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. Author manuscript; available in PMC: 2009 Sep 21.
Published in final edited form as: Women Health. 2009 Jan–Feb;49(1):1–15. doi: 10.1080/03630240802694756

Social Status Correlates of Reporting Racial Discrimination and Gender Discrimination among Racially Diverse Women

Annie E Ro 1,2, Kyung-Hee Choi 1,2
PMCID: PMC2747613  NIHMSID: NIHMS136205  PMID: 19485231

Abstract

The growing body of research on discrimination and health indicates a deleterious effect of discrimination on various health outcomes. However, less is known about the sociodemographic correlates of reporting racial discrimination and gender discrimination among racially diverse women. We examined the associations of social status characteristics with lifetime experiences of racial discrimination and gender discrimination using a racially-diverse sample of 754 women attending family planning clinics in Northern California (11.4% African American, 16.8% Latina, 10.1% Asian and 61.7% Caucasian). A multivariate analysis revealed that race, financial difficulty and marital status were significantly correlated with higher reports of racial discrimination, while race, education, financial difficulty and nativity were significantly correlated with gender discrimination scores. Our findings suggest that the social patterning of perceiving racial discrimination is somewhat different from that of gender discrimination. This has implications in the realm of discrimination research and applied interventions, as different forms of discrimination may have unique covariates that should be accounted for in research analysis or program design.

Keywords: sexism, racism, social correlates, discrimination

Introduction

The growing body of research on discrimination and health indicates a deleterious effect of discrimination on various health outcomes. Higher self-reports of discrimination have been linked to poor mental health, high blood pressure, and low birth weight (Collins, David, Handler, Wall, & Andes, 2004; Kessler, Mickelson, & Williams, 1999; Mustillo et al., 2004; Wyatt et al., 2003). These findings are particularly troubling when considering the relatively high numbers of people who report experiences of discrimination. Among a national sample of American adults, 34% of respondents reported having a major discriminatory event in their lifetime and 61% reported having regular, day-to-day experiences of discrimination (Kessler et al., 1999).

It appears that discrimination is still a normative aspect of social interactions among the general population (Kessler et al., 1999). However, the types of discrimination that are reported and their rates differ based on characteristics of the respondents, such as their race, gender, age, income and education (Watson, Scarinci, Klesges, Slawson, & Beech, 2002). Knowing the social patterning of reported discrimination is important because it enables health researchers to identify subgroups who may experience higher levels of discrimination. A number of studies have examined the social patterning of reported discrimination (Finch, Kolody, & Vega, 2000; Gary, 1995; Kessler et al., 1999; Krieger, 1990; Landrine, Klonoff, Gibbs, Manning, & Lund, 1995; LaVeist, Rolley, & Diala, 2003; Moody-Ayers, Stewart, Covinsky, & Inouye, 2005; Perez, Fortuna, & Alegria, 2008; Vines et al., 2006; Watson et al., 2002). It is difficult to make generalizations across the studies because the samples and discrimination measures vary by study. For example, age was a significant factor in some studies (Perez et al., 2008), but had no effect in others (Finch et al., 2000). Varying levels of income had different effects on perceived discrimination (Moody-Ayers et al., 2005; Watson et al., 2002). We perceive two gaps in the existing literature on the social patterning of discrimination. The first is that most studies have only included a single race or made comparisons between Caucasians and African Americans alone. The second gap is that most studies only examine the social patterning of a single type of discrimination. As a result of these gaps, we are limited in our ability to draw comparisons across different populations or for a discrimination type other than the one measured.

As stated, most studies either analyzed a single race or made comparisons between Caucasians and African Americans alone. The increasing racial heterogeneity of the United States necessitates information about the discrimination experiences of other racial groups. Past research has shown that discrimination is significantly related to poorer health outcomes for both Latino and Asian Americans (Finch et al., 2000; Gee, Spencer, Chen, & Takeuchi, 2007; Holt, Gee, Ryan, & Laflamme, 2006). We found three studies that included an additional racial category apart from Caucasians and African Americans, but two of them combined different racial groups into a single category (Kessler et al., 1999; Landrine et al., 1995) and another only included Caucasians, Latinos and African Americans (LaVeist et al., 2003). The field would benefit from a study that includes a wider range of racial groups. Such a study would enable a broader discussion about the experiences of discrimination across different racial groups to include aspects of nativity and immigration, as well as consider the racial landscape beyond African Americans and Caucasians.

