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.

Graph and download economic data for All Employees: Government: State Government in San German, PR (MSA) (SMU72419009092000001SA) from Jan 2003 to Dec 2024 about San Germán, Puerto Rico, state govt, government, employment, and USA.

In late May 1939, just three months before the Second World War began in Europe, Germany's workforce was made up of almost 25 million men, 15 million women, and a very small number of foreign workers. The share of German men in the workforce decreased each year thereafter, as more were conscripted into the armed forces, and there were approximately 11 million fewer German male citizens in the workforce by September 1944. The number of German women fluctuated, but remained between 14 and 15 million throughout the given period, and it exceeded the number of German men in 1944. Despite the number of German men in the workforce dropping by 45 percent, the total number of workers in German was consistently around 36 million between 1940 and 1944, as this difference was offset by foreign and forced laborers. These workers were mostly drafted from annexed territories in Eastern Europe, and prisoners were transferred from concentration and POW camps to meet the labor demands in various areas of Germany.

Germany DE: Expenditure: Compensation of Employees: % of Expenditure data was reported at 5.240 % in 2022. This records an increase from the previous number of 5.213 % for 2021. Germany DE: Expenditure: Compensation of Employees: % of Expenditure data is updated yearly, averaging 5.826 % from Dec 1972 (Median) to 2022, with 51 observations. The data reached an all-time high of 12.018 % in 1972 and a record low of 4.884 % in 1995. Germany DE: Expenditure: Compensation of Employees: % of Expenditure 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: Government Revenue, Expenditure and Finance. Compensation of employees consists of all payments in cash, as well as in kind (such as food and housing), to employees in return for services rendered, and government contributions to social insurance schemes such as social security and pensions that provide benefits to employees.;International Monetary Fund, Government Finance Statistics Yearbook and data files.;Median;

Public sector employees: Germany, Reference date,Employment area, Employment relationship,Employment volume

National Accounts of the Federal Government - Employment, Wages and Salaries,Hours worked: Germany, years, economic sectors

Public sector employees: Germany, cut-off date,Employment sector

Germany DE: General Government: Expense: Compensation of Employees data was reported at 337,571.000 EUR mn in 2023. This records an increase from the previous number of 320,671.000 EUR mn for 2022. Germany DE: General Government: Expense: Compensation of Employees data is updated yearly, averaging 225,451.000 EUR mn from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 337,571.000 EUR mn in 2023 and a record low of 189,950.000 EUR mn in 2002. Germany DE: General Government: Expense: Compensation of Employees data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Germany – Table DE.IMF.IFS: Government Finance: Operations Statement: Annual.

All Employees: Government: State Government in San German, PR (MSA) was 2.00000 Thous. of Persons in December of 2024, according to the United States Federal Reserve. Historically, All Employees: Government: State Government in San German, PR (MSA) reached a record high of 2.30000 in August of 2022 and a record low of 2.00000 in February of 2022. Trading Economics provides the current actual value, an historical data chart and related indicators for All Employees: Government: State Government in San German, PR (MSA) - last updated from the United States Federal Reserve on July of 2025.

Germany DE: Total R&D Personnel: Per Thousand Total Employment data was reported at 17.207 Per 1000 in 2022. This records an increase from the previous number of 16.760 Per 1000 for 2021. Germany DE: Total R&D Personnel: Per Thousand Total Employment data is updated yearly, averaging 13.326 Per 1000 from Dec 1981 (Median) to 2022, with 38 observations. The data reached an all-time high of 17.207 Per 1000 in 2022 and a record low of 11.921 Per 1000 in 1996. Germany DE: Total R&D Personnel: Per Thousand Total Employment data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Germany – Table DE.OECD.MSTI: Number of Researchers and Personnel on Research and Development: OECD Member: Annual.

The data in this publication for Germany cover unified Germany from 1991 and western Germany only until 1990.

Between 1991 and 2021, the data for the PNP sector were included in the Government sector. In 2016, the method for calculating R&D coefficients was revised, introducing a break in series in the Higher Education sector. In particular, coefficients are thereafter based on time-use surveys.

From reference year 2014, the distribution of R&D personnel by occupation is requested in the government survey whereas it was previously estimated from data by qualification.

