In 2021, around 90 percent of respondents in Germany stated that one of the key issues in corporate diversity management was employees with different cultural backgrounds. 78 percent saw a balanced gender ratio as one of the most important issues.

Infineon, the largest German semiconductor manufacturer, was the most diverse company in Germany in 2021. Fashion company Hugo Boss, then the arms and automotive manufacturer Rheinmetall round off the top three.

According to a report by StepStone in 2021, almost 51 percent of respondents German companies needed to catch up with workplace diversity throught the promotion of older employees. Around 50 percent of people thought equal opportunity of promotions was something needed to be improved.

Almost 66 percent of respondents in Germany thought that diversity in the workplace was a contributing success factor to the development of a corporate image. Around 65 percent of people thought that is increased employee motivation. Figures are based on a report by StepStone in 2021.

Political attitudes and behaviors with regard to demographic change.

Topics: Assessment of the national economic situation (retrospective, current, prospective); concern regarding demographic change; anticipated problems caused by an aging society; perceived age limit of older and younger people; knowledge test: Proportion of the country´s population over 65; perception of commonalities in own age group; perceived frequency of media reports on generational conflicts; political interest; assessment of one´s own economic situation (retrospective, current, prospective); voter turnout (Sunday question); party preference (voters and non-voters); perceptions of social conflicts between selected social groups (people with and without children, politically left and right, young and old, poor and rich, employed and retired, Germans and foreigners, East Germans and West Germans); most important political goals (post-materialism, Inglehart indicators); opinion on selected statements about old and young (frequent abuse of social benefits in Germany, assessment of representation of younger people´s interests in politics, assessment of representation of older people in political positions, older people should organize their own party, older people should support younger people and younger people should support older people); perceived strength of general intergenerational support; financial support of a family member of another generation resp. frequency of self-received financial support (intergenerational transfers); frequency of support from a person in everyday life who belongs to another generation or frequency of self-received support; satisfaction with democracy; political trust (Bundestag, politicians, Federal Constitutional Court, federal government, media); opinion on selected statements about young and old (importance of contact with significantly younger persons, evaluation of the representation of the interests of older persons in politics, older persons live at the expense of the following generations, older persons have built up what the younger persons live on today, importance of contact with significantly older persons, evaluation of the representation of younger persons in political positions; political efficacy; electoral norm (voter turnout as a civic duty); sympathy scalometer of political parties (CDU/CSU, SPD, FDP, Greens, Die Linke); satisfaction with selected policy areas (reduction of unemployment, health, education, financial security for the elderly, family, care in old age); preferred level of government spending in the aforementioned areas; preferred government responsibility in the aforementioned areas; most competent party to solve the problems in the aforementioned areas (problem-solving competence); salience of the aforementioned policy areas; self-ranking on a left-right continuum; assessment of the representation of older people´s interests by political parties (CDU/CSU, SPD, FDP, Greens, Die Linke); assessment of the representation of younger people´s interests by political parties (CDU/CSU, SPD, FDP, Greens, Die Linke); recall Bundestag elections 2013 (voter turnout, voting decision); expected occurrence of various future scenarios (conflicts between older and younger people, refusal of younger people to pay for the pensions of older people, older people more likely to assert their political interests than younger people, increasing old-age poverty, refusal of younger people to pay for the medical care of older people, Germany will no longer be able to afford current pension levels, Elderly will no longer receive all available medical benefits); reliance most likely on state, family or self for own retirement; knowledge test: Year of phased introduction of retirement at 67; civic engagement; hours per week of volunteering; perception of social justice; general life satisfaction; party affiliation and strength of party identification; concerns regarding own retirement security (financial/medical) or feared unemployment; religious affiliation; religiosity; salience of selected life domains (family and friends, health, leisure, politics, income, education, work, and occupation); self-assessment of class affiliation; residence description.

Demography: age (grouped) and year of birth; sex; household size; number of persons under 18 in household; household composition (one, two, or three generations); number of children and grandchildren; regrets about own childlessness; partnership; living with partner; married to partner; German citizenship; German citizenship since birth or year of acquiring German citizenship; country of birth (in the old federal states (West Germany, in the new federal states (East Germany or former GDR) or abroad); highest school degree; university degree; current and former employment; current and former occupation.

Additionally coded were: Federal state; area; region West East; weighting factors; interview date.

