Germany DE: Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: Female: % Cumulative data was reported at 25.306 % in 2022. This records an increase from the previous number of 24.831 % for 2021. Germany DE: Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: Female: % Cumulative data is updated yearly, averaging 20.690 % from Dec 2010 (Median) to 2022, with 13 observations. The data reached an all-time high of 25.306 % in 2022 and a record low of 0.730 % in 2011. Germany DE: Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: Female: % Cumulative data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Social: Education Statistics. The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;;

In the winter semester 2024/25, around 2.87 million students were enrolled in German universities. This was a slight decrease compared to the previous year, but still around 250,000 more than ten years ago, demonstrating that higher education is becoming a more popular option for people in Germany. Students at German universities The majority of students in Germany are studying to get a bachelor’s degree, however, a significant growing number of students also go on to do a master’s degree. German universities offer students the opportunity to study a wide range of subjects - humanities as well as sciences. The most popular subjects to study among German students in recent years have been law, economics, and social sciences, followed by engineering. Although there are different institutions at which students in Germany can pursue higher education, most students opt to study at a university or university of applied sciences. These types of institutions also offer the most courses for students to choose from. Private universities As well as having state-funded universities, there are also private universities in Germany. As the name suggests, this means that they are not funded by the state, and therefore students must pay the fees for each semester themselves. This model of higher education is more similar to the one found in England or the U.S. Despite the higher tuition fees, the most popular university in Germany is currently a private one, suggesting that there are possibly some advantages to paying more for your education. It is important to note that comparatively only a very small percentage of students attend private universities. This is likely since they are more expensive, and shows the importance of keeping university affordable so that everyone can have the opportunity to pursue further education.

In 2023, roughly 24 percent of the population with a migrant background were doing an apprenticeship or vocational training.

Universities are part of the tertiary education sector and offer students an academic education. They are divided into state and private universities, which differ in terms of their funding. While state universities are mainly financed by public funds, private universities are primarily financed by tuition fees. In the last five years, more and more school leavers have decided to study at university, which has increased the demand for places. The number of private universities in particular has grown. They are increasingly offering study programmes that are rarely available in this form at state universities. Turnover in the sector has risen by an average of 0.9% per year over the last five years and is expected to total 75.6 billion euros in the current year. Compared to the previous year, this corresponds to growth of 0.7%.Public spending on education has risen over the last five years, which has also increased the income of universities. In addition, spending on research and development by the state and private institutions has increased, meaning that they are awarding more research contracts to universities. With the outbreak of the coronavirus pandemic, universities have had to switch from face-to-face teaching to digital teaching at short notice and have had to make some very high investments to do so. In the five years up to and including 2029, average annual growth in industry turnover of 3.5% to 89.6 billion euros is expected in 2029. Due to the increasing academisation of many training occupations and a steadily growing range of courses on offer, demand for the services provided by universities is rising. Due to demographic change, however, student numbers are expected to decline in the long term, as the number of people in the relevant age group will decrease. The first signs of this can already be seen in the declining number of first-year students. It can be assumed that universities will increasingly focus on adult continuing education in the coming years.

This survey shows men and women by highest educational attainment compared to the general population in Germany in 2021. According to the survey, almost 28 percent of men in Germany achieved school leaving qualification for entering university (Abitur in German).

Germany DE: Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: Male: % Cumulative data was reported at 33.311 % in 2022. This records a decrease from the previous number of 33.453 % for 2021. Germany DE: Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: Male: % Cumulative data is updated yearly, averaging 29.980 % from Dec 2010 (Median) to 2022, with 13 observations. The data reached an all-time high of 33.453 % in 2021 and a record low of 1.708 % in 2013. Germany DE: Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: Male: % Cumulative data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Social: Education Statistics. The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;;

Labor force with advanced education (% of total working-age population with advanced education) in Germany was reported at 72.9 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Germany - Labor force with advanced education (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

