Discrimination,Harassment,and Gendered Health Inequalities: Do Perceptions of Workplace Mistreatment Contribute to the Gender Gap in Self-reported Health?

Abstract

This study examines the extent to which discrimination and harassment contribute to gendered health disparities. Analyzing data from the 2006, 2010, and 2014 General Social Surveys (N = 3,724), we ask the following: (1) To what extent are perceptions of workplace gender discrimination and sexual harassment associated with self-reported mental and physical health? (2) How do multiple forms of workplace mistreatment (e.g., racism, ageism, and sexism) combine to structure workers’ self-assessed health? and (3) To what extent do perceptions of mistreatment contribute to the gender gap in self-assessed health? Multivariate analyses show that among women, but not men, perceptions of workplace gender discrimination are negatively associated with poor mental health, and perceptions of sexual harassment are associated with poor physical health. Among men and women, perceptions of multiple forms of mistreatment are associated with worse mental health. Gender discrimination partially explains the gender gap in self-reported mental health.

Keywords

discrimination gender harassment health inequities intersectionality

Two decades of social science and public health research shows, with great consistency, that discrimination is associated with numerous poor mental and physical health outcomes, including anxiety, depression, and cardiovascular disease (Kessler, Mickelson, and Williams 1999; Paradies 2006; Williams and Mohammed 2009). This research remains limited in several respects. First, most studies have focused on the effects of racial-ethnic discrimination, with less attention to sexism, ageism, and other forms of mistreatment (Pascoe and Richman 2009; Schmitt et al. 2014). Second, in the little research that has examined the health effects of sexism, most studies have focused solely on gender discrimination or sexual harassment, with few examining the potential independent effects of the two. And, third, while there is a broad consensus that perceived discrimination is associated with poor health outcomes, relatively few studies have examined the extent to which either discrimination or harassment explain health disparities (Krieger 2014). To our knowledge, no existing study has examined the extent to which gender discrimination and sexual harassment, in conjunction with other forms of mistreatment, account for gender-based health inequities.

The present study investigates how perceived gender discrimination and sexual harassment in the U.S. workforce, along with other forms of workplace mistreatment, structure mental and physical health outcomes as well as gendered health disparities. While few studies have examined these questions directly, we situate our analyses within existing feminist, epidemiological, social-psychological, and intersectional scholarship. After describing this theoretical landscape, we analyze data from the 2006, 2010, and 2014 General Social Surveys (GSS), testing the associations between perceived workplace discrimination and harassment, and self-rated health as well as gendered health disparities. In what follows, we use the term mistreatment to include both harassment and discrimination.

Background

Discrimination, Harassment, and Gender Inequality

The U.S. Equal Employment Opportunity Commission describes sex discrimination as treating someone (an applicant or employee) unfavorably because of that person’s sex, sexual orientation, or gender identity (including transgender status). Sexual harassment falls under the larger umbrella of sex discrimination and itself includes two specific forms of mistreatment: quid pro quo harassment and hostile-environment harassment. The former involves sexual threats or bribery that become a condition of employment, and the latter refers to offensive behavior (e.g., jokes, slurs, offensive images, intimidation) severe or pervasive enough to create an intimidating, hostile, or abusive environment (U.S. Equal Employment Opportunity Commission 2018). In legal frameworks, as well as social science research, sexual harassment and gender discrimination are closely linked. As Welsh (1999) suggests, sexual harassment often has as much or more to do with gender than with sexuality per se.

It is perhaps because gender discrimination and sexual harassment are so closely intertwined that existing research often elides the two. Fitzgerald et al.’s (1988) Sexual Experiences Questionnaire, for example, includes derogatory comments and jokes about women as one dimension of sexual harassment. Klonoff and Landrine (1995) also group sexual harassment and gender discrimination together but include experiences with “inappropriate sexual advances” and experiences of being denied a raise or promotion “because you are a woman” under the broader umbrella of “sexist events.” Legal and social science conceptualizations notwithstanding, individuals’ perceptions of discrimination, harassment, and the relationship between the two are shaped by numerous cultural factors, such as race, ethnicity, and most certainly, gender (Welsh 1999; Welsh et al. 2006).

Neither gender discrimination nor sexual harassment can be fully understood without attention to the larger gender structure. Following Risman (2004, 2017), we conceptualize gender as a hierarchical social structure that cuts across all levels of social life, organizing social institutions (such as the workforce), interpersonal interactions (such as interpersonal discrimination), and individual identities and expectations (such as entitlement). We view gender as a structure that significantly inhibits the possibilities for people of all genders (Connell 2005) and that works with and through other social hierarchies, such as race, class, and age (Baca Zinn and Thornton Dill 1996). Along the same lines, we view gender discrimination and sexual harassment as practices that are often intertwined with race, age, sexuality, and other inequalities (Crenshaw 1989; Welsh 1999; Welsh et al. 2006) and that can, in different ways and to varying degrees, negatively affect all people.

Gendered Health Disparities, Discrimination, and Harassment

Existing research on gender and health shows that, in the United States and elsewhere, women tend to live longer than men but also suffer from higher rates of nonfatal chronic health problems (Annandale and Hunt 2000; Read and Gorman 2010). Despite this general finding, gender inequalities in health vary across a range of disorders. Women are more likely to experience high levels of depression and anxiety (Caroli and Weber-Baghdiguian 2016; Marchand et al. 2016), but men are more likely to experience antisocial personality and substance-abuse dependence disorders (Simon 2002). Gender differences in physical health are similarly diverse, with men more likely to experience life-threatening illnesses, such as emphysema and heart disease, and women more likely to suffer from less serious, but chronic, conditions (Gorman and Read 2006; Read and Gorman 2010). Gender-based health disparities also vary across geographical and historical contexts and are further contingent upon the specific social groups under consideration (Bambra et al. 2009; Brown et al. 2016). In the aggregate, the self-reported health of women in the United States has steadily improved since the mid-1970s, whereas in the past two decades, men’s self-reported health has declined (Schnittker 2007).

While a combination of biological and social factors undoubtedly shapes gender differences in health outcomes (Simon and Lively 2010), to date, no study has examined the extent to which discrimination and/or harassment shape gendered health disparities. Existing research on the health effects of discrimination has, to this point, largely focused on race, with far fewer studies examining discrimination due to gender, disability, or other social statuses (Pascoe and Richman 2009; Schmitt et al. 2014). This lack of attention to gender discrimination is especially concerning, given that sexism remains pervasive in U.S. culture (Anderson 2014) and that women continue to report high levels of discrimination and sexual harassment in the workplace and elsewhere (Bobbitt-Zeher 2011; Krieger et al. 2006).

Epidemiological research shows that discrimination operates at multiple levels and “gets under the skin” through numerous pathways (Paradies, Bastos, and Priest 2016). The uneven distribution of internalized, interpersonal, and structural forms of discrimination—both within and between populations— gives rise not only to specific patterns of health and disease but also to persistent and large health-related inequalities. Stressful events, such as discrimination, take an emotional and psychological toll on individuals and are also associated with increased cortisol levels, which take a further toll on individuals’ health. While perceptions are not always a perfect reflection of reality, underreporting is more likely to take place than overreporting (Major, Quinton, and McCoy 2002), and subjective experiences of discrimination have been shown to affect health regardless of the objectivity of such reporting (Paradies 2006).

