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

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.

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

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

The LAGENDA dataset is a large-scale dataset with age and gender annotations for face and body bounding boxes. The dataset consists of 67,159 images from the Open Images Dataset and comprises 84,192 pairs (FaceCrop, BodyCrop). This dataset offers a high level of diversity, encompassing various scenes and domains. It contains minimal celebrity data, thus reflecting real-world, in-the-wild scenarios. The dataset spans a wide age range, from 0 to 95 years old.

The global gender gap index benchmarks national gender gaps on economic, political, education, and health-based criteria. In 2025, the country offering the most gender equal conditions was Iceland, with a score of 0.93. Overall, the Nordic countries make up 3 of the 5 most gender equal countries worldwide. The Nordic countries are known for their high levels of gender equality, including high female employment rates and evenly divided parental leave. Sudan is the second-least gender equal country Pakistan is found on the other end of the scale, ranked as the least gender equal country in the world. Conditions for civilians in the North African country have worsened significantly after a civil war broke out in April 2023. Especially girls and women are suffering and have become victims of sexual violence. Moreover, nearly 9 million people are estimated to be at acute risk of famine. The Middle East and North Africa have the largest gender gap Looking at the different world regions, the Middle East and North Africa have the largest gender gap as of 2023, just ahead of South Asia. Moreover, it is estimated that it will take another 152 years before the gender gap in the Middle East and North Africa is closed. On the other hand, Europe has the lowest gender gap in the world.

Benin BJ: Women Business and the Law Index Score: scale 1-100 data was reported at 83.750 NA in 2023. This stayed constant from the previous number of 83.750 NA for 2022. Benin BJ: Women Business and the Law Index Score: scale 1-100 data is updated yearly, averaging 40.000 NA from Dec 1970 (Median) to 2023, with 54 observations. The data reached an all-time high of 83.750 NA in 2023 and a record low of 28.125 NA in 1972. Benin BJ: Women Business and the Law Index Score: scale 1-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Benin – Table BJ.World Bank.WDI: Governance: Policy and Institutions. The index measures how laws and regulations affect women’s economic opportunity. Overall scores are calculated by taking the average score of each index (Mobility, Workplace, Pay, Marriage, Parenthood, Entrepreneurship, Assets and Pension), with 100 representing the highest possible score.;World Bank: Women, Business and the Law. https://wbl.worldbank.org/;;1. For the reference period, WDI and Gender Databases take the data coverage years instead of reporting years used in WBL (https://wbl.worldbank.org/). For example, the data for YR2020 in WBL (report year) corresponds to data for YR2019 in WDI and Gender Databases. 2. The 2024 Women, Business and the Law (WBL) report has introduced two distinct datasets, labeled as 1.0 and 2.0. The WBL data in the Gender database is based on the dataset 1.0. This dataset maintains consistency with the indicators used in previous WBL reports from 2020 to 2023. In contrast, the WBL 2.0 dataset includes new areas of childcare and safety. For those interested in exploring the WBL 2.0 dataset, it is available on the WBL website at https://wbl.worldbank.org.

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

Kosovo KS: Women Business and the Law Index Score: scale 1-100 data was reported at 91.875 NA in 2023. This stayed constant from the previous number of 91.875 NA for 2022. Kosovo KS: Women Business and the Law Index Score: scale 1-100 data is updated yearly, averaging 69.063 NA from Dec 1970 (Median) to 2023, with 54 observations. The data reached an all-time high of 91.875 NA in 2023 and a record low of 67.500 NA in 1996. Kosovo KS: Women Business and the Law Index Score: scale 1-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kosovo – Table KS.World Bank.WDI: Governance: Policy and Institutions. The index measures how laws and regulations affect women’s economic opportunity. Overall scores are calculated by taking the average score of each index (Mobility, Workplace, Pay, Marriage, Parenthood, Entrepreneurship, Assets and Pension), with 100 representing the highest possible score.;World Bank: Women, Business and the Law. https://wbl.worldbank.org/;;1. For the reference period, WDI and Gender Databases take the data coverage years instead of reporting years used in WBL (https://wbl.worldbank.org/). For example, the data for YR2020 in WBL (report year) corresponds to data for YR2019 in WDI and Gender Databases. 2. The 2024 Women, Business and the Law (WBL) report has introduced two distinct datasets, labeled as 1.0 and 2.0. The WBL data in the Gender database is based on the dataset 1.0. This dataset maintains consistency with the indicators used in previous WBL reports from 2020 to 2023. In contrast, the WBL 2.0 dataset includes new areas of childcare and safety. For those interested in exploring the WBL 2.0 dataset, it is available on the WBL website at https://wbl.worldbank.org.

