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Career Changes and the Loss of Human Capital�
Jakob Roland Munchy
University of Copenhagen, CEBR and EPRU
April 2006
Abstract
This paper studies the relationship between the probability of job change and
tenure. Theory about worker-�rm speci�c capital predicts that the job change haz-
ard declines with time on the job, which accords with much empirical evidence.
This paper �nds further support for this prediction. However, a distinction between
career changes, that is, job changes involving both a change of industry and occupa-
tion, and other job changes reveals that the career change hazard is declining while
other types of job changes exhibit a constant hazard rate. This suggests that there
are no important �rm-, industry- or occupation-speci�c elements in human capital.
Instead skills are more likely to be career-speci�c.
Keywords: Job mobility, Speci�c human capital, Mixed proportional hazard
model.
JEL Classi�cation: C41, J41, J63
�This paper is a substantially revised version of the working paper entitled "Are skills �rm-speci�c?
Evidence from Danish micro data". The paper has bene�tted from comments from Dale Mortensen,
Gerard van den Berg and participants at the Sandbjerg conference on Labour Market Models and Matched
Employer-Employee Data. Thanks to Daniel le Maire for research assistance and to Lars Skipper and
Michael Svarer for sharing computer code.yAddress: Department of Economics, University of Copenhagen, Studiestraede 6, DK - 1455 Copen-
hagen K, Tel.: +45 35323019, Fax: +45 35323000, E-mail: [email protected].
1 Introduction
This paper is concerned with the distinction between speci�c human capital and general
human capital. According to Becker (1964) �rm-speci�c human capital is by de�nition not
useful to the �rm or the worker outside their relationship, whereas general human capital
increases productivity of the worker also in other �rms. In empirical studies �rm-speci�c
human capital is almost always measured by the worker�s tenure at the current employer,
and a substantial literature have found large returns to tenure that are often equal in
size with the general experience e¤ect (see Farber (1999) for an overview). These positive
returns to tenure are regarded as evidence for signi�cant investments in �rm-speci�c
human capital. However, this literature has also been put into question by empirical
�ndings by e.g. Neal (1995) and Parent (2000), who show that human capital tends to be
industry-speci�c rather than �rm-speci�c. Against this �nding Kambourov & Manovskii
(2005) argue that it is more plausible that the human capital of workers is speci�c to the
type of work they do (i.e., their occupation) rather than to the industry they work in.
Based on a rich Danish micro data set I study whether there can be found evidence for
�rm-speci�c human capital, and I also investigate if the �ndings by Neal (1995), Parent
(2000) and Kambourov & Manovskii (2005) can be further supported.
The returns-to-tenure approach implicitly takes it for given that the job change prob-
ability declines with tenure, because otherwise it is not clear that tenure measures �rm-
speci�c human capital in any way. Search theory predicts that if �rm-speci�c human
capital accumulates with tenure and if the return to the speci�c capital is split between
the worker and the �rm, then the job change hazard starts out high and declines with
time on the job, since the loss associated with a job change rises (Jovanovic (1979a)). The
theory about speci�c capital in the form of worker-�rm matches also gives a prediction
about the relationship between the separation rate and tenure, cf. Jovanovic (1979b).
Early in the match the quality of the match is unknown so the separation rate is low due
to job change costs for both the worker and the �rm. The match quality of workers and
�rms reveals itself over time, and so most bad matches are ended after some time in the
job. That is, the separation rate �rst rises and then declines when mostly good matches
remain.
It is indeed a well established empirical fact that the probability of job change is
declining with tenure if the �rst few months are disregarded. Farber (1994) uses monthly
data and controls for some worker characteristics to investigate this issue and �nds that
the hazard rate peaks after 3 months of employment after which it declines. In fact in his
survey Farber (1999) notes that �virtually all of the literature uses annual data on job
1
change to investigate the relationship between tenure and the probability of job change,
and, without exception, �nds a monotonic negative relationship�. Farber lists this fact as
one of three stylized facts that exist about job mobility.
This paper takes another look at the job change hazard rate. In doing so it is important
to control for worker heterogeneity because, as pointed out by Farber (1994), worker
heterogeneity by itself can generate a declining job change hazard, if heterogeneity is not
accounted for.1 Taken together if worker heterogeneity is accounted for and if �rm-speci�c
capital is important, then one should expect a declining job change hazard perhaps after
an initial rise during the �rst few months of tenure.
