Actual value and historical data chart for World Population Female

Forbes Women of the Year

Global Rankings and Designations

By Priyanka Dobhal [source]

About this dataset

This dataset contains the rankings of the 2020 Forbes list of 100 most powerful women from around the world. This dataset includes detailed insights on each woman, such as their age, country/territory, category, and designation. This comprehensive ranking celebrates female leaders that are making an impact in their field and around the world while inspiring us to continue striving for gender parity and driving positive social change. Explore this dataset to get an idea of who are some of the top female voices right now at the forefront of progress

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Research Ideas

• Creating personalized stories of each woman to showcase their inspiring accomplishments, achievements and successes.
• Analyzing the age range of female Forbes 100 Power Women list to adjust marketing, staffing, and other outreach initiatives aimed at empowering women globally.
• Developing an interactive map with information about the country/territory of origin for each Forbes Power Woman, with an interactive feature that provides stories from successful women from these countries/territories that can serve as inspiration for other aspiring entrepreneurs

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: Forbes 100 Women List 2020.csv | Column name | Description | |:----------------------|:-------------------------------------------------------------------------------| | Name | Name of the Power Woman. (String) | | Age | Age of the Power Woman. (Integer) | | Country/Territory | Country or territory of origin of the Power Woman. (String) | | Category | Category of the Power Woman's achievements. (String) | | Designation | Designation of the Power Woman. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Priyanka Dobhal.

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

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

United States US: Population: Female: Aged 15-64 data was reported at 106,545,028.000 Person in 2017. This records an increase from the previous number of 106,254,414.000 Person for 2016. United States US: Population: Female: Aged 15-64 data is updated yearly, averaging 81,112,897.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 106,545,028.000 Person in 2017 and a record low of 54,897,168.000 Person in 1960. United States US: Population: Female: Aged 15-64 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 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2017 Revision.; Sum; Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services.

Women in Headlines: Bias

Investigating Gendered Language, Temporal Trends, and Themes

By Amber Thomas [source]

About this dataset

This dataset contains all of the data used in the Pudding essay When Women Make Headlines published in January 2022. This dataset was created to analyze gendered language, bias and language themes in news headlines from across the world. It contains headlines from top50 news publications and news agencies from four major countries - USA, UK, India and South Africa - as published by SimilarWeb (as of 2021-06-06).

To collect this data we used RapidAPI's google news API to query headlines containing one or more of keywords selected based on existing research done by Huimin Xu & team and The Swaddle team. We analyzed words used in headlines manually curating two dictionaries — gendered words about women (words that are explicitly gendered) and words that denote societal/behavioral stereotypes about women. To calculate bias scores, we utilized technology developed through Yasmeen Hitti & team’s research on gender bias text analysis. To categorize words used into themes (violence/crime, empowerment, race/ethnicity/identity etc), we manually curated four dictionaries utilizing Natural Language Processing packages for Python like spacy & nltk for our analysis. Plus, inverting polarity scores with vaderSentiment algorithm helped us shed light on differences between women-centered/non-women centered polarity levels as well as differences between global polarity baselines of each country's most visited publications & news agencies according to SimilarWeb 2020 statistics..

This dataset enables journalists, researchers and educators researching issues related to gender equity within media outlets around the world further insights into potential disparities with just a few lines of code! Any discoveries made by using this data should provide valuable support for evidence-based argumentation . Let us advocate for greater awareness towards female representation better quality coverage!

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How to use the dataset

This dataset provides a comprehensive look at the portrayal of women in headlines from 2010-2020. Using this dataset, researchers and data scientists can explore a range of topics including language used to describe women, bias associated with different topics or publications, and temporal patterns in headlines about women over time.

To use this dataset effectively, it is helpful to understand the structure of the data. The columns include headline_no_site (the text of the headline without any information about which publication it is from), time (the date and time that the article was published), country (the country where it was published), bias score (calculated using Gender Bias Taxonomy V1.0) and year (the year that the article was published).

