(Re)Modeling Race A Latent Variable Approach for Research on Inequality

(Re)Modeling RaceA Latent Variable Approach for Research on Inequality Aliya Saperstein University of California, Berkeley SSED Conference December 8, 2007

An emerging consensus • Should we count?: Yes, though always with sensitivity and reflexivity • Why count?: Primarily to monitor discrimination but also to recognize the diversity of the population • How to count?: Using multiple measures, including self-identity and how the individual is perceived by others • But what happens next?

Why we need multiple measures • Race is not an intrinsic characteristic of individuals, but a context-specific marker of social status • Not simply a matter of “misclassification” or measurement error, but of understanding how racial divisions create and maintain inequality • Different measures of race likely tap different inequality producing processes: • Appearance (e.g., discrimination) • Identity (e.g., attitudes, motivations, behaviors) • Ancestry (e.g., intergenerational transmission of disadvantage)

The data: National Survey of Family Growth • In-person interview of women, ages 15-44, living in households in the United States • 1988 sample includes questions about both self-reported and interviewer-classified race • Sample size: 8,279 women who are not missing either measure of race • NSFG interviewers: mostly white women with at least a high school degree, assigned to respondents in their local area

Racial categories and coding • Observed race coded: Black, White, Other • Recoded self-reports to match observed race • Self-reported “others” are an aggregation of American Indian and Asian responses

Cross-tabulation of self-reported and observed race in 1988 NSFG Self-reported race

Largest racial populations in the 1988 NSFG • Consistently classified whites 62% • Consistently classified blacks 32% • Consistently classified others 2% • Seen as white, IDs as white and other 1% • Seen as black, identifies as black and 1% another race • Seen as white, identifies as other 0.7% • Seen as white, identifies as black 0.5%

Empirical example #1: Family income • Survey question: The total combined income of your family in the past 12 months, “including income from all sources such as wages, salaries, social security or retirement benefits, help from relatives, rent from property and so forth.” • Previous studies find that “Black” families have lower incomes than “white” families, in part because of a larger proportion of female-headed households, but also because “black” men’s wages are much lower on average than “white” men’s wages

Which “blacks” have lower incomes?

Distribution of income, by race percent of racial group in income quintile quintiles of annual family income

Empirical example #2: Health screenings • Survey question: “In the past 12 months, during a visit for family planning services [as part of a general check-up or other medical visit], did you have a … ?” • Papanicolaou test (pap smear) • Breast exam • Blood pressure checked • Studies find consistently that “black” women are equally likely – if not more likely – to receive screenings as “white” women

Which “blacks” are more likely to be screened?

Comparing findings: Race in context • Self-reported race is more closely related to family income, but women who are seen as white are (a little bit) better off • Observed race is more closely related to receiving health screenings, but again that measure alone does not tell the whole story • Findings suggest that the mechanisms that perpetuate racial inequality are not always the same across contexts

Implications • One measure of race is not better than the other: each provides equally “correct” and complementary information • U.S. Census should include a proxy for observed race: “How do others in this country typically classify you?” • Encourages all of us to think about the multidimensionality of race (and ethnicity) not solely as a measurement issue but in terms of how it can move us closer to identifying the mechanisms that perpetuate inequality in the United States and around the world

(Re)Modeling RaceA Latent Variable Approach for Research on Racial Inequality Aliya Saperstein University of California, Berkeley asaper@berkeley.edu

(Re)Modeling Race A Latent Variable Approach for Research on Inequality