The second gap in the literature is that most of the studies that identified social correlates of discrimination used a single measure of discrimination and did not allow for comparisons across different kinds of discrimination, such as discrimination due to race compared to discrimination due to gender. Some researchers have called for empirical work that further differentiates among the different types of discrimination (Reid & Comas-Diaz, 1990). Kessler et al.'s measure allowed respondents to attribute poor treatment to particular characteristics, such as race, gender, appearance or income, but the authors combined all forms of discrimination into single binary measure in their multivariate analysis. An all-encompassing discrimination measure can lead to problems of generalizability; it is hard to know what discrimination is, how it is experienced and who is being discrimination against(Watson et al., 2002). If studies did include more than one type of discrimination, they did not have a racially diverse sample. Two studies examined two forms of discrimination, but had only Caucasian and African Americans in their sample (Krieger, 1990; Watson et al., 2002). Based on the existing literature, it is unclear whether different kinds of discrimination have similar social correlates in a racially diverse group.

Our study addressed some of these gaps by identifying social status correlates of reported discrimination among a racially diverse sample of young women from the San Francisco Bay Area. We examined the correlates of two types of discrimination: racial and gender. Although the literature has employed a variety of terms to describe discrimination due to race or gender, we chose to define our concepts as racial discrimination and gender discrimination. Discrimination consists of the practices and actions of dominant groups that have a negative and differential impact on subordinate groups, whereas prejudice (such as racism or sexism) is the motivation behind such treatment (Feagin & Eckberg, 1980). Our newly developed measures asked specifically about overt and observable events, not about the underlying motivations or beliefs of the perpetrator. The specific content of the scales will be further discussed in the Methods section.

The correlates we examined are a combination of ascribed and achieved social status correlates. Ridgeway defined a status characteristics as “an attribute on which individuals vary that is associated in a society with widely held beliefs according to greater esteem and worthiness to some states of the attribute (e.g., Caucasians or males) than others (e.g., African Americans or females)” (Ridgeway, 1991). The value of these status characteristics is closely tied to a society's history and broader ideology; possession of certain characteristics gives individuals more status within a society, She defined an ascribed, or nominal, status characteristic as a distinguishing feature that is categorical rather graduated or ordinal. These attributes assign individuals into particular groups, often based on immutable characteristics. The ascribed status characteristics we included in the study were race, nativity and age. Achieved, or graduated, status characteristics are attributes that are valued by the extent to which a person possesses them. These attributes are not naturally occurring and are believed to be attained through effort. The achieved status characteristics in our study were financial difficulty, education and marital status.

There were two goals of this study: 1) to compare the experiences of racial discrimination and gender discrimination across different four racial groups: African American, Caucasian, Asian and Latina; and 2) . to determine which achieved and ascribed social status characteristics were correlated with racial discrimination and which were correlated with gender discrimination.

Methods

Participants

Data were collected from 754 women who completed baseline surveys for the Female Condom Intervention Trial (FEMIT), which was designed to evaluate the efficacy of female condom skills training in increasing female condom use. Details of the intervention activities have been described elsewhere (Choi et al., 2008). Participants were recruited over a period of one and half years between 2003 and 2004 from four family planning clinics in the San Francisco Bay Area. During the recruitment period, flyers were posted at the clinics and three trained female recruiters approached women in the waiting areas at the study sites who appeared to be eligible for the study and screened them in private areas of the clinic. Women were eligible for the study if they self-identified as African American, Asian, Latina, or Caucasian; were 18-39 years of age; had more than one male sex partner in the previous year; had no known allergies to polyurethane, latex, or lubricants; were HIV negative; had no plan to get pregnant within the subsequent 6 months; and were English speakers. The study's eligibility rate was 30%; of the 4071 women who were approached to participate, 1057 met the eligibility requirements. Of those who were eligible, 71% agreed to participate, bringing the study sample total to 754. During the screening, recruiters described the purpose of the study and acquired written informed consent from eligible women who agreed to participate. The consented participants completed a standardized baseline questionnaire using an audio computer-assisted self-administered interview (ACASI) system and received $10 in cash immediately following the survey. The recruiting procedures and baseline survey were conducted in English. The Committee for Human Research of the University of California, San Francisco approved the study procedures.

