Racial inequity manifests in many ways. Consider the effects of the COVID-19 pandemic: job losses were greater for people of color, children of color experienced outsize learning loss, and Black workers—who make up a disproportionate share of frontline workers—had both more exposure to the virus and inadequate access to healthcare. These inequities cost lives and widened a preexisting racial gap in life expectancy.1
These trends and others are national in scope, but the specific ways racial inequity manifests may be vastly different from one state, one city, and one neighborhood to another. Each locality experiences a specific combination of interconnected factors that shape the lives of its Black and Hispanic residents. To properly diagnose racial inequity in the United States, then, it is crucial to understand the historical context of each locality and the lived experiences of its residents in addition to collecting data that reveals differences in economic outcomes and living standards across racial groups, cities, and communities.
This thinking is consistent with the findings in a previous McKinsey report, The case for inclusive growth,2 which outlines a three-stage approach for embedding equity into economic and community-focused growth strategies: diagnosing the current state and developing a bold vision for change, designing comprehensive community- and human-centered interventions, and taking coordinated action to ensure long-term accountability and momentum. This article focuses on the first stage.
To gain greater insights into the current state of racial inequity in US communities, we analyzed data3 from eight cities with large Black and Hispanic populations and compared it with national-level data. Our analysis revealed crucial differences in equity from one city and one neighborhood to another in areas such as education, banking access, food security, and financial inclusion. These findings suggest that complementing data aggregated nationally with a greater understanding of issues at the city and even neighborhood levels could inform targeted approaches for redressing inequities and help stakeholders deliver more meaningful change for Black and Hispanic residents.
Many communities are actively working to understand the challenges Black and Hispanic residents face, and publicly available data exists—but local leaders may struggle to weave it all together to form an effective action plan. As part of our work to keep our Action 9 commitment to advancing equity,4 we’ve developed a three-part diagnostic approach that combines macro- and microlevel data with input from residents and historical context to help stakeholders prioritize interventions that improve residents’ lives and outcomes: (1) evaluate a city’s overall equity gaps, (2) understand relative inequity at the neighborhood level, and (3) benchmark a community with peer cities to reveal opportunities for improvement. While this approach will not solve racial inequities, it can provide a fact base to help good-faith actors begin to address inequities and embark on the path to inclusive growth.
The local journey to understanding inequity
Given competing priorities and limited resources, local leaders often focus on programs with the potential to deliver the highest impact. To this end, several questions regularly arise about defining the nature and scope of the challenges facing communities and understanding which efforts have the strongest potential to effect meaningful change:
- Defining the nature and magnitude of the challenge. How big of a challenge is racial inequity in my city? How is racial inequity showing up in residents’ lives? How do we compare with peer cities, and have other cities found solutions for the challenges we face?
- Understanding where and on what to focus. Are certain neighborhoods experiencing racial inequity more acutely than others? Given resource constraints, how do we prioritize across issues and neighborhoods? What should we tackle first, and what needs to be worked on in conjunction with other challenges?
Once local leaders have asked these questions, stakeholders can collect and explore the data as one input to help answer them (see sidebar “Collecting quantitative data on racial equity”). The following three-part approach could help guide this effort.5
While this approach is not intended to reveal the root causes of inequities or deliver a specific intervention design, the analysis can be used in conjunction with other important inputs from local stakeholders, such as a community’s broader historical context, the local ecosystem, and its residents’ lived experiences, to create a starting point for driving more equitable outcomes.
1. Evaluating equity gaps and opportunities
Evaluating equity gaps and related opportunities by outcome (for example, health, education, and wealth) and in the aggregate could help stakeholders better understand outcome disparities for Black and Hispanic residents relative to White residents. While this type of analysis is not new, many evaluations tend to focus on four to five metrics, such as graduation rates, employment rates, and household wealth. In our experience, expanding metrics to capture 16 outcomes, including access to civic infrastructure and technology, can provide a more robust picture of the magnitude and scope of equity gaps and the opportunities to close them (see sidebar “Our framework for metrics of inequity”). These metrics are not exhaustive, and additional data points could yield useful insights depending on a city or neighborhood’s specific context, such as environmental quality, resilience to natural disasters, and access to basic services.6
For example, in Dallas, 41 percent of Hispanic residents own homes, compared with 53 percent of non-Hispanic White residents. Even more stark: the median home value for Hispanic homeowners is $166,000, compared with $375,000 for White homeowners. If stakeholders looked only at homeownership rates, they would underestimate the equity gap between White and Hispanic residents.7
Additionally, while stakeholders tend to expect to see gaps along racial lines, the prevalence and magnitude of those gaps can affect the nature and urgency of the conversation about making changes. Exhibit 1 shows inequity ratios and actual values for a selection of metrics for Black and Hispanic residents in Dallas when compared with their non-Hispanic White counterparts. For example, while overall life expectancy in Dallas is 6 percent lower for Black residents than for non-Hispanic White residents (61.0 years versus 65.2 years, respectively),8 Black residents experience 118 percent higher infant mortality than non-Hispanic White residents (10.9 deaths per 1,000 births versus 5.0 deaths per 1,000 births, respectively).9 Local leaders may, therefore, choose to prioritize efforts to reduce infant mortality.
