Abstract
Objectives. To examine racial and ethnic inequities in paid family and medical leave (PFML) access and the extent to which these inequities are mediated by employment characteristics.
Methods. We used data from the 2011 and 2017–2018 American Time Use Survey in the United States to describe paid leave access by race/ethnicity. We present unadjusted models, models stratified by policy-targetable employment characteristics, and adjusted regression models.
Results. We found that 54.4% of non-Hispanic White workers reported access to PFML in 2017–2018 but that access was significantly lower among Asian, Black, and Hispanic workers. Inequities were strongest among private-sector and nonunionized workers. Leave access improved slightly between 2011 and 2017–2018, but the inequity patterns were unchanged.
Conclusions. We observed large and significant racial and ethnic inequities in access to PFML that were only weakly mediated by job characteristics. PFML has a range of health benefits for workers and their families, but access remains limited and inequitable.
Public Health Implications. Our findings suggest that broad PFML mandates (such as those in other high-income countries) may be needed to substantially narrow racial and ethnic gaps in paid leave access. (Am J Public Health. 2022;112(7):1050–1058. https://doi.org/10.2105/AJPH.2022.306825)
Substantial research has documented the beneficial effects of access to paid leave for new parents and their children, as well as health benefits for workers, for sick family members, and in the workplace. A growing body of evidence links paid family and medical leave (PFML) with decreases in low-birthweight births and infant mortality, increased breastfeeding, improved maternal mental health, improved self-rated health, and increased postpartum care attendance.1–11 Evidence also suggests that access to paid maternity leave increases infant immunization rates and decreases childhood hospitalizations.12–14
The United States remains the only Organisation for Economic Co-operation and Development country that does not mandate paid leave for new mothers, 1 of only 2 countries without paid leave for new fathers, and 1 of 3 high-income countries without any paid sick leave.15 An unsurprising result of this policy context has been large racial and ethnic inequities in access to paid leave. The results of 2 published reports assessing the 2011 Leave Module of the Bureau of Labor Statistics American Time Use Survey (ATUS) showed that only 23% to 25% of Hispanic parents had access to paid parental leave, as compared with 47% to 50% of non-Hispanic White and 41% to 43% of non-Hispanic Black parents.16,17 A study of mothers in the San Francisco (California) Bay Area revealed that, relative to White women, Asian, Hispanic, and Black women received 0.9 (P < .05), 2.0 (P < .01), and 3.6 (P < .01) fewer weeks, respectively, of full-pay equivalence during their parental leaves.18
These racial and ethnic inequities in access to paid parental leave may be reflective of structural racism,19 which shapes and upholds systems that result in vastly inequitable distributions of risk, opportunity, wealth, and poverty. Occupational segregation extends from structural racism and, in this case, may be the mechanism by which these inequities take hold.20 For example, workers with occupations in the highest average wage quartile are 3.5 times more likely to have access to paid leave through their jobs than workers in the lowest average wage quartile.21 Furthermore, 33% of management and professional workers in 2020 had access to paid family leave through their jobs, as compared with only 12% of service workers.21 At the same time, Hispanic workers are most likely to fall in the lowest wage brackets16 and, relative to White and Asian workers, both non-Hispanic Black and Hispanic workers are underrepresented in professional-class jobs.22
There is some evidence that PFML policies can narrow these inequities that derive from a reliance on employer-provided benefits. For example, California’s paid family leave program has increased leave taking among mothers by an average of 3 weeks, with the greatest gains among Black and Hispanic mothers.23
However, even in places with PFML policies, inequities persist. One reason has to do with policy design elements that disproportionately exclude workers of color, another example of how structural racism shapes and reifies inequities by institutionalizing exclusionary policies and practices.19 For instance, minimum hours or job tenure requirements may exclude seasonal and part-time workers, and policies that cover only private-sector workers leave out many Black workers who are overrepresented in public-sector (i.e., governmental) jobs.