Among those studies that have examined the health effects of gender-based mistreatment, there is a consensus that women’s perceptions of “sexist events” (including sexual harassment and gender discrimination) are associated with increased psychological distress (Klonoff, Landrine, and Campbell 2000; Moradi and Subich 2002; Zucker and Landry 2007). Few studies have examined the impact of sexism on physical health. Drawing on multiple indicators of mental and physical health, Landrine et al. (1995) found that perceptions of sexist events explained a significant portion of the variance in women’s mental and physical health, “above and beyond that accounted for by generic stressors” (p. 487). Fitzgerald et al. (1997) analyzed the consequences of sexual harassment among women workers at a “West Coast Utility Company” and found that perceptions of sexual harassment were negatively associated with women’s physical health. Taken together, these studies suggest that perceptions of gender discrimination, sexual harassment, and other forms workplace mistreatment adversely affect multiple dimensions of women’s health.

The Moderating Effect of Status and Expectations

Despite the fact that feminist scholarship views people of all genders as constrained by gender norms and subject to gendered social control (Connell 2005), few studies have examined either the prevalence or correlates of perceived gender discrimination or sexual harassment among men. In fact, we have found only three. Kobrynowicz and Branscombe (1997) analyzed data from a sample of white undergraduate students and concluded that men’s perception of gender discrimination was associated with high levels of assertiveness and low levels of self-esteem. Schmitt et al. (2002), relying on a similar sample, found that perceptions of gender discrimination had negative consequences for women’s psychological well-being but no significant effect for men’s. Examining harassment and abuse within four U.S. universities, Richman et al. (1999) found that general abuse and sexual harassment (composed of gender harassment, unwanted sexual attention, and sexual coercion) had independent effects on the mental health of men and women. Among men, but not women, gender harassment (e.g., comments that demean the target’s gender) was the only dimension of sexual harassment linked to poor mental health.

Schmitt et al. (2002) argue that gender discrimination takes less of a toll on men because “when privileged group members are rejected by the disadvantaged . . . it carries fewer implications for the in-group’s value and status within the culture as a whole” (p. 199). Women, in contrast, are more likely to encounter gender discrimination and harassment across a broader range of life domains, and the gender discrimination they experience is often more severe (Schmitt et al. 2002:198). The pervasiveness of this discrimination, and its connection to culturally and institutionally supported inequality, is thought to make the material and psychological consequences of gender discrimination and harassment more detrimental for women, compared with men.

The consequences of perceived gender discrimination and harassment among men may be more complex than these studies suggest, however. Expectation-states theory (Correll and Ridgeway 2006) suggests that, due to their privileged position in the status hierarchy, men are generally assumed (by men and women alike) to be more competent than women and that both groups expect to be treated and evaluated accordingly. From this perspective, men’s greater sense of personal entitlement, and women’s lower sense of it (Bylsma and Major 1992), influences not only men’s and women’s perceptions of whether discrimination has occurred but also the emotional and psychological impact of this perceived event. From this perspective, men’s high-status position and expectation of rewards may increase their vulnerability to the health consequences of perceived discrimination. Gendered expectations of how one should be treated, and how much power and control one should have, are inevitably intertwined with perceptions of discrimination. For these reasons, men’s perceptions of gender discrimination may result in poor health outcomes.

The health consequences of gender discrimination are further complicated when multiple axes of inequality are examined concurrently. While men hold a privileged status with respect to gender, many men—in fact, most men—also hold at least one disadvantaged social status. Hierarchies of class, race, ethnicity, age, (dis)ability, and sexuality all work to structure the extent to which men benefit from gender inequality (Connell 2005). Research that conceptualizes men as a unitary “privileged group” masks important inequalities among men and the ways in which these inequalities might structure men’s experiences with, and perceptions of, gender discrimination (Harnois 2017). As discussed below, identities of class, race, age, disability, and sexuality are not always easily separable. Nor is it always easy, or even possible, to disentangle discrimination based on particular social statuses.

An Intersectional Analysis of Discrimination, Harassment, and Gendered Health Inequalities

Intersectionality emphasizes that women, men, and transgender people always occupy multiple social statuses and hold multiple identities (Baca Zinn and Thornton Dill 1996). In addition, interpersonal interactions and social institutions are structured by multiple systems of inequality, which produce historically and contextually specific matrices of domination (Collins 2000). Intersectional health scholars call for research that emphasizes how structural inequalities in power are embodied at the level of the individual, situating health outcomes within the broader structure of families, the workplace, and other social institutions (Weber and Parra-Medina 2003).

Intersectional studies of discrimination and health indicate that the health effects of perceived discrimination often vary across gender–racial and gender–age groups (Landrine et al. 1995; Pavalko, Mossakowski, and Hamilton 2003; Versey and Curtin 2016). An additional stream of research reveals the importance of conceptualizing discrimination itself from an intersectional approach (Bastos et al. 2017; Grollman 2014; Harnois 2014). A central claim of intersectionality is that individuals with multiple disadvantaged statuses experience discrimination that stems from multiple systems of inequality (Crenshaw 1989; King 1988). Recent empirical studies support this general claim and suggest that, compared with those who perceive having experienced discrimination as stemming from a single axis of inequality, those who perceive multiple forms of discrimination tend to experience even worse health outcomes (Bastos et al. 2014; Grollman 2014; Szymanski and Owens 2009). Moreover, Szymanski (2005) found that the interactive effects of multiple forms of discrimination (in this case, sexism and heterosexism) can negatively impact health above and beyond the independent effects of sexism and heterosexism, although in other studies (e.g., Bastos et al. 2014), similar interactions were nonsignificant.

Beyond centralizing the ways in which systems of inequality work with and through one another, Hankivsky (2012) argues that intersectionality makes three contributions to the study of gender and health. First, by emphasizing multiple identities, statuses, and systems of inequality, intersectionality avoids the a priori assumption that gender is always the most salient frame for analyzing women’s lives. Second, intersectionality calls attention to diversity among women and the need to develop analyses of health that explicitly acknowledge these differences. This research shows, for example, that while the overall gender gap in health in the United States has been shrinking in the past 30 years (Schnittker 2007), black women still experience worse health and, compared with other social groups, do not gain the same health benefits from higher levels of education (Cummings and Jackson 2008). Finally, intersectionality rejects the “conflation of gender with women,” calling attention to the ways in which social constructions of masculinity, binary notions of sex and gender, and the broader gender structure affect health. In brief, while existing research emphasizes that gender discrimination and harassment can be understood only within a broader system of gender inequality, intersectionality posits that gender discrimination and harassment must be understood within a broader system of intersecting inequalities.

Building on the research described above, the present study investigates discrimination, harassment, and gendered health inequalities among workers in the United States. We ask the following: (1) To what extent are perceptions of workplace gender discrimination and sexual harassment associated with self-reported mental and physical health? (2) How do multiple forms of workplace mistreatment (e.g., racism, ageism, and sexism) combine to shape self-assessed health? and (3) To what extent do perceptions of workplace discrimination and harassment explain the inequity in men’s and women’s self-reported mental and physical health?

Hypotheses

We hypothesize that among women, perceptions of gender discrimination and sexual harassment will be negatively associated with self-reported health (Hypothesis 1). Some existing research (i.e., Schmitt et al. 2002) suggests that due to their privileged position in the gender hierarchy, perception of gender discrimination and harassment will have no significant relationship with men’s health outcomes (Hypothesis 2). Expectation-states theory (Correll and Ridgeway 2006) provides an alternative hypothesis (Hypothesis 2A), predicting that perceptions of gender discrimination and harassment may adversely affect men’s health as much as—or more than—women’s. Drawing from intersectionality, we further hypothesize that respondents who perceive multiple forms of discrimination will report worse health outcomes than those who perceive only one (Hypothesis 3). And, last, we hypothesize that together with other forms of workplace discrimination, perceptions of gender-based mistreatment will explain a significant portion of the gender gap in self-reported mental and physical health (Hypothesis 4).

