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TRANSCRIPT
WHAT’S THE DIFFERENCE?
A DESCRIPTIVE ANALYSIS OF THE EVOLUTION OF THE
FAMILY GAP IN SWEDEN
Submitted by
Anna Fornwall
Department of Economics
Supervisor
Håkan Selin
Spring term, 2019
ABSTRACT
In this study, I compare men and women with and without children to analyze the effect
of children on wages and earnings. By comparing the gender wage gap to the family gap for
men and women respectively, I find that there is still a persistent, yet rather small, family gap
for women. The constant family gap for women supports the notion that a greater fraction
of the gender wage gap can be explained by effects of having children now than previously.
When using yearly earnings instead of hourly wages, the gender wage gap increases whereas
the family gap for women decreases.
This implies that although there are several policies with the aim of reducing gender wage
differences and creating possibilities for women to combine work and family, there are still
concrete effects that arise from taking the responsibility for children. Because the effect of
having children is seemingly constant over time for women, the results from this study imply
that specific policies are needed to prevent and battle the difference in labor market outcomes
that arise because of the differing effects from caring for children.
Keywords: Gender wage gap, Family gap, Wage inequality, Child penalty
CONTENTS
1 INTRODUCTION 4
2 BACKGROUND AND LITERATURE REVIEW 6
2.1 THE GENDER WAGE GAP . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 DIFFERENCES IN THE USE OF PARENTAL LEAVE AND FAMILY RESPONSI-
BILITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 EFFECTS OF BEING OUT OF THE LABOR FORCE . . . . . . . . . . . . . . . 7
2.4 THE FAMILY GAP – ESTIMATING THE WAGE IMPACT OF CHILDREN . . . . 8
3 THEORETICAL FRAMEWORK 11
3.1 LABOR SUPPLY THEORY . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2 HUMAN CAPITAL THEORY . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.3 SIGNALING THEORY AND STATISTICAL DISCRIMINATION . . . . . . . . . . 14
3.4 SELF-SELECTION THEORY . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4 DATA 16
4.1 DATA SOURCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.2 VARIABLE DESCRIPTION . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
5 EMPIRICAL METHOD 21
5.1 ORDINARY LEAST SQUARES . . . . . . . . . . . . . . . . . . . . . . . . . 21
5.2 FAMILY AND GENDER GAP IN EACH WAVE . . . . . . . . . . . . . . . . . 21
5.3 INTERPRETATION OF ESTIMATES . . . . . . . . . . . . . . . . . . . . . . 23
5.4 LABOR EARNINGS AS THE DEPENDENT VARIABLE . . . . . . . . . . . . . 23
5.5 PROBABILITY OF BEING IN EMPLOYMENT . . . . . . . . . . . . . . . . . 24
6 RESULTS 25
6.1 FAMILY AND GENDER GAP . . . . . . . . . . . . . . . . . . . . . . . . . 25
6.2 YEARLY EARNINGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
6.3 PROBABILITY OF BEING IN EMPLOYMENT . . . . . . . . . . . . . . . . . 36
6.4 DISCUSSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2
7 CONCLUSION 39
3
1 INTRODUCTION
For the past few decades, there has been a growing body of research examining the wage
differential between men and women. Decomposing the gender wage gap into an explained and
an unexplained part, the explained part has slowly converged over time and what remains could
to some extent be attributed to the gender difference in effects of children [Kleven et al., 2018].
Sweden has one of the most generous parental leave insurances in the world, and was the
first country to allow fathers to receive benefit on the same terms as mothers when caring for
their children [SCB, 2018]. Despite the possibility to share family responsibilities equally,
the outtake of parental leave days is heavily skewed with women still accounting for the vast
majority of the parental leave [Försäkringskassan, 2018]. The difference in outtake varies with
income and educational level of the parents and the sector in which they work.
There is also a lot of research examining the effects of having children and taking parental
leave. These effects differ between men and women, with women experiencing a much more
negative effect than men. This negative effect is referred to as the “family penalty”, which is
the effect of children on labor market outcomes that cannot be explained statistically
[Staff and Mortimer, 2012]. Because of the longstanding wage differences between men
and women, one approach to examine the effect on wage due to children is to compare women
with children to women without children. The differences in wage between the two groups,
when accounting for observable characteristics, is defined as “the family gap” – the difference
in wage that occurs between comparable individuals when one of the two has at least one child
and the other one has no children [Waldfogel, 1998]. The family gap has received growing
research interest in the past few years, with Kleven et al. [Kleven et al., 2018] accounting
for the most recent research. In their paper “Children and Gender Inequality: Evidence from
Denmark” they are investigating the family gap in Denmark using an event study approach, and
they argue that although the gender wage gap has decreased in the past few decades, the family
gap remains constant.
The aim of this thesis is to investigate how the family gap has developed over time in
Sweden and how it can be related to the general gender wage gap during the same time period.
The data used for the analysis is the Swedish level of living surveys (SLLS) conducted in six
waves. SLLS is unique in that they have collected hourly wage data for individuals in all waves,
which is normally not possible to access. In the most similar study conducted by Kleven et al in
4
Denmark, a measure of wage is constructed by using yearly earnings divided by hours worked,
which they themselves point out likely biases the results somewhat.
The research question of this paper is therefore “how has the family gap evolved in Sweden
over time and how does this development relate to the general gender wage gap?”. The research
question is of relevance in a policy perspective as the wage gap between men and women is
still a challenge in the Swedish labor market. An increased understanding of the reasons for
its development over time with regards to having children is of importance in order to reduce
this difference in the future. Because Sweden has a particularly well-developed parental leave
and child benefit system, the results may very well differ from other countries, either in a more
positive or more negative direction.
There are two main contributions of this study. Firstly, there has, to the best of my knowl-
edge, not been any similar historical analysis of the development of the family gap in Sweden.
Secondly, the study uses gross hourly wages as the dependent variable in the analysis. Data on
hourly wages is often difficult to retrieve for earlier years, which is the reason for why most
historical analyses depend on monthly or yearly labor income instead. Using hourly wage as
the dependent variable makes it possible to separate the effect of children on wages from the
effect of children on hours worked. For this reason, I believe that this study will fill a gap in
the existing literature.
The outline of the paper is organized as followed: Section 2 provides background and previ-
ous literature in the research area; section 3 describes the theoretical framework. Furthermore,
the data used for the analysis is presented in section 4, along with some variable descriptions;
section 5 covers the method used to retrieve the results, which are presented in section 6. Lastly,
section 7 provides a brief summary and conclusion of the results.
5
2 BACKGROUND AND LITERATURE REVIEW
This section starts by a presentation of research on the general wage differences between men
and women. Then, statistics and research regarding the gender differences in factors relevant
to labor market outcomes are discussed. Lastly, previous literature on the family gap and its
implications are reviewed.
2.1 THE GENDER WAGE GAP
Understanding the wage differentials between men and women has been an important research
agenda for decades. The difference can be decomposed into two parts – explained and un-
explained. Much of the variation between men and women can be explained by observed
differences in educational and career choices, differences in the amount of unpaid work and
family responsibilities [Blau and Kahn, 2017]. The explained gap has somewhat decreased
over time, but the pace has slowed down and there is still a persistent unexplained gap that
remains [Kleven et al., 2018]. This has been explained in different ways; statistical discrimi-
nation towards women, differences in preferences and psychological attributes being some of
the most common examples from literature [Bertrand, 2011]. The gender wage gap opens up
mainly in ages 30-40 (SCB, 2013), when many individuals have young children, with men and
women following similar trends before becoming parents. [Kleven et al., 2018]
2.2 DIFFERENCES IN THE USE OF PARENTAL LEAVE AND FAMILY RE-
SPONSIBILITY
One reason for why studying Sweden is particularly interesting is because of its generous poli-
cies on parental leave benefits and public childcare. The parental leave insurance was first
introduced in 1974, replacing the previous maternal leave insurance [SCB, 2018]. The new
insurance made it possible for men to receive social benefits on the same terms as women when
taking leave from work to care for their children. This encouraged men to be more involved
in their children and acknowledged the child’s right to both parents. Although the insurance
was changed to induce economic incentives for couples to share family responsibilities more
equally, women accounted for 99,5 percent of the outtake in 1974. Women have since grad-
ually decreased their share of parental leave and men have increased theirs, but the averages
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between the groups still differ significantly, with women being responsible for a vast majority
of the parental leave still. [Försäkringskassan, 2018]
Since being introduced in 1974, the scheme of the insurance has underwent a number
of changes; the maximum length of the parental leave benefit has been prolonged gradually
through several reforms and is presently set at 480 days, with 90 days being reserved for each
parent since 2002. (Ibid.) The parental leave benefit can be divided into two levels of replace-
ment. For 90 of the total 480 days, the replacement is set at a low, fixed level whereas benefit
for the remaining 390 days is calculated based on previous earnings. There is, however, a cap
for maximum level of benefits. (Ibid.)
