As of 2023, the average annual wage of Germany was 48,301 euros per year, a growth of almost 6,000 Euros when compared with 2000. From 2000 until 2007, wages rose by less than a thousand euros, with wage growth accelerating mainly in the period after 2010. Comparisons with rest of the EU Within the European Union Luxembourg had an average annual salary of almost 80 thousand Euros, with Germany having an annual salary comparable to other large European Countries, such as the United Kingdom and France. In neighboring Poland, the average annual salary was just over 39 thousand U.S dollars, meaning that German’s earned, on average, 20 percent more than what their Polish counterparts did. German economy slowing in 2023 While Germany initially had one of the strongest recoveries from the 2008 financial crash and as of 2020 had the largest economy in Europe its economy has started to slow in recent years. For 2023 the German economy is contracted by 0.26 percent, and while 2024 marked a slight improvement, the expectations are that 2025 remains a year of slow growth.

Wages in Germany increased to 4479 EUR/Month in 2023 from 4244 EUR/Month in 2022. This dataset provides the latest reported value for - Germany Average Gross Monthly Earnings - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

A structured overview of the average, net, median, and minimum wage in Germany for 2025. This dataset combines original market research conducted by KUMMUNI GmbH with publicly available data from the German Federal Statistical Office. It includes values with and without bonuses, hourly minimum wage, and take-home pay after tax.

Wages in Germany increased 2.50 percent in December of 2024 over the same month in the previous year. This dataset provides the latest reported value for - Germany Wage Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in New Germany. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In New Germany, the median income for all workers aged 15 years and older, regardless of work hours, was $53,438 for males and $33,889 for females.

These income figures highlight a substantial gender-based income gap in New Germany. Women, regardless of work hours, earn 63 cents for each dollar earned by men. This significant gender pay gap, approximately 37%, underscores concerning gender-based income inequality in the city of New Germany.

- Full-time workers, aged 15 years and older: In New Germany, among full-time, year-round workers aged 15 years and older, males earned a median income of $62,778, while females earned $47,813, leading to a 24% gender pay gap among full-time workers. This illustrates that women earn 76 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in New Germany.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

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.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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

Wages in Manufacturing in Germany increased to 103.52 points in April from 101 points in March of 2025. This dataset provides the latest reported value for - Germany Hourly Wages in Manufacturing Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in German Flatts, New York, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 13,816, while the mean income for the highest quintile (20% of households with the highest income) is 164,009. This indicates that the top earners earn 12 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 252,643, which is 154.04% higher compared to the highest quintile, and 1828.63% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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. You can refer the same here

This study examines the short-term effects of the introduction of a statutory minimum wage in Germany on hourly wages, monthly wages and paid working hours. We exploit a novel panel dataset by linking the Structure of Earnings Survey (SES) 2014 and the Earn-ings Survey (ES) 2015 and apply a difference-in-differences approach at the establishment level. The results indicate an effect of the introduction of the statutory minimum wage on the average hourly wages of employees in minimum wage establishments of up to 5.9 percent. Due to negative effects on average working time of approximately minus 3.1 percent, the effects on monthly gross earnings are smaller but still amount to up to 2.7 percent on aver-age. The results further suggest that the minimum wage effects on earnings were greater among low-wage employees than on average, in eastern Germany than in western Germany, and among part-time employees and marginal employees than among full-time employees.

Germany DE: Wages Index data was reported at 119.343 2010=100 in 2017. This records an increase from the previous number of 115.816 2010=100 for 2016. Germany DE: Wages Index data is updated yearly, averaging 98.357 2010=100 from Dec 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 119.343 2010=100 in 2017 and a record low of 83.569 2010=100 in 2000. Germany DE: Wages Index data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Germany – Table DE.IMF.IFS: Wages, Labour Cost and Employment Index: Annual.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in German township. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in German township. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in German township, where there exist only two delineated age groups, the median household income is $76,554 for householders within the 45 to 64 years age group, compared to $50,855 for the 65 years and over age group.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

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 township median household income by age. You can refer the same here

Content

A dataset of counties that are representative for Germany with regard to

the average disposable income,

the quota of divorces,

the respective quotas of employees working in the services (excluding logistics, security, and cleaning) and the MINT sectors,

the proportions of age groups in the total proportion of the respective population, with age groups in five-year strata for the population aged between 30 and 65 and the population in the age range between 65 and 75 each considered separately for the calculation of representativeness.

In addition, data from the four big cities Berlin, München (Munich), Hamburg, and Köln (Cologne) were collected and reflected in the dataset.

The dataset is based on the most recent data available at the time of the creation of the dataset, mainly deriving from 2022, as set out in detail in the readme.md file.

Method applied

The selection of the representative counties, as reflected in the dataset, was performed on the basis of official statistics with the aim of obtaining a confidence rate of 95%. The selection was based on a principal component analysis of the statistical data available for Germany and the addition of the regions with the lowest population density and the highest and lowest per capita disposable income. A check of the representativity of the selected counties was performed.

In the case of Leipzig, the city and the district had to be treated together, in deviation from the official territorial division, with respect to a specific use case of the data.