Data and Methods

The data came from the U.S. GSS, a nationally representative survey of English- and Spanish-speaking, non-institutionalized adults. The 2006, 2010, and 2014 surveys each contained a special module concerning the quality of working life, which was administered to 5,579 economically active respondents across all three years. Most of the variables used here had between 2 and 335 missing values, and the process of listwise deletion resulted in an analytic sample of 3,724 observations. Because the GSS is an area-probability sample, with an equal-probability multistage cluster-sampling scheme, standard errors are adjusted by the specific sampling design variables and weights across all statistical analyses.

Dependent Variables

Our focus was on self-reported physical and mental health. In general, studies show that men report better self-rated health than women (Read and Gorman 2010; Ross and Bird 1994). While self-reported health is undoubtedly influenced by norms related to gender (Caroli and Weber-Baghdiguian 2016; Ross and Bird 1994) as well as class, race, and disability, it is nonetheless an important dimension of individuals’ well-being and is strongly correlated with more “objective” indicators of health, including mortality (Idler and Benyamini 1997).

Respondents’ physical health was assessed with the question, “Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good?” Mental health was assessed with the question, “Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?” Both variables ranged from 0 to 30, with higher scores representing worse health.

Perceived Mistreatment

The main independent variables included respondents’ perceptions of workplace discrimination and harassment. Perceptions of discrimination were assessed by three separate questions asking whether respondents felt “in any way discriminated against on your job” because of their gender, their race or ethnic origin, or their age. In addition, respondents were asked about recent experiences with sexual harassment (“In the last 12 months, were you sexually harassed by anyone while you were on the job?”) as well as other forms of harassment (“In the last 12 months, were you threatened or harassed in any other way by anyone while you were on the job?”). Respondents who answered yes were coded as 1, and those who answered no as 0 on each of these questions.

In addition to examining the independent and interactive effects of particular forms of mistreatment, we also examined the health effects of multiple forms of mistreatment. We did this in two ways. We first used a series of dummy variables indicating whether respondents perceived having experienced no form of discrimination or harassment (the reference group), one form of mistreatment, or two or more forms of mistreatment. Our second approach used a count variable corresponding to the number of forms of mistreatment the respondent perceived.

Social Statuses and Control Variables

Independent variables included social statuses of gender, age, racial-ethnic group, educational attainment, and family income. Respondents’ gender status was assessed with a dummy variable where women = 1 and men = 0. Racial-ethnic status was assessed with a series of variables indicating respondents’ self-identification as non-Hispanic white (the reference group), African American or black, nonblack Hispanic, or “other” racial-ethnic group. Age was assessed with a series of dummy variables corresponding to ages 18 to 29 (the reference group), 30 to 44, 45 to 60, and over 60. Education was assessed with a series of dummy variables indicating whether respondents held either less than a high school degree; a high school degree, GED, or degree from a junior college; a bachelor’s degree; or a graduate degree (the reference group). Family income was measured with dummy variables corresponding to family incomes below $40,000 per year, $40,000 to $74,999 per year, and $75,000 and above (the reference group).¹ Because we analyzed data from three survey years, we included additional control variables for survey year.²

Analyses

We began by examining gender differences in self-reported mental and physical health as well as the frequency of perceived workplace mistreatment. We then conducted a series of multivariate zero-inflated negative binomial (ZINB) regression analyses to assess the relationship between perceived mistreatment and self-reported health, while controlling for a range of sociodemographic characteristics. Model selection followed the recommendations set forth by Long and Freese (2014), which included comparisons among different regressions (Poisson, negative binomial, zero-inflated Poisson, and ZINB) according to the Bayesian information criterion, Akaike information criterion, likelihood ratio test, and associated residual plots. The results indicated that the ZINB model fit best, and these models thus formed the basis of our multivariate analyses. ZINB regressions simultaneously estimate two separate models, because they assume that there are two latent groups under analysis. While a standard binary logit model is generated to account for observations that are part of the “zero-count” group (i.e., cases whose probability of zero days of poor health is 1), a negative binomial regression model is fitted for those respondents who may report either zero days of poor health or any positive count (Long and Freese 2014). The ZINB models thus yielded two sets of coefficients: one for the likelihood of being part of the zero-count group (via odds ratios) and the other indicating the factor change in the expected mean count of days of poor mental or physical health (via incidence rate ratios).³

Our first models focused on perceptions of gender discrimination and sexual harassment. We then examined the interplay of multiple social statuses and multiple forms of mistreatment in three ways. First, we analyzed gender discrimination and sexual harassment alongside other forms of discrimination and harassment (e.g., age discrimination). We then tested for interactive effects across all forms of mistreatment, in order to discern whether specific combinations of mistreatment were associated with particularly severe self-reported health. In order to circumvent the problem of multiple significance testing, which increases the likelihood of finding a low p value due to chance alone, Bonferroni-corrected significance levels were used throughout.

Interactions between social statuses of gender, age, class, race, and each form of mistreatment (e.g., Racial Group × Sexual Harassment) were also examined to ascertain whether the effects of mistreatment were stronger among women, specific age groups, low-socioeconomic-status individuals, and/or racial-ethnic minorities. We also provide more general analyses that examined how perceptions of multiple forms of discrimination relate to self-reported health.

Our final set of analyses investigated the extent to which perceptions of mistreatment explain the gender gaps in self-reported health. Our first step here was to test the association between gender (the factor whose effect on both dependent variables is to be decomposed) and each form of mistreatment (the hypothesized mediators between gender and the health outcomes), controlling for age, racial-ethnic group, educational attainment, family income, and survey year. Those potential mediators that were simultaneously associated with gender (as per the regressions above) and the two dependent variables under study—according to the models that were estimated to answer the first two research questions—were then analyzed with the KHB method (Breen, Karlson, and Holm 2013) to estimate whether they explained gender gaps in self-reported health.

Results

Table 1 presents the weighted descriptive statistics for all socioeconomic and demographic variables in the analysis for the analytic sample. As shown, the majority of men (70.4%) and women (68.5%) respondents identify as non-Hispanic whites. Compared with women, men were more likely to be working full-time and were less likely to have family incomes under $40,000. Women were more likely than men to have graduated from college. These patterns also hold true across individual survey years (results not shown but available upon request).

Table 1.

Weighted Sociodemographic Characteristics of the Sample, General Social Survey (2006, 2010, 2014).

Sociodemographic Characteristics	Men		Women
Sociodemographic Characteristics	n	%	n	%
Age groups (years)
18–29	402	21.5	370	19.5
30–44	645	34.5	696	36.7
45–60	650	34.8	680	35.8
>60	173	9.2	151	8.0
Race-ethnicity
Non-Hispanic white	1317	70.4	1299	68.5
Black (Hispanic or not)	201	10.7	305	16.1***
Hispanic (not black)	266	14.2	198	10.4***
Other racial-ethnic group	86	4.6	95	5.0
Highest degree achieved
Graduate	199	10.6	230	12.1**
Bachelor	404	21.6	392	20.6
Junior college or high school	1078	57.7	1147	60.5**
Less than high school	189	10.1	129	6.8**
Labor force status
Working full-time	1606	85.9	1417	74.7***
Working part-time	225	12.0	418	22.0***
Temporarily not working	39	2.1	63	3.3*
Family income
$75,000 and over	767	41.0	643	33.9***
$40,000–74,999	576	30.8	592	31.2
$0–39,999	527	28.2	663	34.9***

p < .05, ** p < .01, ***p < .001.

