Brief Observing Race and Ethnicity through a New Lens
Subtitle
An Exploratory Analysis of Different Approaches to Measuring “Street Race”
Dulce Gonzalez, Nancy López, Michael Karpman, Karishma Furtado, Genevieve M. Kenney, Marla McDaniel, Claire O'Brien
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Race is a social construct, and the way it is measured has important implications for understanding inequities in health and other outcomes. But conventional definitions of racial and ethnic identity in survey research may conceal important differences in the experiences of diverse groups. In this study, we explored what we can learn by measuring race in alternative ways and what we miss by focusing on only one set of measures. We assessed nonelderly adult survey respondents’ perceptions of how others see their race based on their physical appearance, a concept known as “street race,” and how respondents’ reported street race varies depending on the response options presented to them.

What we did

We conducted an experiment in which we randomly divided participants in the Urban Institute’s December 2021 Well-Being and Basic Needs Survey into two groups, each of which received one of two versions of a street race survey question. The first version included “Brown” as a street race option and excluded the Hispanic/Latinx and Middle Eastern or Arab categories that appeared in the second version. We assessed responses to each version of the street race question for adults who self-identified as Hispanic/Latinx or self-identified as non-Hispanic/Latinx and white, Black, Asian, or multiple races.

What we found

Findings from this study reinforce that race and ethnicity are more complex to measure than people may think and point to the need for further exploration of multidimensional measures of race in survey data, especially for Hispanic/Latinx and multiracial populations.

Our key findings include the following:

  • Many self-identified white, Black, and Asian adults reported a street race that aligns with their self-identified race. However, we found some variation in street race within these groups that might otherwise go unidentified in survey data without multiple measures of race and ethnicity.
  • Diversity in reported street race was most pronounced for self-identified Hispanic/Latinx adults and adults who self-identified as more than one race.
  • Self-identified Hispanic/Latinx adults’ responses to the street race questions were especially sensitive to the categories presented to them. Many who received the version of the street race question lacking a Hispanic/Latinx response option wrote in that their street race is Hispanic, Latino/a, or a Hispanic national origin.
  • Among adults who received the first version of the street race question, self-identified Hispanic/Latinx adults were more likely than other adults to report their street race is Brown. However, this response was still relatively uncommon among Hispanic/Latinx adults.

Why this matters

Traditional measures of self-identified race and ethnicity lump together diverse populations, often yielding results with insufficient nuance to understand and address inequities. The concept of street race has the potential to help us gain a clearer picture of how people believe they are perceived by others and how the racialized status others attribute to them affects their well-being and access to opportunity. But more research is needed to understand how people respond to alternative approaches to measuring street race.

This exploratory study is part of a larger body of work, and we continue to explore the concept of street race further in surveys conducted in 2022. By learning more about how people are perceived as they navigate their daily lives, policymakers and other stakeholders can better identify and interrupt inequities in health and economic circumstances.

Research Areas Health and health care Race and equity
Tags Race, gender, class, and ethnicity Structural racism Latinx communities Asian American and Pacific Islander communities Black/African American communities
Policy Centers Health Policy Center Center on Labor, Human Services, and Population
Research Methods Data analysis Quantitative data analysis