Article Critique Psychology PAPER Homework

Racism at the Intersections: Gender and Socioeconomic Differences in the Experience of

Racism Among African Americans

Naa Oyo A. Kwate Rutgers, the State University of New Jersey

Melody S. Goodman Washington University in St. Louis School of Medicine

Several studies investigating the health effects of racism have reported gender and socioeco- nomic differences in exposures to racism, with women typically reporting lower frequencies, and individuals with greater resources reporting higher frequencies. This study used diverse measures of socioeconomic position and multiple measures and methods to assess experienced racism. Socioeconomic position included education and financial and employment status. Quantitative racism measures assessed individual experiences with day-to-day and with major lifetime incidents and perceptions of the extent to which African Americans as a group experience racism. A brief qualitative question asked respondents to describe a racist incident that stood out in recent memory. Participants comprised a probability sample of N � 144 African American adults aged 19 to 87 residing in New York City. Results suggested that women reported fewer lifetime incidents but did not differ from men on everyday racism. These differences appear to be partly because of scale content. Socioeconomic position as measured by years of education was positively associated with reported racism in the total sample but differently patterned across gender; subjective social status showed a negative association. Qualitative responses describing memorable incidents fell into 5 key categories: resources/opportunity structures, criminal pro- filing, racial aggression/assault, interpersonal incivilities, and stereotyping. In these narratives, men were more likely to offer accounts involving criminal profiling, and women encountered incivilities more often. The findings highlight the need for closer attention to the intersection of gender and socioeconomic factors in investigations of the health effects of racism.

I n How to Read the Air (Mengestu, 2010), Jonas Wolde-mariam, a second generation Ethiopian immigrant, visits ahistorical site in the Midwestern United States. He is met by a security guard at the entrance, and is viewed with suspicion.

I play my role perfectly, standing nonchalantly while he takes his notes. I know that we’re all supposed to be wary these days, of strangers and strange bags and especially of strangers carrying strange bags, and I want to do my part in easing some of the collective tension

as best I can . . . I say and do nothing, however, hoping as I always do for the best—that perhaps he will find a measure of comfort in my prep-school uniform of khaki pants and dark blue shirt, of which I have a suitcase full . . .” pp. 121–122.

Jonas’ surveillance and racial scrutiny are inflected by his race, gender, nativity, and class—both real and perceived—and are contextualized by a social moment in which people are intensely concerned with the threat of terrorism. African Americans’ expe- rience with interpersonally mediated racism is fraught with the expenditure of psychological resources Jonas deploys to anticipate, decode, and cope with subtle subordination. Research has shown that this environment takes a toll; encounters with interpersonally mediated racism exact substantial health costs encompassing men- tal health, physical health, and health behaviors (Mays, Cochran, & Barnes, 2007; Paradies, 2006; Pascoe & Smart Richman, 2009; Williams & Mohammed, 2009). The scales used in these studies have been developed and validated with Black U.S. populations, but they may not have dealt as fully with sociodemographic inflections in the experience of racism, particularly with regard to gender and socioeconomic position.

Studies investigating the health effects of racism often find that women report less racism than men (Paradies, 2006), but the source of gender differences is unclear. A parsimonious explana-

Naa Oyo A. Kwate, Departments of Human Ecology and Africana Studies, Rutgers, the State University of New Jersey; Melody S. Goodman, Division of Public Health Sciences, Department of Surgery, Washington University in St. Louis School of Medicine.

This research was funded by the NIH Director’s New Innovator Award Program, Award DP2 OD006513 from the Office of the Director, National Institutes of Health and the National Institute of General Medical Sciences (NIGMS). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Office of the Director, National Institutes of Health or the National Institutes of Health.

Correspondence concerning this article should be addressed to Naa Oyo A. Kwate, Department of Human Ecology, Rutgers, the State University of New Jersey, 55 Dudley Road, Cook Office Building, New Brunswick, NJ 08901-8520. E-mail: [email protected]

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American Journal of Orthopsychiatry © 2015 American Orthopsychiatric Association 2015, Vol. 85, No. 5, 397–408 http://dx.doi.org/10.1037/ort0000086

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tion is that men’s higher scores reflect actual differences in the prevalence of experienced racism. But also possible are measure- ment processes that tend to inflate men’s scores. Men may be more willing than women to respond affirmatively to questions about racism, despite equivalent experiences. Or, measures may empha- size incidents that men experience more frequently or that are gendered as male, making women less likely to endorse them. If so, measures of racism would fail to capture how racism is mod- ified by gender (Jackson, Phillips, Hogue, & Curry-Owens, 2001; Moradi & Subich, 2003). Black feminist scholarship tells us that Black women’s subordination lies in occupying “a position whereby the inferior half of a series of binaries converge” (Collins, 2000, p. 71). Goff and Kahn (2013) argue that experimental psychology has often not been invested in the intersection of race and gender in thinking through the construction of social identities, therefore undertheorizing the lived experience of individuals. To the extent that commonly used measures are less attentive to intersecting identities, observational studies may also leave critical gaps in our understanding of the experience of racism. For exam- ple, if the kinds of experiences that are found at the convergences that Collins (2000) articulates are underrepresented in measures, the scope of experiences that respondents are able to report is restricted, limiting our ability to assess racism’s impact on health. In one study, men endorsed more forms of racism, but associations with poor mental and physical health were stronger among women than in men—in some instances, the estimates for women were twice as high (Borrell et al., 2007).

