Locus of Control and
Mothers' Return to Employment
Eva M. Berger and Luke Haywood
Deutsches Institut für Wirtschaftsforschung 2016
Opinions expressed in this paper are those of the author(s) and do not necessarily reflect views of the institute.
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Locus of Control and Mothers’ Return to Employment
Eva M. Berger∗Luke Haywood
Johannes Gutenberg University Mainz DIW Berlin
This paper investigates the eﬀect of locus of control (LOC) on the length of mothers’
employment break after childbirth. Using data from the German Socio-Economic Panel
Study (SOEP), duration data reveals that women with an internal LOC return to employ-
ment more quickly than women with an external LOC. We ﬁnd evidence that this eﬀect
is mainly related to diﬀerential appreciation of the career costs of longer maternity leave.
Given the high level of job protection enjoyed by mothers in Germany, economic con-
sequences of diﬀerences in this noncognitive skill can be expected to be larger in other
Keywords: Locus of Control, Noncognitive Skills, Personality, Maternal Employment,
Female Labor Supply, Survival Analysis
JEL-codes: J22, J24
∗Corresponding author: Johannes Gutenberg University Mainz, Jakob-Welder-Weg 4, 55128 Mainz, Germany.
Phone: +49(0)6131-39-27295. Fax.: +49(0)6131-39-27695. E-mail: firstname.lastname@example.org.
Recent research on so-called noncognitive skills1demonstrates their crucial role for educational
attainment, employment outcomes, and a variety of risky behaviors (Blomeyer et al. 2009, Cole-
man and DeLeire 2003, Heckman et al. 2006, Piatek and Pinger 2015). In the labor market, a
number of studies ﬁnd that noncognitive skills aﬀect earnings (Andrisani 1977, 1981, Cebi
2007, Flossmann et al. 2007, Heckman and Rubinstein 2001, Heckman et al. 2006, Heineck
and Anger 2010, Mueller and Plug 2006, Nyhus and Pons 2005, Osborne Groves 2005) and
unemployment duration (Uhlendorﬀ2004, Uysal and Pohlmeier 2009).2In the present study,
we test whether and how locus of control (LOC) aﬀects women’s decision how long to stay out
of employment after childbirth, a crucial determinant of female labor supply and related to the
female wage gap (Blundell et al. 2013, Adda et al. 2016).
While the eﬀect of various noncognitive skills on employment outcomes has been stud-
ied, little is known about its association with labor supply decisions.3We focus on one such
noncognitive skill, LOC. It describes “individual diﬀerences in a generalized belief for internal
versus external control of reinforcement” (Rotter 1966, p. 1). LOC is then a measure of the
degree to which an individual perceives that success or failure in life follows from her own
behavior rather than being controlled by outside forces. If life events are perceived by a per-
son as being contingent upon her own behavior or her own characteristics, this characterizes
an internal LOC. If, on the other hand, life events are perceived by a person as the result of
luck, chance, fate, or under the control of powerful others, this is labeled an external LOC.4
Using a common factor of the Rotter LOC scale and the Rosenberg self-esteem scale, Heckman
et al. (2006) ﬁnd that the impact of noncognitive skills on the employment probability is greater
1We use the term non-cognitive skills as it is used in a large part of the related literature (see, e.g., Cunha
and Heckman 2007, 2008). These skills refer to abilities and personality traits other than pure cognitive ability.
Nevertheless, we are aware that most of these skills or traits do have a cognitive component.
2Furthermore, a number of studies in psychology investigate the relationship between personality traits and
occupational attainment (see (Roberts et al. 2007)).
3Wichert and Pohlmeier (2010) ﬁnd a relationship between women’s big ﬁve personality traits and their labor
force participation. For a detailed discussion of the research on noncognitive skills in economics and psychology,
see Borghans et al. (2008).
4Following the original concept of LOC by Rotter (1966), we use a single index of LOC where internal and
external are the opposite poles of a single dimension. This aspect is discussed in detail in appendix A.
than the eﬀect of cognitive skills. Moreover, they ﬁnd that this pattern is more pronounced
for women than for men, suggesting that noncognitive skills matter particularly for women’s
labor supply. Departing from this observation, the present paper focuses on a particularly im-
portant labor supply decision for women—when to re-enter the labor market after childbirth.
Since noncognitive skills—though largely stable throughout adulthood—are malleable during
childhood (Cunha and Heckman 2007, 2008), the current study adds to the evidence suggesting
that early childhood investments may have long-lasting eﬀects (Carneiro et al. 2011, Cunha and
Post-birth employment behavior matters because it aﬀects human capital accumulation,
future employment chances and wages (Davies and Pierre 2005, Gutierrez-Domenech 2005,
Lefebvre et al. 2009). Furthermore, maternal employment plays a large role in counteracting
the risk of child poverty, an important policy issue. Most studies on mothers’ return to employ-
ment focus on institutional factors such as parental leave schemes or tax rules (e.g., Bergemann
and Riphahn 2009, 2010, Burgess et al. 2008, Gutierrez-Domenech 2005), or on individual
and family-related factors such as income, educational attainment, and labor market experience
(Kuhlenkasper and Kauermann 2010).
The present paper thus contributes to two strands of the literature: First, the economic con-
sequences of noncognitive skills; second, the determinants of mothers’ return to employment
after childbearing. We focus on LOC and test several channels through which LOC could
inﬂuence the length of employment breaks women take after a childbirth. First, LOC might
aﬀect women’s labor market attachment, viz. their willingness to be available all day for their
children. Second, LOC may aﬀect the relative importance given to uncertain material gains
associated with earlier return to the labor market.
The paper is organized as follows: Section 2 presents a simple model which provides us with
a framework to test various channels by which LOC may inﬂuence maternal return to employ-
ment. Section 3 gives details on the data from the German Socio-Economic Panel (SOEP) that
we use. Section 4 describes our reduced-form estimation strategy based on a semi-parametric
survival model. Section 5 presents and discusses our main results: women with more inter-
nal LOC return to the labor market quicker because they are more sensitive to the career costs
associated with long leave durations. Section 6 concludes.
2 A Model of Return to Employment After Childbirth
The economic consequences of mothers’ decision when to return to the labor market after giv-
ing birth are very large (Adda et al. 2016) since human capital accumulates in employment.
Work experience can then be seen as a form of investment in human capital (Blundell et al.
2013). Coleman and DeLeire (2003) ﬁnd that persons with an internal LOC make higher in-
vestments in human capital than persons with an external LOC. They argue that the beneﬁts of
human capital investment are appreciated more strongly by individuals with an internal LOC
compared to individuals with an external LOC since the latter believe that future earnings de-
pend more strongly on factors over which they have no inﬂuence. The same argument applies
to an earlier return to work since this also leads to more work experience and can thus be seen
as an investment in human capital.
Importantly, while a more internal LOC is associated with a higher job search intensity by
unemployed persons (Caliendo et al. 2015, Spinnewijn 2015), our setting allows us to highlight
other aspects. Mothers in Germany beneﬁt from a legal right to return to their previous position
(at the same wage) within three years after childbirth. Hence, mothers can freely decide about
the length of their leave without fearing to lose their job position or have their wage level cut on
returning—they do not need to search for a new job.5In practice very few mothers are observed
to enter new jobs after maternity leave, so that career costs do not arise as a result of diﬀer-
ences in job search during maternity leave. In particular, mothers on leave report extremely low
5Obviously, job protection is only helpful for mothers who have a permanent contract or whose temporary job
does not expire during maternity leave. We do a robustness test of our main estimations below including only those
women in the sample who have a permanent job prior to ﬁrst childbirth. Our results are robust.
search intensity, signiﬁcantly less than unemployed and even employed individuals: Only 4%
of women on leave in the ﬁrst year after birth report active job search activities in the previous
month, rising to only 9% and 12% in the second and third year after birth. This is signiﬁcantly
less than a comparable sample of unemployed individuals of whom 66% report to have been
actively searching for work in the last month. We believe our evidence about the eﬀect of LOC
on return to work thus highlights a diﬀerent channel: diﬀerential appreciation of future wage
Even though mothers have the legal right to return to their previous position at the same
wage, the duration of leave might have consequences for their future career prospects. For ex-
ample, the probability of future promotions might be reduced for mothers taking long leaves.
Expectations about these consequences might vary across individuals: Women with a more in-
ternal LOC might fear stronger negative consequences than women with a more external LOC.
In the following section we formalize this intuition, allowing mothers to determine the du-
ration of leave to maximize their expected net present value of lifetime utility, comparing the
value of leave to that of work. We use this model to determine the factors we investigate in a
framework of duration models.
2.1 The Model
To inform our empirical results, we now present a simple model of return to the labor market
based on the German institutions of job protection. Mothers’ instantaneous utility u(.) depends
on the material and non-material beneﬁts of staying at home with the child b(d), a function of
leave duration d. Utility also depends on other household income m, which can strongly inﬂu-
ence mothers’ net income given joint household taxation in Germany. On returning to the labor
market, mothers receive their pre-childbirth wage level w0, in line with German job protection,
which extends until 36 months of maternity leave.
