Abstract
This special issue brings together original research that advances the emerging subfield on the measurement and analysis of varying components of race. The articles provide insight into how social scientists can tease apart the multiple components of race and leverage them to better understand how race continues to divide life chances, creatively using existing and new sources of data. The articles speak to three key themes: how we can better understand the various ways that race is experienced, alternative approaches to measuring the different components of race, and the implications of race measures for understanding social inequality.
Consider the following example: Raquel, a Dominican woman in New York, has light skin and European features, but because she has some African ancestry, she identifies as Black, although she may be perceived by her Latino/a neighbors as Dominican, by her non-Latino/a White employers as Latina, and by strangers as White (Roth, 2012). For increasing numbers of people, the lived reality of race is not monolithic—not all of their experiences with racial categorization are consistent. Rather, there are a number of different components to how they experience race in their daily lives. Such components include how a person self-identifies, the more limited identity she expresses when asked “what is your race?” on a questionnaire, her racial ancestry, how she is classified by others, how she believes others classify her, and a range of racialized physical characteristics that may shape how people treat her. As is the case with Raquel, these components do not always align. But with multiple components at work, how can we measure race reliably and understand how an individual’s experiences are shaped by racialized interactions if we only collect information about one component of that experience?
Two recent examples from widely publicized Pew Research Center reports show how our measurement of race can limit our understanding of racial groups and their experiences. One report pointed out that more than 60% of individuals who report a mixed-racial ancestry do not self-identify as multiracial on a questionnaire (Pew Research Center, 2015). The other showed that many Latino/as who think of themselves as mestizo or mixed race choose to identify with just one race when confronted with the standard U.S. Census question on race (Gonzalez-Barrera, 2015). Clearly, then, measuring multiracial populations and drawing conclusions about their experiences is far from a straightforward project (D. R. Harris & Sim, 2002), and a single racial identification question on a survey will not capture all of an individual’s racial experience.
These considerations lie at the center of work on the ways race is socially constructed, but are often rather peripheral to how race is measured and analyzed. Theorizing about race, ethnicity, racism, and racial inequality has tackled this complexity by examining the social construction of racial and ethnic categories over time and the implications of the multifaceted nature of race for the lived experiences of individuals (Bonilla-Silva, 2004; Brubaker, Loveman, & Stamatov, 2004; Cornell & Hartmann, 2007; Loveman, 1999; Omi & Winant, 1994). However, the collection and use of racial and ethnic data have largely remained one dimensional, often treating race and ethnicity as monolithic and static for individuals across contexts and over the life course, even while major demographic trends in the past 50 years have increased the need for more sophistication in our operationalization of racial measurement. High rates of immigration to the United States have increased the size of populations who do not see themselves fitting neatly into U.S. racial categories (Itzigsohn, Giorguli, & Vazquez, 2005; Lee & Bean, 2010; Roth, 2012). Increasing cultural contact between races and ethnicities, among both immigrants and the native-born, has also led to rising intermarriage as well as more individuals whose identities cross or blur racial and ethnic boundaries (Brunsma, 2005; Campbell, 2007; Campbell & Martin, 2015; Lee & Bean, 2010; Roth, 2005).
A subfield on the multiple components of race has been emerging, with several works showing that which component of race we measure significantly affects conclusions about inequality, or examining the implications of a mismatch between different components of race (Campbell & Troyer, 2011; Freeman, Penner, Saperstein, Scheutz, & Ambady, 2011; Noymer, Penner, & Saperstein, 2011; Rodríguez, Castro, Garcia, & Torres, 1991; Roth, 2010; Saperstein, 2006, 2012; Vargas, 2015). This prompted us to organize a workshop held at Texas A&M University in 2014, titled “Measuring the Diverging Components of Race in Multiracial America,” sponsored in part by the American Sociological Association Fund for the Advancement of the Discipline. The workshop brought together several scholars working on these issues, as well as graduate students and representatives from the U.S. Census Bureau, and its sessions may be viewed online at https://kinder.rice.edu/measuringraceconference/. This special issue of American Behavioral Scientist grew out of the workshop, although an open call for articles for this volume allowed us to include relevant work that had not been part of our discussions.
