parenthood wage penalties in a double income society

Parenthood wage penalties in a double income society

Sara Cools Marte Strm

Received: 14 March 2013 / Accepted: 22 February 2014

Springer Science+Business Media New York 2014

Abstract We estimate how parenthood affects hourly wages using panel data forNorwegian employees in the years 19972007. Though smaller than for most other

OECD countries, we find substantial wage penalties to motherhood, ranging from a

1.2 % wage reduction for women with lower secondary education to 4.9 % for

women with more than four years of higher education. Human capital measures

such as work experience and paid parental leave do not explain the wage penalties,

indicating that in the Norwegian institutional context, mothers are protected from

adverse wage effects due to career breaks. We do however find large heterogeneity

in the effects, with the largest penalties for mothers working full time and in the

private sector. Contrary to most studies using US data and to previous research from

Norway, we find a small wage penalty also to fatherhood. Also for men, the penalty

is greater for those who work full time and in the private sector. A substantial share

of the fatherhood wage penalty is explained by paternity leave.

Keywords Parenthood penalties Welfare state Gender wage gap

We are grateful to Jenny Clarhall, Eva Klve, Andreas Kotsadam, Ingrid Kruger, Jo Thori Lind, Kalle

Moene and Fredrik Willumsen for helpful comments and suggestions. The authors bear all responsibility

for errors and shortcomings. This paper is part of the research activities at the center of Equality, Social

Organization, and Performance (ESOP) at the Department of Economics at the University of Oslo, in

collaboration with the Ragnar Frisch Centre for Economic Research. We acknowledge funding from

the Norwegian Research Council (Frisch project 1110). Data made available by Statistics Norway have

been essential for the research project. ESOP is supported by the Research Council of Norway.

S. Cools

BI Norwegian Business School, Oslo, Norway

e-mail: [email protected]

M. Strm (&)Institute for Social Research, Oslo, Norway

e-mail: [email protected]

123

Rev Econ Household

DOI 10.1007/s11150-014-9244-y

JEL Classification J13 J22 J24 J31

1 Introduction

Womens labor force participation increased greatly in Norway during the last

decades of the twentieth century, closely approaching that of men. Over the same

period, as several European countries experienced falling fertility rates, the total

fertility rate in Norway increased and stayed far above the European average. In a

country where the combination of work and family is encouraged through public

policies such as universal child care, paid parental leave and extensive job

protection for parents, and where not only the two career household, but also the two

carer household, is encouraged, the double fact of high female labor force

participation and high fertility is the hallmark of these policies success.

Unfortunately, and puzzling to some, the gender gap in hourly wage has not declined

as successfully over the period. It seems rather that the convergence in mens and

womens hourly wage stagnated some time during the 1980s, only to pick up slightly

from 2000 onwards. As in most other countries, the gender wage gap is greater among

parents than among non-parentsa phenomenon known as the family gap in wages.

Quantifying the wage effects of parenthood is therefore of interest to policy

makers wishing to address the overall gender wage gap. In this paper we study how

children affect mens and womens hourly wages, using panel data on hourly wages

in Norway from 1997 to 2007. Using fixed effects estimation and register data

covering both the private and the public sector (in total 80 % of the working

population), this is the first broad panel data study of parenthood wage effects in

Norway. In addition to rich register data, the institutional setting provided by

Norway as a typical Nordic welfare state makes it an interesting case for the

exploration of causes behind the family wage gap.

The international empirical evidence on a negative relationship between having

children and womens labor market outcomes, like wages and labor supply, is

substantial (e.g. Korenman and Neumark (1992), Waldfogel (1997) and Budig and

England (2001)). The evidence on the effects of fatherhood is both more scarce and less

consistent (see for instance Millimet (2000), Lundberg and Rose (2002), Simonsen and

Skipper (2008), Astone et al. (2010), Wilde et al. (2010)). Generally, the literature finds a

smaller family gap in pay for mothers in the Scandinavian countries (Datta Gupta and

Smith 2002; Harkness and Waldfogel 2003a). Differences in family policies are argued

to be key in explaining differences in family gaps across countries (Waldfogel 1998).

For example, Waldfogel (1998) show that in countries with paid parental leave, women

return to work more quickly and at the same time maintain a good job match. The

institutional context may therefore leave a smaller role for the motherhood wage penalty

being caused by an adverse effect on firm-specific human capital.

This paper aims to investigate further two main explanations for why we observe

a negative wage effect on mothers wages.1 Commonly, children are considered to

1 Unless tied specifically to institutional or biological gender differences, these explanations would also

apply to fatherhood wage penalties.

S. Cools, M. Strm

123

have an effect on womens productivityby a comparative reduction either in their

human capital or in their work effortwhich again will be reflected in their wages.

As proposed by Mincer and Polachek (1974), childbearing and child rearing may

cause a comparative reduction in mothers labor market experience, both through

periods out of the labor market and through periods of reduced working hours,

which then adversely affects mothers accumulation of human capital. Several

empirical studies have shown that when controlling for experiencemeasured as

time in active employment, net of career breaks and/or adjusted for part time

worka large part of the wage penalty is explained (Waldfogel 1997; Budig and

England 2001; Datta Gupta and Smith 2002; Anderson et al. 2002; Wilde et al.

2010).

We explore a human capital explanation for the family wage gap by including

actual job experience, the length of parental leave and part-time work as explanatory

variables in our estimations of parenthood wage effects. As the Norwegian

institutional setting provides, on the one hand, long periods of parental leave, while

on the other, high job-protection during leave, we expect two conditions to be met:

First, that the overall wage penalty is lower in Norway if the loss of job-specific

human capital explains a large share of the wage-penalty in other settings. Second,

that the inclusion of experience measures explains a smaller share of the wage

penalty because they only pick up the deterioration of human capital in general.

In the other part of the productivity explanation for mothers reduced hourly

wage, Becker (1985) theory of a conflict between effort in home production and

effort at work implies that among women working the same number of hours,

mothers will put in less effort per hour. Several studies have found a negative

relationship between housework load and wages (Coverman 1983; Shelton and

Firestone 1989; McAllister 1990; Hersch and Stratton 1997). Career break effects

may also wrongly be attributed to loss of human capital. Using Swedish data,

Albrecht et al. (1999) find that the type of career break matters for its effect on

wages, and that the effects vary by gender. As they find no effect of maternity leave

and a negative effect of paternity leave, they interpret this as parental leave

signalling worker effort.2

We explore the effort explanation in different ways. First, we expect workers to

be more free to regulate their own effort in occupations requiring higher education,

whereas workers in occupations requiring lower education will be relatively more

forced to reducing working hours rather than effort in their work. We therefore

expect parenthood to affect hourly wages more negatively in higher education

groups (whereas it affects working hours more negatively in the lower education

groups). Second, Anderson et al. (2003) suggest that the role of effort can be

investigated by studying how the parenthood wage penalty evolves as the child

grows older, since smaller children demand more effort at home than older children.

