Over the past decades, more and more women have entered the labor market around the world. Today, over 40 percent of the global workforce are women. However, only one third are in senior roles, and less than 30 percent work within science, technology, engineering, and mathematics (STEM). The Global Gender Index benchmarks national gender gaps on economic, political, education, and health-based criteria. In 2025, the leading country was Iceland .

According to an annual survey conducted in China in the beginning of 2025, around ** percent of surveyed female professionals said that gender inequality at work persists because of the ongoing childbirth burden for women. Only ** percent of male respondents agreed with that opinion. However, an equal proportion of men and women thought that gender discrimination at work is caused by educational reasons.

The Gender Equality Index is a tool to measure the progress of gender equality in the EU, developed by EIGE. It gives more visibility to areas that need improvement and ultimately supports policy makers to design more effective gender equality measures.

The Gender Equality Index has tracked the painfully slow progress of gender equality in the EU since 2010, mostly due to advances in decision-making. While equality is more pronounced in some Member States than in others, it is far from a reality for everyone in every area. Gender norms around care, gender segregation in education and the labour market, and gender inequalities in pay remain pertinent.

The Index allows Member States to easily monitor and compare gender equality progress across various groups of women and men in the EU over time and to understand where improvements are most needed. The 2022 Index has a thematic focus on care in the Covid-19 pandemic. It explores the division of informal childcare, long-term care and housework between women and men.

The Gender Equality Index is a composite indicator. With a total of six core domains (work, money, knowledge, time, power and health) and two satellite domains (violence against women and intersecting inequalities), it offers a synthetic and easy-to-interpret measure for gender equality, indicating how far (or close) the EU and its Member States are from achieving gender equality on a scale of 1 to 100.

Building on previous editions alongside EIGE’s approach to ensuring intersecting inequalities are captured, the Gender Equality Index 2022 continues to show the diverse realities that different groups of women and men face. It examines how elements such as disability, age, level of education, country of birth and family type can intersect with gender and create many different kinds of pathways in people's lives.

According to a survey about Chinese career women conducted in 2025, about **** percent of female respondents said they had experienced gender discrimination at work, whereas only **** percent of male respondents had similar experience. Similarly, more women than men felt that age was affecting their career prospects.

Under the Workplace Gender Equality Act 2012, non-public sector employers with 100 or more staff must report to the WGEA annually, which covers over 12,000 Australian organisations. Information collected and contained in the data files are the gender composition of the workforce and governing bodies/boards, percentage of organisations with policy and/or strategies across a broad range of gender equality issues, paid parental leave and flexible work arrangement offerings.

Visit Data Explorer (https://data.wgea.gov.au/) for key trends and data visualisations

Visit Metadata registry (https://wgea.aristotlecloud.io/about/wgea/gender_equality_indicators) for further information about how we use the data to measure gender equality.

The Data Quality Declaration (https://www.wgea.gov.au/data/data-quality-declaration) addresses the overall quality of the Agency data in terms of relevance, timeliness, accuracy, coherence, interpretability, accessibility, and the institutional environment.

According to a survey conducted in 2023 on gender equality in Japan, almost ** percent of respondents believed that men are favored to at least some extent in the workplace. Just about two percent of survey participants were the opinion that women in Japan received preferential treatment at work.

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 Van Meter. 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 Van Meter, the median income for all workers aged 15 years and older, regardless of work hours, was $71,458 for males and $48,500 for females.

These income figures highlight a substantial gender-based income gap in Van Meter. Women, regardless of work hours, earn 68 cents for each dollar earned by men. This significant gender pay gap, approximately 32%, underscores concerning gender-based income inequality in the city of Van Meter.

- Full-time workers, aged 15 years and older: In Van Meter, among full-time, year-round workers aged 15 years and older, males earned a median income of $85,238, while females earned $56,750, leading to a 33% gender pay gap among full-time workers. This illustrates that women earn 67 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 Van Meter, 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 Van Meter median household income by race. You can refer the same here

Taoyuan City Violates Gender Equality Employment Law Business Unit Quarterly Statistical Report

The datafile and code are available to replicate findings in the publication "Can't Catch a Break: Intersectional Inequalities at Work." Abstract: The labor market is the site of longstanding and persistent inequalities across race and gender groups in hiring, compensation, and advancement. In this paper, we draw on data from 13,574 hourly service-sector workers to extend the study of intersectional labor market inequalities to workers’ experience on the job. In the service sector, where workers are regularly expected to be on their feet for long hours and to contend with workloads that are intense and unrelenting, regular break time is an essential component of job quality and general well-being. Yet, we find that Black women are less likely than their counterparts to get a break during their work shift. Although union membership and laws mandating work breaks are effective in increasing access to breaks for workers overall, they do not ameliorate the inequality Black women face in access to work breaks within the service sector. A sobering implication is that worker power and labor protections can raise the floor on working conditions but leave inequalities intact. Our findings also have implications for racial health inequalities, as the routine daily stress of service sector takes a disproportionate toll on the health of Black women.

