In 2024, there were ******* German households with a household net income of under 500 euros per month. ***** households had a monthly income of 5,000 euros and more. Disposable net income While at first glance the aforementioned monthly income may seem manageable, based on general German standards of living, it is worth noting that flexibility and expenditure depends on the number of people living in a household, or rather the number of earners in relation to that number. In the case of employed population members, what remains as disposable net income is influenced by various regular payments made by households after the already taxed salary arrives. These payments include, but are not limited to, rent, different types of insurance, repaying loans, fees for internet and mobile phone services. Food and housing When looking at private household spending in Germany, consistent patterns emerge. Housing, water, electricity, gas and other fuel made up the largest share and will increase even further in the coming months, followed by food, beverages, and tobacco.

As of 2024, the average annual wage of Germany was 50,257 euros at current prices. This was a real increase of around 7,300 Euros (in constant 2024 prices) when compared with 2000. However, the wage increase in real terms was low between 2000 and 2009, and wage growth accelerated mainly in the period between 2009 and 2019. Comparisons with rest of the EU Within the European Union Luxembourg had an average annual salary of almost 80 thousand Euros, with Germany having an annual salary comparable to other large European Countries, such as the United Kingdom and France. In neighboring Poland, the average annual salary was just over 39 thousand U.S dollars, meaning that German’s earned, on average, 20 percent more than what their Polish counterparts did. German economy slowing in 2023 While Germany initially had one of the strongest recoveries from the 2008 financial crash and as of 2020 had the largest economy in Europe its economy has started to slow in recent years. For 2023 the German economy is contracted by 0.26 percent, and while 2024 marked a slight improvement, the expectations are that 2025 remains a year of slow growth.

The indicator is a measure of the inequality of income distribution. It is calculated as the ratio of total income received by the 20 % of the population with the highest income (the top quintile) to that received by the 20 % of the population with the lowest income (the bottom quintile).

The study is focused on the wagesand salaries proportions in the era before World War I. In respect thereof, a working hypothesis is: The proportion of wages and salaries as to the national income increases in the course of the economic progress because (and as long as) the proportion of the wage and salary earners compared to the total number of income recipients increases too; both proportions are higher in national economies with highly-developed industries as compared to less developed agricultural regions. The correlation between the income proportion generated from employed work and the national income on the one hand, and the weight of the employed part of the population within the body of socio-economic groups on the other, is explained in this study. Moreover, an empirical documentation is realised hereby. Factual classification of corresponding data tables in ZA-Database HISTAT:A. Growth and distribution of the national incomeA.1 Growth and distribution of the national income in Saxony (1874-1913)A.2 Growth and distribution of the national income in Baden (1885-1911)A.3 Growth and distribution of the national income in Württemberg (1904-1913)A.4 Nominal and real per capita income in Saxony (1874-1913)A.5 Nominal and real per capita income in Baden (1885-1911)A.6 Nominal and real per capita income in Württemberg (1904-1913) B. Per capita income and relative distributionB.1 Proportion of employed workforce (1875-1913)B.2 Per capita income and relative distribution in Baden (1885-1911)B.3 Per capita income and relative distribution in Saxony (1876-1913)B.4 Per capita income and relative distribution in Württemberg (1904-1913) C. Estimated income of physical persons per type of incomeC.1 Estimated income of physical persons per type of income in Saxony (1874-1913)C.2 Income of physical persons per type of income in Baden (1885-1911)C.3 Income of physical persons per type of income in Württemberg (1904-1913) D. Employment structure according to occupation censusD.1 Employment structure according to occupation census in Saxony (1875-1925)D.2 Employment structure according to occupation census in Baden (1882-1925)D.3 Employment structure according to occupation census in Württemberg (1882-1925) E.Distribution of income per categoryE.1 Development of the categorial distribution of income in Germany (1870-1913)E.2 Development of the national wage ratio (1870-1913)E.3 Development of the proportion of employed persons in the national economy (1870-1913)E.4 Development of the national per capita income of the employed workforce (1870-1913)E.5 Development of the national per capita income of self-employed persons (1870-1913)

The rising share of national income taken by the top one percent of earners is a common thread amongst almost all European countries over the past half century. As economic globalization took hold throughout the 1980s and 1990s, European countries experienced de-industrialization due to the emergence of international competitors, mostly in East Asia. At the same time, information technology and finance became much more important for most European economies, while growth in these sectors tends to favor high earners. This rise in inequality is also often also attributed to the ascendence of 'neoliberal' economic and political ideas which prioritized free markets and the privatization of government-owned businesses. Russia: the explosion of inequality after the fall of communismAmong the largest European economies, the Russian Federation stands out as the country which experienced the sharpest increase in inequality, as a small number of 'oligarchs' took control of the major industries after the collapse of the Soviet Union and the end of communist rule in 1991. The top one percent in Russia increased their share of national income five-fold over the 20 years from 1987 to 2007, when inequality in the country reached its peak as the oligarchs took home over a quarter of the country's income. Turkey: falling share of national income taken by top earners****** has bucked the trend of the rising income share for the richest over this period, as its extremely concentrated income distribution has in fact become somewhat more equitable. The highest earners in Turkey saw their share of national income drop from almost ** percent in the early *****, to a low of ** percent in 2007, after which it has stabilized between ** and ** percent. Western Europe: gradually rising share of national income for the richThe five western European democracies, Germany, France, Italy, Spain, and the United Kingdom, have all seen increases in their top earners' shares of national income over this period. The United Kingdom, Italy, and Germany have in particular seen their shares increase sharply, while Spain and France have experienced a more gradual increase.

