European Sociological Review, Vol. 16 No. 2, 185^200
185
The Effects of Level of Education on Mobility
between Employment and Unemployment
in the Netherlands
Maarten H. J.Wolbers
It is a known fact that less well-educated people have higher unemployment rates than better educated people. A possible explanation of this ¢nding is job competition: employers prefer higher over
lower educated workers for jobs that were previously occupied by lower-educated employees. As a
consequence, the lowest educated become unemployed. In this article we investigate the relationship between education and unemployment in a dynamic way. The question is to what extent
unemployment entry and exit rates vary with educational level. In order to answer this question we
used Dutch panel data with information on the shifts in labour-market position of more than 10,000
respondents in the period 1980^94. Our ¢rst ¢nding shows that the least well-educated employees
have a higher risk of becoming unemployed than better educated workers. This e¡ect of education
di¡ers by current aggregate unemployment rate and sex. Secondly, we conclude that unemployed
individuals with quali¢cations have higher probabilities of regaining employment than the unemployed without quali¢cations. This e¡ect varies by current aggregate unemployment rate, sex, and
unemployment duration.
Introduction
Since the 1970s unemployment in most industrialized societies has been subject to great changes
(see for instance OECD, 1991). These are caused by
economic £uctuations. Each economic recession
causes rapidly increasing unemployment rates,
while recovery of the economy causes the ¢gures to
slowly decrease again. The worst stage was in the
beginning of the 1980s, when in a number of European countries more than 10 per cent of the
workforce was registered with the various labour
exchanges as unemployed. In addition, the high
unemployment ¢gures in that period were characterized by a new phenomenon: long-term
unemployment. Many more people than before ran
the risk of being unemployed for a long time (i.e.
more than one year).
The likelihood of (long-term) unemployment is
unequally divided among the various groups in
& Oxford University Press 2000
society. There are considerable di¡erences in the
chances of unemployment for individuals with different educational quali¢cations. Table 1 shows that
in the Netherlands the problem of unemployment is
greatest among the lower educated. The unemployment rate for persons with no more than primary
education, but also for those with PVE, LGSE, or
HGSE/PUE is considerably higher than for the
better educated (for an explanation of abbreviations,
see Table 1). In addition, the relationship between
education and unemployment is stronger when
there is an ample supply of labour on the labour
market. If we compare the odds of unemployment
versus employment for the lowest educated (primary
education) to the odds of unemployment versus
employment for the highest educated (university) ^ i.e. if we calculate an odds ratio ^ then it
appears that this odds ratio was the greatest in
1985, the year when unemployment reached a
peak.1
186
MAARTEN H. J.WOLBERS
Table 1. Unemployment rates classi¢ed by level of education,1975^1993 (%)
1975
1985
1990
1992
1993
.............................................................................................................................................
Primary education (PE)
Lower general secondary education (LGSE)
Preparatory vocational education (PVE)
Higher general secondary education (HGSE)/
Preparatory university education (PUE)
Intermediate vocational education (IVE)
Higher vocational education (HVE)
University education (UE)
Total
7.1
5.3
4.2
26.1
18.3
14.2
14.0
10.8
6.8
13.3
10.2
6.6
15.6
10.3
8.4
5.7
3.6
2.7
2.0
5.1
18.0
7.3
6.8
6.7
13.0
12.2
4.3
4.8
5.9
8.5
9.4
4.4
4.9
5.4
6.5
12.2
5.2
5.1
6.1
7.5
Source: SCP (1994), Table 8.8, p. 341.
Unemployment percentages are only aggregated
snapshots and provide little insight into the labour
market itself (Layard, Nickell, and Jackman, 1991).
There may have been great changes at the level of
individuals without the unemployment rate having
changed at all. For example, a high and stable unemployment rate among the lower educated may be
accompanied by a high level of individual mobility
between employment and unemployment within
this group. In other words, an unemployment rate
consists on the one hand of individuals who are
becoming unemployed, and on the other hand of individuals who are remaining unemployed (Ultee,
Dessens, andJansen, 1988).
To gain more insight into the dynamic nature of
unemployment, we investigate in this article the
impact of the level of education on mobility
between employment and unemployment in the
Netherlands. We ask ourselves to what extent the
less educated become unemployed more quickly
and remain unemployed longer than individuals
with a higher level of education, and to what extent
this relationship between education and unemployment is stronger during a period of recession in the
labour market. To answer these questions, Dutch
panel data on (shifts in) the labour-market positions
of more than 10,000 respondents in the period
1980^94 will be studied.
There are at least two reasons why the Netherlands
provides an interesting context for the analysis of
this issue. First, in the Netherlands, compared to
other European countries, unemployment rates
have varied a lot since the 1980s (OECD, 1996).
This gives us a good opportunity to study the e¡ects
of education on unemployment entry and exit rates
at di¡erent stages of the business cycle. Unemployment was very high in 1983, when 12 per cent of the
Dutch workforce was registered as unemployed.
Since then, unemployment steadily decreased to
just over 5 per cent in 1992. In 1994, however, the
number of unemployed rose again: to around 8 per
cent. Since then the situation has improved greatly
and the current rate of unemployment in the Netherlands stands below 4 per cent. This is the lowest
percentage for more than two decades and is much
lower than the ¢gures in most other European countries.
Secondly, long-term unemployment is a relatively
large problem in the Netherlands (OECD, 1993: ch.
3). Anyone who loses their job in the Netherlands
runs the risk of being unemployed for a long time.