Women on average take more time away from the labor market when becoming parents,
thus also taking on a larger share of unpaid work in the home [Försäkringskassan, 2018]. The
difference in parental leave may impact long-run patterns within families, with women tak-
ing temporary parental leave more than men to care for sick children or choosing to work
part-time. Differences in outtake between men and women and the design of the parental
leave insurance causes women to fall behind men when they have children. Angelov et al.
[Angelov et al., 2016] show that there are small gender differences in wage prior to having
children, but significantly larger differences 15 years after the birth of the first child. The great-
est differences occur in couples where the woman would have had a lower income and wage
development than the man even without children.
2.3 EFFECTS OF BEING OUT OF THE LABOR FORCE
Having children has a negative effect on women’s wages, but not men’s, and the penalty remains
after controlling for the length of the leave and other characteristics [Staff and Mortimer, 2012].
The wage penalty could be because of a loss of human capital due to the time spent away from
the labor market but if so, men and women would be equally affected by taking the same
amount of parental leave, which is not the case. Another explanation of the penalty could
be statistical discrimination, with employers promoting men more than women in terms of
wages and advancement [Benard and Correll, 2010]. Since mothers more than fathers take the
initial parental leave associated with having a child, this also affects the amount of unpaid
work the parents perform at home and the pattern of which parents divide temporary parental
leave when the child is sick later [Forssell, 2002, Försäkringskassan, 2013]. Knowing this, it
may be rational for the employer to discriminate against women, knowing that they on average
7
will spend less time at work than men. In contrast to the punishment that women experience
after becoming mothers, fathers seem to rather be rewarded for having children; with wages
increasing after having a child [Benard and Correll, 2010]. This can be compared to the wage
premium of married compared to single men, perhaps suggesting that being married and having
children signal responsibility [Chun and Lee, 2001].
However, men and women taking out the same amount of parental leave are not equally
affected. Assuming that men and women, conditioning on a set of characteristics, have the
same potential outcomes, being out of the labor force should have the same impact on all
individuals independent on gender. Furthermore, the loss of human capital should be the same
for a given time period independent on what the time out is devoted to, i.e. taking parental leave
should not cause a different loss in human capital than taking a leave for other non-educational
or work-related reasons. Albrecht et al. [Albrecht et al., 1999] studied this in Sweden in the
nineties, by comparing the effect of different types of career interruptions on wages to estimate
the specific effect of parental leave. The results suggested that women’s labor market outcomes
were seemingly unaffected by taking parental leave, whereas men experienced a negative effect.
This either suggests that men taking parental leave sends a negative signal to employers whereas
it’s expected of women, or that women are already being statistically discriminated against in
terms of expectations of parental leave, so that they have been penalized already before taking
the leave.
2.4 THE FAMILY GAP – ESTIMATING THE WAGE IMPACT OF CHILDREN
One of the first and most eminent researchers on the family gap is Jane Waldfogel [Waldfogel, 1998,
Waldfogel, 1997]. In a paper from 1997, she examines the evolution of the family gap in the US
using the National Longitudinal Survey of Young Women between 1968 and 1988. While high-
lighting that the gender wage gap has decreased over time as a result of higher labor force par-
ticipation and education among women, Waldfogel discuss the persistent tendency of women
having less labor market experience than comparable men. Likewise, the wage differential be-
tween women with and without children – the family gap – appears to be rather constant over
time. In order to account for the difference in labor market experience, she uses a measure of
“potential labor market experience” in her estimates, as well as controlling for both parental
and marital status. She finds that even after controlling for actual labor market experience and
other characteristics, women with children earn lower wages than women without children.
8
Additionally, after performing some heterogeneity tests, Waldfogel suggests that the wage dif-
ferential cannot be attributed to differences in unobserved characteristics between women with
and without children. The results ultimately imply that women suffer a family penalty when
having children that persists when controlling for labor market choices and personal attributes.
Building on the previous paper, Waldfogel continued studying the matter in mainly the US
and Britain. When the first few studies were published just before the millennium, she argued
that although the gender wage gap had decreased in the United States in the previous decades,
the family gap had actually widened. One reason for this may, according to Waldfogel, be
that several policies have been implemented to encourage and legalize equal pay, but fewer on
maternity and childcare. The direction in which maternity leave affects wage is according to
most research ambiguous. On the one hand, maternity leave enable new mothers who would
otherwise have remained out of the labor force to come back to their previous job without
a decrease in wage. On the other hand, some mothers likely stay at home longer than they
otherwise would have. However, previous studies suggest that the main impact of maternity
leave is that more women choose to return to the labor force after childbirth. In her paper
“Understanding the “Family Gap” in Pay for Women with Children” [Waldfogel, 1998], she
compares the US to some Scandinavian countries including Sweden and Denmark. She argues
that as opposed to the US, which at the time had a family gap of about 10-15%, neither Denmark
nor Sweden has any noteworthy family gap. What is interesting in this context is that Kleven
et al. [Kleven et al., 2018], has published a study showing that Denmark now has a family gap.
Whereas the gender gap was lower in Denmark than in the US before the millennium, as shown
by Waldfogel, the levels are now much more similar.
Kleven et al. use an event study approach based on the birth of the first child. The identi-
fication strategy relies on the assumption that although the decision of having children is not
random, the timing of the first child is. Their results show that men and women follow the same
trends in labor market outcomes prior to childbirth. However, directly following the birth of
the first child, women fall behind men and the trend for women never recovers. In addition to
studying the impact of children on wages, Kleven et al. also investigate the incentives among
women to switch to “family friendly” jobs after forming a family. Family friendly jobs are
described as firms with more flexible characteristics and a larger share of women employed.
Self selection into more family friendly work environments have been discussed as a possible
determinant of the gender wage gap in previous studies [Goldin, 2014], but this is the first study
9
to show how the segregation is associated to having children. Ultimately, the results of Kleven
et al. [Kleven et al., 2018] imply that the wage differential caused by having children, i.e. the
family gap, has evolved over time in Denmark and is presently responsible for the remainder
of the unexplained gender wage gap.
Another recent study on the family gap is one by Lundborg et al. [Lundborg et al., 2017].
In their paper “Can Women Have Children and a Career”, they use an IV approach with IVF
treatments. The identification strategy is based on the assumption that the chance of having a
successful IVF treatment is random and not correlated with previous labor market outcomes.
Individuals that have all received IVF treatments, but where only some has succeeded, are then
compared in terms of labor market outcomes. Thus, it is possible to interpret the difference in
labor market outcomes between parents and non-parents as the causal effect of having children.
Lundborg et al. find that having children has large negative impacts on women’s labor market
outcomes, both in the short and the long term. The results suggest that women choosing to work
in lower paid jobs when they become mothers can explain a large portion of the wage decrease.
They also suggest that although women receiving IVF treatments likely differ to other women,
the results are likely generalizable to all women.
Lastly, Angelov et al. [Angelov et al., 2016] investigate the income and wage trajectories
for Swedish couples before and after parenthood in their paper “Parenthood and the Gender
Gap in Pay”. The approach is based on comparing the within-couple gaps in wage before
and after having children. They find that the effects on wages are substantially negative and
long lasting for women. 15 years after the birth of the first child, the wage gap between men
and women have increased by 32 and 10 percentage points. They provide some evidence of
explanations for this in terms of comparative advantages of working at home and in the labor
market.
10
3 THEORETICAL FRAMEWORK
This section provides a theoretical framework for the following analysis. First, a short overview
of labor supply theory is presented. Then an introduction to human capital theory follows, and
lastly signaling theory and self-selection theory are presented.
3.1 LABOR SUPPLY THEORY
Simplified, the labor market consists of firms and workers; firms accounting for the demand
and workers accounting for the supply of labor. On a market with free competition, wages
are determined by setting supply equal to demand [Borjas, 2016]. In the textbook example,
wage represents marginal productivity of the worker and since there is variation in marginal
productivity, there is variation in wages. Wages and labor market outcomes are thus determined
by several factors affecting the skills and marginal productivity of workers. These consist of
observable factors as educational level and working experience, and unobservable factors as
effort. [Mankiw and Taylor, 2017]
Workers account for the supply of labor in the economy, offering their time and produc-
tivity to firms at a cost. The labor supply varies between demographic groups and over time
[Borjas, 2016]. The traditional framework for analyzing labor supply is called the “neoclassi-
cal model of labor-leisure choice”, in which any worker has a fixed number of hours a day to
allocate between labor and leisure. The allocation depends on the individual’s preferences, as
taking time for leisure has an alternative cost in terms of the wage that individual could have
earned if the time was instead spent on labor. There is thus a trade-off between labor and leisure
(ibid.).
Gary Becker [Becker, 1965] formalized a model for explaining household’s choice of al-
location of time. In his unitary model, households are both the consumers and workers and
they gain utility from consuming and means of consuming, though disutility, from working.
This model has later been criticized in that it assumed that couples have joint preferences and
that there is no “work” in the household. In terms of labor supply, the work performed in the
household is then grouped with consumption of labor, although the labor choice may be that of
working in the labor force or in the household [Johnson, 2010]. Becker suggested however that
as one person’s consumption depends on the others time spent working in the joint model, it is
rational that women perform more of the work in the household and men in the labor market.
