About this dataset

Context

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

Key observations

There is a majority of male population, with 53.18% 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 Globe is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Globe 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 Globe Population by Race & Ethnicity. You can refer the same here

Germany DE: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data was reported at 84.557 % in 2023. This records an increase from the previous number of 84.326 % for 2022. Germany DE: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data is updated yearly, averaging 76.088 % from Dec 1983 (Median) to 2023, with 41 observations. The data reached an all-time high of 84.557 % in 2023 and a record low of 57.039 % in 1983. Germany DE: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Labour Force. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100.;World Bank, World Development Indicators database. Estimates are based on data obtained from International Labour Organization, ILOSTAT at https://ilostat.ilo.org/data/.;Weighted average;The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

About this dataset

Context

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

Key observations

There is a slight majority of male population, with 50.58% 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 Blue Earth County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Blue Earth County 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 Blue Earth County Population by Race & Ethnicity. You can refer the same here

United States US: Labour Force Participation Rate: Modeled ILO Estimate: Ratio of Female to Male data was reported at 81.641 % in 2017. This records a decrease from the previous number of 81.668 % for 2016. United States US: Labour Force Participation Rate: Modeled ILO Estimate: Ratio of Female to Male data is updated yearly, averaging 80.555 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 82.223 % in 2010 and a record low of 74.649 % in 1990. United States US: Labour Force Participation Rate: Modeled ILO Estimate: Ratio of Female to Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Labour Force. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100.; ; Derived using data from International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections. National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

Context

This dataset contains all the stats of Gender Statistics 2022 - World Bank.

Details

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.

Wage and salaried workers (employees) are those workers who hold the type of jobs defined as "paid employment jobs," where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work. Contraceptive prevalence rate is the percentage of women who are practicing, or whose sexual partners are practicing, at least one modern method of contraception. It is usually measured for women ages 15-49 who are married or in union. Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception.

Number of male sole proprietors is the number of newly registered sole proprietors owned by female individuals in the calendar year. A sole proprietorship is a business entity owned and managed by a single individual who is indistinguishable from the business and personally liable.

Percentage of women aged 15–49 who have gone through partial or total removal of the female external genitalia or other injury to the female genital organs for cultural or other non-therapeutic reasons. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households. Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered. Women who own house both alone and jointly (% of women age 15-49): Q4 is the percentage of women age 15-49 who alone as well as jointly with someone else own a house which is legally registered with their name or cannot be sold without their signature. "Both alone and jointly" Implies a woman owns a house alone and another house jointly with someone else. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households.

Number of infants dying before reaching one year of age. Male population between the ages 75 to 79.

The percentage of respondents who report using mobile money, a debit or credit card, or a mobile phone to make a payment from an account, or report using the internet to pay bills or to buy something online, in the past 12 months. It also includes respondents who report paying bills, sending or receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly from or into a financial institution account or through a mobile money account in the past 12 months, male (% age 15+).

Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.

Metadata

Coverage & Extent

• Granularity List : National
• Temporal Coverage : 1959 - 2021
• Periodicity : Annual
• Acronym : Gender Stats
• Recommended Citation: Gender Statistics, The World Bank
• Languages Supported : English
• Source Type : World Bank Group
• Source: : Gender Statistics, The World Bank
• Harvest Source : World Bank Data API
• Dates
  • First Published Date : Jul 18, 2010
  • Last Updated on : Jun 22, 2022
• Update Frequency : Quarter

Download

kaggle API Command !kaggle datasets download -d azminetoushikwasi/gender-statistics-wb

Disclaimer

The data collected are all publicly available and it's intended for educational purposes only.

Acknowledgement

https://datacatalog.worldbank.org/search/dataset/0037654

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.

This dataset shows the percentage of adult males, adult females and juveniles that reported ever using meth in 2023.

