The total population of Germany was estimated at over 84.4 million inhabitants in 2025, although it is projected to drop in the coming years and fall below 80 million in 2043. Germany is the most populous country located entirely in Europe, and is third largest when Russia and Turkey are included. Germany's prosperous economy makes it a popular destination for immigrants of all backgrounds, which has kept its population above 80 million for several decades. Population growth and stability has depended on immigration In every year since 1972, Germany has had a higher death rate than its birth rate, meaning its population is in natural decline. However, Germany's population has rarely dropped below its 1972 figure of 78.6 million, and, in fact, peaked at 84.7 million in 2024, all due to its high net immigration rate. Over the past 75 years, the periods that saw the highest population growth rates were; the 1960s, due to the second wave of the post-WWII baby boom; the 1990s, due to post-reunification immigration; and since the 2010s, due to high arrivals of refugees from conflict zones in Afghanistan, Syria, and Ukraine. Does falling population = economic decline? Current projections predict that Germany's population will fall to almost 70 million by the next century. Germany's fertility rate currently sits around 1.5 births per woman, which is well below the repacement rate of 2.1 births per woman. Population aging and decline present a major challenge economies, as more resources must be invested in elderly care, while the workforce shrinks and there are fewer taxpayers contributing to social security. Countries such as Germany have introduced more generous child benefits and family friendly policies, although these are yet to prove effective in creating a cultural shift. Instead, labor shortages are being combatted via automation and immigration, however, both these solutions are met with resistance among large sections of the population and have become defining political issues of our time.

Germany Population: East data was reported at 16,147.618 Person th in 2021. This records a decrease from the previous number of 16,163.795 Person th for 2020. Germany Population: East data is updated yearly, averaging 16,722.586 Person th from Dec 1950 (Median) to 2021, with 72 observations. The data reached an all-time high of 18,388.172 Person th in 1950 and a record low of 15,119.530 Person th in 2000. Germany Population: East data remains active status in CEIC and is reported by Statistisches Bundesamt. The data is categorized under Global Database’s Germany – Table DE.G001: Population.

The aim of the project was to identify and compile the best available historical time series for Germany, and to complement or update them at reasonable expense. Time series were only to be included, if data for the entire period from 1834 to 2012 was at least theoretically available. An integral aspect of the concept of our project is the combination of data with critical commentaries of the time series by established expert scientists. The following themes are covered (authors in parentheses): 1. Environment, Climate, and Nature (Paul Erker) 2. Population, Households, Families (Georg Fertig/Franz Rothenbacher) 3. Migration (Jochen Oltmer) 4. Education and Science (Volker Müller-Benedict) 5. Health Service (Reinhard Spree) 6. Social Policy (Marcel Boldorf) 7. Public Finance and Taxation (Mark Spoerer) 8. Political Participation (Marc Debus) 9. Crime and Justice (Dietrich Oberwittler) 10. Work, Income, and Standard of Living (Toni Pierenkemper) 11. Culture, Tourism, and Sports (Heike Wolter/Bernd Wedemeyer-Kolwe) 12. Religion (Thomas Großbölting/Markus Goldbeck) 13. National Accounts (Rainer Metz) 14. Prices (Rainer Metz) 15. Money and Credit (Richard Tilly) 16. Transport and Communication (Christopher Kopper) 17. Agriculture (Michael Kopsidis) 18. Business, Industry, and Craft (Alfred Reckendrees) 19. Building and Housing (Günther Schulz) 20. Trade (Markus Lampe/ Nikolaus Wolf) 21. Balance of Payments (Nikolaus Wolf) 22. International Comparisons (Herman de Jong/Joerg Baten) Basically, the structure of a dataset is guided by the tables in the print publication by the Federal Agency. The print publication allows for four to eight tables for each of the 22 chapters, which means the data record is correspondingly made up of 120 tables in total. The inner structure of the dataset is a consequence of a German idiosyncrasy: the numerous territorial changes. To account for this idiosyncrasy, we decided on a four-fold data structure. Four territorial units with their respective data, are therefore differentiated in each table in separate columns: A German Confederation/Custom Union/German Reich (1834-1945).B German Federal Republic (1949-1989).C German Democratic Republic (1949-1989).D Germany since the reunification (since 1990). Years in parentheses should be considered a guideline only. It is possible that series for the territory of the old Federal Republic or the new federal states are continued after 1990, or that all-German data from before 1990 were available or were reconstructed.All time series are identified by a distinct ID consisting of an “x” and a four-digit number (for numbers under 1000 with leading zeros). The time series that exclusively contain GDR data were identified with a “c” prefix instead of the “x”.For the four territorial units, the time series are arranged in four blocks side by side within the XLSX files. That means: first all time series for the territory and the period of the Custom Union and German Reich, the next columns contain side by side all time series for the territory of the German Federal Republic / the old federal states, then – if available – those for the territory of the German Democratic Republic / the new federal states, and finally for the reunified Germany. There is at most one row for each year. Dates can be missing if no data for the respective year are available in either of the table’s time series, but no date will appear twice. The four territorial units and the resultant time periods cause a “stepwise” appearance of the data tables.