Abstract

EU-SILC has become the EU reference source for comparative statistics on income distribution and social exclusion at European level, particularly in the context of the "Program of Community action to encourage cooperation between Member States to combat social exclusion" and for producing structural indicators on social cohesion for the annual spring report to the European Council. The first priority is to be given to the delivery of comparable, timely and high quality cross-sectional data.

Social exclusion and housing-condition information is collected at household level. Income at a detailed component level is collected at personal level, with some components included in the "Household" section. Labour, education and health observations only apply to persons 16 and older. EU-SILC was established to provide data on structural indicators of social cohesion (at-risk-of-poverty rate, S80/S20 and gender pay gap) and to provide relevant data for the two 'open methods of coordination' in the field of social inclusion and pensions in Europe.

EU-SILC produces two types of datasets: 1) Cross-sectional data pertaining to fixed time periods, with variables on income, poverty, social exclusion and living conditions. 2) Longitudinal data pertaining to individual-level changes over time, observed periodically - usually over four years.

The sixth revision of the 2007 Cross-Sectional User Database (UDB) as released by Eurostat in August 2011 is documented here.

Geographic coverage

National

Analysis unit

• Households;
• Individuals 16 years and older.

Universe

The survey covered all household members over 16 years old. Persons living in collective households and in institutions are generally excluded from the target population.

Kind of data

Sample survey data [ssd]

Sampling procedure

On the basis of various statistical and practical considerations and the precision requirements for the most critical variables, the minimum effective sample sizes to be achieved were defined. Sample size for the longitudinal component refers, for any pair of consecutive years, to the number of households successfully interviewed in the first year in which all or at least a majority of the household members aged 16 or over are successfully interviewed in both the years.

The cross-sectional sample sizes were calculated in order to achieve an effective size of 121,000 households at the European level (127,000 including Iceland and Norway). Then, the allocation among the countries aims to ensure a minimum precision for each of them.

Member States using registers for income and other data may use a sample of persons (selected respondents) rather than a sample of complete households in the interview survey. The minimum effective sample size in terms of the number of persons aged 16 or over to be interviewed in detail is in this case taken as 75 % of the figures shown in columns 3 and 4 of the table I, for the cross-sectional and longitudinal components respectively.

The reference is to the effective sample size, which is the size required if the survey were based on simple random sampling (design effect in relation to the 'risk of poverty rate' variable = 1.0). The actual sample sizes will have to be larger to the extent that the design effects exceed 1.0 and to compensate for all kinds of non-response. Furthermore, the sample size refers to the number of valid households which are households for which, and for all members of which, all or nearly all the required information has been obtained. For countries with a sample of persons design, information on income and other data shall be collected for the household of each selected respondent and for all its members.

At the beginning, a cross-sectional representative sample of households is selected. It is divided into say 4 sub-samples, each by itself representative of the whole population and similar in structure to the whole sample. One sub-sample is purely cross-sectional and is not followed up after the first round. Respondents in the second sub-sample are requested to participate in the panel for 2 years, in the third sub-sample for 3 years, and in the fourth for 4 years. From year 2 onwards, one new panel is introduced each year, with request for participation for 4 years. In any one year, the sample consists of 4 sub-samples, which together constitute the cross-sectional sample. In year 1 they are all new samples; in all subsequent years, only one is new sample. In year 2, three are panels in the second year; in year 3, one is a panel in the second year and two in the third year; in subsequent years, one is a panel for the second year, one for the third year, and one for the fourth (final) year.

According to the Commission Regulation on sampling and tracing rules, the selection of the sample will be drawn according to the following requirements:

1. For all components of EU-SILC (whether survey or register based), the crosssectional and longitudinal (initial sample) data shall be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. All private households and all persons aged 16 and over within the household are eligible for the operation.
2. Representative probability samples shall be achieved both for households, which form the basic units of sampling, data collection and data analysis, and for individual persons in the target population.
3. The sampling frame and methods of sample selection shall ensure that every individual and household in the target population is assigned a known and non-zero probability of selection.
4. By way of exception, paragraphs 1 to 3 shall apply in Germany exclusively to the part of the sample based on probability sampling according to Article 8 of the Regulation of the European Parliament and of the Council (EC) No 1177/2003 concerning

Community Statistics on Income and Living Conditions. Article 8 of the EU-SILC Regulation of the European Parliament and of the Council mentions: 1. The cross-sectional and longitudinal data shall be based on nationally representative probability samples. 2. By way of exception to paragraph 1, Germany shall supply cross-sectional data based on a nationally representative probability sample for the first time for the year 2008. For the year 2005, Germany shall supply data for one fourth based on probability sampling and for three fourths based on quota samples, the latter to be progressively replaced by random selection so as to achieve fully representative probability sampling by 2008. For the longitudinal component, Germany shall supply for the year 2006 one third of longitudinal data (data for year 2005 and 2006) based on probability sampling and two thirds based on quota samples. For the year 2007, half of the longitudinal data relating to years 2005, 2006 and 2007 shall be based on probability sampling and half on quota sample. After 2007 all of the longitudinal data shall be based on probability sampling.

