Forbes Women of the Year

Global Rankings and Designations

By Priyanka Dobhal [source]

About this dataset

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

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Research Ideas

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

Acknowledgements

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

License

See the dataset description for more information.

Columns

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

Acknowledgements

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

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.

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

About Violence Against Women & Girls

The Demographic and Health Surveys (DHS) Program exists to advance the global understanding of health and population trends in developing countries.

The UN describes violence against women and girls (VAWG) as: “One of the most widespread, persistent, and devastating human rights violations in our world today. It remains largely unreported due to the impunity, silence, stigma, and shame surrounding it.”

In general terms, it manifests itself in physical, sexual, and psychological forms, encompassing: • intimate partner violence (battering, psychological abuse, marital rape, femicide) • sexual violence and harassment (rape, forced sexual acts, unwanted sexual advances, child sexual abuse, forced marriage, street harassment, stalking, cyber-harassment), human trafficking (slavery, sexual exploitation) • female genital mutilation • child marriage

About The Data

The data was taken from a survey of men and women in African, Asian, and South American countries, exploring the attitudes and perceived justifications given for committing acts of violence against women. The data also explores different sociodemographic groups that the respondents belong to, including: Education Level, Marital status, Employment, and Age group.

It is, therefore, critical that the countries where these views are widespread, prioritize public awareness campaigns, and access to education for women and girls, to communicate that violence against women and girls is never acceptable or justifiable.

Question | Respondents were asked if they agreed with the following statements: - A husband is justified in hitting or beating his wife if she burns the food - A husband is justified in hitting or beating his wife if she argues with him - A husband is justified in hitting or beating his wife if she goes out without telling him - A husband is justified in hitting or beating his wife if she neglects the children - A husband is justified in hitting or beating his wife if she refuses to have sex with him - A husband is justified in hitting or beating his wife for at least one specific reason

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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.

Dataset Description

This comprehensive dataset provides insights into women's participation in Science, Technology, Engineering, and Mathematics (STEM) education across major countries from 2000 to 2023. The data captures both enrollment and graduation patterns, offering a nuanced view of gender dynamics in STEM fields globally. This dataset explores the representation of women in STEM education globally over two decades. It includes data on female enrollment, graduation rates, and fields of study within STEM. Columns: Country, Year, Female Enrollment (%), Female Graduation Rate (%), STEM Fields (e.g., Engineering, Computer Science), Gender Gap Index.

Key Features:

• Temporal Coverage: 24-year span (2000-2023) showing educational trends over time
• Geographic Scope: Data from 6 major countries (USA, China, India, Germany, Canada, Australia)
• Field Diversity: Four core STEM disciplines (Engineering, Computer Science, Mathematics, Biology)
• Dual Metrics: Both enrollment percentages and graduation rates for comprehensive analysis
• Gender Gap Index: Quantitative measure of gender parity in each field

Research Applications:

• Gender equity analysis in STEM education
• Cross-country comparative studies
• Temporal trend analysis of women's STEM participation
• Policy impact assessment on gender inclusion initiatives
• Educational pipeline analysis from enrollment to graduation

File Information

women_in_stem.csv

Size: ~50KB | Records: 500+ entries Format: UTF-8 encoded CSV with headers

This is the main dataset file containing all research data with clean, analysis-ready format.

Column Descriptors

Data Quality Notes:

• All percentages are recorded as decimal values (e.g., 20.4 = 20.4%)
• Gender Gap Index calculated using standard methodology
• Missing values handled through verified data sources
• Temporal consistency maintained across all records

Provenance & Methodology

Data Collection:

• Primary Sources: National education ministries, UNESCO statistics, OECD education databases
• Collection Period: 2023-2024 compilation of historical records
• Verification: Cross-referenced with multiple governmental and international sources
• Quality Assurance: Data validated against published academic research

Note: This dataset represents a completed historical analysis. While no regular updates are scheduled, significant data corrections or methodology improvements may prompt occasional revisions.