If you find anything missing, unclear, incomprehensible, improvable, etc., please contact me (kontakt@deutschland-in-daten.de). Further reading:Rahlf, Thomas, The German Time Series Dataset 1834-2012, in: Journal of Economics and Statistics 236/1 (2016), pp. 129-143. [DOI: 10.1515/jbnst-2015-1005] Open Access: Rahlf, Thomas, Voraussetzungen für eine Historische Statistik von Deutschland (19./20. Jh.), in: Vierteljahrschrift für Sozial- und Wirtschaftsgeschichte 101/3 (2014), S. 322-352. [PDF] Rahlf, Thomas (Hrsg.), Dokumentation zum Zeitreihendatensatz für Deutschland, 1834-2012, Version 01 (= Historical Social Research Transition 26v01), Köln 2015. http://dx.doi.org/10.12759/hsr.trans.26.v01.2015Rahlf, Thomas (Hrsg.), Deutschland in Daten. Zeitreihen zur Historischen Statistik, Bonn: Bundeszentrale für Politische Bildung, 2015. [EconStor]

The anthropometric datasets presented here are virtual datasets. The unweighted virtual dataset was generated using a synthesis and subsequent validation algorithm (Ackermann et al., 2023). The underlying original dataset used in the algorithm was collected within a regional epidemiological public health study in northeastern Germany (SHIP, see Völzke et al., 2022). Important details regarding the collection of the anthropometric dataset within SHIP (e.g. sampling strategy, measurement methodology & quality assurance process) are discussed extensively in the study by Bonin et al. (2022). To approximate nationally representative values for the German working-age population, the virtual dataset was weighted with reference data from the first survey wave of the Study on health of adults in Germany (DEGS1, see Scheidt-Nave et al., 2012). Two different algorithms were used for the weighting procedure: (1) iterative proportional fitting (IPF), which is described in more detail in the publication by Bonin et al. (2022), and (2) a nearest neighbor approach (1NN), which is presented in the study by Kumar and Parkinson (2018). Weighting coefficients were calculated for both algorithms and it is left to the practitioner which coefficients are used in practice. Therefore, the weighted virtual dataset has two additional columns containing the calculated weighting coefficients with IPF ("WeightCoef_IPF") or 1NN ("WeightCoef_1NN"). Unfortunately, due to the sparse data basis at the distribution edges of SHIP compared to DEGS1, values underneath the 5th and above the 95th percentile should be considered with caution. In addition, the following characteristics describe the weighted and unweighted virtual datasets: According to ISO 15535, values for "BMI" are in [kg/m2], values for "Body mass" are in [kg], and values for all other measures are in [mm]. Anthropometric measures correspond to measures defined in ISO 7250-1. Offset values were calculated for seven anthropometric measures because there were systematic differences in the measurement methodology between SHIP and ISO 7250-1 regarding the definition of two bony landmarks: the acromion and the olecranon. Since these seven measures rely on one of these bony landmarks, and it was not possible to modify the SHIP methodology regarding landmark definitions, offsets had to be calculated to obtain ISO-compliant values. In the presented datasets, two columns exist for these seven measures. One column contains the measured values with the landmarking definitions from SHIP, and the other column (marked with the suffix "_offs") contains the calculated ISO-compliant values (for more information concerning the offset values see Bonin et al., 2022). The sample size is N = 5000 for the male and female subsets. The original SHIP dataset has a sample size of N = 1152 (women) and N = 1161 (men). Due to this discrepancy between the original SHIP dataset and the virtual datasets, users may get a false sense of comfort when using the virtual data, which should be mentioned at this point. In order to get the best possible representation of the original dataset, a virtual sample size of N = 5000 is advantageous and has been confirmed in pre-tests with varying sample sizes, but it must be kept in mind that the statistical properties of the virtual data are based on an original dataset with a much smaller sample size.