We believe that future wage growth prospects vary signiﬁcantly as a function of maternity
leave duration. We do not discuss what causes these diﬀerences. A possible mechanism is that
wage oﬀers reﬂect the latent productivity of returning mothers. The reduction in wage oﬀers
would thus reﬂect the depreciation of human capital during maternity leave. Another reason
may be discrimination by employers.
In line with the literature on job search, we model wage growth as workers receiving job of-
fers from a wage distribution F(.) at Poisson rate λ. Dechter (2014) and Schmieder et al. (2015)
ﬁnd that periods out of the labor market are followed by a reduction in job oﬀers. We thus
allow for mothers to receive fewer oﬀers when returning later to the labor market. Assuming
mothers accept oﬀers with a higher wage than in their current job, this then translates to a lower
wage growth rate for mothers with longer maternity leave spells. We also allow the job arrival
rate λto be a function of individual unobserved characteristics X(including LOC—as previous
evidence suggests that LOC inﬂuences job search intensity and this may likewise be the case
for job search intensity on-the-job6).
Since beneﬁts of earlier return to the labor market are in the future and thus uncertain,
these might be appreciated diﬀerentially by individuals depending on their LOC. LOC is a
noncognitive skill related to beliefs, it plays a role in situations of uncertainty; i.e., individuals
form expectations about the net present value of labor market re-entry according to their LOC.
We operationalize this using an expectations operator ˜
λ, indicating that diﬀerences in LOC
translate to diﬀerences in beliefs about λ. We can then give the net present value of being in
6Though we assume that mothers do not search during leave, we do allow for job search on-the-job, both prior
to childbirth as well as after re-entry. This is how we model wage growth.
maternity leave, V(.), and that of being employed, W(.), discounted by the interest rate ras
These continuous-time Bellman equations are often presented in terms of asset value formula-
r V(b,m,d)=u(b(d),m)+max [W(w0,m,d),V(b,m,d)]−V(b,m,d) (1)
The structure of these expressions closely follows the “fundamental equation of search” for
the unemployed (Pissarides 2001). First, the ﬂow utility u(b(d),m) depends on other household
income mand enjoyment of staying at home during maternity leave, b(d). We expect both the
material and non-material components of b(d) to be decreasing with the duration of maternity
leave d. Second, at any point in time during the ﬁrst 36 months of maternity leave, mothers
may decide to move back into employment. We expect this to occur when W(.) exceeds V(.),
explaining the maximum operator in equation (1).
Equation (2) gives the value of being employed. This has a similar structure to that of being
in maternity leave: First, ﬂow utility is given by the instantaneous utility of work depending on
wage wand other household income. Second, with some probability λ(.), individuals receive
job oﬀers from a wage distribution with the cumulative distribution function F(.). When an
oﬀer w0is accepted, the employment value increases to W(w0,m,d)—the maximum operator
indicates that only oﬀers are accepted which increase the employment value. This is the case
for any oﬀer that pays higher wages—and this is the way we model wage growth. Note that
we allow the rate of new job oﬀers after return to the labor market to depend on LOC (as part
of X) and maternity duration d. Additionally, we allow individuals with diﬀerent LOC to have
diﬀerent beliefs about the probability of job oﬀers arising. In particular, the literature reviewed
above suggests that individuals with internal LOC may expect stronger consequences of own
behavior on wage growth than individuals with external LOC. Thus, individuals with an internal
LOC may expect stronger consequences of maternity leave duration on wage growth (beliefs
A young mother then chooses her optimal duration of maternity leave, d∗, such that V(b,m,d∗)=
W(w0,m,d∗), which implies
Expression (3) implicitly deﬁnes the optimal time of return to the labor market (d∗) as a
function of the level of pre-birth earnings w0(as this is equal to wage at re-entry), other house-
hold earnings m, and the option value related to future wage growth—beliefs about which may
be moderated by LOC. In order to gain an estimable expression, we note that the expression
in the expectations-term is related only to wage gains in the future, thus is a function of wage
growth. Therefore we can write
where T(.) is a function ensuring that (3) is satisﬁed. The above formulation implies the
following paths by which LOC may determine return to the labor market:
1. Pre-birth wage rate w0—moderated by other household income m. The pre-birth wage
rate may also be a function of characteristics Xand thus of LOC, e.g., as a result of
diﬀerences in eliciting outside oﬀers due to diﬀerences in job search intensity on-the-job
before childbirth (Spinnewijn 2015).7
7We do not explicitly write w0(X) in order to keep the notation short.
2. Wage growth ∆w(.). This may be a function of LOC (independently of leave duration),
again as a result of diﬀerences in job search intensity on-the-job.
3. Expectations about the monetary beneﬁts of returning earlier (conditional on all other
eﬀects, via ˜
λ). As noted above, by changing perceived control, LOC may inﬂuence
the relevant expectations (especially for risk-averse individuals). We test two channels
for this: (i) wage growth might be appreciated diﬀerentially by people according to their
LOC; (ii) the eﬀect of don wage growth post-return (i.e., the wage growth penalty) might
be appreciated diﬀerentially by people according to their LOC.
4. Utility of staying at home vis-a-vis working. Individuals with diﬀerent LOC may have
related (unobserved) preferences related to staying at home with their child (cf., e.g.,
Section 4 presents our empirical framework to test these channels, especially the channels
(3.i) and (3.ii). Channel (4) constitutes a residual explanation in case none of the previous
channels (fully) explain the eﬀect of LOC on d. However, based on our empirical results below
(presented in section 5) we conclude that the residual channel can be neglected as we ﬁnd
channels (1) and (3ii) fully explain the eﬀect of LOC on leave duration.
The empirical analysis in this paper is based on data from the German Socio-Economic Panel
Study (SOEP) 1984–2013, an annual household panel study that is representative of the pop-
ulation in Germany (Wagner et al. 2007). We use data from women who have their ﬁrst child
between 1992 and 2012.
We focus on the return decision of mothers, i.e., the decision to re-enter employment after
childbirth and therefore sample women who were employed prior to ﬁrst childbirth. This allows
us to take into account employment characteristics prior to ﬁrst childbirth and thus to analyze
the channels of pre-birth wage level and wage growth. Certainly, the population of employed
women prior to childbirth is selective. Women who are not employed prior to ﬁrst childbirth are
partly still in education or have some other reason (e.g., health problems) not to participate in
the labor force. For this group the decision to enter employment after childbirth is presumably
diﬀerent and our determinants may not be as relevant.
Further, we concentrate on ﬁrst births because transitions into employment after higher order
births are more complex to model. For example, later transitions into employment are related to
the space between births and to previous career interruptions due to previous births. Focusing
on ﬁrst birth allows us to control for employment characteristics of the job prior to ﬁrst birth.
We later show that our association of LOC and labor market return is not related to timing of
Women’s employment status is observed in the data on a monthly basis from the fourth
month after childbirth until they return to employment or until they are censored.8. The ﬁrst
three months after childbirth are ignored because new mothers in Germany are not allowed to
be employed during the ﬁrst eight weeks after childbirth; this period extents to twelve weeks
for mothers of multiple births or preterm births. Censoring occurs when an individual exits the
survey or when the most recent month observed (December 2012) is reached and no transition
into employment is observed until this point. Individuals for whom we observe a transition
into unemployment9or education are discarded from the sample. In total, the sample contains
18,967 person-month observations from 966 individuals observed between a minimum of one
and a maximum of 215 monthly spells (corresponding to 18 years). Average maternity leave
duration is 19.6 months (std. dev. 27.3). For 78.8% of individuals a transition into employment
is observed with a mean number of spells of 18.2 (std. dev. 21.9). The remaining 21.2% are
censored and have a mean spell duration of 25.1 (std. dev. 41.3). Figure (1) illustrates the
density of spells.
8The monthly employment status is surveyed retrospectively in each wave; therefore, the latest SOEP wave of
2013 contains information on the monthly employment status until December 2012.
9A person is deﬁned as unemployed if she is registered as unemployed with the Employment Oﬃce.
Figure 1: Density of maternity leave spells
Our measure of LOC is based on ﬁve items each surveyed in the SOEP in waves 1999,
2005, and 2010. We use the earliest measure available for each individual, i.e., preferably that
of 1999.10 The LOC items from year 1999 were answered on a 4-point scale (1 “disagree com-
pletely” to 4 “agree completely”), while in the 2005 and 2010 surveys the items were answered
on 7-point Likert scales (1 “disagree completely” to 7 “agree completely”). To make the scales
compatible, we standardize the score of each item and take the average over the ﬁve standard-
ized scores.11 The higher the LOC index, the more a woman believes she has (internal) control
over life events.12 Since the LOC items are not collected yearly in the SOEP, we assume that
LOC is stable over time. Given previous evidence this seems reasonable.13 In section 5.1 we
10For 68% of individuals we have information from 1999, for 27% from 2005, and for 5% our LOC measure
stems from 2010. Section (5.1) shows that all results hold using only individuals for whom the 1999 LOC measure
11This way of construction the LOC index is also used by Coleman and DeLeire (2003) with the LOC items from
the National Educational Longitudinal Study (NELS) and by Cebi (2007) with the LOC items from the National
Longitudinal Survey of Youth (NLSY).
