Between 1950 and 1962, West Germany's national income grew by over 7.2 percent, in contrast to the United Kingdom's growth of just 2.29 percent. The primary reason for national growth in this period was improvements made to productivity, such as general advances in knowledge, reallocation of labor resources, and economies of scale (i.e. lowering costs by producing in mass). Increases in labor and capital also contributed to economic growth during this period, and in the UK it was responsible for around half of national growth, however the improvements in productivity constituted a much larger share of national growth in other nations. In Germany, France, and Italy, improvements to scale economies alone saw national income grow by more than one percent, although the largest contributor in Italy was the reallocation of labor from agricultural to industrial sectors.

During the 1950s and 1960s, the population of Eastern Europe grew by approximately 1.3 percent each year, although it varied per country. The Soviet Union and Poland saw the largest growth, with annual increases of 1.5 and 1.4 percent respectively. While most countries saw significant population growth in this period, East Germany's population actually decreased, from 18.4 million in 1950 to 17.1 million in 197. This was due to the high rates of Westward migration in the 1950s, before border restrictions became much more stringent after 1961.

In 2025, the crude birth rate in Europe (the number of live births per 1,000 population) was estimated to be 8.3, which is also the lowest birth rate in the provided time period. Between 1950 and 2025, the birth rate was highest in Europe in 1950 when it stood at 22.2.

In Eastern Europe in the 1950s and 1960s, economic output per employed person grew by approximately 5.2 percent per year. The annual rate was highest in the 1950s, where it was over 5.8 percent, while it fell to 4.7 percent in the 1960s. Overall, Bulgaria and Romania saw the largest increases in output, particularly during the periods of rapid industrialization in the 1960s.

This dataset provides monthly total precipitation amounts (in millimeters) recorded across Europe. Each row represents the total precipitation for one month and year, facilitating analyses of hydrological cycles, droughts, flooding risks, and long-term climate variability. The data can be used for time series analysis, climate modeling, and environmental studies focused on European precipitation patterns over multiple decades.

E-OBS is a daily gridded land-only observational dataset over Europe. The blended time series from the station network of the European Climate Assessment & Dataset (ECA&D) project form the basis for the E-OBS gridded dataset. All station data are sourced directly from the European National Meteorological and Hydrological Services (NMHSs) or other data holding institutions. For a considerable number of countries the number of stations used is the complete national network and therefore much more dense than the station network that is routinely shared among NMHSs (which is the basis of other gridded datasets). The density of stations gradually increases through collaborations with NMHSs within European research contracts. Initially, in 2008, this gridded dataset was developed to provide validation for the suite of Europe-wide climate model simulations produced as part of the European Union ENSEMBLES project. While E-OBS remains an important dataset for model validation, it is also used more generally for monitoring the climate across Europe, particularly with regard to the assessment of the magnitude and frequency of daily extremes. The position of E-OBS is unique in Europe because of the relatively high spatial horizontal grid spacing, the daily resolution of the dataset, the provision of multiple variables and the length of the dataset. Finally, the station data on which E-OBS is based are available through the ECA&D webpages (where the owner of the data has given permission to do so). In these respects it contrasts with other datasets. The dataset is daily, meaning the observations cover 24 hours per time step. The exact 24-hour period can be different per region. The reason for this is that some data providers measure between midnight to midnight while others might measure from morning to morning. Since E-OBS is an observational dataset, no attempts have been made to adjust time series for this 24-hour offset. It is made sure, where known, that the largest part of the measured 24-hour period corresponds to the day attached to the time step in E-OBS (and ECA&D).

Annual mean temperature data for the period 1950 to 2009 for Europe. Data is gridded at a cell size of 0.25 degrees. E-OBS daily data downloaded from http://eca.knmi.nl/download/ensembles/download.php#datafiles in NetCDF format. Data converted to Arc GRID and annual averages calculated for each year, using map algebra. Please see http://eca.knmi.nl/download/ensembles/download.php#datafiles for terms and conditions of use. Other. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2011-01-14 and migrated to Edinburgh DataShare on 2017-02-21.

