Unequal depression for equal work? How the wage gap explains gendered disparities in mood disorders

Highlights

• Individual-level indicators of productivity do not fully explain the gender wage gap.

• The gender wage gap is evidence of structural workplace discrimination.

• Odds of mood disorders increased among women paid less than equally qualified men.

• Structural discrimination partially explains gender disparities in mood disorders.

Abstract

Mood disorders, such as depression and anxiety, are more prevalent among women than men. This disparity may be partially due to the effects of structural gender discrimination in the work force, which acts to perpetuate gender differences in opportunities and resources and may manifest as the gender wage gap. We sought to quantify and operationalize the wage gap in order to explain the gender disparity in depression and anxiety disorders, using data from a 2001–2002 US nationally representative survey of 22,581 working adults ages 30–65. Using established Oaxaca-Blinder decomposition methods to account for gender differences in individual-level productivity, our models reduced the wage gap in our sample by 13.5%, from 54% of men's pay to 67.5% of men's pay. We created a propensity-score matched sample of productivity indicators to test if the direction of the wage gap moderated the effects of gender on depression or anxiety. Where female income was less than the matched male counterpart, odds of both disorders were significantly higher among women versus men (major depressive disorder OR: 2.43, 95% CI: 1.95–3.04; generalized anxiety disorder OR: 4.11, 95% CI: 2.80–6.02). Where female income was greater than the matched male, the higher odds ratios for women for both disorders were significantly attenuated (Major Depressive Disorder OR: 1.20; 95% CI: 0.96–1.52) (Generalized Anxiety Disorder OR: 1.5; 95% CI: 1.04–2.29). The test for effect modification by sex and wage gap direction was statistically significant for both disorders. Structural forms of discrimination may explain mental health disparities at the population level. Beyond prohibiting overt gender discrimination, policies must be created to address embedded inequalities in procedures surrounding labor markets and compensation in the workplace.

Introduction

The prevalence of depression and anxiety is approximately twice as high among women as compared with men in the United States (Kessler et al., 2005). This marked gender disparity in these disorders begins in early adolescence and is evident throughout the entire life course (Vesga-Lopez et al., 2008). Proposed explanations for this pattern include biological factors, such as sex hormone changes during puberty (Brooks-Gunn and Warren, 1989); gender differences in psychological factors such as stress reactivity, and coping styles (Kessler et al., 1985); and environmental factors such as exposure to interpersonal violence, and child abuse, (Nolen-Hoeksema, 1990). None of these factors fully explain the disparities (Piccinelli and Wilkinson, 2000).

Ample empirical evidence and insights from social stress theory (Folkman, 1984) indicate that structural inequality and, specifically, discrimination, are important upstream determinants of the population patterning in mood disorders (Corrigan et al., 2004). Although limited (McLaughlin et al., 2011), this evidence extends to gender-specific forms of inequality and discrimination to explain gender disparities in depression (Earls, 1987). For example, unfair treatment in the workplace on the basis of gender, has been shown to account for more variance in depressive and somatic symptoms among women than standard measures of life events and daily hassles (Klonoff et al., 2000). However, large epidemiologic surveys rarely measure gender-specific, stress-inducing forms of discrimination (Pavalko et al., 2003), including sexual harassment and unequal treatment.

The United States has passed legislation to address the most overt forms of sex discrimination in the workplace (e.g. Title VII of the 1964 Civil Rights Act). While these have been somewhat effective in reducing gender discrimination in its most explicit forms (Neumark and Stock, 2001), less conspicuous forms of structural discrimination persist, in the form of social and economic devaluation and workplace marginalization and negative events (e.g., with respect to promotion, task allocation). Empirical tests of whether such factors explain gendered health disparities can be methodologically challenging (Link and Phelan, 2001, Meyer, 2003), because culpable parties or polices are often ambiguous (Klonoff et al., 2000), negative events may not necessarily be perceived as discriminatory, or such forms of structural discrimination are social processes that may not be reducible to a single event at all (Krieger, 2014).

Nevertheless, because structural discrimination can result in gender differences in status, power, opportunities, and resources (Bird and Rieker, 2008) it merits greater attention as an upstream driver of gendered patterns in health outcomes. In the present study, we empirically examine the gender wage gap as one form of structural gender discrimination and test the extent to which it can explain gender disparities in mood disorders in the US. The wage gap is a complex construct that reflects processes at multiple levels of social organization, including gender segregation in training and the labor market, differential penalties and rewards for “non-productive” roles (e.g., parenthood (Budig and England, 2001)), and institutional and interpersonal discrimination in the workplace. All of these have material and psychosocial implications for the risk of mood disorders (Piccinelli and Wilkinson, 2000).

The gender wage gap refers to the persistent disparity in income that women receive for their labor relative to men. In 1963, a woman made 59 cents for every dollar paid to her male counterpart. By 2013 it was 82 cents on the dollar (BLS, 2014). Even as the wage gap appears to narrow, there are several robust trends that persist.

