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Attacking the Black–White Opportunity Gap That Comes from Residential Segregation

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Residential segregation between black and white Americans remains both strikingly high and deeply troubling. Black–white residential segregation is a major source of unequal opportunity for African Americans: among other things, it perpetuates an enormous wealth gap and excludes black students from many high-performing schools. While some see residential segregation as “natural”—an outgrowth of the belief that birds of a feather flock together—black–white segregation in America is mostly a result of deliberate public policies that were designed to subjugate black people and promote white supremacy.

Because the federal, state, and local policy arenas were the laboratory for engineering black–white residential segregation, that is where people must work to help undo it. In order for these heinous differences to be reversed, people in government at all levels have to be proactive in eliminating policy that supports segregation and in creating anti-segregation policies.

It is time for bold action. The first part of this report outlines why all Americans should care about black–white residential segregation: the perpetuation of an opportunity gap between blacks and whites. The second part delineates the ways in which black–white segregation is rooted primarily in deliberate government policies enacted over generations. And the last part of the report sketches a four-prong strategy for undoing this horrible creation.

First, policymakers should address the legacy of generations of racial discrimination in housing by implementing the “Affirmatively Furthering Fair Housing” provision of the Fair Housing Act and providing new mortgage assistance to buy homes in formerly “redlined” areas. Second, government should seek to reduce contemporary residential racial discrimination by increasing resources allocated to fair housing testers and reestablishing the federal interagency task force to combat lending discrimination. Third, officials should counter contemporary residential economic discrimination that disproportionately hurts African Americans by curbing exclusionary zoning, funding “disparate impact” litigation, adopting “inclusionary zoning” policies, banning source of income discrimination, and beefing up housing mobility programs. Fourth, policy officials should respond to the re-segregating effects of displacement that can come with gentrification by revising tax abatement policies that promote gentrification, implementing longtime owner occupancy programs, and investing in people, not powerbrokers.

How Black–White Segregation Perpetuates an Opportunity Gap

Residential segregation between black and white Americans remains very high more than fifty years after passage of the 1968 Fair Housing Act. An analysis of U.S. Census Data from 2013–17 found that the “dissimilarity index” between blacks and non-Hispanic whites for metropolitan areas was 0.526 for the median area—meaning that 52.6 percent of African Americans or whites would have to move for the area to be fully integrated. (A dissimilarity index of 0 represents complete integration between two groups, while 100 represents absolute apartheid.) The index for black–white segregation was higher than it was for segregation between non-Hispanic whites and Asians (0.467), and segregation between non-Hispanic whites and Hispanics (0.407). 1 And while the nation is also seeing increasing residential segregation by income, racial segregation today remains starker and more pervasive than economic segregation. 2 Analyzing data over time, Paul Jargowsky of Rutgers University writes of African Americans: “Few groups in American history have ever experienced such high levels of segregation, let alone sustained them over decades.” 3

Residential segregation matters immensely, because where people live affects so much of their lives, such as their access to transportation, education, employment opportunities, and good health care. In the case of black–white segregation in particular, the separateness of African-American families and white families has contributed significantly to two entrenched inequalities that are especially glaring: the enormous wealth gap between these races, and their grossly unequal access to strong public educational opportunities.

It is well established that historical and contemporary racial discrimination has given rise to a substantial income gap between black and white Americans. African Americans make, on average, about 60 percent of what whites make. 4 But housing segregation helps explain the ways in which African-American families are further disadvantaged compared to white families who have the same income and education levels. Typically, higher levels of education and income translate into higher levels of wealth and less exposure to concentrated poverty. In the case of African Americans, however, residential segregation by race imposes a penalty that interrupts these positive patterns. Stunningly, African-American households headed by an individual with a bachelor’s degree have just two-thirds of the wealth, on average, of white households headed by an individual who lacks a high school degree . 5 Equally astonishingly, middle-class blacks live in neighborhoods with higher poverty rates than low-income whites. 6 As the following sections will show, these negative outcomes are largely a result of residential segregation; furthermore, when black–white segregation is reduced, outcomes for black families are shown to improve.

How Residential Segregation Affects Wealth Accumulation

Racial residential segregation inhibits home value appreciation in predominantly African-American neighborhoods. Research finds that some white families remain distressingly resistant to buying homes in predominantly African-American neighborhoods; for example, even when all other characteristics of homes and neighborhoods are identical, white respondents view predominantly black neighborhoods as less safe and less desirable than predominantly white neighborhoods. 7 Fewer potential buyers—particularly among the whiter and thus usually wealthier segment of the market—means significantly lower rates of home appreciation.

Because homes are typically the largest financial asset for most Americans, segregated markets significantly reduce the accumulated wealth of blacks. This phenomenon—on top of the penalties endured during the historical legacy of slavery and Jim Crow—helps explain why the black–white wealth gap is so much larger than the black–white income gap. While median income for black households is 59 percent that of white households, black median household net worth is just 8 percent of white median household net worth. 8 (See Figure 1.)

The segregation-driven wealth gap imposes enormous burdens on African Americans. Having or lacking wealth influences many of life’s big decisions—from financing a child’s education to saving for retirement.

How Residential Segregation Affects Exposure to Concentrated Poverty, Particularly in Schools

Racial residential segregation also means that African Americans are more likely to be steered toward high-poverty neighborhoods, further contributing to the opportunity gap. Typically, families with higher levels of income have access to more-affluent neighborhoods, which tend to have more amenities, and, in particular, higher-performing public schools. Yet persistent racial residential segregation (and the wealth gap it creates) means even middle-class black families are more likely to live in concentrated poverty, and thus are more likely to send their children to high-poverty schools than are low-income whites . In fact, sociologist Patrick Sharkey finds that middle-class African Americans earning $100,000 or more per year live in neighborhoods with the same disadvantages as the average white household earning less than $30,000 per year. 9 Living in a neighborhood with concentrated poverty is associated with a variety of learning disadvantages, including lower scores on cognitive tests. One study by Harvard University’s Robert Sampson and colleagues on African-American children in Chicago found that living in a high-poverty neighborhood was associated with lower scores on vocabulary and reading tests that were roughly the equivalent of a full grade of school learning. 10

Even middle-class black families are more likely to live in concentrated poverty, and thus are more likely to send their children to high-poverty schools than are low-income whites .

Some students can use public school choice policies to circumvent residential segregation to attend integrated magnet or charter schools outside their neighborhood, but most cannot. Seventy-five percent of American students attend a neighborhood public school—that is, they are simply assigned to the school nearest their homes. 11 This inability of most students to attend schools beyond their neighborhood is troubling, because low-income students who are given the chance to attend socioeconomically integrated schools are shown to achieve at much higher levels than do low-income students in high-poverty schools. On the 2017 National Assessment of Educational Progress (NAEP) given to fourth graders in math, for example, low-income students attending schools that are more affluent scored roughly two years of learning ahead of low-income students in high-poverty schools. 12 Controlling carefully for students’ family background, another study found that students in mixed-income schools showed 30 percent more growth in test scores over their four years in high school than peers with similar socioeconomic backgrounds in schools with concentrated poverty. 13

Because of racial residential segregation, low-income African Americans are much less likely to be afforded the opportunity to attend socioeconomically integrated schools. According to a 2017 analysis by Emma Garcia of the Economic Policy Institute , 81.1 percent of poor black children attended high poverty schools in 2013, compared with just 53.5 percent of poor white children. 14 (See Figure 2.) That is to say, less than one in five poor black children had access to a predominantly middle-class school, compared to almost half of poor white children.

When Racial Segregation Is Reduced, African Americans Have Better Outcomes

Would outcomes for African Americans improve if residential racial segregation were reduced? Because levels of black–white segregation vary across the country, it is possible for researchers to examine different outcome levels for African Americans in communities with higher or lower levels of black–white segregation.

Scholars have found that African Americans in moderately segregated metropolitan areas have much better employment levels, earnings, and mortality rates than do African Americans in metropolitan areas with very high segregation levels. The University of California at Los Angeles’s Richard H. Sander and Jonathan M. Zazloff, along with Yana A. Kucheva of the City College of New York, looked at outcomes for African Americans in metropolitan areas where the black–white dissimilarity index was below 0.60 outcomes and compared them with outcomes for African Americans living in areas with a dissimilarity index above 0.80. The outcomes were consistently better for African Americans living in moderately segregated areas than highly segregated areas, both in absolute terms and when compared with non-Hispanic whites living in the same regions. 15

The unemployment rate for black men ages 25–34, for example, was 17.4 percent in highly segregated areas, compared with 10.1 percent in moderately segregated areas. Unemployment was 3.48 times the level of non-Hispanic whites in highly segregated areas, but 1.44 times the level of non-Hispanic whites in moderately segregated areas. Earnings for black men aged 25–34 were $4,000 higher in moderately segregated areas than in highly segregated areas, and, relative to non-Hispanic whites, the earnings were higher—68 percent in moderately segregated areas compared with 47.6 percent in highly segregated areas. (See Figure 3.) Likewise, for all blacks, age-adjusted mortality (relative to non-Hispanic whites) was better in moderately segregated regions (1.14) than in highly segregated areas (1.42). 16

Part of the reason for better outcomes, the authors of the study suggest, is that blacks are more likely to live in concentrated poverty in metropolitan areas with high levels of racial segregation than those with moderate levels of racial segregation. The researchers found, for example, that 17 percent of low-income blacks living in moderately segregated metro areas reside in concentrated poverty, compared with 33 percent of low-income blacks living in highly segregated areas. 17

The Deliberate Social Engineering of Black–White Residential Segregation

Both currently and historically, segregation is best understood as a tool used to promote and preserve white supremacy, deployed to make it easier to isolate, divest from, surveil, and police black (and brown) people concentrated in certain communities. The ingenuity of this racist tool is that its evil use creates its own justification—that is, once employed, it creates perspectives and data that seem to support its further use. As communities of color suffer under the deprivations that come with segregation—economic disinvestment, political disenfranchisement, educational inequity, and unfair, ineffective policing practices—those who build and install resilient and enduring racist systems that sustain segregation explain their decisions in terms of protecting and promoting safety, strong schools, and stable housing markets. These indeed are desirable neighborhood attributes—but they are the very same attributes that the conditions of segregation disrupted for blacks.

The ingenuity of this racist tool is that its evil use creates its own justification—that is, once employed, it creates perspectives and data that seem to support its further use.

In fact, regarding neighborhood characteristics, African Americans express the same values and desires as most Americans, even though they have much more difficulty in realizing them. According to a study of black Long Islanders, residents considered the most important neighborhood characteristics to be a low crime rate (89 percent), landlords/homeowners who maintain their property (81 percent), high quality public schools (80 percent), and good public services (78 percent). Yet only 16 percent rated their local public schools as excellent , and 43 percent of residents reported feeling that their local government services were not a good value for the taxes that they pay. 18

Extensive evidence suggests that black residents in many segregated communities do not believe that their needs and desires are met in their current environments. Survey results indicate that most Americans prefer integrated neighborhoods, but white and black Americans define “integrated” differently. For African Americans, an integrated community is one where between 20 to 50 percent of residents are African American. White definitions of integration indicate that they accept diversity only when they can continue to dominate, defining integration as a scenario where only 10 percent of neighborhood residents are black. 19 A recent Pew Survey found that blacks are much more supportive of integrated schools than are whites, particularly when that integration necessitates children going to schools outside of their neighborhoods. Sixty-eight percent of blacks say that “students should go to schools that are racially and ethnically mixed, even if it means some students don’t go to school in their local community,” compared to just 35 percent of whites. 20 Given the close relationship between housing and school integration, such data exposes how the African-American value of integrated school options is crushed by the reality of racially isolated neighborhoods.

Certainly, integration is not a panacea for past and present injustices. In fact, pro-integration advocates should respect the ways that integration might lead to new hardships for black folks—increased discomfort and fear of police encounters, elevated levels of surveillance and suspicion from neighbors, disproportionate discipline of black children in predominantly white schools, and so on. 21 In large part due to the very attitudes that sustain segregation, communities of color have a reasonable desire to live in a safe and affirming space when living in a discriminatory society; and despite typically having fewer resources to work with, black and brown people so often foster loving, culturally rich, and affirming communities for themselves. And so one challenge of contemporary housing integration efforts becomes how to dismantle the racist system of policies that created and continue to sustain residential segregation without simultaneously destroying valuable cultural and economic institutions that black and brown communities have created in response to it.

Integration best functions (and is best incentivized) when public policies and private citizens tackle the myriad of inequities and indignities that complicate, and sometimes limit, the lives of African Americans. Despite this caveat, it remains true that (1) both historically and currently, black people have risked their comfort, livelihoods, and sometimes lives to gain access to integrated spaces; and, most importantly, that (2) segregation itself is a white supremacist practice that has proven both durable and highly effective at limiting black wealth and opportunity.

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From racial zoning to economic zoning.

Members of government and private entities began to deliberately segregate residential areas by race in the late nineteenth and early twentieth century, largely by prohibiting blacks from purchasing homes in majority-white neighborhoods. After the Civil War, those newly liberated black people dispersed throughout the United States, but an abrupt end to Reconstruction ushered in an era of heightened white paramilitary violence, exploitative sharecropping arrangements, and Jim Crow laws. As anti-black discrimination formalized and intensified, many communities systematically expelled African Americans, excluded them from public goods and services, and adopted policies that forbade blacks from residing in towns, or even remaining within town borders after dark. 23 Communities who forbade blacks from being within their borders after dark came to be known as “sundown towns”; by 1930, at least 235 counties had “sundowned” black people, often enforcing their rules through violence. 24

Pioneered by Baltimore in 1910, racial zoning quickly emerged as an effective way to further subjugate and segregate black folks. Baltimore’s then-mayor did not mince words when discussing the motivation for such an ordinance: “Blacks should be quarantined in isolated slums in order to reduce the incidence of civil disturbance, to prevent the spread of communicable disease into nearby white neighborhoods, and to protect property values among the white majority.” 25 Soon, similar policies spread to other cities, including Atlanta, Birmingham, Dade County (Miami), Charleston, Dallas, Louisville, New Orleans, Oklahoma City, Richmond, St. Louis, and others. 26

The U.S. Supreme Court in 1917 struck down explicit racial zoning with its decision in Buchanan v. Warley , arguing that such ordinances interfered with the rights of property owners. 27 The ruling failed to put an end to segregation, however, instead motivating a new wave of racist creativity by white leaders and communities. Localities quickly found a way to circumvent the ruling and preserve the racial caste system in housing. Some localities created and enforced laws in flagrant violation of Buchanan. Richmond, Virginia, for example, passed a law prohibiting anyone from moving onto a block where they could not marry the majority of people on that block. Because the state had then-enforceable anti-miscegenation laws on the books, the ordinance effectively prevented neighborhood integration without explicitly mentioning race. 28

Other localities were slightly more subtle. Switching from race-based zoning to economic zoning, cities and localities designed policies now known as “exclusionary zoning,” which require that neighborhoods consist exclusively of single-family homes, have minimum lot sizes, and/or have minimum square footage requirements. These policies rapidly proliferated. In 1916, just eight cities had zoning ordinances; by 1936, that number had risen to 1,246 . 29

The U.S. Supreme Court affirmed the practice of exclusionary zoning in Euclid v. Ambler (1926), finding that zoning ordinances were reasonable extensions of police power and potentially beneficial to public welfare. While arguments against placement of factories or landfills next to residences can reasonably be said to protect public safety, when it came to siting residences, the opinion in Euclid stated additional concerns: that an apartment could be “a mere parasite, constructed in order to take advantage of the open spaces and attractive surroundings created by the residential character of a neighborhood,” adding later that “apartment houses . . . come very near to being nuisances.” 30 Of course, because many blacks could not afford to buy around the expensive housing restrictions, such “race-neutral” economic zoning policies had a racially discriminatory effect.

Restrictive Covenants, Redlining, and Racial Violence

This supposedly “race-neutral” form of economic discrimination emerged alongside longstanding, more explicit political and economic racism. In order to continue to exclude middle- and upper-class blacks from white neighborhoods, public and private interests conspired to establish a web of racist policies and practices surrounding housing and homeownership. One practice for many white homeowners was to band together and adopt racially restrictive covenants in their neighborhoods, which forbade any buyer from reselling a home to black buyers. Initially upheld in Corrigan v. Buckley (1926), the U.S. Supreme Court reasoned that covenants were private contracts not subject to the Constitution. 31 But the Court’s logic was faulty, because (1) private contracts are not enforceable except through the power of the state, and (2) the state was using that power of enforcement. In city after city, courts and sheriffs successfully evicted African Americans from homes that they had rightly purchased in order to enforce racially restrictive covenants. 32 The racist contracts were so widely accepted that the commissioner of the Federal Housing Administration continued to recommend their use well after the U.S. Supreme Court declared them unconstitutional in Shelley v. Kramer (1948), dismissing the ruling and declaring that it was not “the policy of the government to require private individuals to give up their right to dispose of their property as they see fit.” 33 Still today, racially restrictive covenants appear in real estate records , even if they are unenforceable . 34

In order to continue to exclude middle- and upper-class blacks from white neighborhoods, public and private interests conspired to establish a web of racist policies and practices surrounding housing and homeownership.

The official position of the Federal Housing Administration—which underwrote $120 billion in new housing construction between 1934 and 1962—was that blacks were an adverse influence on property values. 35 In response, the FHA warned against insuring mortgages for homes in racially mixed neighborhoods, and counseled lenders to reject or give poor ratings to loan applicants from black and brown neighborhoods. Baking racial exclusion into programs designed to promote homeownership, an FHA manual suggested that the best financial bets were those where safeguards—such as highways separating communities—could prevent “the infiltration of lower class occupancy, and inharmonious racial groups.” 36 The FHA’s chief economist Homer Hoyt designed a racial ranking system that positioned “Mexicans” and “Negroes” as the least desirable neighborhood residents, and worked with the Home Owners’ Loan Corporation to map cities and design areas into various risk categories congruent with that racial hierarchy. Homebuyers seeking to purchase in “red” zone neighborhoods—those with high percentages of black residents, regardless of the wealth of those residents—would likely be denied a mortgage loan and received no federal support. The FHA provided the strongest financial support to green-zoned areas that, as one appraiser noted , lacked “a single foreigner or Negro.” 37 In 1940, the FHA actually denied insurance for a white developer with a project located near an African-American community until the builder agreed to construct a half-mile, six-foot high concrete wall to separate the two neighborhoods. 38 Not only did this practice of redlining explicitly encourage and perpetuate racial segregation, it also shut black Americans out of key opportunities for one of the country’s most effective wealth-building strategies: homeownership. Of all of the homeownership loans approved by the government between 1934 and 1968, whites received 98 percent of them. 39

The U.S. Supreme Court ultimately struck down racially restrictive covenants in Shelley v. Kramer (1948), but even then, many black families faced grave risks when attempting to move into white neighborhoods. Extralegal violence became an all-too-common method of maintaining segregation through intimidation and fear. 40 In one case, when a middle-class black family moved into an all-white neighborhood in a suburb of Philadelphia, some 600 white demonstrators gathered in front of the house and pelted the home and family with rocks. Shortly after, several whites rented a unit next door to the family, hoisting up a Confederate flag and blaring music throughout the night. Klan and community members burned a cross in the family’s yard. Law enforcement largely declined to intervene, with one sergeant suffering a demotion to patrolman after objecting to his orders not to interfere with the rioters. 41 In Richmond, California, members of the neighborhood homeowners association insisted that they could enforce a racially restrictive covenant against a black war veteran and his wife after they purchased a home there—four years after the Supreme Court had ruled such covenants unconstitutional. When the black family arrived, a mob of 300 gathered outside of their home, threw bricks at the house, and burned a cross in the front yard. As in Pennsylvania, the police refused to step in for several days, only intervening after the NAACP pressed the governor to do so. Still, no arrests were made. 42 In Los Angeles, of the more than one hundred incidents of move-in bombings and vandalism between 1950 and 1965, only one led to arrest and prosecution. 43 This harassment and racial terrorism was not declared a federal crime until the Fair Housing Act made it so in 1968. Still, the Southern Poverty Law Center found that, in 1985–86, only one-quarter of these incidents were prosecuted. 44

Ongoing Discrimination by Realtors, Banks, and Government Officials

To this day, forms of discrimination stymie racial integration and housing opportunities for black Americans. Attorneys and academics alike identify realtor bias and racial steering as factors that continue to disadvantage black people in the housing market. African Americans frequently encounter discrimination when searching for housing at all stages: they are more likely to receive subpar service when interacting with realtors, and are shown fewer homes for sale or rent than are whites. A 2003 study found that realtor steering of residents away from neighborhoods due to their racial composition is shockingly persistent, even if illegal. The practice showed up in up to 15 percent of tests that made their determination based on clear and explicit indications by the realtor. 45 Some scholars have explained that “agents typically accept the initial request as an accurate portrayal of a white’s preferences but adjust the initial request made by a black to conform to their preconceptions. In the case of houses with visible problems, agents refuse to accept the initial request that whites want such a house, but have no trouble making this inference for blacks.” 46 Now, there is evidence that such discrimination might have moved onto new platforms, with technology reinforcing human and societal biases. In March 2019, the U.S. Department of Housing and Urban Development (HUD) announced a lawsuit against social media giant Facebook , alleging that the platform allowed advertisers to use data in order to exclude certain racial groups from seeing home or apartment advertisements. 47

Relatedly, black homebuyers are also more likely to be steered toward high-interest and high-risk loans when seeking to purchase a home, regardless of income or creditworthiness. A black family that earns $157,000 per year is less likely to qualify for a prime loan than is a white family earning $40,000 per year, which means that white families can borrow heavily at favorable rates, while black families are far less likely to receive a safe, fair loan product. 48 In 2006, 53.7 percent of blacks and 46.6 percent of Latinx applicants received high-priced loans; only 17.7 percent of white borrowers did. This pattern remains even after controlling for borrower characteristics (income, credit score) and the amount of the loan, though the gaps do become less stark. Interestingly, these disparities actually worsened at higher income levels. 49 Because predatory lenders are more likely to set up shop in predominantly black neighborhoods, their actions wind up leading to generational wealth loss in communities of color. One study indicated that, since 2005, more than half of all borrowers who were issued subprime loans could have qualified for lower-cost loans with more favorable terms. 50 Because of their costs and risky nature, subprime loans are more likely to result in foreclosures, which have been disproportionately located in low-income and predominantly black neighborhoods. In the run-up to the subprime mortgage crisis, federal regulators failed in their obligation to recognize the targeting of African Americans and enforce the laws against bad actors who participated in this predatory behavior. The result was a staggering collapse of wealth among black communities; in Prince George’s County, Maryland, for example, during the crisis, “high-earning blacks were 80 percent more likely to lose their homes than their white counterparts. ” 51

Current public policy choices hardly indicate that government will readily act as a reliable partner in seeking housing desegregation. To this day, public policy choices by state and local officials tend to steer public housing units, which are disproportionately occupied by black and brown residents, into high-poverty areas with fewer resources and opportunities. And the federal government’s two major programs that seek to help low-income people rent homes in the private market—the Low-Income Housing Tax Credit (LIHTC) program and Section 8 housing vouchers—often perpetuate economic and racial segregation.