Furthermore, PFML policies do not necessarily include job protection, so workers are dependent on such protection through the Family and Medical Leave Act. This legislation has notoriously strict eligibility criteria: individuals must have worked at least 1250 hours for the same employer in the preceding year and must have been employed at the same job for at least 12 months, and only firms with at least 50 employees are covered. In a recent study in which data from the Current Population Survey were used to estimate the Family and Medical Leave Act restrictions that exclude the most workers, the results indicated that minimum hours requirements disproportionately exclude women; job tenure requirements exclude Black, Indigenous, and multiracial workers; and firm size requirements exclude Latinx workers.24
Another reason for these persistent inequities involves access to information about PFML benefits. Ten years after California’s PFML law went into effect, Latinx, immigrant, and nonunionized workers were among the least likely to be aware of the state’s policy.25 More recent research among new parents showed that Black and Hispanic workers were less likely than White workers to understand their maternity leave benefits, stemming from the fact that they were about half as likely to report receiving help from their employers in understanding their benefits.18 Similar findings have been observed for Medicaid-eligible workers (relative to workers with private insurance).26
We used data from the 2017–2018 ATUS Leave Module (the most recent data available) to document the magnitude of racial and ethnic inequities in PFML access and compared these data with those from the 2011 Leave Module. In addition, we investigated the extent to which such inequities might be mediated by employment characteristics that could be leveraged to better target and promote paid leave policies.
METHODS
We primarily used data from the 2017–2018 ATUS Leave Module,27 a nationally representative, cross-sectional household survey that included detailed questions about access to paid leave. As noted, we also compared leave access inequities in 2017–2018 with those in 2011. We excluded respondents who were not employed or were self-employed; those whose race/ethnicity was not listed as non-Hispanic White, non-Hispanic Asian, non-Hispanic Black, or Hispanic; and those who had missing data on paid leave variables. Our analytic sample included 9987 workers in 2017–2018 and 6383 workers in 2011.
Dependent Variables
Our primary outcome was self-reported access to PFML. Respondents were first asked whether they received paid leave on their current or main job and, if so, to list the reasons for which they could take paid leave. Respondents were characterized as having PFML if they reported having each of the following: paid leave for their own illness or medical care (medical leave), paid leave for the illness or medical care of another family member (caregiving leave), and paid leave for the birth or adoption of a child (parental leave). This reflects the set of reasons most commonly covered under state PFML laws. We also looked separately at each of these 3 types of leave.
Independent Variable
We compared access to PFML across 4 racial and ethnic categories: non-Hispanic White (White), non-Hispanic Asian (Asian), non-Hispanic Black (Black), and Hispanic.
Covariates
In the case of the 2017–2018 data, we focused on 3 policy-targetable occupational characteristics: employment sector (public vs private), work hours (full time vs part time), and whether the respondent was covered by a union. We also examined occupation (using census occupation codes for respondents’ main jobs), industry (using census industry codes), presence of children younger than 18 years in the household, age, gender, marital status, educational attainment, family income, and citizenship.
Analyses
We present unadjusted models, initially showing combined PFML and then breaking out each type of paid leave separately; we compared unadjusted inequities in 2011 versus 2017–2018. For the most recent (2017–2018) data, we then describe PFML access stratified by the 3 policy-targetable employment characteristics just described (sector, hours, and union coverage). Next, we tested whether racial and ethnic differences in 2017–2018 were attenuated after inclusion of regression controls for employment and sociodemographic characteristics. We used linear probability models to examine how adjustment for employment and sociodemographic characteristics changed the differential access observed in our unadjusted analyses. We present 3 nested models that adjusted for (1) the 3 primary employment characteristics (sector, hours, and union coverage), (2) all employment characteristics, and (3) sociodemographic characteristics.
Stata version 14.2 (StataCorp LLC, College Station, TX) was used in conducting our analyses. In all of our models, we used weights to account for the ATUS Leave Module sampling frame.
RESULTS
Table 1 presents descriptive statistics for our 2017–2018 analytic sample. The weighted distribution of the sample was 64.8% White, 17.0% Hispanic, 12.2% Black, and 5.9% Asian. Most respondents worked in the private sector, predominantly at for-profit companies. Black workers were somewhat overrepresented in public-sector jobs. Most respondents worked full time, with no statistically significant differences across racial and ethnic groups. About 13% of workers across all racial and ethnic groups were covered by a union.