12Appendix A provides details about the construction of the LOC index as well as about the LOC items.
13Cobb-Clark and Schurer (2013) use panel data to show that LOC is relatively stable even during major life
events. Personality psychologists widely agree upon mean and rank-order stability of noncognitive skills (or per-
sonality traits as they call it) in adulthood (Caspi et al. 2005, Costa Jr and McCrae 1994, Fraley and Roberts 2005,
McCrae and Costa 1994, McCrae and Costa Jr 1996, 2003, Roberts and DelVecchio 2000). Heckman et al. (2006)
and Coleman and DeLeire (2003) make similar assumptions for data availability reasons.
test whether changes in LOC may be driving our results.
We use a number of socio-economic and demographic control variables: In the main es-
timations we include the ﬁrst child’s birth cohort (in three-year groups), log of return wage
w0—equal to the wage prior to childbirth (we use gross hourly wage, inﬂation-adjusted to the
base year 2011, in Euros), log of other household income m(we use inﬂation-adjusted net
household income net of own labor earnings, in Euros per month), predicted wage growth ten-
∆wT RE ND (see section 4.1), and the estimated wage growth penalty ˆg(see section 4.2).
In extensions of our main speciﬁcation we include educational attainment (university degree,
vocational degree, no professional degree), ISCED categories of educational level, labor market
experience prior to ﬁrst birth in years, mothers’ birth cohorts (in groups of ﬁve years), partner
status (cohabiting), an indicator for the presence of other adults present in the household, re-
gional dummies (16 federal states in Germany), a dummy for East versus West Germany, a
dummy variable indicating whether the woman has a second child within three years after the
ﬁrst birth,14 the Big Five personality traits, job type (civil servant, self-employed, white-collar,
blue-collar), and employment hours (full-time, part-time, marginal employment). Summary
statistics of these variables are reported in table 1.
4 Empirical Strategy
We want to test whether and how LOC inﬂuences return to the labor market. We now present a
strategy to estimate the relative importance of our key determinants of maternal return decisions
and in particular to assess the channels by which LOC might inﬂuence maternal return. The key
determinants and channels are deducted from our model above, as derived in section 2.
14We tried a number of diﬀerent speciﬁcations to account for having another child within a short period of
time; we controlled for having a second or third child within diﬀerent periods of time and for the spacing between
children (in months) and the spacing squared. The main results are always largely the same. See columns (5) and
(6) of table 4, for example.
Table 1: Summary statistics of covariates
Mean /Percent std. dev. Min. Max.
LOC (higher =more internal) 0.053 0.568 -1.900 1.525
4-6 months 25%
7-9 months 9 %
10-12 months 13%
13-18 months 18%
19-24 months 8%
25-30 months 6%
31-36 months 5%
37-48 months 5%
49-60 months 4%
Child birth cohorts
Return wage w054.428 22.591 4.783 205.808
Other household income m2410.929 1308.446 1 13929.91
Wage growth tendency ˆ
∆wT EN D 0.102 0.189 -0.02 1.704
ˆγ0.026 0.013 0.008 0.050
University degree 23%
Vocational degree 67%
No professional degree 10%
ISCED educational levels
general elementary 9.6%
middle vocational 45.5%
vocational +high school degr Abitur 11.3%
higher vocational 10.5%
higher education 23.1%
Experience 7.647 4.307 0 24.6
Mother birth cohorts
Partner in HH 94.5%
Other adults in HH 2.7%
East Germany 21.8 %
2nd child w/in 3 yrs 28.9%
Neuroticism -0.036 0.746 -2.013 1.797
Openness 0.007 0.765 -2.371 1.546
Conscientiousness 0.009 0.757 -3.155 0.941
Extraversion 0.039 0.775 -2.668 1.359
Agreeableness 0.016 0.693 -2.277 1.114
Full-time employment 84.5%
Part-time employment 8.5%
Marginal employment 1.3%
Civil servant 8.7%
Note: N =966 individuals.
First, we have information about return wages as these are equal to pre-birth wages. Second,
in section 4.1 we show how we estimate wage growth tendencies from our pre-birth observa-
tions. Third, section 4.2 shows how we assess the inﬂuence that maternity leave duration has on
post-return wage growth (the wage growth penalty) in diﬀerent occupations. Fourth, in section
4.3 we explain how we test the role of LOC on return durations via the diﬀerent channels—i.e.,
via return wage, wage growth tendency, and diﬀerential appreciation of wage growth and wage
growth penalty by LOC—in a simple estimation framework. Finally, in subsection 4.4, we give
details of the survival model used for estimation.
4.1 Individual Wage Growth Tendency
Following the model presented above, mothers receive wage w0on returning to the labor market.
Wage growth after re-entry is modeled in a search framework and the amount of job oﬀers
arriving may depend on a number of characteristics including LOC as well as d. The model
implies that with probability λ(.) mothers receive a wage oﬀer from distribution F(.), such that
wage growth after return is given by
Wage growth depends on factors Xincluding LOC. We assume that these factors are time-
constant and aﬀect wage growth similarly before and after childbirth. We call this the wage
growth tendency of a woman. Women with a high wage growth tendency have incentives to
return to the labor market quicker than women with a low wage growth tendency because they
have higher opportunity costs of staying out of the labor market (see section 2). To empirically
assess the inﬂuence of wage growth tendency on the return decision, we would not wish to use
actual observed wage growth after return (subject to selection issues since λ(.) also depends on
d). We thus follow a diﬀerent strategy to estimate the wage growth tendency of young mothers.
We could use observationally similar men or women who have no children; however this group
may be speciﬁc in other (unobserved) dimensions. In our main speciﬁcation we thus estimate
wage growth for women based on their pre-birth wage dynamics. We then predict wage growth
tendencies ( ˆ
∆wT RE ND ) taking into account both observable individual and job characteristics
(education, occupation, labor market experience, tenure with the same ﬁrm, and region) xi,tas
well as an unobserved individual component ηi; in addition, we control for macroeconomic
trends νt(year ﬁxed eﬀects). Hence, we estimate women’s pre-childbirth earnings dynamics as
where ∆wi,t=log(wi,t+1)−log(wi,t). We estimate equation (6) using all periods tprior to
pregnancy (at least nine months prior to ﬁrst childbirth). Note that if LOC inﬂuences on-the-
job search intensity, this will be part of the individual time-invariant component (ηi) of wage
growth.15 Appendix B provides further details and presents results of the estimation of equation
(6). We can now use our estimated parameters ˆη, ˆα, and ˆνtto predict young mothers’ wage
growth tendency at the time of childbirth ( ˆ
T RE ND ) and argue that this is the wage growth
that women could expect on returning immediately to the labor market—the following section
considers how these wage growth prospects evolve as a function of the duration of maternity
leave. Note that this prediction takes into account the fact that women may change their jobs
and occupations over the course of their careers.16
Our identifying assumption is that the only diﬀerence between post- and pre-birth wage
growth arises as a result of diﬀerent maternity leave durations d. Then, our estimable equation
(6) is a linear (ﬁrst-order) approximation of wage growth given by expression (5) with d=0.
Since young mothers do not know the precise rate of their future wage growth, they will base
15This is true if the role of LOC is constant before and after birth. If, however there was some eﬀect of LOC on
wage growth that is speciﬁc to post-birth periods, this would not be taken into account.
16In predicting counterfactual levels of wage growth for mothers out of the labor market, we could focus only
on an individual ﬁxed eﬀect with no other observables. However, pre-birth characteristics may be informative of
future wage growth. Being in occupation jat time ton wage growth in time t+1 might be associated with a high
probability of moving to occupation k, where earnings may be higher. For a young mother in occupation jbefore
leaving the labor market to maternity, this may thus be a valuable predictor. The time-varying characteristics we
use are subsumed in the vector xin equation (6).
their decisions about don predictions similar to ours.
Table 1 shows predicted yearly wage growth at the time of ﬁrst childbirth ˆ
T RE ND . Mean
wage growth is 10% with considerable variance.17 Diﬀerences in wage growth across occupa-
tions is largely consistent with previous ﬁndings—e.g., higher for women in technical occupa-
tions (14.8%) than in health service occupations (8.8%).
We also test an on-the-job search channel: if LOC importantly inﬂuences job search in em-
ployment, it should show up in a higher predicted wage growth tendency (as we include the
individual ﬁxed eﬀect in our prediction). However, we ﬁnd that the predicted wage growth
tendency does not depend on LOC (the correlation of ˆηwith LOC is insigniﬁcant). Hence, em-
pirically, on-the-job search does not constitute an important channel by which LOC inﬂuences
mothers’ return decisions.
4.2 Wage Growth Penalty
We think that the predicted wage growth tendency reﬂects important individual prospects in
wage growth. We now consider by how much wage growth post return is reduced depending
on maternity leave duration d. Recall that the wage on returning to the labor market is ﬁxed at
w0due to German job protection legislation; however, wage growth at return might be reduced
depending on leave duration d. We denote the extent of wage growth reduction the wage growth
penalty. The wage growth penalty varies across occupations. Our hypothesis is that in occupa-
tions with a high wage growth penalty, LOC has a larger eﬀect on dthan in occupations with a
low wage growth penalty. In an extreme case, in an occupation with no wage growth penalty at
all, there is no reason for LOC to aﬀect dbecause there is no cost (no penalty) that needs to be
attributed to internally or externally controlled factors (according to internal or external LOC).
