Rising Inequality in Mothers' Employment Statuses: The Role of Intergenerational Transmission

Abstract

During the late twentieth century, U.S. mothers' propensities to hold full-time jobs became increasingly unequal across the distribution of socioeconomic status (SES). Consequently, daughters in high-SES households became more likely to be raised by working mothers than daughters in low-SES households. To what extent did this unequal exposure further shape maternal employment inequality in the twenty-first century—when these daughters had grown into adults and begun to raise their own children? Leveraging the genealogical structure of the Panel Study of Income Dynamics, this article estimates intergenerational employment coefficients on a sample of late twentieth century mothers and their daughters. It documents a much stronger intergenerational relationship in high-SES families than in low-SES families. Supplementary analyses reveal that being raised by a working mother significantly reduces the motherhood employment penalty among high-SES women but not among low-SES women. Unequal rates of mother-daughter employment transmission by SES can account for 36% of growing inequality in maternal employment across SES groups, observed in the Current Population Survey, between 1999 and 2016. These findings indicate that family-level transmission processes magnify the effects of structural forces on maternal employment inequality.

Introduction

In the 1960s, few U.S. mothers were employed in paid labor. By the 2010s, college-educated mothers' employment propensities had risen dramatically, but those of mothers without a college education had risen by much less. Unequal maternal employment has become an important determinant of socioeconomic inequality. For example, unequal entry of mothers into the workforce is related to the widening family income distribution (Gonalons-Pons et al. 2020; Western et al. 2008), diverging investments in children (Guryan et al. 2008; McLanahan 2004), falling intergenerational mobility (Beller 2009), and inequality in maternal well-being (Frech and Damaske 2012).

The literature has studied a variety of structural factors related to mothers' employment opportunities over time and across the socioeconomic status (SES) spectrum. These factors include changing wages (Juhn and Murphy 1997; Leibowitz and Klerman 1995; Pencavel 1998), fertility technology and practices (Bailey 2006; Goldin and Katz 2002), childcare affordability (Attanasio et al. 2008; Averett et al. 1997; Cascio 2009; Kubota 2020), and the welfare system (Blank 2002; Eissa and Hoynes 2004; Hoynes and Schanzenbach 2012). Other work has identified SES-based inequality in maternal access to work-family support from employers (Schneider and Harknett 2019) as well as from familial and social networks (Dow 2016; Harknett and Hartnett 2011). Despite considerable investigation into these factors, little attention has been paid to a factor that has arisen as their direct consequence. Specifically, as maternal employment inequality has increased, daughters in high-SES households have become more likely than daughters in low-SES households to be raised by working mothers. To what extent does this unequal exposure, via intergenerational transmission of employment propensity from mother to daughter, shape employment inequality over the long run?

This study constructs an account of maternal employment inequality that integrates structural factors operating in society with intergenerational channels of status transmission operating in families. Recent work has suggested that compared with stay-at-home mothers, working mothers foster greater employment capacities in their daughters through the transmission of attitudes, skills, and opportunities they glean from the workplace (e.g., Galassi et al. 2019; McGinn et al. 2019). Accordingly, if structural forces increase maternal employment inequality in one generation, intergenerational channels may produce a further increase in the next generation. Moreover, the structural forces that limit employment opportunities of low-SES mothers in one generation may also limit the rate at which low-SES working mothers augment the employment propensities of the next generation. That is, structural forces and intergenerational channels may interact, furthering the level of inequality beyond what would exist if this interaction were not present. These relationships have yet to be explored.

I apply this account to a quantification of rising maternal employment inequality between SES groups over the period 1999–2016. The analysis proceeds in two steps. First, I leverage the genealogical structure of the Panel Study of Income Dynamics (PSID) to construct a data set of employment information for more than 2,200 mother-daughter pairs. The daughters were born in the mid-1960s through the early 1980s and were observed as adult mothers during the twenty-first century. I use these data to conduct the first exploration of heterogeneity in mother-daughter employment transmission across SES groups. Second, I feed these estimated intergenerational coefficients into a decomposition framework, which I apply to population-level data from the Current Population Survey (CPS). The decomposition quantifies the contribution of unequal rates of mother-daughter employment transmission by SES to growing inequality in maternal employment across SES groups.

In addition to this primary contribution, this article delivers three ancillary contributions. First, it updates the literature on temporal change in female employment. This literature has primarily considered the twentieth century and analyzed employment status in the survey week (e.g., Goldin 2006). I focus on full-time, full-year (FTFY) employment as a measure of labor market activity, which is more related to economic prosperity than occasional employment and has exhibited more change since 1999. Second, my usage of the PSID data improves existing intergenerational measures of maternal employment. Previous studies (e.g., Beller 2009; Kalmijn 1994; McGinn et al. 2019; Morrill and Morrill 2013; van Putten et al. 2008) used the respondent's report of her mother's occupation status when the respondent was 14 or 16 years old. The longitudinal data set that I construct permits a direct measurement of a mother's entire employment history throughout a daughter's upbringing. Third, the longitudinal data permit a supporting analysis of the effect of exposure to a working mother on one's own motherhood employment penalty. This analysis furthers the literature on determinants of motherhood penalties in the labor market.

