Minority Neighborhoods Research Articles

Community reinvestment challenges in the age of gentrification: Pittsburgh, Pennsylvania as a case study for wide bank lending disparities

Abstract Racially unequal home mortgage loan patterns are alive and well today with many financial institutions. Efforts to change these trends are difficult when few question the ‘reinvestment thesis’—an environment in which financial institutions are assumed to meet community needs, even when data suggest the opposite. In this article, we analyze thirteen years of financial institution lending data from 2007 to 2019 in Pittsburgh that show how the city’s African American neighborhoods are starved for private capital as vastly more loans and loan dollars were approved in white neighborhoods. Conversely, an analysis of public expenditures for affordable housing between 2010 and 2020 demonstrate the majority of government dollars went to minority neighborhoods. These data provide a partial explanation as to why Pittsburgh lost > 10,000 Black residents over the past decade. These wide disparities are a significant barrier to building African American wealth and present challenges to community development efforts.

Impact of Neighborhood Sociodemographic Characteristics on Food Store Accessibility in the United States Based on the 2020 US Census Data.

BackgroundIt has been previously reported that access to quality food is greatly impacted by neighborhood food store quality and availability, which in turn is determined by a complex interplay of sociodemographic factors. Low-income predominantly Black neighborhoods face the most limited access to quality food. The purpose of this study was to examine the newly available 2020 Census to see if any significant change has occurred to this pattern and if any new insights can be gained by analyzing these data.Methods2020 US Census and current ReferenceUSATM food store data were merged and multivariate Negative Binomial Count Regression Models were used to establish the relationship between different types of food stores (high, medium, and low quality) and neighborhood characteristics including urbanicity, poverty level, and race/ethnicity.Results11.5% of the predominantly Non-Hispanic (NH) White census tracts (CT) (6,486 out of 56,192), 61.3% (4,002 out of 6,531) of the predominantly Non-Hispanic Black CTs, and 44.1% (3,644 out of 8,258) of the predominantly Hispanic CTs were in the high poverty category. Compared to the reference group of NH White/low-poverty group, the incident rate ratio (IRR) and 95% Confidence interval [CI] of having access to high quality food stores for NH Black was significantly lower starting at the low poverty level (0.57 [0.48, 0.67], p<0.001) and decreasing further with increasing poverty: NH Black/medium poverty (0.48 [0.42, 0.55], p<0.001); NH Black/high poverty (0.38, [0.34, 0.42], p<0.001). A similar pattern was seen with the Hispanic groups as well, though to a lesser degree. We further examined access to computer/electronic devices including smartphones from 2017 to 2020. High poverty NH Black households experienced the fastest growth from 73.6% access rate in 2017 to 82.6% in 2020, compared with 87.0% to 92.0% in the total population.ConclusionAnalyses of the 2020 Census data reveal that access to high-quality food stores in high-poverty minority neighborhoods, NH Black neighborhoods in particular, remains severely limited. Innovative interventions and emerging technologies, online grocery shopping for example, warrant further evaluation as potential strategies to improve access and decrease disparities in social determinants of healthy eating.

Racialization of Serious Crime Arrestees: Who Does It and Does It Predict Worry about Victimization in Minority Neighborhoods?

Using data from two statewide surveys conducted in 2018 and 2020, this study explores the characteristics of adults who racialize serious crime arrestees and examines if racialization influences worry of victimization in minority neighborhoods. Results show a significant increase in perceptions of Whites as the largest portion of arrestees, suggesting a reduction in racialization. Those who do racialize serious crime arrestees were significantly more likely to express worry of victimization in predominantly Black or Hispanic neighborhoods. Our findings suggest the public lacks accurate information about arrests for serious crimes which may perpetuate stereotypes about minorities.

A Spatiotemporal Analysis of the Association of California City and County Cannabis Policies with Cannabis Outlet Densities.