UIS: Percentage of population age 25+ with at least a completed short-cycle tertiary degree (ISCED 5 or higher). Male in Germany was reported at 30.09 % in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Germany - Percentage of population age 25+ with at least a completed short-cycle tertiary degree (ISCED 5 or higher). Male - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Germany DE: Labour Force With Advanced Education: Female: % of Female Working-age Population data was reported at 72.835 % in 2023. This records a decrease from the previous number of 72.838 % for 2022. Germany DE: Labour Force With Advanced Education: Female: % of Female Working-age Population data is updated yearly, averaging 74.412 % from Dec 1992 (Median) to 2023, with 31 observations. The data reached an all-time high of 75.636 % in 1999 and a record low of 72.094 % in 2020. Germany DE: Labour Force With Advanced Education: Female: % of Female Working-age Population 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: Labour Force. The ratio of the labor force with advanced education to the working-age population with advanced education. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011).;International Labour Organization. “Education and Mismatch Indicators database (EMI)” ILOSTAT. Accessed January 07, 2025. https://ilostat.ilo.org/data/.;Weighted average;

This statistic shows the results of a survey detailing the share of millennials among the German population in 2021, broken down by professional education. That year, *** percent of millennials aged 26 to 31 years were still in the process of completing their apprenticeship, and *** percent had completed an apprenticeship without a graduation certificate. **** percent of millennials in this age group had a university degree.

The system of social indicators for the Federal Republic of Germany - developed in its original version as part of the SPES project under the direction of Wolfgang Zapf - provides quantitative information on levels, distributions and changes in quality of life, social progress and social change in Germany from 1950 to 2013, i.e. over a period of more than sixty years. With the approximately 400 objective and subjective indicators that the indicator system comprises in total, it claims to measure welfare and quality of life in Germany in a differentiated way across various areas of life and to monitor them over time. In addition to the indicators for 13 areas of life, including income, education and health, a selection of cross-cutting global welfare measures were also included in the indicator system, i.e. general welfare indicators such as life satisfaction, social isolation or the Human Development Index. Based on available data from official statistics and survey data, time series were compiled for all indicators, ideally with annual values from 1950 to 2013. Around 90 of the indicators were marked as "key indicators" in order to highlight central dimensions of welfare and quality of life across the various areas of life. The further development and expansion, regular maintenance and updating as well as the provision of the data of the system of social indicators for the Federal Republic of Germany have been among the tasks of the Center for Social Indicator Research, which is based at GESIS, since 1987. For a detailed description of the system of social indicators for the Federal Republic of Germany, see the study description under "Other documents".
Indicators for the area of life “education”

The data on the area of life “education” is made up as follows:

Participation in education and educational opportunities: Access to the elementary area [second level]; Children in kindergartens; Provision rate in child day care for children under three years old; Participation in education in lower secondary education [second level]; School attendance of 13-year-olds by school type; School success in lower secondary education [second level]; School leavers without a secondary school diploma; School success in upper secondary education [second level]: High school graduate rate; Participation in tertiary education [second level]: Student quota of 20 to 30 year olds; Equal opportunities in tertiary education [second level]: Student entry rate for university studies; Entry rate for university/technical college studies; Further training [second level]: VHS course occupancy per 100 inhabitants; Participation rate for the entire further training; Participation rate in general and political further training; Participation rate in continuing professional education; Participation rate of continuing vocational training among employed people; Qualification [first level]: Language competence [second level]: Proportion of population with foreign language skills; IT competence [second level]: Proportion of population with computer skills; Quality of school education [second level]: Proportion of students with a lack of mathematical competence; Proportion of students with poor reading skills; Proportion of students with a lack of scientific competence; Vocational training [second level]: Share of population with apprenticeship/skilled training; Proportion of the population with vocational training; Proportion of the population with a technical college education; Proportion of the population with a university degree; Effectiveness [first level]: Unemployment rate: people without training; Unemployment rate: university graduates; Organization and costs of the education system [first level]: Public/private sector ratio [second level]: Proportion of high school students in public schools to all high school students; Costs of the education system [second level]: Share of public budget expenditure on education; public/private financing of studies; Financing the studies; Subjective preservation and evaluation of education [first level]: Satisfaction with training.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in New Germany, MN, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 30,969, while the mean income for the highest quintile (20% of households with the highest income) is 215,949. This indicates that the top earners earn 7 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 309,570, which is 143.35% higher compared to the highest quintile, and 999.61% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 New Germany median household income. You can refer the same here

