Does the “Glass Escalator” Compensate for the Devaluation of Care Work Occupations?

Abstract

Feminized care work occupations have traditionally paid lower wages compared to non–care work occupations when controlling for human capital. However, when men enter feminized occupations, they often experience a “glass escalator,” leading to higher wages and career mobility as compared to their female counterparts. In this study, we examine whether men experience a “wage penalty” for performing care work in today’s economy, or whether the glass escalator helps to mitigate the devaluation of care work occupations. Using data from the Survey of Income and Program Participation for the years 1996-2011, we examine the career patterns of low- and middle-skill men in health care occupations. We found that men in occupations that provide the most hands-on direct care did experience lower earnings compared to men in other occupations after controlling for demographic characteristics. However, men in more technical allied health occupations did not have significantly lower earnings, suggesting that these occupations may be part of the glass escalator for men in the health care sector. Minority men were significantly more likely than white men to be in direct care occupations, but not in frontline allied health occupations. Male direct care workers were less likely to transition to unemployment compared to men in other occupations.

Keywords

Low-wage work Social mobility New economy Health care Feminized occupations

The devaluation of feminized care work occupations is well established. Those who work in occupations where they care for other people experience lower earnings as compared with workers of similar skill and education in non–care work occupations (Duffy 2011; England 2005; England, Budig, and Folbre 2002). However, care work jobs are largely filled by women (often as high as 90 percent), and less is known about how men fare in these kinds of female-dominated occupations, particularly in an economy that offers fewer job alternatives for many men (Gatta and Roos 2005; Price-Glynn and Rakovski 2012). We use the term “care work” to refer to occupations in which workers provide face-to-face services that contribute to the physical, mental, social, or emotional well-being of others (Duffy, Albelda, and Hammonds 2013; Duffy, Armenia, and Stacey 2015). As a case study for examining the careers of low- and middle-skill men in care work occupations, we use care work occupations in the health care sector that typically do not require a four-year college degree, which we refer to as frontline health care occupations.

Our expectations for men’s careers in female-dominated health care occupations are guided by two sociological perspectives. On one hand, a large body of research has shown that workers in female-dominated occupations earn less in comparison to occupations that do not have a high percentage of female workers, even when controlling for skill level and education (Budig 2002; Cohen and Huffman 2003a, 2003b; Gauchat, Kelly, and Wallace 2012; Reskin 1988). Further, as described above, there is substantial evidence that workers also experience a “wage penalty” for working in care work occupations (England, Budig, and Folbre 2002; Findlay, Findlay, and Stewart 2009; Palmer and Eveline 2012). On the other hand, another body of research has focused on the “glass escalator” effect and has shown that men in female-dominated professions, which include most care work occupations, tend to have higher wages and wage growth as compared to their female counterparts (Baughman and Smith 2011; Price-Glynn and Rakovski 2012; Ribas, Dill, and Cohen 2012). Men are said to ride the glass escalator in pink-collar occupations because of their higher likelihood of being promoted, or because they locate themselves in specialties that have higher compensation or prestige (Snyder and Green 2008; C.L. Williams 1992). Thus, the glass escalator may help to compensate for the devaluation of care work in terms of men’s wages in frontline health care occupations. In this study, we ask whether men working in low- and middle-skill caring occupations experience a penalty in earnings compared to men in other occupations, or whether the benefits of the glass escalator help to mitigate the devaluation of care work in today’s economy. We also explore the characteristics of men who enter frontline health care occupations; we give special attention to racial minorities, who face additional disadvantages in the labor market and may be more likely to be “pushed” into pink-collar occupations because of a lack of alternative job options (Gatta and Roos 2005; S.L. Williams and Villemez 1993). Finally, we examine the job stability of frontline health care workers as compared to men in other occupations. In today’s economy, where layoffs and periods of unemployment are common (Autor 2010), we ask whether frontline health care occupations provide a higher degree of job security for low- and middle-skill men.

The New Economy and Frontline Health Care Occupations

Since the 1970s, there has been a decline in many traditionally male-dominated jobs for low- and middle-skill workers. Low-skill jobs are those that require a high school degree or less, while “middle-skill” jobs are those that generally require some education and training beyond high school but less than a four-year college degree (Holzer and Lerman 2009). Middle-skill job training requirements can include associate’s degrees, vocational certificates, significant on-the-job training, previous work experience, or generally “some college” less than a bachelor’s degree (Holzer and Lerman 2009). Manufacturing and production jobs—traditionally the highest paid jobs for low- and middle-skill men—have been lost over the last several decades (Kalleberg 2011; Morris and Western 1999; Osterman 2000); indeed, the percentage of Americans employed in manufacturing jobs has dropped by half since the 1970s (Holzer and Lerman 2009). The shift in the economy away from manufacturing and toward the service sector, which is sometimes referred to as the “New Economy,” has been especially difficult for men with lower levels of education, who have seen their real wages drop by as much as 15-20 percent over the last 30 years (Mishel et al. 2012). Low- and middle-skill men who are racial/ethnic minorities are particularly vulnerable in today’s labor market. As compared to non-Hispanic white low- and middle-skill workers, they are disproportionately likely to be low-wage workers, to experience low wage growth during their careers, or to become unemployed (Acs, Loprest, and Ratcliffe 2010).

