The revenue share of wages and salaries in the synthetic materlias industry in Germany was at around ** percent in 2022. This was a decrease compared to previous years. Worldwide, synthetic materials production is predicted to increase in 2025.

Graph and download economic data for Employed: Percent of hourly paid workers: Paid total at or below prevailing federal minimum wage: Private wage and salary workers: Wholesale and retail trade industries: 16 years and over (LEU0204865400A) from 2000 to 2024 about paid, wholesale, minimum wage, salaries, workers, hours, retail trade, 16 years +, percent, federal, wages, sales, retail, private, employment, industry, and USA.

The motivation behind analyzing salary data is to gain insights into compensation trends, identify factors that influence pay, and understand disparities across industries, locations, or job roles. For businesses, this analysis is crucial in shaping competitive compensation packages, attracting top talent, and ensuring fair pay practices. Additionally, individuals can benefit from understanding how their salaries compare to industry standards, aiding in negotiation strategies.

Context With increasing attention on pay transparency and equity, salary data has become a critical dataset for human resources departments, economists, and policymakers. Companies and industries alike need to assess compensation against benchmarks, inflation, and the evolving job market. Salary datasets often contain variables such as job titles, experience levels, education, locations, and industries, which are essential in determining pay structures. This analysis allows for a deeper dive into trends like gender pay gaps, regional disparities, and the impact of education or experience on earnings.

For the Kaggle community, salary datasets provide rich opportunities for performing exploratory data analysis, statistical modeling, and predictive analytics. It serves as a hands-on opportunity to practice data wrangling, feature engineering, and model building, especially in the realm of HR analytics.

Description This CSV file contains anonymized company salary data across various industries, roles, and locations. The dataset includes key variables such as:

Job Title: The role of the employee (e.g., Data Analyst, Software Engineer). Years of Experience: Number of years the employee has been in the workforce or industry. Education Level: The highest degree obtained by the employee (e.g., Bachelor's, Master's). Location: City or country where the employee works. Industry: The sector in which the company operates (e.g., Finance, Technology). Annual Salary: The employee’s yearly earnings, including bonuses or incentives. Gender: Gender identification of the employee (if available). Remote Work Percentage: The percentage of work conducted remotely, which may influence salary based on location independence. The dataset is perfect for understanding how salaries vary by job role, region, industry, and experience level. It can also be used to uncover trends such as salary growth over time, the impact of education or certifications on compensation, or potential gender pay gaps. Through data visualization, predictive models, and regression analysis, users can extract meaningful insights that could inform corporate strategy, HR policies, or even career decisions.

The U.S. banking industry's noninterest income as a percentage of average assets is estimated to increase to **** percent in 2025, marking the highest level since 2019. This expected peak would follow a period of lower noninterest income between 2020 and 2024, with the lowest value measured in 2022 at **** percent. However, this metric has seen a notable increase in 2023, rising to *** percent, pointing to a steady recovery in banks' ability to generate revenue from sources beyond net interest income.

This statistic depicts the revenue share of the public sector salary bill in Saudi Arabia and Kuwait in 2017. In this year, the contribution to revenue in Saudi Arabia from public sector employment amounted to around ** percent.

Graph and download economic data for Personal Sector; Personal Saving (NIPA Concept; FOF Data) as a Percentage of Disposable Personal Income, Transactions (BOGZ1FA176007006A) from 1946 to 2024 about disposable, savings, transactions, sector, personal income, percent, personal, income, and USA.

India IHISs: Percentage of Revenue: More than 150 Room: Net Income data was reported at 32.000 % in 2017. This records a decrease from the previous number of 33.000 % for 2016. India IHISs: Percentage of Revenue: More than 150 Room: Net Income data is updated yearly, averaging 34.950 % from Mar 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 47.700 % in 2008 and a record low of 24.400 % in 2002. India IHISs: Percentage of Revenue: More than 150 Room: Net Income data remains active status in CEIC and is reported by Federation of Hotel & Restaurant Associations of India. The data is categorized under India Premium Database’s Hotel Sector – Table IN.QHF003: Indian Hotel Industry Survey: Financial Performance: Revenue: by Number of Rooms.

