Editorial Board: Editor: George H. Conklin, Emeritus, North Carolina Central University Robert Wortham, Associate Editor, North Carolina Central University Board: Rebecca Adams, UNC-Greensboro Bob Davis, North Carolina Agricultural and Technical State University Catherine Harris, Wake Forest University Ella Keller, Fayetteville State University Ken Land, Duke University Steve McNamee, UNC-Wilmington Miles Simpson, North Carolina Central University William Smith, N.C. State University
Editorial Assistants John W.M. Russell, Technical Consultant
Volume 10, Number 1
"What Race Do You Consider Yourself?"
Shannon N. Davis
George Mason University
Virginia Commonwealth University
The language used to describe race and ethnicity in the United States has a complicated history. A variety of terms are applied when individuals are asked to label themselves or others. While the history of many terms (e.g., the rise of African American as a label as opposed to Black) can be documented, little research has investigated the appropriation of these terms to describe racial self-classification. The purpose of this paper is to examine the factors that predict how individuals, when given the opportunity, choose to racially self-classify. This paper examines responses from a nationally representative survey, where respondents could use any language they chose to respond to the question "What race do you consider yourself?" We determine the demographic characteristics that are correlated with responding with a color versus a racial group (e.g., Black versus African American), noting that educational attainment seems to be the key correlate of language choice. For clarity, we use lower case terms of black and white to denote the reference to color while upper case lettering (Black and White) refers to a racial group.
The Language of Race
The terms used to classify race have evolved greatly over the years. The specific terms "black" and "white" began to emerge in the late 1600s when Francois Bernier began to categorize humans by their skin color (Ashcroft, 2010), using the colors black, white, red and yellow to organize the supposed biological differences across human beings. According to Ashcroft (2010), this began the binary use of the terms black and white in every day language to describe race. Western culture has used light and its absence in both religious and philosophical terms throughout history (Ashcroft, 2010). Given that white and black were used to denote good and bad, respectively, it is no surprise that these terms were used to amplify the assumption that the white category of race was far superior to that of the Black category of race (Ashcroft, 2010). Indeed, these categories exist in part because of the history of their mutual construction and comparison.
Each racial category has a history of the terms used to describe the category. Over the years, the word Black has been disputed and changed many times in American history (Bhopal and Donaldson 1998). It made a transition from the word African, to Colored, to Negro, to Black, and finally to African American (Smitherman 1991). The word Negro was replaced with the word Black in 1966 when the activist Stokeley Carmichael coined the phrase "Black Power" (Smitherman 1991). Smitherman (1991) explains that this gave a sense of empowerment for the Black Community. The term "African American" went a step further to define this community. "For a people grappling with disempowerment and its tragic effects on entire Black communities across the nation, the term provides the security of 'I am somebody' by reaffirming the origin and cultural continuity of our African heritage" (Smitherman 1991:123). The American history of African American/Black classification is one with profound meaning and purpose.
As there are few terms used to classify Caucasians/Whites, the history is more straightforward. The debates and controversy over the words used to label African Americans/Blacks are not seen when looking at the history of Caucasian/White individuals (Bhopal and Donaldson 1998). The term "white" has been used throughout history and is still used fluently today. The term Caucasian was established to describe those that descended from Central Europe (Bhopal and Donaldson 1998). Both the words "white" and "Caucasian" have little social history behind them and are mainly used to classify based on origin.
Predicting Use of Color to Racially Self-Classify
Like other parts of one's identity, racial identity evolves over time and may vary based not only on interactional conditions, but also may be a response to "structural circumstances (e.g., occupational or residential concentration, political-legal status)" (Jaret and Reitzes 1999: 713). The salience of one's racial identity in a particular social setting may lead them to invoke particular language to describe their identity. Racial identity has been found to be more important for minority individuals, especially African Americans/Blacks than for Caucasians/Whites (Cross 1991; Jaret and Reitzes 1999). Therefore, the use of a term to denote "race" (African American or Caucasian) rather than "color" (black or white) may be more likely among African American/Black individuals. However, racial identity among Caucasian/Whites has been found to be most important in public settings (which arguably a telephone survey like the one examined here may be considered), as opposed to home, work, or neighborhood settings. Therefore, Caucasian/Whites may be more likely to self-classify as "Caucasian" rather than "White" in a telephone survey due to the public nature of the interaction.
of invoking color when asked to racially self-classify may also be influenced
by social desirability. Social desirability effects have been defined
by researchers as a methodological problem within research projects where
a participant's knowledge that he or she is participating in research results
in change of their behavior in ways other than how they would have initially
behaved prior to knowledge of the research project (Adair, 1984). Ganster
et al. (1983) have developed three alternative response effect models that
present ways in which research can be greatly affected by social desirability.