11
This argument is based on women’s potential earnings being lower than the potential earnings
of men traditionally.
The labor supply of women has gradually increased in the past few decades, but is still
below that of men in most countries. Much of this increase has been explained as a result
of changing attitudes towards women working, as well as increased wages for women and
thus higher incentives to work [Gardiner, 1997, Bertrand, 2011]. As the potential earnings of
women increase, Becker’s argument of the rational choice in households no longer holds, but
the division of house and labor market work is still determined by gender to a large extent
[Gardiner, 1997].
In recent work, Blau and Kahn [Blau and Kahn, 2013] examine how the labor supply of
women is affected by the presence of more family-friendly policies. They build on the exist-
ing theory by suggesting that these policies can affect women’s labor supply in two opposite
directions: on the one hand by making it possible for women to combine work and family, they
facilitate labor market entry for women who would have otherwise remained at home. On the
other hand, long, paid parental leave likely causes even career-oriented women to stay at home
longer than they otherwise would have. Likewise, women with a strong labor market commit-
ment may have incentives to adjust their labor supply to part-time and possible positions of
lower levels.
3.2 HUMAN CAPITAL THEORY
Gary Becker [Becker, 1962] formalized the first modern human capital theory. Human capital
refers to the stock of skills an individual has, and so human capital investments refer primarily
to investments made in an individual’s skills and productivity, but it can also include invest-
ments in physical and emotional health. These are factors that increase individual productivity
and subsequently wage. There are different types of human capital investments, where the
main investments are usually seen as education and on-the-job training. On the job training
can be either of a general character or firm specific. General training increases the individual’s
productivity regardless of workplace, whereas specific training increases productivity on the
current job, but cannot be transferred to a different job or sector. There are also other types
of human capital investments such as acquiring knowledge of the labor market and economic
system and increasing emotional and physical health. Previous research has found that higher
levels of human capital are positively related to earnings. Likewise, skills are negatively corre-
12
lated with unemployment. When trying to measure a causal effect of human capital on wages,
there are two main problems. Firstly, human capital can normally not be directly observed, so
instead some proxy is used as a measure of human capital, normally years of schooling and
labor market experience. Secondly, there is a potential ability bias. It could be argued that
this should not cause any trouble, as human capital is the stock of skills and knowledge of an
individual. However, there is a positive correlation between being more able and the amount
of education and training, which may attribute an excessive amount of credit to investments in
human capital.
Mincer and Ofek [Mincer and Ofek, 1982] developed Becker’s model further. By inves-
tigating how wages respond to career interruptions, they showed that human capital must be
maintained or it depreciates. In their paper from 1982, they find that wage rates are lower at
reentry for individuals that have left the labor market and increases with the length of the in-
terruption. These results supported the theory of human capital depreciation, as a break from
the labor force would cause a decrease in the human capital stock because of a loss of specific
on-the-job investments, but it would be constant regardless of the length of the interruption,
unless human capital depreciated over time. When “leavers” reenter the labor market, they thus
enter with lower wages than when they left. However, Mincer and Ofek found that there was a
rapid initial wage growth for the leavers once they came back. Because human capital is less
costly to reconstruct once lost than to construct new human capital, they explain this as leavers
having decreased their human capital stock, they are less productive and earn lower wages.
The alternative cost for investing in human capital is thus lower for leavers than for those who
stayed, and so when reentering the labor market they make large investments in their human
capital.
According to this theory, being out of the labor force does not only keep human capital
from increasing, but even reduces it – human capital is an investment that must be maintained
continuously or it depreciates [Mincer and Ofek, 1982]. This implies that taking leave to care
for children would cause a decrease in human capital for the individual taking leave, thus
implying that a decrease in earnings following the leave could be a consequence of the decrease
in productivity. However, if this is the case, then men and women would be affected equally by
the leave and the effect of the leave should be the same as the effect of any other leave, which
is not the case [Albrecht et al., 1999].
13
3.3 SIGNALING THEORY AND STATISTICAL DISCRIMINATION
As opposed to the human capital theory, which assumes that investing in schooling increases
actual productivity, signaling theory suggests that schooling does not increase productivity per
se. Signaling theory instead proposes that education signals productivity to employers and that
it is that signal that is reflected in offered wages. [Spence, 1973]
The signaling theory builds on the assumption that the costs of investing in education, sig-
naling costs, are negatively correlated with productivity capability. If this assumption does not
hold, then all individuals would invest the same amount so that they are not distinguishable in
their signal. [Spence, 1973]
Signals are thus characteristics that employers use as an implication for true productivity. In
this context, the signaling of being a parent rather than that of education is of interest. This can
be either positive or negative and likely differs between men and women. Gender should not
be a signal in terms of productivity per se, since the same amount of education should signal
the same amount of productivity, regardless of sex [Spence, 1973]. There are however other
aspects of the signal of gender, which have been brought up earlier in the literature review.
Given that women on average, conditional on education and previous labor market outcomes,
tend to take on more family responsibility and care for children once becoming a parent, simply
being a woman sends a signal of a lower average labor market commitment of the group. As the
employer cannot distinguish which individuals will be more committed, being a woman may
cause employers to statistically discriminate and invest more in men. [Bielby and Baron, 1986]
Previous research indicates that whereas women with children are perceived as less compe-
tent and engaged in work, men with children are considered to be loyal and reliable.
[Benard and Correll, 2010]
3.4 SELF-SELECTION THEORY
Human capital assumes that different amounts of human capital stock affect wages, and as-
sumes that human capital can be seen as homogeneous. Self-selection theory criticizes this
homogeneity assumption that all the variation in wages can be explained by different amounts
of human capital. Self-selection theory instead suggests that there are different kinds of human
capital, with different pay-offs in terms of wage [Polachek, 1981]. The self-selection theory
introduces occupational choice and its importance for wages. Since occupational patterns dif-
14
fer with demographic groups, the main interest in this context is occupational sorting based
on gender. The basic assumption of the model assumes that the goal for all individuals is to
maximize lifetime earnings. For different occupations, there are different levels of atrophy.
Atrophy is defined as “the loss in potential lifetime earnings from labor force intermittency”. It
is thus the wage decrease caused by skills not being continuously used. The smaller the loss in
wage because of an interruption, the lower is the atrophy [Polachek, 1981]. In terms of gender
differences, the model assumes that men and women have the same characteristics on average
and differ only in expected lifetime labor force participation. It follows that gender differences
in occupation can be attributed to the differences in lifetime labor force participation. Because
atrophy differs between jobs, it is rational for individuals to choose an occupation that maxi-
mizes lifetime earnings given the expected dropout rates from the labor force. Since women
have lower labor force participation than men, it may be rational for men and women to choose
different occupational paths, and for women to choose occupations with lower atrophy and thus
lower wages [Polachek, 1981]. Traditionally, women tend to take more parental leave – both
directly following childbirth and later temporary to care for sick children. Knowing that they
will likely have career interruptions, it is rational for women to invest in education and choose
occupations for which the wage decrease following an interruption is low, even if the initial
wage is lower than for other occupations. A related but different possible explanation of occu-
pational sex segregation could be because of differing preferences between men and women.
Assuming that some women and men are family-oriented and therefore willing to forego higher
earnings in exchange for employment that provide better work-life balance, this would suggest
that there are self-selection effects [Gash, 2009]. The self-selection theory thus provides some
alternative explanation of the family penalty in wages. Family friendly employment is charac-
terized by jobs with a more flexible working environment and, in more recent research, a higher
share of women in the workplace. [Gash, 2009]
15
4 DATA
This section presents the data used to conduct the analysis. It also contains some variable
descriptions in addition to the descriptive statistics.
4.1 DATA SOURCE
This paper uses the Swedish level-of-living surveys (SLLS) collected by the Swedish institute
for social research at Stockholm University. The survey has been carried out in six different
waves, the first one in 1968 and the most recent one in 2010.
The original aim of SLLS, beginning with the first wave in 1968, was to survey a random
sample of approximately one per mille of the Swedish population in ages 15-74. In the latest
wave however, the sample consisted of about 6000 individuals in ages 18-75, with a response
rate of 72%.
Throughout the years, the survey has developed. After the three first waves, further di-
mensions were introduced in the 1991 survey. Family events, information on each individuals
partner and the composition of labor market interruptions were all included in the question-
naire. For the purpose of this study, all available waves are used in order to examine how the
family and gender gaps have developed over time. The additional variables for the later waves
regarding children and parental leave are of particular interest for this study. Unfortunately,
no suitable proxy is available for similar estimation for the earlier waves, and for this reason
parental leave outtake is not included in the analysis.
There are however great advantages of using the SLLS. Because many of the dimensions in
the survey are the same over time, this presents a rare opportunity of comparability over time.
Likewise, because of the extensive time period for which SLLS has been carried out, it provides
historical data for completely comparable variables. Furthermore, SLLS contain hourly wage
data for all survey years, which is unusual for data from the time of the first few surveys years.
This is also one of the main advantages of the SLLS data, as previous research has suggested
that the use of monthly or yearly labor earnings may bias the results somewhat.