In September 2024, the disposable personal income in the United States increased by 0.3 percent from the previous month. The data are in current U.S. dollars, seasonally adjusted at annual rates. Disposable personal income in the United States According to the BEA, personal income is the income that is received by persons from all sources. It is calculated as the sum of wage and salary disbursements, supplements to wages and salaries, proprietors' income with inventory valuation and capital consumption adjustments, rental income of persons with capital consumption adjustment, personal dividend income, personal interest income, and personal current transfer receipts, minus contributions for government social insurance. In simple terms, disposable personal income is the total remaining income after taxes paid; it is the income available to persons for spending or saving. It is useful to economists because it measures the amount of money available for spending in a specific area. Disposable personal income is a significant indicator of an economy’s health. Personal income determines an individual’s ability to consume goods and services, i.e. personal consumption expenditure, and industries producing consumer goods and services contribute heavily to United States gross domestic product. The retail trade industry, for example, contributed 1.38 trillion chained U.S. dollars to the GDP of the United States in 2021. Total real GDP amounted to about 22.99 trillion U.S. dollars that year. The arts, entertainment, recreation, accommodation and food services industry contributed 839.6 billion U.S. dollars to the GDP in 2021. Personal income in the United States was 21.06 trillion U.S. dollars in 2021, the highest value in over ten years.

Graph and download economic data for Personal Sector; Personal Saving (FOF Concept; FOF Data) as a Percentage of Disposable Personal Income, Transactions (BOGZ1FU176007026Q) from Q4 1946 to Q2 2025 about disposable, savings, transactions, sector, personal income, percent, personal, income, and USA.

This table presents data on the labour income share and the components used to calculate it. The labour income share is a measure of the distribution of earned income between providers of labour (employees and the self-employed) and providers of capital.

Data available from: 1995.

Status of the figures: Data from 1995 up to and including 2022 are final. Data of 2023 and 2024 are provisional. The labour income share is based on data from the system of supply and use tables and the Sector accounts. Because the Sector accounts are revised annually, definitive figures can be subject to changes.

Changes as of July 28th 2025: Data of 2024 have been added to this table. Furthermore, the amount of economic activities in this table has been extended from Eurostat's A21 to A64.

When will new figures be published? Provisional data are published 6 months after the end of the reporting year. Final data are released 18 months after the end of the reporting year.

The Fixed Income Market Report Segments the Industry Into by Issuer Types (Governments As Issuers, Corporate Debt Instruments, Structured Finance Instruments), by End User (Institutional Investors, Retail Investors), by Time of Maturity (Short-Term (Less Than 1 Year), Intermediate-Term (1-10 Years), Long-Term (More Than 10 Years)), and by Geography (North America, Europe, Asia Pacific, South America, Middle East).

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 Industry town. 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 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Industry town, the median income for all workers aged 15 years and older, regardless of work hours, was $49,429 for males and $24,321 for females.

These income figures highlight a substantial gender-based income gap in Industry town. Women, regardless of work hours, earn 49 cents for each dollar earned by men. This significant gender pay gap, approximately 51%, underscores concerning gender-based income inequality in the town of Industry town.

- Full-time workers, aged 15 years and older: In Industry town, among full-time, year-round workers aged 15 years and older, males earned a median income of $56,447, while females earned $33,508, leading to a 41% gender pay gap among full-time workers. This illustrates that women earn 59 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 similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Industry town, showcasing a consistent income pattern irrespective of employment status.

https://i.neilsberg.com/ch/industry-me-income-by-gender.jpeg" alt="Industry, Maine gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-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 2022
• 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 Industry town median household income by gender. You can refer the same here

Average hourly and weekly wage rate, and median hourly and weekly wage rate by North American Industry Classification System (NAICS), type of work, gender, and age group.

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 Industry. 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 Industry, the median income for all workers aged 15 years and older, regardless of work hours, was $47,045 for males and $26,629 for females.

These income figures highlight a substantial gender-based income gap in Industry. Women, regardless of work hours, earn 57 cents for each dollar earned by men. This significant gender pay gap, approximately 43%, underscores concerning gender-based income inequality in the borough of Industry.

- Full-time workers, aged 15 years and older: In Industry, among full-time, year-round workers aged 15 years and older, males earned a median income of $71,023, while females earned $44,408, 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 similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Industry, showcasing a consistent income pattern irrespective of employment status.

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 Industry median household income by race. You can refer the same here

CF Industries reported $17M in Interest Income for its fiscal quarter ending in June of 2025. Data for CF Industries | CF - Interest Income including historical, tables and charts were last updated by Trading Economics this last November in 2025.

Asian households measured the highest median household income among racial and ethnic groups in the United States. In 2024, Asian household incomes reached a median of 121,700 U.S. dollars. On the other hand, Black households had the lowest median income of 56,020 U.S. dollars. Overall, median household incomes in the United States stood at 83,730 U.S. dollars that year.Asian and Caucasian (white not Hispanic) households had relatively high median incomes, while the median income of Hispanic, African American, American Indian, and Alaskan Native households all came in lower than the national median. A number of related statistics illustrate further the current state of racial inequality in the United States. Unemployment is highest among Black or African American individuals in the U.S. nearing nine percent unemployed, according to the Bureau of Labor Statistics in 2024. Hispanic individuals (of any race) were most likely to go without health insurance as of 2024.

