Worldwide, the male population is slightly higher than the female population, although this varies by country. As of 2024, Hong Kong has the highest share of women worldwide with almost ** percent. Moldova followed behind with around ** percent. Among the countries with the largest share of women in the total population, several were former Soviet states or were located in Eastern Europe. By contrast, Qatar, the United Arab Emirates, and Oman had some of the highest proportions of men in their populations.

This dataset explores the intriguing phenomenon of life expectancy disparity between genders across various countries spanning the years 1950 to 2020. Delving into the age-old statement that "women live longer than men," this dataset provides insights into the evolving trends in life expectancy and population dynamics worldwide.

Dataset Glossary (Column-wise):

• Year: The year of observation (1950-2020).
• Female Life Expectancy: The average life expectancy at birth for females in a given year and country.
• Male Life Expectancy: The average life expectancy at birth for males in a given year and country.
• Population: The total population of the country in a given year.
• Life Expectancy Gap: The difference between female and male life expectancy, highlighting the disparity between genders.

The dataset aims to facilitate comprehensive analyses regarding gender-based life expectancy disparities over time and across different nations. Researchers, policymakers, and analysts can utilize this dataset to explore patterns, identify contributing factors, and devise strategies to address gender-based health inequalities.

License - 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.

Acknowledgement: Image :- Freepik

Graph and download economic data for Ratio of Female to Male Tertiary School Enrollment for the United States (SEENRTERTFMZSUSA) from 1971 to 2022 about enrolled, ratio, tertiary schooling, females, males, education, and USA.

About this dataset

Context

The dataset tabulates the population of Chicago by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Chicago. The dataset can be utilized to understand the population distribution of Chicago by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Chicago. 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 Chicago.

Key observations

Largest age group (population): Male # 25-29 years (132,614) | Female # 25-29 years (139,234). 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 Chicago population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Chicago is shown in the following column.
• Population (Female): The female population in the Chicago 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 Chicago 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 Chicago Population by Gender. You can refer the same here

The gender or sex ratio in China has been a contentious issue since the introduction of the one-child policy in 1979, intended to limit the population of the country. Although the policy is no longer in place, the population gender difference throughout the country is still evident. In 2023, fifteen to nineteen-year-old children had the largest gender disparity of 115.3 males to every 100 females. Gender imbalance While the difference of gender at birth has been decreasing in the country over the past decade, China still boasts the world’s most skewed sex ratio at birth at around 110 males born for every 100 females as of 2023. That means there are about 31 million more men in the country than women. This imbalance likely came from the country’s traditional preference for male children to continue the family lineage, in combination with the population control policies enforced. Where does that leave the population? The surplus of young, single men across the country poses a risk for China in many different socio-economic areas. Some of the roll-on effects include males overrepresenting specific labor markets, savings rates increasing, consumption reducing and violent crime increasing across the country. However, the adult mortality rate in China, that is, the probability of a 15-year-old dying before reaching age 60, was significantly higher for men than for women. For the Chinese population over 60 years of age, the gender ratio is in favor of women, with more females outliving their male counterparts.

SonishMaharjan/male-female dataset hosted on Hugging Face and contributed by the HF Datasets community

About this dataset

Context

The dataset tabulates the population of Myrtle Beach by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Myrtle Beach. The dataset can be utilized to understand the population distribution of Myrtle Beach by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Myrtle 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 Myrtle Beach.

Key observations

Largest age group (population): Male # 60-64 years (1,676) | Female # 60-64 years (1,880). 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 Myrtle Beach population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Myrtle Beach is shown in the following column.
• Population (Female): The female population in the Myrtle Beach 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 Myrtle Beach 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 Myrtle Beach Population by Gender. You can refer the same here

Actual value and historical data chart for World Population Female Percent Of Total

Male Female 2

## Overview

Male Female 2 is a dataset for instance segmentation tasks - it contains Male Female annotations for 9,943 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

As of 2025, there was a noticeable digital gender divide between men and women using the internet in both the Least Developed Countries (LDCs) and Landlocked Developing Countries (LLDCs). Meanwhile, 64 percent of both males and females in Small Island Developing States (SIDS) have access to the internet.

Here are a few use cases for this project:

1. Crowd Analysis: The model can be used in public spaces, malls, events, or gatherings to analyze the demographic distribution, shaping crowd management strategies.

2. Smart Retail: In retail environments, the model can detect and analyze the gender and age demographics of customers to personalize services, optimize store layout, or measure the effectiveness of marketing campaigns.

3. Safety Measures: It could be used in areas like swimming pools, parks, or schools to detect the presence of children for enhanced safety or surveillance, alerting the appropriate authorities if there is any potential danger.

4. Content Recommendation: Online platforms could use it to identify the viewer's demographic from their profile picture leading to better content recommendation tailored to their age and gender.

5. Education: The model could be used in smart classrooms to identify the number of male, female, and child participants in online or offline education sessions, helping in creating pedagogy or curricula that is audience-specific.

Graph and download economic data for Ratio of Female to Male Secondary School Enrollment for Low Income Countries (SEENRSECOFMZSLIC) from 1970 to 2020 about enrolled, secondary schooling, secondary, ratio, females, males, education, and income.

In 2024, over 11,000 men and women each in Germany were aged 40 to 59 years, making it the largest age group in the country for each gender. The next most represented age group was 65 years and older.

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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...

Colombia: Ratio of female to male students in tertiary level education: The latest value from 2022 is 1.15 percent, unchanged from 1.15 percent in 2021. In comparison, the world average is 1.21 percent, based on data from 117 countries. Historically, the average for Colombia from 1970 to 2022 is 0.99 percent. The minimum value, 0.37 percent, was reached in 1970 while the maximum of 1.15 percent was recorded in 2021.

Actual value and historical data chart for United Kingdom Sex Ratio At Birth Male Births Per Female Births

About this dataset

Context

The dataset tabulates the population of Palm Beach County by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Palm Beach County. The dataset can be utilized to understand the population distribution of Palm Beach County by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Palm Beach County. 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 Palm Beach County.

Key observations

Largest age group (population): Male # 55-59 years (50,401) | Female # 60-64 years (53,567). 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 Palm Beach County population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Palm Beach County is shown in the following column.
• Population (Female): The female population in the Palm Beach County 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 Palm Beach County 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 Palm Beach County Population by Gender. You can refer the same here

Graph and download economic data for Ratio of Female to Male Primary School Enrollment for China (SEENRPRIMFMZSCHN) from 1974 to 2021 about primary schooling, enrolled, ratio, primary, females, males, and China.

Since 1950 there has been a relatively large difference in the number of males and females in Latvia, particularly when put in context with the total overall population. The number of women exceeds the number of men by over 260 thousand in 1950, which is one of the long-term effects of the Second World War. During the war, Latvia lost approximately 12.5 percent of its overall population, an the number of women was already higher than men before this, however the war caused this gap in population to widen much further. From 1950 onwards both male and female populations grow, and by 1990 the gap has shrunk down to 180,000 people. In 1990 Latvia gained it's independence from the Soviet Union, and from this point both populations begin to decline, falling to 870 thousand men in 2020, and just over one million women, with a difference of 150 thousand people.