These models include: 1) the spurious model (social desirability
contamination can produce spurious correlations between variables), 2)
the suppression model (social desirability may hide any real correlations
between independent and dependent variables) and 3) the moderator model
(social desirability may or may not be correlated with the independent
or dependent variables present) (Ganster et al. 1983). This final
model would be the expected influence of social desirability in the current
This phenomenon should be of considerable concern to researchers especially within in the realm of survey research. Yet, this social desirability effect and its implications upon research have yet to be studied thoroughly (Adair, 1984; Raley, 2002). Crowne and Marlowe first attributed the individual differences related to social desirable behavior of participants as an expression of one's need for approval (cited in Ganster et al. 1983). Nunnally (1978) expanded upon this speculation in suggesting that social desirability may be the product of several sources of variance, for example, one's level of psychological adjustment, his or her self-knowledge and his or her level of frankness.
Racial Classification and Survey Design
Data collection strategies, beginning with the United States Census, have historically classified people into racial categories based on skin color; giving social and political preference to individuals of Caucasian/White heritage (Lee 1993). The articulation of color categories of Caucasian/White and non-white (which has evolved over time) was intentionally framed to reflect political and social understandings of skin color and racial purity. The category of "White" has always been part of Census classifications; the category of "Black" was an original classification, though the language changed to "Negro" in 1930, and then a blended category of "Black/Negro" in 1970 emerged (Lee 1993). Further, large scale surveys that have not been implemented by the federal government have historically only used the language of color to classify respondents, changing their data collection strategies only in response to the U.S. Census change in 2000 (though for ease in trend analysis continuing to provide color classifications; see the General Social Survey (Smith 1995, 1997; Smith et al. 2010)). Therefore, it is possible that there could be some design effect on Americans' responses to survey questions on racial self-classification. Due to the ways in which Americans are accustomed to being asked about race in survey design, simply by participating in the survey project respondents may be more likely to answer the question of self-classification using a color. However, because, "Caucasian" as a label is not as commonly used in everyday language as compared to the racial-classification of "African American" and because of the relative lack of the use of "Caucasian" in surveys (e.g., the General Social Survey (Smith et al. 2010)) we would expect to see Caucasians/Whites to be more likely to invoke color when racially self-classifying that would African Americans/Blacks.
The Present Study
Given the dearth of research on the choice of terminology used by individuals to racially self-classify, this innovative paper is both exploratory and descriptive. We interrogate choice of language for racial self-classification by investigating the demographic correlates of responding with a color versus a racial group that invokes geographical ancestry (e.g., Black versus African American). We describe two groups of individuals: those who invoke a color when asked to racially self-classify and those who do not use color to self-classify. Finally, we ask which factors, net of other characteristics, motivate individuals to use color to racially self-classify. Thus, our key contribution to sociological knowledge is the specification of some of the mechanisms through which racial self-identification and self-classification occur.
To assess racial self-classification, we utilize nationally representative survey data collected in August and September 2010. The Work and Family Survey was a telephone survey designed to investigate married individuals' attitudes and behaviors in the wake of the latest recession. Respondents completed a questionnaire that included items regarding employment, but also were asked many demographic and background questions. Of the 343 respondents who participated in the survey, 212 are included in this analysis due to missing data on key variables.
asked "What race do you consider yourself?" as part of the demographic
questions in the Work and Family Survey. Interviewers were instructed
to record verbatim all responses. Based on these respondents, we
organized respondents into three main categories: Caucasian/White, African
American/Black, and all other responses. A small proportion of the
sample provided a response other than Caucasian/White and African American/Black;
those respondents were not included in these analyses.
We examined whether there were differences in the use of color to describe race by many demographic characteristics. We included the broader racial group (Caucasian/White or African American/Black), with "Caucasian/White" as the reference category. Sex of respondent was included as a dummy variable, with men as the reference category. Respondent's age was included as a continuous measure, as were the number of children in each of three age groups (under 6, 6-12, and 13-17). Respondents were asked for their religious affiliation. Due to small sample sizes, the responses were collapsed to Protestant, Catholic, other (which includes Jewish, Muslim, and any other affiliation), or none. Religious affiliation is included as a set of dummy variables. Religious service attendance was measured by asking respondents how frequently they attended religious services, ranging from never (1) to more than once a week (7).
