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TwitterOver the past 24 years, there were constantly more men than women living on the planet. Of the 8.06 billion people living on the Earth in 2024, 4.09 billion were men and 4.05 billion were women. One-quarter of the world's total population in 2024 was below 15 years.
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TwitterWorldwide, 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.
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The dataset tabulates the population of England by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for England. The dataset can be utilized to understand the population distribution of England by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in England. 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 England.
Key observations
Largest age group (population): Male # 40-44 years (154) | Female # 0-4 years (183). 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 England Population by Gender. You can refer the same here
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The dataset tabulates the population of Tennessee by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Tennessee across both sexes and to determine which sex constitutes the majority.
Key observations
There is a slight majority of female population, with 50.93% 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 Tennessee Population by Race & Ethnicity. You can refer the same here
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TwitterThe 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.
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TwitterIn 2020, the male-female ratio expressed as men per 100 women in Mexico amounted to approximately *****. Between 1995 and 2020, the figure dropped by around ****, though the decline followed an uneven course rather than a steady trajectory.
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The dataset tabulates the population of Ontario by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Ontario. The dataset can be utilized to understand the population distribution of Ontario by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Ontario. 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 Ontario.
Key observations
Largest age group (population): Male # 30-34 years (7,947) | Female # 25-29 years (8,143). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Ontario Population by Gender. You can refer the same here
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The dataset tabulates the population of North Pole by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for North Pole. The dataset can be utilized to understand the population distribution of North Pole by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in North Pole. 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 North Pole.
Key observations
Largest age group (population): Male # 5-9 years (261) | Female # 30-34 years (183). 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 North Pole Population by Gender. You can refer the same here
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Actual value and historical data chart for United States Population Female Percent Of Total
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TwitterIntroducing a data set that specifically compares females and males can be done in various ways, depending on the purpose and context of the data set. Here's a general introduction that you can use as a starting point:
Title: Female vs Male Data Set: A Comparative Analysis
Introduction:
The "Female vs Male Data Set" is a comprehensive collection of information that aims to provide insights into the similarities and differences between females and males across various domains. This data set has been curated to facilitate analysis and exploration of characteristics, traits, preferences, and other factors that may vary between the two genders.
Dataset Description:
The Female vs Male Data Set comprises a wide range of data points sourced from diverse fields, including demographics, biology, psychology, sociology, economics, education, and more. It encompasses both quantitative and qualitative data, allowing for statistical analysis as well as qualitative interpretations.
The data set covers a multitude of aspects, such as:
Demographic Information: Age, ethnicity, geographical distribution, and other relevant demographic factors that distinguish females and males.
Physiological and Biological Factors: Biological traits, genetic variations, hormonal differences, and anatomical characteristics that are unique or more prevalent in one gender compared to the other.
Social and Cultural Factors: Gender roles, societal expectations, cultural norms, and their impacts on behavior, relationships, and social dynamics between females and males.
Psychological and Personality Traits: Differences or similarities in personality traits, cognitive abilities, emotional patterns, and psychological attributes between females and males.
Educational and Professional Data: Educational attainment, career choices, employment statistics, wage disparities, and other factors related to education and professional domains.
Health and Wellness: Variances in health outcomes, disease prevalence, risk factors, and responses to treatment between females and males.
Usage and Applications:
The Female vs Male Data Set can be utilized for a wide range of research, analysis, and decision-making purposes. Some potential applications include:
Gender Studies: Conducting in-depth studies on gender differences and gender-related topics. Social Sciences: Exploring the societal impacts of gender and investigating gender inequalities. Marketing and Consumer Behavior: Understanding gender-based preferences and consumption patterns. Health and Medicine: Investigating gender-specific health concerns and developing targeted interventions. Education: Analyzing gender gaps and formulating strategies for educational equality. Policy-making: Informing evidence-based policies and initiatives aimed at gender equity. It's important to note that this data set should be used responsibly and with an understanding that gender is a complex and multifaceted concept. Care should be taken to avoid generalizations and to respect individual variations within each gender.
Disclaimer: The data set does not endorse or perpetuate stereotypes or biases, but rather aims to provide a foundation for further exploration and understanding of gender-related aspects.