Context

This data set contains global economic income indicators per country. The data has been prepared for ease of use.

The data is divided into: Male, female, dimestic credit, gross domestic product, gross national income, fixed capital formation, labour share. The individual files are briefly described below:

Income index:

Dimension: Income/composition of resources Definition: GNI per capita (2011 PPP International $, using natural logarithm) expressed as an index using a minimum value of $100 and a maximum value $75,000.

Domestic credit provided by financial sector (% of GDP)

Dimension: Income/composition of resources Definition: Credit to various sectors on a gross basis (except credit to the central government, which is net), expressed as a percentage of GDP.

Estimated gross national income per capita, female (2011 PPP $)

Full and productive employment and decent work for all women and men,including for young people and persons with disabilities, and equal pay for work of equal value Dimension: Income/composition of resources Definition: Derived from the ratio of female to male wages, female and male shares of economically active population and gross national income (in 2011 purchasing power parity terms).

Estimated gross national income per capita, male (2011 PPP $)

Full and productive employment and decent work for all women and men,including for young people and persons with disabilities, and equal pay for work of equal value Dimension: Income/composition of resources Definition: Derived from the ratio of female to male wages, female and male shares of economically active population and gross national income (in 2011 purchasing power parity terms).

GDP per capita (2011 PPP $)

Dimension: Income/composition of resources Definition: GDP in a particular period divided by the total population in the same period.

Gross domestic product (GDP), total (2011 PPP $ billions)

Dimension: Income/composition of resources Definition: Sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products, expressed in 2011 international dollars using purchasing power parity (PPP) rates.

Gross fixed capital formation (% of GDP)

Dimension: Income/composition of resources Definition: Value of acquisitions of new or existing fixed assets by the business sector, governments and households (excluding their unincorporated enterprises) less disposals of fixed assets, expressed as a percentage of GDP. No adjustment is made for depreciation of fixed assets.

Gross national income (GNI) per capita (2011 PPP $)

Full and productive employment and decent work for all women and men,including for young people and persons with disabilities, and equal pay for work of equal value Dimension: Income/composition of resources Definition: Aggregate income of an economy generated by its production and its ownership of factors of production, less the incomes paid for the use of factors of production owned by the rest of the world, converted to international dollars using PPP rates, divided by midyear population.

Labour share of GDP, comprising wages and social protection transfers (%)

Adopt policies, especially fiscal, wage and social protection policies, and progressively achieve greater equality Dimension: Income/composition of resources Definition: Total compensation of employees given as a percent of GDP, which is a measure of total output. Total compensation refers to the total remuneration, in cash or in kind, payable by an enterprise to an employee in return for work done by the latter during the accounting period.

Additional Information

For more information see : http://hdr.undp.org/sites/default/files/hdr2019_technical_notes.pdf

The title picture is from https://searchengineland.com/international-ppc-deal-currency-fluctuations-245601

India IHIS: Percentage of Revenue: 50 to 150 Rooms: Net Income data was reported at 26.900 % in 2017. This records an increase from the previous number of 25.900 % for 2016. India IHIS: Percentage of Revenue: 50 to 150 Rooms: Net Income data is updated yearly, averaging 27.900 % from Mar 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 38.500 % in 2008 and a record low of 19.400 % in 2002. India IHIS: Percentage of Revenue: 50 to 150 Rooms: Net Income data remains active status in CEIC and is reported by Federation of Hotel & Restaurant Associations of India. The data is categorized under India Premium Database’s Hotel Sector – Table IN.QHF003: Indian Hotel Industry Survey: Financial Performance: Revenue: by Number of Rooms.

India IHIS: Percentage of Revenue: Independent Hotels: Net Income data was reported at 27.400 % in 2017. This records a decrease from the previous number of 28.800 % for 2016. India IHIS: Percentage of Revenue: Independent Hotels: Net Income data is updated yearly, averaging 28.800 % from Mar 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 36.000 % in 2009 and a record low of 22.400 % in 2002. India IHIS: Percentage of Revenue: Independent Hotels: Net Income data remains active status in CEIC and is reported by Federation of Hotel & Restaurant Associations of India. The data is categorized under India Premium Database’s Hotel Sector – Table IN.QHF004: Indian Hotel Industry Survey: Financial Performance: Revenue: by Ownership.

Average weekly earnings at sector level headline estimates, Great Britain, monthly, seasonally adjusted. Monthly Wages and Salaries Survey.