HEALTH POLICY CENTER Support for this research was provided by the Robert Wood Johnson Foundation. The views expressed here do not necessarily reflect the views of the Foundation. Observing Race and Ethnicity through a New Lens An Exploratory Analysis of Different Approaches to Measuring "Street Race" Dulce Gonzalez Nancy López Michael Karpman URBAN INSTITUTE UNIVERSITY OF NEW MEXICO URBAN INSTITUTE Karishma Furtado Genevieve M. Kenney Marla McDaniel Claire O'Brien URBAN INSTITUTE URBAN INSTITUTE URBAN INSTITUTE URBAN INSTITUTE December 2022 Survey questions about race and ethnicity can shed light on how experiences differ across groups and ultimately help policymakers and stakeholders address inequities in health and other outcomes. But such questions often focus only on self-identified race and ethnicity and lump together diverse populations, masking substantial differences within groups and yielding results with insufficient nuance to appropriately understand and address inequities. In this brief, we explore what we can learn by measuring race and ethnicity in alternative ways on surveys and what we miss by focusing only on one set of measures of race and ethnicity. We assess nonelderly adults' perceptions of how others see their race based on their physical appearance (henceforth "street race") and how responses to questions about street race vary depending on the response options given. To do so, we analyze data from the December 2021 round of the Urban Institute's Well-Being and Basic Needs Survey (WBNS), a nationally representative, internet-based survey of adults ages 18 to 64. Introduction What is race? What is ethnicity? Are these two concepts interchangeable? Occasionally we are reminded that the ways we see and understand race and ethnicity are oversimplified and even misleading. Take, for example, casting decisions for Lin Manuel Miranda's musical In the Heights. Although the musical was set in a predominantly Afro-Hispanic/Latinx,1 mostly Dominican, community, most of the lead actors were light skinned. The omission of dark-skinned Afro-Hispanic/Latinx lead actors prompted claims that the musical erases the Afro-Hispanic/Latinx experience and contributes to the falsehood that no Hispanic/Latinx people are Black.2 The lack of representation of the Afro- Hispanic/Latinx population in the musical highlights the broad brush with which mainstream culture paints the Hispanic/Latinx population and the frequent conflation of race and ethnicity. Defaulting to simplistic understandings of race and ethnicity such as these that lump together different groups within a limited number of categories may mask that individual experiences differ substantially (Jones et al. 2008; LaVeist-Ramos et al. 2012; Monk 2022). In this study, we sought to explore what we can learn by measuring race and ethnicity in alternative ways and what we miss by focusing only on one set of measures of race and ethnicity. We build on prior work studying multidimensional measures of race and ethnicity (Irizarry 2015; Jones et al. 2008; López et al. 2017, 2018) to assess nonelderly adults' street race-or their perceptions of how others see their race based on their physical appearance-and how responses to questions about street race vary depending on the response options presented. To do so, we analyze data from the December 2021 round of the WBNS. We conducted an experiment in which we randomly divided WBNS respondents into two groups, each of which received one of two versions of the street race survey question (box 1). Version A included "Brown" as a street race option and excluded the Hispanic/Latinx and Middle Eastern or Arab categories that appeared in version B.3 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,4 Asian, or multiple races.5 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 find some variation in street race within these groups that might 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; many who received version A, which lacked a Hispanic/Latinx response option, wrote in that their street race is Hispanic, Latino/a, or a Hispanic national origin.  Among adults who received version A of the street race question, self-identified Hispanic/Latinx adults were more likely than other adults to report their street race is Brown, but this response was still relatively uncommon among Hispanic/Latinx adults. The results presented here form the groundwork for our ongoing investigation of how to improve measurement of race and ethnicity in surveys. A future paper will explore street race further using survey data collected in June 2022.6 Findings from this exploratory study highlight the complex 2 OBSERVING RACE AND ETHNICITY THROUGH A NEW LENS relationship between self-identified race and ethnicity and how people believe others perceive their race (Roth 2016). They reinforce the notion that race and ethnicity are not as simple to measure as people may think. Like other research, this study points to the need to move beyond capturing only self- identified race and ethnicity in survey data, especially for Hispanic/Latinx and multiracial populations, who have significant racial diversity. In all likelihood, we will never be able to ask enough survey questions to fully grasp the complexity of the social construction of race and its impact on outcomes. Still, gaining a better understanding of how people are perceived as they navigate their daily lives can help policymakers and other stakeholders identify and interrupt inequities in health and economic circumstances. BOX 1 English and Spanish Street Race Survey Questions English version: This question is about how others see your race, not how you identify yourself. If you were out in public, what race do you think other people who do not know you personally would assume you were based on what you look like (for example, your skin color, facial features, and hair)? Version A Version B 1. White 1. White 2. Black or African American 2. Black or African American 3. Brown (not Black or African American) 3. East or Southeast Asian (such as Chinese, Japanese, 4. East or Southeast Asian (such as Chinese, Japanese, Korean, Filipino, or Vietnamese) Korean, Filipino, or Vietnamese) 4. South Asian (such as Indian or Pakistani) 5. South Asian (such as Indian or Pakistani) 5. American Indian or Alaska Native 6. American Indian or Alaska Native 6. Hispanic or Latino 7. Some other race (please specify): ___ 7. Middle Eastern or Arab 8. Some other race (please specify): ___ Spanish version:a Esta pregunta se refiere a la manera en que otras personas ven su raza, no como usted se identifica. Si usted estuviera afuera en público, ¿de qué raza o color cree que otras personas que no lo/la conocen pensarían que usted es basada en su apariencia (por ejemplo, su color de piel, rasgos físicos y pelo)? Versión A Versión B 1. Blanca/o 1. Blanca/o 2. Negra/o o Afroamericana/o 2. Negra/o o Afroamericana/o 3. Morena/o (no de apariencia negra o Afroamericana) 3. Asiática oriental o sudoriental (como China/o, 4. Asiática oriental o sudoriental (como China/o, Japonés, Coreana/o, Filipina/o o Vietnamita/o) Japonés, Coreana/o, Filipina/o o Vietnamita/o) 4. Asiática del sur (como de la India o Pakistaní) 5. Asiática del sur (como de la India o Pakistaní) 5. Indígena de las Américas o Nativa/o de Alaska 6. Indígena de las Américas o Nativa/o de Alaska 6. Hispana/o o latina/o 7. Otra raza (especifique):___ 7. Árabe o del Oriente Medio 8. Otra raza (especifique):___ Note: a A total of 650 respondents, representing about 8 percent of the sample, completed the survey in Spanish. OBSERVING RACE AND