EDUCATIONAL ATTAINMENT, OCCUPATIONAL ACHIEVEMENTS, CAREER PEAKS
The Netherlands in the second part of the 20th century
Maarten Wolbers, Radboud University Nijmegen
Ruud Luijkx, Tilburg University
Wout Ultee, Radboud University Nijmegen
1
A series of research questions
Sociology is about human societies. By now it has witnessed several generations of research on the
way they are stratified and their pattern of mobility (Ganzeboom, Treiman and Ultee 1991). This paper
seeks to avoid drawbacks of the latest, fourth, generation and brings in relatively unnoticed aspects of
the second generation. It addresses a sequence of (no less) seven questions. These questions pertain to
men who entered the Dutch labour market in the 1950s, 1960s and 1970s, in so far as their
occupational history is available until at least the age of 45 years.
Readers familiar with the second generation ‘path model for the socio-economic life cycle’
(Blau and Duncan 1967: 170) will readily appreciate that this small graph in one stroke answers
several questions. It does so because its causal arrows target three distinct phenomena: a man’s level
of education, the status of his first job, and his present job. It does so too since several paths finish at a
man’s current job: one starting from a man’s first job after leaving school, another one from a man’s
education, and yet another arrow from the job of a man’s father. It does so once more because two
arrows lead up to a man’s first job, one from father’s job and an arrow from a man’s education.
Finally, questions multiply if models for separate cohorts are envisaged. Each generation of research
on stratification and mobility did so, and the present paper does so too.
The first part of this paper details the shifts from one generation of research on stratification
and mobility to the next. It will become clear that later generations broke down big questions into their
constituents, arranging them into a chain of questions. The second part argues this paper’s choice of
questions, and the third paragraph details hypotheses answering these questions. The fourth part
informs about the data to be analysed, and the fifth part presents results. The final paragraph of the
present paper reviews findings, recasts hypotheses and proposes follow-up questions.
Questions that pose issues poorly
The successive generations of research on stratification and mobility have employed quite different
techniques of data analysis. The Lipset generation of the 1950s eyeballed tables cross-classifying the
present status of persons in society against the status of their parental home (Lipset and Bendix 1959).
In contrast, The Duncan generation from around 1970 estimated path models for the socio-economic
life cycle in a country at some point in time (Blau and Duncan 1967: 170). Then came the Goldthorpe
generation. It began to bloom in the early 1980s and applied log-linear models to father-son class
mobility tables (Goldthorpe 1980). Finally, around 2000 event-history techniques became
commonplace, after their introduction by Blossfeld and Mayer (1988). Right now no new generation
rallying around some technique of data analysis seems to be on the horizon.
In addition, every successive technique of data analysis was accompanied by the collection of
richer data. Although each generation collected data for a representative sample from a country’s
2
population, and took the jobs of persons as indicative of their status in a society’s stratification system,
the first generation made do with data on a man’s present job and the job of his father. The second
generation also collected data on a man’s level of education and a man’s job right after leaving school.
The third generation added the job of men ten years after their first job. The fourth generation
collected full occupational histories, for men and women, as well as properties of their father and
mother. It should be added that, unlike the studies crowning the third generation (Erikson and
Goldthorpe 1992, Breen 2004), the fourth generation, because of a dearth of job histories for a
sufficient number of countries, still is not up to the height of the second and third generation.
Given the increasing richness of the collected data and the growing sophistication in data
analysis, it may be surmised that early generations were interested in descriptive issues, later ones in
comparative questions, and recent ones in explanatory problems. However, each generation described
and explained. Generational shifts were propelled by scrapping questions that ‘pose the issue poorly’,
to use an apt expression from the exemplar of the second generation (Blau and Duncan 1967: 402).
This aspect tends to be overlooked, but is the most important one of generational change.
By subtracting father’s from son’s job, the first generation ascertained whether a man was
upwardly mobile, downwardly mobile or socially stationary. This result answered a descriptive
question and was the starting point of explanatory questions. The exemplar of the second generation
argued for the inadmissibility of the follow-up question of why some persons moved up and others
down. It is logically impossible for a person starting out at the highest level to climb, and for a person
at the lowest level to fall. And the category of stable persons lumps together those staying at the
highest level with those remaining at the lowest level, making for a disparate category unsuitable to
detailed analysis. For similar reasons, the question of why the balance of upward and downward
mobility differs between societies makes for trick questions too. After all, upward and downward
mobility rates are logically dependent upon the distribution of origin and destination positions.
According to the Duncan generation, questions should be about the strength of the association
between the status of the inhabitants of a society and the status of their parental home. A weak relation
between a man’s education and the extent to which he moved up compared with his father’s job, need
not be surprising. Given a strong relation between a man’s education and his job, the stronger the
relation between father’s job level and son’s level of education, the weaker will be the relation
between education and the mobility variable (Blau and Duncan 1967: 194-199). Given that sons who
attain a higher level of education have a father with a high job to begin with, only a few of them will
ascend, since their father already attained the highest job level.
