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

TL;DR

This paper introduces a machine learning-based framework to create a comprehensive, longitudinal Ease of Living Index for major economies, aiding policymakers in identifying and addressing quality of life issues over time.

Contribution

It presents a novel machine learning approach to handle missing data and constructs a longitudinal index of living standards using principal component analysis.

Findings

The index covers major economies since 1970.

The framework effectively addresses data gaps.

Open data and code facilitate reproducibility.

Abstract

The drastic changes in the global economy, geopolitical conditions, and disruptions such as the COVID-19 pandemic have impacted the cost of living and quality of life. It is important to understand the long-term nature of the cost of living and quality of life in major economies. A transparent and comprehensive living index must include multiple dimensions of living conditions. In this study, we present an approach to quantifying the quality of life through the Global Ease of Living Index that combines various socio-economic and infrastructural factors into a single composite score. Our index utilises economic indicators that define living standards, which could help in targeted interventions to improve specific areas. We present a machine learning framework for addressing the problem of missing data for some of the economic indicators for specific countries. We then curate and update the data and use a dimensionality reduction approach (principal component analysis) to create the Ease of Living Index for major economies since 1970. Our work significantly adds to the literature by offering a practical tool for policymakers to identify areas needing improvement, such as healthcare systems, employment opportunities, and public safety. Our approach with open data and code can be easily reproduced and applied to various contexts. This transparency and accessibility make our work a valuable resource for ongoing research and policy development in quality-of-life assessment.