Family leaves, the FMLA and gender neutrality: The intersection of race and gender

Social Science Research 35 (2006) 871–891 www.elsevier.com/locate/ssresearch

Family leaves, the FMLA and gender neutrality: The intersection of race and gender 夽 Amy Armenia a

a,¤

, Naomi Gerstel

b

Department of Sociology, Rollins College, 1000 Holt Avenue, Box 2761, Winter Park, FL 32789, USA b University of Massachusetts, Amherst, USA Available online 23 February 2005

Abstract Using nationally representative data on the employed, we assess the eVects of gender as well as the intersection of race and gender on family leave taking post-FMLA. We Wnd that White men are signiWcantly less likely to take family leaves than White women and men and women of color. Although men across race are less likely to take leaves for newborns, they are almost as likely as women to take leaves for seriously ill children and parents and as likely to take leaves for spouses. Men, regardless of race, tend to take shorter leaves than women. Our results have important implications for the design of leave policy: the broadening of family leaves beyond parental leaves reduces inequality in likelihood of leave; the introduction of leaves for routine family demands probably does little to reduce gender inequality; unpaid leaves mandated by the FMLA may sustain inequality. © 2005 Elsevier Inc. All rights reserved. Keywords: Family leaves; Job leaves; FMLA; Caregiving; Gender; Race; Children; Parents

夽 Previous version of this paper presented at American Sociological Association, August, 2000, Washington, DC. ¤ Corresponding author. E-mail address: [email protected] (A. Armenia).

0049-089X/$ - see front matter © 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.ssresearch.2004.12.002

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1. Introduction Although US government policy has included relatively few initiatives to address the tension between workplace and family demands (Gornick and Meyers, 2003; Michel, 1999), the entrance of growing numbers of women into the labor force intensiWed the pressure on politicians and employers to attend to employees’ family needs. The passage of the Family and Medical Leave Act (FMLA) of 1993 was one of the most visible results of this pressure. Moving beyond prior legislation that allowed parental leave, the FMLA provides job leaves for workers not only for maternity disability and newborns but also to care for seriously ill children, parents or spouses. It guarantees a 12 week job protected leave to employees who work for employers with 50 or more employees working within a 75-mile radius. These leaves, however, are unpaid (US Department of Labor, 1993). Gender neutrality in family leave taking was a primary goal of the FMLA, as stated by both the early sponsors of the bill (Schroeder, 1988), and the Wnal text of the legislation: It is the purpose of this Actƒ [to grant leave] in a manner that, consistent with the Equal Protection Clause of the Fourteenth Amendment, minimizes the potential for employment discrimination on the basis of sex by ensuring generally that leave is available for eligible medical reasons (including maternity-related disability) and for compelling family reasons, on a gender neutral basis; and to promote the goal of equal employment opportunity for women and men, pursuant to such clause (The Family and Medical Leave Act of 1993, p. 2). In a 2003 Supreme Court ruling, the majority opinion reiterated this initial view: “The FMLA aims to protect the right to be free from gender-based discrimination” (Greenhouse, 2003). The availability of national data from the Commission on Family Leaves makes possible an assessment of gender neutrality of family leaves in the post-FMLA era. Because we only have data from one time point—post-FMLA—we are unable to evaluate the “success” of the FMLA in terms of changes in outcomes for individuals or families. We are able to assess, however, the extent to which the diVerent types of family leaves—for babies and children, for spouses and parents—continue to vary by gender post-FMLA. Like many of the Act’s critics, we argue that any legislation that allows for only unpaid leaves cannot fully counter, and may even reproduce, the gender inequalities manifested in occupational segregation and wage disparities. (For a similar theoretical argument, see also Bergmann, 1997; Deitch and HuVman, 2001.) Equally important, we argue that treating women or men as a homogenous group cannot capture the variation in families, and their relationship to workplaces, likely to shape leaves and their length. For example, couples are more likely to be in a position to take advantage of the opportunities oVered by unpaid leaves than are those without a partner able to support them. Variation by race—and the cultural and structural conditions associated with race—also shapes the organization of both

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families and paid work (see, for example, Collins, 1998; Glenn, 2002; King, 1988; Sarkisian and Gerstel, 2004). For example, not only do we Wnd variation in health status across race that is likely to shape the need for leaves, we also Wnd signiWcant racial diVerences in family composition and household income, along with racial diVerences in the gender gap in income, that likely shape the ability and willingness to take leaves. Although there is a growing theoretical literature on the intersectionality of gender and race, very few scholars have developed empirical models to assess its operation (Browne and Misra, 2003). No one, in fact, has looked at race or the intersection of race and gender as related to family leave taking. Examining the eVects of gender and the intersection of race and gender, this paper assesses workers’ family leave taking for diVerent reasons, and the length of the diVerent kinds of leaves, in a post-FMLA era. We show that although there are important elements of gender neutrality that the FMLA introduces, this piece of legislation, like so much of US labor law, contains inadequate means to counter existing inequalities connected not simply to gender but to a gender that intersects with race.