Mode of data collection

Mixed

This dataset contains the financial records of 12 major German companies, including top players like Volkswagen AG, Siemens AG, Allianz SE, BMW AG, BASF SE, Deutsche Telekom AG, Daimler AG, SAP SE, Bayer AG, Deutsche Bank AG, Porsche AG, and Merck KGaA. Covering quarterly data from 2017 to 2024, this dataset is designed to provide insights into key financial metrics, allowing for indepth analysis and modeling of corporate financial health, performance, and growth trends. this comprehensive dataset is highly suitable for tasks such as financial forecasting, risk analysis, profitability assessment, and performance benchmarking. Each entry represents one quarter’s financial snapshot for a company, enabling robust time series and cross-sectional analyses.

Data Sources:

Company: Name of the company to which the financial data corresponds (e.g., "Volkswagen AG"). This field categorizes the data and enables cross-company comparisons and individual company trend analysis.

Period: The specific quarter (in year-month format) when the financial data was recorded (e.g., "2017-03-31" for Q1 of 2017). This field is crucial for time-series analysis, allowing users to track financial trends and performance over time.

Revenue: The total revenue of the company for that quarter, measured in billions of Euros. This field provides insight into the company’s sales performance and market reach within each period.

Net Income: The net income (profit after all expenses) of the company for the given quarter, also in billions of Euros. Net income is a key indicator of a company’s profitability and financial efficiency.

Liabilities: The total liabilities (debt and obligations) of the company for the quarter, in billions of Euros. This metric helps gauge the company’s financial leverage and debt exposure, essential for risk assessment.

Assets: The total assets (all owned resources with economic value) for the company in billions of Euros. This metric reflects the scale of the company’s holdings and resources available for operations and investments.

Equity: The shareholder equity calculated as Assets minus Liabilities, in billions of Euros. Equity indicates the residual value owned by shareholders and serves as a core metric for assessing financial stability and value creation.

ROA (%): Return on Assets (ROA), expressed as a percentage, calculated as (Net Income / Assets) * 100. ROA shows how efficiently a company is utilizing its assets to generate profit, an essential measure of operational effectiveness.

ROE (%): Return on Equity (ROE), expressed as a percentage, calculated as (Net Income / Equity) * 100. ROE is a key indicator of financial performance and profitability, reflecting the rate of return on shareholders' investment.

Debt to Equity: The ratio of Liabilities to Equity. This metric provides insights into the company’s capital structure and financial leverage, aiding in risk assessment by showing how much of the company’s operations are funded through debt compared to shareholder equity.

Time series data for the data Current Account and Its Components - Current USD, TTM for the country Germany. The Current Account and Its Components The current account is a component of a country's balance of payments that records the transactions of goods, services, income, and current transfers between residents of the country and the rest of the world. It consists of four main components:

a. Trade in Goods Balance

b. Trade in Services Balance

c. Primary Income Balance

d. Secondary Income Balance

1. Trade in Goods Balance Definition: This includes the export and import of physical items such as machinery, food, clothing, etc.

Credit Example: A German car manufacturer exports cars to the United States (value of exported cars).

Debit Example: A German electronics retailer imports smartphones from South Korea (value of imported smartphones).

1. Trade in Services Balance Definition: This includes the export and import of services such as tourism, financial services, consulting, transportation, etc.

Credit Example: A German IT company provides software development services to a client in Japan (value of exported services).

Debit Example: A German tourist books a hotel room in France (value of imported tourism services).

1. Primary Income Balance Definition: This includes earnings from the provision of factors of production such as labor, financial assets, land, and natural resources. It covers income from interest, profits, and dividends.

Credit Example: A German investor receives dividends from shares held in a U.S. company (value of received dividends).

Debit Example: Foreign investors receive interest payments on bonds issued by a German company (value of interest payments).

1. Secondary Income Balance Definition: This includes current transfers such as foreign aid, remittances, and other one-way payments that do not involve an exchange of goods or services.

Credit Example: Remittances sent by German residents working abroad to their families in Germany (value of received remittances).

