In 2025 thus far, there were around 728,400 employees from non-European asylum countries in Germany. This was an increase compared to the previous year.

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.

In 2021, according to a survey among those responsible for diversity management in their company in Germany, the most important advantage of diversity management was employer branding; 67 percent of respondents stated that this would be positively impacted by diversity management. For 55 percent of respondents, preventing discrimination was also a key advantage of diversity management in the workplace.

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.

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.

How do people living in contemporary Germany react to diversification in their every- day life? What undergirds pro-diversity perspectives among those who live in rapidly diversifying cities? Conversely, what are their limits, and what groups are excluded? The Diversity Assent (DivA) project was designed to understand the foundations and mechanisms underlying the acceptance of socio-demographic heterogeneity on multiple dimensions in cities located both in West and East Germany. Two core motivations underlie the project. So far, we insufficiently understand what motivates those who oppose right- wing positions – usually a majority among inhabitants of cities in Germany and other Western European countries. Second, this project builds on a previous large-scale project of the Socio-Cultural Diversity department at MPI-MMG, “Diversity and Contact”. In particular, it explores to what extent attitudes and patterns of interaction have changed, or remained constant, in the decade from 2010 to 2020, which was a time of major ruptures and political polarization. We designed a large telephone survey of 2,917 respondents asking a set of interrelated questions on dispositions towards diversity, everyday experiences and diversification dynamics. This includes a set of survey experiments designed to tap and measure social norms of tolerance.

In 2023, around 15 percent of people in Germany found gender inclusive language rather important. 50 percent of people found it not important at all.

In institutions of higher education, both internationality and diversity are highly valued. Yet the relationship between these two values often remains undefined. On the one hand, the ‘internationalisation imperative’ and the ‘diversity imperative’ can be regarded as two sides of the same coin. On the other hand, they are perceived as two different tasks and are associated with different groups, interests, and organisational units. To better understand their nexus, this article presents a comparative analysis of German and Polish universities. It identifies the administrative units and actors responsible for managing internationalisation and diversity. Mixed methods were used, including surveys in university administrations, publicly available data from universities’ websites, and qualitative interviews with practitioners in both fields. The results illustrate how the traditional ‘International Offices’ and the more recently established ‘Diversity Offices’ are equipped and related to each other. Regarding internationalisation, German and Polish universities have comparable national trajectories as both institutionalise this task at the administrative level and within university leadership. At the same time, there is a gap between the two countries in terms of how they deal with the diversity imperative. Finally, the article raises the practical question of whether the respective units need to reconceptualise their relationship in the future.

IntroductionCommunity Supported Agriculture (CSA) is an emerging model within alternative food networks (AFNs). It shapes close relationships between food producers and consumers, thereby contributing to food sovereignty and agri-food system transformations. Despite rapid growth from about 10 to over 500 CSAs in just over a decade, the model in Germany still remains niche. We argue that further and faster scaling up requires better understanding of its diversity, yet a comprehensive conceptualization of CSA types is lacking, with insufficient differentiation in research and practice.MethodsThis study employs a transdisciplinary mixed-methods approach (literature, qualitative, and quantitative data) in cooperation with the German CSA Network. By integrating organizational perspectives, we found that CSAs are highly complex and diverse organizations. Therefore, we firstly aimed at identifying characteristics that we summarized in a CSA framework. In a second stage, we used this framework as guiding structure for co-developing a survey with the Network covering 70 participating CSAs.ResultsAs the defining characteristic within the CSA framework, community financing (domain A) clarifies the uniqueness of the CSA model, thus enables delimitation from other AFN forms. Then differentiation characteristics (domain B) encompass the diversity of CSA configurations. CSA governance (domain B1), regarding the predominant characteristic of organizational governance, distinguish between Producer-led, Consumer-led, and Integrated (all-in-one) CSA types. Varying characteristics (domain B2) specify CSA configurations and enable additional distinction between CSAs. Based on the developed CSA framework, the survey results verify the applicability of governance types in particular, while confirming a high level of diversity of differentiating characteristics in general.DiscussionThis study can be used to reveal existing generalizations about CSAs, providing a starting point for more nuanced and critical views in research and practice. When seen against the background of AFN and food sovereignty discourses in particular, CSA is an alternative production-distribution model, but not every CSA is governed or structured in alternative ways. CSAs can simultaneously contain both more conventional, traditional elements, as well as more alternative elements. Moreover, the framework provides easy-to-access differentiation criteria for matching members with their most suitable CSAs and vice versa. Overall, this study illustrates that CSA cannot be considered as homogeneous AFN type but be rather marked as a diverse field of its own.