2. Literature review 2.1. Gender, race, and family leave Little research has addressed the eVects of gender on either the propensity to take leave or the length of leave since the passage of the FMLA. Moreover, in examining who takes leaves, the literature to date has focused almost exclusively on maternity or parental leave, typically for newborns, rather than the broader conception of family leave covered by the FMLA (which covers leaves for older children, spouses, as well as parents). Most of these studies suggest that women are far more likely than men to take parental leaves (Glass and Estes, 1997; Hyde, 1995). With regard to the length of leave, Hyde et al. (1996) found that women took an average of 9 weeks (3 weeks less than that provided by the Act) of maternity leave. Importantly, Hyde et al. found that most women returned to work earlier than they might need to for two reasons: they needed the money and the leaves allowed by their employers were too short. A few studies showed that men often do take parental leaves but typically of very short duration—from 3 days to 1 week (Bond et al., 1991; Pleck, 1991). In one of the few studies to examine parental leaves for newborns after the passage of the FMLA, Han and Waldfogel (2003) Wnd no eVect of recent legislation on men’s leave taking. They Wnd some increase in leave taking and length of leave for women; however, these eVects are not signiWcant after controlling for working hours and state of residence. No study directly examines the extent to which race is related to family leave taking. Some research, however, seems to suggest that race and ethnicity would play a role in the need for and ability to take family leave. That is, African-Americans and Latinas—whether because of greater health problems (Cockerham, 1995) or greater demands from relatives (Collins, 1994; Jarrett, 1997; Lee et al., 1998; Stack and Burton, 1994)—may need leaves more than Whites. Given their fewer

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resources (Oliver and Shapiro, 1995), however, people of color may be less able to take the leaves they need. Moreover, despite the highly gendered nature of family and kin work, even studies that examine race diVerences do not examine men and women separately. Instead, they seem to at least implicitly assume that women and men are the same (for exceptions, see Roschelle, 1997 and Sarkisian and Gerstel, 2004). Given the fact that prior research ignores racial diVerences in family leave taking, none takes the next step and asks about the intersection of race and gender. This paper takes that step. Moreover, we go beyond prior research by examining the broad range of family leaves covered by the FMLA, asking whether race and gender inequalities in leave taking and length of leave vary across the diVerent types of family leave. 2.2. Other factors aVecting family leaves Prior research suggests that other factors, including family resources, life stage, and workplace characteristics, which we use in this paper as controls, are also likely to aVect the probability and length of leave taking. Empirical research to date, although limited, suggests that Wnancial resources may aVect the ability to take family leaves and the length of those leaves. Although women with less household income are more likely to perceive a need for a family leave (Gerstel and McGonagle, 1999), they take less time oV after childbirth than women from higher income households (Bond et al., 1991; Glass and Camarigg, 1992; McGovern et al., 1998). Leave taking is also shaped by an individual’s place in the family life course. Data on these factors are sparse, but there is at least some relevant research. For example, Gerstel and McGonagle (1999) Wnd that the presence of children increases the likelihood of taking a family leave. So, too, having a spouse or partner increases women’s and men’s ability and need to take family leaves (Galinsky et al., 1996; Gerstel and McGonagle, 1999; Thompson et al., 1999). A growing number of researchers are examining what types of workplaces are responsive to institutional pressures in general and to changing legislation concerning family and work in particular (see, for example, Deitch and HuVman, 2001; Goodstein, 1994; Kelly and Dobbin, 1999). A number of authors contend that relatively privileged workers—especially those working in large organizations, with salaried rather than waged jobs, or union members—are in a better position to negotiate with their employers for family leaves. Studies about employer size provide mixed results. Some Wnd that large employers are more likely to oVer leave, whether because of cost eVectiveness or greater social pressure (Deitch and HuVman, 2001; Glass and Fujimoto, 1995; Goodstein, 1994; Knoke, 1996; Osterman, 1995). In contrast, comparing national (Current Population Survey) data from the pre-FMLA (1992–1993) with the post-FMLA (1994–1995) period, Waldfogel (1999) Wnds an increase in leave taking concentrated among mothers with children under 1 year of age who worked in “medium-sized” Wrms (l00–499 employees).

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Fewer studies compare salaried with wage workers. The Family and Work Institute’s 1998 Business Work Life Study (BWLS) study of companies with more than 100 employees found that companies with more hourly wage workers (rather than salaried workers) were less likely to oVer leave to care for mildly ill children (Galinsky, 2001). Contradictory evidence exists with regard to unionization (for review see Gerstel and Clawson, 2001). Whereas Galinsky (2001) and Glass and Fujimoto (1995) found that unionized workplaces were more likely to provide paternity and maternity leaves, neither Osterman (1995), Knoke (1996), nor Deitch and HuVman (2001) found any signiWcant eVect for unionization on their measures of family leave. Kelly (1999) found that unionization actually had a negative eVect on employers’ adoption of maternity leave policy both before and after the passage of the FMLA. Perhaps most important, the existence of a formal leave policy does not mean that workers actually can or do take such leaves. There is some evidence that workplace cultures may have more inXuence than oYcial policy (Hochschild, 1997; Mennino et al., 2001). In her ethnographic study, Fried (1998) found that men in the upper reaches of management were particularly susceptible to these informal pressures, and did not take parental leave, even when entitled to it. Yet most quantitative studies report only on formal policies rather than on workers’ actual utilization of leaves. Overall, previous research on leave taking is limited by a number of factors; much of this research was conducted before the passage of the FMLA, entailed small, regional samples, examined only formal policy and not actual usage, or has examined only maternity or parental leave and not leave taking for the range of reasons covered by the FMLA. More important, it examines gender with no attention to its intersection with race.