To this day, public policy choices by state and local officials tend to steer public housing units, which are disproportionately occupied by black and brown residents, into high-poverty areas with fewer resources and opportunities.

The Low-Income Housing Tax Credit program, which allocates a certain number of tax credits for states to distribute to developers according to housing needs, allows consideration of several factors that help determine where new housing will be located. Because housing agencies can consider community support levels when determining housing locations, and more affluent areas are more likely to organize in opposition to such developments, this housing is more likely to be steered into already-low-income communities. 52 The nation’s largest low-income housing program—Section 8 vouchers—is directed toward individuals rather than state agencies or developers, in theory giving people more control over where they live. But despite this program’s potential advantage for integration, the limited nature of the vouchers does not provide sufficient support for families to rent in higher-income and more-advantaged areas. Moreover, some states actually allow landlords to reject Section 8 housing vouchers , as income (unlike race) is not a protected class. 53

Public Policy Remedies

Government is the laboratory in which many of the schemes for black–white segregation were (and still are) concocted; it is also, therefore, where much of the effort must be placed in order for racial segregation to be undone. Members of government who want to reverse segregation must work to remove policies that promote and protect white supremacy, and replace them instead with ones that actively fight segregation. The rest of this report outlines a four-part strategy to address the following four key facets of black–white segregation: (1) the legacy of generations of racial discrimination in housing; (2) contemporary residential racial discrimination; (3) contemporary residential economic discrimination that disproportionately hurts African Americans; and (4) the re-segregating effects of displacement that can come with gentrification.

Addressing the Legacy of Generations of Racial Discrimination in Housing

When Congress passed the Fair Housing Act (FHA) in 1968, it intended for the executive branch to take steps to reduce housing segregation, with several courts interpreting the FHA as assigning HUD a nonnegotiable “statutory duty to promote fair housing.” 54 But it was not until decades later, in 2015, that the Obama administration introduced a rule to implement the Fair Housing Act’s “Affirmatively Furthering Fair Housing” (AFFH) requirement. The 2015 rule charged HUD with “taking meaningful actions, in addition to combating discrimination, that overcome patterns of segregation and foster inclusive communities free from barriers that restrict access to opportunity based on protected characteristics” and “replacing segregated living patterns with truly integrated and balanced living patterns.” 55

The failure to implement the AFFH requirements for nearly a half century after passage of the Fair Housing Act allowed segregation to remain the norm—particularly in predominantly black areas. “Segregation decreases most quickly in metro areas with small black populations,” observes NYU’s Furman Center . “Conversely, metropolitan areas with large black populations living in poverty showed the highest levels of black–white segregation, as measured by the dissimilarity index, in 2010.” 56 As noted in the first section of this report, while the black–white dissimilarity index has declined over time, it remains extremely high. Furthermore, although the portion of neighborhoods that have only a tiny share of black residents has declined, the proportion of black people living in racially integrated neighborhoods in certain communities has also declined. In New York City, for example, the proportion has actually decreased from 41 percent in 1970 to 21 percent in 2010. 57 Rigorous enforcement of the AFFH rule is as important as ever.

Despite this need, President Donald Trump and Secretary of Housing and Urban Development Ben Carson suspended the AFFH rule in 2018. HUD also removed, without public comment, the Assessment of Fair Housing (AFH) tool, which aided communities in determining housing needs and segregation patterns. This suspension aligns with Secretary Carson’s public disdain for the AFFH rule, which he unfairly derided as “social engineering” and “a tortured reading of fair housing laws.” 58

Housing justice and the fulfillment of the Fair Housing Act should not be held hostage to the political whims of an administration led by a man who was himself investigated for racial discrimination in his own real estate holdings . 59 Reinstatement and rigorous enforcement of the AFFH are clear next steps in the quest to narrow the black–white housing opportunity gap.

In addition, government should undertake efforts to address the legacy of discrimination in the financing of homes. Senator Elizabeth Warren (D-MA), for example, has appropriately proposed providing new mortgage assistance to buy homes in formerly redlined neighborhoods.

Addressing Contemporary Racial Residential Discrimination

Attacking contemporary racial discrimination will require additional tools specifically aimed at both racial bias in the sale and rental of properties and in the financing of residential purchases.

Increase the Number of, and Resources for, Fair Housing Testers and Enforcement

Fair housing testing is an effective means to uncovering evidence of discrimination in renting or purchasing homes. Typically responding to tips from prospective homebuyers belonging to a protected group, individual testers (with no true intent to purchase or rent a home) pose as potential buyers or renters for the purpose of gathering information on possible FHA violations. In accordance with the Fair Housing Act, testers are looking to uncover discrimination based on race, color, religion, national origin, sex, disability, and familial status.

When testing is conducted, results can be eye opening. A study by the Chicago Lawyers’ Committee for Civil Rights, “Fair Housing Testing Project for the Chicago Commission on Human Relations,” tested for source of income and racial discrimination in seventy properties in six Chicago neighborhoods. Of the tests conducted, thirty revealed one or both forms of discrimination. 60

HUD funds many of these exercises through the Fair Housing Initiatives Program (FHIP), and should increase the resources allotted to the program to match the prevalence and gravity of the problem. Because discrimination can be difficult to prove, and because evidence indicates that it is quite widespread, increased resources for testing have been productively used to unearth cases of bias and secure remedies for victims of housing discrimination. When HUD offered grants to a small number of localities for testing programs in the mid-1990s, the Iowa Civil Rights Commission was able to conduct over 900 tests, found 136 possible violations, and filed 41 complaints. During the expansion of this program within Bill Clinton’s first term as president, HUD settled 6,517 cases out of court , took enforcement action on another 1,085, and received nearly $18 million in compensation for housing discrimination victims. 61 Localities need more resources to continue the work of rooting out tough-to-prove acts of discrimination.

Reestablish and Strengthen Federal Interagency Taskforces That Combat Lending Discrimination

Established early in the Obama administration, the Financial Fraud Enforcement Task Force (FFETF) brought together a broad coalition of law enforcement, regulatory, and investigatory agencies to combat financial fraud. As part of its mandate, the FFETF looked closely at discrimination in lending practices, such as racialized loan steering.

In 2015, based upon the work of the coalition, the U.S. Department of Justice filed its largest residential fair lending suit in history against Countrywide Financial Corporation and its subsidiaries. The complaint alleged that Countrywide engaged in a widespread practice of discrimination against more than 200,000 qualified African-American and Hispanic borrowers in their mortgage lending between 2004 and 2008. Countrywide did so by charging them higher fees and interest rates, and by steering thousands of black and Hispanic borrowers into subprime mortgages when non-Hispanic white borrowers with similar credit profiles received prime loans. Disturbingly, the suit also alleged that Countrywide was aware of this racial discrimination and took no meaningful action to stop it or prevent it from continuing.

The federal government, which at one time was itself a purveyor of racist lending and housing practices, should provide the appropriate resources and coordination to seek justice for continued fallout of financial racism on the well-being of black Americans.

This was the first time that the Department of Justice alleged and obtained relief for victims of loan steering, but the process of investigating and organizing the suit made clear how challenging these cases are to prove and bring forth. The federal government, which at one time was itself a purveyor of racist lending and housing practices, should provide the appropriate resources and coordination to seek justice for continued fallout of financial racism on the well-being of black Americans.

Addressing Ongoing Economic Discrimination That Disproportionately Hurts African Americans

Action should also be taken to curb the discrimination against African Americans (which is illegal) cloaked as income discrimination (which, unfortunately, frequently is still legal). 62 As noted above, after the U.S. Supreme Court struck down racial zoning laws in 1917, jurisdictions rapidly adopted economically exclusionary zoning policies that ban apartment buildings and other multifamily units, in order to achieve much the same result. Today, exclusionary zoning is pervasive in the United States and has been found to exacerbate both economic and racial segregation. Jonathan Rothwell and Douglas Massey have found that “a change in permitted zoning from the most restrictive to the least would close 50 percent of the observed gap between the most unequal metropolitan area and the least, in terms of neighborhood inequality.” 63

In another study, Rothwell concludes that local and exclusionary land-use regulations are largely responsible for differences in racial segregation between cities. 64 One study by Harvard researcher Matthew Resseger finds that in Massachusetts, census blocks “zoned for multi-family housing have black population shares 3.36 percentage points higher and Hispanic population shares 5.77 percentage points higher than single-family zoned blocks directly across a border from them.” 65

To address contemporary income discrimination, we need a five-pronged approach: (1) adoption of an Economic Fair Housing Act that launches a direct assault on exclusionary zoning; (2) funding of disparate impact litigation under the Fair Housing Act that challenges exclusionary zoning when it disproportionately affects people of color; (3) adoption of “inclusionary zoning” policies that set aside a portion of new housing developments for families of modest means; (4) adoption of laws outlining “source of income” discrimination targeting public housing residents; and (5) adoption of “mobility programs” modeled after the federal Moving to Opportunity Act, which provided residents of public housing the chance to live in high opportunity neighborhoods. Each of these approaches will reduce economic segregation and also reduce, indirectly, racial segregation.

Institute an Economic Fair Housing Act

We need an Economic Fair Housing Act —to parallel the 1968 Fair Housing Act—to curb explicit economic discrimination in the form of exclusionary zoning laws. 66 The concept of an Economic Fair Housing Act is straightforward: just as it is illegal to discriminate in housing based on race, it should be illegal for municipalities to employ exclusionary zoning policies (such as banning apartment buildings, townhouses, or houses on modestly sized lots) that discriminate based on income and exclude the non-rich from many neighborhoods and their associated schools. At the individual housing unit level, free market forces would continue to discriminate by income, because some apartments and houses will be more expensive than others—that simply is what markets do. But government zoning policies should not, on top of that, discriminate based on income by rendering off-limits entire communities where it is impossible to rent an apartment, live in a townhouse, or purchase a home on a modest plot of land.

One alternative to a complete ban on exclusionary zoning would be a federal (or state) policy to reduce the amount of mortgage interest that a family can deduct in jurisdictions that practice exclusionary zoning, as the University of North Carolina’s John Boger has suggested. 67 Another variation would bar federal funding for infrastructure to municipalities that insist on exclusionary zoning policies. For example, HUD currently allocates $50 billion for a variety of forms of public housing , including $5 billion in community planning and development grants. Although exclusive suburbs do not often rely on these housing grants, there are other federal spending programs that can provide leverage over wealthy communities. 68

Federal legislators have begun to propose action along these lines. Senator Cory Booker (D-NJ), for example, has proposed legislation to curtail exclusionary zoning . 69 Under Booker’s proposal, states, cities, and counties would receive $16 billion in a variety of infrastructure programs, and would be required to develop strategies to reduce barriers to housing development and increase the supply of housing. Plans could include authorizing more high density and multifamily zoning and relaxing lot size restrictions. The goal is for affordable housing units to comprise not less than 20 percent of new housing stock.

Senator Elizabeth Warren (D-MA), likewise, has proposed a comprehensive housing plan that includes a new $10 billion infrastructure program with powerful incentives to reduce exclusionary zoning rules, such as “minimum lot sizes or mandatory parking requirements.” As she explained in March 2019, “to even apply for these grants,” localities “must reform land-use rules to allow for the construction of additional well-located affordable housing units.” 70

Similar legislation to reduce exclusionary zoning, particularly near mass transit hubs, has been introduced and debated in California. Spurred by affordability concerns (even more than concerns about segregation), Massachusetts and Seattle have also considered proposals to curtail exclusionary zoning. And in Minneapolis, the city recently adopted a proposal to end single-family zoning restrictions entirely.

California activist Brian Hanlon notes that progressives are rightfully proud of their openness to immigrants, so why, he asks, are some standing by exclusionary zoning, which says, “we welcome outsiders—but you’ve got to have a $2 million entrance fee to live here.” 71

Fund Disparate Impact Litigation

Government should devote greater resources to bringing litigation to challenge economic zoning laws that don’t explicitly discriminate based on race but have a “racially disparate” impact. Over time, the courts interpreted the Fair Housing Act to allow plaintiffs to bring such lawsuits targeting policies that have a discriminatory impact on minorities, even absent a discriminatory intent. The U.S. Supreme Court affirmed this interpretation of the act in the 2015 case of Texas Department of Housing and Community Affairs v. Inclusive Communities Project. 72

Adopt Inclusionary Zoning Policies

More localities should support “inclusionary zoning” policies. Under such programs, a developer must set aside a portion of new housing units to be affordable for low- and moderate-income residents. In exchange, the developer receives a “density bonus,” allowing them to develop a larger number of high-profit units than the area is zoned for. This benefit for developers has proven critical to the idea’s political acceptance. Among the states most dedicated to inclusionary zoning are New Jersey, Massachusetts, Maryland, and California. 73 In all, about 400 municipalities have inclusionary zoning programs. 74 According to researcher David Rusk, 11 percent of Americans now live in jurisdictions with inclusionary zoning policies. 75

A leading example is Montgomery County, Maryland, which adopted a groundbreaking program in 1974. Under the policy, when a developer builds more than a certain number of units, 12.5 percent to 15 percent of a developer’s new housing stock must be affordable for low-income and working-class families. Between 1976 and 2010, the program produced more than 12,000 moderately priced homes, of which the housing authority has the right to purchase one-third for public housing. 76 Unfortunately, almost 90 percent of American municipalities lack any inclusionary zoning policies.

Expand Housing Choice Vouchers and Ban of Source-of-Income Discrimination

More states and localities should pass legislation to ban discrimination based on “source of income”—that is, discrimination against individuals using government subsidies to pay for part of their rent. According to the Poverty and Race Research Action Council, as of May 2017, fourteen states and sixty localities had passed legislation to bar source of income discrimination. 77 Senator Warren has also called for making it illegal for landlords to discriminate against renters with federal housing vouchers. 78 In addition to banning discrimination based on source of income, the Housing Choice Vouchers Program (formerly known as Section 8 housing assistance) should be fully funded. Housing Choice Vouchers (along with a few other smaller programs) served only 4.7 million households in 2016 of the 25.7 million who qualified. 79 The combination of full funding and reduced discrimination could greatly reduce economic and racial segregation in America.

Expand Housing Mobility Programs

“Housing mobility” programs, which allow public housing residents to live in high-opportunity neighborhoods, should be expanded. The primary federal foray into this area was the federal Moving to Opportunity Act, a 1990s experiment in housing mobility that eventually produced substantial wage gains for people who moved to higher-opportunity areas as children. 80 Harvard’s Raj Chetty and his colleagues found that the total mean income for those who moved before age 13 was 31 percent higher than for the control group . The researchers also observed in this group a 16 percent increase in the likelihood of attending college between the ages of 18 and 20. 83 Such programs, which reduce both income and black–white segregation, should be expanded.

Addressing Displacement from Gentrification that Fosters Re-Segregation

New tools are also needed to dismantle the ills caused by gentrification and displacement. As formerly segregated neighborhoods become more diverse, they do not automatically become more equitable, as rising costs often displace long-term residents and threaten cultural institutions and practices. Washington, D.C. provides many recent examples of this common phenomenon. Residents of an expensive, high-rise, majority white apartment in the historically black Shaw neighborhood allegedly complained about go-go music —a cultural institution of working-class black D.C.—loudly playing from a longstanding neighborhood shop run by a black owner. After the owner was forced to turn down the music, black Shaw and D.C. residents began to protest, not only arguing that the music was the enduring soundtrack of the block, but that this was but one example of how white gentrifiers wanted the economic benefits of the neighborhood but lamented their actual neighbors. 84 Not far from the site of these protests, students at Howard University, one of the nation’s oldest and most esteemed historically black colleges and universities (HBCUs), decried that new white residents of the surrounding neighborhood used the private university’s historic yard as a dog park. When a news station interviewed a white male neighbor about the controversy, he suggested that if students of the 152-year-old historic institution did not want dogs on the yard, they should “just move the campus.” 85 Incidents like these highlight how residents of color frequently experience gentrification as colonization rather than as revitalization.

Racially concentrated poverty is an evil that public policy must address, but pro-integration housing plans should seek solutions that respect and amplify the economic and cultural power of the longstanding institutions and people in that neighborhood.

Reconsider Tax Abatements and Implement Longtime Owner Occupancy Programs

In a desire to revitalize disinvested neighborhoods, policymakers frequently introduce laws that entice wealthy individuals and investors into the area but ultimately underserve or harm current residents. One example of such a policy is long-term tax abatements, which allows owners of newly constructed or significantly renovated properties in underserved neighborhoods to avoid paying property taxes for an extended time period.

Offering wealthy investors long-term tax relief, with no guarantees that those investments will materially improve the lives and economic stations of current residents, prioritizes property over poor people. Such policies allow the wealthy to live and operate in a neighborhood while having no obligation to contribute to the public good of it—the upkeep of its streets and parks, its public safety, its schools, and so on. Meanwhile, the neighborhood’s original residents continue to shoulder this burden because they have received no such tax abatements. This type of trickle-down real estate might spur growth, but such growth will be inequitable.

From 2014 to 2016, for example, the City of Philadelphia’s controversial ten-year tax abatement on new property applied to nearly 4,300 properties, forgoing more than $420 million in revenue. A conservative estimate based on recent market trends found that, over the next decade, the struggling Philadelphia School District could lose out on nearly $1 billion in property tax revenue due to this abatement plan. 86

However, Philadelphia’s Longtime Owner Occupants Program (LOOP) seeks to productively respond to the possibility of the displacement of long-term residents. LOOP assists those below 150 percent area median income (AMI) who have lived in their homes for over ten years and have experienced at least a three-fold increase in assessed home values. Too often long-term residents experiencing this increase lack the liquidity to pay outright the higher taxes imposed on the newly appreciated property. The average LOOP participant is a senior citizen who purchased their home in the 1970s and 1980s. 87 An April 2018 report by the Federal Reserve Bank of Philadelphia found that LOOP had proven effective in both reducing tax delinquencies and reducing displacement in gentrifying areas. 88

Localities need to strongly consider reevaluating tax abatement programs, making them shorter or partial, or writing stipulations into them that encourage investors to focus on equity (as explained further below). Simultaneously, they should design programs that protect black, brown, and low-income people whose intellect, labor, and creativity helped shape the original neighborhood.

Invest in People, Not Power-Brokers

Why are people who craft public policy so eager to provide funding to area newcomers—who are unlikely to hail from the same racial or socioeconomic station as long-term residents—but unlikely to offer black, brown, and poor folks in the same area that same opportunity? First, this choice likely reflects society’s consistent favoritism of fiscal capital above the social and cultural capital created and accumulated by poor and nonwhite communities. This preference makes sense only if the benefits of those financial advantages are redistributed, and thereby consistently felt by the residents with the greatest need. Unfortunately, this is no guarantee. Second, the choice of policymakers to invest in newcomers over long-term and legacy residents seems to reveal a historical tendency to distrust people of color with self-governance. The tendency of many Americans to assign moral judgment to poverty and wealth—alongside the nation’s enduring current of racism—has led some policymakers to conclude that segregated, marginalized communities struggle due to the moral and intellectual failings of their residents, rather than due to the moral and political failings of those who ensured that their poverty was intractable. Lawmakers pursuing any and all neighborhood revitalization plans that might lead to gentrification should also consider the following actions to prevent displacement and re-segregation:

• If tax abatements are deemed necessary for growth, offer them with enforceable stipulations that new businesses must employ, at a living wage, members of the community that host it. Offer tax abatements first to already existing small businesses to allow them to expand and employ more people.
• Invest in educational programs, community gardens, health care facilities, and job programs in equal or greater amounts as the investments made in real estate.
• Require that new housing developments set aside a percentage of homes at affordable rates. AMI for the entire city is an insufficient threshold for inclusion. “Affordable” should be scaled to median and below-median incomes for the neighborhood in which the new development is located.
• Regard long-term residents as decision makers in their neighborhood. Developers and policymakers should not only consult with, but also take direction from the democratic representatives of community members when determining what gets built and where.

Black–white racial segregation, deliberately created by whites over decades to subjugate black people, continues to thwart opportunities for millions of African Americans. Of the many ways in which American society unfairly treats black people, the continued segregation of residential areas remains a central source of racial inequality. Taking bold action of the type outlined in this report would constitute an important step in cleansing this enduring stain from the fabric of American society, and making it solely the resident of history.

