Racism Dataset

[Fisseha Aranda]

Wantto work for racial justice? Organize, protest, vote, and spend with intention. And stop giving your investment dollars to companies that exacerbate racial inequities. When publicly traded companies employ these practices, the repercussions extend throughout the financial system.

Our Racial Justice Investor Dataset is a list of companies excluded from Adasina portfolios for failing to meet our Racial Justice Investment Criteria. These companies are believed to contribute to systemic racism through practices identified by social justice movements, such as prison involvement, money bail, surveillance, and other practices. We continue to conduct research with our partners to ensure this list reflects the ongoing insights of racial justice movements.

Below is a list of companies that failed to meet our investment criteria. Each company listed includes the failed criteria name, current company status on the issue, and ways to take action. Beneath the list you will also find:

Between 2003-2013, nearly 90 percent of stops did not lead to a summons or arrest. Since 2013, the arrest rate has risen because the number of overall stops has decreased significantly. The rate at which the NYPD are frisking or searching civilians has also risen sharply. In first two years of the Adams Administration, 76 percent of the people stopped by the NYPD were frisked or searched.

Every time a police officer stops a person in NYC, the officer is supposed to fill out a form recording the details of the stop. The forms were filled out by hand and manually entered into an NYPD database until 2017, when the forms became electronic. The NYPD reports stop-and-frisk data in two ways: a summary report released quarterly and a complete database released annually to the public.

The quarterly reports are released by the NYCLU every three months (available here) include data on stops, arrests, and summonses. The data are broken down by precinct of the stop and race and gender of the person stopped.

The annual database includes nearly all of the data recorded by the police officer after a stop such as the age of the person stopped, if a person was frisked, if there was a weapon or firearm recovered, if physical force was used, and the exact location of the stop within the precinct. The NYPD uploads this databe to their website annually. The most recent annual dataset and codebook is located below. It contains over 100 variables and 15,102 observations, each of which represents a stop conducted by an NYPD officer.

Racial disparities in the housing market have been stark for nearly a century. Because of the pervasive influence of structural racism, people of color have experienced the greatest barriers to accessing and maintaining homeownership. Moreover, these disparities are expected to widen, given that the COVID-19 pandemic has had a disproportionate impact on households of color.

Amid increased attention on these racial disparities, policymakers, local stakeholders, and financial institutions are designing and implementing solutions to expand homeownership and wealth-building opportunities for households of color.

To measure the success of these efforts, a timely estimate of the racial homeownership rate is critical. It provides a common understanding of the current reality and can help housing stakeholders set a common goal for defining and measuring progress.

The CPS/HVS provides quarterly information for the United States, census regions, states, and the 75 largest metropolitan statistical areas (by population). It employs the Current Population Survey sample, which has about 72,000 housing units.

The small sample size also means it is not possible to analyze homeownership in smaller geographic areas (e.g., states, counties, zip codes, and census tracts) or by housing characteristics (e.g., number of rooms or units in structure). This weakness of small sample size was pronounced in the second and third quarters of 2020, when response rates were depressed because of the COVID-19 pandemic (PDF), especially among renters, pushing homeownership rates improbably high.

The decennial census counts 100 percent of the US population and housing, so it is the most complete and reliable source. It is conducted every 10 years, and as of today, the most up-to-date estimates are for 2010. It usually takes two years for the Census Bureau to compile and release the results. The 2020 Decennial Census data have only been partially released, and tenure information is expected to be released in mid-2022.

The annual ACS is positioned between the other two. Its sample includes about 3.5 million housing units, and thus its one- or five-year estimates provide a sufficient sample size to produce estimates for smaller population subgroups or geographies.

The ACS is best for someone seeking to understand homeownership rates by different population subgroups or across smaller geographies, but it is less accurate than the decennial census and less timely than the CPS/HVS.

The homeownership rate differences across datasets may confuse housing market analysts, community stakeholders, and policymakers who want to assess homeownership trends or the effectiveness of policy interventions aimed at closing the racial homeownership gaps. Which data source should we use? It depends.

If they want to know what is going on at the state or metropolitan level, they should use ACS data. For smaller geographies, it is safer to aggregate multiple years of ACS data to get accurate homeownership rates for the population they are interested in.

Once the 2020 Decennial Census data come out next year, we will get the most accurate information on the racial homeownership rate. But to track trends, we will need to continue relying on CPS/HVS data and ACS data. At Urban, the Housing Finance Policy Center generally prefers to use the ACS.

Each survey has pros and cons, and users should consider these and find the data source that best serves their purpose, refrain from mixing different sources, and indicate which source they are using. This will help ensure accuracy, which can lead to stronger analyses and more effective policy solutions.

The Urban Institute podcast, Evidence in Action, inspires changemakers to lead with evidence and act with equity. Cohosted by Urban President Sarah Rosen Wartell and Executive Vice President Kimberlyn Leary, every episode features in-depth discussions with experts and leaders on topics ranging from how to advance equity, to designing innovative solutions that achieve community impact, to what it means to practice evidence-based leadership.

Racial and ethnic health inequities are an ongoing problem in the United States, with racially and ethnically minoritized populations experiencing higher rates of infectious diseases, chronic illnesses, infant and maternal mortality, and premature death compared to their White counterparts. Researchers have identified racism as a root cause of racial and ethnic health inequities.

U.S. state laws have constituted and continue to form a significant component of structural racism, including historical and contemporary discriminatory banking laws and mortgage lending policies, criminal justice laws and practices, abortion coverage laws, Medicaid expansion legislation, voting rights laws, and state mandates in public education. Many of these laws explicitly or implicitly disadvantage, exclude, or exploit Black, Native American, and Hispanic people, resulting in unequal access to socioeconomic resources and opportunities as well as to essential social services and high-quality healthcare. However, there has been relatively little research investigating how these laws specifically influence the health of marginalized racial and ethnic groups and their effects on racial and ethnic health inequities.

As part of her ongoing work on the impact of health and social policies on health inequities, Dr. Madina Agnor, social epidemiologist and Assistant Professor of Behavioral and Social Sciences at Brown University School of Public Health, along with a team of public health researchers and legal scholars, has developed a first-of-its-kind database of structural racism-related state laws to advance health equity research and practice in the U.S. Through the use of primary and secondary sources, the team identified state laws related to structural racism, developed a coding scheme for critical features and categories of each law, and systematically applied this coding scheme to laws in all 50 states and the District of Columbia. Their work ultimately identified 843 contemporary U.S. state laws explicitly or implicitly linked to structural racism. Learn more about the team's methods and results here.

Black, Hispanic, and AIAN people fare worse than White people across the majority of examined measures of health and health care and social determinants of health (Figure 1). Black people fare better than White people for some cancer screening and incidence measures, although they have higher rates of cancer mortality. Despite worse measures of health coverage and access and social determinants of health, Hispanic people fare better than White people for some health measures, including life expectancy, some chronic diseases, and most measures of cancer incidence and mortality. These findings may, in part, reflect variation in outcomes among subgroups of Hispanic people, with better outcomes for some groups, particularly recent immigrants to the U.S. Examples of some key findings include:

Asian people in the aggregate fare the same or better compared to White people for most examined measures. However, they fare worse for some measures, including receipt of some routine care and screening services, and some social determinants of health, including home ownership, crowded housing, and experiences with racism. They also have higher shares of people who are noncitizens or who have limited English proficiency (LEP), which could contribute to barriers to accessing health coverage and care. Moreover, the aggregate data may mask underlying disparities among subgroups of the Asian population. Asian people also report experiences with discrimination in daily life, which is associated with adverse effects on mental health and well-being.

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