Maternal Employment Across Time, Generations, and SES Subgroups

Measuring Change Over Time

Growth in women's employment statuses over the last 50 years has varied by demographic group and employment definition. Table 1 displays these patterns, based on data from the March Supplement to the CPS on women aged 25–44. Both in the 1960s and the 2010s, women with preschool-age children had lower propensities for employment than the full population of women. However, they experienced larger employment increases than the full population, especially with respect to full-time, full-year (FTFY) employment.¹ Moreover, although all education subgroups experienced substantial FTFY employment increases, college-educated mothers experienced the largest increase. The ratio of the employment propensity of college graduates to that of those with high school education or less climbed from around 1 to around 1.7 between 1969 and 2016 (last row of Table 1).

I focus on mothers with preschool-age children, the FTFY employment concept as a measure of employment status, and the period of 1999–2016. Prior literature has primarily focused on changes in female employment during the late twentieth century (e.g., Attanasio et al. 2008; Blau and Kahn 2007; Cohen and Bianchi 1999; Goldin 2006; Juhn and Murphy 1997; Leibowitz and Klerman 1995; Pencavel 1998). Although some recent accounts covered the 2000s (e.g., Abraham and Kearney 2020; Goldin and Mitchell 2017; Moffitt 2012), these studies measured employment status during the survey week instead of employment status throughout the entire year. In addition, these studies did not directly focus on mothers. Moreover, although the prior literature sometimes reported changes by education and demographic subgroup, it focused on aggregate change. My analysis of maternal employment throughout the early twenty-first century—with explicit attention to FTFY employment and to SES-based inequality—enriches the recent literature and provides a further update to the older literature.

Understanding Transmission Across Generations

Relative to being raised by a stay-at-home mother, being raised by an otherwise equivalent working mother potentially increases a daughter's own employment status in adulthood. One theory posits that daughters adopt employment-relevant beliefs and expectations via socialization by parents (Moen et al. 1997). Developmental psychologists (e.g., Eccles et al. 1990; Galambos 2004; Jodl et al. 2001; McHale et al. 1999) have documented that children's conceptualizations of gender are affected by the behaviors of their parents. Bertrand (2019) showed that children raised by full-time employed mothers expressed more egalitarian gender-role attitudes than children raised by comparable stay-at-home mothers. Other work has linked these attitudes to greater maternal employment (e.g., Farre and Vella 2013; Fernandez and Fogli 2009; Fortin 2005). In addition, maternal employment may affect the skills and opportunities a daughter possesses in adulthood. Families with two working parents instead of one may earn more income, leading to greater monetary investments in their children, in turn augmenting their children's general human capital (Kalmijn 1994) and employment statuses (Beller 2009). Working mothers may also empower their daughters to demand greater work-family support from partners and workplaces, obtain and transmit skills regarding the balance of work and family to their daughters, or secure attractive jobs for their daughters through labor market networks (e.g., Staiger 2021).

Drawing on new sources of intergenerational data, recent work has estimated strong intergenerational employment correlations between mothers and daughters (Galassi et al. 2019; McGinn et al. 2019; Morrill and Morrill 2013; van Putten et al. 2008) even after controlling for a variety of human capital and contextual factors. It is therefore plausible that some portion of the long-run change in maternal employment has resulted from changing exposure of daughters to working mothers. The present article is one of the first to explore this possibility and the first to do so using intergenerationally linked longitudinal data.² These data permit measurement of the entire employment history of a first-generation mother throughout the first 18 years of her daughter's life. Given the instability of maternal employment patterns over the lives of their children (Damaske and Frech 2016; Killewald and Zhuo 2019)—especially when children are of preschool age—this measure contains substantially more information on exposure to a working mother than the single-teenage-year measures used by much of the literature.

Intergenerational Transmission and SES-Based Inequality

The literature is also limited by its focus on the average intergenerational transmission rate in the entire sample. My focus on maternal employment inequality necessitates computation of mother-daughter employment coefficients separately by family SES, and with attention to the role of motherhood in the second (i.e., daughters') generation.³ I test the following hypotheses:

