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

TL;DR

This paper reviews global inequality measurement databases, analyzes discrepancies in Gini coefficient estimates, and highlights how methodological choices significantly impact cross-country comparability of inequality data.

Contribution

It compiles and merges over 122,000 observations from multiple databases, quantifies sources of discrepancies, and discusses methodological impacts on Gini coefficient comparability.

Findings

Differences in welfare metrics cause the largest Gini estimate discrepancies.

Sub-metric definitions and equivalence scales also significantly affect results.

Methodological choices are rarely fully disclosed, impacting cross-country comparisons.

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.