The explanation for the fact that so many Dutch
individuals su¡er long-term unemployment lies in
the strong employment security legislation. Those
in employment protect their labour-market position
via collective negotiations that are conducted to a
large extent on behalf of those with jobs, pursuing
wage maximization at the expense of job expansion
for the unemployed. By studying unemployment
entry and exit rates for a country such as the Netherlands, we can gain more insight into the factors
behind long-term unemployment and how it is
concentrated among certain educational groups.
Theoretical Background
One of the causes of high unemployment among the
lower educated that is often referred to, is crowding
out (Teulings and Koopmanschap, 1989). According
EFFECTS OF EDUCATION ON EMPLOYMENTUNEMPLOYMENT MOBILITY
to the job competition model (Thurow, 1975), the
labour market can be represented as consisting of
two rows. One row contains the jobs, classi¢ed
according to job level, the other row contains individuals, classi¢ed according to the quali¢cations
that they have acquired. Job-seekers try to ¢nd the
most attractive jobs, while employers prefer to
employ the highest educated. As a result, the best
jobs go to the higher educated, while the lower educated ^ because of their less favourable position in
the job queue ^ are forced to accept less attractive
jobs. In this concept, education is referred to as positional (Boudon, 1974; Hirsch, 1977; see alsoWolbers,
De Graaf, and Ultee, 1997). In a labour market characterized by an ample supply of labour, the job queue
will be longer and the higher educated who no
longer have access to the best jobs, will try to ¢nd a
job further down the queue. Highly educated
employees will then suddenly ¢nd themselves competing with less-well-educated employees, who
originally had these jobs. This competition often
ends in success for the higher educated. After all,
they have superior quali¢cations. The e¡ect of this
process of crowding out is that the individuals at
the lower end of the job queue have the greatest
chance of unemployment.
The job competition model can be applied to
both the entry and the exit stages of unemployment.
The lower educated occupy a lower position in the
job queue than the higher educated and are therefore
the ¢rst to be dismissed. Moreover, employers looking for suitable candidates for vacancies will ¢rst
select job-seekers at the front of the queue. As long
as there are more job-seekers than jobs, the last in the
queue ^ with the lowest quali¢cations ^ will not
be considered.
In addition to education, other criteria of selection play a role in the chance of unemployment. An
important selection criterion concerns an individual's labour-market biography. We can distinguish
between three aspects of a labour biography. The
¢rst important aspect is the amount of labourmarket experience. Labour-market experience is
considered as a way of accumulating human capital
during a professional career (Becker, 1964; Mincer,
1974). Whereas education re£ects learning capacities, labour-market experience indicates the level of
training. Employers aim to keep the training costs of
their personnel as low as possible, and hence indivi-
duals with experience are more attractive than those
without.This means that employers will less readily
¢re employees in whom they have made a large
investment, and who have a lot of knowledge and
experience. Conversely, employers are very keen on
employing job-seekers with a great deal of labourmarket experience, because the latter require hardly
any training.
Secondly, the duration of unemployment serves
asan indicator thatprovidesinformationto employers
with regard to the labour biography of job-seekers.
Although the chances of regaining employment ¢rst
increase, the duration of unemployment has a negative e¡ect on exit chances if it lasts several months
(Sprengers, 1992). Both employers and employees
contribute to this negative duration e¡ect: employers
are less interested in recruiting long-term unemployed individuals, and the unemployed lose faith
in ¢nding a job when they have been looking for
one for some time.
The third aspect relating to someone's labour biography is his or her job level. In general, higher
positions o¡er greater job security than lower ones,
a number of conditions (period of notice, severance
pay, seniority principles, etc.) are ¢xed more ¢rmly
in the law and/or collective labour agreements, and
the adjustment costs are higher (Lindbeck and
Snower, 1988). This means that individuals with a
higher position will be ¢red less easily than those
with a lower position. During the transition from
unemployment to employment, on the other hand,
there are no advantages for job-seekers who were
previously employed in higher positions. All current employees (`insiders') may defend themselves
against job-seekers (`outsiders'), even if they are in
lower positions.
Besides these economic factors more sociological
variables are important too. First, di¡erences in the
labour-market position between men and women
are sociologically relevant. Women in general have
less favourable prospects in the labour market than
men, because they often combine their professional
careers with domestic tasks and bringing up children (Blossfeld and Hakim, 1997). One of the areas
in which this is re£ected is unemployment: in most
European countries (Britain is one exception) the
unemployment ¢gure for women is much higher
than that for men. We therefore expect that women
will become unemployed more easily and have
187
188
MAARTEN H. J.WOLBERS
greater di¤culty ¢nding work again than men. In
addition, we may predict that the e¡ect of education
on unemployment is greater for women than it is for
men.
Secondly, social background is a sociological
factor a¡ecting someone's labour-market position.
It is a known fact that individuals originating from
higher social classes more often have high class positions than people from lower social classes (Erikson
and Goldthorpe, 1992). Apart from direct inheritance of occupations (as in the case of farmers and
small retailers), parents from the higher social
classes provide their o¡spring with resources to
gain access to these higher social classes (Collins,
1971).With respect to unemployment, a similar strategy can be envisaged. People from higher social
strata use their resources as protection against
unemployment. Support for this hypothesis was
found by Batenburg, Smeenk, and Ultee (1995),
who concluded that the chance of unemployment
not only depends on the individual's social class
position before unemployment, but also on the
father's social class position.
