Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Main CSV file extracted from zip file download of World Bank gender statistics file.Copy of data as of 25th September 2019.
As of January 2024, 56.8 percent Facebook's audience were male and 43.2 percent were female. By the end of 2023, Facebook had over three billion monthly active users (MAU).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of Hermosa Beach by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Hermosa Beach. The dataset can be utilized to understand the population distribution of Hermosa Beach by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Hermosa Beach. 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 Hermosa Beach.
Key observations
Largest age group (population): Male # 30-34 years (971) | Female # 30-34 years (967). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
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
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.
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/.
This dataset is a part of the main dataset for Hermosa Beach Population by Gender. You can refer the same here
https://data.gov.tw/licensehttps://data.gov.tw/license
From August 2024, statistics on the number of cases in various regions, age groups, and genders will be compiled (disease name: COVID-19 with severe complications, date type: confirmation date, case type: confirmed cases, source of infection: whether it is imported from overseas). This dataset is updated once a day according to a fixed system schedule, presenting statistics as of the previous day.
https://data.gov.tw/licensehttps://data.gov.tw/license
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
According to a survey from 2020, the coronavirus (COVID-19) crisis will increase female poverty worldwide. Globally, 247 million women aged 15 years and older will be living on less than 1.90 U.S. dollars per day in 2021, compared to 236 million men. The gender poverty gap is expected to increase by 2030 as women will still be the majority of the world's extreme poor.
https://data.gov.tw/licensehttps://data.gov.tw/license
The current statistics of personnel ranks in this office
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of Heidelberg by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Heidelberg. The dataset can be utilized to understand the population distribution of Heidelberg by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Heidelberg. 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 Heidelberg.
Key observations
Largest age group (population): Male # 0-4 years (44) | Female # 60-64 years (62). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
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
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.
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/.
This dataset is a part of the main dataset for Heidelberg Population by Gender. You can refer the same here
https://data.gov.tw/licensehttps://data.gov.tw/license
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 from overseas)
https://data.gov.tw/licensehttps://data.gov.tw/license
Statistical table of dengue fever cases by region, age group, and gender since 2003 (Disease name: Dengue fever, Date type: Onset date, Case type: Confirmed case, Source of infection: Domestic, Imported)
As of February 2025, approximately ** percent of YouTube users were male. By comparison, female users on the popular social video platform were approximately ** percent of the total. In the last examined period, the ******************** and ****** were among the country with the highest YouTube penetration worldwide.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of Green Bay by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Green Bay across both sexes and to determine which sex constitutes the majority.
Key observations
There is a slight majority of female population, with 50.43% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
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
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.
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/.
This dataset is a part of the main dataset for Green Bay Population by Race & Ethnicity. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of Phoenix by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Phoenix. The dataset can be utilized to understand the population distribution of Phoenix by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Phoenix. 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 Phoenix.
Key observations
Largest age group (population): Male # 30-34 years (201) | Female # 65-69 years (295). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
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
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.
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/.
This dataset is a part of the main dataset for Phoenix Population by Gender. You can refer the same here
In 2024, there were around 719 million male inhabitants and 689 million female inhabitants living in China, amounting to around 1.41 billion people in total. China's total population decreased for the first time in decades in 2022, and population decline is expected to accelerate in the upcoming years. Birth control in China From the beginning of the 1970s on, having many children was no longer encouraged in mainland China. The one-child policy was then introduced in 1979 to control the total size of the Chinese population. According to the one-child policy, a married couple was only allowed to have one child. With the time, modifications were added to the policy, for example parents living in rural areas were allowed to have a second child if the first was a daughter, and most ethnic minorities were excepted from the policy. Population ageing The birth control led to a decreasing birth rate in China and a more skewed gender ratio of new births due to boy preference. Since the negative economic and social effects of an aging population were more and more felt in China, the one-child policy was considered an obstacle for the country’s further economic development. Since 2014, the one-child policy has been gradually relaxed and fully eliminated at the end of 2015. However, many young Chinese people are not willing to have more children due to high costs of raising a child, especially in urban areas.
https://data.gov.tw/licensehttps://data.gov.tw/license
From August 2024, statistics on the number of cases by region, age group, and gender (disease name: severe COVID-19 complications, date type: onset date, case type: confirmed case, source of infection: whether it is imported from overseas). This dataset is updated once a day according to the fixed schedule of the system, presenting statistical information as of the previous day.
https://data.gov.tw/licensehttps://data.gov.tw/license
Statistical table of the number of cases by region, age group, and gender since 2003 (Disease name: Epidemic Typhus, Date Type: Date of diagnosis, Type of case: Confirmed case, Source of infection: Domestic, Imported)
In May 2025, 55.4 percent of Instagram users in the United States were women, and 44.6 percent were men. Overall, Instagram finds much popularity in the U.S., and the country is home to the platform's second-largest worldwide audience, with around 171.7 million users in the country.
Women accounted for ** percent of the behind-the-scenes professionals working in Hollywood's top 250 films in 2023. More than a ******* of producers behind the United States' highest-grossing movies were female in 2023.
https://data.gov.tw/licensehttps://data.gov.tw/license
From 2003, statistics of the number of cases by region, age group, and gender (disease name: leptospirosis, date type: onset date, case type: confirmed case, source of infection: domestic, imported)
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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