Usage Examples & Starter Code

Quick Data Overview

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load the dataset
df = pd.read_csv('women_in_stem.csv')

# Basic information
print(f"Dataset shape: {df.shape}")
print(f"Countries included: {df['Country'].unique()}")
print(f"STEM fields covered: {df['STEM Fields'].unique()}")
print(f"Year range: {df['Year'].min()} - {df['Year'].max()}")

Trend Analysis by Country

# Analyze enrollment trends over time
plt.figure(figsize=(12, 8))
for country in df['Country'].unique():
  country_data = df[df['Country'] == country]
  yearly_avg = country_data.groupby('Year')['Female Enrollment (%)'].mean()
  plt.plot(yearly_avg.index, yearly_avg.values, marker='o', label=country)

plt.title('Women\'s STEM Enrollment Trends by Country')
plt.xlabel('Year')
plt.ylabel('Average Female Enrollment (%)')
plt.legend()
plt.grid(True, alpha=0.3)
plt.show()

Field Comparison Analysis

# Compare gender gaps across STEM fields
field_analysis = df.groupby('STEM Fields').agg({
  'Female Enrollment (%)': 'mean',
  'Female Graduation Rate (%)': 'mean',
  'Gender Gap Index': 'mean'
}).round(2)

print("Average Metrics by STEM Field:")
print(field_analysis)

Research Questions This Dataset Can Answer

1. How has women's participation in STEM evolved globally over the past two decades?
2. Which countries show the most progress in gender equity in STEM education?...

this graph was created in PowerBi,Loocker and OurDataWorld :

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Women have gained the right to vote and sit in parliament almost everywhere.

How much progress has been made toward women’s political equality? How far do we still have to go?

In this article, I show global data on women’s political rights and representation.

In the 18th and 19th centuries, women did not have the right to vote or sit in parliaments, and only very few — of royal descent — led their countries.

Women became much better represented in the later 20th century, gaining the right to vote and seats in parliaments in almost all countries, and making it to their country’s highest political office more frequently.

However, the data also shows that this progress has been uneven and limited: women still do not have the right to vote in a handful of countries; women parliamentarians continue to be a small minority in most countries; and women political leaders remain rare.

Let’s look at how far we have come and what remains to be done.

Women have gained the right to vote in almost all countries A fundamental political right is choosing one’s political representatives in elections. Until recently, women did not have this right to vote in countries’ elections.

Using data from political scientist Svend-Erik Skaaning and colleagues, the chart shows that neither women nor men had the universal right to vote almost anywhere until the middle of the 19th century.

Then a gap in political participation opened: men gained voting rights in some countries, while women remained mostly excluded. New Zealand became the first exception to this, where women gained the universal right to vote in 1893.

The gap between women and men further opened in the early 20th century, as women gained the right to vote in more countries, but men’s voting rights spread even farther. By the beginning of World War II, men had the right to vote in 1 out of 3 countries, while women only had the right to vote in 1 out of 6 countries.

The gap rapidly closed in the decades after World War II, when the voting-rights discrimination against women ended in many countries, and both women and men gained the right to vote in many others.

Today there are no countries that formally discriminate between men and women regarding the right to vote. In 2006, Kuwait was the last country that extended the right to vote to women. The corresponding area in the chart therefore disappears in recent years.

However, six countries and territories still have no right to vote for women or men: Brunei, Gaza, Qatar, Saudi Arabia, Somalia, and the United Arab Emirates.

When it comes to voting rights, what is needed is more of a general expansion of rights rather than ending discrimination against women.

On this map, you can see the voting rights in each country. You can explore how each country changed over time by moving the time slider.

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

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

National Coverage.

Analysis unit

Individual

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling 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. 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 by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in Latvia was 1,006 individuals.

Mode of data collection

Face-to-face [f2f]

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 over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

this graphs was created in Ourdataworld,Tableu and PowerBi:

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Introduction:

Understanding gender disparities in labor force participation is essential for shaping inclusive economic policies worldwide. Across regions and countries, significant variations exist, highlighting the complex interplay of cultural, social, and economic factors. This analysis delves into the intricacies of women's labor force participation, utilizing data from the International Labor Organization (ILO) to elucidate trends and disparities on a global scale.

Global Overview:

Globally, approximately half of women are part of the labor force, although significant variations exist across regions. While many regions surpass the global average, such as Europe and North America, others, notably the Middle East, North Africa, and South Asia, exhibit substantially lower participation rates. Men generally participate more frequently in labor markets than women, illustrating pervasive gender disparities in workforce engagement.

Regional Disparities:

Examining regional disparities reveals nuanced patterns. In Europe and North America, women's participation rates often exceed global averages, reflecting advancements in gender equality and inclusive labor policies. Conversely, regions like the Middle East, North Africa, and South Asia lag behind, hindered by cultural norms, limited educational opportunities, and restrictive gender roles.

