The difference between male and female hourly earnings as a share of male earnings in the European Union was 12 percent in 2023, compared with 12.9 percent in 2020. The gender pay gap has reduced significantly in the European Union since the early 2010s, when it peaked at 16.4 percent in 2012.

Annual gender pay gap estimates for UK employees by age, occupation, industry, full-time and part-time, region and other geographies, and public and private sector. Compiled from the Annual Survey of Hours and Earnings.

The 'gender pay gap' is defined as the difference between men's and women's average hourly earnings for full time workers within the information and communication sector, as a percentage of men's average hourly earnings. Within this sector in 2020, publishing activities had the highest gender pay gap in full-time employment at **** percent. On the other hand, Programming and broadcasting activities had the lowest gender pay gap at * percent.

The difference between the earnings of women and men shrank slightly over the past years. Considering the controlled gender pay gap, which measures the median salary for men and women with the same job and qualifications, women earned one U.S. cent less. By comparison, the uncontrolled gender pay gap measures the median salary for all men and all women across all sectors and industries and regardless of location and qualification. In 2025, the uncontrolled gender pay gap in the world stood at 0.83, meaning that women earned 0.83 dollars for every dollar earned by men.

The 'gender pay gap' is defined as the difference between men's and women's average hourly earnings for workers within all service industries, as a percentage of men's average hourly earnings. In 2020 there was a *** percent difference between men and women in full time employment.

In 2024, Italian women earned annually about ***** euros less than men. However, the gender pay gap decreased in the last years. In 2016, it amounted to **** percent in favor of men, whereas the difference in 2022 was equal to **** percent. For 2024, it reduced to *** percent. According to JobPricing, women's annual gross salary amounted to around ****** euros in 2024. On the other hand, men had an average annual salary of approximately ****** euros. Regional differences In Italy, significant wage differences can also be observed among regions. As of 2024, regions in northern Italy registered higher average annual salaries compared to the southern regions. Lombardy had the highest average wages in the country, ****** euros per year. On the other hand, people living in Basilicata, in the south, had the lowest wages in the country, ****** euros annually. Differences in the sectors Different sectors registered various levels of pay gaps. For instance, in the banking and financial services, the difference in between the salaries of men and women favored men by ***** euros in 2020. Nonetheless, in very few sectors, the gap favors women. In the construction industry, women earned, on average, around ***** euros more than men. In the field of metallurgy and steel, women and men were equally paid.

This statistic displays the average annual gross salary of workers with a master’s degree in Italy in 2020, broken down by gender. According to data provided by JobPricing, the average annual gross salary of women with a master degree was of **** thousand, which was roughly ** thousand euros less than the average annual gross salary of Italian men with the same education level.

Average hourly and median hourly gender wage ratio by National Occupational Classification (NOC), type of work, sex, and age group, last 5 years.

The OECD Earnings and Wages database is part of the Organisation for Economic Co-operation and Development (OECD) and offers comparable statistics on average wages, employee compensation by activity, the gender wage gap and wage levels. Data is for the most part available since 1970 for most OECD member countries.

This statistic illustrates the average annual gross salary of employees working in the agricultural sector in Italy in 2020, broken down by gender. According to data provided by JobPricing, the average annual gross salary of men working in this sector amounted to **** thousand euros.

About this dataset

Context

The dataset tabulates the Shade Gap household income by age. The dataset can be utilized to understand the age-based income distribution of Shade Gap income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Shade Gap, PA annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Shade Gap, PA household incomes: Comparative analysis across 16 income brackets

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Shade Gap income distribution by age. You can refer the same here

About this dataset

Context

The dataset tabulates the Big Stone Gap household income by age. The dataset can be utilized to understand the age-based income distribution of Big Stone Gap income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Big Stone Gap, VA annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Big Stone Gap, VA household incomes: Comparative analysis across 16 income brackets

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Big Stone Gap income distribution by age. You can refer the same here

Statistics Canada's 2021 Census data, by community and age group, on the number and proportion of people whose income is below the low-income line. Keywords: Low-income, LICO-AT Statistics Canada. 2022. Census Profile. 2021 Census of Population. Statistics Canada Catalogue number 98-316-X2021001. Ottawa. Released October 26, 2022. https://www12.statcan.gc.ca/census-recensement/2021/dp-pd/prof/index.cfm

This statistic displays the average annual gross salary of workers with compulsory education in Italy in 2020, broken down by gender. According to data provided by JobPricing, the average annual gross salary of women who attended compulsory school was of **** thousand euro, which was roughly ***** thousand euros less than the average annual gross salary of Italian men with the same education level.

During a 2024 survey carried out among marketers from the United Kingdom, it was found that women earned nearly ** percent less than men. The gap widened in the most recent year by *** percentage points.

Income inequality is a good indicator reflecting the quality of people's livelihood. There are many studies on the determinants of income inequality. However, few have studied the impacts of industrial agglomeration on income inequality, and even fewer have studied the spatial correlation of income inequality. The goal of this paper is to investigate the impact of China’s industrial agglomeration on income inequality from a spatial perspective. Using data on China’s 31 provinces from 2003 to 2020 and the spatial panel Durbin model, our results show that industrial agglomeration and income inequality present an inverted “U-shape†relationship, proving that they are non-linear changes. As the degree of industrial agglomeration increases, income inequality will rise; after it reaches a certain value, income inequality will drop. Therefore, the Chinese government and enterprises had better pay attention to the spatial distribution of industrial agglomeration, thereby reducing China's region..., These data originate from the 2004–2021 China Statistical Yearbook, China Labour Statistical Yearbook and China Population and Employment Statistics Yearbook. Using data on China's 31 provinces from 2003 to 2020, the spatial panel Durbin model is adopted to explore the impact of industrial agglomeration on income inequality in China.,

Rising income inequality challenges economic and social stability in developing countries. For China, the fastest-growing global digital economy, it could be an effective tool to promote inclusive development, narrowing urban–rural income disparity. It investigates the role of digital financial inclusion (DFI) in narrowing the urban–rural income gap. The study uses panel data from 52 counties in Zhejiang Province, China, from 2014 to 2020. The results show that the development of DFI significantly reduces rural–urban and rural income inequality. The development of DFI helps optimize industrial structure and upgrade the internal structure of agriculture, facilitating income growth for people in rural areas. Such effects are greater in poorer counties. Our findings provide insights into why rapid DFI and the narrowing of the rural–urban income disparity exist in China. Moreover, our results provide clear policy implications on how to reduce the disparity. The most compelling suggestion is that promoting the optimization of industrial structure through DFI is crucial for narrowing the urban–rural income gap.