About this dataset

Context

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

Key observations

Largest age group (population): Male # 30-34 years (52) | Female # 30-34 years (47). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Hill Country Village population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Hill Country Village is shown in the following column.
• Population (Female): The female population in the Hill Country Village 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 Hill Country Village 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 Hill Country Village Population by Gender. You can refer the same here

About this dataset

Context

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

Key observations

Largest age group (population): Male # 50-54 years (27) | Female # 10-14 years (25). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Lost Nation population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Lost Nation is shown in the following column.
• Population (Female): The female population in the Lost Nation 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 Lost Nation 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 Lost Nation Population by Gender. You can refer the same here

The dataset contains information on various demographic and health indicators for different countries. It is organized into several columns, each providing essential information about these countries. Here's a description of each column:

1. Country: This column represents the names of different countries or regions included in the dataset. Each row corresponds to a specific country or region, and this column serves as the identifier for each entry.

2. Life Expectancy Males: This column contains data on the average life expectancy of males in each of the listed countries. Life expectancy is a crucial health indicator and provides an estimate of the average number of years a male can expect to live, given current mortality rates and health conditions.

3. Life Expectancy Females: Similar to the "Life Expectancy Males" column, this column provides data on the average life expectancy of females in the same countries. It reflects the average number of years a female can expect to live, considering the prevailing health and mortality conditions.

4. Birth Rate: The "Birth Rate" column contains information about the birth rate in each country. Birth rate is a demographic indicator that represents the number of live births per 1,000 people in a given population over a specific period, usually a year. It can provide insights into a country's population growth or decline.

5. Death Rate: This column presents data on the death rate in each of the listed countries. The death rate is another crucial demographic indicator and represents the number of deaths per 1,000 people in a population over a specific period, often a year. It helps gauge the overall health and mortality conditions within a country.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 60-64 years (538) | Female # 45-49 years (537). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Town And Country population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Town And Country is shown in the following column.
• Population (Female): The female population in the Town And Country 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 Town And Country 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 Town And Country Population by Gender. You can refer the same here

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

Dataset Glossary (Column-wise):

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

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

License - This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

Acknowledgement: Image :- Freepik

BackgroundDelivery of preventive chemotherapy (PC) through mass drug administration (MDA) is used to control or eliminate five of the most common neglected tropical diseases (NTDs). The success of an MDA campaign relies on the ability of drug distributors and their supervisors—the NTD front-line workers—to reach populations at risk of NTDs. In the past, our understanding of the demographics of these workers has been limited, but with increased access to sex-disaggregated data, we begin to explore the implications of gender and sex for the success of NTD front-line workers.Methodology/Principal findingsWe reviewed data collected by USAID-supported NTD projects from national NTD programs from fiscal years (FY) 2012–2017 to assess availability of sex-disaggregated data on the workforce. What we found was sex-disaggregated data on 2,984,908 trainees trained with financial support from the project. We then analyzed the percentage of males and females trained by job category, country, and fiscal year. During FY12, 59% of these data were disaggregated by sex, which increased to nearly 100% by FY15 and was sustained through FY17. In FY17, 43% of trainees were female, with just four countries reporting more females than males trained as drug distributors and three countries reporting more females than males trained as trainers/supervisors. Except for two countries, there were no clear trends over time in changes to the percent of females trained.Conclusions/SignificanceThere has been a rapid increase in availability of sex-disaggregated data, but little increase in recruitment of female workers in countries included in this study. Women continue to be under-represented in the NTD workforce, and while there are often valid reasons for this distribution, we need to test this norm and better understand gender dynamics within NTD programs to increase equity.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 10-14 years (93) | Female # 40-44 years (65). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Country Club Hills population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Country Club Hills is shown in the following column.
• Population (Female): The female population in the Country Club Hills 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 Country Club Hills 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 Country Club Hills Population by Gender. You can refer the same here