All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions in Germany. Power Plant emissions from all power plants in Germany were obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The dataset provides the name, company, parent company, city, state, metro area, lat/lon, and plant id for each individual power plant. Only Power Plants that had a listed longitude and latitude in CARMA's database were mapped. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. Carbon Monitoring for Action (CARMA) is a massive database containing information on the carbon emissions of over 50,000 power plants and 4,000 power companies worldwide. Power generation accounts for 40% of all carbon emissions in the United States and about one-quarter of global emissions. CARMA is the first global inventory of a major, sector of the economy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information http://carma.org/region/detail/78

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in German Valley. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In German Valley, the median household income for the households where the householder is White decreased by $13,652(16.09%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $84,845 in 2013 and $71,193 in 2023.
• Black or African American: As per the U.S. Census Bureau population data, in German Valley, there are no households where the householder is Black or African American; hence, the median household income for the Black or African American population is not applicable.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in German Valley.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are presented in 2023-inflation-adjusted dollars.

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 Valley median household income by race. You can refer the same here

A newer version of this dataset is available at https://doi.org/10.7802/2117 . ------------------------------------------------------------------------------------- With The Privacy Longitudinal Study, we surveyed and investigated privacy attitudes, perceptions, and behaviors in the German population. In our longitudinal study a representative panel of participants was surveyed five times over the course of three years between 2014 and 2017. The aim of this survey is to help generate profound knowledge about the German population's attitudes, behaviors, and perceptions surrounding privacy. We are grateful that we were able to follow up on this aim with the support of the German Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF) and with the support of the “Forum Privatheit” (www.forumprivatheit.de) – an interdisciplinary research consortium that has been collaborating since 2012 on questions of informational self-determination and privacy. At the core of the survey, we measured people’s behavior in different mediated and non-mediated communication settings. We believe that in Germany and around the globe, the term privacy is now mostly connected to the online world. However, online privacy has to be managed also through offline communication. Moreover, privacy in offline settings is also affected by our online communication. In our survey, we asked respondents to report their perceptions, behaviors, and beliefs regarding typical communication situations that they might encounter in all kinds of social media and – of course – in face-to-face communication. Pls find further information on ongoing projects and publications here: https://osf.io/y35as/

Germany Multidimensional Poverty Headcount Ratio: World Bank: % of total population data was reported at 0.300 % in 2020. This stayed constant from the previous number of 0.300 % for 2019. Germany Multidimensional Poverty Headcount Ratio: World Bank: % of total population data is updated yearly, averaging 0.200 % from Dec 2010 (Median) to 2020, with 10 observations. The data reached an all-time high of 0.300 % in 2020 and a record low of 0.100 % in 2015. Germany Multidimensional Poverty Headcount Ratio: World Bank: % of total 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: Social: Poverty and Inequality. The multidimensional poverty headcount ratio (World Bank) is the percentage of a population living in poverty according to the World Bank's Multidimensional Poverty Measure. The Multidimensional Poverty Measure includes three dimensions – monetary poverty, education, and basic infrastructure services – to capture a more complete picture of poverty.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

This data set contains estimates of the base rates of 550 food safety-relevant food handling practices in European households. The data are representative for the population of private households in the ten European countries in which the SafeConsume Household Survey was conducted (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, UK).

Sampling design

In each of the ten EU and EEA countries where the survey was conducted (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, UK), the population under study was defined as the private households in the country. Sampling was based on a stratified random design, with the NUTS2 statistical regions of Europe and the education level of the target respondent as stratum variables. The target sample size was 1000 households per country, with selection probability within each country proportional to stratum size.

Fieldwork

The fieldwork was conducted between December 2018 and April 2019 in ten EU and EEA countries (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, United Kingdom). The target respondent in each household was the person with main or shared responsibility for food shopping in the household. The fieldwork was sub-contracted to a professional research provider (Dynata, formerly Research Now SSI). Complete responses were obtained from altogether 9996 households.

Weights

In addition to the SafeConsume Household Survey data, population data from Eurostat (2019) were used to calculate weights. These were calculated with NUTS2 region as the stratification variable and assigned an influence to each observation in each stratum that was proportional to how many households in the population stratum a household in the sample stratum represented. The weights were used in the estimation of all base rates included in the data set.

