Learning from mum:

Cross-national evidence linking maternal employment and adult children’s outcomes

Kathleen L. McGinn1

Mayra Ruiz Castro2

Elizabeth Long Lingo3

Forthcoming in Work, Employment and Society

Keywords: female labour force participation; gender attitudes; household labour; maternal employment; social class; social learning theory; social mobility

Author Affiliation:1. Harvard Business School, Boston, MA 02163; 2. Kingston Business School; Kingston University, Kingston upon Thames, KT2 7LB, United Kingdom; 3. Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA 01609

Corresponding Author: Kathleen L. McGinn, Harvard Business School, Boston, MA 02163. (617) 495-6901. kmcginn@hbs.edu.

Maternal employment and adult children’s outcomes 1

Learning from mum:

Cross-national evidence linking maternal employment and adult children’s

outcomes

Abstract

Analyses relying on two international surveys from over 100,000 men and women across

29 countries explore the relationship between maternal employment and adult daughters’

and sons’ employment and domestic outcomes. In the employment sphere, adult

daughters, but not sons, of employed mothers are more likely to be employed and, if

employed, are more likely to hold supervisory responsibility, work more hours and earn

higher incomes than their peers whose mothers were not employed. In the domestic

sphere, sons raised by employed mothers spend more time caring for family members and

daughters spend less time on housework. Analyses provide evidence for two

mechanisms: gender attitudes and social learning. Finally, findings show contextual

influences at the family and societal levels: family-of-origin social class moderates

effects of maternal employment and childhood exposure to female employment within

society can substitute for the influence of maternal employment on daughters and

reinforce its influence on sons.

Keywords

female labour force participation; gender attitudes; household labour; maternal

employment; social class; social learning theory; social mobility

2

Introduction

Maternal employment—women’s employment during their sons’ and daughters’

childhood years—remains a lightning rod for policy discourse and emotional debate

(Lyonette et al., 2011; O'Reilly et al., 2014; Schober and Scott, 2012). Consistent with

the United Nation’s 2030 Agenda for Sustainable Development, the European

Commission included gender equality in its Europe 2020 Strategy, specifically targeting

the gender gap in employment among parents with young children at home (Miani and

Hoorens, 2014). In spite of attention and effort by policy makers, maternal employment

in Europe remains below EU recommended levels (Dotti, Giulia and Scherer, 2017).

Fathers in the EU-27 are more likely to be employed than men without children, but

mothers of children under 12 years old are ten percent less likely to be employed than

other women (Miani and Hoorens, 2014).

Maternal employment is closely linked to beliefs, held to different degrees across

the globe, that children’s outcomes are negatively affected by mothers’ involvement in

paid employment (Budig et al., 2012; Corrigall and Konrad, 2007; Kanji, 2010; Sigle-

Rushton and Waldfogel, 2007). In the 2012 British Social Attitudes Survey, for example,

over a quarter of respondents agreed that family life suffers when a woman has a full-

time job (Scott and Clery, 2013). Research largely fails to support such beliefs. Meta-

analyses of early childhood outcomes in the USA highlight the moderating role of social

context, finding no consistent disadvantages for young children raised by employed

mothers (Goldberg et al., 2008; Lucas-Thompson et al., 2010). Turning to outcomes at

adolescence, recent studies capitalizing on changes in parental leave laws in Europe

conclude, inconclusively, that associations between maternal employment and teens’

academic outcomes are null (Dustmann and Schonberg, 2012; Rasmussen, 2010),

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negative (Bettinger et al., 2014; Carneiro et al., 2015; Liu and Skans, 2010) or positive

(Dunifon et al., 2013). In light of inconsistent links to childhood and adolescent

outcomes, continued interest in maternal employment has led to a recent upsurge in

research exploring links to adult outcomes. Longitudinal data from the USA suggest

positive associations between mothers’ employment and adult daughters’ employment

probability and income (Stinson and Gottschalk, 2016), hours daughters spend in paid

work (Olivetti et al., 2016) and equitable division of household work (Cunningham,

2001). Adult sons of employed mothers in the USA engage in more housework (Gupta,

2006) and are more likely to be married to women who are also employed (Fernández et

al., 2004).

While growing evidence suggests a positive association between maternal

employment and adult children’s employment and domestic outcomes, extant studies are

primarily single-country and limited to one or two measures of adult outcomes. Little is

known about the applicability of these findings outside the limited contexts studied or

across the spectrum of employment and domestic outcomes potentially affected. This

article seeks to identify patterns across multiple countries, across men and women and

across labour market and domestic spheres simultaneously to gain a more holistic picture

of the relationship between maternal employment and children’s lives over the long term.

Analyses explore two mechanisms: mothers’ employment may influence the next

generation’s behaviour by shaping gender attitudes (Moen, Erickson & Dempster-

McClain et al., 1997; Panayotova and Brayfield, 1997) and by providing behavioural

examples for their children to imitate via social learning (Bandura, 1977). Informed by

past research showing that family context (Lucas-Thompson et al., 2010) and societal-

level factors (Fuwa, 2004) can both buttress and limit the long-term effects of maternal

4

employment, the analyses also consider micro and macro moderators: family-of-origin

social class and alternative models of female employment during childhood. Analyses

rely on two multi-national datasets that vary in critical ways. First, specific measures—

most notably the measures of maternal employment—differ, allowing replication across

the different measures and datasets. Second, maternal occupation and education variables

available in only one of the datasets enable exploration of the role social class plays in the

association between maternal employment and sons’ and daughters’ outcomes as adults.

Three research questions guide the literature review, empirical methods,

presentation of results and discussion. First, what are the associations between maternal

employment and adult children’s employment and domestic outcomes? Second, can

maternal influences on gender attitudes and social learning account for part or all of the

maternal employment-related variation in adult children’s employment and domestic

outcomes? Third, what is the power of maternal employment in the face of contextual

influences, specifically childhood social class and childhood exposure to other models of

female employment within society?

Maternal employment shapes children’s gender attitudes and provides social

learning

Socialization in families shapes and refines children’s gender attitudes—

individually held beliefs about desirable roles for men and women in public and private

spheres (Cunningham, 2001; Davis and Greenstein, 2009; Davis and Wills, 2010).

Mothers' and children's gender role attitudes are positively correlated, even when

measured 25 years apart (Johnston et al., 2014). Particularly relevant for this study, adults

raised by employed mothers hold more egalitarian gender attitudes, supporting women’s

5

engagement in the labour market and shared responsibilities between men and women in

households (Davis and Greenstein, 2009; Fernández et al., 2004). In turn, gender attitudes

are associated with men’s decisions about whom to marry (Farré and Vella, 2013),

women’s involvement in the paid labour force (Johnston et al., 2014; Moen et al., 1997)

and men and women’s division of household labour (Gupta, 2006; Kan, 2008; Schober

and Scott, 2012).

Beyond shaping their sons’ and daughters’ gender attitudes, mothers provide

behavioural models of skills their children can emulate (Beller, 2009). Parents engaged in

activities not traditionally associated with their gender, such as employed mothers or

stay-at-home fathers, demonstrate opportunities for enacting non-traditional roles (Gupta,

2006; Olivetti et al., 2016). Social learning theory suggests that exposure to parents’

behaviours builds capacities children draw upon later in life (Bandura, 1977), and this

exposure wields more influence than examples provided by friends, teachers and other

relevant adults (Basow and Howe, 1979). Parents’ occupational choices are often

replicated by their children and the choice of a same-gender parent is the stronger

predictor (Carmichael, 2000; Emran and Shilpi, 2011; Miller and Hayes, 1990). Studies

of occupational data from Canada, Ireland, Italy, Nepal, UK and USA offer

complementary evidence that the occupational status of mothers, including homemaker,

is a strong predictor of daughters’ occupation (Boyd, 1985; DiPietro and Urwin, 2003;

Emran and Shilpi, 2011; Hayes, 1987; Stevens and Boyd, 1980). The predictive power of

mothers’ employment on daughters’ occupation, found to be greater than that of mothers’

or fathers’ education, can be so strong that ‘knowledge of the father’s occupation is

superfluous’ (Stevens and Boyd, 1980: 192). Parental division of household labour is also

reflected in children’s domestic engagement when they become parents themselves

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(Gupta, 2006). Qualitative research in the USA suggests social learning underlies a

generational effect on sons raised in homes where fathers were relatively more involved

in household labour (Cunningham, 2001). Yet, cross-national research exploring the

possibility that children emulate their parents' engagement in employment, housework

and childcare is limited, potentially due to historical limitations in cross-national data sets

(Hook, 2006).

Potential moderators of maternal employment: Household social class and societal

models of female employment

Maternal employment, like all employment, is a marker of socioeconomic status.