This record for the Dataset 1 (Interviews & Focus Groups) from the Work Package 2 (Effects of, and Resistances to 'Anti-Gender' Mobilisations Across Europe) of the RESIST Project has been created in the Zenodo open repository, in line with the RESIST Project’s Data Management Plan, and according to the framework of the Open Science principles of the European Union. We followed the accepted gold-standard rule: “as open as possible – as closed as necessarily” to ensure research ethics, integrity, and compliance with the research policies of the EU and the consortium members.

Data gathered during the Work Package 2 (Effects of, and Resistances to 'Anti-Gender' Mobilisations Across Europe) have been classified as SENSITIVE and therefore the dataset will not be available in open access repositories for ten years after the end of the project (that is until 01/10/2036). After 01/10/2036, if certain conditions outlined by the project consortium are met, the dataset will be released publicly on Zenodo.

In the fiscal year 2023, around *** thousand consultations regarding sexual harassment were recorded by the Employment Environment and Equal Employment Offices in Japan. The number of consultations regarding maternal health management reached over *** thousand. This refers to employers' obligation to ensure that pregnant workers receive health checkups and guidance related to their pregnancy.

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 Wheeling. 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 Wheeling, the median income for all workers aged 15 years and older, regardless of work hours, was $34,933 for males and $25,827 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 26% between the median incomes of males and females in Wheeling. With women, regardless of work hours, earning 74 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Wheeling.

- Full-time workers, aged 15 years and older: In Wheeling, among full-time, year-round workers aged 15 years and older, males earned a median income of $55,684, while females earned $49,275, resulting in a 12% gender pay gap among full-time workers. This illustrates that women earn 88 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Wheeling.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Wheeling.

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

This study sheds light on the growing trend and gender dynamics of workplace flexibility in Latin America, underscoring the importance of remote work options in the region’s labor market. We explore gender differences in willingness to pay (WTP) for remote work arrangements in Latin America, using a discrete choice experiment across five countries: Colombia, Peru, Mexico, Chile, and Argentina. Results reveals a general trend among Latin American workers to trade off some wage in exchange for more remote work options, both fully and partially remote, in two male-dominated occupations: Manufacturing and information technology. On average, participants agreed to sacrifice around 10\% of their wage for hybrid jobs (80\% remote, 20\% on-site). The WTP for fully remote work was slightly lower, at about 6\% of the wage. Women exhibit a higher WTP for flexibility compared to men, with a 62.5\% higher willingness across estimates for hybrid arrangements. Moreover, women's inclination towards fully remote options was distinct, as they showed a positive WTP (up to 10\% of their salary) for such arrangements, whereas men exhibited no willingness to reduce their wages for fully remote roles.

The GIST Impact DEI data offers a glimpse into the gender pay gap trends at top European companies and delves deeper into how these pay disparities materialize at different levels of the hierarchy.

By analysing labour force participation and pay gap data, we provide a picture of how well these businesses are performing in terms of Diversity, Equity, and Inclusion (DEI). The analysis also serves as a benchmark to help gauge corporate progress on DEI commitments, particularly related to gender diversity.

GIST Impact’s analysis delivers meaningful quantitative data insights concerning women's workforce participation and career progression, drawing upon publicly available and secondary data sources. This method provides a more nuanced depiction of the impact of gender-inclusive policies and practices than simply presenting gender equality scores based on qualitative data.

Our workplace diversity Data analysis also gives context to theoretical frameworks such as the "glass ceiling" effect that underscores the discrimination faced by women in the workplace. The glass ceiling effect can have a significant impact on an individual's professional development, and addressing it requires proactive efforts to promote diversity, equity, and inclusion in the workplace.

GIST Impact's DEI data can be used to: - Measure diversity and gender pay gap of companies and portfolios - Benchmark companies within their sector - Benchmark a portfolio against indices - Screen companies for risk and opportunity - Integrate sustainability into portfolio decision-making

In recent years, the concept of gender equality has gradually been implemented in legislation. With the joint efforts of the Labor Commission and various sectors of society, there has been significant progress in safeguarding women's labor rights and promoting measures for work equality. This has also led to an increasingly important impact of women's participation in the labor market in Taiwan. The Taiwan Water Corporation has also been promoting gender equality measures in accordance with the law to create a friendly workplace environment. In addition, senior employees are mature human resources, and their long-term service experience can stabilize the organization, making them key personnel in problem solving. Effectively passing on knowledge and skills is also a major challenge for the future. This dataset provides statistical analysis of the number of existing employees of the Taiwan Water Corporation by gender and age.

