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

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 # 55-59 years (337) | Female # 50-54 years (448). 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 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

The Gridded Population of the World, Version 4 (GPWv4): Basic Demographic Characteristics, Revision 11 consists of estimates of human population by age and sex as counts (number of persons per pixel) and densities (number of persons per square kilometer), consistent with national censuses and population registers, for the year 2010. To estimate the male and female populations by age in 2010, the proportions of males and females in each 5-year age group from ages 0-4 to ages 85+ for the given census year were calculated. These proportions were then applied to the 2010 estimates of the total population to obtain 2010 estimates of male and female populations by age. In some cases, the spatial resolution of the age and sex proportions was coarser than the resolution of the total population estimates to which they were applied. The population density rasters were created by dividing the population count rasters by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions.

This dataset provides comprehensive information on sex ratios at birth across different countries and regions from 1950 to 2023. It contains 18,944 observations from the United Nations World Population Prospects, offering researchers and demographers valuable insights into gender demographics and potential societal influences on birth sex ratios. The dataset enables analysis of deviations from the biological norm of 105 males per 100 females at birth.

Key features include:

• Coverage of over 200 countries and territories
• Annual sex ratio measurements spanning 73 years
• Standardized methodology across regions
• High-quality demographic data from national statistics
• Consistent reporting format and units

This dataset is ideal for:

• Analyzing demographic trends and gender imbalances
• Studying the impact of social policies on birth sex ratios
• Investigating potential sex-selective practices
• Conducting cross-cultural demographic research
• Developing population projection models
• Understanding regional variations in birth patterns

Population distribution by country for 2019

This dataset contains the population distribution for all countries world wide available through the UNdata portal. http://data.un.org/Data.aspx?d=POP&f=tableCode%3A22

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

This data is taken from the UN data portal.

Inspiration

Important dataset in the prediction of fatality rates for COVID-19 by country. Fatality rates vary based on age.

RO: Population: Male: Ages 20-24: % of Male Population data was reported at 5.858 % in 2017. This records a decrease from the previous number of 5.951 % for 2016. RO: Population: Male: Ages 20-24: % of Male Population data is updated yearly, averaging 7.911 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 9.052 % in 1976 and a record low of 5.858 % in 2017. RO: Population: Male: Ages 20-24: % of Male Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Romania – Table RO.World Bank: Population and Urbanization Statistics. Male population between the ages 20 to 24 as a percentage of the total male population.; ; World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; ;

The Gender Statistics database is a comprehensive source for the latest sex-disaggregated data and gender statistics covering demography, education, health, access to economic opportunities, public life and decision-making, and agency.

The Data

The data is split into several files, with the main one being Data.csv. The Data.csv contains all the variables of interest in this dataset, while the others are lists of references and general nation-by-nation information.

Data.csv contains the following fields:

Data.csv

• Country.Name: the name of the country
• Country.Code: the country's code
• Indicator.Name: the name of the variable that this row represents
• Indicator.Code: a unique id for the variable
• 1960 - 2016: one column EACH for the value of the variable in each year it was available

The other files

I couldn't find any metadata for these, and I'm not qualified to guess at what each of the variables mean. I'll list the variables for each file, and if anyone has any suggestions (or, even better, actual knowledge/citations) as to what they mean, please leave a note in the comments and I'll add your info to the data description.

Country-Series.csv

• CountryCode
• SeriesCode
• DESCRIPTION

Country.csv

• Country.Code
• Short.Name
• Table.Name
• Long.Name
• 2-alpha.code
• Currency.Unit
• Special.Notes
• Region
• Income.Group
• WB-2.code
• National.accounts.base.year
• National.accounts.reference.year
• SNA.price.valuation
• Lending.category
• Other.groups
• System.of.National.Accounts
• Alternative.conversion.factor
• PPP.survey.year
• Balance.of.Payments.Manual.in.use
• External.debt.Reporting.status
• System.of.trade
• Government.Accounting.concept
• IMF.data.dissemination.standard
• Latest.population.census
• Latest.household.survey
• Source.of.most.recent.Income.and.expenditure.data
• Vital.registration.complete
• Latest.agricultural.census
• Latest.industrial.data
• Latest.trade.data
• Latest.water.withdrawal.data