Apart from individual characteristics, structural
developments also a¡ect mobility in the labour
market. An obvious structural factor is the aggregate
level of unemployment (Vissers, 1987).When unemployment is high, there is less voluntary mobility than
in more favourable economic circumstances. The
economic explanation for this is that there are few
alternative jobs during a recession, which renders
the costs of ¢nding one high. A more psychological
explanation is that individuals look for security during a period of recession and do not want to run the
risk of losing established rights by changing jobs.
The amount of forced mobility, however, increases
when unemployment develops unfavourably. Figure
1 shows that in the Netherlands there is a strong
positive relationship between the in£ow into unemployment and the unemployment level of the
workforce as a whole (the correlation is 0.82). In particular, the least productive employees, who are at
the end of the job queue, will be ¢red ¢rst: those
with the lowest quali¢cations. Our hypothesis is
therefore that the impact of the educational level
on the chances of becoming unemployed is greater
Figure 1. Indexed unemployment level, in£ow and out£ow from unemployment,1980^1994 (1980=100)
Sources; CBS (1994), column 56, p. 49; OSA Labour Supply Panel 1985^1994, author's calculations
EFFECTS OF EDUCATION ON EMPLOYMENTUNEMPLOYMENT MOBILITY
in times of high unemployment. Figure 1 also shows
that there is no relationship between the out£ow
from unemployment and the aggregated unemployment rate in the Netherlands (the correlation is 0.01).
Apparently, job-seekers do not bene¢t from growing employment and many of them run the risk of
remaining (long-term) unemployed. This will be
the case, in particular, for those with little education,
because of their relative lack of competitiveness in
the labour market.
Not only the present opportunity structure, but
also structural circumstances from the past play a
role in the chance of unemployment. A comparative
study of ¢ve European countries (Britain, Denmark,
France, Italy, and the Netherlands) has shown that
cohorts who enter the labour market during an economic recession, su¡er permanent disadvantage
with respect to later chances of unemployment compared to school-leavers who enter the labour market
in more favourable circumstances (DeVreyer, Layte,
Wolbers, and Hussain, forthcoming). These permanent e¡ects of labour-market entry in times of high
aggregate unemployment are strongest in France
and Italy, probably resulting from the rather weak
linkage between the education and training system
on the one hand and the labour market on the other
hand in these countries (Hannan, Ra¡e, and Smyth,
1997). But also in the Netherlands, where education
and employment are more closely linked, cohorts
that enter the labour market during a recession
seem to have a greater chance of being unemployed
at a later stage in their occupational career. The last
hypothesis of this article is therefore that individuals
who enter the labour market during a time of high
unemployment, become unemployed more quickly
and remain unemployed longer than individuals
who make the transition from education to the
labour market during an economic recovery.
Research Design
Data
The data used in the present article were derived
from the Labour Supply Panel of the Organisatie
voor Strategisch Arbeidsmarktonderzoek. The
OSA Labour Supply Panel has existed since the
spring of 1985, when more than 4,000 respondents
from a representative Dutch sample population of
approximately 2,100 households were asked about
their labour-market biography since 1980. Members
of the household who were between 16 and 64 years
of age at the time of the survey, and who were not
following a full-time course study or drafted for
military service, were eligible for participation in
the study. In the autumn of 1986, the survey was
repeated with the household and participating
respondents selected in 1985 constituting the population bases. If panel members of the original sample
were no longer available, their places were taken by
new respondents and/or households who corresponded as closely as possible to the original
participants in such characteristics as age, sex, family
size, and geographical region. The changes in the
labour-market positions of the original respondents
between the spring of 1985 and the autumn of 1986
were recorded; for the new respondents, this concerned their labour biography from 1980. Since
then, the survey has been repeated every two years,
with the emphasis being on changes in the labourmarket position during each previous period of two
years. For this article, we were able to use the data
obtained in surveys conducted in 1985, 1986, 1988,
1990, 1992, and 1994. This produces a combined
dataset of a total of 10,514 respondents who were
questioned at least once and at most six times.2
Variables
In this article about the dynamic relationship
between level of education and unemployment we
study two transitions: the transition from employment to unemployment, and the transition from
unemployment to employment. For the measurement of unemployment we use the o¤cial
de¢nition by the Centraal Bureau voor de Statistiek
(CBS ^ Statistics Netherlands), dating from 1991.
According to this de¢nition, unemployed persons
are those who have no work, or who have work for
between 1 and 11 hours per week, who wish to work
at least 12 hours per week, are available for such
work, and are searching for suitable employment.
We restrict ourselves here to those who are working
as paid employees. This means that in the transition
from employment to unemployment, we will only
look at paid employees; for the transition from
unemployment to employment, we will only look
189
190
MAARTEN H. J.WOLBERS
at unemployed individuals who are looking for jobs
as paid employees.
The level of education of respondents is determined on the basis of the Standaard Onderwijs
Indeling (SOI ^ Standard Education Classi¢cation) by the CBS (CBS, 1987). We distinguish the
following categories:
1. primary education (PE);
2. lower level secondary education (PVE/LGSE);
3. higher level secondary education (IVE/HGSE/
PUE);
4. higher education, ¢rst stage (HVE); and
5. higher education, second stage (UE).