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 Shasta County. 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 Shasta County, the median income for all workers aged 15 years and older, regardless of work hours, was $43,393 for males and $29,360 for females.

These income figures highlight a substantial gender-based income gap in Shasta County. Women, regardless of work hours, earn 68 cents for each dollar earned by men. This significant gender pay gap, approximately 32%, underscores concerning gender-based income inequality in the county of Shasta County.

- Full-time workers, aged 15 years and older: In Shasta County, among full-time, year-round workers aged 15 years and older, males earned a median income of $66,613, while females earned $53,090, leading to a 20% gender pay gap among full-time workers. This illustrates that women earn 80 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 Shasta County.

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

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 Welcome. 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 Welcome, the median income for all workers aged 15 years and older, regardless of work hours, was $35,708 for males and $31,875 for females.

Based on these incomes, we observe a gender gap percentage of approximately 11%, indicating a significant disparity between the median incomes of males and females in Welcome. Women, regardless of work hours, still earn 89 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

- Full-time workers, aged 15 years and older: In Welcome, among full-time, year-round workers aged 15 years and older, males earned a median income of $57,679, while females earned $53,438, resulting in a 7% gender pay gap among full-time workers. This illustrates that women earn 93 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Welcome.

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

Taipei City Gender Equality at Work Complaints Overview Time Series Statistics

To develop a better understanding how people in Germany handle the social and political consequences of the Corona (COVID-19) crisis, the Cluster of Excellence "The Politics of Inequality" has installed a surveys program with the participation of researchers from several different departments and disciplines at the University of Konstanz (Sociology, Political Science, Economics and Psychology).
The surveys focus on the social and political consequences of the Corona crisis and cover multiple topics, such as the perceived individual and social consequences of the pandemic and the measures taken to contain it, trust in health and social policy and the welfare state, support for government aid given to businesses, gender inequalities, questions of solidarity within the EU, opinions on the "Corona app", on debates on loosening the emergency measures, and on perceived infection risks in the working place.
The survey program took the form of several online surveys conducted in 2020 and 2021. The surveys were organized in two series, Survey A and B. For each serie, three cross-sectional surveys werde conducted that included a sample of respondents who where surveyed repeatedly. This dataset is the survey that took place in November 2020. It is the second Wave of Survey B.

The other surveys can also be found in the GESIS repository (https://doi.org/10.7802/2116 , https://doi.org/10.7802/2118 , https://doi.org/10.7802/2334).

AimsThe aim of the study was to evaluate the prevalence and temporal trend of common mental health problems (CMHPs) in the UK by industrial classification from 2012–2014 to 2016–2018 while evaluating the corresponding gender disparities.MethodsWe used data from the Health Survey for England. CMPH was judged by a 12-item General Health Questionnaire. Industrial classifications were defined using the UK Standard Industrial Classification of Economic Activities. Data were fitted by the logistic models.ResultsIn this study, 19,581 participants covering 20 industries were included. In total, 18.8% of participants screened positive for CMHP in 2016–2018, which significantly increased from 16.0% in 2012–2014 [adjusted OR (AOR) = 1.17, 95% CI 1.08–1.27]. In 2016–2018, the prevalence of CMHP ranged from 6.2% in the industry of mining and quarrying to 23.8% in the industry of accommodation and food service activities. From 2012–2014 to 2016–2018, none of the 20 industries studied experienced significant decreases in the above prevalence; conversely, three industries saw significant increases, including wholesale and retail trade, repair of motor vehicles and motorcycles (AOR for trend = 1.32, 95% CI 1.04–1.67), construction (AOR for trend = 1.66, 95% CI 1.23–2.24), and other service activities, which cannot be classified (AOR for trend = 1.94, 95% CI 1.06–3.55). In total, 11 of the 20 industries studied had significant gender disparities against women, with the smallest gap being in the industry of transport and storage (AOR = 1.47, 95% CI 1.09–2.0) and the highest in the industry of arts, entertainment, and recreation (AOR = 6.19, 95% CI 2.94–13.03). From 2012–2014 to 2016–2018, gender disparities were narrowed only in two industries, including human health and social work activities (AOR for trend = 0.45, 95% CI 0.27–0.74), and transport and storage (AOR for trend = 0.5, 95% CI 0.27–0.91).ConclusionThe prevalence of CMHPs has increased and had a wide variation across industries in the UK. There were disparities against women, and the gender disparities have been keeping almost no improvement from 2012–2014 to 2016–2018.