The study at hand is a pioneer work of compiling statistical materials on the German national income in a comparable form for a period of more than 100 years. This is meant to be a cornerstone of the detailed analysis of the growth process of the German national economy. As a matter of fact, the structural changes of the German economy within the last 100 years can be presented by means of cross-sectional analysises for certain points of time. Thus essential facts as related to economic history are arranged in a systematic order as well. In addition, time series are analysed in order to make the explanation of the underlying development possible. Consequently, the findings resulting from this study could offer a primary basis for the verification of theories on growth, and for the judgment of future chances of development. Due to considerable efforts in this regard, such estimated figures on the national income are disposable for several countries. In this context, the study in hand closes the gap in the German statistical reports in the mentioned field. The study proves that, in this field of research, many difficulties have to be overcome; above all, it must be stated that the statistical materials are partly incomplete and make the realisation of such a project appear venturesome. However, the results prove that taking the risk pays off in the end. So the authors pin their hopes on an ensuing evaluation, as far as feasible, of the obtained data. Additionally, they would appreciate if these data were completed by means of criticism and new research, whereby different methods could be applied as well. According to its purpose, the study in hand is limited to the presentation of statistical materials without interpreting them. The attempt to evaluate the results of this study in order to provide an analysis on the growth rate of the German national economy seems premature yet; the work accomplished so far is simply not sufficient. Apart from the lack of absolute figures on the national income, a detailed structural analysis of the German national income has still not been completed for this purpose. In fact, the focus of the analysis lies on the determination of nominal values for the national income. Details on the real income, on the other hand, are restricted to a brief analysis, as the authors are of the opinion that the disposible price series are insufficient as regards a study on the real income. It is beyond doubt that they cannot imply all major components, in particularly those of earlier periods. The named long-term work has been made possible by the financial support of the Social Science Research Council and the Deutsche Forschungsgemeinschaft; the latter has also contribute to the printing costs. In their turn, the authors would like to express their gratefulness for the generous assistance on the part of these two institutions.” (W.G. Hoffmann / J.H. Müller (1959), S. V-VI). Classification of tables:A. Germany, overwiewB. PrussiaC. State or region A. Overview: German national income per type of income (1851-1957)A. Overview: national income in Germany and in single federal states (1871-1936)A. Overview: the national income per capita in Germany and in single federal states (1871-1936)B. The national income in Prussia (1851-1913)C. Uncorrected income per capita of the population and national income per capita of the population in selected years (1900-1913)C. The national income in Hamburg and Bremen (1871-1913)C The national income in Hesse (1872-1913)C. The national income in Saxony (1874-1913)C. The national income in Baden (1885-1913)C. The national income in Württemberg (1904-1913)C. The national income in Bavaria (1911-1913)

505 Economics is on a mission to make academic economics accessible. We've developed the first monthly sub-national GDP data for EU and UK regions from January 2015 onwards.

Our GDP dataset uses luminosity as a proxy for GDP. The brighter a place, the more economic activity that place tends to have.

We produce the data using high-resolution night time satellite imagery and Artificial Intelligence.

This builds on our academic research at the London School of Economics, and we're producing the dataset in collaboration with the European Space Agency BIC UK.

We have published peer-reviewed academic articles on the usage of luminosity as an accurate proxy for GDP.

Key features:

• Granular: Data is provided at the following geographical units:
  • NUTS3 (e.g. German Districts/Kreise),
  • NUTS2 (e.g. Regions/Regierungsbezirke),
  • NUTS1 (e.g. States/Länder), and
  • NUTS0 (e.g. Germany) levels.
• Frequent: Data is provided every month from January 2015. This is more frequent than the annualised official datasets.
• Timely: Data is provided with a one month lag (i.e. the data for January 2021 was published at the end of February 2021). This is substantially quicker than the 18 month lag of official datasets.
• Accurate: Our dataset uses Deep Learning to maximise accuracy (RMSE 1.2%).

The dataset can be used by:

• Governments and policy makers - to monitor the performance of local economies, to measure the localised impact of policies, and to get a real-time indication of economic activity.
• Financial services - to get an indication of national-level GDP before official GDP statistics are released
• Engineering companies - to monitor and evaluate the localised impact of infrastructure projects
• Consultancies - to forecast the localised impact of specific projects, to retrospectively monitor and evaluate the localised impact of existing projects
• Economics firms - to create macro forecasts at the national and sub-national level, to assess the impact of policy interventions.
• Academia / Think Tanks - to conduct novel research at the local level. E.g. our dataset can be used to measure the impact of localised COVID-19 lockdowns.

We have created this dataset for all UK sub-national regions, 28 EU Countries and Switzerland.

