Is the Gender Wage Gap Really a Family Wage Gap in Disguise?

Abstract

Despite large literatures on gender and family wage gaps (e.g., the motherhood wage penalty, fatherhood wage premium, and the marriage premium) and widespread recognition that the two gaps are intertwined, the extent and pattern of their relationships are underexplored. Using data from the 2018 Survey of Income and Program Participation, we show that family wage gaps are strongly associated with the gender wage gap, as long assumed in the literature, but with important caveats. The gender-differentiated wage returns to parenthood contribute 29 percent of the gender wage gap. One third of this is associated with occupation, but very little with other worker and job attributes. The gender-differentiated returns to marriage contribute another 33 percent, two thirds of which is associated with worker and job attributes but very little with occupation. However, 36 percent of the gender wage gap is unrelated to these family wage gaps, and the gender wage gap among childless workers remains substantial. Moreover, for Black and Hispanic workers, the pattern of association is more complex and generally weaker than for White workers. These results caution against focusing solely on the wage gap between “mothers and others” and suggest new directions for research.

Keywords

gender wage gap family wage gap motherhood wage penalty fatherhood wage premium gender inequality

In the contemporary United States, as in other advanced industrialized economies, wage differentials between men and women, parents and childless adults, and married and unmarried workers remain large and consequential (for a review, see Misra and Murray-Close 2014). On average, employed women earn approximately 83 cents for every dollar that men earn, representing an increase of approximately 5 cents on the dollar over the past 30 years (see England, Levine, and Mishel 2020: Figure 9). Mothers earn less than childless women (the “motherhood wage penalty”) and fathers earn more than childless men (the “fatherhood wage premium”), although the latter finding is not as robust (Budig and England 2001; England et al. 2016; Glauber 2008; Jee, Misra, and Murray-Close 2019; Killewald 2013; Lundberg and Rose 2000; Pal and Waldfogel 2016; Waldfogel 1998). Married men typically earn higher wages than unmarried men, and wage disparities between married and unmarried women vary by cohort, race and ethnicity, and life-course stage (Avellar and Smock 2003; Cheng 2016; Cohen 2002; Glauber 2007; Juhn and McCue 2017; Killewald and Gough 2013; Korenman and Neumark 1991).

Parental, marital, and gender wage gaps are not independent of each other. Logically, if mothers are paid less than childless women and fathers are paid more than childless men, a gender wage gap will emerge even if childless women and childless men are paid equivalently. Indeed, much of the public discourse around the gender wage gap focuses on the disparate effects of parenthood, sometimes with an explicit claim that “the gender wage gap is mostly a penalty for bearing children” (e.g., Kliff 2018). In the academic literature, examinations of the “family wage gap” (i.e., wage differentials by marital and parental status) using single-gender samples are often justified by their presumed effect on the gender wage gap. Similarly, much of the contemporary scholarship on gender wage gaps focuses on the conflicting demands of work and family, sometimes with the explicit claim that this conflict is the last major barrier to gender parity in wages, particularly among highly educated workers (Goldin 2021).

Despite widespread recognition that family wage gaps are consequential for the gender wage gap, the two phenomena are typically analyzed separately and with very different goals and analytic strategies. At the risk of oversimplification, most research on the gender wage gap focuses on identifying its individual and structural correlates and how these correlates vary across time and place (e.g., Blau and Kahn 2017; Cha and Weeden 2014; Goldin 2014; Mandel and Rotman 2021). Most research on family wage gaps, by contrast, focuses on identifying the causal effects of marital or parental status on wages among workers of the same gender (e.g., Budig and England 2001; Cheng 2016; England et al. 2016; Killewald 2013). As we will elaborate, this difference in substantive focus is manifest in the datasets and modeling strategies each research tradition favors.

The largely separate strands of research on gender and family wage gaps leave a critical hole in our knowledge of the relationship between the two. Estimates of family wage gaps based on data from a single cohort or gender cannot be extrapolated to estimate the overall contribution of family wage gaps to the gender wage gap. Inferring this contribution is even more problematic for Black and Hispanic workers, given family wage gaps and the gender wage gap are smaller for Black and Hispanic workers than for non-Hispanic White workers (Cheng 2016; Glauber 2007, 2008; Mandel and Semyonov 2016; Van Winkle and Fasang 2020).

The separate strands of research on gender and family wage gaps also shift attention away from gender wage disparities among childless or never-married adults. In family wage gap research, childless women and never-married women are often used as the reference group for within-gender comparisons, and their wages relative to childless or never-married men are masked. In the gender wage gap literature, family demography is rarely incorporated in wage gap models, or it is estimated with imperfect measures of family structure. This leaves a key question unanswered: Have childless women indeed reached wage parity with childless men? Although some studies of particular occupations suggest this may be the case (see, e.g., Correll, Benard, and Paik [2007] on entry-level management consultants, or Bertrand, Goldin, and Katz [2010] on young MBAs from top U.S. business schools), neither family wage gap nor gender wage gap research offers a clear answer for the labor force as a whole.

The main contribution of this article is to describe the empirical relationship between family wage gaps and the aggregate gender gap in wages for the full labor market and, where sample sizes allow, for Black, Hispanic, and White workers separately. We first document gender wage differentials within eight demographic groups defined by marital and parental statuses (“family status groups”). We then apply a standard wage decomposition to estimate the contribution of gender-differentiated “returns” to parental and marital statuses—that is, the motherhood wage penalty, the fatherhood wage premium, and men’s marriage premium—to the gender wage gap, and describe how much of these gender-differentiated returns to parenthood and marriage are associated with human capital or other pre-market attributes, job attributes, and occupation.

All analyses use data from the 2018 Survey of Income and Program Participation (SIPP), a household survey that is representative of the U.S. labor market. As we will discuss, the SIPP has important advantages in both measurement and generalizability over the single-cohort longitudinal surveys favored in the family wage gap literature and the cross-sectional labor force surveys that dominate the gender wage gap literature. Our goal is not to provide or test a causal model of wages or wage disparities, but to provide a rich description of the empirical relationship between family wage gaps and the gender wage gap. Our analyses, although explicitly descriptive, bring together the gender and family wage gap literatures and provide an empirical foundation for new questions into persistent and consequential gender inequalities in labor market outcomes.

The Gender Wage Gap And Family Status

In accounting for the gender wage gap, researchers often focus on gender differences in the distribution of and returns to human capital (as measured by education or labor force experience), occupation, job and labor force attributes, and other structural predictors of the gender pay gap (Blau and Kahn 1997, 2006, 2017; Cha and Weeden 2014; Cortés and Pan 2019; Goldin and Katz 2007; Mandel and Rottman 2021; Mandel and Semyonov 2014). In the United States, gender differences in human capital account for a relatively modest share of the gender wage gap. This is unsurprising given that average education levels among women now exceed men’s, the college wage premium is similar for women and men, and job tenure and continuous labor force experience are converging (Blau and Kahn 2017; England et al. 2020; Mandel and Semyonov 2014). By contrast, gender differences in work hours, when combined with differential returns to work hours, widen the gender wage gap (Cha and Weeden 2014; Goldin 2014; Mandel and Semyonov 2014). Similarly, occupational segregation and the higher average pay of “men’s” occupations account for a large and growing share of the gender wage gap, with recent estimates ranging from 10 percent in the private sector and 25 percent in the public sector (Mandel and Semyonov 2014) to 33 percent overall (Blau and Kahn 2017).

