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

Actual value and historical data chart for World Population Female

Women's Soccer Participation in High Schools

Women's Soccer Participation in High Schools

By Eva Murray [source]

About this dataset

This dataset contains information on participation in high school soccer in the United States from 2006 to 2014. It includes data on the number of schools participating, the number of students participating, and the gender split of participants. This dataset can be used to understand the popularity of soccer among high school students and compare participation rates between boys and girls

How to use the dataset

Research Ideas

• Analyzing the correlation between boys and girls soccer participation in high school and the level of success of each gender's national soccer team.
• Determining which states have the largest disparities between boys and girls soccer participation rates.
• Analyzing how participation rates have changed over time, both nationally and by state

Acknowledgements

Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: partcipation_statistics_06_14_2020 10_22.csv | Column name | Description | |:------------------------|:---------------------------------------------------------------------| | Year | The year the data was collected. (Integer) | | State | The state the data was collected from. (String) | | Sport | The sport the data is for. (String) | | Boys School | The number of schools that offered a boys soccer program. (Integer) | | Girls School | The number of schools that offered a girls soccer program. (Integer) | | Boys Participation | The number of boys who participated in soccer. (Integer) | | Girls Participation | The number of girls who participated in soccer. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit Eva Murray.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 20-24 years (347) | Female # 50-54 years (433). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Globe population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Globe is shown in the following column.
• Population (Female): The female population in the Globe 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 Globe 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 Globe Population by Gender. You can refer the same here

About Violence Against Women & Girls

The Demographic and Health Surveys (DHS) Program exists to advance the global understanding of health and population trends in developing countries.

The UN describes violence against women and girls (VAWG) as: “One of the most widespread, persistent, and devastating human rights violations in our world today. It remains largely unreported due to the impunity, silence, stigma, and shame surrounding it.”

In general terms, it manifests itself in physical, sexual, and psychological forms, encompassing: • intimate partner violence (battering, psychological abuse, marital rape, femicide) • sexual violence and harassment (rape, forced sexual acts, unwanted sexual advances, child sexual abuse, forced marriage, street harassment, stalking, cyber-harassment), human trafficking (slavery, sexual exploitation) • female genital mutilation • child marriage

About The Data

The data was taken from a survey of men and women in African, Asian, and South American countries, exploring the attitudes and perceived justifications given for committing acts of violence against women. The data also explores different sociodemographic groups that the respondents belong to, including: Education Level, Marital status, Employment, and Age group.

It is, therefore, critical that the countries where these views are widespread, prioritize public awareness campaigns, and access to education for women and girls, to communicate that violence against women and girls is never acceptable or justifiable.

Field	Definition
Record ID	Numeric value unique to each question by country
Country	Country in which the survey was conducted
Gender	Whether the respondents were Male or Female
Demographics Question	Refers to the different types of demographic groupings used to segment respondents – marital status, education level, employment status, residence type, or age
Demographics Response	Refers to demographic segment into which the respondent falls (e.g. the age groupings are split into 15-24, 25-34, and 35-49)
Survey Year	Year in which the Demographic and Health Survey (DHS) took place. “DHS surveys are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health and nutrition. Standard DHS Surveys have large sample sizes (usually between 5,000 and 30,000 households) and typically are conducted around every 5 years, to allow comparisons over time.”
Value	% of people surveyed in the relevant group who agree with the question (e.g. the percentage of women aged 15-24 in Afghanistan who agree that a husband is justified in hitting or beating his wife if she burns the food)

Question | Respondents were asked if they agreed with the following statements: - A husband is justified in hitting or beating his wife if she burns the food - A husband is justified in hitting or beating his wife if she argues with him - A husband is justified in hitting or beating his wife if she goes out without telling him - A husband is justified in hitting or beating his wife if she neglects the children - A husband is justified in hitting or beating his wife if she refuses to have sex with him - A husband is justified in hitting or beating his wife for at least one specific reason

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United States US: Population: Female: Ages 60-64: % of Female Population data was reported at 6.224 % in 2017. This records an increase from the previous number of 6.143 % for 2016. United States US: Population: Female: Ages 60-64: % of Female Population data is updated yearly, averaging 4.552 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 6.224 % in 2017 and a record low of 3.843 % in 1997. United States US: Population: Female: Ages 60-64: % of Female Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Population and Urbanization Statistics. Female population between the ages 60 to 64 as a percentage of the total female population.; ; World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; ;

Actual value and historical data chart for World Population Ages 20 24 Female Percent Of Female Population