As low- and middle-skill workers have lost jobs in traditionally male-dominated occupations, female-dominated, frontline health care jobs have increased (Dwyer 2013; U.S. Department of Labor, Bureau of Labor Statistics 2012). In this study, frontline health care occupations are defined as low- or middle-skill occupations in health care settings that require a high degree of face-to-face interaction with patients (Schindel et al. 2006). These occupations include nursing assistants, home health aides, respiratory therapists, and surgical technicians, and they are among the fastest growing jobs in the United States (U.S. Department of Labor, Bureau of Labor Statistics 2012). This has been shown to be due to the demands of chronic illness in an expanding, aging population (Bodenheimer, Chen, and Bennett 2009), the benefits to families and the elderly gained through the New Deal (Jacoby 2001), and women’s participation in the paid workforce (Thistle 2006). Women typically fill between 70 and 90 percent of frontline health care jobs (Schindel et al. 2006), but as men face fewer job alternatives, there is some evidence that more men are pursuing jobs in the health care sector that have traditionally been filled by women (Henderson 2012).

The Devaluation of Feminized Occupations and Care Work

Occupations with a higher percentage of women, which include frontline health care occupations, pay less than occupations with a lower percentage of women, even after adjusting for measurable differences in education requirements, skill levels, and working conditions (Cohen and Huffman 2003a, 2003b; England, Budig, and Folbre 2002). Research on the relationship between an occupation’s female share and worker compensation generally supports a devaluation view of the work performed by women (England, Allison, and Wu 2007; Karlin, England, and Ross 2002; Levanon, England, and Allison 2009). A devaluation view assumes that gendered cultural beliefs portray women’s work as being less competent and status-worthy (Ridgeway and England 2007). Consequently, this view assumes that female-dominated occupations will be devalued by both employers and prospective employees because of the lower status of female employees, and that pay will be lower predominately because women fill jobs in these occupations. Care work occupations are typically female-dominated occupations, so it is not surprising that care work occupations are devalued when controlling for skill level and education. However, research has shown that workers in care work occupations experience an even greater “wage penalty,” since jobs involving care work pay less than other feminized jobs of similar skill levels that do not involve care (England, Budig, and Folbre 2002). Some researchers argue that because caring labor jobs are assumed to require altruism, compassion, and kindness—qualities that are thought to be embodied by women—the skills needed to perform these jobs are often overlooked (Duffy 2005). In other words, we tend to equate care with “kindness” and fail to recognize the skill dimensions required of care work occupations (England, Budig, and Folbre 2002; Cotter, Hermsen, and Vanneman 2004). Scholars have also argued that care work is devalued because of cultural assumptions that workers should provide these services because of “love,” not “money.” Thus, paid care workers are implicitly expected to show their proper motivation by accepting lower wages (England and Fobre 1999; Folbre and Nelson 2000).

Past research has shown that men typically enter female-dominated occupations only when jobs in traditionally male or integrated occupations or industries are unavailable (Simpson 2005; C.L. Williams 1993), and this is particularly the case when those occupations are most associated with quintessential “women’s work” (Gauchat, Kelly, and Wallace 2012; Jacobs 1993). The low wages in female-dominated care work occupations and the stigma attached to doing “women’s work” may make these jobs unattractive to men, and the majority of men in female-dominated occupations are not there by choice (Lupton 2000). Indeed, past research has found that male workers with labor market disadvantages—namely racial and ethnic minorities, those who are nonnative born and nonnative English speakers, and those with lower socio-economic status—are more likely to be in female-dominated occupations (Gatta and Roos 2005; S.L. Williams and Villemez 1993).

The Glass Escalator

Men may be reluctant to go into feminized occupations because of lower pay and prestige, but research has shown that men in female-dominated occupations have higher wages and are more likely to be promoted compared to their female peers (Budig 2002; Snyder and Green 2008). Normative understandings and practices associated with masculinity often underscore men’s cultural, discursive, and material advantages (Connell and Messerschmidt 2005). Consequently, men who cross the gender line into pink-collar jobs face less opposition than women crossing into male-dominated occupations (Pierce 1995; Taylor 2010). Once inside, men profit from the expectations of masculinity by riding the glass escalator to better pay, promotion, and enhanced social support (Budig 2002; Snyder and Green 2008; Taylor 2010; C.L. Williams 1992). Christine Williams’s (1992, 1995) research on the career paths of men in feminized occupations suggests that men benefit from being in a work world that rewards stereotypically masculine attributes, regardless of the gender composition of the occupation. Williams and others argue that cultural beliefs about masculinity and femininity are built into the structure of the work world across all settings, and these beliefs serve to disadvantage women and enhance men’s opportunities (Budig 2002; C.L. Williams 1992). Consequently, men are “pushed” in ways often subtle and invisible toward positions that carry greater wages, prestige, and authority (Henson and Rogers 2001; Snyder and Green 2008; C.L. Williams 1992).