FootNote.csv

• CountryCode
• SeriesCode
• Year
• DESCRIPTION

Series-Time.csv

• SeriesCode
• Year
• DESCRIPTION

Series.csv

• Series.Code
• Topic
• Indicator.Name
• Short.definition
• Long.definition
• Unit.of.measure
• Periodicity
• Base.Period
• Other.notes
• Aggregation.method
• Limitations.and.exceptions
• Notes.from.original.source
• General.comments
• Source
• Statistical.concept.and.methodology
• Development.relevance
• Related.source.links
• Other.web.links
• Related.indicators
• License.Type

Acknowledgements

This dataset was downloaded from The World Bank's Open Data project. The summary of the Terms of Use of this data is as follows:

• You are free to copy, distribute, adapt, display or include the data in other products for commercial and noncommercial purposes at no cost subject to certain limitations summarized below.

• You must include attribution for the data you use in the manner indicated in the metadata included with the data.

• You must not claim or imply that The World Bank endorses your use of the data by or use The World Bank’s logo(s) or trademark(s) in conjunction with such use.

• Other parties may have ownership interests in some of the materials contained on The World Bank Web site. For example, we maintain a list of some specific data within the Datasets that you may not redistribute or reuse without first contacting the original content provider, as well as information regarding how to contact the original content provider. Before incorporating any data in other products, please check the list: Terms of use: Restricted Data.

-- [ed. note: this last is not applicable to the Gender Statistics database]

• The World Bank makes no warranties with respect to the data and you agree The World Bank shall not be liable to you in connection with your use of the data.

• This is only a summary of the Terms of Use for Datasets Listed in The World Bank Data Catalogue. Please read the actual agreement that controls your use of the Datasets, which is available here: Terms of use for datasets. Also see World Bank Terms and Conditions.

RO: Population: as % of Total: Male: Aged 15-64 data was reported at 69.050 % in 2017. This records a decrease from the previous number of 69.398 % for 2016. RO: Population: as % of Total: Male: Aged 15-64 data is updated yearly, averaging 66.381 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 70.690 % in 2007 and a record low of 63.332 % in 1980. RO: Population: as % of Total: Male: Aged 15-64 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Romania – Table RO.World Bank: Population and Urbanization Statistics. Male population between the ages 15 to 64 as a percentage of the total male 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;

This dataset contains population estimates, by age and sex, per 30 arc-second grid cell consistent with national censuses and population registers. There is one image for each modeled age and sex category based on the 2010 round of Census. General Documentation The Gridded Population of World Version 4 (GPWv4), Revision …

Kenya KE: Condom Use: Population Aged 15-24: Male: % of Males Aged 15-24 data was reported at 56.200 % in 2009. This records an increase from the previous number of 39.400 % for 2003. Kenya KE: Condom Use: Population Aged 15-24: Male: % of Males Aged 15-24 data is updated yearly, averaging 39.400 % from Dec 1998 (Median) to 2009, with 3 observations. The data reached an all-time high of 56.200 % in 2009 and a record low of 39.100 % in 1998. Kenya KE: Condom Use: Population Aged 15-24: Male: % of Males Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank: Health Statistics. Condom use, male is the percentage of the male population ages 15-24 who used a condom at last intercourse in the last 12 months.; ; Demographic and Health Surveys, and UNAIDS.; Weighted average;

A collection of datasets for economic, demographic, and population metrics for the Marshall Islands derived from the World Bank DataBank interface. DataBank is an analysis and visualisation tool that contains collections of time series data on a variety of topics. Data are derived from a series of databases such as: World Development Indicators; Statistical Capacity Indicators, Education Statistics, Gender Statistics, Health Nutrition and Population Statistics, and others

About this dataset

Context

The dataset tabulates the population of Globe by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Globe across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of male population, with 53.18% of total population being male. 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.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Globe is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Globe total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Race & Ethnicity. You can refer the same here

Until the 1800s, population growth was incredibly slow on a global level. The global population was estimated to have been around 188 million people in the year 1CE, and did not reach one billion until around 1803. However, since the 1800s, a phenomenon known as the demographic transition has seen population growth skyrocket, reaching eight billion people in 2023, and this is expected to peak at over 10 billion in the 2080s.

Context

This dataset is in context of the real world data science work and how the data analyst and data scientist work.

Content

The dataset consists of four columns Year, Level_1(Ethnic group/gender), Level_2(Age group), and population

Acknowledgements

I would sincerely thank GeoIQ for sharing this dataset with me along with tasks. Just having a basic knowledge of Pandas and Numpy and other python data science libraries is not enough. How can you execute tasks and how can you preprocess the data before making any prediction is very important. Most of the datasets in Kaggle are clean and well arranged but this dataset thought me how real world data science and analysis works. Every data science beginner must work on this dataset and try to execute the tasks. It would only give them a good exposer to the real data science world.