Thus, effects attributed to maternal employment may be due to household social class

rather than to mothers’ labour force participation, per se (Sullivan et al., 2013). The

association between maternal employment and family-of-origin social class is, however,

imperfect. A mother working as a corporate lawyer and a mother working two minimum

wage jobs may have similar limitations on the number of hours each spends with her

children, but other resources in the two households are likely to vary dramatically.

Studies exploring links between maternal employment and outcomes during childhood

and adolescence find that effects vary with mothers’ income (Lombardi and Coley, 2017)

and education (Hsin and Felfe, 2014). The carryover to adult outcomes is unclear.

Countervailing effects of social class may mask or moderate the influence of mothers’

employment. Analyses that include maternal occupation and education, key markers of

socioeconomic status (Breen and Jonsson, 2005; Hollingshead, 1975), may help

disentangle potentially additive or interactive effects attributable to a mother’s

engagement in the labour market and related socioeconomic factors within the household.

7

At the societal level, labour force participation rates among mothers vary

considerably across countries (Miani and Hoorens, 2014; OECD, 2007). Societal models

of female employment observed by girls and boys during childhood may serve as cultural

complements or substitutes to their own mothers’ employment status. Recent research

finds both maternal employment and the employment of childhood friends’ mothers are

positively associated with the number of hours adult females spend in paid employment,

but the interaction is negative, suggesting the influence of maternal employment is

weaker when alternative models are locally available (Olivetti et al., 2013). If societal

models of female employment operate in a similar fashion, childhood observation of high

levels of female labour force participation (FLFP) may reduce the impact of maternal

employment on daughters’ employment outcomes. Turning to domestic outcomes, the

distribution of work within households is more egalitarian in countries with a legacy of

high maternal employment (except within countries previously part of the Soviet Union)

(Treas and Tai, 2012a) and men’s unpaid household work within countries increases as

FLFP rises (Hook, 2006). These findings suggest that the influence of any individual

mother’s employment on her adult offspring’s domestic outcomes may be heightened

when women’s employment is commonplace. Across employment and domestic

outcomes, a positive interaction between maternal employment and female employment

at a societal level would suggest cultural complementarity, while the reverse would

suggest household and societal models act as substitutes.

Methodology

Data sources and measures

8

Our analyses rely on data from two cross-national social surveys: the “Family and

Changing Gender Roles” module of the International Social Survey Programme (ISSP) in

2002 and 2012 (Group IR, 2013; 2014) (available at http://www.issp.org/data-

download/by-year/); and the “Generations and Gender Survey Core Questionnaire” from

the Generations and Gender Programme (GGP) administered in two waves from 2002

through 2013 (Generations and Gender Program, 2000-2013) (available at

http://www.ggp-i.org/data). National level archival data on FLFP, gathered from multiple

sources, are added in a subset of analyses. As described below, initial analyses use ISSP

data from 20,966 female and 15,508 male respondents, 18 to 60 years old, across 24

countries in North and South America, Australia, Europe, Asia and the Middle East.

Replication tests use GGP data from 37,808 women and 31,182 men, 18 to 60 years old,

across 11 European countries (six countries are included in both data sets). ISSP and

GGP data sets vary in critical ways: measures of maternal employment and domestic

engagement differ and additional family-of-origin variables are available in the GGP data

only. Online Appendix B presents the ISSP and GGP survey questions underlying all

variables used in the analyses.

The ISSP measure of maternal employment is based on binary responses to the

question: ‘Did your mother ever work for pay for as long as one year, after you were born

and before you were 14?’ (= 1 if mother ever worked for pay as long as one year in

respondent’s first 14 years; = 0 otherwise). The GGP maternal employment measure is

based on binary recoding of responses to the question: ‘What was your mother’s

occupation when you were 15?’ (= 0 if response is housewife / homemaker OR

unemployed; = 1 otherwise).

9

Analyses examine the association between maternal employment and

respondents’ employment and domestic outcomes, separately for male and female

respondents. Employment measures include:

(1) likelihood of employment (binary, = 1 if hours worked for pay > 0) (ISSP and

GGP);

(2) likelihood of supervisory responsibility if employed (binary, = 1 if directly

responsible for work of other people) (ISSP and GGP) (GGP, data not

available for Poland);

(3) hours of paid work each week if employed (ISSP and GGP); and

(4) income if employed (log transformed annualized earnings, standardized

within country-year) (ISSP and GGP) (GGP, data not available for Austria,

Poland and Sweden).

Measures of domestic engagement include:

(5) hours spent on household work each week, not including childcare (ISSP); OR

division of labour on five household tasks, between male and female adults in

two-adult, heterosexual households (1 – 5, higher = more egalitarian) (GGP,

data not available for Poland)

(6) hours spent caring for family members each week (ISSP, available 2012

only); OR division of labour on six childcare activities, between male and

female adults in two-adult, heterosexual households with children (1 – 5,

higher = more egalitarian) (GGP)

Table 1 summarizes maternal employment and all outcome measures for men and women

separately, by country, noting data source. (See Online Appendix C for summary

statistics on additional variables and Online Appendix I for correlation matrices.)

10

[TABLE 1 here]

Analysis plan

Effects are estimated using linear probability fixed effects regressions for

continuous outcome variables and logistic fixed effects regressions for dichotomous

outcome variables, unless otherwise noted. Regressions controlling for country-year fixed

effects allow direct assessment of the relationship between maternal employment and the

outcome variables within a given country in a given year. Models control for individual

respondents’ age, age squared, years of education, marital status, whether or not there are

children living in the household and religion, unless noted otherwise. Robust standard

errors are clustered at country-year level. (See Online Appendix A for additional model

details.) All analyses are run separately for males and females.

Main effect regressions are presented first. The next set of regressions investigate

gender attitudes and social learning as mechanisms. Initial tests at each step rely on ISSP

data, followed by replication tests using GGP data. After establishing main effects and

mechanisms, analyses assess the role of family-of-origin social class in fixed effects

models using additional variables available in the GGP data set only. Mixed models

analysing the role of societal context during respondents’ childhood years rely on ISSP

data and country-level measures of FLFP from multiple historical data sets. A final set of

analyses offers robustness checks exploring alternative explanations.

Results

Main effects for maternal employment

In regressions using ISSP data, the likelihood of being employed was 1.21 times

greater for women raised by an employed mother (Table 2, Model 1a). Employed women

11

raised by mothers who were employed were 1.29 times more likely to supervise others at

work than those whose mothers were not employed (Model 2a) and spent roughly 44

additional minutes at their jobs each week (Model 3a, 0.74*60 minutes). Adult daughters

of employed mothers reported significantly higher annual earnings, partially due to

greater time investment in paid work (Models 4a and 5a). In the domestic realm,

controlling for employment status, daughters of employed mothers spent approximately

an hour less on housework each week than daughters of mothers who were not employed

(Model 6a, -0.96*60 minutes). The relationship between maternal employment and the

time adult daughters spent caring for family members was not significant. (See Online

Appendix D for regressions with non-significant effects.) In stark contrast to the findings

for female respondents, maternal employment was not a significant predictor of adult

sons’ employment outcomes or housework hours, but men raised by an employed mother

spent approximately 50 additional minutes weekly caring for family members, relative to

sons whose mothers were not employed (Model 7a, 0.83*60 minutes).

[TABLE 2 here]

Regressions using GGP data to test the replicability of significant relationships

identified in the ISSP data confirmed the predictive power of maternal employment.

Female respondents whose mothers were employed when the respondent was 15 were

1.19 times as likely to be employed (Table 2, Model 1b), 1.17 times as likely to supervise

others if employed (Model 2b) and earned significantly more than their peers whose

mothers were not employed (Model 4b), even after controlling for hours worked (Model

5b). In the GGP sample, hours women spent in paid employment did not vary

significantly with maternal employment (Model 3b). Domestic variables in GGP data

were measured on 1 to 5 scale; higher scores indicated greater male involvement.

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Maternal employment was not significantly related to the distribution of household

labour as measured in the GGP survey (Model 6b). Men raised by employed mothers

were marginally more involved in childcare (Model 7b). In sum, results from two

international surveys revealed that daughters raised in homes where mothers were

employed reaped employment benefits as adults, but daughters’ benefits at home were

inconsistent; sons experienced no significant impact of their mother’s employment status

on their own employment, but those raised by employed mothers were more involved in

caring for family members.