There is some evidence of a glass escalator for men in lower level health care occupations. Price-Glynn and Rakovski (2012) found that white male nursing assistants experienced some advantages compared to women, such as working in nonprofit and smaller facilities that typically have higher pay and better working conditions. Men also had higher household incomes and were less likely to rely on government assistance (Price-Glynn and Rakovski 2012). These studies suggest that gender norms have changed enough to accommodate men doing women’s work, but men still experience privilege and may be cushioned from the wage penalty experienced by women performing the same work. However, not all men may be experiencing the same advantages. Studies of wages among frontline health care workers suggest that citizenship and race may be more important factors than gender in determining advantages for workers (Rakovski and Price-Glynn 2010; Ribas, Dill, and Cohen 2013). Wingfield (2009) found that Black male nurses experienced stereotypes from both clients and supervisors that limited their opportunities for promotion. Indeed, in a commentary on the theoretical development of the glass escalator, Williams (2013) concurs that the glass escalator does not operate uniformly across all occupations, or for all racial, ethnic, or immigrant groups.

Beyond Compensation: Job Stability in Today’s Economy

In addition to the earnings of care workers, we are interested in job stability as a measure of job quality. Male frontline health care workers may feel stigmatized in these low-status, female-dominated occupations and exit quickly for other occupations or unemployment (Simpson 2004, 2005). Further, low wages and heavy workloads have been shown to contribute to high turnover in this population of workers (Dill, Morgan, and Marshall 2013). However, frontline health care jobs may provide greater job stability for men in today’s economy because of instability in many male-dominated occupations. In previous generations, many blue-collar occupations were characterized by rewards for tenure and seniority, which created incentives for workers to remain in their jobs and with their employers (e.g., working in a unionized automotive factory) (Doeringer and Piore 1971). In today’s economy, manufacturing and production jobs have declined, and many low- and middle-skill men have been at increased risk of being laid off (Holzer and Lerman 2009).

In this study, we explore the characteristics and careers of men in frontline health care occupations, or low- and middle-skill jobs in the health care sector that require a high degree of patient interaction. We examine who enters frontline health care occupations and whether the advantages that men have traditionally experienced in female-dominated occupations (e.g., the glass escalator) compensate for the general devaluation of care work. Using longitudinal data from the Survey of Income and Program Participation, there are three main questions we seek to answer in this analysis.

First, what are the odds of being a male frontline health care work, net of (i.e., after regression-based statistical adjustments for) personal characteristics? We give special attention to racial and ethnic minorities, who may be more disadvantaged in the labor market and consequently may be more likely to be “pushed” into care work occupations. We expect to find that men who are racial minorities will be more likely to be frontline health care workers than white men. Second, what is the earnings effect of working in a frontline healthcare job? If the glass escalator mitigates the devaluation of care work occupations, we expect to find that men in frontline health care occupations have earnings that are not significantly lower than men in other occupations, net of personal characteristics. In our analyses of earnings, we look closely at racial/ethnic groups to see if the glass escalator has different effects for these groups. And third, what are the odds of transitions to unemployment for frontline health care workers as compared to the general male workforce? If frontline health care occupations provide greater stability in the labor market for low- and middle-skill men, we expect to find that frontline health care workers are less likely to transition to unemployment as compared to men in other occupations.

Methods

The data used in this analysis come from the 1996, 2001, 2004, and 2008 panels of the Survey of Income and Program Participation (SIPP), administered by the U.S. Census Bureau. The SIPP universe includes the noninstitutionalized resident population living in the United States. Individuals were interviewed either by phone or in person every four months over the survey period; each interview is called a wave. The panels cover three to four years and include between eight and 12 waves of data. All data used in this study were made available by the Center for Economic and Policy Research (CEPR 2012). The strengths of SIPP include its longitudinal design, detailed three- to four-year information on individuals, its greater attention to Hispanic and immigrant populations relative to other surveys (McKernan and Ratcliffe 2002), and its unique identification of respondents’ employers. Our sample includes all male respondents between the ages of 18 and 65 in all four (1996, 2001, 2004, and 2008) SIPP Panels. The pooled sample includes 148,272 individuals and 1,040,990 observations. The respondents completed 5.04 survey waves on average. We use criteria identified by Schindel et al. (2006) to create a subsample of frontline health care workers in the SIPP. The subsample includes respondents who were (1) employed in a health care setting, (2) in an occupation that requires hands-on patient care, and (3) in an occupation that typically requires an associate’s degree or less. A list of the occupations categorized as frontline health care occupations is included in the appendix. The frontline worker sample includes 883 individuals and 6,433 observations. Frontline health care respondents completed 5.03 survey waves on average.