One of the challenges discussed at the workshop is the question of where to find the nuanced measures of race that would best fit the theoretical mechanisms we are interested in capturing. We understand just how difficult this can be, so to make the search easier, we compiled an online resource, the Multiple Components of Race Data Library, that profiles survey data sets that have measures of at least two components of race or racialized experience (Bratter, Campbell, & Roth, 2014). In this resource (which also includes a comment section where anyone can suggest additional surveys that meet the criteria for inclusion), scholars can find information about how each of the sources collects data on race, ethnicity, phenotype, and others’ perceptions. The profiles also detail measurement issues in surveys that complicate the use of single-race components in a theoretically informed way—for example, clarifying how the race measure in the General Social Survey moved from a measure of interviewer-observed race to self-identified race over time in the same longitudinal variable, or specifying in what context a particular question was asked. The workshop also led to productive recommendations to help designers of new or continuing surveys make decisions about how to include analytically sophisticated measures of race in their data collection and analysis efforts (Bratter et al., 2015).
There is considerable work yet to be done to understand how the different components of race are experienced, intersect, and how they differentially influence important social processes, such as discrimination, health, residential segregation, self-esteem, or group belonging. For example, while our discussion of the social construction of race emphasizes that the same people can be perceived differently in different contexts (Cornell & Hartmann, 2007; Freeman et al., 2011), we know less about how different measures might capture these aspects of racial variation in the population as a whole. In many cases, the types of social inequalities that people experience stem more from categories that are imposed from outside than from how people self-identify (Roth, 2010). Being able to study how people are perceived, a measure frequently omitted from standard social surveys, reveals that inequalities can also shape who is placed in racial categories, a dynamic that can reinforce and even expand racial inequality (Saperstein & Penner, 2012).
The current volume brings together original research that contributes to this emerging subfield and pushes us forward on all of these fronts. The articles provide insight into how social scientists can tease apart the multiple components of race and leverage them to better understand how race continues to divide life chances, creatively using existing and new sources of data. The articles speak to three key themes: how we can better understand the various ways that race is experienced, alternative approaches to measuring the different components of race, and the implications of race measures for understanding social inequality.
How Race Is Experienced and the Social Construction of Appearance
The first set of articles shed light onto the complex ways that race is experienced, using measures that are somewhat different from those often used in survey research. These three articles focus explicitly on how individuals are racially perceived by others and the social construction of appearance. This goes on to shape the experiences of the individual, as we see in the third article in this section.
In the first article, “Shades of Race: How Phenotype and Observer Characteristics Shape Racial Classification,” Feliciano uses Match.com profiles as a source of photographs with demographic and racial self-identification information attached. In this study, multiple undergraduates racially classified each picture, creating a rich new data set with information about observers’ perceptions of White, Black, Latino/a, and multiracial individuals. The analysis moves the conversation about racial classification by others forward in many new ways: it examines agreement and disagreement among multiple observers of the same individual; it tests relationships with multiple measures of phenotype ranging from facial characteristics to body type; and it tests how the characteristics of the observer shape classifications. The findings confirm the primacy of skin tone in predicting how individuals are classified (Gravlee, 2005; M. Harris, 1970; Herman, 2010), but they go much further. They show us that the rapidly growing Latino/a and multiracial populations are classified less consistently by observers into the categories that they self-select than are Whites and Blacks, and that these classification decisions vary by the gender and race of the observers.
Garcia and Abascal’s “Colored Perceptions: Racially Distinctive Names and Assessments of Skin Color” illustrates an important way in which racial logic creates a “way of seeing” the world that literally shapes how we perceive people around us. These authors demonstrate an important implication of ethnically distinctive names: those whose names mark their membership in the Latino/a group are perceived as significantly darker-skinned than the same individuals assigned a non-Latino/a name. Using a palette-based measure of perceived skin tone, the authors demonstrate the importance of social context in interpreting measures of phenotype. The association between skin tone and inequality within groups is well-known (Frank, Akresh, & Lu, 2010; Monk, 2014; Rondilla & Spickard, 2007; Telles & Murguia, 1988), and this article adds direct evidence of one way that the social context of interactions and the information available to those in the interaction shapes racialized perceptions, an important point that should shape how we interpret data on phenotype from a variety of social contexts.
In the third article in this section, “When Others Disagree: Documenting Perceived Racial Contestation and Its Implications for Racial Identity Characteristics Among Self-Identified Latina/os, Asians, Blacks, and Whites,” Vargas and Stainback demonstrate the importance of perceived racial contestation—an individual believing she is viewed differently by others than how she identifies herself—on a person’s sense of connection with her racial group. The authors find that a small but nontrivial portion of the Latino/a and Asian communities in the United States believe that their racial identities are contested by others, and that they report lower racial identity salience. This could have important implications for the cohesiveness of some of the most rapidly growing groups in the United States, and should lead us to reexamine our understanding of the racialized daily experience of these groups.