TABLE 1—
Descriptive Statistics by Race/Ethnicity: United States, American Time Use Survey, 2017–2018
| Non-Hispanic White (n = 6571), No. (%) or Weighted Mean±SD | Non-Hispanic Asian (n = 556), No. (%) or Weighted Mean±SD | Non-Hispanic Black (n = 1339), No. (%) or Weighted Mean±SD | Hispanic (n = 1521), No. (%) or Weighted Mean±SD | P | |
| Employment sector (detailed) | < .001 | ||||
| Government, federal | 202 (3) | 16 (3) | 87 (6) | 34 (2) | |
| Government, state | 458 (7) | 34 (5) | 114 (6) | 57 (4) | |
| Government, local | 620 (9) | 21 (6) | 128 (9) | 103 (7) | |
| Private, for profit | 4674 (73) | 440 (80) | 881 (68) | 1245 (82) | |
| Private, nonprofit | 617 (9) | 45 (7) | 129 (10) | 82 (6) | |
| Part-time work | 1113 (19) | 72 (15) | 221 (18) | 282 (22) | .14 |
| Covered by a union | 819 (13) | 64 (13) | 180 (14) | 176 (13) | .85 |
| Occupation | < .001 | ||||
| Management, business, and financial | 1317 (18) | 108 (17) | 166 (11) | 183 (11) | |
| Professional and related | 868 (12) | 165 (24) | 149 (10) | 105 (5) | |
| Service | 1999 (31) | 158 (32) | 486 (40) | 502 (34) | |
| Sales and related | 518 (8) | 28 (5) | 93 (8) | 120 (8) | |
| Office and administrative support | 836 (14) | 51 (10) | 203 (12) | 165 (12) | |
| Farming, fishing, and forestry | 32 (1) | 1 (0) | 4 (0) | 25 (2) | |
| Construction and extraction | 194 (3) | 5 (1) | 22 (2) | 129 (10) | |
| Installation, maintenance, and repair | 175 (3) | 5 (2) | 26 (2) | 54 (4) | |
| Production | 342 (6) | 20 (4) | 85 (7) | 120 (7) | |
| Transportation and material moving | 290 (5) | 15 (4) | 105 (7) | 118 (7) | |
| Industry | < .001 | ||||
| Agriculture, forestry, fishing, and hunting | 59 (1) | 1 (0) | 3 (0) | 30 (2) | |
| Mining | 38 (0) | 1 (0) | 1 (0) | 8 (1) | |
| Construction | 269 (4) | 10 (2) | 25 (2) | 146 (10) | |
| Manufacturing: durable goods | 258 (4) | 37 (6) | 40 (3) | 49 (3) | |
| Manufacturing: nondurable goods | 423 (7) | 30 (5) | 86 (5) | 107 (6) | |
| Wholesale trade | 245 (3) | 21 (5) | 28 (2) | 56 (3) | |
| Retail trade | 622 (10) | 45 (11) | 118 (10) | 145 (10) | |
| Transportation and warehousing | 225 (4) | 20 (4) | 83 (7) | 72 (4) | |
| Utilities | 90 (1) | 3 (0) | 9 (1) | 10 (1) | |
| Information | 120 (2) | 14 (2) | 21 (1) | 23 (1) | |
| Finance and insurance | 444 (7) | 51 (7) | 64 (4) | 55 (4) | |
| Real estate and rental and leasing | 106 (2) | 4 (0) | 22 (2) | 45 (3) | |
| Professional, scientific, and technical services | 651 (9) | 95 (14) | 61 (4) | 70 (3) | |
| Management, administrative, and waste management services | 189 (3) | 7 (1) | 67 (4) | 92 (6) | |
| Educational services | 876 (13) | 41 (8) | 152 (10) | 134 (9) | |
| Health care and social services | 980 (14) | 91 (15) | 282 (22) | 177 (12) | |
| Arts, entertainment, and recreation | 125 (2) | 14 (2) | 16 (2) | 34 (3) | |
| Accommodation and food services | 251 (5) | 40 (12) | 75 (8) | 151 (11) | |
| Private households | 10 (0) | 1 (0) | 3 (0) | 5 (0) | |
| Other services, except private households | 217 (4) | 14 (2) | 50 (4) | 60 (4) | |
| Public administration | 373 (5) | 16 (3) | 133 (9) | 52 (3) | |
| Children < 18 y in household | 3279 (40) | 333 (45) | 458 (36) | 740 (50) | < .001 |
| Age, y | 41.56 ±0.28 | 38.79 ±0.90 | 43.36 (0.63) | 37.29 (0.50) | < .001 |
| Female | 3347 (49) | 274 (46) | 789 (53) | 680 (43) | < .01 |
| Married | 3744 (56) | 370 (59) | 450 (40) | 719 (46) | < .001 |
| Family income, $ | < .001 | ||||