• “Residential Segregation Data for U.S. Metro Areas,” Governing , https://www.governing.com/gov-data/education-data/residential-racial-segregation-metro-areas.html.
• Richard Fry and Paul Taylor, “The Rise of Residential Segregation by Income,” Pew Research Center, August 1, 2012, http://www.pewsocialtrends.org/2012/08/01/the-rise-of-residential-segregation-by-income/ . See also Paul Jargowsky, “Segregation, Neighborhoods and Schools,” in Choosing Homes, Choosing Schools , ed. Annette Lareau and Kimberly Goyette (New York: Russell Sage Foundation, 2014), 107–8 (finding a poor/non-poor dissimilarity index of 0.354 but noting that the poor/non-poor and black–white dissimilarity indexes “cannot be compared directly. In part, the lower levels [of income segregation] stem from the fact that income is continuous, so something just above the poverty line is hardly distinguishable from someone just below it. In contrast, race and ethnicity are categorical measures and reflect sharper and more visible distinctions between groups”). The poor/affluent dissimilarity index (roughly first and fourth quintile by income) was 0.46 in 2009.
• Paul Jargowsky, “Segregation, Neighborhoods and Schools,” in Choosing Homes, Choosing Schools , ed. Annette Lareau and Kimberly Goyette (New York: Russell Sage Foundation, 2014), 103-4.
• Carmen DeNavas-Walt, Bernadette D. Proctor, and Jessica C. Smith, “Income, Poverty and Health Insurance Coverage in the United States, 2009,” U.S. Census Bureau, Current Population Reports, P60-238, 2010, http://www.census.gov/prod/2010pubs/p60-238.pdf , 5, Table 1.
• William Darity Jr., “A New Agenda for Eliminating Racial Inequality in the United States: The Research We Need,” William T. Grant Foundation (2019), 1, https://wtgrantfoundation.org/library/uploads/2019/01/A-New-Agenda-for-Eliminating-Racial-Inequality-in-the-United-States_WTG-Digest-2018.pdf .
• John R. Logan, “Separate and Unequal: The Neighborhood Gap for Blacks, Hispanics and Asians in Metropolitan America,” US2010 Project, July 2011, 5.
• See, e.g., Maria Krysan, Reynolds Farley and Mick P. Couper, “In the Eye of the Beholder: Racial Beliefs and Residential Segregation, Du Bois Review 5, no. 1 (2008): 5–26, https://igpa.uillinois.edu/system/files/cas/media/pubs/Krysan_Farley_Couper_2008.pdf ; and Robert Cervero and Michael Duncan, “Neighbourhood Composition and Residential Land Prices: Does Exclusion Raise or Lower Values?” Urban Studies , February 2004, http://usj.sagepub.com/content/41/2/299.
• Kayla Fontenot, Jessica Semega, and Melissa Kollar, “Income and Poverty in the United States: 2017,” United States Census Bureau, September 12, 12, 2018, “Table 1: Income and Earnings Summary Measures by Selected Characteristics: 2016 and 2017,” https://www.census.gov/library/publications/2018/demo/p60-263.html ; Survey of Consumer Finance Combined Extract Data, 2013.
• Patrick Sharkey, “Spatial Segmentation and the Black Middle Class,” American Journal of Sociology 119, no. 4 (2014): 903–54, http://www.ncbi.nlm.nih.gov/pubmed/25032266.
• Robert Sampson, Patrick Sharkey and Stephen Raudenbush, “Durable Effects of Concentrated Disadvantage on Verbal Abilities among African American Children,” Proceedings of the National Academy of Sciences 105 (3): 845-52 (2008).
• “Public School Choice Programs,” National Center for Education Statistics, https://nces.ed.gov/fastfacts/display.asp?id=6 (indicating that 13 percent of public school parents choose a school outside their neighborhood; roughly 10 percent use private school).
• See Richard D. Kahlenberg, Halley Potter and Kimberly Quick, “A Bold Agenda for School Integration,” The Century Foundation, April 8, 2019, Figure 1, https://tcf.org/content/report/bold-agenda-school-integration/ .
• G. Palardy, “Differential school effects among low, middle, and high social class composition schools,” School Effectiveness and School Improvement 19, no. 1 (2008): 37.
• Emma Garcia, “Poor black children are much more likely to attend high-poverty schools than poor white children,” Economic Policy Institute, January 13, 2017, https://www.epi.org/publication/poor-black-children-are-much-more-likely-to-attend-high-poverty-schools-than-poor-white-children/ .
• Richard H. Sander, Yana A. Kucheva and Jonathan M. Zasloff, Moving Toward Integration: The Past and Future of Fair Housing (Cambridge, Massachusetts: Harvard University Press, 2018), 1–4.
• Richard H. Sander, Yana A. Kucheva and Jonathan M. Zasloff, Moving Toward Integration: The Past and Future of Fair Housing (Cambridge, Massachusetts: Harvard University Press, 2018), 2, Table 0.1
• Richard H. Sander, Yana A. Kucheva and Jonathan M. Zasloff, Moving Toward Integration: The Past and Future of Fair Housing (Cambridge, Massachusetts: Harvard University Press, 2018), 4, Table 0.2.
• “Housing and Neighborhood Preferences of African Americans on Long Island, 2011 Survey Research Report,” ERASE Racism, February 2012, http://www.racialequitytools.org/resourcefiles/ERASE_Racism_Housing.pdf.
• Richard Rothstein, The Color of Law: A Forgotten History of How Our Government Segregated America (New York: Liveright Publishing Corp/W. W. Norton, 2017), 223–24.
• “Americans See Advantages and Challenges in Country’s Growing Racial and Ethnic Diversity,” Pew Research Center, May 8, 2019, https://www.pewsocialtrends.org/2019/05/08/americans-see-advantages-and-challenges-in-countrys-growing-racial-and-ethnic-diversity/.
• Sandra E. Garcia, “Black Boys Feel Less Safe in White Neighborhoods, Study Shows,” The New York Times , August 14, 2018, https://www.nytimes.com/2018/08/14/us/black-boys-white-neighborhoods-fear.html.
• Richard Rothstein, The Color of Law: A Forgotten History of How Our Government Segregated America (New York: Liveright Publishing Corp/W. W. Norton, 2017), 237.
• Jeannine Bell, Hate Thy Neighbor: Move-In Violence and the Persistence of Racial Segregation in American Housing (New York: NYU Press, 2013), 14.
• Elizabeth Brown and George Barganier, Race and Crime: Geographies of Injustice (Oakland: University of California Press, 2018), 168.
• Ibid., 47. See also Douglas Massey, “Residential Segregation and Neighborhood Conditions in U.S. Metropolitan Areas,” in America Becoming: Racial Trends and Their Consequences , vol. I, ed. Neil J. Smelser, William Julius Wilson, and Faith Mitchell (Washington, D.C.: National Academies Press, 2001), 392 (“As [African Americans] moved into urban areas from 1900 to 1960 . . . their segregation indices rose to unprecedented heights, compared with earlier times”).
• Buchanan v. Warley , 245 U.S. 60 (1917).
• Katie Nodjimbadem, “The Racial Segregation of American Cities Was Anything But Accidental,” Smithsonian Magazine , May 30, 2017, https://www.smithsonianmag.com/history/how-federal-government-intentionally-racially-segregated-american-cities-180963494/.
• William A. Fischel, “An Economic History of Zoning and a Cure for its Exclusionary Effects,” Urban Studies 41, no. 2 (February 2004), http://journals.sagepub.com/doi/abs/10.1080/0042098032000165271.
• Euclid v. Ambler , 272 U.S. 365 (1926), at 394–95.
• Corrigan v. Buckley , 271 U.S. 323 (1926).
• Richard Kahlenberg, “An Economic Fair Housing Act,” The Century Foundation, August 2017, https://tcf.org/content/report/economic-fair-housing-act/#easy-footnote-bottom-25.
• Richard Rothstein, The Color of Law: A Forgotten History of How Our Government Segregated America (New York: Liveright Publishing Corp/W. W. Norton, 2017), 86.
• Kimberly Quick, “The Myth of the Natural Neighborhood,” The Century Foundation, March 2016, https://tcf.org/content/commentary/11312/ ; see also, Margalynne J. Armstrong, “Race and Property Values in Entrenched Segregation,” University of Miami Law Review 52, no. 4 (January 1997): 1051–65, https://repository.law.miami.edu/cgi/viewcontent.cgi?article=1686&context=umlr.
• Richard Rothstein, The Color of Law: A Forgotten History of How Our Government Segregated America (New York: Liveright Publishing Corp/W. W. Norton, 2017), 67.
• Ta-Nehisi Coates, “The Case for Reparations,” The Atlantic, June 2014,  https://www.theatlantic.com/magazine/archive/2014/06/the-case-for-reparations/361631/.
• Ibid, 76; Richard D. Kahlenberg, “An Economic Fair Housing Act,” The Century Foundation, August 3, 2017. https://tcf.org/content/report/economic-fair-housing-act/.
• Sam Fulwood III, The United States’ History of Segregated Housing Continues to Limit Affordable Housing, Center for American Progress (December 2016), https://www.americanprogress.org/issues/race/reports/2016/12/15/294374/the-united-states-history-of-segregated-housing-continues-to-limit-affordable-housing/ , citing Testimony of George Lipsitz of the University of California-Santa Barbara before the National Commission on Fair Housing and Equal Opportunity.
• Jeannine Bell, Hate Thy Neighbor: Move-In Violence and the Persistence of Racial
• Richard Rothstein, The Color of Law: A Forgotten History of How Our Government Segregated America (New York: Liveright Publishing Corp/W. W. Norton, 2017), 140–42.
• Ibid., 139–40.
• Ibid., 147.
• Michael B. de Leeuw, Megan K. Whyte, Dale Ho, Catherine Meza, and Alexis Karteron, “The Current State of Residential Segregation and Housing Discrimination: The United States’ Obligations Under the International Convention on the Elimination of All Forms of Racial Discrimination,” Michigan Journal of Race and Law 13, 2 (2008): 337, https://repository.law.umich.edu/mjrl/vol13/iss2/1.
• Jan Ondrich et al., “Now You See It, Now You Don’t: Why Do Real Estate Agents Withhold Available Houses from Black Customers? Review of Economics and Statistics 85, (2003): 872; Bo Zhao et. al., “Why Do Real Estate Brokers Continue to Discriminate? Evidence from the 2000 Housing Discrimination Study,” Journal of Urban Economics 59, (2006): 394.
• Katie Benner, Glenn Thrush, and Mike Isaac, “Facebook Engages in Housing Discrimination With Its Ad Practices, U.S. Says,” New York Times , March 28, 2019, https://www.nytimes.com/2019/03/28/us/politics/facebook-housing-discrimination.html.
• Amanda Kolson Hurley, “The Problem of Resegregation in Suburbia,” CityLab , February 15, 2016, https://www.citylab.com/equity/2016/02/the-problem-of-resegregation-in-suburbia/462396/.
• Rick Brooks and Ruth Simon, “Subprime Debacle Traps Even Very Credit-Worthy,” Wall Street Journal , December 3, 2007 (citing analysis showing that 55 percent of subprime loans issued in 2005 went to borrowers with credit scores high enough to qualify for conventional loans with far better terms; this figure rose to 61 percent by the end of 2006).
• Nathalie Baptiste, “Them That’s Got Shall Get,” American Prospect , October 12, 2014, https://prospect.org/article/staggering-loss-black-wealth-due-subprime-scandal-continues-unabated.
• Richard D. Kahlenberg, “An Economic Fair Housing Act,” The Century Foundation, August 3, 2017. https://tcf.org/content/report/economic-fair-housing-act/.
• Hills v. Gautreaux , 425 U.S. 284, 302 (1976); “Poverty and Race Research and Action Council to Office of the General Counsel,” Department of Housing and Urban Development, October 15, 2018, https://prrac.org/pdf/affh_anpr_letter_of_civil_rights_and_Fair_housing_organizations.pdf.
• Ibid.; 24 CFR § 5.512, https://www.law.cornell.edu/cfr/text/24/5.152.
• “NYU Furman Center to Department of Housing and Urban Development,” October 15, 2018, https://prrac.org/pdf/furman_center_comments.pdf.
• Ibid., citing Ronald J. O. Flores and Arun Peter Lobo, “The Reassertion of a Black/Non-Black Color Line: The Rise in Integrated Neighborhoods without Blacks in New York City,” 1970–2010, Journal of Urban Affairs 35 (2012): 266.
• Ben S. Carson, “Experimenting with failed socialism again,” Washington Times , July 23, 2015, https://www.washingtontimes.com/news/2015/jul/23/ben-carson-obamas-housing-rules-try-to-accomplish-/.
• Decades-Old Housing Discrimination Case Plagues Donald Trump,” NPR, September 29, 2016, https://www.npr.org/2016/09/29/495955920/donald-trump-plagued-by-decades-old-housing-discrimination-case.
• “Fair Housing Testing in Chicago Finds Discrimination Based on Race and Source of Income,” National Low Income Housing Coalition, January 28, 2019, https://nlihc.org/resource/fair-housing-testing-chicago-finds-discrimination-based-race-and-source-income ; source of income discrimination is defined as discrimination based on a renter’s alternative means to pay for the rental property, such as a housing choice voucher.
• Kathryn Lodato, Krista Joy Martinelli, Larissa Ng, Richard Todd Schwartz, and Lara Vinnard, “Investigatory Testing as a Tool for Enforcing Civil Rights Statutes Current Status and Issues for the Future,” Public Law Research Institute, http://gov.uchastings.edu/public-law/docs/plri/testing.pdf.
• Portions of this section are drawn from Richard D. Kahlenberg, “An Economic Fair Housing Act,” The Century Foundation, August 3, 2017, https://tcf.org/content/report/economic-fair-housing-act/.
• Jonathan Rothwell and Douglas Massey, “Density Zoning and Class Segregation in U.S. Metropolitan Areas,” Social Science Quarterly 91, no. 5 (December 2010): 1123–43, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3632084/
• Jonathan Rothwell, “Racial Enclaves and Density Zoning: The Institutionalized Segregation of Racial Minorities in the United States,” American Law and Economics Review 13 (2011): 290–358, https://academic.oup.com/aler/article-abstract/13/1/290/182611/Racial-Enclaves-and-Density-Zoning-The.
• Matthew Resseger, “The Impact of Land Use Regulation on Racial Segregation: Evidence from Massachusetts Zoning Borders,” November 26, 2013, https://scholar.harvard.edu/files/resseger/files/resseger_jmp_11_25.pdf.
• Richard D. Kahlenberg, “An Economic Fair Housing Act,” The Century Foundation, August 3, 2017, https://tcf.org/content/report/economic-fair-housing-act/.
• Richard Rothstein, The Color of Law: A Forgotten History of How Our Government Segregated America (New York: Liveright Publishing Corp/W. W. Norton, 2017), 204–06 (referencing proposal by Jack Boger).
• “FACT SHEET: The President’s Fiscal Year 2017 Budget: Overview,” U.S. Department of Housing and Urban Development, February 9, 2016, https://portal.hud.gov/hudportal/documents/huddoc?id=ProposedFY17FactSheet.pdf. Of course, the conditioning of federal funds would have to comply with the U.S. Supreme Court’s ruling in National Federation of Independent Business v. Sebelius, 567 U.S. 519 (2012).
• In 2018, Booker introduced the Housing, Opportunity, Mobility and Equity (HOME) Act the provided incentives to reduce exclusionary zoning in states, cities and counties receiving federal funding under the $3.3 billion federal Community Development Block Grant. See Richard D. Kahlenberg, “Taking on Class and Racial Discrimination in Housing: Cory Booker’s big idea to rein in exclusionary zoning,” The American Prospect , August 2, 2018. In June 2019, Booker expanded the proposal along the lines outlined in the text. See Cory Booker, “Cory’s Plan to Provide Safe, Affordable Housing for All Americans,” Medium , June 5, 2019, https://medium.com/@corybooker/corys-plan-to-provide-safe-affordable-housing-forall-americans-da1d83662baa.
• Elizabeth Warren, “My Housing Plan for America,” Medium, March 16, 2019, https://medium.com/@teamwarren/my-housing-plan-for-america-20038e19dc26.
• Hanlon, quoted in Farhad Manjoo, “America’s Cities are Unlivable. Blame Wealthy Liberals,” New York Times , May 22, 2019, https://www.nytimes.com/2019/05/22/opinion/california-housing-nimby.html.
• Texas Department of Housing and Community Affairs v. The Inclusive Communities Project, Inc. (2015).
• Brian R. Lerman, “Mandatory Inclusionary Zoning—The Answer to the Affordable Housing Problem,” Boston College Environmental Affairs Law Review 33 (2006): 383–416, http://socialeconomyaz.org/wp-content/uploads/2011/06/Mandatory-Inlusionary-Zoning-The-Answer-to-Affordable-Housing-Problem-Brian-R.-Lerman.pdf.
• National Low Income Housing Coalition, “40 Years Ago: Montgomery County, Maryland Pioneers Inclusionary Zoning,” May 16, 2014.
• David Rusk, cited in Nicholas Brunick and Patrick Maier, “Renewing the Land of Opportunity,” Journal of Affordable Housing 19, no. 2 (2010), http://socialeconomyaz.org/wp-content/uploads/2011/06/RenewingtheLandofOpportunity.pdf.
• Carl Chancellor and Richard D. Kahlenberg, “The New Segregation,” Washington Monthly , November/December 2014; Heather Schwartz, “Housing Policy Is School Policy,” in The Future of School Integration , ed. Richard D. Kahlenberg (New York: The Century Foundation, 2012).
• “Expanding Choice: Practical Strategies for Building a Successful Housing Mobility Program APPENDIX B: State, Local, and Federal Laws Barring Source-of-Income Discrimination,” Poverty and Race Research Action Council, May 2017, http://www.prrac.org/pdf/AppendixB.pdf.
• Rachel M. Cohen, “Elizabeth Warren Introduces Plan to Expand Affordable Housing and Dismantle Racist Zoning Practices,” The Intercept , September 28, 2018, https://www.theintercept.com/2018/09/28/elizabeth-warren-affordable-housing-bill.
• Corianne Payton Scally, Samantha Batko, Susan J. Popkin, and Nicole DuBois, “The Case for More, Not Less Shortfalls in Federal Housing Assistance and Gaps in Evidence for Proposed Policy Changes,” Urban Institute, January 2018, https://www.urban.org/sites/default/files/publication/95616/case_for_more_not_less.pdf.
• Raj Chetty, Nathaniel Hendren and Lawrence F. Katz “The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment,” American Economics Review 106, no. 4 (2016): 855–902, 875, Figure 1, https://scholar.harvard.edu/files/lkatz/files/chk_aer_mto_0416.pdf.
• Christian Paz, “Thousands Rally to Preserve Go-Go Culture as Gentrification Debate Continues in DC,” NBC Washington, May 8, 2019, https://www.nbcwashington.com/news/local/Thousands-Rally-to-Preserve-DCs-Go-Go-Culture-as-Gentrification-Debate-Continues-in-the-District-509645931.html.
• Theresa Vargas “The Howard University controversy was never just about dogs. It was about respect.” Washington Post , April 24, 2019, https://www.washingtonpost.com/local/the-howard-university-controversy-was-never-just-about-dogs-it-was-about-respect/2019/04/24/e0286c14-66a2-11e9-a1b6-b29b90efa879_story.html.
• Darrell L. Clarke, “Time for an Honest Discussion about Fair Taxation in Philly,” The Philadelphia Inquirer , January 22, 2018, https://www.philly.com/philly/opinion/commentary/darrell-clarke-allan-domb-property-taxes-philadelphia-city-council-20180122.html.
• Jared Brey, “Who’s Paying For Public Services in a Changing City?” Next City, July 2, 2018, https://nextcity.org/daily/entry/who-paying-for-public-services-in-a-changing-city.
• Lei Ding and Jackelyn Hwang, “Effects of Gentrification on Homeowners: Evidence from a Natural Experiment,” Federal Reserve Bank of Philadelphia, April 2018, https://www.philadelphiafed.org/-/media/community-development/publications/discussion-papers/discussionpaper-effects-of-gentrification-on-homeowners.pdf?la=en.

Tags: housing segregation , black americans , segregation , high poverty neighborhoods , racial inequality , racial divide , housing discrimination

Kimberly Quick, Contributor

Kimberly Quick is a senior policy associate at The Century Foundation working on education policy in the foundation’s Washington, D.C. office.

Richard D. Kahlenberg, Former Senior Fellow

Richard D. Kahlenberg is a senior fellow at the Progressive Policy Institute and was formerly a senior fellow at The Century Foundation. He is the author of Excluded: How Snob Zoning, NIMBYism and Class Bias Build the Walls We Don’t See (PublicAffairs, 2023).

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• Published: 25 March 2021

Residential housing segregation and urban tree canopy in 37 US Cities

• Dexter H. Locke   ORCID: orcid.org/0000-0003-2704-9720 1 ,
• Billy Hall 2 ,
• J. Morgan Grove 1 ,
• Steward T. A. Pickett   ORCID: orcid.org/0000-0002-1899-976X 3 ,
• Laura A. Ogden 4 ,
• Carissa Aoki 5 ,
• Christopher G. Boone   ORCID: orcid.org/0000-0001-7643-0806 6 &
• Jarlath P. M. O’Neil-Dunne 7  

npj Urban Sustainability volume  1 , Article number:  15 ( 2021 ) Cite this article

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Redlining was a racially discriminatory housing policy established by the federal government’s Home Owners’ Loan Corporation (HOLC) during the 1930s. For decades, redlining limited access to homeownership and wealth creation among racial minorities, contributing to a host of adverse social outcomes, including high unemployment, poverty, and residential vacancy, that persist today. While the multigenerational socioeconomic impacts of redlining are increasingly understood, the impacts on urban environments and ecosystems remain unclear. To begin to address this gap, we investigated how the HOLC policy administered 80 years ago may relate to present-day tree canopy at the neighborhood level. Urban trees provide many ecosystem services, mitigate the urban heat island effect, and may improve quality of life in cities. In our prior research in Baltimore, MD, we discovered that redlining policy influenced the location and allocation of trees and parks. Our analysis of 37 metropolitan areas here shows that areas formerly graded D, which were mostly inhabited by racial and ethnic minorities, have on average ~23% tree canopy cover today. Areas formerly graded A, characterized by U.S.-born white populations living in newer housing stock, had nearly twice as much tree canopy (~43%). Results are consistent across small and large metropolitan regions. The ranking system used by Home Owners’ Loan Corporation to assess loan risk in the 1930s parallels the rank order of average percent tree canopy cover today.

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Introduction.

Spatial, social, and environmental inequities pose significant challenges for American cities 1 , 2 . Urban inequity is the result of historical and systemic forces, including structural racism and segregation, which have enduring effects on the ways cities function socially, economically, and ecologically 3 , 4 , 5 . For instance, decades of racial discrimination in housing policy created barriers to homeownership, employment, and access to quality education for people of color, making it difficult to build wealth across generations 6 , 7 , 8 . While the mechanisms linking structural racism to wealth creation and socioeconomic status are well understood 9 , it is less clear how housing segregation may have played a role in shaping urban ecosystems.

This paper investigates how the historic practice of redlining, one of the most consistent, wide-spread, spatial, and racial forms of US housing practices, relates to the contemporary distribution of urban tree canopy, commonly understood as a vital component of urban ecosystem health and sustainability 10 , 11 . Trees provide a host of ecosystem services and social benefits, including heat island mitigation 12 , 13 . In the United States, ~1500 heat-related deaths occur each year 14 , and the impact of heat stress is likely to increase given current climate projections 15 . Existing tree canopy cover 13 and the replacement of impervious surfaces with tree canopy can lower urban temperatures 12 and save lives. But trees and tree canopy are not distributed equitably 16 , 17 , 18 . Recent meta-analyses show that lower-income urban areas 16 and areas with more racial minorities 17 have less tree canopy cover, an environmental injustice that can exacerbate health problems for already disadvantaged groups.

These racial and geographic disparities in urban tree canopy parallel other striking patterns of racialized environmental inequity documented by environmental justice (EJ) research. For more than three decades, EJ researchers have developed an enormous body of evidence highlighting the disproportionate concentration of environmental hazards and burdens in communities of color, and conversely, the privileged access to environmental amenities in predominantly white communities 19 , 20 , 21 , 22 . More recent waves of this scholarship have begun to explicitly connect racial disparities in environmental “goods” and “bads” to structural racism and discrimination in policy 23 , 24 . Institutionalized policies and practices intended to racially segregate (and concentrate wealth in white communities) have been increasingly shown to produce racially uneven landscapes of environmental privilege and risk, even decades later 2 , 25 , 26 , 27 .

