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 # 40-44 years (386) | Female # 50-54 years (413). 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 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

Context

This dataset compiles valuable information on how different countries worldwide rank concerning conditions and opportunities for women. It aims to shed light on the status of women's rights and gender equality across the globe, making it a valuable resource for researchers, policymakers, and organizations advocating for gender equality.

Content

This dataset contains three main columns:

1.**Rank:** This column provides the ranking of countries based on their performance or score in terms of conditions and opportunities for women. Rankings range from 1 (indicating the best country for women) to the total number of countries included in the dataset.

2.**Country:** This column lists the names of the countries under evaluation. Each row corresponds to a specific country, allowing users to identify which country the data pertains to. Examples of entries in this column include "United States," "Sweden," "India," and more.

3.**Score:** The "Score" column comprises numerical values or scores reflecting the overall assessment of each country's performance regarding conditions and opportunities for women. These scores are likely calculated based on factors such as gender equality in education, employment, healthcare, political representation, and legal rights. Higher scores generally indicate better conditions for women, while lower scores suggest room for improvement.

Use Cases:

• Researchers can analyze this dataset to identify global trends in gender equality, allowing for cross-country comparisons and the identification of areas where countries excel or need improvement.

• Policymakers can utilize this data to make informed decisions and track progress in achieving gender equality goals.

• Advocacy groups and organizations working on women's rights can leverage this dataset to support their initiatives and promote gender equality on a global scale.

• Data enthusiasts on Kaggle can explore this dataset for data visualization, machine learning, and statistical analysis projects aimed at uncovering insights and trends related to women's well-being and opportunities.

Data Source:

https://ceoworld.biz/2021/06/11/the-worlds-best-countries-for-women-2021/

Acknowledgments:

If applicable, acknowledge any individuals or organizations that contributed to collecting or compiling this dataset.

By publishing this dataset on Kaggle, you are contributing to the open data community and providing a valuable resource for data-driven insights into gender equality worldwide.

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

• Country: Name of the country.
• Density (P/Km2): Population density measured in persons per square kilometer.
• Abbreviation: Abbreviation or code representing the country.
• Agricultural Land (%): Percentage of land area used for agricultural purposes.
• Land Area (Km2): Total land area of the country in square kilometers.
• Armed Forces Size: Size of the armed forces in the country.
• Birth Rate: Number of births per 1,000 population per year.
• Calling Code: International calling code for the country.
• Capital/Major City: Name of the capital or major city.
• CO2 Emissions: Carbon dioxide emissions in tons.
• CPI: Consumer Price Index, a measure of inflation and purchasing power.
• CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.
• Currency_Code: Currency code used in the country.
• Fertility Rate: Average number of children born to a woman during her lifetime.
• Forested Area (%): Percentage of land area covered by forests.
• Gasoline_Price: Price of gasoline per liter in local currency.
• GDP: Gross Domestic Product, the total value of goods and services produced in the country.
• Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.
• Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.
• Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.
• Largest City: Name of the country's largest city.
• Life Expectancy: Average number of years a newborn is expected to live.
• Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.
• Minimum Wage: Minimum wage level in local currency.
• Official Language: Official language(s) spoken in the country.
• Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.
• Physicians per Thousand: Number of physicians per thousand people.
• Population: Total population of the country.
• Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.
• Tax Revenue (%): Tax revenue as a percentage of GDP.
• Total Tax Rate: Overall tax burden as a percentage of commercial profits.
• Unemployment Rate: Percentage of the labor force that is unemployed.
• Urban Population: Percentage of the population living in urban areas.
• Latitude: Latitude coordinate of the country's location.
• Longitude: Longitude coordinate of the country's location.

Potential Use Cases

• Analyze population density and land area to study spatial distribution patterns.
• Investigate the relationship between agricultural land and food security.
• Examine carbon dioxide emissions and their impact on climate change.
• Explore correlations between economic indicators such as GDP and various socio-economic factors.
• Investigate educational enrollment rates and their implications for human capital development.
• Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.
• Study labor market dynamics through indicators such as labor force participation and unemployment rates.
• Investigate the role of taxation and its impact on economic development.
• Explore urbanization trends and their social and environmental consequences.