3. Research questions Looking at the likelihood of taking leaves as well as the length of those leaves for the full range of family members covered by the FMLA, this paper assesses inequalities by: 1. Gender: we ask whether a gender gap in family leave taking remains in the context of a “gender-neutral” FMLA. 2. Race/ethnicity: we ask to what extent the likelihood and length of family leave varies by racial/ethnic group. 3. Intersectionality of gender and race: we ask whether the gender eVects on leave taking are equally apparent across racial/ethnic groups. Although the cross-sectional data we use do not allow us to make causal arguments about change in leave taking since the FMLA, we can investigate equality in family leave taking across these dimensions after passage of the legislation.

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4. Methods 4.1. Sample This paper uses data from a national survey conducted under the auspices of the Congressional Commission on the Family and Medical Leave Act (Commission on Family and Medical Leave, 1996). The Employee Survey randomly sampled the telephone household population of the coterminous US aged 18 years and older who had been employed for pay any time between January 1, 1994, and the time of the interview (a time span of approximately 18 months). The design allowed for more than one respondent to be selected from a household. When a household was contacted, all eligible residents were listed and screened for eligibility for one of the three categories (in order of precedence): leave taker, leave needer, and employed only (neither needer nor taker). To achieve Wxed sample size allocations for each of these three respondent categories, a category-speciWc subselection rate was applied for each eligible person and the person was either selected for an interview or subsampled out. As a result, family leave takers are oversampled to allow for an adequate group for analysis; they make up a little over 20% of the survey respondents, representing just over 6% of the population. Data on leaves taken, the length and reason for leave, and various social and demographic characteristics of the respondent were obtained during the telephone interview with a total of 2256 respondents. Individuals were characterized as leave takers if they responded yes to: “Since January 1, 1994 [have you/has RELATIONSHIP] taken time oV from work to care for a newborn, newly adopted, or new foster child, or for [your/their] own serious health condition, the serious health condition of [your/their] child, spouse or parent that lasted more than three days1 or required an overnight hospital stay?” The Weld period was from June 1995 through August 1995. An analysis weight calculated from the product of the following component weights was used in all analyses: (1) a sample selection weight factor which is the reciprocal of the probability that the respondent is included in the sample; (2) a screening non-response weight which adjusts for geographic and urbanicity diVerences in response rates; (3) an interview non-response weight which adjusts for diVerences in response rate by sex and age group categories. 4.2. Variables and analyses 4.2.1. Leave taking and length of leave Respondents who reported taking one or more leaves in the past 18 months were asked to specify the length and reason for their longest leave. For the purposes of these analyses, we have coded a dummy variable for family leave takers as all those who took their longest leave for one of the following reasons: maternity disability, care for newborn/newly adopted child, care for a seriously ill child, care for a spouse

1

The actual leave reported, however, could be as short as 1 day.

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or care for a parent. Those who took a medical leave for their own health condition are not included as family leave takers for these analyses. Due to the skewed nature of this variable, length of leave is measured as the logarithm of the number of days taken. 4.2.2. Independent variables The key variables used to explain leave taking and length of leave are gender, race, and the interaction between the two. In addition, other variables representing family resources, family life stage, and workplace characteristics are used as controls. Gender is a dichotomous variable with 0 D male and 1 D female. Racial/ethnic group is included as a dichotomous variable indicating White (0 D no, 1 D yes). Initially, dummy variables were included for each of four groups (White, Latino/a, African-American, and other), but due to small sample sizes and the similarity of coeYcients for Latino/as, African-Americans, and the other category, these groups were combined. Interactions between race and gender were also tested in the models. Control variables include the following indicators of family resources and family life stage: marital status (categorized as never married, married/partnered, divorced/separated/widowed), and family income (in thousands, centered on the median, $37,000). We should note that the survey did not include a measure of personal income; it contained, and we used, a measure of household income. Although it might be interesting to look at both, we are fortunate that household income, as the data analysis reveals, is a useful and appropriate measure. We would hope to control for family life stage and the existence of those situations that might compel an employee to take a family leave. Ideally, measures of the age and health of children and other qualifying family members would be included. Unfortunately, the survey did not include these questions, so we are limited to available proxies for these variables. We include the number of children and age of respondent (centered around 25 years). Among those who take a family leave, we have data on the reason for that leave, and we add this variable to the equation for length of leave as an indicator of caregiving need. As indicators of workplace characteristics, we included the following dichotomous variables: salaried position (0 D no, 1 D yes) and union membership (0 D no, 1 D yes). In addition, we include one organizational characteristic speciWed in the FMLA, whether the respondent works for an organization with 50 or more employees (0 D no, 1 D yes). These variables clearly do not provide a comprehensive picture of workplace characteristics. However, the survey data are primarily limited to workplace characteristics relevant to FMLA eligibility, and do not include information on the subject’s occupation or work conditions (e.g., professional/managerial status, work schedule) that might have a strong eVect on the ability to take a family leave. Nor do we have information on family leave policies or other beneWts available to the employee.