There are three reasons to suspect this relationship. First, lower-SES women are more exposed to labor market volatility (Charles and Luoh 2003) and are more likely to experience involuntary job loss (Kletzer 1998). In addition, lower-SES women tend to experience less-stable nonwage job features, such as unpredictable shift scheduling and lack of job protection in case of work-family conflict (e.g., Schneider and Harknett 2019). These forces of employment instability may prevent lower-SES women from consolidating the advantages of being raised by a working mother. Second, both employment and family instability may disrupt the socialization of daughters by working mothers. Lower rates of marriage and higher rates of union disruption—disproportionately faced by low-SES mothers (e.g., Lundberg et al. 2016; Smock and Schwartz 2020)—have been associated with less parent-child interaction and parental involvement (e.g., Kalil, Ryan et al. 2014). The greater prevalence of nonstandard work schedules among lower-SES mothers has also been shown to affect their children's behavioral developments (Dunifon et al. 2013) and pit maternal well-being against quality time with children (Kalil, Dunifon et al. 2014). Third, Lareau's (2003) ethnographic work found that high-SES families made concerted efforts to instill individualistic ideologies and attitudes of status entitlement in their children, but these efforts were less present in low-SES families. This evidence suggests that the socialization channel may be weaker in lower-SES families, perhaps as a result of instability forces.

There are two reasons to suspect this relationship. First, mothers who wish to maintain careers in a gendered society often face substantial work-family conflict (e.g., Damaske 2011; Stone 2008). Thus, effective transmission of attitudes, skills, and opportunities in high-SES families may particularly help high-SES women navigate the intersection of work and motherhood. On the other hand, employment and family instability may particularly disrupt low-SES mothers' employment statuses. Second, lower-SES mothers who wish to maintain stable employment tend to face greater childcare instability. Lower-SES workers' more unpredictable schedules and lesser access to employer-provided work-family support induce greater reliance on low-quality sources of childcare, including no supervision at all (Harknett et al. 2020). Nonstandard work schedules themselves may impact low-SES mothers' informal support systems (Su and Dunifon 2017). Moreover, despite the greater role of informal childcare, lower-SES households with working mothers still spend greater income shares on childcare than do higher-SES households with working mothers (Blau and Currie 2006; Laughlin 2010). These constraints lead to greater experiences of work-family conflict and psychological distress among lower-SES mothers (e.g., Ciabattari 2007; Dziak et al. 2010; Harknett et al. 2020). These challenges likely also disrupt employment status transmission across generations of low-SES mothers.

I test Hypothesis 2 in two ways. First, I assess how SES-based inequality in the intergenerational employment coefficient changes when I limit the second-generation sample to mothers with young children. Second, drawing on the longitudinal structure of the data, I assess SES-based inequality in how one's own motherhood employment penalty varies with one's childhood exposure to a working mother. Although fully examining SES-based heterogeneity in the motherhood employment penalty is beyond the scope of this study, this hypothesis points to one additional dimension—exposure to a working mother—that may drive motherhood penalty heterogeneity documented in recent work (e.g., Byker 2016; Sandler and Szembrot 2019). Apart from Kleven et al.'s (2019) study, which estimated intergenerational motherhood penalty coefficients using Danish administrative data, I am not aware of a study that has studied the motherhood penalty in an intergenerational framework.⁴

As shown earlier in Table 1, SES-based inequality in maternal employment rose considerably after the 1960s, suggesting—as confirmed in the next section—that high-SES daughters growing up in the late twentieth century became more likely than low-SES daughters to be raised by working mothers. This difference in exposure to working mothers, interacted with differential responsiveness of employment status to such exposure, would generate maternal employment inequality across SES groups in the twenty-first century. I test this hypothesis by using a decomposition analysis that interacts population exposure to working mothers by SES with newly estimated intergenerational employment coefficients. This approach resembles that of Bloome (2017), who quantified the portion of income inequality between individuals raised inside and outside of stable two-parent homes that can be explained by differential rates of intergenerational income transmission across childhood family structures.

Data and Methods

Construction and Summary of the PSID Intergenerational Data

The Panel Study of Income Dynamics (PSID) began following several thousand families in 1968, asking heads of households about their labor market outcomes in 1967. Using the genealogical structure of the survey, one can observe parental employment history for any individual born in 1967 or later and who belonged to one of these original families. The PSID is one of only two U.S. surveys from which it is possible to observe multiple linked generations of labor force activity. The other is the National Longitudinal Survey of Youth, which contains longitudinal modules covering both an initial cohort of individuals (the NLSY79 study) and all children born to women from this initial cohort (the NLSY79 Child and Young Adult study; see Galassi et al. 2019). I use the PSID instead of the NLSY, given that the former covers a broader range of birth cohorts. I am interested in second-generation daughters who were raising their own young children during the period 1999–2016 and thus were typically born in the 1960s, 1970s, or early 1980s. These birth cohorts are covered by the PSID, whereas most children of NLSY79 women were born after 1983.