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

In 2024, there were around 175,236 people with severe disabilities who were unemployed in Germany. This was an increase from the previous year, when there were 165,725 unemployed. Figures peaked in 2014 at around 180,000.

About this dataset

Context

The dataset presents the median household income across different racial categories in New Germany. 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 New Germany population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 97.22% of the total residents in New Germany. Notably, the median household income for White households is $70,710. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $70,710.

https://i.neilsberg.com/ch/new-germany-mn-median-household-income-by-race.jpeg" alt="New Germany 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 New Germany.
• 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 New Germany median household income by race. You can refer the same here

This dataset tracks annual diversity score from 2007 to 2023 for Twin Cities German Immersion Charter School District vs. Minnesota

Soil degradation can have an impact on the soil microbiota, but its specific effects on soil fungal communities are poorly understood. In this work, we studied the impact of soil degradation on the richness and diversity of communities of soil fungi, including three different degrees of degradation in Germany and Panama. Soil fungi were isolated monthly using the soil-sprinkling method for 8 months in Germany and 3 months in Panama, and characterized by morphological and molecular data. Soil physico-chemical properties were measured and correlated with the observed values of fungal diversity. We isolated a total of 71 fungal species, 47 from Germany, and 32 from Panama. Soil properties were not associated with fungal richness, diversity, or composition in soils, with the exception of soil compaction in Germany. The geographic location was a strong determinant of the soil fungal species composition although in both countries there was dominance by members of the orders Eurotiales and Hypocreales. In conclusion, the results of this work do not show any evident influence of soil degradation on communities of soil fungi in Germany or Panama.

Population: Mid Year: Projection data was reported at 72,589,745.000 Person in 2100. This records a decrease from the previous number of 72,646,641.000 Person for 2099. Population: Mid Year: Projection data is updated yearly, averaging 78,298,957.000 Person from Jun 1950 (Median) to 2100, with 151 observations. The data reached an all-time high of 84,498,245.000 Person in 2020 and a record low of 68,374,572.000 Person in 1950. Population: Mid Year: Projection data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s Germany – Table DE.US Census Bureau: Demographic Projection.

This is the first study aimed to explore the genetic diversity and population structure of domestic water buffalo (Bubalus bubalis) in Germany and their potential relations to herds in other parts of Europe or worldwide. To this end, ear tissue samples of 285 animals from four different herds, including Brandenburg (n=27), Mecklenburg-Western Pomerania (n=28), Lower Saxony (n=26), and Saxony (n=28), together with animals from herds in Bulgaria (n=58), Romania (n=63), and Hungary (n=55) were collected and genotyped using the Axiom Buffalo Single Nucleotide Polymorphism (SNP) Array (90K). This dataset was then merged with 220 genotypes from Brazil, Colombia, Egypt, Turkey, India, Pakistan, Iran, and Italy obtained from public repositories. The multidimensional scaling based on identity by state matrix distances followed by a model-based estimation of population structure revealed a mixed genetic make-up of German buffalos with contribution from Bulgaria (Murrah breed), Romania, and Italy....

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 90 men and 106 women in the workforce. Among them, 48 men were engaged in full-time, year-round employment, while 53 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 6.25% fell within the income range of under $24,999, while 5.66% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 2.08% of men in full-time roles earned incomes exceeding $100,000, while 3.77% 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 North Germany township median household income by race. You can refer the same here

WIGeoGIS provides current population figures, age structure, housing situation, life stages, purchasing power, and over 100 additional attributes as market data for Germany. Germany Demographic Market Data are spatially processed and updated annually. Available spatial levels include municipalities, postal codes, geomarkets, and INSPIRE raster grids of 250x250m or 100x100m. Regional extracts are available.

Bacterial diversity and community composition should be assessed for grassland and forest soils in the three German Biodiversity Exploratories (Schorfheide-Chorin, Hainich-Dun, Schwabische Alb). Grassland soil samples were derived from meadows, pastures or mown pastures that were either fertilized or non-fertilized. Forest soil samples were derived from age class forest, selection forest or natural forest and dominated by either beech, oak, pine or spruce trees.The study focused on the effect of land use, management, fertilization and tree species as well as edaphic parameters onto the bacterial community and diversity to identify drivers of diversity and community composition.

In 2024, the gender pay gap in Germany was around 16 percent. This meant that wages for men were on average 16 percent higher than for women. Figures have gradually decreased since 2009.