The method for calculating public-financed R&D in the business enterprise sector was reviewed, resulting in the revision of business enterprise R&D and the national total back to 1991.

In 1992 the methodology of the survey on resources devoted to R&D in the Government sector was changed.

For 1997, the methodology for allocating GBARD by socio-economic objective changed. For 1997 and from 2001 to 2015, the global budget reduction was not distributed proportionally across SEO by the Federal Ministry of Education and Research. Therefore, the sum of the breakdown for those years does not add to the total. From 2016 onwards the global reduction is distributed across SEO proportionally.

Germany DE: Expenditure: Compensation of Employees data was reported at 65,175.085 EUR mn in 2022. This records an increase from the previous number of 62,710.462 EUR mn for 2021. Germany DE: Expenditure: Compensation of Employees data is updated yearly, averaging 36,298.767 EUR mn from Dec 1972 (Median) to 2022, with 51 observations. The data reached an all-time high of 65,175.085 EUR mn in 2022 and a record low of 11,846.633 EUR mn in 1972. Germany DE: Expenditure: Compensation of Employees 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: Government Revenue, Expenditure and Finance. Compensation of employees consists of all payments in cash, as well as in kind (such as food and housing), to employees in return for services rendered, and government contributions to social insurance schemes such as social security and pensions that provide benefits to employees.;International Monetary Fund, Government Finance Statistics Yearbook and data files.;;

All Employees: Government in San German, PR (MSA) was 4.10000 Thous. of Persons in December of 2024, according to the United States Federal Reserve. Historically, All Employees: Government in San German, PR (MSA) reached a record high of 4.50000 in December of 2022 and a record low of 4.10000 in February of 2022. Trading Economics provides the current actual value, an historical data chart and related indicators for All Employees: Government in San German, PR (MSA) - last updated from the United States Federal Reserve on July of 2025.

There were around ** percent of female apprentices working in the German public sector as of 2022. Figures had increased compared to the 1990s and fluctuated only slightly during the last decade. Overall, female apprentices in Germany still make up the lower share.

Germany DE: Total Employment data was reported at 45,932.000 Person th in 2023. This records an increase from the previous number of 45,596.000 Person th for 2022. Germany DE: Total Employment data is updated yearly, averaging 39,362.000 Person th from Dec 1981 (Median) to 2023, with 43 observations. The data reached an all-time high of 45,932.000 Person th in 2023 and a record low of 26,993.000 Person th in 1983. Germany DE: Total Employment data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Germany – Table DE.OECD.MSTI: Population, Labour Force and Employment: OECD Member: Annual.

The data in this publication for Germany cover unified Germany from 1991 and western Germany only until 1990.

Between 1991 and 2021, the data for the PNP sector were included in the Government sector. In 2016, the method for calculating R&D coefficients was revised, introducing a break in series in the Higher Education sector. In particular, coefficients are thereafter based on time-use surveys.

From reference year 2014, the distribution of R&D personnel by occupation is requested in the government survey whereas it was previously estimated from data by qualification.

The method for calculating public-financed R&D in the business enterprise sector was reviewed, resulting in the revision of business enterprise R&D and the national total back to 1991.

In 1992 the methodology of the survey on resources devoted to R&D in the Government sector was changed.

For 1997, the methodology for allocating GBARD by socio-economic objective changed. For 1997 and from 2001 to 2015, the global budget reduction was not distributed proportionally across SEO by the Federal Ministry of Education and Research. Therefore, the sum of the breakdown for those years does not add to the total. From 2016 onwards the global reduction is distributed across SEO proportionally.

Germany Public Sector: Compensation of Employees data was reported at 3,308.500 EUR mn in 2019. This records an increase from the previous number of 3,073.300 EUR mn for 2018. Germany Public Sector: Compensation of Employees data is updated yearly, averaging 2,726.000 EUR mn from Dec 2010 (Median) to 2019, with 10 observations. The data reached an all-time high of 3,308.500 EUR mn in 2019 and a record low of 2,357.000 EUR mn in 2010. Germany Public Sector: Compensation of Employees data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Germany – Table DE.OECD.ESG: Environmental: Environmental Protection Expenditure: by Sector: OECD Member: Annual.