This dataset provides meteorological and snow indicators for Europe, characterizing operating conditions of winter ski resorts under past and future climate scenarios. The dataset consists of 39 indicators of atmospheric and snow conditions computed in a similar manner for all mountain regions in Europe at the scale of NUTS-3 regions (Nomenclature of Territorial Units for Statistics) and by steps of 100 m elevation. The snow indicators are generated using the Crocus snowpack model, a multi-layer snowpack model embedded in the land surface model, SURFEX (Surface Externalisée). In order to assess the impact of climate change, the model is run for four different climate scenarios: the present climate (labelled 'historical'), and three Representative Concentration Pathway (RCP) scenarios that correspond to an optimistic emission scenario where emissions start declining beyond 2020 (RCP2.6), a further optimistic emission scenario where emissions start declining beyond 2040 (RCP4.5) and a pessimistic scenario where emissions continue to rise throughout the century, often called the high emission scenario (RCP8.5). In order to simulate these climate scenarios the SURFEX model is forced with atmospheric fields provided by adjusted EURO-CORDEX ensemble climate projections (European branch of the Coordinated Downscaling Experiment). Regional climate models downscaled from global climate models are used to provide the high resolution, pan-European, indicators required to assess the snow reliability for all mountainous regions across Europe. In addition to the climate scenarios, a reanalysis dataset is computed using UERRA reanalysis. A total of 39 indicators are made available in this dataset, divided into seven distinct groups:

Start and end date of snow season Annual amount of machine made snow produced Precipitation Snow depth Snow water equivalent Air temperature Potential snow making hours

The Crocus model makes it possible to account for both snow grooming and mechanical snow-making based upon the physical representation of these snow management practices, adding further value to the end-user by providing indicated snow management requirements under future climate conditions. However, it is not designed to replace higher resolution products available in some European regions that provide a more detailed view of ski conditions; for example, accounting for slope, local meteorological phenomena and local snow management practices. Instead this dataset presents a homogenous product at a pan-European level and hence its main goal is to compare the main features of past and future snow conditions across Europe or to compare distant destinations; for example, Scandinavia and Eastern Europe (for a given elevation and time horizon). This dataset was produced on behalf of the Copernicus Climate Change Service.

In 1950, the agriculture and forestry sector employed the largest amount of people in every country in Eastern Europe, except for East Germany. The industrial sector would eventually emerge as the largest contributor to the economy by the late 1960s, though agriculture would remain the largest employment sector in most countries.

ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. ERA5-Land uses as input to control the simulated land fields ERA5 atmospheric variables, such as air temperature and air humidity. This is called the atmospheric forcing. Without the constraint of the atmospheric forcing, the model-based estimates can rapidly deviate from reality. Therefore, while observations are not directly used in the production of ERA5-Land, they have an indirect influence through the atmospheric forcing used to run the simulation. In addition, the input air temperature, air humidity and pressure used to run ERA5-Land are corrected to account for the altitude difference between the grid of the forcing and the higher resolution grid of ERA5-Land. This correction is called 'lapse rate correction'.
The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. The temporal and spatial resolutions of ERA5-Land makes this dataset very useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states.

Pedersen index of net volatility in legislative elections between 1950 and 2014 in Western Europe. The index was originally outlined in Pedersen, M.N. (1979) The Dynamics of European Party Systems: Changing Patterns of Electoral Volatility. European Journal of Political Research 7(1): 1-26. How to cite this dataset? Dassonneville, Ruth (2015). Net Volatility in Western Europe: 1950-2014. Dataset. Leuven: Centre for Citizenship and Democracy.

The European System of Social Indicators provides a systematically selected collection of time-series data to measure and monitor individual and societal well-being and selected dimensions of general social change across European societies. Beyond the member states of the European Union, the indicator system also covers two additional European nations and – depending on data availability – the United States and Japan as two important non-European reference societies. Guided by a conceptual framework, the European System of Social Indicators has been developed around three basic concepts – quality of life, social cohesion, and sustainability. While the concept of quality of life is supposed to cover dimensions of individual well-being, the notions of social cohesion as well as sustainability are used to conceptualize major characteristics and dimensions of societal or collective well-being. The indicator system is structured into 13 life domains altogether. Time-series data are available for nine life domains, which have been fully implemented.

Time series start at the beginning of the 1980s at the earliest and mostly end by 2013. As far as data availability allows, empirical observations are presented yearly. Most of the indicator time-series are broken down by selected sociodemographic variables, such as gender, age groups, employment status, or territorial characteristics. Regional disaggregations are being provided at the NUTS-1 or similar levels as far as meaningful and data availability allows. The European System of Social Indicators is preferably based on harmonized data sources, ensuring the best possible level of comparability across countries and time. The data sources used include international aggregate official statistics, for example, provided by EUROSTAT and the OECD, as well as microdata from various official as well as science-based cross-national surveys, such as the European Union Statistics on Income and Living Conditions (EU-SILC), Eurobarometer Surveys, the World Value Surveys, or the European Social Survey.