Similarly, reports of racism vary by socioeconomic position, but associations are not consistent, and may be attributed at least in part to the nature of the questions in commonly used measures. A review of studies across multiracial and multiethnic samples re- vealed that self-reported racism is generally positively related to socioeconomic position (measured with income or education), though there are also inverse associations (Paradies, 2006). Sub- sequent studies have conveyed variation in associations between reported racism and socioeconomic position, depending on which domains are queried. For example, in one sample, middle- and low-income African American respondents differed little when asked about work and housing. However, high-income respon- dents were more likely to report incidents related to education and service, while low-income respondents were more likely to report incidents related to police and the courts (Williams et al., 2012). Brondolo et al. (2009) explicitly tested dimensions of individual- level socioeconomic position (income, education, occupational prestige, and assets) and neighborhood income as determinants of experienced racism among Black and Latino New York City residents. In both the total sample and Black subsample, socioeco- nomic position was not associated with overall lifetime discrimi- nation, but interaction effects revealed income to be inversely related to threat/harassment, and positively related to workplace discrimination. As well, individuals with low socioeconomic po- sition reported more discrimination over the past week.

Study Objectives We sought to test and tease apart gender and socioeconomic

differences in reports of racism. To do so, we used multiple measures and multiple methods. Studies reporting demographic differences tend to use only one measure to operationalize expe-

riences with racism, making it a challenge to tease out the source of observed disparities (Brondolo et al., 2009). We sought to improve extant work by including varied operationalizations of both constructs. In assessing experiences with racism, we used two measures each for interpersonally mediated everyday racism, and major lifetime racism. We also assessed beliefs about the preva- lence of racism in the lives of African Americans in general. Most studies of the health effects of racism include only quantitative self-report measures; only a few studies (e.g., see Rooks, Xu, Holliman, & Williams, 2011) include qualitative questions that plumb the kinds of racist experiences faced by Black women and men. Such data are useful in illuminating how scale content may operate across gender, and the potential mechanisms by which racism harms health. Thus, our research goals were to investigate with quantitative data the extent to which gender and socioeco- nomic position are associated with reports of racism; and, using brief qualitative reports, to conduct exploratory analyses of how Black women and men encounter racism, and whether gender differences emerge in brief narratives about racism.

Method

Sample and Study Design

Data were generated from The Black LIFE Study (Linking Inequality, Feelings and the Environment), a longitudinal investi- gation of the health effects of racism, and that took place in two predominantly Black New York City (NYC) neighborhoods. Data were collected between December 2011 and June 2013. N � 144 participants (52% female, with a mean age of 44.6) were recruited from a probability sample of African American residents in the two neighborhoods. Participants were randomly selected from randomly selected households, and eligible respondents were aged 18 or older, English speaking, self-identified as Black/African American, and grew up in the United States. Because other por- tions of the project involved blood draws, exclusion criteria were surgery or blood transfusions within the past 6 months. Overall response rates to initial recruitment across the two neighborhoods were 30–35%, reflecting difficulty in making household contact for screening; rates for successfully interviewing people were about 60% once a household was known to have an eligible person. Trained African American interviewers conducted face-to- face Computer-Assisted Personal Interviews (CAPI). Participants were followed for two additional visits: a 2-month follow up, which comprised a brief telephone interview; and a 1-year follow up, a second in-person CAPI that was somewhat shorter than that at baseline. The present analyses focus on quantitative self-reports from baseline, and qualitative short-answer responses from base- line and the 1-year follow-up.

Measures

Socioeconomic position comprised years of education, employ- ment status, financial strain, and subjective social status. Employ- ment status assessed whether participants were currently working (labeled fully employed); unemployed but had worked at some point during the prior year (partially employed); and unemployed over the course of the past year (unemployed). Financial strain was

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398 KWATE AND GOODMAN

a 1-item measure that asked participants how comfortably their household lived on income received, whether they “always have enough money for the things you need,” “sometimes don’t have enough money,” or “often don’t have enough money.” Subjective social status was assessed with a commonly employed ladder (Adler, Epel, Castellazzo, & Ickovics, 2000), which asked respon- dents to indicate where they fell on a 10-rung ladder depicting social positions ranging from the least to best money, education, and jobs.

We assessed experiences with racism in different domains. Beliefs about the extent of racism faced by African Americans were measured with the Group Impact scale (Harrell, 1997); day- to-day experiences with racism and discrimination were assessed with the Everyday Discrimination Scale (Williams, Yu, Jackson, & Anderson, 1997) and the Racism and Life Experiences Scales (Harrell, 1997); referred to hereafter as Daily Life Experiences; and major lifetime experiences were assessed with the Experiences of Discrimination Scale (Krieger, Smith, Naishadham, Hartman, & Barbeau, 2005) and the Major Experiences of Discrimination Scale (Williams et al., 1997). At baseline, Cronbach’s alpha values were: .94 for Group Impact, .89 for Everyday Discrimination, .91 for Daily Life Experiences, .79 for Experiences of Discrimination, and .66 for Major Experiences of Discrimination.