According to WIN World Survey (an association of Survey Firms), 62% respondents over the world say that gender equality in social settings has definitely or to some extent been achieved in their country. This is a series of polls being released in honor of International Women’s Day, celebrated on the 8th of March every year. A sample of 29,368 men and women from 40 countries across the globe was asked, “Would you say gender equality has been definitely/to some extent/not really/not at all achieved in your country in social settings?” 62% of respondents in participating countries say that gender equality in social settings has definitely or to some extent been achieved in their country, while 33% say that it has not really, or not at all been achieved. 5% did not know or did not respond. Globally, the net index for gender equality in social settings is 28%. Results from Pakistan: Not so different from the world Respondents from Pakistan had similar views, with 60% saying gender equality is definitely or to some achieved, while 39% disagreed. Net index* (% Definitely achieved + To some extent achieved) – (% Not really achieved + Not at all achieved) for Pakistan is 21%. Global gender breakdown: Females are less optimistic about gender equality than men Analysis on the basis of gender shows that 65% males, and 59% females were of the opinion that gender equality in social settings has been achieved. Country wise Analysis: Lebanese are the most optimistic about gender equality, French and Japanese the most pessimistic Of the 40 countries surveyed, 35 have a positive net index for social gender equality. Lebanon ranks the highest with a net index of 80%, followed by Philippines at 65%. In contrast, France has an index of -15%, and Japan the lowest at -47%.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 55-59 years (22) | Female # 45-49 years (15). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Country Club Heights population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Country Club Heights is shown in the following column.
• Population (Female): The female population in the Country Club Heights 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 Country Club Heights 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 Country Club Heights Population by Gender. You can refer the same here

Gender Mental Disorder Prevalence

Investigating Differences Across Cultures and Continents

By Reisha Hermana [source]

About this dataset

This dataset provides the gender-based prevalence of mental health disorders around the world in 2019. It includes data about eating disorders, bipolar disorder, and population estimates for each country or region included in the dataset. The data reveals mental health disparities across countries and continents by showing different levels of prevalence amongst males versus females. This valuable resource allows us to understand more deeply our current global mental health situation, while also providing an insight into potential areas for improvement and progress in terms of both diagnosis and access to care

More Datasets

For more datasets, click here.

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How to use the dataset

This dataset provides the prevalence of eating disorders and bipolar disorder among males and females in different countries from 2019. This dataset can be useful for researchers looking to compare the prevalence of these mental disorders between genders, or across regions.

Research Ideas

• To understand geographic patterns in mental health disorders. This data can be used to enhance our understanding of which parts of the world are more likely to experience different types of mental health disorders and how prevalence can vary from continent to continent.
• To compare prevalence between males and females in terms of mental health disorders and highlight potential differences among different areas/countries/continents.
• To explore trends in mental health over time and assess which populations have seen the most improvement or decline within a certain period (e.g., check if there is any correlation between trends in bipolar disorder prevalence for males versus those for females across countries)

Acknowledgements

If you use this dataset in your research, please credit the original authors. 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: prevalence-of-eating-disorders-in-males-vs-females.csv | Column name | Description | |:----------------------------------------|:-----------------------------------------------------------------------------------------------------| | Entity | The name of the country or region. (String) | | Code | The ISO 3166-1 alpha-3 code for the country or region. (String) | | Year | The year the data was collected. (Integer) | | Prevalence - Eating disorders - Sex | The prevalence of eating disorders among females, age-standardized to the global population. (Float) | | **Prevalence ** | Female - Age | | Population (historical estimates) | The estimated population of the country or region. (Integer) | | Continent | The continent the country or region is located in. (String) |

File: prevalence-of-bipolar-disorder-in-males-vs-females.csv | Column name | Description | |:----------------------------------------|:---------------------------------------------------------------------------------------------------------------| | Entity | The name of the country or region. (String) | | Code ...

