The research aim was to explore how to promote gender equality using Information and Communication Technologies (ICTs). The researcher conducted semi-structured interviews with a total of 19 respondents, both men and women. The researcher used the information that was collected from interviews and document analysis to evaluate the facts and findings of the study. The researcher used SPSS Version 21 to analyse the data in Section A for the respondents’ biographical data and perceived use of ICTs. The researcher then used NVIVO to transcribe and code data and then used Microsoft Excel to present the data set from which themes were generated to analyse data in Section B and C in order to answer the research questions.

CONTENT

Gender Inequality Index: A composite measure reflecting inequality in achievement between women and men in three dimensions: reproductive health, empowerment and the labour market. See Technical note 4 at http://hdr.undp.org/sites/default/files/hdr2022_technical_notes.pdf for details on how the Gender Inequality Index is calculated.

Maternal mortality ratio: Number of deaths due to pregnancy-related causes per 100,000 live births.

Adolescent birth rate: Number of births to women ages 15–19 per 1,000 women ages 15–19.

Share of seats in parliament: Proportion of seats held by women in the national parliament expressed as a percentage of total seats For countries with a bicameral legislative system, the share of seats is calculated based on both houses.

Population with at least some secondary education: Percentage of the population ages 25 and older that has reached (but not necessarily completed) a secondary level of education.

The global gender gap index benchmarks national gender gaps on economic, political, education, and health-based criteria. In 2025, the country offering the most gender equal conditions was Iceland, with a score of 0.93. Overall, the Nordic countries make up 3 of the 5 most gender equal countries worldwide. The Nordic countries are known for their high levels of gender equality, including high female employment rates and evenly divided parental leave. Sudan is the second-least gender equal country Pakistan is found on the other end of the scale, ranked as the least gender equal country in the world. Conditions for civilians in the North African country have worsened significantly after a civil war broke out in April 2023. Especially girls and women are suffering and have become victims of sexual violence. Moreover, nearly 9 million people are estimated to be at acute risk of famine. The Middle East and North Africa have the largest gender gap Looking at the different world regions, the Middle East and North Africa have the largest gender gap as of 2023, just ahead of South Asia. Moreover, it is estimated that it will take another 152 years before the gender gap in the Middle East and North Africa is closed. On the other hand, Europe has the lowest gender gap in the world.

In 2023, the female to male earnings ration was at 82.7 percent, a decrease from the previous year. The ratio indicates that a regular female worker earns about 82.7 percent of the amount a male employee in the same position would receive. The female to male earnings ratio in the U.S. from 1990 to 2023 are based on earnings in 2023 CPI-U-RS adjusted dollars.

Access data, visualizations, and stories that portray results of the IMF's research on gender and economics or create your own charts and analysis. This dataset includes gender inequality and development indices.

For further details, please see https://data.imf.org/?sk=388DFA60-1D26-4ADE-B505-A05A558D9A42&sId=1479329132316

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Context

The gender pay gap or gender wage gap is the average difference between the remuneration for men and women who are working. Women are generally considered to be paid less than men. There are two distinct numbers regarding the pay gap: non-adjusted versus adjusted pay gap. The latter typically takes into account differences in hours worked, occupations were chosen, education, and job experience. In the United States, for example, the non-adjusted average female's annual salary is 79% of the average male salary, compared to 95% for the adjusted average salary.

The reasons link to legal, social, and economic factors, and extend beyond "equal pay for equal work".

The gender pay gap can be a problem from a public policy perspective because it reduces economic output and means that women are more likely to be dependent upon welfare payments, especially in old age.

This dataset aims to replicate the data used in the famous paper "The Gender Wage Gap: Extent, Trends, and Explanations", which provides new empirical evidence on the extent of and trends in the gender wage gap, which declined considerably during the 1980–2010 period.

Citation

fedesoriano. (January 2022). Gender Pay Gap Dataset. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/gender-pay-gap-dataset.

Content

There are 2 files in this dataset: a) the Panel Study of Income Dynamics (PSID) microdata over the 1980-2010 period, and b) the Current Population Survey (CPS) to provide some additional US national data on the gender pay gap.