Background:Cannabis outlets may affect health and health disparities. Local governments can regulate outlets, but little is known about the effectiveness of local policies in limiting outlet densities and discouraging disproportionate placement of outlets in vulnerable neighborhoods.Methods:For 241 localities in California, we measured seven policies pertaining to density or location of recreational cannabis outlets. We geocoded outlets using web-scraped data from the online finder Weedmaps between 2018 and 2020. We applied Bayesian spatiotemporal models to evaluate associations of local cannabis policies with Census block group-level outlet counts, accounting for confounders and spatial autocorrelation. We assessed whether associations differed by block group median income or racial–ethnic composition.Results:Seventy-six percent of localities banned recreational cannabis outlets. Bans were associated with fewer outlets, particularly in block groups with higher median income, fewer Hispanic residents, and more White and Asian residents. Outlets were disproportionately located in block groups with lower median income [posterior RR (95% credible interval): 0.76 (0.70, 0.82) per $10,000], more Hispanic residents [1.05 (1.02, 1.09) per 5%], and fewer Black residents [0.91 (0.83, 0.98) per 5%]. For the six policies in jurisdictions permitting outlets, two policies were associated with fewer outlets and two with more; two policy associations were uninformative. For these policies, we observed no consistent heterogeneity in associations by median income or racial–ethnic composition.Conclusions:Some local cannabis policies in California are associated with lower cannabis outlet densities, but are unlikely to deter disproportionate placement of outlets in racial–ethnic minority and low-income neighborhoods.

Fintech’s relationship with subprime lending in immigrant gateway metropolitan areas

ABSTRACT Financial technology lending (fintech) is a subset of the mortgage industry characterized by all-online application processes and the inclusion of non-traditional applicant data in underwriting decisions. While national studies suggest that fintech lenders mimic traditional lenders and distribute subprime loans to minority borrowers and neighborhoods at higher rates than to white borrowers and neighborhoods, these studies exclude regional differences by race/ethnicity and nativity. We assess variation in neighborhood-level fintech and traditional subprime lending rates across immigrant gateway metropolitan areas. Using Home Mortgage Disclosure Act (HMDA) data, we find that immigrant gateways are associated with higher rates of subprime lending than metropolitan areas with low rates of immigration. Results suggest that neighborhood-level composition of Asian and Latinx residents mediate the relationship between subprime lending and immigrant gateways in distinct ways depending on lender type. Findings suggest metropolitan and tract-level racial and ethnic patterns remain key factors in shaping subprime lending rates in a rapidly evolving mortgage credit market.

Examining Spatial Opportunity for Local Action: From Theory to Practice

Opportunity assessments have served as crucial tools for advocacy and policy-based action to address variability in spatially distributed urban opportunity. However, despite the vast body of theoretical and normative frameworks for discerning the effects of space, the current practices of opportunity mapping do not provide grounded assessments of differences that disproportionately affect the poor and racial minorities. We analyze seventeen opportunity assessments from the last twenty years and find issues of construct validity, implicit racial bias, and limited interpretability of outputs. More importantly, symptomatic outcomes of historical marginalization are conflated with problems of material access in poor and minority neighborhoods.

Disparities in air quality downscaler model uncertainty across socioeconomic and demographic indicators in North Carolina

Studies increasingly use output from the Environmental Protection Agency's Fused Air Quality Surface Downscaler (“downscaler”) model, which provides spatial predictions of daily concentrations of fine particulate matter (PM2.5) and ozone (O3) at the census tract level, to study the health and societal impacts of exposure to air pollution. Downscaler outputs have been used to show that lower income and higher minority neighborhoods are exposed to higher levels of PM2.5 and lower levels of O3. However, the uncertainty of the downscaler estimates remains poorly characterized, and it is not known if all subpopulations are benefiting equally from reliable predictions. We examined how the percent errors (PEs) of daily concentrations of PM2.5 and O3 between 2002 and 2016 at the 2010 census tract centroids across North Carolina were associated with measures of racial and educational isolation, neighborhood disadvantage, and urbanicity. Results suggest that there were socioeconomic and demographic disparities in surface concentrations of PM2.5 and O3, as well as their prediction uncertainties. Neighborhoods characterized by less reliable downscaler predictions (i.e., higher PEPM2.5 and PEO3) exhibited greater levels of aerial deprivation as well as educational isolation, and were often non-urban areas (i.e., suburban, or rural). Between 2002 and 2016, predicted PM2.5 and O3 levels decreased and O3 predictions became more reliable. However, the predictive uncertainty for PM2.5 has increased since 2010. Substantial spatial variability was observed in the temporal changes in the predictive uncertainties; educational isolation and neighborhood deprivation levels were associated with smaller increases in predictive uncertainty of PM2.5. In contrast, racial isolation was associated with a greater decline in the reliability of PM2.5 predictions between 2002 and 2016; it was associated with a greater improvement in the predictive reliability of O3 within the same time frame.