Dependent Variables

In this study, we include analyses of three dependent variables. First, we examine the likelihood of a male respondent being a frontline health care worker. We distinguish between two groups of frontline health care workers based on the level of training required to work in an occupation. Direct care occupations typically require a limited amount of post–high school training or a vocational certificate and include workers such as nursing assistants, patient care technicians, and home health workers. Frontline allied health occupations usually require an associate’s degree or equivalent training and include workers such as respiratory therapists, ultrasound technicians, and surgical technicians. Second, we use the natural log of monthly earnings to analyze the earning effects of being a frontline health care worker. With this measure, regression coefficients can be interpreted approximately as percentage differences in earnings. All earnings are inflation-adjusted to 2011 dollars (the last year of data collection). Third, to analyze job stability, we constructed a categorical variable that indicates the survey wave in which an individual transitioned to unemployment. Individuals who were unemployed may be so voluntarily or involuntarily.

Independent Variables

In our analyses, we included a variety of work-specific variables. In some models, whether a worker is a direct care worker or frontline allied worker (see above) is an independent variable. For workers that were not in frontline health care occupations, we also included occupational categories based on the U.S. Census 2000 Major Occupational Groups.¹ Occupational dummy variables include (1) Management, business and financial; (2) Professional; (3) Service; (4) Sales, Office and administrative support; (5) Farming, fishing and forestry; (6) Construction and extraction; (7) Installation, maintenance, and repair; (8) Production; (9) Transportation and material moving; and (10) Armed Forces. Production is the reference category.² We controlled for union membership and full-time status (more than 35 hours per week for all weeks during the month) with dummy indicator variables. We constructed a variable of job tenure, which indicates the number of months an individual has worked for their current employer. In the analyses, we divide tenure by 100 to keep the range consistent with other variables.

Demographic variables in the analysis include educational attainment level, which was coded into four categories: less than high school, high school graduate, some college, or a college degree or higher. These were included as dummy variables in the models with high school degree as the reference category. Race/ethnicity is divided into four categories: white, Black, Hispanic, or other, with white as the reference category. We also included interaction terms between race/ethnicity and frontline health care occupational categories. Additional demographic variables included in the analyses were marital status (married = 1, else = 0), caring for children under the age of 18 (1 = yes, 0 = no), and age (ranged 15-65). Four categories indicated region: Northeast (the reference category), Midwest, South, and West. Finally, we included dummy variables that indicate the year of data collection. The work-related and demographic variables included in our analyses have been linked to earnings and career transitions in past studies of low-level health care workers (Dill, Morgan, and Marshall 2013; Price-Glynn and Rakovski 2012; Ribas, Dill, and Cohen 2013). A summary of the sample characteristics is shown in Table 1.

Table 1:

Summary Statistics.

	Frontline Workers 1996	Frontline Workers 2008	Male Workforce 1996	Male Workforce 2008	Full Sample
Variable	Mean or Percent	Mean or Percent	Mean or Percent	Mean or Percent	Min	Max
Occupation
Direct care	60%	61%			0	1
Frontline allied health	40%	39%			0	1
Management, business, and financial			9%	9%	0	1
Professional			11%	12%	0	1
Service			9%	11%	0	1
Sales			8%	7%	0	1
Office and administrative support			5%	5%	0	1
Farming, fishing, and forestry			3%	1%	0	1
Construction and extraction			5%	8%	0	1
Installation, maintenance, and repair			5%	5%	0	1
Production			10%	7%	0	1
Transportation and material moving			13%	8%	0	1
Armed Forces			1%	1%	0	1
Work-related characteristics
Earnings (adjusted for inflation)	$2,815*	$3,562*	$3,336	$4,073	0	$38,610
Union member	14%	13%	16%	14%	0	1
Full-time worker (35+ hours per week)	77%**	77%***	66%	61%	0	1
Tenure (in months, divided by 100)	0.58*	0.59***	0.79	0.87	0	5.9
Education
Less than high school degree	6%***	3%***	20%	16%	0	1
High school degree	29%	18%*	34%	27%	0	1
Some college	45%***	54%***	26%	32%	0	1
College degree	20%	24%	20%	24%	0	1
Personal characteristics
Married	46%*	50%	53%	52%	0	1
Kids under 18	30%*	35%	37%	35%	0	1
Age	33**	38	37	39	18	65
Race
White	60%***	67%	73%	68%	0	1
Black	24%***	16%*	11%	11%	0	1
Hispanic	10%	8%*	11%	13%	0	1
Other minority	6%	8%	4%	8%	0	1
Region
Northeast	24%**	18%	18%	18%	0	1
South	28%*	31%	35%	35%	0	1
Midwest	23%	27%	24%	23%	0	1
West	25%	23%	23%	24%	0	1
Number of observations^a	204	261	32,228	32,812

NOTE: Mean values include the first observation for each respondent in the 1996 and 2008 cohorts of the Survey of Income and Program Participation.

The number of observations varies for some variables. t-tests were conducted to test whether frontline health care workers were significantly different from the general male workforce in the corresponding cohort.

***

p < 0.01, **p < 0.05, *p < 0.1.

Likelihood of Being a Frontline Health Care Worker

Logistic regression models were used to analyze the odds of a male being in a frontline direct care or frontline allied health occupation, which are binary variables. The data used for this analysis are pooled cross-sectional observations; we use the first survey observation for respondents in all four SIPP panels.