Analyses of the predictors of the gender wage gap rarely explore in depth the contributions of parental or marital status. One reason may be that parental and marital status do not fit neatly into the human capital theoretical framework that underpins much of the work on gender wage differentials, given endogeneity between the two: human capital (e.g., education, work experience) mediates the association between family status and wages, and family status also mediates the association between human capital and wages. Another may be that the household survey data (e.g., Current Population Survey [CPS], ACS/Census) on which many analyses of the gender wage gap in the United States rest do not include complete information about parental status. The upshot is that the gender wage gap literature offers few conclusive answers about the strength of the contribution of family wage gaps.

Family Wage Gaps And Gender

An extensive and largely independent body of research identifies the strength and pattern of the association between family status and wage differentials, mostly using within-gender comparisons (Anderson, Binder, and Krause 2003; Avellar and Smock 2003; Budig and England 2001; Cheng 2016; England et al. 2016; Glauber 2007; Jee et al. 2019; Killewald 2013; Killewald and Gough 2013; Pal and Waldfogel 2016; Van Winkle and Fasang 2020; Waldfogel 1996). Studies that focus on the association between marriage and wages generally show that married men earn higher wages, by 7 to 11 percent, than unmarried men after adjusting for education, work experience, occupation, and other indicators of labor market position (Budig and Lim 2016; Chun and Lee 2001; Killewald and Gough 2013; Korenman and Neumark 1991; but on the role of selection, see Killewald and Lundberg 2017; Ludwig and Brüderl 2018). For women, by contrast, marriage does not have a consistent positive or negative association with wages, with estimates ranging from negative or null effects (Anderson et al. 2003; Avellar and Smock 2003; Hersch and Stratton 2000; Loughran and Zissimopoulos 2009) to an adjusted marriage premium of 3 to 7 percent (Budig and England 2001; Budig and Lim 2016; Glauber 2007; Juhn and McCue 2017; Killewald and Gough 2013; Waldfogel 1997). A fair but cautious summary is that the “marriage premium” for women is lower than for men, if it exists at all.

A related stream of research focuses on the relationship between parental status and wages, again within gender. Mothers earn 5 to 7 percent less per hour than childless women with similar education, experience, and jobs (Budig and England 2001; Budig and Hodges 2010, 2014; England et al. 2016; Glauber 2007; Jee et al. 2019; Pal and Waldfogel 2016; Waldfogel 1997). The size of this “motherhood wage penalty” varies by marital status (Budig and England 2001; Budig and Hodges 2010; Glauber 2007; Pal and Waldfogel 2016), race (Anderson et al. 2003; Glauber 2007; Van Winkle and Fasang 2020), and education (Doren 2019; England et al. 2016). Married, White, and highly educated women experience the largest motherhood penalty, although it has declined more rapidly than the penalty experienced by less educated mothers and by Black or Hispanic mothers (Glauber 2007; Pal and Waldfogel 2016). Although there is some evidence that fathers earn a wage premium relative to childless men, this finding is less robust across studies than the motherhood wage penalty, and it may be restricted to younger, married, White, and highly educated fathers of residential children (Glauber 2008; Hodges and Budig 2010; Killewald 2013; Killewald and Gough 2013; Van Winkle and Fasang 2020).

A central goal of the family wage gap literature is to identify whether these within-gender associations between parenthood, marriage, and wages are due to a causal effect of marriage or parenthood, to reverse causality wherein higher wages increase the likelihood of marriage or parenthood, to wage-relevant factors that influence the selection of workers into marriage or parenthood, or to a spurious correlation between family status and wages. This concern with causality is evident in the questions that motivate some of the field’s most prominent papers: Is there a motherhood wage penalty? (Budig and England 2001; Correll et al. 2007; Waldfogel 1996). Is there a fatherhood wage premium? (Glauber 2008; Hodges and Budig 2010; Killewald 2011). And what explains the marriage premium? (Budig and Lim 2016; Killewald and Gough 2013; Ludwig and Brüderl 2018).

Consistent with this focus on causal effects, most contemporary research on the family wage gap relies on longitudinal studies of a single cohort for which there are comprehensive fertility and labor force histories, and applies models to these data that, under specific assumptions, can plausibly support causal inference. One common strategy is to apply fixed-effect models to longitudinal data to “net out” observed and unobserved time-invariant differences across workers, including their human capital (e.g., England et al. 2016; Killewald and Gough 2013). Other strategies include relying on unique samples, such of monozygotic twins (Antonovics and Town 2004), or focusing on particular groups, such as older grooms or men in “shotgun” marriages followed immediately by the birth of a child (Ginther and Zavodny 2001; Killewald and Lundberg 2017).

These efforts show that selection into marriage of earners with higher wages or higher wage growth potential accounts for at least some, if not all, of men’s marriage wage premium (Antonovics and Town 2004; Budig and Lim 2016; Cheng 2016; Ginther and Zavodny 2001; Killewald and Lundberg 2017; Korenman and Neumark 1991; Ludwig and Brüderl 2018). The unadjusted fatherhood wage premium for men also seems to be partly driven by selection (Killewald 2013; Van Winkle and Fasang 2020). For women, differential selection appears to be less consequential for the motherhood and marriage penalties. Instead, the birth or adoption of a child leads to reductions in work experience or in work hours and, in turn, lower wages (Budig and England 2001; Cheng 2016; England et al. 2016; Killewald and Gough 2013). This is broadly consistent with experimental studies that document differences in job callback rates and in subjects’ perceptions of job candidates depending on whether a fictive job-seeker’s application materials include signals of motherhood (e.g., Correll et al. 2007; Ishizuka 2021).

As informative as they are, efforts to parse causal from selection effects of family status come at the cost of understanding patterns in the full labor force. In the standard fixed-effect approach, workers who remain unmarried or childless throughout the observation period or who are missing all but one observation do not contribute to estimates of marriage or parental status effects. In the National Longitudinal Survey of Youth (NLSY) 1979, a dataset commonly used in the family wage gap literature, these exclusions eliminate about 33 percent of workers (Killewald and Lundberg 2017). Because of various data limitations and decisions to obtain cleaner causal estimates, analysts may also exclude workers who had a child before entering the panel (Cheng 2016; Doren 2019; Killewald and Gough 2013), who remain unmarried until age 45 (Cheng 2016), or who are not White (Budig and England 2001; England et al. 2016). And, of course, single-cohort studies may not generalize to the full labor force. Audit studies likewise suffer from challenges to generalizability and to the problem of “scaling up” evidence of differences in callback rates or recommended wages to the total gender wage gap.

A related concern is that a focus on the causal effects of marriage or parenthood, at least as it is estimated in much of this literature, renders invisible the wage disadvantages experienced by childless or unmarried adults. Part of the issue is that in within-gender analyses, childless or unmarried workers are typically treated as the baseline group, both by convention and by theory, and accordingly tend to “disappear” in discussions of results. More importantly, within-gender analyses preclude comparing unmarried men and unmarried women to each other, or childless men and childless women to each other.