Measures

Dependent variables

The dependent variables were reports of racial and gender discrimination. We adapted Landrine and Klonoff's Schedule of Racist Events to obtain a measure of racial discrimination (1996). The scale consisted of 10 items that asked participants to report how often they experienced discrimination due to their race in various settings: never, rarely, sometimes or often. Sample items included, “Because of your race, have you ever been treated unfairly by your employers bosses or supervisors?”, “Because of your race, have you ever been treated unfairly by people in service jobs, such as store clerks or waiters?” “Have you ever been called a racist name?” All items were meant to capture lifetime encounters with racial discrimination. A value for racial discrimination was derived by averaging across the 10 items. Racial discrimination ranged from zero (never) to three (often). Our scale showed strong internal consistency for the whole sample (Cronbach's alpha = 0.90) as well as across all four racial groups in the sample (Cronbach's alpha=0.87-0.91).

Gender discrimination was measured in a similar fashion, yet participants were asked to report how often they experienced discrimination due to their gender in 13 different settings. The questions were derived from Klonoff and Landrine's Schedule of Sexist Events (SSE) (1995). Sample items included, “As a woman, how often have you been treated unfairly by your family?”, “As a woman, how often have you been denied a raise, promotion, a job, or something at work that you deserved?” All items were meant to capture lifetime experiences with gender discrimination. A value for gender discrimination was derived from calculating the average for the 13 items. Gender discrimination ranged from zero (never) to three (often). The internal consistency of our sexism scale was high for the whole sample (Cronbach's alpha = 0.89) and across the different racial groups in the sample (Cronbach's alpha=0.85-0.90).

Independent variables

The independent variables were different features of social status, modified slightly from the ascribed and achieved statuses categorized in Kessler el al (1999). The ascribed social status characteristics were race, nativity and age and the achieved status characteristics were financial difficulty, education and marital status. We selected these social status characteristics because they were significantly related to reports of discrimination in past research (Finch et al., 2000; Kessler et al., 1999; LaVeist et al., 2003; Watson et al., 2002).

Participants identified their own race and age. Four racial groups were included: African American, Caucasian, Latina/Hispanic and Asian American. The ages of the participants ranged between 18 and 39 years. During analysis, age was categorized into three groups: 18-20 years, 21-25 years and over 25 years. Given the recruiting sites and stipulations, the age of the sample size was skewed to the left, with the median age being 21 years. We attempted to distribute the statistical power better by creating three age categories, which also offered clearer interpretation of the results based on the life stages of each age group. Nativity was a binary measure; participants indicated whether they were born in the United States. Marital status was binary and measured as either never or ever married.

Financial difficulty was a binary variable based off of participants' responses from a seven-item scale. The scale asked participants whether they had been in various situations of financial hardship within the past three months (e.g., unable to pay bills, accept public assistance). If participants answered yes to any of the seven items, they were considered to have financial difficulty. Educational attainment was measured as the last grade completed in school. Responses were categorized into three groups: high school or less, vocational/ technical/some college and college degree/postgraduate work. The education categories were entered as categorical variables in the regression models. We considered education and financial difficulty primarily as our measures of socioeconomic status (SES), although they hold other social meanings that we consider in the interpretation of the findings.

Data Analysis

Our analysis included bivariate and multivariate analyses. In our bivariate analyses, we used a one-way ANOVA to test for significant differences in the dependent variable within each of the social status correlates. For example, we used a one-way ANOVA to test for any significant difference in racial discrimination among the three age categories: 18-20 years, 21-25 years and over 25 years. We repeated this analysis for gender discrimination. We conducted Scheffe post-hoc tests to investigate pair-wise comparisons between different racial groups: African American-Caucasian, African American-Latino, African American-Asian American, Latino-Asian American, Latino-Caucasian and Caucasian-Asian American. We did this for both outcomes, racial and gender discrimination.