Debit Example: Germany sends humanitarian aid to a developing country (value of sent aid). Trade in Services Balance (USD)The indicator "Trade in Services Balance (USD)" stands at -83.97 Billion United States Dollars as of 3/31/2025, the lowest value at least since 6/30/2001, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -13.36 Billion United States Dollars compared to the value the year prior.The 1 year change is -13.36 Billion United States Dollars.The 3 year change is -83.30 Billion United States Dollars.The 5 year change is -65.84 Billion United States Dollars.The 10 year change is -57.27 Billion United States Dollars.The Serie's long term average value is -35.61 Billion United States Dollars. It's latest available value, on 3/31/2025, is -48.36 Billion United States Dollars lower, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 3/31/2025, to it's latest available value, on 3/31/2025, is +0.0 Billion.The Serie's change in United States Dollars from it's maximum value, on 9/30/2021, to it's latest available value, on 3/31/2025, is -100.95 Billion.Trade in Goods Balance (USD)The indicator "Trade in Goods Balance (USD)" stands at 238.85 Billion United States Dollars as of 3/31/2025, the lowest value since 12/31/2023. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -23.39 Billion United States Dollars compared to the value the year prior.The 1 year change is -23.39 Billion United States Dollars.The 3 year change is 37.45 Billion United States Dollars.The 5 year change is 8.01 Billion United States Dollars.The 10 year change is -47.32 Billion United States Dollars.The Serie's long term average value is 219.05 Billion United States Dollars. It's latest available value, on 3/31/2025, is 19.79 Billion United States Dollars higher, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 3/31/2001, to it's latest available value, on 3/31/2025, is +176.43 Billion.The Serie's change in United States Dollars from it's maximum value, on 6/30/2018, to it's latest available value, on 3/31/2025, is -61.14 Billion.Secondary Income Balance (USD)The indicator "Secondary Income Balance (USD)" stands at -73.09 Billion United States Dollars as of 3/31/2025. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -0.9373 Billion United States Dollars compared to the value the year prior.The 1 year change is -0.9373 Billion United States Dollars.The 3 year change is -2.26 Billion United States Dollars.The 5 year change is -18.03 Billion United States Dollars.The 10 year change is -19.06 Billion United States Dollars.The Serie's long term average value is -46.89 Billion United States Dollars. It's ...

Abstract

In 2011, the EU-SILC instrument covered all EU Member States plus Iceland, Turkey, Norway, Switzerland and Croatia. EU-SILC has become the EU reference source for comparative statistics on income distribution and social exclusion at European level, particularly in the context of the "Program of Community action to encourage cooperation between Member States to combat social exclusion" and for producing structural indicators on social cohesion for the annual spring report to the European Council. The first priority is to be given to the delivery of comparable, timely and high quality cross-sectional data.

There are two types of datasets: 1) Cross-sectional data pertaining to fixed time periods, with variables on income, poverty, social exclusion and living conditions. 2) Longitudinal data pertaining to individual-level changes over time, observed periodically - usually over four years.

Social exclusion and housing-condition information is collected at household level. Income at a detailed component level is collected at personal level, with some components included in the "Household" section. Labor, education and health observations only apply to persons aged 16 and over. EU-SILC was established to provide data on structural indicators of social cohesion (at-risk-of-poverty rate, S80/S20 and gender pay gap) and to provide relevant data for the two 'open methods of coordination' in the field of social inclusion and pensions in Europe.

The 3rd revision of the 2011 Cross-Sectional User Database as released in September 2014 is documented here.

Geographic coverage

The survey covers following countries: Austria; Belgium; Bulgaria; Croatia; Cyprus; Czech Republic; Denmark; Estonia; Finland; France; Germany; Greece; Spain; Ireland; Italy; Latvia; Lithuania; Luxembourg; Hungary; Malta; Netherlands; Poland; Portugal; Romania; Slovenia; Slovakia; Sweden; United Kingdom; Iceland; Norway; Turkey; Switzerland

Small parts of the national territory amounting to no more than 2% of the national population and the national territories listed below may be excluded from EU-SILC: France - French Overseas Departments and territories; Netherlands - The West Frisian Islands with the exception of Texel; Ireland - All offshore islands with the exception of Achill, Bull, Cruit, Gorumna, Inishnee, Lettermore, Lettermullan and Valentia; United Kingdom - Scotland north of the Caledonian Canal, the Scilly Islands.

Analysis unit

• Households;
• Individuals 16 years and older.

Universe

The survey covered all household members over 16 years old. Persons living in collective households and in institutions are generally excluded from the target population.

Kind of data

Sample survey data [ssd]

Sampling procedure

On the basis of various statistical and practical considerations and the precision requirements for the most critical variables, the minimum effective sample sizes to be achieved were defined. Sample size for the longitudinal component refers, for any pair of consecutive years, to the number of households successfully interviewed in the first year in which all or at least a majority of the household members aged 16 or over are successfully interviewed in both the years.

For the cross-sectional component, the plans are to achieve the minimum effective sample size of around 131.000 households in the EU as a whole (137.000 including Iceland and Norway). The allocation of the EU sample among countries represents a compromise between two objectives: the production of results at the level of individual countries, and production for the EU as a whole. Requirements for the longitudinal data will be less important. For this component, an effective sample size of around 98.000 households (103.000 including Iceland and Norway) is planned.

Member States using registers for income and other data may use a sample of persons (selected respondents) rather than a sample of complete households in the interview survey. The minimum effective sample size in terms of the number of persons aged 16 or over to be interviewed in detail is in this case taken as 75 % of the figures shown in columns 3 and 4 of the table I, for the cross-sectional and longitudinal components respectively.

