GINI index (WB estimate)
| Quick info | |
|---|---|
| Data type | Numeric |
| Scale | Metric |
| Value labels | Not applicable |
| Technical name | socstr_gini_ndx_wb |
| Category | Social structure |
| Label | GINI index (WB estimate) |
| Related indicators |
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The Gini index measures how much the distribution of income or consumption expenditure among individuals or households in an economy deviates from perfect equality. The Lorenz curve shows the cumulative share of total income or consumption expenditure by a growing number of recipients, beginning with the poorest. The Gini index calculates the area between the Lorenz curve and a hypothetical line of perfect equality, expressed as a percentage of the total area under this line. As a result, a Gini index of 0 represents total equality, while an index of 100 denotes complete inequality.
Coding rules
- The Gini Index ranges between "0" and "100". "100" refers to a complete unequal distribution of income or consumption expenditure and "0" to a complete equal distribution of income or consumption expenditure. Teorell et al. (2024:1506) define the variable as "Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality."
- The Gini coefficient measures relative rather than absolute wealth. It is not additive across groups, therefore a society's total Gini does not equal the sum of its subgroup Ginis. It is not additive across groups, which means that a society's total Gini does not equal the sum of its sub-group Ginis. Thus, country-level Gini coefficients cannot be aggregated into regional or global Gini coefficients, but a Gini coefficient can be calculated for the aggregate.Because the methodologies and types of welfare measures obtained in the underlying household surveys differ, data are not perfectly comparable between countries or even years within a country. Two types of noncomparability should be considered, particularly for income distributions.First, the surveys can differ in a variety of ways, such as whether they use income or consumption expenditure as a measure of living standards. Income distributions are often more uneven than consumption distributions. Furthermore, income criteria vary more frequently between polls. Consumption is usually a considerably superior welfare indicator, especially in poor countries. Second, households differ in size (number of members) and in the amount to which members share their income. Individuals' ages and consumption demands vary. Differences between countries in these respects may affect distributional comparisons. To improve comparability, the WB has prioritized consumption over income. Income distribution and Gini indexes for high-income economies are generated directly from the Luxembourg Income Study database, using an estimating procedure similar to that used for impoverished countries (see World Bank 2023).
Bibliographic info
Citation:- Teorell, Jan, Aksel Sundström, Sören Holmberg, Bo Rothstein, Natalia Alvarado Pachon, Cem Mert Dalli, Rafael Lopez Valverde, and Paula Nilsson. 2024. The Quality of Government Standard [Dataset]. https://www.gu.se/en/quality-government. https://doi.org/10.18157/qogstdjan24.
- World Bank. 2023. World development indicators. Washington DC: The World Bank. https://databank.worldbank.org/source/world-development-indicators.
- World Bank. 2024. Poverty and Inequality Platform Methodology Handbook. Washington DC: The World Bank. https://datanalytics.worldbank.org/PIP-Methodology/.
- Kohli, Martin, Harald Künemund, Andrea Schäfer, Jürgen Schupp, and Claudia Vogel. 2006. "Erbschaften und ihr Einfluss auf die Vermögensverteilung." Vierteljahrshefte zur Wirtschaftsforschung 75 (1): 58-76. https://doi.org/10.3790/vjh.75.1.58.
Misc
Project manager(s): Responsible for data editing, description (WeSIS) and entry: Andrea Schäfer (2021-2025, Version 0.002), Jean-Yves Gerlitz (2018-2020; Version 0.001); Principal Investigator: Irene Dingeldey, Ulrich Mückenberger; Student assistants: Karolin Meyer (2018-2020)
Data release:- Version 0.001: Initial release with data The Quality of Government Standard Dataset, version January 2019
- Version 0.002: Updated with data from The Quality of Government Standard Dataset, version January 2024
Revisions: No revisions yet
Sources
- Teorell, Jan, Stefan Dahlberg, Sören Holmberg, Bo Rothstein, Natalia Alvarado Pachon, and Richard Svensson. 2019. The Quality of Government Standard [Dataset]. http://www.qog.pol.gu.se. https://doi.org/10.18157/qogstdjan19.
- Teorell, Jan, Aksel Sundström, Sören Holmberg, Bo Rothstein, Natalia Alvarado Pachon, Cem Mert Dalli, Rafael Valvarde Lopez, and Paula Nilsson. 2024. The Quality of Government Standard [Dataset]. https://www.gu.se/en/quality-government/qog-data/data-downloads/standard-dataset.
- World Bank. 2023. World development indicators. Washington DC: The World Bank. https://databank.worldbank.org/source/world-development-indicators.