Historical redlining and food environments: A study of 102 urban areas in the United States.

Unhealthy food environments are disproportionally concentrated in neighborhoods with clustering of racial/ethnic minorities and poverty. This disparity has been blamed, in part, on market self-regulation. This explanation risks overlooking past and current practices of racial segregation that have created and reinforced the obstacles blocking investments from food retailers in marginalized neighborhoods. We fill this gap by investigating how the long-term ramifications of redlining, discriminatory housing practices enacted by federal Home Owner Lending Corporation (HOLC) in the 1930s, has evolved generations later to disproportionally exposing neighborhoods to unhealthy food environments. We overlaid historical redlining maps over 2010 food environment observations at the census tract level to identify areas with less healthy food environments and to assess the historical context of those areas. For 11,651 census tracts within 102 U.S. urban areas, we described the healthiness of food environments as measured by the modified retail food environment index (mRFEI). Using hurdle models with random effects, we further examined the association between redlining housing practice and food environments. The results indicate that historically redlined neighborhoods show a higher likelihood for unhealthy retail food environments even for census tracts with present-day economic and racial privilege. The current evidence shows how structural discrimination manifested by unjust housing practices and racial residential segregation fueled an uneven food environment where minority neighborhoods disproportionally bore the brunt of restrictive food access. It highlights an urgent need to ameliorate patterns of housing inequality as a fix to unequal food environments.

Environmental inequality in the neighborhood networks of urban mobility in US cities

Research has made clear that neighborhoods impact the health and well-being of their residents. A related strand of research shows that neighborhood disadvantage is geographically clustered. Because the neighborhoods of low-income and minority populations tend to be more disadvantaged, neighborhood conditions help explain racial and socioeconomic inequalities. These strands of research restrict processes of neighborhood influence to operate only within and between geographically contiguous neighbors. However, we are underestimating the role of neighborhood conditions in explaining inequality if disadvantage extends beyond the residential and extralocal environments into a network of neighborhoods spanning the urban landscape based on where residents move within a city. I use anonymized mobile phone data to measure exposure to air pollution among residents of poor and minority neighborhoods in 88 of the most populous US cities. I find that residents from minority and poor neighborhoods travel to neighborhoods that have greater air pollution levels than the neighborhoods that residents from White and nonpoor neighborhoods visit. Hispanic neighborhoods exhibit the greatest overall pollution burden, Black/White and Asian/White disparities are greatest at the network than residential scale, and the socioeconomic advantage of lower risk exposure is highest for residents from White neighborhoods. These inequalities are notable given recent declines in segregation and air pollution levels in American cities.

Oral Health Service Access in Racial/Ethnic Minority Neighborhoods: A Geospatial Analysis in Washington, DC, USA.

Previous studies on individual-level variables have improved our knowledge base of oral health service use. However, environmental or contextual variables are also important in understanding oral health disparities in racial and ethnic neighborhoods. Based on Bronfenbrenner’s ecological framework, this study examines the geographic availability of oral health providers in Washing-ton DC, U.S.A. Census tract-level data were drawn from the American Community Survey, joined with tract-level shapefiles, and overlaid with the geographic location of dental services throughout the city. Visual maps, descriptive statistics, and spatial lag regression models showed that census tracts with higher concentrations of African Americans were significantly farther from their nearest oral health providers (r = 0.19, p < 0.001), after controlling for neighborhood poverty rate, median age, and gender. Such findings confirm that in urban areas with highly di-verse populations such as Washington DC, racial disparities in oral health care access are signifi-cant. The study highlights that identifying neighborhoods with limited oral health care providers should be a priority in diminishing racial disparities in oral health service access. Improving access to racial/ethnic minority communities, especially African American neighborhoods, will require changes in health policies and programs, workforce development, resource allocation, community outreach, and educational programs.