Germany DE: Unemployment: National Estimate: Youth Male: % of Male Labour Force Aged 15-24 data was reported at 6.529 % in 2023. This records a decrease from the previous number of 7.019 % for 2022. Germany DE: Unemployment: National Estimate: Youth Male: % of Male Labour Force Aged 15-24 data is updated yearly, averaging 8.720 % from Dec 1983 (Median) to 2023, with 41 observations. The data reached an all-time high of 16.730 % in 2005 and a record low of 4.442 % in 1990. Germany DE: Unemployment: National Estimate: Youth Male: % of Male Labour Force Aged 15-24 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: Employment and Unemployment. Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment. Definitions of labor force and unemployment differ by country.;International Labour Organization. “Labour Force Statistics database (LFS)” ILOSTAT. Accessed January 07, 2025. https://ilostat.ilo.org/data/.;Weighted average;The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

Graph and download economic data for Nominal General Government Final Consumption Expenditure for Germany (NCGGSAXDCDEQ) from Q1 1991 to Q1 2025 about general, Germany, consumption expenditures, consumption, and government.

The number of social media users in Germany was forecast to continuously increase between 2024 and 2029 by in total **** million users (+***** percent). After the ninth consecutive increasing year, the social media user base is estimated to reach ***** million users and therefore a new peak in 2029. Notably, the number of social media users of was continuously increasing over the past years.The shown figures regarding social media users have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Since 2005, the Diagnosis Related Groups (DRG) statistics have provided annual information on morbidity events and morbidity trends in inpatient care, as well as on the volume and structure of demand for services, over and above the existing official hospital statistics. In particular, type of illness, case-flat-rate hospital statistic (DRGs), operations and procedures as well as length of stay and department are collected.

The aggregated data are freely accessible.

Analysis unit

Persons West Germany; persons not organized into households

UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: no - Households: no - Individuals: yes - Group quarters: yes

UNIT DESCRIPTIONS: - Dwellings: A dwelling, in terms of a survey unit, is defined as a self-contained set of rooms intended for living purposes which enable persons to keep their own household. - Households: A household consists of all persons who live in the same dwelling and have a common housekeeping budget. Also. persons living and keeping house alone as well as lodgers are counted as households. - Group quarters: Persons in institutions, homes or the like are covered if they live there, are registered at that address with the police or relevant authorities and, fully or partly, make use of communal catering arrangements or of any joint facilities.

Universe

Total population entitled to reside in households

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: Federal Statistical Office (Statistisches Bundesamt)

SAMPLE SIZE (person records): 3094845.

SAMPLE DESIGN: A randomized 50% sub-sample was drawn from the 10% sample of the factually anonymous Scientific-Use-File (SUF) by using the ending numbers method. This yields the Public Use File, which is 5% sample of the total 1970 FRG population.

Mode of data collection

Face-to-face [f2f]

Research instrument

2 questionnaires: (f1) population census questionnaire: 90% of the population received a questionnaire comprising 18 questions, the remaining 10% received a questionnaire containing 39 questions; (f2) local unit questionnaire

Graph and download economic data for Real General Government Final Consumption Expenditure for Germany (NCGGRSAXDCDEQ) from Q1 1991 to Q1 2025 about general, Germany, consumption expenditures, consumption, government, and real.

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Government Revenues in Germany decreased to 490.22 EUR Billion in the first quarter of 2025 from 594.41 EUR Billion in the fourth quarter of 2024. This dataset provides - Germany Government Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Germany DE: Government Expenditure on Education: Total: % of Government Expenditure data was reported at 9.191 % in 2022. This records an increase from the previous number of 9.066 % for 2021. Germany DE: Government Expenditure on Education: Total: % of Government Expenditure data is updated yearly, averaging 9.047 % from Dec 1991 (Median) to 2022, with 32 observations. The data reached an all-time high of 9.748 % in 2014 and a record low of 7.506 % in 1995. Germany DE: Government Expenditure on Education: Total: % of Government Expenditure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Social: Education Statistics. General government expenditure on education (current, capital, and transfers) is expressed as a percentage of total general government expenditure on all sectors (including health, education, social services, etc.). It includes expenditure funded by transfers from international sources to government. General government usually refers to local, regional and central governments.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Median;

Government Debt in Germany increased to 2553976 EUR Million in the second quarter of 2025 from 2523342 EUR Million in the first quarter of 2025. This dataset provides - Germany Government Debt- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Read the associated blogpost for a detailed description of how this dataset was prepared; plus extra code for producing animated maps.

Context

The 2019 Novel Coronavirus (COVID-19) continues to spread in countries around the world. This dataset provides daily updated number of reported cases & deaths in Germany on the federal state (Bundesland) and county (Landkreis/Stadtkreis) level. In April 2021 I added a dataset on vaccination progress. In addition, I provide geospatial shape files and general state-level population demographics to aid the analysis.