The reference is to the effective sample size, which is the size required if the survey were based on simple random sampling (design effect in relation to the 'risk of poverty rate' variable = 1.0). The actual sample sizes will have to be larger to the extent that the design effects exceed 1.0 and to compensate for all kinds of non-response. Furthermore, the sample size refers to the number of valid households which are households for which, and for all members of which, all or nearly all the required information has been obtained. For countries with a sample of persons design, information on income and other data shall be collected for the household of each selected respondent and for all its members.

At the beginning, a cross-sectional representative sample of households is selected. It is divided into say 4 sub-samples, each by itself representative of the whole population and similar in structure to the whole sample. One sub-sample is purely cross-sectional and is not followed up after the first round. Respondents in the second sub-sample are requested to participate in the panel for 2 years, in the third sub-sample for 3 years, and in the fourth for 4 years. From year 2 onwards, one new panel is introduced each year, with request for participation for 4 years. In any one year, the sample consists of 4 sub-samples, which together constitute the cross-sectional sample. In year 1 they are all new samples; in all subsequent years, only one is new sample. In year 2, three are panels in the second year; in year 3, one is a panel in the second year and two in the third year; in subsequent years, one is a panel for the second year, one for the third year, and one for the fourth (final) year.

According to the Commission Regulation on sampling and tracing rules, the selection of the sample will be drawn according to the following requirements:

1. For all components of EU-SILC (whether survey or register based), the crosssectional and longitudinal (initial sample) data shall be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. All private households and all persons aged 16 and over within the household are eligible for the operation.
2. Representative probability samples shall be achieved both for households, which form the basic units of sampling, data collection and data analysis, and for individual persons in the target population.
3. The sampling frame and methods of sample selection shall ensure that every individual and household in the target population is assigned a known and non-zero probability of selection.
4. By way of exception, paragraphs 1 to 3 shall apply in Germany exclusively to the part of the sample based on probability sampling according to Article 8 of the Regulation of the European Parliament and of the Council (EC) No 1177/2003 concerning

Community Statistics on Income and Living Conditions. Article 8 of the EU-SILC Regulation of the European Parliament and of the Council mentions: 1. The cross-sectional and longitudinal data shall be based on nationally representative probability samples. 2. By way of exception to paragraph 1, Germany shall supply cross-sectional data based on a nationally representative probability sample for the first time for the year 2008. For the year 2005, Germany shall supply data for one fourth based on probability sampling and for three fourths based on quota samples, the latter to be progressively replaced by random selection so as to achieve fully representative probability sampling by 2008. For the longitudinal component, Germany shall supply for the year 2006 one third of longitudinal data (data for year 2005 and 2006) based on probability sampling and two thirds based on quota samples. For the year 2007, half of the longitudinal data relating to years 2005, 2006 and 2007 shall be based on probability sampling and half on quota sample. After 2007 all of the longitudinal data shall be based on probability sampling.

Detailed information about sampling is available in Quality Reports in Documentation.

Mode of data collection

Mixed

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.

Part A. Heinz Müller: The income structure in several German states, 1874-1913. The entire material of this study was developed by the institute for regional politics and transportation science at the University of Freiburg. The first part of the study deals with an analysis of the income structure (=personnel income distribution) in chosen German states for the period from 1873 to 1913. Analysis of income structures can be planned and realized with different aims and with the use of different methods. The following structuring of income recipients is most commonly used: a) By sources of income b) By the income of sociologically important recipient groups c) By income level The structuring by income sources addresses a registration of the functional income distribution. This is instructive but difficult to carry out. For example it is hardly possible to subdivide the income resulting from entrepreneurial activity in the structural components as this term includes employer´s salary, basic pension for entrepreneurs, enterprise interests and entrepreneurial profit. Even theoretically it is difficult to subdivide income resulting from entrepreneurial activity in these components but in the practical implementation this division faces insurmountable difficulties. On the other hand such a structuring is an important perquisite for a proper analysis of income sources especially in the area of agriculture. Another criteria used for classification is the division by sociologically important recipient groups. The aim of such an analysis could be to estimate the share of specific groups of persons in the national income and the changes that are taking place in the process of economic development. Alternatively such an analysis can be based on a division in economic sectors; one could estimate for example the share of agriculture or services in the national income and its changes over time. Also this procedure allows interesting conclusions on social and economic development of the national economy. The third and particularly important criterion consists in the division by income level. This type of investigation serves to generate important findings on the social and economic situation and development of different income groups. Development of wealth within a national economy can be assessed looking at the economic situation of the lower income classes in relation to the higher classes and on how fast one class integrates into another. These three different types of structuring of income recipients can be combined with each other. Doings so one can generate more insights on the development of the industrialization process co pared to using only one classification type (Müller, Heinz/Geisenberger, Siegfried, 1972: Die Einkommensstruktur in verschiedenen deutschen Ländern 1874-1913. Berlin: Duncker & Humblot, S. 13f). The first part of the study exclusively deals with the investigation of the income size structure. These are the summarized results for the investigation on the temporal development of distribution coefficients:

(1) Differentiated according to the different states The share of the very highest incomes in the total income is increasing in all states (Besides Hesse) during the investigation period.

(2) Differentiated according to the surveyed areas According to the distribution coefficient and its development over time one can say that developments differ a lot between rural and industrial areas.