Transformations

All survey variables were normalised to the [0,1] range before the analysis. Responses to food frequency questions were transformed into the proportion of all meals consumed during a year where the meal contained the respective food item. Responses to questions with 11-point Juster probability scales as the response format were transformed into numerical probabilities. Responses to questions with time (hours, days, weeks) or temperature (C) as response formats were discretised using supervised binning. The thresholds best separating between the bins were chosen on the basis of five-fold cross-validated decision trees. The binned versions of these variables, and all other input variables with multiple categorical response options (either with a check-all-that-apply or forced-choice response format) were transformed into sets of binary features, with a value 1 assigned if the respective response option had been checked, 0 otherwise.

Treatment of missing values

In many cases, a missing value on a feature logically implies that the respective data point should have a value of zero. If, for example, a participant in the SafeConsume Household Survey had indicated that a particular food was not consumed in their household, the participant was not presented with any other questions related to that food, which automatically results in missing values on all features representing the responses to the skipped questions. However, zero consumption would also imply a zero probability that the respective food is consumed undercooked. In such cases, missing values were replaced with a value of 0.

In the year 1975 the death rate has been higher than the birth rate for the first time since the end of the war. This means that our country has now the same problem as the Federal Republic of Germany and the German Democratic Republic namely a declining population. A decline in the birth rate is a phenomenon that could be observed in many industrialised countries since the 60s. This resulted in questions and problems that concern many areas of the economic an social development. The need for kindergartens, class rooms, apartments and workplaces has to be evaluated anew constantly as well as the necessary number of foreign workers or the financial burden for the contributors to the public pension scheme. In the developing countries on the other hand, it is the population boom in connection with the unemployment rate and the shortage of food that causes immense problems - which in return has an impact on the rich countries. Therefore, worldwide measures are taken understand the factors that influence the population growth and the birth rate so that decisions can be made for the future. The International Statistic Institute conducts, commissioned by the United Nations, a World-Fertility-Survey (WFS) in numerous countries; the up until now largest research on fertility and its conditions. The title birth-biography implies that this special survey collects information that cannot be gained from the existing birth statistic; the reports from the registrar’s offices to the Central Statistical Office cannot be merged with data from previous reports and also can not be evaluated together. To a limited extent, special question on children born alive had already been posed in the Mikrozensus in 1971 (Mikrozensus MZ7102). Since the number of answers was quite high, important partial results had already been gained. This special survey also concentrates on question on regional and social origin, occupation of the women in connection with the birth of their children and previous marriages. It is also noted if and at what age a child died. This is necessary for research on social conditions of infant mortality which is still quite high in Austria.

BackgroundA multi-dimensional model can be a useful tool for estimating the general impact of disease on the different sectors of the healthcare system. We chose the sexually transmitted disease syphilis for our model due to the good quality of reported data in Germany.MethodsThe model included gender- and age-stratified incident cases of syphilis (in- and outpatients) provided by a German statutory health insurance company, as well as seroprevalence data on syphilis in first-time blood donors. Age standardized rates were calculated based on the standard German population. The test quality was assessed by extrapolating the number of false-positive and false-negative results based on data from Europe-wide external quality assessment (EQA) schemes. The model analysis was validated with the reported cases and diagnosis-related group (DRG)-statistics from 2010 to 2012. The annual direct and indirect economic burden was estimated based on the outcomes of our model.ResultsThe standardized results were slightly higher than the results reported between 2010 and 2012. This could be due to an underassessment of cases in Germany or due to limitations of the dataset. The number of estimated inpatients was predicted with an accuracy of 89.8 %. Results from EQA schemes indicated an average sensitivity of 92.8 % and an average specificity of 99.9 % for the recommended sequential testing for syphilis. Based on our model, we estimated a total average minimal annual burden of €20,292,110 for syphilis on the German healthcare system between 2010 and 2012.ConclusionsThe linking of claims data, results from EQA schemes, and blood donor surveillance can be a useful tool for assessing the burden of disease on the healthcare system. It can help raise awareness in populations potentially at risk for infectious diseases, demonstrate the need to educate potential risk groups, and may help with predictive cost calculations and planning.

