Income Inequality

Indicator Definition: JENKINS (1991) considers the study of economic inequality as “the analysis of differences across the population in access to, and control over, economic resources.”

Goal: To reduce income inequality as far as possible. The most common measure for the indicator is the Gini-coefficient with possible values between 0 (i.e., full equality in the distribution of incomes) and 1 (maximum inequality in the distribution of incomes).

Policy Relevance: The renowned Kuznets’ curve (1955) was one of the first descriptions of the correlation between inequality in the distribution of incomes and a country’s economic growth. Kuznets discussed the growth process as exogenous. Newer studies attribute the potential effects of inequality on growth to a variety of reasons (Deininger, Squire 1996). The indicator is especially relevant for the economic and social dimensions of sustainability and for intragenerational justice.

Limitations of the indicator: The Gini-coefficient is aggregated, and it is impossible to attribute changes in the indicator to a specific redistribution, e.g., between the richest and the middle-income group or between the middle-income and low-income groups.

Methodology: Jenkins (1991) distinguishes between ordinal and cardinal inequality measures. He states that “all inequality measures, even ones related to apparently objective diagrams, inevitably involve value judgments of various kinds” (Jenkins 1991, p. 19). Nevertheless, due to the availability of the primary data necessary for the Gini- coefficient and due to the fact that studies found the indicator’s explanatory power to be similar to that of other, more complicated and less common measures, the Gini- coefficient remains the indicator of choice.

Data source and availability: Primary data for the Gini-coefficient (i.e., the distribution of income in the population) is collected by statistical agencies, but also fiscal authorities and censuses. Data is usually available on an annual level, in many countries more often.

Quality of the data: Due to the level of aggregation, the interpretation of the Gini- coefficient must be accompanied by further information (e.g., quintile-specific data).

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