Spatial Variations in Exclusionary Criteria from Online Rental Advertisements

Although the Fair Housing Act prohibits discrimination in the rental housing market on the basis of race, color, or religion, there exist ample opportunities for landlords to restrict their rental units to individuals with various backgrounds using exclusionary screening practices. These criteria, such as minimum credit scores, criminal history, source of income, or prior evictions, for instance, are often correlated with race and thus hold the potential to perpetuate spatial patterns of racial and income segregation. In this article, we analyzed online rental listings from Zillow and Craigslist in a case study of Charlotte, North Carolina, to examine the proliferation and spatial variation in exclusionary criteria by neighborhood race and income. Overall, we found criminal background, credit score, housing voucher, eviction, and minimum income restrictions to be greater in poorer and minority neighborhoods. When distinguishing by platform, however, we found the presence of corporate landlords, who did not advertise on Craigslist, to be a significant driver of these results. Corporate landlords represented nearly 60 percent of advertisements on Zillow and were more common in poorer and minority neighborhoods; they also systematically included nearly all restrictions. Craigslist, by contrast, had fewer criteria that correlated with race and income. Key Words: housing discrimination, natural language processing, online rental listings, spatial analysis, Web scraping.

Historical Redlining and Contemporary Federal Place-Based Policy: A Case of Compensatory or Compounding Neighborhood Inequality?

In the 1930s, the federal Home Owners' Loan Corporation (HOLC) created maps of American cities that were used to restrict investment in minority neighborhoods, leaving a durable mark on redlined neighborhoods. Since the 1990s, place-based policies are one tool the federal government has used to reinvest in disadvantaged neighborhoods. Do these programs target historically redlined neighborhoods—and to what effect? In this article, we combine data on federal place-based initiatives from 1990 to 2015 and historical HOLC maps to answer these questions. Results indicate that formerly redlined areas received substantially more funding than areas graded more favorably, indicating concentrated investment in neighborhoods that had experienced disinvestment. Federal place-based funding was associated with increased property values in formerly redlined areas, but also reductions in the share of Black homeowners, suggesting racial disparities in who benefits from rising property values. We conclude by discussing the potential and the challenges of place-based policy to address urban inequality.

Neighborhood Change in Chicago and Its Suburbs 1990-2019

This research empirically examines neighborhood change, as measured by change in per capita income, for 335 Chicago neighborhoods and suburbs for the period 1990 to 2019. Its purpose is to examine the factors associated with neighborhood change in a metropolitan region anchored by a shrinking central city. Using Geographically Weighted Regression, this research analyzes the spatially varying impacts of explanatory variables commonly found in the urban resurgence literature such as race, ethnicity, education, and nativity. The results show that the areas experiencing the highest change in per capita income were the northern neighborhoods of Chicago as well certain suburbs on the suburban fringe, Conversely, decline in per capita income occurred in the inner-ring suburbs, particularly those to the south and west of Chicago. The results further show that some minority neighborhoods in Chicago experienced income ascent relative to the rest of the metropolitan area, which challenges the findings of some previous studies and provides insights for community and suburban planners.

Is COVID-19 Causing More Business Closures in Poor and Minority Neighborhoods?

We are starting to understand the magnitude of economic damage from the COVID-19 pandemic. Current estimates cover the national or state level, but tell us little about how massive business closures may be affecting urban vitality at the intrametropolitan level. A particular concern is whether urban areas with high poor or minority populations are more deeply affected. This paper combines InfoGroup Historic Business Data and Google Map API to analyze business closures at the neighborhood level in Franklin County, Ohio, encompassing the Columbus Metropolitan Area. As expected, retail and restaurant sectors had the highest number of closures, but closure rates were higher in other sectors. Descriptive and multivariate analyzes reveal that downtown Columbus has been severely affected, but no disadvantaging effect for communities of color or neighborhoods with concentrations of poor residents is found to be statistically significant.

