Explore gender statistics data focusing on academic staff, employment, fertility rates, GDP, poverty, and more in the GCC region. Access comprehensive information on key indicators for Bahrain, China, India, Kuwait, Oman, Qatar, and Saudi Arabia.

academic staff, Access to anti-retroviral drugs, Adjusted net enrollment rate, Administration and Law programmes, Age at first marriage, Age dependency ratio, Cause of death, Children out of school, Completeness of birth registration, consumer prices, Cost of business start-up procedures, Employers, Employment in agriculture, Employment in industry, Employment in services, employment or training, Engineering and Mathematics programmes, Female headed households, Female migrants, Fertility planning status: mistimed pregnancy, Fertility planning status: planned pregnancy, Fertility rate, Firms with female participation in ownership, Fisheries and Veterinary programmes, Forestry, GDP, GDP growth, GDP per capita, gender parity index, Gini index, GNI, GNI per capita, Government expenditure on education, Government expenditure per student, Gross graduation ratio, Households with water on the premises, Inflation, Informal employment, Labor force, Labor force with advanced education, Labor force with basic education, Labor force with intermediate education, Learning poverty, Length of paid maternity leave, Life expectancy at birth, Mandatory retirement age, Manufacturing and Construction programmes, Mathematics and Statistics programmes, Number of under-five deaths, Part time employment, Population, Poverty headcount ratio at national poverty lines, PPP, Primary completion rate, Retirement age with full benefits, Retirement age with partial benefits, Rural population, Sex ratio at birth, Unemployment, Unemployment with advanced education, Urban population

Bahrain, China, India, Kuwait, Oman, Qatar, Saudi Arabia

Follow data.kapsarc.org for timely data to advance energy economics research.

In terms of population size, the sex ratio in the United States favors females, although the gender gap is remaining stable. In 2010, there were around 5.17 million more women, with the difference projected to decrease to around 3 million by 2027.

Gender ratios by U.S. state In the United States, the resident population was estimated to be around 331.89 million in 2021. The gender distribution of the nation has remained steady for several years, with women accounting for approximately 51.1 percent of the population since 2013. Females outnumbered males in the majority of states across the country in 2020, and there were eleven states where the gender ratio favored men.

Metro areas by population National differences between male and female populations can also be analyzed by metropolitan areas. In general, a metropolitan area is a region with a main city at its center and adjacent communities that are all connected by social and economic factors. The largest metro areas in the U.S. are New York, Los Angeles, and Chicago. In 2019, there were more women than men in all three of those areas, but Jackson, Missouri was the metro area with the highest share of female population.

The Gender Statistics database is a comprehensive source for the latest sex-disaggregated data and gender statistics covering demography, education, health, access to economic opportunities, public life and decision-making, and agency.

For further details, please refer to https://datacatalog.worldbank.org/search/dataset/0037654/gender-statistics

The Gender Statistics Database provides a broad overview of statistics on gender as well as information on the various aspects of (in)equality between women and men. These include indicators referred to the EU Strategy for Equality between Women and Men (2010-2015) and the Beijing Declaration and Platform for Action on Equality.

Moreover, it is possible to access the Gender Equality Index Scores on the same platform. The Index is a composite indicator that measures how far (or close) the EU and its Member States were from achieving complete gender equality in the reference year.

As of February 2025, 56.7 percent Facebook's audience were male and 43.4 percent were female. By the end of 2023, Facebook had over three billion monthly active users (MAU).

About this dataset

Context

The dataset tabulates the population of Sharon by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Sharon. The dataset can be utilized to understand the population distribution of Sharon by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Sharon. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Sharon.

Key observations

Largest age group (population): Male # 5-9 years (562) | Female # 55-59 years (636). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Sharon population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Sharon is shown in the following column.
• Population (Female): The female population in the Sharon is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Sharon for each 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 Sharon Population by Gender. You can refer the same here

Gender statistics on the numbers of women and men in key decision-making positions across a number of different life domains. The domains covered include: politics; public administration; judiciary; business and finance; social partners and NGOs; environment and climate change; and media.

Data on decision-making are collected for 35 European countries - the 28 EU Member States, 4 candidate countries (Montenegro, the former Yugoslav Republic of Macedonia, Serbia and Turkey) and the remaining EEA countries (Iceland, Liechtenstein and Norway).

Figures are available at international, European, national, regional and local level. Most data are updated annually, but some key data are updated more frequently. In particular, data on national and European politics are updated quarterly, and data on large companies biannually, in order to ensure that the information is always right up to date.

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 .

About this dataset

Context

The dataset tabulates the population of Roseburg by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Roseburg across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 51.82% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Roseburg is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Roseburg total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Roseburg Population by Race & Ethnicity. You can refer the same here

Denmark - Gender employment gap was 6.50 % of total population in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Denmark - Gender employment gap - last updated from the EUROSTAT on June of 2025. Historically, Denmark - Gender employment gap reached a record high of 7.80 % of total population in December of 2015 and a record low of 5.40 % of total population in December of 2022.

The statistical report, international indicators, and research reports for the Gender Mainstreaming Project administered by the National Science and Technology Commission.

Worldwide Gender Differences in Public Code Contributions - Replication Package

This document describes how to replicate the findings of the paper: Davide Rossi and Stefano Zacchiroli, 2022, Worldwide Gender Differences in Public Code Contributions. In Software Engineering in Society (ICSE-SEIS'22), May 21-29, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3510458.3513011

This document comes with the software needed to mine and analyze the data presented in the paper.