Earnings

Linear regression models were used to analyze the natural log of inflation-adjusted monthly earnings. The data used for this analysis are pooled cross-sectional observations; we use the first survey observation for respondents in all four SIPP panels. We limit this analysis to full-time workers only, or workers who worked at least 35 hours per week during all weeks of the month. As described above, with this measure, regression coefficients can be interpreted approximately as percentage differences in earnings.

Transitions to Unemployment

As a proxy of job stability, we examined transitions to unemployment during the survey period as the dependent variable. The analysis uses longitudinal data and includes all of the observations where an individual is working during the survey period and the first quarter of their transition away from their job (if a transition did occur). We used fixed effects logit models to examine patterns of employment transitions among frontline health care workers (Singer and Willett 2003). In this analysis, the data were restricted to observations when an individual was observed working during the survey period, and their immediate occupational transition observations. Specification tests confirmed that multicollinearity was not a concern for any of the models; VIF scores were below 2 for all models. All statistical analyses were conducted using Stata 13.

Sample Characteristics

Table 1 shows summary statistics for male frontline workers and the general male workforce. The summary statistics are drawn from the first observation of respondents in the 1996 and 2008 cohorts. Men in frontline health care occupations and the general male workforce in the sample were about 37 years old; about 50 percent were married, and around 35 percent had children under the age of 18. In the 1996 cohort, there was a higher percentage of minority men employed as frontline health care workers as compared to the general male workforce; for example, 24 percent of frontline health care workers were Black, as compared to 11 percent of workers in the general male workforce. However, in the 2008 cohort, a higher percentage of frontline health care workers were white, and the percentage of minorities in frontline health care occupations more closely resembled the general male workforce. Rates of unionization were similar across all workers (around 15 percent), but men who were not in frontline health care occupations had longer job tenure in their current job (77 months on average) as compared to frontline health care workers (87 months as compared to 59 months in 2008, respectively). Finally, frontline health care workers had on average higher education compared to the general male workforce. In 2008, 54 percent of frontline health care workers had some college, compared to just 32 percent of the general male workforce. However, the percentage of college graduates was the same (28 percent). The distribution of workers across the U.S. Census Major Occupational Groups and other sample characteristics are shown in Table 1.

Figure 1 shows mean inflation-adjusted monthly earnings for full-time workers in a few highlighted occupational groups. We chose to highlight earnings for workers in occupational groups that contain many jobs comparable to frontline health care work in terms of skill and education. Figure 1 shows that mean earnings for frontline allied health workers, or workers in health care that provide patient care and typically have an associate’s degree or equivalent, had higher average earnings as compared to workers in service, office and administration, construction, and production occupations. Further, workers in frontline allied health and direct care occupations had monthly earnings that consistently rose between 1996 and 2008, while the other highlighted occupations had monthly earnings that stagnated or declined over the same period.

Figure 1:

Mean inflation-adjusted monthly earnings by highlighted occupational categories.

Men in Frontline Health Care Occupations

The results of logistic regression models predict being a male frontline health care worker (shown in Table 2). The pooled cross-sectional sample includes the first observation for each respondent in the 1996, 2001, 2004, and 2008 cohorts of the SIPP. Black men were 3.3 times more likely than white men to be direct care workers (p < 0.001), and other minority men were 1.8 times more likely to be direct care workers (p < 0.001). Men who have more education—those with some college or a college degree—were significantly more likely to be both direct care workers and frontline allied health workers as compared to those with a high school degree (p < 0.001), while those with less than a high school degree were significantly less likely to work in these occupations (p < 0.05). Last, men in the 2004 and 2008 cohorts were significantly more likely to be direct care workers as compared to the 1996 cohorts (p<.001).

Table 2:

Pooled Cross-Sectional Model of the Odds of Being a Male Frontline Health Care Worker.

	Direct Care Worker		Frontline Allied Health
	Odds Ratio	(SE)	Odds Ratio	(SE)
Personal characteristics
Married	0.98	(0.098)	1.00	(0.141)
Kids	0.85*	(0.081)	1.02	(0.132)
Age	1.00	(0.004)	0.99	(0.006)
Race
White	Reference		Reference
Black	3.30***	(0.340)	0.89	(0.180)
Hispanic	1.35**	(0.195)	1.01	(0.201)
Other race	1.79***	(0.261)	0.97	(0.225)
Education
Less than high school	0.43***	(0.078)	0.45*	(0.189)
High school degree	Reference		Reference
Some college	1.34***	(0.134)	6.08***	(1.163)
College degree	1.34***	(0.150)	3.29***	(0.695)
Work-related characteristics
Union	0.81*	(0.101)	0.81	(0.143)
Full-time	0.82*	(0.082)	1.51**	(0.253)
Tenure/100	0.82***	(0.045)	0.84**	(0.064)
Region
South	0.57***	(0.066)	1.30	(0.232)
Midwest	0.92	(0.106)	1.28	(0.240)
West	0.72***	(0.089)	1.34	(0.252)
Cohort
1996 cohort	Reference		Reference
2001 cohort	0.84	(0.115)	0.73*	(0.131)
2004 cohort	1.43***	(0.164)	0.94	(0.146)
2008 cohort	1.58***	(0.183)	1.21	(0.184)
Constant	0.01***	(0.001)	0.00***	(0.000)
Observations	100,387		100,387

SOURCE: Survey of Income and Program Participation (SIPP).