ETHNICITY THROUGH A NEW LENS 3 Background Conventional measures of race and ethnicity hide the complexities of race and racialization. Understanding racialization, the process by which society and its people attach racial meaning to groups of people or experiences, is crucial to understanding experiences of discrimination and how they contribute to inequities in health and other outcomes (Gonzalez-Sobrino and Goss 2018). However, traditional measures of race and ethnicity may conceal important nuances. In this study, we define self- identified race and ethnicity in a manner consistent with the census and surveys using US Office of Management and Budget standards for collecting and reporting race and ethnicity data, which first ask respondents to identify whether they are of Hispanic, Latino, or Spanish origin and then to identify their race as one or more among white, Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian or other Pacific Islander, or some other race.7 Most research on racial and ethnic inequities to date has assessed disparities along these measures of self-identified race and ethnicity. But measures of self-identification can still hide inequities, given that they do not capture the diversity of skin color, hair texture, facial features, and other characteristics that shape how people perceive one another and how those perceptions affect treatment in health care settings and other situations (Monk 2022; Vargas et al. 2019). The concept of street race challenges notions of race that are conflated with national origin, ethnicity, or ancestry by instead emphasizing the social construction of race based on physical appearance and how it forms unequal power relationships.8 One thing appears clear: relying solely on self-identified race may not be enough for identifying and rectifying inequities. We designed the experiment in our survey to explore within-group heterogeneity that may not be captured by traditional measures of self-identified race and ethnicity. For instance, standard approaches may not fully reflect the diversity in street races of Hispanic/Latinx people.9 In the 2010 Census, 53 percent of Hispanic/Latinx respondents identified their race as white alone, 37 percent identified as some other race (including those who described their race using terms representing Hispanic origin, such as Latino, Mexican, Puerto Rican, or Salvadoran), and 6 percent identified as two or more races, most commonly white and some other race (Humes, Jones, and Ramirez 2011). One major effect of changes to the design of the 2020 Census, such as asking respondents to write in their origin under each racial category selected, was a substantial increase in the number of Hispanic/Latinx people reporting some other race alone (42 percent) or more than one race (33 percent).10 Statistical reporting and health policy research, with few exceptions, often conceal significant intragroup differences by race within the Hispanic/Latinx population because of the limitations of traditional measures of self- identified race and ethnicity (LaVeist-Ramos et al. 2012; Zambrana et al. 2021). The omission of an Arab or Middle Eastern or North African (sometimes called MENA) category from the census has also been subject to ongoing debate.11 Currently, the white racial category groups people of Middle Eastern or North African origin (e.g., Egyptian or Lebanese) with those of European origin (e.g., English, Irish, or German). This federal standard limits the availability of data on the Middle Eastern or North African population, many of whom do not necessarily classify themselves or feel they 4 OBSERVING RACE AND ETHNICITY THROUGH A NEW LENS are perceived as white and have faced unique experiences of discrimination and prejudice (Awad et al. 2022; Maghbouleh, Schacter, and Flores 2022; Padela and Heisler 2010; Selod and Embrick 2013). Exploring street race categories such as Brown could help us uncover differences in racialization. In the US, the dominant paradigm for thinking about race centers on a Black-white binary (Perea 1997). People who are not Black or white may be acculturated to having to define themselves outside this binary, such as under the heterogeneous banner of "people of color." There may be strength in the unity implied in that shared identity and oppression (Crenshaw 1991; Sanchez and Vargas 2016). But, identifying oneself under such a banner can lead to marginalization and invisibility for non-Black people of color. In an increasingly multiracial US, categories that can capture the racialized status of people outside the Black-white binary are increasingly needed.12 To our knowledge, ours is the first nationally representative survey in the US to include the term "Brown" as a racial category. The context around the use of Brown in the US has evolved and in many cases the term has been used inaccurately to portray Hispanic/Latinx people as a monolith (Busey and Silva 2021). Adopted by Chicano activists in the 1960s, Brown may now be used to refer to various groups, including Hispanic/Latinx people and people who are of South Asian, Southeast Asian, and Middle Eastern or North African origin (Nadal 2020; Zopf 2018).13 The diversity of racial and ethnic groups who may identify or be identified as Brown underscores the limitations of this term and its role in homogenizing people of widely varying backgrounds, as explored in qualitative and quantitative studies of Brown racialization (Irizarry 2011, 2015; Logan 2003; Zopf 2018). However, that such different groups of people are racialized as Brown also makes the term potentially relevant for our exploration of street race because it (1) can capture how some people who are not Black or white believe strangers categorize their race and (2) can highlight the nuances missed when surveys rely only on self-identified race and ethnicity. Results Many self-identified Asian, Black, and white adults reported a street race that aligns with their self-identified race. However, we find some variation in street race within these groups that might otherwise go unidentified in survey data without multiple measures of race and ethnicity. Many self-identified Asian, Black, and white adults reported that others perceive them in a way that aligns with their self-identified race, regardless of the version of the street race question they received. As shown in table 1, most self-identified white adults (96 percent) reported their street race is white, regardless of the version of the street race question they received. Similarly, among self-identified Black adults, most (88 percent for version A and 91 percent for version B) reported that their street race is Black or African American. Among self-identified Asian adults, most reported that their street race is East, South, or Southeast Asian (94 percent for version A and 91 percent for version B). Overall, this tells us that self-identified race largely captures the way many Asian, Black, and white adults believe others perceive them. OBSERVING RACE AND ETHNICITY THROUGH A NEW LENS 5 However, self-identified race did not align with street race for some self-identified Asian, Black, and white adults. As indicated, around 10 percent of self-identified Black adults believed others perceive them to be of a race that does not match how they identify; this includes about 2 percent and 1 percent for versions A and B, respectively, who reported others perceive them as white. Just under 10 percent of self-identified Asian adults in each version reported a street race different from the race they identify as, including 2 percent in each