Mapping Gender Disparities:

Visualizing gender disparities through a global map provides insights into country-level variations. The female-to-male ratio in labor force participation rates illustrates the extent of gender gaps across nations. Data from the ILO, which harmonizes diverse sources for enhanced comparability, reveal stark contrasts. While most countries exhibit ratios below 100%, indicating lower female participation, disparities range from below 25% to parity or even a slight female predominance in some instances.

Key Factors Influencing Gender Disparities:

Education: Disparities in educational attainment significantly influence women's participation in the labor force. Access to quality education empowers women economically, leading to higher workforce engagement.

Legal Frameworks: Gender-sensitive legislation and policies promoting equal opportunities are crucial for narrowing labor force disparities. Ensuring legal protections against discrimination and providing support for work-life balance can enhance women's workforce participation.

Cultural Norms: Societal attitudes towards women's roles and responsibilities shape labor force dynamics. Challenging traditional gender norms and fostering inclusive workplaces are essential for creating environments conducive to women's employment.

Economic Development: The level of economic development and access to employment opportunities profoundly impact women's labor force participation. Investment in infrastructure, vocational training, and entrepreneurship programs can enhance women's economic empowerment and workforce integration.

Policy Implications:

Addressing gender disparities in labor force participation requires a multifaceted approach encompassing policy reforms, social interventions, and cultural transformations. Governments, businesses, and civil society must collaborate to enact inclusive policies that promote gender equality, expand educational opportunities, and create supportive work environments. Empowering women economically not only enhances individual livelihoods but also fosters sustainable development and prosperity for societies as a whole.

Conclusion:

Analyzing global gender disparities in labor force participation underscores the imperative of advancing gender equality in economic spheres. By addressing systemic barriers and fostering inclusive policies, societies can harness the full potential of women as active contributors to workforce development and economic growth. Embracing diversity and promoting gender parity are essential steps towards building more equitable and prosperous societies worldwide.

The often unseen but disastrous consequences of chemical-intensive food and agricultural production are felt most by half of the world’s food producers and rural population: women. On the average, women make up about 43 percent of the agricultural labour force in developing countries, according to the Food and Agriculture Organization (FAO). Women are involved in all stages of food production€”everything from seed collection, land preparation, fertilizer and pesticides application, weeding, harvesting and storage, food processing, and livestock rearing. In addition, they are also responsible for most household and child-rearing activities.This booklet contains a collection of stories of 25 women from five countries who are involved in an inspiring, ongoing campaign to eliminate use of chemical pesticides and promote agroecology in the Mekong Region. These women are part of the programme Towards a Non-toxic SouthEast Asia, a programme aiming to reduce health and environmental risks from chemicals by monitoring, regulating and managing agricultural, industrial and consumer chemicals. Partners in this initiative are the Swedish Chemicals Agency (KemI), Food and Agriculture Organization of the United Nations (FAO), Pesticide Action Network Asia and the Pacific (PAN AP) and The Field Alliance (TFA).The stories in this booklet highlight how women were influenced by the work of these dedicated organizations and how various activities and support resulted in mobilization of communities to start working for improved livelihoods, through reduction of pesticides use and shift to agro-ecology.