NOTE: Sample includes the first observation for each respondent in the 1996, 2001, 2004, and 2008 cohorts of the SIPP. SE = standard error.

***

p < 0.01, **p < 0.05, *p < 0.1.

Earnings Effect of Frontline Health Care Work

The results of the linear regression models predict inflation-adjusted earnings among full-time employees (shown in Table 3). Model 1 includes interaction terms between race and frontline health care occupations. The model indicates that white men in direct care occupations earned 21 percent less per month, net of personal characteristics. Black men in direct care occupations experienced a similar penalty (22 percent), Hispanics had a penalty of 4 percent, and other minorities earned 61 percent less, net of personal characteristics. However, the interaction terms were not statistically significant, suggesting that racial minorities in frontline health care occupations did not experience significantly more or less disadvantage in earnings compared to minority men in other occupations. Model 2 includes occupational categories. The reference group for all occupational categories was production. Model 2 indicates that direct care workers had monthly earnings that were 10 percent lower than workers in production occupations (p < .05), but frontline allied health workers had earnings that were 22 percent higher than production workers (p < .001).

Table 3:

Linear Regression Model of the Effect of Frontline Health Care Work on Logged Inflation-Adjusted Monthly Earnings.

	Model 1		Model 2
	Coefficient	(SE)	Coefficient	(SE)
Frontline health care occupations
Direct care	−0.21***	(0.076)	−0.10*	(0.058)
Allied health	0.11	(0.086)	0.22***	(0.075)
Personal characteristics
Married	0.17***	(0.011)	0.15***	(0.011)
Kids	0.10***	(0.010)	0.09***	(0.010)
Age	0.01***	(0.000)	0.01***	(0.000)
Work-related characteristics
Union	0.12***	(0.012)	0.18***	(0.013)
Tenure/100	0.15***	(0.005)	0.13***	(0.005)
Education
Less than high school	−0.26***	(0.016)	−0.22***	(0.016)
High school degree	Reference		Reference
Some college	0.15***	(0.011)	0.10***	(0.011)
College degree	0.61***	(0.012)	0.43***	(0.014)
Race
White	Reference		Reference
Black	−0.19***	(0.016)	−0.15***	(0.015)
Hispanic	−0.14***	(0.015)	−0.10***	(0.015)
Other race	−0.08***	(0.020)	−0.06***	(0.019)
Black × Direct care work	0.18	(0.138)
Black × Allied health	0.06	(0.263)
Hispanic × Direct care work	0.31	(0.192)
Hispanic × Allied health	0.21	(0.263)
Other minority × Direct care work	−0.32	(0.194)
Other minority × Allied health	0.04	(0.291)
Occupation
Management, business, and financial			0.37***	(0.018)
Professional			0.24***	(0.018)
Service			−0.22***	(0.018)
Sales			0.07***	(0.020)
Office and administrative support			−0.06***	(0.021)
Farming, fishing, and forestry			−0.24***	(0.035)
Construction and extraction			0.03	(0.019)
Installation, maintenance, and repair			0.12***	(0.021)
Production			Reference
Transportation and material moving			−0.11***	(0.018)
Armed Forces			0.19***	(0.044)
Region
Northeast	Reference		Reference
South	−0.08***	(0.013)	−0.08***	(0.013)
Midwest	−0.08***	(0.014)	−0.07***	(0.014)
West	0.01	(0.014)	0.00	(0.014)
Cohort
1996 cohort	Reference		Reference
2001 cohort	0.11***	(0.017)	0.09***	(0.017)
2004 cohort	0.15***	(0.017)	0.14***	(0.017)
2008 cohort	0.16***	(0.017)	0.15***	(0.017)
Constant	7.18***	(0.023)	7.21***	(0.026)
Observations	80,916		80,916
R-squared	0.116		0.130

SOURCE: Survey of Income and Program Participation (SIPP).

NOTE: Sample includes the first observation for each respondent in the 1996, 2001, 2004, and 2008 cohorts of the SIPP. SE = standard error.

***

p < 0.01, **p < 0.05, *p < 0.1.

Job Stability among Frontline Health Care Workers

The fixed effects logit model predicts worker transitions to unemployment (Table 4). All control variables described in the measurement section were included in the model, but some are not shown in Table 4. Recall that this analysis used the truncated sample—each individual’s observations working, and the first observation after they leave their jobs. The model indicates that direct care workers were less likely to transition to unemployment compared to the general male workforce (p < .05), when controlling for personal demographics.

Table 4:

Predictors of Transitioning to Unemployment.