question version who reported their street race is white. About 3 percent of self-identified Asian adults who received version B reported that their street race is Hispanic/Latinx. Though we expect some misalignment between the street race and self-identified race measures because of response option differences, by combining the two measures, we can shed light on the diversity of perceived race for these racial groups. Relying on a measure of self-identified race and ethnicity alone would hide these nuances. TABLE 1 Self-Reported Street Race among Adults Ages 18 to 64, by Self-Identified Race or Ethnicity and Version of the Street Race Question Received, December 2021 SELF-IDENTIFIED RACE/ETHNICITY AND VERSION OF STREET RACE QUESTION RECEIVED Hispanic/ Multiple White Black Asian Latinx Races A B A B A B A B A B Reported street race (%) White 96 96 2 1 2 2 46 18*** 43 41 Black/African American 1 0 88 91 1 1 2 2 24 17 Brown 0 – 5 – 2 – 20 – 4 – East/Southeast Asian 1 1 1 0 74 65** 2 1 14 11 South Asian 0 0 1 0 20 26* 1 1 1 1 American Indian/ Alaska Native <1 < 1* 0 0 1 1 2 1 5 10 Hispanic/Latinx – 1 – 2 – 3 – 74 – 9 Middle Eastern/Arab – 0 – 0 – 1 – 1 – 0 Some other race 1 1 2 3 1 1 24 2** 6 11* Hispanic/Latinxa 0 0 0 18 2 Not reported 1 < 1* 1 2 0 0 3 1*** 3 1 Sample size 2,372 2,366 508 515 294 305 741 732 105 133 Source: Well-Being and Basic Needs Survey, December 2021. Notes: Dashes indicate the race was not an option in either version A or B of the street race question. Respondents who self- identified as white, Black, Asian, and multiracial did not identify as Hispanic/Latinx. Estimates are not shown for adults of other races because of sample size limitations. Totals may not sum to 100 percent because of rounding. a This row represents respondents who wrote in Hispanic, Latina/o, or a Hispanic national origin as their street race. But because version B already included a Hispanic/Latinx option, those who received that version and still wrote in Hispanic, Latino/a, or a Hispanic national origin as their street race were recoded as Hispanic/Latinx. These respondents are therefore included in the main Hispanic/Latinx row and indicated with a blank cell in the write-in Hispanic/Latinx subcategory. */**/*** Estimate differs significantly from that for respondents who received version A of the survey at the 0.10/0.05/0.01 level, using two-tailed tests. 6 OBSERVING RACE AND ETHNICITY THROUGH A NEW LENS Diversity in reported street race was most pronounced for self-identified Hispanic/Latinx adults and adults who identify with more than one race. By complementing self-identified race with street race, we uncovered significant diversity in the way self-identified Hispanic/Latinx adults and multiracial adults believe others perceive them. Many self- identified Hispanic/Latinx adults (46 percent) who received version A reported that their street race is white (table 1). More than a quarter of these adults reported that their street race is Black (2 percent), Brown (20 percent), American Indian or Alaska Native (2 percent), or East, South, or Southeast Asian (3 percent). Though their sample size is limited, multiracial adults also reported significant diversity in street race. A plurality (43 percent) of multiracial adults who received version A reported that their street race is white. About a quarter (24 percent) reported that their street race is Black, and 15 percent reported that their street race is East, South, or Southeast Asian. Self-identified multiracial respondents who received version B, where Brown was not an option but Hispanic/Latinx was, were more likely to report that their street race is "some other race" than multiracial respondents who received version A (11 versus 6 percent). Self-identified Hispanic/Latinx adults' responses to the street race questions were especially sensitive to the categories presented; many wrote in that their street race is Hispanic, Latino/a, or a Hispanic national origin when this option was not presented to them. Without Hispanic/Latinx as a street race option, nearly one in five self-identified Hispanic/Latinx adults who received version A wrote in Hispanic, Latina/o, or a Hispanic national origin as their street race (18 percent). These adults were more than twice as likely to say their street race was white than to say it was Brown, and the share who selected Brown was about the same as the share who wrote in a response that we coded as Hispanic/Latinx. This suggests the other categories, including Brown, did not resonate with those respondents. The underlying reason for this could be at least partially related to differences in perceptions of race and ethnicity by nativity and primary language. For example, foreign- born self-identified Hispanic/Latinx adults who received version A were more likely than those born in the US to write in that their street race is Hispanic/Latinx (22 versus 14 percent; data not shown). Similarly, self-identified Hispanic/Latinx adults who took the survey in Spanish were nearly twice as likely as those who took the survey in English to write in that their street race is Hispanic/Latinx (25 versus 13 percent; data not shown).14 Unlike the street races of self-identified Asian, Black, and white adults, self-identified Hispanic/Latinx adults' reported street race varied significantly depending on the response options presented. Among self-identified Hispanic/Latinx adults who received version B (which included a Hispanic/Latinx street race option), 74 percent selected Hispanic/Latinx as their street race, and only about 18 percent selected white as their street race. In contrast, nearly half (46 percent) of self- identified Hispanic/Latinx adults who received version A reported that their street race is white when they were not presented with a Hispanic/Latinx street race option. Compared with self-identified Hispanic/Latinx adults not given the Hispanic/Latinx street race option, self-identified Hispanic/Latinx OBSERVING RACE AND ETHNICITY THROUGH A NEW LENS 7 adults who were given the Hispanic/Latinx street race option-a more traditional ethnic category than Brown-were more likely to select it as their street race and were much less likely to select white as their street race. Among adults receiving version A of the street race question, self-identified Hispanic/Latinx adults were more likely than other adults to report their street race is Brown, but this response was still relatively uncommon among Hispanic/Latinx adults. Less than 5 percent of adults who self-identify as Asian, Black, white, or multiracial reported that their street race is Brown. In comparison, about 20 percent of self-identified Hispanic/Latinx adults chose this category. This could be because other adults who strangers might otherwise racialize as Brown saw a race option that better reflected their street race (e.g., Southeast Asian). We find that self-identified Hispanic/Latinx adults' selection of Brown as their street race did not differ by nativity but did vary by the language in which they completed the survey. US- and foreign-born self-identified Hispanic/Latinx adults were equally likely to say their street race is Brown (21 percent among those born in the US and 20 percent among those who are foreign born; data not shown). Among self-identified Hispanic/Latinx adults, those who took the survey in English were more likely than those who took the survey in Spanish to say their street race is Brown (24 versus 14 percent; data not shown). This gives us greater insight into the factors that could influence respondents' likelihood to select this