Employment Rate in Germany decreased to 77.20 percent in the second quarter of 2025 from 77.30 percent in the first quarter of 2025. This dataset provides - Germany Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Germany DE: Income Share Held by Second 20% data was reported at 12.800 % in 2020. This records a decrease from the previous number of 13.100 % for 2019. Germany DE: Income Share Held by Second 20% data is updated yearly, averaging 13.100 % from Dec 1991 (Median) to 2020, with 30 observations. The data reached an all-time high of 13.700 % in 1996 and a record low of 12.800 % in 2020. Germany DE: Income Share Held by Second 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Social: Poverty and Inequality. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Germany DE: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data was reported at 0.200 % in 2018. This stayed constant from the previous number of 0.200 % for 2017. Germany DE: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data is updated yearly, averaging 0.100 % from Dec 1991 (Median) to 2018, with 28 observations. The data reached an all-time high of 0.200 % in 2018 and a record low of 0.000 % in 2016. Germany DE: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of 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: Social: Poverty and Inequality. Poverty headcount ratio at $3.20 a day is the percentage of the population living on less than $3.20 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from around 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in German Flatts town. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In German Flatts town, the median household income for the households where the householder is White increased by $10,039(17.60%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $57,041 in 2013 and $67,080 in 2023.
• Black or African American: Even though there is a population where the householder is Black or African American, there was no median household income reported by the U.S. Census Bureau for both 2013 and 2023.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in German Flatts town.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are presented in 2023-inflation-adjusted dollars.

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 Flatts town median household income by race. You can refer the same here