Mechanisms underlying maternal employment effects

The next set of analyses assessed gender attitudes as a possible mechanism

underlying maternal employment effects. Tests relied on standardized factor scores from

a confirmatory factor analysis of responses to eight ISSP survey items regarding

individual beliefs about appropriate economic and domestic roles for women and men

(alpha = .78; average inter-item covariance = .31). Standardized factor scores, where

higher scores indicated more egalitarian gender attitudes, averaged .11 (SD = 1.0) for

adult children whose mothers were employed and -.20 (SD = .98) for those whose

mothers were not employed. Country averages by sex and maternal employment are

illustrated in Figure 1. Notably, across the 24 countries in the ISSP data set, men whose

mothers were employed held significantly more egalitarian gender attitudes ( = .04; SD

= .97) than women whose mothers were not employed ( = -.12; SD = .98; t-test for

equality of means = 10.40; p < .001), suggesting the effects of maternal employment may

outweigh previously documented sex differences in gender attitudes (Shannon and

Greenstein, 2009).

[FIGURE 1 here]

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Gender attitudes partially mediated all significant relationships between maternal

employment and women’s outcomes in the ISSP data (Sobel-Goodman tests, p < .001

unless noted). Gender attitudes accounted for 38 percent of maternal employment effects

on female likelihood of employment, 13 percent on female likelihood of supervisory

responsibility, 17 percent on female hours worked (p = .003), 55 percent on female

income (controlling for hours worked) (p = .02) and 20 percent on female housework

hours. Gender attitudes also significantly mediated the relationship between maternal

employment and men’s engagement in family care (p = .03), accounting for 12 percent of

the effects. After controlling for the mediating effect of gender attitudes, maternal

employment remained a significant predictor of all of the reported dependent variables

except female income. Replication tests using GGP data revealed weaker mediation

effects of gender attitudes on daughters’ outcomes and stronger mediation effects on

men’s involvement in childcare. Table 3 presents parallel models across data sets for

regressions used in gender attitude mediation analyses. Overall, respondent attitudes

regarding economic and domestic roles for men and women were related to mothers’

employment status and these attitudes, in turn, partially accounted for the observed

relationships between maternal employment and respondents’ employment and domestic

outcomes.

[TABLE 3 here]

The next set of analyses investigated social learning as a mechanism by exploring

the possibility that daughters, when they become parents themselves, tap into skills they

learned from their mothers during childhood. If an adult daughter’s behavioural repertoire

draws on life skills gleaned from first-hand exposure to an employed mother, the

association between maternal employment and daughters’ employment outcomes should

14

be stronger for women with children at home, above and beyond effects due to gender

attitudes. Figure 2 provides graphic representations of results from ISSP models

predicting women’s employment outcomes and controlling for gender attitudes,

separately for respondents with and without children under 18 living at home. Maternal

employment’s influence on hours worked, income and income controlling for hours

worked held only for women with children at home (F-test for equality of coefficients, p

= .02; .02; .08, respectively). The magnitude of the positive relationship between

maternal employment and daughters’ likelihood of employment did not vary significantly

between women with and without children (F-test, p = .81). For women who were

employed, maternal employment was positively and significantly associated with greater

likelihood of supervisory responsibility for both subsamples, though the association was

marginally stronger for women with children at home (F-test, p = .09). In replication tests

using GGP data, maternal employment was associated with significant increases in the

likelihood of employment and, given employment, higher income only for women with

children at home (all: p < .01 children at home; p > .10 wo/children at home). Effects on

supervisory responsibility and hours in paid employment in the GGP data did not vary by

children at home. (See Online Appendix E for ISSP regressions and parallel tests with

GGP data.) While the results across data sets differ on some of the outcome variables,

maternal employment’s positive association with adult daughters’ employment outcomes

is stronger and more consistent for women with children living at home, suggesting social

learning may be in play. When daughters become mothers themselves, childhood lessons

learned from observing their own mothers may help daughters maintain their careers as

they raise their children.

[FIGURE 2 here]

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Household social class and societal models of female employment as moderating factors

Household social class. To explore the possibility that the association between

maternal employment and adult outcomes varies with family-of-origin social class, the

next set of analyses exploited additional variables available in the GGP data set only. In

fixed effects regressions on female employment outcomes, controlling for respondent

gender attitudes, the dichotomous measure of maternal employment was replaced with a

measure of maternal education (mother’s highest education level based on International

Standard Classification of Education, 0 – 6 scale: 0 = pre-primary education; 6 = second

stage of tertiary education, qualifying graduates for faculty or high-level research

positions) and dummies for maternal occupational categories (not employed (omitted);

manual labour or equivalent (0/1); low-skill non-manual (0/1); high-skill non-manual

(0/1)). (See Online Appendix F for regressions.) Relative to women whose mothers were

not employed, employment was 1.17 times more likely for women whose mothers had

worked in manual labour occupations (p < .01), 1.23 times more likely for those whose

mothers had worked in low-skill non-manual labour occupations (p < .01) and 1.10 more

likely for women whose mother s had worked in high-skill occupations (p < .10), offering

evidence that the relationship between mothers’ and daughters’ employment status held

across social classes. Consistent with this conclusion, mother’s education was not a

significant predictor of daughters’ employment. In contrast, women’s likelihood of

supervisory responsibility rose with family-of-origin social class: maternal employment

in manual labour showed no significant effect, while women whose mothers had worked

in non-manual labour were 1.14 (low-skill, p < .10) and 1.22 (high-skill, p < .01) times

more likely to supervise others; holding occupation constant, maternal education was also

positively related to daughters’ supervisory responsibility (p < .01). The moderating

16

influence of socioeconomic status held for income: only maternal employment in high-

skill non-manual labour was positively and significantly related to daughters’ income (p

< .05, with and without controls for hours worked); the coefficient for maternal education

was also positive and significant in both income models (p < .01). Hours spent in paid

employment were significantly higher for daughters raised by mothers employed in low-

skill non-manual occupations (p < .01), relative to women raised by mothers who were

not employed, but coefficients for maternal employment in high-skill occupations and

manual labour were not significant. Overall, the findings revealed a moderating role for

for family-of-origin socioeconomic class and provided further evidence of social

learning: women raised by employed mothers were more likely to be employed

themselves regardless of maternal education or occupation, but the status of daughters’

employment – supervisory responsibility and earning level – reflected their mothers’

education and occupation, with higher levels of responsibility and income accruing to

women raised in families with higher socioeconomic status.

Societal context: Female Labour Force Participation. Societal models teaching

similar lessons may moderate the influence of maternal employment. To explore this

possibility, a variable equal to the FLFP rate in the respondent’s country when the

respondent was fourteen years old (FLFP at 14, = .40, SD = .13; see Online Appendix

A for data sources and details) was included in generalized linear mixed models for the

full set of outcome variables (Angrist and Pischke, 2008). Each regression included a

term interacting maternal employment with FLFP at 14. All models controlled for

respondent demographics and gender attitudes (except models predicting gender

attitudes), as well as current country-year FLFP. Significant interactions are illustrated in

Figure 3. (See Online Appendix G for regression results.) For both male and female

17

respondents, FLFP at 14 positively and significantly moderated the relationship between

maternal employment and gender attitudes (ß = 0.01, p < .01), suggesting the influence of

maternal employment on adults’ beliefs was strongest when reinforced by childhood

observation of higher levels of female employment in society. In contrast, the positive

relationship between maternal employment and daughters’ likelihood of supervising

others, and the negative relationship between maternal employment and women’s hours

spent on housework, were strongest for women raised during periods of lower levels of

female employment and weak or absent for women exposed as children to higher levels

of female employment (interaction, supervisory responsibility, ß = -0.001, p < .01;

interaction, hours housework, ß = 0.05, p < .01). Turning to sons, the positive influence

of maternal employment on men’s hours spent on family care was intensified by

childhood observation of higher levels of FLFP (ß = 0.06, p < .05). In summary, exposure

to higher rates of female employment during childhood provided a partial alternative to

mothers’ employment for daughters, mitigating the influence of maternal employment on

a subset of adult daughters’ outcomes. For sons, however, exposure to higher rates of

female employment provided a complement to maternal employment, bolstering the

relationship between maternal employment and adult sons’ involvement at home.

[FIGURE 3 here]

Testing alternative explanations

Several alternative explanations warrant consideration. Though analyses showed

no significant association between maternal employment and sons’ employment

outcomes, it could be that daughters benefit broadly while sons suffer when their mothers

are employed. To test this possibility, fixed-effect regressions using the ISSP data with

18

the full set of controls explored the relationship between maternal employment and

children’s well-being outside employment and domestic measures, specifically sons’ and

daughters’ education and life satisfaction (see Online Appendix H for details). Results

showed no significant associations between maternal employment and self-reported

overall happiness for men or women. Turning to education, both sons and daughters of

employed mothers had significantly more education than children of mothers who were

not employed, and the effect sizes for male and female respondents were not significantly

different. While the analyses did not control for the presence of male or female siblings

and cannot rule out the possibility of unidentified gender issues at play (beyond gendered

responses to maternal employment in employment and domestic realms), the results

offered no support for the conjecture that daughters of employed mothers reap positive

benefits while sons incur costs.