Part B. Siegfried Geisenberger: Important determinants for changes in the income structure. An attempt of an economic interpretation of the development in Prussia 1874-1913. The results from the first part of the investigation gave the impulse for further investigations at the institute of regional politics and transportation science (University of Freiburg). They wanted to investigate the development of the distribution situation for further regions and to control for different income tax laws in several states. The typical differences in the development of income distribution between rural and industrial areas could also be detected for the Prussian governmental districts. The second part of the investigation aims to explain this phenomenon using theoretical economic and statistical instruments.

Register of tables in HISTAT: A. Data on income structure in Prussia and in chosen Prussian governmental districts A.1 Data on income structure in Prussia (1874-1913) A.2 Data on income structure in Prussia, governme...

The share of total national wealth owned by the top one percent of wealthy people in most major European economies rose over the period from 1995 to 2023. The growth from 21.5 percent in 1995 to 48.6 percent share in Russia is particularly striking, as the poweful 'oligarchs' at the top of Russian society increased their share of that country's national wealth from less than a fifth in 1995, to almost half in 2023.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in German town. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In German town, the median income for all workers aged 15 years and older, regardless of work hours, was $41,000 for males and $36,500 for females.

Based on these incomes, we observe a gender gap percentage of approximately 11%, indicating a significant disparity between the median incomes of males and females in German town. Women, regardless of work hours, still earn 89 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

- Full-time workers, aged 15 years and older: In German town, among full-time, year-round workers aged 15 years and older, males earned a median income of $53,333, while females earned $52,917, resulting in a 1% gender pay gap among full-time workers. This illustrates that women earn 99 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the town of German town.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in German town, showcasing a consistent income pattern irrespective of employment status.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for German town median household income by race. You can refer the same here

The statistic displays the share of wealth that the richest one percent of the population owned in selected European countries as of 2014. The wealth concentration was the highest in Austria and Germany, where the wealthiest one percent of households owned 40 and 35 percent of country's total private wealth, respectively.

This statistic illustrates the number of millionaire (HNWI, UHNWI) and billionaire individuals in Germany in selected years from 2014 to 2019, with a forecast for 2024, by wealth bracket. The high, ultra-high and billionaire's population are set to see continued growth. By 2024, the number of billionaires in Germany is set to reach ***.

The number of one-person households in Germany has been increasing in the last decade, while the opposite was true for three-person homes. In 2024, around ** million German households had one occupant, while roughly **** million households had three people living in them. Aging population These trends may be rooted in various reasons, such as population developments, aging, urbanization, individual lifestyles, flexible living arrangements. When looking at the growing number of one-person households, depending on the age group, this increase may be due to being single, for example, as well as an older person living alone. The ************* of the German population was aged 40 to 59 years, followed by those aged 65 and older. In terms of housing situations, **** were renting. Residential building construction in Germany struggled somewhat in recent years. Decreasing household member numbers It is not just in Germany that households are decreasing in size. A similar trend has been seen in the United States. Household size is often very dependent on the financial status of individuals. Those with more money will often opt to live alone, whilst those on a lower income may have no choice but to have roommates or to continue living with their family.

Time series data for the data Current Account and Its Components - Current USD, TTM for the country Netherlands. The Current Account and Its Components The current account is a component of a country's balance of payments that records the transactions of goods, services, income, and current transfers between residents of the country and the rest of the world. It consists of four main components:

a. Trade in Goods Balance

b. Trade in Services Balance

c. Primary Income Balance

d. Secondary Income Balance

1. Trade in Goods Balance Definition: This includes the export and import of physical items such as machinery, food, clothing, etc.

Credit Example: A German car manufacturer exports cars to the United States (value of exported cars).

Debit Example: A German electronics retailer imports smartphones from South Korea (value of imported smartphones).

1. Trade in Services Balance Definition: This includes the export and import of services such as tourism, financial services, consulting, transportation, etc.

Credit Example: A German IT company provides software development services to a client in Japan (value of exported services).

Debit Example: A German tourist books a hotel room in France (value of imported tourism services).

1. Primary Income Balance Definition: This includes earnings from the provision of factors of production such as labor, financial assets, land, and natural resources. It covers income from interest, profits, and dividends.

Credit Example: A German investor receives dividends from shares held in a U.S. company (value of received dividends).

Debit Example: Foreign investors receive interest payments on bonds issued by a German company (value of interest payments).

1. Secondary Income Balance Definition: This includes current transfers such as foreign aid, remittances, and other one-way payments that do not involve an exchange of goods or services.

Credit Example: Remittances sent by German residents working abroad to their families in Germany (value of received remittances).