The European System of Social Indicators results from research activities within the former Social Indicators Research Centre at GESIS. In its initial stage, this research was part of the EuReporting-Project (Towards a European System of Social Reporting and Welfare Measurement), funded by the European Commission within its 4th European Research Framework Programme from 1998 to 2001. For more detailed information on the European System of Social Indicators, see the methodological report under „other documents“.
The Data of SIMon (German System of Social Indicators (DISI) and European System of Social Indicators (EUSI)) are available via the histat online database (https://histat.gesis.org/histat/) under the topic ´SIMon: Social Indicators Monitor´(https://histat.gesis.org/histat/de/data/themes/36) for the free download.

A) Conceptual framework

The development of the conceptual framework for the European System of Social Indicators builds on the theoretical discussion of welfare and quality of life as well as the goals of social development oriented towards them. Additionally, the tasks and fundamental objectives of European Union policy have been statistically measured and reported. Based on these two areas (theoretical debate on welfare on the one hand and EU policy objectives on the other), six perspectives and dimensions of social development in Europe were identified which form the conceptual core of the European system of social indicators and are related to the concepts of quality of life, social cohesion and sustainability.

Dimensions of quality of life: 1) The dimension of objective living conditions describes the actual living conditions of individuals (working conditions, state of health, material standard of living). 2) The dimension of subjective welfare includes perceptions, assessments, and assessments of living conditions by citizens.

Dimensions derived from the concept of social cohesion: 3) Disparities, inequalities and social exclusion relate to aspects of the distribution of wealth in society (regional disparities, equal opportunities). 4) Social relationships, bonds, and inclusion refer to the social capital of a society. The existence of informal networks, associations, and organizations as well as the functioning of social institutions are covered by the dimension of social cohesion.

Dimensions of sustainability. In this context, sustainability is primarily understood as the preservation or increase of social capital (physical capital, social capital, human capital, natural capital) for future generations. 5) Securing human capital: Measuring dimensions and indicators of this target dimension primarily concern aspects of people´s education, skills, and health. 6) Safeguarding natural capital: This dimension relates both to the current state of the environment and to processes and measures that improve or worsen the natural foundations of...

The current growing interest in the growth of the Western European economies between the end of World War II and the first oil crisis of 1973 is primarily due to the end of the Cold War and the subsequent demand for solutions for the economic problems of Central and Eastern European transition countries. It was and is discussed to what extent we could learn from the successful rebuilding of the Western European economies. In this context one area of special interest is the reconstruction of West Germany, closely accompanied by the principle of the social market economy. The recollection of this principle, and the call for a new Marshall Plan imply the idea that the Western European post-war boom in essence can be traced to a successful economic policy. It is shown how this assumption can stand up to a theoretical and empirical analysis. Using the new growth theory and the cointegration analysis both national (eg social market economy and Planification (i.e. macroeconomic framework development planning)) and international explanations (eg the Marshall Plan) of the so called ‘golden age’ are examined. It turns out that the impact of economic policies on economic growth must be put into perspective. In contrast, the importance of the different economic conditions of the countries for the explication of their growth process is underlined. Variables, inter alia:- Investment behavior of industry- Production and Export industry- Exchange Rates- Structure of the economies Data focus:Foreign trade structure, external value (foreign wholesale prices), export volume, industrial production, capital stock, long-term development (income, investment rates, openness, exchange rates), patents (patent applications in Germany, France). List of tables in the database HISTAT ZA:- Investment rates in four European countries (1880-1995)- Net fixed assets of the industry in Germany (1950-1968)- Sectoral Gross capital expenditures in Germany (1960-1976)- Sectoral Gross investment in France (1949-1965)- Export volume index of France and the Federal Republic of Germany (1950-1973)- Export volume in millions of current U.S. dollars (1951-1990)- Weighted exchange rate index in indirect rate (1950-1973)- Index of industrial production in Europe and North America (1950-1973)- Construction and equipment investment in Germany (1950-1968)- Investment rates in four European countries (1880-1995)- Sectoral gross and net capital stock in France (1950-1970)- Sectoral gross and net capital stock, investment in France (1950-1969)- Percentage of the French colonies in the French total exports (1950-1973)- Openness of four European economies (1880-1994)- Annual patent applications in the United States (1963-1995)- Real per capita income in Europe and the United States (1870-1992)- Regional structure of the French export value (1896-1973)- French sector gross investment (1960-1976)- Exchange rates in four European countries (1891-1995) Territory of investigation:Germany, France, further OECD-states. Sources:Publications of the official French and German statistics, publications of the OECD, USA and further states; scientific journals.