less-traditional ethnic category. Discussion A growing body of literature and many people's lived experiences confirm that race and ethnicity are not fixed across time and do not fall into neatly established categories that are understood the same way by all people (Irizarry 2015; Jones et al. 2008; López et al. 2017, 2018; Saperstein and Penner 2012). Our exploratory investigation of street race shows the role perspective plays in racialization; people's self-identified race and the race they think other people see them as can differ. This was more true if a respondent self-identified as a person of color, as 96 percent of self-identified white respondents indicated that their street race was white as well. In addition, we found greater diversity in reported street race among adults who self-identify as Hispanic/Latinx or multiracial, groups also likely to have significant diversity in self-identified race. When Hispanic/Latinx was not a street race option, nearly one in five self-identified Hispanic/Latinx adults chose to write in Hispanic, Latina/o, or a Hispanic national origin as their street race. This suggests other available options, including Brown, did not resonate for these respondents. Further, self- identified Hispanic/Latinx respondents who received version B, where Hispanic/Latinx was a street race option, tended to select the Hispanic/Latinx option, reducing our ability to discern variation in street race for adults receiving this question. This finding suggests we may be losing information about the diversity of racialization among self-identified Hispanic/Latinx adults when we ask them about their street race using the same language used for measures of self-identified ethnicity. This could have implications for our ability to observe important but unmeasured disparities within the Hispanic/Latinx 8 OBSERVING RACE AND ETHNICITY THROUGH A NEW LENS population: for example, studies of Hispanic/Latinx people using similar measures of street race as those in this study have found that street race is associated with mental health (López et al. 2017). Work on colorism has found that darker skin is associated with worse educational, health, and economic outcomes when comparing dark-skinned and lighter-skinned people from the same marginalized racial or ethnic group (Crutchfield et al. 2022; Hersch 2018; Keyes, Small, and Nikolova 2020). Without detailed data on the diversity of racialization within the Hispanic/Latinx population, it could be difficult to determine whether resources are allocated equitably across Hispanic/Latinx communities and to document whether people have equal access to opportunity.15 Ultimately, the Brown street race option did not resonate with 80 percent of respondents who self- identified as Hispanic/Latinx, and few self-identified non-Hispanic/Latinx adults chose this option. It's unclear what drove this pattern. It could be that seeing Brown as a response option encouraged respondents to answer the street race question in terms of their phenotype and select an option that best reflects their physical characteristics. Alternatively, seeing the other, more conventional options (e.g., East or Southeast Asian, South Asian, American Indian or Alaska Native) may have primed respondents to choose the response option they were used to selecting. Further, the availability of other options (e.g., South Asian) could have reduced the number of people selecting Brown for those who might have done so if more-fitting options had not been available. A lack of exposure to Brown as a racial category could also explain the low uptake of this option. To our knowledge, this is the first time a survey has included Brown as a dedicated response on a measure of race and ethnicity in the US. As such, many respondents may not have encountered that category before seeing it on our survey. Understanding perceptions of Brown as a category for capturing people's racialized status could help us assess its value in uncovering differential treatment among Hispanic/Latinx and other populations that include many people who do not identify as or believe they are perceived as Black or white. Findings from this experimental study helped shape our approach to asking about street race in our June 2022 Health Reform Monitoring Survey. In that survey, we asked respondents who reported a street race that is not Black or white to report how often they are racialized as Black, Brown, or white. We expect this approach could render even sharper insights into the dynamics of racialization. For example, given that self-identified Hispanic/Latinx adults gravitate to Hispanic/Latinx street race categories, as we find in this study, we anticipate these changes to question wording will help us better discern differences in racialization that might otherwise go unseen without further probing. In addition, we plan to use the new data to explore how street race-as opposed to self-identified race and ethnicity-relates to health and economic characteristics. Implications Our findings align with an understanding of race and ethnicity as fluid rather than fixed, as a function of not just how people see themselves but of how others see them. The findings have implications for researchers, policymakers, administrative and regulatory bodies, and evaluators: First, they highlight the importance of expanding our understanding of different approaches to measuring street race and other measures that acknowledge the complex, nuanced, and socially negotiated nature of race. OBSERVING RACE AND ETHNICITY THROUGH A NEW LENS 9 Oversimplifying measures of race and ethnicity risks diluting the quality of information we obtain and limiting our ability to discern important racial and ethnic differences. Second, it will be important to use such data to assess differences in outcomes by street race both to better understand the mechanisms that produce disparities and to ultimately close them. Operationalizing a more dynamic concept of race and ethnicity, including whether Brown street race is distinct from Black and white street races, has the potential to provide important insights about racial and ethnic disparities in life outcomes, including those related to health and well-being. Race and ethnicity are defined in complex and oftentimes hidden ways that involve people's self- perception and that of the world around them. This complexity is difficult to measure and study but easy to relate to, especially for marginalized and minoritized people who constantly navigate the way the world sees, understands, and responds to them. We can attempt to uncover as much of the complexity as possible by developing and using multidimensional measures of race and ethnicity, which can help us gain a clearer picture of how people are racialized and how this racialization affects well-being and access to opportunity. Methods This section provides more detail on the WBNS data and sample for our exploratory analysis, the street race question experiment and analysis, and the limitations of the analysis. Data and Sample This analysis draws on data from the December 2021 round of the Urban Institute's Well-Being and Basic Needs Survey, a nationally representative, internet-based survey of nonelderly adults designed to monitor changes in individual and family well-being as policymakers consider changes to federal safety net programs. For each round of the WBNS, we draw a stratified random sample (including a large oversample of adults in low-income households) from the KnowledgePanel, a probability-based internet panel maintained by Ipsos that includes households