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

2023-01-07 · Source: stat.ML updates on arXiv.org · Field: Science & Research — Social Sciences & Behavioral Studies, Research Methodology & Innovation, Economic Analysis & Policy · Depth: Advanced, extended

Summary

The "Global Ease of Living Index" introduces a machine learning framework to quantify quality of life and living conditions across major economies since 1970. This index integrates diverse socio-economic and infrastructural factors into a single composite score, addressing limitations of existing indices like the World Happiness Index, which often exhibit biases towards economic metrics or subjective measures. The methodology employs Random Forest Regressor (RFR) and Multiple Imputation by Chained Equations (MICE) to handle missing data, with RFR demonstrating superior performance for complex datasets. Principal Component Analysis (PCA) and Factor Analysis are then utilized for dimensionality reduction to construct four sub-indices: Economic, Institutional, Quality of Life, and Sustainability. These sub-indices are weighted (Economic 0.25, Institutional 0.25, Quality of Life 0.35, Sustainability 0.15) to form the final Global Ease of Living Index. The framework, with its open data and code, offers a transparent and reproducible tool for policymakers to identify areas for targeted interventions, such as healthcare or public safety.

Key takeaway

For policymakers evaluating national development strategies, this Global Ease of Living Index offers a robust, transparent alternative to subjective measures like the World Happiness Index. You should prioritize a multi-faceted approach that balances economic growth with institutional strength, quality of life improvements, and environmental sustainability, as reflected in the index's weighted sub-components. Consider utilizing the open data and reproducible methodology to pinpoint specific areas, such as healthcare access or crime rates, needing targeted interventions within your country.

Key insights

A machine learning framework quantifies global living conditions using a weighted composite index of economic, institutional, quality of life, and sustainability factors.

Principles

• Composite indices require multi-dimensional factors.
• Machine learning improves missing data imputation.
• Dimensionality reduction aids index construction.

Method

Data is collected, merged, and standardized, then missing values are imputed using Random Forest Regressor. Sub-indices are created via Factor Analysis, and a weighted sum forms the final index.

In practice

• Use Random Forest Regressor for robust data imputation.
• Apply Factor Analysis to derive latent sub-indices.
• Weight sub-indices based on policy priorities.

Topics

• Global Ease of Living Index
• Machine Learning
• Data Imputation
• Dimensionality Reduction
• Quality of Life Metrics
• Socio-Economic Indicators
• Policy Analysis

Best for: AI Scientist, Policy Maker, Research Scientist, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.