Social class measures included household income, educational attainment, and occupation. Annual income was measured by asking respondents their total household income. Response categories ranged from under $15,000 (1) to $150,000 or more (7); income was included as an indicator for social class as a continuous measure. Respondents were asked to describe their educational background, ranging from having less than a high school education (1) to having a graduate degree (7). Educational attainment was included as a continuous measure. Each respondent was asked to provide the job title from their current primary job. Each occupation was mapped onto the ISCO (International Standard Classification of Occupations) occupational prestige scale. Three dummy variables for the main occupational categories (plus unemployed) were constructed: Professional (manager, professional, technical), Service (clerical, service), and Manual Labor (craft, machine operator, and unskilled).
Finally, respondents were asked to provide their zip code. Using this information, we constructed a set of dummy variables to capture region of the country in a manner consonant with that used by other large national surveys (e.g., the General Social Survey). Region of the country is included in the analysis through a set of dummy variables (Northeast, South, Midwest, and West).
Table 1 presents the overall sample statistics. Almost 58% of the sample used color to racially self-classify. The sample is overwhelmingly Caucasian/White. The average age is about 55 years old. Respondents averaged some college (4 = an associate's degree). It is worth noting the relatively high percentage of the sample (30.5%) who chose not to report either an occupation or being unemployed. Close to 18% of the sample reported being unemployed, a percentage somewhat higher than the official unemployment rate though not much higher than the total unemployment rate plus discouraged workers and those marginally attached to the labor force (BLS 2011).
As this research is both exploratory and descriptive, we began our investigation by examining whether there were statistically significant differences in demographic characteristics across the two groups of respondents: those who used color to self-describe race and those who did not use color to self-describe race. Table 2 presents mean differences in demographic characteristics for the two groups of respondents. All statistically significant differences across the two groups are bolded. There are differences between the two groups in educational attainment, household income, and number of children in the household, as well as their reporting their current occupation or if they were unemployed. Those who used color to racially self-identify had lower education levels and household incomes than individuals who did not use color to racially self-identify. Professionals were more likely not to use color to racially self-identify, while individuals who did not disclose their occupational status were more likely to use color to racially self-identify. People who used color to racially self-identify had more children ages 6-12 and fewer teenagers than did individuals who did not use color to racially self-identify. There were no differences in the use of color to racially self-classify based on racial category.
Based on these descriptive findings, we constructed a logistic regression model predicting the likelihood of participants using color to describe their racial classification. Table 3 reports those results. The model includes the factors that were not distributed equally across the two groups (educational attainment, occupation, number of children) as well as other demographic characteristics that may be correlated with those factors, such as gender and region. Table 3 shows that net of other characteristics, the three main correlates of whether a respondent uses color to describe race are educational attainment, number of children in the home, and region. Individuals with more education are less likely to use color to racially self-identify. Each child aged 6-12 in the household increases the likelihood someone will use color to racially self-identify threefold, while each teenager in the household decreases the likelihood someone will use color to racially self-identify by over 60%. Individuals living in the Midwest are almost 70% less likely to use color to racially self-identify as are those living in the Northeast. Subsequent analysis showed that individuals living in the Northeast are more than twice as likely as individuals living in the remainder of the country to use color to racially self-identify (effect on odds = .853, odds ratio = 2.347, p < .05). In this model, occupation is not significantly correlated with the likelihood of using color to racially self-identify. Alternative specifications of the model (where household income, a continuous measure of occupational prestige, or other background characteristics from Table 1 such as religious service attendance are included) do not substantively change the results. As before, there is no difference in whether the respondent used color to racially self-classify based on racial category.