First, the gap increases as a woman's absolute wage increases. In 2014, the 95th percentile of women made 79 cents for every $1 earned by men in the 95th percentile, while women in the lowest 10th percentile made 91 cents for each $1 earned by their male counterparts (Davis, 2015). Second, the gap increases as a woman's level of education increases (Vincent-Lancrin, 2008), even as women are increasingly overrepresented among Americans with higher education (Goldin et al., 2006). Finally, men earn more than women even in traditionally female occupations: for example, male registered nurses have historically out-earned female registered nurses by an average of $5100 per year across most specialties (Muench et al., 2015).

While a complete review of different theories explaining the gender wage gap is beyond the scope of this study, it is important to note that such theories focus on both structural- and individual-level explanations (Altonji and Blank, 1999, Goldin, 1990). Structural theories emphasize that gender relations are one of the key social processes that fundamentally shape wages by influencing the relative power of employers and workers and the balance of power between labor and capital (Marx, 1865/1996). Gender relations affect both the bargaining power differentials between gender groups and cultural norms and values concerning the relative worth of female and male labor power (Beechey, 1977, Figart et al., 2005). Feminist economists have divided the function of gender in the wage setting process into three elements: factors that determine intra- and inter-industry wage differentials among male and female workers with similar levels of skill and education; factors that determine the occupational wage structure within industries; and factors that determine deviations of individual wages from average wages within occupations (Karamessini and Ioakimoglou, 2007).

Individual-level theories have focused on the latter of these three elements, separating the part of the wage gap that can be explained by gender segregation across industries and occupations from the differences in individual characteristics of workers, such as accumulated skills, knowledge, and other traits conducive to productivity (Sweetland, 1996). Indicators of these characteristics include education, years of work experience, geographic region, occupation, and industry (Sweetland, 1996). As a way to explain the wage gap as a function of observed gender differences, economists regress income on these individual factors and quantify the income differential in unadjusted and adjusted estimates. Previous research has attributed between 20 and 75% of the income gap to individual-level characteristics using these methods, depending on the age, population subset, and quality/availability of variables (e.g., Blau and Kahn, 2007, Wood et al., 1993). However, a focus on individual measures of productivity minimizes the structural causes of the wage differences, such as labor structure (Bowles and Gintis, 1975).

Recognizing the importance of structural-, institutional-, and individual-level influences on the wage-setting process, researchers have sought ways to quantify gender differences. Oaxaca and Blinder described a counterfactual decomposition technique to study mean outcome differences between groups (Blinder, 1973, Oaxaca, 1973), which has frequently been applied to the gender wage gap (e.g., Bertrand and Hallock, 2001, Blau and Kahn, 2007). Their model is a linear regression comparing the difference in male and female wages as a function of two additive components. First, the explained part of the wage gap is due to group differences in the predictors, assuming that the counterfactual female gap has the same slope and intercept of males. The residual wage gap that is unaccounted for by individual measures is the difference between what women should earn, given they are compensated for their productivity and potential for advancement, and their actual wage. This difference in these amounts is usually attributed to gender discrimination. Linear models used to estimate the residual wage gap can be found in Supplementary Table 2.

This residual wage gap is understood as the result of two types of non-mutually exclusive discriminatory processes: the distribution effect, which is the structuring of women into certain occupations, which are then implicitly viewed as secondary or inferior to those occupied by men (Bergman, 1974), and the undervaluation effect, which is the process by which women are paid less for the same work or work of equal value, driven by the lower valuation of the jobs that women do within all fields (Horrell et al., 1989).

In sum, there are robust and long-standing gender disparities in depression and anxiety disorders, in addition to persistent gender disparities in wages. Although the latter are somewhat reflective of differential individual-level attainment of characteristics conducive to productivity, a large proportion of this gap remains after accounting for these factors and is likely the result of discriminatory processes operating at structural, institutional, and individual levels. This ‘unexplained’ portion appears to be increasing (Blau and Kahn, 2007). We believe the macro-social processes of gender segregation of employment are an equally or more powerful determinant of gender earnings differentials than strict productivity-related personal attributes. The aim of this study is to quantify and understand how the wage gap may function as a risk factor for disparities in mood and anxiety disorders, guided by the following two goals. First, we seek to measure the extent of the wage gap, after accounting for gender differences in individual-level productivity indicators. Second, assuming that we find a wage gap residual, we seek to leverage that residual to explain the gender disparity in depression and anxiety disorders. Our goal is to estimate the effect of gender discrimination, as reflected through income differences, on mood disorders. To achieve these goals, we employ propensity score methods to construct a sample of men and women comparable on observed individual-level characteristics that influence wages (e.g. education, occupation, age, children in the home, marital status), and test a potential mechanism to explain why women experience greater depressive and anxiety disorders than men among a nationally-representative sample of adults in the US.