This is a nationally representative cross-sectional survey of Chinese international students in the UK, with a comparison group of UK home students. It is part of a wider study with other surveys in Germany and China. The study population are taught (undergraduate and postgraduate) Chinese students studying in UK universities. Areas covered in the questionnaires: Socio-demographic characteristics and course details; family background (parental education, occupation, household income, siblings); prior education (academic achievement and educational migration); motivations for study abroad and decision-making process; personality traits and values (e.g., risk-taking attitude); study experience in current course; health and wellbeing; future life course aspirations; cosmopolitan vs national orientations.Young people moving away from home to seek 'bright futures' through higher education are a major force in the urbanization of China and the internationalization of global higher education. Chinese students constitute the largest single group of international students in the richer OECD countries of the world, making up 20 percent of the total student migration to these countries. Yet systematic research on a representative sample of these student migrants is lacking, and theoretical frameworks for migration more generally may not always apply to students moving for higher education. Bright Futures is a pioneering study that investigates key dimensions of this educational mobility through large-scale, representative survey research in China, the UK and Germany. We explore this phenomenon in two related aspects: the migration of students from the People's Republic of China to the UK (this data collection) and Germany for higher education, and internal migration for studies within China. This research design enables an unusual set of comparisons, between those who stay and those who migrate, both within China and beyond its borders. We also compare Chinese students in the UK and Germany with domestic students in the two countries. Through such comparisons we are able to address a number of theoretical questions such as selectivity in educational migrations, aspirations beyond returns, the impact of transnationalization of higher education on individual orientations and life-course expectations, and the link between migration and the wellbeing of the highly educated. Bright Futures is a collaborative project, involving researchers from University of Essex, University of Edinburgh, UNED, University of Bielefeld and Tsinghua University. The research was funded by the Economic and Social Research Council (UK), German Research Foundation (Germany) and the National Natural Science Foundation (China). The sample design is a two-stage stratified sample, with universities as the Primary Sampling Units (PSUs). The sample was stratified by university ranking and the size of Chinese students enrolled at the institution to ensure that students from different types of universities were proportionately represented. Within each university that agreed to participate we either sampled all Chinese students in undergraduate and taught postgraduate programmes, or (in universities with a very large population of Chinese students) took a random sample. In each university, we sampled the same number of British home students as Chinese students for comparison. The questionnaire for UK home students is designed to serve as a comparison group to Chinese students. All questionnaires were in the students’ main language, i.e. Chinese or English respectively. The survey was conducted online. The response rate at the student level was approximately 13 percent. Survey fieldwork took place between April 2017 and March 2018. The achieved sample size in the UK is 1,446 Chinese students and 1,678 home students.

As anyone who has studied knows, undertaking a degree can be a stressful time with many deadlines to undertake and exams to pass. Therefore, it is important to have a healthy way in which to reduce stress. Popular stress relievers for both men and women included meeting with friends and family, going for a walk, or playing sports. Around ** percent of men also used the internet and played video games to relax. Student stress There are almost three million students in Germany. This is an increase of around a million compared to 20 years ago. Studying has become more and more common, with many feeling that getting a degree will give them the best career prospects. However, for many studying can be a stressful experience at times mainly due to exams, studying as well as working part-time, and feeling pressure to get good grades. It also seems that students are more stressed now than they were in the past. Around eight years ago, only ** percent of students said that they often felt stressed, whilst now this figure has risen to ** percent. How stressed are the Germans? In general, the level of anxiety among the German population has been rising over the past few years. Since 2021, the index value for anxiety has risen by **** points. There could be several reasons for this increase in worry. Society has seen instability over the past few years due to the pandemic, inflation, and war. For Germans, the main worries were financial, with rising living costs, unaffordable housing, and tax increases topping the list. With the mounting stresses of day-to-day life, many people agree on the importance of integrating ways to relax into their daily routines. This can help to prevent people from becoming completely overwhelmed and contribute positively to their overall mental health.