Oman OM: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data was reported at 33.880 % in 2016. This records an increase from the previous number of 31.955 % for 2013. Oman OM: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data is updated yearly, averaging 31.955 % from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 33.880 % in 2016 and a record low of 21.435 % in 2000. Oman OM: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Oman – Table OM.World Bank.WDI: Labour Force. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100.; ; Derived using data from International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average; The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

Denmark DK: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data was reported at 87.694 % in 2016. This records an increase from the previous number of 86.620 % for 2015. Denmark DK: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data is updated yearly, averaging 83.086 % from Dec 1960 (Median) to 2016, with 43 observations. The data reached an all-time high of 88.451 % in 2013 and a record low of 42.757 % in 1960. Denmark DK: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Denmark – Table DK.World Bank: Labour Force. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100.; ; Derived using data from International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted Average; The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

Denmark DK: Labour Force Participation Rate: Modeled ILO Estimate: Ratio of Female to Male data was reported at 88.092 % in 2017. This records an increase from the previous number of 88.010 % for 2016. Denmark DK: Labour Force Participation Rate: Modeled ILO Estimate: Ratio of Female to Male data is updated yearly, averaging 84.631 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 88.553 % in 2013 and a record low of 79.338 % in 1995. Denmark DK: Labour Force Participation Rate: Modeled ILO Estimate: Ratio of Female to Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Denmark – Table DK.World Bank.WDI: Labour Force. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100.; ; Derived using data from International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections. National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

About this dataset

Context

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

Key observations

There is a slight majority of male population, with 51.51% 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 Black Earth is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Black Earth 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 Black Earth Population by Race & Ethnicity. You can refer the same here

Context

Women roughly occupy half of the world's population but when it comes to the total workforce of a country, the percentage of male and female workers are rarely similar. This is even more prominent for the developing and underdeveloped countries. While several reasons such as the insufficient access to education, religious superstitions, lack of adequate infrastrucutres are responsible for this discrepancy, it goes way beyond these. One significant factor is the fertility rate of women which is a count for the total number of births per an individual woman. And to show its effects on the participation of women in the total workforce, percentage of female workers in the labor force has been considered. Using simple linear regression model, the relationship between these two factors can be analyzed.

Content

The datasets span over 23 years (from 1995 to 2017). Data has been collected separately from two surveys carried out by the World Bank for both the fertility rate and the percentage of female in the total workforce of Bangladesh. These two datasets were compiled into one dataset and it corresponds to the 23 data points for these two variables ("fertility rate" and "worker percent").

Inspiration

Linear model as well as other statistical methods can be applied on this dataset to analyze if there is any viable relationship between these two variables.

Context

The literacy rate is defined by the percentage of the population of a given age group that can read and write. The adult literacy rate corresponds to ages 15 and above, the youth literacy rate to ages 15 to 24. It is typically measured according to the ability to comprehend a short simple statement on everyday life.

Content

The dataset contains information about the literacy rates across the globe for various countries. It is grouped into 3 major categories, Total %, Male% and Female %. The dataset has 2 csv files. 1) Adults_15YrsAndUp.csv - Contains literacy rate information for Adults

2) Youth_15to24Yrs.csv - Contains literacy rate information for Youth

Acknowledgements

The dataset is sourced from Unicef.

Inspiration

Exploratory Data Analysis to find greater insights into the data.

The indicator is defined as the absolute difference between males and females in the at-risk-of-poverty rate for single-person households.

Analysis of ‘Global Child Mortality Rate’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/drateendrajha/global-child-mortality-rate on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Dataset Size

This dataset contains data of 197 countries from 1967 to 2020.

Column Description

Country - Name of country Year - Year in numeric form Gender -Male ; Female Child Mortality - Mortality of child Total Population - Population of respective country in respective year Mortality Rate - Child Mortality / Total Population

Acknowledgements

Thankful to UNICEF for the data.