The data is conducted on mainly different population samples at each point in time, thus
constituting repeated cross-sectional data on multiple dimensions. About a thousand individu-
als have however been surveyed in each wave, thus compiling panel data for these individuals.
Although panel data would be preferable in order to answer the research question, the panel
16
data sample is not large enough for the purpose of the thesis, so the approach uses the repeated
cross sections for all individuals instead. This also means that there is no possibility of estimat-
ing causal effects in terms of the research question using the available data. Therefore, the aim
of this thesis is to give a descriptive analysis of the associations between cause and effect.
4.2 VARIABLE DESCRIPTION
Table 1 shows the variable descriptions of the variables of interest in the following analysis.
Table 1: Variable Description
Variable Description
Gross hourly wage Gross hourly wage, collected for each wave. The wage is expressed in SEK and is
based on several wage variables in the SLLS data, including bonuses and an average
of weekly hours worked. All wage variables adjusted with CPI, using 2010 as the
index year and thereafter logged.
Yearly labor earnings The variable is constructed by using the gross hourly wage and then multiplying it by
hours worked in the previous year.
Employed Dummy variable encoded as 1 if the individual had a positive number of hours worked
in the year before the survey and 0 otherwise.
Married Dummy variable with levels [ Married, Not married ]. Generated by using the variable
"Civil state according to the interview" and with "Married" as the reference. For the
survey years of 2000 and 2010, couples living together but not being married are also
indicated as married.
Age Age in years by the survey year. Calculated by using survey year and year of birth.
The sample has been limited to only include observations aged between 25-65 years at
the time of the survey.
Education Total number of years of education. Observations with more than 20 years of education
has been removed from the sample.
Experience Total number of years employed in the labor force. Individuals with more than 50
years of experience have been removed from the sample.
Children Dummy variable with levels [0,1], where 1 indicates having children and 0 indicates
not having children. The variable was generated by first adding the number of children
living at home and the number of children not living at home.
Table 2 shows the descriptive statistics for the observations with a positive gross hourly
17
wage, i.e. the subsample used for the main analysis. Because the dependent variable used
in the main analysis is gross hourly wage, all observations with no value in that variable has
been removed from the analysis. This is mainly because the Swedish Level of Living Survey
(SLLS) cover ages 18-74, which indicates that removing observations lacking wage data likely
decreases the age span of the analysis somewhat further than the initial restriction of 25-65
years old at the time of the survey. This is also confirmed in the descriptive statistics, where the
mean age is higher than for the full samples.
The descriptive statistics imply that the women in the sample have on average lower educa-
tion and experience in the first few waves, but catches up to men in more recent year, and even
exceed men in terms of education for the last three waves. This is in line with previous studies.
Tables 3 and 4 give the same descriptive statistics of means but grouped on individuals with
and without children.
Table 2: Sample Means: All Individuals
1968 1974 1981 1991 2000 2010
Men Women Men Women Men Women Men Women Men Women Men Women
Log wage 4.53 4.24 4.71 4.46 4.67 4.50 4.71 4.55 4.88 4.76 5.10 5.0
(0.342) (0.396) (0.279) (0.294) (0.240) (0.258) (0.235) (0.216) (0.227) (0.214) (0.302) (0.237)
Age 42.7 43.2 41.7 42.0 41.8 41.4 41.6 42.2 42.2 43.4 43.9 45.2
(11.2) (10.8) (11.4) (11.2) (11.1) (10.7) (10.8) (10.6) (11.0) (10.5) (11.1) (10.9)
Experience 25.9 17.7 24.1 17.0 23.4 17.4 22.1 18.8 21.9 20.8 22.8 22.2
(12.1) (10.9) (12.6) (10.6) (12.6) (10.3) (12.1) (10.1) (12.4) (10.8) (12.3) (11.7)
Education 8.22 8.35 9.59 9.40 10.3 10.2 11.3 11.4 12.2 12.5 13.3 13.8
(2.57) (2.75) (3.34) (3.01) (3.31) (3.18) (3.08) (2.96) (2.91) (2.91) (2.67) (2.80)
Share with children 74.1% 72.6% 76.1% 79.6% 75.5% 81.2% 70.7% 80.6% 70.3% 78.8% 71.2% 81.2%
No. of observations 1376 879 1390 1035 1385 1347 1350 1438 1211 1288 1074 1062
18
Table 3: Sample Means: Individuals With Children
1968 1974 1981 1991 2000 2010
Men Women Men Women Men Women Men Women Men Women Men Women
Log wage 4.56 4.22 4.73 4.45 4.69 4.50 4.74 4.55 4.90 4.76 5.14 5.0
(0.297) (0.394) (0.280) (0.310) (0.238) (0.268) (0.233) (0.215) (0.227) (0.212) (0.264) (0.238)
Age 43.7 44.5 42.9 42.8 43.4 42.3 44.2 43.8 44.9 45.1 46.4 46.8
(10.8) (9.98) (11.0) (10.8) (10.6) (10.2) (9.93) (9.95) (10.1) (9.82) (10.1) (10.2)
Experience 27.2 17.0 25.5 16.4 25.4 17.5 25.0 19.7 24.8 22.2 25.4 23.8
(11.5) (10.3) (12.0) (9.90) (12.0) (9.72) (11.3) (9.53) (11.5) (10.1) (11.4) (11.1)
Education 8.10 8.08 9.44 9.18 10.0 10.0 11.1 11.2 12.0 12.3 13.2 13.6
(2.45) (2.67) (3.32) (2.96) (3.30) (3.17) (3.15) (2.90) (2.94) (2.85) (2.70) (2.75)
No. of observations 1019 638 1057 824 1045 1094 955 1158 851 1015 765 868
Table 4: Sample Means: Individuals Without Children
1968 1974 1981 1991 2000 2010
Men Women Men Women Men Women Men Women Men Women Men Women
Log wage 4.43 4.29 4.65 4.50 4.62 4.54 4.64 4.57 4.83 4.77 5.02 5.0
(0.434) (0.399) (0.268) (0.218) (0.239) (0.206) (0.226) (0.220) (0.219) (0.221) (0.368) (0.235)
Age 40.0 39.9 37.9 39.1 36.8 37.5 35.3 35.7 35.7 37.3 37.8 37.7
(11.9) (12.3) (11.9) (12.5) (11.0) (11.6) (10.1) (10.6) (10.3) (10.7) (11.2) (11.0)
Experience 22.3 19.6 19.6 19.0 17.4 17.4 15.2 15.1 14.8 15.7 16.2 15.3
(13.1) (12.2) (13.1) (12.6) (12.6) (12.4) (11.0) (11.4) (11.5) (11.6) (12.0) (11.7)
Education 8.55 9.05 10.1 10.2 11.1 11.1 11.8 12.6 12.6 13.5 13.6 14.9
(2.86) (2.82) (3.37) (3.06) (3.20) (3.08) (2.86) (2.94) (2.80) (2.90) (2.59) (2.79)
No. of observations 357 241 333 211 340 253 395 280 360 273 309 194
19
Lastly, table 5 shows the difference in means for men and women, with and without chil-
dren. The values are calculated by subtracting the means of individuals without children from
the means of individuals with children.
Table 5: Difference in Means for Men and Women, With and Without Children
1968 1974 1981 1991 2000 2010
Has Children: Men Women Men Women Men Women Men Women Men Women Men Women
∆ Log wage 0.13 −0.07 0.08 −0.05 0.07 −0.04 0.1 −0.02 0.07 −0.01 0.12 0
∆ Age 3.7 4.6 5.0 3.7 6.6 4.8 8.9 8.1 9.5 7.8 8.6 9.1
∆ Experience 4.9 −2.6 5.9 −2.6 8.0 0.1 9.8 4.6 10.0 6.5 9.2 8.5
∆ Education −0.45 −0.97 −0.66 −1.02 −1.1 −1.1 −0.7 −1.4 −0.6 −1.2 −0.4 −1.3
∆ Observations 662 397 724 613 705 841 560 878 491 742 456 674
A few notes are of interest here. Firstly, when only looking at sample means, men and
women with children show the opposite difference in wages compared to men and women
without children. Whereas men with children have higher mean wages than men without chil-
dren, women with children have lower mean wages than women without children. The indi-
viduals with children are also older than individuals without children. Secondly, for the four
most recent survey years, both men and women with children have more experience than all
individuals without children. Lastly, regarding the fact that women in more recent years exceed
men in terms of education when looking at all individuals, i.e. with and without children, it is
interesting to note that this difference is mainly driven by women without children.
The opposite directions of the wage differences for men and women with children could
possible be a result of the "child premium" for men discussed in previous literature, as opposed
to the child penalty for women. The difference in wages are not likely explained by differences
in years of education, as women with children have almost the same or slighlty longer edcu-
cation than men. There could however be differences in the type or level of education, which
I do not have access to for the whole period. The difference in experience could however be a
determinant, if the women’s experience is lower because they take on more responsibility for
the family and household once they become mothers.
20
5 EMPIRICAL METHOD
This section is outlined as followed: first, there is a description of the method used to retrieve
the results for the analysis. Second, a base model is specified for the family and gender gaps.