The parenthood wage penalty should consequently be smaller when the child grows

2 Mothers being expected by employers to take leave for a considerable period of time means maternity

leave is not a strong signal to the employer of their type. For men, on the other hand, the length of

parental leave is a strong signal of how much effort they will spend at work.


123

older.3 Third, if effort is part of the explanation and there is a constant amount of

effort to be shared between parents, we would expect mens penalties to increase as

womens decline. Last, we should also expect to see larger wage penalties in the

private sector, where individual wage setting is more common but job protection

still applies, as long as individually bargained wages are more closely linked to

effort.

Other explanations from both the empirical and theoretical literature do not

evoke an effect of children on womens productivity: Mothers may earn lower

wages than non-mothers due to employers discrimination or because they seek

employment in mother-friendly jobs, for instance offering greater flexibility or

requiring skills that deteriorate less rapidly during time spent out of the labor

forceat the expense of lower wages (Budig and England 2001; Nielsen et al.

2004). We do not test hypotheses of discrimination in this paper, and cannot rule out

that it plays a role. Public sector jobs are often viewed as more easily compatible

with family life. We explore whether a change in sector of employment can explain

some of the parenthood wage effects by including sector of employment as an

explanatory variable in our regressions.

Our results show that even in the context of a double income welfare state like

Norway, there is a substantial wage penalty to motherhood. Ranging from 1.2 %

during the first years for women with lower secondary education to 4.9 % for

women with more than four years of higher education, motherhood wage penalties

in Norway are lower than estimates found for the UK (Harkness and Waldfogel

2003b) and the US (Korenman and Neumark 1992; Budig and England 2001; Wilde

et al. 2010), and comparable to estimates found for Germany (Harkness and

Waldfogel 2003b) and Denmark (Nielsen et al. 2004), among others. Contrary to

most other studies, we find a negative (though comparatively small) effect of having

children of about .3 to .4 % on the hourly wages of men with middle length

education.

The wage penalty to motherhood is not explained when we include measures of

experience, parental leave, working part time and sector of employment. The results

indicate that Norwegian family policies protect mothers from adverse wage effects

following a career break. We find evidence that changes in effort might be important

for the explanation of a parenthood wage effect. As hypothesized, the wage effects

to parenthood is larger for higher educational groups and for people working in the

private sector, where we expected a change in effort to affect wages more.

2 Estimating the wage penalty to parenthood

The obvious problem of childbearing being endogenous to wagesand to labor

market outcomes in generalis dealt with most completely in studies that use

3 However, Wilde et al. (2010) argue that if parenthood affects both wage levels and wage growth, the

effect of reduced effort during early years may have a lasting effect. If the wage trajectory changes

growth rate, the difference between parents and childless individuals may even be increasing, a pattern

found in Wilde et al. (2010) on US data. A long term persistence in motherhood wage effects is found in

several other studies, like Viitanen (2014) using UK data.

S. Cools, M. Strm

123

instrumental variables for the childbearing decision. A shortcoming of this approach

is that it cannot be applied to the study of effects of having the first child.4 The

literature on the wage penalties to parenthood is mainly concerned with effects of

the first child, although effects of subsequent children are often estimated in

addition. Given this focus, endogeneity is primarily addressed by the inclusion of

individual fixed effects in the estimation (this is done by Waldfogel (1997), Hersch

and Stratton (1997), Budig and England (2001) and Anderson et al. (2002)).

Even when controlling for individual fixed effects, there is the possibility of

selection of individuals into parenthood and of systematic differences in the career

path of individuals who have children at different ages, which would both yield

biased estimates (Korenman and Neumark 1991; Antonovics and Town 2004).

Wilde et al. (2010) address the issue of selection into parenthood by restricting their

sample to contain only individuals who are observed to become parents. This

restriction in combination with fixed effects estimation means identification of the

parenthood wage penalties comes from comparing the development in wages of

individuals with children to the development in wages of individuals who have not

yet had childrenin essence, causal interpretation rests on the assumption of

exogenous variation in the timing of when to have children.5Wilde et al. (2010)

further address the endogeneity in the timing of childbearing by running separate

analyses of the wage effects for different skill levels (defined by Armed Forces

Qualification Test scores taken at ages 1421). If the timing of children is random

within a skill group, separate analyses will yield unbiased estimates. Put less

strongly, if part of the unobserved heterogeneity that is correlated both with the

timing of children and with wages varies systematically between certain groups,

estimation of wage effects within groups will reduce the bias.

Our data do not contain any exogenous measure of ability. Given that there is a

strong tradition in Norwegian centralized wage setting of tying qualification to

education length, the length of individuals education will in large part determine

how their wages develop. Length of education is also a major factor influencing

when to have children. For these reasons, subsample analysis based on education

length might reduce the bias resulting from time-variant heterogeneity amongst

individuals who have children at different points in time. We divide the sample into

four educational categories.6 We include individual fixed effects in order to take out

time-invariant unobservable heterogeneity that is correlated both with the average

wage level and with the propensity to have children at different ages. The resulting

identifying assumption for a causal interpretation of our results is that, within an

education group, there is no systematic difference in the wage paths of those who

4 The most widely applied instruments for having additional children are twins and child gender

composition (first used by Rosenzweig and Wolpin (1980) and Angrist and Evans (1998), respectively).5 As postponing childbearing is found to reduce the negative career effects of having children (Hofferth

1984; Taniguchi 1999; Buckles 2008; Miller 2011), the assumption is under siege.6 Our divisionfurther described in Sect. 3is similar to one in Anderson et al. (2003), but we have a

separate group with a master degree or higher. This is a small group, but interesting because of the large

career potential, meaning motherhood may be a substantial hinder as is shown in e.g. Bertrand et al.

(2010). The timing of the first child is clearly different between education groups: Age at first birth

increases by one year on average between one educational group and the next.


123

have children at a certain point in time and those who do notother than what is

caused by the event of having children itself.

We estimate the following relationship between wages and having children:

ln wit p1aChild11 5it p1bChild16 10it p1cChild111 15it p1dChild1[ 15it p2aChild21 5it p2bChild26 10it p2cChild211 15it p2dChild2[ 15it p3aChild31 5it p3bChild36 10it p3cChild311 15it p3dChild3[ 15it p4Child4it qaPregnantit qbBabyit df Ageit et vi uit;

1

where i indicates individual and t indicates year of the observation. w denotes hourly

wage, which enters log transformed. The Child1it variables are variables indi-cating the age categories of the first child. Child11 5it takes a value between 0and 1 according to how much of year t individual is first child is between the age of

1 to 5. Child16 10it thus takes a value between 0 and 1 according to how muchof year t individual is first child is aged 6 to 10, and so on. The Child2it variablesindicate the age categories of a second child, and the Child3it variables those ofthe third child. Child4it indicates whether the individual has had a additional chil-

dren beyond the third child.