Open Science in (Higher) Education – data of the February 2017 survey

This data set contains:

• Full raw (anonymised) data set (completed responses) of Open Science in (Higher) Education February 2017 survey. Data are in xlsx and sav format.
• Survey questionnaires with variables and settings (German original and English translation) in pdf. The English questionnaire was not used in the February 2017 survey, but only serves as translation.
• Readme file (txt)

Survey structure

The survey includes 24 questions and its structure can be separated in five major themes: material used in courses (5), OER awareness, usage and development (6), collaborative tools used in courses (2), assessment and participation options (5), demographics (4). The last two questions include an open text questions about general issues on the topics and singular open education experiences, and a request on forwarding the respondent’s e-mail address for further questionings. The online survey was created with Limesurvey[1]. Several questions include filters, i.e. these questions were only shown if a participants did choose a specific answer beforehand ([n/a] in Excel file, [.] In SPSS).

Demographic questions

Demographic questions asked about the current position, the discipline, birth year and gender. The classification of research disciplines was adapted to general disciplines at German higher education institutions. As we wanted to have a broad classification, we summarised several disciplines and came up with the following list, including the option “other” for respondents who do not feel confident with the proposed classification:

• Natural Sciences
• Arts and Humanities or Social Sciences
• Economics
• Law
• Medicine
• Computer Sciences, Engineering, Technics
• Other

The current job position classification was also chosen according to common positions in Germany, including positions with a teaching responsibility at higher education institutions. Here, we also included the option “other” for respondents who do not feel confident with the proposed classification:

• Professor
• Special education teacher
• Academic/scientific assistant or research fellow (research and teaching)
• Academic staff (teaching)
• Student assistant
• Other

We chose to have a free text (numerical) for asking about a respondent’s year of birth because we did not want to pre-classify respondents’ age intervals. It leaves us options to have different analysis on answers and possible correlations to the respondents’ age. Asking about the country was left out as the survey was designed for academics in Germany.

Remark on OER question

Data from earlier surveys revealed that academics suffer confusion about the proper definition of OER^[2]. Some seem to understand OER as free resources, or only refer to open source software (Allen & Seaman, 2016, p. 11). Allen and Seaman (2016) decided to give a broad explanation of OER, avoiding details to not tempt the participant to claim “aware”. Thus, there is a danger of having a bias when giving an explanation. We decided not to give an explanation, but keep this question simple. We assume that either someone knows about OER or not. If they had not heard of the term before, they do not probably use OER (at least not consciously) or create them.

Data collection

The target group of the survey was academics at German institutions of higher education, mainly universities and universities of applied sciences. To reach them we sent the survey to diverse institutional-intern and extern mailing lists and via personal contacts. Included lists were discipline-based lists, lists deriving from higher education and higher education didactic communities as well as lists from open science and OER communities. Additionally, personal e-mails were sent to presidents and contact persons from those communities, and Twitter was used to spread the survey.

The survey was online from Feb 6^th to March 3^rd 2017, e-mails were mainly sent at the beginning and around mid-term.

Data clearance

We got 360 responses, whereof Limesurvey counted 208 completes and 152 incompletes. Two responses were marked as incomplete, but after checking them turned out to be complete, and we added them to the complete responses dataset. Thus, this data set includes 210 complete responses. From those 150 incomplete responses, 58 respondents did not answer 1st question, 40 respondents discontinued after 1st question. Data shows a constant decline in response answers, we did not detect any striking survey question with a high dropout rate. We deleted incomplete responses and they are not in this data set.

Due to data privacy reasons, we deleted seven variables automatically assigned by Limesurvey: submitdate, lastpage, startlanguage, startdate, datestamp, ipaddr, refurl. We also deleted answers to question N^o 24 (email address).

References

Allen, E., & Seaman, J. (2016). Opening the Textbook: Educational Resources in U.S. Higher Education, 2015-16.

First results of the survey are presented in the poster:

Heck, Tamara, Blümel, Ina, Heller, Lambert, Mazarakis, Athanasios, Peters, Isabella, Scherp, Ansgar, & Weisel, Luzian. (2017). Survey: Open Science in Higher Education. Zenodo. http://doi.org/10.5281/zenodo.400561

Contact:

Open Science in (Higher) Education working group, see http://www.leibniz-science20.de/forschung/projekte/laufende-projekte/open-science-in-higher-education/.

[1] https://www.limesurvey.org

[2] The survey question about the awareness of OER gave a broad explanation, avoiding details to not tempt the participant to claim “aware”.

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.

