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

A Machine Learning Approach to Measuring Quality of Life

The paper introduces a machine learning framework designed to create a Global Ease of Living Index, specifically targeting longitudinal analysis across major economies. Rather than relying on traditional static indices that update infrequently, the authors propose using ML techniques to track changes in cost of living and quality of life over time, accounting for disruptions like the COVID-19 pandemic and shifting geopolitical conditions. The framework processes diverse economic and social indicators to generate a dynamic, data-driven measure of living standards.

Why This Matters

Traditional ease of living indices—such as the Human Development Index or Mercer’s Quality of Living surveys—suffer from several limitations: they are updated annually at best, rely on survey data that can be subjective, and often fail to capture rapid changes in economic conditions. The COVID-19 pandemic exposed these weaknesses dramatically, as lockdowns, inflation spikes, and supply chain disruptions altered living conditions within months, not years.

A machine learning framework offers three distinct advantages. First, it can ingest higher-frequency data—monthly inflation reports, housing cost trends, employment statistics—to produce more timely assessments. Second, ML models can identify non-linear relationships between variables that traditional linear weighting schemes miss. For instance, the marginal impact of rising rent on overall quality of life may differ significantly depending on whether healthcare costs are also rising simultaneously. Third, longitudinal analysis becomes more robust: the framework can detect structural breaks (like a pandemic or war) and adjust its weighting accordingly, rather than treating all years as comparable.

Implications for AI Practitioners

For those building applied ML systems, this work highlights several practical considerations:

Data pipeline design matters more than model complexity. The core challenge here is not finding a novel algorithm but assembling clean, comparable, multi-source data across countries and time periods. AI practitioners should invest heavily in data engineering—handling missing values, aligning definitions across jurisdictions, and normalizing currencies and purchasing power parity. Interpretability is non-negotiable. Policymakers and economists will not trust a black-box index. The framework must provide clear explanations of which factors are driving changes in the index and why. Techniques like SHAP values or LIME, or simpler approaches like feature importance rankings, become essential for adoption. Temporal validation is critical. Standard train-test splits fail for time series data. Practitioners must use walk-forward validation, ensuring that models trained on pre-pandemic data are tested on post-pandemic periods to assess generalization to novel disruptions. Domain expertise cannot be fully automated. While ML can identify correlations, causal interpretation requires economic theory. The index should be designed as a decision-support tool, not an autonomous judgment system.

Key Takeaways

• A machine learning framework for a Global Ease of Living Index promises more timely, dynamic, and nuanced measurements than traditional static indices, particularly for capturing rapid economic shifts.
• The approach addresses a real gap: existing indices update too slowly to reflect disruptions like pandemics, inflation crises, or geopolitical shocks.
• For AI practitioners, the primary challenges are data pipeline robustness, model interpretability, and proper temporal validation—not algorithmic innovation.
• The framework’s value depends on transparent, explainable outputs that economists and policymakers can trust and act upon.