Third, an alternative measure of wage is introduced to the model as the dependent variable and
lastly, a model for investigating differences in employment probability is presented.
5.1 ORDINARY LEAST SQUARES
Ideally, we would have had a large longitudinal dataset where fertility was randomly assigned
to individuals. That way, we would have comparable individuals where children would cause
the only difference. A second best option would have been to have access to the same type
of data, but instead using an event study to see how wages respond to children shortly after
having a child and a few years after. However, as this requires a large set of panel data, it is
unfortunately not feasible for this paper. For this reason, the main method for the analysis is
using a simple ordinary least squares.
Although the method in this paper may not be the best for the research question, I believe
it is the most suitable one given the prerequisites. There are however other perks of using the
Swedish Level of Living Surveys (SLLS). As previously discussed, the SLLS data contain vari-
ables that have been the same for all waves, and are thus completely comparable. Furthermore,
it has the very unique perk of containing hourly wage data for all samples. This is especially
unusual, as many studies in the area do not have access to hourly wage data for earlier years,
although it has been argued that it is the most reliable wage measure when analyzing the gender
wage gap.
5.2 FAMILY AND GENDER GAP IN EACH WAVE
For each of the six waves, a base specification is used, including only the variables of interest in
order to get a picture of the uncontrolled family and gender wage gaps. The base specification
is:
ln(GHWi,t) = β1Womani,t + β2Woman× Childreni,t + β3Childreni,t + εi,t, (1)
21
where the dependent variable is logged gross hourly wages. To make the monetary difference
comparable across the survey years, the wage variable has been adjusted to the consumer price
index (CPI) with 2010 as the index year. The variable of interest is “Children”, which is a
dummy variable that equals 1 if the individual has at least one child and 0 otherwise. It is
also of interest to examine and identify whether the effect of children on wages change with
the number of children. The initial idea was therefore to compare how different numbers of
children as well as having children of different ages affect wages differently. Unluckely, in-
cluding all of the population parameters decreased the sample size of each subset to greatly.
Therefore, only the binary variable indicating children or no children is included. Furthermore,
the base specification includes an interaction term between being female and having children.
The interaction term estimates the additional effect of having children for women.
In a modification of the base specification, a wage equation in an adjusted form of the one
proposed by Mincer will be used. The specification including all controls is:
ln(GHWi,t) = β0 + β1Womani,t + β2Woman× Childreni,t + β3Childreni,t
+ β4Educationi,t + β5Experiencei,t + β6Experience2i,t + β7Agei,t
+ β8Age2i,t + β9Marriedi,t + εi,t
(2)
The additional variables in this specification are Education, Experience, Age and Marriage
status. Education is total number of years in education and Experience is total number of
years employed, thus years of experience. The experience variable is included both linearly
and squared, since existing literature in the area suggests that wage increases non-linearly with
experience. The specification also includes a dummy-variable for being married. The reason
for including civil status is because previous research has shown that women’s wages tend to
decrease when they get married, whereas an increase have been found for men. This increase
in wages for married men has been referred to as a “marriage premium”. Again, the initial
aim was to include different types of civil status such as single, divorced or widowed as well
as married. However, in order for the sample population to remain of reasonable size, only an
indicator of being married or not being married is included. However, for the waves in 2000
and 2010, being in a couple and living together will also be indicated as married in the variable
for Married, as it has become a common alternative to marriage in more recent years. Lastly,
22
the variable Age is included both linearly and squared, as proposed by previous research in the
area [Waldfogel, 1997].
The identification strategy of the method is selection on observables. By controlling for
the observable characteristics, we make the assumption that all individuals are absolutely com-
parable except for the fact that some of them have children and some do not. Thus having
children is as if randomly allocated and the only differing effect on wage between individuals
will be that of being a parent. This is however a rather strong assumption, as parenthood is for
the most part a conscious choice. Individuals making the choice of becoming parents likely
differ in unobserved characteristics from those not making the choice of parenthood. Although
this problem remains when only comparing women with and without children as well, the as-
sumption is that there are less unobserved characteristics differing between women in general
than between women and men. Thus the chance of estimating the wage impact of children
specifically likely increases when only comparing women that are parents with women who
are not. However, this is also the main reason for why the aim of the analysis is to estimate the
associations between variables, rather than the causal effects.
Only variables that are available for all of the waves will be included in the specification.
Unfortunately, this means leaving out some of the most important variables in the later survey
years, such as total number of months on parental leave and number of parental leave weeks
per child.
5.3 INTERPRETATION OF ESTIMATES
It is of importance to note that because of the nature of the data and the research question at
hand, no causal inference can be drawn from the results. Instead, coefficients of the parame-
ters are interpreted as associations − or correlations − between the variables of interest. The
potential causes for the effects are discussed further on but without the possibility to draw any
conclusions about the causality.
5.4 LABOR EARNINGS AS THE DEPENDENT VARIABLE
When studying the gender wage gap as well as the family gap, it is of interest to examine how
wages differ independent of labor supply. That is, to ensure we are looking at the wage gap
and not the difference in labor supply, we need hourly wages. However, as this is not feasible
in many cases when studying longer time periods or using old register data, many studies have
23
instead had to rely on monthly wage or yearly labor earnings. I argue that one of the main
contributions of this paper is the fact that hourly wage data is available for all waves, i.e. a
time period of about 40 years. In order to investigate the implication this might have on the
results, I perform a similar analysis to the main one, but using yearly labor earnings instead of
hourly wages. The variable is constructed by multiplying the wage variable used in the main
analysis with hours worked last year. Although this is a rough measure that likely has some
measurement error in the dependent variable, it should provide some evidence of the differing
results obtained by different measures of wage. Since the same independent variables are used
as in the main analysis, only the potential measurement error in the dependent variable should
have a potential of posing a problem. If that is the case, the estimates will simply suffer from
attenuation bias, i.e. they will be less precise yet unbiased.
5.5 PROBABILITY OF BEING IN EMPLOYMENT
In addition to the main analysis using logged gross hourly wages as the dependent variable as
well as an alternative wage measure, a supplementary test of probability of being employed
will also be analyzed. The dependent variable used for these regressions is constructed as a
binary variable taking the value of one if the individual has a positive amount of working hours
in the year before the survey, and zero otherwise. For the purpose of this part of the analysis,
a linear probability model will be used in order to estimate the likelihood of being in the labor
force, given the presented control variables.
When estimating the probability for a binary dependent variable, it could be argued that a
logistic probability model (logit) should be used instead of a linear probability model (LPM).
This is because the linear probability model allows for probabilities below 0 and above 1, which
is obviously not possible theoretically. A second issue with the LPM is that probabilities are
linear in the independent variables. However, the estimates of LPM and logit are often similar,
and because the independent variables of interest in this case are binary as well, the issue of
probabilities being linear in the independent variables does not pose a problem in this context.
Furthermore, the LPM has more intuitive interpretations, and so for the purpose of this analysis
it poses a more suitable option.
Because the labor supply affects the wages in terms of labor market outcomes such as
advancement, it is of interest to examine if it differs between groups and if so, what it is that
determines the difference.
24
6 RESULTS
This section starts by a presentation of the results obtained from the analysis of the evolution
of the family and gender gaps, using two different model specifications. Furthermore, results
on earnings are presented and lastly on the probability of being employed.
6.1 FAMILY AND GENDER GAP
The results from the base specification with no controls are shown in table 6. This specification
includes only gender, having children and an interaction between being female and having
children, as these are the variables of interest. The interaction term between being a woman
and having children is included in the model in order to estimate both the gender wage gap and
the family gap in the same regression. The gender wage gap is thus the coefficient for gender,
and the family gap is the difference between the gender coefficient and the two coefficients
indicating the effects of children. The effect of being a woman, that is the estimate of the gender
wage gap, is negative for all waves but with estimates decreasing in magnitude. This implies
that when not including controls in the model, the estimated gender wage gap has decreased
over the time period studied. The estimate for 2010 is not significant, but previous estimates
suggest that the gender wage gap is emerging when not including controls. The coefficient
for the variable indicating having children is positive but decreasing between the first and the
last wave. Similarly, the estimate for the additional effect of being female with children is
decreasing between the first and last survey year, but with some fluctuations across years.