The second generation avoided mobility variables (differences between scores), and computed
measures for the association between elementary variables. In this way, the con question of the effect
of education on the degree of upward or downward mobility is replaced by two fitting questions: the
question of the effect of father’s occupation on son’s education, and that of the effect of father’s
occupation and son’s education upon son’s job level.
3
The third generation did not estimate path models or other linear regression models. It deemed
it inappropriate to conceive of jobs as forming one status gradient. Instead this generation took
occupations as belonging to discrete categories that cannot be ordered along one dimension. However,
in its class schema classes I and II were above all other classes making up a society’s class structure,
and classes VI and VII below all others. This still allowed for hypotheses about higher and lower jobs.
Indeed, its exemplar declared an interest in the unequal outcomes of competitions between persons
from different origins for higher and lower destinations (Goldthorpe 1980: 77). To answer questions
about unequal outcomes, the third generation computed odds ratio’s. These measures are independent
of the frequencies in the marginals of a mobility table, and therefore of the structures within which
people compete. This generation also estimated log-linear models. These models yield parameters that
can be reworked into odds ratio’s.
The fourth generation held that the questions of each preceding generation seem
underspecified, and upon closer inspection turn out to be compound questions that do not lend
themselves to easy disbanding into doable questions. Mobility always should refer to two points in
time. However, and peculiarly, first generation tables referred to, say, father-son mobility in the
Netherlands in 1954, second generation path models for the socio-economic life cycle to the United
States of America in 1962, and third generation odds ratio’s to the employed male population of the
United Kingdom in 1972. These dates invoke the year of a man’s job, but not the year of the job of
their father when this man when young. Indeed, the latter year differs from father to father. But effects
of origin on destination over longer periods need not equal those produced during shorter ones. After
all, aging may affect a man’s job level. And dissimilar lengths spoil the possibility of answering
questions about the separate influence of factors like educational reforms and unemployment levels on
patterns of mobility. Or, as is now being said, age-, cohort and period effects cannot be identified.
That is why the fourth generation ascertained job histories. With these data, it becomes
possible to answer separately questions about the effects of the length of a person’s occupational
career, outcomes of educational reforms and consequences of business cycles. The fourth generation
accomplished this feat by explaining job mobility taking place during always equally long periods.
This period sometimes measured one month and sometimes one year, depending upon the details of
the job histories collected.
Models for the socio-economic life cycle reappraised and a string of questions proposed
One criticism of models of the socio-economic life cycle, also called status attainment models, says
that they are not pertinent to sociology proper (Lenski 1984: xvi-xvii). An inspection of the graphs
with dots and arrows makes clear that the points stand for properties of persons. None of them refers
to the societies these persons belong to. They pertain to individual features like their education and the
job of their father. It is as if societies do not consist of structures that hinder attainment.
4
Yet for this reason a path model does not belong to psychology, the science of humans taken
in isolation. Arrows refer to societal properties, like returns, in a specific country at a certain moment,
in terms of a higher job to one more year of education. This is the case since the persons behind the
points of a model form a representative sample from the population of that country at that date. And
this property, the strength of the relation between two individual variables, may vary between
societies. It also did vary within one society from one birth cohort to another (Blau & Duncan 1967:
178). The objection of individualism against the Duncan generation disappears, if comparisons are
envisaged. Such comparisons have taken off (Rijken 1999). The models of this paper will pertain to
one society, the Netherlands, and three labour market entry cohorts, for the 1950s, 1960s and 1970s.
Since a series of questions on women, stratification and mobility needs to be argued anew and would
turn out to be not only quite different, but also more complex, this paper only studies men.
One little noticed advantage of the second and third generation, compared with the first one, is
that the two later generations deal with several quite distinct questions about societies in a neat order.
Indeed, these generations replaced the first generation question of the over time and between societies
varying effect of origin on destination (in the singular) by three questions: the effect of father’s job
status on son’s level of education, the effect of father’s job (net of the effect of son’s level of
education) on the level of son’s first job, and the effect of father’s job (net of the effect of son’s
education and the level of son’s first job) on son’s current job status. The present paper regards
questions about ‘repeat effects’ of father’s status worthwhile.
This paper’s first question therefore will be about origin effects on a man’s education: Are
family background effects on the level of education for later labour market entry cohorts of the same
magnitude as for earlier ones? Of course, this question, when applied to the Netherlands, has been
answered before. Here we answer it not only with a quite uniform data set, but also with a data set
suitable for answering a string of other questions and capable of yielding a bird’s eye view.
It now will not be difficult for readers to guess our second question: Did for successive cohorts
the effect of a man’s education on his first job increase, while the effects of a man’s background, net of
education, decreased? Some of our later questions will be about effects on a man’s later job levels.
The fourth generation had a valid point against all earlier generations: because the period a
person is active on the labour market forms a confounding factor, the strength of origin effects may be
over- or underestimated. Yet it is easy to replace the improper question about what explains a person’s
present job by a proper one, if more detailed data about a person’s job history are available. For this
reason this paper raises several questions about net effects on a man’s jobs ten years and twenty years
after leaving school. Our third question will be about effects of background and education on the level
of a man’s job after ten and after twenty years. Are effects of background and education on job after
ten and twenty years so powerful, that they remain present after taking into account their effects on a
man’s first job, or do background an education achieve all their results in one blow?