Finally, participants gave open-ended descriptions of recent racist incidents. Because the extensive nature of the quantitative questionnaire precluded an in-depth interview on these experi- ences alone, respondents were asked, “We have been talking a lot about how frequently you encounter racism. Can you give me examples of some of the incidents that stick out in your recent memory? Describe what happened.” Participants gave responses ranging in length from a few sentences to a short paragraph. Though brief, several participants described in rich detail several racist incidents.

Analytic Plan

Quantitative analyses were weighted to take into account non- response and poststratification adjustments for age and gender and the stratified sampling design by neighborhood. Regression anal- yses for survey data with complex sampling designs were con- ducted using Stata MP/13.1; significance was assessed as p � .01, a conservative Bonferroni adjustment for multiple comparisons. Weighted linear regression models for complex survey data were used to assess the impact of gender and socioeconomic position. For each of the five assessments of racism, we fit regression models where explanatory variables were age, gender, years of education, employment status, financial strain, and subjective so- cial status. Age, years of education, and subjective social status were modeled continuously; gender, financial strain, and employ- ment status were categorical.

Qualitative analyses were completed for the majority of the sample that articulated at least one racist incident in response to the open-ended question (22 people, 15% did not report any). Using an Excel worksheet, the first author organized and coded response content by identifying the settings, incidents, perpetrators, and reactions to the reported experiences, and then assigning category codes to all unique portions of responses to represent the full range of discussed material. The approach was inductive in nature, attempting to draw out the themes that characterized participant

responses with an eye on the kinds of experiences with racism that African Americans face in the United States. Analyses sought to faithfully describe important details and organize the data to reveal underlying patterns (Barg & Kauer, 2005; Brent & Slusarz, 2003). Initial readings of participant responses allowed a broad demarca- tion of the domains in which racism took place (e.g., in public space), and these were refined through subsequent readings to produce more precise descriptions (e.g., accessing resources). Us- ing a parsimonious set of categories that echoed scientific litera- ture and public discourse fostered accurate characterization of the breadth of participant experiences.

Responses were coded in their entirety, assigning multiple codes where appropriate. For example, if a respondent reported police harassment and poor restaurant service, both incidents were incor- porated in extracting common underlying themes. Narratives were analyzed in the aggregate, and were characterized by five under- lying themes: resources, criminal profiling, aggression/assault, in- civilities, and stereotyping.

Results

Descriptive Quantitative Results

Sample characteristics appear in Table 1. We first examined relationships among the set of measures assessing experiences with racism. Bivariate correlations (see Table 2) suggest that the measures of racism are related but not redundant; the two measures of lifetime major experiences were most highly correlated (r � .766).

Gender and Socioeconomic Position Differences

Table 3 shows reports of racism by gender and categorical measures of socioeconomic position across all measures at the baseline interview. Men scored higher, though differences were slight for some measures, and generally not statistically signifi- cant. Consistent patterning was not evident for financial strain; on some measures those with fewer resources had higher scores, and on others they had lower scores. To operationalize education categorically, we use receipt of a bachelor’s degree (yes/no). Studies investigating the health effects of racism have used other dichotomous or trichotomous categories such as some college, or high school diploma versus less than a high school degree (e.g., see Brondolo et al., 2009; Chae et al., 2014; Cunningham et al., 2012; Krieger, Kosheleva, Waterman, Chen, & Koenen, 2011). In the present sample, using a college degree as a cutpoint was more appropriate given the relatively high education levels, and allowed us to split the sample approximately in half. Those with a degree were higher on all scales save Major Discrimination. The differ- ences for the Experiences of Discrimination Scale, t(110) � �2.09, p � .04, the Daily Life Experiences, t(118) � �2.35, p � .02, and Group Impact, t(118) � �2.47, p � .02, were statistically significant. Employment status was also associated with reported racism; those who were fully employed reported the most, fol- lowed by those who were partially employed, and the lowest prevalence by those who were unemployed.

In Table 4 we look at gender differences across the nine do- mains comprising major lifetime events. Women were more likely

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399GENDER, SEP, AND RACISM

to report having experienced discrimination in school settings, though the disparity differed across the two scales. Women were also more likely to experience discrimination in housing contexts, in stores, or with neighbors. Men gave higher levels of endorse- ment for all remaining experiences; marked differences were ev- ident for hiring/work, encounters with the police, and obtaining capital and medical care.

Primary Quantitative Results

We first investigated gender differences alone, conducting re- gression models that adjusted for age. As shown in Table 5, with men as the reference group, women reported 1.50 and 1.16 fewer incidents on the two measures of major lifetime discrimination, but no differences emerged on day-to-day discrimination or group impact. Weighted linear regressions modeling the impact of gender and socioeconomic position together show that the effects of these factors vary across measures (see Table 6). Controlling for socio- economic factors, gender was still associated with lifetime racism, though the effect was attenuated compared to the age-adjusted model. No gender differences emerged on either measure of day- to-day racism.

Controlling for gender, socioeconomic position was associated with both lifetime and day-to-day racism. Education was positively associated with lifetime racism, while subjective social status was inversely related. Each additional “rung” of social status was associated with approximately 1/3 of an incident less on the lifetime scales. Results differed on measures of day-to-day racism. Subjective social status was not associated, but years of edu-

cation were again positively related, though only on the Daily Life Experiences Scale. To further explore the variable associ- ations between socioeconomic position and reported racism, we graphed the association between both subjective social status and education and day-to-day racism. The negative association between perceived social rank and racism persisted for both men and women.