Debit Example: Germany sends humanitarian aid to a developing country (value of sent aid). Trade in Goods Balance (USD)The indicator "Trade in Goods Balance (USD)" stands at 90.28 Billion United States Dollars as of 3/31/2025, the highest value since 12/31/2018. Regarding the One-Year-Change of the series, the current value constitutes an increase of 8.80 Billion United States Dollars compared to the value the year prior.The 1 year change is 8.80 Billion United States Dollars.The 3 year change is 12.99 Billion United States Dollars.The 5 year change is 20.34 Billion United States Dollars.The 10 year change is 4.73 Billion United States Dollars.The Serie's long term average value is 75.63 Billion United States Dollars. It's latest available value, on 3/31/2025, is 14.65 Billion United States Dollars higher, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 3/31/2023, to it's latest available value, on 3/31/2025, is +39.84 Billion.The Serie's change in United States Dollars from it's maximum value, on 9/30/2008, to it's latest available value, on 3/31/2025, is -2.22 Billion.Secondary Income Balance (USD)The indicator "Secondary Income Balance (USD)" stands at -12.88 Billion United States Dollars as of 3/31/2025, the lowest value since 12/31/2021. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -3.23 Billion United States Dollars compared to the value the year prior.The 1 year change is -3.23 Billion United States Dollars.The 3 year change is -2.71 Billion United States Dollars.The 5 year change is -0.2169 Billion United States Dollars.The 10 year change is 0.5401 Billion United States Dollars.The Serie's long term average value is -11.65 Billion United States Dollars. It's latest available value, on 3/31/2025, is -1.23 Billion United States Dollars lower, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 6/30/2008, to it's latest available value, on 3/31/2025, is +3.66 Billion.The Serie's change in United States Dollars from it's maximum value, on 9/30/2017, to it's latest available value, on 3/31/2025, is -7.87 Billion.Primary Income Balance (USD)The indicator "Primary Income Balance (USD)" stands at -14.63 Billion United States Dollars as of 3/31/2025, the lowest value since 3/31/2021. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -24.37 Billion United States Dollars compared to the value the year prior.The 1 year change is -24.37 Billion United States Dollars.The 3 year change is -21.84 Billion United States Dollars.The 5 year change is 9.62 Billion United States Dollars.The 10 year change is -6.40 Billion United States Dollars.The Serie's long term average value is -6.37 Billion United States Dollars. It's latest available value, ...

It is estimated that, in 1933, just 0.77 percent of the German population was Jewish. Despite this, Nazi leaders and propaganda perpetually claimed that Jews owned up to 20 percent of all capital in the German economy, and used claims such as this to demonize Jews and turn the rest of German society against the Jewish community. Official estimates from the national statistical office or central bank in the 1930s, as well as some modern estimates, also suggest that Jewish wealth may have been equal to as much as 20 percent of national wealth; however, this is misrepresentative.

According to a 2019 paper by Albrecht Ritschl of the London School of Economics, the share of Jewish-owned assets in the private sector alone was actually much lower. Ritschl uses a range of estimates (two potential figures for the German total, and three potential figures for the Jewish total) to show that the Jewish share of capital in the private sector in 1937 was likely somewhere between 0.96 and 1.57 percent. The author claims that the middle estimates, where Jewish assets are valued at 3.54bn RM, is likely the most plausible. It is also estimated that the combined capital in the public and private sector was around 400 billion RM, and if one uses the estimate of Jewish assets being valued at 2.99 billion, then this is equal to a 0.75 percent, which is almost the exact same as their population share in 1933. The paper then concludes that the share of Jewish assets in the German economy was much more in line with their population size than the bogus claims made by Nazi leaders, propaganda, and the German media.

In 2025, Luxembourg was the country with the highest gross domestic product per capita in the world. Of the 20 listed countries, 13 are in Europe and five are in Asia, alongside the U.S. and Australia. There are no African or Latin American countries among the top 20. Correlation with high living standards While GDP is a useful indicator for measuring the size or strength of an economy, GDP per capita is much more reflective of living standards. For example, when compared to life expectancy or indices such as the Human Development Index or the World Happiness Report, there is a strong overlap - 14 of the 20 countries on this list are also ranked among the 20 happiest countries in 2024, and all 20 have "very high" HDIs. Misleading metrics? GDP per capita figures, however, can be misleading, and to paint a fuller picture of a country's living standards then one must look at multiple metrics. GDP per capita figures can be skewed by inequalities in wealth distribution, and in countries such as those in the Middle East, a relatively large share of the population lives in poverty while a smaller number live affluent lifestyles.

Abstract

In 2010, the EU-SILC instrument covered 32 countries, that is, all EU Member States plus Iceland, Turkey, Norway, Switzerland and Croatia. EU-SILC has become the EU reference source for comparative statistics on income distribution and social exclusion at European level, particularly in the context of the "Program of Community action to encourage cooperation between Member States to combat social exclusion" and for producing structural indicators on social cohesion for the annual spring report to the European Council. The first priority is to be given to the delivery of comparable, timely and high quality cross-sectional data.

There are two types of datasets: 1) Cross-sectional data pertaining to fixed time periods, with variables on income, poverty, social exclusion and living conditions. 2) Longitudinal data pertaining to individual-level changes over time, observed periodically - usually over four years.

Social exclusion and housing-condition information is collected at household level. Income at a detailed component level is collected at personal level, with some components included in the "Household" section. Labor, education and health observations only apply to persons aged 16 and over. EU-SILC was established to provide data on structural indicators of social cohesion (at-risk-of-poverty rate, S80/S20 and gender pay gap) and to provide relevant data for the two 'open methods of coordination' in the field of social inclusion and pensions in Europe.

The 6th version of the 2010 Cross-Sectional User Database as released in July 2015 is documented here.