Parental leave benefits in Norway entail wage replacement during approximately

the first year after a child is born, contingent on the parents labor market behavior

during the last 10 months prior to the childs birth. Wages observed during pregnancy

and during the parental leave period may therefore differ significantly from wages

received prior to the pregnancy, and from the wage realized after returning to the labor

force. The variable Pregnantit, which takes a value between 0 and 0.75 according to

how much of the year was spent in pregnancy, and the variable Babyit, taking a value

between 0 and 1 according to how much of that year the individual had a first-born

baby younger than 1 year, take out the variation in wages caused by these periods.

We also include a fourth order polynomial in each parents age (f Age). etdenotes year fixed effects and vi individual fixed effects. Hence, the wage path of

non-parents is given by the age function, net of year and individual fixed effects.7

Our main interest lies with the parameters p1a, p1b and p1c, the effect on wages ofhaving a first child aged 15 years, 610 years and 1115 years, respectively. For

(weakly) negative values of the p1s, p1a p1b p1c would correspond to a constantwage effect of parenthood over time. jp1aj[ jp1bj[ jp1cj corresponds to a scenario ofparents wages catching up with their original wage path, whereas jp1aj\jp1bj\jp1cjcorresponds to the case in which parents wages keep deteriorating relative to the

wages of non-parents.

3 Data

The basis for our main sample is all Norwegian residents born between 1950 and

1980, except parents who at one point had twins, triplets or any type of multiple

7 The fourth order polynomial is chosen in order to be as flexible as possible, given that the use of age

dummies is not feasible due to multicollinearity with year and individual fixed effects.

S. Cools, M. Strm

123

birth. The sample is further restricted by the availability of data on hourly wages

(described below).

We run separate estimations in four subsamples based on the length of

individuals education. The education data is provided by Statistics Norway and

makes it possible to track individuals highest registered education over time. As an

individuals ultimate education level may be influenced by childbearing, we face a

trade-off between a late observation of education, that more likely reflects the

individuals actual wage path, and an early and less endogenous measure of

education. Since it is not clear how much there is to gain in terms of exogeneity by

using observations on education at younger ages, and because there is something to

be gained by moving close to the actual education of the individual during his or her

working life, we use education measured at age 25 for the main part of our analysis.

The median age at which an individual attains the highest registered education is 24

years in our sample.

The first education group is the group of individuals who have completed nine

years of lower secondary education or less (this group also includes individuals for

whom education information is missing). This group constitutes about 40 % of our

sample, based on the observation of education at age 25. Typical occupations for

this education group are for instance shop clerks, transport workers (men), cleaning

personnel (women). The second group has completed additional three years of upper

secondary education, and constitutes roughly 30 % of our sample. Typical

occupations are technicians and electricians (men) and secretaries and health care

personnel (women). The third group has a lower degree in higher education (four

years or less of higher education, typically a bachelors degree or the equivalent),

and is typically employed as engineers (men) or nurses (women). This group

constitutes almost 30 % of the sample. Finally, the fourth education group consists

of those with four years or more of higher education (and thus a higher degree).

Constituting only about 2 % of the whole sample, this group is typically employed

as doctors, architects and engineers.

Our measure of hourly wages is constructed using information on contracted

hours and monthly wages in Statistics Norways Wage statistic (Lnnsstatis-

tikken). The Wage statistic is based on employer reports for a sample of

Norwegian enterprizes on all employees by the 1st of October. 8 Every year all

public enterprizes and all private enterprizes with more than a certain number of

employees are included (the number varies with industry and year), for the

remaining private sector a 50 % sample of medium size enterprizes and a 20 %

sample of small enterprizes is drawn every year. Employment in agriculture,

hunting and forestry is left out. So are enterprizes with 3 or less employees. On

average, the Wage statistic covers about 80 % of Norwegian employees (100 % of

the public sector employees and 70 % of the private sector employees) every year.

The information on birth year, education and the linking of parents to their children

comes from Statistics Norways demography, family and education registers.

8 Results are therefore representative of employees only. The share of self-employed in the labor force is

about 4 % for women and almost 10 % for men during the period under study, with smaller shares for

individuals with small children (Villund 2005).


123

The resulting panel is unbalanced, both due to the sampling of medium size

enterprizes, and because people may move in and out of employment covered by the

Wage statistic. A missing observation for a given year may mean either that the

person in question does not work that year, that she is self-employed, or that she is

employed in an enterprize that is not included in that years sample.9

In Sect. 5, we include four covariates that are clearly endogenous to the

childbearing decision (so-called bad controls) in order to see to what extent they

explain the parenthood wage penalties. In Sect. 6 we divide the sample into

different subsamples based on these four variables. As some of these variables are

only observable from 1993 onwards, the samples used in the analyses in Sects. 5

and 6 will be restricted to parents of children born between 1994 and 2007.

Experience is constructed counting the cumulative number of years the

individual is registered with occupational income above the basic amount of the

Norwegian social security system (G), which is a common measure of labor force

participation based on Norwegian income data. Parental leave counts the

cumulative number of days the individual has been registered with paid parental

leave. This register has reliable information from 1993 onwards. Third, the variable

Part time is constructed as a dummy variable equal to one if the individual is

registered as working 30 h or less per week, zero if the registered number of weekly

hours is more than 30. Lastly, the Public sector variable used in Sect. 5 is based on

whether the individual is registered with a public sector employment code in the

Wage statistic. When we split the sample according to which sector individuals

worked in the year they became parents, we again use information from the

Employer/Employee register, going back to 1993.

Descriptive statistics for the samples and subsamples we use in the analyses in

Sects. 46 are given in Table 1.

4 Baseline results

Table 2, columns (1)(5) shows the results from fixed effects regression on Eq. 1

for our main sample of women. Estimated on the whole sample together, having

children on average reduces womens earnings by 1 % during the first years. This

estimate for the whole sample is smaller than in any of the subsamples.10 The effect

9 Using other official registries we find that about 20 % of women and 7 % of men who have a missing

wage observation when their first child is 3 years old have earnings below the basic amount (G) of the

Norwegian social security system, meaning that they are marginally or not employed at all that year (from

January 1 2010, G is NOK 72 881, approximately USD 12 500). About 50 % of the women and 75 % of

the men with missing wage observations are registered as employed. Some of the remaining missing

observations may indicate self-employment.10 When the timing of children differs between educational groups, the estimate for the full sample will

typically be different from the average over the within-group estimates. Estimating the effect of children

relative to the counterfactual wage path for the full sample means individuals in the highest education

group are over-represented among the (still) childless, while individuals in the lower education group are

over-represented among those who have already had children. The same pattern is found in Anderson

et al. (2002).