However, education had opposite effects across gender. We conducted an additional regression to assess the effect of an interaction between gender and education. With day-to-day racism as the outcome, and age, gender, years of education, and a gender- education interaction as explanatory variables, the interaction (b � .108, 95% CI � .105–.206) was statistically significant. As shown in Figure 1, for men, more years of education was associated with less frequent day-to-day racism; for women, more education car- ried a higher prevalence of racism.

Finally, men and women were similar in their ratings of the degree to which African Americans as a group experience racism. As with individual experiences, respondents with more years of education were more likely to declare racism is a problem for African Americans. As well, individuals under greater financial strain gave stronger ratings.

Qualitative Results

For some respondents, the incidents that were uppermost in their minds were racist injuries sustained during childhood or much earlier in life, including during the era of Jim Crow. For some younger respondents, childhood and early adolescence may not be

Table 1. Sample Characteristics

Men Women Total n � 69 n � 75 N � 144

Age 43.70 (17.10) 45.43 (15.95) 44.59 (16.48) Years of education 13.46 (2.77) 13.60 (3.13) 13.53 (2.95) Subjective social status 4.32 (1.80) 4.43 (1.69) 4.38 (1.74) Financial strain

Always has enough 35% 24% 29% Sometimes does not have enough 41% 53% 48% Often does not have enough 24% 23% 23%

Employment status Fully employed 54% 53% 53% Partially employed 23% 17% 20% Unemployed 23% 29% 26%

Bachelor’s degree Yes 62% 44% 53% No 38% 55% 47%

Table 2. Bivariate Correlations Among Measures

Experiences of discrimination

Major discrimination

Everyday discrimination

Daily life experiences

Group impact

Experiences of discrimination 1 Major discrimination 0.766 1 Everyday discrimination 0.574 0.535 1 Daily life experiences 0.501 0.499 0.578 1 Group impact 0.495 0.444 0.390 0.376 1

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400 KWATE AND GOODMAN

the distant past, but because the mean age was 45 years old, it is striking that childhood events were recalled so easily. Although the number of respondents who made explicit mention of events taking place during childhood were relatively few (n � 7), these incidents may have been more severe, therefore keeping them top of mind. It is also possible that these individuals did not have any recent experiences of racism.

To explore the source of gender differences in reported racism, we first examined the prevalence of volunteering an incident in response to the open-ended question. If Black men have a higher prevalence of experienced racism, or a greater willingness to report it, it is logical to conclude that this would result in a greater likelihood of volunteering a story about memorable incidents. In fact, men (24%) were more likely than women (19%) not to report an incident. Across three of the five thematic categories, there was little gender difference; and these pointed toward greater preva- lence for women. Thirty percent of women reported incidents involving resources, 26% reported aggression, and 7% reported stereotypes; male counterparts were 24%, 27% and 5%, respec- tively. We discuss the remaining two themes (criminal profiling and incivilities) in the following section.

Second, we examined the nature of the incidents that respon- dents recounted. Incidents categorized as aggression affected both

men and women and were characterized by racial epithets (e.g., monkey, black bitch, and in many instances, nigger), verbal as- saults and insults, and physical threats and attacks. Incidents occurred both in direct confrontations (e.g., passersby, motorists, and coworkers) and indirectly (e.g., witnessing epithets written in public spaces or directed at others). The following are examples of quotes that illustrate this theme.

I work with these little boys . . . [One boy] said he didn’t like me because I was Black. That was the first time I had encountered anything like that in all my years. I said to the little boy—I was shocked, but I said to him—“It’s not what’s on the outside that counts, it’s what’s on the inside that counts. And I’m just as good as anyone.”

I was on the bus in Queens and I was eating a sandwich. And this older White guy turned around and said “Are you really going to eat that on the bus?” I said yeah, and he turned around and sucked his teeth. When I told him that I was really hungry, he said, “Yeah, you people usually are.” I didn’t respond, but I was shocked, completely shocked.

I was crossing the street and an old White guy was crossing and I forget what happened but all I remember is that I heard him call me a nigger and I said, “It would be wrong if I punched you in the mouth,” and I kept walking. I had never had that happen to me.

Table 3. Mean Racism Scores by Gender and Categorical Measures of Socioeconomic Position

Gender

Men Women

Mean (SE) Mean (SE)

Experiences of discrimination 4.46 (.317) 3.66 (.313) Major discrimination 3.14 (.248) 2.39 (.255) Everyday discrimination 2.48 (.100) 2.42 (.094) Daily life experiences 2.45 (.099) 2.31 (.102) Group impact 3.54 (.100) 3.70 (.096)

Socioeconomic position

Financial strain Always have enough Sometimes don’t have enough Often don’t have enough

Experiences of discrimination 4.59 (.455) 3.37 (.301) 4.79 (.459) Major discrimination 3.14 (.364) 2.08 (.234) 3.61 (.370) Everyday discrimination 2.47 (.137) 2.34 (.091) 2.64 (.155) Daily life experiences 2.43 (.139) 2.32 (.105) 2.43 (1.34) Group impact 3.53 (.155) 3.66 (.088) 3.70 (.150)

Education No bachelor’s degree Bachelor’s degree

Experiences of discrimination 3.74 (.349) 4.75 (.333) Major discrimination 2.86 (.275) 2.79 (.284) Everyday discrimination 2.35 (.105) 2.61 (.104) Daily life experiences 2.18 (.095) 2.53 (.117) Group impact 3.54 (.010) 3.88 (.096)