The multivariate analysis used ordinary least squared (OLS) regression models to determine whether the patterns observed in the bivariate ANOVA persisted after taking the other social status variables into account. We conducted two regression models, one with racial discrimination as the outcome variable and the other with gender discrimination as the outcome variable. The sample and independent variables were identical in both models. We retained all of the variables we used in the in the ANOVA analysis in the multivariate analysis, even though not all were significantly associated with either racial or gender discrimination. Although some characteristics were not significantly related to racial discrimination, they were significantly related to gender discrimination and vice versa. We retained the same variables in the models to maintain model symmetry between the two types of discrimination. Using the same model for both racial and gender discrimination enabled a direct comparison between the two outcomes. We assessed model fit using the R2 value.

We also created an interaction term of financial difficulty and race and tested its effect in both models. The relationship of socioeconomic status (SES), race, discrimination and health is a complex one. SES is often treated as a confounder of race, but it can also be part of the causal pathway by which race affects health (Williams, 1999). By including an interaction term, we acknowledged the complexity of the relationship between SES, race and discrimination and tested whether financial difficulty moderated the relationship between racial group and discrimination.

Results

Our sample was a mix of different races, with the majority being Caucasian women (Table 1). The highest percentage of the sample was between the ages of 18 and 20 years (mean age 22.4 years, se 0.2). Over half had completed some or all of their high school education and the next highest percentage completed some college. Over three-quarters reported having experienced financial difficulty in the past three months. The average score for racial discrimination was .27 (se 0.01; range, 0-2), and the average score for gender discrimination was 1.16 (se 0.02; range 0-3).

Table 1.

Social Status Characteristics of the Sample (N=754)

Social Status Characteristics %
Ascribed Characteristics
Race
African American 11.4
Asian 10.1
Latina/Hispanic 16.8
Caucasian 61.7
Age (years)
18-20 43.8
21-25 37.3
Over 25 18.9
Nativity
Foreign Born 11.8
US Born 88.2
Achieved Characteristics
Educational Attainment
High school or less 51.1
Vocational/technical/some college 34.4
Bachelor's degree, Bachelor's degree plus 14.6
Financial Difficulty
Yes 75.5
No 24.5
Marital Status
Not married 98.4
Married 1.5

The means of racial discrimination and gender discrimination were reported for different categories within each of the independent variables (Table 2). Different categories within the race, age and financial difficulty variables had significant differences in both racial and gender discrimination (p<.05). Respondents' education and nativity categories were significantly different for gender discrimination only (p<.001), while different marital statuses were significantly different for racial discrimination only (p<.05).

Table 2.

Social Status Correlates of Reports of Lifetime Experiences of Racial and Gender Discrimination: Means of Racial and Gender Discrimination and One-way ANOVA Results (N=754)

Social Status Characteristics Mean scores for:
Racial Discrimination Gender Discrimination
Ascribed Characteristics
Race
African American .65 ** 1.14 **
Asian .32 .96
Latina/Hispanic .32 .106
Caucasian .17 1.27
Age (years)
18-20 .21 ** 1.11 *
21-25 .32 1.23
Over 25 .28 1.29
Nativity
Foreign Born .30 1.00 **
US Born .26 1.21
Achieved Characteristics
Educational Attainment
High school or less .27 1.09 **
Vocational/technical/some college .26 1.28
Bachelor's degree, Bachelor's degree plus .27 1.32
Financial Difficulty
Yes .3 ** 1.25 **
No .16 .99
Marital Status
Not married .26 * 1.19
Married .54 1.26
Overall Score for Sample .27 (.01) 1.19 (.02)
*

p<0.05

**

p<0.001

A post-hoc Scheffe test revealed that African Americans had significantly higher reports of both racial and gender discrimination (African American/Asian, p<.001, African American/Latina, p<.001, African American/Caucasian, p<.001) (Chart 1). Asians and Latinas did not have significantly different reports of racial discrimination. Asians' reports of gender discrimination were significantly lower than Caucasians' ( p<.05) and Latinas' reports were significantly higher than Caucasians (p<.05).

Chart 1.