I use an exceptionally rich data set for the Danish labour market, which allows to
uncover the shape of the hazard rate and to estimate e¤ects of explanatory variables with
high precision. In particular a long list of covariates is available such that much worker
heterogeneity is accounted for, and in addition the econometric framework attempts to
control for any remaining unobserved heterogeneity. After controlling for heterogeneity I
�nd that the job change hazard �attens out, but it is still declining.
The question is now whether this decline can be attributed the accumulation of �rm-
speci�c human capital. Neal (1995) argues that skills could be speci�c to the industry
instead. He �nds that tenure with the predisplacement employer is positively correlated
with the wage earned in the post-displacement job only for those workers who stay in
the same industry. Parent (2000) �nds additional support for this view, since the return
to tenure in the earnings function is reduced substantially when within-industry labour
market experience is included. Therefore it is argued that what matters most for the
wage pro�le is not �rm-speci�c human capital but industry-speci�c human capital. To
investigate this issue a distinction between within- and between-industry job changes is
made, such that two destination speci�c job change hazard rates are estimated.
Kambourov & Manovskii (2005) expand on the approach by Parent (2000) and in-
cludes also within-occupation experience in the wage equation. This reveals substantial
returns to occupational tenure, while tenure with an industry or a �rm have little impor-
tance in explaining the wage growth from overall work experience. Again this �nding is
investigated further by estimating destination speci�c job change hazard rates for within
1This is realized by the following example. Suppose there are only two types of workers, stable and
unstable workers, with equally many of each type. The two types have a high and a low but constant
hazard rate. In the beginning the observed hazard rate is just the average of the two constant hazard
rates. However, over time more unstable than stable workers leave their jobs such that stable workers
with low hazard rates come to dominate the sample. Thus, the observed hazard rate is declining and so
failure to control for heterogeneity leads to negative duration dependence in the job change hazard rate.
2
and between-occupation job changes.
I �nd that the within-industry job change hazard is roughly constant, while the
between-industry job change hazard is declining throughout the job spell. Likewise, I �nd
that the within-occupation job change hazard is constant, while the between-industry job
change hazard is declining. This questions the notion �rm-speci�c skills because that
would entail declining within-industry and within-occupation hazards. Also, there seems
to be support for both industry-speci�c and occupation-specifc human capital, but it is
not possible to say more than that.
In a �nal speci�cation I also consider career changes, that is, job changes involving
both a change of industry and a change of occupation. It turns out that, once career
changes are distinguished from other job changes, these remaining job changes exhibit a
constant hazard rate, while the career change hazard rate is declining. It is only when
workers change jobs where the type of work they do and the goods they produce change,
that they appear to lose speci�c human capital. Thus human capital tends to be career
speci�c rather than speci�c to the �rm, industry or occupation.
The rest of the paper is organized as follows. The next section describes the data set
and points out some distinguishing characteristics of the Danish labour market. Section
3 sets up the duration model and section 4 presents the estimation results. Section 5
concludes.
2 Data and the Danish labour market
There is access to a very rich representative matched worker-�rm panel data set based on
administrative �les covering 10 % of the Danish population for the years 1992-2001. In
each year detailed information about the labour market states of all individuals along with
information on socio economic characteristics is available. These socio economic variables
are extracted from the integrated database for labour market research (IDA) and the
income registers in Statistics Denmark. Of particular importance is that an establishment
identi�er is associated with each worker at the end of each year.2 A �rm can have more
than one establishment, so if a worker changes between two establishments within the
same �rm, then this is counted as a job change in the present analysis. Job spells are
then straightforwardly constructed from successive years at the same establishment. The
quality and validity of the IDA data is highly regarded �for more details see Abowd &
Kramarz (1999).
2Establishment identi�ers are obtained from the Danish Establishment Register. The dataset includes
all establishments that have had employees performing paid work and for whom tax has been paid.
3
Here I am interested in the duration of job spells and transitions between jobs, and for
the present purposes job spells are �ow sampled such that only spells starting in 1993 and
later are included in the analysis. The destination state for all spells that end before 2001 is
known and I focus in particular on spells that end with a transition into a new job with the
possibility to distinguish between a new job in the same industry, in a di¤erent industry,
in the same occupation and in a new occupation. Since the job spells are based on annual
observations it is possible that the workers have had intermediate unemployment spells
of duration less than a year between two job spells. Thus to focus on "pure" job changes
I right censor those spells where the worker has collected UI bene�ts in the year of job
change. In addition, if job spells end with transitions into other states than employment
(e.g. unemployment, out of the labour force) or if spells are uncompleted in 2001 then
they are treated as right censored observations. Also, if job spells end because of a �rm
closure, they are treated as right censored observations. To increase the homogeneity of
the sample all part time workers and students with jobs have been excluded.