In thinking about urban tree canopy, the space to plant trees is often a legacy of the urban built environment, which in the United States stems from histories of deliberate and systematic racial discrimination in housing and urban development 26 , 28 . In 1933, the US Congress created the Home Owners’ Loan Corporation (HOLC) to assist Americans struggling to pay their mortgages in the wake of the Great Depression. To guide lending criteria, the HOLC developed neighborhood appraisal maps for 239 urban areas, ranking the perceived risk of investing in particular neighborhoods using a color-coded scale of “A” (green), “B” (blue), “C” (yellow), and “D” (red) 29 . Appraisals were based primarily on an area’s demographic characteristics and the age and physical condition of its housing stock. Areas with predominantly U.S.-born, white populations, and newer housing stock were often codified as the “safest” places for banks to invest and were graded “A” and “B.” Meanwhile, areas with somewhat older structures and/or a presence of foreign-born residents were commonly ascribed a “C” grade, while areas with significant numbers of racial and ethnic minorities, foreign-born residents, families on relief, and having older housing were almost always viewed as “hazardous” and given the lowest grade, “D.” The term “redlining” is used because areas graded “D” were shaded red on the HOLC maps. In effect, while race was not the only criterion considered in designating grades, the maps formally embedded race into neighborhood appraisal processes by systematically factoring in the race of an area’s occupants into the perceived long-term value of an area 30 , 31 .

Some context is important to better understand HOLC’s residential security maps and its practices from 1934 to 1951. While the HOLC created uniform guidelines for neighborhood appraisal, because appraisals were produced in direct consultation with local municipal officials, loan officers, appraisers, and realtors, evidence suggests some variation in the grading across cities 32 . Still, these agents were familiar with their city’s specific patterns of residential segregation. More importantly, many local actors were already part of the power structures that had created, maintained, or profited from the prevailing racist housing policies and practices. These policies and practices included segregation ordinances, racially-restrictive deed covenants, and zoning plans that promoted their agendas of racial and immigrant exclusion 33 , 34 , 35 , 36 , 37 . Thus, the HOLC maps helped codify the local real estate industry’s consensus of perceived neighborhood value, which often institutionalized existing local inequities in borrowers’ access to credit 31 , 38 .

The extent to which the HOLC’s maps, guidelines, and practices influenced the actual distribution of mortgages remains uncertain. Some evidence suggests that lending practices varied by lender and geography, despite the HOLC’s systematic guidelines 32 . In addition, some have argued that it is unlikely that the Federal Housing Administration (FHA), which issued long-term mortgages, cooperated directly with the HOLC 32 . Yet it has been demonstrated that the FHA overwhelmingly prioritized granting mortgages for new homes, which would have been located in areas graded “A” by the HOLC. For instance, between 1934 and 1962, the FHA and the Veterans Administration lent over $120 billion for new housing, and 98% of this money was distributed to white residents compared to <2% for African Americans and other people of color 39 , 40 . During this period, African Americans represented ~10% of the US population 41 . Studies in Houston and Boston show that even when controlling for income, whites were nearly three times as likely to receive a mortgage loan 3 , 42 .

A large consensus among housing policy scholars is that the federal government helped institutionalize a two-tiered, racialized lending system. One tier provided federally-backed mortgages to higher-graded neighborhoods with predominantly U.S.-born, affluent, white populations occupying newer housing stock. A second-tier subjected residents in the “yellow” and “red” neighborhoods, which housed predominantly low-income African Americans and immigrants in older buildings. Homeowners in the second tier experienced predatory lending schemes or no mortgage lending at all 35 , 43 . For decades, many whites benefited from privileged access to credit, home ownership, and wealth accumulation based on home equity, while African Americans were largely denied this route to economic prosperity 3 , 33 , 44 , 45 , 46 . Redlining created systematic disinvestment in minority communities that were located in the denser, older urban core while protecting the property values and resources of white communities moving into desirable homes in the suburbs. Indeed, the post-World War II suburban development supported by federal subsidies created new, exclusively white geographies that generated enormous new wealth 31 , 47 .

Although Congress officially outlawed racial discrimination in housing with the Fair Housing Act of 1968, studies continue to document its enduring effects. Many formerly redlined areas continue to struggle with segregation, poverty, unemployment, low educational attainment, and poor health outcomes today 3 , 48 , 49 . Research shows that compared to areas receiving higher grades by the HOLC, lower-graded areas exhibit declines in home ownership, housing value, and credit scores 50 .

Despite the abundance of evidence on the social and economic impacts of racist housing policy, little is known about the relationships among redlining, social disadvantage, and environmental quality. It is well-documented that various social disadvantages are bundled in racially segregated urban areas (3), and environmental justice scholars have demonstrated that these outcomes are the product of profound historical and present-day racist and discriminatory policies and institutions, such as redlining, blockbusting, and zoning 51 , 52 , 53 , 54 , 55 . Environmental justice scholars and activists have convincingly argued that fair processes governed by just institutions are equally if not more important than equitable environmental outcomes because process change can lead to enduring systemic change 56 , 57 .

It has also been documented that lower-income areas 16 and areas with more racial minorities 17 tend to have less tree canopy cover. However, the relationships among long-term discriminatory housing practices and contemporary environmental conditions remain poorly understood. The distribution of current urban tree canopy cover offers one perspective on environmental inequities related to housing segregation.

Research in Baltimore, MD has shown that redlining and other racially-biased housing practices have historically shaped the location of investments in environmental amenities such as trees and parks and the allocation of environmental disamenities via non-conforming zoning 2 , 38 , 58 , 59 . Redlined, African American neighborhoods of East and West Baltimore, graded D in the HOLC system, had overcrowded and poor quality housing and higher exposure to noise and other pollution from nearby industries 2 . These denser, D-graded areas had less available space for trees and tree planting, while A-graded areas comprised of single-family homes on larger lots could maintain, grow, and plant additional trees. Race-based evaluations of credit-worthiness also shaped access to wealth accumulation and related political power. Residents in A-graded areas directed municipal investments into street tree plantings, creating public parks with trees, and invested their own resources into trees on their private lands 26 , 59 . At the same time, residents in D-graded areas had less access to public investments and were more likely to spend their lower wages on other necessities such as rent, food, or transportation. Thus, differences in lot sizes, money, and access to power along HOLC neighborhood lines played an important role in shaping the distribution of Baltimore’s urban tree canopy over the long term 2 .

Our goal in this paper is to examine whether there are similar patterns in the distribution of tree canopy by HOLC-graded neighborhoods in other cities. These analyses are possible because redlining was a national process, initiated by the Federal government in collaboration with state and local governments. It was a practice that was spatially-explicit and applied to 239 cities in the same time period throughout the country. These characteristics make the practice of redlining particularly well-suited for within- and cross-city comparisons. We examined whether historic redlining is statistically associated with contemporary spatial distributions of tree canopy for a range of metropolitan areas across a spectrum of area, population, and climate. This paper assesses whether there are differences in current tree canopy cover among historic HOLC classes and whether differences among HOLC classes are consistent among cities.

There is a strong relationship between HOLC grades and tree canopy: areas formerly graded D have 21 percentage points less tree canopy than areas formerly graded A. One-way ANOVA showed significant differences in tree canopy by HOLC grade [F(3, 3184) = 253.9, p  < 0.001]. Post hoc comparisons using the Tukey HSD test indicated that the same hierarchical ranking system used by HOLC to assess loan risk in the 1930s is paralleled by the rank order of average percent tree canopy cover today. Areas formerly graded D have significantly less tree canopy (M = 20.9 percentage points, SD = 12.2), than areas graded C (M = 24.6, SD = 10.9), B (M = 32.4, SD = 13.8), or A (M = 41.1, SD = 14.7). All six pairwise combinations were significantly different at the p  < 0.0001 level. The same model was re-fit as a linear regression so that areas graded A are the reference, with differences in means as estimated coefficients, as a baseline model (Table 1 , Model 1).

To test for unobserved city-specific factors, a separate unconditional one-way ANOVA was performed. This second ANOVA showed significant differences in tree canopy by city [F(36, 3151) = 21.60, p  < 0.001]. The intraclass correlation coefficient (ICC) indicated that 23% of the variance in tree canopy cover was from city to city (Table 1 , Model 2). A mixed effects model with fixed effects for HOLC grade and random effects for city (Table 1 , Model 3) showed that the areas given less-favorable grades by HOLC have significantly less canopy cover than their higher-graded counterparts, with overlap between C and D areas (i.e., D ≤ C < B < A). Comparing the three model specifications (fixed effects for HOLC grade only, random effects for city, and a specification with both HOLC fixed effects and city random effects) with an AIC-minimization criterion showed that Model 3’s added complexity provided the best fit (Table 1 ). Model 3’s regression-adjusted estimates of tree canopy cover suggest that areas formerly graded D had 21 percentage points less tree canopy ( γ 30  = −20.79, 95% [−22.27, −19.31]) (or 22% cover) than areas formerly graded A ( γ 00  = 43.44, 95% [40.80–46.07]) and the HOLC categories explained 19% of the tree canopy variance while city-to-city variation explained an additional 25%.

Results of further tests and robustness checks

A-graded neighborhoods were often the rarest, making within-city analyses under-powered statistically. In cities with 10 or more A-graded neighborhoods (Fig. 1 ), within-city analyses of tree canopy cover by grade confirmed the pooled analyses’ findings (Fig. 2 ). Wilcoxon tests showed lower median tree canopy in D neighborhoods compared to A neighborhoods, except in Seattle ( p  = 0.093). Although the sample sizes for many cities do not permit statistical analyses of within-city analyses of canopy by HOLC grade, the boxplots in Fig. S1 illustrate variation among classes within each city. Tree canopy today is almost always in rank order of HOLC grades.

Larger and more segregated metropolitan areas tend to have more HOLC-defined neighborhoods. Cities are sorted by the number of A-graded neighborhoods. Only eight cities have ≥10 Grade-A neighborhoods (left) to permit within-city analyses. In the main analysis, all neighborhoods are used. *Johnson City/Birmingham, NY; **Holyoke/Chicopee, MA.

Seattle is an exception, where two formerly D-Graded neighborhoods have the most tree canopy today and are public parks. The number of A-Graded neighborhoods constrains analyses within cities; only cities with ≥10 A-graded neighborhoods are shown. See Fig. S2 for the distribution of tree canopy for all cities Note that the rank order of tree canopy cover mirrors the HOLC grades A through D. Significance tests for A to D provided via two-sample Wilcoxon test (aka Mann–Whitney test). # end August 13, 2020.

It is possible that the main results reported in Model 3 are driven by the patterns and sample size in the largest 16 cities with at least 50 HOLC-defined neighborhoods. We therefore re-fit Model 3 excluding the largest 16 cities and the results were substantively the same (Table S1 ); formerly D-graded areas have about 23% tree canopy, while formerly A-graded areas have nearly twice as much canopy today (43%). Therefore, the findings are not attributable to the patterns found in the largest cities.

The link between redlining and socioeconomic outcomes such as poverty and home foreclosure has previously been documented 3 , 29 , 33 , 44 , 45 , 46 , 48 , 49 , 50 , 60 . The lack of access to wealth via homeownership had a powerful influence on real estate markets. People of color were deprived of an important path to wealth accumulation in many urban areas across the US 29 , 33 , 34 . However, the relationships among historic discriminatory housing practices and current environmental conditions remain poorly understood. Redlining was one of the most consistent, wide-spread, spatial, and racial forms of US housing practices. The relationship between redlining and the current distribution of urban tree canopy cover offers a preliminary window into these larger, long term, and complex dynamics. Our research supports prior work on social disparities corresponding to redlining grades by adding evidence pertaining to environmental inequities.

Trees are an important component of the urban environment. They reduce the urban heat island effect 12 , 13 and provide a number of other public health benefits 61 such as crime reduction 62 . In order to consider whether historic social disparities are paralleled by contemporary disparities in tree canopy, this paper examined variations in tree cover by HOLC-defined neighborhoods and the metropolitan regions containing those neighborhoods. The difference was significant: formerly D-graded areas have about 23% tree canopy today while formerly A-graded areas have nearly twice as much (43%). We found that just two variables, HOLC neighborhood grade and city, explained 43% of the variance (Table 1 ).

To be very clear, this study used a cross-sectional, observational quantification of social-ecological patterns that is fundamentally incapable of finding, identifying, and/or ascribing causality for complementary or competing explanations of the process. The findings are consistent with other recent examinations of HOLC grades and vegetation cover 63 , the urban heat island effect 64 , and even premature births 65 : formerly D-graded areas on average have less vegetation, are hotter, and are associated with statistically significantly more preterm births 65 . The determinants of tree canopy cover in urban areas are complex 28 . Our paper highlights one possible factor that may have played a role while also ruling out random chance. We argue that redlining is an understudied process in urban ecology and that our findings suggest that the role of redlining in shaping tree canopy, in concert with other explanatory factors, warrants further process-based research. HOLC’s redlining was a moment in a long-term history of discriminatory housing practices in the United States 31 . Thus, in-depth and comparative research is needed to understand the systemic processes among long-term discriminatory housing practices and contemporary environmental conditions 2 .

There may be several systemic explanations for our pattern-based results. If redlining reflected existing differences in lot size and reinforced those differences through preferred investment over the long term, we could expect to see more extensive contemporary tree canopy within formerly A-graded areas. This contemporary distribution of canopy cover may be due in part to the fact that residential lots in these areas would have been larger and had more space for trees. A-graded areas were also more affluent, and households may have had higher disposable incomes to invest in landscaping such as trees. Further, because redlining helped shape wealth accumulation and related political power by race and geography, the privilege of those living in formerly A-graded neighborhoods may have served to direct public investments in tree canopy over the long term for street trees and trees in parks or through continued private household investment in landscaping on their own larger residential properties 66 , 67 , 68 , 69 . In this way, complex and reinforcing positive feedback loops may have occurred, perpetuating relationships among housing markets, affluence, race, and trees. Such a positive feedback loop may have also been mirrored in formerly D-graded areas with lower tree canopy today due to smaller lots, industrial land uses not conducive to tree canopy cover, fewer resources for maintaining trees on properties, and less influence over public investments over the long term. Our results are consistent with both of these rationales. A process-based study is beyond the scope of this paper, but our findings provide a robust starting point to examine the longitudinal dynamics between redlining and tree canopy cover.

Our results point to at least three other areas that could benefit from further research. First, more research may be needed to understand the mechanisms for why the strong association between HOLC categories and urban tree canopy exists some 80 years after the HOLC maps were drawn and the roles that different actors may have played to maintain these differences. Many A-graded areas were suburban areas that had been zoned for single-family housing with large lot sizes 70 . D-graded areas had denser housing stock, but they may have also contained non-residential land uses, such as industrial sites, which might have been unfavorable for trees. A next step could be to examine different residential densities, land uses associated with different jurisdictions, policies, and tree planting programs in the different cities over time. For example, D-graded areas could have been more susceptible to urban renewal projects, supporting highways, and other large-scale infrastructure projects that could have required tree removals or made space for new trees. Analyses of changing land uses, local policies, demographic trends, or historic aerial imagery could enable a greater understanding of the extent to which HOLC grades ‘locked in’ urban forms that are more or less amenable to tree canopy.

A second approach would be to examine areas that do not match the overall pattern. So-called statistical “deviant case analyses” 71 may help to build better theory about spatial, social, and environmental inequities, including historic processes of urban renewal and contemporary processes of gentrification and climatic conditions. For example, tree canopy cover in Seattle, WA in formerly A-graded neighborhoods is generally greater than in formerly D-graded neighborhoods (Fig. 2 ), but the differences were not statistically significant ( p  = 0.093). Moreover, the two areas with the highest percent of tree canopy cover in Seattle were graded D and are now public parks. The distribution of tree canopy in Gary, IN appears relatively invariant to HOLC grades and may warrant further investigation, too (Fig. S1 ). Third, additional research may disaggregate redlined neighborhoods by race and ethnicity to examine whether there are significant differences between neighborhoods with larger numbers of African-American residents, US-born white residents, and white immigrant residents, including Irish, Italian, Polish, German, and Jewish communities. A methodological challenge would be to identify realistic counterfactuals for analyzing the spatial distribution of urban tree canopy across metropolitan areas that were not redlined. Canadian cities may offer a point of comparison.

While urban trees provide ecosystem services such as urban heat island mitigation, it is important to acknowledge that trees can produce disservices 72 , 73 . Not everyone wants trees, so their absence may be a desired condition for some residents 74 , 75 , 76 , 77 , 78 . In addition, a pixel of tree canopy cover cannot reveal whether a tree was purposefully planted or sprouted through seed dispersal.

Given the long history of disinvestment in African-American communities in the United States, we sought to understand the extent to which a program in the 1930s that altered the distribution and flow of land and capital along racial lines is associated with contemporary tree canopy cover in urban areas. Our investigation into 37 cities reveals a strong association between HOLC grades inscribed on maps roughly nine decades ago and present-day tree canopy. The study design cannot identify causal pathways, but the inequity invites careful scrutiny of the social, economic, and ecological processes that have created the demonstrably uneven and inequitable distribution of urban tree canopy in the United States.

Sample and data

Two hundred and thirty-nine cities were redlined. As part of the Mapping Inequality project, the University of Richmond’s Digital Scholarship Lab georectified and digitized more than 150 HOLC maps where HOLC-defined neighborhoods are represented as polygons 79 . Shapefiles for areas with available land cover data, described below, were downloaded.

The heterogeneity of urban environments necessitates high-resolution and high-accuracy measures of tree canopy. 30 m 2 resolution datasets such as Landsat scenes or derivative products such as the National Land Cover Database (NLCD) are insufficient for mapping trees in a way that effectively operationalizes lived experience in cities 80 , 81 . For consistency, high-resolution tree canopy data were obtained from eleven sources.

Land cover data for 23 areas were downloaded from The Spatial Analysis Lab (The SAL, http://gis.w3.uvm.edu/utc/ , Table S2 ) at the University of Vermont. The SAL routinely maps large spatial extents such as counties and their methods are detailed elsewhere 82 , 83 , 84 . Next, tree canopy data for the entire state of Pennsylvania were obtained for all HOLC-mapped cities in Pennsylvania from SAL (Altoona, Johnstown, New Castle, Philadelphia, and Pittsburgh, http://letters-sal.blogspot.com/2015/09/pennslyvania-statewide-high-resolution.html ). Tree canopy data for eight cities (Baltimore, MD; Johnson City-Binghamton, Syracuse, and Utica, NY; Lynchburg, Norfolk, Richmond, and Roanoke, VA) were obtained (Chesapeake Bay Program, https://chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/ ). Data for New Jersey (Atlantic City, Camden, and Trenton) were obtained (Pennsylvania Spatial Data Access, http://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=3193 ). Finally, a literature review was used to identify ( n  = 8) sources for additional land cover data overlapping HOLC-graded areas and corresponding authors were contacted for data access (Los Angeles and Sacramento, CA; Denver, CO; Miami and Tampa, FL; Hollyoke-Chicopee, MA; Toledo, OH; and Seattle, WA). In total, there were 3188 HOLC-defined neighborhoods, from 37 cities, in 16 states from 11 sources (Table S2 ). Statistical analyses were conducted in R v. 3.6.1 85 using the tidyverse 86 , simple features 87 , ggpubr 88 , lme4 89 , sjPlot 90 , and sjstats 91 packages.

Dependent variables

The dependent variable was the percentage of tree canopy cover within each HOLC zone. Consistent with previously published literature 18 , 92 , we define and operationalize tree canopy as “the layer of leaves, branches, and stems of trees that cover the ground when viewed from above” 93 . After projecting the HOLC polygons obtained from the Mapping Inequality Project to match the land cover data, the Tabulate Area tool was used in ArcMap Version 10.2.2 (ESRI, 2014) to calculate the percent of tree canopy cover for each polygon. In seven cities (Boston, Denver, Detroit, New Haven, New York City, Seattle, and Toledo), tree canopy data were not available for the entire extent of the HOLC-defined neighborhoods, which occasionally extended into suburban areas surrounding the municipalities of interest and 156 polygons had to be omitted. This represents 4.67% of the dataset and was unavoidable. As a robustness check, described below, our main regression model was re-fit with those seven cities entirely removed.

Empirical strategy

We conducted two analyses of variance (ANOVA) with tree canopy as the dependent variable. In the first ANOVA, the independent variable was the HOLC categories in order to test our main hypothesis that mean canopy cover varied by grade. A post hoc Tukey HSD was then used to examine which pairs of grades differed from each other. This initial ANOVA was re-fit as a linear regression model so that Grade A would be the base-case for comparison, and letters B, C, and D would be estimated as differences in means from A. This is Model 1.

In the second ANOVA, the independent variable was the city in which each neighborhood was located (hereafter Model 2). This analysis was conducted because we were concerned that unobserved city-specific characteristics pertaining to such things as land use policy, urban form, climate, and other factors may have influenced tree canopy cover. The purpose of Model 2 was to test whether tree canopy cover varied across each study city.

As anticipated, tree canopy varies significantly by city. We therefore fit a mixed-effect model with the four-category HOLC grades as the fixed effects, with random intercepts for city, as shown in Eq. ( 1 ) and termed Model 3.

Where η ij is tree canopy as a percentage land area for HOLC polygon i in city j . HOLC grade A is the reference, and γ 00 is the intercept and mean value of percent tree canopy cover in formerly A-graded neighborhoods. γ 10 , γ 20 , γ 30 , are the coefficients of interest, which represent the differences in mean tree canopy from A by HOLC grades B, C, and D, respectively. μ 0 j represents the city-specific random intercept, which was included to capture unobserved aspects of each city, e ij is the observation-level residuals, σ 2 is the within-city variance, and τ 00 represents the variance across cities. The variance partitioning coefficient, also known as the intraclass correlation coefficient (ICC) is “a population estimate of the variance explained by the grouping structure” 94 , which was calculated as the between-group-variance ( τ 00 , random intercept variance) divided by the total variance (i.e., sum of between-group-variance τ 00 and within-group σ 2 residual variance), shown in Eq. ( 2 ).

T-statistics were treated as Wald Z-statistics for calculating the confidence intervals and p -values, assuming a normal-distribution. An approximate R 2 was computed as the proportion of variance explained in the random effect after adding the categorical HOLC fixed effect to the model. This is computed as the correlation between fitted and observed values 95 . AIC minimization was used to compare Models 1, 2, and 3, and to determine the best fitting model 96 .