S. Cools, M. Strm

123

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8(5

.57

)


123

Ta

ble

1co

nti

nued

All

Sec

ondar

yed

uca

tion

Hig

her

educa

tion

Lo

wer

Up

per

Lo

wer

deg

.H

igher

deg

.

Mea

nS

DM

ean

SD

Mea

nS

DM

ean

SD

Mea

nS

D

Cum

ula

ted

par

enta

lle

ave

(yea

rs)

0.0

6(0

.12

)0

.05

(0.1

0)

0.0

6(0

.12

)0

.07

(0.1

4)

0.0

9(0

.18

)

Par

tti

me

0.0

8(0

.27

)0

.10

(0.2

9)

0.0

7(0

.25

)0

.08

(0.2

8)

0.0

5(0

.22

)

Pu

bli

cse

cto

r0

.31

(0.4

6)

0.2

2(0

.41

)0

.24

(0.4

3)

0.5

0(0

.50

)0

.35

(0.4

8)

Tak

esle

ave

wit

hfi

rst

chil

d0

.55

(0.5

0)

0.4

7(0

.50

)0

.57

(0.5

0)

0.6

0(0

.49

)0

.63

(0.4

8)

Fir

stch

ild

leav

e(y

ears

)0

.04

(0.0

8)

0.0

3(0

.06

)0

.04

(0.0

7)

0.0

5(0

.09

)0

.06

(0.1

0)

Fu

llti

me

bef

ore

chil

dre

n0

.92

(0.2

8)

0.9

0(0

.29

)0

.93

(0.2

5)

0.9

0(0

.29

)0

.96

(0.1

9)

Pu

bli

cse

cto

rb

efo

rech

ild

ren

0.3

2(0

.47

)0

.23

(0.4

2)

0.2

5(0

.43

)0

.50

(0.5

0)

0.3

7(0

.48

)

N1

37

,19

83

7,8

25

56

313

38

767

4,2

93

Sam

ple

isw

om

enan

dm

enb

orn

bet

wee

n1

95

0an

d1

98

0w

ho

are

regis

tere

dw

ith

emplo

ym

ent

inS

tati

stic

sN

orw

ays

Wag

est

atis

tic.

Wo

rkin

gle

ssth

an3

0h

per

wee

kis

clas

sifi

edas

par

tti

me

emplo

ym

ent,

work

ing

30

ho

rm

ore

iscl

assi

fied

asfu

llti

me

S. Cools, M. Strm

123

Ta

ble

2T

he

effe

cto

fch

ild

ren

on

wo

men

and

men

sh

ou

rly

wag

e

Wo

men

Men

All

Sec

ondar

yed

uca

tion

Hig

her

educa

tion

All

Sec

ondar

yed

uca

tion

Hig

her

educa

tion

Lo

wer

Up

per

Lo

wer

deg

.H

igh

erd

eg.

Lo

wer

Up

per

Lo

wer

deg

.H

igh

erd

eg.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10

)

Fir

stch

ild

Ag

e1

5

0

.009

8*

**

0

.01

2*

**

0

.033

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*

0.0

25

**

*

0.0

49

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*0

.006

7*

**

0.0

02

6

0.0

02

8*

0

.00

39

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0.0

01

7

(0.0

00

79

)(0

.00

17

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.00

14

)(0

.00

12

)(0

.00

61

)(0

.00

10

)(0

.00

18

)(0

.00

16

)(0

.00

19

)(0

.00

58

)

Ag

e6

1

0

0.0

13

**

*

0.0

14

**

*

0.0

44

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*

0.0

35

**

*

0.0

45

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0.0

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0

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3*

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04

3

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01

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.00

19

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.00

17

)(0

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88

)(0

.00

14

)(0

.00

24

)(0

.00

22

)(0

.00

26

)(0

.00

79

)

Ag

e1

1

15

0

.016

**

*

0.0

17

**

*

0.0

51

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*

0.0

42

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01

50

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59

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01

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024

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23

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21

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1)

(0.0

016

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.00

27

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26

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.00

32

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95

)

Ag

e[

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0

.015

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16

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53

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46

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29

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.004

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01

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026

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27

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(0.0

018

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30

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.00

31

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.00

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on

dch

ild

Ag

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0

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0

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21

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00

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59

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15

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18

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)

Ag

e6

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0

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0.0

06

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0.0

27

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00

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17

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17

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15

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86

)(0

.00

12

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.00

21

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.00

20

)(0

.00

24

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72

)

Ag

e1

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0

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*

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08

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0.0

31

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01

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21

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.00

19

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1)

(0.0

015

)(0

.00

25

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.00

25

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.00

30

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89

)

Ag

e[

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0

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06

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0

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0.0

34

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43

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15

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01

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021

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22

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30

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Th

ird

chil

d

Ag

e1

5

0

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0

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18

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0.0

41

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08

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00

72

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00

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14

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76

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.00

12

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.00

20

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.00

19

)(0

.00

23

)(0

.00

64

)


123

Ta

ble

2co

nti

nued W

om

enM

en

All

Sec

ondar

yed

uca

tion

Hig

her

educa

tion

All

Sec

ondar

yed

uca

tion

Hig

her

educa

tion

Lo

wer

Up

per

Lo

wer

deg

.H

igh

erd

eg.

Lo

wer

Up

per

Lo

wer

deg

.H

igh

erd

eg.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10

)

Ag

e6

1

0

0.0

13

**

*

0.0

07

0*

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0

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*

0.0

25

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*

0.0

50

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09

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01

60

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8

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01

1)

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019

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20

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18

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1)

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015

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25

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25

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30

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84

)

Ag

e1

1

15

0

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06

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*

0.0

27

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55

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07

5

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Ag

e[

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0

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0

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0.0

30

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29

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Ob

serv

atio

ns

4,1

85

,653

1,9

27

,527

1,0

39

,05

71

,16

7,1

12

51

,95

73

,325

,98

91

,377

,70

91

,120

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73

6,9

30

90

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5

Ind

ivid

ual

s6

46

,86

93

,11

,87

91

70

,46

21

57

,18

87

,340

56

7,3

21

25

6,2

82

19

2,2

27

10

6,0

54

12

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8

Eac

hco

lum

npro

vid

esF

Ees

tim

ates

from

are

gre

ssio

nbas

edon

Eq.

1o

nth

esa

mp

leo

fN

orw

egia

nw

om

enan

dm

enb

orn

bet

wee

n1

95

0an

d1

98

0.T

he

colu

mn

s(2

)(5

)an

d

(7)

(10)

corr

espond

tosu

bsa

mple

sac

cord

ing

toin

div

idual

sle

ngth

of

educa

tion

mea

sure

dat

age

25.

Yea

rdum

mie

s,a

poly

nom

ial

inag

ean

din

div

idual

fixed

effe

cts

are

incl

ud

edin

all

spec

ifica

tio

ns.