Inspiration

1. Identify the largest Ethnic group in Singapore. Their average population growth over the years and what proportion of the total population do they constitute.
2. Identify the largest age group in Singapore. Their average population growth over the years and what proportion of the total population do they constitute.
3. Identify the group (by age, ethnicity and gender) that: a. Has shown the highest growth rate b. Has shown the lowest growth rate c. Has remained the same
4. Plot a graph for population trends

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Employed population below international poverty line (%) - Male





  Dataset Description

This dataset provides country-level data for the indicator "1.1.1 Employed population below international poverty line (%) - Male" across African nations, sourced from the World Health Organization's (WHO) data portal on Sustainable Development Goals (SDGs). The data is… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/employed-population-below-international-poverty-line-male-for-african-countries.

Finland Population: Up to 4 Years data was reported at 278,510.000 Person in 2017. This records a decrease from the previous number of 287,537.000 Person for 2016. Finland Population: Up to 4 Years data is updated yearly, averaging 327,400.000 Person from Dec 1865 (Median) to 2017, with 153 observations. The data reached an all-time high of 502,668.000 Person in 1950 and a record low of 223,358.000 Person in 1870. Finland Population: Up to 4 Years data remains active status in CEIC and is reported by Statistics Finland. The data is categorized under Global Database’s Finland – Table FI.G001: Population: by Gender and Age Group.

"Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.This dataset includes demographic data of 22 countries from 1960 to 2018, including Sri Lanka, Bangladesh, Pakistan, India, Maldives, etc. Data fields include: country, year, population ratio, male ratio, female ratio, population density (km). Source: ( 1 ) United Nations Population Division. World Population Prospects: 2019 Revision. ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations Statistical Division. Population and Vital Statistics Reprot ( various years ), ( 5 ) U.S. Census Bureau: International Database, and ( 6 ) Secretariat of the Pacific Community: Statistics and Demography Programme. Periodicity: Annual Statistical Concept and Methodology: Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models. Errors and undercounting occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census. The quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data. The currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census. The United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems. International migration is the only other factor besides birth and death rates that directly determines a country's population growth. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect. Population projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant."

This formatted dataset (AnalysisDatabaseGBD) originates from raw data files from the Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease Study (GBD2017) affiliated with the University of Washington. We are volunteer collaborators with IHME and not employed by IHME or the University of Washington.

The population weighted GBD2017 data are on male and female cohorts ages 15-69 years including noncommunicable diseases (NCDs), body mass index (BMI), cardiovascular disease (CVD), and other health outcomes and associated dietary, metabolic, and other risk factors. The purpose of creating this population-weighted, formatted database is to explore the univariate and multiple regression correlations of health outcomes with risk factors. Our research hypothesis is that we can successfully model NCDs, BMI, CVD, and other health outcomes with their attributable risks.

These Global Burden of disease data relate to the preprint: The EAT-Lancet Commission Planetary Health Diet compared with Institute of Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis. The data include the following: 1. Analysis database of population weighted GBD2017 data that includes over 40 health risk factors, noncommunicable disease deaths/100k/year of male and female cohorts ages 15-69 years from 195 countries (the primary outcome variable that includes over 100 types of noncommunicable diseases) and over 20 individual noncommunicable diseases (e.g., ischemic heart disease, colon cancer, etc). 2. A text file to import the analysis database into SAS 3. The SAS code to format the analysis database to be used for analytics 4. SAS code for deriving Tables 1, 2, 3 and Supplementary Tables 5 and 6 5. SAS code for deriving the multiple regression formula in Table 4. 6. SAS code for deriving the multiple regression formula in Table 5 7. SAS code for deriving the multiple regression formula in Supplementary Table 7
8. SAS code for deriving the multiple regression formula in Supplementary Table 8 9. The Excel files that accompanied the above SAS code to produce the tables

For questions, please email davidkcundiff@gmail.com. Thanks.

As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.

              Teens and social media

              As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
              Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 65-69 years (51) | Female # 10-14 years (76). 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 Earth population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Earth is shown in the following column.
• Population (Female): The female population in the 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 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 Earth Population by Gender. You can refer the same here