A central issue in the analysis is the distinction between job changes within and
between industries or occupations. The industry switches are based on the NACE industry
classi�cation, and the occupation switches are based on the so-called DISCO code, which
is the Danish version of the ISCO-88 classi�cation. I use the most disaggregated de�nition
of the industry- and occupation codes, the six digit NACE code and the four digit DISCO
code. This aggregation level is chosen because the most direct test of the existence of
�rm-speci�c human capital is to consider job changes within narrowly de�ned industries
and occupations. If human capital is speci�c to the �rm this should be evident even for
such job changes.
In some countries the ISCO codes are plagued by serious measurement error, since
misleading registration sometimes happen particularly in the �rst year in employment
relationships. While such registration errors cannot be ruled out in the Danish case,
the validity and reliability of the DISCO code is considered to be high. For example
the DISCO code is used for wage-setting purposes in bargaining between trade unions
and employer confederations. The two bargaining parties at national level, The Danish
Confederation of Trade Unions (LO) and The Confederation of Danish Employers (DA),
use the code to assess the economic implications of proposals for the workers and employers
they represent. It is noteworthy that LO uses the code and appears to be con�dent in its
validity even though it is the employers who collect and register the information. Also,
the assessment of the Danish Statistical authorities is that the DISCO code is a very
useful tool to group workers according to occupation, cf. Statistics Denmark (1996).
Another potential data concern is that some job changes may be between establish-
4
ments within the same �rm. However, from a separate extract of the IDA data set
with establishment and �rm identi�ers (which is only available from 1999), it is possible
to determine the proportion of between-establishments job changes that also involve a
change of �rm. It is found that among workers that change establishment 88 % also
change �rm. This holds for job changes between 1999 and 2000 and between 2000 and
2001. This suggests that only a minor part of the job changes in the present analysis
are in fact within-�rm establishment changes. To evaluate whether these within-�rm job
changes may bias the results, some further statistics from the limited data set are derived.
The within-�rm job changes are more likely to be with unchanged industry codes than
between-�rm job changes, because establishments within the same �rm are typically in
the same industry, but perhaps somewhat surprising 16 % of all within-�rm job changes
do in fact involve industry changes as opposed to 45 % of between-�rm job changes. The
di¤erence is even less pronounced for occupation changes; 28 % of all within-�rm job
changes involve a change of occupation as opposed to 41 % of between-�rm job changes.
Thus, the inclusion of within-�rm job changes in the analysis is unlikely to seriously bias
the results.
In the data set used below there are 257,325 job spells for 161,508 persons which
amounts to 646,623 observations (an observation is a year in a job spell). Table 1 displays
summary statistics for the job spells. A signi�cant proportion (14 %) of all individuals
in the sample is recorded with more than one job spell, which is useful when controlling
for unobserved heterogeneity (see next section). Slightly more than half of the spells are
treated as right censored observations, while the job changes are fairly evenly spread across
the di¤erent types of change (i.e., within-industry and within-occupation job change,
within-industry and between-occupation job change etc.).
Insert Table 1 about here
Table 2 displays descriptive statistics for all explanatory variables. Self explanatory
dummies for age, gender, the presence of children, the presence of two adults in the
household, citizenship and education are included. Also, three geographic dummies are
included to distinguish between the capital Copenhagen, 5 large cities and all other lo-
calities. Information on the hourly wage rate is used �this variable is calculated as the
annual labour income divided by hours worked. The de�nition of hours worked changed
between 1992 and 1993 and this is one reason why I have chosen to only look at job spells
starting in 1993 and later. A source of measurement error is that hours worked do not
include overtime work, so the wage rate may be biased upwards. However, it should be
noted that the wage rate is only included to control for heterogeneity, and so the estimated
5
coe¢ cients to this variable are not of interest per se. Further, labour market experience
and dummies for not being a member of an unemployment insurance fund, membership
of a trade union, and dummies for the labour market state prior to the job spell with a
distinction between employment, unemployment, self-employment and out of the labour
force is included. There are also dummies for the size of the �rm (or more precisely es-
tablishment) in terms of the workforce, and �nally, to capture business cycle e¤ects the
GDP growth rate and local unemployment rates based on 51 local labour markets3 are
included.