Past research finds that sons raised by employed mothers are more likely to be

married to women who are employed (Fernández et al., 2004), raising the possibility that

findings for men’s involvement in family care may have been due to men’s wives’—

rather than their mothers’—employment. Refuting this alternative, after controlling for

spousal employment status in regressions using the ISSP data to predict men’s

employment and domestic outcomes, maternal employment remained a significant

predictor of men’s involvement in family care ( = .89; p = .03) and had no significant

relationship to men’s employment outcomes. (See Online Appendix A for additional

robustness checks.)

Discussion

19

The findings presented in this article offer a holistic view of the links between

maternal employment and adult daughters’ and sons’ lives, across labour market and

domestic spheres, across decades and multiple countries simultaneously. Based on

analyses of ISSP data from 24 countries, and replicated with data from GGP surveys

across 11 countries (of which six overlap), the pattern of results revealed that mothers’

employment status strongly influenced a broad spectrum of adult daughters’—but not

sons’—employment outcomes. Daughters raised by mothers who were employed were

more likely to engage in paid work and, if employed, were more likely to supervise

others, worked more hours and earned higher incomes. The pattern of results also

revealed gendered associations with maternal employment in the domestic sphere.

Daughters raised by mothers who were employed spent less time on household tasks,

while sons spent more time caring for family members, relative to their same-sex peers

raised by mothers who were not employed.

The literature suggests two potential mechanisms for the influence of maternal

employment on offspring’s subsequent behaviours in public and private spheres: gender

attitudes and social learning. Mediation analyses confirmed past research showing that

children of employed mothers hold more egalitarian gender attitudes (Davis and

Greenstein, 2009; Fernández et al., 2004) and provided evidence that gender attitudes, in

turn, partially accounted for the association between maternal employment and adult

daughters’ employment outcomes. After controlling for gender attitudes, however,

maternal employment remained a significant predictor of daughters’ employment,

supervisory responsibility, hours in paid employment and housework hours, as well as

sons’ family care hours.

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Social learning is also at play (Bandura, 1977; Cunningham, 2001). After

controlling for gender attitudes, associations between maternal employment and

daughters’ employment outcomes were stronger for women with children at home,

suggesting daughters tap into behavioural examples garnered by observing their own

mothers. Employed daughters of employed mothers, when faced with the opportunities

and challenges of having children themselves, appear both willing and able to emulate

their mothers as they manage employment and caregiving roles simultaneously. Positive

associations between maternal occupation and education and daughters’ supervisory

responsibility and earning levels offer additional support for social learning as

mechanism. Children’s observations of and experiences with their mothers’ labour

market decisions and behaviours mould attitudes about desirable roles for men and

women and transmit skills children draw upon later in life as they navigate private and

public spheres.

Consistent with past research detailing how contextual factors at household and

societal levels shape the influence of household demographics (Bittman et al., 2003;

Fuwa, 2004), the findings presented in this article provide new insights into the complex,

situated ways in which mothers’ employment shapes the adult lives of their children.

Analyses including mothers’ occupational categories and education explored the

interplay between maternal employment and family-of-origin social class. The likelihood

of daughters’ employment increased with maternal employment across occupational

categories and was not affected by maternal education. While women were more likely to

be employed if their mothers had been employed, regardless of their mothers’

socioeconomic status, social class was replicated in the jobs daughters held if employed:

only women raised by mothers with more education and employed in higher-skill jobs

21

realised benefits in supervisory roles and higher incomes. Turning to societal-level

moderators, the interaction between country-level FLFP and maternal employment on

adult daughters’ employment and domestic outcomes suggest maternal and societal

models act as substitutes: mothers’ conveyance of skills and capacities has its greatest

influence on daughters when alternative role models are relatively unavailable. For sons,

however, childhood exposure to employed women across society enhanced maternal

employment’s influence, suggesting cultural complementarity: changes in men’s

childcare engagement rely on reinforcement across societal context and maternal

employment.

Identifying main effects in one multinational data set (ISSP) and replicating in

another pan-European data set (GGP), while drawing on unique features of both data sets

to test mechanisms and moderators, allowed a robust examination of maternal

employment's links to adult outcomes in 29 countries. Despite these advantages, the data

sets offered small samples within each country-year and only a limited window into

details of maternal involvement at home. Neither measure of maternal employment, for

example, differentiated between part and full-time work, and measures captured maternal

employment at only certain points in respondents’ childhoods, either up to the age of 14

(ISSP) or at the age of 15 (GGP). Small sample sizes within country-year clusters and

noisiness in the key predictor variables imply conservative tests, but analyses were

nonetheless unable to discern whether findings would vary with the intensity or timing of

maternal employment and maternal involvement at home. Additional questions related to

maternal activities in future international survey programmes will allow future research

to explore finer details of mothers’ employment and domestic engagement.

22

Our analyses explored only mothers’ employment. Ongoing changes in gender

roles globally call for a better understanding of the dynamic and fluid nature of all family

members’ participation at home and in the workplace, and the long-term impact of their

participation on adult children’s labour market and domestic outcomes (Treas and Tai,

2012b). In light of research showing little change in the time children spend with their

parents even as women’s hours in paid work rises (Bianchi, 2000), future research should

gather data on and investigate the influence of the time mothers and fathers spend with

their children. In two parent heterosexual households, maternal employment is positively

associated with fathers’ involvement at home (Hoffman, 1989); in turn, sons’ time spent

on household tasks as adults reflects their fathers’ participation in similar tasks

(Cunningham, 2001). Finally, future research on maternal employment should also

consider the larger cultural, social and economic contexts in which gender is negotiated

and enacted in practice. Critical contextual features not considered in the analyses nor

presented in this article include family and friend networks (Olivetti et al., 2016), the

availability and media coverage of political role models (e.g., Beaman et al., 2012;

Campbell and Wolbrecht, 2006; Wolbrecht and Campbell, 2007) and social welfare

policies (e.g., Bittman et al., 2003; Edlund and Öun, 2016; Fuwa and Cohen, 2007; Hook,

2006; Lyonette et al., 2011).

Conclusion

Scholars, policy makers, family members and women themselves continue to

debate the effects of mothers’ employment on their daughters’ and sons’ lives. The

takeaway across decades of research is that young children of employed mothers tend to

be higher achieving and have fewer behavioural problems than young children whose

23

mothers are not employed, though these associations vary by income and race (Lucas-

Thompson et al., 2010), while effects on cognitive and behavioural development in

adolescents are mixed (e.g., Bettinger et al., 2014; Dunifon et al., 2013; Dustmann and

Schonberg, 2012). The research presented in this article sheds light on adult outcomes.

Based on data collected in two surveys across 29 countries between 2002 and 2013, the

findings illuminate a gendered pattern of associations between maternal employment and

adult daughters’ and sons’ employment and domestic outcomes. Due at least in part to

employed mothers’ conveyance of egalitarian gender attitudes and life skills for

managing employment and domestic responsibilities simultaneously, daughters raised by

employed mothers benefit in the employment realm, while sons raised by employed

mothers spend more time engaged in family care. Family-of-origin social class matters:

women’s likelihood of employment rises with maternal employment across the

socioeconomic spectrum, but higher incomes and supervisory responsibility accrue

primarily to women raised by mothers with more education and higher skill jobs. Social

context also matters: exposure to high levels of female labour force participation during

childhood weakens daughters’ reliance on maternal employment as a model for their own

employment, but strengthens the links between maternal employment and sons’

engagement in family care. These findings add to a growing body of research providing a

counterpoint to persistent beliefs and rhetoric that employed women are negatively

affecting their families and society. Together with research on young children and

adolescents, this study calls attention to multiple ways in which children across the world

benefit in adulthood from exposure to mothers engaged in the labour market.

24

Acknowledgements

We sincerely thank Kristina Tobio, Research Associate at the Harvard Business School, for her valuable assistance. We are also grateful to Claudia Olivetti, John Beshears, and participants in seminars at IESE, the University of Maryland, INSEAD, Harvard Kennedy School’s WAPPP, and MIT’s IWER, for their thoughtful comments on earlier versions of this article.

25

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Prof. Kathleen McGinn

Kathleen L. McGinn is the Cahners-Rabb Professor of Business Administration at Harvard Business School. She previously served as Harvard Business School’s Senior Associate Dean for Faculty Development, Director of Research, and Chair of Doctoral Programs. Professor McGinn studies the role of gender and class at work, at home and in negotiations. Her current field research investigates these issues internationally—in families across the globe, among women “firsts,” in North American professional service firms, across organizations and communities in Mexico and India, and in relation to health and welfare outcomes for young women in Zambia.