Another important independent variable in the
analysis is the level of the job that is held by the
respondent. We have chosen to use the job level
classi¢cation by Huijgen and colleagues (Huijgen,
Riesewijk, and Conen, 1983: 161^162). The level of
a job re£ects the required quali¢cations of the
occupation concerned. On the basis of a number of
characteristics of the content of the job (learning
time, independent initiative, and the training
regarded as necessary in order to function properly),
all occupations that are performed by paid
employees have been given a job level. There are
seven job levels:
^ level 1 consists of unskilled occupations (with
very simple work and simple instructions,
requiring no or little insight and no consultation);
^ level 2 consists of semi-skilled and skilled occupations (with simple work and few complex
instructions, requiring some insight and consultation);
^ levels 3 and 4 are skilled occupations (with
slightly to fairly complex work, requiring
insight, consultation, and theoretical knowledge; level 4, compared to level 3, requires not
only more speci¢c professional training, but also
considerably more practical experience);
^ levels 5, 6, and 7 are specialized to highly specialized occupations (where the nature of the work
varies from complex with considerable theoretical knowledge to university level).
The level of the job occurs twice in the analysis: as
the level of the present job (at the transition from
employment to unemployment), and as the level of
the job before the period of unemployment (at the
Figure 2. Observed probabilities of becoming unemployed classi¢ed by number of years of labour market experience
Source: OSA Labour Supply Panel 1985^1994, author's calculations
EFFECTS OF EDUCATION ON EMPLOYMENTUNEMPLOYMENT MOBILITY
transition from unemployment to employment). To
determine the job level before the period of unemployment, we added a separate category for the selfemployed and for individuals whose job level was
unknown.
As both the respondents' educational level and
their labour-market position are a¡ected by social
background, we have included in the analysis the
job level of the father at the time when the respondent was 12 years old.3 For fathers who were not in
paid employment at the time of the survey, and for
fathers whose job level was unknown, we have added
a separate category.
Di¡erences between men and women are determined on the basis of the sex variable, where men
constituted the reference category.
In the case of the labour-market experience variable, we take the number of years that a person has
worked. Because of the curvilinear relationship
between this variable and the labour-market position (see Figure 2), working experience is included
in the analysis in both a linear and a quadratic form.
As expected, the chances of ¢nding a job during
the ¢rst few months after dismissal ¢rst increase, but
then decrease (see Figure 3). After a number of years,
the chances of ¢nding a job again are very small.The
speci¢c pattern of the duration e¡ect demands a
special treatment of this variable in the analysis (see
Blossfeld and Rohwer, 1995).We model the duration
of unemployment on the basis of two variables.The
e¡ect of the second variable (log(847duration of
unemployment)) indicates how great the increase is
at the beginning of the period of unemployment,
whereas the e¡ect of the ¢rst variable (log(duration
of unemployment71)) shows the level of the subsequent decrease. If both e¡ects are equally great, the
turning point is located exactly in the middle of the
selected range of the duration of unemployment (1^
84 months). The e¡ect of the second variable, however, will be much greater than that of the ¢rst,
because the chances of ¢nding a job again reach a
maximum after only a few months.
Structural circumstances in the labour market can
be determined on the basis of so-called cohort and
period e¡ects (Blossfeld, 1986). First, we use the year
of entry into the labour market to determine cohort
developments. Then we investigate to what extent
the di¡erences found between the labour-market
Figure 3. Observed probabilities of ¢nding a job classi¢ed by duration of unemployment
Source: OSA Labour Supply Panel 1985^1994, author's calculations
191
192
MAARTEN H. J.WOLBERS
cohorts can be explained by developments in
unemployment at the time of entry into the labour
market. These developments are indicated by registered unemployment ¢gures (CBS, 1979, 1994). To
represent the cohort e¡ect, each respondent is
given a value for this property, which indicates the
registered unemployment rate for the year when he
or she entered the labour market. The period e¡ect
stands for the current unemployment level. For the
period e¡ect, each respondent receives a new value
for each year, re£ecting the registered unemployment rate for that speci¢c year.
Method
In order to adequately investigate mobility between
employment and unemployment, a dynamic
approach is very useful: event history analysis
(Allison, 1984; Yamaguchi, 1991). Within eventhistory analysis, a distinction can be made between
discrete-time and continuous-time models. In this
article we will use discrete-time models to study
transitions between states of employment and
unemployment.4 Three considerations were relevant
for this decision (see also Yamaguchi, 1991: 15^17).
First, the event of interest in this study ^ losing a
job or ¢nding a job ^ occurs only at discrete time
points. In the Netherlands, people are normally
¢red at the end of the month, and people typically
start in a new job at the beginning of the month.
Secondly, the discrete-time models that will be
used in this article are a reasonable approximation
of continuous-time models. Since dates in the dataset are all measured in months, the time interval is
small relative to average durations.This implies that
the conditional probability of experiencing the
event is very small and that discrete-time models
approximate continuous-time models closely.
Thirdly, discrete-time models are easy to handle.
They can be applied to the data using computer programs that are available from standard statistical
packages. In addition, discrete-time models allow
covariates to be time-dependent. Time-dependent
covariates are variables that can vary not only
between respondents, but also for the same respondent over time.
In a discrete-time event history analysis, hazard
rates are modelled. A hazard rate h(t) re£ects the con-
ditional probability of an event occurring at time t,
given that this event has not occurred prior to time t:
h(t) ˆ P(T ˆ ti jT 5 ti )
The discrete-time event-history model can be speci¢ed by means of a logistic regression analysis, in
which the log-odds of h(t) is a function of a number
of covariates, possibly time-dependent:
log
h(t)
1 h(t)
ˆ ai ‡
X
k bk Xkt
This model di¡ers from a conventional logistic
regression model with respect to the structure of
the data to be analysed. In a conventional logistic
regression analysis (i.e. estimated in cross-sectional
data) one observation for each respondent is used.