So

are

con

tro

lsfo

rp

reg

nan

cy,

hav

ing

ab

aby

yo

un

ger

than

1y

ear

and

for

hav

ing

more

than

3ch

ild

ren

.R

ob

ust

stan

dar

der

rors

are

in

par

enth

eses

.*

p\

0:1

0;*

*p\

0:0

5;

**

*p\

0:0

1

S. Cools, M. Strm

123

Ta

ble

3T

he

effe

cto

fch

ild

ren

on

wo

men

and

men

sh

ou

rly

wag

e

Wo

men

Men

All

Sec

ondar

yed

uca

tion

Hig

her

educa

tion

All

Sec

ondar

yed

uca

tion

Hig

her

educa

tion

Lo

wer

Up

per

Lo

wer

deg

.H

igher

deg

.L

ow

erU

pp

erL

ow

erd

eg.

Hig

her

deg

.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10

)

Pa

nel

A:

ba

seli

ne

Fir

stch

ild

Ag

e1

5

0

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**

*

0.0

18

**

*

0.0

34

**

*

0.0

27

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*

0.0

52

**

*

0.0

05

9*

**

0

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0

0.0

06

6*

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0

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0.0

04

3

(0.0

00

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.00

21

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.00

16

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.00

13

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.00

63

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.00

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.00

21

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.00

17

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.00

21

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.00

60

)

Ag

e6

1

0

0.0

46

**

*

0.0

24

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*

0.0

44

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*

0.0

37

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*

0.0

49

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*

0.0

18

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03

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13

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01

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030

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86

)

Ag

e1

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13

0

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0.0

46

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0.0

45

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29

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01

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024

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3)

Obse

rvat

ions

1,4

39,8

05

298,6

90

485,6

26

619,8

74

35,6

15

1,4

49,5

91

402,6

89

595,7

52

405,2

42

45,9

08

Ind

ivid

ual

s2

57

,69

16

6,3

53

91

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69

4,4

54

5,3

18

26

8,5

44

88

,10

11

10

,89

36

2,6

52

6,8

98

Pa

nel

B:

incl

ud

ing

exp

lan

ato

ryva

ria

ble

s

Fir

stch

ild

Ag

e1

5

0

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0.0

21

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*

0.0

38

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*

0.0

29

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*

0.0

48

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05

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0.0

04

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02

40

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01

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026

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)

Ag

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0

0.0

48

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0.0

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0.0

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0.0

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11

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01

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)

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e1

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0

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0.0

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0.0

46

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0.0

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02

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024

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.00

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Ex

per

ien

ce0

.00

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***

0.0

04

7*

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0.0

02

8*

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0

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45

0.0

07

0

0.0

04

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00

50

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9*

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00

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11

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06

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(0.0

013

)(0

.00

23

)(0

.00

25

)(0

.00

22

)(0

.00

76

)


123

Ta

ble

3co

nti

nued

Wo

men

Men

All

Sec

ondar

yed

uca

tion

Hig

her

educa

tion

All

Sec

ondar

yed

uca

tion

Hig

her

educa

tion

Lo

wer

Up

per

Lo

wer

deg

.H

igher

deg

.L

ow

erU

pp

erL

ow

erd

eg.

Hig

her

deg

.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10

)

Par

enta

lle

ave

0

.003

0*

**

0

.00

41

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0

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0

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*

0.0

06

2

0.0

09

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*

0.0

32

**

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0.0

36

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0.0

39

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(0.0

00

81

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20

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27

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S. Cools, M. Strm

123

of having children is strongest for the highest education group, and weakest for the

lowest education group. During the first years after having children wages are

reduced by 4.9 % for those who have a higher university degree and by 1.2 % for

women who have lower secondary education.11 In all education groups except the

highest one, and in the sample as a whole, the effect of the first child grows over

time.

Restricting the sample to mothers only, as is done by Wilde et al. (2010), gives

more negative estimates in the sample as a whole, whereas the estimates in the

subsamples based on education length change only marginally. The results indicate

that women who do not have children are on a lower wage-path, yielding a smaller

estimated impact of children when the control group includes these women. If we

further restrict the sample to parents of at least two children, effects are again

somewhat larger in the aggregate, but the main pattern across subsamples and child

ages remains the same. The estimated effect of having a second child is stronger for

women in all education groups and at all ages of the second child. Lastly, splitting

the sample into subgroups based on education measured at age 22 instead of 25

though yielding too few observations to get meaningful estimates in the group with

the highest educationthe pattern across the other groups is still one of larger wage

penalties with higher education.12

Table 2, columns (6)(10) gives the results on the wage penalties to fatherhood.

In the whole sample, the estimated effect is positive initially, at .67 %, and only

becomes negative as the child is older than 15, at .45. However, when we estimatethe effect on wages separately for the four education groups, there are negative

effects in the two middle education groups already at the outset, at .28 and .39 %

respectively. Also for men in these groups, the wage penalty to parenthood grows as

the child gets older, ending up at 1 % in the group with upper secondary education,

and 2 % for those with 4 years or less of higher education.

Restricting the sample to fathers only, the estimated effects of the first child tend

to become stronger, in both directions, but the general picture remains the same.

Further restricting the sample to fathers of at least two children, effects of having a

second child are estimated to be negative, indicating that fathers of two children are

positively selected with respect to wages. There are no significant differences when

we use the observed level of education at age 22 instead of 25.13

The statistically and economically significant wage penalty to fatherhood that we

find here contrasts with the findings of other studies on US data (Lundberg and Rose

2000, 2002), and also those by Simonsen and Skipper (2008) for Denmark. Wilde

et al. (2010), who restrict their sample to those who become fathers, also find small

negative effects on mens wages of having children.

The estimated effects of further children are positive and statistically significant

for men in the two lower education groupsand in the sample as a whole. However,

11 For comparison, the average return to one more year of education for women in Norway in this period,

lies between 5 and 6 % (Hgeland and Kirkeben 2007, p.14) and the gender wage gap in Norway lies

around 16 % (NOU 2008:6).12 Results are available from the authors upon request.13 Results are available from the authors upon request.


123

when we restrict the sample of men to those who are eventually observed to have at

least two children, the estimated impact of the second child is negative.

5 Explaining the wage penalties to parenthood

We examine the determinants of the motherhood wage penalty in two different

ways. In this section, we investigate how changes in observable circumstances

explain the wage effect of children. Specifically, we study the respective roles of

work experience, parental leave, part time work and sector of employment.

As discussed in Sect. 3, the sample we use are those who have their first child

between 1993 and 2008. Panel A gives the baseline results from estimating Eq. 1 for

this sample. Results are very similar to those for the whole sample, given in Table 2.

In Panel B, the potential explanatory variables are included in the regression.