I construct several summary measures of a daughter's exposure to a full-time employed mother. Each measure takes the form motherFT_a_b and equals the share of years between a daughter's ages a and b during which her mother was employed FTFY. I construct motherFT_0_17, motherFT_6_17, and motherFT_0_10.⁵ Each measure has relative strengths and weaknesses. For example, the 6–17 age range results in the largest sample size because it permits inclusion of the 1961–1966 cohorts in the sample: cohorts that are left out if the 0–10 age range is used. On the other hand, the 0–10 age range is more directly related to my substantive focus, which is to explain employment behaviors of mothers with young children in terms of their own mothers' behaviors. Thus, my main results are based on motherFT_0_10.

Next, I construct a sample of second-generation women who are current heads of their own households or are partners of the heads. I keep only those women born between 1961 and 1987, the range of years for which it is possible to reliably observe both the first-generation mother's full work history and the second-generation woman's work behavior. Because I am interested in modeling these individuals' employment behaviors relatively early in their career cycles—when they are most likely to be raising their own children—I focus on a sample of women aged 22–49. Finally, I merge information on these women to information on their mothers' work histories.

Table 2 summarizes the resulting sample. Statistics are weighted by original PSID family weights. The sample contains 21,980 daughter-year observations, covering 2,228 unique daughters. The average daughter was observed around 9.9 times, with earlier birth cohorts observed more often. The average birth year of the sample is 1970. The daughter-year observations range from the 1983 interview year to the 2017 year, with the mean observation coming from 2004. The average age of the sample is 33.5 years, roughly 8% of the sample is Black, and one-third of the sample has a four-year college degree. Forty-four percent of the observations correspond to young mothers: women who have at least one child under age 6 living in the household. These young-mother households have an average of 1.43 young children.

Table 2 also records statistics on the mothers of these daughters. Forty-one percent of first-generation mothers had completed some college education. The average first-generation mother was employed FTFY 27% of the time throughout the first 18 years of her daughter's life, with some mothers FTFY employed none of the time and some mothers FTFY employed the entire time. This average propensity rises when the preschool age range is excluded from the measure (motherFT_6_17) and falls when the preschool range occupies a larger share of the measure (motherFT_0_10). All measures indicate substantial variation across second-generation women in their mothers' employment histories.

Although my goal is to use these rich intergenerational data to inform national trends in maternal FTFY employment, my sample and results are representative of only the original 1968 PSID sampling frame (which contained relatively few Black and extremely few Hispanic families). Accordingly, I do not attempt heterogeneity analyses by race and ethnicity.

Intergenerational Regression Methodology

Linear Probability Models of FTFY Employment Propensity

Estimating this regression model yields an estimate of γ, the intergenerational coefficient of interest. I use a linear probability model instead of a logit or probit model for simplicity and because it is more straightforward to map a linear regression equation than a nonlinear one to a decomposition analysis (e.g., Angrist and Pischke 2009). In any case, Table A1 in the online appendix presents logit estimates of γ from the full sample; these estimates are almost the same as the linear estimates.

As hypothesized earlier, the intergenerational employment coefficient likely depends on the demographic subsample s. I present two alternative cuts of the sample on SES: one according to whether the first-generation mother had obtained any college education, and one according to whether the second-generation woman had obtained a four-year college degree.

The preferred regression specification includes controls for other economic and demographic characteristics, X_it. One set of background controls includes sets of birth cohort, parental education, own education, division of origin, and race fixed effects. Another set of contemporaneous controls captures other economic and demographic determinants of women's labor supply. These include the log real hourly wage offer, partnership status, a quadratic in the male partner's earnings (set to 0 if none is present), a quadratic in the number of children aged 0–5, the number of children aged 6–10, and a quadratic in age (e.g., Killingsworth and Heckman 1986). The real hourly wage offer is observed wage income divided by hours employed for women who were employed at all in the given reference year.⁶ I impute hourly wage offers for women who were not employed at all, based on observed wages of demographically similar women, via a procedure described in the online appendix.

The interpretation of the sth intergenerational coefficient is the effect of a 0-to-1 change in the mother's FTFY employment history on the daughter's FTFY employment propensity, with these background and contemporaneous variables held fixed. This describes how much more likely—in percentage point terms—an s-type daughter is to work full-time if her mother always worked full-time while raising her versus if her mother never worked full-time. Given that my goal is to capture the extent to which a full-time employed mother augments her daughter's capacity for full-time employment—through socialization and other unobserved investments, skills, and opportunities—I am interested in an intergenerational association between daughters' and mothers' FTFY employment propensities net of other observed determinants of daughters' employment.

Individual Fixed-Effect Models of the Motherhood FTFY Employment Penalty

The longitudinal structure of the data permits a parallel investigation into the intergenerational determinants of the motherhood employment penalty. This is the extent to which FTFY employment propensity changes when women transition into or out of raising young children. One drawback of the preceding analysis is that it relies on comparisons across different mother-daughter pairs and thus does not control for systematic unobserved differences between families in full-time employment propensities. This analysis, on the other hand, leverages daughter fixed-effect models to control for these differences. In addition to being of its own interest, this analysis functions as a check of the main intergenerational transmission analysis.