However, the discrete-time logistic model uses one
observation per time-unit per respondent.The data
matrix to be analysed here is a so-called `personmonth-¢le', containing as many records as there
were `person-months'.
At the time of the transition from employment to
unemployment, the dependent variable is the conditional probability that someone has of becoming
unemployed within a particular month, assuming
that this person has worked until that time. At the
time of the transition from unemployment to
employment, the opposite applies. The dependent
variable in this model is the conditional probability
of someone ¢nding a job in a particular month,
given the fact that this person was looking for work
until that time. The covariates in the analysis were
measured time-dependently or time-independently.
The scores for the variables education level, duration of unemployment, labour-market experience,
and current unemployment rate may vary with
time. The variables year of entry, sex, father's job
level, current job level, job level before unemployment, and unemployment rate at the time of entry
into the labour market were included in the analysis
as time-independent characteristics.
Results
From Employment to Unemployment
Table 2 shows which factors a¡ect the probability of a
transition from employment to unemployment.The
estimated parameters represent the change in the
EFFECTS OF EDUCATION ON EMPLOYMENTUNEMPLOYMENT MOBILITY
193
Table 2. E¡ects of level of education on the transition from employmentto unemployment,1980^1994
Model
1
2
3
4
.............................................................................................................................................
Educational levela
PVE/LGSE
70.0955
70.0663
70.0695
71.3626*
IVE/HGSE/PUE
70.5040**
70.4286**
70.4453**
70.5886
HVE
70.8280**
70.6600**
70.6944**
70.7892
UE
70.4545
70.1942
70.2673
70.4348
Labour-market experience
70.0646**
70.0636**
70.0519**
70.0508**
Labour-market experience squared
0.0008*
0.0007*
0.0006
0.0005
Year of entry (1930=0)
0.0069
0.0069
0.0082
0.0075
Sex
70.0701
70.0748
70.0483
70.3241
Job level fatherb
Job level 2
0.0235
0.0368
0.0446
0.0335
Job level 3
70.0222
0.0067
0.0082
0.0102
Job level 4
70.3792*
70.3445
70.3326
70.3442
Job level 5
0.0261
0.0630
0.0637
0.0488
Job level 6
70.0534
0.0019
70.0055
70.0265
Job level 7
0.1178
0.1941
0.2028
0.1873
Job level unknown or not in paid employment
0.0179
0.0481
0.0342
0.0104
Current job levelb
Job level 2
70.0912
70.0965
70.1053
Job level 3
70.3472*
70.3519*
70.3764*
Job level 4
70.2422
70.2382
70.2765
Job level 5
70.2860
70.2950
70.3112
Job level 6
70.3503
70.3231
70.3453
Job level 7
70.7325*
70.7329*
70.7402*
Current unemployment rate
0.1231**
0.0754
Unemployment rate at time of entry
0.0353*
0.0355*
Interaction with current unemployment rate
PVE/LGSE
0.1713*
IVE/HGSE/PUE
70.0061
HVE
70.0260
UE
0.0180
Interaction with sex
PVE/LGSE
70.0230
IVE/HGSE/PUE
0.5005
HVE
0.7094*
UE
0.0673
Intercept
75.4556**
75.3165**
76.5503**
76.0421**
169.8000**
179.5210**
207.4330**
231.1250**
Model chi2
df
15
21
23
31
Number of transitions
574
574
574
574
Number of subepisodes
309,696
309,696
309,696
309,696
aPE is the reference category.
b
Job level 1 is the reference category.
*=p50.05; **=p50.01.
Source: OSA Labour Supply Panel 1985^1994; author's calculations.
194
MAARTEN H. J.WOLBERS
log-odds of the conditional probability of experiencing an event, caused by a one-unit increase in the
associated covariate. Due to the small time-unit of
one month all hazards analysed are very low. For
such low hazards the hazard rate (h(t)) and the odds
of the hazard rate (h(t)/17h (t)) have nearly the same
value.Therefore, the e¡ects of covariates on the odds
of hazards will be interpreted as e¡ects on hazards.
Model 1 shows that employees with an HVE
diploma have the smallest chance of becoming
unemployed, followed by individuals with a background in higher secondary education (IVE/
HGSE/PUE). Individuals without quali¢cations
have the greatest chance of unemployment, as have
academics and individuals with an PVE/LGSE
diploma. Evidently, links between education levels
and unemployment entry rates are not entirely linear, as predicted by Thurow's job competition
model. It also appears that people with a great deal
of work experience have a smaller chance of
becoming unemployed than those who have little
experience. The negative correlation between
work experience and the chance of becoming
unemployed is not a linear, but a curvilinear one.
Each additional year of labour-market experience
o¡ers less additional protection against unemployment. After 40 years of labour-market
experience ^ i.e. more or less at the end of one's
occupational career ^ the chance of unemployment
is the least (0.0646/2*0.0008=40.375). This is summarized in Figure 4.
In model 2 we investigate whether employees
with high-level jobs have a smaller probability of
becoming unemployed than employees with lowlevel jobs. This proves to be the case, although the
relationship between the present job and the chance
of unemployment is not entirely linear. In particular
those individuals who work at job levels 3 and 7 have
a smaller chance of losing their jobs. Another conclusion that can be drawn from model 2 is that the
e¡ect of level of education hardly diminishes if one
takes the present job into account. Of two employees
who have jobs at the same job level, the one with the
highest educational level ^ with the exception of
university education ^ has the smallest risk of
becoming unemployed.