Does Race Matter for Police Use of Force? Evidence from 911 Calls

This paper examines race and police use of force using data on 1.6 million 911 calls in two cities, neither of which allows for discretion in officer dispatch. Results indicate White officers increase force much more than minority officers when dispatched to more minority neighborhoods. Estimates indicate Black (Hispanic) civilians are 55 (75) percent more likely to experience any force, and five times as likely to experience a police shooting, compared to if White officers scaled up force similarly to minority officers. Additionally, 14 percent of White officers use excess force in Black neighborhoods relative to our statistical benchmark. (JEL H76, J15, K42, R23)

Abstract P201: Social Determinants Of Health Modulate Risk Of Heart Failure Development In A Federally Qualified Health Center Population

Introduction: Social determinants of health (SDoH) are known predictors of heart failure, though their incorporation into existing risk models is limited, and these models are not widely implemented. In particular, there are limited studies of heart failure risk within federally qualified health centers (FQHCs) tailored to quantify the effects of several SDoH including sexual and gender minority status, socioeconomic factors, and neighborhood characteristics. Hypothesis: We assessed the hypothesis that risk factors among several SDoH domains would modulate heart failure risk when controlled for traditional biologic risk factors and significantly improve the performance of a predictive model among patients at Howard Brown Health, a FQHC. Methods: We retrospectively identified all patients at Howard Brown Health from January 1, 2012 through December 31, 2019 using electronic health records. The association between risk factors and heart failure diagnosis, defined as having one heart failure-associated ICD code, was assessed by multivariate logistic regression. Missing data were imputed with the R mice package. Neighborhood-level data were obtained from the 2019 American Community Survey. Machine learning was used to predict heart failure with several algorithms including gradient boosting machine via the R caret package. Results: Among 99,047 patients, 130 (0.13%) had heart failure. On multivariate logistic regression, these patients were significantly more likely than peers to be older (OR 0.0002, p&lt;0.001), smoke (OR = 0.0035, p&lt;0.001), have diabetes (OR 0.3, p&lt;0.001), have dyslipidemia (OR 0.3, p&lt;0.001), have hypertension (OR 0.001, p&lt;0.001) or have coronary heart disease (OR 0.8, p&lt;0.001. Non-Hispanic whites were at lower risk than non-Hispanic blacks (OR 0.0008, p&lt;0.01) and at higher risk than Hispanic patients (OR -0.0006, p&lt;0.05). Patients with lower household incomes, government insurance or no insurance, or those who lived in neighborhoods with lower median household incomes were also all more likely to have heart failure (all p&lt;0.05). The machine learning predictive models incorporating SDoH and without SDoH respectively had sensitivities of 87% and 82%, specificities of 85% and 80%, positive predictive values of 99.98% and 99.98%, and negative predictive values of 0.9% and 0.8%. Conclusions: Biologic comorbidities portend the highest heart failure risk. Race and economic factors, including neighborhood level variables, predict the highest heart failure risk among measured SDoH in this FQHC population. A machine learning model is able to predict heart failure with biologic comorbidities, and SDoH improve model performance. In conclusion, future research incorporating SDoH—in particular race and economic characteristics—into risk assessments or predictive modeling may aid early detection and prevention of heart failure and reduce health disparities.

Racial, ethnic and socioeconomic disparities in SARS-CoV-2 infection amongst children.