Table 2 shows the mean scores for men’s and women’s self-reported health and the weighted percentage of respondents who perceive different forms of workplace mistreatment. Women, on average, report significantly more days of poor mental health than men. Women also report having more days of poor physical health, although the difference is less for physical as compared with mental health. Men and women report similar levels of workplace racism and ageism, but women are significantly more likely to perceive sexual harassment, gender discrimination, and “other” forms of threatening behavior and harassment in the workplace. Of those respondents who perceive gender discrimination, more than half (64.8% of women and 78.8% of men) also perceive an additional form of discrimination or harassment. The correlation between perceived gender discrimination and sexual harassment is .182, suggesting that many respondents draw a distinction between the two. Nearly a quarter of women workers (23.0%) perceive experiencing at least one form of workplace mistreatment, compared with 16.5% of men.

Table 2.

Weighted Self-reported Mean Count of Days of Poor Health and Percentage Reporting Workplace Mistreatment, General Social Survey (2006, 2010, and 2014).

Variable	Men	Women
Mean count of days of poor . . .
Mental health	2.8 (SE = .181)	3.6** (SE = .183)
Physical health	2.2 (SE = .163)	2.7* (SE = .150)
Percentage of respondents reporting . . .
Gender discrimination	2.0	8.4***
Racial discrimination	4.2	4.3
Age discrimination	7.7	7.3
Sexual harassment	1.3	4.1***
Other threat or harassment	7.2	9.3*
Gender and other forms of discrimination	1.6	5.5***
Any form of discrimination	16.5	23.0***

p < .05, **p < .01, ***p < .001.

Table 3 tests the relationship between perceived gender discrimination, sexual harassment, and self-rated health. Models 1 and 5 show the results from regression equations assessing the effects of gender on self-reported mental and physical health. Controlling for sociodemographic characteristics, women were 41% to 48% less likely to be part of the zero-count group for either mental (Model 1, odds ratio [OR] = .520) or physical health (Model 5, OR = .590). Models 2 and 6 include perceptions of workplace gender discrimination and sexual harassment. Perceived gender discrimination is associated with a lower likelihood of being part of the zero-count group and a 36% (incidence rate ratio [IRR] = 1.356) higher mean count of days of poor mental health (Model 2). Perceptions of workplace gender discrimination are not associated with days of poor physical health, however, in either the zero-inflated or the binomial portion of the regression equation (Model 6).

Table 3.

Zero-inflated Negative Binomial Models for Self-reported Health on Gender-based Discrimination and Sexual Harassment, General Social Survey (2006, 2010, and 2014).

	Days of Poor Mental Health				Days of Poor Physical Health
IndependentVariable	Model 1 (Total)	Model 2 (Total)	Model 3 (Women)	Model 4 (Men)	Model 5 (Total)	Model 6 (Total)	Model 7 (Women)	Model 8 (Men)
Zero-inflated portion
Sexual harassment: Yes	—	.791	1.095	.383	—	.665	.858	.289
		[.434,1.433]	[.561,2.138]	[.097,1.517]		[.295,1.498]	[.375,1.965]	[.035,1.517]
Gender discrimination: Yes	—	.394***	.405**	.372	—	.521	.525	.618
	[.245, .631]	[.239,.686]	[.121,1.142]		[.266,1.019]	[.242,1.143]	[.180,1.142]
Gender: Women	.520***	.551***	—	—	.590***	.620**	—	—
	[.431,.626]	[.456,.665]			[.451,.772]	[.473,.814]
Negative binomial portion
Sexual harassment: Yes	—	1.191	1.308	1.049	—	1.464	1.632*	1.083
		[.928,1.527]	[.994,1.723]	[.563,1.955]		[.964,2.226]	[1.007,2.646]	[.474,2.471]
Gender discrimination: Yes	—	1.356**	1.299*	1.663	—	1.204	1.183	1.258
		[1.096,1.678]	[1.033,1.633]	[.994,2.783]		[.883,1.640]	[.833,1.679]	[.624,2.537]
Gender: Women	.934	.899	—	—	.979	.944	—	—
	[.811,1.076]	[.780,1.038]			[.807,1.189]	[.779,1.145]

Note: Results adjusted by age, race-ethnicity, education, labor force status, family income, and survey year. Odds ratios (and 95% confidence intervals) are presented for the zero-inflated part of the models, and incidence rate ratios (with 95% confidence intervals) are shown for the negative binomial portion of the regressions.

p < .05, **p < .01, ***p < .001.

The remaining models in Table 3 show separate models for men and women respondents. Models 3 and 4 show that, controlling for sociodemographic characteristics, perceived gender discrimination is associated with a 60% lower likelihood of being part of the zero-count group (Model 3, OR = .405) as well as a 30% increase in the mean count of days of poor mental health for women (IRR = 1.299). Neither sexual harassment nor gender discrimination is associated with self-reported mental health among men respondents (Model 4). Models 7 and 8 show that among women, perceptions of sexual harassment are associated, on average, with 63% more days of poor physical health. Perceptions of workplace gender discrimination and sexual harassment are not significantly associated with self-reported physical health for men, although the low prevalence of these forms of mistreatment may lower the statistical power for detecting significant associations among men.

Tables 4 and 5 show two approaches for testing Hypothesis 3, examining the role of multiple forms of mistreatment in shaping mental and physical health. The analyses in Table 4 include separate independent variables for perceived gender discrimination, racial discrimination, age discrimination, sexual harassment, and “other” types of workplace harassment. Model 1 shows that, controlling for sociodemographic characteristics and perceived workplace mistreatment, women are 46% less likely to be part of the zero-count group (OR = .544). Perceived workplace gender discrimination and “other” types of workplace harassment are each significantly associated with 49% (OR = .509) and 57% (OR = .430) lower likelihood of being part of the zero-count group for self-reported mental health. “Other” types of workplace harassment are also associated with a 35% higher mean count of days of poor mental health (IRR = 1.353). These relationships hold true among the aggregated sample (Model 1) and when the correlates of women’s, but not men’s, self-reported mental health are examined separately (Model 2). Among men, only “other” types of harassment remain associated with a lower likelihood of being part of the zero-count group (Model 3, OR = .335). In these models, perceptions of age-based discrimination, sexual harassment, and racial discrimination are not statistically significant predictors of poor mental health, nor do they show coefficients of considerable magnitude.

Table 4.

Zero-inflated Negative Binomial Models for Self-reported Health on All Forms of Workplace Mistreatment, General Social Survey (2006, 2010, and 2014).

	Days of Poor Mental Health			Days of Poor Physical Health
Independent Variable	Model 1 (total)	Model 2 (women)	Model 3 (men)	Model 4 (total)	Model 5 (women)	Model 6 (men)
Zero-inflated portion
Age discrimination: Yes	.792	.960	.655	.740	.628	.745
	[.510,1.229]	[.531,1.733]	[.360,1.194]	[.436,1.257]	[.174,2.266]	[.352,1.577]
Sexual harassment: Yes	.968	1.277	.544	.772	.884	.471
	[.533,1.757]	[.659,2.475]	[.136,2.179]	[.338,1.765]	[.321,2.433]	[.065,3.380]
Other threat or harassment: Yes	.430***	.567*	.335**	.529*	.556	.427
	[.297,.624]	[.365,.904]	[.170,.662]	[.305,.918]	[.213,1.450]	[.177,1.030]
Gender discrimination: Yes	.509*	.483*	.456	.604	.623	.586
	[.302,.858]	[.266,.877]	[.101,2.058]	[.316,1.156]	[.262,1.483]	[.146,2.348]
Racial discrimination: Yes	.880	.722	1.013	1.348	.814	1.888
	[.536,1.444]	[.394,1.323]	[.396,2.592]	[.732,2.484]	[.220,3.009]	[.633,5.630]
Gender: Women	.544***	—	—	.619***	—	—
	[.450,.659]			[.475,.807]
Negative binomial portion
Age discrimination: Yes	1.075	1.194	.918	1.170	.911	1.452
	[.840,1.377]	[.842,1.694]	[.619,1.362]	[.893,1.534]	[.545,1.524]	[.892,2.364]
Sexual harassment: Yes	1.161	1.273	1.003	1.466	1.521	1.297
	[.893,1.511]	[.965,1.697]	[.519,1.940]	[.939,2.290]	[.886,2.613]	[.549,3.060]
Other threat or harassment: Yes	1.353**	1.379*	1.387	1.093	1.061	1.026
	[1.103,1.660]	[1.079,1.762]	[.984,1.957]	[.837,1.428]	[.742,1.516]	[.655,1.607]
Gender discrimination: Yes	1.183	1.097	1.460	1.063	1.102	.961
	[.933,1.500]	[.823,1.463]	[.834,2.556]	[.762,1.484]	[.758,1.601]	[.468,1.934]
Racial discrimination: Yes	1.130	1.112	1.082	1.335	1.339	1.155
	[.854,1.496]	[.812,1.525]	[.631,1.857]	[.905,1.968]	[.805,2.227]	[.440,3.032]
Gender: Women	.909	—	—	.949	—	—
	[.787,1.048]			[.784,1.150]