5
One finding of the model of the socio-economic life cycle for the USA in 1962 was that a
man’s current job is higher if, independent of a person’s level of education, that man’s first job was
higher. This interesting result has been neglected a bit. Careers are to some extent self-reinforcing:
among persons with the same level of education, those who started out in higher job achieve even
higher jobs, with those who start out in a lower job do not climb up as much. Therefore, this paper’s
fourth question is: to what extent does a man’s job status ten years and twenty years after entering the
labour markets, depend upon this person’s first job, net of this man’s education and background?
Although it is quite easy to do away with effects of aging on job status, it requires some
pondering to formulate proper questions about aging effects. An older person, all another things
remaining equal, indeed has had more opportunities to find the job that matches his wishes and skills.
But then, the man who has found that job also has been exposed to more opportunities to lose it. The
Netherlands is a free market economy and its governments have been unable to do away with business
cycles and sometimes quite high unemployment rates. For that reason yet another question is in order:
Apart form the effect of the present level of unemployment upon the level of a man’s job ten years after
entering the labour market, and his job twenty years later, to what extent are the effects of a man’s
level of education on this man’s job level ten and twenty years later stable, or do they depend on the
prevailing unemployment level? This is this paper’s fifth question.
The two final questions of the present paper seek to deal with a criticism on fourth generation
research. It collected job histories, but by breaking them down into observations for mobility during
periods of the same length, it did away with characteristics of job histories that feature in common
sense notions: careers progress and attain a peak. This paper addresses questions about career progress
by focussing a man’s first job, his job ten years later and his job twenty years later. But these questions
leave the issue of career peaks untouched. For that reason, the sixth question will be about what
happens, or does not happen, with a person’s job status during each and every month of a person’s job
history. Given that people at each point of their job history have not yet attained, or reach the peak of
their career, do background, education, period on the labour market and unemployment rate affect a
man’s chances of remaining below peak or arriving on top? So this paper’s sixth question is about
factors making for people reach the high point of their career.
The seventh question recognizes that peaks differ for persons. Indeed, the Duncan generation
held so against first generation questions. This paper’s final question takes as its starting point the
highest job level reached by persons 45 years or older. Is the level at which a man’s career peaks not
only determined by background and education, but also by the level of a man’s first job and the length
of the period a man has been on the labour market? Note that this question need not be about a man’s
job level at age 45 years, since men may slide down after having reached their career peak.
To keep the size of this paper within bounds, it skips questions about sliding after reaching
career peaks.
6
Hypotheses: families, markets and states
The results of the Duncan generation of research on stratification and mobility have been heavily
criticized for a long time from a theoretical point of view. The terminology may have changed, but the
old argument that ‘status attainment research’ is ‘a-theoretical’ stands behind the current name of
abuse ‘variable sociology’ (Hedström 2005). However, the criticism was cogently answered (Horan
1978) by pointing towards quite specific hypotheses. They state that the shift from agriculture as the
prime mode of subsistence in societies like the Netherlands, the United Kingdom and the United States
of America, to industry and after that, with the decline of manufacturing, to knowledge-intensive
services, was accompanied by a shift in the norms for status attainment. Whereas agrarian norms
stipulated ascription (following in father’s footsteps), the new ones prescribed achievement. The
higher level jobs are supposed to go to the persons qualified for it. In addition, a person’s education
would become less depended upon the social origin of a person.
Of course, this hypothesis may turn out false in empirical research, and Duncan’s test was a bit
of a failure. This test sure seemed strange. The model of the socio-economic cycle for the USA in
1962 did not include men with a farm background. However, Duncan did test hypotheses by studying
whether a person’s first job after leaving school became more dependent upon a person’s level of
education and less dependent upon this person’s social origin (Blau and Duncan 1967: 178).
But then, it may be the case that the from-ascription-to-achievement hypothesis faces obvious
difficulties, or that a more general theory implies its falsity. To begin with, a lot of norms are violated.
Policemen and judges enforce norms about life and property, but there is no equivalent when it comes
to fair decisions after job applications. Of course, profits of corporations that reject applicants with
better credentials, will suffer. Yet not infrequently corporations are in the red. Secondly, behind the
from-ascription-to-achievement thesis stands the idea of education as a functional requirement of
industrial society (Kerr et al. 1960). This is an obscure hypothesis. Persons have unsatisfied needs, and
children of lower family background are less likely to attain a higher level of education. So why would
the needs of societies, if they have them, become fulfilled? There will be no general trend towards
smaller effects of family background on education.
Thirdly, when ‘positive externalities’ of education are highlighted, a new difficulty arises. The
theory of collective goods says that positive externalities are not produced optimally by free markets
(Van den Doel 1978), and industrial societies mostly are free market societies. This theory also holds
that states sometimes provide collective goods from taxes. Indeed, schooling is compulsory in the
Netherlands, and the school-leaving age has been rising. State-funded stipends for post-compulsory
schooling are substantial, perhaps more so than in the United Kingdom under Thatcher, and less so
than the Sweden under Palme. The hypothesis answering this paper’s first question therefore reads
that, at least as far as the Netherlands goes, for later cohorts family background effects on education
are smaller than for earlier ones.