This data collection contains electoral and demographic data at several levels of aggregation (kreis, land/regierungsberzirk, and wahlkreis) for Germany in the Weimar Republic period of 1919-1933. Two datasets are available. Part 1, 1919 Data, presents raw and percentagized election returns at the wahlkreis level for the 1919 election to the Nationalversammlung. Information is provided on the number and percentage of eligible voters and the total votes cast for parties such as the German National People's Party, German People's Party, Christian People's Party, German Democratic Party, Social Democratic Party, and Independent Social Democratic Party. Part 2, 1920-1933 Data, consists of returns for elections to the Reichstag, 1920-1933, and for the Reichsprasident elections of 1925 and 1932 (including runoff elections in each year), returns for two national referenda, held in 1926 and 1929, and data pertaining to urban population, religion, and occupations, taken from the German Census of 1925. This second dataset contains data at several levels of aggregation and is a merged file. Crosstemporal discrepancies, such as changes in the names of the geographical units and the disappearance of units, have been adjusted for whenever possible. Variables in this file provide information for the total number and percentage of eligible voters and votes cast for parties, including the German Nationalist People's Party, German People's Party, German Center Party, German Democratic Party, German Social Democratic Party, German Communist Party, Bavarian People's Party, Nationalist-Socialist German Workers' Party (Hitler's movement), German Middle Class Party, German Business and Labor Party, Conservative People's Party, and other parties. Data are also provided for the total number and percentage of votes cast in the Reichsprasident elections of 1925 and 1932 for candidates Jarres, Held, Ludendorff, Braun, Marx, Hellpach, Thalman, Hitler, Duesterburg, Von Hindenburg, Winter, and others. Additional variables provide information on occupations in the country, including the number of wage earners employed in agriculture, industry and manufacturing, trade and transportation, civil service, army and navy, clergy, public health, welfare, domestic and personal services, and unknown occupations. Other census data cover the total number of wage earners in the labor force and the number of female wage earners employed in all occupations. Also provided is the percentage of the total population living in towns with 5,000 inhabitants or more, and the number and percentage of the population who were Protestants, Catholics, and Jews.

In 2021, according to a survey among those responsible for diversity management in their company, the most important diversity management measure in Germany was to focus on flexible working hour models; 80 percent of respondents stated that this was an important issue. For over two thirds of respondents, restructuring recruiting processes was also a key priority for diversity in the workplace.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within New Germany. The dataset can be utilized to gain insights into gender-based income distribution within the New Germany population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within New Germany, among individuals aged 15 years and older with income, there were 230 men and 186 women in the workforce. Among them, 164 men were engaged in full-time, year-round employment, while 111 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 4.88% fell within the income range of under $24,999, while 5.41% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 16.46% of men in full-time roles earned incomes exceeding $100,000, while 4.50% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New Germany median household income by race. You can refer the same here

Replication files for: Breuer, S. and S. Elstner: "Germany’s growth prospects against the backdrop of demographic change" Journal of Economics and Statistics, forthcoming. * All data are in ASCII format (tabstop seperated, .txt-files). * All code for this paper is written in EViews. File Description: * Nairu_data.txt and Nairu_code.txt contain the replication files for the NAIRU-Estimation * TFP_data.txt and TFP_code.txt contain the replication Files for the TFP-Estimation * Potential_outptput_data.txt and Potential_outptput_code.txt contain the replication Files for the Potential-Output-Estimation. Notes: * Some comments in the EVIEWs-codes are in German, please contact the authors if you have any questions (sebastian.breuer@gmx.de, steffen_elstner@web.de) * More Data and further technical details about the GCEE´s cohort model are available on request. Seite

About this dataset

Context

The dataset presents the median household income across different racial categories 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. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of German Valley population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 99.24% of the total residents in German Valley. Notably, the median household income for White households is $69,843. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $69,843.

https://i.neilsberg.com/ch/german-valley-il-median-household-income-by-race.jpeg" alt="German Valley median household income diversity across racial categories">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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.
• Median household income: Median household income, adjusting for inflation, presented in 2022-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

Despite their ecological and economic importance, hymenopteran parasitoids are severely understudied. Even in countries with a long taxonomic history such as Germany, dating back to the 18th century and including prolific figures like Christian Gottfired Nees von Esenbeck and Otto Schmiedeknecht, those species-rich groups are seldom the subject of comprehensive research efforts, leaving their true diversity unknown. This is often due to their small size of a few millimetres on average, leading to difficulties in their identification and examination. The chalcidoid family Pteromalidae is no exception to this neglect. So far, 735 species have been reported from Germany. Estimating the diversity of this group is not possible, but it has to be assumed that many more species are still to be discovered in Germany.With this study, we improve the knowledge on pteromalid diversity and present new records of 17 genera and 41 species, previously unknown to occur in Germany. We also match and describe previously unknown sexes of two species, based on DNA barcode data. The results of this study were generated as part of the German Barcode of Life Project. The newly-recorded species are illustrated and notes on the biology and distribution are given. The ecological significance of Pteromalidae and potential value as indicators for nature conservation efforts are briefly discussed.