In short, the family wage gap literature provides insight into whether and why mothers are paid less or fathers are paid more than childless men and women, but not the extent to which family wage gaps contribute to the gender wage gap. There are a few notable exceptions. Waldfogel (1998) used NLSY79 data to show that at age 30, 56 percent of the total gender gap in the log of hourly wages is “explained by” the gender-differentiated effects of marital and parental status on wages. More recently, Juhn and McCue (2017) found that the association of marriage and parenthood with the gender gap in wages among workers age 30 and 40 increased between 1968 and 2014. Another study of Swedish administrative data shows that the gender gap in wages within couples increases by 10 percentage points within 15 years after the birth of the first child (Angelov, Johansson, and Lindahl 2016). In each of these studies, restrictions in the analytic sample (e.g., a single birth cohort, the exclusion of childless adults) or in the quantities estimated (e.g., cohort- and age-specific effects) offer cleaner causal estimates of marital and parental effects on gender wage gaps, but at the cost of limiting coverage to a small slice of the labor force. Our analysis complements these studies, trading away any pretense of causal inference in exchange for the ability to describe patterns for the full labor force.

Racial DIFFERENCES In The Gender And Family Wage Gap Relationship

The extant research literature offers even less systematic evidence on the association between family wage gaps and the gender wage gap among workers from racialized minority backgrounds. We see this as a critical gap, given that pervasive labor market discrimination and other structural inequalities and adaptations to them fundamentally affect the meanings and experiences of marriage and parenthood (see Dow 2019), further calling into question the validity of generalizing the patterns from the full sample to patterns for Black and Hispanic workers (see Cheng 2016).

The few available “family wage gap” studies of Black and Hispanic workers suggest the motherhood wage penalty is smaller for Black and Hispanic women than for White women (Budig and England 1991; England et al. 2016; Glauber 2007; Waldfogel 1997), and the fatherhood wage premium is less robust for Black and Hispanic fathers (Glauber 2008; Hodges and Budig 2010; Van Winkle and Fasang 2020). The wage boost associated with marriage is smaller for Black men than for White men (Kilbourne et al. 1994; Korenman and Neumark 1991), and the wage penalty associated with marriage may be exclusive to White women (Cheng 2016).

Given the family wage gaps among Black and Hispanic workers are smaller than among White workers, we might also expect family wage gaps to have a weaker association with (race-specific) gender wage gaps among Black and Hispanic workers. However, this straightforward story is complicated by the fact that the gender wage gap is also smaller in these racial and ethnic groups (Budig, Lim, and Hodges 2021; Hegewisch and DuMonthier 2016; Mandel and Semyonov 2016), affecting the denominator in estimates of the share of the gender wage gap associated with family wage gaps. Racial and ethnic disparities in the observed covariates of wages (e.g., human capital, job and labor force attributes, occupation) and their association with wages may also differ across racial and ethnic groups. Rather than gloss over these variations in the relationship between family wage gaps and the gender wage gap, we repeat our analysis of the entire labor force with analyses of Black, Hispanic, and White workers alone, where sample sizes allow.

Data, Variables, And Methods

Data

Our analyses are based on data from the 2018 SIPP, the most recent SIPP panel in which data quality was unaffected by budget cuts (as in the 2019 SIPP) or the pandemic. The SIPP uses an event history calendar to collect information about the relationship among household members, demography, and employment and wage data for each month. To minimize recall bias, we use event data from the month preceding the first wave of the SIPP panel, meaning the reference period is December 2017.¹

For our goals, the SIPP offers important benefits over more commonly used datasets in the gender and family wage gap literatures. First, it allows researchers to identify parents of grown or nonresidential children. This avoids bias in estimates of the within-group wage gap from including these parents in the “childless adult” category. Second, it includes a direct measure of work experience, although in the 2018 SIPP panel this measure is limited to tenure with the current employer.² Third, it is representative of the entire labor force and includes fertility histories that cover all children, instead of just those born or entering the household after the respondent enters the longitudinal panel, as in most family wage gap research that relies on fixed-effects models applied to panel data. Finally, the SIPP sample is large enough to support models that adjust for detailed occupations and to fit the more parsimonious models to the Black, Hispanic, and White subsamples. The full sample includes Asian and “other race” workers, but there are too few respondents from these groups to obtain reliable estimates in race-stratified samples.

Our analytic sample includes wage and salary workers age 18 to 67 who have non-zero earnings. This age range captures most of the working population and acknowledges that the wage effects of marriage and motherhood can linger well beyond the departure of adult children from the home (Abendroth, Huffman, and Treas 2014; Cheng 2016; Van Winkle and Fasang 2020). Note 8 provides additional results after restricting the sample to adults of prime working age (i.e., age 25 to 54). The final analytic sample includes 23,765 workers. We weight the data by the final person weight provided by the SIPP.

Variables

Our measure of the gender wage gap is based on men’s and women’s hourly rate of pre-tax wage and salary income, excluding income from self-employment or investments.³ In SIPP 2018, respondents were allowed to report wages as they prefer: hourly, bi-weekly, weekly, monthly, bi-monthly, annually, or as the gross amount from W-2 forms. Based on this information, the SIPP data providers calculated weekly earnings. For workers who did not report hourly wages directly, we calculate hourly wages from weekly earnings and the number of usual hours worked per week at the main job. For a small percentage of respondents whose usual work hours are missing, we use the number of hours worked in the week preceding the survey.

To preserve confidentiality, the SIPP top-codes wage and income data, meaning earnings above a threshold are replaced with the mean (for hourly wages) or median (for all other earnings) among the demographic group to which high-earning respondents belong. We exclude 463 workers, or 1.9 percent of the analytic sample, whose hourly wages fall below $2 or above $250, under the assumption that these reflect errors in reports of wages or of hours worked (Blau et al. 2021).⁴ With top-coding and this trimming, women in our analytic sample earn an average of $24.31 per hour and men $29.68 per hour (see Table 1). This yields an unadjusted gender wage ratio of approximately 82 cents on the dollar, consistent with estimates from other data sources.

Table 1.

Means and Standard Errors of Variables Used in the Analysis by Gender: All Workers

Variable	Men	Women
Hourly wages (logged)	3.101 (.007)	2.925 (.006)
Hourly wages (2017 US$)	29.681 (.302)	24.305 (.205)
Parent (reference: “never had child”)	.603	.653
Marital status
Never married (reference)	.265	.255
Married	.548	.500
Previously married	.075	.137
Cohabiting	.112	.108
Age	40.538 (.090)	40.793 (.085)
Race
White (reference)	.621	.616
Black	.109	.136
Hispanic	.184	.162
Asian	.062	.058
Other	.024	.028
Education
Less than high school	.085	.054
High school graduate (reference)	.279	.223
Some college	.276	.303
College graduate	.231	.256
Advanced degree	.129	.164
Job tenure (years)	7.499 (.070)	7.102 (.073)
Weekly work hours
Less than 35 hours	.127	.257
35 to 49 hours (reference)	.660	.642
50 or more	.214	.101
Public sector	.137	.187
Region
East (reference)	.181	.185
Midwest	.216	.223
South	.361	.368
West	.242	.224
Metropolitan	.874	.872
Union	.146	.133
Occupations	510 Occupations
N	12,079	11,686

Source: SIPP 2018 Panel.

Note: All data are weighted using the final weight provided by the SIPP. Standard errors are estimated using Fay’s modified balanced repeated replication (BRR) method, using replicate weights provided by the SIPP.

The key variables in our analyses are gender, marital status, and parental status. Gender is indicated with a binary variable in which the reference category is men. Marital status is coded into four categories: currently married; separated, divorced, or widowed but not currently married (“previously married”); currently cohabiting; and never married, the reference category. Cohabitation is indicated by SIPP’s household relationship question, from which we identified respondents who have unmarried romantic partners currently living in the same household. By necessity, respondents who previously cohabited but are currently neither cohabiting nor married are included in the “never married” category. In the SIPP, a greater proportion of men (.55) than women (.50) were married, a greater proportion of women (.14) than men (.08) were previously married but not currently married, and approximately equal proportions of women and men were cohabiting (.11) (see Table 1).

