Over the past 23 years, there were constantly more men than women living on the planet. Of the 8.06 billion people living on the Earth in 2023, 4.05 billion were men and 4.01 billion were women. One-quarter of the world's total population in 2024 was below 15 years.

In 2020, the male-female ratio expressed as men per 100 women in Mexico amounted to approximately *****. Between 1995 and 2020, the figure dropped by around ****, though the decline followed an uneven course rather than a steady trajectory.

Worldwide, the male population is slightly higher than the female population, although this varies by country. As of 2024, Hong Kong has the highest share of women worldwide with almost ** percent. Moldova followed behind with around ** percent. Among the countries with the largest share of women in the total population, several were former Soviet states or were located in Eastern Europe. By contrast, Qatar, the United Arab Emirates, and Oman had some of the highest proportions of men in their populations.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 30-34 years (7,947) | Female # 25-29 years (8,143). 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 Ontario population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Ontario is shown in the following column.
• Population (Female): The female population in the Ontario 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 Ontario 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 Ontario Population by Gender. You can refer the same here

The gender or sex ratio in China has been a contentious issue since the introduction of the one-child policy in 1979, intended to limit the population of the country. Although the policy is no longer in place, the population gender difference throughout the country is still evident. In 2023, fifteen to nineteen-year-old children had the largest gender disparity of 115.3 males to every 100 females. Gender imbalance While the difference of gender at birth has been decreasing in the country over the past decade, China still boasts the world’s most skewed sex ratio at birth at around 110 males born for every 100 females as of 2023. That means there are about 31 million more men in the country than women. This imbalance likely came from the country’s traditional preference for male children to continue the family lineage, in combination with the population control policies enforced. Where does that leave the population? The surplus of young, single men across the country poses a risk for China in many different socio-economic areas. Some of the roll-on effects include males overrepresenting specific labor markets, savings rates increasing, consumption reducing and violent crime increasing across the country. However, the adult mortality rate in China, that is, the probability of a 15-year-old dying before reaching age 60, was significantly higher for men than for women. For the Chinese population over 60 years of age, the gender ratio is in favor of women, with more females outliving their male counterparts.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 15-19 years (35) | Female # 45-49 years (41). 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 Sicily Island population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Sicily Island is shown in the following column.
• Population (Female): The female population in the Sicily Island 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 Sicily Island 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 Sicily Island Population by Gender. You can refer the same here

Population, female (% of total population) in United States was reported at 49.76 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Population, female (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 25-29 years (139) | Female # 60-64 years (169). 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 England population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the England is shown in the following column.
• Population (Female): The female population in the England 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 England 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 England Population by Gender. You can refer the same here

Population, female (% of total population) in World was reported at 49.71 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Population, female (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 20-24 years (862) | Female # 60-64 years (869). 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 Yankton County population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Yankton County is shown in the following column.
• Population (Female): The female population in the Yankton County 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 Yankton County 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 Yankton County Population by Gender. You can refer the same here

There are more women than men in Germany, although the number of men has been slowly increasing in recent years, especially since 2015. In 2024, there were around **** million males and **** million females in Germany. Births and deaths Globally, the death rate had been slowly decreasing until 2019, but there was a sharp spike in 2020 and 2021, which can be attributed to the COVID-19 pandemic. The general decline, however, is probably due to medical advancements which mean that many diseases are now treatable or curable, that were not 50 years ago. The birth rate has also been decreasing across the world, but it is lowest in Europe and North America. Future challenges There are a number of challenges facing the German population in the future. Some of the most pressing ones are the growing urban population and especially its ageing structure in combination with slow birth rates, which will put increased pressure on the pension system. Because of this trend, old age security and pensions are already today in the top ten most pressing political issues in Germany.

Sex ratio at birth (male births per female births) in United States was reported at 1.049 in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Sex ratio at birth (male births per female births) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

In Japan, the population sex ratio has seen slight changes over the past decades. In 2021, the number of men was around 94.6 for every 100 women, constituting a decrease from 96.1 in 1950.