As of 2024, the share of internet users in the CIS region (Commonwealth of Independent States) was the highest in the world, with 91 percent of the female population and 93 percent of the male population accessing the internet. As of the same year, there were 90 percent female and 92 percent male internet users in Europe, making it the second region worldwide by internet usage. Africa was the region where internet access was the lowest. Share of female and male internet users worldwide There are still disparities between the internet access rates of male and female online users in global regions. According to the latest data, 34 percent of Africa’s female population had online access, compared to 45 percent of men. Whereas in the Americas, the share of male and female internet users was the same, 83 percent. There was also a big difference in the share of female and male internet users in the Arab States. In the region, 65 percent of women had access to the internet, whereas the share of the male population using the internet was 75 percent. The gender gap was also seen in mobile internet usage in low-and middle-income countries (LMICs). Internet access and SDGs As of 2022, Africa’s online access rate was the lowest worldwide, with estimates showing that just over 30 percent of the total population was using the internet. By comparison, the global average online usage rate was 51 percent. This technological gap between Africa and the rest of the world highlights the need for continued investment in information and communication technologies on the continent, as such processes can speed up progress towards the 17 Sustainable Development Goals (SDGs) set by the United Nations. The Sustainable Development Goals, also known as the Global Goals, are a worldwide agenda to protect the planet, end poverty, and ensure global peace and prosperity. ICTs, especially mobile internet, contribute to the goals by enabling countries to participate in digital economies as well as empowering individuals to access crucial information and services. However, almost 40 percent of the world was not using the internet as of 2021. Particularly disenfranchised groups were frequently excluded from digital society, including women and girls, people with disabilities, elders, indigenous populations, people living in poverty, and inhabitants of least developed or developing countries. The digital gender gap was another obstacle for women to overcome on a global level to achieve economic advancement which would ultimately also benefit their communities.

The goal of this dataset is to help researchers and data scientists gain insights into the preferences, behaviors, and demographics of single individuals in Pakistan, particularly those who are using online platforms to search for a marriage partner. The dataset can be used for various purposes, such as developing machine learning models for matchmaking, analyzing trends and patterns in the marriage market, and understanding the socio-economic factors that influence partner selection.

It's worth noting that the dataset contains personal information and should be used responsibly and in accordance with ethical and legal guidelines for data usage and privacy protection.

This meticulously curated dataset offers a panoramic view of education on a global scale , delivering profound insights into the dynamic landscape of education across diverse countries and regions. Spanning a rich tapestry of educational aspects, it encapsulates crucial metrics including out-of-school rates, completion rates, proficiency levels, literacy rates, birth rates, and primary and tertiary education enrollment statistics. A treasure trove of knowledge, this dataset is an indispensable asset for discerning researchers, dedicated educators, and forward-thinking policymakers, enabling them to embark on a transformative journey of assessing, enhancing, and reshaping education systems worldwide.

Key Features: - Countries and Areas: Name of the countries and areas. - Latitude: Latitude coordinates of the geographical location. - Longitude: Longitude coordinates of the geographical location. - OOSR_Pre0Primary_Age_Male: Out-of-school rate for pre-primary age males. - OOSR_Pre0Primary_Age_Female: Out-of-school rate for pre-primary age females. - OOSR_Primary_Age_Male: Out-of-school rate for primary age males. - OOSR_Primary_Age_Female: Out-of-school rate for primary age females. - OOSR_Lower_Secondary_Age_Male: Out-of-school rate for lower secondary age males. - OOSR_Lower_Secondary_Age_Female: Out-of-school rate for lower secondary age females. - OOSR_Upper_Secondary_Age_Male: Out-of-school rate for upper secondary age males. - OOSR_Upper_Secondary_Age_Female: Out-of-school rate for upper secondary age females. - Completion_Rate_Primary_Male: Completion rate for primary education among males. - Completion_Rate_Primary_Female: Completion rate for primary education among females. - Completion_Rate_Lower_Secondary_Male: Completion rate for lower secondary education among males. - Completion_Rate_Lower_Secondary_Female: Completion rate for lower secondary education among females. - Completion_Rate_Upper_Secondary_Male: Completion rate for upper secondary education among males. - Completion_Rate_Upper_Secondary_Female: Completion rate for upper secondary education among females. - Grade_2_3_Proficiency_Reading: Proficiency in reading for grade 2-3 students. - Grade_2_3_Proficiency_Math: Proficiency in math for grade 2-3 students. - Primary_End_Proficiency_Reading: Proficiency in reading at the end of primary education. - Primary_End_Proficiency_Math: Proficiency in math at the end of primary education. - Lower_Secondary_End_Proficiency_Reading: Proficiency in reading at the end of lower secondary education. - Lower_Secondary_End_Proficiency_Math: Proficiency in math at the end of lower secondary education. - Youth_15_24_Literacy_Rate_Male: Literacy rate among male youths aged 15-24. - Youth_15_24_Literacy_Rate_Female: Literacy rate among female youths aged 15-24. - Birth_Rate: Birth rate in the respective countries/areas. - Gross_Primary_Education_Enrollment: Gross enrollment in primary education. - Gross_Tertiary_Education_Enrollment: Gross enrollment in tertiary education. - Unemployment_Rate: Unemployment rate in the respective countries/areas.

