Nancy López, PhD, is an associate professor of sociology at the University of New Mexico (UNM). She co-founded and directs the Institute for the Study of “Race” and Social Justice at the Robert Wood Johnson FoundationCenter for Health Policy at the UNM. On December 5, RWJF will hold its first Scholars Forum: Disparities, Resilience, and Building a Culture of Health.
How should we measure race and ethnicity for the 2020 Census? How can health disparities researchers engage in productive dialogues with federal, state and local agencies regarding the importance of multiple measures of race and ethnicity for advancing health equity for all?
If we depart from the premise that the purpose of race, ethnicity, gender and other policy-relevant data collection is not simply about complying with bureaucratic mandates, but rather it is about establishing communities of practice that work in concert toward the creation of pathways (from harmonized and contextualized data collection, analysis and reporting) to effective policy solutions and interventions that address the pressing needs of diverse communities across the country, then we have planted the seeds of a culture of health equity and social justice.
As a sociologist of racial, ethnic and gender inequality, I remain viscerally concerned about the current U.S. Census plans to combine Hispanic origin and race into one question. In Mapping “Race”: Critical Approaches to Health Disparities Research (Rutgers University Press, 2013), which grew out of a National Institutes of Health R13 workshop grant, I offer separate conceptual models for race and ethnicity as multi-dimensional social constructions. I argue that while it is tempting to combine ethnicity and race as analytically equivalent concepts, this conflation contributes to many unanticipated consequences that will impede the development of effective policies to advance health equity.
Researchers across the social sciences find that there is value in keeping questions about national origin, ethnic background, ancestry and language as separate from race. Using the National Health Interview Survey, LaVeist et al. examine whether Black Hispanics are more similar to their Hispanic co-ethnics or to Black non-Hispanics. They find that Hispanics, regardless of racial status, shared similar health behaviors; however, for health status and health services outcomes, Black Hispanics were more like Non-Hispanic Blacks. This means that Black Hispanics did not experience the same type of health access as their Hispanic family members, neighbors or even spouses who may occupy a very different racial status based on what they look like.
How can we map the complexity of race, ethnicity, national origin and generational status, as well as gender, in health disparities research? One solution is to include more than one measure of race and to always be attentive to how race and gender interact. I put forward the concept of “street race-gender” or the meanings ascribed to a conglomeration of markers of physical appearance, including but not limited to skin color, hair texture, facial features among other characteristics and interacting with gender as a key concept for mapping and interrupting inequality. Race-gender profiling, whether in housing, employment, law enforcement, educational institutions or even when accessing health care, voting or traveling in an airport, takes place according to one’s “street race-gender.”
Health equity researchers interested in this measure can employ the following questions:
If you were walking down the street, what race do you think other Americans who do not know you personally would automatically assume you were, based on what you look like? Would you say: White, Black, Asian, American Indian, some other race? (Write in).
How do other people in the United States usually classify your race based on what you look like? Would you say: White, Black, Asian, American Indian, some other race? (Write in).
Have you ever been mistaken for someone of another race based on what you look like? If yes, please list the top three in descending order, with number 1 representing the race you are most frequently mistaken for, followed by the second and third races that you are most commonly mistaken for.
If you were walking down the street, how would other Americans who do not know you personally identify your gender? Would you say: ___ Man __ Woman ___ Transgender ___ Other?
The next time that you fill out a questionnaire or survey about your race or gender for yourself or a family member, I invite you to self-reflect on the purpose of this data collection. Consider how this data will be used for monitoring social inequalities in key policy arenas, such as health access, service, treatment and outcomes, as well as housing, employment, education and law enforcement. Then, ask yourself: What is my “street race-gender”?
It is my hope that as more health equity researchers engage in dialogues with federal and state agencies—including the Office of Management and Budget, Census, and departments of health and vital records—community dialogues about use of this data will become the new “gold standard” for assessing our progress in building a culture of health equity and social justice for all.