcare, employment, and public safety.
• →Longitudinal analysis spanning 50+ years reveals how pandemics, geopolitical shifts, and economic cycles impact living conditions across different regions.
• →The transparent methodology establishes a new standard for quality-of-life measurement that can be adapted across diverse economic contexts and developing nations.