Comparable Indicators of Inequality Across Countries
1. Introduction
Measuring income inequality levels and trends in a consistent, harmonised fashion is at the core of the GINI
project, essential if it is to achieve its objectives of tracing and understanding the social, political and cultural
impacts of inequality. The fact that different national and comparative studies of inequality and its impacts define
and measure income inequality in differing ways makes it difficult to generalize from their specific findings. The
GINI project aims to exploit differences between and within countries in inequality levels and trends in order to
understand their impacts and tease out the implications for policy and institutions, encompassing 25 EU countries,
the USA, Japan, Canada and Australia. In order to do so, a common approach to the measurement of inequality
across those countries, insofar as is possible with available data, will be sought. In this paper we review a range of
issues that arise in considering such an approach, focusing first on the definition of income and the income recipi-
ent unit, then turning to the unit of analysis, the time period covered, the measures employed, and sources of data
for comparative analysis.
It must be emphasized at the outset that the appropriate inequality measures will vary depending on the topic
to be addressed. Both in seeking to track and analyse inequality itself, and in studying the impact of inequality on
economic, social and political outcomes of interest - i.e. where inequality is an explanatory variable - the concept
and how it is best operationalized will depend in the first instance on the nature of the relationships being hypoth-
esized and tested. Most importantly, inequality in total household income versus the dispersion in earnings among
employees capture quite different aspects of inequality, each the subject of very substantial research literatures;
here we deal with overall income inequality first and then with earnings dispersion. We discuss some of the central
issues and choices to be faced in the operationalization stage of the analysis, but the choice of concept and deci-
sions about how it is best measured must be driven by the research question, and for that reason will naturally vary
across different contributions to the GINI project. The aim is however to minimise variation that is not motivated
by such considerations in order to maximize comparability across those contributions. Any remaining variation
needs to be mentioned and its potential effects for the arguments and conclusions discussed. It is of fundamental
importance for all contributions to GINI to be explicit about the concepts and definitions used, in theory as well as
in practice - given the data -, and indicate the implications of choices that have been made, for the argument and
conclusions. Evaluating data outcomes from international sources with the help of national sources, and the other
way around, is equally advisable.
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