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Table 1 Weighted means and standard deviations of independent variables (N D 2256) Variables

Proportion/mean

Female Whitea African-American Latino Other race/ethnicity Controls Marital status Never married Married/partnered Divorced/separated Widowed Incomeb Salaried Union member Large employer Age # of children a b

Standard error

.46 .81 .09 .07 .04

.015 .012 .008 .007 .006

.17 .69 .11 .02

.012 .014 .009 .005 1816 .015 .011 .015 .386 .033

46,109 .38 .16 .54 39.5 .80

Data are weighted to reXect the racial-ethnic composition of the employed population. The geometric mean of income (the mean of ln (income)) is $44,868.

The estimated means and standard errors of these independent variables are presented in Table 1. 4.3. Analysis STATA survey estimation features (StataCorp, 1999) were used to compute descriptive statistics and regression models. The survey estimation process accounts for complex sample designs in its computation of standard errors. All results presented below are adjusted for variations in non-response and reXect subselection among the sample types. In addition to descriptive statistics, we used logistic regression to estimate the likelihood of taking a family leave and a Heckman selection model to predict the length of leave among family leave takers. We used the Heckman model for length of leave because the group of family leave takers is not a random subsample of the employed population; leave takers diVer from non-leave takers in need, choice or ability to take a leave. Predicting length of leave from leave takers’ data only, without controlling for the selection process, will produce biased regression coeYcient estimates (Kennedy, 1998). The Heckman model has two equations, one for selection (i.e., choosing to take a leave) and one to predict the length of leave. Both equations are estimated for the entire sample; the length of leave equation is not restricted to those who took a leave (although the length of leave is missing for those who did not take a leave). Including the selection probability (in the form of the inverse Mills’ ratio) in the length of leave equation allows us to identify and assess the contribution of factors aVecting the

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length of leave anyone in the employed population would take, were they to choose to take a leave. The selection equation, a probit model, incorporates signiWcant predictors from our logistic regression of leave taking. To identify the Heckman selection model, we excluded the number of children, a variable contained in the selection equation, from the length of leave equation. We used maximum likelihood to estimate the coeYcients of the two equations of the Heckman model in a single step. We evaluated the predictive power of the model by the Pseudo R2 coeYcient, which compares the increase in the log-likelihood of the full model to the log-likelihood of a model that includes no predictors. This is analogous to R2 in regression analysis, where the increase in explained variance is compared to the variance of the model with no predictors. Approximately 8.6% of the family leave takers were on leave at the time of the interview, resulting in censored length of leave data. Subsequent analyses were performed to test the eVect of censoring on the length of leave models. We estimated a two-step Heckman model (rather than the simultaneous Heckman model above) by including the inverse Mills’ ratio in a censored regression equation. We found no appreciable diVerence in the coeYcient estimates.

5. Results 5.1. Taking leave As Table 2 shows, during the 18-month period covered by the survey, 6.2% of the employed population took a leave to care for a family member.

Table 2 Percentage of employed taking family leave, by reason and gender (N D 2256) Took leave (%)a All

Women

Men b

Black Latino All men White Black Latino All women White (n D 1187) (n D 903) (n D 138) (n D 91) (n D 1069) (n D 847) (n D 95) (n D 81) All family leaves Maternity disability Newborn/newly adopted child Sick child Sick parent Sick spouse ***

6.23% 8.57%***

8.44%

9.19%

9.13%

4.21%

0.77

1.65

1.47

1.82

2.92

—

2.29

2.72*

2.81

2.01

2.59

1.38 1.23 0.56

2.00** 1.77** 0.43

1.77 1.86 0.52

3.82 1.54 0

2.12 1.50 0

3.75%

5.27%

8.19%

1.93

1.88

1.28

2.73

0.84 0.76 0.68

0.61 0.64 0.61

0.09 1.03 2.07

3.89 1.57 0

p < .001, **p < .01, *p < 05, two-tailed. Percentages reported of leave taking out of total population are weighted to account for stratiWed sampling. b Asterisks in this column refer to signiWcant diVerences between men and women.

a

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This is a conservative estimate because respondents answered questions only about their longest leave. SpeciWcally, about 3% (2.76) of the employed reported taking more than one leave, with the longest for their own health condition rather than for another family member. They may also have taken a leave for a family member, but did not report the family leave because it was not their longest leave. Assuming the distribution of leaves by reason is similar among longest leaves and additional leaves, about one-third of those who took multiple leaves would have taken an additional shorter leave to care for a family member, leading to an estimate of 7.1% of the population. The most common reason individuals take a family leave is for a newborn (2.29%). Following leaves for newborn are leaves for a sick child or parent (both a little over 1%), and then leaves for maternity disability or a sick spouse (.76 and .56%, respectively). The diVerences in likelihood between each of these sets along this gradient (from newborn to sick child/parent to maternity disability/sick spouse) are statistically signiWcant (p < .01, tests not shown in table). Turning to gender diVerences in the likelihood of taking leave, as prior research would lead us to expect, women are signiWcantly more likely than men to take family leaves (p < .001). SpeciWcally, we Wnd that women are signiWcantly more likely than men to take a leave for most categories—newborns, sick children, and sick parents. We Wnd, however, that men are somewhat more likely than women to take leave for a sick spouse, although the diVerence is not signiWcant. This result may suggest a way that the FMLA does contribute to gender neutrality in leave taking. Men and women are more equal in precisely the broader types of leaves that the advocates of the FMLA sought and succeeded in adding (e.g., care for spouses) to those that already existed (leaves to care for newborns). The frequencies in Table 2 also present evidence of an interaction between gender and race. Although men overall have a lower rate of leave taking than women, this diVerence appears to be most pronounced among White men, only 3.75% of whom take a family leave.2 5.2. Predicting leave taking To further explore factors that shape leave taking, Table 3 presents two logistic regression models of family leave taking. The Wrst model presents main eVects of gender, race, and control variables. In the second model, we estimate separately the eVect of each combination of gender and race/ethnicity (White female, non-White female, non-White male), contrasting each group with White males. Because of the similarity of regression coeYcients in the multivariate analysis and small sample sizes in these groups, African-Americans, Latino/as, and other racial ethnic/groups are combined into one non-White group.