This is an Annotation for Transparent Inquiry (ATI) data project. The annotated article can be viewed on the publisher's website here. Project Summary Scholarship on human rights diplomacy (HRD)—efforts by government officials to engage publicly and privately with their foreign counterparts—often focuses on actions taken to “name and shame” target countries, because private diplomatic activities are unobservable. To understand how HRD works in practice, we explore a campaign coordinated by the US government to free twenty female political prisoners. We compare release rates of the featured women to two comparable groups: a longer list of women considered by the State Department for the campaign; and other women imprisoned simultaneously in countries targeted by the campaign. Both approaches suggest that the campaign was highly effective. We consider two possible mechanisms through which expressive public HRD works: by imposing reputational costs and by mobilizing foreign actors. However, in-depth interviews with US officials and an analysis of media coverage find little evidence of these mechanisms. Instead, we argue that public pressure resolved deadlock within the foreign policy bureaucracy, enabling private diplomacy and specific inducements to secure the release of political prisoners. Entrepreneurial bureaucrats leveraged the spotlight on human rights abuses to overcome competing equities that prevent government-led coercive diplomacy on these issues. Our research highlights the importance of understanding the intersection of public and private diplomacy before drawing inferences about the effectiveness of HRD. Data Generation We generated four sources of data for this project: 1. A dataset of political prisoners from 13 countries based on Amnesty International Urgent Action reports between 2000 and 2015. 2. Arrest and release information for a dataset of female political prisoners 3. A dataset on media attention based on both news articles from LexisNexis and online search trends from Google Trends 4. Interviews conducted with U.S. government officials and other human rights advocates involved in the #Freethe20 campaign to free political prisoners launched in September 2015 We used two sources of data for each of our two research questions. Our first research question was: Did the #Freethe20 campaign have an impact on the release rate of political prisoners? In an ideal world, we would have a comprehensive set of female political prisoners to compare with #Freethe20 prisoners. However, as we explain in the manuscript, in countries with more dire human rights situations, arrests often go unreported. In some cases, the sheer volume of political prisoners makes chronicling information about them challenging, if not impossible. Therefore, in order to construct a comparable set of cases, one strategy we used was to collect information from Amnesty International’s “Urgent Action” campaigns. To our knowledge, Amnesty International has the most comprehensive, publicly available list of contemporary political prisoners globally. Their records are public and searchable, which allowed us to construct a population of political prisoners from the countries targeted by the #Freethe20 campaign. We began our data collection with a base set of Urgent Actions metadata generated by Judith Kelley and Dan Nielson via webscraping from the Amnesty International website. Using a list of URLs that linked to each Urgent Action Report, we coded the name and sex of individuals featured in each Urgent Action Report from 2000 through September 2015 (the start of the #Freethe20 campaign) in the 13 countries featured in the campaign (Azerbaijan, Burma, China, Egypt, Ethiopia, Eritrea, Iran, North Korea, Russia, Syria, Uzbekistan, Venezuela, and Vietnam). Instructions about how we coded this information and sample documents are available in the QDR repository (QDR: MyrickWeinstein_codebook_urgentaction.pdf). After compiling a base dataset of individuals featured in Urgent Action reports, we identified the women in the dataset (~17% of entries) and conducted additional research about (1) whether these women could be classified as political prisoners, and (2) whether and when these women were released from prison, detention, or house arrest. Here, we relied on both follow-up reporting from Amnesty International as well as a variety of online news sources. We deposited the coding instructions for this process (MyrickWeinstein_codebook_releaseinfo.pdf) and also include documentation on additional online news sources that we used to make a judgment on a particular case. Our second question was: How and under what conditions did #Freethe20 affect the release rate of female political prisoners? To answer this question, we look at strategies of both public pressure and private, coercive diplomacy. For the former, we collected data on media attention and online search trends. We searched for newspapers and news articles that featured...

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

See Methodology document for country-specific geographic coverage details.

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.

Mode of data collection

Other [oth]