Figure 4. Average predicted probabilities of becoming unemployed classi¢ed according to the number of years of labour market experience and
educational level
Source: OSA Labour Supply Panel 1985^1994, author's calculations
EFFECTS OF EDUCATION ON EMPLOYMENTUNEMPLOYMENT MOBILITY
Next, we will look at the in£uence of structural
labour-market circumstances on the likelihood of
unemployment (model 3). As Figure 1 showed,
there is a positive relationship between the current
aggregate unemployment rate and the in£ow into
unemployment. Again, the unemployment level
appears to have a considerable in£uence on the
chance of becoming unemployed. If unemployment
rises by 1 per cent, the chance of becoming unemployed in the following month increases by about
13 per cent (e0.1231=1.1310). In addition, the aggregate
rate of unemployment at the time of entry into the
labour market has an impact on one's later chances of
becoming unemployed. Although the e¡ect of
unemployment at the time of entry is considerably
smaller than that of current unemployment, individuals who entered the labour market under adverse
circumstances su¡er a permanent disadvantage during their further careers.
Lastly, we investigated whether the in£uence of
work experience, sex, and trends in unemployment
levels di¡er from one level of education to another.
Model 4 only describes the terms of the statistical
interactions of level of education with the current
rate of unemployment and sex, because the other
interaction terms were not signi¢cant. Interestingly,
we see that the e¡ect of unemployment found in
model 3 is greatest for individuals with a PVE/
LGSE education. Compared to the other levels of
education, the impact of the current rate of unemployment on the probability of losing one's job is
more than twice as great for persons with a PVE/
LGSE diploma. At the same time, the conclusion
drawn above that men and women have an equal
chance of becoming unemployed, must be adapted
slightly: this is not true for those who have an HVE
diploma. Female HVE graduates have twice as much
risk of becoming unemployed as their male counterparts (e0.7094=2.0328).
From Unemployment to Employment
Table 3 shows which factors are decisive for the transition from unemployment to employment. Again,
we express the results found in terms of e¡ects on
hazards. Model 1 indicates that individuals with a
diploma have better chances of ¢nding work again
than those who failed to obtain any quali¢cation
after primary school. There are few di¡erences
between the various levels of education. Only for
university graduates, the probability of regaining
employment is slightly smaller, and it does not di¡er
signi¢cantly from those who only have primary
education. The variables for the duration of unemployment show that the probability of ¢nding a job
¢rst increases rapidly (1.3127) and then decreases
slowly (0.1077). The turning point5 is reached quite
soon: after about seven months, the probability of
¢nding a job is greatest ((84*0.1077+1*1.3127)/
(0.1077+1.3127)=7.2934). The e¡ects of labourmarket experience show that the chances of ¢nding
a job decrease up to more than 18 years of experience
(0.0450/2*0.0012 = 18.75), after which the likelihood
of ¢nding work slowly increases again. Furthermore, individuals from more recent labour-market
cohorts have better chances of regaining employment than older cohorts, irrespective of their
limited work experience (0.0255). To what extent
this cohort e¡ect re£ects structural labour-market
circumstances will become clear soon (in model 3).
Lastly, model 1 indicates that it is more di¤cult for
women to ¢nd a job again than it is for men ( 0.6198).
Individuals who worked in high-level jobs ^
before they became unemployed ^ do not ¢nd a
new job more quickly than those who worked in
low-level jobs (model 2). There is only a signi¢cant
di¡erence for the category of respondents for whom
information regarding the level of their latest job
was not available or who used to be self-employed.
The latter group has greater di¤culty ¢nding a job.
Model 2 also shows that, again, the education e¡ect
hardly decreases if we take into account the level of
the most recently held job.
We can see in model 3 that, on average, structural
labour-market circumstances play no role across the
sample as a whole with respect to the chances of ¢nding a job. This result is in accordance with Figure 1,
because it was already shown there that there is no
correlation between leaving the state of unemployment and the current aggregate unemployment rate.
The cohort e¡ect that was found in model 1 clearly
suggests a di¡erent type of development in the
labour market. Possibly, the modernization of the
labour market (including £exibilization) leads to
higher unemployment exit rates.
If we classify according to level of education,
however, there is an e¡ect of the current rate of
unemployment on the probability of ¢nding a new
195
196
MAARTEN H. J.WOLBERS
Table 3. E¡ects of level of education on the transition from unemploymentto employment,1980^1994
Model
1
2
3
4
.............................................................................................................................................
Educational levela
PVE/LGSE
0.3228*
IVE/HGSE/PUE
0.3309*
HVE
0.3559*
UE
0.2460
log (Duration of unemployment ^ 1)
0.1077**
log (84 ^ Duration of unemployment)
1.3127**
Labour-market experience
70.0450**
Labour-market experience squared
0.0012**
Year of entry (1930 = 0)
0.0255**
Sex
70.6198**
Job level fatherb
Job level 2
70.2272
Job level 3
70.0213
Job level 4
70.0055
Job level 5
0.1074
Job level 6
0.0903
Job level 7
0.2734
Job level unknown or not in paid employment
70.0850
Job level before unemploymentb
Job level 2
Job level 3
Job level 4
Job level 5
Job level 6
Job level 7
Job level unknown or not in paid employment
Current unemployment rate
Unemployment rate at time of entry
Interaction with current unemployment rate
PVE/LGSE
IVE/HGSE/PUE
HVE
UE
Interaction with sex
PVE/LGSE
IVE/HGSE/PUE
HVE
UE
Interaction with duration of unemployment
PVE/LGSE
IVE/HGSE/PUE
HVE
UE
Intercept
710.1035**
231.8830**
Model chi2
df
17
Number of transitions
537
Number of subepisodes
21,581
a
PE is the reference category.
bJob level 1 is the reference category.