Chart 1

Racial Discrimination and Gender Discrimination Reporting Score by Race

The multivariate analysis (Table 3) included all the social status variables into a regression model. Each cell in the table represents the beta coefficients from the regression analysis. We dropped 19 respondents due to missing data (N=735). Most of the initial findings in the bivariate analysis persisted in the multivariate analysis. Race, financial difficulty and marital status remained significantly correlated with racial discrimination. African American, Asian and Latina participants reported more racial discrimination than their Caucasian counterparts (Black, p<0.001, Asian, p<.001, Latina, p<.001). Those with financial difficulty reported more racial discrimination than those participants who did not (p<0.05). Finally, married women reported more racial discrimination than their unmarried counterparts (p<.05).

Table 3.

Social Status Correlates of Reports of Lifetime Experiences of Racial and Gender Discrimination: Results of Multiple Ordinary Least Squared Regression Analyses (n=735)

Social Status Characteristics Regression Coefficients for:
Racial Discrimination Gender Discrimination
Ascribed Characteristics
Race
Caucasian -- --
African American .461 ** -.120
Asian .174 ** -.210 *
Latina/Hispanic .151 ** -.145 *
Age (years)
18-20 -- --
21-25 .043 .006
Over 25 .016 .058
Nativity
Foreign Born -- --
US Born -.020 -.159 *
Achieved Characteristics
Educational Attainment
High School or Less --
Vocational/technical/some college .007 .157 **
Bachelor's, Bachelor's degree plus .036 .180 **
Financial Difficulty
No -- --
Yes .115 ** .240 **
Marital Status
Not married -- --
Married .231 ** .131

Coefficients are unstandardized

--

Indicates referent group

**

p<0.001

*

p<0.05

Race, education, financial difficulty and nativity were significantly correlated with gender discrimination. Asian and Latina women had lower reports of gender discrimination than Caucasian counterparts (Asian, p<.05, Latina, p<.05). Caucasian and African Americans did not differ in reports of gender discrimination. Increased educational attainment was associated with more gender discrimination. Respondents with some college and respondents with a bachelor's degree or postgraduate work had more gender discrimination compared to those with a high school degree or less (some college, p<0.001, bachelor's plus, p<0.05). Financial difficulty was significantly correlated with more gender discrimination. (p<0.001). Those born in the United States reported more gender discrimination than their foreign-born counterparts (p<0.05).

The interaction term between financial difficulty and race was not significant. Financial difficulty did not moderate the relationship between race and racial discrimination or gender discrimination.

Discussion

Our results suggest that the social patterning of reporting racial discrimination and gender discrimination differed from one another somewhat. Both appeared to be related to a mix of ascribed and achieved status characteristics, but the characteristics associated with more racial discrimination differed from those associated with more gender discrimination. Race, financial difficulty and marital status were significantly correlated with higher reports of racial discrimination. Race, financial difficulty, education and nativity were associated with higher reports of gender discrimination.

When discussing the implications of our findings, it is important to note that reports of discrimination may arise from two sources: differential exposure to discrimination (i.e., one experiences more discrimination because they are a certain race or gender) or differential rates of perception (i.e., one is more likely to interpret an interaction as being discriminatory) (Mays, Cochran, & Barnes, 2007). The differential exposure to discrimination can influence health by residential segregation or receiving poorer quality health care (Williams, 1999). Some studies have shown that the perception of discrimination alone has a direct effect on health, apart from other mechanisms related to differential exposure (Kessler et al., 1999). Our measures could not differentiate between the two sources, but we assume our measures encompassed aspect of both, and we discussed both alternatives in our discussion. Race was the only ascribed status significantly correlated to both racial and gender discrimination. This corresponds with previous research that found higher reports of racial discrimination among non-Caucasians than Caucasians (Kessler et al., 1999; Krieger, 1990; LaVeist et al., 2003; Watson et al., 2002). Racial discrimination is an outgrowth of the ideology of superiority that categorizes and ranks various racial groups (Williams, Yan, Jackson, & Anderson, 1997). Because of the racial hierarchy that places Caucasians at the apex, groups of color may receive more negative treatment by both individuals and societal institutions.