Cities with enough A- and D-graded neighborhoods were examined in order to determine if the patterns from cross-city, pooled analyses hold within individual cities. D-graded areas are common, but A-graded areas were limiting. For each city with ≥10 HOLC-defined A-neighborhoods ( n  = 8: Los Angeles, Chicago, Cleveland, New York City, Lynchburg, Seattle, Pittsburgh, Philadelphia), Wilcoxon rank-sum tests were used to compare pairwise differences in tree canopy cover from A to D neighborhoods. All other pairwise tests were omitted for parsimony (Fig. 2 ).

Methods for further tests and robustness checks

Four types of checks were conducted: one set to assess the potentially undue influence of cities with many HOLC-defined neighborhoods, a second to assess the influence of metropolitan areas with partially missing data, and a third to examine the sensitivity of grouping the five boroughs of New York City, and Chelsea and Cambridge with Boston, and a fourth to examine data from different sources.

Two strategies were used in order to evaluate whether the results of Models 1, 2, and 3 were driven by the metropolitan areas with the most HOLC-defined neighborhoods. First, the boxplots for all cities are provided in Fig. S1 so that the within-city patterns can be examined visually. Second, as a robustness check, Model 3 was re-fit without data from the metropolitan areas with ≥50 neighborhoods to see if the patterns would still hold (Table S1 ). The inferences from this smaller model remain unchanged, however, the confidence intervals are larger by construction.

Tree canopy data were not available for the entire extent of the HOLC-defined areas in seven metropolitan areas. The missing data are usually at the edges of the geographic extent, and therefore non-random. Specifically, tree canopy data were not available for the entire extent HOLC-defined neighborhoods in Boston, Denver, Detroit, New Haven, New York City, Seattle, and Toledo, which collectively represent 4.67% of the total dataset’s observations. To address non-random, partially missing data at the edges of these metropolitan regions, Model 3 was re-fit with these cities removed entirely (Table S1 , Model 5). Model 5 provides substantively similar results and interpretation to the main Model 3 and the point estimates remain within the bounds of Model 3’s confidence intervals.

The sensitivity of the analytical decision to group the five boroughs of New York City, and Chelsea and Cambridge with Boston was also examined. A version of Model 3 (Table S1 , Model 5) was fit without grouping, which adds 6 additional random intercepts. Again, no substantive changes were observed.

Finally, land cover data for Sacramento, Denver, Miami, Tampa, Holyoke-Chicopee, Toledo, and Seattle all came from different sources (Table S1 , Model 6). It is possible that data from those cities may have influenced the results if the land cover data were not comparable to those produced by SAL. Based on Model 6, no substantive changes were observed. All robustness check models supported the inferences of the main results: formerly D-graded areas had roughly half as much tree canopy as formerly A-graded areas.

Limitations

Cross-city analyses 97 and meta-analyses 16 , 17 have demonstrated inequitable distribution of tree canopy by already disadvantaged groups. This paper builds on those studies by using a consistent approach across 37 cities. These 37 cities (or ~15% of all redlined cities) were chosen based on availability of data. However, this convenience sample nevertheless covers a range of characteristics in population from ~42,000 people (Lynchburg, VA) to ~7.2 million people (New York City) at the time they were redlined in 15 states. When they were redlined, these 37 urban areas analyzed housed ~28.7 million people.

Data availability

All data generated and analyzed as part of this study are openly available from the Environmental Data Initiative (EDI) Data Portal via the following https://doi.org/10.6073/pasta/4ccbc7087959dc2a25063e589dee7718 98 . The data are as follows: (1) City-specific file geodatabases with feature classes of the HOLC polygons obtained from the Mapping Inequality Project https://dsl.richmond.edu/panorama/redlining/ , and tables summarizing tree canopy, and in some cases other land cover classes. (2) An *.R script that replicates all of the analyses, graphs and tables in the article describing the related study. Other double checks, exploratory and miscellaneous outputs can also be created by the script. (3) A *.csv file containing city, the HOLC grade, and the percent tree canopy cover. This can be used to create the main findings of the article and this flat file is provided as an alternative to running the R script to extract information from the geodatabases, combine and analyze them. The intention is that this file is more widely accessible; the underlying information is the same.

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Acknowledgements

This work was supported by the National Socio-Environmental Synthesis Center (SESYNC) under funding received from the National Science Foundation DBI-1639145. This work was also supported by the Baltimore Ecosystem Study (BES) DEB-1637661 and DEB-1855277. Thanks to all data providers, the team in the Spatial Analysis Lab at the University of Vermont, and the Mapping Inequality project. Thanks to Mary Cadenasso and the Cadenasso Landscape and Urban Ecology lab at UC Davis for the Sacramento, CA data, and Monika Moskal and the Remote Sensing and Geospatial Analysis Laboratory at the University of Washington for the Seattle data, and NCDC Imaging and City and County of Denver GIS team. Thanks to Shawn Landry and the Water Institute at the University of South Florida for the Tampa, FL data and for providing thoughtful input on an earlier draft that improved the paper. Thank you Liz Wise, Brian Falasca, Michele Romolini, and LARIAC for access the LA Land cover data. Amanda Phillips de Lucas provided important insights about large-scale infrastructure projects and urban renewal. The findings and opinions reported here do not necessarily reflect those of the funders of this research.

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D.H.L., B.H., and J.M.G. designed the research; D.H.L and B.H. performed the research; D.H.L. and J.O.D. analyzed the data; D.H.L., B.H., J.M.G., S.T.A.P., L.A.O., C.F.A., and C.G.B. interpreted the data and findings; D.H.L., B.H., J.M.G., S.T.A.P., L.A.O., C.F.A., and C.G.B. revised and provided critically important content; and D.H.L., B.H., J.M.G., S.T.A.P., L.A.O., C.F.A., and C.G.B. wrote the paper.

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Locke, D.H., Hall, B., Grove, J.M. et al. Residential housing segregation and urban tree canopy in 37 US Cities. npj Urban Sustain 1 , 15 (2021). https://doi.org/10.1038/s42949-021-00022-0

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Neighborhood Disadvantage, Residential Segregation, and Beyond—Lessons for Studying Structural Racism and Health

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A recent surge of interest in identifying the health effects of structural racism has coincided with the ongoing attention to neighborhood effects in both epidemiology and sociology. Mindful of these currents in the literature, it makes sense that we are seeing an emergent tendency in health disparities research to operationalize structural racism as either neighborhood disadvantage or racial residential segregation. This review essay synthesizes findings on the relevance of neighborhood disadvantage and residential segregation to the study of structural racism and health. It then draws on recent literature to propose four lessons for moving beyond traditional neighborhood effects approaches in the study of structural racism and health. These lessons are (1) to shift the focus of research from census tracts to theoretically meaningful units of analysis, (2) to leverage historic and geographic variation in race relations, (3) to combine data from multiple sources, and (4) to challenge normative framing that aims to explain away racial health disparities without discussing racism or racial hierarchy. The author concludes that research on the health effects of structural racism should go beyond traditional neighborhood effects approaches if it is to guide intervention to reduce racial and ethnic health disparities.

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Riley, A.R. Neighborhood Disadvantage, Residential Segregation, and Beyond—Lessons for Studying Structural Racism and Health. J. Racial and Ethnic Health Disparities 5 , 357–365 (2018). https://doi.org/10.1007/s40615-017-0378-5

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Homeownership, racial segregation, and policy solutions to racial wealth equity

About the authors, rashawn ray, david m. rubenstein fellow – governance studies, andre m. perry, senior fellow – metropolitan policy program, david harshbarger, research analyst – metropolitan policy program at brookings, samantha elizondo, research assistant, the race, prosperity, and inclusion initiative – the brookings institution, alexandra gibbons, research intern – governance studies.

Homeownership is often viewed as the entree to the American dream and the gateway to intergenerational wealth. However, this pathway is often less achievable for Black Americans who post a homeownership rate of 46.4% compared to 75.8% of white families. [1] Compounding matters, homes in predominately Black neighborhoods across the country are valued at $48,000 less than predominately white neighborhoods for a cumulative loss in equity of approximately $156 billion. [2] These are significant contributing factors to the racial wealth gap.

In 2016, white families posted the highest median family wealth at $171,000. Black families, in contrast, had a median family wealth of $17,600. [3] Because wealth (as measured by the total amount of assets a person owns minus debts) is a critical predictor of education, health, employment, and other quality of life metrics, a strategy to maximize homeownership and home value is needed.

Lower Black homeownership and the racial wealth gap are byproducts of systemic racism, including the legacies of slavery, Jim Crow segregation, redlining, and other anti-Black policies that targeted Black people and predominately Black neighborhoods. Residential segregation facilitates the extraction of wealth and other vital resources that fuel economic and social mobility. The loss of wealth in Black communities hastens a downward socioeconomic spiral. For instance, schools predominated by Black, Latinx, and Asian students receive $23 billion less in funding than predominately white districts. [4] This is because schools primarily rely on local property taxes rather than a broader pool of funding to equalize school resources.

Furthermore, education as a solution to closing the wealth gap is inherently flawed. White college graduates have seven times more wealth than Black college graduates. [5] This racial wealth gap reveals how little a strategy singularly focused on increasing college degree attainment will have on reducing the racial wealth gap.

Additionally, subpar neighborhood resources lead to fewer banking options, more payday lenders, and less opportunity for financial literacy. Because most people start their businesses using the equity in their homes, Black business development is throttled by Black families’ lack of homeownership and lack of wealth overall.

Recent public policy has not helped matters. The federal government’s first Covid-19 relief package may have exacerbated the problem. The first round of Paycheck Protection Program (PPP) loans (part of the CARES Act, the federal COVID-19 relief package) gave relief only to employer firms. This framework disproportionately excluded Black businesses: 95% of Black-owned firms are non-employer businesses, compared to 78% of white-owned firms. [6]  This negatively impacted entire communities. According to a Bloomberg analysis, 27% of businesses in white-majority congressional districts received PPP loans, compared with 17% of businesses in districts where minorities make up more than half the population. [7] A Stanford University study estimated that over 40% of Black-owned small businesses closed during the COVID-19 pandemic. [8] A report examining COVID-19 disparities in Detroit found that small businesses in the city, compared to those in the broader Detroit area, were less likely to receive PPP loans and less likely to receive similar amounts even if loans were allocated. [9]

Segregation and racial bias are robbing Black people of opportunities to build wealth, restricting millions from reaching their potentials. At a time when America is at a precipice of a racial awakening, it is important to provide empirical research on a set of problems that if solved, can significantly improve the racial wealth gap.

At a time when America is at a precipice of a racial awakening, it is important to provide empirical research on a set of problems that if solved, can significantly improve the racial wealth gap.

This report aims to provide an empirical analysis and overview of the role that racial residential segregation plays in 17 cities across the United States. Using a dissimilarity index that captures how far apart racial groups in a city live away from each other, we aim to provide information on cities and how racial disparities in housing fuel the racial wealth gap. We focus on homeownership rates and values, banks, credit scores, and educational attainment.

Methodology

For this analysis, we rely on publicly available data sources including the US Census Bureau’s American Community Survey (ACS) and the Federal Reserve System. Additionally, we utilize credit scoring data from numerous credit bureaus, accessed and compiled by the Urban Institute , as well as the ProsperityNow Scorecard . Further, we utilize credit scoring data from numerous credit bureaus, accessed and compiled by the Urban Institute , as well as the ProsperityNow Scorecard . Where possible, we center the Black population of each of the 17 cities included in the analysis, drawing comparisons when necessary to the non-Black population or white population of the city, where appropriate. We define the Black population as Black, non-Hispanic in accordance with Census conventions.

We employ a metric known as the “Dissimilarity Index,” one of many possible calculations used to measure residential segregation between two groups. The index requires that researchers choose two groups to be compared as well as a certain sub-geography of a larger area (here, cities) to compare. We choose Census Tracts, as they are the unit that best approximates our conception of “neighborhoods” for which the latest Census data are available from the 2019 ACS. In addition, we choose to compare Black populations in cities with the remainder of the city entirely, not simply the white population, as is sometimes done in studies of residential segregation. This is to account for the fact that while the cohort of cities in the analysis includes widely differing shares of Latino or Hispanic population, Black and Latino or Hispanic populations are not always spatially segregated to the same degree within cities. In recognition of the fact that residential segregation exists, the index can be thought of as a measure of the proportion of the two groups which would have to move from their current location in order to achieve an evenly spread population where each neighborhood has a representative share of each group. Cities in which a high proportion of a group would have to move are more segregated.

When we study topics other than residential segregation, such as discrepancies in home values, educational attainment, income, and more, we present data that speak to the gap between Black and white populations, rather than Black and non-Black populations. As white populations often (though importantly, not always) have higher incomes, rates of homeownership, and other outcomes often show the true effect of systemic racism for Black residents of these cities.

Where possible, we use data disaggregated by race at the city level. However, in the case of unbanked and underbanked populations, disaggregated data at the city level are not reliable. Instead, we examine the unbanked and underbanked population of the nation, disaggregated by race.

In the case of home values (as reported by the Census Bureau), data are not disaggregated by the race of the homeowner or head of household. Instead, we examine home values in Black-majority neighborhoods and compare them with home values in all other neighborhoods within the city. Some cities in the cohort do not have Black-majority neighborhoods, even if they may have neighborhoods with a high Black population, and so are not included in that portion of the analysis. Median home values are computed by the Census Bureau at the neighborhood level, but the custom sub-geography of “all Black-majority neighborhoods” requires that we compute medians by hand. Rather than employing a cruder estimate of the true median, we more accurately estimated the median home value of homes in all Black-majority neighborhoods in a given city by summing the recorded tallies of homes within each income band and then interpolating linearly within the median band. The same process is used by the U.S. Census Bureau to calculate median estimates for all non-custom geographies for which they disseminate data.

Segregation of Black residents persists in cities.

There are significant differences in the racial compositions of the cities in this analysis. [10] In Redding, Chico, Albuquerque, El Paso, Fairfax, San Antonio, Phoenix, Los Angeles, and Denver, under 10% of the population is Black. Sacramento, Minneapolis, Houston, and Oakland have populations that are between 10 and 25% Black. Proportionally, the largest Black populations are located in Cleveland, Atlanta, Baltimore, and Detroit. With the exception of Cleveland, in which 48.8% of residents are Black, these cities are more than 50% Black. Detroit has the largest percentage of Black residents in the sample at 78.3%.

To measure the extent to which Black residents are segregated from other racial groups in the cities, we utilized indices of dissimilarity. The dissimilarity index represents the percentage of one of the two groups included in the analysis that would have to move in order to achieve racial compositions within smaller geographic units that match the racial composition of the entire city. A value of 0 represents a completely integrated city, while a value of 1 represents a completely segregated city in which 100 percent of one group would have to move to achieve integration. Generally, indices above 0.6 are considered high, indices between 0.3 and 0.6 are moderate, and indices below 0.3 are low. [11]

In this report, dissimilarity indices represent the levels of segregation between the Black, non-Hispanic population of a city and the remainder of the city population, using Census tracts as the geographic unit of analysis. Racial segregation has a range of consequences, and often results in a dearth of resources such as parks [12] and well-funded schools [13] in majority-Black communities.

Based on these cutoffs, only two cities, Sacramento and Fairfax, have low levels of racial segregation between non-Hispanic Black residents and the rest of the city population. [14] The majority of cities exhibit moderate levels of segregation of Black residents. In Albuquerque, Oakland, Chico, Phoenix, San Antonio, El Paso, Redding, Minneapolis, Denver, Los Angeles, and Houston, between 30% and 60% of the Black population would have to move to a new Census tract to achieve a uniform distribution of Black residents.

Black residents are extremely segregated from the rest of the population in four cities, with dissimilarity indices above 0.6: Detroit, Baltimore, Cleveland, and Atlanta. In Atlanta, the city with the highest levels of Black segregation in our sample, almost 70% of Black residents would have to move to new Census tracts to produce racial distributions within Census tracts that match that of the larger city. Notably, these four cities also have the highest proportions of Black residents in the sample, which reflects the impact of historic and modern policies aimed at segregating cities, particularly those with large Black populations.

Black residents are extremely segregated from the rest of the population in four cities: Detroit, Baltimore, Cleveland, and Atlanta.

Black residents face significant barriers in becoming homeowners.

There are significant gaps in homeownership rates between Blacks and whites. The homeownership rate is defined as the ratio of owner-occupied units to all occupied units based on data from 2019. Homeownership rates are important to examine because owning a home is an essential step in building wealth. [15]

Among Black homeowners, housing comprises about 37% of total wealth, compared to about 32% for white homeowners. [16] Financial difficulties brought on by COVID-19 threaten to exacerbate the racial homeownership gap further, especially as the federal forbearance program expired June 30, 2021. [17] Only one city, Detroit, has a gap in homeownership rates between Black and white residents under 10% with Blacks owning homes at a rate of 45.9% and whites owning homes at a rate of 53.4%. [18]

There are five cities with gaps in Black and white homeownership that range from 15% to 20%: Denver, Los Angeles, Baltimore, Oakland, and Cleveland. Rates of Black homeownership in this group of cities range from 27% in Los Angeles to 42.36% in Baltimore. In contrast, Los Angeles is the only city in this group in which the homeownership rate for whites is under 50%.

In Houston, El Paso, Sacramento, Atlanta, San Antonio, and Albuquerque, the gap in homeownership rates between Blacks and whites is between 20% and 30%. Again, the majority of whites own their homes in every city in this group except Houston (which is slightly below 50% for whites). The highest Black homeownership rate of these cities, 41.08%, is in El Paso.

Among cities in this report, Fairfax, Phoenix, Chico, Minneapolis, and Redding have the biggest Black-white disparities in homeownership rates with differences of over 30%

Fairfax, Phoenix, Chico, Minneapolis, and Redding have the biggest Black-white disparities in homeownership rates with differences of over 30%. Chico, CA has the lowest Black homeownership rate with only 10.82% of Black residents owning their homes. The largest observed Black-white gap in homeownership rate is in Redding, CA, with 56.14% of whites owning homes compared to only 16.53% of Blacks. With the exception of Minneapolis, these cities all have relatively small proportions of Black residents. Furthermore, all of these cities have dissimilarity indices under 0.6. Although levels of spatial segregation are lower in these cities than in others in our sample, Black residents face significant barriers in becoming homeowners, limiting their opportunities to build intergenerational wealth.

Homes in majority-Black neighborhoods are significantly devalued.

Black residents are less likely to own homes in all of these cities, and when they do successfully obtain a home, it is often devalued. There are countless stories of Black homeowners receiving low home appraisals until a white friend stands in. [19] For instance, in Denver, a biracial couple hoped to renovate their home and received an initial appraisal of $405,000. During this appraisal, Lorenzo, a Black man, was at home with the couple’s children. The couple got a second appraisal, and this time, Gwen, a white woman, stayed home. They received an appraisal of $550,000, a $145,000 increase. [20]

Beyond discrimination in individual appraisals, homes located in majority-Black neighborhoods have been chronically undervalued, [21] further exacerbating the racial wealth gap. To examine differences in home values by race, we compared the median value of homes in Black-majority Census tracts to the median value of homes in all other Census tracts. Chico, Redding, Sacramento, Denver, Albuquerque, El Paso, San Antonio, and Fairfax have no majority-Black Census tracts and are thus excluded from the analysis.

Detroit has the smallest gap in median home value between Black-minority and Black-majority neighborhoods, with homes in Black-minority neighborhoods worth about $16,043 more than those in Black-majority neighborhoods. In Cleveland, homes in Black-minority neighborhoods were valued at median prices almost twice as high as homes in Black-majority neighborhoods, with a gap in median home value of $38,297. Shockingly, these are the only two cities in the sample in which the gap in median home values is under $100,000.

In Minneapolis, the median value of homes in Black-minority neighborhoods is also almost twice as high as the median value of homes in Black-majority neighborhoods. Since housing costs are higher in Minneapolis than in Cleveland, this amounts to a difference in median value of $110,625. A similar pattern exists in Baltimore, with the median home in Black-minority neighborhoods worth over twice as much, or $138,627 more, than the median home in a Black-majority neighborhood.

In Houston, homes in Black-minority neighborhoods are valued at median prices almost three times the median prices of homes in Black-majority neighborhoods, with a difference in value of $182,092. Here, the median home value in Black-majority neighborhoods is under $100,000, while the median home value in Black-minority neighborhoods approaches $300,000.

In California cities included in this analysis, the gap in the median values of homes is extreme, partially due to exorbitant housing prices. The median value of homes in Black-minority neighborhoods in both Oakland and Los Angeles is about 1.5 times as high as the median value of homes in Black-majority neighborhoods. In Oakland, this amounts to a difference in median values of $242,212. In Los Angeles, homes in Black-minority neighborhoods are worth about $272,933 more than those in Black-majority neighborhoods.

Atlanta, the most segregated city in our sample, also exhibits the biggest racial disparity in median home values. Homes in Black-minority neighborhoods are worth almost four times, or $350,521 more than homes in Black-majority neighborhoods.

The devaluation of homes in majority-Black neighborhoods has a range of consequences. For instance, a Black man and his Japanese American wife purchased a home in 2004 in College Park, a majority-Black area outside of Atlanta, because they wanted their children to grow up in a Black community. However, low home values meant that the schools were underfunded. When they had a second child, they moved to a neighborhood with better-funded schools — this neighborhood, Candler Park, was majority-white. They sold their home in College Park in 2014 and received $144,000 less than they initially paid for it, with no tax breaks for their losses. Thus, owning a home in College Park was not an efficient method of building wealth. Their new home in majority-white Candler Park is accruing value, and one day, they will be able to sell it at a higher value, without paying taxes on up to a $500,000 gain. [22]

Even beyond the cities in this report, the devaluing of houses in predominately Black neighborhoods, or even at times, homes owned by Black people in predominately white neighborhoods, is a systemic problem.

Even beyond the cities in this report, the devaluing of houses in predominately Black neighborhoods, or even at times, homes owned by Black people in predominately white neighborhoods, is a systemic problem. In Jacksonville, Florida, Abena (who is Black) and Alex (who is White) Horton had their home appraised. They believe that the appraisal was too low. During the second appraisal, Alex was present instead of Abena and the couple removed all signs of Abena and their biracial son. The second appraisal yielded a 40% higher value than the appraisal where Abena was present. In Hartford, Connecticut, Stephen Richmond’s home value substantially increased after he removed family photos and had a white neighbor stand in for the second appraisal. Even Black celebrities fall victim to racial discrimination in housing. Comedian and actor D.L. Hughley purchased a home in southern California for $500,000. He renovated the home and added a pool. During an appraisal three years later, Hughley’s home was appraised for a similar price to what he originally purchased it for. The bank flagged it as a mistake and ordered another appraisal. The second appraisal came in $160,000 higher. Hughley went on to sell the home for $770,000. [23]

Unequal access to lending stymies efforts to build lasting wealth.