This equation contains a suite of individual fixed effects, α_i, that allow baseline full-time work propensities to vary arbitrarily across daughters. The coefficient δ_is describes the residual change in FTFY employment realized when daughter i of type s transitions into (or out of) raising her own young children. To distinguish motherhood effects from age effects, the α_st term contains a quadratic in the daughter's age. Thus, δ_is is identified from within-daughter variation in FTFY employment trajectories combined with across-daughter variation in motherhood timing.

The intergenerational coefficient, λ_s, describes the extent to which the motherhood employment penalty faced by daughter i varies according to how much her own mother worked. For example, λ_s = 0.1 implies that a daughter raised by a full-time employed mother faces a motherhood penalty that is 10 percentage points lower than an otherwise-equivalent daughter raised by a stay-at-home mother. Equation (3) also allows the motherhood penalty to vary arbitrarily according to the suite of background characteristics X_i introduced earlier: birth cohort, parental education, own education, division of origin, and race. To recover λ_s, I plug Eq. (3) into Eq. (2) and estimate the resultant linear probability model of FTFY employment propensity.

Measuring Population-Level Inequality in Maternal Employment: Bringing in the CPS

To apply the preceding analysis to population-level trends in maternal employment, I supplement the PSID data with 1968–2017 data drawn from the Current Population Survey (CPS). The CPS is better equipped to examine population employment trends than the PSID because it contains a bigger sample (roughly 60,000 households) and has maintained national representation over time. I use the March supplement to the CPS, which elicits annual employment information for CPS households during their March interviews.⁷ I construct a sample of women aged 22–49, White or Black, with nonmissing economic and demographic information.⁸

I use two methods of dividing the sample by SES. One method involves grouping all women who had wage offers above (below) the median of the hourly wage offer distribution, within a cell defined by survey year and potential experience bin, into a high-SES (low-SES) subsample. (Potential experience equals age minus years of education minus 6.) I adjust wage and business incomes for top-coding and compute hourly wages as total wage and business income divided by annual hours worked,⁹ trimming wage outliers from the sample. I then impute hourly wage offers for nonworking women via a procedure described in the online appendix.

Alternatively, I use the same definition of high SES as used in the second-generation PSID sample: having completed four years of college education. This definition has two advantages over the other. First, it is not dependent on imputation procedures. Second, it more coherently divides the population into distinct labor markets. However, this definition has a key drawback: the underlying composition of the college and noncollege populations may have changed over time as the female college attainment rate has increased. The wage-based method of stratification groups 50% of the population into each group in each year and thus may be more immune to changing composition.

These stratifications reveal two important patterns of maternal employment inequality. First, Figure 1 displays widening inequality across SES groups in daughters' exposures to full-time employed mothers.¹⁰ (The figure uses the wage-based classification of SES.) Consider the median-aged high-SES mother in 1999 (2016), who was born in 1966 (1983). According to Figure 1, between the 1966 and 1983 birth cohorts, the share of high-SES daughters raised by full-time employed mothers increased from 0.15 to 0.39—a 160% increase. On the other hand, the share of low-SES daughters raised by full-time employed mothers increased far more slowly.

Second, Figure 2 and Table 3 show a large increase in maternal employment inequality between 1999 and 2016—the exact years in which the above cohorts of daughters experienced motherhood themselves. According to Table 3, the average FTFY employment-to-population ratio across the two high-SES groups of mothers increased by 0.111 during this period, or roughly 25% over its 1999 base rate (row 6). On the other hand, the FTFY employment-to-population ratio changed little in both groups of low-SES mothers: the average increase was only 0.001 (row 3). Thus, the SES gap in maternal FTFY employment rose by 11 percentage points between 1999 and 2016 (last row). By 2016, the SES gap was a full 17.6 percentage points, which is the highest it had ever been—at least since the 1960s, when these statistics began to be tracked by the CPS.

Decomposition Methodology

Equation (5) contains two terms of interest: the portion of the SES gap attributable to structural forces, operating in 2016 in the absence of any intergenerational transmission; and the portion attributable to intergenerational transmission mechanisms. The intergenerational term answers the following question: How much lower would the SES gap in maternal FTFY employment be if working mothers did not directly augment their daughters' full-time employment statuses?

That is, Eq. (6) decomposes the observed increase in the SES gap into changes in structural forces that favor high-SES mothers—in the absence of intergenerational transmission mechanisms—and changes in intergenerational transmission of employment propensities that favor high-SES mothers.