Understanding how inequities change over time. Examining how inequities evolve over time can help stakeholders understand a city’s inequity trajectory. This approach can also reveal whether critical metrics are moving together—positively or negatively—to further help identify opportunities to improve intervention design.
In Dallas, for example, majority-Black census tracts saw a 19 percent increase in median household income from the 2010–14 period to the 2015–19 period, in line with the city’s overall average increase of 21 percent.10 However, during this time, majority-Black census tracks lagged behind in home value appreciation, with an average increase of only 26 percent compared with the overall city average of 38 percent.11 Dallas leaders may want to prioritize understanding these uneven outcomes and acting on them.
Similarly, in several Black and Hispanic neighborhoods in Houston, an increase in bachelor’s degrees is not necessarily matched by an increase in labor force participation or income. Indeed, in one predominantly Black neighborhood, an 81 percent increase in bachelor’s degrees from the 2010–14 period to the 2015–19 period coincided with a decline in both labor force participation and median household income (Exhibit 2).12 These divergences suggest that individuals may be earning bachelor’s degrees in fields with low hiring rates, lower wages, or lower local presence. While such neighborhoods do not necessarily represent a trend throughout Houston, they do indicate potential opportunity areas that local stakeholders can examine and address.
Though local stakeholders may have already used these data points, tracking these metrics over time could reveal important trends to inform solutions.
Mapping inequities over a lifetime. For the eight cities for which we ran analysis, the data shows that Black and Hispanic residents often face inequity across life stages. They may experience less support in schools; less access to affordable, quality healthcare; and limited access to capital or funding. Individually, such inequities may constrain a person’s outcomes. Combined, these inequities compound and may significantly diminish opportunities across a lifetime (see sidebar “How inequity is experienced across a lifetime”).
While this is true across US metro areas, racial inequity is more acute in some regions. For example, while child poverty is generally more prevalent in Philadelphia across races, the racial inequity of child poverty is more acute in Dallas, where Black children are 4.5 times more likely to live in poverty than White children (Exhibit 3). In Philadelphia, Hispanic and Black residents are 4.0 and 1.8 times more likely, respectively, to not have graduated high school than their non-Hispanic White counterparts; in Dallas, the Hispanic and Black differential is even more stark, at 12.3 and 3.3 times less likely, respectively.13
Local stakeholders often have an intuitive sense that certain residents face inequities across life stages. Bringing the facts together in one view, however, illuminates the life stages during which those challenges might be more concentrated and, therefore, where leaders could take a holistic approach to addressing them.
2. Understanding inequity and opportunities across neighborhoods
Analyzing relative inequities across neighborhoods could help stakeholders understand the spatial nature of challenges and prioritize places for intervention and investment. Stakeholders can also examine factors that may reinforce inequity across neighborhood lines and consider how to address them. For example, railroad tracks in some communities, coupled with limited street crossing points, can create natural dividing lines, making it difficult for residents to access more prosperous parts of town.
Spatial mapping can reveal stark disparities among adjacent neighborhoods with differing demographics (Exhibit 4). In Houston, for example, the 63 percent White Bellaire neighborhood sits alongside the 5 percent White Gulfton neighborhood. Despite the proximity, Gulfton lags behind Bellaire on numerous metrics. For instance, 3 percent of Bellaire households lack broadband access, compared with nearly half in Gulfton; and the child poverty rate is 1 percent in Bellaire, compared with 54 percent in Gulfton.14 These disparate outcomes are not unique to Houston. All eight cities analyzed as part of our research showed similar imbalances across neighborhoods, reinforcing the value of this more granular approach to understanding racial inequities.
Our experience evaluating cities suggests that if a neighborhood has significantly lower outcomes in one dimension, it often struggles across several dimensions. These compounding effects come as no surprise, considering long-standing policies and practices of disinvestment in many of these communities in areas such as transportation, schools, parks, and public health.15 The confluence of challenges across multiple dimensions suggests that while improving one dimension may fix a point in the system, it may not dramatically change outcomes for residents. The comprehensive view of challenge areas that a spatial representation provides could equip city stakeholders to develop a coordinated set of interventions to drive change.