7
However, no vanishing effect is predicted. Educational reforms may have done away with
effects of parental ‘material resources’, but they brought in effects of parental ‘cultural resources’.
Bourdieu’s (1979: 177) theory of compensatory strategies argues that higher class parents compensate
for laws stipulating stipends for all, by transferring more cultural resources to their children.
The Dutch state since the mid 1980s, when youth unemployment was at record levels, also
invested heavily in policies facilitating the transition from school to work. The answer to this paper’s
second question states that the effect of a man’s level of education on the level of his first job has
increased, whereas the net effect of his background decreased. But the expectation also is that net
effects of parental background did not disappear. Educational credentials are resources, but so is
family background, since higher origins make for the possession of less tractable and more tacit
resources not only valuable in schools, but also within corporations.
When applying for jobs, persons not only state their level of education, but also their
experience. Although employers will take experience of persons with previous jobs into account, they
will not bypass a person’s educational credentials. When it comes to answering this paper’s third
question about the level of a man’s job ten and twenty years after his first job, the hypothesis predicts
net effects of their level of education. The same reasoning answers our fourth question: a person’s job
ten years after a person’s first job will depend on his first job, and so will his job after twenty years.
This paper’s fifth question is about effects of unemployment levels. Although the Netherlands,
because of anti-cyclical budget policies of its state, witnessed full employment levels until the 1970s,
since then the business cycle has reappeared, with unemployment levels at the upswing of the business
cycle higher than in the 1950s and 1960s. In addition, the shift from manufacturing, never strong in the
Netherlands, to knowledge intensive services took a big toll in the 1980s. In those years, the growth of
the Dutch economy was at its post-war low. Hence, the hypothesis pertinent tot the fifth question of
the present paper postulates independent effects of the unemployment level on job levels. It also
predicts weaker effects of education on the job levels during a person’s career.
It is difficult to spell out hypotheses answering our sixth question. The prime hypothesis is
about the effect of the length of the period a person has been on the labour market. On the one hand,
hypotheses about technological exigencies hold persons almost start at their peak. On the other hand,
hypotheses about fortuitous markets predict that the time it takes for persons to reach their peak may
be quite long. Since the size of effects remain unclear, it cannot be stated how they add up. But then, if
there is an effect of unemployment levels, given the development of unemployment levels in the
Netherlands, it should be stronger for later cohorts than for earlier ones.
The hypotheses answering this paper’s last question, about the level of the peak reached by a
person, are quite straightforward. Higher current unemployment levels makes for lower peaks, more
education makes for a higher peak, as does a higher social background. If a higher level of societal
technology makes for more efficiency, then the effect of the period a person has spent on the labour
market should become smaller from earlier to later cohorts.
8
Research design
Data
In this paper we analyse data from five retrospective surveys for the Netherlands: Netherlands Family
Survey 1992-1993 (Ultee and Ganzeboom 1992), Households in the Netherlands 1995 (Weesie et al.
1995) and Family Surveys Dutch Population 1998, 2000, and 2003 (De Graaf et al. 1998, 2000, 2003).
All five surveys feature random, nationally representative samples from the Dutch population and
face-to-face interviews with respondents at home. The number of respondents interviewed in the
surveys was 1800, 3354, 2029, 1561 and 2174, respectively, yielding in total 10918 respondents. From
this dataset, we selected men who were at least aged 45 at the moment of survey. We do not include
women of these ages, since the few with a full job history will be quite untypical. We selected only
men of 45 years and older to be pretty sure that they have reached their career peak. Given this
assumption of occupational maturity, it would be unwise and unnecessary to select only men who have
definitively retired from the labour market. Nevertheless, the applied selection of men of at least age
45 severely reduced the number of respondent to be analyzed. After list-wise deletion of respondents
for whom information is missing on any of the variables used in the multivariate analysis, a final
dataset of maximally 1087 men remained.
The surveys contain information on all the jobs persons held in the past, although with some
difference in detail. For all jobs held by a respondent, the beginning and ending dates are reported, as
well as information on the content of the job. In the Netherlands Family Survey 1992-1993, the work
histories were organised by job spells, while in the other surveys, work histories were ordered by
employer spells. Within each employer spell then, information was gathered on the jobs held. For the
survey Households in the Netherlands 1995, this was limited to the first and last job.
Measurement of variables
The level of the jobs of persons is measured by the International Socio-Economic Index of
Occupational Status (ISEI) (Ganzeboom, De Graaf and Treiman 1992). Scores are assigned to
occupational titles (using detailed information from the standard occupational classification developed
by Statistics Netherlands). The scale ranges from 16 for occupations with the lowest status to 90 for
occupations with the highest status. In the analysis, five measures for job levels are considered: the
job of the father when the respondent was aged 15, the first job of respondents, their job after ten
years, the occupation after twenty years, and the job of men at the moment of their career peak.
