In Germany, the Gini index increased from **** points in 2005 to **** points in 2024. The Gini Index is a measurement of inequality within economies, a lower score indicates more equality while a higher score implies more inequality. Germany's index score has increased since 2019 to 2021, however, it has decreased in the most recent period recorded, reaching its lowest rate since 2012.

Germany - Gini coefficient of equivalised disposable income was 29.50% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Germany - Gini coefficient of equivalised disposable income - last updated from the EUROSTAT on October of 2025. Historically, Germany - Gini coefficient of equivalised disposable income reached a record high of 31.20% in December of 2021 and a record low of 28.30% in December of 2012.

Historical dataset showing Germany income inequality - gini coefficient by year from N/A to N/A.

Germany DE: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 32.400 % in 2020. This records an increase from the previous number of 31.800 % for 2019. Germany DE: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 30.350 % from Dec 1991 (Median) to 2020, with 30 observations. The data reached an all-time high of 32.400 % in 2020 and a record low of 28.000 % in 1996. Germany DE: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Social: Poverty and Inequality. 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.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

In 20234 Bulgaria had the highest Gini Index score in the European Union at 38.4, implying that the country had the highest level of inequality among European countries. The Gini Index is a measure of inequality within economies, a lower score indicates more equality, and a higher score less equality. Slovakia had the lowest score among EU countries for 2024 with a score of 21.7, suggesting that it is the most egalitarian society in Europe.

Gini Coefficient data was reported at 0.324 NA in 2020. This records an increase from the previous number of 0.318 NA for 2019. Gini Coefficient data is updated yearly, averaging 0.303 NA from Dec 1991 (Median) to 2020, with 30 observations. The data reached an all-time high of 0.324 NA in 2020 and a record low of 0.280 NA in 1996. Gini Coefficient data remains active status in CEIC and is reported by Our World in Data. The data is categorized under Global Database’s Germany – Table DE.OWID.ESG: Social: Gini Coefficient: Annual.

The European Union Statistics on Income and Living Conditions (EU-SILC) collects timely and comparable multidimensional microdata on income, poverty, social exclusion and living conditions.

The EU-SILC collection is a key instrument for providing information required by the European Semester (_[1]) and the European Pillar of Social Rights, and the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.

AROPE remains crucial to monitor European social policies, especially to monitor the EU 2030 target on poverty and social exclusion. For more information, please consult EU social indicators.

The EU-SILC instrument provides two types of data:

• Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions.
• Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).

EU-SILC collects:

• annual variables,
• three-yearly modules,
• six-yearly modules,
• ad-hoc new policy needs modules,
• optional variables.

The variables collected are grouped by topic and detailed topic and transmitted to Eurostat in four main files (D-File, H-File, R-File and P-file).

The domain ‘Income and Living Conditions’ covers the following topics: persons at risk of poverty or social exclusion, income inequality, income distribution and monetary poverty, living conditions, material deprivation, and EU-SILC ad-hoc modules, which are structured into collections of indicators on specific topics.

In 2023, in addition to annual data, in EU-SILC were collected: the three yearly module on labour market and housing, the six yearly module on intergenerational transmission of advantages and disadvantages, housing difficulties, and the ad hoc subject on households energy efficiency.

Starting from 2021 onwards, the EU quality reports use the structure of the Single Integrated Metadata Structure (SIMS).

_{([1]) The European Semester is the European Union’s framework for the coordination and surveillance of economic and social policies.}

The Gini coefficient is defined as the relationship of cumulative shares of the population arranged according to the level of equivalised disposable income, to the cumulative share of the equivalised total disposable income received by them.

This statistic shows the inequality of income distribution in China from 2005 to 2023 based on the Gini Index. In 2023, China reached a score of ************ points. The Gini Index is a statistical measure that is used to represent unequal distributions, e.g. income distribution. It can take any value between 1 and 100 points (or 0 and 1). The closer the value is to 100 the greater is the inequality. 40 or 0.4 is the warning level set by the United Nations. The Gini Index for South Korea had ranged at about **** in 2022. Income distribution in China The Gini coefficient is used to measure the income inequality of a country. The United States, the World Bank, the US Central Intelligence Agency, and the Organization for Economic Co-operation and Development all provide their own measurement of the Gini coefficient, varying in data collection and survey methods. According to the United Nations Development Programme, countries with the largest income inequality based on the Gini index are mainly located in Africa and Latin America, with South Africa displaying the world's highest value in 2022. The world's most equal countries, on the contrary, are situated mostly in Europe. The United States' Gini for household income has increased by around ten percent since 1990, to **** in 2023. Development of inequality in China Growing inequality counts as one of the biggest social, economic, and political challenges to many countries, especially emerging markets. Over the last 20 years, China has become one of the world's largest economies. As parts of the society have become more and more affluent, the country's Gini coefficient has also grown sharply over the last decades. As shown by the graph at hand, China's Gini coefficient ranged at a level higher than the warning line for increasing risk of social unrest over the last decade. However, the situation has slightly improved since 2008, when the Gini coefficient had reached the highest value of recent times.