The aim of the special survey of the GESIS panel on the outbreak of the corona virus SARS-CoV-2 in Germany was to collect timely data on the effects of the corona crisis on people´s daily lives. The study focused on questions of risk perception, risk minimization measures, evaluation of political measures and their compliance, trust in politics and institutions, changed employment situation, childcare obligations, and media consumption. Due to the need for timely data collection, only the GESIS panel sub-sample of online respondents was invited (about three quarters of the sample). Since, due to time constraints, respondents could only participate in the online survey but not by mail, the results cannot be easily transferred to the overall population. Further longitudinal surveys on Covid-19 with the entire sample of the GESIS panel are planned for 2020.

Topics: Risk perception: Probability of events related to corona infection in the next two months (self, infection of a person from close social surrondings, hospital treatment, quarantine measures regardless of whether infected or not, infecting other people)

Risk minimization: risk minimization measures taken in the last seven days (avoided certain (busy) places, kept minimum distance to other people, adapted school or work situation, quarantine due to symptoms or without symptoms, washed hands more often, used disinfectant, stocks increased, reduced social interactions, worn face mask, other, none of these measures).

Evaluation of the effectiveness of various policy measures to combat the further spread of corona virus (closure of day-care centres, kindergartens and schools, closure of sports facilities, closure of bars, cafés and restaurants, closure of all shops except supermarkets and pharmacies, ban on visiting hospitals, nursing homes and old people´s homes, curfew for persons aged 70 and over or people with health problems or for anyone not working in the health sector or other critical professions (except for basic purchases and urgent medical care).

Curfew compliance or refusal: Willingness to obey a curfew vs. refusal; reasons for the compliance with curfew (social duty, fear of punishment, protection against infection, fear of infecting others (loved ones, infecting others in general, a risk group); reasons for refusal of curfew (restrictions too drastic or not justified, other obligations, does not stop the spread, not affected by the outbreak, boring at home, will not be punished).

Evaluation of the effectiveness of various government measures (medical care, restrictions on social life such as closure of public facilities and businesses, reduction of economic damage, communication with the population).

Trust in politics and institutions with regard to dealing with the coronavirus (physician, local health authority, local and municipal administration, Robert Koch Institute (RKI), Federal Government, German Chancellor, Ministry of Health, World Health Organization (WHO), scientists).

Changed employment situation: employment status at the beginning of March; change in occupational situation since the spread of coronavirus: dependent employees: number of hours reduced, number of hours increased, more home office, leave of absence with/ without continued wage payment , fired, no change; self-employed: working hours reduced, working hours increased, more home office, revenue decreased, revenue increased, company temporarily closed by the authorities, company temporarily voluntarily closed, financial hardship, company permanently closed or insolvent, no change.

Childcare: children under 12 in the household; organisation of childcare during the closure of day-care centres, kindergartens and schools (staying at home, partner stays at home, older siblings take care, grandparents are watching, etc.)

Media consumption on Corona: information sources used for Corona (e.g. nationwide public or private television or radio, local public or private television or radio, national newspapers or local newspapers, Facebook, other social media, personal conversations with friends and family, other, do not inform myself on the subject); frequency of Facebook usage; information about Corona obtained from regional Facebook page or regional Facebook group.

Demography: sex; age (categorized); education (categorized); intention to vote and choice of party (Sunday question); Left-right self-assessment; marital status; size of household.

Additionally coded: Respondent ID;...