By exploring these columns individually or combining them into groups such as by publication or by topic, there are many ways to make meaningful discoveries using this data set. For example, one could explore if certain news outlets employ more gender-biased language when writing about female subjects than other outlets or investigate whether female-centric stories have higher/lower bias scores than average for a particular topic across multiple countries over time. This type of analysis helps researchers to gain insight into how our culture's dialogue has evolved over recent years as relates to women in media coverage worldwide

Research Ideas

• A comparative, cross-country study of the usage of gendered language and the prevalence of gender bias in headlines to better understand regional differences.
• Creating an interactive visualization showing the evolution of headline bias scores over time with respect to a certain topic or population group (such as women).
• Analyzing how different themes are covered in headlines featuring women compared to those without, such as crime or violence versus empowerment or race and ethnicity, to see if there’s any difference in how they are portrayed by the media

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: headlines_reduced_temporal.csv | Column name | Description | |:---------------------|:-------------------------------------------------------------------------------------...

United States US: Population: Female: Ages 50-54: % of Female Population data was reported at 6.562 % in 2017. This records a decrease from the previous number of 6.714 % for 2016. United States US: Population: Female: Ages 50-54: % of Female Population data is updated yearly, averaging 5.529 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 7.158 % in 2010 and a record low of 4.484 % in 1988. United States US: Population: Female: Ages 50-54: % 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 50 to 54 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.; ;

United States US: Population: as % of Total: Female: Aged 65 and Above data was reported at 16.925 % in 2017. This records an increase from the previous number of 16.550 % for 2016. United States US: Population: as % of Total: Female: Aged 65 and Above data is updated yearly, averaging 14.035 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 16.925 % in 2017 and a record low of 10.023 % in 1960. United States US: Population: as % of Total: Female: Aged 65 and Above 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 65 years of age or older as a percentage of the total female population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; Weighted average; Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 10-14 years (17) | Female # 40-44 years (13). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 White Earth population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the White Earth is shown in the following column.
• Population (Female): The female population in the White Earth 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 White Earth 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 White Earth Population by Gender. You can refer the same here

Users can access data related to international women’s health as well as data on population and families, education, work, power and decision making, violence against women, poverty, and environment. Background World’s Women Reports are prepared by the Statistics Division of the United Nations Department for Economic and Social Affairs (UNDESA). Reports are produced in five year intervals and began in 1990. A major theme of the reports is comparing women’s situation globally to that of men in a variety of fields. Health data is available related to life expectancy, cause of death, chronic disease, HIV/AIDS, prenatal care, maternal morbidity, reproductive health, contraceptive use, induced abortion, mortality of children under 5, and immunization. User functionality Users can download full text or specific chapter versions of the reports in color and black and white. A limited number of graphs are available for download directly from the website. Topics include obesity and underweight children. Data Notes The report and data tables are available for download in PDF format. The next report is scheduled to be released in 2015. The most recent report was released in 2010.

Over 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.

Germany DE: Population: as % of Total: Female: Aged 15-64 data was reported at 61.528 % in 2023. This records a decrease from the previous number of 61.854 % for 2022. Germany DE: Population: as % of Total: Female: Aged 15-64 data is updated yearly, averaging 64.197 % from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 67.752 % in 1960 and a record low of 61.528 % in 2023. Germany DE: Population: as % of Total: Female: Aged 15-64 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: Population and Urbanization Statistics. Female population between the ages 15 to 64 as a percentage of the total female population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.;United Nations Population Division. World Population Prospects: 2024 Revision.;Weighted average;Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services.