The proportions of workers in the four marital status groups differ by race (see Table 2). Black workers and, to a lesser extent, Hispanic workers are less likely to be married than White workers. Gender differences in the distribution of marital status are also larger among Black and Hispanic workers than among White workers. The proportion of married individuals is .42 for Black men and .32 for Black women, and .51 for Hispanic men and .44 for Hispanic women, compared to .57 for White men and .55 for White women. The proportion of never-married individuals is .34 for Black men and .42 for Black women, about .29 for Hispanic men and Hispanic women, and .24 for White men and .21 for White women.

Table 2.

Means and Standard Errors of Hourly Wages and Parental and Marital Status Variables for Black, Hispanic, and White Workers

Variable	Black		Hispanic		White
Variable	Men	Women	Men	Women	Men	Women
Hourly wages (logged)	2.903 (.021)	2.832 (.017)	2.867 (.015)	2.729 (.016)	3.187 (.009)	2.982 (.008)
Hourly wages (2017 US$)	24.395 (.855)	22.603 (.613)	22.653 (.582)	19.377 (.479)	31.889 (.367)	25.401 (.264)
Parent (reference: “never had child”)	.622	.679	.631	.690	.596	.640
Marital status
Never married (reference)	.344	.419	.296	.293	.240	.209
Married	.420	.318	.513	.440	.571	.547
Previously married	.095	.169	.062	.162	.081	.130
Cohabiting	.141	.095	.128	.105	.108	.114
N	1,193	1,498	2,261	2,082	7,583	7,125

Source: SIPP 2018 Panel.

Note: All data are weighted using the final weight provided by the SIPP. Standard errors are in parentheses and estimated using Fay’s modified balanced repeated replication (BRR) method, using replicate weights provided by the SIPP.

Parental status is indicated by a binary variable that identifies respondents who have children of any age, relationship (biological, step, or adoptive), and residential status.⁵ We construct the parental status measure from three sources in the SIPP: a parent screener variable; the household roster data, which link parents and all residential children including non-biological children; and fertility history data, which identify all biological children and their birth year regardless of their residential status.⁶

Differences in parental status across the Black, Hispanic, and White samples are more modest than differences in marital status (see Table 2). The gender gap in parental status is also similar across all groups of workers, with the proportion of women who are parents exceeding the proportion of men who are parents by 4 to 6 percentage points. The weighted percentages of the parental status groups and their unweighted counts are provided in Table S1 in the online supplement.

In some analyses, we adjust for common covariates of wages: age and its square, education (less than high school, high school graduate, some college, college graduate, advanced degree), years of job tenure and its square, weekly work hours (under 35, 35 to 49, 50 or more), sector (public or private), region (East, Midwest, South, and West), metropolitan status, and union membership or contract coverage. In analyses based on the full sample, we also adjust for race (White, Black, Hispanic, Asian, and other race). Additional models fit a full set of dummy variables for 510 occupations, coded into the 2010 Census Occupation Classification scheme. Descriptive statistics for these covariates, with the exception of occupation, are provided in Table 1 for all workers and Table S2 in the online supplement for Black, Hispanic, and White workers.

Methods

We first estimate unadjusted and adjusted gender gaps in wages using estimates from OLS regressions in which gender, marital status, and parental status fully interact.⁷ Model 1 does not include any adjustment variables. It allows us to estimate “raw” gender wage gaps across the family status groups—including never-married adults and childless adults—and the total contribution of family wage gaps to the gender wage gap. Model 2 adjusts for worker and job attributes that are commonly included in family and gender wage gap models (age, race, education, job tenure, work hours, sector, region, metropolitan status, and union membership). We combine these attributes into one model to keep the volume of results more manageable, maintain focus on the family wage coefficients, and avoid the need to make tacit assumptions about the causal order among covariates (see note 9 for the results of an interim model that includes only “pre-market” factors).

Model 3 adds indicators of detailed occupation. We separate occupation from the other covariates in the adjustment models for two reasons. First, although occupation does not feature especially prominently in the family wage literature, recent gender wage gap research shows that the uneven distribution of men and women across occupations (i.e., occupational segregation) contributes a large and growing share of the gender wage gap (e.g., Blau and Kahn 2017; Mandel and Semyonov 2014). Comparison of Models 2 and 3 highlights if, and in what patterns, occupational segregation by family status contributes to gender wage gaps. Second, and more practically, the Black and Hispanic subsamples are too sparse to be confident in results from occupation-adjusted models. To be able to compare identically specified models across the race-stratified and full samples, we need to separate occupation from the other covariates in the models fit to the full sample.

In estimating Models 2 and 3, we do not mean to imply that the adjusted models get at the “true” association between gender or family status and wages. After all, many of the covariates in Models 2 and 3 are not clear antecedents of marital and parental status and are better understood as mediators of the relationship between pre-market factors, family status, and wages (Killewald 2013; Lundberg and Rose 2002; Petersen and Morgan 1995). Instead, we fit these models to put the magnitude of family status contributions to gender wage gaps in context, and to reveal how commonly estimated individual and structural covariates of wages in gender wage gap research (e.g., Blau and Kahn 2017; Cha and Weeden 2014) are embedded in family demographic processes in which they confound or mediate the association between marriage and parenthood and wages, or both.

Our second analysis decomposes the gender wage gap, estimated from separate wage equations for men and women, into the portion of the mean gender wage gap associated with gender differences in the means of the predictors (“endowment effects,” in common decomposition terminology), the portion associated with gender differences in coefficients of predictors (“coefficient effects”), the portion associated with simultaneous differences in endowments and coefficients (“interaction effects”), and the residual (Blinder 1973; Jann 2008; Oaxaca 1973; see also Kitagawa 1955). These wage equations only fit the main effects of marital and parental status, given most coefficients for the marriage-parental interaction effects in the decomposition model are not significant.

We are primarily interested in the coefficient effects of marital and parental status, which show the portion of the gender wage gap associated with gender-differentiated wage returns to family status. The coefficient effects for categorical variables are sensitive to the choice of the reference group. One solution is to transform the variables to express them as deviation from the grand mean (Jann 2008; Yun 2005). Compared to the results using untransformed variables, this solution generates substantively similar patterns (see Table S3 in the online supplement). However, we can no longer interpret the coefficients in relation to a marriage premium, a motherhood wage penalty, or a fatherhood wage premium. Given the similarities of the results, we present results using untransformed variables.

In the full sample and White subsample, similar family status distributions by gender translate into very small endowment effects of marital and parental status on gender wage gaps. However, in the Black and Hispanic subsamples, men and women have quite different distributions across marital status. As a result, the endowment effects are more substantial, and we present them in addition to the coefficient effects.

The main decomposition results use the men’s equation as the baseline. The coefficient effects thus represent the expected change in women’s mean of log hourly wages if women had men’s coefficients, and the endowment effects represent the expected change in women’s mean of log hourly wages if women had men’s mean values on the predictors. We also estimated decomposition results using the women’s equation as the baseline. This yields substantively identical results for all workers and for Hispanic workers, but a slightly larger endowment effect of marriage for Black workers.

Results

How Do Gender Wage Gaps Differ across the Family Status Groups?