Racial economic inequality in the U.S. is primarily the result of unequal investments among communities. Estimates published by ProsperityNow showing the percentage of unbanked or underbanked populations in the United States for different racial and ethnic groups often highlight inequalities in the financial health of their neighborhoods. The percentage of Blacks (46%) who are unbanked or underbanked alone is over three times the percentage of whites (14%) who experience the same struggle. Thirty-two percent of Hispanics are also either un- or under- banked. 

Bank accounts are useful tools for building emergency savings and banks themselves provide a connection to mainstream financial systems and programs that provide financial assistance and community investments. The combined 24% of Black and Hispanic populations who are completely unbanked are shut out of these basic tools though which to save and accrue earnings. Only 3% of whites are completely unbanked.

Fifty-four percent of minority populations are underbanked, meaning that while they may have access to bank accounts, these communities also experience a prevalence of alternative financial services like money orders, check cashers, and same day lenders, to manage finances .

Fifty-four percent of minority populations are underbanked, meaning that while they may have access to bank accounts, these communities also experience a prevalence of alternative financial services like money orders, check cashers, and same-day lenders, to manage finances.[24] These high-cost, low-quality financial services tend to trap borrowers in cycles of debt, increasing the financial vulnerability especially among communities of color and blocking efforts to build lasting wealth. Research by McKinsey & Co. reaches a similar conclusion that access to mainstream financial services is an important factor in accumulating savings that many Black Americans, unfortunately, lack—increasing access to basic banking services could save individual Black Americans up to $40,000 over their lifetime. [25]

Black people are more likely to encounter discriminatory lending practices that impact credit scores.

Unequal access to lending is not the only thing that hampers efforts by Blacks to establish financial security and accrue lasting wealth. The legacies of redlining, underinvestment, and a prevalence of alternative banking within these communities that make Black people more likely to encounter discriminatory lending practices also impact credit scores.

People pay attention to credit because loans and credit can provide opportunities to start businesses, increase human and physical capital, and build wealth. These all provide paths out of historical inequality in wealth accumulation. Unfortunately, Blacks must make extraordinary efforts to overcome the discrimination that is often hidden in financial policies or products that are supposed to be bias-free. While the 1974 Equal Credit Opportunity Act barred credit-score systems from using protected information, including race, the decades of discrimination in employment, lending policies, debt collection, and criminal prosecution that have left Black families vulnerable to financial insecurity also disadvantage credit scores. [26]

Without bank accounts, families often cannot generate the data that helps establish credit worthiness. As previously explained, the lack of banking institutions also leaves communities exposed to more predatory lending practices that encourage cyclical debt and impinge on credit worthiness.

Without bank accounts, families often cannot generate the data that helps establish creditworthiness. As previously explained, the lack of banking institutions also leaves communities exposed to more predatory lending practices that encourage cyclical debt and impinge on creditworthiness. While the ability to get a mortgage and pay it on time can have a positive impact on credit scoring, families without access to these financial vehicles can’t build robust credit histories. Unfortunately, the history of redlining that designated Black neighborhoods as too risky for mortgage lending is still evident in the structure of U.S. cities. [27]

Places like Atlanta and Minneapolis highlight stark differences between the average credit scores in white and majority-minority neighborhoods. In these cities, the average credit score for predominantly white neighborhoods sits above 725. By contrast, the average credit score for non-white zip codes is 560 and 570 for Atlanta and Minneapolis, respectively. The demographic composition of these two cities is quite different — Atlanta is majority Black (50.1% Black) and Minneapolis is majority white (19.2% Black). However, the predominant spatial segregation along racial and ethnic lines bears markers of systemic discrimination. While these two cities have the largest gaps between credit scores and showcase the interaction of race and social status in geographic areas, nine of the cities examined have non-white communities at an over 100-point disadvantage in credit scores.

Another interesting city to examine is Detroit, which has the lowest credit scores for both white and majority minority communities across all of these cities. In comparison to other cities, Detroit has the largest percentage of Black residents in the sample at 78.3%. It has maintained a majority Black population since the 1980s. However, Detroit remains among the most segregated cities in the nation today, precisely because of redlining practices that largely blocked Black families from federally-backed mortgages and drove them into neighborhoods with sub-par amenities (e.g., health, education, banking, green spaces, safety) and sectioned “good” neighborhoods as being predominantly white. [28] This combination of subpar investments and difficulty in acquiring mortgages makes the funding for wealth-creating activities such as investing in education or entrepreneurial ventures less accessible.

In the short term, for those with low and unstable income, lack of credit can make it impossible to pay for an unexpected immediate expense. In the long term, without access to the credit needed to fund wealth-creating activities such as educational, workforce, and small business investments, communities of color continue to be harmed by the historical legacies of intentionally discriminatory financial practices.

Higher costs for education result in Black students taking on higher levels of debt.

We used educational attainment data from the 2019 ACS estimates measuring the ratio of people with higher education degrees to the population age 25 or older to highlight differences in educational investments. More than 30% of Blacks in the cities examined have a higher education degree. Albuquerque, the city reporting the highest level of Black educational attainment, still only had 44% of Blacks over the age of 25 achieve an associate degree or higher.

Given the lack of mainstream banking in Black communities, it is no surprise that Blacks are more likely than whites to receive unsubsidized loans for education. Unsubsidized loans increase the amount of debt that Black college graduates have to take on in order to pursue higher education and make higher education less attainable. [29] Black college graduates also experience more difficulty in accumulating wealth than white college graduates since they accrue more student loan debt, and graduates from historically Black colleges are more likely to receive subprime loans with higher interest rates. [30]

There are two cities, San Antonio (with 35% Black and 33% white educational attainment) and El Paso (43% Black and 35% white), in which Blacks have higher levels of post-high school degrees than whites. When examining homeownership rates with educational attainment, it is clear that education does little to mute the racial gap in homeownership, even in cities where Blacks have higher educational attainment than whites.

When examining homeownership rates with educational attainment, it is clear that education does little to mute the racial gap in homeownership, even in cities where Blacks have higher educational attainment than whites.

Detroit and Cleveland have the lowest levels of Black educational attainment out of all cities (21% and 20%, respectively). Both cities are also in the bottom half of educational attainment across all racial groups. As mentioned, inequitable access to financial resources continues to exacerbate underinvestment in factors like education, which also contribute to the lack of ability to obtain wealth. Given that these cities have a majority minority population, it is no surprise that systemic barriers to wealth creation in the form of both financial and educational resources continue to manifest across the country in twenty-first century America. [31]

Policy recommendations

Closing the racial wealth gap will not be trivial. The gap has its roots in racist policies which trace back through the decades and centuries to even before the founding of the country. The vestiges of slavery, which prevented Black Americans from building any wealth for generations, as well as Jim Crow and New Deal-era policies, which sacrificed Black wealth-building opportunities to consolidate a white middle class, means the domino effects of this stolen possibility reach far and wide in American society. Therefore, solving this disparity will require a collection of policies and initiatives to tackle the most heinous examples of racial inequity that persist today, and which continue to rob Black America of the prosperity it is due. Because housing has been the primary mechanism by which Americans have built and passed along generational wealth in the last century, correcting injustices in housing is a prime area to begin attacking the harmful influence of systemic racism.

To bolster Black homeownership nationwide, we recommend a holistic approach to the home-buying process which will create a more equitable system before, during, and after securing a mortgage:

• Increase support for small dollar mortgage loan programs. It is a pervasive myth that homes of lower value are riskier investments for mortgages, but recent analysis from the Urban Institute shows that these buyers have comparable credit scores and their mortgages have similar loan-to-value ratios to more valuable properties. [32] As homes in Black neighborhoods are already devalued, this barrier to entry to homeownership disproportionately affects Black buyers, especially those who are first-time buyers.
• Reduce uneven costs of mortgages for Black homeowners. Homeownership while Black is expensive. Creating a rate-and-term refinancing option would help more households reduce monthly mortgage costs and lower the barrier to homeownership.
• Extend credit and down payment assistance to borrowers impacted by discriminatory housing and lending practices. Historical and ongoing policies such as redlining, restrictive covenants, “steering,” and more have extracted wealth from Black neighborhoods for generations. While it is not enough to simply extend credit based on redlining maps drawn in the New Deal Era, past injustices must be redressed by helping to develop areas left behind by racist policies.
• Adopt credit scoring practices with less discriminatory impacts. Current metrics of credit scoring do not account for regular payments from rent and utilities, instead they prioritize loan and credit card payments. Expanding notions of credit-building can dispel the myth that Black homeowners are risky investments.
• Increase diversity in the appraisal profession. Nearly 9 in 10 property appraisers are white, while 2% are Black, according to Urban Institute analysis of 2019 Census data. [33] With the numerous instances of appraiser bias making headlines on a consistent basis, better representation in the profession which holds sway over much of the valuation process could go a long way to mitigating the effects of societal bias against Black neighborhoods.
• Continue stimulus and relief efforts for homeowners and buyers in the wake of the COVID-19 pandemic. Black Americans are joining the ranks of homeowners at a steady rate, but the disparate impacts of the pandemic and associated recession mean that preexisting Black homeowners faced disproportionate difficulty with mortgage payments. Foreclosure moratoriums allowed many to hold onto their homes, but now they face repayment of deferred mortgages, and a bifurcated economy has not yet returned Black employment to pre-pandemic levels.

Through this report, we have provided a comprehensive overview of the role of segregation, discrimination, and racism in the housing market, as well as policy opportunities and recommendations to reduce these racial disparities. Implementing these evidence-based solutions will improve opportunities for potential Black homebuyers and reduce the racial wealth gap.

Acknowledgements

The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its authors, and do not reflect the views of the Institution, its management, or its other scholars. Support for this publication was generously provided by The Change Company, LLC. Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment.

[1] “Quarterly Residential Vacancies and Homeownership, First Quarter 2021.” U.S. Census Bureau, April 27, 2021. https://www.census.gov/housing/hvs/files/currenthvspress.pdf .

[2] Perry, Andre. 2020. Know Your Price: Valuing Black Lives and Property in America’s Black Cities . Brookings Press: Washington DC.

[3] Dettling, Lisa J., Joanne W. Hsu, Lindsay Jacobs, Kevin B. Moore, and Jeffrey P. Thompson. Recent Trends in Wealth-Holding by Race and Ethnicity: Evidence from the Survey of Consumer Finances. The Federal Reserve, September 27, 2017. https://www.federalreserve.gov/econres/notes/feds-notes/recent-trends-in-wealth-holding-by-race-and-ethnicity-evidence-from-the-survey-of-consumer-finances-20170927.htm.

[4] Edbuild, “$23 Billion Report.” 2019. Accessed on 28 June 2021. https://edbuild.org/content/23-billion .

[5] Ray, Rashawn, and Andre M. Perry. “Why We Need Reparations for Black Americans.” The Brookings Institution, March 4, 2021. http://www.brookings.edu/policy2020/bigideas/why-we-need-reparations-for-black-americans/ .

[6] McManus, Michael. “Minority Business Ownership: Data from the 2012 Survey of Business Owners.” U.S. Small Business Administration Office of Advocacy, September 14, 2016. https://cdn.advocacy.sba.gov/wp-content/uploads/2016/09/07141514/Minority-Owned-Businesses-in-the-US.pdf .

[7] Grotto, Jason, Zachary R. Mider, and Cedric Sam. “White America Got a Head Start on Small-Business Virus Relief.” Bloomberg, June 30, 2020. https://www.bloomberg.com/graphics/2020-ppp-racial-disparity/ .

[8] Fairlie, Robert. “The Impact of Covid-19 on Small Business Owners: Evidence of Early-Stage Losses from the April 2020 Current Population Survey.” Stanford Institute for Economic Policy Research, May 2020. https://siepr.stanford.edu/research/publications/impact-covid-19-small-business-owners-evidence-early-stage-losses-april-2020 .

[9] Ray, Rashawn, Jane Fran Morgan, Lydia Wileden, Samantha Elizondo, and Destiny Wiley-Yancy, “Examining and Addressing COVID-19 Racial Disparities in Detroit.” The Brookings Institution, March 2, 2021. http://www.brookings.edu/research/examining-and-addressing-covid-19-racial-disparities-in-detroit/ .

[10] U.S. Census Bureau. American Community Survey 2019 (5-Year Estimates). Retrieved from Social Explorer.

[11] Massey, Douglas, and Nancy Denton. American Apartheid: Segregation and the Making of the Underclass . Cambridge, MA: Harvard University Press, 1993.

[12] Saporito, Salvatore, and Daniel Casey. “Are There Relationships Among Racial Segregation, Economic Isolation, and Proximity to Green Space?” Human Ecology Review 21, no. 2 (2015): 113-32. http://www.jstor.org/stable/24875135 .

[13] Perry, Andre M. “A Revolution of Values for Black American Families.” The Brookings Institution, May 22, 2020. http://www.brookings.edu/blog/the-avenue/2020/05/22/a-revolution-of-values-for-black-american-families/ .

[14] Analysis of 2019 5-Year ACS Estimates.

[15] McCargo, Alanna, and Jung Hyun Choi. “Closing the Gaps: Building Black Wealth through Homeownership.” Urban Institute, December 9, 2020. https://www.urban.org/research/publication/closing-gaps-building-black-wealth-through-homeownership .

[16] Dettling, Lisa J, Joanne W Hsu, Lindsay Jacobs, Kevin B Moore, and Jeffrey P Thompson. “Recent Trends in Wealth-Holding by Race and Ethnicity: Evidence from the Survey of Consumer Finances.” Federal Reserve, September 27, 2017. https://www.federalreserve.gov/econres/notes/feds-notes/recent-trends-in-wealth-holding-by-race-and-ethnicity-evidence-from-the-survey-of-consumer-finances-20170927.htm .

[17] Henry-Nickie, Makada, Tim Lucas, Radha Seshagiri, and Samantha Elizondo. “Low to Moderate-Income Families Are Losing Ground: How to Save Their Homeownership Dreams.” The Brookings Institution, June 24, 2021. http://www.brookings.edu/blog/how-we-rise/2021/06/24/working-class-families-are-losing-ground-how-to-save-their-homeownership-dreams/ .

[18] Analysis of 2019 5-Year ACS Estimates.

[19] Sheridan, Jill. “A Black Woman Says She Had To Hide Her Race To Get A Fair Home Appraisal.” NPR, May 21, 2021. https://www.npr.org/2021/05/21/998536881/a-black-woman-says-she-had-to-hide-her-race-to-get-a-fair-home-appraisal . 

[20] Haythorn, Russell. “An Unconscious Bias? Biracial Denver Couple Says They Faced Discrimination on Home Appraisal.” The Denver Channel, November 19, 2020. https://www.thedenverchannel.com/news/local-news/an-unconscious-bias-biracial-denver-couple-says-they-faced-discrimination-on-home-appraisal .

[21] Perry, Andre M., Jonathan Rothwell, and David Harshbarger. “The Devaluation of Assets in Black Neighborhoods.” The Brookings Institution, February 17, 2021. http://www.brookings.edu/research/devaluation-of-assets-in-black-neighborhoods/ .

[22] Brown, Dorothy A. “Your Home’s Value Is Based on Racism.” The New York Times. The New York Times , March 20, 2021. https://www.nytimes.com/2021/03/20/opinion/home-value-race-taxes.html .

[23] Kamin, Debra. 2020. ”Black Homeowners Face Discrimination in Appraisals.” The New York Times , August 27, 2021. https://www.nytimes.com/2020/08/25/realestate/blacks-minorities-appraisals-discrimination.html .

[24] Williams, Claire. “’It’s What We Call Reverse Redlining’: Measuring the Proximity of Payday Lenders, Pawn Shops to Black Adults.” Morning Consult, July 24, 2020. https://morningconsult.com/2020/07/23/black-consumers-payday-loan-banking-services/ .

[25] Noel, Nick, Duwain Pinder, Shelley Stewart Ill, and Jason Wright. “The Economic Impact of Closing the Racial Wealth Gap.” McKinsey & Company, August 2019. https://www.mckinsey.com/~/media/mckinsey/industries/public%20and%20social%20sector/our%20insights/the%20economic%20impact%20of%20closing%20the%20racial%20wealth%20gap/the-economic-impact-of-closing-the-racial-wealth-gap-final.pdf .

[26] Klein, Aaron. “Credit Denial in the Age of AI.” The Brookings Institution, October 25, 2019. http://www.brookings.edu/research/credit-denial-in-the-age-of-ai .

[27] Mitchell, Bruce, and Juan Franco. “HOLC ‘Redlining’ Maps: The Persistent Structure of Segregation and Economic Inequality.” National Community Reinvestment Coalition, December 18, 2018. https://ncrc.org/holc/ .

[28] Ray, Rashawn. “Why are Blacks Dying at Higher Rates of COVID-19?” The Brookings Institution. April 56, 2020. http://www.brookings.edu/blog/fixgov/2020/04/09/why-are-blacks-dying-at-higher-rates-from-covid-19/ .

[29] Weller, C., & Roberts, L. (2021, March 19). Eliminating the Black-White Wealth Gap Is a Generational Challenge . Center for American Progress. March 19, 2021. Accessed July 13, 2021. https://www.americanprogress.org/issues/economy/reports/2021/03/19/497377/eliminating-black-white-wealth-gap-generational-challenge/

[30] Ray, Rashawn, and Perry, Andre. “Why we need reparations for Black Americans.” The Brookings Institution. March 4, 2021. http://www.brookings.edu/research/black-white-disparity-in-student-loan-debt-more-than-triples-after-graduation/ .

[31] Analysis of 2019 5-Year ACS Estimates.

[32] McCargo, Alanna, Bing Bai, and Sarah Strochak. “Small-Dollar Mortgages: A Loan Performance Analysis.” Urban Institute, March 6, 2019. https://www.urban.org/research/publication/small-dollar-mortgages-loan-performance-analysis.

[33] Neal, Michael, and Peter J. Mattingly. “Increasing Diversity in the Appraisal Profession Combined with Short-Term Solutions Can Help Address Valuation Bias for Homeowners of Color.” Urban Institute, July 1, 2021. https://www.urban.org/urban-wire/increasing-diversity-appraisal-profession-combined-short-term-solutions-can-help-address-valuation-bias-homeowners-color.

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Residential Segregation and Health: A Hypothesis Still in Search of Convincing Evidence

by Robert Kaestner | October 2017

As Arcaya and Schnake-Mahl note in their article “Health in the Segregated City”, race or ethnicity and poverty are strongly associated with health. Table 1 demonstrates this well-known fact with data drawn from the 2010 to 2016 National Health Interview Surveys (NHIS).

The figures in Table 1 are unsurprising. Being poor is strongly associated with worse self-reported health, and this is true for all racial or ethnic groups. The race/ethnicity-specific differences in health between poor and non-poor are largest for White persons. Race/ethnicity is also associated with self-reported health (within poverty group). Notably, within each poverty group, differences between White and Black persons are small compared to differences between poverty groups within race. It is also evident that Hispanic persons and those labeled as Other (race) have the best health among the four race/ethnic groups.

Similar conclusions apply to two different measures of (poor) health: whether a person has been hospitalized in the last year and whether they had 10 or more health care visits (Table 2). Poor persons are much more likely to be hospitalized and to have 10 or more visits than non-poor persons. Here too, within an income group, White and Black persons have similar health, and Hispanic persons and persons of other races (Other) have better health than White or Black persons.

A central thesis of Arcaya and Schnake-Mahl is that residential segregation independently of race and/or income worsens health, such as those outcomes shown in Tables 1 and 2.

“Methodological limitations and some mixed findings aside, many social epidemiologists see residential segregation by race and class as a "fundamental cause" of health disparities because it shapes exposures to critically important health risks and protective factors.” (Arcaya and Schnake-Mahl)

However, if residential segregation is an independent cause of poor health, as suggested by Arcaya and Schnake-Mahl, then we would expect groups that have the highest rates of segregation to be in the worst health. According to a U.S. Census report , Black persons live in the most segregated (and racially isolated) neighborhoods. Hispanic persons are the group with the next highest rate of residential segregation. Despite these high rates of residential segregation for Black and Hispanic persons, their health is either not very different from, or better than, White persons. 

Overall, the evidence in Tables 1 and 2 puts the burden on researchers, such as Arcaya and Schnake-Mahl, to provide more substantial evidence that residential segregation is a significant cause of health disparities beyond individual-level factors, such as race and income. 

It is also worthwhile to highlight a key phrase in the above quote from Arcaya and Schnake-Mahl:

“Methodological limitations and some mixed findings aside…”. Social scientists cannot put aside these fundamental issues related to causality to reach conclusions. Credible empirical analyses and a consistent set of findings are required before a conclusion such as “segregation by race and class as a "fundamental cause" of health disparities” can be drawn.

The sparseness of credible and consistent evidence linking residential segregation to health is illustrated by several quotes from recent studies:

“The health effects of segregation are relatively consistent, but complex. Isolation segregation is associated with poor pregnancy outcomes and increased mortality for blacks, but several studies report health-protective effects of living in clustered black neighborhoods net of social and economic isolation. The majority of reviewed studies are cross-sectional and use coarse measures of segregation.” (Kramer and Hogue 2009, p. 178)

“Socioeconomic status explains much of the association between neighborhood racial segregation and health outcomes.” (Sudano et al. 2013, p.89)

“In the fully adjusted model … higher Hispanic …but not Black … segregation was associated with higher cause-specific mortality.” (Pruit et al. 2015, p. 1852)

Of course, there are some articles that report evidence supportive of the hypothesis that residential segregation is harmful to health (e.g., Haynaga et al. 2013; Johnson et al. 2016), but the bottom line is that the causal link between residential segregation and health is decidedly uncertain.

One argument put forth by Arcaya and Schnake-Mahl is that residential segregation is a cause of inadequate access to healthcare and poor-quality healthcare. The evidence to substantiate this claim is tenuous and based on cross-sectional analyses that have well-known limitations (e.g., Weinick et al. 2000).  I am unaware of any study that has linked changes in residential segregation to changes in access/quality of healthcare for a given person, for example, remaining residents of a transitioning neighborhood. In addition, the link between access (e.g., insurance) and use of care and health remains sparse. For example, results from the Oregon Medicaid Experiment (Finkelstein et al. 2013) and the more dated RAND Health Insurance Experiment (Newhouse 1993) indicated that despite significant increase in the use of health care services associated with having health insurance (or more generous insurance coverage), there was little differences in health between those with and without insurance (or less generous insurance).