Level of education is based on six educational categories referring to the most distinct
qualification levels within the Dutch school system: elementary education (lo), lower secondary
education (lbo/mavo), higher secondary vocational education (mbo), higher secondary general
education (havo/vwo), lower tertiary education (hbo) and higher tertiary education (wo).
9
As stated, three labour market entry cohorts are distinguished: the 1950s, 1960s and 1970s.
Work experience is measured as the number of months in employment since entering the labour
market. We model curvilinear effects of working experience by including both a linear and quadratic
term for this variable. Yearly unemployment rates are taken from Statistics Netherlands (CBS 2007).
Results
Level of education
This paragraph remains to be written, as the pertinent model has not been estimated yet.
Occupational achievement at labour market entry
Table 1 presents linear regression models for the status of a person’s first job. Model 1 shows that
members from more recent labour market entry cohorts started in jobs with a higher occupational
status score than those from older cohorts. For instance, workers who started their occupational career
in the 1970s attain a job with a seven points higher status score than those who entered the labour
market for the first time in the 1950s. Furthermore, the model showss the status of father’s occupation
has a positive effect on his son’s occupational status.
From Model 2, it becomes clear that educational qualifications are quite important in the
allocation of individuals to jobs. The R-square increases from 0.139 to 0.380. The education effect
shows that high educated individuals enter the labour market at a much higher job level than low
educated ones. The difference between the highest and lowest qualified persons (that is, the contrast
between those with primary education at most and university graduates) amounts to 23 status points.
In addition, Model 2 reveals that the observed cohort effect disappears when taking account of
differences in education. In fact, the advantageous position of more recent labour market entry cohorts
in terms of the status of their first job can be attributed to their, on average, higher qualification level
at labour market entry. The direct effect of father’s occupational status on his son’s status is reduced
considerably, once controlled for educational qualifications. Around half [that is, 49 (1 - 0.162 / 0.317)
percent] of the inheritance of status by sons from is channeled through education.
None of the added interaction terms between education and father’s status on the one hand and
labour market entry cohort on the other, is significant (Model 3). This implies that the direct effect of
this achieved, respectively ascriptive characteristic at labour market entry has not changed over time,
which clearly undermines our hypothesis regarding this paper’s second question. Nevertheless, it is
clear that achievement is more important than ascription at this stage, given the much stronger impact
of education than father’s status on the status of a man’s first job.
[Table 1]
10
Occupational achievement during the career
Occupational status attainment is a process that does not stop at labour market entry. Persons try to
improve their status. Aggregate developments during the working career are displayed in Figure 1, for
labour market entry cohorts, and in Figure 2, for levels of education. First of all, it becomes evident
that status increases over their working career, irrespective of the labour market entry cohort they
belong to and the educational qualifications they possess. Second, the career profiles of these various
categories run by and large parallel, despite their initially different point of departure. There is at least
one visible exception. Persons who entered the labour market in the 1970s reached the top of their
career earlier than those from older cohorts. It also seems to be the case that tertiary educated men
peaked earlier than lower educated ones.
[Figure 1]
[Figure 2]
Linear regression models for the status of persons ten and twenty years after their first job inform in
more detail on career progression. They are presented in Tables 3 and 4. As these results resemble the
findings on a person’s first job (see Table 1), we will be brief. First, more recent cohorts reach a higher
job after ten and twenty years of working experience than older cohorts (Model 1). Second, father’s
status positively affects son’s status, although these origin effect seem to be weaker than that at labour
market entry. This result supports the hypothesis related to the third question of this paper. Third,
adding education to Model 2 makes the large part of the origin effect indirect. Qualifications – as in
the case of the status of the first job – strongly and positively impact status after ten and twenty years.
Fourth, Model 3 shows that macro-economic conditions with respect to a person’s status after twenty
years of working experience. The higher the current aggregate unemployment rate is, the lower the
status achieved at that stage. This finding corroborates the hypothesis concerning this paper’s fifth
question. Fifth, Model 4 reveals that – as predicted in the hypothesis with regard to our fourth
question – the status of the first job strongly affects status at later career moments. Moreover, the
status of the first job largely interprets the effect of education. In Model 4, the difference in status
achieved between the least and highest qualified persons is 10 and 13 points after, respectively, ten
and twenty years in the labour market. It was 22 points in Model 3, so that half the direct effect of
education can be attributed to differences in status of the first job. The impact of father’s occupation is
interpreted by status of the first job as well. In model 4 the remaining effect is not significant anymore.
Sixth, Models 5 and 6 show that there are no significant changes over time, with the exception of the
observation that the effect of the occupational status of the respondent’s first job on the status of his
job after ten years of working experience is smaller for the 1970s cohort than for the 1950s one.
11
[Table 3]
[Table 4]
Career peaks
To model for each man his career peak, we constructed, with the available job histories, a personmonth file. For each month since labour market entry, we established whether a man is at the top of
his career by comparing the status of his job in that month with the status of his job(s) in all the
working months thereafter (until retirement or the moment of interview). An event is defined for the
month when the respondent reaches (for the first time) his maximum status during the observed career.