Insert Table 2 about here
The empirical job change hazard rate, which is simply de�ned as the proportion chang-
ing jobs in year t among those surviving until that year, is depicted in Figure 1, and it is
clearly declining with time on the job. The question is to what extent this decline can be
attributed to worker heterogeneity or speci�c human capital.
Insert Figure 1 about here
Compared to other continental European labour markets the Danish labour market
is often described as being very �exible as employment protection is weak (Nicoletti,
Scarpetta & Boylaud (2000)), while at the same time replacement rates of UI bene�ts are
high. This have led to turnover rates and an average tenure which are in line with those of
the Anglo-Saxon countries. In 1995 the average tenure in the Danish labour market was
the lowest in continental Europe with 7.9 years exceeding only the numbers for Australia,
USA and UK (6.4, 7.4 and 7.8 years respectively), cf. OECD (1997). However, there
are important di¤erences with respect to institutions and wage formation. The Danish
labour market is heavily unionized and the wage structure is relatively compressed even
for European standards.
3 Econometric model
Di¤erent econometric approaches to modelling job change transition have been undertaken
in the literature. Abraham & Farber (1987) estimate a Weibull hazard model for job
change transitions and �nd that the hazard declines sharply with tenure. However this
parametric speci�cation of the baseline hazard is not capable of handling potential non-
monotonicities in the true duration dependence. Parent (1999) also estimates a duration
3The local labour markets are socalled commuting areas that are de�ned such that the internal mi-
gration rate is 50 % higher than the external migration rate, cf. Andersen (2000).
6
model which controls for unobserved heterogeneity, but he does not assess the question
of duration dependence in the hazard. Farber (1994) estimates logit models by years of
tenure to obtain a picture of the duration dependence in the hazard rate, and as previously
noted he �nds a peak in the hazard after three months of employment.
This section sets up a competing risks duration model that distinguishes between dif-
ferent types of job changes (within and between industries or occupations). As noted
earlier, in the present context it is important to control for as much individual hetero-
geneity as possible. There is access to a very detailed data set, but there might still be
some unobserved heterogeneity left, as no direct measures for e.g. ability or motivation
are available. Therefore I try to capture unobserved worker characteristics by specifying
a mixed proportional hazard model for the job-to-job transitions:
�i(tjxt; �i) = �i(t) exp(xt�i + �i); (1)
where i = 1; ::;m indicates the di¤erent destination states for the job change (i.e. within
or between industries and occupations), �i(t) is the baseline hazard capturing the time
dependence, and exp(xt�i + �i) is the systematic part giving the proportional e¤ects of
observed and time-varying characteristics, xt; and unobserved characteristics, �i. All job
spells that end with a transition to other states than a new job (e.g. unemployment and
out of the labour force) are treated as right censored observations.
The annual observations in the data imply that the duration variable T is grouped into
K +1 intervals f[0; t1); [t1;t2); ::; [tk;1)g which must be accounted for in the econometricsetup. Following Kiefer (1990) the interval speci�c survival rate is de�ned as
�k = P (T � tkjT � tk�1; x; �)
= exp
"�
mXi=1
Z tk
tk�1
�i(tjxt; �i)dt#
= exp
"�
mXi=1
exp(xk�i + �i)�i;k
#(2)
=
mYi=1
�i;k;
where �i;k =R tktk�1
�i(t)dt and �i;k = exp [� exp(xk�i + �i)�i;k] :The next step is to �nd the contribution to the likelihood function from a job spell.
The probability that a spell ends in interval k is given by the conditional probability
of failure in that interval times the probability that the spell survives until interval k; or
(1��k)Qk�1j=1 �j: Some spells are right censored and they contribute to the likelihood with
7
the survivor function,Qkj=1 �j: Thus the contribution to the likelihood function from a
job spell can be written
Le =mYi=1
(1� �i;k)di�1��mi dik
k�1Yj=1
�j; (3)
where d1; ::; dm are destination state indicators. If the job spell is right censored then
d1 = :: = dm = 0: In this paper special attention is on the estimated duration dependence,
so it is important to allow for a �exible speci�cation of the baseline hazard. Therefore,
instead of imposing a functional form I simply estimate the interval speci�c baseline
parameters �i;k.
The unobserved heterogeneity is speci�ed by the stochastic variables V1; ::; Vm: It is as-
sumed that the unobserved heterogeneity is time invariant and since each worker possibly
contributes with more than one job spell, the draw from the distribution of unobservables
is restricted to be the same across job spells for the same individual. Thus, the complete
contribution to the likelihood function for a worker with J job spells is
L =JYl=1
ZV1
::
ZVm
Lle(tjxt; V1; ::; Vm)dF (V1; ::; Vm); (4)
where F is the joint CDF for the unobserved heterogeneity, which remains to be speci�ed.