By utilizing the Female vs Male Data Set, researchers, analysts, and policymakers can gain valuable insights into the similarities and differences between females and males, leading to a more informed and nuanced understanding of gender dynamics in various fields.
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The dataset tabulates the population of Cleveland by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Cleveland. The dataset can be utilized to understand the population distribution of Cleveland by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Cleveland. 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 Cleveland.
Key observations
Largest age group (population): Male # 25-29 years (16,343) | Female # 25-29 years (16,950). 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 Cleveland Population by Gender. You can refer the same here
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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.
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 Chicago Population by Gender. You can refer the same here
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Gender inequity in science and academia, especially in senior positions, is a recognised problem. The reasons are poorly understood, but include the persistence of historical gender ratios, discrimination and other factors, including gender-based behavioural differences. We studied participation in a professional context by observing question-asking behaviour at a large international conference with a clear equality code of conduct that prohibited any form of discrimination. Accounting for audience gender ratio, male attendees asked 1.8 questions for each question asked by a female attendee. Amongst only younger researchers, male attendees also asked 1.8 questions per female question, suggesting the pattern cannot be attributed to the temporary problem of demographic inertia. We link our findings to the ‘chilly’ climate for women in STEM, including wider experiences of discrimination likely encountered by women throughout their education and careers. We call for a broader and coordinated approach to understanding and addressing the barriers to women and other under-represented groups. We encourage the scientific community to recognise the context in which these gender differences occur, and evaluate and develop methods to support full participation from all attendees.
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Actual value and historical data chart for World Population Female Percent Of Total
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TwitterAttribution 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 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.
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 Palm Beach County Population by Gender. You can refer the same here
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TwitterThe male-female ratio expressed as men per 100 women in Mexico City stood at approximately ***** in 2020. Between 1910 and 2020, the ratio rose by around ****, though the increase followed an uneven trajectory rather than a consistent upward trend.
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TwitterBackgroundThe sex difference in athletic performance has been thoroughly investigated in single sport disciplines such as swimming, cycling, and running. In contrast, only small samples of long-distance triathlons, such as the IRONMAN® triathlon, have been investigated so far.AimThe aim of the study was to examine potential sex differences in the three split disciplines by age groups in 5-year intervals in a very large data set of IRONMAN® age group triathletes.MethodsData from 687,696 (553,608 men and 134,088 women) IRONMAN® age group triathletes (in 5-year intervals from 18–24 to 75+ years) finishing successfully between 2002 and 2022 an official IRONMAN® race worldwide were analyzed. The differences in performance between women and men were determined for each split discipline and for the overall race distance.ResultsMost finishers were in the age group 40–44 years. The fastest women were in the age group 25–29 years, and the fastest men were in the age group 30–34 years. For all split disciplines and overall race time, men were always faster than women in all groups. The performance difference between the sexes was more pronounced in cycling compared to swimming and running. From the age group 35–39 years until 60–64 years, the sex differences were nearly identical in swimming and running. For both women and men, the smallest sex difference was least significant in age group 18–24 years for all split disciplines and increased in a U-shaped manner until age group 70–74 years. For age groups 75 years and older, the sex difference decreased in swimming and cycling but increased in running. Considering the different characteristics of the race courses, the smallest performance gaps between men and women were found in river swimming, flat surface cycling and rolling running courses.ConclusionsThe sex difference in the IRONMAN® triathlon was least significant in age group 18–24 years for all split disciplines and increased in a U-shaped manner until age group 70–74 years. For 75 years and older, the sex difference decreased in swimming and cycling but increased in running.
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TwitterThis 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
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TwitterIn the Nordic countries, there were more women than men in Denmark and Finland, while there were more men than women in Iceland, Norway and Sweden. Sweden has the largest population of the five countries, while Iceland has the smallest. In 2024, there were **** million men and **** million women living in Sweden, compared to ******* men and ******* women in Iceland.
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Actual value and historical data chart for United States Sex Ratio At Birth Male Births Per Female Births
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TwitterOver the past 24 years, there were constantly more men than women living on the planet. Of the 8.06 billion people living on the Earth in 2024, 4.09 billion were men and 4.05 billion were women. One-quarter of the world's total population in 2024 was below 15 years.