The highest position of executive power has been held by a woman in just 65 countries since 1960. Since Sirimavo Bandaranaike was first elected Prime Minister of Sri Lanka in 1960, the number of women in power has grown slowly, with the fastest growth coming in the past 15 years. As of July 2025, there were 14 countries led by women, with Liechtenstein, Namibia, and Suriname electing their first women leaders in 2025; while Thailand's prime minister was suspended in July following an ethics scandal. Despite this number growing in recent decades, there have never been more than 17 countries with women in the highest positions of power in a single year, which is less than 10 percent of the number of men who have held these positions (as today, there are 193 UN member states). Records The women who have served the longest consecutive terms in these positions are Angela Merkel of Germany (16 years, 16 days), Dame Eugenia Charles of Dominica (14 years, 328 days), and Ellen Johnson Sirleaf of Liberia (12 years, 6 days). The longest combined non-consecutive terms were held by Indira Gandhi of India (16 years, 15 days) and Bangladesh's Sheikh Hasina (20 years, 234 days). Just 15 countries have had more than one woman in the highest position of executive power, and most of these countries can be found either in the Indian sub-continent or in Europe. Of these 14, Finland, Iceland, Moldova, New Zealand, and the UK are the only countries to have had three female leaders, although the unique federal system of Switzerland has had five women serve in nine annual-terms as President of the Swiss Confederation. The first woman Prime Minister The first democratically elected female Prime Minister was Sirimavo Bandaranaike of Sri Lanka, who took over the leadership of the Sri Lanka Freedom Party when her husband was assassinated in 1959. Bandaranaike successfully led her party to victory in three elections, in 1960, 1970 and 1994, however constitutional changes in the 1980s meant that her final term as Prime Minister was spent in a more ceremonial role, while the President now held the real executive power (although the President at this time was also a woman; Bandaranaike's daughter, Chandrika Kumaratunga).

This is a dataset of 1309 songbird (passeri) species for phylogenetic comparative analysis of female song incidence, elaboration, and length, and associated code and phylogenetic trees to recreate the analyses in the associated publication. Ordinal scales are based on published species accounts, primarily Birds of the World (see publication for full list and description of all song variables). Song variables are provided in both nominal and numerical ordinal formats. We scored three aspects of female song: (1) song incidence, (2) song quality or elaboration, and (3) song length. For each of these song variables, headers are listed in parentheses, with nomial headings listed first and numerical ordial headings listed second. Song incidence (DimorphSongOccurrence; DimorphSongOcc_ord; "occ") – How often or to what extent do females sing compared to males? 0 = female song absent, 1 = female song rare (most individuals do not sing; female song has only been observed in a few individuals or certain populations some years), 2 = female song occurs occasionally (it is observed periodically in some individuals but occurs noticeably less than male song or only during truncated parts of the year), 3 = female song occurs regularly (it can be reliably observed in many or most individuals but is somewhat less obvious than male song), 4 = female song occurs to the same extent as male song, 5 = females sing more than males. Elaboration (DimorphSongElab; DimorphSongElb_ord; "elb") – To what extent are female songs described as ‘elaborate’ compared to male songs? This often included qualitative descriptions of song complexity, amplitude, or strength (e.g., female songs were often described as softer or weaker than male song). 0 = female song absent, 1 = female song is substantially less elaborate than male song, 2 = female song is somewhat less elaborate than male song, 3 = female song is similarly elaborate to male song, 4 = female song is more elaborate than male song. Because length was scored independently of elaboration, we did not include information on song length in this elaboration score. Length (DimorphSongLength; DimorphSongLen_ord; "len") – How does the duration of female songs compare to male song? 0 = female song absent, 1 = female song is substantially shorter than male song, 2 = female song is somewhat shorter than male song, 3 = female song is similar in length to male song, or 4 = female song is longer than male song. Female song present vs absent (FemSongFinal_PrsAbs; prs_abs) - For some analyses, we also collapsed song incidence into a binary variable representing female song absent (absent=0) vs present (present=1)