Comparing the two panels of Table 3 for women, we see that inclusion of these

variables does not significantly alter the coefficients. If anything, the estimated

impact of children is larger when they are included. We explored the results further

by including only one variable at the time. This showed that adding measures for

experience, maternity leave and sector of work does not alter the estimated impact

of having children on womens wages. However, when a dummy for working less

than 30 h per week is added, the estimated wage penalty becomes greater. It is thus

the case that working part time, in itself positively associated with hourly wage in

all education groups (except in the highest one where there is no significant

association), is also positively correlated with having children. When the part time

dummy is omitted from the analysis, the effect of children on wages is smaller

because switching to part time work ameliorates the wage penalty to motherhood.

The economically insignificant role of experience, part time work and parental

leave in explaining the motherhood wage penalties point to the importance of labor

market policies, as these variables are found to explain a large part of the

motherhood wage penalty in other countries (Waldfogel 1997; Lundberg and Rose

2000; Budig and England 2001; Datta Gupta and Smith 2002; Anderson et al. 2002;

Wilde et al. 2010). A possible explanations for the small role of career breaks is that

policies of job protection during child related absence from work have an effect14. A

less optimistic explanation is that career breaks around birth are already accounted

for in the wage offer that the woman gets even before pregnancy. This is the

reasoning in Albrecht et al. (1999), who do not find a negative effect of child related

career breaks for Swedish women. It could also be that women have already chosen

jobs where the negative effects of a career break are small. If women select into

more child-friendly occupations even before they have children, some of the

motherhood wage effect is already realized. Adda et al. (2011) suggest such a pre-

birth wage penalty is substantial.

14 The positive effects of job protection on mothers wages may explain parts of the differences in the

size of motherhood wage penalties across countries (Sanchez-Marcos 2013).

S. Cools, M. Strm

123

Table 4 The effect of children on womens hourly wage

All Secondary education Higher education

Lower Upper Lower deg. Higher deg.

(1) (2) (3) (4) (5)

Panel A: no parental leave

First child

Age 15 0.026*** 0.034*** 0.033*** 0.0099* 0.033(0.0036) (0.0074) (0.0063) (0.0052) (0.038)

Age 610 0.042*** 0.040*** 0.036*** 0.017** 0.026(0.0044) (0.0088) (0.0079) (0.0067) (0.050)

Age 1113 0.050*** 0.039*** 0.035*** 0.026*** 0.15**(0.0057) (0.011) (0.010) (0.0091) (0.066)

Observations 245,061 63,128 73,200 103,659 5,074

Individuals 58,494 20,352 16,888 20,253 1,001

Panel B: short length parental leave

First child

Age 15 0.020*** 0.0096*** 0.027*** 0.022*** 0.047***(0.0017) (0.0036) (0.0028) (0.0025) (0.011)

Age 610 0.035*** 0.013** 0.036*** 0.031*** 0.051***(0.0025) (0.0053) (0.0041) (0.0038) (0.017)

Age 1113 0.042*** 0.014* 0.038*** 0.035*** 0.023(0.0035) (0.0070) (0.0057) (0.0056) (0.026)

Observations 397,366 89,395 140,172 158,203 9,596

Individuals 68,809 18,111 25,993 23,322 1,383

Panel C: middle length parental leave

First child

Age 15 0.029*** 0.017*** 0.037*** 0.029*** 0.043***(0.0017) (0.0038) (0.0029) (0.0026) (0.013)

Age 610 0.047*** 0.021*** 0.051*** 0.040*** 0.039*(0.0026) (0.0055) (0.0044) (0.0041) (0.020)

Age 1113 0.059*** 0.027*** 0.054*** 0.048*** 0.019(0.0036) (0.0073) (0.0060) (0.0059) (0.031)

Observations 351,706 75,906 125,978 142,738 7,084

Individuals 59,327 14,507 22,847 20,931 1,042

Panel D: long length parental leave

First child

Age 15 0.025*** 0.015*** 0.035*** 0.021*** 0.059***(0.0020) (0.0055) (0.0037) (0.0027) (0.012)

Age 610 0.040*** 0.026*** 0.046*** 0.033*** 0.057***(0.0029) (0.0075) (0.0052) (0.0040) (0.017)

Age 1113 0.055*** 0.041*** 0.053*** 0.046*** 0.057**(0.0040) (0.010) (0.0071) (0.0056) (0.025)


123

In themselves, experience and part time work are generally positively associated

with the level of wages, whereas maternity leave hardly seems to matter. Public

sector work is negatively associated with wages in all education categories except

for the one with lower degree higher education, where the association is strong and

positive. If women choose to work in the public sector because they plan to have

children (and believe that a public sector job entails better job protection, greater

flexibility or lower attrition of skills during career breaks), the true effect of having

children on womens wages is larger than our estimates, as some of the cost is taken

even before birth.

In Table 3, column (6)(10), the corresponding results are given for men who had

their first child in 1993 or later. Panel A shows that in this sample the average effect

of children on wages for all men, irrespective of education level, is negative, at

.59 %. This sample thus differs significantly from both the baseline sample used in

Table 3 and a sample excluding childless men. This is reflected also in a stronger

point estimate for the wage penalty in the two middle education groups, at .66 and

.60 % respectively (columns (8) and (9)). The difference is due to the exclusion of

parents to children born before 1994. The results are consistent with the observation

that men increasingly share in the caring for children, consequently also sharing in

the wage effects.

When we include the potentially explanatory variables in the regression the

impact is significant, although most of the effect estimated for the whole sample is

left unexplained. The coefficient on having a first child aged between 1 and 5 years

is no longer significant for the group with higher education, lower degree (column

(9)), and for the group with upper secondary education it is cut in half. Also the

persistence of the effect, reflected in the coefficients on having a first child aged

610 years and 1114 years, is reduced, although not by as much.

Including each of these variables separately shows that the wage penalty to

fatherhood hardly covaries with experience, part time work or sector of employ-

ment. It is the inclusion of parental leave that matters for the estimated effect on

mens wages of having children. In the two groups where having children does have

a statistically significant impact on mens wages, paternity leave explains a

substantial part of the effect.

Table 4 continued



(1) (2) (3) (4) (5)

Observations 396,786 60,440 130,141 193,584 12,621

Individuals 62,649 11,366 22,853 26,721 1,709

Subsample analysis according to relative length of parental leave

Each column in each panel provides FE estimates from a regression based on Eq. 1 for the sample of

individuals who had their first child in 1994 or later. Year dummies, a polynomial in age, individual fixed

effects and controls for further children, being pregnant and having a baby younger than one year are

included in all specifications. Robust standard errors are in parentheses. * p\0:10; ** p\0:05;*** p\0:01

S. Cools, M. Strm

123

The variation in parental leave in itself explains a great deal of mens hourly

wages. For the three highest education groups (columns (8) to (10)), one year of

parental leave is associated with a 34 % reduction in wagesabout the whole

wage penalty for women, who typically take somewhat less than a year of leave. We

are not able to determine to what extent this is due to the adverse effect of paternity

leave on human capital accumulation or whether the length of paternity leave

reflects mens propensity to spend more effort at home relative to market work,

thereby reducing wages. The finding that parental leave is more strongly associated

with the wage effects of parenthood for men than for women is in accordance with

Albrecht et al. (1999)s finding in their study on Swedish data. They find that career

breaks around birth are associated with negative wage effects only for men, not for

women. Their interpretation is that the negative wage effect stems from the

employer offering lower wages to women even before they have children because

they expect women to have a career break around birth. Employers do not expect the

same from men, and parental leave is therefore a stronger signal of the strength of

mens commitment to market work. Another possible explanation that would give

the same empirical pattern, however, is that men to a larger extent than women

choose jobs where a career break has a larger effect.