p < .05, **p < .01, ***p < .001.

Table 5.

Zero-inflated Negative Binomial Models for Self-reported Health on Counts of Multiple Forms of Mistreatment, General Social Survey (2006, 2010, and 2014).

	Days of Poor Mental Health			Days of Poor Physical Health
Independent Variable	Model 1 (Total)	Model 2 (Women)	Model 3 (Men)	Model 4 (Total)	Model 5 (Women)	Model 6 (Men)
Zero-inflated portion
One form of mistreatment: Yes	.579***	.819	.396***	.652*	.625	.616
	[.434, .773]	[.559,1.201]	[.244,.641]	[.427, .995]	[.344,1.133]	[.304,1.246]
Two or more forms of mistreatment: Yes	.341***	.317***	.366**	.422**	.326*	.515
	[.228, .510]	[.175,.575]	[.188, .709]	[.234, .763]	[.125,.852]	[.193,1.374]
Gender: Women	.538***	—	—	.624***	—	—
	[.445, .651]			[.480, .812]
Negative binomial portion
One form of mistreatment: Yes	1.107	1.206	.975	1.289	1.282	1.241
	[.902,1.358]	[.956,1.522]	[.728,1.306]	[.996,1.668]	[.923,1.782]	[.828,1.859]
Two or more forms of mistreatment: Yes	1.563***	1.557***	1.635**	1.368*	1.305	1.342
	[1.305,1.871]	[1.246,1.945]	[1.169,2.286]	[1.051,1.781]	[.929,1.831]	[.808,2.229]
Gender: Women	.907	—	—	.957	—	—
	[.787,1.045]			[.788,1.162]

p < .05, **p < .01, ***p < .001.

In addition to the models shown here, we tested for the possibility of two types of interaction effects: between social statuses of race, class, age, and perceptions of particular forms of mistreatment (e.g., Racial Group × Sexual Harassment) and interactions between particular forms of mistreatment (e.g., Racism × Sexism). Only one such interaction was statistically significant, and it was restricted to the negative binomial portion of the model. Figure 1 shows that when perceived age and gender discrimination combine, the effects are particularly detrimental to women’s, but not men’s, mental health: reports of both age and gender discrimination were associated with an average of 9.0 days of poor mental health among women and 5.8 among men.

Models 4 through 6 of Table 4 test the relationship between multiple forms of mistreatment on respondents’ self-reported days of poor physical health. Model 4 shows that when examined in the aggregate, perceptions of “other” types of harassment are significantly associated with self-reported physical health: reports of “other” types of harassment are associated with a 47% lower likelihood of being part of the zero-count group. Models 5 and 6 present the results disaggregated by gender. When included concurrently, none of the specific forms of mistreatment are significantly associated with women’s or men’s self-reported days of poor physical health. We tested numerous interaction effects between social statuses and particular forms of mistreatment as well as between specific forms of mistreatment. None were significant at the alpha = .05 level.

Table 5 takes a more general approach to assess the effects of multiple forms of mistreatment, using dummy variables to indicate whether respondents perceive experiencing no forms of mistreatment (the reference group), a single form of mistreatment, or multiple forms of mistreatment. Although some of the particularities are lost, this approach is potentially more in line with an intersectional framework. Rather than attempting to isolate the unique contributions of particular forms of discrimination to women’s and men’s health, it focuses more generally on the interplay and overlap among multiple inequalities (Moradi and Subich 2003). Results are consistent across each of the first three models: women and men respondents who perceive two or more types of workplace mistreatment not only are 63% to 68% less likely to be part of the zero-count group but also show 56% (IRR = 1.557) and 64% (IRR = 1.635) higher mean counts of days of poor mental health. Models 4 through 6 show, on the other hand, that perceiving two or more forms of mistreatment is associated with poor physical health only among women (Model 5, OR = .326).

In addition to the models shown here, we ran additional models using a single count variable for the number of forms of mistreatment that respondents perceived. The results of these models (available upon request) are consistent with those presented in Table 5 and show that for women, there is a significant positive association between the number forms of mistreatment that respondents perceive, and poor mental and physical health.

Our fourth hypothesis was that together with other forms of discrimination, perceptions of gender-based mistreatment would explain some of the gender gap in self-reported health (see the main effects of gender on self-reported mental and physical health in Models 1 and 5 from Table 3). Ancillary analyses using standard binary or multinomial logistic regression models indicated that gender also predicts perceived gender discrimination (OR = 4.927), sexual harassment (OR = 3.436), “other” types of harassment (OR = 1.351), one form of mistreatment (relative risk ratio [RRR] = 1.412), and two or more forms of mistreatment (RRR = 1.974). Gender predicts neither age nor racial discrimination, however.⁴

In view of the above, as well as the finding that gender discrimination, “other” types of harassment, and one and two or more forms of mistreatment showed substantive effects on both self-reported health measures (see Tables 3, 4, and 5), the KHB method was used to assess the extent to which these forms of mistreatment explained gender gaps in mental and physical health. For the mediation analyses, both self-reported mental and physical health were dichotomized into none versus one or more days of poor health, since gender was a strong and statistically significant predictor of the two dependent variables only in the zero-inflated portion of the ZINB models.

Use of the KHB method via binary logistic regressions revealed that around 9% to 10% of the total effect of gender on either self-reported mental or physical health is mediated by gender discrimination. Mediation by gender discrimination was statistically significant (p < .001) only for self-reported mental health, however. A considerably smaller proportion (~3.5%) of the gender effect on self-reported mental and physical health was mediated by “other” types of workplace harassment, with a statistically significant mediational effect (p = .031) for mental health only. One and two or more forms of discrimination explained around 10% of the gender effect on self-reported physical and mental health, and both were statistically significant mediators for mental (p < .001) and physical health (p = .034).

Discussion

In recent years, a consensus has emerged within social science and epidemiological research that discrimination constitutes a “social hazard” resulting in a range of negative physical and mental health outcomes (Krieger et al. 2006). This research has primarily focused on racial-ethnic discrimination and racial-ethnic health inequalities. The few studies that examine gender discrimination tend to focus exclusively on women and do so without situating gender in relation to other systems of inequality. This study demonstrates how gender-based discrimination and harassment, in combination with other forms of workplace mistreatment, shape health outcomes for men and women, and contribute to gendered health disparities.

