Of the ***** billionaires in the world in 2023, only *** were women, meaning ** percent. Whereas male billionaires tend to be self-made, nearly ** percent of the female billionaires in 2023 had inherited their fortune.

The World Inequality Database on Education (WIDE) brings together data from Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), national household surveys and learning achievement surveys from over 160 countries to enable users to compare education outcomes between countries, and between groups within countries, according to factors that are associated with inequality, including wealth, gender, ethnicity and location.

This statistic shows the gender distribution of billionaires around the world in 2013, by geographic region. In 2013, ** percent of the ***** billionaires residing in Europe were women.

Abstract

The Health Inequality Project uses big data to measure differences in life expectancy by income across areas and identify strategies to improve health outcomes for low-income Americans.

Section 7

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution. Both race-adjusted and unadjusted estimates are reported.

Source

Section 13

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution separately by year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 6

This dataset was created on 2020-01-10 18:53:00.508 by merging multiple datasets together. The source datasets for this version were:

Commuting Zone Life Expectancy Estimates by year: CZ-level by-year life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy: Commuting zone (CZ)-level life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy Trends: CZ-level estimates of trends in life expectancy for men and women, by income quartile

Commuting Zone Characteristics: CZ-level characteristics

Commuting Zone Life Expectancy for larger populations: CZ-level life expectancy estimates for men and women, by income ventile

Section 15

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by state of residence and year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 11

This table reports US mortality rates by gender, age, year and household income percentile. Household incomes are measured two years prior to the mortality rate for mortality rates at ages 40-63, and at age 61 for mortality rates at ages 64-76. The “lag” variable indicates the number of years between measurement of income and mortality.

Observations with 1 or 2 deaths have been masked: all mortality rates that reflect only 1 or 2 deaths have been recoded to reflect 3 deaths

Source

Section 3

This table reports coefficients and standard errors from regressions of life expectancy estimates for men and women at age 40 for each quartile of the national income distribution on calendar year by commuting zone of residence. Only the slope coefficient, representing the average increase or decrease in life expectancy per year, is reported. Trend estimates for both race-adjusted and unadjusted life expectancies are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 9

This table reports life expectancy estimates at age 40 for Males and Females for all countries. Source: World Health Organization, accessed at: http://apps.who.int/gho/athena/

Source

Section 10

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by county of residence. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for counties with populations larger than 25,000 only

Source

Section 2

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by commuting zone of residence and year. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 8

This table reports US population and death counts by age, year, and sex from various sources. Counts labelled “dm1” are derived from the Social Security Administration Data Master 1 file. Counts labelled “irs” are derived from tax data. Counts labelled “cdc” are derived from NCHS life tables.

Source

Section 12

This table reports numerous county characteristics, compiled from various sources. These characteristics are described in the county life expectancy table.

Two variables constructed by the Cen

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.

Talent is one of the most essential factors for growth and competitiveness. To build future economies that are both dynamic and inclusive, we must ensure that everyone has equal opportunity. When women and girls are not integrated—as both beneficiary and shaper—the global community loses out on skills, ideas and perspectives that are critical for addressing global challenges and harnessing new opportunities.

This report finds that, globally, gender parity is shifting into reverse this year for the first time since the World Economic Forum started measuring it. Yet there are also many countries that have made considerable progress, understanding that talent is a critical factor for growth. These countries are poised for further success. This year’s analysis also reveals gender gaps at the industry level and, in particular, highlights that even though qualified women are coming out of the education system, many industries are failing to hire, retain and promote them, losing out on a wealth of capacity.

As the world moves from capitalism into the era of talentism, competitiveness on a national and on a business level will be decided more than ever before by the innovative capacity of a country or a company. In this new context, the integration of women into the talent pool becomes a must.

While no single measure can capture the complete situation, the Global Gender Gap Index presented in this report seeks to measure one important aspect of gender equality: the relative gaps between women and men across four key areas: health, education, economy and politics.