Employment No employment over the past year Not currently employed; worked over

the past year Currently employed

Experiences of discrimination 3.55 (.478) 4.26 (.527) 4.18 (.308) Major discrimination 2.49 (.365) 2.79 (.393) 2.86 (.247) Everyday discrimination 2.24 (.132) 2.40 (.137) 2.57 (.096) Daily life experiences 2.17 (.111) 2.36 (.173) 2.49 (.101) Group impact 3.51 (.142) 3.63 (.146) 3.68 (.096)

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A woman called me the “n word” for not letting her in the club. I’ve had people, customers, have used the “n word” many times. But I also think that it is the field that I work in, too.

When I was younger, I was in the hallway with about four or five of my friends playing cards, the cops came into the building, rounded us all up on the roof and said I should throw all of you off of the roof because then there would be less niggers on welfare.

I been accused and threatened. Almost been in fights, been in fights one or two times due to racial incidents.

When I was in school, I was treated very, very badly—well, bullied me because of my race.

Responses in the resource category recounted denial of access to, or subordinating treatment while accessing goods and services. As with racial aggression, men and women were equally likely to recount incidents involving access to resources. In the workplace, participants were denied positions, and when employed, received disparate treatment such as being skipped over for promotions or receiving the most unattractive shifts. Black LIFE participants reported experiences with housing discrimination, concordant with housing audit studies in which Black homeseekers are denied access to homes and financing; are steered to Black or low-income neighborhoods; or are presented with worse rental and purchase terms (Galster & Godfrey, 2005).

When my children were young, we went to see an apartment with White friends. The super denied the apartment, saying that we were loud, after they found out that we were Black.

I remember I went to get an apartment. I went and met with the landlord and the apartment went up three times the price.

We were looking for an apartment, we wanted a different environment for my son. We went to a predominantly Hispanic neighborhood, and after a few months we didn’t get any callbacks, everyone was con- cerned with us being young Black parents.

But by far, racism in retail settings dominated responses in the resource category, as participants recounted poor service, harassment, and pejorative statements, and being followed in stores. News reports occasionally appear in NYC tabloid pa- pers, exposing overt consumer mistreatment of black shoppers (Burke, Morales, Ross, & Adams Otis, 2013; Moore & Adams, 2013); retail racism often comprises subtle degradation that causes individuals to feel that they received poorer quality service or goods than was expected, but the experience cannot be definitively attributed to racial bias. Instead, time and mental resources must be deployed to interpret the meaning behind store encounters (Harris, Henderson, & Williams, 2005). For study respondents, retail racism was a routine part of consum- erism in the city:

I mean, it’s a generic thing where you’re walking around, you’re walking in the store and they’ll follow you.

When I go shopping, they follow you all around the store. They aren’t looking at the White man stuffing his bags.

It’s always something. When I go into the store, people follow me. They think I’m going to steal something but I’m just there to purchase something.

We were in Bloomingdale’s one time and she came up to us and asked if we could afford any of this. We were like, “What?” She let us know Black Friday was coming up. It doesn’t happen all the time but it

Table 4. Gender Differences in Lifetime Discrimination

Men Women %

Difference% Yes % No % Yes % No

Experiences of Discrimination Scale

At school 35 65 44 56 �9 Getting hired or getting a job 67 33 49 51 18 At work 62 38 40 60 22 Getting housing 26 74 27 73 �1 Getting medical care 12 88 1 82 11 Getting service in a store or a restaurant 66 33 71 29 �5 Getting credit, bank loans, or a mortgage 38 62 23 77 15 On the street or in a public setting 67 33 60 40 7 From the police or in the courts 72 28 42 58 30

Major Discrimination Scale

Denied a bank loan 16 84 15 85 1 Fired 35 65 31 69 4 Not hired for a job 48 52 28 72 20 Denied a promotion 40 60 31 69 9 Stopped, threatened or abused by police 78 22 40 60 38 Discouraged by teacher 35 65 36 64 �1 Landlord or realtor refused to sell or rent 16 84 24 76 �8 Neighbors made life difficult 16 84 19 81 �3 Received worse service 23 77 23 77 0

Note. Gender differences were calculated by subtracting the percentage of women who endorsed a particular item from the percentage of men. Negative differences therefore indicate women reported more; positive differences indicate higher prevalence for men.

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402 KWATE AND GOODMAN

happens in retail a lot, they follow us to the dressing room and around the store. What!?

In addition to displaying disproportionate and negative atten- tion, retail clerks also failed to provide assistance when needed, or attempted to serve others out of turn:

Recently, I can say that I’ve been in stores where I felt overlooked and somebody has helped somebody White before me, even if I was there first.

Like when you go into the store like Lord and Taylor’s and they think that your money doesn’t spend like the White people’s do.

Being with my cousin in a baby clothing store and the guy refusing to help us. And we believe that it was because of our skin color.

Brewster and Rusche (2012) found that White wait staff rated Black patrons as poor tippers, 70% used code words to refer to Black customers, and 53% reported that coworkers treated Black customers poorly. Respondents in the present study frequently expressed experiences with poor restaurant service.

Sometimes at restaurants, seat me near the back, I think that’s racism.