Gender Discrimination and Sexual Harassment Are Associated with Poor Health Outcomes for Women

In support of Hypothesis 1, our results show that among women, perceptions of gender discrimination, and to a certain extent, sexual harassment, are significantly and negatively associated with self-reported health. Compared with women who had not perceived workplace gender discrimination, women who did reported more days of poor mental health. Women who perceived sexual harassment also reported worse physical health. This finding is consistent with existing epidemiological research that theorizes perceived discrimination as a social stressor and an important predictor of health outcomes (Krieger 2014; Williams and Mohammed 2009).

More specifically, our findings echo those of Landrine et al. (1995) and Pavalko et al. (2003), who, analyzing data from previous decades, found that women’s perceptions of sexist events were associated with increased psychological distress and decreased functional mobility. Our analyses suggest that the deleterious health effects of discrimination and harassment persist. They also suggest that gender discrimination and sexual harassment, while related, may affect women’s health in different ways—the former showing stronger associations with mental health and the latter with physical health. Future research is needed to understand the processes through which individuals classify mistreatment as gender discrimination, sexual harassment, or both and the potential impact of this classification itself on self-assessed health.

Few men surveyed reported experiencing either gender discrimination (2.0%) or sexual harassment (1.3%), and neither form of mistreatment was significantly associated with either mental health or physical health among them. This finding is consistent with previous research by Kobrynowicz and Branscombe (1997) and Schmitt et al. (2002), which suggests that men’s high-status position may buffer them from the health consequences of perceived gender-based mistreatment (Hypothesis 2). The low frequency with which gender discrimination and sexual harassment were reported among men, coupled with a somewhat lower precision of our estimates in gender-stratified models, may have decreased the power to detect significant associations, however.

Multiple Forms of Mistreatment Structure Health Outcomes

As described above, intersectionality highlights the interplay of multiple social statuses and multiple forms of inequality (Baca Zinn and Thornton Dill 1996; Crenshaw 1989; Hankivsky 2012). Drawing from this approach, we examined gender-based mistreatment alongside other forms of workplace discrimination and harassment. We also examined potential interactive effects of multiple forms of mistreatment on self-reported health as well as potential interactive effects between social statuses of race, class, and age and particular forms of mistreatment.

Consistent with previous research (e.g., Bastos et al. 2014; Grollman 2014; Harnois 2014) and with our third hypothesis, results showed that perceptions of multiple forms of workplace mistreatment are relatively common: nearly two thirds of women who perceive gender discrimination (64.8%) and more than three quarters of men who perceive gender discrimination (78.8%) also report perceiving another form of mistreatment. Among both groups, those who perceive multiple forms of mistreatment report significantly worse mental health than those who perceived no mistreatment or just one form of mistreatment. Among women, the combination of age- and gender-based discrimination at work may be particularly detrimental for mental health. And for women, but not men, perceptions of multiple forms of discrimination were also associated with decreased physical health. More generally, our findings underscore the importance of intersectionality for assessing discrimination, harassment, and health disparities. While, among women, gender-based mistreatment is associated with poor mental and physical health, our results demonstrate that gender-based mistreatment works in combination with other forms of mistreatment to structure health outcomes and gendered health disparities. A comprehensive understanding of discrimination, harassment, and health requires attention to multiple systems of inequality.

Gender-based Mistreatment Drives Disparities in Self-reported Health

Our last hypothesis (Hypothesis 4) was that in combination with other forms of workplace mistreatment, perceptions of gender-based mistreatment would explain some of the gender gap in self-reported health. Here the results partially support our hypothesis. Perceptions of gender discrimination were the strongest mediator between gender and either of the studied health outcomes, although mediation was statistically significant only for self-reported mental health. This finding suggests that part of the “gender effect” on mental health may be attributed to gender discrimination. When we further examined multiple forms of mistreatment, the gender gap for mental and physical health was also partially explained.

Beyond showing that gender-based mistreatment is an important driver of health outcomes and gendered health disparities, the present study illustrates one way in which quantitative analyses can clarify the processes through which gender inequality is embodied at the level of the individual. Rather than taking “a dichotomous classification of bodies as a complete definition of gender” (Connell 2012:1675), here we have conceptualized gender categories as socially constructed statuses, and we have interpreted them within the context of gendered processes (e.g., discrimination and harassment) and within gendered social institutions (e.g., the workplace). We contend, then, that the “gender effect,” to which we referred above, materializes in the form of a statistically significant regression coefficient due to capturing these and other complex social processes occurring at multiple societal levels. Were it not for the social construction of gender, based on social, cultural, and biological meanings of what makes gender, the aforementioned “gender effect” on health-related outcomes would likely not be consistent or even detectable through our analyses.

Finally, we note here that in addition to contributing to poor health outcomes, gender discrimination and harassment—as experienced by women, men, and almost certainly, transgender people, too—are simultaneously one of the processes through which gendered social statuses and gendered hierarchies are created and maintained. In this sense, our analyses conceptualize gender as a recursive social process and, again, shed light on the processes through which gender is embodied at the level of the individual.

Limitations

Our research is limited in that we focus on workplace discrimination and harassment, and examine only a handful of the many different types of mistreatment that respondents might experience. Perceptions of discrimination on the basis of sexuality, class, and disability are not assessed directly though may be included in our measure of “other” types of harassment or threatening behavior. The survey questions we analyzed also focus on discrimination in respondents’ current job and experiences of harassment within the past year. The consequences of workplace mistreatment can span long past a single year and can affect individuals’ job trajectories, earnings, and participation in the workforce in addition to their well-being. It is likely that some people who have had significant experiences with discrimination (within and outside of the workplace) may not be in the workforce precisely because of these experiences. Our analyses are further limited to English- and Spanish-speaking adults, and it is likely that those who, living in the United States, are fluent in neither language are particularly vulnerable to discrimination. For all of these reasons, our results likely underestimate the prevalence of workplace mistreatment and its association with mental and physical health.

The cross-sectional nature of our analyses limits our ability to establish temporal orderings between the independent variables and the health outcomes we analyzed: psychiatric morbidities as well as physical disability may be affected by perceived discrimination but may also increase the extent to which respondents are vigilant to instances of unfair treatment. Lending credence to our findings, previous studies consistently show that perceived discrimination is prospectively associated with adverse mental and physical health outcomes; psychiatric morbidities or health-related work limitations do not seem to prospectively predict discrimination, however (Gee and Walsemann 2009; Gibbons et al. 2004). Finally, and as previously mentioned, the small number of men who perceive gender discrimination and sexual harassment may decrease our ability to detect significant associations among men. Future research should help elucidate this issue.

Conclusion

With these limitations in mind, it is clear that perceived gender discrimination, sexual harassment, and other forms of workplace mistreatment play an important role in shaping self-reported health as well as gendered health inequalities. Here, we have relied upon limited measures of mistreatment and limited measures of health. Yet, the findings show great consistency: among women, but not men, perceptions of workplace gender discrimination are negatively associated with poor mental health, and perceptions of sexual harassment are associated with poor physical health; among men and women, perceptions of two or more forms of mistreatment are negatively associated with mental health; across genders, multiple forms of mistreatment work together to structure health outcomes; and perceptions of gender discrimination partially explain the gap between working men’s and women’s mental health in the United States. To our knowledge, this is the first study to examine the extent to which discrimination and harassment drive gendered health inequities. More are needed.

Supplemental Material

JHSB767407_Supplemental_FINAL – Supplemental material for Discrimination, Harassment, and Gendered Health Inequalities: Do Perceptions of Workplace Mistreatment Contribute to the Gender Gap in Self-reported Health?