17Expectations of strongly negative wage growth tendencies appear unrealistic, thus we exclude as measurement
error rates below -2%.
To test this channel, we ﬁrst assess the average wage growth penalty in diﬀerent occupa-
tion categories j. We estimate wage growth after return in diﬀerent occupations as a function
of the leave duration din combination with the predicted wage growth tendency in the same
occupation j, i.e., we estimate
∆wT RE ND
where ∆wi,d=log(wi,d+1)−log(wi,d). The wage growth penalty gj(d) is thus the extent by
which wage growth at return is reduced compared to the wage growth tendency. In our estima-
tion we specify a linear penalty function gj(d)=γjd. This means that the wage growth penalty
for each month of leave is constant. In an occupation with a zero wage growth penalty, we
would ﬁnd γ=0; i.e., in this occupation, wage growth after return (∆wi,d) corresponds to the
predicted wage growth tendency ( ˆ
∆wT RE ND
i) plus noise (ιi).
Since wage growth post return is estimated as a function of dand dis a choice variable,
issues of selection may arise. Most obviously, women with certain unobservable characteristics
choose higher dand at the same time have lower wage growth. Our estimation procedure will
not be aﬀected if these unobservable characteristics are constant before and after birth and aﬀect
wage growth before and after birth equally; this follows from the fact that we estimate wage
growth post return as a function of individual wage growth tendency ( ˆ
∆wT RE ND
i). Since the wage
growth tendency (see the preceding section) is a prediction based on estimated wage growth in
pre-birth periods (including individual ﬁxed eﬀects, thus including diﬀerences in LOC), it is
generated independently of d. Only if the unobservable characteristics aﬀect wage growth be-
fore birth diﬀerently than wage growth after birth, the γ-coeﬃcients might be estimated with
bias. Does this bias our results? Not necessarily, since we are not primarily interested in the
predicted eﬀect of don wage growth post return, ˆγ; rather our aim is to compare wage growth
penalties across occupations in order to test whether the eﬀect of LOC on the return decision
is moderated by the industry’s wage growth penalty—this will depend more on the quality of
the ranking, less on the absolute values. Therefore, if the distribution of the relevant unobserv-
able characteristics and thus selection into diﬀerent dwas similar in all occupation categories
in the way that ˆγjrelative to ˆγkis close to the relation between the true wage growth penalties
in occupations jand kfor all j, selection would not lead to false conclusions in our context. By
contrast, if the bias varies strongly between occupation categories, such that the order of wage
growth across occupations is aﬀected, we face a problem. We are reassured by the fact that the
ranking across diﬀerent occupations’ wage growth penalties seem very plausible (see below,
full results are relegated to appendix C).
Finally, another selection problem might occur if gj(d) is importantly non-linear and thus
the penalty for a further month of leave depends on the level of d. However, we ﬁnd the simple
linear form g(d)=γdperforms as well as a quadratic form. We also tested further polynomials
and other functional forms such as piece-wise linear transformations. If this function is indeed
linear, then the penalty for one more month of leave is constant across d. Furthermore, the dis-
tribution of the leave duration dseems to be very broad in all occupation categories (cf. ﬁgure
A2 in appendix C).
Table A5 in appendix C gives the results of the estimation of equation (7); the distribution
of individuals in the eight occupation categories at return is provided in table A6. The estimated
wage growth penalties vary between 0.8 and 5 percent per month of maternity leave. The mean
wage penalty in our sample is 2.6%.18 This implies that a signiﬁcant proportion of the wage
growth tendency is forgone as a result of maternity leave. We now test how these diﬀerent leave
penalties ˆγjinﬂuence maternal return decisions, especially as moderated by LOC.
18The rank order of occupations’ estimated γseems to be plausible: The occupation category with the highest
estimated γ(5%) is cat. 4 (“merchants of services or associated occupations”). The occupation category with the
lowest estimated γ(0.8%) is cat. 8 (“occupations in transport, regulation, security, writing, translation, librarians,
artists, and other service occupations”). The occupation category with the second lowest estimated γ(1.2%) is cat.
6 (“occupations in health services”.
4.3 Determinants of Returning to Work
We now estimate determinants of young mothers’ return to work as a function of individuals’
wage level w0,19 other household earnings m, wage growth tendency ˆ
∆wT RE ND , occupation-
speciﬁc wage growth penalty ˆg, and LOC.
LOC inﬂuences mothers’ maternity leave controlling for the wage level and the wage growth
tendency. This means that the eﬀect of LOC on return decisions is not merely an artefact of
selection (by LOC) into particular jobs with higher wages or higher wage growth.20 If LOC
inﬂuences maternity leave duration conditional on wage level and wage growth, this may be
because mothers with diﬀerent LOC have diﬀerent perceptions of human capital depreciation
when they are out of employment. By contrast, if we ﬁnd no evidence for any of these human
capital stories, we may conclude that the evidence points towards diﬀerences in preference for
time spent with young children. The condition for women to return, including multiple channels
of LOC inﬂuencing return, is
Expression (8) implies the following candidates for inﬂuencing return decisions:
T RE ND ,LOCi,ˆ
T RE ND ×LOCi,ˆγj,ˆγj×LOCi(9)
where the interaction terms show that LOC may inﬂuence maternal return decisions by
moderating the weight of other determinants.
19This is the pre-childbirth wage level, but also the wage at which most women can be expected to return to the
20Note that since dmay inﬂuence wage growth, we use wage growth predictions from pre-maternity periods as
explained above in subsection (4.1).
4.4 The Duration Model
Since return to the labor market is a binary choice, a linear model would not be appropriate;
therefore, we focus on the hazard rate of returning to work, θ(.), i.e., the probability at any
duration diof returning to work given Xi. We use a mixed proportional hazard framework, such
θ(d,X, η)=θ0(d)exp(β0X) exp(ξ),(10)
where θ0(d)is the baseline hazard which depends on maternity leave duration dbut not on
the covariates X.βdescribes the parameter vector to be estimated and ξis unobserved hetero-
Note three important features of this empirical speciﬁcation: First, in common with a large
literature using survival models, we assume a proportional hazards framework: conditional on
ξ, absolute diﬀerences in the covariates imply proportionate diﬀerences in the hazards at each
Second, we allow for a ﬂexible semi-parametric form of duration dependence (i.e., the func-
tional form of θ0(.)) by including interval-speciﬁc intercepts. Given that each time interval in
the data is of unit length (one month)22 the discrete time hazard can be written as
21We ﬁnd descriptive evidence in favor of this assumption by looking at the hazard by diﬀerent covariates.
Merely one determinant, w0, appears to change its inﬂuence on the hazard. However, conditional on our estimated
ˆηthis is no longer the case, consistent with the mixed proportional hazard formulation.
22This requires a complementary log-log transformation, see Jenkins (2005, p.41–42).
where dis the number of months after childbirth and Dkis an indicator equal to one if month
dlies within the kth interval of maternity duration.23 The parameter vector δis estimated by the
model along with the parameter vector β.
Third, we include unobserved heterogeneity in a semi-parametric form also, without as-
suming a speciﬁc distribution for the random eﬀect. We assume a discrete distribution of types
following Heckman-Singer:24 the likelihood contribution of a person with spell length dmonths
where Mis the number of groups of unobserved types25 and πis the probability of belonging
to type ξ. Conditional on the belonging to type ξ, the likelihood is then
Lξ(d,X, ξ)= h(d,X, ξ)
where cis the censoring indicator. The parameters πand ξare estimated by the model
together with βand δ.
5 Estimation Results
The main estimation results are reported in table 2. We ﬁrst show the basic impact of LOC
on the timing of labor market return not controlling for the other determinants. Column (1)
shows that LOC signiﬁcantly inﬂuences the decision about maternity leave duration. Mothers
23That is, the model contains one dummy variable for each group of duration: 4-6 months (0-3 are omitted for
reasons outlined in section (3)); 7-9 months; 10-15 months; 16-21 months; 22-27 months; 28-33 months, 46-57
months and 58+months.
24The model has also been estimated with a parametrically speciﬁed random eﬀect. Gamma distributed and
normally distributed ηwere tested, as well as a speciﬁcation without any random eﬀect. The sign and signiﬁcance
levels of paremter estimates do not change compared to the main results discussed in section (5) below.