Discussion and Conclusions
This research, one of the first of its kind, examines the demographic correlates of using specific terminology to racially self-identify. When given the opportunity to use whatever language they wanted, the majority of the sample chose to use a color (either White or Black) to identify their race, rather than invoking a term that denotes geographic ancestry. Our analysis provides insight into some of the mechanisms through which racial self-identification and self-classification occur. Table 2 notes the substantial importance of social class as pathway to language use and self-classification. Individuals with markers of higher social class, that is, greater educational attainment, higher household income, and more prestigious occupations, were less likely to invoke color when asked to identify their own race. However, in the logistic regression analysis, only educational attainment remains a significant predictor of whether a respondent uses color to self-classify. Rational choice theory argues that individuals will make choices based on both the expected outcomes of their actions as well as the subjective utility of the attached expected outcome, though these outcomes are understood as contextually based (Heath 1976; Blaclock and Wilken 1979; River and Ordeshock 1973 cited in McClendon 1985). Therefore, this finding is consistent with rational choice theory, as individuals with higher education likely see the benefit of classifying themselves without using color as outweighing any cost of invoking color to identify race in the interactional setting of the telephone interview (Hechter and Kanazawa 1997). College and post-baccalaureate education provide many opportunities for individuals to learn about race and its history in the United States. More importantly, attainment of college and post-baccalaureate education are interpreted as markers of membership in the middle class. To be seen as inhabiting the habitus (Bourdieu 1990) of the middle class, to "do class" (West and Fenstermaker 1995), individuals with college or post-baccalaureate education would be more likely to feel the need to use some terminology other than color to describe their race. Expecting to be held morally accountable to their social class, their behavior, via their language, would be consonant with culturally expected norms for those who are middle class, specifically using more refined, precise language for concepts.
Having more preteen children in the household increases the likelihood of using color to racially self-identify, while having more teenagers in the household decreases the likelihood of using color to racially self-identify. Preteens actively construct their racial identity (French et al. 2006), and the use of color to designate race is both easy and culturally prevalent. Further, adolescents tend to belong to social groups that are monoracial, though "race matching increases with age" (Crosnoe 2000: 382). The extent of racial social integration likely influences not only the children's development of a racial identity through self-labeling (Mead 1934), but the parents' use of language as well in order to provide a consistent framework of racial understanding for their children. Given that labels are accompanied by culturally constructed behavioral expectations and evaluations (Backman and Secord 1968, Kuhn and McParland 1954; Rubington and Weinberg 1968 cited in Scheirer and Kraut 1979), parents' use of language would be an additional reinforcement of the cultural understandings of racial identity in contemporary American society. The same logic holds for parents of teenagers. The agency of teenagers in constructing their own identities has been well-documented (Hitlin et al. 2006). Further, teenagers may be interested in understanding their place in racial history, and may stake an ideological as well as identity-based claim on their own racial identity by invoking language other than that of color to describe their racial classification. Their parents, perhaps in an attempt to relate to them or as a result of their children's inquiries into racial identity construction, may be less likely to invoke color as well.
Most previous research examining regional differences on any topic regarding race tends to find the South to be the outlier, the region with the most unique findings regarding race. Interestingly, we found that individuals living in the South were just as likely to use color or not use color when racially self-identifying (see Table 2). The fact that individuals living in the Northeast were significantly more likely than individuals from the rest of the United States to use color when racially self-identifying is unexpected. We investigated whether this finding was a function of the relative homogeneity of the sample from the Northeast or whether it was truly a regional difference. The race distribution in the Northeast is the same as the entire sample, with 10.5% of the respondents living in the Northeast reporting themselves to be African American/Black. Why then would individuals living in the Northeast be significantly more likely to use color when talking about race? Perhaps because the Northeast as a region is relatively racially homogenous (Humes, Jones and Ramirez 2011) and has a relatively high level of education (Crissey 2009), the normative ways of "doing" race and class differ in the Northeast. That is, the standards against which individuals in the Northeast expect to be held morally accountable for their performance of race and class may focus less on vocabulary than in other regions of the country. This possible explanation has not been explored in the research literature; future investigation is certainly warranted.
That the likelihood of invoking color did not differ by respondent racial group suggests that if social desirability was playing a role in how people responded to the question on racial classification, that role did not differ by racial group the respondent identified with. This, too, is surprising, as previous research suggested that racial identification was more important among minority individuals than among Caucasian/Whites (Cross 1991; Jaret and Reitzer 1999). Perhaps the similarity across racial groups in the use of color to describe race is due to the prevalence of equating race and color in American society (Ashcroft 2010). As the concept of race as a social construction becomes more accepted outside of academic circles and the connection between color (that is, biology) and race is deconstructed in cultural discourse, it is likely that we may see not only a change in the overall usage of color to describe race but also a divergence across groups in their usage of color. Specifically, we would expect to see a reduction among African American/Black individuals in the invocation of color if a pervasive cultural shift away from equating race with color were to occur. We encourage additional research in this area to specifically examine change over time in the invocation of color in racial self-classification among African American/Blacks in the United States.