Supplemental material, JHSB767407_Supplemental_FINAL for Discrimination, Harassment, and Gendered Health Inequalities: Do Perceptions of Workplace Mistreatment Contribute to the Gender Gap in Self-reported Health? by Catherine E. Harnois and João L. Bastos in Journal of Health and Social Behavior

Footnotes

Acknowledgements

The authors thank Robin W. Simon, Cathy Zimmer, the anonymous reviewers, and Journal of Health and Social Behavior coeditors for their constructive feedback. This project was supported by the Wake Forest University Center for Global Affairs and Studies, the Brazilian National Research Council (CNPq) (Research Grant 303857/2015-3 for João L. Bastos), and a sabbatical scholarship from the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES) for João L. Bastos (Process Number 88881.119350/2016-01).

Supplemental Material

Appendices A through D are available in the online version of the article.

Notes

Author Biographies

Catherine E. Harnois is a professor of sociology at Wake Forest University. Her current work focuses on issues of discrimination, political consciousness, and intersectional research methods.

João L. Bastos currently works as an associate professor of public health at the Federal University of Santa Catarina. His main research interests include the health effects of discrimination as well as the development of psychometric scales to assess self-reported experiences with discrimination.

References

Anderson

Kristin J.

2014. Modern Misogyny: Anti-feminism in a Post-feminist Era. Oxford: Oxford University Press.

Annandale

Ellen

Hunt

Kate

, eds. 2000. Gender Inequalities in Health. Buckingham, UK: Open University Press.

Baca Zinn

Maxine

Thornton Dill

Bonnie

. 1996. “Theorizing Difference from Multiracial Feminism.” Feminist Studies 22(2):321–31.

Bambra

Clare

Pope

Daniel P.

Swami

Viren

Stanistreet

Debbi L.

Roskam

Albert-Jan

Kunst

A. E.

Scott-Samuel

Alex

. 2009. “Gender, Health Inequalities and Welfare State Regimes: A Cross-national Study of 13 European Countries.” Journal of Epidemiology and Community Health 63(1):38–44.

Bastos

João L.

Barros

Aluisio J.

Celeste

Roger K.

Paradies

Yin C.

Faerstein

Eduardo

. 2014. “Age, Class and Race Discrimination: Their Interactions and Associations with Mental Health among Brazilian University Students.” Cadernos de Saúde Pública 30(1):175–86.

Bastos

João L.

Harnois

Catherine E.

Bernardo

Carla O.

Peres

Marco A.

Paradies

Yin C.

2017. “When Does Differential Treatment Become Perceived Discrimination? An Intersectional Analysis in a Southern Brazilian Population.” Sociology of Race and Ethnicity 3(3):301–18.

Bobbitt-Zeher

Donna

. 2011. “Gender Discrimination at Work.” Gender & Society 25(6):764–86.

Breen

Richard

Karlson

Kristian Bernt

Holm

Anders

. 2013. “Total, Direct, and Indirect Effects in Logit and Probit Models.” Sociological Methods & Research 42(2):164–91.

Brown

Tyson H.

Richardson

Liana J.

Hargrove

Taylor W.

Thomas

Courtney S.

2016. “Using Multiple-hierarchy Stratification and Life Course Approaches to Understand Health Inequalities.” Journal of Health and Social Behavior 57(2):200–22.

10.

Bylsma

Wayne H.

Major

Brenda

. 1992. “Two Routes to Eliminating Gender Differences in Personal Entitlement: Social Comparisons and Performance Evaluations.” Psychology of Women Quarterly 16(2):193–200.

11.

Caroli

Eve

Weber-Baghdiguian

Lexane

. 2016. “Self-reported Health and Gender: The Role of Social Norms.” Social Science & Medicine 153:220–29.

12.

Collins

Patricia Hill

. 2000. Black Feminist Thought. New York: Routledge.

13.

Connell

Raewyn

. 2005. Masculinities. Los Angeles: University of California Press.

14.

Connell

Raewyn

. 2012. “Gender, Health and Theory: Conceptualizing the Issue, in Local and World Perspective.” Social Science & Medicine 74(11):1675–83.

15.

Correll

Shelley J.

Ridgeway

Cecilia L.

2006. “Expectation States Theory.” Pp. 29–51 in Handbook of Social Psychology, edited by Delamater

Boston: Springer US.

16.

Crenshaw

Kimberlé W.

1989. “Demarginalizing the Intersection of Race and Sex: A Black Feminist Critique of Antidiscrimination Doctrine, Feminist Theory and Antiracist Politics.” University of Chicago Legal Forum 140:139–67.

17.

Cummings

Jason L.

Jackson

Pamela Braboy

. 2008. “Race, Gender, and SES Disparities in Self-assessed Health, 1974–2004.” Research on Aging 30(2):137–67.

18.

Fitzgerald

Louise F.

Drasgow

Fritz

Hulin

Charles L.

Gelfand

Michele J.

Magley

Vicki J.

1997. “Antecedents and Consequences of Sexual Harassment in Organizations: A Test of an Integrated Model.” Journal of Applied Psychology 82(4):578–89.

19.

Fitzgerald

Louise F.

Shullman

Sandra L.

Bailey

Nancy

Richards

Margaret

Swecker

Janice

Gold

Yael

Ormerod

Mimi

Weitzman

Lauren

. 1988. “The Incidence and Dimensions of Sexual Harassment in Academia and the Workplace.” Journal of Vocational Behavior 32(2):152–75.

20.

Gee

Gilbert

Walsemann

Katrina

. 2009. “Does Health Predict the Reporting of Racial Discrimination or Do Reports of Discrimination Predict Health? Findings from the National Longitudinal Study of Youth.” Social Science & Medicine 68(9):1676–84.

21.

Gibbons

Frederick X.

Gerrard

Meg

Cleveland

Michael J.

Wills

Thomas A.

Brody

Gene

. 2004. “Perceived Discrimination and Substance Use in African American Parents and Their Children: A Panel Study.” Journal of Personality and Social Psychology 86(4):517–29.

22.

Gorman

Bridget K.

Ghazal Read

Jen’nan

. 2006. “Gender Disparities in Adult Health: An Examination of Three Measures of Morbidity.” Journal of Health and Social Behavior 47(2):95–110.

23.

Grollman

Eric A.

2014. “Multiple Disadvantaged Statuses and Health: The Role of Multiple Forms of Discrimination.” Journal of Health and Social Behavior 55(1):3–19.

24.

Hankivsky

Olena

. 2012. “Women’s Health, Men’s Health, and Gender and Health: Implications of Intersectionality.” Social Science & Medicine 74(11):1712–20.

25.

Harnois

Catherine E.

2014. “Are Perceptions of Discrimination Unidimensional, Oppositional, or Intersectional? Examining the Relationship among Perceived Racial–Ethnic-, Gender-, and Age-based Discrimination.” Sociological Perspectives 57(4):470–87.

26.

Harnois

Catherine E.

2017. “Intersectional Masculinities and Gendered Political Consciousness: How Do Race, Ethnicity and Sexuality Shape Men’s Awareness of Gender Inequality and Support for Gender Activism?” Sex Roles 77(3/4):141–54.

27.

Idler

Ellen L.

Benyamini

Yael

. 1997. “Self-rated Health and Mortality: A Review of Twenty-seven Community Studies.” Journal of Health and Social Behavior 38(1):21–37.

28.

Kessler

Ronald C.

Mickelson

Kristin D.

Williams

David R.

1999. “The Prevalence, Distribution, and Mental Health Correlates of Perceived Discrimination in the United States.” Journal of Health and Social Behavior 40(3):208–30.

29.

King

Deborah K.

1988. “Multiple Jeopardy, Multiple Consciousness: The Context of a Black Feminist Ideology.” Signs 14(1):42–72.

30.

Klonoff

Elizabeth A.

Landrine

Hope

. 1995. “The Schedule of Sexist Events.” Psychology of Women Quarterly 19(4):439–70.

31.