Although race was significantly correlated to both racial and gender discrimination, its direction and pattern differed between the two outcomes. Latinas and Asians reported significantly lower levels of gender discrimination than Caucasians. No difference was observed between African Americans and Caucasians in their reports of gender discrimination, a finding that corresponds with previous work (Watson et al., 2002). A possible explanation for these differences may come from the centrality perspective. Perceptions of discrimination depend heavily on self-identification with a particular group (Sellers & Shelton, 2003). It is possible that the social reinforcement of race is strong enough to make the racial identity of the African Americans, Latina and Asian participants more salient than that of their gender identity, making it more likely that they will attribute any poor treatment to their race. Although the gender discrimination reporting results were not significantly lower for African Americans compared to Caucasian women, the coefficient was in the same direction as those for the Asian and Latina women. Future research that measures racial and gender identity is needed to empirically test this explanation. Our two measures for SES, financial difficulty and education, produced different results. This is not fully congruent with Watson et al, who concluded that SES, as measured by income and education, did not have a consistent relationship with either perceived racial or gender discrimination (2002). Financial difficulty was associated with higher reports of racial and gender discrimination, while education was only associated with gender discrimination. It is possible that the two measures were capturing different constructs, particularly for the younger women in our sample. Due to their young age and ongoing educational attainment, education may not have been a true economic indicator because they may not have yet realized the full economic benefits of higher education. Instead, education may have been an indicator of personal knowledge or life phase, while financial difficulty measured availability of economic resources. Indeed, the correlation between the two measures was quite low (p=.06).

It is not clear why we did not find an association between racial discrimination and nativity in our sample. This does not correspond to other research that suggests that immigrants may experience racial discrimination differently than their native born counterparts (Holt et al., 2006). Black immigrants report lower levels of racial discrimination than do US-born African Americans; the same is true for Asians and Latinos (Meyers, 2006). We may not have detected a nativity effect due to a sampling effect. We had a very small percentage of foreign-born; over 88% of our sample was born in the United States. It is also possible that the duration of time in the United States, rather than place of birth, may be the crucial factor in recognizing and reporting discrimination. Other studies have shown an interactive effect of years in the United States and reported discrimination on health outcomes (Gee, Ro, Gavin, & Takeuchi, 2008). Future research with a larger sample of foreign-born respondents may be helpful to examining this relationship.

Married women reported more racial discrimination than their unmarried counterparts. The vast majority of our sample was unmarried (98%); the differences among married and unmarried women may be related to the unique characteristics of the sample instead of a substantive difference due to marital status.

Age was not significantly related to reporting either racial and gender discrimination. As previously discussed, our sample was skewed to the left, with over 40% being in the 18-20 years age category. It is plausible that discrimination is experienced differently across different ages as individuals move through different life stages such as schooling, career and personal relationships. However, our sample's narrow age interval may not have captured enough variation across age.

Our study had several limitations. First, our sample was non-random; participants were drawn from a pool of participants in an intervention program. The social experiences of these young women might well differ from the general population of women, which makes the findings non-generalizable to populations that are different from the sample. Additionally, some selection bias may have been introduced during participant recruiting, as not all women who were approached by recruiters and eligible for the study were willing to participate. This bias also makes the findings of our study difficulty to generalize to the broader population. Another form of bias may have entered our results through social desirability bias. This bias occurs when individuals do not accurately report on certain measures in order to preserve a positive sense of self or provide a response they think is “correct” (Fisher, 1993). For a sensitive measure such as discrimination, social desirability bias may lead respondents to underreport their experiences in order to avoid accepting their marginalized status or portraying themselves as victims..

Our findings highlighted the social patterning of racial discrimination and gender discrimination across a set of social status characteristics. Discrimination, social status and race are very complex constructs, however, and our findings focus on a small piece of a multifaceted relationship among the three concepts. The cross-sectional nature of our data did not allow causal interpretations, particularly for the achieved social status correlates that were significantly associated with higher reports, such as SES or marriage. We cannot know whether these social characteristics made our sample more likely to report discrimination, or whether chronic exposure to discrimination resulted in the characteristics.

The public health implications of the study can help planning efforts for interventions that mitigate the ill-effects of discrimination. By knowing the various social status correlates that are associated with different forms of discrimination, program planners can identify individuals who are at the highest risk of reporting discrimination and plan their efforts accordingly. For example, a social marketing campaign educating women about their rights when they are discriminated against could be tailored to a group's characteristics. The tailoring could be based on the social characteristics known to be associated with reporting higher levels of a particular type of discrimination. Future studies should examine these social status correlates in more detail and propose why they exist.

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