2 Because of the small sample size in these groups, signiWcance tests for these diVerences are not shown. We use signiWcance tests for the multivariate models, where we control for racial/ethnic background.

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Table 3 Taking family leave: logistic regression model (N D 2193) Variables

Model 1 CoeYcient

Model 2 Odds ratio

(SE) Female White Controls Marital statusa Married/partnered Divorced/separated Widowed Income Salaried Union member Large employer Age # of children

2.02

1.57*** (.28) 1.24*** (.33) 1.37* (.66) .004** (.001) .34* (.15) ¡.26 (.19) .27 (.16) ¡.06*** (.008) .32*** (.06)

4.79

Non-White males Constant

.69

1.15 3.93 1.004 1.41 .77 1.30 .94 1.38

1.58*** (.28) 1.28*** (.34) 1.41* (.66) .004* (.001) .35 * (.15) ¡.26 (.19) .27 (.16) ¡.06 *** (.008) .32 *** (.06)

.81 *** (.15) 1.003*** (.24) .68 ** (.30)

Non-White females

¡4.11 (.29)

Odds ratio

(SE)

.70*** (.14) ¡.38 (.19)

Gender £ raceb White females

CoeYcient

4.87 3.60 4.11 1.004 1.41 .77 1.31 .94 1.38

2.26 2.73 1.99

¡4.58 (.26)

*

p < .05, **p < .01, ***p < .001, two-tailed. a The excluded category is never married. b The excluded category is White males. DiVerences between the included categories (White females, non-White females, and non-White males) are not statistically signiWcant.

As shown in Model 1, gender remains a central predictor of leave taking; even controlling for other variables, women are almost twice as likely as men to take a family leave. Racial diVerences in leave taking are not statistically signiWcant, although it is in the expected direction with Whites only 70% as likely to take a family leave as African-Americans, Latino/as, and other racial/ethnic groups. The control variables also exert a signiWcant eVect on the likelihood of taking leave.

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Those who are never married are signiWcantly less likely to take a leave than those who are married, partnered, divorced, separated or widowed. To test the assumption that marital status is only signiWcant because of leaves for spouses (for which the never married are not eligible), we ran this model without leaves for spouses and found nearly identical results, with the exception of widowed respondents, because all widowed respondents who had taken family leave did so to care for a sick spouse. Because this analysis also controls for the number of children present, this result seems to suggest that the presence of a spouse or partner, or even of a separated or ex-spouse, provides the support (and perhaps responsibility) necessary for leave taking. Our Wnding that respondents with higher family income as well as salaried workers are more likely to take family leaves further substantiates this argument. The number of children and respondent’s age, included to control for family life stage, is signiWcant and in the expected direction. Additional children increase the likelihood of taking leave by a factor of 1.4 for each child. Employees’ increased age is associated with decreased likelihood of family leave, probably reXecting the fact that the most common leaves—for newborns or seriously ill young children—are taken by younger respondents. The results of Model 2 provide speciWcation of both the race and gender eVect of the previous model.3 We Wnd only one gender diVerence in predicting leave taking. Not all men are signiWcantly less likely to take family leave; only White men are. Non-White men are about twice as likely, White women are 2.3 times as likely, and non-White women are 2.7 times as likely as White men to take family leaves There are no signiWcant diVerences in the likelihood of leave between the other three categories of respondents (White women and women and men of color). We also tested the model using separate categories for African-American and Latino respondents, but there were no signiWcant diVerences between these groups. Because there are a number of very short leaves included in this analysis, we also tested whether the results of these models were due to the eVect of these short leaves. We estimated a series of models that excluded short leaves and found that the interaction between race and gender becomes non-signiWcant when leaves of 5 days or fewer are removed (data not shown). SpeciWcally, when only looking only at longer leaves, men of color are similar to White men, taking signiWcantly fewer leaves than women. As such, it appears that the similarity between men of color and women (and their divergence from White men) lies primarily in their ability or propensity to take leaves for short-term family needs. We explore this diVerence further when we examine in greater detail the variations in length of leave below.

3 The model was initially estimated with main eVects for gender and race and an interaction between the two. To more clearly emphasize the speciWc group diVerences, we have presented this model with dummy variables for each of four groups, representing White and non-White men and women. This method gives identical Wndings to the traditional presentation of main eVect and interaction coeYcients, with the advantage of clearly showing the exceptional group (in this case, White men).