The World Economic Forum's Global Gender Gap Report 2022 assesses gender parity across 146 countries. It measures progress in four areas: economic participation and opportunity, educational attainment, health and survival, and political empowerment. The report found that progress towards gender parity has stalled, particularly in the workforce, where the pandemic has exacerbated existing inequalities. The report also examines emerging trends in the labour market and explores factors contributing to gender gaps in wealth accumulation, lifelong learning, and stress levels. It provides detailed Economy Profiles of each country, along with an interactive data platform that allows users to explore the findings in detail.

SI.POV.GINI. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality. The Gender Statistics database is a comprehensive source for the latest sex-disaggregated data and gender statistics covering demography, education, health, access to economic opportunities, public life and decision-making, and agency.

As of 2024, the share of internet users in the CIS region (Commonwealth of Independent States) was the highest in the world, with 91 percent of the female population and 93 percent of the male population accessing the internet. As of the same year, there were 90 percent female and 92 percent male internet users in Europe, making it the second region worldwide by internet usage. Africa was the region where internet access was the lowest. Share of female and male internet users worldwide There are still disparities between the internet access rates of male and female online users in global regions. According to the latest data, 34 percent of Africa’s female population had online access, compared to 45 percent of men. Whereas in the Americas, the share of male and female internet users was the same, 83 percent. There was also a big difference in the share of female and male internet users in the Arab States. In the region, 65 percent of women had access to the internet, whereas the share of the male population using the internet was 75 percent. The gender gap was also seen in mobile internet usage in low-and middle-income countries (LMICs). Internet access and SDGs As of 2022, Africa’s online access rate was the lowest worldwide, with estimates showing that just over 30 percent of the total population was using the internet. By comparison, the global average online usage rate was 51 percent. This technological gap between Africa and the rest of the world highlights the need for continued investment in information and communication technologies on the continent, as such processes can speed up progress towards the 17 Sustainable Development Goals (SDGs) set by the United Nations. The Sustainable Development Goals, also known as the Global Goals, are a worldwide agenda to protect the planet, end poverty, and ensure global peace and prosperity. ICTs, especially mobile internet, contribute to the goals by enabling countries to participate in digital economies as well as empowering individuals to access crucial information and services. However, almost 40 percent of the world was not using the internet as of 2021. Particularly disenfranchised groups were frequently excluded from digital society, including women and girls, people with disabilities, elders, indigenous populations, people living in poverty, and inhabitants of least developed or developing countries. The digital gender gap was another obstacle for women to overcome on a global level to achieve economic advancement which would ultimately also benefit their communities.

SI.POV.GINI. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality. The Gender Statistics database is a comprehensive source for the latest sex-disaggregated data and gender statistics covering demography, education, health, access to economic opportunities, public life and decision-making, and agency.

75% of households from the Bangladeshi ethnic group were in the 2 lowest income quintiles (after housing costs were deducted) between April 2021 and March 2024.

The Bolivia Social Accounting Matrix (SAM), 2012 was built with a focus on analyzing the structure and importance of the agricultural sector and gender differences in the Bolivian economy, and understanding the linkages between agricultural production, factor income distribution, and households' incomes and expenditures. It is the most detailed, openly accessible SAM for Bolivian economy to date. The 2012 Input-Output (I-O) was the main data source used in building the disaggregated activity sector and commodity accounts of the 2012 Bolivia SAM.

The escalating global hunger crisis, exacerbated by ongoing conflicts, economic shocks, and climate crises, demands comprehensive and effective solutions. Since limited research has explored the interplay between income inequality, gender inequality, and economic growth in relation to a country’s food insecurity prevalence, this study seeks to bridge this knowledge gap. Using country-level data from 113 countries in both pre- and post-pandemic periods and employing the Seemingly Unrelated Regression (SUR) model, this study provides empirical evidence, highlighting the significant roles of gender inequality and income inequality in addressing food insecurity. The study found that both gender inequality and income inequality correlate positively with food insecurity. Intriguingly, our results indicate that economic growth can exacerbate food insecurity, particularly in the post-pandemic context. This suggests that mere economic growth is insufficient to combat food insecurity if gender and economic inequalities persist.