PSID variables:

NOTES: THE VARIABLES WITH fz ADDED TO THEIR NAME REFER TO EXPERIENCE WHERE WE HAVE FILLED IN SOME ZEROS IN THE MISSING PSID YEARS WITH DATA FROM THE RESPONDENTS’ ANSWERS TO QUESTIONS ABOUT JOBS WORKED ON DURING THESE MISSING YEARS. THE fz variables WERE USED IN THE REGRESSION ANALYSES THE VARIABLES WITH A predict PREFIX REFER TO THE COMPUTATION OF ACTUAL EXPERIENCE ACCUMULATED DURING THE YEARS IN WHICH THE PSID DID NOT SURVEY THE RESPONDENTS. THERE ARE MORE PREDICTED EXPERIENCE LEVELS THAT ARE NEEDED TO IMPUTE EXPERIENCE IN THE MISSING YEARS IN SOME CASES. NOTE THAT THE VARIABLES yrsexpf, yrsexpfsz, etc., INCLUDE THESE COMPUTATIONS, SO THAT IF YOU WANT TO USE FULL TIME OR PART TIME EXPERIENCE, YOU DON’T NEED TO ADD THESE PREDICT VARIABLES IN. THEY ARE INCLUDED IN THE DATA SET TO ILLUSTRATE THE RESULTS OF THE COMPUTATION PROCESS. THE VARIABLES WITH AN orig PREFIX ARE THE ORIGINAL PSID VARIABLES. THESE HAVE BEEN PROCESSED AND IN SOME CASES RENAMED FOR CONVENIENCE. THE hd SUFFIX MEANS THAT THE VARIABLE REFERS TO THE HEAD OF THE FAMILY, AND THE wf SUFFIX MEANS THAT IT REFERS TO THE WIFE OR FEMALE COHABITOR IF THERE IS ONE. AS SHOWN IN THE ACCOMPANYING REGRESSION PROGRAM, THESE orig VARIABLES AREN’T USED DIRECTLY IN THE REGRESSIONS. THERE ARE MORE OF THE ORIGINAL PSID VARIABLES, WHICH WERE USED TO CONSTRUCT THE VARIABLES USED IN THE REGRESSIONS. HD MEANS HEAD AND WF MEANS WIFE OR FEMALE COHABITOR.

1. intnum68: 1968 INTERVIEW NUMBER
2. pernum68: PERSON NUMBER 68
3. wave: Current Wave of the PSID
4. sex: gender SEX OF INDIVIDUAL (1=male, 2=female)
5. intnum: Wave-specific Interview Number
6. farminc: Farm Income
7. region: regLab Region of Current Interview
8. famwgt: this is the PSID’s family weight, which is used in all analyses
9. relhead: ER34103L this is the relation to the head of household (10=head; 20=legally married wife; 22=cohabiting partner)
10. age: Age
11. employed: ER34116L Whether or not employed or on temp leave (everyone gets a 1 for this variable, since our wage analyses use only the currently employed)
12. sch: schLbl Highest Year of Schooling
13. annhrs: Annual Hours Worked
14. annlabinc: Annual Labor Income
15. occ: 3 Digit Occupation 2000 codes
16. ind: 3 Digit Industry 2000 codes
17. white: White, nonhispanic dummy variable
18. black: Black, nonhispanic dummy variable
19. hisp: Hispanic dummy variable
20. othrace: Other Race dummy variable
21. degree: degreeLbl Agent's Degree Status (0=no college degree; 1=bachelor’s without advanced degree; 2=advanced degree)
22. degupd: degreeLbl Agent's Degree Status (Updated with 2009 values)
23. schupd: schLbl Schooling (updated years of schooling)
24. annwks: Annual Weeks Worked
25. unjob: unJobLbl Union Coverage dummy variable
26. usualhrwk: Usual Hrs Worked Per Week
27. labincbus: Labor Income from...

In 2024, the average annual full-time salary for men in the United Kingdom was 40,035 British pounds, compared with 34,000 pounds for women, a difference of just over 6,000 pounds. In the previous year, men earned an average annual salary of 37,382, compared with women who earned 31,672.

Achieve gender equality and empower all women and girls : The region has made progress in achieving gender equality and empowering women and girls, particularly in education and health and to a lesser extent women’s participation in formal employment and national policy making. This is attributed to growing awareness of the need to address gender inequalities; While almost all countries in the Pacific have adopted specific gender policies and strategies, the resources for integrating and implementing these priorities are limited. Budgets for national women’s offices are less than one percent of national appropriations; Gender inequality is highlighted by the high prevalence rates of violence against women (more than 60 percent in Melanesia, and more than 40 percent in Polynesia and Micronesia). Sexual and reproductive health and rights issues also remain substantial challenges to be addressed under Goal 5. Fertility rates, especially teenage fertility, remain high in some.