In SIPP 2018, not surprisingly, the gender gap in unadjusted (logged) hourly wages among all workers is larger among parents than among childless adults. Specifically, the ratio of women’s mean wages to men’s is .77 among parents, corresponding to a 23 percent wage gap, but only .93 among childless adults, corresponding to a 7 percent wage gap (not shown). The story becomes more complex when we examine the unadjusted gender wage gaps by eight family status groups defined by the cross-classification of family and marital status, as shown in Figure 1a. Among childless workers (see left panel of Figure 1a), the wage gaps between men and women who are cohabiting or who were previously married are close to zero, and the standard errors are too large to reject the implicit null of no gender wage gap. Among never-married childless adults, the gender wage gap is estimated at 6 percent, or an earnings ratio of .94 (e ^{(2.701 – 2.768)} = .94), which is statistically significant at conventional thresholds. Among married childless adults, the gender wage gap is more than twice as large at 15 percent, or a gender wage ratio of .85.

Figure 1.

Log of Hourly Wages by Gender and Family Status, Full Sample

Gender wage gaps among parents also vary by parents’ marital status, as shown in the right-hand panel of Figure 1a. Married parents show the largest gap, at 23 percent (i.e., a women-to-men wage ratio of .77). Previously married and cohabiting parents also show sizable gender wage gaps of 17 and 11 percent, respectively. Never-married parents, by contrast, show no significant gender wage gap. The gender wage gap among cohabiting parents (11 percent) is not significantly different from—and, potentially, smaller than—the gender wage gap among married childless adults (15 percent).

Adjusting for demographic and human capital factors (age, race, education, job tenure) and job attributes (work hours, public or private sector, region, and union) reduces the observed differences in average wages across family status groups, as shown by the contrast of Figure 1a to Figure 1b. However, it does little to reduce gender wage gaps within the family status groups (see also Blau and Kahn 2017). Indeed, for most family status groups, the estimated gender wage gaps increase. For example, among never-married childless adults, the unadjusted model shows a 6 percent gender wage gap (see Figure 1a), but adjusting for worker and job attributes expands this to a 10 percent gap (see Figure 1b). Among never-married parents, the gap increases from a non-significant 7 percent gap to a significant 10 percent gap. For most other family status groups, the increase is 2 to 5 percentage points. The larger observed gaps in the adjusted model are attributable to women’s greater likelihood of earning at least a baccalaureate degree, and the higher wages of degree-holders relative to non-degree-holders (see also note 9).

Adjusting for occupation, by contrast, reduces observed gender wage gaps for all family status groups relative to the human capital and job attribute model (compare Figures 1b and 1c). The greatest effect is seen among married parents, where adjusting for occupation reduces the estimated gender wage gap by 6 percentage points, from 23 to 17 percent. In other words, approximately one quarter of the adjusted gender wage gap among married parents is associated with occupational segregation by gender. With the exception of cohabitors, all parent groups show a larger decline in the net gender wage gap from adjusting for occupation than do the childless adult groups with the same marital status.

Notably, the residual gender wage gap within family status groups remains substantial after adjusting for occupations: 17 percent among married parents, 13 percent among previously married parents, 10 percent among cohabiting parents, and 12 percent among married childless adults. In all eight family status groups, over 70 percent of the total gender wage gap remains “unexplained” in the occupation adjusted models. This residual gender wage gap could, of course, be tied to unobserved covariates (e.g., firm size, worker commitment) or to unobserved processes (e.g., employer discrimination) that are associated with gender, family status, and wages (see, e.g., Correll et al. 2007; Stainback and Tomaskovic-Devey 2012).

Decomposition Results

How do the gender-differentiated marriage premium, the motherhood wage penalty, and the fatherhood wage premium contribute to the overall gender wage gap? Table 3 shows decomposition results derived from an unadjusted model (Model 1); a model that adjusts for worker education, experience, work hours, and job and labor market attributes (Model 2); and a fully adjusted model that adds occupation dummies (Model 3). The regression coefficients used to produce these decomposition results are shown in Appendix Table A1.

Table 3.

Wage Decomposition: Estimated Contributions of Family Wage Gaps to the Gender Wage Gap for All Workers

	Model 1^a		Model 2^b		Model 3^c
	Contribution	Percent of Total Gap	Contribution	Percent of Total Gap	Contribution	Percent of Total Gap
Total gap = .176
Parent	.051** (.015)	29.1	.052** (.014)	29.5	.034* (.014)	19.3
Married	.058** (.013)	32.9	.033** (.012)	18.6	.031* (.012)	17.6
Previously married	.004 (.005)	2.2	.003 (.005)	1.6	.002 (.005)	.9
Cohabiting	<.001 (.004)	.1	.001 (.004)	.3	–.001 (.003)	–.4

Source: SIPP 2018 Panel.

Note: N = 23,765. The “contribution” columns show the contribution to the gender gap in mean wages of gender differences in coefficients associated with marital and parental status. See Appendix Table A1 for regression coefficients and Table S4 in the online supplement for the full decomposition results.

Model 1 fits only parental status and marital status without any other covariates.

Model 2 adjusts for all covariates except for occupations (see Table 1).

Model 3 adds dummy variables for 510 detailed occupations to Model 2.

p < .05; **p < .01 (two-tailed test).

Model 1 in Table 3 shows that gender differences in the wages of parents relative to those of childless adults account for .051 log points, or about 29 percent, of the total gap of .176 log points observed in the unadjusted model. Put differently, if mothers’ wages relative to those of childless women were the same as fathers’ wages relative to childless men, the total gender gap in wages would be 29 percent smaller. The higher wage returns to marriage for men than for women account for approximately 33 percent of the total gender wage gap. Gender differences in the relative wages of previously married workers and cohabiting workers (both relative to never-married workers) are negligible and noisy, together contributing only about 2 percent of the total gender wage gap.

Taken together, these results suggest that approximately 62 percent of the total gender wage gap is associated with gender-differentiated wage “returns” to marriage or parenthood (29.1 + 32.9 = 62 percent). Conversely, approximately 36 percent of the gender wage gap is unrelated to gender-differentiated wage returns to any type of family status (i.e., 100 – [29.1 + 32.9 + 2.2 + .1] = 35.7 percent).⁸

Adjusting for human capital and job attributes (but not occupation) decreases the contribution of gender-differentiated wage returns to marriage to 19 percent of the total gender wage gap, compared to 33 in the unadjusted model. In other words, a little under half of the total marriage premium effect is associated with gender differences in human capital and job attributes between married and never-married workers (i.e., [.058 – .33]/.058 = .43). The large attenuation likely reflects selectivity into marriage based on factors associated with wages (see Killewald and Lundberg 2017; Ludwig and Brüderl 2018). By contrast, adjusting for these covariates has virtually no effect on the percent of the gender wage gap associated with gender-specific returns to parenthood. This result is anticipated by recent gender wage gap research that shows only a modest share of the gender wage gap is associated with gender differences in education and other measures of human capital (e.g., Blau and Kahn 2017; Mandel and Semyonov 2014).⁹

Adjusting for occupation (Model 3) has a more substantial effect on the contribution of the gender-specific wage returns to parenthood, but not marital status. Specifically, the contribution of parental status declines from 30 percent in Model 2 (human capital and job attributes) to 19 percent in Model 3 (occupation; see Table 3). The contribution of marriage, however, remains relatively stable at around 18 percent. Put differently, approximately one third of the total parental status effect is associated with occupational segregation by gender and parental status, but virtually none of the marital status effect. We identify possible reasons behind the different patterns for parenthood and marriage in the Discussion section.