Alternative Measures of Components of Race
While the majority of social surveys measure race through a racial self-identification question, a few surveys also ask interviewers to classify the respondents’ race. These measures provide the majority of our data on self-identified and observed race, respectively, perhaps the most central components of people’s lived experience (or at least the two we know the most about). Two articles in this volume focus on alternative measures, considering what is to be gained by bringing in additional information about observed race or about racial ancestry.
In “An Outside View: What Observers Say About Others’ Races and Hispanic Origins,” Rastogi, Liebler, and Noon leverage a truly unique data set—individually linked records from the full 2000 and 2010 Census, yielding more than three million cases with both a proxy response (usually from a neighbor) and a household response for the same individual—to test the correspondence between proxy reports and household reports of race and ethnicity in a way that has never been possible before. These analyses test the cues that outsiders use to identify another person’s race and ethnicity. They teach us that correspondence is common in the aggregate data, while mismatches are common for very small racial groups, and that patterns of mismatch illuminate assumptions about how people identify. For example, proxy respondents rely on contextual clues when they are guessing the identification of others. They show the importance of these patterns not only for understanding how stereotypes and context influence classification but also how these systematic patterns of proxy responses will influence aggregate statistics in data where proxy responses are used.
In “Essential Measures: Ancestry, Race, and Social Difference,” Gullickson uses two measures of self-identified racial and ethnic background from the Census and the American Community Survey: the standard race and ethnicity questions that are used by most work on racial inequality, and the ancestry question, which receives far less attention. The article leverages the correspondence between ancestry responses and racial identification responses to consider the consistency across measures and the ways these measures map onto existing inequality in the United States. Gullickson shows that while using the racial and ethnic identification questions as the sole measure of group membership does capture a large amount of the inequality across groups today, there remain significant amounts of variation at the ancestry level that is not captured by this broader classification of race and ethnicity. This is true even among Whites, a group that is usually conceptualized as containing little meaningful variation by ancestry today.
Implications for Understanding Social Inequality
A consideration that motivates much of this work is how racial complexity helps us better understand racial inequality. Two final articles reveal how our measures of race and its various components shape our understanding of racial inequalities. Estimates of racial disparities are sensitive to the measures we select (Bratter & Gorman, 2011; Noymer et al., 2011). These articles show how analyzing various dimensions reveals the different ways that race can contribute to experiences of discrimination or privilege, helping us better understand the specific nature of the social inequality between and within racial groups. These two articles show how the analytical choices we make shape our conclusions about inequality across the Americas.
In “Making the Most of Multiple Measures: Disentangling the Effects of Different Dimensions of Race in Survey Research,” Saperstein, Kizer, and Penner provide a powerful illustration of how using multiple measures of racial categorization in a single analysis provides new insights into the mechanisms that create and sustain racial inequality. This article explores four approaches for combining multiple measures to test different theoretical mechanisms for the production of racial inequality in an outcome. For example, they use cases where an individual is perceived as a member of a different race than the group with which they self-identify to test whether an outcome (such as home ownership) is more tied to others’ perceptions or to self-identification. Finding that the perception of others is a more important predictor of outcomes, the article clarifies the mechanisms of inequality and illustrates the importance of using appropriate measures that capture those social mechanisms.
In the final article, “Interrogating Race: Color, Racial Categories, and Class Across the Americas,” Bailey, Fialho, and Penner extend our discussion beyond the United States. They remind us that in international comparisons of race and ethnicity, we often exaggerate the differences between Latin America and the United States, often by asserting the uniqueness of the U.S. context. Doing so effectively hides similar mechanisms of ethnic inequality across the Americas and obscures important country-level differences between countries in the way that racial categorization and color shape inequality. This article uses data on both skin color and racial group categorization for 19 countries, and finds that both measures are important for understanding the role of social origins in creating contemporary inequality. Like the previous piece, this article makes an effective case for the use of multiple measures of racial and ethnic difference. Including more than one component of race in our analyses offers insights that are not possible with a single measure, such as the authors’ insight that color differences are more closely tied to socioeconomic origins than are differences in racial categories.
In all, the articles in this issue define a new way forward for the measurement of racial and ethnic categories, lived experiences, and inequality. They show us the potential power of using multiple measures of racialized experience and of carefully matching our measures to the concepts that drive our work. Because race continues to be a “fundamental axis of social organization” in the United States (Omi & Winant, 1994), it is important that we conceptualize and measure the components of these processes to the best of our ability.
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors’ work on this special issue was funded in part by the American Sociological Association’s Fund for the Advancement of the Discipline, supported by the National Science Foundation, as well as the Kinder Institute for Urban Research and the Race and Ethnic Studies Institute, the College of Liberal Arts, and the Department of Sociology at Texas A&M University.