It is suggested by Heckman & Singer (1984) that discrete distributions can approximate
any arbitrary distribution functions, and here I assume that each stochastic variable can
take two values (�i1 and �i2)4 each with an associated probability. Such a speci�cation of
unobservables is very �exible and widely applied.5
4 Estimation results
Before turning to the shape of the estimated baseline hazard rate I go through the e¤ects of
some of the control variables. The two columns of Table 3 display the e¤ects of covariates
and their standard errors for the model where no distinction between di¤erent types of
job changes are made, i.e. a single risk model. Most variables have the expected signs;
e.g. younger workers, men and more educated workers change jobs more frequently. A
lower wage rate seems to increase the likelihood of a job change. However, this e¤ect
4One of the support points in each destination speci�c hazard is normalized to one, i.e., �i1 = 1;
i = 1; ::;m:5See van den Berg (2001) for more details and e.g. Belzil (2001) and Jensen, Rosholm & Svarer (2003)
for applications in grouped duration models.
8
should be interpreted with caution, since it may be endogenous. For the present purposes
I include the wage variable to control for heterogeneity. It is also seen that, if workers
were unemployed, self-employed or out of the labour force prior to the job spell, their
job spells tend to be longer when compared to workers who had another job prior to the
present job. Also, a higher GDP growth rate and local unemployment rate lead to a lower
job change hazard rate.
Insert Table 3 about here
The estimated hazard rate is depicted in Figure 2 for the reference person.6 It is seen
that when worker heterogeneity is controlled for the job change hazard rate �attens out
but it is still declining. The impact of observed and unobserved heterogeneity on the
shape of the job change hazard rate can be illustrated by comparing the decline from the
1st to the 8th year of empirical unconditional hazard rate of Figure 1 with that of the
heterogeneity corrected hazard rate of Figure 2. The empirical hazard rate declines 60
% over this eight year span while the heterogeneity corrected hazard rate only declines
39 %, so heterogeneity can explain a large part of the decline in the raw empirical job
change hazard. However, the hazard rate is still declining, which means that the notion
of �rm-speci�c human capital cannot be ruled out based on this evidence. So far, I have
just con�rmed what has previously been found in the literature �the job change hazard
rate is indeed declining with time on the job (see e.g. Farber (1999) for an overview and
Frederiksen, Honoré & Hu (2006) for another study on Danish register-data).
Insert Figure 2 about here
An important extension of the analysis is to investigate whether skills may be speci�c
to the industry instead of the �rm, since, as noted in the introduction, Neal (1995) and
Parent (2000) have found evidence showing that there is a signi�cant industry-speci�c
element in the return to tenure. This issue can be analysed in terms of the shape of the
job change hazard by distinguishing between within- and between-industry job changes.
To this end, the econometric model is extended with a distinction between within- and
between-industry job changes, such that two destination speci�c job change hazard rates
are estimated.7 The e¤ects of covariates on the within- and between-industry job change
6The reference person in Figures 2-5 is de�ned in a year with GDP growth rate of 2 % and a local
unemployment rate of 6 % and for persons with average experience and wage rate. The remaining
characteristics are given from Table 3, i.e. it is a single male between 30 and 39 years without children
aged 0-6 years etc. The estimated baseline parameters and standard errors are shown in Appendix B.7Again, the estimated baseline parameters and standard errors are shown in Appendix B. The e¤ects
of covariates are not shown for this and all subsequent models. They are availiable from the author
9
hazards mostly have the same sign as in the single risk model, but there are also di¤erences.
Workers with just basic schooling and short term further education change jobs more often
between industries than workers with a vocational education, which is di¤erent compared
to the within-industry job change hazard and the overall job change hazard. Also of
interest is that experience has an opposite e¤ect on the two destination speci�c hazard
rates, such that there is a positive e¤ect on within-industry job changes and a negative
e¤ect on the propensity to switch industry.
If skills are speci�c to the industry instead of the �rm, the between-industry job change
hazard should be declining while the within-industry job change hazard could be declining
or constant depending on whether skills also to some extent are speci�c to the �rm (or
occupation) or not. Figure 3 shows that the between-industry job change hazard is indeed
declining, while, if anything, the within-industry job change hazard is mildly rising. Thus,
there seems to be support for the suggestion by Neal (1995) and Parent (2000) that skills
are speci�c to the industry. Furthermore, there is no evidence of �rm-speci�c human
capital because that would entail a declining within-industry job change hazard.