with and without internet access. The 2021 WBNS collected data from a sample of 8,142 adults ages 18 to 64. Survey weights adjust for unequal selection probabilities and are poststratified to the characteristics of nonelderly adults based on benchmarks from the Current Population Survey Annual Social and Economic Supplement and the American Community Survey. Participants can complete the survey in English or Spanish. For further information about the survey design and content, see Karpman, Zuckerman, and Gonzalez (2018). We conducted a split-sample experiment in which we randomly assigned respondents to one of two versions of a question about their street race. About half of the sample (4,052 respondents) was assigned to version A of the question and the remaining half was assigned to version B (4,090 respondents). Adults in each group did not differ significantly across nearly all sociodemographic and geographic characteristics we examined (table 2). However, when comparing smaller subgroups, such as adults who self-identified as Hispanic/Latinx, we observed statistically significant differences by version 10 OBSERVING RACE AND ETHNICITY THROUGH A NEW LENS for some characteristics. We used regression adjustment to assess the effect of these differences on street race estimates in each version for racial and ethnic subgroups and found that accounting for these differences had little effect on the patterns presented in table 1. TABLE 2 Select Characteristics of Adults Ages 18 to 64, by Whether Respondents Received Version A or B of the Street Race Experiment Question, December 2021 Received version A Received version B Female (%) 52.0 50.4 Age (%) 18 to 34 36.2 35.8 35 to 49 30.2 30.6 50 to 64 33.6 33.6 Race/ethnicity (%) White 59.5 59.2 Black 11.7 13.1 American Indian/Alaska Native 1.0 1.1 Asian 6.3 6.1 Native Hawaiian/Pacific Islander 0.1 0.2 More than one race 1.6 1.6 Hispanic/Latinx 19.9 18.7 Educational attainment (%) Less than high school 8.7 10.1 High school graduate 27.4 26.0 Some college 27.8 27.6 College graduate or higher 36.1 36.3 Family income (%) Less than 100% of FPL 15.7 15.2 100 to < 200% of FPL 15.5 15.1 200 to < 400% of FPL 25.7 25.4 At or above 400% of FPL 43.1 44.3 Primary language (%) Hispanic/Latinx; primary English speaker 5.3 4.8 Hispanic/Latinx; bilingual English and Spanish speaker 11.3 9.9 Hispanic/Latinx; primary Spanish speaker 3.3 4.0 Primary language not reported 80.1 81.3 Internet access (%) Has internet access 94.2 94.3 Lacks internet access 5.8 5.7 Urban/rural residence (%) Lives in urban area 87.4 87.7 Does not live in urban area 12.6 12.3 Census region (%) Northeast 16.9 16.7 Midwest 20.5 20.7 South 38.6 37.6 West 24.0 25.0 Sample size 4,052 4,090 Source: Well-Being and Basic Needs Survey, December 2021. Note: FPL = federal poverty level. OBSERVING RACE AND ETHNICITY THROUGH A NEW LENS 11 Survey Experiment and Measure of Street Race We define street race as the race an individual believes others who do not know them personally would assume they are based on their physical appearance (i.e., how strangers on the street would perceive their race; López et al. 2018).16 This definition includes perceptions based on skin color as well as other physical traits such as facial features and hair texture. Street race is not equivalent to skin color. For example, some Southeast Asians may have darker skin than many African Americans and other people racialized as Black, including Afro-Hispanic/Latinx people; however, each of these groups would not occupy the same racial status (Bonilla-Silva and Glover 2004). Adults assigned to both versions of the questionnaire received the same question about their street race: "This question is about how others see your race, not how you identify yourself. If you were out in public, what race do you think other people who do not know you personally would assume you were based on what you look like (for example, your skin color, facial features, and hair)?" In each version, respondents could select one of the following categories, which partially draw on definitions of race and origin used in the census and other federal surveys: white, Black or African American, East or Southeast Asian (such as Chinese, Japanese, Korean, Filipino, or Vietnamese), South Asian (such as Indian or Pakistani), American Indian or Alaska Native, and some other race (with an option to write in a response). Because the question focused on street race rather than self-identified race, we did not present an exhaustive set of race categories,17 and we separated the Asian category into two groups often assigned different racialized statuses (Lee and Ramakrishnan 2019; NPR, RWJF, and Harvard 2017). The experiment tested the inclusion of one additional response category in version A-Brown (not Black or African American)-against the inclusion of two other categories in version B- Hispanic or Latino and Middle Eastern or Arab (box 1). We tested the inclusion of Brown as a street race category in version A to represent a colloquial term applied to many people in the US who are racialized as neither white nor Black or African American.18 We were particularly interested in how many adults who self-identify as Hispanic/Latinx and received version A would select Brown as their street race in the absence of a Hispanic or Latino category. Other surveys that have asked about street race (without including a Brown option) have found that most Hispanic/Latinx adults select Hispanic, Latino, or Mexican, and about one in six selects white (Center for Health Policy at UNM 2015; Pew Research Center 2021). Surveys have also examined experiences with discrimination by asking respondents to identify their skin color based on a scale (Pew Research Center 2021), but this measure alone does not necessarily indicate whether respondents are typically racialized as white, Black, Hispanic/Latinx, or another race (Bonilla-Silva and Glover 2004). In version B of the survey, we took a different approach by including categories associated with ethnicity or origin (Hispanic or Latino and Middle Eastern or Arab) alongside the other race categories.19 Despite the census prompt that "Hispanic origins are not races,"20 a majority of Hispanic respondents to the 2020 Census identified as some other race or more than one race, often because they did not believe the standard racial categories apply to them or because they may not have made a meaningful distinction between race and ethnicity.21 Moreover, even if people do not self-identify as 12 OBSERVING RACE AND ETHNICITY THROUGH A NEW LENS Hispanic or Latino or as Middle Eastern or Arab, they may believe others assign these categories to them. In both versions of the questionnaire, some adults selected "some other race" and provided a written response. This was particularly common for adults who self-identified as Hispanic/Latinx and received version A of the street race question; many of these adults wrote their street race was Hispanic, Latino/a, Mexican, or another Hispanic country of origin. We recoded these responses to a separate Hispanic or Latino category presented in version B but not version A. We recoded remaining written responses in both versions to existing categories when there was little ambiguity. Otherwise, those responses remained in the category for some other race, which included many adults who identified their street race as multiracial. Analysis We estimated the share of adults reporting each street race category in versions A and B for the following self-identified racial and ethnic groups: Hispanic/Latinx adults and non-Hispanic/Latinx