The intergenerational terms in Eqs. (5) and (6) capture the interaction of structural forces operating in previous generations and those operating in the current generation. To see this, consider the exposure variables $motherFT¯HI$ and $motherFT¯LO$⁠. To the extent that higher-SES mothers in the previous generation encountered better employment opportunities and had more advantageous upbringings, we would expect $motherFT¯HI>motherFT¯LO$⁠. Now, consider the intergenerational coefficients $γHI$ and $γLO$⁠. To the extent that structural forces of instability, operating in the current generation, inhibit low-SES mothers from translating the advantages of being raised by a working mother into their own full-time employment statuses, we would expect $γHI>γLO$⁠. The intergenerational term of Eq. (5) captures the interaction of unequal exposures to working mothers with unequal responses to being raised by a working mother. It thus describes the additional impact of structural forces on maternal employment inequality that operate intergenerationally through family channels.

To take stock, note that each intergenerational term requires four statistics: exposures to full-time employed mothers and intergenerational employment coefficients for each SES group. I use the CPS to compute group-level exposures (reported earlier in Figure 1). I use the PSID to estimate group-level intergenerational coefficients, as described in the preceding subsections. Thus, the intergenerational terms interact the CPS exposure measures with the PSID response coefficients.

Results

As a baseline exercise, I estimate intergenerational employment coefficients in the full PSID sample of mother-daughter pairs. I use the most comprehensive measure of the mother's employment history available—motherFT_0_17—given that my initial aim is to provide a broad description of intergenerational employment transmission that utilizes all available employment information. Table 4 reports estimated marginal effects (expressed in percentage points / 100) of a 0-to-1 change in motherFT_0_17. Robust standard errors are clustered at the individual level and appear in parentheses below the coefficients. Table A1 in the online appendix presents similar estimates based on alternative measures of the mother's employment history (motherFT_0_10 and motherFT_6_17).

A simple bivariate regression yields a positive and highly significant association between a first-generation mother's FTFY employment history and the FTFY employment behavior of her daughter, as shown in column 1. The estimate of 0.136 can be interpreted as follows: the typical daughter raised by a continuously full-time employed mother is 13.6 percentage points more likely to work full-time throughout her early adult life (i.e., ages 22–49) than the typical daughter raised by a stay-at-home mother. This effect amounts to 26% of daughters' observed FTFY employment-to-population ratio in the sample (0.531). Columns 2–4 successively add in the control variables described in the preceding section. The inclusion of these controls reduces but does not largely affect the estimated intergenerational relationship: column 4, the preferred specification, shows that exposure to a full-time employed mother (vs. a stay-at-home mother) is associated with an 11.6 percentage point increase in the probability of full-time work. This large residual association suggests that working mothers raise the full-time employment capacities of their daughters in part through (unobserved) channels of attitude formation, work-family balancing skill formation, and labor market networks.

SES-Based Inequality in Intergenerational Employment Transmission

Proceeding with the preferred specification, %Table 5 displays heterogeneity in the intergenerational employment coefficient across motherhood and SES subgroups. The top panel defines SES groups based on the first-generation mother's education level (no college vs. some college or more), and the bottom panel stratifies on the daughter's education level (less than four years completed college vs. at least four years completed college).¹¹ The six columns in each panel correspond to the motherhood-by-SES cuts of the sample. Because of the focus on FTFY employment in the context of raising young children, I use the 0–10 daughter age range to measure the first-generation mother's employment history. In doing so, I align mother's and daughter's full-time work propensities at similar points in the life cycle (and in motherhood).

Two important patterns of heterogeneity emerge. First, the intergenerational coefficient tends to be larger in higher-SES families. Columns 1 and 2 show that exposure to a full-time employed mother is associated with a 10.2 or a 16.1 percentage point increase in a high-SES daughter's propensity to work full-time but only a 7.0 or 7.6 percentage point increase in a low-SES daughter's propensity (depending on the definition of SES). Second, this SES gradient is larger in the subsamples of young mothers. Columns 5 and 6 of panel A show that exposure to a full-time employed mother is associated with a 16.8 percentage point increase in a high-SES mother's full-time work propensity but only a 3.8 percentage point increase in a low-SES mother's full-time work propensity. Columns 5 and 6 of panel B show a similar pattern. Although the differences in point estimates are substantial,¹² I cannot statistically reject the hypothesis that the estimates are equal (p values from F tests of equality, shown below the main estimates, are .126 and .162).

The bottom subpanels of each panel of Table 5 record fixed-effect model estimates of the motherhood FTFY employment penalty (refer to Eqs. (2) and (3)). The same SES gradient shown earlier is evident here as well. As shown in the bottom subpanel of panel A, exposure to a full-time employed mother is associated with a 0.142 decline in the motherhood FTFY employment penalty for high-SES daughters. That is, relative to a daughter raised by a stay-at-home mother, a daughter raised by a full-time employed mother experiences a 14.2 percentage point lower decline in full-time work propensity upon raising her own young children. Given that the baseline estimate of the motherhood FTFY employment penalty in the high-SES subsample is −0.174, this finding indicates that high-SES daughters raised by full-time employed mothers experience small motherhood penalties. For low-SES daughters, on the other hand, exposure to a full-time employed mother is associated with only a 3.5 percentage point decline in the motherhood FTFY employment penalty. Given a baseline motherhood FTFY employment penalty estimate of −0.230, this finding indicates that a low-SES daughter experiences a large motherhood penalty regardless of her own mother's work behavior. The bottom subpanel of panel B records a similar pattern of estimates.