Part time work is associated with higher wages for those with secondary

education (columns (7) and (8)) and with lower wages for those with higher

education (columns (9) and (10)). Public sector work is generally associated with

having lower wages, except in the group with lower degree higher educationlike

for women.

6 Heterogeneous effects

In this section we investigate whether the wage penalty is stronger for some groups

than for others. We do this by running separate regressions on different subgroups;

on those who take relatively longer or shorter parental leave, on those who work full

time and part time, and on those who work in the public or in the private sector.

Individuals are sorted into groups based on the observed length of parental leave

taken with the first child, or according to sector or working time category the year

before their first child is born. As the variables we condition on are available from

1993 onwards, we limit the sample to individuals having their first child after 1993

(like in Sect. 5).

6.1 Parental leave

Probably the most direct labor market effect of having children is the period of

parental leave. In Tables 4 and 5, we investigate whether parenthood wage effects

are stronger for those who take relatively longer leave. We have divided the samples

in four: Those who take no, little, middle and long leave. The division into little,

middle and long is based on the assigned percentile in the distribution of all

parents of the same sex with children born in the same quarter of the year. The


123

distribution is divided in three. For men, though, there is very little spread in the

number of leave days taken, and the middle groupcontaining less than 1 % of the

samplehas therefore been included in the group of men taking a short period of

leave.

Table 5 The effect of children on mens hourly wage



(1) (2) (3) (4) (5)

Panel A: No parental leave

First child

Age 15 0.015*** 0.011*** 0.0093*** -0.012*** 0.011(0.0019) (0.0031) (0.0030) (0.0039) (0.011)

Age 610 0.032*** 0.017*** 0.019*** -0.023*** 0.023(0.0027) (0.0044) (0.0043) (0.0054) (0.016)

Age 1113 0.047*** 0.024*** 0.031*** -0.026*** 0.0070(0.0037) (0.0060) (0.0059) (0.0077) (0.023)

Observations 618,598 207,085 242,647 153,265 15,601

Individuals 127,953 50,982 48,601 25,839 2,531

Panel B: short or middle length parental leave

First child

Age 15 0.0013 0.0044 0.0031 0.0014 0.00090(0.0016) (0.0032) (0.0024) (0.0030) (0.0096)

Age 610 0.0076*** 0.0077* 0.0097*** 0.0041 0.0015(0.0024) (0.0046) (0.0036) (0.0044) (0.014)

Age 1113 0.016*** 0.0050 0.016*** 0.0079 0.0094(0.0035) (0.0066) (0.0052) (0.0066) (0.020)

Observations 651,320 161,248 286,487 184,084 19,501

Individuals 111,740 30,629 51,018 27,233 2,860

Panel C: long length parental leave

First child

Age 15 0.014*** 0.0018 0.016*** 0.014*** 0.025**(0.0032) (0.0078) (0.0057) (0.0047) (0.012)

Age 610 0.020*** 0.012 0.021** 0.018** 0.028(0.0048) (0.011) (0.0083) (0.0072) (0.018)

Age 1113 0.019** 0.023 0.023* 0.017 0.000059(0.0075) (0.016) (0.013) (0.012) (0.028)

Observations 132,112 22,129 46,954 53,934 9,095

Individuals 20,517 3,967 7,803 7,487 1,260

Subsample analysis according to relative length of parental leave





S. Cools, M. Strm

123

Among the parents who are not registered with taking any paid parental leave are

both individuals who have have not earned the right to paid parental leave and

individuals who may have earned the right but do not use it. It is very uncommon for

women not to use the right to maternity leave, hence the group of women taking no

leave can generally be thought of as consisting of women who did not earn the right.

For men, it is much more common not to take the paid leave granted them. Also,

mens right to wage compensated paternity leave does not only depend on their own

earned right but also on the childs mother having earned the right. Hence the group

of men taking no leave is more mixed, consisting of men who did not earn the right,

men whose spouse did not earn the right, and men who have the right to paid

parental leave but do not use it.

The results for women are given in Table 4. On average, there is not much

variation in the immediate effect of having children between the four groups, and

though there is some variation within each education group according to the relative

length of maternity leave taken with the first child, there is no clear pattern of

statistically significant difference between the groups. In the group of women with

the highest education (column (5)), taking longer leave, relative to taking no leave at

all, is clearly associated with a greater wage penalty to having children the first four

years after the wage compensated maternity leave period is over. For the two groups

with no more than secondary education (columns (2) and (3)), the wage penalty

during the first years is stronger for the group who does not take paid maternity leave,

and then it increases in the relative length of the leave period. All in all, the wage

penalty to motherhood is not very clearly linked to the length of maternity leave (as is

also found by Albrecht et al. (1999)). This is consistent with the insignificant role of

maternity leave in explaining motherhood wage penalties discussed in Sect. 5, and is

a further indication that in the Norwegian institutional context career breaks are less

important for how wages develop after having children.

The results for men are given in Table 5. For men, there is a U-shaped

relationship between the penalty and the propensity to take leave. The wage penalty

to fatherhood is greatest for the group of men taking no leave and the group of men

taking longer leave than what is usual. The average wage penalty in the two groups

of men is considerable, at about 1.5 %. As mentioned, the group of men taking no

leave is a diverse group, making the interpretation of the results for this group

harder to interpret. A stronger parenthood wage effect for men taking longer

parental leave is on the other hand in line with the findings of Albrecht et al. (1999).

6.2 Full time versus part time employment

Having children is associated with a reduction in working hours both for men and

womenbut mainly for women (Cools and Strm 2012). As documented in Sect. 5,

changing working time status to part time is what correlates most with a motherhood

wage penalty. Here we investigate whether wage penalties are smaller for those who

already work part time before having children. However, if part time work is chosen

in advance because the individual plans to have a child, this would cause an upward

bias in our estimates of the effect of having children.