The United Nations declareds 2021–2030 the ‘Decade of Healthy Ageing ’. Both individuals and society suffer from increasing rates of Alzheimer’s disease and other types of dementia (ADs). In 2019, these diseases contributed to a loss of 33.1 million years of healthy life globally. However, existing research has not fully analyzed the relationship among socioeconomic data and ADs. This study was designed to explore the relationship between Alzheimer’s disease rates and socioeconomic conditions in 120 countries. We used mixed effect models to investigate the relationship between the rates of ADs and socioeconomic data. The data was obtained from global databases, including from The Global Burden of Disease and World Bank. The socioeconomic data included information onf gender inequality, wealth inequality, and countries’ overall wealth. This study is among the first studies to put forward statistical evidence of a significant association between AD and other dementias among the elderly and socioeconomic inequality. These findings could help to inform the policies to be designed to improve the quality of interventions for ADs.

Abstract

The 2018 Dhaka Low Income Area Gender, Inclusion, and Poverty (DIGNITY) survey attempts to fill in the data and knowledge gaps on women's economic empowerment in urban areas, specifically the factors that constrain women in slums and low-income neighborhoods from engaging in the labor market and supplying their labor to wage earning or self-employment. While an array of national-level datasets has collected a wide spectrum of information, they rarely comprise all of the information needed to study the drivers of Female Labor Force Participation (FLFP). This data gap is being filled by the primary data collection of the specialized DIGNITY survey; it is representative of poor urban areas and is specifically designed to address these limitations. The DIGNITY survey collected information from 1,300 urban households living in poor areas of Dhaka in 2018 on a range of issues that affect FLFP as identified through the literature. These range from household composition and demographic characteristics to socioeconomic characteristics such as detailed employment history and income (including locational data and travel details); and from technical and educational attributes to issues of time use, migration history, and attitudes and perceptions.

The DIGNITY survey was designed to shed light on poverty, economic empowerment, and livelihood in urban areas of Bangladesh. It has two main modules: the traditional household module (in which the head of household is interviewed on basic information about the household); and the individual module, in which two respondents from each household are interviewed individually. In the second module, two persons - one male and one female from each household, usually the main couple, are selected for the interview. The survey team deployed one male and one female interviewer for each household, so that the gender of the interviewers matched that of the respondents. Collecting economic data directly from a female and male household member, rather than just the head of the household (who tend to be men in most cases), was a key feature of the DIGNITY survey.

Geographic coverage

The DIGNITY survey is representative of low-income areas and slums of the Dhaka City Corporations (North and South, from here on referred to as Dhaka CCs), and an additional low-income site from the Greater Dhaka Statistical Metropolitan Area (SMA).

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling procedure followed a two-stage stratification design. The major features include the following steps (they are discussed in more detail in a copy of the study's report and the sampling document located in "External Resources"):

FIRST STAGE: Selection of the PSUs

Low-income primary sampling units (PSUs) were defined as nonslum census enumeration areas (EAs), in which the small-sample area estimate of the poverty rate is higher than 8 percent (using the 2011 Bangladesh Poverty Map). The sampling frame for these low-income areas in the Dhaka City Corporations (CCs) and Greater Dhaka is based on the population census of 2011. For the Dhaka CCs, all low-income census EAs formed the sampling frame. In the Greater Dhaka area, the frame was formed by all low-income census EAs in specific thanas (i.e. administrative unit in Bangladesh) where World Bank project were located.

Three strata were used for sampling the low-income EAs. These strata were defined based on the poverty head-count ratios. The first stratum encompasses EAs with a poverty headcount ratio between 8 and 10 percent; the second stratum between 11 and 14 percent; and the third stratum, those exceeding 15 percent.

Slums were defined as informal settlements that were listed in the Bangladesh Bureau of Statistics' slum census from 2013/14. This census was used as sampling frame of the slum areas. Only slums in the Dhaka City Corporations are included. Again, three strata were used to sample the slums. This time the strata were based on the size of the slums. The first stratum comprises slums of 50 to 75 households; the second 76 to 99 households; and the third, 100 or more households. Small slums with fewer than 50 households were not included in the sampling frame. Very small slums were included in the low-income neighborhood selection if they are in a low-income area.

Altogether, the DIGNITY survey collected data from 67 PSUs.