	Odds Ratio	(SE)
Frontline health care occupations
Direct care	0.68*	(0.13)
Allied health	0.93	(0.35)
Personal characteristics
Married	0.87	(0.07)
Kids	1.02	(0.06)
Age	0.88***	(0.02)
Race
White	Reference
Black	0.37	(0.29)
Hispanic	0.48	(0.31)
Other race	1.98	(1.42)
Education
Less than high school	1.33***	(0.10)
High school degree	Reference
Some college	0.96	(0.07)
College degree	0.47	(0.07)
Work-related characteristics
Union	0.85*	(0.06)
Tenure/100	0.24***	(0.01)

SOURCE: Survey of Income and Program Participation (SIPP).

NOTE: Base outcome is no employment change. All control variables described in the measurement section are included in the model but not shown in Table 4. SE = standard error.

p < 0.05, **p < 0.01, ***p < 0.001.

Discussion

A goal of this study was to explore whether the glass escalator that men experience in female-dominated occupations would compensate for the “wage penalty” that workers experience in care work occupations. Past research has shown that jobs involving care work are devalued compared to other jobs of similar skill levels that do not involve care (England, Budig, and Folbre 2002), but the advantages that men experience in female-dominated occupations may boost their earnings and job stability (Snyder and Green 2008; C.L. Williams 2013). On one hand, our study tells a story about men in frontline health care occupations that is consistent with past research on the devaluation of care work (Duffy 2011; England 2005; England, Budig, and Folbre 2002). When we look at men who are direct care workers, or those care workers who provide a high level of nurturant care for patients (Duffy 2011), we find that men across all racial/ethnic groups experience a “wage penalty” as compared to the general male workforce, net of personal characteristics including education and job tenure. When we included occupational control variables for all workers with production occupations as the reference group, we found that direct care workers earned 10 percent less than their blue-collar counterparts. This suggests that even in today’s economy, where manufacturing jobs have declined in availability and job quality, men in direct care occupations still experience a substantial “wage penalty” for working in a feminized care work occupation, when controlling for personal demographics. These findings indicate that the glass escalator does not mitigate the lower wages in direct care occupations that are strongly associated with “women’s work.”

Our findings are also consistent with past research that has shown that men are reluctant to enter care work occupations, particularly those that are strongly stigmatized as “women’s work,” and that men in these occupations are not there by choice (Gauchat, Kelly, and Wallace 2012; Jacobs 1993; Lupton 2000). We find evidence that higher rates of racial/ethnic minorities work in female-dominated occupations, presumably because they face greater disadvantages in the labor market and lack access to better paying, male-dominated occupations, which is consistent with past research (Gatta and Roos 2005; S.L. Williams and Villemez 1993). We found that African American men were three times more likely to be direct care workers as compared to white men, while Hispanic and other minority men were about one and half times more likely to be direct care workers. That white men are significantly less likely to be direct care workers reflects the lower status and corresponding devaluation of direct care work for men. Male frontline health care workers who are minorities also experience wage penalties in frontline health care occupations, although the wage penalties for minority frontline health care workers are not significantly different from the wage penalties that minority men experience in other occupations.

On the other hand, there were suggestions throughout our findings that men did experience some advantages in frontline health care occupations. First, when we look descriptively at the data, inflation-adjusted monthly earnings went up consistently over time for both direct care and frontline allied health workers, while men in service, administrative and office, construction, and production occupations experienced stagnant or declining wages in later cohorts (see Figure 1). These descriptive data suggest that earnings among male frontline health care workers have been increasing since the mid-1990s, while earnings in other occupations—particularly in male-dominated blue-collar occupations—have stagnated or decreased. Rising earnings over time for men in frontline health care occupations may help to compensate for the devaluation of care work occupations.

Second, we found that frontline allied health workers did not have earnings that were significantly lower than the general male workforce, net of characteristics such as education and job tenure. In fact, when we controlled for occupation, we found that frontline allied health workers had earnings that were significantly higher (22 percent) than workers in production occupations when controlling for demographic characteristics. These findings lend support to the idea that, at least within frontline allied health occupations, the glass escalator may help to mitigate the devaluation of care work, resulting in earnings that are not lower than other occupations when controlling for personal characteristics. The high demand for health care workers and the decline of many alternative job options traditionally held by men (e.g., manufacturing and production) may also be contributing to a narrowing of the wage gap between these industries (Bodenheimer, Chen, and Bennett 2009; Schindel et al. 2006).

Further, we found that white men were more likely to occupy the more lucrative, more skilled, frontline allied occupations compared to minority men, who were overrepresented in the direct care occupations. White men may be more attracted to frontline allied occupations because these occupations have higher educational requirements compared to other frontline occupations and, like nursing, have experienced an increase in technical skill requirements as these occupations have become more professionalized (Judd and Sitzman, 2014). The technical skills required for many of the jobs—such as using computers and diagnostic technology—means that these jobs do not require as much of the intimate patient care associated with “women’s work” and may be less stigmatized and more appealing to men (Cottingham 2013). Indeed, frontline allied health occupations may be part of the glass escalator for the careers of low- and middle-skill men in the health care sector. Third, we found that direct care workers had greater overall job stability as compared to the general male workforce in that they were less likely to transition to unemployment during the survey period. The greater job stability that we see among frontline health care workers likely reflects the greater demand for workers in the health care sector, particularly in comparison to industries that have been dominated by men, such as manufacturing. This greater job stability may contribute to the benefits that men experience in frontline health care occupations and help to compensate for the devaluation of care work occupations. Finally, we want to note that we found some evidence that more men are going into frontline health care occupations. This is a common narrative in the press about today’s labor market, arguing that men in search of work are moving into service sector jobs that have been traditionally dominated by women (Dewan and Gebeloff 2012; Rosin 2012; Vigeland 2012). In our study, we found that men were significantly more likely to be in direct care occupations in the 2004 and 2008 cohorts as compared to the 1996 cohort.