To put the magnitude of the family wage gap contributions in context, it is helpful to compare them to other covariates of wages. In Model 2, the endowment effect of education (i.e., the overrepresentation of women among college graduates) is –.044 log points, or 25 percent of the total wage gap (see Table S4 in the online supplement). This is smaller than the wage gap-exacerbating effect of parental status (.052 log points), meaning the gender-differentiated returns to parenthood entirely offset women’s educational advantages. The effect of gender-differentiated wage returns to marriage (.033) is about 75 percent of the size of the education endowment effect (–.044). In Model 3, the coefficient effects of parental and marital status are the largest observable sources of the gender wage gap except occupation.

Racial Differences in the Family and the Gender Wage Gap Relationship

In Figures 2, 3, and 4, we present results from models fit to the subsamples of non-Hispanic Black (Figure 2), Hispanic (Figure 3), and White (Figure 4) workers. To ease comparisons, Table 4 presents the gender wage ratio within family status groups for each race subsample, where this ratio is calculated from the same coefficients as in the figures. Because the samples of Black and Hispanic workers are too small to estimate Model 3, which includes detailed occupation effects, we do not show these results. Smaller sample sizes may also account for the larger standard errors in models fit to the Black and Hispanic samples.

Figure 2.

Log of Hourly Wage by Family Status for Black Men and Women

Figure 3.

Log of Hourly Wages by Family Status for Hispanic Men and Women

Figure 4.

Log of Hourly Wages by Family Status for White Men and Women

Table 4.

Ratio of Women’s to Men’s Hourly Wages for Black, Hispanic, White, and All Workers

	Black		Hispanic		White
	Model 1^a	Model 2^b	Model 1	Model 2	Model 1	Model 2
Childless adults
Never married	.96	.92	.91	.87	.95	.92
Married	.97	.92	.86	.83	.86	.85
Previously married	1.07	.82	.78	.72	1.05	.99
Cohabiting	1.03	1.00	.94	.89	.91	.88
Parents
Never married	1.05	1.04	.78	.72	.93	.89
Married	.93	.92	.84	.79	.72	.74
Previously married	.92	.88	.82	.78	.83	.79
Cohabiting	.96	.91	.99	.92	.82	.81

Source: SIPP 2018 Panel.

Note: N = 2,691 Black workers, 4,343 Hispanic workers, and 14,708 White workers. The ratios are calculated by exponentiating the gender gaps in log of hourly earnings.

Model 1 does not include any covariates.

Model 2 adjusts for age and its square, education, years of job tenure and its squared term, work hours, sector, region, metropolitan status, and union status.

What can we learn from the race-specific models? As prior research has shown, gender wage gaps among Black (Figure 2) and Hispanic (Figure 3) workers are smaller than those among White workers (Figure 4) or all workers (Figure 1). Similarly, the unadjusted gender wage gaps are smaller among Black parents (Figure 2a) and Hispanic parents (Figure 3a) than among White parents (Figure 4a; compare also Model 1 for the three groups in Table 4; see also Glauber 2007, 2008). These racial differences are especially pronounced for married parents, where a wage gap of 28 percent (a wage ratio of .72) among married White parents is reduced to 7 percent (a wage ratio of .93) with overlapping 95 percent confidence intervals among married Black parents, and to 16 percent (a wage ratio of .84) among married Hispanic parents. Childless Black workers (of any marital status) also show smaller gender wage gaps than do childless White workers and Black parents.

The SIPP data also reveal racial heterogeneity in the gender wage gaps by marital status. Never-married and childless Black workers have a 4 percent gender wage gap with overlapping confidence intervals, compared to a 5 percent wage gap observed for never-married and childless White workers (see Table 4, also compare Figures 2a and 4a). Married and childless Black workers have a 3 percent gender wage gap, compared to a 14 percent wage gap observed for White workers. However, childless Hispanic workers have similar gender wage gaps as childless White workers.

As was the case for all workers (Figure 1b), adjusting for worker and job attributes compresses wage gaps between the marital and parental status groups for Black (Figure 2b), Hispanic (Figure 3b), and White (Figure 4b) workers, but it does not reduce the gender wage gaps within family status groups. In fact, for many family status groups, covariate adjustment increases the estimated gender wage gaps. This is most pronounced in the Hispanic subsample, where adjustment results in a 5 percentage-point increase (from 16 to 21 percent) in the wage gap among married parents and a 6 percentage-point increase (from 22 to 28 percent) among previously married childless adults. Among Black workers, adjusting for covariates also increases the estimated gender wage gaps, but by a smaller amount. In all family status groups, the confidence intervals for Black men and women overlap in the covariate-adjusted models.

Table 5 shows substantial variations among Black, Hispanic, and White workers in the contributions of family wage gaps to the (race-specific) gender wage gaps. In both absolute terms and as a percentage of the gender wage gap, the unadjusted coefficient effects of parenthood and marriage are smaller for Black and Hispanic workers than for White workers or all workers (shown in Table 3). Specifically, gender differences in the wage returns to parenthood in the unadjusted model (Model 1) range from .005 log points, or about 7 percent of the total gender wage gap (.071 log points), among Black workers and .014 log points, or about 10 percent of the gender wage gap (.138 log points), among Hispanic workers, to an astonishing .098 log points, or half of the gender wage gap (.205 log points), for White workers. The estimates for Black and Hispanic workers are not statistically significant. Similarly, gender-differentiated returns to marriage contribute 25.4 percent of the gender wage gap for Black workers and 16.2 percent for Hispanic workers compared to 34.3 percent for White workers, but the former two percentages are based on imprecise estimates. The coefficients for the gender differences in the returns to being previously married (instead of never married) may be slightly larger among Black and Hispanic workers than among White workers, but again these estimates are not significant. The general pattern is that family wage gaps, and in particular the motherhood wage penalty and fatherhood wage premium, contribute smaller shares to gender wage gaps for Black and Hispanic workers than for White workers.

Table 5.

Wage Decomposition: Estimated Contributions of Family Status to the Gender Wage Gap for Black, Hispanic, and White Workers

	Model 1^a		Model 2^b
	Contribution	Percent of Total Gap	Contribution	Percent of Total Gap
A. Black Workers (total gap = .071)
Coefficient effects
Parent	.005 (.047)	7.3	–.013 (.046)	–18.4
Married	.018 (.023)	25.4	.025 (.022)	34.9
Previously married	.008 (.016)	11.3	.020 (.016)	28.3
Cohabiting	.002 (.009)	2.3	.006 (.009)	8.0
Endowment effects
Parent	.004 (.003)	5.1	.003 (.003)	4.9
Married	.042** (.007)	59.7	.018** (.005)	25.3
Previously married	–.019** (.005)	–27.0	–.004 (.003)	–6.2
Cohabiting	.007^* (.003)	10.0	.003 (.003)	4.8
B. Hispanic Workers (total gap = .138)
Coefficient effects
Parent	.014 (.047)	9.9	.044 (.044)	32.1
Married	.022 (.028)	16.2	.034 (.028)	24.4
Previously married	.015 (.015)	10.5	.018 (.014)	12.7
Cohabiting	–.010 (.010)	–7.3	–.008 (.009)	–6.0
Endowment effects
Parent	.001 (.002)	1.0	.001 (.002)	1.0
Married	.015** (.003)	10.7	.001 (.003)	.9
Previously married	–.018** (.005)	–12.8	–.002 (.005)	–1.8
Cohabiting	.003 (.002)	2.5	.002 (.002)	1.5
C. White Workers (total gap = .205)
Coefficient effects
Parent	.098** (.018)	47.7	.079** (.017)	38.8
Married	.070** (.019)	34.3	.040* (.018)	19.7
Previously married	–.002 (.006)	1.2	–.001 (.006)	–.4
Cohabiting	.004 (.005)	1.8	.002 (.004)	1.1
Endowment effects
Parent	<.001 (.001)	.1	<.001 (.001)	.1
Married	.009** (.002)	4.5	.003** (.001)	1.4
Previously married	–.012** (.002)	–6.0	–.002 (.002)	–.8
Cohabiting	–.001 (.001)	–.4	<.001 (<.001)	–.1

Source: SIPP 2018 Panel.