25We use two mass points in estimation, we also tested speciﬁcations with three and four mass points. However
Bayesian and Akaike information criteria point to the model with two mass points being suited best.
with more internal LOC have a higher hazard rate of return to employment; i.e., women with
a high belief in internal control return to employment signiﬁcantly earlier than women with
a high belief in external control. To illustrate this result, ﬁgure 2 plots the hazard functions
for two individuals who are equal in all characteristics but LOC.26 The solid line in the graph
represents a woman with high internal LOC (75th percentile of the LOC score), the dashed
line a woman with more external LOC (25th percentile of the LOC score). The solid line runs
above the dashed line, illustrating that a person with internal LOC is more likely to return to
employment in each period. These diﬀerences in hazard rates create diﬀerences in the proba-
bility of returning to employment. For example, 36% of women with a high internal LOC score
(at the 75th percentile) return to employment twelve months after childbirth; this is more than
16% higher than for women with a low internal LOC score (at the 25th percentile). Moving
from one standard deviation below to one standard deviation above the mean of LOC increases
median return time by two months, i.e., by over 13%. The magnitude may seem modest but cu-
mulative knock-on career eﬀects must also be taken into account. Finally, the estimation results
that we present next show that this LOC eﬀect masks important eﬀect heterogeneity depending
on mothers’ occupation: LOC really inﬂuences return decisions in occupations with high wage
The step pattern of the graph in ﬁgure 2 is a result of our modeling the duration dependency
(see section 4). The ﬁrst clear peak of the hazard function is just after 12 months, suggesting
that there is a relatively high probability of returning to employment around the time the child
turns one year. The second clear peak is observed in the graph during months 33 to 37, that is,
around the third year after childbirth. This is likely to be a result of the legal right to return to
the previous job, which expires after three years. We discuss whether this speciﬁc institutional
aspect may be driving our results in the following subsection. A smaller peak of the hazard
function is observed when the child is six years old, which is the usual school entry age in
26The graph uses the results of column (4) of table 2 (see below). For the graph all non-binary covariates other
than LOC are set to their mean and the child birth cohort is set to the most recent category (i.e., 2010-2012).
Considering women with diﬀerent values in these characteristics would not change the pattern of the graph but
only rescale it—due to the proportional hazards feature of the model.
Germany. The irregular pattern of the hazard function over time conﬁrms the importance of
allowing for non-parametric duration dependency.
Note: The estimated parameters of model (4) of table 2 are used to calculate the hazard functions. All non-binary
covariates other than LOC are set to their mean. We observe the child birth cohort of 2010-2012.
Figure 2: Hazard function by level of LOC
Table 2: Determinants of return to employment after ﬁrst childbirth (dependent variable: hazard rate of return), discrete semi-parametric hazard
estimation (Heckman-Singer model)
(1) (2) (3) (4) (5) (6) (7) (8)
Locus of control LOC 0.300*** 0.287* 0.215*** 0.302*** 0.298*** 0.256*** 0.244** -0.112
(0.100) (0.160) (0.081) (0.098) (0.110) (0.098) (0.098) (0.204)
Return wage log(w0) 0.203 0.174* 0.389** 0.388** 0.284* 0.306* 0.303*
(0.212) (0.100) (0.159) (0.160) (0.152) (0.162) (0.163)
Other household income log(m) -0.337*** -0.436*** -0.435*** -0.316*** -0.313*** -0.312***
(0.034) (0.072) (0.076) (0.055) (0.052) (0.052)
W growth tendency ˆ
∆wT EN D -0.005 -0.012 0.259 0.300 0.280
(0.253) (0.278) (0.261) (0.261) (0.261)
∆wT EN D ×LOC 0.038
W growth penalty ˆγj-6.493 -6.635
Interaction ˆγj×LOC 13.444**
Duration dependency Yes Yes Yes Yes Yes Yes Yes Yes
Child birth cohort Yes Yes Yes Yes Yes Yes Yes Yes
Constant -3.352*** -3.983*** -2.115*** -2.103*** -2.106*** -1.473*** -1.318*** -1.215**
z0.929** 0.967 0.198 0.841*** 0.839*** 0.489** 0.434** 0.435**
p(pct) 0.496 0.622 2.191 0.590 0.590 0.632 0.691 0.692
Number of person-spells 18967 18967 18967 18967 18967 12262 12262 12262
Number of persons 966 966 966 966 966 748 748 748
Note: Columns 6-8 are based on a smaller sample since information necessary to estimate wage-growth depreciation terms is not available for all individuals. Standard
errors are in parentheses. * p<0.10, ** p<0.05, *** p<0.01. Data source: SOEP 1992–2013, authors’ calculations.
We now test whether the eﬀect of LOC on return is related to material factors. First, column
(2) of table 2 introduces return wages w0and ﬁnds that these do not have an eﬀect on return as
long as other household income mis not included. As soon as the latter is included, we ﬁnd
a positive eﬀect of the wage level on return. This is consistent with the idea that the marginal
utility of income (and, given the German tax system, net income) depends on other household
income.27 Further, we ﬁnd that the coeﬃcient related to LOC is slightly reduced from .300 in
column (1) to .215 in column (3). This suggests that one reason why individuals with more
internal LOC return earlier is that they have higher wages. However, even controlling for the
wage level, LOC still has a signiﬁcantly positive eﬀect on leave duration.
In a second step, we introduce workers’ predicted wage growth tendency ˆ
∆wT RE ND (from
section 4.1) in column (4). It does not appear to inﬂuence return decisions; i.e., wage growth
tendencies seem not to be a channel by which LOC inﬂuences return decisions. Further, the in-
teraction between wage growth tendency and LOC in column (5) is not a signiﬁcant determinant
of return decisions either; put diﬀerently, the eﬀect of wage growth tendency is not diﬀerentially
appreciated depending on LOC.
Third, column (7) shows that the average wage penalty in an occupation does not signiﬁ-
cantly aﬀect the return hazard. This suggests that women do not self-select into occupations
with a certain ˆgjaccording to their planned d. However, introducing the interaction between
ˆgjand LOC reveals that wage growth penalties do play a role for mothers’ return decision
for individuals with high LOC scores. In fact, this appears to be the main channel by which
LOC inﬂuences maternal return decisions: the direct, unconditional measure of LOC is now no
longer signiﬁcant. Given that we have fewer data-points on occupational categories for these
analyses, we also test our benchmark results using this smaller sample to ensure that any results
are related to sample selection. Column (6) shows that estimates are similar in this sample.
27We tested a number of functional forms for this relationship, including own wage in levels and logs and relative
to total household wage and ﬁnd the results of column (4) to be largely robust.
We next turn to non-wage dimensions of human capital. We ﬁnd no evidence that the in-
troduction of education variables inﬂuences the eﬀect of LOC on return decisions, as both the
introduction of simple dummies for formal education (in column (2) of table 3) as well as more
detailed ISCED education categories (in column (3) of table 3) conﬁrm. While there may be a
small eﬀect of experience on return (controlling for wage and wage growth), the eﬀect is only
signiﬁcant at 10% (column (4) of table 3). When we introduce maternal birth cohorts (thus im-
plicitly controlling for mothers’ age at ﬁrst childbirth) on top of child birth cohorts , the results
change very little, as can be seen from column (5) of table 3.
We include a range of further covariates to test the sensitivity of our results. Among those
covariates we ﬁnd regional dummies to have most signiﬁcance (see column (2)-(4) of table 4):
living in East Germany is related to a quicker return—a ﬁnding that may relate both to dif-
ferences in child-care provision and social norms. Cohabiting with a partner and the presence
of another adult living in the same household do not appear to aﬀect return to employment
(columns (6) and (7) of table 3) conditional on household income.
Table 3: Determinants of return to employment after ﬁrst childbirth (dependent variable: hazard rate of return), discrete semi-parametric hazard
estimation (Heckman-Singer model) — with further covariates (I)
(Benchmark) (2) (3) (4) (5) (6) (7)
Locus of control LOC 0.302*** 0.298*** 0.302*** 0.304*** 0.287** 0.261** 0.313***
(0.098) (0.103) (0.101) (0.100) (0.121) (0.102) (0.097)
Return wage log(w0) 0.389** 0.203 0.207 0.336** 0.331 0.379*** 0.401**
(0.159) (0.159) (0.154) (0.164) (0.202) (0.147) (0.159)
W growth tendency ˆ
∆wT EN D -0.005 -0.178 -0.134 0.084 0.074 -0.050 0.004
(0.253) (0.265) (0.270) (0.286) (0.270) (0.242) (0.253)
Other household income log(m) -0.436*** -0.451*** -0.463*** -0.432*** -0.450*** -0.468*** -0.437***
(0.072) (0.079) (0.081) (0.079) (0.072) (0.067) (0.069)
University degree 0.633***
No professional degree -0.155
ISCED: middle vocational 0.247
ISCED: vocational +high school degr (Abitur) 0.298
ISCED: higher vocational 0.415*
ISCED: higher education 0.926***
Maternal cohorts 6 cat.