Find more Pacific data on PDH.stat.

Context

The Gender Inequality Index (GII) is a comprehensive measure devised to evaluate gender disparities and inequities within a society by taking into account various critical dimensions. This index provides insights into the differences and imbalances experienced by individuals based on their gender. The GII is an extension of the Human Development Index (HDI) and concentrates on three principal dimensions: reproductive health, empowerment, and economic activity. Reproductive health is a significant dimension of the GII, encompassing indicators such as maternal mortality rates and adolescent birth rates. These indicators reflect the disparities in health outcomes experienced by women, especially in terms of maternal health and reproductive rights.

Content

This dataset provides comprehensive historical data on gender development indicators at a global level. It includes essential columns such as ISO3 (the ISO3 code for each country/territory), Country (the name of the country or territory), Continent (the continent where the country is located), Hemisphere (the hemisphere in which the country is situated), Human Development Groups, UNDP Developing Regions, HDI Rank (2021) representing the Human Development Index Rank for the year 2021, GII Rank (2021) representing the Gender Inequality Index Rank for 2021 and Gender Inequality Index spanning from 1990 to 2021.

Dataset Glossary (Column-wise)

• ISO3 - ISO3 for the Country/Territory
• Country - Name of the Country/Territory
• Continent - Name of the Continent
• Hemisphere - Name of the Hemisphere
• Human Development Groups - Human Development Groups
• UNDP Developing Regions - UNDP Developing Regions
• HDI Rank (2021) - Human Development Index Rank for 2021
• GII Rank (2021) - Gender Inequality Index Rank for 2021
• Gender Inequality Index from 1990 to 2021 - Gender Inequality Index from 1990 to 2021

Data Dictionary

• UNDP Developing Regions:
  • SSA - Sub-Saharan Africa
  • LAC - Latin America and the Caribbean
  • EAP - East Asia and the Pacific
  • AS - Arab States
  • ECA - Europe and Central Asia
  • SA - South Asia

Structure of the Dataset

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Acknowledgement

This Dataset is created from Human Development Reports. This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

Cover Photo by: Image by pikisuperstar on Freepik

Thumbnail by: Equality icons created by Freepik - Flaticon

The OECD Gender Data Portal, www.oecd.org/gender/data, includes 40+ selected indicators shedding light on gender inequalities in education, employment and entrepreneurship. Data and metadata for all the indicators are easily and freely accessible and displayed through interactive visualizations. The Gender Data Portal is one of the main outputs of the OECD Gender Initiative, launched in 2010 to improve policies and promote gender equality in the economy in both OECD and non-OECD countries. The Portal is part of the new OECD Gender Equality website www.oecd.org/gender, which also features Closing the Gender Gap: Act Now, a publication that presents new analysis of the productivity losses caused by gender inequality and proposes policy solutions to close the gender gaps. While much progress has been accomplished in recent years, there are still relevant dimensions of gender inequalities that are poorly monitored and measured. The OECD Gender Portal is thus a work in progress, that aims at progressively filling these gaps through new indicators. The last data release, for Women's Day 2013, includes new gender-sensitive indicators of job quality, timely indicators of labor market participation, indicators on top and low-achieving students in different subjects and on entrepreneurial culture. The data cover OECD member countries, as well as Russia, Brazil, China, India, Indonesia, and South Africa.

According to the Gender Inequality Index (GII), Denmark and Norway were the most gender equal countries in the world in 2023, reporting an index of ***** and *****, respectively. The Gender Inequality Index measures inequality between women and men in three dimensions: reproductive health, empowerment, and the labor market. A low GII value indicates low inequality between women and men and vice versa. Yemen was considered the least gender equal country that same year. Gender inequality in the workplace The most prominent source of gender inequality is the workplace, often captured by the gender pay gap. In 2023, women still earned one percent less than their male counterparts with the same qualification and the same job. Women are less represented in senior roles and top management positions, with only one third percent of companies worldwide having a woman in leadership positions. The same situation can be observed in government roles - only 17 out of 195 countries worldwide have ever had a woman in the highest position of executive power. Future outlook Numbers on how long it will take to close gender gaps highly differ between regions. In Europe, it is estimated that it will take around 67 years to achieve equality between the genders. In East Asia and the Pacific, on the other hand, it is projected to take 189 years. New data shows that the COVID-19 pandemic has increased female poverty worldwide and widened the gender poverty gap even further. Heightened female poverty will also negatively impact the Gender Inequality Index (GII).