A second argument of Arcaya and Schnake-Mahl is that residential segregation gives rise to a segregated healthcare system with low-quality providers serving residents in segregated neighborhoods. However, as shown by researchers from Dartmouth Atlas of Healthcare (e.g., Baicker et al. 2004; Baicker et al. 2005; Goodman et al. 2010), the association between the quality of care (provided to Medicare patients) and racial disparities is not strong.

“Furthermore, there is no consistent pattern of disparities: some areas may have a wide disparity in one treatment but no disparity in another. The problem of differences in quality of care across regions, as opposed to racial disparities in care, should remain the target of policy makers, as reducing quality disparities would play a major role in improving the health care received by all Americans and by minority Americans in particular.” (Baicker et al. 2005, S42)

In sum, the claim that residential segregation (isolation) is an important cause of health disparities net of individual level factors, such as race and income, is not strong. That does not mean it is not true. There are plausible mechanisms linking residential segregation to health that support the scientific plausibility of the argument (Williams and Cohen 2001). And it is possible (likely) that residential segregation causes poverty and other outcomes that may affect health. However, theory and suggestive and inconsistent empirical evidence is a reason to do more research, but it is not sufficient to make causal claims.

Robert Kaestner is a Professor of Public Policy and Economics at the University of California, Riverside. 

More in Discussion 25: Health and Segregation

Health in the Segregated City

by Mariana C. Arcaya , Alina Schnake-Mahl

Segregated Health Systems

by Jose F. Figueroa

Why Aren’t Segregation’s Effects on Health Larger?

by Sherry Glied

Residential Segregation and Health: A Hypothesis Still in Search of Convincing Evidence

by Robert Kaestner

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The Problems of Residential Segregation in USA Research Paper

Introduction.

Residential segregation is an ongoing problem within the U.S. that indicates a deep societal inclination towards bias for certain ethnic and social groups within the general population of the country.

This form of community formation (i.e. Caucasians in one neighborhood, African Americans in another) extends to various social situations related to economic opportunity, education and even the way people are treated in public. Some communities and jobs simply require individuals with more money and a better educational foundation which result in a form of isolation and consolidation of different cultures in communities that are within their economic and educational strata.

It is based on this perception that opportunities related to work, education and being within a safe community are often isolated to Caucasian populations. On the other end of the spectrum, low income housing, lack of job availability and unsafe communities due to high crime rates are often seen among minority populations as seen in the case of L.A. (i.e. Los Angeles). This in effect creates social segregation wherein American society is becoming increasingly divided over racial lines (Jesdale, Morello-Frosch, and Cushing, 811-817).

Today, forms of racial segregation are also evident in society where wealth is increasingly being isolated towards the majority which in this particular case consists of the Caucasian population. The end result of such actions is that minorities remain minorities with wealth, education and connections being increasingly isolated to the upper echelons of society. It is based on this that this paper will explore the current level of racial segregation within various areas in the U.S. and attempt to explain the factors that lead to their development.

Literature Review

Origin of residential segregation.

Residential segregation as it is known today has its origins in the current level of structural inequality within the U.S. social system. Structural inequality can be described as an inherent bias within social structures which can provide some advantages to a select group of people within society while at the same time marginalizing others (Li, Campbell, and Fernandez, 2642-2660).

This can be seen in instances related to racism, education and discrimination wherein certain segments of the population are categorized and marginalized depending on the color of their skin and their particular race (Li, Campbell, and Fernandez, 2642-2660). For example the law involving illegal immigration passed by Arizona has in effect created a form of discrimination against many Mexicans living within the U.S. who are in fact there legally.

Structural inequality is one of the main reasons behind the continued limitations in the development of community systems wherein minorities are in fact being discriminated against due to connotations involving their propensity towards illegal or criminal behavior.

One clear example where structural inequality promotes discrimination can be seen in the current employment rates within the U.S. and their correlation to race. Despite the presence of an assortment of laws barring discriminatory practices, this still occurs among several local or regional businesses (Li, Campbell, and Fernandez, 2642-2660).

While on paper it can be seen as a viable way of providing businesses with the “proper type” of employee, the fact remain that such a discriminatory hiring system has actually resulted in racial lines being drawn with white Americans normally being segregated into the upper tier of community system creation while minorities are usually set in the lower tier system.

Such a system actually perpetuates the concept of societal inequality where it has come to be believed that white Americans are more associated towards success and having better levels of education while minorities are leaning towards marginal careers at best. As explained by Friedman, Tsao, and Chen (2013), communities tend to develop along racial lines due to the availability of opportunities.

Limitation of opportunities is often the result of racial associations with illegal/criminal activity with African Americans, Mexicans and Latin Americans consisting of the three most identifiable demographics when it comes to cataloguing crime in certain parts of the U.S. An article in the New York Times examining the number of African Americans in U.S. jails shows that since 2000 there were 791,600 African inmates behind bars compared to the 603,032 that were enrolled in college.

Comparatively, during the 1980’s there were only 143,000 African Americans in jail and 463,700 enrolled in college (Friedman, Tsao, and Chen, 1477-1498). What this indicates is a growing limitation of opportunities for a minority population in the U.S. that contributes to towards residential segregation

Discrimination in Work Place Environments

Various studies conducted examining the hiring practices of various HR departments have also shown that despite the presence of affirmative action programs most HR personal tend to call people for interviews with names that sound like they belong to a majority while at times ignoring those that belong to minorities unless upper management says that they need to round out the talent pool with minorities (Li, Campbell, and Fernandez, 2642-2660).

Other instances have also shown that despite laws against discriminating individuals due to sex or race, this has not prevented HR personnel from proactively choosing men or Caucasians to fill higher positions in the company as compared to women or minorities. This is one of contributing factors that has lead to residential segregation at the present.

Methodology

The method of research that will be utilized in this paper is actually quite simple, utilizing survey data from Milwaukee, New York, Chicago and Detroit, the researcher will utilize studies that have examined the demographics of these locations and will attempt to explain why certain segments of the local population are segregated the way they are. This will include levels of segregation and census numbers.

Limitation of the Study

Due to the sheer amount of possible locations that could possibly be examined, the researcher has decided to limit the study primarily to Milwaukee, New York, Chicago and Detroit which are listed among the top 10 most segregated locations within the U.S.

Data Analysis

Area examination.

As it can be seen in various inner city neighborhoods that were examined in this study, the population structure in several areas is geared towards low income families and the concentration of minorities into a single area. While on the other end of the spectrum the outer and more affluent suburbs consist predominantly of Caucasians.

Based on an examination of the research data, it can be seen that a considerable degree of “clustering” is present wherein populations of specific ethnicities are not spread out; rather, they are concentrated into small pocket communities or encompass large swaths of the given map.

It is obvious that the Caucasian population easily outstrips the population density of the other ethnicities (i.e. Asians, American Indians, African American and Hispanic) however, what is surprising is the formation of the segregated population sets wherein minority populations are increasingly concentrated within the city while white populations decrease within the city center yet increase as they approach the suburbs.

One way of analyzing this is by comparing the demographic data with the immigration and socio-economic history of the regions that are being examined.

An examination of Milwaukee, New York, Chicago and Detroit showed that on average 75.8 percent of the local population was composed of Caucasians while 18.5 percent was composed of African Americans. The remainder was a mixture of Alaskan Natives, Native Americans, Hispanics, Latinos and other races (Spivak and Monnat, 1414-1437).

What is interesting to note is that based on an examination of survey data from the year 2000 till 2010, the African American population within the areas that were being examined rose by 20% as a result of migrations of the African American population from states such as Mississippi.

The same can be said for the Hispanic and Latino population that increased also increased from 2000 to 2010 which also came about as a result of migration. The reason behind such migrations is connected to the economic robustness of the local market of the population centers that are being examined which many minorities interpreted as a “signal” so to speak to try their luck within the local area.

However, an examination of the migrating African American, Hispanic and Latin American population revealed that a disproportional amount of the immigrants were from low income families with little, if any, substantial work experience and education.

As explained by Iceland, Sharp, and Timberlake (2013), individuals with low income levels and little in the way of substantial education have a greater predilection to go for manual labor jobs due to the limited opportunities that are presented to them. As a result, they tend to concentrate within particular areas in a city center due to the availability of low income housing as well as enabling them to live closer to where they work which helps them to save money (Iceland, Sharp, and Timberlake, 97-123).

Further examination of the demographic data of Milwaukee, New York, Chicago and Detroit shows that what is present is a White majority with an increasing level of Black, Hispanic and Latino immigrants who are looking for better opportunities within the local area through work.

The concentration of Latinos, African Americans, and other minorities within the urban centers is indicative of communities created based on their educational capacity as seen in the study of Nelson (2013) which examined population concentrations and the level of education of the ethnicities there.

Nelson explains that the concentration of Caucasians in America’s suburbia is due to the fact that by virtue of their race, they have lower levels of discriminatory practices leveled against them which gives them access to more opportunities in relation to education and wealth creation.

This helps to develop the necessary income levels to actually afford a house located within the suburbs. While such a viewpoint may be controversial since few individuals would openly admit to being racist due to the negative social connotations attached to the description, the fact remains that there is still a level of racial animosity within the local population of the U.S. (Nelson, 646-657).

Evidence of this can be seen in the study of Britton and Goldsmith (2013) which examined the local population of the U.S. and saw that there was still a considerable income and education gap between the White majority population and the African American-Mexican-Latino minority population.

Neighborhoods are still drawn across “ethnic lines” so to speak with White neighborhoods often being the most economically stable and prosperous as compared to their minority counterparts (Britton and Goldsmith, 2886-2903). Other studies support such claims by showing that the areas that have been examined have had a history of racial discrimination since the mid 1990s which continues to persist due to the population imbalance (Spivak and Monnat, 1414-1437).

Access to Education as one of the reasons behind Residential Segregation

While it may be true that some minorities do have difficulties in learning due to their origins, the fact remains that such a system actually perpetuates the concept of societal inequality where it has come to be believed that white Americans are more oriented towards success while minorities are leaning towards marginal careers at best.

This is not only limited to the current school system in lower grades but also in higher education wherein the basis of college admission is the use of SAT scores as a indicator of talent in an individual (Spivak and Monnat, 1414-1437). The one problem with using SAT scores as the main criteria for evaluating college admissions is that they fail to accurately represent the true value or abilities that a person possesses.

Take for example an individual who works to support his family, gets marginally good grades in school and average SAT results, it can be assumed that the average SAT results and the marginally good grades could be attributed to the fact that this individual has to work to support his family and, as a result, could not devote the same amount of time into studying.

Most individuals would not be capable of balancing work, family obligations and going to school yet here is a person that is able to do that. Based on an examination of various applications of minorities to several colleges, it has been shown that, on average, the SAT score of white Americans outclassed that of their minority counterparts yet this is not an indicator of superior talent, rather, white students were merely given more opportunities to learn and develop as a result of their social advantage (Spivak and Monnat, 1414-1437).

This particular form of structural inequality denies the possibility of certain minorities from entering particular colleges resulting in not only a degree of inequality in lower education but in higher education as well.

The end result of this level of segregation is that minority populations are stuck with low paying jobs resulting in a greater concentration towards living within low income communities within cities. This can be seen in the study data which is indicative of the correlation between education and opportunity and how this impacts residential segregation.

Crime and Residential Segregation

Based on the information that was provided in the previous section, an individual’s status as a minority would limit their capacity to obtain a decent paying job and increases the likelihood of them turning towards a life of crime to sustain themselves. It should also be noted that a majority of the businesses within Milwaukee, New York, Chicago and Detroit are owned by the White majority with managers, shop keepers and other individuals of authority normally being White.

In their study involving discrimination and its impact on criminal behavior, Sharp, and Timberlake (2013), explain that a combination of structural inequality with tendencies of racial discrimination limits the opportunities available to minority populations which creates a greater tendency towards criminal activity.

It must be noted that the rate of crime in certain areas has been proven to go up depending on the income rate of the populations within it. As such, areas with population structures geared towards low income families and people create the possibility for criminal behaviors to occur as a result of desperation or the distinct influence from people in the surrounding environment. For example, various social scientists indicate that a person’s race is invariably connected to that person’s propensity or possibility of being able to commit a crime.

The two most identifiable minorities in connection to a vast majority of crime in the country (i.e. African American and Hispanic) are also the two most identifiable minorities in connection to poverty, social inequality and a distinct lack of education standing and achievement. It can also be seen from the study data that these minorities are also the most concentrated within city centers and are rarely seen in the suburbs.

Data from various school districts around the U.S. reveals that communities composed of African Americans and Hispanics were among those that were predicted to perform the most poorly in terms of scholastic achievement while communities composed primarily of white Americans were predicted to perform at a much higher level. This is in part due to two factors: racial prejudice against the capabilities of minorities and class prejudice against a class with a lower income threshold.

While school districts may say they are not prejudiced the fact remains that the current system of segregation within schools wherein students at the same grade level are grouped into different blocks depending on aggregate skill is in fact a form of discrimination since it encourages social class disparity. From a sociological perspective this particular form of behavior encourages the creation of criminal tendencies in people since it reinforces the social idea that minorities cannot rise above what they currently are.

This is one of the facilitators of residential segregation that this study has come across since a concentration of low income families in an area increases the predilection towards criminal behavior especially when factoring in a lack of potential opportunities.

As a result, white populations that have high levels of opportunity and income would tend to avoid living in such areas and would focus on areas with low crime rates. This is can be seen in the examination that was done wherein concentrations of white populations was in areas far away from the city.

What this paper has shown is that the connection between race, culture and population structures can be understood under the sociological context that since certain races and population demographics are impacted by limited opportunities, they are not given the chance to rise above their current level which causes residential segregation.

While it may be true that there are cases where minorities do in fact achieve the so called “American Dream” they represent only a small fraction of a population that is being placed in a disadvantageous situation by a system that is inadequately capable of fully providing them with the tools they need to become a success.

Works Cited

Britton, Marcus L., and Pat Rubio Goldsmith. “Keeping People In Their Place? Young – Adult Mobility And Persistence Of Residential Segregation In US Metropolitan Areas.” Urban Studies (Sage Publications, Ltd.) 50.14 (2013): 2886-2903. Print.

Friedman, Samantha, Hui-shien Tsao, and Cheng Chen. “Housing Tenure And Residential Segregation In Metropolitan America.” Demography 50.4 (2013): 1477-1498. Print.

Iceland, John, Gregory Sharp, and Jeffrey M. Timberlake. “Sun Belt Rising: Regional Population Change And The Decline In Black Residential Segregation, 1970- 2009.” Demography 50.1 (2013): 97-123. Print.

Jesdale, Bill M., Rachel Morello-Frosch, and Lara Cushing. “The Racial/Ethnic Distribution Of Heat Risk-Related Land Cover In Relation To Residential Segregation.” Environmental Health Perspectives 121.7 (2013): 811-817. Print.

Li, Huiping, Harrison Campbell, and Steven Fernandez. “Residential Segregation, Spatial Mismatch And Economic Growth Across US Metropolitan Areas.” Urban Studies (Sage Publications, Ltd.) 50.13 (2013): 2642-2660. Print.

Nelson, Kyle Anne. “Does Residential Segregation Help Or Hurt? Exploring Differences In The Relationship Between Segregation And Health Among U.S. Hispanics By Nativity And Ethnic Subgroup.” Social Science Journal 50.4 (2013): 646-657. Print.

Spivak, Andrew L., and Shannon M. Monnat. “The Influence Of Race, Class, And Metropolitan Area Characteristics On African-American Residential Segregation.” Social Science Quarterly (Wiley-Blackwell) 94.5 (2013): 1414-1437. Print.

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• Residential and Commercial Design
• Controversy of Gender and Race Discrimination
• Gender and the Black Freedom Movement
• White Privilege Social Issue
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• Race and Gender: What Binds People Together
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• Introduction
• Conclusions
• Article Information

Residential segregation measure (isolation index) represents the probability that Black residents would interact with White residents and vice versa across US census tracts in a maternal residential county, categorized into low (<40%), medium (40%-59%), and high (≥60%).

Residential segregation measure (isolation index) represents the probability that Black residents would interact with White residents and vice versa across US Census tracts in a maternal residential county, categorized into low (<40%), medium (40%-59%), and high (≥60%). Data for non-Hispanic other race group and some data for Hispanic group are suppressed due to less than 10 cases. All race and ethnicity groups’ rates were calculated across all childbirths of non-Hispanic White (White), non-Hispanic Black (Black), Hispanic, and other race populations.

Adjusted means and 95% CIs (upper and lower) of severe maternal morbidity rates were calculated from model estimates presented in eTable 3 in the Supplement and average covariate values in the Table .

eTable 1. Study Sample Inclusion and Exclusion Criteria

eTable 2. Maternal Characteristics by Race and Ethnicity and Residential County Black Residential Segregation

eTable 3. The COVID-19 Pandemic Changes in Severe Maternal Morbidity and Its Racial and Ethnic Disparities by Racial Residential Segregation

eFigure. Monthly and 6-Month Moving Prevalence of Severe Maternal Morbidity Without Blood Transfusion Among White, Black and Hispanic Women Giving Birth in January 2018 to June 2021 by County-Level Black-White Residential Segregation Level

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Hung P , Liu J , Norregaard C, et al. Analysis of Residential Segregation and Racial and Ethnic Disparities in Severe Maternal Morbidity Before and During the COVID-19 Pandemic. JAMA Netw Open. 2022;5(10):e2237711. doi:10.1001/jamanetworkopen.2022.37711

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Analysis of Residential Segregation and Racial and Ethnic Disparities in Severe Maternal Morbidity Before and During the COVID-19 Pandemic

• 1 Department of Health Services Policy and Management, University of South Carolina Arnold School of Public Health, Columbia
• 2 South Carolina SmartState Center for Healthcare Quality, University of South Carolina Arnold School of Public Health, Columbia
• 3 Department of Epidemiology and Biostatistics, University of South Carolina Arnold School of Public Health, Columbia
• 4 Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, University of South Carolina School of Medicine, Columbia
• 5 Department of Health Promotion Education and Behavior, University of South Carolina Arnold School of Public Health, Columbia

Question   Was living in a highly segregated Black community associated with severe maternal morbidity (SMM) before and during the COVID-19 pandemic?

Findings   In this cohort study of 166 791 South Carolina women with childbirths from January 2018 to June 2021, Black and Hispanic women living in high-segregated Black communities had higher odds of SMM than their counterparts living in less segregated communities. During the pandemic, Black vs White disparities in SMM persisted, while the Hispanic vs White disparities were exacerbated.

Meaning   These findings suggest that policy initiatives on improving maternal health should combat the corresponding structural racism associated with residential segregation.

Importance   Persistent racial and ethnic disparities in severe maternal morbidity (SMM) in the US remain a public health concern. Structural racism leaves women of color in a disadvantaged situation especially during COVID-19, leading to disproportionate pandemic afflictions among racial and ethnic minority women.

Objective   To examine racial and ethnic disparities in SMM rates before and during the COVID-19 pandemic and whether the disparities varied with level of Black residential segregation.

Design, Setting, and Participants   A statewide population-based retrospective cohort study used birth certificates linked to all-payer childbirth claims data in South Carolina. Participants included women who gave birth between January 2018 and June 2021. Data were analyzed from December 2021 to February 2022.

Exposures   Exposures were (1) period when women gave birth, either before the pandemic (January 2018 to February 2020) or during the pandemic (March 2020 to June 2021) and (2) Black-White residential segregation (isolation index), categorizing US Census tracts in a county as low (<40%), medium (40%-59%), and high (≥60%).

Main Outcomes and Measures   SMM was identified using International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes developed by the US Centers for Disease Control and Prevention. Multilevel logistic regressions with an interrupted approach were used, adjusting for maternal-level and facility-level factors, accounting for residential county-level random effects.

Results   Of 166 791 women, 95 098 (57.0%) lived in low-segregated counties (mean [SD] age, 28.1 [5.7] years; 5126 [5.4%] Hispanic; 20 523 [21.6%] non-Hispanic Black; 62 690 [65.9%] White), and 23 521 (14.1%) women (mean [SD] age, 28.1 [5.8] years; 782 [3.3%] Hispanic; 12 880 [54.8%] non-Hispanic Black; 7988 [34.0%] White) lived in high-segregated areas. Prepandemic SMM rates were decreasing, followed by monthly increasing trends after March 2020. On average, living in high-segregated communities was associated with higher odds of SMM (adjusted odds ratio [aOR], 1.61; 95% CI, 1.06-2.34). Black women regardless of residential segregation had higher odds of SMM than White women (aOR, 1.47; 95% CI, 1.11-1.96 for low-segregation; 2.12; 95% CI, 1.38-3.26 for high-segregation). Hispanic women living in low-segregated communities had lower odds of SMM (aOR, 0.48; 95% CI, 0.25-0.90) but those living in high-segregated communities had nearly twice the odds of SMM (aOR, 1.91; 95% CI, 1.07-4.17) as their White counterparts.

Conclusions and Relevance   Living in high-segregated Black communities in South Carolina was associated with racial and ethnic SMM disparities. During the COVID-19 pandemic, Black vs White disparities persisted with no signs of widening gaps, whereas Hispanic vs White disparities were exacerbated. Policy reforms on reducing residential segregation or combating the corresponding structural racism are warranted to help improve maternal health.

Severe maternal morbidity (SMM) rates in the US tripled from 49.5 in 1993 to 146.6 cases per 10 000 childbirths in 2014. 1 These SMM rates are unevenly distributed geographically and socioeconomically, with the highest rate among low-income women who delivered at hospitals in Southern states. 2 - 5 South Carolina’s pregnancy-related mortality rate, much like SMM rates, is among the highest in the US, with 25.5 deaths per 100 000 in 2014 to 2018. 6 Black and women of other minority groups living in South Carolina experience a 3-fold higher pregnancy-related mortality rate (46.3 deaths per 100 000) compared with White women (13.7 deaths per 100 000). 6 The rising SMM rate and persistent racial and ethnic disparities trigger public health concerns, not only due to the immediate burden faced by vulnerable women, but also due to potentially lasting effects on women’s health over a life course or along family lines across generations. 7

The origin of these racial disparities in SMM is complex and multifaceted. 8 At the micro level, maternal sociodemographic factors (eg, education and socioeconomic status), health behaviors (eg, prenatal care adequacy, smoking, diet, physical activity, and gestational weight gain), and preexisting maternal conditions (eg, hypertension, obesity, and diabetes) are associated with SMM disparities. 5 , 9 At the macro level, structural racism and discrimination—historical and/or current oppression that results in disparities in access to opportunities and resources—might shape pregnant individuals’ lived experience at multiple levels, 5 , 10 including community (eg, residential segregation, housing, access to healthy food, and transportation), health care organization (eg, access to high quality care and receipt of risk-appropriate treatments), and state (eg, Medicaid eligibility policy), further exacerbating racial and ethnic inequities in maternal and birth outcomes in the US. 10 - 12 Despite several sociological theories trying to explain the multilevel pathways of structural racism on racial and ethnic health inequities, 13 - 15 limited evidence has categorized their role in SMM, especially during the COVID-19 pandemic.