Klonoff

Elizabeth A.

Landrine

Hope

Campbell

Robin

. 2000. “Sexist Discrimination May Account for Well-known Gender Differences in Psychiatric Symptoms.” Psychology of Women Quarterly 24(1):93–99.

32.

Kobrynowicz

Diane

Branscombe

Nyla R.

1997. “Who Considers Themselves Victims of Discrimination?” Psychology of Women Quarterly 21(3):347–63.

33.

Krieger

Nancy

. 2014. “Discrimination and Health Inequities.” Pp. 63–125 in Social Epidemiology, edited by Berkman

L. F.

Kawachi

Glymour

M. M.

New York: Oxford University Press.

34.

Krieger

Nancy

Waterman

Pamela D.

Hartman

Cathy

Bates

Lisa M.

Stoddard

Anne M.

Quinn

Margaret M.

Sorensen

Glorian

Barbeau

Elizabeth M.

2006. “Social Hazards on the Job: Workplace Abuse, Sexual Harassment, and Racial Discrimination: A Study of Black, Latino, and White Low-income Women and Men Workers in the United States.” International Journal of Health Services 36(1):51–85.

35.

Landrine

Hope

Klonoff

Elizabeth A.

Gibbs

Jeannine

Manning

Vickie

Lund

Marlene

. 1995. “Physical and Psychiatric Correlates of Gender Discrimination.” Psychology of Women Quarterly 19(4):473–92.

36.

Long

J. Scott

Freese

Jeremy

. 2014. Regression Models for Categorical Dependent Variables Using Stata. College Station, TX: Stata Press.

37.

Major

Brenda

Quinton

Wendy J.

McCoy

Shannon K.

2002. “Antecedents and Consequences of Attributions to Discrimination: Theoretical and Empirical Advances.” Advances in Experimental Social Psychology 34:251–330.

38.

Marchand

Alain

Bilodeau

Jaunathan

Demers

Andrée

Beauregard

Nancy

Durand

Pierre

Haines

Victor Y.

III . 2016. “Gendered Depression: Vulnerability or Exposure to Work and Family Stressors?” Social Science & Medicine 166:160–68.

39.

Moradi

Bonnie

Subich

Linda Mezydlo

. 2002. “Perceived Sexist Events and Feminist Identity Development Attitudes: Links to Women’s Psychological Distress.” Counseling Psychologist 30(1):44–65.

40.

Moradi

Bonnie

Subich

Linda Mezydlo

. 2003. “A Concomitant Examination of the Relations of Perceived Racist and Sexist Events to Psychological Distress for African American Women.” Counseling Psychologist 31(4):451–69.

41.

Paradies

Yin Carl

. 2006. “A Systematic Review of Empirical Research on Self-reported Racism and Health.” International Journal of Epidemiology 35(4):888–901.

42.

Paradies

Yin Carl

Bastos

João L.

Priest

Naomi

. 2016. “Prejudice, Stigma, Bias, Discrimination, and Health.” Pp. 559–80 in Cambridge Handbook of the Psychology of Prejudice, edited by Sibley

Barlow

F. K.

Cambridge: Cambridge University Press.

43.

Pascoe

Elizabeth A.

Smart Richman

Laura

. 2009. “Perceived Discrimination and Health: A Meta-analytic Review.” Psychological Bulletin 135(4): 531–54.

44.

Pavalko

Eliza K.

Mossakowski

Krysia N.

Hamilton

Vanessa J.

2003. “Does Perceived Discrimination Affect Health? Longitudinal Relationships between Work Discrimination and Women’s Physical and Emotional Health.” Journal of Health and Social Behavior 44(1):18–33.

45.

Read

Jen’nan Ghazal

Gorman

Bridget K.

2010. “Gender and Health Inequality.” Annual Review of Sociology 36(1):371–86.

46.

Richman

Judith A.

Rospenda

Kathleen M.

Nawyn

Stephanie J.

Flaherty

Joseph A.

Fendrich

Michael

Drum

Melinda L.

Johnson

Timothy P.

1999. “Sexual Harassment and Generalized Workplace Abuse among University Employees: Prevalence and Mental Health Correlates.” American Journal of Public Health 89(3):358–63.

47.

Risman

Barbara J.

2004. “Gender as a Social Structure: Theory Wrestling with Activism.” Gender and Society 18(4):429–50.

48.

Risman

Barbara J.

2017. “2016 Southern Sociological Society Presidential Address: Are Millennials Cracking the Gender Structure?” Social Currents 4(3):208–27.

49.

Ross

Catherine E.

Bird

Chloe E.

1994. “Sex Stratification and Health Lifestyle: Consequences for Men’s and Women’s Perceived Health.” Journal of Health and Social Behavior 35(2):161–78.

50.

Schmitt

Michael T.

Branscombe

Nyla R.

Kobrynowicz

Diane

Owen

Susan

. 2002. “Perceiving Discrimination against One’s Gender Group Has Different Implications for Well-being in Women and Men.” Personality and Social Psychology Bulletin 28(2):197–210.

51.

Schmitt

Michael T.

Branscombe

Nyla R.

Postmes

Tom

Garcia

Amber

. 2014. “The Consequences of Perceived Discrimination for Psychological Well-being: A Meta-analytic Review.” Psychological Bulletin 140(4):921–48.

52.

Schnittker

Jason

. 2007. “Working More and Feeling Better: Women’s Health, Employment, and Family Life, 1974–2004.” American Sociological Review 72(2):221–38.

53.

Simon

Robin W.

2002. “Revisiting the Relationships among Gender, Marital Status, and Mental Health.” American Journal of Sociology 107(4):1065–96.

54.

Simon

Robin W.

Lively

Kathryn

. 2010. “Sex, Anger, and Depression.” Social Forces 88(4):1543–68.

55.

Szymanski

Dawn M.

2005. “Heterosexism and Sexism as Correlates of Psychological Distress in Lesbians.” Journal of Counseling & Development 83(3):355–60.

56.

Szymanski

Dawn M.

Owens

Gina P.

2009. “Group-level Coping as a Moderator between Heterosexism and Sexism and Psychological Distress in Sexual Minority Women.” Psychology of Women Quarterly 33(2):197–205.

57.

U.S. Equal Employment Opportunity Commission. 2018. Sexual Harassment. Washington, DC: Author. Retrieved February 5, 2018 (https://www.eeoc.gov/laws/types/sexual_harassment.cfm).

58.

Versey

H. Shellae

Curtin

Nicola

. 2016. “The Differential Impact of Discrimination on Health among Black and White Women.” Social Science Research 57:99–115.

59.

Weber

Lynn

Parra-Medina

Deborah

. 2003. “Intersectionality and Women’s Health: Charting a Path to Eliminating Health Disparities.” Pp. 181–230 in Gender Perspectives on Health and Medicine, edited by Segal

M. T.

Demos

Kronenfeld

J. J.

New York: Emerald Group.

60.

Welsh

Sandy

. 1999. “Gender and Sexual Harassment.” Annual Review of Sociology 25(1):169–90.

61.

Welsh

Sandy

Carr

Jacquie

MacQuarrie

Barbara

Huntley

Audrey

. 2006. “‘I’m Not Thinking of It as Sexual Harassment.’” Gender & Society 20(1):87–107.

62.

Williams

David R.

Mohammed

Selina A.

2009. “Discrimination and Racial Disparities in Health: Evidence and Needed Research.” Journal of Behavioral Medicine 32(1):20–47.

63.

Zucker

Alyssa N.

Landry

Laura J.

2007. “Embodied Discrimination: The Relation of Sexism and Distress to Women’s Drinking and Smoking Behaviors.” Sex Roles 56(3):193–203.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.16 MB