*=p50.05; **=p50.01.
Source: OSA Labour Supply Panel 1985^1994; author's calculations.
0.2832*
0.2876*
0.3440
0.2279
0.1143**
1.1727**
70.0609**
0.0015**
0.0246**
70.5920**
0.2900*
0.2989*
0.3537
0.2369
0.1155**
1.1800**
70.0627**
0.0014**
0.0195*
70.6203**
70.1401
70.4395
71.3349
70.2799
0.1573**
0.3422
70.0595**
0.0014**
0.0203*
70.8306**
70.2415
70.0386
0.0063
0.0977
0.1338
0.2724
70.0890
70.2421
70.0328
70.0071
0.1062
0.1354
0.2680
70.0833
70.2329
70.0746
0.0308
0.1079
0.0645
0.2382
70.1017
0.0338
0.2786
0.1848
0.2019
70.0308
0.3093
70.4784*
0.0354
0.2954
0.1891
0.1979
70.0481
0.2983
70.4878*
70.0384
0.0103
0.0030
0.2832
0.0932
0.1863
0.0437
0.2487
70.5235*
70.1727**
0.0088
0.1437
0.1529*
0.2689**
0.1172
70.0508
0.3027
0.5312
1.1743**
79.1794**
278.3890**
24
537
21,581
78.7110**
280.5770**
26
537
21,581
70.0342**
70.0262**
70.0285**
70.0410*
74.3269**
326.1520**
38
537
21,581
EFFECTS OF EDUCATION ON EMPLOYMENTUNEMPLOYMENT MOBILITY
job (model 4). In particular, among the unemployed
with only primary education, the chances of ¢nding
work greatly decrease when unemployment rises: for
each 1 per cent that unemployment increases, the
probability of ¢nding a job drops by almost 16 per
cent (e70.1727=0.8414). A similar conclusion can be
drawn for PVE/LGSE and UE. Individuals with a
diploma at the level of IVE/HGSE/PUE and
HVE, on the other hand, do not experience any
negative e¡ects of the current rate of unemployment. Another outcome of model 4 is the fact that
the di¡erence found previously between the sexes
applies largely to the lowest educational levels. In
particular, women with an education at a low level
have di¤culty in ¢nding a new job. Female UE
graduates, on the other hand, are popular in the
labour market: their chances of regaining employment are even greater than those for men
(70.8306+1.1743=0.3437).
The most interesting result found in model 4 is
the fact that the duration e¡ect observed varies
between the di¡erent educational levels. Figure 5
illustrates this ¢nding. This ¢gure shows that the
initial increase in the likelihood of ¢nding a new
job is greater if the educational level is higher.
After several months of unemployment, the
chances of individuals with only primary-level education ¢nding work again in the following month
are slightly more than 2 per cent, whereas for those
with a secondary-school diploma (PVE/LGSE and
IVE/HGSE/PUE) or graduates of higher vocational education (HVE), they are more than 4 per
cent, and for university graduates about 6 per cent.
The later decrease in the chances of ¢nding
work ^ in particular after a period of unemployment of two years ^ is equal for all types of
education, with the exception of primary education. The fall in the probability of ¢nding work
for individuals with only primary education (PE)
is much smaller, while after about three-and-a-half
years of unemployment, the monthly chances of
¢nding a job for job-seekers without a diploma are
even slightly greater than for job-seekers with a
diploma.
Figure 5. Average predicted probabilities of ¢nding a job classi¢ed according to duration of unemployment and educational level
Source: OSA Labour Supply Panel 1985^1994, author's calculations
197
198
MAARTEN H. J.WOLBERS
Conclusions and Discussion
In the Netherlands, as in other industrialized countries, unemployment among individuals with a
lower level of education is considerably higher than
among those with higher educational quali¢cations.
This may be the result of crowding out in the labour
market. The higher educated take the jobs that used
to be done by the lower educated, pushing the least
quali¢ed out of the labour market. In this article we
have analysed the relationship between education
and unemployment from a dynamic perspective.
We have looked at the extent to which the level of
education a¡ects mobility between employment
and unemployment in the Dutch labour market. To
do so, we have made use of panel data on the labourmarket situation of a representative group of individuals in the period 1980^94.
First, it appears that in the Netherlands education
o¡ers protection against dismissal. In general, the
less educated have a greater chance of losing their
jobs than the better educated. However, the link
between education and unemployment entry rates
is not entirely linear. University graduates have, for
example, a higher probability of becoming unemployed than individuals with higher vocational
education. In addition, the positive e¡ect of the
current aggregate unemployment rate on the probability of becoming unemployed is greater for
individuals with a PVE/LGSE diploma than for
those with di¡erent quali¢cations. It also appears
that women with an HVE diploma have a greater
chance of becoming unemployed than men who
have completed a course of study in higher vocational education.