Other adult in household 0.316
Duration dependency Yes Yes Yes Yes Yes Yes Yes
Child birth cohort Yes Yes Yes Yes Yes Yes Yes
Constant -2.103*** -1.223 -1.371* -2.140*** -2.745** -2.323*** -2.171***
z0.841*** 0.847*** 0.862*** 0.809*** 0.842*** 0.867*** 0.823***
p(pct) 0.590 0.589 0.599 0.595 0.602 0.587 0.597
Number of person-spells 18967 18946 18857 18967 18967 18967 18967
Number of persons 966 963 956 966 966 966 966
Note: Standard errors are in parentheses. * p<0.10, ** p<0.05, *** p<0.01. Data source: SOEP 1992–2013, authors’
Table 4: Determinants of return to employment after ﬁrst childbirth (dependent variable: hazard rate of return), discrete semi-parametric hazard
estimation (Heckman-Singer model) — with further covariates (II)
(Benchmark) (2) (3) (4) (5) (6)
Locus of control LOC 0.302*** 0.195*** 0.195*** 0.194*** 0.305*** 0.294***
(0.098) (0.072) (0.072) (0.073) (0.098) (0.083)
Return wage log(w0) 0.389** 0.237** 0.238** 0.242** 0.391** 0.180*
(0.159) (0.105) (0.103) (0.100) (0.161) (0.097)
W growth tendency ˆ
∆wT EN D -0.005 -0.346 -0.335 -0.243 -0.004 -0.262
(0.253) (0.238) (0.237) (0.233) (0.253) (0.263)
Other household income log(m) -0.436*** -0.342*** -0.341*** -0.334*** -0.433*** -0.334***
(0.072) (0.035) (0.039) (0.034) (0.075) (0.032)
Regional dummies 16 cat. 16 cat.
Other adult in household 0.674* 0.542
Cohabiting 0.282 0.223
East Germany 0.243**
Second child within 3 yrs -0.076 -0.179
Interaction second child ×LOC -0.252
Duration dependency Yes Yes Yes Yes Yes Yes
Child birth cohort Yes Yes Yes Yes Yes Yes
Constant -2.103*** -1.855*** -2.164*** -2.626*** -2.115*** -2.023***
z0.841*** 0.175 0.164*** 0.161*** 0.849*** 0.169***
p(pct) 0.590 0.920 0.934 0.928 0.581 0.926
Number of person-spells 18967 18967 18967 18967 18929 18929
Number of persons 966 966 966 966 960 960
Note: Standard errors are in parentheses. * p<0.10, ** p<0.05, *** p<0.01. Data source: SOEP 1992–2013, authors’
Finally, we ﬁnd evidence of signiﬁcant unobserved heterogeneity in most speciﬁcations.
Since our estimation is based on random eﬀects, these are not treated as individual eﬀects to
be estimated but as nuisance parameters that are integrated out. To gain an understanding of
the dimensions which the random eﬀects are covering,28 we recover estimated probabilities by
considering the posterior distribution of types after convergence in our benchmark speciﬁcation
(see table 2). The only signiﬁcant correlate of type probabilities we ﬁnd is the East dummy,
viz., some regional dummies if we include one for each region. Most obviously, this may be
related to diﬀerences in child-care provision across diﬀerent regions.
5.1 Robustness Tests
In this subsection, we discuss a number of challenges to our ﬁndings. A ﬁrst concern refers to
the endogeneity of LOC with childbirth. We assume that LOC is constant over time for each
individual, in line with previous research. Our LOC measure comes from diﬀerent years (1999,
2005, and 2010), for some women this is prior to having the ﬁrst child, while for others it is
after having the ﬁrst child. If LOC evolves over time, certain characteristics of a newborn child
(e.g., poor health) might cause a mother to adopt a more external LOC and, at the same time,
extend her leave duration. To test the robustness of our results, we ﬁrst create a dummy for
women who had their ﬁrst child after their LOC was measured. We ﬁnd this indicator not to
be signiﬁcantly correlated with LOC; the same is true if we regress LOC on this indicator in-
cluding further covariates such as children’s birth cohorts. This provides ﬁrst evidence for the
robustness of our results. In a second step we carry out the following robustness tests of our
main estimations: First we restrict the sample to mothers of children born after LOC is surveyed
(column (2) of table 5). To make the sample even more homogeneous, second, we restrict the
sample to mothers for whom LOC is measured in 1999 (column (3) of table 5). Third, we re-
28We are cautious about interpreting the predicted probabilities. Heckman and Singer (1984) note that their
estimation method “recovers the structural parameters of the underlying models very well but does not accurately
estimate the distribution of unobservables even in very large samples.” If we had multiple spells per individual, we
might have more conﬁdence in the identiﬁcation of these parameters (for a discussion on the beneﬁts of multi-spell
data, see van den Berg (2001)).
strict the sample to mothers for whom LOC is measured in 1999 and who have their ﬁrst child
after LOC is measured (i.e., after 1999) (column (4) of table 5). The results are robust to all
these restrictions. This suggests that our main result is unlikely to be aﬀected by LOC evolving
as a result of childbirth.
A second concern relates to LOC picking up other noncognitive skills that might be the true
reason for a delayed or quicker return to work. We thus now control for important other noncog-
nitive skills in order to minimize the probability of LOC picking up other skills or traits. We
focus on the Big Five personality traits identiﬁed in personality psychology: neuroticism,open-
ness to experience,conscientiousness,extraversion, and agreeableness (John and Srivastava
1999, McCrae and Costa Jr 1996, 1999). The Big Five concept is among the best-established
models in personality psychology and widely used in empirical research (Caspi et al. 2005).
Column (5) of table 5 reports the results of including these ﬁve dimensions in the benchmark
speciﬁcation.29 We ﬁnd that extraversion is associated with faster return to the labor market. We
note that the eﬀect of LOC on the outcome persists—in fact, the magnitude has increased. The
eﬀect of LOC on the outcome seems not to be due to LOC picking up other personality traits.
Finally, LOC could pick up the eﬀect of cognitive ability, which is likely correlated. However,
the most important channel of cognitive skills aﬀecting mothers’ return decision is predicted to
go through their reward on the labor market, and should be reﬂected in wage levels and growth.
We have demonstrated that LOC inﬂuences return conditional on wage level and growth, argu-
ing that LOC operates by diﬀerent appreciation of wage growth penalties.
29The SOEP wave 2005 provides a set of ﬁfteen items of the big ﬁve—three for each of the ﬁve dimensions—
that were answered on a 7-point Likert scale. We use standardized mean answer scores as indicators for the ﬁve
variables. For more information on the implementation of the Big Five traits in the SOEP survey as well as on the
reliability and validity, see Dehne and Schupp (2007).
Table 5: Determinants of return to employment after ﬁrst childbirth (dependent variable: hazard rate of return), discrete semi-parametric hazard
estimation (Heckman-Singer model) — robustness tests
(Benchmark) (2) (3) (4) (5) (6) (7)
Locus of control LOC 0.302*** 0.265* 0.396*** 0.283** 0.270**
(0.098) (0.142) (0.115) (0.111) (0.106)
Locus of control LOC from 1999 0.343*** 0.365**
Return wage log(w0) 0.389** 0.386 0.428** 0.375 0.365** 0.409** 0.127
(0.159) (0.249) (0.175) (0.257) (0.165) (0.170) (0.149)
W growth tendency ˆ
∆wT EN D -0.005 -0.294 0.173 -0.247 0.182 -0.168 -0.533
(0.253) (0.386) (0.276) (0.342) (0.321) (0.299) (0.401)
Other household income log(m) -0.436*** -0.615*** -0.412*** -0.592** -0.447*** -0.455*** -0.329***
(0.072) (0.236) (0.073) (0.238) (0.064) (0.069) (0.050)
Part-time employment -0.282
Marginal employment 1.010
Civil servant 0.392**
Blue-collar worker 0.100
Duration dependency Yes Yes Yes Yes Yes Yes Yes
Child birth cohort Yes Yes Yes Yes Yes Yes Yes
Constant -2.103*** 2.192 -2.346*** 2.246 -1.736** -2.054*** -2.428***
z0.841*** 1.215*** 0.937*** 1.496*** 0.652*** 0.914*** 0.171***
p(pct) 0.590 0.631 0.548 0.586 0.701 0.582 0.942
Number of person-spells 18967 7394 15309 5614 16547 18967 6944
Note: Standard errors are in parentheses. * p<0.10, ** p<0.05, *** p<0.01. Data source: SOEP 1992–2013, authors’
A third major concern could be that women with certain LOC select themselves into spe-
ciﬁc job types—e.g., civil service or self-employment—or choose especially family friendly
employment hours—like part-time schedules—and that these job types and employment hours
are, at the same time, more compatible with a longer or shorter leave. If this is the case, then
LOC would not directly aﬀect the decision to return to employment but rather indirectly through
the choice of the job type and employment hours prior to childbirth. To check this channel, we
introduce a set of variables characterizing the job type occupied prior to childbirth: civil servant,
self-employed, blue collar worker, and white collar worker (reference category). Additionally,
working hours categories are introduced: we diﬀerentiate part-time and marginal employment
(tax-free minijobs) from full-time employment. The results with the additional covariates are
reported in column (6) of table 5. Although it emerges that some of the variables have additional
predictive power—not surprisingly, self-employed women appear to return to employment more
quickly—the main results remain stable. If anything, the magnitude of the coeﬃcient on LOC
has again increased, suggesting that it is not via choice of job type that LOC inﬂuences mater-
nity leave durations.