COVID-19 disproportionately affects racial and ethnic minority populations, but comparatively few epidemiologic studies have been performed on children as compared to adults. To characterise factors associated with SARS-CoV-2 infections amongst children from Chicago, Illinois, USA. A test-negative case-control study of children tested for SARS-CoV-2 (0-18years) at three medical centres of the Rush University System for Health between 12March and 7 December 2020 was conducted. Of 8462 children, 1,302 tested positive by real-time PCR or rapid (NAAT) testing. Infection with SARS-CoV-2 was analysed as the outcome variable; effects of predictors were assessed by logistic regression analysis. A Paediatric Risk Score Index with a concordance index of 72% of accuracy was created to predict SARS-CoV-2 infection. The median age of cases was 13years. On multivariable analysis, factors associated with SARS-CoV-2 infection were being Hispanic/Latinx (odds ratio [OR] 2.45, 95% CI 1.99, 3.03); Black/African-American (OR 1.31, 95% CI 1.03, 1.66); overweight/obese (OR 1.27, 95% CI 1.02, 1.58); older age, 10-14years (OR 1.70, 95% CI 1.39, 2.08), 15-18years (OR 2.06, 95% CI 1.71, 2.47); from households with income <$50,000 (OR 1.36, 95% CI 1.17, 1.60); or residing in predominantly minority neighbourhoods (OR 1.45, 95% CI 1.17, 1.80). Infections were higher during the second "fall" wave (5 October 2020 onward) compared with the first "spring" wave (OR 2.30, 95% CI 2.01, 2.63). Within Chicago, racial/ethnic minority neighbourhoods had striking positivity rates, as high as 39% in majority Hispanic/Latinx West Lawn neighbourhood. In suburban Chicago, highest positivity rates (20%-28%) were in zip codes within Hispanic/Latinx communities. Infection with SARS-CoV-2 is more likely amongst children of Hispanic/Latinx ethnicity, Black/African-American race, aged 10-18years, who are overweight/obese, from lower income households, and from minority neighbourhoods. Future studies should focus on the prevention of COVID-19 infection in children of highest risk.

Socioeconomic Stability Buffers Racial Discrimination Effect on Depression in a Marginalized Community.

Depression disproportionately burdens poverty-affected minority communities. Racism and racial discrimination are well-known determinants of depression among members of marginalized minority communities. Less is known about potential buffers of the discrimination effects on depression, particularly those that could serve as targets for efficient community-based policies and interventions. Our secondary analysis of data from a community needs assessment survey (N = 677) in an urban minority neighborhood of low socio-economic status revealed that high school completion and current employment significantly weakened the association between discrimination and depression. Our findings frame community-level efforts to foster high school completion and employment as potential strategies to reduce the footprint of racism on the mental health of marginalized community members. Implications for future research and policy are discussed.

Majority-Minority Boards of Directors and Decision Making: The Effects of Homophily on Lending Decisions

In this study, I examine the role racial minorities in the boardroom can play in reducing social injustice by promoting more equal access to mortgage credit to minority households. I develop a simple theoretical model that posits directors who are racial minorities provide the credit unions they govern with a perspective that shapes lenders’ trust of minority applicants. This trust is shaped by homophily and the tendency of individuals to prefer interactions with similar individuals. Using mortgage loan data from a cross-section of credit unions in the United States from 185,446 applications, I find that credit unions where the majority of board members are minorities are less likely to reject a similarly qualified minority applicant than their counterparts. Governance by minority directors significantly reduces the effects of discrimination faced by minority applicants. The board’s effect is strongest in minority neighborhoods and where the homophily is stronger between directors and applicants.

Exploring the distributional environmental justice implications of an air quality monitoring network in Los Angeles County

Non-governmental air quality monitoring networks include low-cost, networked air pollution sensors hosted at homes and schools that display real-time pollutant concentration estimates on publicly accessible websites. Such networks can empower people to take health-protective actions, but their unplanned organization may produce an uneven spatial distribution of sensors. Barriers to acquiring sensors may disenfranchise particular social groups. To test this directly, we quantitatively examine if there are social inequalities in the distribution of sensors in a non-governmental air quality monitoring network (PurpleAir) in Los Angeles County, California. We paired sociodemographic data from the American Community Survey and estimates of PM2.5 concentrations from the USEPA's Downscaler model at the census tract level (n = 2203) with a sensors per capita (SPC) variable, which is based on population proximity to PurpleAir sensors (n = 696) in Los Angeles County. Findings from multivariable generalized estimating equations (GEEs) controlling for clustering by housing age and value reveal patterns of environmental injustice in the distribution of PurpleAir sensors across Los Angeles County census tracts. Tracts with higher percentages of Hispanic/Latino/a and Black residents and lower median household income had decreased SPC. There was a curvilinear (concave) relationship between the percentage of renter-occupants and SPC. Sensors were concentrated in tracts with greater percentages of adults and seniors (vs. children), higher occupied housing density, and higher PM2.5 pollution. Results reveal social inequalities in the self-organizing PurpleAir network, suggesting another layer of environmental injustice such that residents of low-income and minority neighborhoods have reduced access to information about local air pollution.