| < 50 000 | 1761 (26) | 108 (20) | 666 (43) | 756 (48) | |
| 50 000–99 999 | 2392 (36) | 168 (31) | 450 (38) | 490 (34) | |
| ≥ 100 000 | 2418 (38) | 280 (49) | 223 (19) | 275 (18) | |
| Education | < .001 | ||||
| < high school | 226 (5) | 12 (5) | 76 (7) | 311 (22) | |
| High school or equivalent | 1228 (25) | 56 (16) | 342 (32) | 380 (31) | |
| Some college or associate degree | 1776 (25) | 87 (17) | 437 (31) | 411 (25) | |
| ≥ college | 3341 (45) | 401 (62) | 484 (31) | 419 (22) | |
| Citizenship status | < .001 | ||||
| Native-born citizen | 6306 (97) | 150 (32) | 1140 (83) | 787 (53) | |
| Naturalized citizen | 152 (2) | 230 (37) | 133 (10) | 298 (19) | |
| Non–US citizen | 113 (1) | 176 (31) | 66 (7) | 436 (28) |
Note. P values are derived from χ2 test for categorical variables and from analyses of variance for continuous variables. The sample size was 9987.
Just over half (54.4%) of White workers in 2017–2018 reported access to PFML, but access was significantly lower among Asian (−8.6 percentage points; P < .05), Black (−12.7 percentage points; P < .001), and Hispanic (−23.4 percentage points; P < .001) workers (Figure 1). Medical leave was the most frequently reported type of paid leave for all groups, followed by caregiving and parental leave. Black and Hispanic workers were significantly less likely to receive all 3 types of leave than White workers.
FIGURE 1—
Paid Family and Medical Leave by Race/Ethnicity in (a) 2011 and (b) 2017–2018: United States
Note. Whiskers indicate 95% confidence intervals.
Source. Authors’ analysis of data from the American Time Use Survey 2011 and 2017–2018 Leave Modules.
*P < .05; **P < .01; ***P < .001.
Figure 1 also shows parallel paid leave inequities in 2011, allowing a comparison of changes over time. Access to all types of paid leave increased from 2011 to 2017–2018, but the inequity patterns remained the same. Access to paid leave among workers across all racial and ethnic groups increased over time, but the gains among Black and Hispanic workers were no larger than the gains among White workers. Formal interaction tests did not reveal any significant changes in inequities between 2011 and 2017–2018.
Focusing specifically on the more recent 2017–2018 data, there were significant racial and ethnic inequities, particularly among workers in the private sector and those who were not covered by unions (Figure 2). Part-time workers were substantially less likely to receive paid leave than full-time workers, and there were within-group inequities among full-time and part-time workers, with Black and Hispanic workers significantly less likely than their White counterparts to receive paid leave.
FIGURE 2—
Paid Family and Medical Leave, Stratified by Occupational Characteristics and Race/Ethnicity: United States, 2017–2018
Source. Authors’ analysis of data from the American Time Use Survey 2017–2018 Leave Module.
*P < .05; **P < .01; ***P < .001.
Overall, however, racial and ethnic sorting by occupational characteristics is insufficient to explain the differences observed in access to PFML (Figure 3). Inequities in access to PFML persisted in models that accounted for (1) sector, work hours, and union coverage (model 1) and (2) these 3 variables along with occupation and industry (model 2). When demographic characteristics were included, Asian workers, but not Black and Hispanic workers, were no longer significantly less likely to receive PFML (model 3).