Fourth, we may be interested to assess to what extent the result is driven by mothers’ deci-
sion to have a second child. Diﬀerences in LOC may relate to diﬀerent preferences about child
spacing rather than labor supply. If individuals (with diﬀerent LOC levels) have the same total
number of children, our results could be consistent with the same life-time labor supply despite
later returns of mothers who have their children with shorter intervals. We address this concern
in two ways: First, we focus on women who are observed to only have one child in the sample,
yielding a sample of 6944 spells from 481 women.30 Column (7) in table 5 shows that the eﬀect
of LOC remains largely unchanged in this smaller sample. Second, we ﬁnd similar patterns
when we censor women at the moment they become pregnant with their second child.31 Finally,
note that average child spacing in Germany is over 3 years, whereas median return to the labor
market occurs after around 10 months in our data. These all point against a strong inﬂuence of
30We attempted to restrict the sample to women who have completed their fertility (age 40 plus), but many
women are younger at the last observation in the sample, yielding a too small sample for estimation.
31Results are available on request.
second children on return decisions after the ﬁrst child.
Fifth, our model is based on labor supply decisions driving return. This makes sense for
returns to employment within the institutional framework of job protection. Within this period,
we are conﬁdent that the most important eﬀects will be labor-supply driven. The loss of job
protection at 36 months of maternity leave is an important labor demand eﬀect: Mothers are
no longer guaranteed their previous wage but may need to search for a job. If this institutional
feature is an important determinant of maternal return, we should observe a signiﬁcant eﬀect of
this threshold on maternal returns. Figure (1) shows the density of (completed) maternity leave
durations. We ﬁnd, ﬁrst, that only a small fraction—less than 15%—of maternal returns occur
after 36 months. Second, there appears to be some bunching at the threshold, i.e., a positive
mass of individuals choose exactly 36 months. We thus test the importance of the other deter-
minants of the model in explaining durations by adding a speciﬁc shifter for return at around
36 months.32 While the hazard at this speciﬁc date is indeed discretely higher than for other
periods controlling for all the other determinants,33 controlling for this speciﬁc group does not
alter the magnitude or signiﬁcance of LOC in return decisions.34 Furthermore, it is not the
case that mothers react to the institutional set-up diﬀerentially according to their LOC. Finally,
conditional on this institutional threshold, we still ﬁnd that the main eﬀect of LOC runs by a
diﬀerential appreciation of the wage growth penalty. We thus believe that while labor demand
constraints resulting from diﬀerential job protection rules are an important part of return deci-
sions, our results are robust to taking this aspect into account.
32After inspection of the data, we include individuals who report 37 months of maternity leave in this category.
33We believe this bunching is not a function of a sudden change in underlying preferences of mothers. Next to
the labor demand eﬀect discussed here, the diﬀerence could also stem from eﬀects of child-care provision at age
34The results of this robustness check are available on request.
The present study investigates the eﬀect of locus of control (LOC) on the duration of mothers’
leave after ﬁrst childbirth. Using data from the German Socio-Economic Panel Study (SOEP)
we ﬁnd that women with a more internal LOC return to employment faster. We show that this
is not mainly a result of diﬀerences in wage levels or wage growth rates. Rather, we ﬁnd that
the main reason that individuals with more external LOC return later is that they react less to
what we call wage growth penalties. These are estimated reductions in mothers’ wage growth
prospects—reducing wage growth more the longer women stay out of the labor market after
childbirth. The paper contributes to understanding the impact that LOC has on important eco-
First, if mothers with external LOC do not fully take into account the eﬀects of long labor
market breaks, they will stay out of the labor market for too long. This may cause large career
costs for mothers with external LOC.
Second, our results indicate that changes in ﬁnancial incentives inﬂuence LOC-external
mothers’ return decisions less strongly if they accrue in the future and are seen as uncertain.
This insensitivity is relevant to policy: For example, subsidizing child-care costs ﬁnanced by
higher taxation after return to work could be welfare-enhancing. Also, this lack of sensitivity
means there will be less negative moral hazard issues associated with transfers later in life. Con-
sider two reforms that were instituted in Germany: (i) providing pensioners a minimum level
of state pension; (ii) increasing pension payments to mothers with long periods out of the labor
utterrente). Both reduce incentives to work for individuals with low contributions. If
pension payments are seen as uncertain, women with external LOC will reduce their labor sup-
ply less as a reaction to the more generous new pension rules. This may then suggest an overall
smaller reaction of female labor supply to pension changes—consistent with early evidence of
Third, noncognitive skills such as LOC—whilst relatively stable across adult life—are not
predetermined but develop during childhood and adolescence (Cunha and Heckman 2007, 2008).
Family and education can importantly determine non-cognitive skills. Policies which foster in-
ternal LOC may especially beneﬁt girls by fostering their long-term career prospects.
The Construction of the LOC Variable and Factor Analysis
LOC is typically conceived as a unidimensional concept with “internal” and “external” as op-
posite poles of the same dimension (see, e.g., Piatek and Pinger 2015, Caliendo et al. 2015,
Coleman and DeLeire 2003, Cebi 2007). LOC describes the extent to which individuals believe
they can determine events in their own lives (internal) as opposed to feeling dependent on fac-
tors that are outside of their control (external). The unidimensional LOC index we use in our
estimations is based on ﬁve items surveyed in the SOEP; the items are given in table A1. We
standardize the score of each item and take the average over the ﬁve standardized scores. All
items but the ﬁrst are reverse coded such that a higher LOC index reﬂects a more internal LOC.
Running an exploratory factor analysis over the ﬁve items in our sample conﬁrms the as-
sumption of one single latent factor behind the items: only the eigenvalue of factor 1 is larger
than one; the others are clearly below. The relative signs of the factor loadings correspond to
the way the items are included in the LOC score (i.e., item 1 is reverse compared to the other
items). The results of the factor analysis are given in table A1.
Discussion of Alternative Versions of Constructing a LOC Variable
In the SOEP, the ﬁve LOC items are part of a 10-item battery that are related to LOC but that
do not all reﬂect the original concept of LOC by Rotter (1966). Table A2 gives the 10 items
(the ﬁve items used in our LOC score are in bold). We excluded ﬁve items from our main
LOC index for the following reasons: Item Q2 (Compared to other people, I have not achieved
what I deserve) does not reﬂect internal versus external LOC but rather measures an individuals
sense of fairness (Weinhardt and Schupp 2011, Piatek and Pinger 2015). Item Q4 (If a person
is socially or politically active, he/she can have an eﬀect on social conditions) rather measures
an individuals degree of social involvement (Weinhardt and Schupp 2011, Piatek and Pinger
Table A1: Factor analysis with ﬁve LOC items
Q1 How my life goes depends on me (Wie mein Leben verl¨
angt von mir selbst ab).
Q3 What a person achieves in life is above all a question of fate or
luck (Was man im Leben erreicht, ist in erster Linie eine Frage von
Schicksal oder Gl¨
Q5 I frequently have the experience that other people have a con-
trolling inﬂuence over my life (Ich mache h¨
auﬁg die Erfahrung,
dass andere ¨
uber mein Leben bestimmen).
Q8 The opportunities that I have in life are determined by the social
conditions (Welche M ¨
oglichkeiten ich im Leben habe, wird von
den sozialen Umst¨
Q10 I have little control over the things that happen in my life
(Ich habe wenig Kontrolle ¨
uber die Dinge, die in meinem Leben
Factor 1 1.027257
Factor 2 .0399061
Factor 3 -.0181629
Factor 4 -.0878149
Factor 5 -.2522844
2015). Item Q6 (One has to work hard in order to succeed) measures the degree to which
someone attributes success to eﬀort, which would be part of an internal LOC. However, for
respondents who disagree with item Q6’s statement it is unclear whether they attribute success
to stable skills (which would also be part of an internal LOC) or to forces outside of themselves
(which would be part of an external LOC). Therefore, this item is inappropriate for measuring
internal versus external LOC. Item Q7 (If I run up against diﬃculties in life, I often doubt my
own abilities) displays a similar problem as item Q6; for respondents who disagree with the
item’s statement it is unclear whether they attribute diﬃculties to the lack of own eﬀort (which
would also be part of an internal LOC) or whether they attribute diﬃculties rather to forces
outside of themselves (which would be part of an external LOC). Item Q9 (Innate abilities are
more important than any eﬀorts one can make) explicitly measures the weight a person places
on eﬀort versus skills—while both would be subsumed under an internal LOC according to Rot-
ter (1966). For these reasons, we exclude these ﬁve items from our main LOC index. The ﬁve
items we do include in our LOC index (the items printed bold) are appropriate for measuring
the unidimensional trait of internal versus external LOC. These items correspond to the LOC
items in the National Educational Longitudinal Study (NELS), which has been used e.g., by
Cebi (2007), Coleman and DeLeire (2003) and Heckman et al. (2006). The ﬁve inappropriate
SOEP items have no corresponding items in the NELS.
Caliendo et al. (2015) construct a similar LOC index as we do but they include nine out of
the ten SOEP items, dropping only item Q4 (and reverse coding items Q2, Q3, Q5, Q7, Q8,
and Q10). We run a robustness test with a LOC index using their items; the main results of the
paper do not change (see column (2) of table A3). Piatek and Pinger (2015) use only items Q3,
Q5, Q7, Q8, and Q10, i.e., they use similar items as we do but substitute Q1 by Q7. We run
a robustness test with a LOC index using the items selected by Piatek and Pinger (2015); the
results of our paper do not change (see column (3) of table A3).