This study deals with the regional acquisition structure in Germany in the 19th century. The focus is on sectoral difference in the acquisition structure between the Prussian provinces (partly also government districts) and other German states in the period from 1861 to 1907. The goal of the investigation is to test hypothesis on the regional distribution of German industrialization in the 19th century. Tipton uses the degree of specialization of workers in industrial occupations as an indicator and distinguishes between 32 regions (Prussian provinces, other German states) in the German Reich. He analyzes the concrete changes in the regional development pattern in a broad framework of explaining variables of which regional specialization is the central explaining variable. Tipton observed increasing differences and sees the geographical distribution of business as the main reason. Tipton concludes that the differences in the acquisition structure in this period of industrialization increase continuously and that there was an increasing gap between the industrialized regions such as the Ruhr area, Saxony, Berlin, Upper Silesia and Alsace-Lorraine on the one side and the Eastern Prussian provinces of the other side. He does not want to oversimplify this process: also within the industry regions the differences were increasing, also tertiary regions like Hamburg and Bremen specialized more and more and also in the West there were less developed agrarian regions. Register of tables in HISTAT: Employment structure in Germany (1882-1907)Employment structure in Eastern and Western Prussia (1861-1882)Employment structure in Eastern Prussia (1882-1907)Employment structure in Western Prussia (1882-1907)Employment structure in Posen (1861-1907)Employment structure in Pomerania (1861-1907)Employment structure in Opole, Upper Silesia (1861-1907)Employment structure in Breslau, Legnica (1861-1907)Employment structure in Frankfurt/Oder (1861-1907)Employment structure in Potsdam (1861-1907)Employment structure in Berlin (1861-1907)Employment structure in Mecklenburg-Schwerin, Mecklenburg-Strelitz (1882-1907)Employment structure in Schleswig-Holstein (1861-1907)Employment structure in Hanover (1867-1882)Employment structure in Hanover, Oldenburg, Brunswick, Schaumburg-Lippe (1882-1907)Employment structure in Lübeck, Bremen, Hamburg (Hanseatic cities) (1882-1907)Employment structure in the kingdom of Saxony (1849-1907)Employment structure in Saxony (Prussia) (1861-1882)Employment structure in Magdeburg, Anhalt (1882-1907)Employment structure in Merseburg, Erfurt, Thuringia (1882-1907)Employment structure in Münster, Minden, Northern Westphalia, without Lippe, Waldeck (1861-1875)Employment structure in Münster, Minden, Northern Westphalia, with Lippe, Waldeck (1882-1907)Employment structure in Düsseldorf, Arnsberg (Ruhr) (1861-1907)Employment structure in Aachen (1861-1907)Employment structure in Cologne (1861-1907)Employment structure in Trier, Koblenz (1861-1907)Employment structure in Hesse-Nassau, Upper Hesse Posen (1867-1907)Employment structure in Bavaria (1847-1907)Employment structure in Württemberg, Hohenzollern (1861-1907)Employment structure in Baden (1847-1907)Employment structure in Hesse without upper Hesse 1867-1882)Employment structure in Hesse with upper Hesse (1882-1907)Employment structure in Rhenish Palatine (1847-1907)Employment structure in Lorraine (1882-1907)Employment structure in Alsace (1882-1907)

The German Internet Panel (GIP) is an infrastructure project. The GIP serves to collect data about individual attitudes and preferences which are relevant for political and economic decision-making processes.

Experimental variations in the instruments were used. The questionnaire contains numerous randomizations (order of questions or answer categories) as well as a cross-questionnaire experiment.

Topics: Personal trust; life satisfaction (scalometer); happiness scale; frequency of social contacts in general and compared to peers; frequency of selected activities in the last year (voluntary work, participation in a training course, participation in activities of clubs, political organisations or citizens´ initiatives, reading books, magazines or newspapers, solving crossword or number puzzles, card games); perceived personal esteem by loved ones; satisfaction with the current national economic situation and with the work of the federal government (scalometer); demand for government measures to reduce income disparities; assessment of the current general economic situation in Germany compared with 12 months ago and expected development in one year (business expectation); assessment of the current financial situation of the household compared to 12 months ago and expected development in one year; difficulties to make ends meet; employment status (ILO); sufficient qualification for more demanding work; training needs for the current job; qualification corresponds to requirements in the workplace; responsibility of selected institutions for the economic situation in the country (European Union, International Monetary Fund (IMF), Federal Government, banks); probability to vote for the parties CDU, CSU, SPD, Die Linke, Bündnis 90/Die Grünen, FDP and Alternative für Deutschland (AfD); opinion on European unification; agreement on the Federal Government´s achievements; agreement on last year´s EU policy; probability of participation in the European elections; assessment of the country´s EU membership; concerns about: financial difficulties, declining living standards, jobs, paying off bank loans and mortgage rates; opinion on German financial support for other EU member states in financial difficulties; opinion on obligatory adaptation of immigrants to German culture; policy should not interfere in the economy; demand for harsher penalties for criminals; demand for redistribution of income and wealth in favour of ordinary people; dealing with the issue of personal impact on the environment; lifestyle correspondence with demands for personal commitment to the environment; assessment of personal lifestyle as environmentally friendly; major environmental problems in Germany (air pollution, chemicals and pesticides, water scarcity, water pollution, nuclear waste, household waste disposal, climate change, genetically modified food, depletion of raw materials and natural resources); extent of concern about climate change; personal behaviour and lifestyle contribute to climate change; willingness to spend more on environmentally friendly products; expectation of a major environmental catastrophe in the near future; perceived exaggeration of the environmental crisis; no longer able to influence climate change; no worries about the far-off effects of climate change; willingness to change the environment only if compatible with personal lifestyle; personal commitment to the environment is not worthwhile if others do not do the same; German efforts to combat climate change are being destroyed by the actions of other countries.