FIGURE 3—
Racial/Ethnic Inequities in Access to Paid Family and Medical Leave After Adjustment for Occupational and Demographic Characteristics: United States, 2017–2018
Note. Results are from linear probability models. Model 1 includes job sector, work hours, and union coverage; model 2 includes the variables in model 1 along with occupation and industry; and model 3 includes the variables in model 2 along with presence of children younger than 18 years in the household, age, age squared, gender, marital status, educational attainment, family income, and citizenship.
Source. Authors’ analysis of data from the American Time Use Survey 2017–2018 Leave Module.
*P < .05; **P < .01; ***P < .001.
DISCUSSION
We found large and significant racial and ethnic inequities in access to PFML. Asian, Black, and Hispanic workers were 8.6, 12.7, and 23.4 percentage points less likely to report access to PFML, respectively, than White workers. Notably, access to PFML was limited for everyone; just over half of White workers reported access. Although access to paid leave increased over time across all racial and ethnic groups, inequitable patterns persisted. Consistent with previous research, we found that Black and Hispanic workers were least likely to have access to paid leave in both 2011 and 2017–2018.17
Although our main finding—that PFML access is highly inequitable—stands on its own, we also conducted a series of subgroup analyses and created multivariate regression models controlling for occupational and sociodemographic characteristics. The intent of these analyses and models was not to “explain away” observed inequities but, rather, to understand what is driving inequities and the extent to which these characteristics may be responsive to policy levers.
In our analyses of access to PFML, we continued to see inequities among workers in occupational subgroups (employment sector, work hours, and union coverage) that have been or could be targeted by policies. For example, many paid leave policies at both the organizational and public policy levels have minimum hours requirements, disproportionately excluding part-time workers. This is reflected in our results showing that part-time workers are significantly less likely to report access to PFML than full-time workers. However, we also found that within both full- and part-time subgroups, workers of color have less access to PFML than White workers. This suggests that part-time workers are being left behind by policies targeting full-time workers and that expanding coverage to part-time workers is not enough to eliminate racial and ethnic inequities in PFML access. Moreover, when we controlled for these and other occupational characteristics in multivariate regression models, we continued to see racial and ethnic inequities in PFML access.
Even after controlling for a comprehensive set of occupational and sociodemographic characteristics, we continued to see that workers of color have less access to PFML, suggesting that structural racism and even interpersonal racism28 may be contributing drivers. It is also worth questioning the value of controlling for these characteristics given that the occupational segregation that so deeply influences access to PFML is itself a product of structural racism. Should we accept that workers in certain occupations or those working part time have limited access to PFML? Or should we expect that workplace benefits that have been tied to improved health and economic outcomes for new parents, infants, caregivers, and adults dealing with serious medical conditions are equally accessible to all workers?
Limitations
Our reliance on self-reported paid leave access may be problematic. Workers may not be familiar with their benefits, especially those they have not needed to use. For example, medical leave was the most commonly reported type of paid leave, followed by caregiving and parental leave. This could reflect real differences in offering of leave or lower awareness of parental and caregiving leave among workers who have not had a need for such leave. Limited awareness of workplace benefits may be more common among workers of color who are less likely to have received information and support about leave taking from their employers than White workers. The ATUS data did not allow us to discern whether our findings reflect differential access or differential awareness; arguably, both are of equal importance and suggest that PFML policies need to include robust outreach and enforcement mechanisms. Finally, the ATUS data did not include several important occupational characteristics associated with PFML access such as firm size and job tenure.
Public Health Implications
The health benefits of PFML have been increasingly well documented, but the limited access to paid leave among workers of color means that these benefits are inequitably distributed, potentially contributing to widening gaps in health across racial and ethnic groups. We observed large and significant racial and ethnic inequities in access to PFML that were only weakly mediated by the job characteristics analyzed. If these inequities cannot be explained by policy-targetable job characteristics, this would suggest that broad PFML mandates (such as those in other high-income countries) may be needed to substantially narrow racial and ethnic gaps in paid leave access.
ACKNOWLEDGMENTS
This work was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development under award K12HD043488.
Note. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
CONFLICTS OF INTEREST
The authors have no conflicts of interest to disclose.
HUMAN PARTICIPANT PROTECTION
No protocol approval was needed for this study because deidentified, publicly available data were used.
See also Heymann, p. 959.
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