Often times going out—we have to wait extra time, or we get seated in places and we are thinking to ourselves, “Why do we have to sit here?”

The waitress will just bypass you until they’ve seen everybody else and then the service is still bad.

When I went downtown to a wing place, it was crowded and they never came to the table. We sat for almost 40 minutes and then I asked for a waiter, then a manager. Then I barked at him and then everything was free.

Gender differences did emerge in two categories: criminal profiling and incivilities. Male respondents were more likely (35% vs. 20%) to remark that authorities and laypersons alike profiled them as criminals or as dangerous, consonant with social science analysis of popular representations of Black men (Covington, 1995; Dixon & Linz, 2000). For study participants, profiling was most commonly borne out by the New York

Police Department’s “Stop, Question and Frisk”. In a class- action federal lawsuit, stop and frisk procedures—which over- whelmingly targeted Black and Latino men—were ruled uncon- stitutional in 2013 (Floyd v. City of New York, 2013) predicated on violations of the plaintiffs’ Fourth and 14th Amendment rights. Or, in the words of one participant, “they don’t stop the White people.” The use of race as a marker for suspicion and wrongdoing arose repeatedly in men’s responses:

The police always stop me for no reason at all.

Being stopped by cops. Stopped and frisked because you were Black. For no other reason.

I got on the elevator and was going to the store. I go downstairs, the police was downstairs and had some people lined up against the wall. I got off the elevator and they put me on the wall. Had we been White that would have never happened. But White cops, Black neighbor- hood—we all get grouped together.

The state was not the only source of criminal profiling; the general public also related to participants as dangerous. Fear, avoidance, and tightly clutched pocketbooks marred respondents’ use of public space:

Recently on the train. You know how—there’s a seat on the train, we sit down next to a White man who then got up and moved to another seat . . . White people getting uncomfortable in the supermarkets and the parks. I’m doing the same thing you’re doing. Buying my gro- ceries and walking in the park.

These kinds of interpersonal tensions frequently took place in public transit. As one respondent observed,

This happens sometimes—it was probably a year ago, there was a White lady on the train, who looked like she was afraid to ride the train, she looked across the train, saw a White gentleman, walked past me and sat next to him . . . She looked like she didn’t want to be anywhere near me or the seat. She seemed scared.

In contrast to criminal profiling, women were more likely (13% vs. 3%) to recall incivilities—interpersonal interactions character- ized by rudeness, reprimands, or inappropriate inquiries by strang-

Table 5. Regression Models: Association Between Gender and Self-Reported Racism

Explanatory variable

Experiences of discrimination Major experiences of discrimination

B 95% CI B 95% CI

Age 0.004 �0.027 0.034 0.004 �0.016 0.025 Women �1.5 �2.59 �4.13 �1.17 �1.9 �0.432

Everyday Discrimination Daily Life Experiences

B 95% CI B 95% CI

Age �0.002 �0.011 0.008 �0.011 �0.202 �0.002 Women �0.238 �0.571 0.095 �0.234 �0.555 0.078

Group Impact Scale

B 95% CI

Age 0.009 �0.001 0.019 Women 0.039 �0.3 0.378

Note. Coefficients in bold are statistically significant at p � .01.

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ers; undue or inappropriate attention; or being treated as invisible or unimportant. These microaggressions—brief, everyday ex- changes that send denigrating messages (Sue et al., 2007)—subtly subordinate those on the receiving end. Two women explained:

I was on the train coming home from school. I was sitting on the edge of the seat, next to a White lady. The White woman was really nasty, she brushed past and ignored me. She kept hitting me with her bag and magazine, she still didn’t say excuse me. I knew if I was a White person she would have said “Excuse me”; she treated me like I was not even there.

Little subtle things, people waiting for the bus and they’ll push in front of you, or you’ll be waiting in line at the supermarket and they’ll jump in front of you. Or you’re in a White area and they look at you like you have three heads. I used to think it was the older, but it’s the younger ones too, they’ll skip you in line, or edge in front of you . . . They’ll say it in a subtle way.

Some women bristled at attention from staring onlookers and visual surveillance and critique that often focused on race and class-inscribed expectations of Black comportment. One woman endured questioning and reprimands about her hair:

“I went, and she said ‘I don’t like your hair—you need to pin it back.’ I had an Afro at that time. I turned to someone and asked them, ‘Who is she?’ She had done this to others before. I was really offended.”

Table 6. Regression Models: Association Between Gender, Socioeconomic Position, and Self-Reported Racism

Major lifetime discrimination

Experiences of discrimination

Explanatory variable B 95% CI

Age 0.005 �0.030 0.040 Women �1.254 �2.202 �0.306 Unemployed — — — Partially employed 0.834 �0.680 2.348 Fully employed �0.330 �1.625 0.966 Years of education 0.422 0.238 0.606 Always have enough financially for needs — — — Sometimes do not have enough �0.810 �1.966 0.346 Often do not have enough 0.368 �0.990 1.724 Subjective social status �0.351 �0.630 �0.071

Major experiences of discrimination

B 95% CI

Age 0.0059 �0.017 0.029 Women �1.006 �1.682 �0.330 Unemployed — — — Partially employed 0.4281 �0.530 1.385 Fully employed �0.161 �1.191 0.870 Years of education 0.2379 0.084 0.392 Always have enough financially for needs — — — Sometimes do not have enough �0.951 �0.190 �0.002 Often do not have enough 0.0045 �1.198 1.207 Subjective social status �0.318 �0.539 �0.097