The Epidemiological Survey of Substance Abuse (ESA) is a population-representative study that has been conducted regularly since 1980 to record the use of psychoactive substances and substance-related problems in the general population of Germany. In each survey wave, sampling was based on German-speaking persons aged 18 to 59 (ESA 1995-2003) and 18 to 64 (ESA 2006-2021) from private households. Sampling was carried out in 1995 and 1997 with an age-proportional three-stage draw using the random route method. First, sample points were drawn before the survey households were determined with the help of a fixed random-route survey. In households with more than one person aged 18 to 59, the target person was defined based on a birthday question (´Who had his birthday last?´). From 2000, the survey was switched to a population sample using a two-stage selection procedure and, from 2003, the draw was disproportionately to the distribution of birth cohorts, with more persons of a younger age. In the first step, municipalities within Germany were randomly selected. In the second step, addresses were drawn from population registers using systematic random selection. The 1995-2003 surveys were conducted using written surveys (Paper and Pencil Interview: PAPI). In the 2006 survey, telephone interviews (Computer Assisted Telephone Interviews: CATI) were additionally conducted, and from 2009 onward, the survey also included internet-based questionnaires (Computer Assisted Web Interviews: CAWI). To compensate for disproportionate selection probabilities and to align the data with the distribution of the population in Germany in the respective survey year, poststratification weights were determined for each survey. Data collection was carried out respectively from January- June 1995, April-August 1997, May-October 2000, March-September 2003, March-September 2006, May-October 2009, April-July 2012, March-July 2015, March-June 2018, and May-September 2021. Data collection was carried out 1995-1997 by the field research institute GFM-GETAS and from 2000 by infas Institut für angewandte Sozialwissenschaft GmbH . Response rates were 65% (1997 and 1995), 51% (2000), 55% (2003), 45% (2006), 50% (2009), 54% (2012), 52% (2015), 42% (2018), and 35% (2021). The trend data set contains comparable questions that were asked 4 times or more. Questions were not always asked regarding the same time period. The time periods and substances asked included past 30-day, past 12-month, and lifetime prevalence of use of conventional tobacco products (cigarettes, cigars, cigarillos, pipes), alcohol, illegal drugs, and medications. Furthermore, the prevalence of the use of gambling machines was also asked. For conventional tobacco products, alcohol, selected illegal drugs (cannabis, cocaine, and amphetamines), and medications (painkillers, sleeping pills, and sedatives), additional diagnostic criteria were recorded using the written version of the Munich Composite International Diagnostic Interview (M-CIDI) for the period of the last twelve months. Problematic patterns of use were also identified using screeners for use disorders for alcohol (AUDIT - Alcohol Use Disorder Identification Test), tobacco (Fagerström Test for Nicotine Dependence), medications (KFM - Kurzfragebogen Medikamente), and cannabis (SDS - Severity of Dependence Scale). A range of sociodemographic data and physical and mental health status were also recorded. Der Epidemiologische Suchtsurvey (ESA) ist eine seit 1980 regelmäßig durchgeführte bevölkerungsrepräsentative Studie zur Erfassung des Konsums psychoaktiver Substanzen und substanzbezogener Probleme in der Allgemeinbevölkerung in Deutschland. Die Grundlage der Stichprobenziehungen waren jeweils die deutschsprachigen Personen im Alter von 18 bis 59 Jahren (ESA 1995-2003) bzw. 18 bis 64 Jahren (ESA 2006-2021) aus Privathaushalten. Die Stichprobenziehung erfolgte 1995 und 1997 als altersproportionale dreistufige Ziehung nach dem Random-Route-Verfahren. Zuerst wurden sogenannte Sample Points gezogen, ehe mithilfe einer festgelegten Random-Route-Begehung die Befragungshaushalte ermittelt wurden. In Haushalten mit mehr als einer Person im Alter zwischen 18 und 59 Jahren wurde die Zielperson anhand der Geburtstagfrage („Wer hatte zuletzt Geburtstag?“) definiert. Ab 2000 wurde die Erhebung auf eine Einwohnermeldestichprobe in einem zweistufigen Auswahlverfahren umgestellt und ab 2003 wurde die Ziehung disproportional zur Verteilung der Geburtsjahrgänge durchgeführt, um mehr jüngere Leute zu befragen. Im ersten Schritt wurden Gemeinden innerhalb Deutschlands zufällig ausgewählt. In einem zweiten Schritt erfolgte die Ziehung von Adressen aus den Einwohnermelderegistern über eine systematische Zufallsauswahl. Die Befragungen wurden 1995-2003 schriftlich (Paper and Pencil Interview: PAPI) erhoben. In der Erhebung 2006 wurden zusätzlich telefonische Interviews (Computer Assisted Telephone Interviews: CATI) durchgeführt und ab 2009 fand die Erhebung auch mit internetbasierten Fragebögen (Computer Assisted Web Interviews: CAWI) statt. Zum Ausgleich disproportionaler Auswahlwahrscheinlichkeiten und um die Daten an die Verteilung der Grundgesamtheit der Bevölkerung in Deutschland im jeweiligen Erhebungsjahr anzugleichen, wurden für jede Erhebung Poststratifikationsgewichte ermittelt. Die Datenerhebung erfolgte jeweils Januar- Juni 1995, April-August 1997, Mai-Oktober 2000, März-September 2003, März-September 2006, Mai-Oktober 2009, April-Juli 2012, März-Juli 2015, März-Juni 2018 und Mai-September 2021. Die Datenerhebung wurde 1995-1997 vom Feldforschungsinstitut GFM-GETAS und ab 2000 durch infas Institut für angewandte Sozialwissenschaft GmbH durchgeführt. Die Antwortraten betrugen 65% (1997 und 1995), 51% (2000), 55% (2003), 45% (2006), 50% (2009), 54% (2012), 52% (2015), 42 % (2018) und 35% (2021). Im Trenddatensatz enthalten sind vergleichbare Fragen, welche 4 mal oder öfter erhoben wurden. Dabei wurden die Fragen nicht immer bezüglich des gleichen Zeitraums gestellt. Die erfragten Zeiträume und Substanzen waren u.a. die 30-Tage-, 12-Monats- und Lebenszeitprävalenz des Konsums von konventionellen Tabakprodukten (Zigaretten, Zigarren, Zigarillos, Pfeifen), Alkohol, illegalen Drogen und Medikamenten. Des Weiteren wurde auch die Prävalenz der Nutzung von Glückspielautomaten erfragt. Für konventionelle Tabakprodukte, Alkohol, ausgewählte illegale Drogen (Cannabis, Kokain und Amphetamine) und Medikamente (Schmerzmittel, Schlafmittel und Beruhigungsmittel) wurden zusätzlich Diagnosekriterien mit der schriftlichen Version des Münchener Composite International Diagnostic Interview (M-CIDI) für den Zeitraum der letzten zwölf Monate erfasst. Es wurden auch problematische Konsummuster anhand von Screenern für Konsumstörungen für Alkohol (AUDIT – Alcohol Use Disorder Identification Test), Tabak (Fagerströmtest for Nicotine Dependence), Medikamente (KFM – Kurzfragebogen Medikamente) und Cannabis (SDS – Severity of Dependence Scale) festgestellt. Erfasst wurden zudem eine Reihe soziodemografischer Daten sowie der körperliche und psychische Gesundheitszustand. Probability: MultistageProbability.Multistage Wahrscheinlichkeitsauswahl: Mehrstufige ZufallsauswahlProbability.Multistage Face-to-face interview: Paper-and-pencil (PAPI)Interview.FaceToFace.PAPI Persönliches Interview : Papier-und-Bleistift (PAPI)Interview.FaceToFace.PAPI