25
Table 6: Specification With No ControlsDependent variable:
log(as.numeric(GRHW))1968 1974 1981 1991 2000 2010
Woman −0.139∗∗∗ −0.150∗∗∗ −0.085∗∗∗ −0.073∗∗∗ −0.057∗∗∗ −0.028(0.030) (0.025) (0.021) (0.017) (0.018) (0.025)
Woman × Children −0.202∗∗∗ −0.132∗∗∗ −0.106∗∗∗ −0.118∗∗∗ −0.090∗∗∗ −0.111∗∗∗
(0.035) (0.028) (0.023) (0.020) (0.020) (0.028)
Children 0.126∗∗∗ 0.086∗∗∗ 0.065∗∗∗ 0.097∗∗∗ 0.073∗∗∗ 0.114∗∗∗
(0.022) (0.018) (0.015) (0.013) (0.014) (0.018)
Constant 4.434∗∗∗ 4.648∗∗∗ 4.622∗∗∗ 4.641∗∗∗ 4.831∗∗∗ 5.024∗∗∗
(0.019) (0.016) (0.013) (0.011) (0.012) (0.015)
Observations 2,255 2,425 2,732 2,788 2,499 2,136R2 0.144 0.169 0.109 0.127 0.082 0.055Adjusted R2 0.143 0.168 0.108 0.126 0.081 0.053Residual Std. Error 0.361 (df = 2251) 0.284 (df = 2421) 0.248 (df = 2728) 0.224 (df = 2784) 0.219 (df = 2495) 0.269 (df = 2132)F Statistic 126.393∗∗∗ (df = 3; 2251)164.413∗∗∗ (df = 3; 2421)110.968∗∗∗ (df = 3; 2728)134.485∗∗∗ (df = 3; 2784)74.679∗∗∗ (df = 3; 2495)41.152∗∗∗ (df = 3; 2132)Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01
26
When instead using estimates from the regressions including controls that can be found in
table 7, figure 1 show the gender wage gap in terms of estimated gross hourly wage when in-
cluding the effects of children, and controlling for observable covariates. Men are the reference
group, plotted as the intercept line at 0.
Figure 1: Gender Wage Gap Accounting for Effects of Children
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
1968 1974 1981 1991 2000 2010
Survey Year
Est
imat
eof
Hou
rly
Wag
e(l
og)
Women
Error Bars Represent 95 % Standard Errors For Estimates
Estimated Wage Difference
The figure shows an overall decrease of the gender wage gap, with women still falling
below men but less and less so. This is in line with the findings of previous studies. However,
the difference between men and women is larger in magnitude compared to official statistics
as well as previous studies; the magnitude is similar to what is usually found for the explained
gender wage gap. This could possibly be explained by the fact that when accounting for the
effects of children, the wage estimate for men becomes higher and the wage estimate for women
becomes lower. This can be seen more clearly in figure 2, where the family gaps for men as
well as women are graphed.
27
Figure 2: Family Wage Gaps
-0.2
-0.1
0.0
0.1
1970 1980 1990 2000 2010
Survey Year
Est
imat
eof
Hou
rly
Wag
e(l
og)
Men with Children Women with Children Women without Children
Error Bars Represent 95 % Standard Errors For Estimates
Estimated Wage Difference
What is of interest in this graph is the family gap for men and women respectively, i.e. the
difference between men with and without children as well as the difference between women
with and without children. Men with children are plotted as the top, two-dashed line and
men without children are plotted as the intercept line. The effect of children for men is thus
seemingly positive, with fathers having higher wage estimates than non-fathers for all the years
studied. The difference is however decreasing; the positive effect of children on the wages of
men is about half the magnitude in 2010 as compared to 1968.
The trend in the family gap for women shows a rather different pattern. Women encounter
the opposite effect of having children from that of men, namely a negative one. This is in line
with previous research and the notion that men with children benefit from a "child premium" as
opposed to the "child penalty" faced by women. Furthermore, whereas the family gap for men
has decreased steadily over time, the family gap for women has remained on a level of between
3-5%. Although the difference may not seem severe, it is of great economic significance if
women with children have constantly lower wages than women without children.
It is also worth noting that a number of reforms were implemented in the 70’s in order to
increase the possibility of combining family and work, yet there is no significant drop in the
family gap for the subsequent years.
28
The full results of the main analysis are found in table 7. The regressions include controls
for experience, education, age and marriage status. These findings, graphed above in figures
1 and 2 show that the gender wage gap (excluding the effect of children) has consistently
decreased since the first observed wave in 1968 and until the last observed wave in 2010.
When taking all control variables into account, the remaining gender wage gap is estimated to
be about 16% in 1968, and "only" about 8% in 2010. The estimates are significantly different
from zero and effects are of both statistic and economic significance. The additional effect of
children for women is negative for all survey years but overall decreasing. However, the full
effect for women with children is retrieved by adding all of the estimates for which [Woman=1]
and [Children=1], and so the decreasing positive effect of children for men and women with
children has to be taken into account as well. Still, the complete wage difference for women
with children compared to men without children has decreased from about −21% in 1968 till
about −11% in 2010. However, the wage difference for women without children compared
to men without children has decreased from about −17% in 1968 till about −8% in 2010,
implying no significant differences in the family gap − it seemingly persists.
29
Table 7: Specification With ControlsDependent variable:
Gross Hourly Wage (log)
1968 1974 1981 1991 2000 2010
Woman −0.165∗∗∗ −0.160∗∗∗ −0.083∗∗∗ −0.096∗∗∗ −0.092∗∗∗ −0.079∗∗∗
(0.026) (0.022) (0.019) (0.016) (0.016) (0.023)
Woman × Children −0.114∗∗∗ −0.077∗∗∗ −0.075∗∗∗ −0.070∗∗∗ −0.045∗∗ −0.053∗∗
(0.032) (0.026) (0.022) (0.019) (0.019) (0.026)
Children 0.093∗∗∗ 0.066∗∗∗ 0.049∗∗∗ 0.051∗∗∗ 0.029∗∗ 0.042∗∗
(0.021) (0.017) (0.016) (0.014) (0.014) (0.019)
Education 0.069∗∗∗ 0.046∗∗∗ 0.034∗∗∗ 0.033∗∗∗ 0.032∗∗∗ 0.040∗∗∗
(0.003) (0.002) (0.002) (0.001) (0.002) (0.002)
Experience 0.020∗∗∗ 0.013∗∗∗ 0.014∗∗∗ 0.015∗∗∗ 0.018∗∗∗ 0.012∗∗∗
(0.003) (0.002) (0.002) (0.002) (0.002) (0.003)
Experience2 −0.0003∗∗∗ −0.0002∗∗∗ −0.0002∗∗∗ −0.0002∗∗∗ −0.0002∗∗∗ −0.00005(0.0001) (0.00004) (0.00004) (0.00004) (0.00004) (0.0001)
Age 0.002 0.014∗∗∗ 0.006 0.002 −0.008 0.013∗
(0.006) (0.005) (0.005) (0.004) (0.005) (0.007)
Age2 −0.00004 −0.0001∗∗∗ −0.0001 −0.00003 0.00003 −0.0002∗∗∗(0.0001) (0.0001) (0.0001) (0.00005) (0.0001) (0.0001)
Not married −0.078∗∗∗ −0.022 0.001 −0.017∗ −0.029∗∗∗ −0.068∗∗∗
(0.018) (0.014) (0.011) (0.010) (0.010) (0.013)
Constant 3.635∗∗∗ 3.764∗∗∗ 3.940∗∗∗ 4.085∗∗∗ 4.484∗∗∗ 4.176∗∗∗
(0.124) (0.093) (0.089) (0.080) (0.094) (0.128)
Observations 2,255 2,423 2,732 2,788 2,499 2,136R2 0.356 0.366 0.254 0.286 0.222 0.198Adjusted R2 0.354 0.363 0.252 0.284 0.219 0.195Residual Std. Error 0.314 (df = 2245) 0.249 (df = 2413) 0.227 (df = 2722) 0.202 (df = 2778) 0.202 (df = 2489) 0.249 (df = 2126)F Statistic 137.977∗∗∗ (df = 9; 2245)154.471∗∗∗ (df = 9; 2413)103.055∗∗∗ (df = 9; 2722)123.592∗∗∗ (df = 9; 2778)78.774∗∗∗ (df = 9; 2489)58.399∗∗∗ (df = 9; 2126)Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01
30
6.2 YEARLY EARNINGS
When using yearly labor earnings as the dependent variable, the results differ from the main
analysis of the paper. Because the yearly earnings is a variable generated by multiplying hourly
wage with hours worked, the idea is to get a rough measure of the yearly labor earnings. The
comparison between the results is however not entirely intuitive, as the unit of measure differ
between wage for one hour worked and wage for a year of hours worked. The results without
control variables can be found in table 8 and the results including control variables are shown
in table 9.
When only including the variables of interest but no other controls, the gender wage gap
shows no obvious trend, although the gap is overall diminishing over time. The additional effect
of being a woman with children is negative for all survey years, but decreasing over time. The
coefficient for having children is positive here as well, but with no obvious downward trend
over time.