Secondly, we conclude that in the Netherlands
individuals with any type of diploma have a greater
chance of ¢nding a new job than those who left
school at the level of primary education. Between
people with secondary and tertiary education there
are hardly any di¡erences in unemployment exit
rates. Furthermore, developments in the business
cycle in£uence the probability of ¢nding a job
between one education level and another. The
unemployed with no more than primary education
or a PVE/LGSE or UE diploma have greater di¤culty in ¢nding a job when the level of
unemployment increases, while the chances of
those with a diploma at the level of IVE/HGSE/
PUE or HVE do not su¡er from an economic recession.The impact of sex also varies between levels of
education. In general, for women the likelihood of
¢nding a job is smaller than for men, but female university graduates ¢nd jobs more quickly than their
male counterparts. Lastly, the role of the duration
of unemployment di¡ers for the various levels of
education. The increase of the chances of work at
the beginning of a period of unemployment is
greater if the educational level is higher. The later
decrease is similar for all types of education, with
the exception of primary education.
Overall, the results in this article support the job
competition model.The lower educated are the ¢rst
to be ¢red and stand at the back of the job queue
when there are vacancies to be ¢lled. Nevertheless,
there are signs that the sorting and signalling function of education, as assumed by the job competition
model, does not in itself determine the entire relationship between education and unemployment.
For example, we found in this article that university
graduates face a greater risk of unemployment than
vocationally educated graduates, although in theory
the former should occupy the best positions in the
job queue. It seems that, in addition to a hypothesis
on educational levels, we also need a hypothesis on
types of education (see also Arum and Shavit, 1995).
The latter would state that individuals with an education which is more oriented towards the labour
market (i.e. people with occupationally speci¢c or
vocational education) have a lower probability of
becoming unemployed than those with a more general education. Especially in the German-speaking
countries (Austria, Germany, and Switzerland)
where the combination of on-the-job-training with
instruction in public vocational schools (the `dual
system') predominates, the distinction between academic and vocational education seems to be very
valuable. In these countries, vocational education
prepares students with skills. Moreover, employers
know which skills are taught in the apprenticeship
programmes and can rely on vocational quali¢cations. As a result, vocational education can reduce
the risk of unemployment, in contrast to academic
programmes.
The fact that educational quali¢cations play a protective role in the case of unemployment suggests
that educational policy measures should be taken
with respect to individuals at the lower end of the
EFFECTS OF EDUCATION ON EMPLOYMENTUNEMPLOYMENT MOBILITY
labour market. The question, however, is whether
additional training for the lower educated is su¤cient for them to ¢nd or keep a job. At the level of
the individual it may be useful to have more education ^ their relative position in the job queue
improves ^ but for society as a whole, this only
leads to credential in£ation and to crowding out in
the labour market (Boudon, 1974). After all, additional training for the lowest educated increases the
risk of unemployment for the slightly better educated. It would be more useful to create jobs at the
upper end of the labour market in order to cater for
the greater supply of higher-educated individuals.
Notes
1. The ratio of the odds of unemployment versus
employment for persons with primary education and
for those with a university education was 4.92 in 1985
((26.1/73.9) / (6.7/93.3)). In 1975, the odds ratio was 3.74;
in 1990, 2.37; in 1992, 2.69 and in 1993, 2.85.
2. For more detailed information on the OSA Labour
Supply Panel (the justi¢cation of the ¢eldwork, representativeness, panel mortality rate and substitution,
and the structure of the dataset), we refer readers to
the appendices of the annual trend report on the
supply of labour (see for instance OSA, 1995).
3. It is actually the job level of the head of the family, but
in most cases this turned out to be the father.
4. Since the data used in this article obviously include
more than just transitions between states of employment and unemployment, a multi-state or competing
risk model might be more desirable. However, in the
present analysis the occurrences of other events are for
theoretical (other transitions are of little or no interest
for the purpose of this article) and practical (analysing
separate transitions gives one much greater analytical
simplicity and £exibility) reasons treated as censored
observations. First of all, the transition from pupil/
student to work (possibly with a period of unemployment in between) remains outside our scope, because
the available data do not allow such an analysis (only a
small number of school-leavers are included in the
dataset). Other transitions that we do not look at are
those from being in the labour force to homemaking
and vice versa. Women often leave the labour force
when labour and care tasks can no longer be
combined and re-enter when the children go to
school or have left home. Furthermore, transitions
that are not considered in the analysis are those
between labour-force participation and early retire-
ment or disability. The question is whether the use of
such a restricted model as a replacement for a multistate model is appropriate, especially since transitions
between employment and unemployment might be
dependent on other labour-market transitions.
According to Begg and Gray (1984) this is in general
the case. They conclude that estimating separate
regressions for each transition relative to a simultaneous estimation of multiple transitions is highly
e¤cient. Substantial losses of e¤ciency of parameter
estimates occur only when the probability of the baseline category of the dependent variable is low, which is
clearly not the case in the present analysis.
5. The top of the curve can be calculated by taking the
derivative of the duration of unemployment and
setting it at zero: duration of unemployment=
(84 b1 ‡ 1 b2 )=(b1 ‡ b2 ).
Acknowledgements
A Dutch version of this article was previously published in
Mens en Maatschappij, 73 (1998), pp. 176^194. The author
wishes to thank the OSA (for granting permission to use
the Labour Supply Panel), Ruud Luijkx (for his help in
making the data analysable), Paul de Graaf, Wout Ultee,
Emer Smyth, and the anonymous reviewers of the ESR
(for their comments and suggestions on earlier versions
of this article).
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Author's Address
Maarten H.J. Wolbers, Research Centre for Education and
the Labour Market, Maastricht University, PO Box
616, 6200 MD Maastricht, The Netherlands. Tel.:
+31 43 3883737; fax: +31 43 3884914; e-mail:
[email protected].
Manuscript received: October 1998.