Office for National Statistics’ national and subnational mid-year population estimates for England and Wales for a selection of administrative and census areas by age (in 5 year age brackets) for 2012 to 2020. The data is source is from ONS Population Estimates. Find out more about this dataset here.This data is issued at (BGC) Generalised (20m) boundary type for:Country,Region,Upper Tier Local Authority (2021),Lower Tier Local Authority (2021),Middle Super Output Area (2011), andLower Super Output Area (2011).If you require the data at full resolution boundaries, or if you are interested in the range of statistical data that Esri UK make available in ArcGIS Online please enquire at content@esriuk.com.The Office for National Statistics (ONS) produces annual estimates of the resident population of England and Wales at 30 June every year. The most authoritative population estimates come from the census, which takes place every 10 years in the UK. Population estimates from a census are updated each year to produce mid-year population estimates (MYEs), which are broken down by local authority, sex and age. More detailed information on the methods used to generate the mid-year population estimates can be found here.For further information on the usefulness of the data and guidance on small area geographies please see here.The currency of this data is 2021.MethodologyThe total and 5-year breakdown population counts are reproduced directly from the source data. The age range estimates have been calculated from the published estimates by single year of age. The percentages are calculated using the gender specific (total, female or male) total population count as a denominator except in the case of the male and female total population where the total population is used to give female and male proportions.This dataset will be updated annually, in two releases.Creator: Office for National Statistics. Aggregated age groupings and percentages calculated by Esri UK._The data services available from this page are derived from the National Data Service. The NDS delivers thousands of open national statistical indicators for the UK as data-as-a-service. Data are sourced from major providers such as the Office for National Statistics, Public Health England and Police UK and made available for your area at standard geographies such as counties, districts and wards and census output areas. This premium service can be consumed as online web services or on-premise for use throughout the ArcGIS system.Read more about the NDS.

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.

Cover Photo by: Image by iconicbestiary on Freepik

Thumbnail by: Worker icons created by Uniconlabs - Flaticon

Abstract (en): These data are a collection of demographic statistics for the populations of 125 countries or areas throughout the world, prepared by the Statistical Office of the United Nations. The units of analysis are both country and data year. The primary source of data is a set of questionnaires sent monthly and annually to national statistical services and other appropriate government offices. Data include statistics on approximately 50 types of causes of death for the years 1966 through 1974 for males, females, and total populations. Causes of death in 125 countries or areas throughout the world between the years 1966 and 1974. 2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revised 2005-11-04 to reflect these additions. The causes of death are classified according to the 6th, 7th, and 8th versions of an abbreviated list of the World Health Organization's INTERNATIONAL STATISTICAL CLASSIFICATION OF DISEASES, INJURIES, AND CAUSES OF DEATH. Therefore, data for causes of death are not necessarily comparable across countries or data years. Users should refer to Variable 5 in the Variable List for full discussion of this problem. Users interested in comparing deaths for countries or years that use different versions of the Abbreviated list should consult two publications: A. Joan Klebba, and Alice B. Dolman. COMPARABILITY OF MORTALITY STATISTICS FOR THE SEVENTH AND EIGHTH REVISIONS OF THE INTERNATIONAL CLASSIFICATION OF DISEASES, UNITED STATES. Rockville, MD: United States Department of Health, Education, and Welfare. Public Health Service. Health Services and Mental Health Administration. National Center for Health Statistics, 1975, and World Health Organization. MANUAL OF THE INTERNATIONAL STATISTICAL CLASSIFICATION OF DISEASES, INJURIES, AND CAUSES OF DEATH. Geneva, Switzerland: World Health Organization, 1967.The user should note that countries have data covering a variety of time spans (the maximum span being 1965-1973), and the data have not been padded to supply missing data codes for those years for which a country does not have data. Thus, Egypt has data for years 1965 through 1972, while Kenya has data for only 1970. (See Appendix D in the codebook to determine the years for which a country has data.)It is important that any user of these data consult the United Nations' DEMOGRAPHIC YEARBOOK, 1976, for further explanation of the data's limitations. Certain countries have modified reporting procedures which are presented in both the footnotes and the technical notes accompanying the tables in the Yearbook. There is no way to identify these problems using only the machine-readable data.In order to eliminate unnecessary repetition of identifying information, data were merged so that each record now contains all the data for a country for one particular year. In this process, breakdowns of deaths by ethnic group and/or urban/rural classification were omitted since only a few countries provided such information. Each record now contains the data for the number of deaths from each cause of death for male, female, and total.While the data appear to be in a rectangular matrix, such is not the case. This occurs because different versions of the abbreviated list are referenced in different data years. The lack of a rectangular data matrix does little to restrict the manageability of the dataset. See codebook for examples.While the data have been reformatted and documented by ICPSR staff, there has been no attempt to verify the accuracy and consistency of the data received from the U.N. Statistical Office.