Day-to-day discrimination

Everyday discrimination

B 95% CI

Age 0.001 �0.010 0.012 Women �0.237 �0.545 0.071 Unemployed — — — Partially employed 0.240 �0.232 0.712 Fully employed 0.239 �0.209 0.687 Years of education 0.062 �0.003 0.127 Always have enough financially for needs — — — Sometimes do not have enough 0.075 �0.333 0.483 Often do not have enough 0.279 �0.232 0.790 Subjective social status �0.059 �0.160 0.042

Daily life experiences

B 95% CI

Age �0.011 �0.023 0.000 Women �0.269 �0.571 0.033 Unemployed — — — Partially employed �0.078 �0.590 0.434 Fully employed �0.06 �0.563 0.442 Years of education 0.0814 0.020 0.143 Always have enough financially for needs — — — Sometimes do not have enough 0.186 �0.219 0.591 Often do not have enough 0.2194 �0.309 0.748 Subjective social status �0.025 �0.141 0.091

Group Discrimination

Group Impact

B 95% CI

Age 0.009 �0.001 0.019 Women 0.013 �0.279 0.305 Unemployed — — — Partially employed 0.255 �0.195 0.705 Fully employed 0.055 �0.415 0.524 Years of education 0.145 0.080 0.210 Always have enough financially for needs — — — Sometimes do not have enough 0.402 0.018 0.786 Often do not have enough 0.486 �0.003 0.975 Subjective social status �0.050 �0.166 0.066

Note. Coefficients in bold are statistically significant at p � .01.

Figure 1. Association between education and everyday discrimination for Black men and women in the Black LIFE Study. See the online article for the color version of this figure.

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Discussion This study investigated gender and socioeconomic differences

in reported racism among Black men and women residing in two of New York City’s largest Black neighborhoods. Women were less likely to report major discrimination over their lifetimes, but the lower prevalence may be attributable to differences in specific domains. For example, women were less likely to face police encounters, one of nine domains assessed by both of our quanti- tative measures of lifetime discrimination. Gender differences persisted in regression models in which we assessed socioeco- nomic position concomitantly. Individuals with more years of schooling reported higher lifetime discrimination, day-to-day rac- ism as measured by one scale, and stronger beliefs that African Americans face racism as a group.

Our results differ from one NYC-based study (Brondolo et al., 2009), in which socioeconomic position was unassociated with lifetime discrimination, and in which individuals with fewer so- cioeconomic resources reported more discrimination over the pre- vious week. We found years of education to be positively related to lifetime discrimination, and we found socioeconomic position to be unrelated to day-to-day racism on one scale, and positively associated on another. As noted earlier, the literature is discrepant on the effect of socioeconomic position, and disparate measures likely play a role. For example, our measure of day-to-day racism assessed recent chronicity, but was not specified as recently and discretely as over the past week. Across the total sample, individ- uals with higher education levels may have reported more racism for several reasons. Racism may have occurred frequently in educational institutions or in other social spheres made possible by a greater amount of schooling. If so, our results support the assertion that “when members of racial/ethnic minority groups overcome historical and structural barriers to advancement, they do not escape experiences of racism. Rather, they tend to experience racism in exactly the arena of life . . . that is used to measure adult achievement” (Brondolo et al., 2009, p. 425). It is also possible that more education provides greater access to African American history and other academic training that fosters identification and acknowledgment of more subtle forms of subordination.

Importantly, we found that education had opposite effects for Black men and women; for men, education was protective, but for women, it carried a penalty. Black women have higher rates of attending college than their male counterparts (Landry & Marsh, 2011), and these disparities are often used to problematize the precarious social status of Black men (Butler, 2013). Although disparities in attendance are often used to make dire pronounce- ments about the state of young Black men, “there is a serious lack of research in this area that measures the extent and consequences of this pattern” (Landry & Marsh, 2011, p. 391). At least among women in our sample, one consequence that has not come to pass is greater freedom from racism in daily life. Black women with high levels of education may face unique social cleavages in settings where education is a salient resource. One commentator noted that her embodiment of woman, Black person, and engineer generated responses from her college students that did not exist for White men (Berry, 2014). Individuals who hold intersecting social identities that are not seen as prototypical (e.g., a White male would be seen as a prototypical engineering professor) face unique

forms of oppression that stem from their intersecting location (Purdie-Vaughns & Eibach, 2008). For Black women, high levels of education may animate racism in ways that are not so for Black men.

Income and education are determinants of perceived social standing, but these kinds of traditional measures factor less strongly for Black men and women than for White counterparts in the United States and in the United Kingdom, perhaps an indica- tion of experiences of discrimination overshadowing economic factors in rating social status (Adler et al., 2008). In other words, because subjective social status is an overall assessment of past, current, and future socioeconomic chances and prospects, African Americans may incorporate experiences of racism into this assess- ment of rank in the social hierarchy (Subramanyam et al., 2012). The associations we observed speak to this possibility. The inverse correlations between lifetime racism and subjective social status in the present study were stronger than those in other work (Subra- manyam et al., 2012); but correlations with day-to-day racism were similarly weak.