Prerequisites

These instructions assume the use of the bash shell, the Python programming language, the PosgreSQL DBMS (version 11 or later), the zstd compression utility and various usual *nix shell utilities (cat, pv, ...), all of which are available for multiple architectures and OSs.
It is advisable to create a Python virtual environment and install the following PyPI packages: click==8.0.3 cycler==0.10.0 gender-guesser==0.4.0 kiwisolver==1.3.2 matplotlib==3.4.3 numpy==1.21.3 pandas==1.3.4 patsy==0.5.2 Pillow==8.4.0 pyparsing==2.4.7 python-dateutil==2.8.2 pytz==2021.3 scipy==1.7.1 six==1.16.0 statsmodels==0.13.0

Initial data

• swh-replica, a PostgreSQL database containing a copy of Software Heritage data. The schema for the database is available at https://forge.softwareheritage.org/source/swh-storage/browse/master/swh/storage/sql/.
  We retrieved these data from Software Heritage, in collaboration with the archive operators, taking an archive snapshot as of 2021-07-07. We cannot make these data available in full as part of the replication package due to both its volume and the presence in it of personal information such as user email addresses. However, equivalent data (stripped of email addresses) can be obtained from the Software Heritage archive dataset, as documented in the article: Antoine Pietri, Diomidis Spinellis, Stefano Zacchiroli, The Software Heritage Graph Dataset: Public software development under one roof. In proceedings of MSR 2019: The 16th International Conference on Mining Software Repositories, May 2019, Montreal, Canada. Pages 138-142, IEEE 2019. http://dx.doi.org/10.1109/MSR.2019.00030.
  Once retrieved, the data can be loaded in PostgreSQL to populate swh-replica.
• names.tab - forenames and surnames per country with their frequency
• zones.acc.tab - countries/territories, timezones, population and world zones
• c_c.tab - ccTDL entities - world zones matches

Data preparation

• Export data from the swh-replica database to create commits.csv.zst and authors.csv.zst sh> ./export.sh
• Run the authors cleanup script to create authors--clean.csv.zst sh> ./cleanup.sh authors.csv.zst
• Filter out implausible names and create authors--plausible.csv.zst sh> pv authors--clean.csv.zst | unzstd | ./filter_names.py 2> authors--plausible.csv.log | zstdmt > authors--plausible.csv.zst

Gender detection

• Run the gender guessing script to create author-fullnames-gender.csv.zst sh> pv authors--plausible.csv.zst | unzstd | ./guess_gender.py --fullname --field 2 | zstdmt > author-fullnames-gender.csv.zst

Database creation and data ingestion

• Create the PostgreSQL DB sh> createdb gender-commit Notice that from now on when prepending the psql> prompt we assume the execution of psql on the gender-commit database.

• Import data into PostgreSQL DB sh> ./import_data.sh

Zone detection

• Extract commits data from the DB and create commits.tab, that is used as input for the gender detection script
  sh> psql -f extract_commits.sql gender-commit
• Run the world zone detection script to create commit_zones.tab.zst sh> pv commits.tab | ./assign_world_zone.py -a -n names.tab -p zones.acc.tab -x -w 8 | zstdmt > commit_zones.tab.zst Use ./assign_world_zone.py --help if you are interested in changing the script parameters.
• Read zones assignment data from the file into the DB
  psql> \copy commit_culture from program 'zstdcat commit_zones.tab.zst | cut -f1,6 | grep -Ev ''\s$'''

Extraction and graphs

• Run the script to execute the queries to extract the data to plot from the DB. This creates commits_tz.tab, authors_tz.tab, commits_zones.tab, authors_zones.tab, and authors_zones_1620.tab.
  Edit extract_data.sql if you whish to modify extraction parameters (start/end year, sampling, ...). sh> ./extract_data.sh
• Run the script to create the graphs from all the previously extracted tabfiles. This will generate commits_tzs.pdf, authors_tzs.pdf, commits_zones.pdf, authors_zones.pdf, and authors_zones_1620.pdf. sh> ./create_charts.sh

Additional graphs

This package also includes some already-made graphs

• authors_zones_1.pdf: stacked graphs showing the ratio of female authors per world zone through the years, considering all authors with at least one commit per period
• authors_zones_2.pdf: ditto with at least two commits per period
• authors_zones_10.pdf: ditto with at least ten commits per period

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

Over the past 23 years, there were constantly more men than women living on the planet. Of the 8.06 billion people living on the Earth in 2023, 4.05 billion were men and 4.01 billion were women. One-quarter of the world's total population in 2024 was below 15 years.

From August 2024, statistics will be provided for the number of cases in various regions, age groups, and genders suffering from severe complications of COVID-19. The dataset will be updated daily and will show statistics up to the previous day.

From August 2024, statistical tables of cases by region, age group, and gender (disease name: severe cases of COVID-19, date type: onset date, case type: confirmed cases, source of infection: whether imported from abroad). This dataset is updated once daily according to a fixed schedule by the system, presenting statistical information up to the previous day.

About this dataset

Context

The dataset tabulates the population of Sheboygan by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Sheboygan across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of male population, with 51.48% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Sheboygan is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Sheboygan total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Sheboygan Population by Race & Ethnicity. You can refer the same here

Germany - Gender employment gap was 7.10 % of total population in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Germany - Gender employment gap - last updated from the EUROSTAT on June of 2025. Historically, Germany - Gender employment gap reached a record high of 10.70 % of total population in December of 2009 and a record low of 7.10 % of total population in December of 2024.

Statistical table of the number of cases by region, age group, and gender since 2003 (Disease name: Scrub typhus, Date type: Onset date, Case type: Confirmed case, Source of infection: Domestic, Imported).

Statistical table of the number of cases by disease name (epidemic parotitis), date type (onset date), case type (confirmed case), and source of infection (domestic, imported) by region, age group, and gender since 2003