The predictor variables used to test hypotheses associated with female song were compiled from several sources including: (1) daily nest predation rates from Unzeta et al. 2020: Unzeta, M., Martin, T. E., & Sol, D. (2020). Daily nest predation rates decrease with body size in passerine birds. The American Naturalist, 196, 743-754. (2) life- and natural history traits from Dale et al. 2015: Dale, J., Dey, C. J., Delhey, K., Kempenaers, B., & Valcu, M. (2015). The effects of life history and sexual selection on male and female plumage colouration. Nature, 527, 367-370. (3) territoriality and duet data from Tobias et al. 2016: Tobias, J. A., Sheard, C., Seddon, N., Meade, A., Cotton, A. J., & Nakagawa, S. (2016). Territoriality, social bonds, and the evolution of communal signaling in birds. Frontiers in Ecology and Evolution, 4, 74. A brief description of each predictor variable is below. For more details, see the supplementary methods associated with the publication. Daily nest predation rates (DPR) – Calculations from both personal field data (as exposure days calculated by the Mayfield method; Mayfield 1961) and nest success reported from the literature. Cavity nesters were omitted from Unzeta et al. 2020, but added to the current study when available from field studies (T. Martin unpublished data) and the literature (Martin 1995, Martin and Clobert 1996, Remes et al. 2012). Breeding latitude (degrees_from_equator) – Each species’ geographical location was computed as the latitude (degrees from equator) of the breeding range centroid. Body size (log mass; log_CRC_species_mass) – Body mass data was collated from Dunning 2008. These data were log-transformed prior to statistical analysis. Sexual size dimorphism (SSD_wing) – Sexual size dimorphism was calculated as the log (male wing length) − log(female wing length) to provide a proportional index of relative sizes of the sexes. See Dale et al. 2007 for a detailed explanation of this metric. Biparental care (paternal_care) – Biparental care was scored as 0 = absent or 1 = present primarily based on data provided in Cockburn 2006. Cooperative breeding (cooperation) – Cooperative breeding was scored as 0 = absent, 0.5 = suspected, or 1 = present also primarily based on data from Cockburn 2006. For species in our data set not present in Cockburn 2006, additional parental care scores were obtained from del Hoyo et al. 2003-2011. Social mating system (mating_system) – Social polygyny was scored on a four-point scale following Owens and Hartley 1998, with 0 = strict social monogamy, 1 = monogamy with infrequent instances of polygyny (20% of males; e.g., red-winged blackbird, Agelaius phoeniceus) or lek polygyny (e.g., lance-tailed manakin, Chiroxiphia lanceolata). Migratory behaviour (Migratory) – Migration was scored on a scale from 0 to 2, with 0 = resident (breeding and non-breeding ranges identical), 1 = partial migration (some overlap between breeding and non-breeding ranges), 2 = complete migration (no overlap between breeding and non-breeding ranges). Assignments were made based on the range maps within del Hoyo et al. 2003-2011. Territoriality (Territory) – Species were classified as 0 = non-territorial, 1 = seasonally or weakly territorial, or 2 = year-round territorial. We defined year-round territoriality as territory defence lasting throughout the year rather than residency within a restricted area, including migrants that are territorial on both the breeding and non-breeding grounds. Species that are vocal and aggressive (responsive to playbacks) for part of the year but remain in the same area silently for the rest of the year were classified as seasonal rather than year-round territorial. Seasonal or weak territoriality primarily included species with broadly overlapping home ranges or that joined mixed species flocks. Non-territorial species never defend territories and included species that defend a very small area around a nest site. Duetting (Duet) – Duets were scored as 0 = absent or 1 = present for each species and were defined as acoustic signals involving two individuals. In line with previous work, duets had to be composed of long-range acoustic signals that are coordinated or stereotyped in some way, whether they be loosely synchronous, regularly alternating, or precisely interwoven. While duets of songbirds are often comprised of songs, duets could include other long-range vocalizations with song-like functions. Duet presence/absence was scored based on a variety of sources, including published literature, field observations, regional experts, and sound archives. Female Song Present, Absent, or duetting species (prs_abs_duet) - a 3 category ordinal scale of In addition to the above predictor variables, we measured seasonality and wing length (see Dale et al. 2015 for a description of these variables). These two variables were highly correlated (r > 0.85) with latitude and Log mass, respectively, and we therefore left them out of final analyses. Additional variables are included from the original datasets of Unzeta et al. 2020, Dale et al. 2015, Tobias et al. 2016.

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.

CONTENT

Gender Inequality Index: A composite measure reflecting inequality in achievement between women and men in three dimensions: reproductive health, empowerment and the labour market. See Technical note 4 at http://hdr.undp.org/sites/default/files/hdr2022_technical_notes.pdf for details on how the Gender Inequality Index is calculated.

Maternal mortality ratio: Number of deaths due to pregnancy-related causes per 100,000 live births.

Adolescent birth rate: Number of births to women ages 15–19 per 1,000 women ages 15–19.

