Some Black households live in neighborhoods with lower incomes, as well as higher unemployment rates and lower educational attainment, than their own incomes might suggest, and this may impede their economic mobility. We investigate reasons for the neighborhood sorting patterns we observe and find that differences in financial factors such as income, wealth, or housing costs between Black and white households do not explain racial distributions across neighborhoods. Our findings suggest other factors are at work, including discrimination in the housing market, ongoing racial hostility, or preferences by Black households for the strength of social networks or other neighborhood amenities that some lower-socioeconomic locations provide.

PSID data extract for computing per capita white-to-Black wealth gaps and active saving rates of Black and white Americans during 1984-2019.

Does the measurement of the racial wealth gap shift depending on the model, method, and data set used? We contrast the traditional mean Oaxaca-Blinder decomposition with the distributional Recentered Influence Function (RIF) methods. The untransformed, logarithm-transformed, and inverse hyperbolic sine-transformed versions in both Survey of Consumer Finances and Panel Study of Income Dynamics data sets exhibit similarities. The Oaxaca-Blinder (mean) decomposition highlights that receiving an inheritance explains a larger portion of the racial wealth gap than educational attainment. Conversely, the RIF method at the median suggests that educational attainment accounts for more of the wealth gap than inheritance receipt.

Crain's Forum: Racial wealth gap

A growing body of research documents the importance of wealth and the racial wealth gap in perpetuating inequality across generations. We add to this literature by examining the impact of wealth on child income by race, while also extending our analysis to three generations. Our two stage least squares regressions reveal that grandparental and parental wealth and the younger generation’s household income is strongly positively correlated. We further explore the relationship between income and wealth by decomposing the child’s income by race. We find that the disparity in income between black and white respondents is mainly attributable to differences in family background. In context, differences in family background are stronger than differences in educational attainment. When we examine different income percentiles, however, we find that the effect of grandparental and parental wealth endowment is much stronger at the top of the income distribution. These findings indicate that wealth is an important source of income inequality.

Data and code accompanying "The Racial Wealth Gap and the Role of Firm Ownership"This paper develops an overlapping generations model that isolates the impact of the U.S. racial wealth gap in 1962 on the long-run dynamics of wealth. The model predicts that one component of the initial gap, firm ownership, coupled with the intergenerational transfer of that ownership, results in a permanent wealth gap independent of other dimensions of inequality. This implies that even if all discrimination against black Americans had ceased upon the end of Jim Crow, the wealth gap would have persisted without a reparations policy addressing the fact that the initial firm ownership gap arose in the first place.

In this paper, we address what has been termed “the Fed view,” the Federal Reserve’s model for black-white wealth inequality, articulated in a series of mutually consistent papers making two arguments. First, the Fed view has it that the black-white wealth gap, when measured with an “expanded wealth concept,” is smaller than previously thought. Second, the Fed view has it that the black-white wealth gap is primarily explained by income differences shaped by personal decisions around human capital acquisition, family structure, risk taking, and the legacy of residential segregation.

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 Black Wolf town. 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 Black Wolf town, the median income for all workers aged 15 years and older, regardless of work hours, was $57,981 for males and $36,176 for females.

These income figures highlight a substantial gender-based income gap in Black Wolf town. Women, regardless of work hours, earn 62 cents for each dollar earned by men. This significant gender pay gap, approximately 38%, underscores concerning gender-based income inequality in the town of Black Wolf town.

- Full-time workers, aged 15 years and older: In Black Wolf town, among full-time, year-round workers aged 15 years and older, males earned a median income of $71,875, while females earned $58,229, 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 Black Wolf town.

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 Black Wolf town median household income by race. You can refer the same here

In 2024, about 44.7 percent of White households in the United States had an annual median income of over 100,000 U.S. dollars. By comparison, only 26.8 percent of Black households were in this income group. Asian Americans, on the other hand, had the highest median income per household that year.

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 Black Springs. 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 Black Springs, the median income for all workers aged 15 years and older, regardless of work hours, was $56,250 for males and $33,393 for females.

These income figures highlight a substantial gender-based income gap in Black Springs. Women, regardless of work hours, earn 59 cents for each dollar earned by men. This significant gender pay gap, approximately 41%, underscores concerning gender-based income inequality in the town of Black Springs.

- Full-time workers, aged 15 years and older: In Black Springs, among full-time, year-round workers aged 15 years and older, males earned a median income of $57,143, while females earned $34,464, leading to a 40% gender pay gap among full-time workers. This illustrates that women earn 60 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment 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 Black Springs, 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 Black Springs median household income by race. You can refer the same here

What is the income gap between blacks and whites within the same metropolitan region? What variable puts individuals in greatest disadvantage: skin color or place of residence? Should mitigating policies against inequality be global or local? To answer these questions we compare the wages of blacks and whites living in the center and in the periphery of six Brazilian metropolitan regions. Results from the PNAD (2008) show that the impact of skin color on wages is larger than that of the geographic location within the city. We also show that there is substantial spatial heterogeneity in income differentials by race.