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

Brazil BR: Literacy Rate: Youth Female: % of Females Aged 15-24 data was reported at 99.560 % in 2022. This records an increase from the previous number of 99.490 % for 2021. Brazil BR: Literacy Rate: Youth Female: % of Females Aged 15-24 data is updated yearly, averaging 98.855 % from Dec 1980 (Median) to 2022, with 24 observations. The data reached an all-time high of 99.560 % in 2022 and a record low of 85.000 % in 1980. Brazil BR: Literacy Rate: Youth Female: % of Females Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Social: Education Statistics. Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Weighted average;

Civilian Population: 15 Years & Over: Female: 25-34 Years data was reported at 2,037.492 Person th in Mar 2025. This records an increase from the previous number of 2,032.394 Person th for Feb 2025. Civilian Population: 15 Years & Over: Female: 25-34 Years data is updated monthly, averaging 1,431.171 Person th from Feb 1978 (Median) to Mar 2025, with 566 observations. The data reached an all-time high of 2,037.492 Person th in Mar 2025 and a record low of 1,116.489 Person th in Feb 1978. Civilian Population: 15 Years & Over: Female: 25-34 Years data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.G001: Civilian Population: by Age, Sex and Status.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in White Earth. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In White Earth, the median income for all workers aged 15 years and older, regardless of work hours, was $63,333 for males and $23,594 for females.

These income figures highlight a substantial gender-based income gap in White Earth. Women, regardless of work hours, earn 37 cents for each dollar earned by men. This significant gender pay gap, approximately 63%, underscores concerning gender-based income inequality in the city of White Earth.

- Full-time workers, aged 15 years and older: In White Earth, for full-time, year-round workers aged 15 years and older, while the Census reported a median income of $80,536 for males, while data for females was unavailable due to an insufficient number of sample observations.

As there was no available median income data for females, conducting a comprehensive assessment of gender-based pay disparity in White Earth was not feasible.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 White Earth median household income by race. You can refer the same here

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in White Earth township. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In White Earth township, the median income for all workers aged 15 years and older, regardless of work hours, was $36,250 for males and $25,250 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 30% between the median incomes of males and females in White Earth township. With women, regardless of work hours, earning 70 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thetownship of White Earth township.

- Full-time workers, aged 15 years and older: In White Earth township, among full-time, year-round workers aged 15 years and older, males earned a median income of $47,500, while females earned $50,417

Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.06 dollars for every dollar earned by men. This suggests that within full-time roles, womens median incomes significantly surpass mens, contrary to broader workforce trends.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 White Earth township median household income by race. You can refer the same here

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Black Earth town. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Black Earth town, the median income for all workers aged 15 years and older, regardless of work hours, was $68,125 for males and $58,750 for females.

Based on these incomes, we observe a gender gap percentage of approximately 14%, indicating a significant disparity between the median incomes of males and females in Black Earth town. Women, regardless of work hours, still earn 86 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

- Full-time workers, aged 15 years and older: In Black Earth town, among full-time, year-round workers aged 15 years and older, males earned a median income of $93,000, while females earned $78,542, leading to a 16% gender pay gap among full-time workers. This illustrates that women earn 84 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Remarkably, across all roles, including non-full-time employment, women displayed a lower gender pay gap percentage. This indicates that Black Earth town offers better opportunities for women in non-full-time positions.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 town median household income by race. You can refer the same here

The Second World War had a sever impact on gender ratios across European countries, particularly in the Soviet Union. While the United States had a balanced gender ratio of one man for every woman, in the Soviet Union the ratio was below 5:4 in favor of women, and in Soviet Russia this figure was closer to 4:3.

As young men were disproportionately killed during the war, this had long-term implications for demographic development, where the generation who would have typically started families in the 1940s was severely depleted in many countries.

