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. Our two stage least squares regressions reveal that grandparental and parental wealth have an important effect on the younger generation’s stock (first stage results), which in turn affects the younger generation’s household income (second stage results). We further explore the relationship between income and wealth by decomposing the child’s income by race. We find that the intergroup disparity in income is mainly attributable to differences in family background. These findings indicate that wealth is an important source of income inequality.

Americans remain largely unaware of the magnitude of economic inequality in the nation and the degree to which it is patterned by race. In the present research we exposed a community sample of respondents to one of three interventions designed to promote a more realistic understanding of the Black-White wealth gap. The interventions were developed to conform to best practices in messaging about racial inequality drawn from the social sciences, yet differed in the extent to which they highlighted a single story versus data-based trends in Black-White wealth inequality or both. The interventions that highlighted data versus only a single story of racial inequality were most effective in both shifting how people talk about racial wealth inequality—eliciting less speech about personal achievement—and, critically, improving accuracy in perceptions of the Black-White wealth gap. These increases in accuracy persisted up to 18 months following the intervention, though accuracy did decline across time. The initial findings from this study highlight how data can be leveraged, along with current recommendations in the social sciences, to promote more accurate understandings of the magnitude of racial inequality in society, laying the necessary groundwork for messaging about equity-enhancing policy.

This statistic shows the median household wealth in the United States in 2016, by race. In 2016, the median Black household wealth was 17,600 U.S. dollars.

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 the U.S., median household income rose from 51,570 U.S. dollars in 1967 to 80,610 dollars in 2023. In terms of broad ethnic groups, Black Americans have consistently had the lowest median income in the given years, while Asian Americans have the highest; median income in Asian American households has typically been around double that of Black Americans.

Graph and download economic data for Net Change in Total Assets by Race: White, Asian, and All Other Races, Not Including Black or African American (CXUCHGASSETLB0902M) from 1984 to 2023 about change, asian, white, Net, assets, and USA.

The statistic shows the distribution of U.S. millionaires in 2013, by race and ethnicity. As of 2013, about 76 percent of U.S. millionaires were White/Caucasian.

Additional information on racial income inequality

The issue of racial inequality in regards to income and wealth has been a problem through the entirety of the history of the United States. The statistic above demonstrates how the percentage of millionaires that identify as Black/African Americans is disproportionate to the share of the population overall. While the disproportionate number of millionaires demonstrates an undesirable degree of income inequality it is at the bottom of the wealth ladder within American society that the issue is most pressing. The overrepresentation of African Americans in contrast to the population in unemployment statistics are cause for concern on the part of the government and society as a whole. In 2014, nearly 25 percent of surveyed families who placed themselves in the income bracket of under ten thousand dollars identified as black.

The percentage of non-white female business owners perhaps demonstrates that barriers to wealth exist but are diminished in unison. As barriers to wealth generation are removed for women, similar barriers are also being broken to allow for greater equality in the economic opportunities offered across the population of the United States. A central issue for policy makers is the time delay associated with policies aimed at reversing these inequalities. This was reflected in the 2015 Democratic and Republican presidential primary campaigns. Despite many major candidates discussing the issue none put forward meaningful proposals to address the problem. Even Senator Bernie Sanders who made addressing income inequality the cornerstone failed to separate the issue from income inequality generally. However, the global attention gained by movements such as ‘Black Lives Matter’ shows issues of racial inequality are prominent in the discourse of sections of the wider population if not forming a cornerstone of the political discourse in the United States.

In 2023, the gross median household income for Asian households in the United States stood at 112,800 U.S. dollars. Median household income in the United States, of all racial and ethnic groups, came out to 80,610 U.S. dollars in 2023. Asian and Caucasian (white not Hispanic) households had relatively high median incomes, while the median income of Hispanic, Black, American Indian, and Alaskan Native households all came in lower than the national median. A number of related statistics illustrate further the current state of racial inequality in the United States. Unemployment is highest among Black or African American individuals in the U.S. with 8.6 percent unemployed, according to the Bureau of Labor Statistics in 2021. Hispanic individuals (of any race) were most likely to go without health insurance as of 2021, with 22.8 percent uninsured.