Running a factor analysis over the ten items conﬁrms the strength of the ﬁrst latent factor.
The results of the factor analysis are given in table A2. The factor loadings suggest dropping
items Q4, Q6, and Q9 which do not load on the latent factor. A similar result of a factor analy-
sis of the LOC-items from the SOEP is found for example by Specht et al. (2013).35 Since the
factor analysis does not imply to exclude items Q2 and Q7, we do another robustness test: even
though items Q2 and Q7 do not ﬁt into the original one-dimensional concept of LOC as argued
above, we include these items in the LOC index (reverse coding items Q2, Q3, Q5, Q7, Q8, and
Q10); our main results are robust to this modiﬁcation (see column (4) of table A3).
A further robustness test is conducted employing a factor score of LOC weighting the items
according to the factor analysis reported in table A1 (similar to Piatek and Pinger (2015)) in-
stead of weighting all component items of the LOC index equally. The results are reported in
column (5) of table A3. Our main results are robust to using this alternative LOC-score.
35They also drop items Q4, Q6, and Q9 ex-ante, using only seven of the ten items.
Table A2: Factor analysis with ten LOC items
Factor 1 Factor 2 Factor 3
Q1 How my life goes depends on me (Wie mein Leben
angt von mir selbst ab).
-.2244663 .3317205 .1397904
Q2 Compared to other people, I have not achieved what
I deserve (Im Vergleich mit anderen habe ich nicht das
erreicht, was ich verdient habe).
.4855427 .0709979 -.0730634
Q3 What a person achieves in life is above all a ques-
tion of fate or luck (Was man im Leben erreicht, ist in
erster Linie eine Frage von Schicksal oder Gl¨
.405968 .0981938 -.1910283
Q4 If a person is socially or politically active, he/she
can have an eﬀect on social conditions (Wenn man sich
sozial oder politisch engagiert, kann man die sozialen
.0963083 .0461371 .1972745
Q5 I frequently have the experience that other people
have a controlling inﬂuence over my life (Ich mache
auﬁg die Erfahrung, dass andere ¨
uber mein Leben
.6344261 .0101093 .1099268
Q6 One has to work hard in order to succeed (Erfolg muss
man sich hart erarbeiten).
.0317271 .3904663 -.0564504
Q7 If I run up against diﬃculties in life, I often doubt my
own abilities (Wenn ich im Leben auf Schwierigkeiten
stoße, zweiﬂe ich oft an meinen F¨
.4587426 -.0118316 .1196966
Q8 The opportunities that I have in life are deter-
mined by the social conditions (Welche M¨
ich im Leben habe, wird von den sozialen Umst¨
.3267092 .1440986 -.0532071
Q9 Innate abilities are more important than any eﬀorts
one can make (Wichtiger als alle Anstrengungen sind die
ahigkeiten, die man mitbringt).
.1013728 .3544741 .0105248
Q10 I have little control over the things that happen
in my life (Ich habe wenig Kontrolle ¨
uber die Dinge,
die in meinem Leben passieren).
.6018275 -.1673338 .0313426
Factor 1 1.553382
Factor 2 .4539728
Factor 3 .1338106
Factor 4 .0469835
Factor 5 .0194704
Factor 6 -.088547
Factor 7 -.1033458
Factor 8 -.1480346
Factor 9 -.2053569
Factor 10 -.2317325
Table A3: Determinants of return to employment after ﬁrst childbirth (dependent variable: hazard rate of return), discrete semi-parametric
hazard estimation (Heckman-Singer model) — robustness tests w.r.t. the construction of the LOC variable
(Benchmark) (2) (3) (4) (5)
Locus of control LOC 0.302***
LOC Caliendo et al. (2015) 0.047***
LOC Piatek and Pinger (2015) 0.039***
LOC incl Q2 +Q7 0.260***
LOC factor score -0.137***
Return wage log(w0) 0.389** 0.414*** 0.166* 0.387** 0.382**
(0.159) (0.153) (0.098) (0.167) (0.164)
W growth tendency ˆ
∆wT EN D -0.005 0.018 -0.195 0.011 -0.008
(0.253) (0.255) (0.253) (0.253) (0.251)
Other household income log(m) -0.436*** -0.439*** -0.339*** -0.434*** -0.435***
(0.072) (0.069) (0.032) (0.074) (0.075)
Duration dependency Yes Yes Yes Yes Yes
Child birth cohort Yes Yes Yes Yes Yes
Constant -2.103*** -2.137*** -2.070*** -2.117*** -2.091***
z0.841*** 0.849*** 0.190*** 0.820*** 0.849***
p(pct) 0.590 0.597 0.908 0.603 0.584
Number of person-spells 18967 18966 18967 18967 18967
We estimate wage growth prior to ﬁrst childbirth as a function of occupation, education, labor
market experience (in several polynomials), tenure with the same ﬁrm (in several polynomials),
region (East versus West Germany), year ﬁxed eﬀects and interactions between year ﬁxed ef-
fects and region. Wage growth of person iin year tis deﬁned as log(waget+1)−log(waget),
where wage means gross hourly wage. We choose the covariates in the estimation to maximize
the model ﬁt relying on the Akaike information criterion. Occupation is split into eight groups
based on the classiﬁcation of occupations (Klassiﬁkation der Berufe 1992 (KldB 92)) by the
Germany Statistical Oﬃce (Statistisches Bundesamt). Though the classiﬁcation is available in
the SOEP on a 4-digit level, we are obliged to use relatively wide categories in order to have
a reasonable number of observations in each category. The categories are as follows: cat. 1 =
occupations in agriculture, animal husbandry, forestry, horticulture, manufacturing, and other
occupations; cat. 2 =technical occupations; cat. 3 =merchants of goods; cat. 4 =merchants
of services or associated occupations; cat. 5 =occupations in organizing, administration, or
oﬃce; cat. 6 =occupations in health services; cat. 7 =occupations in social services and ed-
ucation, other humanistic and natural scientiﬁc occupations; cat. 8 =occupations in transport,
regulation, security, writing, translation, librarians, artists, and other service occupations. The
results of the estimation of wage growth prior to ﬁrst childbirth are reported in table A4. The
overall R2(including the individual eﬀect) of the estimation is 23.6%, i.e., we explain 23.6% of
the variation of wage growth. This might seem low if we consider that we include an individual
ﬁxed eﬀect. Note, however, that the periodicity of wage adjustments may matter: Assume we
have diﬀerent types of individuals in the sample, with diﬀering wage growth trajectories, with
all individuals receiving a promotion every three years. While the individual eﬀects would be
perfect predictors of latent wage growth, the explained variation in wage changes from any one
year to the next would only be one third.
Table A4: Estimation of wage growth prior to ﬁrst childbirth (dependent variable:
Cat. 2 -0.015
Cat. 3 -0.136**
Cat. 4 -0.069
Cat. 5 -0.082
Cat. 6 0.101
Cat. 7 0.018
Cat. 8 -0.128**
ISCED educational level
General elementary 0.103
Middle vocational -0.089
Vocational +high school degr (Abitur) -0.208
Higher vocational -0.174
Higher education -0.219
East Germany -0.618
Year ﬁxed eﬀects Yes
Year ﬁxed eﬀects X East Germany Yes
No. of person-year obs 6059
No. of persons 1295
Note: The reference category for occupation is cat. 1 (de-
tails about the occupation categories can be found in the
text in appendix B.); the reference category for ISCED
educational level is “inadequately”. Standard errors are
in parentheses. * p<0.10, ** p<0.05, *** p<0.01. Data
source: SOEP 1984–2013, authors’ calculations.
Table A5: Estimation of the leave-related wage growth penalty (dependent variable: wage
growth at return)
g1(occ. cat. 1) 0.042**
g2(occ. cat. 2) 0.020
g3(occ. cat. 3) 0.028***
g4(occ. cat. 4) 0.050*
g5(occ. cat. 5) 0.022
g6(occ. cat. 6) 0.012
g7(occ. cat. 7) 0.039***
g8(occ. cat. 8) 0.008
No. of persons 541
Note: Details about the occupation categories can be
found in appendix B. Standard errors are in parentheses.
* p<0.10, ** p<0.05, *** p<0.01. Data source: SOEP
1992–2013, authors’ calculations.
Table A6: Occupation categories
Item Number Percent
Cat. 1 43 8
Cat. 2 24 4
Cat. 3 68 13
Cat. 4 46 8
Cat. 5 144 27
Cat. 6 81 15
Cat. 7 79 15
Cat. 8 58 11
Total 543 100
Note: Details about the occupation categories can be
found in in appendix B.. Data source: SOEP 1992–2013,
Note: Details about the occupation categories can be found in appendix B.
Figure A1: Distribution of LOC by occupation category
Note: Details about the occupation categories can be found in appendix B.
Figure A2: Distribution of leave duration by occupation category
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