Women's Business Centers (WBCs) represent a national network of nearly 100 educational centers throughout the United States and its territories, which are designed to assist women in starting and growing small businesses. WBCs seek to "level the playing field" for women entrepreneurs, who still face unique obstacles in the business world. SBA’s Office of Women’s Business Ownership (OWBO) oversees the WBC network, which provides entrepreneurs (especially women who are economically or socially disadvantaged) comprehensive training and counseling on a variety of topics in several languages

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.

Series Name: Proportion of women aged 20-24 years who were married or in a union before age 15 (percent)Series Code: SP_DYN_MRBF15Release Version: 2020.Q2.G.03This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 5.3.1: Proportion of women aged 20–24 years who were married or in a union before age 15 and before age 18Target 5.3: Eliminate all harmful practices, such as child, early and forced marriage and female genital mutilationGoal 5: Achieve gender equality and empower all women and girlsFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

The 2015 Global Nutrition Report Dataset contains data for all the indicators that were used in Global Nutrition Report 2015: Actions and Accountability to Advance Nutrition & Sustainable Development. The data are compiled from secondary sources including United Nations Children's Fund (UNICEF), World Health Organization (WHO), and the World Bank (WB) among many others. The dataset broadly contains information on adult and child nutrition, economic demography, nutrition intervention coverage, and policy legislation in the nutrition sector.

Series Name: Proportion of women who make their own informed decisions regarding sexual relations contraceptive use and reproductive health care (percent of women aged 15-49 years)Series Code: SH_FPL_INFMRelease Version: 2020.Q2.G.03This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 5.6.1: Proportion of women aged 15–49 years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health careTarget 5.6: Ensure universal access to sexual and reproductive health and reproductive rights as agreed in accordance with the Programme of Action of the International Conference on Population and Development and the Beijing Platform for Action and the outcome documents of their review conferencesGoal 5: Achieve gender equality and empower all women and girlsFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Context : The ICC Women's T20 World Cup is a (generally) bi-annual cricket tournament for women's international teams. This dataset looks at the editions in 2014, 2016, 2018, 2020 and 2023, in which 10 teams have competed. This dataset contains both match overview data and ball by ball data, as well as a players list.

A handful of matches are missing from the source data (as far as I'm aware, the 2nd, 6th, 9th and 11th games from the 2014 world cup). Runs/Wickets additions contain the wickets taken and runs scored by each player in these missing matches, but this information is not in any of the main files.

Notebooks - To see some charts based on this data go to - Match overview : https://www.kaggle.com/code/acidbear55/women-s-t20-world-cups-data-visualisation - Ball by Ball : https://www.kaggle.com/code/acidbear55/women-s-icc-t20-world-cup-ball-by-ball/notebook

Sources - All this data was taken from https://cricsheet.org/downloads/ , under BY EVENT, and from 'ICC Women's T20 World Cup'. Originally the data came as one json file per match, which has now been combined into a single CSV file.

Python code used to clean and create the csv files can be found at : https://github.com/annaFlett/T20WCData

Any feedback is much appreciated :)

Series Name: Proportion of girls and women aged 15-49 years who have undergone female genital mutilation cutting by age (percent)Series Code: SH_STA_FGMSRelease Version: 2020.Q2.G.03This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 5.3.2: Proportion of girls and women aged 15–49 years who have undergone female genital mutilation/cutting, by ageTarget 5.3: Eliminate all harmful practices, such as child, early and forced marriage and female genital mutilationGoal 5: Achieve gender equality and empower all women and girlsFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Context

This dataset contains all the stats of Gender Statistics 2022 - World Bank.

Details

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.

Wage and salaried workers (employees) are those workers who hold the type of jobs defined as "paid employment jobs," where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work. Contraceptive prevalence rate is the percentage of women who are practicing, or whose sexual partners are practicing, at least one modern method of contraception. It is usually measured for women ages 15-49 who are married or in union. Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception.