[Figure 3]
[Figure 4]
Figures 3 and 4 depict the (cumulative) percentage of men who have reached their career peak, once
again for labour market entry cohorts and for level of education. Figure 3 shows that a minority of
men reached its peak immediately at labour market entry: the status of their first job was the maximum
status ever reached during their career span. In other words: a majority experienced upward mobility.
Members of the most recent labour market entry cohort reached their career peak earlier than those
from older cohorts. Almost 50 percent of the 1970s cohort men reached their top within 30 months,
whereas for the 1960s and 1950s cohort this was only within 100, respectively 120 months. After
some twenty years in the labour market (that is, 240 months), the 1970s cohort has converged to the
other two cohorts. Figure 4 indicates that persons with tertiary education are most likely at their peak
immediately. In their first job, 40 percent was at their maximum, whereas this percentage for those
with higher secondary education was below 20 percent and for the others less than 10 percent. During
their career, the various educational groups converge. Nevertheless, the cumulative percentage of
reaching the peak of their career always remains highest for tertiary educated men.
[Table 4]
This analysis of the speed of reaching the peak of one’s career peak is refined in Table 4. It presents
discrete-time event history models with multivariate effects of several covariates on the logged hazard
rate for making the peak. This rate reflects the conditional probability that a person reaches his career
peak in a particular month, given that this did not occur prior to this month. The estimated parameters
represent the change in the log odds of the conditional probability of peaking, caused by a one-unit
increase in the associated covariate. They are interpreted as relative risks (or actually, odds ratios).
12
Model 1 confirms the descriptive findings of Figure 3. Men from the 1970s cohort reached
their career peak earlier than men from the 1950s cohort. The implied odds ratio is 1.846 (e0.613). Men
form the 1960s cohort reach their top also faster. These findings lend considerable support to the
hypothesis answering our sixth question that technological developments in modern labour markets
require that persons reach their career peak in a shorter time span. The same holds for persons with a
father who had a high status job. These individuals peak earlier during their career too.
In Model 2, the educational level effect indicates that individuals, who graduated in tertiary
education, in particular at the higher, that is, university level, attained their maximum occupational
status more quickly than less educated persons.
The coefficients of Models 1 and 2 are underestimates, since duration effects are not modelled.
Model 3 includes the variable working experience and its squared term, plus the current aggregate
unemployment rate. In this model all other parameters are larger and several are significant now. All
in all, members from more recent labour market entry cohorts, persons from a higher origin and those
with more education, are more likely to reach their career peak in a certain period than their
counterparts. Furthermore, individuals, with more working experience, are more likely to attain their
maximum status than less experienced workers. In addition, the unemployment rate negatively affects
the likelihood of peaking. In times of high unemployment, it takes longer before individuals enter a
job in which they reach their maximum status than in times of low unemployment.
All results remain unchanged when the status of a person’s first job is taken into account
(Model 4), with the exception of the effect of father’s status. Surprisingly enough, it changes sign and
is negative now. Perhaps a ceiling effect, so feared by the Duncan generation, shows up here. A
person’s first job itself has a strongly positive effect on reaching the top.
Model 5 is the same as Model 3, with the exception that interaction terms between father’s
status and education on the one hand and cohort on the other are added. Four interactions are
significant; they all involve interactions between education and cohort. First, persons with higher
secondary vocational education reached the top relatively more quickly when they entered the labour
market in the 1970s than in the 1950s. Second, individuals with higher secondary general education
relatively more likely reached their career peak early when they belong to the 1950s cohort than to the
1960s one. Third and fourth, the same holds for persons who graduated from lower, respectively
higher tertiary education.
Model 6, finally, includes interactions between the status of the first job and labour market
entry cohort as well. None of two new terms are significant. Two of interactions between education
and cohort from Model 5 loose their significance.
[Table 5]
13
Models for the status of a person’s peak job are presented in Table 5. Model 1 shows that there is no
significant difference in status between cohorts. Combined with the fact that persons from more recent
cohorts entered the labour market at a higher job level than those from older cohorts (see Table 1), this
implies that the former reached their career peak more quickly. And this is exactly what was shown in
Table 4. Furthermore, Model 1 of Table 5 tells that father’s status matters. The peak in the career of
advantaged origin sons is higher than that in the career of persons from a low background. However,
the impact of father’s status is weaker at the point of career peak than at labour market entry.
Model 2 once again shows that a large part of the effect of father’s status can be attributed to
differences in education. The effect of father’s job, although reduced by 40 percent, is still significant.
Education itself has a strong positive impact on maximum status. However, at this stage of a person’s
career the education effect is weaker. For the first job, the difference in average status between persons
with the highest and the lowest education was 23 points (Table 1). For the peak job it is 17 points.
When working experience is taken into account (Model 3), the education effect still holds.
Despite the notion that employers take working experience as a direct measure of labour productivity,
and use educational qualifications as a ‘proxy’ for the productive value of workers at labour market
entry, when controlling for working experience, the predictive power of education remains unchanged.
The effect of working experience has the expected positive sign, but is not significant.