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

Two online overviews offer comprehensive metadata on the EVS datasets and variables. The extended study description for the EVS 2008 provides country-specific information on the origin and outcomes of the national surveys The variable overview of the four EVS waves 1981 1990 1999/2000 and 2008 allows for identifying country specific deviations in the question wording within and across the EVS waves. These overviews can be found at: http://info1.gesis.org/EVS/Studies (Extended Study Description), http://info1.gesis.org/EVS/Variables (Variable Overview). Moral, religious, societal, political, work, and family values of Europeans. Topics: 1. Perceptions of life: importance of work, family, friends and acquaintances, leisure time, politics and religion; frequency of political discussions with friends; happiness; self-assessment of own health; memberships and unpaid work (volunteering) in: social welfare services, religious or church organizations, education, or cultural activities, labor unions, political parties, local political actions, human rights, environmental or peace movement, professional associations, youth work, sports clubs, women´s groups, voluntary associations concerned with health or other groups; tolerance towards minorities (people with a criminal record, of a different race, left/right wing extremists, alcohol addicts, large families, emotionally unstable people, Muslims, immigrants, AIDS sufferers, drug addicts, homosexuals, Jews, gypsies and Christians - social distance); trust in people; estimation of people´s fair and helpful behavior; internal or external control; satisfaction with life. 2. Work: reasons for people to live in need; importance of selected aspects of occupational work; employment status; general work satisfaction; freedom of decision-taking in the job; importance of work(work ethics, scale); important aspects of leisure time; attitude towards following instructions at work without criticism (obedience work); give priority to nationals over foreigners as well as men over women in jobs. 3. Religion: Individual or general clear guidelines for good and evil; religious denomination; current and former religious denomination; current frequency of church attendance and at the age of 12; importance of religious celebration at birth, marriage, and funeral; self-assessment of religiousness; churches give adequate answers to moral questions, problems of family life, spiritual needs and social problems of the country; belief in God, life after death, hell, heaven, sin and re-incarnation; personal God versus spirit or life force; own way of connecting with the divine; interest in the sacred or the supernatural; attitude towards the existence of one true religion; importance of God in one´s life (10-point-scale); experience of comfort and strength from religion and belief; moments of prayer and meditation; frequency of prayers; belief in lucky charms or a talisman(10-point-scale); attitude towards the separation of church and state. 4. Family and marriage: most important criteria for a successful marriage (scale); attitude towards childcare (a child needs a home with father and mother, a woman has to have children to be fulfilled, marriage is an outdated institution, woman as a single-parent); attitude towards marriage, children, and traditional family structure(scale); attitude towards traditional understanding of one´s role of man and woman in occupation and family (scale); attitude towards: respect and love for parents, parent´s responsibilities for their children and the responsibility of adult children for their parents when they are in need of long-term care; importance of educational goals; attitude towards abortion. 5. Politics and society: political interest; political participation; preference for individual freedom or social equality; self-assessment on a left-right continuum (10-point-scale); self-responsibility or governmental provision; free decision of job-taking of the unemployed or no permission to refuse a job; advantage or harmfulness of competition; liberty of firms or governmental control; equal incomes or incentives for individual efforts; attitude concerning capitalism versus government ownership; postmaterialism (scale); expectation of future development (less emphasis on money and material possessions, greater respect for authority); trust in institutions; satisfaction with democracy; assessment of the political system of the country as good or bad (10-point-scale); preferred type of political system(strong leader, expert decisions, army should rule the country, or democracy); attitude towards democracy (scale). 6. Moral attitudes (scale: claiming state benefits without entitlement, cheating on taxes, joyriding, taking soft drugs, lying, adultery, bribe money, homosexuality, abortion, divorce, euthanasia, suicide, corruption, paying cash, casual sex, avoiding fare on publictransport, prostitution, experiments with human embryos, geneticmanipulation of food, insemination or in-vitro fertilization and deathpenalty). 7. National identity: geographical group the respondent feels belonging to (town, region of country, country, Europe, the world); citizenship; national pride; fears associated with the European Union(the loss of social security and national identity, growing expenditure of the own country, the loss of power in the world for one´s own country and the loss of jobs); attitude towards the enlargement of the European Union (10-point-scale); voting intensions in the next election and party preference; party that appeals most; preferred immigrant policy; opinion on terrorism; attitude towards immigrants and their customs and traditions (take jobs away, undermine a country´s cultural life, make crime problems worse, strain on country´s welfare system, threat to society, maintain distinct customs and traditions); feeling like a stranger in one´s own country; too many immigrants; important aspects of national identity (being born in the country, to respect country´s political institutions and laws, to have country´s ancestry, to speak the national language, to have lived for a long time in the country); interest in politics in the media; give authorities information to help justice versus stick to own affairs; closeness to family, neighborhood, the people in the region, countrymen, Europeans and mankind; concerned about the living conditions of elderly people, unemployed, immigrants and sick or disabled people. 8. Environment: attitude towards the environment (scale: readiness to give part of own income for the environment, overpopulation, disastrous consequences from human interference with nature, human ingenuity remains earth fit to live in, the balance of nature is strong enough to cope with the impacts of modern industrial nations, humans were meant to rule over the rest of nature, an ecological catastrophe is inevitable). Demography: sex; age (year of birth); born in the country of interview; country of birth; year of immigration into the country; father and mother born in the country; country of birth of father and mother; current legal marital status; living together with the partner before marriage or before the registration of partnership; living together with a partner and living with a partner before; steady relationship; married to previous partner; living together with previous partner before marriage; end of relationship; number of children; year of birth of the first child; size and composition of household; experienced events: the death of a child, of father or mother, the divorce of a child, of the parents or of another relative; age of respondent when these events took place; age at completion of education; highest educational level attained; employment status; employed or self-employed in the last job; profession (ISCO-88) and occupational position; supervising function and span of control; size of company. Social origin and partner: respondent´s partner or spouse: partner was born in the country and partner´s country of birth; highest educational level; employment status of the partner; employment or self-employment of the partner in his/her last job; partner´s profession (ISCO-88) and occupational position; supervising function of the partner and span of control; unemployment and dependence on social-security of the respondent and his partner longer than three months in the last five years; scale of household income; living together with parents when the respondent was 14 years old; highest educational level of father/mother; employment status of father/mother when the respondent was 14 years old; profession of father/mother (ISCO-88) and kind of work; number of employees (size of business); supervising function and span of control of father and mother; characterization of the parents when respondent was 14 years old (scale: liked to read books, discussed politics at home with their child, liked to follow the news, had problems making ends meet, had problems replacing broken things); region the respondent lived at the age of 14, present place of residence (postal code); size of town; region. Interviewer rating: respondent´s interest in the interview. Additionally encoded: interviewer number; date of the interview; total length of the interview; time of the interview (start hour and start minute, end hour and end minute); language in which the interview was conducted. Additional country specific variables are included in this national dataset.