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Table 4 Length of family leave and percentage of long leaves, by reason and gender (N D 467) Length of leave, mean in days (SE) All

Women

Men

African- Latino All men White African- Latino All womena White (n D 306) (n D 241) American (n D 24) (n D 160) (n D 123) American (n D 14) (n D 27) (n D 15) 38.08 48.48¤¤¤ (2.74) (3.74)

46.49 (3.93)

42.23 (14.08)

72.14 (18.36)

85.55 (12.63)

122.49 (24.92)

102.44 — (39.29) —

49.06 75.60¤¤¤ (4.21) (5.85)

72.84 (5.81)

78.31 (28.78)

106.97 (42.20)

13.55 10.96 (3.49) (2.80)

11.09 (3.52)

5.35 (1.77)

Sick parent

18.30 12.73 (4.77) (1.72)

12.31 (1.76)

5.71 (3.44)

Sick spouse

24.82 35.21 (8.08) (15.25)

36.44 (16.12)

All family leaves

Maternity 91.74 91.74 disability (10.46) (10.46) Newborn/ newly adopted child Sick child

a ¤¤¤

— —

20.69 (4.62)

22.84 (15.94)

10.62 (4.68)

16.75 (3.74)

15.01 (4.17)

13.16 (3.49)

20.77 (11.37)

22.32 (14.82)

18.84 (9.10)

28.41 (13.83)

4.06 (1.59)

4.42 (0.53)

23.48 (12.45)

29.48 (13.81)

26.94 (16.41)

91.38 (78.13)

8.29 (2.54)

19.09 (9.28)

23.91 (12.30)

2.91 (0.60)

— —

19.85 (3.83)

— —

Asterisks in this column refer to signiWcant diVerences between men and women. p < .001, two-tailed.

Although we examined all other interactions between gender, race, and other variables, and the three-way interaction between gender, race, and income, we found no other signiWcant eVects.4 5.3. Length of leave Apart from one individual who took a leave of 16 months, employed family members took leaves ranging in length from a day to a year. As Table 4 shows, the mean length of family leaves is 38 days. Most striking is the dramatic break between leaves around pregnancy and newborns and leaves for all other family members. Leaves taken to care for a newborn or for maternity disability are signiWcantly longer than leaves taken for other reasons. Although leaves for maternity disability (with an average of 92 days leave) are signiWcantly longer than those for newborns (average of 49 days leave), we must interpret 4 In this analysis, we again encounter the potential error due to having data only on the longest leave taken. Consequently, we tested each model in our analysis for this error by creating a category for those who might have taken a family leave (respondents who took more than one leave, the longest of which was for their own health condition). Dropping this group from the analysis made no appreciable diVerence in our results. In addition, we tested a multinomial logit model with three categories of leave taking (0 D didn’t take leave, 1 D maybe took leave and 2 D took leave), and found our equation for leave taking was virtually the same.

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this diVerence with caution, because respondents reporting maternity disability may have included time taken to care for a newborn in their report of length of leave.5 Leaves for sick children, parents, and spouses (which do not diVer signiWcantly from one another) are much shorter, averaging between two and a little over 3 weeks in length. Overall, only about 15% of family leaves are longer than the 12 weeks permitted by the FMLA.6 These long leaves overwhelmingly consist of leaves for maternity disability and newborn care. Nearly half of maternity disability leaves, and almost one-Wfth of leaves for newborn care exceed the allowable length mandated by the law, while few of the other leaves extend beyond this time period. These results suggest, as do the results for the age variable in the previous model, that the greatest demand for family leave (both in likelihood and length) occurs in the early building phase of both family and careers. 5.4. Length of leave by gender The pattern in length of leaves is ampliWed when we separate leave taking by gender. Overall, family leaves taken by women are more than twice as long as leaves taken by men (48 versus 20 days). Apart from maternity disability which does not apply to men, however, the diVerences in length of leave for men and women are signiWcant only for newborns and newly adopted children, where women average almost 2 months longer leave than men (76 days versus 17 days.) Although providing newborn care leads to the longest leaves for women, it leads to the shortest leaves for men. Even in the era of the FMLA, family building in the early years of a man’s career seems to mean staying on the job. In contrast, men appear to take longer leaves than women do to care for a sick parent (29 versus 13 days) or a sick child (19 versus 11 days). We should note, however, that these gender diVerences can be attributed to a few men taking very long leaves (as indicated by the standard errors). If we look at only the shortest 90% of leaves for sick children (i.e., leaves under 21 days) women average signiWcantly longer leaves than men (6.08 days versus 4.67 days, p D .041). If we look only at the shortest 90% of leaves for sick parents (i.e., leaves under 30 days), women and men have nearly the same average length of leave (9.02 days versus 8.04 days, p D .474). Considering leaves longer than the 12 weeks allowed by the FMLA, women are about three times more likely than men to take such leaves. This gender diVerence 5 This is indicated by the response to a question asked of women who reported returning to work from maternity disability leave because they no longer needed to be on leave. About half (48 percent) gave the reason that they had other care for their child. Only 17 percent said they returned because they themselves felt better. This clearly suggests that a rather high proportion of those who took maternity leave continued their leave after the baby was born. The format of the survey makes it impossible for us to separate these leaves. 6 This is again, a somewhat conservative estimate. Approximately 8% of the individuals taking family leave are still on leave at the time of interview, and therefore the length of leave is truncated. These censored leaves are already signiWcantly longer than uncensored leaves. Based on our analysis of censoring, we expect that complete data on the length of leave for these individuals would result in no more than a two point increase in the percentage taking leaves longer than 12 weeks.