In Model 4, the status of a person’s first job is added. Maximum status is very strongly
affected by status at labour market entry. Moreover, the status of the first job to a large extent mediates
the effect of education. In Model 3, the difference in status points between the least and the most
qualified persons amounted to 17 points; in Model 4, that difference now is less than eight points. This
implies that more than half of the direct effect of education can be statistically explained by the status
of a person’s first job. The effect of father’s occupation is interpreted by the status of the first job as
well: the remaining impact of 0.51 of father’s occupation in Model 3 is reduced to 0.042 in Model 4.
Furthermore, the effect of working experience is significant after controlling for the status of a
person’s first job. Individuals with more working experience obtain a higher occupation at their career
peak than those with less experience. All in all, these findings confirm the expectations as formulated
in the hypotheses regarding the seventh question of this paper.
In Models 5 and 6, finally, statistical interactions of father’s occupation, the respondent’s level
of education and his first occupation with labour market entry cohort are tested again. None of
interactions is significant, indicating that the effects of these ascribed and achieved characteristics
have been constant over time.
Discussion
This paper broke down the theme of the effect of origin on destination, which has driven various
generations of research on societal stratification and mobility, into a series of questions. It raised
14
questions about effects on the level of education of persons, the level of their first job, the level of
their job ten years after their first job, their job twenty years later and their peak job. And each time
questions were raised about effects of parental background, a person’s level of education, and previous
jobs. The questions pertained to men entering the Dutch labour market in the 1950s, 1960s and 1970s,
in so far as their job history has been ascertained until the age of 45 years.
This paper found hardly any differences between cohorts. The effect of origin on education did
not change. Later cohorts did have higher job levels at the outset of their job history, but not after
taking father’s job level into account. The effect of father’s job on a man’s first job and later jobs did
not differ from cohort to cohort, nor did the effect of man’s education. The same goes for the effect of
father’s job on a man’s job after ten years, after twenty years, and a man’s peak job. One theoretical
implication of our findings therefore is that the various generations of research on stratification and
mobility have spent too much time in devising hypotheses about changes produced by states.
A man’s level of education, of course, was found to depend upon his father’s job level and it
also affected the level of his own first job. When explaining the status of a man’s first job, his father’s
job was influential, independent of a man’s education. Apart from that, when explaining a man’s job
after ten years, after twenty years, direct effects were found of a person’s own education, but not of the
job of this person’s father. Indeed, origin did not directly affect a person’s maximum status either.
These results show the import of ‘repeat effects’, but also underline the limits of this notion.
When explaining a man’s job after ten years, after twenty years and a man’s peak job, effects
of a man’s first job were found. This result shows that advantages accumulate during a person’s job
history: the level of a man’s first job, independent of a man’s level of education, affects his later jobs.
Therefore, hypotheses on cumulative advantage need elaboration (DiPrete and Eirich 2006).
One question the present paper did not address was that of career dips for men. It deserves
separate attention. In addition, until now no neat series of questions on women, stratification and
mobility has been addressed for the Netherlands. Indeed, already the wording of a neat series of
questions on this theme is an exercise on its own. Questions not only should seek to explain job levels,
but also (non)employment, and they should incorporate not only effects of family background, but also
of spouses (Bernasco, De Graaf and Ultee 1998).
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17
Table 1: Coefficients of linear regression analysis of occupational status of job at labour market entry
(regression effects)
Labour market entry cohort (1950s=ref.)
1960s
1970s
Status father’s occupation (ISEI)
* 1960s
* 1970s
Level of education (Primary (lo)=ref.)
Lower secondary (lbo/mavo)
* 1960s
* 1970s
Higher secondary vocational (mbo)
* 1960s
* 1970s
Higher secondary general (havo/vwo)
* 1960s
* 1970s
Lower tertiary (hbo)
* 1960s
* 1970s
Higher tertiary (wo)
* 1960s
* 1970s
Constant
R-square
Number of men
* p<0.05, ** p<0.01
Model 1
Model 2
Model 3
3.101**
7.404**
0.317**
1.596
2.154
0.162**
4.377
3.042
0.204**
-0.079
-0.017
-1.530
2.617
8.285**
14.758**
22.806**
29.645**
0.139
1087
30.723**
0.380
1087
-1.900
1.314
-3.152
2.706
-1.329
1.621
8.001*
2.168
-2.779
14.155**
0.481
0.891
21.295**
3.942
-0.936
29.316**
0.386
1087
18
Table 2: Coefficients of linear regression analysis of occupational status of job after ten years of
working experience (regression effects)
Labour market entry cohort (1950s=ref.)
1960s
1970s
Status father’s occupation (ISEI)
* 1960s
* 1970s
Level of education (Primary (lo)=ref.)