Qualitative reports unearthed frequent experiences with racism across a spectrum of life contexts, for both men and women. As put by one respondent, “Racism is mainly institutional and that is a foundational problem. It is in every part of our life.” For most domains, men and women had similar experiences. But gender dif- ferences in qualitative reports did emerge, with men reporting more criminal profiling, and women reporting interpersonal incivilities. Black men and women may differ not in the prevalence of racism, as suggested by quantitative self-report scores, but in its instantiation. Goff and Kahn (2013) show that Black men are often seen as the prototypical target of discrimination; it is possible that this character- ization may subtly structure the content of measures of racist events. For example, few measures inquire about sexuality, but the regulation of Black women’s bodies and their sexuality has been central to the racial oppression of Black women (Collins, 2000). And although Black women are not free of police surveillance and control, they are less likely than men to be perceived as physically perilous and experience police stops and searches. Police stops and harassment are often assessed in measures querying major lifetime events, and have been described as unlikely to occur on a daily or weekly basis for most people (Harrell, 2000). However, for Black men in NYC, police surveillance is less of a lifetime major event and instead a day-to-day reality.

For Black women, bearing the brunt of Black criminality may take shape as profiling and prosecution for perceived moral fail- ures, criminal neglect, and abuse of state resources. The felony trial of a young Black mother in Ohio who sent her child to a school outside her home district is instructive in this regard (Rose, 2013). Black women also face distinct punitive encounters with police as part of broader state policies. For example, “nuisance laws” place women at risk of eviction from their homes for calling the police “too many times” and making the household a source of neighborhood “disorder”—even if those calls were to seek pro- tection from violent partners (Eckholm, 2013). If the particularity of racism for Black women goes less frequently assessed, studies reporting gender differences may inadvertently contribute to what has been called “Black male exceptionalism”—a problematic nar- rative of Black men as racial standard bearers who have been more adversely affected by White supremacy than Black women (Butler, 2013).

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Although we did not query coping behaviors directly, several respondents spoke of the ways in which they challenged racism. To address police stop and frisk procedures, participants ob- tained information, brought complaints to the police, and en- couraged action among others. Several expressed having to intervene when youth were stopped. Still, as some participants noted, deciding when or how to contest racism was a constant calculus, as there are risks for resistance. Other qualitative research has shown that Black, compared to White respondents, had more inhibited expression in police encounters (Rooks et al., 2011). Respondents also coped with racism by obtaining or carrying materials that they believed could attenuate or redress discriminatory treatment, such as requesting a receipt in con- flictual retail engagements, or carrying identification. Even these less volatile strategies carry costs. The cumulative impact of race-related stress contributes to premature health deteriora- tion and high illness morbidity (Geronimus, 2001). One partic- ipant voiced this experience: “It’s tiring fighting every single day. You try and try and to fight for the power that we’re entitled to. I’m tired.”

Study findings should be interpreted in light of some limita- tions. First, a modest sample size could have limited power to test hypotheses. Differential patterning of gender disparities across measures of lifetime versus everyday discrimination mitigates to some degree against this limitation, but studies with larger samples would provide additional data. As well, the strength of having diversity in age range among the participants could also be a limitation, given that the sample was relatively small. More homogeneity in age could allow more precise estimates. Second, response rates were relatively low. Because the invitation to participate made explicit mention of racism, study participants may have had a particular interest in racism and the African American experience; findings may not be generalizable to those who are less inclined to discuss racism. Third, this study was conducted in New York City, the most populous U.S. city, and a highly racially segregated city, and education levels were much higher in this sample than for African Americans nationally. Our data may not generalize as well to individuals living in other geographic regions and who have fewer years of schooling. However, respondent narratives echoed the kinds of encounters with racism and coping strate- gies documented by similar studies in other cities and popula- tions (Rooks et al., 2011; Ziersch, Gallaher, Baum, & Bentley, 2011). Finally, the qualitative data were coded by only one author, which may have restricted the scope of the data interpretation.

Despite these limitations, this study provides important data on the ways in which gender and socioeconomic position are associated with the experience of racism. We concur with the assertion that studies should assess multiple dimensions of socioeconomic position given differential patterning of the rac- ism individuals face. Scale content also matters— questions about discrimination may pull for interpretations and reports from individuals with certain socioeconomic resources (Bron- dolo et al., 2009), and we have seen how scale content can be gendered as well.

Researchers have begun to interrogate the content and cross- population applicability of the standard measures that underlie the corpus of work on racism and health (Bastos, Celeste, Faerstein, &

Barros, 2010; Lewis, Yang, Jacobs, & Fitchett, 2012; Shariff- Marco et al., 2011). Commonly used measures have produced similar patterns of gender differences among groups other than African Americans, such as Latinos (Ornelas & Hong, 2012), but investigators have developed measures specific to these popula- tions as well (Torres, Yznaga, & Moore, 2011). In addition to such work, studies should supplement standard measures with other assessments that yield more nuanced data on the subjective expe- rience of racism. For example, perhaps individuals who can call to mind and articulate qualitative narratives about specific incidents face different health risks (or protections) than those who do not. Although no scale can exhaustively capture the range of experi- ences that Black women and men face, researchers should query and interpret experiences of racism as closely as possible, and at the intersections of social identities.

Keywords: African American/Black; racism/discrimination; gen- der; socioeconomic; measurement

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408 KWATE AND GOODMAN