This paper is one of the first to examine
the demographic correlates of the use of specific terminology to racially
self-classify. Given the importance of racial identity in people's
lives, future research on self-labeling will make a strong contribution
to the literatures on racial identity construction.
Adair, John G. 1984. "The Hawthorne Effect: A Reconsideration of the Methodological Artifact." Journal of Applied Psychology 69(2): 334-345.
Allaman, Jacqueline D. 1972. "The Antecedents of Social Desirability Response Tendencies of Children and Young Adults." Child Development 43 (4): 1135-1160.
Ashcroft, Bill. 2010. "Language and Race." Social Identities 7(3): 311-328.
Bourdieu, Pierre. 1990. The Logic of Practice. Stanford: Stanford University Press.
Cross, William E. Jr. 1991. Shades of Black: Diversity in African-American Identity. Philadelphia: Temple.
Bhopal, Raj and Liam Donaldson. 1998. "White, European, Caucasian, or What? Inappropriate Labeling in Research on Race, Ethnicity, and Health." American Journal of Public Health 88(9): 1303-1307.
Bureau of Labor Statistics. 2011. Economic News
Release. U.S. Department of Labor: Washington, DC. (http://www.bls.gov/
Crissey, Sarah R. 2009. Educational Attainment
in the United States: 2007. Washington, D.C.: U.S. Census Bureau/U.S.
Department of Commerce. http://www.census.gov/prod/2009pubs/
French, Sabine Elizabeth, Edward Seidman, LaRue Allen, and J. Lawrence Aber. 2006. "The Development of Ethnic Identity during Adolescence." Developmental Psychology 42: 1-10.
Ganster, Daniel C., Harry W. Hennessey and Fred Luthans. 1983. "Social Desirability Response Effects: Three Alternative Models" The Academy of Management Journal 26 (2): 321-331.
Hechter, Michael and Satoshi Kanazawa. 1997. "Sociological Rational Choice Theory." Annual Review of Sociology 23: 191-214.
Humes, Karen R., Nicholas A. Jones, and Roberto R. Ramirez.
2011. Overview of Race and Hispanic Origin: 2010. Washington,
D.C.: U.S. Census Bureau/U.S. Department of Commerce. http://www.census.gov/prod/cen2010/
Jaret, Charles and Donald C. Reitzes. 1999. "The Importance of Racial-Ethnic Identity and Social Setting for Blacks, Whites, and Multiracials." Sociological Perspectives 42: 711-737.
Krysan, Maria and Mick P. Couper. 2003. "Race in the Live and Virtual Interview: Racial Deference, Social Desirability, and Activation Effects in Attitude Surveys." Social Psychology Quarterly 66 (4): 364-383.
Lee, Sharon M. 1993. "Racial Classifications in the US Census: 1890––1990." Ethnic and Racial Studies 16(1): 75-94.
Mead, G, H. 1934. Mind, Self, and Society. Chicago, IL: The University of Chicago Press.
McClendon, McKee. Rational Choice and White Opposition to Racial Change: A Case Study of Busing." The Public Opinion Quarterly 49 (2): 214-233.
Nunally, J. and Ira Bernstein. 1994. "Response Biases and Response Styles." Pp. 376-385 in Psychometric Theory. 3rd ed. New York: McGraw Hill.
Raley, R. K. 2002. "The Effects of the Differential Undercount on Survey Estimates of Race Differences in Marriage." Journal of Marriage and Family 64 (3): 774-779.
Schuman, Howard and Jean M. Converse. 1971. "The Effects of Black and White Interviewers on Black Responses in 1968." The Public Opinion Quarterly 35(1): 44-68.
Scheirer, Ann and Kraut, Robert.1979. "Increasing Educational Achievement via Self Concept Change." Review of Educational Research 49 (1): 131-149.
Smith, Tom W. 1995. "MR085 A Review of Ethno-Racial Measures on the General Social Survey." Methodological Report, Chicago, NORC.
Smith, Tom W. 1997. "MR089 Measuring Race by Observation and Self-identification." Methodological Report, Chicago, NORC.
Smith, Tom W.; Marsden, Peter V; Michael Hout; Jibum Kim. 2010. General Social Surveys, 1972-2010: Cumulative Codebook / Principal Investigator, Tom W. Smith; Co-Principal Investigators, Peter V. Marsden and Michael Hout. -- Chicago: National Opinion Research Center.
Smitherman, Geneva. 1991. "'What Is African to Me?'":
Language, Ideology, and African American." American Speech 66(2):
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