Geographic coverage

The survey covers following countries: Austria; Belgium; Bulgaria; Croatia; Cyprus; Czech Republic; Denmark; Estonia; Finland; France; Germany; Greece; Spain; Ireland; Italy; Latvia; Lithuania; Luxembourg; Hungary; Malta; Netherlands; Poland; Portugal; Romania; Slovenia; Slovakia; Sweden; United Kingdom; Iceland; Norway; Turkey; Switzerland

Small parts of the national territory amounting to no more than 2% of the national population and the national territories listed below may be excluded from EU-SILC: France - French Overseas Departments and territories; Netherlands - The West Frisian Islands with the exception of Texel; Ireland - All offshore islands with the exception of Achill, Bull, Cruit, Gorumna, Inishnee, Lettermore, Lettermullan and Valentia; United kingdom - Scotland north of the Caledonian Canal, the Scilly Islands.

Analysis unit

• Households;
• Individuals 16 years and older.

Universe

The survey covered all household members over 16 years old. Persons living in collective households and in institutions are generally excluded from the target population.

Kind of data

Sample survey data [ssd]

Sampling procedure

On the basis of various statistical and practical considerations and the precision requirements for the most critical variables, the minimum effective sample sizes to be achieved were defined. Sample size for the longitudinal component refers, for any pair of consecutive years, to the number of households successfully interviewed in the first year in which all or at least a majority of the household members aged 16 or over are successfully interviewed in both the years.

For the cross-sectional component, the plans are to achieve the minimum effective sample size of around 131.000 households in the EU as a whole (137.000 including Iceland and Norway). The allocation of the EU sample among countries represents a compromise between two objectives: the production of results at the level of individual countries, and production for the EU as a whole. Requirements for the longitudinal data will be less important. For this component, an effective sample size of around 98.000 households (103.000 including Iceland and Norway) is planned.

Member States using registers for income and other data may use a sample of persons (selected respondents) rather than a sample of complete households in the interview survey. The minimum effective sample size in terms of the number of persons aged 16 or over to be interviewed in detail is in this case taken as 75 % of the figures shown in columns 3 and 4 of the table I, for the cross-sectional and longitudinal components respectively.

The reference is to the effective sample size, which is the size required if the survey were based on simple random sampling (design effect in relation to the 'risk of poverty rate' variable = 1.0). The actual sample sizes will have to be larger to the extent that the design effects exceed 1.0 and to compensate for all kinds of non-response. Furthermore, the sample size refers to the number of valid households which are households for which, and for all members of which, all or nearly all the required information has been obtained. For countries with a sample of persons design, information on income and other data shall be collected for the household of each selected respondent and for all its members.

At the beginning, a cross-sectional representative sample of households is selected. It is divided into say 4 sub-samples, each by itself representative of the whole population and similar in structure to the whole sample. One sub-sample is purely cross-sectional and is not followed up after the first round. Respondents in the second sub-sample are requested to participate in the panel for 2 years, in the third sub-sample for 3 years, and in the fourth for 4 years. From year 2 onwards, one new panel is introduced each year, with request for participation for 4 years. In any one year, the sample consists of 4 sub-samples, which together constitute the cross-sectional sample. In year 1 they are all new samples; in all subsequent years, only one is new sample. In year 2, three are panels in the second year; in year 3, one is a panel in the second year and two in the third year; in subsequent years, one is a panel for the second year, one for the third year, and one for the fourth (final) year.

According to the Commission Regulation on sampling and tracing rules, the selection of the sample will be drawn according to the following requirements:

1. For all components of EU-SILC (whether survey or register based), the crosssectional and longitudinal (initial sample) data shall be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. All private households and all persons aged 16 and over within the household are eligible for the operation.
2. Representative probability samples shall be achieved both for households, which form the basic units of sampling, data collection and data analysis, and for individual persons in the target population.
3. The sampling frame and methods of sample selection shall ensure that every individual and household in the target population is assigned a known and non-zero probability of selection.
4. By way of exception, paragraphs 1 to 3 shall apply in Germany exclusively to the part of the sample based on probability sampling according to Article 8 of the Regulation of the European Parliament and of the Council (EC) No 1177/2003 concerning

Community Statistics on Income and Living Conditions. Article 8 of the EU-SILC Regulation of the European Parliament and of the Council mentions: 1. The cross-sectional and longitudinal data shall be based on nationally representative probability samples. 2. By way of exception to paragraph 1, Germany shall supply cross-sectional data based on a nationally representative probability sample for the first time for the year 2008. For the year 2005, Germany shall supply data for one fourth based on probability sampling and for three fourths based on quota samples, the latter to be progressively replaced by random selection so as to achieve fully representative probability sampling by 2008. For the longitudinal component, Germany shall supply for the year 2006 one third of longitudinal data (data for year 2005 and 2006) based on probability sampling and two thirds based on quota samples. For the year 2007, half of the longitudinal data relating to years 2005, 2006 and 2007 shall be based on probability sampling and half on quota sample. After 2007 all of the longitudinal data shall be based on probability sampling.

Detailed information about sampling is available in Quality Reports in Related Materials.

Mode of data collection

Mixed