As of October 2025, 6.04 billion individuals worldwide were internet users, which amounted to 73.2 percent of the global population. Of this total, 5.66 billion, or 68.7 percent of the world's population, were social media users. Global internet usage Connecting billions of people worldwide, the internet is a core pillar of the modern information society. Northern Europe ranked first among worldwide regions by the share of the population using the internet in 2025. In the Netherlands, Norway, and Saudi Arabia, 99 percent of the population used the internet as of February 2025. North Korea was at the opposite end of the spectrum, with virtually no internet usage penetration among the general population, ranking last worldwide. Eastern Asia was home to the largest number of online users worldwide—over 1.34 billion at the latest count. Southern Asia ranked second, with around 1.2 billion internet users. China, India, and the United States rank ahead of other countries worldwide by the number of internet users. Worldwide internet user demographics As of 2024, the share of female internet users worldwide was 65 percent, five percent less than that of men. Gender disparity in internet usage was bigger in African countries, with around a 10-percent difference. Worldwide regions, like the Commonwealth of Independent States and Europe, showed a smaller usage gap between these two genders. As of 2024, global internet usage was higher among individuals between 15 and 24 years old across all regions, with young people in Europe representing the most considerable usage penetration, 98 percent. In comparison, the worldwide average for the age group of 15 to 24 years was 79 percent. The income level of the countries was also an essential factor for internet access, as 93 percent of the population of the countries with high income reportedly used the internet, as opposed to only 27 percent of the low-income markets.

Female life expectancy of Lao People’s Democratic Republic improved by 0.35% from 71.0 years in 2022 to 71.3 years in 2023. Since the 1.37% reduction in 2021, female life expectancy grew by 1.68% in 2023. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.

This Dataset provides comprehensive demographic information on global populations from 1950 to the present. It offers insights into various aspects of population dynamics, including population counts, gender ratios, birth and death rates, life expectancy, and migration patterns.

Column Descriptions:

SortOrder: Numeric identifier for sorting.

LocID: Location identifier.

Notes: Additional notes or comments (blank in this dataset).

ISO3_code: ISO 3-character country code.

ISO2_code: ISO 2-character country code.

SDMX_code: Statistical Data and Metadata Exchange code.

LocTypeID: Location type identifier.

LocTypeName: Location type name.

ParentID: Identifier for the parent location.

Location: Name of the location.

VarID: Identifier for the variant.

Variant: Type of population variant.

Time: Year or time period.

TPopulation1Jan: Total population on January 1st.

TPopulation1July: Total population on July 1st.

TPopulationMale1July: Total male population on July 1st.

TPopulationFemale1July: Total female population on July 1st.

PopDensity: Population density (people per square kilometer).

PopSexRatio: Population sex ratio (male/female).

MedianAgePop: Median age of the population.

NatChange: Natural change in population.

NatChangeRT: Natural change rate (per 1,000 people).

PopChange: Population change.

PopGrowthRate: Population growth rate (percentage).

DoublingTime: Time for population to double (in years).

Births: Total number of births.

Births1519: Births to mothers aged 15-19.

CBR: Crude birth rate (per 1,000 people).

TFR: Total fertility rate (average number of children per woman).

NRR: Net reproduction rate.

MAC: Mean age at childbearing.

SRB: Sex ratio at birth (male/female).

Deaths: Total number of deaths.

DeathsMale: Total male deaths.

DeathsFemale: Total female deaths.

CDR: Crude death rate (per 1,000 people).

LEx: Life expectancy at birth.

LExMale: Life expectancy for males at birth.

LExFemale: Life expectancy for females at birth.

LE15: Life expectancy at age 15.

LE15Male: Life expectancy for males at age 15.

LE15Female: Life expectancy for females at age 15.

LE65: Life expectancy at age 65.

LE65Male: Life expectancy for males at age 65.

LE65Female: Life expectancy for females at age 65.

LE80: Life expectancy at age 80.

LE80Male: Life expectancy for males at age 80.

LE80Female: Life expectancy for females at age 80.

InfantDeaths: Number of infant deaths.

IMR: Infant mortality rate (per 1,000 live births).

LBsurvivingAge1: Children surviving to age 1.

Under5Deaths: Number of deaths under age 5.

NetMigrations: Net migration rate (per 1,000 people).

CNMR: Crude net migration rate.

How to Use the Dataset:

1. Researchers can analyze demographic trends, birth and death rates, and population growth over time.
2. Policymakers can use population data to inform decisions on healthcare, education, and social services.
3. Data scientists can visualize and model population dynamics for various regions.
4. Journalists can use the dataset to report on global population trends and disparities.
5. Educators can incorporate real-world population data into lessons and research.

Please upvote and show your support if you find this dataset valuable for your research or analysis. Your feedback and contributions help make this dataset more accessible to the Kaggle community. Thank you!