Number of male sole proprietors is the number of newly registered sole proprietors owned by female individuals in the calendar year. A sole proprietorship is a business entity owned and managed by a single individual who is indistinguishable from the business and personally liable.

Percentage of women aged 15–49 who have gone through partial or total removal of the female external genitalia or other injury to the female genital organs for cultural or other non-therapeutic reasons. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households. Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered. Women who own house both alone and jointly (% of women age 15-49): Q4 is the percentage of women age 15-49 who alone as well as jointly with someone else own a house which is legally registered with their name or cannot be sold without their signature. "Both alone and jointly" Implies a woman owns a house alone and another house jointly with someone else. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households.

Number of infants dying before reaching one year of age. Male population between the ages 75 to 79.

The percentage of respondents who report using mobile money, a debit or credit card, or a mobile phone to make a payment from an account, or report using the internet to pay bills or to buy something online, in the past 12 months. It also includes respondents who report paying bills, sending or receiving remittances, receiving payments for agricultural products, receiving government transfers, receiving wages, or receiving a public sector pension directly from or into a financial institution account or through a mobile money account in the past 12 months, male (% age 15+).

Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.

Metadata

Coverage & Extent

• Granularity List : National
• Temporal Coverage : 1959 - 2021
• Periodicity : Annual
• Acronym : Gender Stats
• Recommended Citation: Gender Statistics, The World Bank
• Languages Supported : English
• Source Type : World Bank Group
• Source: : Gender Statistics, The World Bank
• Harvest Source : World Bank Data API
• Dates
  • First Published Date : Jul 18, 2010
  • Last Updated on : Jun 22, 2022
• Update Frequency : Quarter

Download

kaggle API Command !kaggle datasets download -d azminetoushikwasi/gender-statistics-wb

Disclaimer

The data collected are all publicly available and it's intended for educational purposes only.

Acknowledgement

https://datacatalog.worldbank.org/search/dataset/0037654

The study used an explanatory sequential mixed method design. This method is appropriate for examining the employment status of STEM graduates in terms of gender as well as the time it takes for graduates to secure their first job after graduating. The method is also employed to look at how staff in higher education supports female graduates in their search for employment after graduation. By design, this study collects data in a sequential fashion, starting with quantitative data and moving on to qualitative data that provide context for the quantitative data.Both primary and secondary sources of data were employed in the study (See Figure A). While information from secondary sources was gathered using Eric, Scopus, and Google search engines, information from primary sources was gathered through questionnaires and interviews. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) was used to conduct the analysis. Using the keywords employment status, duration of job search, and gender-responsive support of higher education, the first 221 articles were collected. Only 15 articles were chosen when PRISMA used the inclusion and exclusion criteria to filter out publications gathered between 2012 and 2024. The information gathered from secondary sources was utilized to triangulate the findings of the primary data sources. The following figure shows the data sources.Figure A: Data sources for the study (see the Description Word Doc. in the dataset)Based on the explanatory sequential mixed method design, quantitative data analysis was first carried out. In order to determine whether there were statistical differences in the employment status and the time it took for male and female STEM engineering graduates to find jobs, the chi square test was employed. An analysis of the degree to which higher education institutions assist female graduates in their job search was also done using an independent samples t-test. The viewpoints of academics from these related universities and prospective employers of STEM graduates were captured through the use of qualitative data.

The difference between the earnings of women and men shrank slightly over the past years. Considering the controlled gender pay gap, which measures the median salary for men and women with the same job and qualifications, women earned one U.S. cent less. By comparison, the uncontrolled gender pay gap measures the median salary for all men and all women across all sectors and industries and regardless of location and qualification. In 2025, the uncontrolled gender pay gap in the world stood at 0.83, meaning that women earned 0.83 dollars for every dollar earned by men.

In 2023, Mongolia had the highest share of women employed in science, technology, engineering, and mathematics (STEM) fields, with ** percent of all those employed in STEM fields being women. Belarus, Lesotho, the United States, and Barbados rounded out the top five countries employing the highest share of women in STEM fields.