Content

The dataset consists of thre main csv files: covid_de.csv, demgraphics_de.csv, and covid_de_vaccines.csv. The geospatial shapes are included in the de_state.* files. See the column descriptions below for more detailed information.

• covid_de.csv: COVID-19 cases and deaths which will be updated daily. The original data are being collected by Germany's Robert Koch Institute and can be download through the National Platform for Geographic Data (the latter site also hosts an interactive dashboard). I reshaped and translated the data (using R tidyverse tools) to make it better accessible. This blogpost explains how I prepared the data, and describes how to produces animated maps.

• demographics_de.csv: General Demographic Data about Germany on the federal state level. Those have been downloaded from Germany's Federal Office for Statistics (Statistisches Bundesamt) through their Open Data platform GENESIS. The data reflect the (most recent available) estimates on 2018-12-31. You can find the corresponding table here.

• covid_de_vaccines.csv: In April 2021 I added this file that contains the Covid-19 vaccination progress for Germany as a whole. It details daily doses, broken down cumulatively by manufacturer, as well as the cumulative number of people having received their first and full vaccination. The earliest data are from 2020-12-27.

• de_state.*: Geospatial shape files for Germany's 16 federal states. Downloaded via Germany's Federal Agency for Cartography and Geodesy . Specifically, the shape file was obtained from this link.

Column Description

COVID-19 dataset covid_de.csv:

• state: Name of the German federal state. Germany has 16 federal states. I removed converted special characters from the original data.

• county: The name of the German Landkreis (LK) or Stadtkreis (SK), which correspond roughly to US counties.

• age_group: The COVID-19 data is being reported for 6 age groups: 0-4, 5-14, 15-34, 35-59, 60-79, and above 80 years old. As a shortcut the last category I'm using "80-99", but there might well be persons above 99 years old in this dataset. This column has a few NA entries.

• gender: Reported as male (M) or female (F). This column has a few NA entries.

• date: The calendar date of when a case or death were reported. There might be delays that will be corrected by retroactively assigning cases to earlier dates.

• cases: COVID-19 cases that have been confirmed through laboratory work. This and the following 2 columns are counts per day, not cumulative counts.

• deaths: COVID-19 related deaths.

• recovered: Recovered cases.

Demographic dataset demographics_de.csv:

• state, gender, age_group: same as above. The demographic data is available in higher age resolution, but I have binned it here to match the corresponding age groups in the covid_de.csv file.

• population: Population counts for the respective categories. These numbers reflect the (most recent available) estimates on 2018-12-31.

Vaccination progress dataset covid_de_vaccines.csv:

• date: calendar date of vaccination

• doses, doses_first, doses_second: Daily count of administered doses: total, 1st shot, 2nd shot.

• pfizer_cumul, moderna_cumul, astrazeneca_cumul: Daily cumulative number of administered vaccinations by manufacturer.

• persons_first_cumul, persons_full_cumul: Daily cumulative number of people having received their 1st shot and full vaccination, respectively.

Acknowledgements

All the data have been extracted from open data sources which are being gratefully acknowledged:

• The [Robert ...