adults who are white, Black, Asian, or multiracial. As noted above, the random assignment produced samples receiving versions A and B that had few differences in sociodemographic characteristics overall, but some differences within self-identified racial and ethnic groups emerged. We used regression adjustment to test the sensitivity of the self-reported street race estimates after accounting for differences in these characteristics and found limited effects on the results. Limitations This analysis has several limitations. Though variations of a street race question have been included in surveys and the Behavioral Risk Factor Surveillance System's Reactions to Race module has included a question about ascribed race,22 the 2021 WBNS is the first survey in the US to test Brown as a dedicated response option rather than a retroactively inferred racial position (Irizarry 2011, 2015; Logan 2003). We were unable to cognitively test this terminology before survey fielding. The large number of written responses in version A of the questionnaire among Hispanic/Latinx adults indicated Brown did not resonate with many of them. Similarly, version B of the street race question invited respondents to select categories reflecting race, ethnicity, or origin, and we did not prompt respondents to make a distinction between those concepts. As noted previously, the street race question did not present an exhaustive list of potential race categories, and we faced decisions about how much to disaggregate existing categories (e.g., creating a separate category for South Asian but grouping East and Southeast Asian together). Moreover, responses to the street race question may depend on the racial and ethnic diversity of a person's neighborhood, locality, or state or the frequency or nature of respondents' interactions with people in other racial and ethnic groups. We included a text response option so that respondents could identify a street race not reflected in the categories presented to them. OBSERVING RACE AND ETHNICITY THROUGH A NEW LENS 13 Another limitation is that estimates for some groups are based on small sample sizes. Because the WBNS has a large design effect, the effective sample sizes are smaller and estimates for these groups are imprecise. The sample sizes for these groups are below our typical threshold for reporting estimates from the WBNS, and the estimates should be interpreted with caution. Because the WBNS is only administered in English and Spanish, the patterns we observe for reported street race do not reflect the experiences of adults who speak other languages. The coverage error associated with the survey language is likely greatest for Asian adults, who are more likely than other groups to have limited English proficiency. Finally, it is unclear how well street race terminology and Brown as a racial category translate into Spanish. For the Spanish translation of Brown, we use the term "Morena/o," but we recognize the variation in how Spanish speakers across Latin America interpret this term. For example, Spanish speakers could interpret the term to mean someone with brown skin, but in some contexts, the term also encompasses people who are Black (Sue 2013). Finally, we acknowledge that our personal backgrounds (e.g., self-identified race and ethnicity, street race, gender, and age) could influence the design and interpretation of this analysis. Our personal experiences could lead us to focus on certain findings more than others. Table 3 provides more information on our backgrounds. TABLE 3 Select Characteristics of Researchers Involved in This Study Number of Characteristics researchers Self-identified race and ethnicity African American, non-Hispanic/Latinx 1 Black Dominican, Hispanic/Latinx 1 South Asian, non-Hispanic/Latinx 1 White, non-Hispanic/Latinx 3 Other Mexican, Hispanic/Latinx 1 Street race Black or African American 2 White 3 Brown 2 Gender or gender identity Woman 6 Man 1 Age group 18 to 34 2 35 to 49 3 50 or older 2 First-generation college status First-generation college graduate 2 Not a first-generation college graduate 5 Immigrant generation Second generation 3 Third generation or later 4 Source: Researchers' self-reports. 14 OBSERVING RACE AND ETHNICITY THROUGH A NEW LENS Notes: People who identify as first-generation college graduates are those who, at age 16, did not have one or more parents who had earned a four-year college degree. People whose immigrant generation is second generation were born in the US and have one or more foreign-born parents. People whose immigrant generation is third generation or later were born in the US, as were both of their parents. Notes 1 We use the term "Hispanic/Latinx" to reflect the different ways people with Latin American ancestry self- identify. Many see "Latinx" as more inclusive; unlike "Latino/a," it is not gender specific. We use the term "Afro- Hispanic/Latinx" to describe people who identify as Hispanic/Latinx and are of African descent. 2 Felice León, "Let's Talk about In the Heights and the Erasure of Dark-Skinned Afro-Latinx Folks," The Root, June 9, 2021, https://www.theroot.com/lets-talk-about-in-the-heights-and-the-erasure-of-dark-1847064126. 3 Although we use the term "Hispanic/Latinx" when referring to street race, the term used in the survey question on street race is "Hispanic or Latino." 4 We capitalize Black to denote the unique Black experience as one characteristic of a diverse group of people, ethnicities, and cultures. The authors have not capitalized "white," a term and label for a range of historically grouped ethnicities used to delineate a contrast with people of color. See Margaret Simms, "Say African American or Black, but First Acknowledge the Persistence of Structural Racism.," Urban Wire (blog), Urban Institute, February 8, 2018, https://www.urban.org/urban-wire/say-african-american-orblack-first- acknowledge-persistence-structural-racism. 5 Self-identified race and ethnicity refers to the race and ethnicity respondents reported for themselves to profile questions for the KnowledgePanel. Respondents were first asked to report whether they are of Hispanic origin, and then they were asked to report whether their race is white, Black, Asian, American Indian or Alaska Native, or some other race. For our analyses, adults who are Black, white, Asian, or multiple races include those who self- identified with those racial categories but did not self-identify as Hispanic/Latinx in a previous demographic profile survey of all members of the panel underlying the WBNS. We lacked the sample size to report estimates for other non-Hispanic/Latinx racial groups, including adults who identified as American Indian or Alaska Native and Native Hawaiian or other Pacific Islander. 6 Questionnaires for the Health Reform Monitoring Survey are available at https://www.urban.org/policy- centers/health-policy-center/projects/health-reform-monitoring-survey/survey-resources. 7 The US Census Bureau collects race data based on self-identification following guidelines from the US Office of Management and Budget. These racial categories generally reflect a social definition of race in the US and include race and national origin or sociocultural groups. The Census Bureau uses the term "Hispanic origin" to describe people who are of Cuban, Mexican, Puerto Rican, South or Central American, or other Spanish culture or origin. People who identify as Hispanic, Latino, or Spanish may be any race. For more information, see "Glossary," US Census Bureau, accessed October 11, 2022, https://www.census.gov/glossary/. 