Series Name: Proportion of women who make their own informed decisions regarding sexual relations contraceptive use and reproductive health care (percent of women aged 15-49 years)Series Code: SH_FPL_INFMRelease Version: 2020.Q2.G.03This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 5.6.1: Proportion of women aged 15–49 years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health careTarget 5.6: Ensure universal access to sexual and reproductive health and reproductive rights as agreed in accordance with the Programme of Action of the International Conference on Population and Development and the Beijing Platform for Action and the outcome documents of their review conferencesGoal 5: Achieve gender equality and empower all women and girlsFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Germany DE: (DC)Part Time Employment: Female: % of Total Female Employment data was reported at 47.110 % in 2017. This records an increase from the previous number of 46.570 % for 2016. Germany DE: (DC)Part Time Employment: Female: % of Total Female Employment data is updated yearly, averaging 39.380 % from Dec 1983 (Median) to 2017, with 35 observations. The data reached an all-time high of 47.990 % in 2013 and a record low of 28.780 % in 1986. Germany DE: (DC)Part Time Employment: Female: % of Total Female Employment 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: Employment and Unemployment. Part time employment refers to regular employment in which working time is substantially less than normal. Definitions of part time employment differ by country.; ; International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average; Relevance to gender indicator: More and more women are working part-time and one of the concern is that part time work does not provide the stability that full time work does.

Context

Women roughly occupy half of the world's population but when it comes to the total workforce of a country, the percentage of male and female workers are rarely similar. This is even more prominent for the developing and underdeveloped countries. While several reasons such as the insufficient access to education, religious superstitions, lack of adequate infrastructures are responsible for this discrepancy, it goes way beyond these. And to show the effects of multiple socioeconomic factors on the participation of women in the total workforce, percentage of female employment in the total labor force has been considered. Using multiple linear regression model, the relationship between these factors can be analyzed.

Content

For the current study, the data set has been chosen from a survey performed on the population of Bangladesh. The datasets selected for this study span over 25 years (from 1995 to 2019). Data has been collected separately from multiple datasets from the World Bank databank for the employed women percentage and the related predictor variables. These datasets were compiled into one dataset and it corresponds to the 25 data points for the variables. There is one response variable which is the percentage of the employed women and 10 exlnanatory variables of predictors. Brief descriptions of these variables are given below.

PerFemEmploy Employment to population ratio (%) of women who are of age 15 or older. Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.

FertilityRate Fertility rate (birth per women). Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year.

RatioMaletoFemale Ratio of female to male labor force participation rate. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100.

PerFemEmployers Employers, female (% of female employment). Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a "self-employment jobs" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s).

Agriculture Employment in agriculture, female (% of female employment). Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4).

Industry Employment in industry, female (% of female employment). The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4).

Services Employment in services, female (% of female employment). The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4).

Wage.Salaried Wage and salaried workers, female (% of female employment). Wage and salaried workers (employees) are those workers who hold the type of jobs defined as "paid employment jobs," where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work.

ContrFamWorkers Contributing family workers, female (% of female employment). Contribut...

Analysis of ‘Female Employment vs Socioeconimic Factors’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mdmuhtasimbillah/female-employment-vs-socioeconimic-factors on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Women roughly occupy half of the world's population but when it comes to the total workforce of a country, the percentage of male and female workers are rarely similar. This is even more prominent for the developing and underdeveloped countries. While several reasons such as the insufficient access to education, religious superstitions, lack of adequate infrastructures are responsible for this discrepancy, it goes way beyond these. And to show the effects of multiple socioeconomic factors on the participation of women in the total workforce, percentage of female employment in the total labor force has been considered. Using multiple linear regression model, the relationship between these factors can be analyzed.

Content

For the current study, the data set has been chosen from a survey performed on the population of Bangladesh. The datasets selected for this study span over 25 years (from 1995 to 2019). Data has been collected separately from multiple datasets from the World Bank databank for the employed women percentage and the related predictor variables. These datasets were compiled into one dataset and it corresponds to the 25 data points for the variables. There is one response variable which is the percentage of the employed women and 10 exlnanatory variables of predictors. Brief descriptions of these variables are given below.

