The Role of Data and Metrics in Measuring Inequality Worldwide. A Tribute to Giovanni Andrea Cornia's Lifelong Work on the World Ginis

2026-03-18

AI Summary

This study addresses the lack of cross-national comparability in global Gini coefficient estimates, which arises from discrepancies in data sources, welfare metrics, and methodological choices. By harmonizing 12 international databases, the authors construct a unified dataset encompassing 222 countries and over 122,000 observations. They provide the first systematic quantification of pairwise inconsistencies among alternative Gini estimates for the same country-year and rigorously assess the influence of key factors—including welfare indicators, reference units, equivalence scales, and survey design. The analysis reveals that Gini coefficients for identical country-years can differ by as much as 50 percentage points, with the choice of welfare metric identified as the primary driver of cross-country incomparability. The paper proposes a methodological framework to enhance temporal and spatial comparability, establishing a standardized foundation for measuring economic inequality.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Large-scale security measurements

Abstract

This paper pays tribute to Professor Giovanni Andrea Cornia's lifelong contributions to the measurement of global inequality. We review twelve world and regional databases of the Gini coefficient, illustrate their coverage, overlapping, and data gaps, and analyse the major sources of discrepancy among published Ginis. Merging all databases into a unified collection of over 122,000 observations spanning 222 countries from 1867 to 2024, we document how differences in welfare metrics, reference units, sub-metric definitions, post-survey adjustments, and survey design produce Gini estimates that diverge considerably -- sometimes by as much as 50 percentage points -- for the same country and year. We quantify pairwise cross-database discordance, document the income-consumption Gini gap by region and income group, and discuss the contributions of welfare metric and equivalence scale choices to cross-database dispersion. We extend the analysis with a dedicated discussion of comparability across time and across measurement dimensions, showing how multiple layers of methodological choice interact to make any single Gini figure a product of a complex chain of decisions that are rarely fully disclosed. Our analysis confirms that the choice of welfare metric remains the single most important source of cross-country non-comparability, while sub-metric definitions and equivalence scales introduce further systematic differences that are routinely overlooked in comparative work.

Problem

• inequality measurement
• Gini coefficient
• data comparability
• welfare metrics
• methodological discrepancies

Innovation

• Gini coefficient
• inequality measurement
• welfare metric
• data harmonization
• equivalence scale