Demography: sex; citizenship; year of birth (categorised); highest school leaving certificate; highest professional qualification; marital status; household size; employment status; private internet use; federal state.

Additionally coded was: interview date; questionnaire evaluation; assessment of the survey as a whole; unique ID, household ID and person ID within the household.

Germany DE: Industrial Employment data was reported at 32,731.392 Person th in 2022. This records an increase from the previous number of 31,915.486 Person th for 2021. Germany DE: Industrial Employment data is updated yearly, averaging 29,057.500 Person th from Dec 1981 (Median) to 2022, with 42 observations. The data reached an all-time high of 32,731.392 Person th in 2022 and a record low of 21,147.608 Person th in 1983. Germany DE: Industrial Employment data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Germany – Table DE.OECD.MSTI: Population, Labour Force and Employment: OECD Member: Annual. The data in this publication for Germany cover unified Germany from 1991 and western Germany only until 1990.In 2016, the method for calculating R&D coefficients was revised, introducing a break in series in the Higher Education sector. In particular, coefficients are thereafter based on time-use surveys.From reference year 2014, the distribution of R&D personnel by occupation is requested in the government survey whereas it was previously estimated from data by qualification.The method for calculating public-financed R&D in the business enterprise sector was reviewed, resulting in the revision of business enterprise R&D and the national total back to 1991.In 1992 the methodology of the survey on resources devoted to R&D in the Government sector was changed. From 1991, the data for the Private Non-Profit sector have been included in the Government sector.For 1997, the methodology for allocating GBARD by socio-economic objective changed. For 1997 and from 2001 to 2015, the global budget reduction was not distributed proportionally across SEO by the Federal Ministry of Education and Research. Therefore, the sum of the breakdown for those years does not add to the total. From 2016 onwards the global reduction is distributed across SEO proportionally.

Building on arguments to political incomes, career concerns and elitist networks, this study assumes that an increasing percentage of highly incentivized former executive board members within the German Federal Government (1957–2012) will decrease the top earners’ average income tax rate during the subsequent year. Conversely, the percentage of lower incentivized former supervisory board members is assumed to increase the top earners’ average income tax rate. Both effects are assumed to be enforced if the ruling parties have strong support in the German Bundestag. The empirical results significantly confirm the unconditional effect for former executive board members and the conditional effect for former supervisory board members.

Corresponding to sociological findings (see Hartmann 2002, Der Mythos von den Leistungseliten. Frankfurt a.M., Campus) and building on Barro’s (1973, The Control of Politicians: An Economic Model. Public Choice 14(1): 19–42) approach to the selfish maximization of political income and arguments regarding career concerns from principal agent theory (see, e. g. Fama 1980, Agency Problems and the Theory of the Firm. Journal of Political Economy 88, 288–307), this study assumes a strong incentive for former executive board members in the German Federal Government (1957–2012) to maximize their political income by lowering the top earners’ average income tax rate (1958–2013) due to their social elitist homogeneity and career concerns in terms of future job opportunities in business corporations. Conversely, former supervisory board members are assumed to increase the top earners’ average income tax rate due to their differing social backgrounds. Despite possible career concerns, they are assumed to increase the top earners’ average income tax rate in order not to lose their previously gained ideological credibility. Both effects are assumed to be enforced if the ruling parties have more than or equal to 55 % of seats in the German Bundestag. By running OLS and Tobit regressions, the empirical results confirm an unconditional decreasing effect of a higher percentage of previous executive board members and a conditional increasing effect of a higher percentage of previous supervisory board members on the top earners’ average income tax rate.