Men in their 40s, who work in full-time jobs earned an average of 876.3 British pounds a week in the United Kingdom in 2024, compared with women in this age group who earned an average of 750.9 pounds a week. This was the highest earning age group for both genders.

India's performance on UNDP's Gender Inequality Index - score, rank, expert analysis and comparison with global peers.

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 .

Women in all countries of the world suffer different forms of violence, inequality and discrimination, both in the public and private spheres. Facing situations of abuse and unequal treatment.

The inequalities experienced by women occur in all areas of their development: health, education, work, among others, seriously undermining women's rights to a dignified life.

One of the most serious scourges suffered by women in Latin America is femicides.

This dataset will allow research development on gender issues -in latin american countries- in terms of: human development, gender development, gender inequalities, femicides and violence.

This contains official indicators from the United Nations Development Program (UNDP), the Economic Commission for Latin America and the Caribbean (ECLAC) -a dependent body of the United Nations Organization- and the Institute for Economics and Peace (IEP).

This dataset contains 7 indexes, to mention.

From UNDP: -Human Development Index (HDI) -Gender Development Index (GDI) -Inequalities in HDI (IHDI) -Gender Inequality Index (GII) -Planetary pressures–adjusted Human Development Index (PHDI)

From CEPAL: Number of femicides (fem)

From the Institute for Economics and Peace (IEP): Global Peace Index (gpi)

To study the political mobilization of underrepresented groups, this paper examines the effect of electoral systems on gender equality in voting. Theoretically, I argue that replacing a plurality electoral system with proportional representation (PR) gives party elites greater incentives to mobilize women to vote in all but the most competitive districts under plurality rule. Yet, they need to tap into women's networks to succeed with such mobilizing efforts. Empirically, I isolate the causal effect of PR by studying an imposed shift from plurality to PR in Norwegian municipalities. Using a difference-in-differences design, I estimate that the move from plurality to PR substantially decreased gender inequality in voting. The effect is most pronounced in previously uncompetitive municipalities and where women's networks are present. This study thus demonstrates how the social environment conditions the effect of democratic institutions on the political participation of marginalized groups.

The gender wage gap indicator compares the median earnings between male and female workers in Champaign County.

Two worker populations are analyzed: all workers, including part-time and seasonal workers and those that were not employed for the full survey year; and full-time, year-round workers. The gender wage gap is included because it blends economics and equity, and illustrates that a major economic talking point on the national level is just as relevant at the local scale.

For all four populations (male full-time, year-round workers; female full-time, year-round workers; all male workers; and all female workers), the estimated median earnings were higher in 2023 than in 2005. The greatest increase in a population’s estimated median earnings between 2005 and 2023 was for female full-time, year-round workers; the smallest increase between 2005 and 2023 was for all female workers. In both categories (all and full-time, year-round), the estimated median annual earnings for male workers was consistently higher than for female workers.

The gender gap between the two estimates in 2023 was larger for full-time, year-round workers than all workers. For full-time, year-round workers, the difference was $11,863; for all workers, it was approaching $9,700.

The Associated Press wrote this article in October 2024 about how Census Bureau data shows that in 2023 in the United States, the gender wage gap between men and women working full-time widened year-over-year for the first time in 20 years.

Income data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Median Earnings in the Past 12 Months (in 2020 Inflation-Adjusted Dollars) by Sex by Work Experience in the Past 12 Months for the Population 16 Years and Over with Earnings in the Past 12 Months.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using data.census.gov; (16 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using data.census.gov; (20 October 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using data.census.gov; (21 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using data.census.gov; (7 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using data.census.gov; (7 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).

Main CSV file extracted from zip file download of World Bank gender statistics file.Copy of data as of 25th September 2019.

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.

Is the gender pay gap largest at the top? No: it is at least as large at bottom percentiles of the earnings distribution. Conditional quantile regressions reveal that while the gap at top percentiles is largest among the most-educated, the gap at bottom percentiles is largest among the least-educated. Gender differences in work hours create more pay inequality among the least-educated than they do among the most-educated. The pay gap has declined throughout the distribution since 2006, but it declined more for the most-educated women. Current economics-of-gender research focuses heavily on the top end; equal emphasis should be placed on mechanisms driving gender inequality for noncollege-educated workers.