Context

The Gender Development Index (GDI) is a composite measure designed to assess gender disparities and inequalities in a society by considering factors related to human development. It is an extension of the Human Development Index (HDI) and focuses on three key dimensions: health, education, and income. In the GDI, these dimensions are assessed separately for males and females, allowing for a comparison of gender-based development gaps. Health indicators typically include life expectancy at birth for both genders. Education indicators encompass literacy rates and enrollment in primary, secondary, and tertiary education for both males and females. The income component typically examines income levels and workforce participation for both genders.

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 Gender Development Index 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
• Gender Development Index from 1990 to 2021 - Gender Development Index 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.

Cover Photo by: Freepik

Thumbnail by: Freepik

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!

General Overview Source: Likely sourced from the Humanitarian Data Exchange (HDX) via the Humanitarian API (HAPI).

Countries Covered: Includes Afghanistan (AFG), Burkina Faso (BFA), Cameroon (CMR), Chad (TCD), Colombia (COL), Democratic Republic of the Congo (COD), El Salvador (SLV), Ethiopia (ETH), Guatemala (GTM), Haiti (HTI), Honduras (HND), Mali (MLI), Mozambique (MOZ), and others.

Timeframe: Data spans multiple years (e.g., 2018–2024), with country-specific reference periods.

Key Columns Demographic Breakdown:

Gender: f (female), m (male), all (combined).

Age Groups: Ranges like 0-4, 5-9, 10-14, up to 100+ (varies by country).

Population: Numerical counts for each gender and age group.

Metadata:

has_hrp: All entries marked Y, suggesting inclusion in a Humanitarian Response Plan.

in_gho: All Y, possibly indicating alignment with the WHO Global Health Observatory.

admin_level: 0 for national-level data (no subnational breakdowns).

reference_period_start/end: Year(s) the data represents.

Identifiers:

Unique dataset_hdx_id and resource_hdx_id for tracking sources on HDX.

Insights from the Data Population Structure:

High youth populations in countries like Afghanistan (e.g., 7.2 million aged 0–4 in 2021).

Older populations in Colombia (e.g., 24,110 aged 100+ in 2024).

Gender Disparities:

Some countries show slight male majorities (e.g., AFG: 20.5M males vs. 19.8M females in 2021).

Others, like Mozambique (MOZ), have more females (17.4M vs. 16.6M males in 2024).

Time-Specific Trends:

Ethiopia (ETH) data for 2022 shows a large youth cohort (14.8 million aged 0–4).

Mali (MLI) uses older data (2018), highlighting potential gaps in recent updates.

Potential Use Cases Humanitarian Planning: Age/gender breakdowns aid in targeting aid (e.g., pediatric healthcare in youthful populations).

Demographic Research: Analyzing aging trends, gender ratios, or youth dependency ratios.

Comparative Studies: Assessing differences between countries (e.g., Colombia’s detailed age brackets vs. Chad’s broader groups).

Limitations Inconsistent Reference Years: Data spans 2018–2024, complicating cross-country comparisons.

Variable Age Granularity: Some countries (e.g., COL) include 5-year brackets up to 100+, while others (e.g., COD) use broader ranges like 5-19.

Example Analysis Afghanistan (2021):

Total population: ~40.4 million.

Youth (0–14): ~17.3 million (43% of total).

Gender split: 51% male, 49% female.