Dr. Mayra Ruiz Castro

Mayra Ruiz Castro is a Senior Lecturer in Ethics at Roehampton Business School, University of Roehampton, UK. Mayra studies gender and class inequality in organizations and the professions. Her research on inequality, the long hours culture and career advancement processes in professional service firms has been published in Gender, Work and Organization. Mayra is currently working on two major research projects. The first research line explores career transitions from academia into the new professional field of (Big) Data Science. The second research line focuses on the interplay between individual, household and organizational factors and its effects on women’s and men’s career-life outcomes.

Dr. Elizabeth Long Lingo

Elizabeth Long Lingo is an Assistant Professor of leadership and organizational behavior at Worcester Polytechnic Institute’s Foisie Business School in Worcester, MA, USA. Elizabeth’s research examines the negotiated nature of collective creativity, and the relational brokerage work of innovators and creative producers navigating across disciplinary, cultural, and organizational boundaries. An ethnographer of work and organizations, Elizabeth is currently exploring how big data shapes collective creative work; gender and leadership outcomes in STEM institutions; and how brokers orchestrate sensemaking across networks. Elizabeth’s research has been published in Administrative Science Quarterly (ASQ), Poetics, Work and Occupations, and the Chronicle of Higher Education. 

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31

Country/Source

Table 1: Means and proportions for Maternal Employment and outcome variables, by country, by gender. Limited to observations with no missing data on independent variables. Data from ISSP from 2002 and 2012; GGP from 2002-2013. Standard deviations in parentheses. Indicators of p values of statistical significance in differences between males and females: *** p<0.01, **p<0.05, * p<0.1.

(1a) (1b) (2a) (2b) (3a) (3b) (4a) (4b) (5a) (5b) (6a) (6b) (7a) (7b)Women Women Women Women Women Women Men

VARIABLES Hours Worked z_income z_income

ISSP GGP ISSP GGP ISSP GGP ISSP GGP ISSP GGP ISSP GGP ISSP GGP

Maternal Employment 1.21*** 1.19*** 1.29*** 1.17*** 0.74** 0.33 0.05** 0.04** 0.04** 0.04** -0.96*** 0.01 0.83** 0.05*(0.05) (0.03) (0.07) (0.07) (0.29) (0.20) (0.02) (0.01) (0.02) (0.01) (0.32) (0.01) (0.35) (0.03)

Age 1.49*** 1.71*** 1.11*** 1.09*** 0.80*** 0.35*** 0.11*** 0.02 0.09*** 0.01 0.39*** -0.06*** 0.66*** 0.02**(0.04) (0.06) (0.02) (0.02) (0.14) (0.07) (0.01) (0.01) (0.01) (0.01) (0.11) (0.01) (0.12) (0.01)

1.00*** 0.99*** 1.00*** 1.00*** -0.01*** -0.00*** -0.00*** -0.00 -0.00*** -0.00 -0.00** 0.00*** -0.01*** -0.00**(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Education Level 1.11*** 1.61*** 1.12*** 1.70*** 0.07 -0.26 0.09*** 0.25*** 0.09*** 0.26*** -0.44*** 0.05*** 0.09 0.06***(0.01) (0.07) (0.01) (0.10) (0.07) (0.30) (0.01) (0.04) (0.01) (0.03) (0.06) (0.01) (0.07) (0.02)

Married 0.78*** 0.78*** 1.06 1.01 -1.92*** -0.94*** -0.08*** -0.08*** -0.03 -0.07*** 3.00*** 0.49*** 3.06*** 0.02(0.06) (0.05) (0.06) (0.08) (0.34) (0.32) (0.02) (0.02) (0.02) (0.02) (0.50) (0.06) (0.64) (0.03)

Children in Household 0.65*** 0.69*** 0.86*** 0.89* -2.78*** -1.55** -0.13*** -0.01 -0.07*** -0.00 2.78*** -0.00 8.74***(0.04) (0.06) (0.05) (0.06) (0.33) (0.66) (0.02) (0.03) (0.02) (0.03) (0.30) (0.04) (0.85)

Christian 0.92 1.01 0.96 1.04 -0.33 -0.04 -0.07*** -0.02 -0.06*** -0.03 0.79*** -0.01 -0.12 -0.00(0.05) (0.05) (0.05) (0.06) (0.29) (0.40) (0.02) (0.03) (0.02) (0.03) (0.25) (0.03) (0.36) (0.02)

Other Religion 0.73*** 0.58*** 1.05 0.94 -0.38 -0.66 -0.13*** 0.05 -0.11*** 0.04 1.58*** -0.12* 0.54 -0.04(0.09) (0.08) (0.13) (0.09) (0.59) (0.43) (0.04) (0.07) (0.04) (0.07) (0.56) (0.06) (0.51) (0.07)

HoursWorked, Weekly 0.02*** 0.01***(0.00) (0.00)

Constant 0.00*** 0.00*** 0.01*** 0.01*** 23.89*** 35.54*** -3.31*** -1.54*** -3.81*** -2.00*** 7.41*** 3.31*** -9.00*** 1.73***(0.00) (0.00) (0.00) (0.00) (2.99) (2.14) (0.20) (0.31) (0.19) (0.36) (2.15) (0.15) (2.42) (0.21)

Observations 20,966 37,808 13,752 15,857 14,124 21,248 12,161 11,977 12,161 11,825 12,073 16,664 5,973 3,651R-squared 0.02 0.01 0.15 0.11 0.25 0.13 0.09 0.14 0.16 0.01Number of CountryYr 48 16 48 14 48 16 48 10 48 10 48 14 24 16

Table 2: Effect of maternal employment on women's and men's outcomes with fixed effects at the country-year level. Robust standard errors in parentheses and clustered at the country-year level. ISSP from 2002 and 2012; GGP from 2002-2013. *** p<0.01, **p<0.05, * p<0.1.

Employment (logistic, odds ratio)

Supervisory (logistic, odds ratio)

HoursHHWork (ISSP); M-F Division HH

work, Higher = more equal (GGP)

Hours Family Care (ISSP); M-F Division Childcare, Higher = more equal (GGP)

Age2

32

(1a) (1b) (2a) (2b) (3a) (3b) (4a) (4b) (5a) (5b) (6a) (6b) (7a) (7b) (8a) (8b) (9a) (9b) (10a) (10b)Women Men Both Women Women Women Women Women Women Men

Gender Attitudes Gender Attitudes Gender Attitudes HoursWorked z_income z_income

ISSP GGP ISSP GGP ISSP GGP ISSP GGP ISSP GGP ISSP GGP ISSP GGP ISSP GGP ISSP GGP ISSP GGP

Maternal Employment 0.19*** 0.04*** 0.23*** 0.05*** 0.20*** 0.05*** 1.13*** 1.18*** 1.24*** 1.16*** 0.61** 0.32 0.03 0.04** 0.02 0.04** -0.77** 0.01 0.73** 0.04(0.02) (0.01) (0.03) (0.02) (0.02) (0.01) (0.05) (0.03) (0.06) (0.07) (0.28) (0.20) (0.02) (0.01) (0.02) (0.01) (0.31) (0.01) (0.34) (0.03)

Gender Attitudes 1.48*** 1.22*** 1.21*** 1.16*** 0.76*** 0.44** 0.15*** 0.08*** 0.13*** 0.07*** -1.18*** 0.08*** 0.42* 0.18***(0.04) (0.04) (0.04) (0.05) (0.21) (0.20) (0.01) (0.02) (0.01) (0.01) (0.16) (0.02) (0.21) (0.02)

Age -0.00 0.00 0.01** 0.00 0.00 0.00 1.51*** 1.71*** 1.11*** 1.09*** 0.80*** 0.34*** 0.11*** 0.02 0.09*** 0.01 0.39*** -0.06*** 0.65*** 0.02**(0.00) (0.00) (0.01) (0.00) (0.00) (0.00) (0.04) (0.06) (0.02) (0.02) (0.14) (0.07) (0.01) (0.01) (0.01) (0.01) (0.11) (0.01) (0.12) (0.01)

-0.00 -0.00** -0.00*** -0.00* -0.00** -0.00* 0.99*** 0.99*** 1.00*** 1.00*** -0.01*** -0.00*** -0.00*** -0.00 -0.00*** -0.00 -0.00** 0.00*** -0.01*** -0.00**(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Education Level 0.06*** 0.08*** 0.05*** 0.06*** 0.05*** 0.07*** 1.09*** 1.58*** 1.11*** 1.68*** 0.03 -0.30 0.08*** 0.25*** 0.08*** 0.25*** -0.38*** 0.04*** 0.07 0.06***(0.00) (0.01) (0.00) (0.01) (0.00) (0.01) (0.01) (0.07) (0.01) (0.10) (0.07) (0.30) (0.01) (0.04) (0.01) (0.04) (0.06) (0.01) (0.07) (0.02)