The dataset presents climate impact indicators related to extreme precipitation in Europe under current climate conditions. The suite of indicators include recent historic records, recurrence intervals, and other relevant statistical measures to evaluate the magnitude and frequency of extreme precipitation events. These are provided as gridded products, with one product covering the whole of Europe, and the other higher resolution product focused on 20 European cities that were identified as vulnerable to urban pluvial flooding based on stakeholder surveys. This dataset makes use of precipitation data available in the Climate Data Store (i.e. E-OBS gridded land-only observational dataset and ERA5 reanalysis) combined with additional datasets capable of improving the spatial and temporal resolution of the precipitation data, making it suitable for pluvial flood analysis at city scales. These are derived from i) the network of meteorological stations included in the European Climate Assessment & Dataset (ECA&D) programme and ii) dynamically downscaled ERA5 reanalysis at 2 km x 2 km (ERA5-2km) using the regional climate model COSMO-CLM and accounting for urban parameterization, specifically performed for the 20 European cities identified as vulnerable to urban pluvial flooding. At the European scale, E-OBS and ERA5 precipitation data are used to compute indicators at different temporal resolutions (i.e. daily, monthly, yearly, and 30-year) according to the type of indicator. The precipitation amounts at fixed return periods are also computed for point observations from meteorological stations using the ECA&D network and are then interpolated onto the E-OBS grid. At the city scale, a dynamically downscaled ERA5-2km precipitation data are instead used to derive daily indicators, allowing city stakeholders to detect and rank local extreme precipitation events and evaluate their magnitude. This dataset was produced on behalf of the Copernicus Climate Change Service.

The HANZE dataset covers riverine, pluvial, coastal and compound floods that have occurred in 42 European countries. It contains:

• 2521 historical floods with impact data (1870-2020);
• 237 further historical floods with significant impacts, but without precise impact data (1950-2020)

• Nearly 15,000 modelled floods with a potential to cause significant impacts, classified by actual historical occurrence or non-occurrence impacts (1950-2020).

• ΩHistorical floods and the classification of modelled floods was completed by extensive data-collection from more than 900 sources ranging from news reports through government databases to scientific papers. Impact data collected or modelled include area inundated, fatalities, persons affected or economic loss. Economic losses were inflation- and exchange-rate adjusted to 2020 value of the euro. The historical catalogue (lsit A) also includes losses in the original currencies and price levels. The spatial footprint of affected areas is consistently recorded using more than 1400 subnational units corresponding, with minor exceptions, to the European Union’s Nomenclature of Territorial Units for Statistics (NUTS), level 3. Apart from the possibility to download the data, the database can be viewed, filtered and visualized online: https://naturalhazards.eu.

The dataset contains the following files (CSV comma-delimited, UTF8, and ESRI shapefiles in zipped folders):

• HANZE_historical_floods_catalogue_listA.csv - historical floods with impact data (1870-2020)
• HANZE_historical_floods_catalogue_listB.csv - historical floods without impact data (1950-2020)
• HANZE_potential_flood_catalogue_all.csv - modelled potential floods (1950-2020)
• HANZE_list_of_references.csv - List of all references used in the catalogues
• HANZE_model_completness_analysis.csv - Comparison between modelled and reported footprints of historical floods
• Regions_v2010_simplified.zip - Map of subnational regions (v2010)
• Regions_v2021_simplified.zip - Map of subnational regions (regions v2021)

v1.2: corrected NUTS regions v2021 for a few events, which were accidently coded with v2010 regions.

v1.1: errors in two records in "HANZE_historical_floods_catalogue_listB.csv" (wrong country code in event ID 8227 and wrong start date in event ID 8237) were corrected.