SECOND STAGE: Selection of the Households

In each sampled PSU a complete listing of households was done to form the frame for the second stage of sampling: the selection of households. When the number of households in a PSU was very large, smaller sections of the neighborhood were identified, and one section was randomly selected to be listed. The listing data collected information on the demographics of the household to determine whether a household fell into one of the three categories that were used to stratify the household sample:

i) households with both working-age male and female members; ii) households with only a working-age female; iii) households with only a working-age male.

Households were selected from each stratum with the predetermined ratio of 16:3:1. In some cases there were not enough households in categories (ii) and (iii) to stick to this ratio; in this case all of the households in the category were sampled, and additional households were selected from the first category to bring the total number of households sampled in each PSU to 20.

The total sample consisted of 1,300 households (2,378 individuals).

Sampling deviation

The sampling for 1300 households was planned after the listing exercise. During the field work, about 115 households (8.8 percent) could not be interviewed due to household refusal or absence. These households were replaced with reserved households in the sample.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaires for the survey were developed by the World Bank, with assistance from the survey firm, DATA. Comments were incorporated following the pilot tests and practice session/pretest.

Cleaning operations

Collected data was entered into a computer by using the customized MS Access data input software developed by Data Analysis and Technical Assistance (DATA). Once data entry was completed, two different techniques were employed to check consistency and validity of data as follows:

1. Five (5%) percent of the filled-in questionnaire was checked against entered data to measure the transmission error or typos, and;
2. A logical consistency checking technique was employed to identify inconsistencies using SPSS and or STATA software.

Primary school completion rates worldwide tend to increase as income rises, too. For instance, the completion rate for male students with low income was around 69 percent and around 99 percent for students with high income. Moreover, completion rates for female students were always slightly below those of male students.

Health And Wellness Products Market size was valued at USD 1.6 Billion in 2024 and is projected to reach USD 2.5 Billion by 2032, growing at a CAGR of 5.5% during the forecast period 2026 to 2032.Global Health And Wellness Products Market Drivers:The mMarket drivers for the health and wellness products market can be influenced by various factors. These may include:• Rising Health Consciousness Among Consumers: Growing awareness of preventive healthcare and wellness lifestyle choices is expected to drive increased demand for natural supplements, fitness products, and wellness solutions.• Increasing Prevalence of Chronic Diseases: High rates of diabetes, cardiovascular diseases, and obesity are projected to boost consumer demand for health management and wellness products that support disease prevention and management.

The United Nations declareds 2021–2030 the ‘Decade of Healthy Ageing ’. Both individuals and society suffer from increasing rates of Alzheimer’s disease and other types of dementia (ADs). In 2019, these diseases contributed to a loss of 33.1 million years of healthy life globally. However, existing research has not fully analyzed the relationship among socioeconomic data and ADs. This study was designed to explore the relationship between Alzheimer’s disease rates and socioeconomic conditions in 120 countries. We used mixed effect models to investigate the relationship between the rates of ADs and socioeconomic data. The data was obtained from global databases, including from The Global Burden of Disease and World Bank . The socioeconomic data included information onf gender inequality, wealth inequality, and countries’ overall wealth. This study is among the first studies to put forward statistical evidence of a significant association between AD and other dementias among the elderly and socioeconomic inequality. These findings could help to inform the policies to be designed to improve the quality of interventions for ADs. Date Submitted: 2022-07-20

In modern times, the whole world is divided into different subjects. In this, Indian economic inequality is divided into different sections of tradition. They are poor-rich, unequal distribution of income, caste, religion, gender, etc. Is divided on the basis of. In this, caste-based inequality is detrimental to Indian economic development. Caste was created in Indian society as a system of income and distribution in the society. Caste is omnipresently governed by different and peculiar traditional rules and norms. Therefore, it can be said that in a caste-based economy, business and property rights are inherited as well as hereditary, and each caste is forced to keep them the same.All the castes in India are based on this socialization. Although conversion is possible in India, caste cannot be changed under any circumstances. A person who is born in the same caste dies in the same caste. In India, it is called caste discrimination that creates castes at this social level. In the literature of modern economics, the concept of exclusion and economic discrimination is considered to be related to race, caste, or gender.