31
Table 8: Earnings Without ControlsDependent variable:Yearly Earnings (log)
1968 1974 1981 1991 2000 2010
Woman −1,461.749∗∗∗ −1,103.100∗∗∗ −1,152.277∗∗∗ −848.012∗∗∗ −487.015∗∗ −690.807∗∗∗
(205.832) (213.915) (190.255) (175.900) (207.372) (222.403)
Woman × Children −2,920.007∗∗∗ −2,622.924∗∗∗ −1,859.235∗∗∗ −1,311.648∗∗∗ −1,127.744∗∗∗ −730.316∗∗∗
(240.766) (241.917) (214.574) (201.356) (239.741) (252.390)
Children 777.012∗∗∗ 669.545∗∗∗ 372.979∗∗∗ 523.389∗∗∗ 760.384∗∗∗ 879.122∗∗∗
(151.979) (153.328) (143.219) (134.970) (161.994) (164.632)
Constant 9,564.397∗∗∗ 9,290.048∗∗∗ 9,295.185∗∗∗ 9,262.472∗∗∗ 9,207.666∗∗∗ 9,752.764∗∗∗
(130.615) (133.687) (124.376) (113.715) (135.574) (139.702)
Observations 2,237 2,364 2,724 2,742 2,437 2,076R2 0.366 0.325 0.269 0.159 0.071 0.072Adjusted R2 0.365 0.324 0.268 0.158 0.070 0.071Residual Std. Error 2,464.447 (df = 2233) 2,402.646 (df = 2360) 2,290.005 (df = 2720) 2,225.454 (df = 2738) 2,554.418 (df = 2433) 2,379.040 (df = 2072)F Statistic 429.627∗∗∗ (df = 3; 2233) 378.479∗∗∗ (df = 3; 2360) 332.802∗∗∗ (df = 3; 2720) 172.889∗∗∗ (df = 3; 2738) 62.091∗∗∗ (df = 3; 2433) 53.875∗∗∗ (df = 3; 2072)Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01
32
Table 9: Earnings With ControlsDependent variable:Yearly Earnings (log)
Woman −1,411.536∗∗∗ −1,068.750∗∗∗ −1,095.468∗∗∗ −853.544∗∗∗ −650.615∗∗∗ −817.837∗∗∗
(196.043) (206.863) (183.474) (169.721) (200.085) (216.672)
Woman × Children −2,268.349∗∗∗ −2,044.933∗∗∗ −1,595.060∗∗∗ −1,066.511∗∗∗ −812.790∗∗∗ −505.651∗∗
(238.640) (242.330) (212.896) (199.935) (233.549) (246.481)
Children 892.696∗∗∗ 527.411∗∗∗ 284.333∗ 201.247 192.608 375.526∗∗(158.468) (161.565) (152.131) (147.619) (172.130) (176.709)
Education 189.045∗∗∗ 162.385∗∗∗ 125.330∗∗∗ 116.183∗∗∗ 149.976∗∗∗ 172.041∗∗∗(20.226) (16.639) (15.258) (15.483) (20.563) (21.586)
Experience 249.407∗∗∗ 180.483∗∗∗ 220.388∗∗∗ 249.135∗∗∗ 315.983∗∗∗ 196.005∗∗∗(21.467) (21.044) (21.437) (23.107) (28.067) (29.472)
Experience2 −3.826∗∗∗ −2.422∗∗∗ −3.517∗∗∗ −3.845∗∗∗ −4.240∗∗∗ −2.175∗∗∗
(0.436) (0.417) (0.418) (0.441) (0.529) (0.562)
Age −164.764∗∗∗ −12.109 −21.136 −70.930 −230.664∗∗∗ 50.853(48.279) (46.148) (47.497) (47.491) (62.722) (63.863)
Age2 1.571∗∗∗ −0.202 −0.095 0.291 1.715∗∗ −1.266∗
(0.559) (0.529) (0.540) (0.524) (0.690) (0.698)
Not married 679.498∗∗∗ 271.582∗∗ 376.358∗∗∗ 133.489 −104.559 −119.774(134.222) (131.958) (110.975) (106.631) (123.008) (126.830)
Constant 8,439.090∗∗∗ 6,118.721∗∗∗ 6,374.781∗∗∗ 7,480.348∗∗∗ 10,058.480∗∗∗ 5,197.390∗∗∗(923.300) (872.359) (868.192) (847.548) (1,145.732) (1,211.519)
Observations 2,237 2,362 2,724 2,742 2,437 2,076R2 0.434 0.375 0.324 0.227 0.151 0.139Adjusted R2 0.432 0.373 0.322 0.225 0.148 0.135Residual Std. Error 2,331.324 (df = 2227) 2,315.340 (df = 2352) 2,203.468 (df = 2714) 2,135.944 (df = 2732) 2,445.340 (df = 2427) 2,295.022 (df = 2066)F Statistic 189.842∗∗∗ (df = 9; 2227) 156.732∗∗∗ (df = 9; 2352) 144.690∗∗∗ (df = 9; 2714) 89.260∗∗∗ (df = 9; 2732) 47.906∗∗∗ (df = 9; 2427) 37.129∗∗∗ (df = 9; 2066)Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01
33
When instead including the same control variables as in the analysis of hourly wages, the
results show similar patterns in the gender wage gap. In order to simplify the comparison of
the two approaches in terms of wage measures, the results from table 9 are graphed in figures
3 and 4, in the same way as the hourly wage difference was graphed in the previous section.
The gender gap in earnings is graphed in figure 3, again with men as the reference group,
plotted as the intercept line at zero. The gender gap is seemingly greater when using yearly
labor earnings as a wage measure than when using hourly wages. This is because the logged
hourly wage only measures the effect of being female and having children on the hourly wage,
and this effect is negative. The total income effect for women with children can however be
decomposed into two parts: the effect on the hourly wage and the negative effect on the number
of hours worked. When taking both effects into account, we obtain the total yearly income
effect, which is greater than each effect separately.
Figure 3: Gender Gap in Earnings
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
1968 1974 1981 1991 2000 2010
Survey Year
Est
imat
eof
Yea
rly
Ear
nin
gs(l
og)
Women
Error Bars Represent 95 % Standard Errors For Estimates
Estimated Difference in Earnings
Lastly, the results from table 9 showing the family gaps are graphed in figure 4. Once again,
men without children are graphed as the intercept line and are thus the reference group. The
family gap for men is the difference between men with children, the two-dashed line at the top,
and men without children, i.e. the intercept line. The results for men are somewhat similar
when using logged yearly earnings as the dependent variable to the results of the main analysis
34
using logged hourly wages. Men with children do not have yearly labor earnings much higher
than men without children, compared to the analysis using hourly wage. This is likely because
men do not change their labor supply to the same extent as women when they have children and
so there is only one effect at work, namely that on wages, and so there are no greater differences
in results between the different wage measures. The child premium for men seems to decrease
over time, causing the family gap for men to emerge. This is similar to what was found in
the main analysis. There is however a slight increase for the year 2010, but over the full time
period studied the family gap seemingly decreases for men.
Regarding the family gap for women however, the results differ significantly from those
in the analysis using logged hourly wages as the dependent variable. According to the results
using yearly labor earnings as the dependent variable, the family gap for women has decreased
considerably over time. These findings suggest that the decrease in the gender wage gap can
likely be explained by the increase in yearly earnings for women with children, since the gap
between men and women without children has remained at a more or less constant level for the
past few decades. Following the analysis of the hourly wage, these findings are likely the result
of women with children working a lot more than previously rather than an increase in wages
for women with children. This would suggest that it is the wage rather than the labor supply in
terms of hours worked that is driving the family gap for women.
35
Figure 4: Family Gaps in Earnings
-3000
-2000
-1000
0
1000
1970 1980 1990 2000 2010
Survey Year
Est
imat
eof
Yea
rly
Ear
nin
gs(l
og)
Men with Children Women with Children Women without Children
Error Bars Represent 95 % Standard Errors For Estimates
Estimated Difference in Earnings
6.3 PROBABILITY OF BEING IN EMPLOYMENT
Figure 5 show the share of the population that are employed. The figure to the left shows the
difference between men and women over time, and the figure to the right show the difference
between women with and without children over time. In both cases, the differences between
the groups seem to have decreased over time, at least when only looking at the raw differences
and not controlling for any covariates. It should be noted that a significant drop in the share
employed can be seen for the survey year of 1974. Although there are no obvious outliers
observed in the data, it is possible that this is because of a measurement error.
36
Figure 5: Share of Population in Employment
0.70
0.75
0.80
0.85
0.90
0.95
1968 1974 1981 1991 2000 2010
Survey Year
Proportion
Employed
Men Women
0.7
0.8
0.9
1968 1974 1981 1991 2000 2010
Survey Year
Proportion
Employed
Children No Children
For the analysis of the probability of being in the labor force, a binary variable has been
constructed and used as the dependent variable. As previously discussed in the section Empir-
ical Method, the variable was constructed by using hours worked in the year before the survey,
encoding individuals with positive hours worked as 1, and those with zero or no value in hours
worked as 0. The results can be found in table 10 in appendix. Unfortunately for the purpose
of the analysis, the subsample of individuals not in the labor force is too small to obtain sig-
nificant results for most of the waves, and so it is not possible to draw any conclusions of the
implications of what determines the probability of being employed over time. Furthermore, the
effects are not only statistically insignificant, but they are also quite small.
6.4 DISCUSSION
The estimated gender wage gap including controls is larger when accounting for the effect of
children. As previously discussed, the reason for this is that the wage estimate for men is higher
when including the effect of children, whereas the opposite is true for women. Although the
evolution of the gap follow a similar trend as found in previous research, the magnitude of the
difference is greater and the decreasing trend over time is not as steep.
The family gap for men suggests that men with children benefit from a "child premium",
37
as opposed to the child penalty suffered by women. This is in line with previous research
suggesting that whereas women with children are perceived as less dedicated to work, men
with children are seen as more reliable.