Married -0.09*** -0.03* -0.00 0.02*** -0.06*** -0.01 0.80*** 0.78*** 1.07 1.01 -1.89*** -0.92** -0.07*** -0.08*** -0.03 -0.07*** 2.96*** 0.44*** 3.06*** 0.02(0.01) (0.02) (0.02) (0.01) (0.01) (0.01) (0.06) (0.05) (0.06) (0.08) (0.34) (0.32) (0.02) (0.02) (0.02) (0.02) (0.51) (0.05) (0.65) (0.04)

Children in Household -0.07*** -0.05*** -0.04** -0.01 -0.04*** -0.02*** 0.66*** 0.70*** 0.86*** 0.89 -2.77*** -1.53** -0.12*** -0.01 -0.07*** -0.00 2.76*** -0.00 8.75***(0.02) (0.01) (0.02) (0.01) (0.01) (0.00) (0.04) (0.06) (0.05) (0.06) (0.34) (0.66) (0.02) (0.03) (0.02) (0.03) (0.30) (0.03) (0.86)

Christian -0.18*** -0.10*** -0.24*** -0.07 -0.19*** -0.07** 0.99 1.03 1.00 1.05 -0.18 -0.01 -0.04* -0.01 -0.04* -0.02 0.57** -0.00 -0.02 0.01(0.03) (0.03) (0.03) (0.05) (0.03) (0.03) (0.05) (0.05) (0.05) (0.06) (0.28) (0.39) (0.02) (0.03) (0.02) (0.03) (0.24) (0.03) (0.36) (0.02)

Other Religion -0.27*** -0.27*** -0.34*** -0.28*** -0.30*** -0.27*** 0.80** 0.61*** 1.12 0.98 -0.19 -0.57 -0.09** 0.07 -0.08** 0.06 1.29** -0.09 0.68 0.01(0.06) (0.06) (0.07) (0.07) (0.06) (0.06) (0.09) (0.08) (0.14) (0.10) (0.60) (0.43) (0.04) (0.07) (0.03) (0.06) (0.54) (0.05) (0.50) (0.08)

HoursWorked, Weekly 0.02*** 0.01***(0.00) (0.00)

Constant -0.49*** -0.06 -0.72*** -0.14* -0.60*** -0.11 0.00*** 0.00*** 0.01*** 0.01*** 24.17*** 35.61*** -3.26*** -1.53*** -3.75*** -1.99*** 7.02*** 0.65*** -8.71*** -0.55**(0.10) (0.06) (0.11) (0.08) (0.09) (0.06) (0.00) (0.00) (0.00) (0.00) (3.00) (2.15) (0.20) (0.31) (0.18) (0.36) (2.17) (0.14) (2.38) (0.22)

Observations 20,966 37,808 15,508 31,182 36,474 68,990 20,966 37,808 13,752 15,857 14,124 21,248 12,161 11,977 12,161 11,825 12,073 16,664 5,973 3,651R-squared 0.09 0.07 0.08 0.04 0.08 0.06 0.02 0.01 0.17 0.11 0.26 0.13 0.10 0.14 0.16 0.03

Table 3: Effect of maternal employment on women's and men's outcomes with fixed effects at the country-year level, mediated by individual-level gender attitudes. Robust standard errors in parentheses and clustered at the country-year level. ISSP from 2002 and 2012; GGP from 2002-2013. *** p<0.01, **p<0.05, * p<0.1.

Employment (logistic, odds ratio)

Supervisory (logistic, odds ratio)

HoursHHWork (ISSP); M-F Division HH work, Higher = more equal (GGP)

HoursCare (ISSP); M-F Division

Childcare, Higher = more equal (GGP)

Age2

33

Figure 1: Average Gender Attitudes by Country (standardized). Bars represent Male / Female by Mother Employed / Not Employed. ISSP data only.

34

Figure 2: Graphs illustrate mean marginal effects and 95% confidence intervals for Maternal Employment on Supervisory Responsibility, Hours in Paid Employment and Income. Bars represent marginal means for women with and without children at home. Based on regressions using ISSP data, controlling for individual demographics and respondent gender attitudes.

35

Figure 3: Graphs illustrate mean marginal effects and 95% confidence intervals for interactions between Maternal Employment and societal levels of FLFP when respondent was 14 years old. Dependent variables are Gender Attitudes (all respondents), Daughters’ Likelihood of Supervising Others, Daughters’ Housework Hours and Sons’ Family Care Hours. Based on regressions using ISSP data, controlling for individual demographics and current country-year National FLFP Rates. All interactions shown are significant (p < .05).

36

Online Appendix A

Fixed Effects Model Details

Models estimate the following country-year fixed-effect regressions:

Yic = δ Mother Employedic + βXic + ηc + εic

where Yic represents adult outcomes—in the workplace or at home—for the ith

respondent in country c; Mother Employedic is a dichotomous variable indicating whether

the respondent’s mother was employed for pay for one year or more between the

respondent’s birth and 14th birthday (ISSP; 1 = yes) or whether the respondent’s mother

was employed for pay when the respondent was 15 years old (GGP; 1 = yes); Xic

represents respondent demographics and family characteristics; ηc denotes country-year

fixed-effects capturing factors expected to differ by country and year, such as GDP, rates

of female labour force participation, welfare policies and widely-held gender attitudes; εic

is the error term. Fixed-effects models include robust standard errors clustered at the

country-year level.

Female Labour Force Participation Rate at 14 Details

Sources for historical country-level female labour force participation rates (FLFP

at 14) include: The International Labour Organization’s Yearbook of Labour Statistics,

1968-1983; the Statistical Abstract of the United States; and International Historical

Statistics.1 Each respondent was assigned the FLFP for their country in the year the

respondent was fourteen years old. For example, if a respondent’s birth year was 1982,

FLFP at 14 was set at FLFP in the respondent’s country in (1996 = 1982 + 14). For some

country-years, the FLFP information is missing from the historical datasets. In these 1 Accessed September 2014 through April 2015: International Labour Organization’s Yearbook of Labour Statistics at http://www.ilo.org/century/research/archives/lang--en/index.htm and in hard copy archives; Statistical Abstract of the United States at https://www.census.gov/library/publications/time-series/statistical_abstracts.html; Mitchell, B.R. (2013) International historical statistics, 1750-2005. Basingstoke, Hampshire: Palgrave Macmillan.

37

cases, respondents were assigned FLFP from the year closest to ((birth year + 14) +/- 5

years) if available. All countries were included in the analyses, but approximately half of

the observations from former Soviet bloc countries were omitted because FLFP data were

not calculated separately for these countries before 1980.

Additional Robustness Checks

Past research suggests maternal employment may simply proxy for the local

availability of employment opportunities for women (Goldin and Olivetti, 2013). Fixed-

effects regressions on the subset of observations in the ISSP data where surveys included

questions about respondents’ communities assessed this possibility by adding a variable

controlling for whether the respondent lived in an urban or suburban community. Living

in an urban community was significantly related to several of the outcome variables, but

the effects for maternal employment and the mediation of those effects through gender

attitudes remained essentially unchanged from those in the analyses reported in the text.

To assess the possibility that time scarcity accounted for the findings regarding

domestic outcomes, we replaced Employed with Hours Worked in analyses of men’s and

women’s time spent on housework and family care. Results in terms of direction and

level of significance remain essentially unchanged with the alternate specification for

employment.

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Online Appendix B

ISSP Survey Questions Used in Creating Measures for Primary Analyses

Demographic / control variables

AgeAge of respondent (in years)

Years of EducationHow many full years of schooling or education have you had? Please include primary and secondary schooling, university and full-time vocational training, but do not include repeated years.