8 Nancy López, "What Is 'Street Race'?," University of New Mexico, Institute for the Study of "Race" and Social Justice, accessed October 11, 2022, https://race.unm.edu/what-is-street-race.html. 9 Nancy López, "The US Census Bureau Keeps Confusing Race and Ethnicity," Salon, March 1, 2018, https://www.salon.com/2018/03/01/the-us-census-bureau-keeps-confusing-race-and-ethnicity_partner/. 10 Rachel Marks and Merarys Ríos-Vargas, "Improvements to the 2020 Census Race and Hispanic Origin Question Designs, Data Processing, and Coding Procedures," Random Samplings (blog), US Census Bureau, August 3, 2021, https://www.census.gov/newsroom/blogs/random-samplings/2021/08/improvements-to-2020-census-race- hispanic-origin-question-designs.html. OBSERVING RACE AND ETHNICITY THROUGH A NEW LENS 15 11 Hansi Lo Wang, "No Middle Eastern or North African Category on 2020 Census, Bureau Says," NPR, January 29, 2018, https://www.npr.org/2018/01/29/581541111/no-middle-eastern-or-north-african-category-on-2020- census-bureau-says; Hansi Lo Wang, "The U.S. Census Sees Middle Eastern and North African People as White. Many Don't," NPR, February 17, 2022, https://www.npr.org/2022/02/17/1079181478/us-census-middle- eastern-white-north-african-mena; and Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity, 62 Fed. Reg. 58782 (Oct. 30, 1997). 12 Nicholas Jones, Rachel Marks, Roberto Ramirez, and Merarys Ríos-Vargas, "2020 Census Illuminates Racial and Ethnic Composition of the Country," US Census Bureau, August 12, 2021, https://www.census.gov/library/stories/2021/08/improved-race-ethnicity-measures-reveal-united-states- population-much-more-multiracial.html. 13 Kat Chow, "Ask Code Switch: Who Can Call Themselves 'Brown'?" NPR, December 11, 2017, https://www.npr.org/2017/12/11/569983724/ask-code-switch-who-can-call-themselves-brown. 14 Among self-identified Hispanic/Latinx adults, about 12 percent reported they were born in the US and their street race is Brown. Another 9 percent reported they are foreign born and their street race is Brown. 15 López, "Census Bureau Keeps Confusing Race and Ethnicity." 16 Nancy López, "What's Your 'Street Race-Gender'? Why We Need Separate Questions on Hispanic Origin and Race for the 2020 Census," Culture of Health Blog, Robert Wood Johnson Foundation, November 26, 2014, https://www.rwjf.org/en/blog/2014/11/what_s_your_street.html; and López, "What Is 'Street Race'?" Studies informing the development of the street race questions include Dowling (2014), Jones and colleagues (2008), and Vargas (2015). 17 Fewer than 10 adults in the WBNS sample self-identified as Native Hawaiian or other Pacific Islander (NHPI). Responses to the street race question excluded an NHPI category, and only a few respondents who self- identified as NHPI reported their street race as "some other race" and specified an NHPI racial group in a written response. Depending on the version of the questionnaire respondents received, the remainder selected white, Brown, East or Southeast Asian, and Hispanic or Latino. However, it is possible more would have selected NHPI if explicitly presented with that category in the street race question. A follow-up survey conducted in June 2022 included this as a response option when asking about street race. 18 For instance, the terms "Black" and "Brown" are often used in conjunction to refer to people of color in political speeches. See, for example, "Remarks by President Biden Commemorating the 100th Anniversary of the Tulsa Race Massacre," White House, June 2, 2021, https://www.whitehouse.gov/briefing-room/speeches- remarks/2021/06/02/remarks-by-president-biden-commemorating-the-100th-anniversary-of-the-tulsa-race- massacre/; and "Transcript: Barack Obama's Speech on Race," NPR, March 18, 2008, https://www.npr.org/templates/story/story.php?storyId=88478467. 19 Lo Wang, "No Middle Eastern or North African Category on 2020 Census." 20 "Hispanic or Latino Origin," US Census Bureau, accessed November 9, 2022, https://www.census.gov/acs/www/about/why-we-ask-each-question/ethnicity/. 21 Hansi Lo Wang, "1 in 7 People Are 'Some Other Race' on the U.S. Census. That's a Big Data Problem," NPR, September 30, 2021, https://www.npr.org/2021/09/30/1037352177/2020-census-results-by-race-some- other-latino-ethnicity-hispanic. 22 See, for example, CDC (2012), López and colleagues (2018), and Pew Research Center (2021). 16 OBSERVING RACE AND ETHNICITY THROUGH A NEW LENS References Awad, Germine H., Nadia N. Abuelezam, Kristine J. Ajrouch, and Matthew Jaber Stiffler. 2022. "Lack of Arab or Middle Eastern and North African Health Data Undermines Assessment of Health Disparities." American Journal of Public Health 112 (2): 209–12. https://doi.org/10.2105/ajph.2021.306590. Bonilla-Silva, Eduardo, and Karen S. Glover. 2004. "'We Are All Americans': The Latin Americanization of Race Relations in the United States." In The Changing Terrain of Race and Ethnicity, edited by Maria Krysan and Amanda E. Lewis, 149–83. New York: Russell Sage Foundation. Busey, Christopher L., and Carolyn Silva. 2021. 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"A Different Kind of Brown: Arabs and Middle Easterners as Anti-American Muslims." Sociology of Race and Ethnicity 4 (2): 178–91. https://doi.org/10.1177/2332649217706089. About the Authors Dulce Gonzalez is a research associate in the Health Policy Center at the Urban Institute. She forms part of a team working on the Urban Institute's Well-Being and Basic Needs Survey. Gonzalez conducts quantitative and qualitative research focused primarily on the social safety net, immigration, and barriers to health care access. Her work has also focused on the impacts of the COVID-19 pandemic on nonelderly adults and their families. Before joining Urban, Gonzalez worked at the Georgetown University Center for Children and Families and at the nonprofit organization Maternal and Child Health Access. Gonzalez holds a BA in economics from California State University, Long Beach, and a master's degree in public policy from Georgetown University. Dr. Nancy López is professor of sociology at the University of New Mexico. She directs and cofounded the Institute for the Study of "Race" and Social Justice. Her engaged scholarship and teaching are guided by intersectionality as inquiry and praxis-interrogating the simultaneity of systems oppression and resistance. Her books include Hopeful Girls, Troubled Boys: Race & Gender Disparity in Urban Education (2003); Mapping "Race": Critical Approaches to Health Disparities Research (2013); and QuantCrit: An Antiracist Quantitative Approach to Educational Inquiry (2023). López coined "street race" (2014) as a measure of perceived race. She received the American Sociological Association William Foote Whyte Distinguished Career Award for sociological practice and public sociology. Her funded research includes research on high school ethnic studies curriculum and pedagogy for reducing inequalities, research on employing intersectionality to revise federal administrative race and ethnicity data, and research on cultivating a community of practice on intersectionality and student success in Hispanic- serving institutions. López is a Black Latina who grew up in public housing and graduated from a de facto segregated public high school in NYC. As the eldest of five US-born children of Dominican immigrants who never had the privilege of pursuing education beyond the second grade, she grew up rich in cultural wealth, including linguistic and resistant capital. López has also participated in many federal and other public programs, such as Head Start and Upward Bound, designed to