Note: N = 2,691 Black workers, 4,343 Hispanic workers, and 14,708 White workers. The “contribution” columns show the contribution to the gender gap in mean log wages of gender differences in coefficients associated with marital and parental status (“coefficient effects”) and gender differences in compositions of marital and parental status (“endowment effects”). See Appendix Tables A2, A3, and A4 for regression coefficients and Tables S5, S6, and S7 in the online supplement for the full decomposition results.

Model 1 fits only parental status and marital status without any other covariates.

Model 2 adjusts for all covariates except for occupations (see the full list in Table 1).

p < .05; **p < .01 (two-tailed test).

At the same time, Black and Hispanic workers reap more substantial endowment effects of family status on the gender wage gaps than do White workers (see Table 5). In the unadjusted model, the higher rate of marriage among Black men (42 percent) than Black women (32 percent) is associated with .042 log points, or 60 percent of the (race-specific) gender wage gap (see Panel A in Model 1 of Table 5). Adjusting for human capital and job attributes reduces this endowment effect of marriage by more than half, to .018 log points or 25 percent of the gender wage gap (see Panel A in Model 2 of Table 5). We see a similar pattern for Hispanic workers, for whom gender gaps in marital status are larger than for White workers but smaller than for Black workers (see Table 2): among Hispanic workers, the endowment effect of marriage contributes to .015 log points, or about 11 percent of the total gender wage gap in the unadjusted model, but becomes a trivial (and non-significant) .001 log points, or .9 percent, in the adjusted model. The endowment effect of parental status is small and imprecise in both models for Black and Hispanic workers, as anticipated by the small gender gaps in parenthood for both groups (see Table 2). For White workers, the endowment effects of both parental and marital status are negligible in all models. As we will discuss, the implication is that the relationship between family status and the gender gap in wages among Black and Hispanic workers is weaker than it is for White workers, and driven more by the gender-differentiated distribution of marriage than gender-differentiated wage returns to parenthood or marriage.

Discussion

It has become something of a truism in academic and popular press reports of the gender gap in wages that “work is greedy, and families are needy” (Folbre 2022), a discourse that locates remaining wage disparities between men and women in the gendered effects of becoming a parent or entering marriage or cohabitation. However, systematic evidence of the contribution of family wage gaps to the gender wage gap is largely lacking, especially for the full labor force. Our core contribution is to provide this evidence.

The top-line result is that approximately 64 percent of the overall gender wage gap is associated with gender-specific wage differences between family status groups, with most of this driven by gender-specific wage differences between married and never-married workers (33 percent) and between parents and childless adults (29 percent). In models that adjust for a host of worker demographic and human capital attributes, job attributes, and occupation, family wage gaps are the second-largest contributors to gender wage gaps, behind occupation, and contribute 37 percent of the total gender wage gap.

The decomposition analysis shows that approximately 40 percent of the association between family wage gaps and the gender wage gap is correlated with gender differences in human capital, work hours, sector, occupation, and other labor market and job attributes. However, the pattern of these associations differs between marital and parental wage effects: the contribution of gender-differentiated wage returns to marriage declines from 33 to 19 percent after adjusting for demographic, human capital, and job attributes, but it remains unchanged after further adjusting for occupation, whereas the contribution of gender-differentiated wage returns to parenthood is unaffected by adjusting for demographic, human capital, and job attributes but declines by one third, approximately from 30 to 20 percent of the gender wage gap, after adjusting for occupation. Although some previous research has noted that (within-gender) marriage premiums are driven more by positive selection into marriage on the basis of pre-market factors and other job attributes than by occupation (Killewald and Gough 2013), to our knowledge, the differential sensitivity of marital and parental status effects on the gender gap in wages has not been previously identified in either the family wage or the gender wage literatures.

How can we understand the differential sensitivity of marital and parental wage gaps to occupation? The weak association between marital wage gaps and occupation implies that “vertical” occupational segregation—that is, segregation across occupations that systematically differ in pay—between married and never-married men and women is no more pronounced than segregation between all men and women. This could reflect the withering away of the discrimination, and in some sectors the legally proscribed discrimination, against married women that characterized labor markets in the post-World War II era, a near irrelevance of marriage to contemporary workers’ choice of occupations, or both. Conversely, the strong association between parental wage gaps and occupation implies that fathers and mothers, even more than childless men and childless women, continue to be unevenly distributed across occupations that differ in pay. This segregation could occur if discrimination against mothers is stronger in some types of occupations (but see Ishizuka 2021), if mothers and fathers are especially likely to hold gender essentialist beliefs that make traditionally female- or male-typed occupations attractive (see Thébaud and Taylor 2021), or if occupation-specific norms and expectations about work hours create stronger conflicts with dependent-care responsibilities in some occupations than in others and trigger gender-specific occupational mobility (see Cha 2013; Goldin 2014). These are mere speculations, though, and we hope our results will encourage future work on the patterns, sources, and consequences of occupational segregation by family status and its interaction with gender.

The general theme of our results is that the strong relationship between family wage gaps and the gender wage gap, long assumed in the family and gender wage gap literatures, is on the mark. At the same time, our analysis reveals two important caveats to a “family wage gap in disguise” story.

First, our results caution against a single-minded focus on the gendered effect of work-family conflict or structural barriers that generate family wage gaps. Indeed, if 64 percent of the gender wage gap is associated with family wage gaps, 36 percent is not. Similarly, the SIPP data show a gender wage gap of 6 to 10 percent, depending on adjustment, among never-married childless workers. Although this is less than half of the wage gap among married parents (17 to 23 percent, depending on adjustment), it is far from negligible. These gender differences in wages among childless and unmarried workers call into question the common interpretation of the wages of these “baseline groups,” as if they are the wage that would obtain in the absence of discrimination or of gender differences in response to work-family conflict.

Second, the strong association between the family wage differentials and the gender wage gap is limited to White workers (see Table 5). Consistent with other research, we find smaller gender wage gaps among Black and Hispanic workers than among White workers or the all-worker sample, so much so that in the fully adjusted model, these gender wage gaps are no longer statistically significant. Our more novel finding is that gender-differentiated returns to parenthood contribute far less to the gender wage gaps among Black and Hispanic workers than among White workers in both absolute and relative terms. The contributions of gender-differentiated returns to marriage are also smaller among Black and Hispanic workers in absolute terms, but comparable or even larger at times as a share of the total gender wage gap among these groups. Moreover, unlike White workers, Black and Hispanic workers also show strong endowment effects of marriage (but not parental status) on gender wage gaps.

At the most basic level, these results imply that even if one accepts the “family wage gap in disguise” framing of the gender wage gap for White workers, it does not obtain for Black and Hispanic workers. Our results thus reinforce recent calls to pay more attention to racial heterogeneity in family wage gap research. A more subtle implication is that where the goal is to understand gender wage gaps among Black and Hispanic workers, the payoff to identifying the sources of gender differences in the distribution of family status may be greater than identifying the sources of gender differences in the payoff (or penalty) to marriage or parenthood.