3. Comparing against peer cities
Local stakeholders can look to data from cities of similar demographic makeup and economic indicators, as well as self-identified peer cities, to uncover the city’s relative inequity across outcomes and metrics (Exhibit 5). While the neighborhood view could prove crucial for prioritizing interventions, stakeholders can also use the high-level city view as a starting point for benchmarking their progress toward racial equity. This view may also reveal unexpected pockets of equity in cities that have been working to address their unique challenges.
Statistical peers could be selected based on similarity against three criteria: median household income, GDP, and percentage of the population that is Black and Hispanic. Cities may be surprised to learn who their peers are in terms of equity. For example, people may commonly look to Atlanta, New York, and Pittsburgh as cities comparable to Philadelphia, with similar situations across many equity metrics. However, comparison against the 16 outcomes reveals that Philadelphia’s inequity challenges are more closely aligned with those of Detroit and Newark.
Cities can also learn from peer cities that perform well on specific metrics. With the fact base in hand, local stakeholders can ask, for instance, “What is it about City X that enables it to excel on equity on the health and food security outcome, and what can we bring back to our locality?”
Bringing it all together
In addition to providing stand-alone insights, the core sets of analyses above provide further insights when considered together. Observing the overlaps between a city’s equity gaps and relative inequity compared with peer cities can provide a fuller picture of where and how a city is struggling or excelling. It may be helpful to think about relative performance in quadrants, with the top left quadrant representing both high racial inequity and worse overall performance compared with peers for a particular outcome, and the bottom right quadrant representing low racial inequity and average or better performance compared with peers (Exhibit 6).
Understanding where the city falls on various outcomes can inform potential priorities for the city or region (see sidebar “Understanding the four quadrants”). By revealing comparative depth and relative performance for a range of inequities, this analysis can support strategic conversations such as whether the geography would be best served by focusing on addressing inequities in a single quadrant or by taking a portfolio approach across quadrants.
The quadrants can also inform how geographies think about addressing challenges. For example, in Dallas’s inequity performance matrix, food security falls in quadrant 2. That is, it has better overall performance compared with peers in terms of food security (ranking above the 50th percentile) but has high inequities across Black and White residents (an inequitable outcome for Black residents occurs three times more often than for White residents). By contrast, in Philadelphia’s inequity performance matrix, food security falls in quadrant 3. This shows that the city has worse overall performance compared with peers in terms of food security (ranking below the 20th percentile) but has lower inequity across Black and White residents (an inequitable outcome for Black residents occurs less than two times more often than for White residents). These two cities could thus take different approaches in addressing food insecurity, with Philadelphia likely benefiting from citywide interventions to improve the outcome for all residents and Dallas benefiting from focusing on Black residents.
Turning insights into action
How can stakeholders harness the insights we’ve described to design effective actions for closing equity gaps? In our experience, three paths can help prioritize efforts and lay a foundation for lasting change:
A use case approach, in which stakeholders identify specific use cases for data and analyses and use insights to design interventions in collaboration with community partners. For example, a large Southern city is considering an effort focused on reducing housing evictions and increasing pathways to good and promising jobs. A locale might select this approach to address an especially pressing need or to demonstrate a proof of concept before taking one of the broader approaches listed below.
A topic-focused racial-equity transformation, in which community partners select one or more topical areas, such as healthcare, for targeted efforts (see sidebar “Using data to inform action: A case study on childcare”). For example, a metro area in the Northwest is embarking on an equity transformation focused on a few topics, with emerging priorities including housing quality and affordability, education, and technological access. This approach might be a good fit when inequities are especially pronounced in a few topical areas, or if convening stakeholders have capabilities and influence within a specific topical area (for example, if multiple local healthcare companies want to make a positive impact).
A comprehensive racial-equity transformation effort cutting across topics (such as education, wealth creation, health and wellness, and neighborhoods and housing) in collaboration with community partners. For example, a midsize Midwestern city is launching a comprehensive, cross-stakeholder effort to advance racial equity in its metro area. This approach may be a good fit when a broad coalition of stakeholders and funders is committed to partnering to make a comprehensive change over a longer period.
To prioritize and implement the most effective and enduring interventions to address racial inequities, stakeholders can consider an approach that involves understanding the degree of inequity within the region, inequities at the neighborhood level, and how their locality compares with peers. This expanded fact base, combined with other critical inputs such as the local historical context and residents’ lived experiences, can bring the story of residents’ unique challenges to life and serve as a catalyst for local transformations, helping stakeholders address root causes to improve outcomes for all those facing compounding inequities.