Since 1996, the Center for Military History and Social Sciences of the Bundeswehr (ZMSBw) has conducted a representative survey of the German population on defense and security policy issues on behalf of the Federal Ministry of Defense. In 1996, this study was continued. For this purpose, N = 2568 persons were interviewed on various issues. The present survey focused in particular on Security and threat perception, attitudes toward security policy, foreign deployments of the Federal Armed Forces, tasks of the Federal Armed Forces, the role of conscription, and military cooperation in Europe. Perception of security and threats: personal feeling of security; personal significance of various aspects of security (e.g. job security, military security, social security, security of income, ecological security, etc.) Interest in politics in general, in foreign policy, in security and defence policy as well as interest in the Federal Armed Forces; security policy interest at the beginning of the 1980s; security policy strategy of ´deterrence´ as a guarantee for peace in Europe, necessary Realpolitik or a threat to humanity; advocacy or rejection of military force; change in personal attitude towards military force; Reasons for change of attitude; reasons for not changing attitudes; personal relationship to the peace movement in the early 1980s and today; opinion on pacifism; opinion on the extent of public debate on security policy issues and on the Federal Armed Forces; future development of the number of international conflicts after the end of the Cold War; likelihood of a military threat to Germany; feeling threatened by: environmental destruction, violence, hatred, crime, unemployment, world wars, right-wing extremism, financial problems, new technologies, diseases and population growth; threat to world peace from various countries and regions (Islamic states, Third World, Russia, Central/Eastern Europe, USA, Western Europe, Germany, Middle East, China); current that will prevail worldwide in the future (national or nationalist thinking vs. voluntary cooperation and interdependence); assessment of nationalist thinking; assessment of voluntary cooperation; suitability of various institutions and instruments to protect Germany against military risks (NATO membership, other/ new treaties with neighbouring countries, United Nations (UN), European Union (EU), Federal Armed Forces, European Army, general disarmament, Organisation for Security and Cooperation in Europe (OSCE)). 2. Security policy attitudes, foreign missions of the Federal Armed Forces: Germany´s role in the world: preference for a rather active vs. rather passive international policy of Germany; approved or rejected measures for Germany´s international action (e.g. aid with food and medicine, aid of a financial and economic nature, technical aid by civil organisations, peacekeeping mission of the Federal Armed Forces within the framework of a UN mission, etc.); opinion on the peace-keeping mission of the Federal Armed Forces in various countries and regions (Eastern Europe, Russia, the Middle East, South-East Asia, Africa, NATO states, Western Europe; opinion on the future role of a state´s military power; opinion on the future staffing level of the Federal Armed Forces; assessment of Germany´s defence expenditure; general attitude towards the Federal Armed Forces. 3. Evaluation of public institutions: Institutional trust (Federal Constitutional Court, other courts, police, Bundesrat, state government, Federal Armed Forces, Bundestag, television, press, churches, trade unions, federal government, education, political parties); reliance on the Federal Armed Forces. 4. Attitude towards compulsory military service: Military service or alternative civilian service more important for society; decision for or against various community services (care of the sick, care of the elderly, military service/defence, care of the disabled, environmental protection/remedy of environmental damage, care of children in need of help, service with the police, border guards or fire brigade); community service which the interviewee would be most likely to opt for social service most likely to be refused; general attitude towards military service; opinion on the right to conscientious objection; frequency of different reasons for conscientious objection (religious reasons, military service as time lost, political reasons, military service not compatible with conscience, civilian service as a more convenient way, economic reasons, civilian service with greater benefit to society); general compulsory military service retained vs. conversion into a voluntary army; future of the Federal Armed Forces (Federal Armed Forces should be abolished, citizen´s army based on the Swiss model, purely voluntary army, current mix of conscripts, professional and temporary soldiers should be retained, fewer professional and temporary soldiers more military exercises for former soldiers); preference for the future of the Federal Armed Forces. 5. Tasks of the Federal Armed Forces: Preferences with regard to the tasks of the Federal Armed Forces (tasks of international arms control, fight against international terrorism, fight against international drug trafficking, border security against illegal immigrants, tasks in the field of environmental protection, international disaster relief, humanitarian aid and rescue services, reconstruction and development aid, international military advice, Combat operations on behalf of and under the control of the UN or other international organisations, peacekeeping operations on behalf of and under the control of the UN or international organisations, protection of the constitutional order in Germany, participation in celebrations and ceremonies, education and character building, defence of Germany, defence of allies, aid for threatened friendly nations); evaluation of the deployment of German soldiers in various UN missions with regard to: care of the suffering population, promotion of the international community, integration of Germany, strengthening of German national interests, stabilisation of world peace, strengthening of the reputation of the Federal Armed Forces, enforcement of human rights, establishment of democracy in the country of deployment, protection of the population in the country of deployment; assessment of the armament and equipment of the Federal Armed Forces; assessment of leadership training in the Federal Armed Forces; assessment of ´soldiering´ as a profession; personal acquaintance with a Federal Armed Forces soldier; personal advice to a relative or friend when considering volunteering for the Federal Armed Forces; importance of co-determination in civilian enterprises; importance of co-determination for soldiers in peacetime; preferences for voluntary service by women in the Federal Armed Forces (women do not belong in the Federal Armed Forces, only in unarmed service, all uses should be open to women); opinion on the complete withdrawal of US troops from Germany; opinion on the complete withdrawal of the Federal Armed Forces from the region; agreement on various possibilities for a new German security policy (extension of NATO security guarantees to Eastern Europe, common European foreign and security policy, restructuring of the military, return to national German interests, strengthening of political cooperation); the importance for Germany of a permanent seat on the UN Security Council; attitudes towards citizens of various neighbouring countries (Belgians, Danes, French, Dutch, Austrians, Poles, Swiss, Czechs and Luxemburgers); the most positive attitudes and the most negative attitudes towards neighbours; a feeling of belonging as West Germans, East Germans, Germans, Europeans or world citizens. 6. Military cooperation in Europe: familiarity of various associations with soldiers from different nations (e.g. German-French Brigade, Eurocorps, German-American Corps, German-Dutch Corps); opinion on military cooperation with various countries (USA, France, Netherlands, England, Belgium, Denmark, Italy); opinion on the creation of a European army; opinion on the political unification of Europe; opinion on the introduction of a common European currency, the Euro; evaluation of the performance of the Federal Armed Forces with regard to reunification in comparison to other institutions (trade unions, churches, political parties, employers´ associations, sports associations and media); opinion on the future NATO deployment of Federal Armed Forces combat troops. Demography: Sex; age (year of birth); education; additional vocational training; occupation; occupational group; net household income; marital status; denomination; residential environment (degree of urbanisation); city size; federal state; household size; number of persons in household aged 16 and over; Left-Right Self-Placement. Additionally coded: Respondent ID; age (categorised); West/East; weight.