This dataset is valuable for understanding demographic dynamics in humanitarian contexts, though users should note temporal and structural inconsistencies.

Using bibliometric analysis of large-scale publication data is a simple approach to exploring gender-related trends, especially gender equality in academic publishing. The aim of this study is to investigate gender trends in the fields of bio-economy and rural development sciences in two under develop regions as Latin America and Africa. This study examines gender differences in these fields in order to: (1) recognize the contribution of female researchers in bioeconomy and rural development, (2) explore the relational structure of gender aspects in academic publications, (3) identify trends in female authorship in these scientific research fields over time, and finally (4) identify gender potentials for women to become more visible in these fields of study. To achieve these objectives, we used bibliometric tools to analyses 1891 publication records in bioeconomy and rural development. After cleaning the database of full names of authors of academic publications relevant to the field studies, we performed a series of statistical analyses in R and SPSS software, such as Lotkas distribution, network analysis, co-authorship analysis and spatial distribution of authors in the study. The results show that the number of male authors is almost three times higher than the number of female authors, suggesting that women are under-represented in the fields studied. Men occupy the most important position of authorship in scientific articles; publications with corresponding male authors were found in 1389 out of 1891 publications related to the bio-economy and rural development. In terms of geographical regions, publications with female authors were more prevalent in European and North American areas, with a small exception in some developing countries such as Argentina and South Africa. In terms of research networks, from the total number of authors evaluated, only 23% are female authors on the map of research influence. This indicates that there is a significant gap to be filled in the promotion of scholarly impact through the sharing of knowledge and expertise among authors.

E-learning has gained popularity since the outbreak of COVID-19. This study aims to identify gender differences in e-learners' self-efficacy, satisfaction, motivation, attitude, and performance across the world. Through a meta-analysis and systematic review, this study concludes that there are generally no significant gender differences in e-learning outcomes except in a few countries. Females significantly outperformed males in Spain and the UK. In Austria, India, and mixed countries (Chile and Spain), females hold significantly more positive attitudes toward e-learning than males. In the USA, females present significantly higher self-efficacy than males. Future research into the gender issue in e-learning across the world may adopt cross-disciplinary research methods except for a meta-analysis.

Abstract

This data is from a quantitative survey administered in 2023 to 2,000 married Nepali women and men from 4 provinces in the country about their own beliefs regarding norms-related behaviors, their expectations of how common it is for others in their social group to engage in those behaviors, and the expected social consequences surrounding those behaviors. It is the primary dataset used to author the working paper titled "Women’s Labor Force Participation in Nepal: An Exploration of The Role of Social Norms" - which presents rigorous evidence on whether and the extent to which social norms matter for women's labor force participation in Nepal.

Geographic coverage

The survey data includes a representative sample of households from 4 out of 7 provinces in Nepal: 1. Bagmati Province 2. Sudurpashchim Province 3. Madhesh Province 4. Gandaki Province

Analysis unit

Individual

Universe

The sampling frame is a list of all wards within each selected province.

Kind of data

Sample survey data [ssd]

Sampling procedure

Ward (cluster) selection: The sampling frame consisted of the list of all wards within each selected province. Each province comprises districts and within each district are municipalities (urban and rural municipalities) which are further broken down into wards – the smallest administrative units. The list of wards and their population figures were taken from the latest available 2021 Census. First, the universe of all districts was stratified by urban and rural to ensure greater statistical power for detecting differences between the 2 localities. The stratification by urban-rural proportionate to the population proportion of each group within a province resulted in a self-weighted sample, allowing for analysis of data at the province level and further at locality level within each province. To select the wards, a random start point was generated to negate any bias in the list and to provide an independent chance of selection from the list. The sampling method used here, probability proportionate to size (PPS), gives an independent chance of selection to each ward as per its population size, i.e., a higher chance of selection to wards with a higher population size.38 As a first step of random selection of wards, the cumulative frequency (CF) of the population of households in a ward was calculated. Since the unit of analysis for our study purpose was households having certain criteria and we expected the main outcome variables (social norms) to vary at household levels (as opposed to at an individual level), the household population figures served as the basis for sampling purpose (as opposed to the population size of individuals for a ward). Applying PPS, in the first step, the required number of wards were selected for Categories 1 and 2 households (households with working and non-working females respectively). Following this, the clusters allocated for Category 3 (households with migrant population) households were taken as a subset of the wards selected for Categories 1 and 2.