Implications for Twenty-First Century Inequality in Maternal Employment

Figure 3 graphs the decompositions specified in Eqs. (5) and (6). The left bar graphs the 17.6 percentage point gap in FTFY employment between high-SES and low-SES mothers observed in 2015–2017 CPS data. The dark portion of the bar marks the estimated share of the overall gap attributable to SES-based inequality in intergenerational factors. I compute this term in the following three steps. First, according to Figure 1, the average exposure of high-SES daughters born in 1982–1984 to full-time employed mothers was 0.39. (These daughters became median-aged mothers themselves in 2015–2017.) The corresponding exposure for low-SES daughters was 0.24. Second, the intergenerational coefficient for second-generation high-SES mothers is estimated at 0.192. (This is the average of the two estimates appearing in column 6 of Table 5.) The corresponding coefficient for second-generation low-SES mothers is .062. Third, Eq. (5) yields 0.39 × 0.192 – 0.24 × 0.062 = 0.060. Thus, unequal intergenerational transmission of FTFY employment propensity across SES groups accounts for 6.0 percentage points of the observed 17.6 percentage point SES gap in maternal FTFY employment. This amounts to 34.1% of the total gap. The whiskers on the graph trace out a 90% confidence interval of this estimate, following the standard error formula described by Jann (2008). The confidence interval does not overlap with 0, indicating statistical significance at the 10% level. (The t statistic is 1.935, for a p value of .053.)

The right bar of Figure 3 graphs an analogous decomposition of the 11.0 percentage point increase in the maternal FTFY employment gap observed between 1998–2000 and 2015–2017. The intergenerational term is computed in the same way except that increases in daughters' exposures to full-time employed mothers, observed between the two time points, are substituted for point-in-time exposures. As depicted in Figure 1, high-SES daughters experienced a 24 percentage point increase in exposure to full-time employed mothers during the sample period, whereas low-SES daughters experienced a 9.4 percentage point increase. Plugging these numbers into Eq. (6) yields 0.24 × 0.192 – 0.094 × 0.062 = 0.040. Thus, unequal growth in the intergenerational transmission of employment propensity across SES groups accounts for an estimated 4.0 percentage points, or 36%, of observed growth in maternal employment inequality. This estimate is also statistically significant. (The t statistic is 2.13, for a p value of .033.)

Discussion

This study constructs an account of rising SES-based inequality in maternal employment that considers how a mother's propensity to work is conditioned by her own mother's prior work history. Before 1960, few U.S. mothers worked full-time (Goldin 2006). By 2016, more than half of college-educated mothers with young children did, but they were almost 1.7 times as likely to do so as mothers with young children and only a high school education. I argue that if working mothers foster greater employment capacities in their daughters than do stay-at-home mothers, then an increase in maternal employment inequality in one generation produces a further increase in the next generation. Moreover, this effect is larger if the intergenerational transmission of employment propensity is stronger in high-SES families than in low-SES families.

To explore these hypotheses, I use the PSID to construct longitudinal employment information for more than 2,200 mother-daughter pairs. I find that the decisions of high-SES women to hold on to their full-time careers while raising young children are particularly sensitive to whether their own mothers did the same. In contrast, the intergenerational relationship is much weaker for lower-SES women and mothers. Investigation of motherhood employment penalties yields similar results: being raised by a working mother dramatically lowers the motherhood penalty for high-SES women but only modestly affects the motherhood penalty for low-SES women. These patterns suggest that high-SES women experience strong intergenerational employment growth: high-SES daughters effectively translate the skills and beliefs conferred by their (working) mothers into their own employment statuses. In contrast, forces of instability—such as the risk of job displacement; irregular work hours, unpredictable shift scheduling, and employer-provided work-family support; and inconsistently affordable childcare—disrupt mother-daughter employment transmissions in lower-SES families. Applying these estimates to a decomposition of trends observed in the March CPS, I attribute 36% of the increase in maternal full-time employment inequality between 1999 and 2016 to disparate intergenerational growth in employment propensity across SES groups. Intergenerational forces have thus importantly shaped SES-based inequality in mothers' employment statuses.

By integrating intergenerational models into the study of long-run change in maternal employment, this paper enriches a literature that has mainly focused on factors such as wages, job quality, childcare availability, and transfer programs. These structural forces have undoubtedly shaped maternal employment inequality (e.g., Moffitt 2012) even in the absence of any intergenerational effects. However, this paper shows that the long-run impacts of these forces are reinforced and magnified by the presence of intergenerational transmission mechanisms. Its findings suggest that high-SES mothers not only have experienced an increase in employment opportunities relative to low-SES mothers but also have become increasingly able to translate these opportunities into stable employment statuses, owing to rising structural inequality in the prior generation. The interaction of these two effects has heightened current inequality in U.S. mothers' labor market positions, compounding other dimensions of family stratification in American society.