The study deals with the changes in the regional structure of Germany’s foreign trade as well as with the causes of these changes between 1880 and 1938. In this context the regional development of german import and export by continents, regions and counties for the major German tradepartners is described. After that the regional development of the trade flows of all major import- and exportproducts is analysed. The German foreign trade therefore is examined on the basis of the goods on the one hand and on the other on the basis of the countries. For the analysis the researcher collected the data (time series) for the important goods and commodity groups. Further more he developed a consistent definition of the commodity groups, so that they are comparable.
Mehtodology a) Definition and Problem:The following processes hab been defined as structural change:- fundamental shifts in the composition of foreign trade partners, as well as- meaningful and sustained change of direction or volume of important commodities and products that affect the trade with countries or regions. b) Temporal Delimination:Period of investigation is from 1880 to 1938. The statistics for the war years 1914-1918 and 1939 and for the post-war years 1919-1924 have not been included in the analysis because values were not covered or values are very incomplete or unreliable coused by inflation and other circumstances of that period. c) Changes of Territory:The data of the German trade statistics refer from 1880 to February 1906 to the German custom territory, which comprised since 1872 the territory of the German Customs Union, consisting of the 26 states, the Grand Duchy of Luxembourg and the Austrian municipalities Jungholz and Mittelberg. The free port areas of Hamburg, Bremerhaven, Geestermünde and Helgoland and parts of the municipality of Hamburg and Cuxhaven did not belong to the German custum territory.Since March 1906 the german trade statistics collected data of the foreign merchandise traffic of the entire German economic area, consists until the Versailler contract of the area of the German Empire including the Grand Duchy of Luxembourg and the Austrian municipalities Jungholz and Mittelberg, excluding Helgoland and the badenese Custum boards. Since 1920 the official trade statistics reports the values of the foreign trade for the German Empire in its new borders. That is to say, the regions of Alsace-Lorraine, the Free City of Danzig, and parts of the Prussian provinces of East Prussia, West Prussia, Brandenburg, Pomerania, Silesia, Posen, Schleswig-Holstein, the Rhine province, the territory of Luxembourg and for the years 1919 to 1935, the Saarland no longer belong to the German economic territory. The expansion of the German Empire territory between 1938 and 1939 by the annexation of Austria, Sudetenland, Bohemia, Moravia, and the Memel territory has been kept out of consideration. For the analysis of the German foreign trade the values of german imports and exports published by the Statistical Office of the German Empire has been used. While comparing the pre-1914 values with values after the first World War, it is important to reconsider the lost of major agricultural areas of East-Germany, which restricts the comparison and it’s explanatory power or validity. On the other hand these changes reveals the changes of Germany’s foreign trade structure. Thus, it becomes obvious how the separation of large agricultural and farming land increased Germany’s import dependency in the food sector as well as Germany’s decreased export opportunities of agricultural products. d) System of commodity groups: The problem of published German trade values of the Official Statistics of the German Empire is, that commodity groups are not defined in terms of their content. Insofar as the information is about single goods (eg.: rye, copper, cotton, etc.), the values are reliable. This is not the case as soon as the information is about commodity groups, such as ‘food’, ‘textiles’, ‘metal goods’, etc., because the structure of the aggregation of specific goods to a commodity group has changed six times over the period of investigation. The list of countries in the german foreign trade statistics has changed as well. Therfore, the author had to revised commodity groups and country lists for the purpose of its analysis and to make them comparable. The author developed the following scheme in order to sort countries into groups or regions: - Europe:Denmarc, Norway, Sweden, Finnland = North EuropeNetherlands, Belgium/Luxembuorg, Great Britain, France, Swizerland = West EuropeJugoslawia, Hungary, Rumania, Bulgaria, Albania, Greek, european and asiatic Turkey = South-East EuropePortugal, Spane, Italy = South EuropePoland, Tschechoslowakia, Russia, Baltic States = East EuropAustria-Hungary - America:Canada, United States of America = North-AmericaMexico, Costarica, Duba, Dominican Republic, Guatemala, Honduras, Nicaragua, Haiti, El Salvador = Ce...

Germany: Government spending, billion USD: The latest value from 2023 is 960.75 billion U.S. dollars, an increase from 896.06 billion U.S. dollars in 2022. In comparison, the world average is 104.89 billion U.S. dollars, based on data from 155 countries. Historically, the average for Germany from 1970 to 2023 is 431.02 billion U.S. dollars. The minimum value, 35.08 billion U.S. dollars, was reached in 1970 while the maximum of 960.75 billion U.S. dollars was recorded in 2023.

The dataset consists of occurrence data of the aquatic organism groups fish, macroinvertebrates and macrophytes that were sampled in the German federal state Saxony between 2007 and 2011. The sampling was conducted according to the protocols of the national monitoring programme for implementation of the Water Framework Directive. More information on this dataset can be found in the Freshwater Metadatabase - BFE_93 (http://www.freshwatermetadata.eu/metadb/bf_mdb_view.php?entryID=BFE_93).

This statistic shows the results of a survey on German national character in 2016. Fifty seven percent of respondents were of the opinion that there is a German national character.

The dataset consists of occurrence data of the aquatic organism group macroinvertebrates that were sampled in the German federal state Berlin between 2006 and 2010. The sampling was conducted according to the protocols of the national monitoring programme for implementation of the Water Framework Directive. More information on this dataset can be found in the Freshwater Metadatabase - BFE_89 (http://www.freshwatermetadata.eu/metadb/bf_mdb_view.php?entryID=BFE_89).

Germany recorded a Government Budget deficit equal to 2.80 percent of the country's Gross Domestic Product in 2024. This dataset provides the latest reported value for - Germany Government Budget - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The urban indicators data available here are analyzed, compiled and published by UN-Habitat’s Global Urban Observatory which supports governments, local authorities and civil society organizations to develop urban indicators, data and statistics. Urban statistics are collected through household surveys and censuses conducted by national statistics authorities. Global Urban Observatory team analyses and compiles urban indicators statistics from surveys and censuses. Additionally, Local urban observatories collect, compile and analyze urban data for national policy development. Population statistics are produced by the United Nations Department of Economic and Social Affairs, World Urbanization Prospects.