The generally weaker association between family wage gaps (and especially parental wage gaps) and the gender wage gap among Black and Hispanic workers is in line with qualitative scholarship that argues that Black and Hispanic experiences of parenthood, work, and work-family conflict fundamentally differ from those of White women. Contemporary notions of work-family conflict presume a norm of separate spheres and the “cult of domesticity,” wherein good mothering implies a devotion to family that is hard to reconcile with incentives and expectations at work, such as the outsized wage returns to working long hours (Cha and Weeden 2014; Goldin 2014). Dow (2019) argues that this is primarily a White, middle-class norm, and that Black women’s standards of womanhood place greater value on paid work and financial independence and allow for greater reliance on extended kin to support mothers’ paid work. Differences in Black and White women’s relationship to paid work are further exacerbated by differences in economic resources, historical legacies of Black enslavement and institutional discrimination, and Black men’s structural position within the labor market. In this social and cultural context, we might expect not only smaller gender wage gaps among Black workers, but also weaker family status effects on the gender wage gaps. We are not wedded to this interpretation of our findings, although we think it holds promise, but to the simpler point that racial heterogeneity undermines sweeping claims about the relationship between family wages and the gender wage gap.

The patterns we document here—the main finding of a strong association between family wage and gender wage gaps as well as the two caveats—shed new light on how wage-setting processes are embedded in family demography processes. Our results also open up new questions for gender and family inequality research. Why is occupation so consequential for the association between the gender wage gap and the gender-differentiated wage returns to parenthood, but not marriage? What accounts for the wage differentials between childless men and women, and between never-married men and women? To what extent do racial differences in the social meaning of motherhood (see Dow 2019) account for the observed racial differences in the relationship between family wage gaps and gender wage gaps? What explains the gender-differentiated access to marriage and its wage consequences among Black and Hispanic workers? Do family wage gaps make similar contributions to the gender wage gap at all points of the wage distribution as at the mean, as we studied here, or do these contributions differ between high- and low-paid workers? At the policy level, our empirical results suggest that efforts to address family wage gaps can complement, but not supplant, efforts to address gender wage gaps.

Supplemental Material

sj-ado-2-asr-10.1177_00031224231212464 – Supplemental material for Is the Gender Wage Gap Really a Family Wage Gap in Disguise?

Supplemental material, sj-ado-2-asr-10.1177_00031224231212464 for Is the Gender Wage Gap Really a Family Wage Gap in Disguise? by Youngjoo Cha, Kim A. Weeden and Landon Schnabel in American Sociological Review

Supplemental Material

sj-do-3-asr-10.1177_00031224231212464 – Supplemental material for Is the Gender Wage Gap Really a Family Wage Gap in Disguise?

Supplemental material, sj-do-3-asr-10.1177_00031224231212464 for Is the Gender Wage Gap Really a Family Wage Gap in Disguise? by Youngjoo Cha, Kim A. Weeden and Landon Schnabel in American Sociological Review

Supplemental Material

sj-do-4-asr-10.1177_00031224231212464 – Supplemental material for Is the Gender Wage Gap Really a Family Wage Gap in Disguise?

Supplemental material, sj-do-4-asr-10.1177_00031224231212464 for Is the Gender Wage Gap Really a Family Wage Gap in Disguise? by Youngjoo Cha, Kim A. Weeden and Landon Schnabel in American Sociological Review

Supplemental Material

sj-pdf-1-asr-10.1177_00031224231212464 – Supplemental material for Is the Gender Wage Gap Really a Family Wage Gap in Disguise?

Supplemental material, sj-pdf-1-asr-10.1177_00031224231212464 for Is the Gender Wage Gap Really a Family Wage Gap in Disguise? by Youngjoo Cha, Kim A. Weeden and Landon Schnabel in American Sociological Review

Footnotes

Appendix

Table A4.

Regression Coefficients Used for the Decomposition Analysis for White Workers (Table 5, Panel C)

	Model 1		Model 2
	Men	Women	Men	Women
Parent	.147** (.021)	–.006** (.021)	.120** (.020)	–.004 (.021)
Marital status (“never married” omitted)
Married	.517** (.025)	.388** (.027)	.194** (.025)	.120** (.025)
Previously married	.232** (.035)	.251** (.035)	.027 (.032)	.033 (.032)
Cohabiting	.172** (.028)	.139** (.033)	.068** (.024)	.048 (.029)
Age			.045** (.004)	.040** (.005)
Age squared			–.000** (<.001)	–.000** (<.001)
Education (“high school graduate” omitted)
Less than high school			–.080* (.033)	–.078* (.037)
Some college			.151** (.016)	.170** (.018)
College graduate			.482** (.021)	.452** (.022)
Advanced degree			.711** (.026)	.680** (.026)
Tenure			.009** (.003)	.013** (.003)
Tenure squared			<.001(<.001)	–.000(<.001)
Weekly work hours (“35 to 49” omitted)
Less than 35			–.179** (.027)	–.161** (.020)
50 or more			.061** (.018)	–.018(.026)
Public sector			–.136** (.021)	–.108** (.021)
Region (“East” omitted)
Midwest			–.060* (.024)	–.057* (.024)
South			–.021(.023)	–.071** (.024)
West			.088** (.026)	.071** (.026)
Metropolitan			.150** (.017)	.175** (.020)
Union			.128** (.021)	.014(.023)
Constant	2.767** (.017)	2.725** (.020)	1.555** (.086)	1.582** (.092)
R-sq	.143	.051	.353	.276
N	7,583	7,125	7,583	7,125

Source: SIPP 2018 Panel.

p < .05; **p < .01 (two-tailed test).

Acknowledgements

We thank Paula England, Misun Lim, Mary C. Noonan, and participants of the Gender Inequality Seminar at Harvard University, Center for Demography and Ecology at the University of Wisconsin-Madison, and Broom Center for Demography at the University of California-Santa Barbara, WZB Talk, and WZB AAM/NEPS/HIS Colloquium for their helpful comments on previous versions of this article.

Editors’ Note

To avoid any possible conflict of interest, the ASR Editors were not involved in the evaluation of this paper. The entire review process was handled by a Deputy Editor who is not affiliated with Indiana University.

ORCID iDs

Youngjoo Cha

Kim A. Weeden

Landon Schnabel

Data Note

Replication materials are available with the article’s online supplementary materials and at .

Notes

Youngjoo Cha is Associate Professor of Sociology at Indiana University-Bloomington and Yonsei University. Her current research investigates how the trend toward long work hours and the normative forces behind them reinforce gender inequality and what organizational and institutional conditions help to change this trend; the sources of occupational variation in the gender pay gap; the role of stereotypes for the labor market outcomes of Asian-origin individuals in the United States; and how the pandemic has changed employees’ and employers’ perspectives on work.

Kim A. Weeden is the Jan Rock Zubrow ’77 Professor of Sociology, Director of the Center for the Study of Inequality, and Stephen H. Weiss Presidential Fellow at Cornell University. Her current projects explore the relationship between the social organization of work and gender inequalities in wages in the United States and abroad; social change in the structure of inequality; race, gender, and class-based sorting into occupations, college majors, and college courses; intergenerational occupational mobility; and differences in young adults’ occupational aspirations and educational decisions, including their entry and persistence in STEM fields.

Landon Schnabel is the Rosenthal Assistant Professor of Sociology at Cornell University. His research examines inequality, gender, religion, and politics.

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