Citation

Acknowledgement

Foto von Jonathan Kemper auf Unsplash

The population of Europe was estimated to be 745 million in 2024, an increase of around 4 million when compared with 2012. Over 35 years between 1950 and 1985, the population of Europe grew by approximately 157.8 million. But 35 years after 1985 it was estimated to have only increased by around 38.7 million. Since the 1960s, population growth in Europe has fallen quite significantly and was even negative during the mid-1990s. While population growth has increased slightly since the low of -0.07 percent in 1998, the growth rate for 2020 was just 0.04 percent. Which European country has the biggest population? As of 2024, the population of Russia was estimated to be approximately 144.8 million and was by far Europe's largest country in terms of population, with Turkey being the second-largest at over 87 million. While these two countries both have territory in Europe, however, they are both only partially in Europe, with the majority of their landmasses being in Asia. In terms of countries wholly located on the European continent, Germany had the highest population at 84.5 million, and was followed by the United Kingdom and France at 69.1 million and 66.5 million respectively. Characteristics of Europe's population There are approximately 384.6 million females in Europe, compared with 359.5 million males, a difference of around 25 million. In 1950, however, the male population has grown faster than the female one, with the male population growing by 104.7 million, and the female one by 93.6 million. As of 2024, the single year of age with the highest population was 37, at 10.6 million, while in the same year there were estimated to be around 136 thousand people aged 100 or over.

ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. ERA5-Land provides a consistent view of the water and energy cycles at surface level during several decades. It contains a detailed record from 1950 onwards, with a temporal resolution of 1 hour. The native spatial resolution of the ERA5-Land reanalysis dataset is 9km on a reduced Gaussian grid (TCo1279). The data in the CDS has been regridded to a regular lat-lon grid of 0.1x0.1 degrees. The data presented here is a post-processed subset of the full ERA5-Land dataset. Monthly-mean averages have been pre-calculated to facilitate many applications requiring easy and fast access to the data, when sub-monthly fields are not required.

This table contains the number of victims of suicide arranged by marital status, method, motives, age and sex. They represent the number deaths by suicide in the resident population of the Netherlands.

The figures in this table are equal to the suicide figures in the causes of death statistics, because they are based on the same files. The causes of death statistics do not contain information on the motive of suicide. For the years 1950-1995, this information is obtained from a historical data file on suicides. For the years 1996-now the motive is tasks from the external causes of death. Before the 9th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD), i.e. for the years 1950-1978, it was not possible to code “jumping in front of train/metro”. For these years 1950-1978 “jumping in front of train/metro” has been left empty, and it has been counted in the group “other method”.

Relative figures have been calculated per 100000 of the corresponding population group. The figures are calculated based on the average population of the corresponding year.

Data available from: 1950

Status of the figures: The figures up to and including 2022 are final.

Changes as of January 25th 2024: The provisional figures for 2022 have been made final unchanged.

Changes as of August 29th 2023: The provisional figures for 2022 have been added. Some final figures of 2021 were incorrect and have been revised. A small adjustment was made in the number of deceased women from 60 to 69 years.

When will new figures be published: In the third quarter of 2024 the provisional figures for 2023 will be published.

A digital corpus on variation in German (1800-1950)The German Innsbruck Corpus (GermInnC) 1800-1950 is a digitised corpus built after the fashion of the German Manchester Corpus (GerManC) 1650-1800 (cf. Scheible et al. 2011; Durrell et al. 2012). Hence, the corpus design of the GermInnC is balanced according to period, region and genre.The GermInnC consists of ca. 840,000 tokens, ca. 120,000 per genre (seven in total: Drama, Humanities, Legal texts, Narrative prose, Newspapers, Scientific texts, Sermons). It is subdivided into three periods, 1800-1850, 1851-1900 und 1901-1950, as well as five regions, North German, West Central German, East Central German, West Upper German (including Switzerland), East Upper German (including Austria).The corpus can be retrieved in a raw version, a lemmatised, fully-annotated version, or an “all data” file (including metadata annotation of file names and periods) for further import and processing. The Stuttgart Tag Set (STTS) and the POS-Tagger TreeTagger was used for linguistic annotation.Two documentation files (word and excel, both included in the download package), provide a more detailed description of the corpus and the digitisation.The corpus may be of interest to all scholars working on the history of the German language, standardisation of German, variation and change, historical sociolinguistics, and Germanic linguistics.The corpus was generously funded by the early career funding of the University of Innsbruck (October 2018 through September 2019).

This survey was undertaken by Cefas as part of the Historic Arctic Survey Series;

Gadus morhua (Atlantic Cod) stocks in the Barents Sea are currently at levels not seen since the 1950s. Causes for the population increase last century, and understanding of whether such large numbers will be maintained in the future, are unclear. To explore this, we digitised and interrogated historical cod catch and diet datasets from the Barents Sea. Data includes temporal and spatial information, cod catch data and length distributions, and hydrographic data.

Survey took place between 16/03/1950 and 29/03/1950 on Ernest Holt

Equipment used during this survey :

• Otter Trawl 78ft Granton Arctic bobbins wooden otter boards

Survey operations were undertaken on 21 stations

17 different species were caught on this survey