In the third quarter of 2024, the top ten percent of earners in the United States held over ** percent of total wealth. This is fairly consistent with the second quarter of 2024. Comparatively, the wealth of the bottom ** percent of earners has been slowly increasing since the start of the *****, though remains low. Wealth distribution in the United States by generation can be found here.

In 2024 the median annual income of Asian households in the United States was 121,700 U.S. dollars. They were followed by White households, who's median earnings were 92,530 U.S. dollars. Furthermore, Black Americans and American Indian and Alaska Native families had the lowest household incomes. That year, median income among all U.S. household rose to 83,730 U.S. dollars.

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 Black township. 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 Black township, the median income for all workers aged 15 years and older, regardless of work hours, was $38,281 for males and $25,500 for females.

These income figures highlight a substantial gender-based income gap in Black township. Women, regardless of work hours, earn 67 cents for each dollar earned by men. This significant gender pay gap, approximately 33%, underscores concerning gender-based income inequality in the township of Black township.

- Full-time workers, aged 15 years and older: In Black township, among full-time, year-round workers aged 15 years and older, males earned a median income of $57,188, while females earned $53,333, resulting in a 7% gender pay gap among full-time workers. This illustrates that women earn 93 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the township of Black township.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Black township.

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

Do Black households make as much as the "typical" household in their county? This map shows that this doesn't seem to be the case. This map compares the median household income of households with Black householders compared to the median household income of that county. If the Black households in a county make as much as a "typical" household in their county, the county is shown in turquoise. If Black households in a county make less than the median income of their county, it is shown in orange. The size of the symbol highlights where there are the highest counts of black population in the US.The data comes from County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. The layer used in the map comes from ArcGIS Living Atlas of the World, and the full documentation for the layer can be found here.

Mortality rates in the United States vary based on race, individual economic status and neighborhood. Correlations among these variables in most urban areas have limited what conclusions can be drawn from existing research. Our study employs a unique factorial design of race, sex, age and individual poverty status, measuring time to death as an objective measure of health, and including both neighborhood economic status and income inequality for a sample of middle-aged urban-dwelling adults (N = 3675). At enrollment, African American and White participants lived in 46 unique census tracts in Baltimore, Maryland, which varied in neighborhood economic status and degree of income inequality. A Cox regression model for 9-year mortality identified a three-way interaction among sex, race and individual poverty status (p = 0.03), with African American men living below poverty having the highest mortality. Neighborhood economic status, whether measured by a composite index or simply median household income, was negatively associated with overall mortality (p

The Biden-Harris Administration released a plan to cancel federal student loans for 43 million borrowers on August 24, 2022. While the Supreme Court struck down the Biden-Harris' student debt relief plan on June 30, 2023, the White House is now planning to use the Higher Education Act of 1965, a federal law that governs the student loan program, to bring about relief for student borrowers. This article estimates the potential impact of broad-based student debt relief on racial and ethnic wealth gaps. On average, federal student debt potentially eligible for relief explains 3% of the White-Black wealth gaps, suggesting that broad-based student debt relief could significantly mitigate racial wealth inequities.Note: This is data and code accompanying the article.

About this dataset

Context

The dataset tabulates the Black median household income by race. The dataset can be utilized to understand the racial distribution of Black income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Black, AL median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Black, AL (2021, in 2022 inflation-adjusted dollars)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Black median household income by race. You can refer the same here

Inequality remains woven into America’s economic fabric, fuelling public debate and shaping politics. In 2024, the richest fifth of U.S. households captured more than half of national income, while the poorest secured a just 3.1 percent. This stark contrast highlights the concentration of wealth among high-income households. Measuring income inequality The Gini coefficient, a standard measure of income inequality, has steadily risen over the past three decades. In 1990, the Gini coefficient for households in the United States stood at 0.43, but by 2024 it had increased to 0.49. This upward trend indicates a growing gap between the rich and poor. Among state, the District of Columbia and New York exhibited the greatest income inequality. Utah, on the other hand, recorded the smallest wealth gap. Income inequality across demographics Income disparities are also drawn along ethnic and racial lines. In 2024, Asian households in the United States had the highest median annual income, followed by White households. Black Americans and American Indian and Alaska Native families had comparatively lower household incomes. The overall median income for U.S. households reached nearly 84,000 U.S. dollars that year. These figures highlight the persistent economic gaps among various racial and ethnic groups in America.

In 2024, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the overall poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States The poverty threshold for a single person in the United States was measured at an annual income of ****** U.S. dollars in 2023. Among families of four, the poverty line increases to ****** U.S. dollars a year. Women and children are more likely to suffer from poverty. This is due to the fact that women are more likely than men to stay at home, to care for children. Furthermore, the gender-based wage gap impacts women's earning potential. Poverty data Despite being one of the wealthiest nations in the world, the United States has some of the highest poverty rates among OECD countries. While, the United States poverty rate has fluctuated since 1990, it has trended downwards since 2014. Similarly, the average median household income in the U.S. has mostly increased over the past decade, except for the covid-19 pandemic period. Among U.S. states, Louisiana had the highest poverty rate, which stood at some ** percent in 2024.