Out of the G20 countries, South Africa, Brazil, and Turkey have the highest levels of income inequality, while France, Canada, and Germany have the lowest levels of inequality. Other G20 countries in the middle have Gini coefficients between 32.5 and 44.0. The Gini coefficient measures the level of income inequality worldwide, where a higher score indicates a higher level of income inequality.

Germany - Inequality of income distribution was 4.49 in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Germany - Inequality of income distribution - last updated from the EUROSTAT on October of 2025. Historically, Germany - Inequality of income distribution reached a record high of 5.12 in December of 2014 and a record low of 4.30 in December of 2012.

This dataset uses the agricultural census of 1898 to reconstruct land inequality (using the measure of gini-coefficient) in the size of agricultural units for each electoral district at two different levels of analysis: the Reichstag constituency level, and the Prussian Chamber of Deputes’ constituency level. The analysis is based on data on the size and number of farms as reported in an agricultural census conducted at the Kreis level of over 5 million agricultural units (Kaiserliches Statistisches Amt. 1898. Statistik des Deutschen Reichs. Bd. 112. Berlin: Verlag des Königlich Preussichen Statistischen Bureaus, pp. 351-413 [Table 9])

Social and economic figures for 67 large West German cities. The data aggregated at city level have been collected for most topics over several years, but not necessarily over the entire reference time period.

Topics: 1. Situation of the city: surface area of the city; fringe location in the Federal Republic.

1. Residential population: total residential population; German and foreign residential population.

2. Population movement:live births; deaths; influx; departures; birth rate; death rate; population shifts; divorce rate; migration rate; illegitimate births.

3. Education figures: school degrees; occupational degrees; university degrees.

4. Wage and income: number of taxpayers in the various tax classes as well as municipality income tax revenue in the respective classes; calculated income figures, such as e.g. inequality of income distribution, mean income or mean wage of employees as well as standard deviation of these figures; GINI index.

5. Gross domestic product and gross product: gross product altogether; gross product organized according to area of business; gross domestic product; employees in the economic sectors.

6. Taxes and debts: debt per resident; income tax and business tax to which the municipality is entitled; municipality tax potential and indicators for municipality economic strength.

7. Debt repayment and management expenditures: debt repayment, interest expenditures, management expenditures and personnel expenditures.

8. From the ´BUNTE´ City Test of 1979 based on 100 respondents per city averages of satisfaction were calculated. satisfaction with: central location of the city, the number of green areas, historical buildings, the number of high-rises, the variety of the citizens, openness to the world, the dialect spoken, the sociability, the density of the traffic network, the OEPNV prices {local public passenger transport}, the supply of public transportation, provision with culture, the selection for consumers, the climate, clean air, noise pollution, the leisure selection, real estate prices, the supply of residences, one´s own payment, the job market selection, the distance from work, the number of one´s friends, contact opportunities, receptiveness of the neighbors, local recreational areas, sport opportunities and the selection of further education possibilities.

9. Traffic and economy: airport and Intercity connection; number of kilometers of subway available, kilometers of streetcar, and kilometers of bus lines per resident; car rate; index of traffic quality; commuters; property prices; prices for one´s own home; purchasing power.

10. Crime: recorded total crime and classification according to armed robbery, theft from living-rooms, of automobiles as well as from motor vehicles, robberies and purse snatching; classification according to young or adult suspects with these crimes; crime stress figures. 12. Welfare: welfare recipients and social expenditures; proportion of welfare recipients in the total population and classification according to German and foreign recipients; aid with livelihood; expenditures according to the youth welfare law; kindergarten openings; culture expenditures per resident. 13. Foreigners: proportion of foreigners in the residential population.

11. Students: number of German students and total number of students; proportion of students in the residential population.

12. Unemployed: unemployment rate; unemployed according to employment office districts and employment office departments.

13. Places of work: workers employed in companies, organized according to area of business.

14. Government employees: full-time, part-time and total government employees of federal government, states and municipalities as well as differentiated according to workers, employees, civil servants and judges.

15. Employees covered by social security according to education and branch of economy: proportion of various education levels in the individual branches of the economy.

Im Jahr 2024 lag der Gini-Index in Deutschland bei 29,5 Punkten und ist somit um 0,1 Punkte gestiegen. Der Durchschnitt in der Europäischen Union (EU-27) betrug 2024 geschätzt 29,3 Punkte (siehe auch EU-Länder nach Einkommensungleichheit im Gini-Index). br>Der Gini-Index oder Gini-Koeffizient ist ein statistisches Maß, das zur Darstellung von Ungleichverteilungen verwendet wird. Er kann einen beliebigen Wert zwischen 0 und 100 Punkten annehmen. Der Gini-Index zeigt die Abweichung der Verteilung des verfügbaren Einkommens auf Personen oder Haushalte innerhalb eines Landes von einer vollkommen gleichen Verteilung. Ein Wert von 0 bedeutet absolute Gleichheit, ein Wert von 100 absolute Ungleichheit.