A different, or additional, possibility is that there is selection in the type of men and women
that have children. Assume for example that men that are successful in the labor market are
also successful in the "marriage market". Social skills are important in the search for a spouse,
but it is also important in the labor market. Assuming that men with good social skills have a
better chance of finding a spouse and having children, then the positive effect of children on
wage for men could at least partly be explained by selection.
Selection could be an explanation for women as well, but rather self-selection. If women
that knows that they will eventually have children makes different educational and career
choices than women who do not plan to have children, then self-selection into less demand-
ing and thus lower paid jobs could explain part of the effect. Because of the expectations and
the expected labor force participation of women, it is rational to make decisions that maximize
lifetime-earnings given for example career interruptions, as discussed in the theory section.
When using yearly labor earnings instead of hourly wages, the estimated gender wage gap
is even greater. When looking at the family gaps in terms of labor earnings, it seems that
women with children has either had a significant increase in wages or in labor supply over time.
Judging by the results obtained in the main analysis with hourly wages, I would say it is the
latter rather than the former. The difference in the results obtained highlights the importance of
using hourly wages when studying wage gaps, as the alternative approach does not distinguish
between the effects on wage and on labor supply.
The minimal number of observations lacking employment in data did not suffice for ob-
taining significant results, unfortunately. Since the findings of the wage and earnings analyses
differed, it would have been interesting to get a clearer picture of what it is that determines the
probability of employment. Previous literature suggest that women tend to work part time to
a greater extent than men, in which case they would be indicated as employed in this analysis,
even though the labor supply likely differs significantly.
38
7 CONCLUSION
In this study, the evolution of the gender and family wage gaps in Sweden are addressed. Pre-
vious research has mainly focused on the gender wage gap, and by controlling for children
rather than investigating the heterogeneous effects thereof. The aim of this study was to pro-
vide a broader picture of how the differences in wages caused by children have evolved over
time, both between men and women but also between individuals of the same gender with and
without children.
The results from the main analysis of the gender wage gap suggest a decrease in the dif-
ference between men and women, which is in line with previous research. The difference is
however greater when accounting for the effects of children, and does not decline as rapidly.
The family gap is seemingly decreasing for men with and without children, whereas the family
gap between women with and without children is seemingly rather constant over time. Al-
though the family gap has been found to be of a greater magnitude in previous research as well
as for other countries, the findings of this study show that the difference is persistent in Sweden
as well.
When using yearly labor earnings as the dependent variable instead of hourly wages, the
gender gap is of yet greater magnitude. Because the measure is constructed by multiplying
hourly wage with hours worked, women having a lower labor supply than men likely explain
the increased difference.
In terms of the family gap when studying yearly labor earnings, the findings of this sub-
analysis is similar for men, but entirely different for women. Whereas the main analysis suggest
that the family gap for women has remained on a somewhat constant level, the family gap for
women in terms of yearly earnings has seemingly decreased significantly to the extent that it
has almost disappeared in 2010. Because of the way that the earnings variable is constructed,
the decrease of the family gap is likely caused by women with children working far more hours
now than in the first few waves. This would also suggest that the family gap for women cannot
be explained by differences in labor supply. However, as I do not have access to neither the
sector of work nor the field or level of education, it is possible that women with children differ
in unobserved characteristics from women without children, causing them to make different
educational and occupational choices.
The findings of this study suggest that there is still a persistent, yet rather small, family gap
39
for women. The aim of the thesis was to answer the research question of how the family gap
has evolved over time in Sweden and how it can be related to the gender wage gap. While
the gender wage gap is decreasing over time, the family gap for women remains at a persistent
level. Although the setting for the paper does not allow me to make any causal interpretations,
I believe that the findings of this paper supports the notion that a greater fraction of the gender
wage gap can be explained by effects of having children now than previously.
The generous parental benefit system in Sweden is often seen as something admirable, yet it
is associated with several challenges. The benefit system was introduced with the aim that more
women would have the possibility of combining work with family. However, if the generosity
of the system causes women to make different educational and labor market choices in order
to care for children, then employers may statistically discriminate towards women with the
expectation that they will be out of the labor market more than men, for example by taking
more temporary parental leave to care for sick children. One major challenge with the parental
leave system is thus that although it makes it possible to combine work and family, it may cause
some women to have more and longer career interruptions. In combination with the subsequent
risk of statistical discrimination, these are possible mechanisms that make women fall behind
men.
The former argument suggests that although the parental leave benefit system as well as
gender equality policies have been introduced to decrease the differences between men and
women, there are still concrete effects that arise from taking the responsibility for children.
Because the effect of having children is seemingly constant over time for women, the results
from this study imply that specific policies are needed to prevent and battle the difference in
labor market outcomes that arise because of the differing effects from caring for children.
The family gap between women in general does likely conceal heterogeneous effects with
differing magnitudes with respect to level of education as well as the sector of employment.
Although this was not possible to investigate with the data available and within the scope for
this thesis, it would be interesting to test this hypothesis in future research. It would clarify
further which groups among women that are most affected by having children and thereby how
policies in this area should be directed to be most efficient.
40
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43
LIST OF FIGURES
1 Gender Wage Gap Accounting for Effects of Children . . . . . . . . . . . . . . 27
2 Family Wage Gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3 Gender Gap in Earnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4 Family Gaps in Earnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
5 Share of Population in Employment . . . . . . . . . . . . . . . . . . . . . . . 37
44
LIST OF TABLES
1 Variable Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2 Sample Means: All Individuals . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3 Sample Means: Individuals With Children . . . . . . . . . . . . . . . . . . . . 19
4 Sample Means: Individuals Without Children . . . . . . . . . . . . . . . . . . 19
5 Difference in Means for Men and Women, With and Without Children . . . . . 20
6 Specification With No Controls . . . . . . . . . . . . . . . . . . . . . . . . . . 26
7 Specification With Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
8 Earnings Without Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
9 Earnings With Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
10 Probability Model with Controls . . . . . . . . . . . . . . . . . . . . . . . . . 46
45
Table 10: Probability Model with ControlsDependent variable:
Employed1968 1974 1981 1991 2000 2010
Woman 0.035∗ 0.015 0.027 −0.019 −0.023 0.034∗∗(0.020) (0.024) (0.019) (0.022) (0.024) (0.015)
Woman × Children −0.002 0.057∗∗ 0.046∗∗ 0.107∗∗∗ 0.089∗∗∗ 0.009(0.024) (0.027) (0.022) (0.026) (0.028) (0.018)
Children −0.025 −0.047∗∗ 0.0003 −0.057∗∗∗ −0.061∗∗∗ −0.007(0.016) (0.019) (0.016) (0.019) (0.021) (0.013)
Education 0.004∗∗ 0.015∗∗∗ 0.014∗∗∗ 0.016∗∗∗ 0.024∗∗∗ 0.001(0.002) (0.002) (0.001) (0.002) (0.002) (0.001)
Experience 0.004∗ 0.047∗∗∗ 0.041∗∗∗ 0.014∗∗∗ 0.029∗∗∗ −0.004∗∗
(0.002) (0.001) (0.001) (0.003) (0.003) (0.002)
Experience2 −0.0001∗∗ −0.001∗∗∗ −0.001∗∗∗ 0.00000 −0.0002∗∗∗ 0.0001∗∗(0.00004) (0.00004) (0.00003) (0.0001) (0.0001) (0.00004)
Age −0.012∗∗ −0.050∗∗∗ −0.049∗∗∗ 0.012∗∗ 0.003 0.008∗
(0.005) (0.004) (0.004) (0.006) (0.006) (0.004)
Age2 0.0001∗∗ 0.0005∗∗∗ 0.0004∗∗∗ −0.0003∗∗∗ −0.0003∗∗∗ −0.0001∗
(0.0001) (0.0001) (0.00005) (0.0001) (0.0001) (0.00005)
Not married −0.005 −0.013 −0.010 −0.034∗∗ −0.033∗∗ −0.004(0.014) (0.015) (0.012) (0.014) (0.015) (0.009)
Constant 1.109∗∗∗ 1.402∗∗∗ 1.483∗∗∗ 0.524∗∗∗ 0.469∗∗∗ 0.788∗∗∗
(0.093) (0.093) (0.082) (0.103) (0.121) (0.084)
Observations 2,857 3,831 3,803 3,728 3,776 2,714R2 0.013 0.371 0.428 0.109 0.147 0.015Adjusted R2 0.010 0.370 0.427 0.106 0.145 0.011Residual Std. Error 0.260 (df = 2847) 0.334 (df = 3821) 0.271 (df = 3793) 0.322 (df = 3718) 0.363 (df = 3766) 0.189 (df = 2704)F Statistic 4.118∗∗∗ (df = 9; 2847) 250.590∗∗∗ (df = 9; 3821) 315.779∗∗∗ (df = 9; 3793) 50.308∗∗∗ (df = 9; 3718) 72.258∗∗∗ (df = 9; 3766) 4.470∗∗∗ (df = 9; 2704)Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01
46