Stepping Stones Supplemental Report

Introduction

The Stepping Stones Report, originally produced by the Charlottesville/Albemarle Commission on Children and Families in 2000, aims to provide insight into outcomes for youth and the larger community in Charlottesville and Albemarle by sharing data on a variety of metrics related to health and well-being. In June 2023, the University of Virginia’s Equity Center, in partnership with the Charlottesville Department of Human Services and Batten School of Leadership and Public Policy, released a new version of the Stepping Stones Report that provides updated data and context for each metric. The current report is a supplement to the Stepping Stones Report to present racially disaggregated data for a subset of the original metrics.

Disaggregated data is data that is broken down and analyzed by race, ethnicity, or other defined subgroups. Interest in racially and other disaggregated data has grown in recent years as evidenced in the establishment of the federal government’s Equitable Data Working Group, an interagency effort to increase access to disaggregated federal data to promote equity assessments and equitable policy making. Equity assessments provide understanding on whether current policies and institutions are impacting or serving subgroups differently; Equitable policy making ensures that new policies will equally help different populations.

The racially disaggregated data in this report is intended to center equity. Aggregated data – summary statistics for the population as a whole – can mask vastly different experiences and outcomes among subgroups. As shown below, many of the measures of youth and community wellbeing vary by race or ethnicity. Some racial subgroups are disproportionately harmed or benefited. Working towards health and well-being for the youth in our region must mean all youth, regardless of their racial or ethnic identity.

Approach

Our approach to this supplemental report follows the principles from the primary report: we seek to make the work open, reproducible, and contextualized. We primarily use publicly available data, our work is documented so that it can be reproduced by others, and we provide context alongside the data, including an overview of how the measure impacts youth and others and known limitations of the data and its source.

Understanding the full context of these data is especially important when considering racial disparities. Youth of color are disproportionately impacted by many of the included outcomes relative to White youth due to the long history of racial inequality in policy and opportunities. We hope that highlighting the disparate outcomes of youth in our region raises questions about what leaders, educators, and stakeholders can do to ensure all youth, regardless of their racial or ethnic identity, have equal opportunity to thrive in our community.

There are additional challenges that arise in racially disaggregated data. First, different data sources use different racial categories and language to describe them. The federal government currently mandates five categories for race and two for ethnicity: American Indian or Alaskan Native, Asian, Black, Native Hawaiian or Other Pacific Islander, and White for race, and Hispanic or Latino and not Hispanic or Latino for ethnicity.¹ None of the data sources for the included measures in this report go beyond these categories, and some include only a portion of them. Additionally, some agencies that collect this data make a distinction between race and ethnicity, while others do not. Thus, a person who is Black and Hispanic might be recorded as belonging to the Black racial category and the Hispanic ethnic group in one dataset, while another dataset will only record them as Hispanic. Finally, it is not always clear who assigned an individual to a racial category (e.g., do individuals self identify, are they identified by their parents, or are they identified by agents within the relevant institution?). In this report, our goal is to be transparent about each data source and their choices around racial and ethnic categories.

Multiple concerns have been raised with respect to the use of racially disaggregated data, primarily around privacy or surveillance and around the potential for misinterpretation. Breaking down data by race and ethnicity within local jurisdictions, and especially for small populations, may unintentionally reveal individuals. Marginalized populations who have been historically surveilled by governments may feel especially distrustful of racially disaggregated data and vulnerable to identification. In addition, research shows there is a tendency to interpret disparities as the fault of individual and group traits in ways that reinforce stereotypes², rather than recognizing how disparities are products of structural inequities. While racial categories are socially constructed – that is, they reflect a social definition of race as recognized in the United States and not a biological or anthropological definition – showing differences by race can contribute to the stigmatization of groups who have been subject to historical and ongoing structural oppression.

We adopt a set of strategies to make the racially disaggregated data useful while minimizing these potential harms. First, we show disaggregated measures only for racial and ethnic subgroups that make up at least five percent of our youth population to minimize the chance that individuals could be identified within small communities. Because of the composition of Charlottesville and Albemarle, this means we only show measures for White, Black, Asian, and Multiracial subgroups. Second, we seek to provide some brief context for each measure. Finally, we visualize the metrics in several ways, intended to emphasize the multiple perspectives from which to read the data.