The “gender gap index” describes the degree of difference between sexual inequality to access to political role and education and health resources in 2010. Countries where part of population does not have access to such resources is more sensitive to climate change consequences, because sacrifice part of its potential. The index results from the third cluster of the Principal Component Analysis preformed among 14 potential variables. The analysis identify three dominant variables, namely “literacy gender ratio”, “women political participation” and “life expectancy gender ratio”, assigning a weight of 0.40 to the first one and 0.3 to the others two variables. Before to perform the analysis the variables were score-standardized (converted to distribution with average of 0 and standard deviation of 1; all variables with inverse method) in order to be comparable. The country base data for “literacy gender ratio” (average from 2008 to 2012) and “women political participation” (i.e. proportion of seats held by women in national parliament in the last election) were gathered from World Bank, whereas the “life expectancy gender ratio” (average from 2008 to 2012) data were collected from the medium fertility scenario of UNPD World Population Prospects, the 2012 Revision. Tabular data were linked by country to the national boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). Women’s representation in parliaments is one aspect of women’s opportunities in political and public life, and it is therefore linked to women’s empowerment. This indicator gives an idea of the progress of women participation in the highest levels of society, such as the decision making process, and becoming a leader and voice of the community. Gender parity in literacy and thus in education, is an indicator for female participation and can hence be seen as a general measure for gender equality. The equality of educational opportunities is a basic state to increase the status and capabilities of women. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

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.

Today's Kinetoplastida form a diverse order of flagellated protozoans that have evolved from an ancient lineage, rooted near the base of the eukaryotic tree. The disease caused by some species of the Order Kinetoplastida have always plagued mankind, and today most are at least as prevalent as they have ever been. Kinetoplastid parasites cause disease in humans, animals and plants, severely affecting human health and retarding agriculture development in less developed countries. Sleeping sickness (caused by pathogenic subspecies ofTrypanosoma brucei), Chagas disease (caused byTrypanosoma cruzi) and the Leishmaniases (caused byLeishmania spp) are the major human diseases caused by kinetoplastids. According to the World Health Organization "sleeping sickness" affects more than 60 million men, women and children in 36 countries of sub-Saharan Africa, most of which are among the least developed countries in the world. In many of these countries sleeping sickness is currently epidemic, re-emerging in some as a greater cause of morbidity than evenHIV/AIDS.T. cruzicurrently infects 14 million people in Latin America. It is the agent ofChagas disease, the leading infectious cardiomyopathy in the world. Theleishmaniasesand the suffering they cause threaten 350 million women, men and children in 88 countries around the world, 72 of which are developing countries. In addition to their medical importance kinetoplastid parasites also cost developing nations millions of dollars in lost agricultural revenues, since other kinetoplastids are pestilences that strike agricultural produce from crops, to fish to cattle.

Women in Headlines: Bias

Investigating Gendered Language, Temporal Trends, and Themes

By Amber Thomas [source]

About this dataset

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

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

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

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How to use the dataset

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

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

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

Research Ideas

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

Acknowledgements

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

License

See the dataset description for more information.

Columns

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

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 is disproportionately allocated across the four broad regions.

Analysis unit

Individual

Universe

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

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or 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 by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid 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 Kish grid 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 in Philippines was 1,000 individuals.

Mode of data collection

Face-to-face [f2f]

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 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, 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 Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

As of April 2024, Facebook had an addressable ad audience reach 131.1 percent in Libya, followed by the United Arab Emirates with 120.5 percent and Mongolia with 116 percent. Additionally, the Philippines and Qatar had addressable ad audiences of 114.5 percent and 111.7 percent.