Marital Status What is your current legal marital status?Recoded: 1=“Married, or living as married” or “Civil partnership”; 0=“Widowed”, “Divorced”, “Separated” or “Never married, never in a civil partnership, single, not married” (Note “Civil partnership" and associated language in 22 of 24 countries, 2012 only)

Children Living in the Household How many children up to the age of school age live in your household? How many children between school age and 17 years old live in your household? Recoded:=1 if response to either question >=1; =0 if response to both questions=0

Religion Do you belong to a religion and, if yes, to which religion or church do you belong or feel close to?(Categories varied across countries)Recoded into three categorical variables: No Religion (omitted) (0/1); Christian (0/1); Other (0/1)

Predictor Variables

Mother EmployedDid your mother ever work for pay for as long as one year, after you were born and before you were 14?Recoded: 1=Yes, she worked for pay; 0=No

Gender Attitudes (Standardized factor score from PCF confirmatory factor analysis of eight survey items ( = .78; avg. inter-item covariance = .31)

To what extent do you agree or disagree...?a) A working mother can establish just as

warm and secure a relationship with her children as a mother who does not work

b) A pre-school child is likely to suffer if his or her mother works

c) Family life suffers if a woman goes out to work

d) Work is alright, but what a woman really wants is a home and family

e) Being a housewife is just as fulfilling as working for pay

f) A man’s job is to earn money, a woman’s job is to look after the home and family

1=strongly agree; 5=strongly disagreeWhat do you think is the best arrangement for women's work outside the home under the following circumstances?

g) When there is a child under school ageh) After the youngest child starts school1=stay home; 2=work part-time; 3=work full-time

Dependent Variables

EmployedLast week were you working full time, part time, going to school, keeping house, or what?Recoded: 1=Currently in paid work; 0=Currently not in paid work, paid work in the past; or never had paid work

Supervisory ResponsibilityIn your main job, do you supervise anyone or are you directly responsible for the work of other people? Recoded: 1=Yes, supervise others at work; 0=No, do not supervise

Hours WorkedHow many hours, on average, do you usually work for pay in a normal week, including overtime? 0=None, no hours, does not apply; 1=1 hour or less; 2=2 hours; 3=3 hours; etc. to 94 hours; 95=95 hours and more

Z-Income Before taxes and other deductions, what on average is your own total monthly income?Z-Income = personal income in country currency, annualized, logged and standardized within country-year

Hours HouseworkOn average, how many hours a week do you personally spend on household work, not including childcare and leisure time activities?0=None, no hours, does not apply; 1=1 hour or less; 2=2 hours; 3=3 hours; etc. to 94 hours; 95=95 hours and more

Hours CareOn average, how many hours a week do you spend looking after family members (e.g. children, elderly, ill or disabled family members)?0=None, no hours, does not apply; 1=1 hour or less; 2=2 hours; 3=3 hours; etc. to 94 hours; 95=95 hours and more

Life SatisfactionHow happy are you on the whole?

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1=completely unsatisfied; 2=very unsatisfied; 3=fairly unsatisfied; 4=neither/nor; 5=fairly satisfied; 6=very satisfied; 7=completely satisfied

1 Questions phrased slightly differently across languages.

40

GGP Survey Questions Used in Creating Measures for Primary Analyses

AgeAge of respondent (in years)

Years of Education

What is the highest level of education you have successfully completed? 0=pre-primary education; 1=primary level; 2=lower secondary level; 3=upper secondary level; 4=post secondary non-tertiary; 5=first stage of tertiary; 6=second stage of tertiary

Marital Status Are you and he/she legally married?1=yes; 2=no

Children Living in the Household Number of children in the household (of any age)

Religion Which religious denomination do you adhere to, if any? (Categories varied across countries)Recoded: 0=No Religion; 1= Christian; 3=Other

Predictor Variables

Mother EmployedWhat was your mother's occupation when you

were 15?1=Response was an occupation; 2=Response was housewife / homemaker, unemployed, student, still in training, or retired.

Mother Employed, detailWhat was your mother's occupation when you

were 15?GPP divided occupations into 3 categories: High non-manual, non-manual, and manual. High non-manual has occupational categories such as corporate managers and professionals; non-manual has occupational categories such as office clerks, service workers, and sales workers; and manual has occupational categories such as trade workers, farm labourers, and textile workers.

Gender Attitudes (Eight survey items; =.73; avg. inter-item covariance=.25)

To what extent do you agree or disagree...?a) A woman has to have children in order

to be fulfilledb) When parents in need, daughters should

take more caring responsibilityc) In a couple it is better for the man to be

older than the womand) If woman earns more than partner, not

good for relationshipe) On the whole, men make better political

leaders than womenf) A pre-school child is likely to suffer if

his/her mother works

g) If parents divorce it's better for child stay with mother than father

h) When jobs scarce, men more right to job than women

1=strongly agree; 5=strongly disagree

Mother EducationWhat is the highest level of education that your mother has successfully completed?0=pre-primary education;1=primary level; 2=lower secondary level; 3=upper secondary level; 4=post secondary non-tertiary; 5=first stage of tertiary; 6=second stage of tertiary

Dependent Variables

EmployedDid you do any paid work in the 7 days ending last Sunday, either as an employee or self-employed?1=yes; 2=no

Supervisory ResponsibilityDo you supervise or co-ordinate the work of any personnel?

1=yes; 2=no

Hours WorkedHow many hours per week do you normally work in this job or business including overtime?Response is in hours/week

Z-Income What was the average net amount of this payment?Sum of all payments to respondent over past 12 months: earnings from a main job/ business and/or additional job/business, retirement pension, widow/survivor/war benefit, disability allowance, incapacity/illness benefit, unemployment benefit/job seeker’s allowance, social assistance payment, study benefits/scholarship, or maternity/parental/childcare leave benefit.(annualized, logged and standardized)

Household Division of Labour (5 survey items; =.83; avg. inter-item covariance=.45)

Now I would like to ask you some questions about who does what in your household. Please tell me who does the following tasks in your household?

a) Preparing daily mealsb) Doing the dishesc) Shopping for foodd) Vacuum-cleaning the housee) Small repairs in, around the house

1=female, 2=mostly female, 3=equal, 4=mostly male, 5=male

Childcare Division of Labour (6 survey items; =.79; avg. inter-item covariance=.43)

I'm going to read out various tasks that have to be done when one lives together with children. Please tell me, who in your household does these tasks?

a) Dressingb) Putting to bedc) Illnessd) Leisure activities

41

e) Homework preparationsf) Transport

1=female, 2=mostly female, 3=equal, 4=mostly male, 5=male1 Questions phrased slightly differently across languages

42

43

Country

Online Appendix Table C: Means and proportions for demographic control variables, by country, by gender. Standard deviations in parentheses. Data from ISSP from 2002 and 2012; GGP from 2002-2013. Indicators of p values of statistical significance in differences between males and females: *** p<0.01, **p<0.05, * p<0.1.

44

Online Appendix D: Non-significant effects of maternal employment on women's and men's outcomes using ISSP data, with fixed effects at the country-year level. Robust standard errors in parentheses and clustered at the country-year level (*** p<0.01, ** p<0.05, * p<0.1). Data are from ISSP, 2002 and 2012.

(1) (2) (3) (4) (5) (6) (7)Women Women Women Women Women Women Men

HoursWorked z_income z_income

Mother employed == High-skill non-manual occupation 1.10* 1.22*** -0.04 0.07** 0.07** 0.03 0.04(0.06) (0.07) (0.29) (0.03) (0.02) (0.02) (0.04)

Mother employed == Low-skill non-manual occupation 1.23*** 1.14* 0.56** 0.02 0.01 0.02 0.05(0.04) (0.09) (0.19) (0.03) (0.03) (0.02) (0.04)

Mother employed == Manual occupation 1.17*** 1.04 0.41 -0.01 -0.01 -0.01 0.01(0.04) (0.06) (0.25) (0.02) (0.02) (0.01) (0.03)

Mother's Highest Level of Education 1.03 1.10*** -0.06 0.05*** 0.05*** -0.01* 0.02**(0.02) (0.03) (0.10) (0.01) (0.01) (0.00) (0.01)

Gender Attiude 1.21*** 1.14*** 0.46** 0.07*** 0.06*** 0.08*** 0.17***(0.04) (0.05) (0.21) (0.02) (0.01) (0.02) (0.02)

Age 1.71*** 1.10*** 0.34*** 0.02 0.02 -0.06*** 0.02***(0.06) (0.02) (0.07) (0.01) (0.01) (0.01) (0.01)

Age2 0.99*** 1.00*** -0.00*** -0.00 -0.00 0.00*** -0.00***(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

EducationYrs 1.57*** 1.60*** -0.24 0.22*** 0.22*** 0.04*** 0.04**(0.07) (0.09) (0.30) (0.03) (0.03) (0.01) (0.02)

Married 0.78*** 1.02 -0.93** -0.07** -0.07** 0.45*** 0.03(0.05) (0.09) (0.32) (0.03) (0.02) (0.05) (0.04)

WithChildren 0.70*** 0.90 -1.54** -0.01 -0.00 -0.00 -0.38***(0.07) (0.06) (0.66) (0.03) (0.03) (0.03) (0.03)

Christian 1.03 1.07 -0.04 -0.01 -0.02 -0.00 0.02(0.04) (0.05) (0.40) (0.03) (0.03) (0.03) (0.02)

Other Religion 0.62*** 1.07 -0.64 0.12 0.11 -0.09 0.04(0.08) (0.13) (0.44) (0.07) (0.06) (0.05) (0.08)

Hours Worked 0.01***(0.00)

Constant 0.00*** 0.00*** 35.72*** -1.69*** -2.17*** 0.67*** -0.25(0.00) (0.00) (2.24) (0.30) (0.35) (0.14) (0.21)

Observations 37,807 15,856 21,247 11,976 11,824 16,663 3,652R-squared 0.01 0.12 0.14 0.14 0.03Number of CountryYr 16 14 16 10 10 14 16

Online Appendix F: Effect of type of maternal employment on women's and men's outcomes with fixed effects at the country-year level, controlling for mother's education. Robust standard errors in parentheses and clustered at the country-year level. Data from GGP, 2002-2013. *** p<0.01, **p<0.05, * p<0.1.