123

Table 6 The effect of children on hourly wage



(1) (2) (3) (4) (5)

Panel A: full time, women

First child

Age 15 0.035*** 0.020*** -0.039*** 0.033*** -0.056***(0.0011) (0.0026) (0.0019) (0.0015) (0.0067)

Age 610 0.054*** 0.030*** -0.052*** 0.045*** -0.053***(0.0016) (0.0039) (0.0028) (0.0023) (0.010)

Age 1113 0.067*** 0.036*** -0.057*** 0.056*** -0.039**(0.0023) (0.0052) (0.0040) (0.0033) (0.016)

Observations 941,388 155,330 311,908 444,460 29,690

Individuals 152,488 29,385 55,171 63,681 4,251

Panel B: part time, women

First child

Age 15 0.014*** 0.0055 0.020*** 0.011*** 0.020(0.0024) (0.0046) (0.0038) (0.0043) (0.036)

Age 610 0.022*** 0.0070 -0.027*** 0.012** 0.029(0.0035) (0.0065) (0.0055) (0.0061) (0.051)

Age 1113 0.026*** 0.012 -0.027*** 0.0071 0.044(0.0048) (0.0086) (0.0076) (0.0085) (0.067)

Observations 230,963 61,801 89,058 78,610 1,494

Individuals 42,296 12,490 16,703 12,852 251

Panel C: full time, men

First child

Age 15 0.0084*** 0.0021 0.0075*** 0.0087*** 0.00088(0.0013) (0.0024) (0.0019) (0.0024) (0.0065)

Age 610 0.026*** 0.0071** 0.019*** 0.020*** 0.0069(0.0019) (0.0035) (0.0029) (0.0035) (0.0093)

Age 1113 0.040*** 0.015*** 0.027*** 0.030*** 0.014(0.0027) (0.0050) (0.0041) (0.0053) (0.014)

Observations 1,083,899 275,061 467,116 304,117 37,605

Individuals 187,504 53,613 83,455 44,952 5,484

Panel D: Part time, men

First child

Age 15 0.0089* 0.00051 0.0096 0.013* 0.021(0.0046) (0.0083) (0.0078) (0.0079) (0.036)

Age 610 0.023*** 0.016 0.022* 0.034*** 0.012

(0.0065) (0.012) (0.011) (0.011) (0.055)

Age 1113 0.036*** 0.023 0.028* 0.070*** 0.032(0.0092) (0.017) (0.016) (0.015) (0.080)

S. Cools, M. Strm

123

The results for women are given in Panel A and B in Table 6. On average, the

negative wage effects of having children are two to three times stronger for the

women who were registered with full time employment the year before they had

children, compared to the women who had part time employment before having

children. Also, on average, there is a clear pattern of the wage penalty increasing

over time for the group of full time working women, reaching a wage reduction of

6.7 % when the first child turns 11 years old.

In the very small group of women with the highest education who worked part

time before having children, we see the first instance of a wage premium to having

children, reaching 7.8 % and significant at the 5 % level by the time the child turns

11 years old. This may indicate that for women who already started part time work

before having children, the extra effort put into child rearing does not intervene as

strongly with the effort put into work.15

Panel C and D in Table 6 give the corresponding results for men. Here, the

difference between the two groups is even more striking. The men who worked full

time before becoming fathers on average experience a wage penalty to fatherhood,

at .88 %. This is a 50 % larger penalty than the .51 % wage penalty estimated for

the whole sample given in Table 6. The penalty grows significantly over time, like

the case is for women who work full time before having children. The wage penalty

estimated for the sample of full time working men is on average stronger than for

the part time working women, especially as the first child grows older.

Consistent with the conjecture that part time workers face less of a trade-off in

effort between home and market work (they may thus even spend more effort at

work after they have children), for men who worked part time before becoming

fathers we find statistically significant wage premia to fatherhood when looking at

the whole sample (column (1) in Panel D) and in the two middle education groups.

The premium grows as the child gets older.

We also investigated the heterogeneity of effects according to sector of

employment and found that wage penalties are larger for both men and women

Table 6 continued



(1) (2) (3) (4) (5)

Observations 95,634 28,386 33,631 32,228 1,389

Individuals 17,845 6,080 6,356 5,190 219

Subsample analysis according to being employed full time or part time the year before having children





15 If part time work is chosen by women because they anticipate childbearing, the wage penalty may

have been realized even before the child is born, and will not be picked up by our estimates.


123

working in the private sector prior to having the first child.16 The results are

consistent with the hypothesis that parenthood wage effects are due to reduced effort

at work. A slowing down of wages in a sector with large wage potential, has large

consequences for the individual.

7 Conclusion

In spite of the encouragement built into the labor market institutions of the Nordic

countries to combine work and family, the two remain in conflict, as is pointed out

by the wage penalties to parenthood documented in this paper. Like in most other

countries, women bear the greater share of the cost of having children also in

Norway. Wage penalties to motherhood are however smaller than what is observed

in many other countries, where the previous literature has found that career breaks

and loss of work experience explain a large share of the wage penalty. As such

measures of human capital do not seem to explain the family gap in Norway, these

findings indicate that family policies such as paid parental and job protection

contribute to reducing the adverse wage effects of parenthood.

We find indicative evidence that a reduced effort is more important than loss of

human capital in explaining the family wage gap. With a larger workload at home,

effort at work can be affected negatively. In groups where wages may be expected

to depend more on efforthigher educated women, women working full time and

in the private sectorwe find larger wage penalties to motherhood.

Contrary to earlier studies using US data and to previous research from Norway,

we find negative (though comparatively small) wage penalties also to fatherhood,

consistent with mens increasing involvement in child rearing. Contrary to the

findings for women, the length of parental leave can explain a large share of the

fatherhood wage penalty. Parental leave periods for fathers are generally short, and

the asymmetry in the importance of parental leave between men and women may

reflect that the length of parental leave serves as a signal of future effort at work,

rather than being an adverse human capital effect (Albrecht et al. 1999).

We conclude that in the Norwegian context, human capital is of little importance

in explaining the family gap in wages. It may as such serve as an explanation for

why the observed motherhood wage penalty is smaller in Norway than in many

other countries. Though suggesting that effort plays a comparatively more important

role, the evidence presented in this article is not conclusive. For a deeper

understanding of the mechanisms, further research is needed.

References

Adda, J., Dustmann, C., & Stevens, K. (2011). The career costs of children. Working paper.

Albrecht, J. W., Edin, P.-A., Sundstrm, M., & Vroman, S. B. (1999). Career interruptions and

subsequent earnings: A reexamination using Swedish data. Journal of Human Resources, 34(2),

294311.

16 Results are available from the authors upon request.

S. Cools, M. Strm

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Anderson, D. J., Binder, M., & Krause, K. (2002). The motherhood wage penalty: Which mothers pay it

and why? American Economic Review, 92(2), 354358.

Anderson, D. J., Binder, M., & Krause, K. (2003). The motherhood wage penalty revisited: Experience,

heterogeneity, work effort, and work-schedule flexibility. Industrial and

parenthood wage penalties in a double income society

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