Share of seats in parliament: Proportion of seats held by women in the national parliament expressed as a percentage of total seats For countries with a bicameral legislative system, the share of seats is calculated based on both houses.

Population with at least some secondary education: Percentage of the population ages 25 and older that has reached (but not necessarily completed) a secondary level of education.

This dataset provides values for RETIREMENT AGE WOMEN reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Greenland Population: as % of Female Population: Female: Aged 0-14 data was reported at 21.354 % in 2023. This records a decrease from the previous number of 21.360 % for 2022. Greenland Population: as % of Female Population: Female: Aged 0-14 data is updated yearly, averaging 28.112 % from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 46.571 % in 1966 and a record low of 21.305 % in 2018. Greenland Population: as % of Female Population: Female: Aged 0-14 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Greenland – Table GL.World Bank.WDI: Population and Urbanization Statistics. Female population between the ages 0 to 14 as a percentage of the total female population. Population is based on the de facto definition of population.;United Nations Population Division. World Population Prospects: 2024 Revision.;Weighted average;Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services.

United States US: Population: as % of Total: Female: Aged 15-64 data was reported at 64.768 % in 2017. This records a decrease from the previous number of 65.038 % for 2016. United States US: Population: as % of Total: Female: Aged 15-64 data is updated yearly, averaging 64.683 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 66.046 % in 2009 and a record low of 59.938 % in 1962. United States US: Population: as % of Total: Female: Aged 15-64 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 15 to 64 as a percentage of the total female population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; Weighted average; Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services.

Context

In the intricate tapestry of gender disparities, the Labour Force Participation Rate (LFPR) serves as a crucial thread that weaves through the fabric of economic activity. Examining LFPR through the lens of the Gender Inequality Index (GII) sheds light on the distinctive experiences of men and women in the workforce, unraveling disparities and inequities that persist in our societies.

Male Labour Force Participation Rate: For men, the LFPR becomes a gauge of economic engagement and contribution to societal progress. Traditionally, societal expectations have often encouraged a high male LFPR, positioning men as primary breadwinners. The index, when analyzed within the context of GII, reveals not only the quantity but also the quality of male participation in the workforce. High LFPR for men might suggest economic activity, but it doesn't necessarily capture the nuances of workplace gender dynamics, occupational segregation, or disparities in income.

Female Labour Force Participation Rate: Conversely, the LFPR for women emerges as a pivotal indicator of empowerment and gender equality. A rising female LFPR signals a departure from traditional norms, reflecting increased opportunities, access to education, and a broader recognition of women's roles in society. However, the GII prompts a deeper examination, delving into the quality of female participation. Disparities may persist in terms of wage gaps, representation in leadership roles, and challenges related to work-life balance.

Content

This dataset provides comprehensive historical data on gender development indicators at a global level. It includes essential columns such as ISO3 (the ISO3 code for each country/territory), Country (the name of the country or territory), Continent (the continent where the country is located), Hemisphere (the hemisphere in which the country is situated), Human Development Groups, UNDP Developing Regions, HDI Rank (2021) representing the Human Development Index Rank for the year 2021 and Labour force participation rate for male and female (% ages 15 and older) spanning from 1990 to 2021.

Dataset Glossary (Column-wise)

• ISO3 - ISO3 for the Country/Territory
• Country - Name of the Country/Territory
• Continent - Name of the Continent
• Hemisphere - Name of the Hemisphere
• Human Development Groups - Human Development Groups
• UNDP Developing Regions - UNDP Developing Regions
• HDI Rank (2021) - Human Development Index Rank for 2021
• Labour force participation rate, male and female (% ages 15 and older) from 1990 to 2021 - Labour force participation rate for male and female from 1990 to 2021

Data Dictionary

• UNDP Developing Regions:
  • SSA - Sub-Saharan Africa
  • LAC - Latin America and the Caribbean
  • EAP - East Asia and the Pacific
  • AS - Arab States
  • ECA - Europe and Central Asia
  • SA - South Asia

Structure of the Dataset

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Acknowledgement

This Dataset is created from Human Development Reports. 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.

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