Wages In the Euro Area increased 3.40 percent in March of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Euro Area Wage Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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 German town. The dataset can be utilized to gain insights into gender-based income distribution within the German town population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within German town, among individuals aged 15 years and older with income, there were 110 men and 86 women in the workforce. Among them, 50 men were engaged in full-time, year-round employment, while 33 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 6% fell within the income range of under $24,999, while 9.09% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 4% of men in full-time roles earned incomes exceeding $100,000, while 3.03% 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 German town median household income by race. You can refer the same here

The study provides an analysis of the effect of migration and commuting on
regional wage disparities. For the analysis most variables are taken from the
INKAR dataset (INKAR 2009, BBSR Bonn). Average wages on ROR level are calculated using the SOEP regional data (SOEP 2009, DIW Berlin).
Average rents on ROR level are calculated using data on BBSR-housing market
observations on county level (BBSR-Wohnungsmarktbeobachtungssystem, IDN ImmoDaten GmbH).

The Personal Income Tax Rate in Germany stands at 45 percent. This dataset provides the latest reported value for - Germany Personal Income Tax Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Content

A dataset of counties that are representative for Germany with regard to

• the average disposable income,
• the quota of divorces,
• the respective quotas of employees working in the services (excluding logistics, security, and cleaning) and the MINT sectors,
• the proportions of age groups in the total proportion of the respective population, with age groups in five-year strata for the population aged between 30 and 65 and the population in the age range between 65 and 75 each considered separately for the calculation of representativeness.
• population density, if the benchmark is defined as the national average density weighted by county population. This value is calculated by averaging the population-per-square-kilometer figures of all German counties, weighted by the population size of each county. In contrast to a simple arithmetic mean of county-level densities, which would disproportionately reflect sparsely populated areas, this method reflects the actual density of the environments where people live. The resulting value for the selected counties falls within the 95% confidence interval of the national weighted average.

In addition, data from the four big cities Berlin, München (Munich), Hamburg, and Köln (Cologne) were collected and reflected in the dataset.

The dataset is based on the most recent data available at the time of the creation of the dataset, mainly deriving from 2022, as set out in detail in the readme.md file.

Method applied

The selection of the representative counties, as reflected in the dataset, was performed on the basis of official statistics with the aim of obtaining a confidence rate of 95%. The selection was based on a principal component analysis of the statistical data available for Germany and the addition of the regions with the lowest population density and the highest and lowest per capita disposable income. A check of the representativity of the selected counties was performed.

In the case of Leipzig, the city and the district had to be treated together, in deviation from the official territorial division, with respect to a specific use case of the data.

Germany DE: Wage And Salary Workers: Modeled ILO Estimate: Male: % of Male Employment data was reported at 89.680 % in 2021. This records an increase from the previous number of 88.524 % for 2020. Germany DE: Wage And Salary Workers: Modeled ILO Estimate: Male: % of Male Employment data is updated yearly, averaging 86.785 % from Dec 1991 to 2021, with 31 observations. The data reached an all-time high of 89.680 % in 2021 and a record low of 85.208 % in 2005. Germany DE: Wage And Salary Workers: Modeled ILO Estimate: Male: % of Male Employment 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. Wage and salaried workers (employees) are those workers who hold the type of jobs defined as 'paid employment jobs,' where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work.;International Labour Organization. “ILO modelled estimates database” ILOSTAT. Accessed January 2021. https://ilostat.ilo.org/data/.;Weighted average;

The Gross Domestic Product per capita in Germany was last recorded at 44108.70 US dollars in 2024. The GDP per Capita in Germany is equivalent to 349 percent of the world's average. This dataset provides the latest reported value for - Germany GDP per capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in German Valley. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In German Valley, the median income for all workers aged 15 years and older, regardless of work hours, was $43,750 for males and $31,250 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 29% between the median incomes of males and females in German Valley. With women, regardless of work hours, earning 71 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thevillage of German Valley.

- Full-time workers, aged 15 years and older: In German Valley, among full-time, year-round workers aged 15 years and older, males earned a median income of $59,688, while females earned $37,321, leading to a 37% gender pay gap among full-time workers. This illustrates that women earn 63 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

Remarkably, across all roles, including non-full-time employment, women displayed a lower gender pay gap percentage. This indicates that German Valley offers better opportunities for women in non-full-time positions.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

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.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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

Germany DE: Wage And Salaried Workers: Modeled ILO Estimate: % of Total Employment data was reported at 91.373 % in 2023. This records an increase from the previous number of 91.151 % for 2022. Germany DE: Wage And Salaried Workers: Modeled ILO Estimate: % of Total Employment data is updated yearly, averaging 89.128 % from Dec 1991 (Median) to 2023, with 33 observations. The data reached an all-time high of 91.373 % in 2023 and a record low of 87.584 % in 2006. Germany DE: Wage And Salaried Workers: Modeled ILO Estimate: % of Total Employment 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. Wage and salaried workers (employees) are those workers who hold the type of jobs defined as 'paid employment jobs,' where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work.;International Labour Organization. “ILO modelled estimates database” ILOSTAT. Accessed January 07, 2025. https://ilostat.ilo.org/data/.;Weighted average;