What is the sex ratio? The population sex ratio is determined by the sex ratio at birth, different mortality rates between men and women, as well as losses and gains through migration. In the absence of alteration, the sex ratio in human populations is quite constant, with only minor deviations. While the sex ratio at birth is usually 105 to 107, the population sex ratio, which refers to the total number of males for every 100 females, is often below 100. The reason for the shift mostly lies in differing lifestyles and physical constitutions of men and women. In general, women tend to be more resistant to disease throughout life, while men tend to engage in higher risk behavior or violence.

Influences and consequences
The sex ratio at birth and its possible determinants such as gestation environment, climate change, chemical pollution or socio-economic factors have long been subject to scientific research. Recently the impact of natural disasters, like the nuclear disaster in Japan in 2011, was presumed to influence the sex ratio at birth. The adult gender ratio has long been recognized as a key population-level determinant of behavior. However, there are many different or competing theories in existing literature about the social impacts of gender imbalance on topics such as violence, family stability, reproduction etc.

Scholarship on women voters in the United States has focused on the gender gap showing that women are more likely to vote for Democratic Party candidates than men since the 1980s. The persistence of the gender gap has nurtured the conclusion that women are Democrats. This article presents evidence upending that conventional wisdom. Data from the American National Election Study are analyzed to demonstrate that white women are the only group of female voters who support Republican Party candidates for president. They have done so by a majority in all but 2 of the last 18 elections. The relevance of race for partisan choice among women voters is estimated with data collected in 2008, 2012, and 2016, and the significance of being white is identified after accounting for political party identification and other predictors.

Proportion of women and men employed in the National Occupational Classification (NOC) broad occupational categories, current year.

The average for 2023 based on 12 countries was 50.47 percent. The highest value was in Uruguay: 51.51 percent and the lowest value was in Paraguay: 49.85 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Brainerd. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Brainerd, the median income for all workers aged 15 years and older, regardless of work hours, was $39,656 for males and $28,477 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 28% between the median incomes of males and females in Brainerd. With women, regardless of work hours, earning 72 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Brainerd.

- Full-time workers, aged 15 years and older: In Brainerd, among full-time, year-round workers aged 15 years and older, males earned a median income of $48,559, while females earned $40,933, leading to a 16% gender pay gap among full-time workers. This illustrates that women earn 84 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Brainerd, showcasing a consistent income pattern irrespective of employment status.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Brainerd median household income by race. You can refer the same here

The male-female ratio expressed as men per 100 women in Mexico City stood at approximately ***** in 2020. Between 1910 and 2020, the ratio rose by around ****, though the increase followed an uneven trajectory rather than a consistent upward trend.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Edgewater. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Edgewater, the median income for all workers aged 15 years and older, regardless of work hours, was $111,250 for males and $70,648 for females.

These income figures highlight a substantial gender-based income gap in Edgewater. Women, regardless of work hours, earn 64 cents for each dollar earned by men. This significant gender pay gap, approximately 36%, underscores concerning gender-based income inequality in the borough of Edgewater.

- Full-time workers, aged 15 years and older: In Edgewater, among full-time, year-round workers aged 15 years and older, males earned a median income of $127,672, while females earned $103,770, leading to a 19% gender pay gap among full-time workers. This illustrates that women earn 81 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Edgewater.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Edgewater median household income by race. You can refer the same here

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Apex. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Apex, the median income for all workers aged 15 years and older, regardless of work hours, was $86,409 for males and $52,846 for females.

These income figures highlight a substantial gender-based income gap in Apex. Women, regardless of work hours, earn 61 cents for each dollar earned by men. This significant gender pay gap, approximately 39%, underscores concerning gender-based income inequality in the town of Apex.

- Full-time workers, aged 15 years and older: In Apex, among full-time, year-round workers aged 15 years and older, males earned a median income of $114,329, while females earned $88,772, leading to a 22% gender pay gap among full-time workers. This illustrates that women earn 78 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Apex.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Apex median household income by race. You can refer the same here