Data on household wealth in Great Britain by ethnic group. Includes total, property, financial, physical and private pension wealth by age, region, household composition and housing tenure.

In 2023, the Gini index for Black households in the United States stood at 0.5, which was higher than the national index that year. The Census Bureau defines the Gini index as “a statistical measure of income inequality ranging from zero to one. A measure of one indicates perfect inequality, i.e., one household having all the income and the rest having none. A measure of zero indicates perfect equality, i.e., all households having an equal share of income.”

About this dataset

Context

The dataset presents the median household income across different racial categories in Money Creek township. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Money Creek township population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 97.65% of the total residents in Money Creek township. Notably, the median household income for White households is $91,250. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $91,250.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Money Creek township.
• Median household income: Median household income, adjusting for inflation, presented in 2023-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/.

Recommended for further research

This dataset is a part of the main dataset for Money Creek township median household income by race. You can refer the same here

Home ownership persists as the primary way that families build wealth. Housing researchers and advocates often discuss the racial home ownership gap, particularly for Black and Hispanic households (Urban Institute, Pew Hispanic Center). Historical policies such as redlining, steering, and municipal underbounding have effects that stay with us today.This map shows the overall home ownership rate and the home ownership rate by race/ethnicity of householder in a chart in the pop-up. Map is multi-scale showing data for state, county, and tract.This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

"Neighborhood Financial Health (NFH) Digital Mapping and Data Tool provides neighborhood financial health indicator data for every neighborhood in New York City. DCWP's Office of Financial Empowerment (OFE) also developed NFH Indexes to present patterns in the data within and across neighborhoods. NFH Index scores describe relative differences between neighborhoods across the same indicators; they do not evaluate neighborhoods against fixed standards. OFE intends for the NFH Indexes to provide an easy reference tool for comparing neighborhoods, and to establish patterns in the relationship of NFH indicators to economic and demographic factors, such as race and income. Understanding these connections is potentially useful for uncovering systems that perpetuate the racial wealth gap, an issue with direct implications for OFE’s mission to expand asset building opportunities for New Yorkers with low and moderate incomes. This data tool was borne out of the Collaborative for Neighborhood Financial Health, a community-led initiative designed to better understand how neighborhoods influence the financial health of their residents.

Graph and download economic data for Income Gini Ratio for Households by Race of Householder, All Races (GINIALLRH) from 1967 to 2023 about gini, households, income, and USA.

The Distributional Financial Accounts (DFAs) provide a quarterly measure of the distribution of U.S. household wealth since 1989, based on a comprehensive integration of disaggregated household-level wealth data with official aggregate wealth measures. The data set contains the level and share of each balance sheet item on the Financial Accounts' household wealth table (Table B.101.h), for various sub-populations in the United States. In our core data set, aggregate household wealth is allocated to each of four percentile groups of wealth: the top 1 percent, the next 9 percent (i.e., 90th to 99th percentile), the next 40 percent (50th to 90th percentile), and the bottom half (below the 50th percentile). Additionally, the data set contains the level and share of aggregate household wealth by income, age, generation, education, and race. The quarterly frequency makes the data useful for studying the business cycle dynamics of wealth concentration--which are typically difficult to observe in lower-frequency data because peaks and troughs often fall between times of measurement. These data will be updated about 10 or 11 weeks after the end of each quarter, making them a timely measure of the distribution of wealth.

Graph and download economic data for Income Before Taxes: Income Before Taxes by Race: Black or African American (CXUINCBEFTXLB0905M) from 1984 to 2023 about African-American, tax, income, and USA.

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.

Household Income and Race Information for Participants in Study 3.

About this dataset

Context

The dataset tabulates the Money Creek township median household income by race. The dataset can be utilized to understand the racial distribution of Money Creek township 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).

• Money Creek Township, Minnesota median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Money Creek Township, Minnesota (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 Money Creek township median household income by race. You can refer the same here