The account of maternal employment developed in this paper reveals important implications for family-friendly workplace policy. Consider a policy mandating that all employers of low-SES service workers provide more family-friendly work environments. These environments could feature greater leave allowances and benefits, more flexible scheduling of hours and telework allowances, or greater childcare support (Adelstein and Peters 2019; Blau and Kahn 2013). The traditional aim of such a policy is to reduce work-family conflict, which may induce more mothers to keep their jobs. This paper suggests two further effects on mothers' employment propensities that operate through intergenerational channels. First, by raising the exposure of low-SES daughters to working mothers, the policy might further raise the employment propensity of the next generation of low-SES mothers. Second, by lessening work-family conflict and raising the stability of service sector employment, the policy might raise the rate at which working mothers are able to augment their daughters' employment propensities. In sum, beyond its contemporaneous effects on the initial maternal generation, a large-scale policy mandating a family-friendlier work environment might help create a new generation of mothers more poised to take advantage of such a work environment.

I conclude by noting four limitations of this study. First, although this study estimates a large difference in intergenerational maternal employment coefficients across SES groups, this difference is not statistically significant at conventional levels. On the other hand, the decomposition results, which capture the interaction of unequal intergenerational coefficients with unequal exposure to working mothers, are statistically significant. That is, intergenerational processes are found to significantly augment the growth of SES-based maternal employment inequality in the twenty-first century. Future work should conduct additional tests of these hypotheses in different samples.

Second, the PSID data do not permit analysis of the mechanisms underlying the intergenerational employment coefficients, such as the formation of egalitarian gender-role attitudes versus unobserved opportunities. It is important to distinguish between these two channels because they may have different implications for (perceived) work-family conflict faced by working mothers and the effects of work-family conflict on maternal employment. A complete account of maternal employment inequality also requires a better understanding of how mothers socialize their daughters. Lareau's (2003) theory of concerted cultivation suggested that dual-career, high-SES families may spend considerable resources to instill gender-egalitarian ideologies in their daughters. Whether class differences in the socialization of daughters regarding employment are large and immutable, or modest and responsive to policy, are important questions.

Third, the intergenerational regression specification could be expanded to consider alternative variable definitions. This work studies the intergenerational transmission of women's employment status (i.e., employment quantity): future work may wish to consider earnings or occupational status (i.e., employment quality).¹³ Moreover, the focus on employment status could benefit from some refinement. The PSID data used here prevent measurement of the first-generation mother's employment history beyond broad summary measures because the three measures used—based on three broadly defined daughter age ranges (0–17, 6–17, and 0–10)—are very highly correlated. Constructing a larger data set would permit more precise intergenerational sequence analysis, which can better account for instability of women's employment over time and during the early years of their children's lives.

Fourth, future work should explore the intergenerational transmission of mothers' labor market outcomes in other populations. This study is based on a sample of mostly White and non-Hispanic U.S. mothers. Within the United States, subgroup analyses by race and ethnicity may shed light on maternal employment inequality across a diverse U.S. population. For example, although Black and White mothers now participate in formal employment at similar rates, Black mothers experienced much higher employment rates throughout much of the late nineteenth and twentieth centuries (Boustan and Collins 2014). It is possible that this gap has closed in part due to greater intergenerational employment growth among White mothers, who tend to have higher SES and thus may be less affected by instability mechanisms. Future cross-national studies should look to add depth to the broad analysis of McGinn et al. (2019), who estimated intergenerational employment coefficients using cross-sectional data from 29 countries. Longitudinal household surveys in Britain (the British Household Panel Survey) and Germany (the German Socio-Economic Panel) may permit the estimation of more detailed intergenerational employment regressions in those countries. Intergenerational population register data are also increasingly available for research in Scandinavian countries (e.g., Kleven et al. 2019). Future work should explore these advanced-country settings and consider extending intergenerational frameworks to the analysis of maternal labor market involvement in developing countries.

Acknowledgments

The author thanks the Editor, Deputy Editor, several anonymous reviewers, David Lam, Sarah Patterson, Pamela Smock, and Jessica Houston Su for insightful comments that greatly improved the clarity and content of this manuscript. This research was completed while the author was a PhD candidate in the Department of Economics, and a predoctoral trainee in the Population Studies Center at the University of Michigan. This research was supported by summer research funding from the University of Michigan Department of Economics, an NICHD training grant to the Population Studies Center at the University at Michigan (T32 HD007339), and the George Katona Economic Behavior Research Award Fund. Errors and opinions are those of the author alone.

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