The COVID-19 pandemic has led to unprecedented societal disruption with a wide range of social and systemic influence at multiple levels. Social isolation, financial distress, community crowdedness, food insecurity, neighborhood violence, technology literacy, and access to perinatal care are among key structural factors that are associated with racial and ethnic disparities in both physical and mental health outcomes. 16 - 19 The pandemic has affected communities of color the hardest 20 - 22 ; Black and Hispanic pregnant women appear to have disproportionate SARS-CoV-2 exposure, infection, and death rates. 23 - 25 Structural racism, often measured by residential segregation, is compounding the COVID-19 pandemic crisis, potentially leaving women of color in a disadvantaged situation in terms of psychosocial stress, employment, education, digital access, and income. 17 , 20 - 22 Understanding how structural racism is associated with SMM disparities, especially amid the COVID-19 pandemic, is essential to identify opportunities to eliminate racial and ethnic disparities in SMM and develop tailored interventions for corresponding risk factors, thus improving maternal health equity.

Few definitive data have concluded how the COVID-19 pandemic has changed the incidences of SMM across maternal racial and ethnic groups, especially through racial residential segregation and its corresponding socioeconomic deprivation. We used a statewide population-based database and the months that have elapsed since the COVID-19 pandemic as a natural experiment to assess its association with SMM rates by racial residential segregation.

This statewide cohort study used data of all-payer childbirth records in South Carolina from inpatient discharges, emergency department visits, outpatient surgery, and other outpatient services, linked to birth certificates for maternal demographic, parity, chronic disease risk factors, and prenatal care utilization patterns. The South Carolina Office of Revenue and Fiscal Affairs collated databases and provided the authors with a deidentified linked database with unique identifiers for every individual. The study was considered exempt from the need for informed consent and institutional review board approval at the University of South Carolina due to the nature of the secondary analysis of deidentified data. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline for cohort studies.

We identified all childbirth deliveries to women living in South Carolina regardless of the place of birth that occurred between January 2018 and June 2021 using International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnoses and procedure codes, Diagnosis-Related Group codes, and Current Procedural Terminology codes (eTable 1 in the Supplement ). The 2015 to 2019 American Community Survey (ACS) US Census tract-level racial and ethnic compositions were derived to calculate county-level residential segregation measure.

SMM was the primary outcome and SMM without blood transfusion was the secondary outcome, defined as whether a woman experienced any of the 21 indicators developed by the Centers for Disease Control and Prevention from a childbirth hospitalization admission date to 60 days post partum. 26

Key exposures include (1) a pandemic indicator (ie, prepandemic [January 2018 to February 2020] and peripandemic [March 2020 to June 2021] periods of childbirth delivery), and (2) a county-level residential segregation measure (isolation index) that represents the probability that Black residents would interact with White residents and vice versa across US Census tracts in a maternal residential county, categorized into low (<40%), medium (40%-59%), and high (≥60%). 27 This isolation index was calculated across US Census tracts using non-Hispanic Black and White as population groups. Higher scores of Black vs White isolation index indicate more extensive isolation of Black residents across US Census tracts in the county, and lower scores suggest less isolation or more Black vs White exposure, compared with the average interracial exposure of the county. 11 , 27 , 28

Covariates included maternal age at childbirth, maternal race and ethnicity (Hispanic, non-Hispanic Black, non-Hispanic White, and non-Hispanic other races, which includes Asian, American Indian, Native Hawaiian and other Pacific Islander, and multiracial groups); maternal education attainment; primary payer; prepregnancy body mass index; enrollment in Women, Infants and Children (WIC) Nutrition Program; gestational trimester of prenatal care initiation; smoking during pregnancy; previous cesarean delivery; preterm labor; plurality; urban vs rural location of health care facility; level of perinatal care; and chronic conditions (preexisting diabetes, gestational diabetes, preexisting hypertension, and pregnancy-induced hypertension). Race and ethnicity data were derived from birth certificate data in which maternal race was categorized as Black, other (Asian, American Indian, Native Hawaiian and other Pacific Islander, and multiracial groups), and White, while Hispanic was a separate variable. We grouped childbirth records with missing values into a separate stratum for each variable.

We first used frequencies and percentages to describe maternal sociodemographic characteristics before the pandemic and during the pandemic across Black vs White residential segregation categories and across maternal race and ethnicity groups in each residential segregation level. Differences were compared using Pearson χ 2 tests for nominal variables and Mantel-Haenszel tests of trend for ordinal variables. Next, trends in SMM rates per race and ethnicity group in a segregation level were assessed using the Mann-Kandall tests. Furthermore, multilevel mixed-effects logistic regressions were used, adjusting for maternal sociodemographic, clinical, and behavioral factors; facility-level perinatal care level; rurality for place of birth; and accounting for maternal residential county-level random effects and interrelated monthly trends. In the modeling procedure, we performed an interrupted time series analysis by including a term for prepandemic monthly trend, a pandemic variable to indicate peripandemic childbirth, and a term to estimate changes in monthly trends during the peripandemic period to test the association between the COVID-19 pandemic and SMM. We also conducted a set of sensitivity analyses to look at 2-way interactions of prepandemic and peripandemic monthly trends with segregation and race and ethnicity. The racial and ethnic disparities, within each level of segregation, and pandemic-associated changes were assessed through odds ratios (OR) and 95% CIs. All statistical analyses were undertaken using Stata statistical software version 16 (StataCorp), with statistical significance set at P  < .05 (2-tailed). Data were analyzed from December 2021 to February 2022.

Of 166 791 childbirths, 95 098 (57.0%) were to women in low-segregated Black communities pre–COVID-19 (mean [SD] age, 28.1 [5.7] years; 20 523 [21.6%] Black, 5126 [5.4%] Hispanic, 62 690 [65.9%] White), 48 172 (28.9%) to women in medium-segregated communities (mean [SD] age, 28.1 [5.8] years; 17 863 [37.1%] Black, 1899 [3.9%] Hispanic, 25 129 [52.2%] White), and 23 521 (14.1%) to women in high-segregated communities (12 880 [54.8%] Black, 782 [3.3%] Hispanic, 7988 [34.0%] White) ( Table ). Compared with those in low-segregated areas, women in high-segregated communities were more likely to have Medicaid or other public insurance (vs private), to be diagnosed with obesity before pregnancy, to enroll in the WIC program, to have inadequate prenatal care visits, to have preexisting hypertension, and to deliver in rural health care facilities or hospitals with level II perinatal care (vs level III).

Compared with those in low-segregated communities, women of all race and ethnicity groups living in high-segregated communities were more likely to have childbirths at age 30 years or older, to have obesity before pregnancy, and to enroll in WIC programs (except non-Hispanic White women). In addition to these differences, non-Hispanic women across all races in high-segregated communities had higher educational attainment. Unlike non-Hispanic women, Hispanic women living in high-segregated areas were more likely to have no high school diploma, to be primarily Medicaid insured or have no health insurance, and have preexisting diabetes than Hispanic women in low-segregated areas (eTable 2 in the Supplement ).

Unadjusted rates of SMM measures were highest among mothers who reside in high-segregated areas ( Figure 1 ). On average, living in high-segregated communities was associated with higher odds of SMM (adjusted OR [aOR], 1.61; 95% CI, 1.06-2.34). SMM rates among Black women were highest overall, in both prepandemic and peripandemic periods. These rates were much higher among Black women in high-segregated communities (196 [244.8 cases per 10 000] in the prepandemic period and 127 [260.6 cases per 10 000] in the peripandemic period) than Black women living in low-segregated communities (244 [192.6 cases per 10 000] in the prepandemic period and 135 [171.8 cases per 10 000] in the peripandemic period). Of those in high-segregated communities, Hispanic mothers had a substantially higher SMM rate (202.7 cases per 10 000) during the COVID-19 pandemic period compared with prepandemic (144.9 cases per 10 000). SMM rates among Hispanic women in high-segregated areas were also higher than Hispanic women in low-segregated areas and non-Hispanic white women in both high and low-segregated communities. Similar trends in SMM without blood transfusion exist (eFigure in the Supplement ).

During January 2018 to June 2021, Black women regardless of residential segregation had consistently higher SMM rates than their White counterparts ( Figure 2 ). Black women in high-segregated communities had a slightly decreasing trend in January 2019 to March 2020, but this decreasing trend was reversed in April 2020. SMM rates for Hispanic women in low-segregated communities had a continual increasing trend (Kendall τ = 0.21; P  = .02) but rates for those in high-segregated communities fluctuated.

Black and Hispanic women living in high-segregated Black communities had higher probabilities of SMM than their counterparts living in low-segregated communities ( Figure 3 ). Black vs White disparities in SMM persisted across communities by segregation, but disparities increased with segregation level (low-segregated: aOR, 1.47; 95% CI, 1.11-1.96; high-segregated: aOR, 2.12; 95% CI, 1.38-3.26) (eTable 3 in the Supplement ). Hispanic women living in low-segregated communities had lower odds of SMM (aOR, 0.48; 95% CI, 0.25-0.91) but those living in high-segregated communities had nearly twice the odds of SMM (aOR, 1.90; 95% CI, 1.07-4.17) as their White counterparts. On average, the odds of SMM were decreasing before March 2020 (prepandemic monthly trends: aOR, 0.98; 95% CI, 0.97-1.00), with no significant immediate level changes in March 2020 (aOR, 1.19; 95% CI, 0.85-1.67), but followed by monthly increasing trends after March 2020 (aOR, 1.05; 95% CI, 1.02-1.08). Pandemic changes in SMM did not vary with Black residential segregation but varied with maternal race and ethnicity. In the sensitivity analysis where 3-way interactions across pandemic indicator, race and ethnicity, and isolation index level were included, peripandemic increasing trends were not significantly different between Black and White women, but were higher among Hispanic women (aOR, 1.48; 95% CI, 1.14-2.28) vs their White counterparts.

In this statewide population-based cohort study, we found that living in high-segregated Black communities was associated with greater risk of maternal morbidity, independent of individual demographic, clinical, and economic conditions. In low-segregated communities, the odds of SMM among non-Hispanic Black women were 1.47 times higher than the odds for White women, and in high-segregated communities, this Black vs White disparity was exacerbated. Interestingly, Hispanic women residing in the low-segregated communities had lower odds of SMM than non-Hispanic White women, but Hispanic women living in high-segregated Black communities had nearly 2-fold the odds of SMM compared with non-Hispanic White women. This Hispanic vs White gap in high-segregated communities was particularly elevated during the pandemic. From January 2018 to February 2020, the prepandemic period, the incidence of SMM had a monthly decreasing trend, followed by an increasing monthly trend during the peripandemic period. These pandemic-associated increases in SMM persisted across levels of Black residential segregation.

In each race and ethnicity group, SMM risk was higher among minoritized residents living in high-segregated communities compared with their counterparts in low-segregated communities. This finding is consistent with existing evidence on the increased maternal health issues for women living in majority-Black neighborhoods. 29 , 30 The fact that such residential segregation was associated with SMM after adjusting for individual-level variations in education, insurance, obesity, nutrition, prenatal care, smoking during pregnancy, and clinical factors has critical implications. First, the residents living in Black segregated communities had worse maternal morbidity outcomes, which might be a totality of historical and structural racism, rather than individual socioeconomic burdens. Cumulative and mutually reinforcing discrimination of housing, education, employment, criminal justice, economic opportunities, and health care put these racial minority populations at higher risk of adverse maternal outcomes. Second, during the pandemic, Black communities appear to have disproportionate COVID-19 outcomes. Furthermore, pregnant women of color who were infected with SARS-CoV-2 were much more likely to be hospitalized or die. 31 - 33 Third, Black and Hispanic women living in the most segregated areas had less access to high-quality obstetric care than White women, 34 likely due to the uneven or unequal distribution of high-quality hospital obstetric units. 35 Multilevel factors such as racial residential segregation across communities, hospitals with high SMM rates, and areas with hospital obstetric closures call into question whether underlying structural racism is hindering Black women from accessing high-quality hospitals 36 , 37 and, in turn, increasing their risk of SMM. 36 To advance maternal health equity and improve population health, addressing the challenges faced in high Black segregated communities is important. For example, an inclusion of pregnant persons’ residence information and social determinant of health ICD-10-CM codes in the electronic health record or medical records may be an important starting point for this effort to better inform clinical prognosis planning for COVID-19 and perinatal care.

Research on the association between residential segregation or structural racism and maternal health has focused on racial disparities between Black women and White women. 30 , 38 , 39 Our findings suggest that such Black residential segregation is not solely associated with Black vs White maternal health disparities. In the current study, the highest SMM incidence of Hispanic women was detected among those residing in high-segregated Black communities during the COVID-19 pandemic, where 200 or more of 10 000 women experienced SMM. Although Hispanic women across all levels of segregations experienced higher SMM incidences peripandemic vs prepandemic, the SMM incidence increased disproportionately in the high-segregated communities. The reasons for Black residential segregation being significantly associated with SMM risks among Hispanic populations are likely multifactorial. Higher prevalence of SARS-CoV-2 infections among Black and Hispanic persons may play a role in the increased rates of SMM in these populations. 4 , 31 - 33 Also, two-thirds of Hispanic women with childbirths in South Carolina were immigrants 40 whose inadequate prenatal care and adverse birth outcomes were documented to be associated with criminalizing immigrant policies. 41 , 42 In South Carolina, only lawfully residing pregnant women would be provided with medical coverage, whereas about 20 other states provide prenatal care regardless of pregnant person’s immigration status. 43 Additionally, these Hispanic populations are more likely to work in essential industries and the associated psychosocial stress might further increase their risks of SMM. 44 , 45 Importantly, members of Hispanic populations face poorer access to health care and economic instability than non-Hispanic populations. In the current study, Hispanic women living in highly Black segregated communities had disproportionately high uninsured or Medicaid insurance rates, as well as much lower educational attainment than their Hispanic counterparts in the least segregated communities. Poverty, unstable housing, lack of transportation, and poor access to quality education, among other social determinants of health, are more common overall in Hispanic populations. 46 Also, lack of health insurance and low health literacy, especially under the circumstance when one’s primary language was other than English, might add another layer of the difficulties faced by these Hispanic families. 46 Together, these factors may intensify disparities in health outcomes, including SMM. This analysis provides empirical data at the intersection between race, ethnicity and structural racism in maternal health outcomes among SC women and should inform efforts to advance maternal health equity in underprivileged communities.

Ongoing efforts have called for addressing the maternal health crisis in the US. 49 The Healthy People 2030 goals seek to reduce severe maternal complication to 0.62% and reduce maternal deaths to 15.7 per 100 000 live births. 50 This study suggests that ways to prevent future SMM cases should pay particular attention to families living in high Black segregation communities. The intersection of SMM risks based on both race and ethnicity–based and place-based inequities requires comprehensive, inclusive, and feasible practices to address the social factors that contribute to the persistent and sadly increasing SMM rates during the pandemic. Yet, it is critical to recognize that Black and Hispanic populations are heterogeneous, and this study identified profound disadvantages among those residing in high Black segregated counties. Improving maternal outcomes for minority populations requires engagement with these families who might have distinct barriers to receive optimal health care before, during, and following childbirth.

Although this study highlights residential segregation as a factor associated with risk for SMMs, 47 the results of this study should be interpreted cautiously in light of data limitations. We derived data from South Carolina population-based health utilization data of all childbirths from January 2018 to June 2021; SMM may occur up to 1 year after childbirth and this study adopted a previously documented approach by identifying SMM from childbirth admission through 2 months post partum, which should capture the majority of the incidences. 48 Moreover, circumstances unique to South Carolina such as social structures, non-Medicaid expansion, health coverage of immigrant populations, and all-statewide counties designated whole or in part as medically underserved areas might have contributed to the findings. We examined isolation index as a measure of residential segregation because it was previously documented to be a sensitive segregation measure to perinatal health, 11 , 28 but structural racism might also manifest in other forms such as institutional racism at the delivery hospitals. Additionally, due to sample size, other racial populations such as American Indian and Alaska Natives were not able to be separately studied in the current study, which warrants future investigations given their high exposure and deaths related to SARS-CoV-2.

In this statewide cohort study of more than 100 000 South Carolina women with childbirths from January 2018 to June 2021, Black and Hispanic women living in high-segregated Black communities had higher odds of SMM than their counterparts living in less segregated communities, respectively. During the pandemic, Black vs White disparities in SMM persisted while the Hispanic vs White disparities were exacerbated. Policy reforms to reduce residential segregation or combat the corresponding structural racism are warranted to help improve maternal health.

Accepted for Publication: September 6, 2022.

Published: October 20, 2022. doi:10.1001/jamanetworkopen.2022.37711

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2022 Hung P et al. JAMA Network Open .

Corresponding Author: Peiyin Hung, PhD, MSPH, Department of Health Services Policy and Management, University of South Carolina Arnold School of Public Health, 915 Greene St, Columbia, SC 29208 ( [email protected] ).

Author Contributions : Drs Hung and Liu had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Hung, Liu, Liang, Li.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Hung, Norregaard, Shih, Zhang.

Critical revision of the manuscript for important intellectual content: Hung, Liu, Norregaard, Liang, Olatosi, Campbell, Li.

Statistical analysis: Hung, Shih, Liang, Zhang.

Obtained funding: Hung, Liu, Liang, Li.

Administrative, technical, or material support: Hung, Liu, Norregaard, Liang, Olatosi, Campbell, Li.

Supervision: Liu, Liang, Li.

Conflict of Interest Disclosures: Dr Hung reported receiving grants from the National Institutes of Health (NIH) and Health Resources and Services Administration during the conduct of the study. Dr Liu reported receiving grants from the National Institutes of Health (NIH) during the conduct of the study. Dr Shih reported receiving grants from University of South Carolina outside the submitted work. Dr Liang reported receiving grants from the NIH during the conduct of the study. Dr Zhang reported receiving grants from NIH during the conduct of the study. Dr Olatosi reported receiving grants from NIH outside the submitted work. Dr Li reported receiving grants from the NIH during the conduct of the study. No other disclosures were reported.

Funding/Support: Research reported in this study was supported by National Institute of Allergy and Infectious Diseases of the NIH under award number R01AI127203-5S2.

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Additional Information: Data for this study were provided by the South Carolina Department of Health and Environmental Control and the South Carolina Office of Revenue and Fiscal Affairs.

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The unexpected explanation for why school segregation spiked

On eve of the 70th anniversary of Brown v. Board of Education, a study finds policy choices explain the rise in segregated schools.

It’s well documented that after falling for years, school segregation has risen again in the United States. But why? New research by academics at the University of Southern California and Stanford University concludes that some popular theories are not to blame.

Ahead of the 70th anniversary of the Supreme Court’s landmark 1954 Brown v. Board of Education decision, a study being released Monday shows a pronounced increase in school segregation since 1988, particularly in large school districts with significant numbers of Black students.

Overall, school segregation between Black and White students has increased by 25 percent since 1991 in the 533 large districts serving at least 2,500 Black students — a significant increase but nowhere near the decline that occurred in the aftermath of Brown , according to the study. (Of note: the paper makes clear that most of the school segregation in the United States is driven by demographic differences between districts , not within them.)

A school district that was entirely segregated would score 1.0 on the researchers’ segregation scale, whereas a perfectly integrated district, where every school perfectly matched the overall district’s demographics, would score 0.0.

Looking at the nation’s 100 largest districts, segregation was 0.45 in 1968. That fell to 0.17 by 1986 and then rose to 0.28 by 2019, researchers found. So while schools are nowhere near as segregated as they were before courts began enforcing the Brown decision, segregation has risen in recent decades.

Researchers offered the example of the Charlotte-Mecklenburg schools system in North Carolina, where segregation was absolute — a score of 1.0 — in 1950, before Brown . By 1968, it remained a still-high 0.66 — at that time, the average White student’s school was 10 percent Black, while the average Black student’s school was 76 percent Black (the difference between 10 and 76 produces the score of 0.66).

Then, in 1971, after the courts ordered a desegregation plan in another landmark court case, this one involving the Charlotte-Mecklenburg district , the segregation score there shrank to just 0.03. (The average White student’s school was 31 percent Black; the average Black student’s school was 34 percent Black.) By 1991, it was still low at 0.10 before rising again. In 2022, segregation had reached 0.44.

The study finds that the rise nationally was not driven by increasing housing segregation. Housing segregation certainly helps explain school segregation. But since 1991, housing has become less segregated.

The study also finds that rising school segregation is not driven by racial economic inequality because racial economic inequality also declined over this period.

Both of these trends “would have led to lower school segregation, had nothing else changed,” said the paper by Ann E. Owens, a sociologist at USC, and Sean F. Reardon, a professor of poverty and inequality in education at Stanford.

So what does explain the rise?

Rather than systemic forces that are difficult to change, these trends are driven by policy choices, they conclude. The researchers point to two specific policies: federal courts releasing school districts, including Charlotte-Mecklenburg, from obligations to desegregate schools beginning in significant numbers in the late 1990s; and school-choice policies that let parents pick what school their children attend.

“It’s not these big structural factors that are outside the school districts’ control that are driving this,” Reardon said in an interview. “It’s things that are under the control of the educational system.”

Court-ordered desegregation plans implemented based on the Brown decision had reduced segregation. But then judges began lifting those orders. “If you switch from an active desegregation effort and go back to neighborhood schools, school segregation is going to go up a lot,” Reardon said.

Had those court orders not been lifted, the study estimates that school segregation would have grown 20 percent less than it did.

At the same time, choice systems such as the introduction of charter schools allowed parents more control — and many used that to choose schools with students like their own. The new study specifically looked at the growth of charter schools and found that if charter schools had not expanded, school segregation would have grown 14 percent less.

These two factors account for all of the rise in school segregation from 2000 to 2019, the paper found.

The rising segregation numbers “appear to be the direct result of educational policy and legal decisions,” the paper concludes. “They are not the inevitable result of demographic changes — and can be changed by alternative policy choices.”

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