Insert Figure 3 about here
The declining within-industry job change hazard is, however, not de�nitive proof of the
existence of industry-speci�c human capital. If skills instead are speci�c to the occupation,
and if occupation switches also mostly involve a change of industry then the decline could
be driven by occupation-speci�c human capital. Kambourov & Manovskii (2005) show
that when within-occupation experience along with experience in the industry and the �rm
are included in a wage equation for the US labour market, there are substantial returns
to occupational tenure, while tenure with an industry or a �rm have little importance
in explaining the wage growth from overall work experience. Again this possibility is
investigated further by estimating destination speci�c job change hazard rates for within-
and between-occupation job changes. If skills are speci�c to the occupation, the between-
occupation job change hazard should be declining, while the within-occupation job change
hazard should be constant (or declining if �rm or industry-speci�c human capital are
relevant notions). Figure 4 shows that both hazard rates are declining, but the within-
occupation job change hazard is not declining by much, while the between-occupation job
upon request. It was not possible to estimate the full model with four mass points in the unobservables
distribution. This is not unusual in such models. To obtain reliable estimates of the mass points, I had
to restrict the correlation structure between the two destination speci�c hazard rates to be perfect. This
means that workers who, for unobserved reasons, have higher transition rates into new jobs in the same
industry also have higher transition rates into new jobs in new industries.
10
change hazard displays a much more pronounced fall. Thus, from Figure 3 and 4 there
seems to be evidence for both industry-speci�c and occupation-speci�c human capital, so
at this stage there is no clear picture of the speci�city of skills.
Insert Figure 4 about here
A natural question to ask now is whether the two declining job change hazards of
Figures 3 and 4 exclusively are due to workers who both change industry and occupation.
This is what Neal (1999) labels a career change. A worker making a career change is
not only moving to a �rm that produces goods that di¤er from those produced by her
previous �rm, she is also performing new tasks in her new occupation. Obviously such
career changes are the type of job changes that are most likely to involve losses of speci�c
human capital. To study this question in terms of job change hazards I estimate a
competing risks model with four risks; job change within the industry and within the
occupation, job change within the industry and between occupations, job change between
the industries and within the occupation, and �nally job change between industries and
between occupations, i.e., a career change.
The estimated hazard rates are displayed in Figure 5, and it is immediately apparent
that career changes drive the downward slope of the overall job change hazard in Figure 2
and the downward sloping hazards of Figure 3 and 4. The top left diagram shows the job
change hazard without industry and occupation moves, and it is clearly not declining. The
same goes for the within-industry, between-occupation job change hazard in the top right
diagram. The between-industry, within-occupation hazard of the bottom left diagram is
also roughly constant, although it appears to decline slightly towards the end of the eight
year interval. In contrast, the career change hazard rate in the bottom right diagram is
declining considerably over course of the job spell.
Insert Figure 5 about here
These results suggest �rst of all that human capital is not speci�c to the �rm. For
that to be the case, basically all four hazard rates should be declining, and this is clearly
not the case. The relevance of �rm-speci�c human capital has been cast into doubt by
other authors using di¤erent approaches, but to my knowledge it has not been shown in
terms of the shape of job change hazards. Further, in contrast to the �ndings of Neal
(1995), Parent (2000) and Kambourov & Manovskii (2005), human capital appears not
to be completely speci�c to the industry or the occupation. Once career changes are
distinguished from other job changes, these remaining types of job changes exhibit a
11
more or less constant hazard rate. If skills were speci�c to the industry the two bottom
diagrams of Figure 5 should be declining, and if skills were speci�c to the occupation the
two right sided diagrams should be declining. It is only when workers change jobs where
the type of work they do and the goods they produce change, that they appear to lose
speci�c human capital.
This questions the substantial literature that estimate the return to tenure in the �rm,
the industry or the occupation. These tenure measures may be correlated with individual
productivity and human capital, but it does not seem to be human capital that is useless to
other �rms, in other industries or in other occupations provided the worker is not making
a career change. Instead the returns-to-tenure approach should focus on tenure within
the industry and the occupation. Speci�c human capital may accumulate as long as the
worker is not changing career, so tenure should be measured as the length of employment
spells that are uninterrupted by a job change involving both a change of industry and
occupation.