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Table 5 Length of family leaves: Heckman selection model (N D 454)a Variables

Length of leaveb B

RSE ***

Female White

.57 ¡.16

(.14) (.10)

Controls Marital status Married/partnered Divorced/separated Widowed Income Salaried Union member Large employer Age Has children Leave for newborn/maternity disability

1.14 .82 1.29 .003** .11 .11 ¡.02 ¡.03*** .21 .37**

(.26) (.31) (.60) (.001) (.14) (.12) (.08) (.007) (.17) (.13)

Interaction terms Female £ leave for newborn/maternity

1.78***

(.16)

Constant Pseudo R2

¡2.07 .10

(.46)

p < .01, ***p < .001, two-tailed. CoeYcients (B) and robust standard errors (RSE) are from a Heckman selection model that estimates length of leave using information from a probit model of taking leave to control for selection bias. b Logarithm of length of leave in days.

**

a

resides primarily in leave taking to care for a newborn, where women are six times as likely as men to take a longer than allowed leave (29% versus 5%, p < .001). The dramatic break between leaves for newborns and for other family members discussed above are found only for women. For men, the percentage of leaves over 12 weeks is virtually the same for all categories of leave taking, varying no more than 2 points. 5.5. Predicting length of leave Table 5 presents the results of a Heckman selection model predicting length of leave among leave takers. As in the model for leave taking, gender remains a signiWcant predictor of length of leave; women take leaves that are 2 days longer than men’s leaves.7 The eVect of gender is strongest among those who take leave to care for a newborn as seen by the signiWcant interaction term for women taking a leave for newborns or maternity disability; these women take leaves that are approximately 15 days longer than men’s leaves. Race, however, does not exert a signiWcant eVect on leave length. 7 To report the eVects of independent variables in this section, we have converted the coeYcient from the log of days to the original scale of days by taking the exponent.

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The control variables again exert signiWcant inXuence on length of leave. Age has an eVect similar to the eVect it has on taking leave; older respondents take signiWcantly shorter leaves. This is likely related to the fact that reason for leave is also a signiWcant predictor of length of leave; both men and women take their longest leaves to care for newborns (with the additional interaction eVect for women). The gender diVerences found in the regression presented in Table 5 and those presented in Table 4 tell slightly diVerent, though complimentary stories. The coeYcient estimates from the Heckman model are derived using the geometric mean of length of leave (due to the use of the logarithm of days of leave), and controlling for selection eVects. When we correct the skewed nature of the length distributions (as in Table 5), men take longer leaves for newborns than for other reasons. In Table 4, which uses the skewed distributions, men’s shortest leaves are for newborns. Combining these two sets of results, it appears that men’s newborn leaves as a group are more homogenous in length than their other types of leaves. The other leaves are more likely to include a handful of extremely long leaves. This suggests that men take on more family work of caring for their parents and children in times of crisis or extreme illness such as that currently covered by the FMLA while they are far less likely to provide much of the more routine care of sick family members. As Table 5 shows, other aspects of family have signiWcant eVects on length of leave. Having a spouse (past or present) or partner appears to provide greater leeway for leave takers; those who are never married take signiWcantly shorter leaves than those who are married, partnered, divorced, separated or widowed. So, too, increased family income is signiWcantly related to longer leaves. Finally, we tested a second model, specifying the interaction between gender and race, and between gender, race, and other independent variables. We found no signiWcant gender diVerences in this analysis (data not shown). Considering the Wndings presented above that the race and gender interaction in leave taking is primarily due to short leaves, it is not surprising that the interaction of race and gender is not signiWcant in this analysis.

6. Discussion Family leave policies potentially aVect families and workers on a number of levels; evaluation of such policies can and should address a variety of outcomes. Other researchers have examined the impact of family leave legislation on employment outcomes, such as work continuity (HoVerth, 1995), wages (Waldfogel, 1997), and labor supply (Klerman and Leibowitz, 1997). Although our ability to reexamine such questions is limited by these data, we are able to address one of the major targets of the FMLA—gender inequalities in leave taking. Our analysis reveals complex gender eVects. On the one hand, conWrming prior research, we Wnd that women are signiWcantly more likely than men to take leaves. However, as a growing body of theoretical work suggests, we can no longer speak of women—or men—as a group but instead must attend to the diversity, especially racial diversity, within each gender. As our results show, not all men are less likely