BackgroundAccording to a recent paper by Gelfand et al., COVID-19 infection and case mortality rates are closely connected to the strength of social norms: “Tighter” cultures that abide by strict social norms are more successful in combating the pandemic than “looser” cultures that are more permissive. However, countries with similar levels of cultural tightness exhibit big differences in mortality rates. We are investigating potential explanations for this fact. Using data from Germany and Japan—two “tight” countries with very different infection and mortality rates—we examined how differences in socio-demographic and other determinants explain differences in individual preventive attitudes and behaviors.MethodsWe compared preventive attitudes and behaviors in 2020 based on real-time representative survey data and used logit regression models to study how individual attitudes and behaviors are shaped by four sets of covariates: individual socio-demographics, health, personality, and regional-level controls. Employing Blinder-Oaxaca regression techniques, we quantified the extent to which differences in averages of the covariates between Japan and Germany explain the differences in the observed preventive attitudes and behaviors.ResultsIn Germany and Japan, similar proportions of the population supported mandatory vaccination, avoided travel, and avoided people with symptoms of a cold. In Germany, however, a significantly higher proportion washed their hands frequently and avoided crowds, physical contact, public transport, peak-hour shopping, and contact with the elderly. In Japan, a significantly higher proportion were willing to be vaccinated. We also show that attitudes and behaviors varied significantly more with covariates in Germany than in Japan. Differences in averages of the covariates contribute little to explaining the observed differences in preventive attitudes and behaviors between the two countries.ConclusionConsistent with tightness-looseness theory, the populations of Japan and Germany responded similarly to the pandemic. The observed differences in infection and fatality rates therefore cannot be explained by differences in behavior. The major difference in attitudes is the willingness to be vaccinated, which was much higher in Japan. Furthermore, the Japanese population behaved more uniformly across social groups than the German population. This difference in the degree of homogeneity has important implications for the effectiveness of policy measures during the pandemic.

This statistic shows the degree of urbanization in Germany from 2013 to 2023. Urbanization means the share of urban population in the total population of a country. In 2023, 77.77 percent of Germany's total population lived in urban areas and cities. Urbanization in Germany Currently, about three quarter of the German population live in urban areas and cities, which is more than in most nations around the world. Urbanization, as it can be seen in this graph, refers to the number of people living in an urban area and has nothing to do with the actual geographical size or footprint of an area or country. A country which is significantly bigger than Germany could have a similar degree of urbanization, just because not all areas in the country are inhabitable, for example. One example for this is Russia, where urbanization has reached comparable figures to Germany, even though its geographical size is significantly bigger. However, Germany’s level of urbanization does not make the list of the top 30 most urbanized nations in the world, where urbanization rates are higher than 83 percent. Also, while 25 percent of the population in Germany still lives in rural areas, rural livelihoods are not dependent on agriculture, as only 0.75 percent of GDP came from the agricultural sector in 2014. So while Germany's urbanization rate is growing, a significant percentage of the population is still living in rural areas. Furthermore, Germany has a number of shrinking cities which are located to the east and in older industrial regions around the country. Considering that population growth in Germany is on the decline, because of low fertility rates, and that a number of cities are shrinking, the urban population is likely shifting to bigger cities which have more economic opportunities than smaller ones.

The dataset contains information on the total resident population, male and female resident population on the county (Kreis), canton (Bezirk) and national level for the years 1969 to 1989. The information is based on the Statistical Yearbooks of the German Democratic Republic. The numbers for the county and national level are taken directly from the Statistical Yearbooks. The numbers for the canton level are the aggregated numbers of the county resident population (total, male and female) per canton and year. All population figures reflect the situation at the end of each year. Up to and including the year 1981, December 31 is given as a specific date of the recordings.
The research for this dataset was financed by the research grant KO 2239/3-1 “Spontane Revolution oder lange Wende? Eine soziologische Analyse der DDR und ihres Niedergangs auf Basis von Eingabenstatistiken zwischen 1970 und 1989” of the German Research Foundation (Deutsche Forschungsgemeinschaft;DFG).

In 2024, around ** percent of German campers, i.e. people who preferred to go camping as a vacation, had currently not completed any vocational/higher education.The Allensbach Market and Advertising Media Analysis (Allensbacher Markt- und Werbeträgeranalyse or AWA in German) determines attitudes, consumer habits and media usage of the population in Germany on a broad statistical basis.