create what Dr. Ruth Zambrana calls "equity lifts." Michael Karpman is a senior research associate in the Health Policy Center at the Urban Institute. His work focuses on the implications of the Affordable Care Act, including quantitative analysis related to health insurance coverage, access to and affordability of health care, use of health care services, and health status. This work includes efforts to help coordinate and analyze data from the Urban Institute's Health Reform Monitoring Survey. Before joining Urban in 2013, Karpman was a senior associate at the National League of Cities Institute for Youth, Education, and Families. He received his MPP from Georgetown University. Karishma Furtado is a member of the Urban Institute's inaugural cohort of equity scholars. She uses human-centered data, research, and storytelling to catalyze and measure impact, facilitate accountability, deepen understanding, and imagine what's possible on the path to racial equity. Before OBSERVING RACE AND ETHNICITY THROUGH A NEW LENS 19 joining Urban, Furtado was a founding staff member of Forward Through Ferguson, a St. Louis–based nonprofit focused on systems change to achieve racial equity; before that, she was a part of the nationally recognized Ferguson Commission following the killing of Mike Brown in 2015. She helped author the Ferguson Commission Report. Her research on the social determinants of health sits at the intersection of race, racism, and health and is in service of advancing health equity, especially in the school setting. She has published articles in leading public health and health policy journals on the Ferguson Commission, the role of public health in advancing racial equity, and operationalizing a commitment to health equity in applied public health spaces. Furtado holds bachelor's degrees in biology and public policy from the University of Chicago and has completed master's and doctoral degrees in public health with a specialization in biostatistics and epidemiology from Washington University in St. Louis. She retains affiliations with the Prevention Research Center in St. Louis, Forward Through Ferguson, and the diversity and inclusion consultancy The Mouse and The Elephant. Genevieve M. Kenney is a vice president and senior fellow in the Health Policy Center at the Urban Institute. She is a nationally renowned expert on Medicaid, the Children's Health Insurance Program (CHIP), and health insurance coverage, health care access and quality, and health outcomes for low- income adults, children, and families. She has played a lead role in several Medicaid and CHIP evaluations, including multiple congressionally mandated CHIP evaluations, and has conducted state- level evaluations of the implementation of managed-care and other service delivery reform initiatives and policy changes in Medicaid and CHIP. Currently, she is leading a project focused on health equity that involves working with a community advisory board. In other work, she is assessing Medicaid policies aimed at improving outcomes in the postpartum period and increasing receipt of evidence- based treatment for substance use disorder. In her prior work, she has used mixed methods to examine Medicaid expansions for pregnant women, parents, and children, Medicaid family planning waivers, and a range of policy choices related to Medicaid and CHIP. She participated in the Agency for Healthcare Research and Quality's Health Equity Planning Meeting and Summit, AARP DC's Symposium on Disrupting Health Disparities Impacting Adult Black Women and Men in DC, and the Medicaid Equity Dashboard Project Advisory Committee for the State Health Access Data Assistance Center. She received a master's degree in statistics and a doctoral degree in economics from the University of Michigan. Kenney is an advisory board member of the University of North Carolina's Rural Health Research Program and the Hilltop Institute and a member of Upstream USA's Evaluation Advisory Group. Marla McDaniel is a senior fellow in the Center on Labor, Human Services, and Population at the Urban Institute. Before joining Urban, she was a postdoctoral fellow at the Columbia University School of Social Work. McDaniel has researched, written about, and spoken about racial inequity and disparity; low-income children, youth, and families; and the programs and policy environments that touch families' lives. She is interested in examining how inequity across domains-including health, education, and employment-has a compounding effect on overall health and well-being. McDaniel earned a bachelor's degree in psychology from Swarthmore College and worked as a case manager for youth in foster care before earning a doctorate in human development and social policy from Northwestern University. 20 OBSERVING RACE AND ETHNICITY THROUGH A NEW LENS Claire O'Brien is a quantitative research assistant in the Health Policy Center at the Urban Institute. She leverages Medicaid claims to study the relationship between racialized economic segregation and health outcomes and to evaluate integrated care plans for beneficiaries dually eligible for Medicare and Medicaid. Additionally, she takes part in the implementation and analysis of the Urban Institute's Health Reform Monitoring Survey, which she has used to study telehealth, unfair treatment, patient-provider racial concordance, and knowledge of insurance Marketplaces. She uses other national survey data to study family coverage and prescription drug affordability. Finally, she monitors changes in the Affordable Care Act's Marketplaces. She has a bachelor's degree in economics and applied math with a minor in poverty studies from the University of Notre Dame and is currently pursuing a master of public policy degree at the George Washington University. Acknowledgments This brief was funded by the Robert Wood Johnson Foundation. The views expressed do not necessarily reflect the views of the Foundation. The views expressed are those of the authors and should not be attributed to the Urban Institute, its trustees, or its funders. Funders do not determine research findings or the insights and recommendations of Urban experts. Further information on the Urban Institute's funding principles is available at urban.org/fundingprinciples. The authors are grateful to Margaret Simms and Stephen Zuckerman for their comments on this brief. We also thank Rachel Kenney for her excellent editorial assistance. ABOUT THE ROBERT WOOD JOHNSON FOUNDATION The Robert Wood Johnson Foundation (RWJF) is committed to improving health and health equity in the United States. In partnership with others, we are working to develop a Culture of Health rooted in equity, that provides every individual with a fair and just opportunity to thrive, no matter who they are, where they live, or how much money they have. ABOUT THE URBAN INSTITUTE The nonprofit Urban Institute is a leading research organization dedicated to developing evidence-based insights that improve people's lives and strengthen communities. For 50 years, Urban has been the trusted source for rigorous analysis of complex social and economic issues; strategic advice to policymakers, philanthropists, and practitioners; and new, promising ideas that expand opportunities for all. Our work inspires effective decisions that advance fairness 500 L'Enfant Plaza SW and enhance the well-being of people and places. Washington, DC 20024 Copyright © December 2022. Urban Institute. Permission is granted for www.urban.org reproduction of this file, with attribution to the Urban Institute. OBSERVING RACE AND ETHNICITY THROUGH A NEW LENS 21