PerFemEmploy Employment to population ratio (%) of women who are of age 15 or older. Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.

FertilityRate Fertility rate (birth per women). Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year.

RatioMaletoFemale Ratio of female to male labor force participation rate. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100.

PerFemEmployers Employers, female (% of female employment). Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a "self-employment jobs" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s).

Agriculture Employment in agriculture, female (% of female employment). Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4).

Industry Employment in industry, female (% of female employment). The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4).

Services Employment in services, female (% of female employment). The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4).

Wage.Salaried Wage and salaried workers, female (% of female employment). Wage and salaried workers (employees) are those workers who hold the type of jobs defined as "paid employment jobs," where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work.

ContrFamWorkers Contributing family workers, female (% of female employment). Contributing family workers are those workers who hold "self-employment jobs" as own-account workers in a market-oriented establishment operated by a related person living in the same household.

OwnAccount Own-account female workers (% of employment). Own-account workers are workers who, working on their own account or with one or more partners, hold the types of jobs defined as "self-employment jobs" and have not engaged on a continuous basis any employees to work for them. Own account workers are a subcategory of "self-employed".

Vulnerable Vulnerable employment, female (% of female employment). Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment.

Inspiration

Linear model as well as other statistical methods can be applied on this dataset to analyze if there is any viable relationship between the predictor and the response variables.

--- Original source retains full ownership of the source dataset ---

The Global Comparative Study on REDD+ (GCS REDD+) was launched in 2009 by the Center for International Forestry Research (CIFOR) to ensure that policy-makers and practitioner communities have access to – and use – the information, analyses and tools they need to: design and implement REDD+ and other forest-based mitigation strategies in effective, efficient and equitable ways that also promote social and environmental co-benefits; and rigorously assess to what degree REDD+ has delivered. Module 2 (Subnational REDD+ and low emissions development initiatives) focuses on assessing the performance of subnational REDD+ and other low-emission development initiatives, including subnational jurisdictional programmes and local-level projects. Module 2 evaluated the impacts of 23 REDD+ project and program sites in six countries: Brazil, Cameroon, Indonesia, Peru, Tanzania and Vietnam. The research uses a before–after/control–intervention (BACI) approach. In this approach, identical data are collected both before and after the initiative starts, and in an ‘intervention’ area (that is, the location that is impacted by the REDD+ initiative) and a ‘control’ area (that is, a location that has similar characteristics to the intervention area, but is not impacted by the REDD+ initiative). Data collection for the ‘before’ period was carried out in 2010/11 (hereafter referred as ‘Phase 1’), and for the ‘after’ period in 2013/14 (hereafter referred as ‘Phase 2’). At each site, four intervention villages and four control villages were surveyed. In each village, three types of data collection instruments were implemented in Phase 1 and Phase 2: a Household Questionnaire; a Village Questionnaire; and a Women’s Questionnaire. The Women’s dataset corresponds to the Women’s Questionnaire, which has three main goals. First, it is an instrument that enables women to have a voice as respondents in GCS REDD+. Second, it is a way to obtain data that are specific to the experience and knowledge of women in the study villages. Third, it supplies information that compares the livelihood activities and outlooks of women and men in the study villages. The Women’s Questionnaire is composed of four main sections: 1. Women’s livelihoods in the village and change over time; and tenure; 2. women’s participation in village and household decisions; 3. perception of changes in women’s well-being; 4. women’s involvement in and assessment of forest interventions. The Women’s Questionnaire was applied through focus group interviews with around 10 women, aged 16 and older, who represent all (or the vast majority) of the different types of women’s livelihoods in the village. The women dataset includes 190 villages in Phase 1 (121 intervention and 69 control villages) and 149 in Phase 2 (87 intervention and 62 control villages). Variables from Phase 1 start with P1W_section&question number, and variables from Phase 2 start with P2W_section&question number . The research design and methods are further described in Sunderlin et al. (2016) as well as in several GCS REDD+ publications (see ‘Related publications’).