In conclusion, we find that men who are in occupations that are most strongly associated with “women’s work”—direct care work occupations—experience a “penalty for caring.” Earnings among direct care workers are lower as compared to the general male workforce after controlling for demographic characteristics. However, frontline allied health workers do not suffer from the same wage disadvantages and are, in fact, better off than many blue-collar workers. Frontline direct care workers also have greater overall job stability in that they are less likely to transition to unemployment. Consequently, while we find some evidence that the devaluation of care work is reflected in the careers of men in frontline health care occupations, there is also evidence that the advantages that men assume in the world of work (C.L. Williams 1992) and the conditions of today’s economy help to overcome the devaluation of care work occupations.

This study has a number of limitations. First, we only capture men that were currently working in frontline health care occupations; we do not account for men who had moved into management or other more lucrative occupations in the health care sector. Another concern is the potential uncontrolled endogeneity between tenure/work experience and wages. Studies on the effect of tenure on wage rates have pointed out that wage rate and tenure may be simultaneously determined by unmeasured factors, which can result in biased or inconsistent estimation (Burdett and Coles 2010). Finally, all studies of turnover and job mobility must be qualified by the unmeasured “real” reasons why some people move in and out of jobs, such as working two jobs, relocating, taking temporary leaves of absences from work, and other conditions we do not measure. Strengths of the study include the large sample size, the use of nationally representative data, and the inclusion of multiple time periods.

Future research should continue to examine shifting gender roles in the New Economy. For example, the advantages that men in frontline allied health occupations experience lead us to ask whether the health care industry is the new pathway to the middle class for low- and middle-skill men (Andersson, Holzer, and Lane 2005). Are these jobs—and the certification required—the new threshold for entry into the middle class in the New Economy? Men with a high school degree previously gained access to the middle class through male-dominated occupations that provided a lifetime career with opportunities for upward advancement (Cappelli 1999; Kalleberg 2009). Much has been written about the breakdown of labor market opportunities for this population, but our study suggests that the associate’s degree—and perhaps feminized health care occupations—may be replacing blue-collar occupations as a key path to stable earnings and careers. The presence of men in low- and middle-skill care work occupations may redefine “women’s work” as both men’s and women’s work. However, research suggests that men, particularly white men, are navigating the glass escalator into the more technical, medical areas of care work in the health field in order to reap the rewards of rising wages and stability. While women and minority men continue to be clustered in lower-paying direct care occupations, the more technical frontline allied health occupations may be culturally reinterpreted as men’s work, preserving conventional understandings of masculinity.

Footnotes

Appendix

Table 5:

Occupations Categorized as Frontline Health Care Occupations.

Frontline direct care work occupations

Medical assistants and other health care support occupations

Nursing, psychiatric, and home health assistants

Personal and home care aides

Recreation therapists

Personal care and service workers, all other

Miscellaneous community and social service specialists

Frontline allied health occupations

Diagnostic related technologists and technicians

Emergency medical technicians and paramedics

Health diagnosing and treating practitioner support technicians

Licensed practical and licensed vocational nurses

Physical therapist assistants and aides

Respiratory therapists

Author’s Note:

The authors would like to thank Arne Kalleberg and Juan Xi for their feedback on earlier drafts of this manuscript. The first author’s efforts were supported by a summer research grant funded by the University of Akron.

Notes

Janette Dill, PhD, is an Assistant Professor of Sociology at the University of Akron. She researches job quality and career mobility in today’s economy, particularly for low- and middle-skill workers. Her current research focuses on the development of career ladders in health care organizations for low-level health care workers.

Kim Price-Glynn, PhD, is Associate Professor of Sociology and Urban and Community Studies at the University of Connecticut. She was the inaugural recipient of the University of Connecticut College of Liberal Arts and Sciences Faculty Achievement Award for Excellence in Teaching in the Social Sciences. Her research examines gender, labor, health, and caregiving in diverse organizational settings, from nursing homes to strip clubs. She is the author of Strip Club: Gender, Power and Sex Work (New York University Press, 2010).

Carter Rakovski, PhD, is Associate Professor of Sociology at California State University Fullerton. She studies gender and work. She also applies statistical methods in her interdisciplinary work. Her research has been published in a range of journals including the Sociology of Health and Illness, Work, Employment and Society, Disability and Rehabilitation, and the Journal of Accounting Research.

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