5 Conclusion
This paper has investigated the relevance of the notion of �rm-speci�c, industry-speci�c
and occupation-speci�c human capital. Instead of estimating the return to tenure in wage
equations I have taken a step back and considered the shape of the job change hazard
rate. To the best of my knowledge the paper is the �rst to investigate the speci�city of
skills in terms of the shape of the job change hazard. This is relevant because if tenure is
to measure �rm-speci�c human capital in any way, then the job change probability must
be declining with time on the job.
Job change hazard rates for workers in the Danish labour market have been estimated
by use of a very rich data set and by setting up a duration model with a �exible non-
parametric baseline hazard. In addition to much observed worker heterogeneity also
unobserved heterogeneity is accounted for and three main �ndings emerged from the
estimation results. First, it is found that after correcting for heterogeneity the job change
hazard �attens out, but it is still declining, so this leaves room for the existence of �rm-
speci�c human capital.
Second, a more detailed investigation of within- and between-industry job changes and
within- and between-occupation job changes indicates that skills may either be speci�c
to the industry or the �rm, since both the between-industry and the between-occupation
job change hazards are declining. Based on this evidence it is, however, not possible to
come up with a more precise answer. Instead it is concluded that skills are not speci�c
12
to the �rm, because both the within-industry and within-occupation job change hazards
are roughly constant.
Third, and most importantly, the downward sloping shape of the overall job change
hazard can exclusively be attributed to career changes, that is job changes involving both
a change of industry and a change of occupation. Once career changes are separated out
from other job changes, these remaining types of job changes exhibit a roughly constant
hazard rate. This suggests that it is only when workers change jobs where the type of work
they do and the goods they produce change, that they tend to lose speci�c human capital.
In that light it may be fruitful in future research to revisit the returns-to-tenure literature.
The results of this paper imply that tenure should be measured as the combined length
of successive job spells with no career changes in between, and once such a variable is
included in wage regressions, the estimated coe¢ cients to other tenure variables should
be signi�cantly reduced.
13
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15
A Appendix: Tables and �gures
TABLE 1
Spell statisticsNumber of individuals 161,508
Number of spells 257,325
Proportion of individuals with multiple spells 0.14
Mean duration of spell (years) 3.04
Proportion of spells:
- right-censored spells 0.53
- within industry, within occ. job change 0.13
- within industry, between occ. job change 0.06
- between industry, within occ. job change 0.14
- between industry, between occ. job change 0.14
16
TABLE 2
Sample meansVariables Mean Stdv.Age 19-24 0.1425 0.3496Age 25-29 0.1565 0.3633Age 30-39 0.2324 0.4224Age 40-49 0.3187 0.4660Age 50-59 0.1416 0.3486Age 60+ 0.0084 0.0911Female 0.4191 0.4934Children 0-6 years 0.2415 0.4280Two adults 0.6848 0.4646Citizenship: non OECD country 0.0212 0.1441Copenhagen 0.2437 0.4293Large city 0.1429 0.3499Rural area 0.6134 0.4870Homeowner 0.5940 0.4911Basic education 0.3271 0.4692Vocational education 0.4053 0.4910Further edu. short term 2 0.0511 0.2202Further edu. medium term 0.1376 0.3445Further edu. long term 0.0788 0.2695Experience (years/100) 0.1360 0.0901Non insured 0.1692 0.3749Union member 0.7967 0.4025Log wage (/10) 0.5097 0.0419Previous state: employed 0.7710 0.4202Previous state: unemployed 0.0992 0.2989Previous state: self employed 0.0259 0.1587Previous state: outside labour market 0.1040 0.3052Firm size 1-10 0.1555 0.3623Firm size 10-50 0.2984 0.4576Firm size 50-200 0.2710 0.4445Firm size 200+ 0.2752 0.4466GDP growth rate (/10) 0.2798 0.0969Local unemployment rate (/10) 0.6777 0.2332# observations 686,906
17
TABLE 3
Effects of covariatesJob change
Variables Coe¤. Std. err.Age 19-24 0.4236 0.0125Age 25-29 0.1716 0.0098Age 40-49 -0.2157 0.0102Age 50-59 -0.4249 0.0140Age 60+ -0.4307 0.0405Female -0.1350 0.0071Children 0-6 years 0.0155 0.0086Two adults -0.0270 0.0079Non OECD country -0.2346 0.0230Large city -0.1538 0.0107Rural area -0.1564 0.0078Home owner -0.0894 0.0078Basic education -0.0300 0.0079Further edu. short -0.0007 0.0154Further edu. medium -0.0714 0.0109Further edu. long 0.0597 0.0134Experience 0.3107 0.0638Non insured 0.0969 0.0093Union member -0.0403 0.0082Log wage 2.1172 0.0935Unemployed -0.4096 0.0128Self employed -0.4376 0.0245Outside -0.2905 0.0120Firm size 10-50 -0.0522 0.0095Firm size 50-200 -0.0533 0.0100Firm size 200+ -0.0142 0.0100GDP growth -0.1627 0.0285Local unempl. -0.0412 0.0139
18
Figure 1: The empirical job change hazard rate
Figure 2: The estimated job change hazard rate.