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Germany: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

What’s Included

Welcome to the German Brainstorming Prompt-Response Dataset, a meticulously curated collection of 2000 prompt and response pairs. This dataset is a valuable resource for enhancing the creative and generative abilities of Language Models (LMs), a critical aspect in advancing generative AI.

These prompt and completion pairs cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more. Each prompt is accompanied by a response, providing valuable information and insights to enhance the language model training process. Both the prompt and response were manually curated by native German people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This dataset encompasses various prompt types, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. Additionally, you'll find prompts and responses containing rich text elements, such as tables, code, JSON, etc., all in proper markdown format.

The German version is grammatically accurate without any spelling or grammatical errors. No copyrighted, toxic, or harmful content is used during the construction of this dataset.

In 2021, a Statista study on Diversity and equality in European companies showed that German workers in the drugs and biotechnology industry rated their diversity policies quite highly, when compared with sectors such as wholesale, and travel and leisure which had the lowest ratings among German employees.

About this dataset

Context

The dataset presents the median household income across different racial categories 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. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of German Flatts town population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 93.50% of the total residents in German Flatts town. Notably, the median household income for White households is $68,175. Interestingly, despite the White population being the most populous, it is worth noting that Two or More Races households actually reports the highest median household income, with a median income of $94,153. This reveals that, while Whites may be the most numerous in German Flatts town, Two or More Races households experience greater economic prosperity in terms of median household income.

https://i.neilsberg.com/ch/german-flatts-ny-median-household-income-by-race.jpeg" alt="German Flatts town median household income diversity across racial categories">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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.
• Median household income: Median household income, adjusting for inflation, presented in 2022-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

What’s Included

Welcome to the German Closed Ended Classification Prompt-Response Dataset—an extensive collection of 3000 meticulously curated prompt and response pairs. This dataset is a valuable resource for training Language Models (LMs) to classify input text accurately, a crucial aspect in advancing generative AI.

These prompt and completion pairs cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more. Each prompt is accompanied by a response, providing valuable information and insights to enhance the language model training process. Both the prompt and response were manually curated by native German people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This closed-ended classification prompt and completion dataset contains different types of prompts, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. The dataset also contains prompts and responses with different types of rich text, including tables, code, JSON, etc., with proper markdown.

The German version is grammatically accurate without any spelling or grammatical errors. No copyrighted, toxic, or harmful content is used during the construction of this dataset.

All data and R code for "Die Entwicklung der lexikalischen Diversität und Elaboriertheit bei SchülerInnen mit portugiesischem Migrationshintergrund in der Schweiz" (Bonvin, Vanhove, Berthele & Lambelet) as a zipped directory.

What’s Included

Welcome to the German Extraction Type Prompt-Response Dataset, a meticulously curated collection of 1500 prompt and response pairs. This dataset is a valuable resource for enhancing the data extraction abilities of Language Models (LMs), a critical aspect in advancing generative AI.

These prompt and completion pairs cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more. Each prompt is accompanied by a response, providing valuable information and insights to enhance the language model training process. Both the prompt and response were manually curated by native German people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This dataset encompasses various prompt types, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. Additionally, you'll find prompts and responses containing rich text elements, such as tables, code, JSON, etc., all in proper markdown format.

The German version is grammatically accurate without any spelling or grammatical errors. No copyrighted, toxic, or harmful content is used during the construction of this dataset.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within North Germany township. The dataset can be utilized to gain insights into gender-based income distribution within the North Germany township population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within North Germany township, among individuals aged 15 years and older with income, there were 76 men and 115 women in the workforce. Among them, 38 men were engaged in full-time, year-round employment, while 55 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, none fell within the income range of under $24,999, while 1.82% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: none of men in full-time roles earned incomes exceeding $100,000, while 3.64% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

https://i.neilsberg.com/ch/north-germany-township-mn-income-distribution-by-gender-and-employment-type.jpeg" alt="North Germany Township, Minnesota gender and employment-based income distribution analysis (Ages 15+)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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 North Germany township median household income by gender. You can refer the same here

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.