Extracted by

drateendrajha.com in case of any query feel free to reach ajha@phaf.in

--- Original source retains full ownership of the source dataset ---

Series Name: Legal frameworks that promote enforce and monitor gender equality (percentage of achievement 0 - 100) -- Area 4: marriage and familySeries Code: SG_LGL_GENEQMARRelease Version: 2020.Q2.G.03This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 5.1.1: Whether or not legal frameworks are in place to promote, enforce and monitor equality and non-discrimination on the basis of sexTarget 5.1: End all forms of discrimination against all women and girls everywhereGoal 5: Achieve gender equality and empower all women and girlsFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Abstract

The Health Inequality Project uses big data to measure differences in life expectancy by income across areas and identify strategies to improve health outcomes for low-income Americans.

Section 7

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution. Both race-adjusted and unadjusted estimates are reported.

Source

Section 13

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution separately by year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 6

This dataset was created on 2020-01-10 18:53:00.508 by merging multiple datasets together. The source datasets for this version were:

Commuting Zone Life Expectancy Estimates by year: CZ-level by-year life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy: Commuting zone (CZ)-level life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy Trends: CZ-level estimates of trends in life expectancy for men and women, by income quartile

Commuting Zone Characteristics: CZ-level characteristics

Commuting Zone Life Expectancy for larger populations: CZ-level life expectancy estimates for men and women, by income ventile

Section 15

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by state of residence and year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 11

This table reports US mortality rates by gender, age, year and household income percentile. Household incomes are measured two years prior to the mortality rate for mortality rates at ages 40-63, and at age 61 for mortality rates at ages 64-76. The “lag” variable indicates the number of years between measurement of income and mortality.

Observations with 1 or 2 deaths have been masked: all mortality rates that reflect only 1 or 2 deaths have been recoded to reflect 3 deaths

Source

Section 3

This table reports coefficients and standard errors from regressions of life expectancy estimates for men and women at age 40 for each quartile of the national income distribution on calendar year by commuting zone of residence. Only the slope coefficient, representing the average increase or decrease in life expectancy per year, is reported. Trend estimates for both race-adjusted and unadjusted life expectancies are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 9

This table reports life expectancy estimates at age 40 for Males and Females for all countries. Source: World Health Organization, accessed at: http://apps.who.int/gho/athena/

Source

Section 10

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by county of residence. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for counties with populations larger than 25,000 only

Source

Section 2

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by commuting zone of residence and year. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 8

This table reports US population and death counts by age, year, and sex from various sources. Counts labelled “dm1” are derived from the Social Security Administration Data Master 1 file. Counts labelled “irs” are derived from tax data. Counts labelled “cdc” are derived from NCHS life tables.

Source

Section 12

This table reports numerous county characteristics, compiled from various sources. These characteristics are described in the county life expectancy table.

Two variables constructed by the Cen

Slovenia SI: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data was reported at 85.184 % in 2023. This records a decrease from the previous number of 85.312 % for 2022. Slovenia SI: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data is updated yearly, averaging 81.848 % from Dec 1991 (Median) to 2023, with 32 observations. The data reached an all-time high of 85.986 % in 2021 and a record low of 78.699 % in 1995. Slovenia SI: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Slovenia – Table SI.World Bank.WDI: Labour Force. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100.;World Bank, World Development Indicators database. Estimates are based on data obtained from International Labour Organization, ILOSTAT at https://ilostat.ilo.org/data/.;Weighted average;The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

Burundi BI: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data was reported at 99.130 % in 2020. This records a decrease from the previous number of 102.487 % for 2014. Burundi BI: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data is updated yearly, averaging 100.508 % from Dec 1978 (Median) to 2020, with 12 observations. The data reached an all-time high of 104.470 % in 1984 and a record low of 79.123 % in 1980. Burundi BI: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Burundi – Table BI.World Bank.WDI: Labour Force. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100.;World Bank, World Development Indicators database. Estimates are based on data obtained from International Labour Organization, ILOSTAT at https://ilostat.ilo.org/data/.;Weighted average;The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

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.