Each included measure has three visuals to represent the data. The first visual is a gap chart highlighting the size of the gaps between racial subgroups who are most and least impacted by each outcome. The second is a line chart showing how the outcome has changed over time for each subgroup. The third is a bar chart showing how the outcome for each racial group compares to the total population in the most recent year for which the data is available. With these three visualizations, we hope to probe the following questions:

Question Set 1

How large is the gap between the racial groups experiencing the best and worst outcomes in Charlottesville, Albemarle, and Virginia? Is the size of the gap changing over time? Are the same groups consistently benefited or harmed?

These questions are primarily answered in the first graph for each outcome – the length of each line emphasizes the difference between how well institutions are serving distinct populations. We show these gaps over multiple years to give a sense of whether the gaps are shrinking or increasing overtime. And we array the rates of each racial subgroup and the rate for the combined population along the same line to avoid making one population, like White residents, the default category against which each other group is compared.

Reading the Gap Charts

Each gap chart shows disaggregated racial data for Albemarle County, the City of Charlottesville, and Virginia–the color of the line corresponds to the locality. Each racial subgroup is drawn as a circle on the line, identified by the first letter of the group name. As seen in the example below, the gap chart allows you to identify the gap in outcomes between different racial groups, as well as between a racial group and the total population. The chart also allows for comparisons to be made between different localities to identify how a group’s outcome differs depending on where they live.

Example gap chart with annotations identifying gaps in outcomes between subgroups on the same locality line and between localities.

Question Set 3

For the most recent year in which data are available, how is each racial subgroup faring relative to the total population? Is any group disproportionately better off or worse off than the population as a whole?

The final graph for each outcome answers these questions. The bars show the rate for a given racial group in a given locality and the dots show the rate for overall population in that same locality. This graph makes it clear how each individual group compares to the population: if a bar is below the dot, the group is experiencing less of that outcome than the overall population; if a bar is above the dot, the group is experiencing more of that outcome.

Example bar chart with annotations identifying gaps in outcomes between a subgroup and that of a locality’s whole population.

Race and Ethnicity in Charlottesville and Albemarle

Before showing the disaggregated data on outcomes, we provide the racial composition of Charlottesville and Albemarle youth (residents under 18 years old) as important context for the data that follows. If 40% of youth in a certain racial group experience a particular outcome, knowing whether that racial group constitutes 2% or 80% of the overall population matters for understanding the number of actual youth impacted. The following graphs show, first, the percentage of the youth population that fall into each racial group and, second, the percentage of the youth population that is or is not Hispanic. These data come from the American Community Survey (ACS), which asks about race and ethnicity separately.

Based on the 2017-2021 ACS, there are an estimated 22,417 residents under 18 yrs. in Albemarle and 7,314 residents under 18 yrs. in Charlottesville. Nearly three-quarters (73%) of youth in Albemarle and nearly two-thirds (65%) of youth in Charlottesville are White, compared to 60% in the state overall. Black youth make up 18% of the Charlottesville youth population, close to that of the state overall, but only 10% of the Albemarle youth population. Multiracial children, those identifying with two or more available racial groups, make up 8% of Albemarle’s youth and 5% of Charlottesville’s youth, slightly less than the proportion in Virginia as a whole. Asian children make up 7% of Albemarle youth and 11% of Charlottesville youth, slightly more than the proportion in Virginia as a whole. Other racial identities – a catchall category that has been growing nationally³ – compose 2% of Albemarle and 1% of Charlottesville youth, and American Indian youth compose less than 1% of either locality.

Because the percentages of youth identifying as American Indian/Alaskan Native or with some other non-provided classification are small – so small that you can hardly see them in the graphs – we do not show the racially disaggregated data for these categories. In addition to raising concerns about identifiability, the estimated percent experiencing an outcome can change dramatically even if only one or a few more individuals are impacted within a small population. This choice is not meant to dismiss or make invisible individual identity or community diversity, but to ensure both accuracy and privacy in the presented data.