Selection of the random starting point within each ward during in-field random sampling of households: The selection of the random starting point within a PSU was done by the survey supervisors. For every ward, a predefined landmark for the starting point was chosen. The predefined landmark consisted of i) school, ii) health post, iii) central marketplace, or iv) ward office. The selection of a predefined landmark was the basis of the starting point which was made at the central office. The chosen landmark for every cluster was rotated to account for randomization and to avoid interviewer bias. Once the landmark was chosen, each enumerator used the spin-the-bottle method to randomize the direction in which the survey took place. After starting with a household, enumerators used a skip interval to survey every third household in rural and every fifth household in urban areas. Once the household was chosen, the interviewer used the screener to ascertain the eligibility as per the category quota set aside for them.

Respondent selection: The respondents were selected based on a screener instrument that surveyed the following factors: 1. Gender: Since the views about social norms and labor market outcomes vary by gender, both males and females within a household were interviewed. However, for households with migrant men, only the women were interviewed. 2. Age group: For all women, the screener was applied so as to ensure that only women within the economically active age range, i.e., between the ages of 18-59 years were interviewed. For spouses of female respondents, they had to be at least 18 years of age with no maximum age limit set. 3. Ethnicity: Nepal has more than a hundred ethnic groups residing across the country, and thus the major 8-10 groups are captured in the sample. The other objective of applying a screener for monitoring ethnic composition was to ensure that marginalized ethnic groups such as Dalits were sufficiently represented in the survey. 4. Marital Status: Only married men and women were interviewed since marriage and the responsibilities that come with are sown to impose greater social barriers and restrictions on mobility and work of females. 5. Location: The survey was carried out in both rural and urban locations in a total of 4 provinces. 6. General demographic factors include: • Perceived economic situation: Low to middle-income • It was ensured that both the respondents (male and female for Categories 1 and 2) and female respondent for Category 3 belonged to the second generation of the selected household (for example, not the in-laws residing in a household but their son and his wife.

Mode of data collection

Computer Assisted Personal Interview [capi]

Objective: The objective of the study is to investigate the gender and socioeconomic disparities in the global burden of epilepsy by prevalence and disability-adjusted life-years (DALYs).Methods: The global, regional, and national gender-specific prevalence and DALYs caused by epilepsy by year and age were extracted from the Global Burden of Disease (GBD) Study 2017. The Gini coefficient and concentration index (CI) were calculated to demonstrate the trends in between-country inequality in the epilepsy burden from 1990 to 2017. Paired Wilcoxon signed rank test, Pearson correlation, and linear regression analyses were performed to analyze the association of gender disparity in epilepsy and socio-demographic index (SDI).Results: The DALYs number of epilepsies increased from 1990 to 2017 by 13.8%, whereas age-standardized DALY rates showed a substantial reduction (16.1%). Men had a higher epilepsy burden than women of the same period. The epilepsy burden appeared to be higher in countries with lower socioeconomic development (CI < 0). The Gini coefficient decreased from 0.273 in 1995 to 0.259 in 2017, representing a decline in the between-country gap. Age-standardized prevalence and DALY rates of men were higher than those of women in each SDI-based country group (p < 0.0001). Male-minus-female difference (r = −0.5100, p < 0.0001) and male-to-female ratio (r = −0.3087, p < 0.0001) of age-standardized DALY rates were negatively correlated with SDI.Conclusion: Although global health care of epilepsy is in progress, the epilepsy burden was concentrated in males and developing countries. Our findings highlight the importance of formulating gender-sensitive health policies and providing more services in developing countries.