19
Figure 3: Within and between industry job change hazard rates.
Figure 4: Within and between occupation job change hazard rates.
20
Figure 5: Destination speci�c job change hazard rates
21
B Appendix: Supplementary tables
TABLE B1
Baseline parameters and unobservablesJob change
Parameters Coe¤. Std. err.1. year, �1 0.0796 0.00472. year, �2 0.0716 0.00423. year, �3 0.0663 0.00394. year, �4 0.0600 0.00355. year, �5 0.0566 0.00336. year, �6 0.0510 0.00317. year, �7 0.0511 0.00328. year, �8 0.0489 0.0035
�2 0.9556 0.0142P (�1) 0.6661 0.0215P (�2) 0.3339 0.0215
22
TABLE B2
Baseline parameters and unobservablesJob changewithinindustry
Job changebetweenindustries
Parameters Coe¤. Std. err. Coe¤. Std. err.1. year, �1 0.0148 0.0014 0.1081 0.00712. year, �2 0.0151 0.0014 0.1019 0.00673. year, �3 0.0157 0.0015 0.0976 0.00644. year, �4 0.0156 0.0015 0.0908 0.00605. year, �5 0.0170 0.0016 0.0830 0.00566. year, �6 0.0154 0.0015 0.0794 0.00567. year, �7 0.0160 0.0016 0.0802 0.00598. year, �8 0.0165 0.0019 0.0693 0.0062
�w2 1.8222 0.0161�b2 1.1763 0.0146P (�w1; �b1) 0.7641 0.0052P (�w2; �b2) 0.2359 0.0052
TABLE B3
Baseline parameters and unobservablesJob changewithin
occupation
Job changebetween
occupationsParameters Coe¤. Std. err. Coe¤. Std. err.1. year, �1 0.0432 0.0032 0.0375 0.00312. year, �2 0.0441 0.0032 0.0350 0.00293. year, �3 0.0439 0.0032 0.0346 0.00284. year, �4 0.0420 0.0031 0.0324 0.00275. year, �5 0.0430 0.0032 0.0293 0.00256. year, �6 0.0368 0.0028 0.0295 0.00267. year, �7 0.0393 0.0031 0.0272 0.00268. year, �8 0.0383 0.0035 0.0245 0.0028
�w2 1.4619 0.0147�b2 1.5141 0.0174P (�w1; �b1) 0.7852 0.0050P (�w2; �b2) 0.2148 0.0050
23
TABLE B4
Baseline parameters and unobservablesJob changewithin ind.within occ.
Job changewithin ind.between occ.
Job changebetween ind.within occ.
Job changebetween ind.between occ.
Parameters Coe¤. Std. err. Coe¤. Std. err. Coe¤. Std. err. Coe¤. Std. err.1. year, �1 0.0087 0.0010 0.0018 0.0003 0.0557 0.0051 0.0319 0.00302. year, �2 0.0096 0.0010 0.0019 0.0003 0.0596 0.0055 0.0306 0.00293. year, �3 0.0104 0.0011 0.0020 0.0003 0.0608 0.0056 0.0307 0.00294. year, �4 0.0106 0.0012 0.0019 0.0003 0.0578 0.0054 0.0292 0.00285. year, �5 0.0116 0.0013 0.0019 0.0003 0.0572 0.0054 0.0251 0.00256. year, �6 0.0095 0.0011 0.0020 0.0003 0.0516 0.0051 0.0253 0.00267. year, �7 0.0101 0.0012 0.0017 0.0003 0.0550 0.0056 0.0232 0.00258. year, �8 0.0099 0.0013 0.0018 0.0004 0.0501 0.0060 0.0170 0.0024
�ww2 1.9264 0.0177�wb2 2.1028 0.0270�bw2 1.6507 0.0188�bb2 1.5845 0.0190P (�ww1; �wb1; �bw1; �bb1) 0.8126 0.0029P (�ww2; �wb2; �bw2; �bb2) 0.1874 0.0029
24