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than women to take a family leave; only White men are. Moreover, there are no signiWcant diVerences in likelihood of leave between African-Americans and Latina/os. The signiWcant diVerence—one limited to short leaves—is only between White men and men of color. The intersection of race and gender may be connected to cultural factors, like the centrality of children or kinship more generally in men’s and women’s lives, or economic factors, like the distribution of Wnancial resources within the family. This intersection may also reXect diVerent labor market positions for particular groups. For example, a larger wage gap exists between White than AfricanAmerican spouses (e.g., Lee et al., 1998); this may lead to greater gender inequality in leave taking in the former group. Turning to length of leave, we again Wnd signiWcant diVerences by gender, but these diVerences are not conWned to one racial/ethnic group. Net of other factors, women—across racial ethnic groups—take signiWcantly longer leaves than men, with the largest gender diVerences among those who take leaves to care for a newborn. In fact, leaves to care for newborns are the only kind of leave where we Wnd women extending the 12 weeks allowed by the FMLA. It is in length of leaves, then, where the FMLA most clearly fails to reach the goal of gender neutrality. The research presented here has a number of important policy implications. It suggests that an underanalyzed aspect of the FMLA—the expansion of allowable reasons for leave from newborns to other relatives—may be one key for the promotion of gender neutrality. On the one hand, the greatest gender gap is in care for newborns: such care leads to the longest leaves for women and the shortest for men. On the other hand, men and women, regardless of race, are similar in their length of leaves for a sick spouse, sick parent or seriously ill child. These results suggest that gender diVerences may be attenuated by the FMLA’s extension of “who counts” as family in leave taking. In addition, although workers do take leaves for these other reasons, these leaves are generally short. This indicates that employers’ concerns about the dire eVects on the workplace of extending family leaves to include not only newborns but also children, spouses, and parents are not borne out. Finally, we Wnd that the similarity of men’s and women’s average leaves appears to be due to a small number of men taking very long leaves. We can speculate that men utilize leaves for family crises, whereas the routine care of family members remains primarily the responsibility of women. The FMLA may create opportunity for gender neutrality in the face of pressing demands, but appears not to change the division of more routine family labor. This suggests that recent state policy innovations that allow family members to take leaves for “small necessities,” like routine doctors and dentist appointments, may serve to bolster gender and race inequality rather than alleviate them. Given the limits of these data, caution is in order. Existing literature strongly suggests that social class is a central contributor both to racial and gender inequality (for review of this literature, see Gerstel and Sarkisian, forthcoming). This proposition is further evidenced by the signiWcance of household income as a consistent predictor of leave taking and length of leave in these analyses. More accurate speciWcation of family resources, including individual wage contributions, and workplace resources, would enrich our ability to evaluate the gender and race eVects. For example, it

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would be useful to include data for both the employee and their spouse or partner on occupation, industry, sectoral location, Xexibility of scheduling, gender composition of the workplace and management, the provision of paid leaves, and the range of beneWts. As these diVerences are likely to be related not only to class but also to race and gender, we would expect the inclusion of these factors to help explain the estimated eVect of gender and race on family leave. Future empirical research should disentangle the social conditions, like individual and household income and wealth, household composition or medical need, and cultural values that may underlie racial diVerences. Moreover, future research should compare, using larger samples, the diVerences among peoples of color—whether various African-American, Latina/o, or Asian-American groups. Overall, despite the presence of a new “gender-neutral” policy that provides the legal right to equal leave beneWts for men and women, our analysis points to striking inequalities. These inequalities could result from a number of processes. They could result from a layering of policy which means employees still use the old system (with its 1978 Pregnancy Disability Act) that is fundamentally geared toward women and their physical disabilities during childbirth. Such leaves are often paid leaves, in contrast to those mandated by the FMLA. Thus, it may make economic sense for women more than men to take leaves. This “economically rational” decision is, of course, reinforced in joint decision making couples in which men still tend to earn higher incomes than their women partners. The gender inequalities we found could also result from two processes based on cultural expectations—one rooted in workplaces, the other in families. First, the gender diVerences could reXect informal conditions in the workplace: bosses or co-workers may increasingly recognize and accommodate to women’s (but not men’s) family responsibilities. Second, the inequalities could reXect processes at home, where women are still expected (and expect themselves) to provide more nurturance and care. Advocating the importance of the Wrst process, Pleck (1991) not only argues that fathering means breadwinning to many men but also goes on to suggest that men are particularly likely to face negative pressures from employers for taking prolonged leaves. However, preliminary analyses of our data suggest otherwise. Women—even when they take leaves that are the same length as men—report signiWcantly greater pressure from colleagues, bosses, and workplaces to return to their jobs after taking a leave than do men (p < .05, data not shown; see also Sandberg and CornWeld, 1999). This accords with research about other issues that suggests co-workers, even supervisors, more often laud men as exceptional, even heroic, when they provide family care while women giving care are viewed as doing one of the many responsibilities expected of them (Coltrane, 1996; Deutsch, 1999). With regard to the second process, rooted in the home rather than in the workplace, a great deal of research suggests that family members still expect women— across race and class—to provide far more care for children and other kin than men (Allen et al., 2001; Hochschild and Machung, 1989; Rossi and Rossi, 1990; Thompson and Walker, 1991). Although attitudes towards such a “traditional” gender division of labor are clearly more egalitarian now than they were thirty years ago, men’s

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care work has not kept pace with attitudinal changes (Coltrane, 1996; Coltrane and Valdez, 1994; Gallagher and Gerstel, 2001; Gerson, 1993; Gerstel and Gallagher, 1994; LaRossa, 1998). We conclude with the question raised by numerous feminist scholars about a range of social policies: to what extent can “gender neutral” approaches attenuate existing gender inequalities? Our answer: not much. Although the FMLA theoretically provides universal and gender neutral access to leaves, actual leave taking is far from gender neutral—especially when it comes to the care of very young children.

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