Employment (logistic, odds

ratio)

Supervisory (logistic, odds

ratio)

M-F Division HH work, Higher =

more equal

M-F Division Childcare, Higher

= more equal

45

(1) (2) (3) (4)

VARIABLES

Maternal Employment -0.06 0.09*** -2.52*** -1.90*(0.06) (0.02) (0.83) (1.11)

Female Labour Force Participation (FLFP) at 1 -0.00*** 0.00 -0.04 -0.01(0.00) (0.00) (0.03) (0.04)

Maternal Employment*FLFP at 14 0.01*** -0.00*** 0.05*** 0.06**(0.00) (0.00) (0.02) (0.03)

Gender Attitude 0.04*** -1.20*** 0.45**(0.01) (0.21) (0.20)

Age -0.01 0.01*** 0.41*** 0.65***(0.00) (0.00) (0.11) (0.12)

Age2 0.00 -0.00*** -0.00** -0.01***(0.00) (0.00) (0.00) (0.00)

EducationYrs 0.05*** 0.02*** -0.38*** 0.06(0.00) (0.00) (0.07) (0.07)

Married -0.07*** 0.01 2.97*** 3.05***(0.02) (0.01) (0.58) (0.67)

WithChildren -0.03** -0.02** 2.86*** 9.06***(0.01) (0.01) (0.38) (0.86)

Christian -0.20*** -0.00 0.51* 0.16(0.04) (0.01) (0.27) (0.41)

Other Religion -0.29*** 0.02 1.10* 0.41(0.08) (0.01) (0.63) (0.49)

Current Female Labour Force Participation Rate -0.00 0.00 -0.15** 0.03(0.01) (0.00) (0.06) (0.11)

Employed 0.16***(0.03)

Year Indicator = 2012 0.02*** -0.01*** -0.01(0.00) (0.00) (0.05)

Constant -33.53*** 10.67*** 33.81 -10.14(6.91) (3.44) (90.86) (6.94)

Observations 34,413 12,915 11,337 5,698Number of Countries 24 24 24 24

Online Appendix G: Interactions of maternal employment with rate of Female Labour Force Participation (FLFP) in the respondent’s country when the respondent was fourteen years old, with fixed effects at the country-year level. Robust standard errors in parentheses and clustered at the country-year level (*** p<0.01, ** p<0.05, * p<0.1). Data from ISSP 2002 and 2012.

Men and Women's

Standardized Gender Issues

Women's Supervisory

Responsibility

Women's Hours of

Household Work

Men's Hours of Family

Care

46

(1) (2) (3) (4)Life Satisfaction Education Level

Women Men Women Men

Maternal Employment 0.00 0.01 0.22*** 0.32***(0.02) (0.02) (0.05) (0.06)

Gender Attitude 0.04*** 0.05*** 0.71*** 0.70***(0.01) (0.01) (0.02) (0.03)

Age -0.05*** -0.08*** 0.14*** 0.11***(0.01) (0.01) (0.01) (0.02)

Age2 0.00*** 0.00*** -0.00*** -0.00***(0.00) (0.00) (0.00) (0.00)

Education Level 0.02*** 0.01***(0.00) (0.00)

Married 0.39*** 0.46*** -0.04 0.16**(0.02) (0.03) (0.05) (0.07)

Children in Household -0.04** 0.05** -0.36*** -0.28***(0.02) (0.02) (0.05) (0.06)

Employed 0.04* 0.25*** 0.73*** 0.44***(0.02) (0.04) (0.05) (0.07)

Christian 0.05** 0.10*** -0.23*** -0.23***(0.02) (0.02) (0.06) (0.07)

Other Religion -0.02 0.07 -0.27*** -0.24**(0.03) (0.04) (0.10) (0.11)

Constant 5.96*** 6.31*** 10.73*** 11.14***(0.13) (0.14) (0.30) (0.37)

0.81 0.14

Observations 20,805 15,408 20,966 15,508R-squared 0.05 0.07 0.23 0.16Number of CountryYr 48 48 48 48

Online Appendix H: Maternal employment and children’s well-being outside employment and domestic measures, with fixed effects at the country-year level. Robust standard errors in parentheses and clustered at the country-year level (*** p<0.01, ** p<0.05, * p<0.1). ISSP from 2002 and 2012.

pvalue coefficients on Mother are the same for men and women

47

Correlation Matrix for ISSP

Employed Supervisory Resp Income Hours Care Age Education Yrs Married Christian No Religion

Maternal Employment 1Employed 0.0403* 1SupervisoryResp 0.0043 0.0846* 1HoursWorked 0.0357* 0.8317* 0.1495* 1Income -0.0073 0.3623* 0.2879* 0.4622* 1HoursHHWork -0.0706* -0.2844* -0.1222* -0.2770* -0.2138* 1HoursCare -0.0109 -0.1177* -0.0088 -0.1344* -0.0700* 0.3480* 1Gender Attitude 0.1492* 0.1591* 0.0909* 0.0826* 0.0931* -0.1925* -0.0665*Age18_60 -0.1781* 0.0712* 0.0968* 0.0684* 0.1724* 0.1075* -0.0687* -0.0614* 1EducationYrs 0.1456* 0.1593* 0.1712* 0.1135* 0.2552* -0.1869* -0.0036 0.3025* -0.1173* 1Married -0.0980* 0.0829* 0.0644* 0.0755* 0.1278* 0.1512* 0.1789* -0.0792* 0.3375* -0.0528* 1WithChildren -0.0074 0.0151* -0.0146 0.0123 0.0166* 0.1135* 0.4139* -0.0646* -0.1554* -0.0313* 0.2594* 1Christian -0.0898* -0.0217* 0.0045 -0.0263* -0.0194* 0.1114* 0.0740* -0.1097* 0.0622* -0.1072* 0.0577* 0.0396* 1Other Religion -0.0295* -0.0105 -0.0234* 0.011 0.0024 -0.0528* -0.0290* -0.0210* -0.0292* 0.0131 0.0331* 0.0390* -0.5072* 1No Religion 0.1251* 0.0329* 0.0131 0.0212* 0.0199* -0.0848* -0.0595* 0.1409* -0.0475* 0.1113* -0.0915* -0.0755* -0.7347* -0.2121* 1

Correlation Matrix for GGP

Employed SupervisoryResp Income Age Married Christian No Religion

Maternal Employment 1Employed 0.0650* 1SupervisoryResp -0.0272* 0.0603* 1Hours Worked 0.0396* 0.2431* 0.0977* 1Income 0.0112 0.1025* 0.2476* 0.1951* 1Division of Housework 0.0193* 0.0614* 0.0556* 0.0555* 0.0905* 1Division of Childcare 0.0124 0.2430* 0.1089* 0.0149 0.0961* 0.5440* 1Gender Attitude -0.0016 0.1256* 0.1474* -0.0511* 0.0280* 0.0895* 0.1851* 1Age -0.1161* 0.0657* 0.0710* 0.0002 0.0438* -0.1420* 0.0845* -0.0687* 1Highest Level of Educat 0.0782* 0.2316* 0.2338* -0.0019 0.2333* 0.0112 0.1103* 0.1260* -0.0144* 1Married -0.0241* 0.1055* 0.0268* 0.0526* 0.0587* -0.1283* 0.0237 -0.0668* 0.3457* 0.0287*Children At Home -0.0064 0.1225* 0.0059 0.0284* 0.0341* -0.2196* -0.0127 -0.0715* 0.1602* 0.0342* 0.4919* Christian 0.0857* -0.0291* -0.0476* 0.0223* 0.0074 -0.0487* -0.0403* -0.1500* 0.0152* 0.0259* 0.0094 -0.0084 1

Online Appendix I: Correlation Matrices for ISSP (2002 and 2012) and GGP (2002 - 2013) data. * p<.01

Maternal Employment

Hours Worked

Hours HH Work

Gender Attitude

With Children at Home

Other Religion

Maternal Employment

Hours Worked

Division of Housework

Division of Childcare

Gender Attitude

Highest Level of Education

Children at Home

Other Religion

48