Germany Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 12.000 % in 2020. This records a decrease from the previous number of 12.200 % for 2019. Germany Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 10.050 % from Dec 1991 (Median) to 2020, with 30 observations. The data reached an all-time high of 12.200 % in 2019 and a record low of 8.200 % in 1993. Germany Poverty Headcount Ratio at Societal Poverty Lines: % of 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: Social: Poverty and Inequality. The poverty headcount ratio at societal poverty line is the percentage of a population living in poverty according to the World Bank's Societal Poverty Line. The Societal Poverty Line is expressed in purchasing power adjusted 2017 U.S. dollars and defined as max($2.15, $1.15 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $2.15. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries’ median income grows.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Aim of the study was the development and validation of the psychometric properties of a six-item bi-factorial instrument for the assessment of social support (emotional and tangible support) with a population-based sample. A cross-sectional data set of N = 15,010 participants enrolled in the Gutenberg Health Study (GHS) in 2007–2012 was divided in two sub-samples. The GHS is a population-based, prospective, observational single-center cohort study in the Rhein-Main-Region in western Mid-Germany. The first sub-sample was used for scale development by performing an exploratory factor analysis. In order to test construct validity, confirmatory factor analyses were run to compare the extracted bi-factorial model with the one-factor solution. Reliability of the scales was indicated by calculating internal consistency. External validity was tested by investigating demographic characteristics health behavior, and distress using analysis of variance, Spearman and Pearson correlation analysis, and logistic regression analysis. Based on an exploratory factor analysis, a set of six items was extracted representing two independent factors. The two-factor structure of the Brief Social Support Scale (BS6) was confirmed by the results of the confirmatory factor analyses. Fit indices of the bi-factorial model were good and better compared to the one-factor solution. External validity was demonstrated for the BS6. The BS6 is a reliable and valid short scale that can be applied in social surveys due to its brevity to assess emotional and practical dimensions of social support.

Abstract

In 2012, 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.

This is the 1st revision of the 2012 Cross-Sectional User Database as released in September 2014.

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