Guyana GY: CPIA: Gender Equality Rating: 1=Low To 6=High data was reported at 3.000 NA in 2017. This stayed constant from the previous number of 3.000 NA for 2016. Guyana GY: CPIA: Gender Equality Rating: 1=Low To 6=High data is updated yearly, averaging 3.500 NA from Dec 2005 (Median) to 2017, with 13 observations. The data reached an all-time high of 4.000 NA in 2009 and a record low of 3.000 NA in 2017. Guyana GY: CPIA: Gender Equality Rating: 1=Low To 6=High data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Guyana – Table GY.World Bank.WDI: Policy and Institutions. Gender equality assesses the extent to which the country has installed institutions and programs to enforce laws and policies that promote equal access for men and women in education, health, the economy, and protection under law.; ; World Bank Group, CPIA database (http://www.worldbank.org/ida).; Unweighted average;

Grenada GD: CPIA: Gender Equality Rating: 1=Low To 6=High data was reported at 3.500 NA in 2017. This stayed constant from the previous number of 3.500 NA for 2016. Grenada GD: CPIA: Gender Equality Rating: 1=Low To 6=High data is updated yearly, averaging 4.000 NA from Dec 2005 (Median) to 2017, with 13 observations. The data reached an all-time high of 5.000 NA in 2008 and a record low of 3.500 NA in 2017. Grenada GD: CPIA: Gender Equality Rating: 1=Low To 6=High data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Grenada – Table GD.World Bank: Policy and Institutions. Gender equality assesses the extent to which the country has installed institutions and programs to enforce laws and policies that promote equal access for men and women in education, health, the economy, and protection under law.; ; World Bank Group, CPIA database (http://www.worldbank.org/ida).; Unweighted average;

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

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.

As of January 2024, Instagram was slightly more popular with men than women, with men accounting for 50.6 percent of the platform’s global users. Additionally, the social media app was most popular amongst younger audiences, with almost 32 percent of users aged between 18 and 24 years.

              Instagram’s Global Audience

              As of January 2024, Instagram was the fourth most popular social media platform globally, reaching two billion monthly active users (MAU). This number is projected to keep growing with no signs of slowing down, which is not a surprise as the global online social penetration rate across all regions is constantly increasing.
              As of January 2024, the country with the largest Instagram audience was India with 362.9 million users, followed by the United States with 169.7 million users.

              Who is winning over the generations?

              Even though Instagram’s audience is almost twice the size of TikTok’s on a global scale, TikTok has shown itself to be a fierce competitor, particularly amongst younger audiences. TikTok was the most downloaded mobile app globally in 2022, generating 672 million downloads. As of 2022, Generation Z in the United States spent more time on TikTok than on Instagram monthly.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 65-69 years (209) | Female # 65-69 years (131). 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 Black Earth population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Black Earth is shown in the following column.
• Population (Female): The female population in the Black 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 Black 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 Black Earth Population by Gender. You can refer the same here

Description:

👉 Download the dataset here

The Crowd Counting Dataset is an extensive collection of high-resolution images capturing a wide variety of crowd scenes, with the number of individuals ranging from zero to 5,000 per image. The dataset is meticulously curated to include diverse scenarios such as public events, street gatherings, protests, festivals, and daily commutes, ensuring that it covers a broad spectrum of crowd densities and environmental conditions.

Download Dataset

Key Features:

Diverse Scenarios: The dataset features crowds in various settings, from dense urban environments to open public spaces, providing a comprehensive resource for developing and testing crowd counting algorithms across different contexts.

High-Quality Annotations: Each image in the dataset is paired with a JSON file that includes precise annotations for every individual in the crowd. The labeling details not only the count of people but also includes classification data such as gender, age group, and activity, enabling multi-dimensional analysis.

Versatility in Applications: This dataset is ideal for training and evaluating machine learning models for applications in public safety, event management, urban planning, and retail analysis, where accurate crowd estimation and behavior analysis are critical.

Scalable Data: With a wide range of crowd sizes, the dataset is suitable for both low-density and high-density crowd counting tasks, providing a robust foundation for developing scalable Al solutions.

Real-World Relevance: The images are sourced from real-world environments, ensuring that models trained on this dataset can generalize well to practical applications, enhancing their reliability in real-time deployments.

Dataset Structure:

Images: High-resolution images captured from various angles and lighting conditions.

Annotations: JSON files containing detailed labels for each individual, including:

Person count

Classification attributes (e.g., age group, gender, activity)

Positional information within the image for precise localization

Categories: The dataset is organized into different categories based on crowd density, scene type, and time of day, allowing users to focus on specific aspects of crowd analysis.

This dataset is sourced from Kaggle.

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.

The Global Legislators Database (GLD) is a cross-national dataset on characteristics of 19,704 national parliamentarians in 97 of the 103 world's electoral democracies. The database includes individual legislator-level information on those who held office in each country's lower (or unicameral) chamber during a single legislative session as of 2015, 2016, or 2017. Variables included for each legislator are name, date of birth, gender, party affiliation, last occupation prior to holding elected office, and highest level of education completed. An article describing and validating the dataset is published in The British Journal of Political Science.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

Sample includes any respondent in a fixed household able to participate in the survey in Arabic. This resulted in a higher percentage of self-reported non-Jordanians in the 2017 sample (12%, compared with less than 5% in previous waves).

Analysis unit

Individuals

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world’s population (see table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer’s gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 1012.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

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.