Lower secondary (lbo/mavo)
* 1960s
* 1970s
Higher secondary vocational (mbo)
* 1960s
* 1970s
Higher secondary general (havo/vwo)
* 1960s
* 1970s
Lower tertiary (hbo)
* 1960s
* 1970s
Higher tertiary (wo)
* 1960s
* 1970s
Status first occupation (ISEI)
* 1960s
* 1970s
Current aggregate unemployment rate
Constant
R-square
Number of men
* p<0.05, ** p<0.01
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
4.460**
9.121**
0.251**
3.036**
4.041**
0.099**
2.566*
2.495
0.099**
2.411**
3.504*
0.018
1.904
1.341
0.104*
-0.023
0.013
0.426
6.421
-0.006
0.017
0.053
-0.144
-0.101
0.642
-1.724
3.941
-1.289
3.505*
3.511*
2.240
3.272
0.780
-0.080
12.590** 12.612** 8.557** 11.089**
0.608
4.434
16.267** 16.245** 9.095** 16.635**
0.086
-1.190
21.615** 21.567** 10.483** 17.951**
4.964
4.145
0.491**
-0.687
3.176
0.139
1.741
1.601
0.460
6.594*
-0.178
7.485
8.880**
0.199
1.608
6.353*
3.192
8.838
0.543**
-0.008
-0.168*
0.214
-0.075
0.224
-0.043
36.221** 36.112** 35.857** 21.158** 36.367** 20.809**
0.127
0.368
0.369
0.529
0.374
0.538
1082
1082
1082
1082
1082
1082
19
Table 3: Coefficients of linear regression analysis of occupational status of job after twenty years of
working experience (regression effects)
Labour market entry cohort (1950s=ref.)
1960s
1970s
Status father’s occupation (ISEI)
* 1960s
* 1970s
Level of education (Primary (lo)=ref.)
Lower secondary (lbo/mavo)
* 1960s
* 1970s
Higher secondary vocational (mbo)
* 1960s
* 1970s
Higher secondary general (havo/vwo)
* 1960s
* 1970s
Lower tertiary (hbo)
* 1960s
* 1970s
Higher tertiary (wo)
* 1960s
* 1970s
Status first occupation (ISEI)
* 1960s
* 1970s
Current aggregate unemployment rate
Constant
R-square
Number of men
* p<0.05, ** p<0.01
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
3.704**
4.459**
0.240**
2.427**
0.201
0.108**
4.578**
1.243
0.106**
4.088**
0.517
0.046
3.601
-1.711
0.108*
0.005
-0.020
4.753
1.122
0.018
0.052
0.007
3.177**
2.984*
3.516*
5.112**
4.987**
4.037**
12.518** 12.144**
9.205**
1.421
2.251
3.241
2.581
3.958
4.768
4.358*
3.165
1.039
1.769
4.025
3.887
10.769** 7.248*
-1.173
-0.945
9.381
11.357
16.647** 10.427**
0.253
1.678
2.688
4.294
19.922** 10.567**
3.341
4.315
4.264
7.496
0.439**
-0.106
-0.125
-0.496*
-0.501**
41.280** 28.426**
0.309
0.405
1069
1069
16.697** 16.572** 11.311**
21.757** 21.624** 13.462**
0.360**
-0.469*
-0.481*
40.912** 38.499** 40.493** 29.519**
0.088
0.301
0.304
0.398
1069
1069
1069
1069
20
Table 4: Coefficients of discrete-time event history analysis of reaching career peak (logit effects)
Model 1 Model 2 Model 3
Labour market entry cohort (1950s=ref.)
1960s
0.141*
0.150
1.295*
1970s
0.613** 0.958** 5.775**
Status father’s occupation (ISEI)
0.009** 0.011*
0.055**
* 1960s
* 1970s
Level of education (Primary (lo)=ref.)
Lower secondary (lbo/mavo)
-0.293
-1.144
* 1960s
* 1970s
Higher secondary vocational (mbo)
0.174
0.576
* 1960s
* 1970s
Higher secondary general (havo/vwo)
0.411
2.502*
* 1960s
* 1970s
Lower tertiary (hbo)
1.085** 4.297**
* 1960s
* 1970s
Higher tertiary (wo)
2.058** 9.203**
* 1960s
* 1970s
Status first occupation (ISEI)
* 1960s
* 1970s
Working experience
0.078**
Working experience squared
-0.000**
Current aggregate unemployment rate
-0.081*
Constant
-5.355** -5.426** -16.537**
Lnsig2u constant
-9.814
1.079** 4.567**
Model Chi-square
83**
194**
1245**
Df
3
8
11
Number of men
1086
1086
1086
Number of man-months
134684
134684
134684
* p<0.05, ** p<0.01
Note: ’lnsig2u constant’ denotes the log of the panel-level variance of the intercept
Model 4
Model 5
Model 6
2.129**
5.667**
-0.036**
-4.758**
-3.054
0.033
0.061
0.018
-2.280
-9.518*
0.016
-0.012
-0.024
1.396
-3.054
-1.139
2.001
-0.176
3.037
3.682
1.220
-2.347** -1.316
3.428
2.101
11.657* 10.606*
3.859*
0.100
-1.491
5.574*
3.924
5.274
4.439
4.703** 2.592** -1.528
3.786*
2.487
8.878
7.978
9.915** 2.408
-1.350
12.177** 7.274**
9.126
5.047
0.282**
0.222**
0.043
0.220
0.100** 0.087** 0.074**
-0.000** -0.000** -0.000**
-0.033
-0.030
-0.069
-28.039** -14.225** -21.107**
4.804** 4.769** 4.159**
918**
1304**
1691**
12
23
26
1086
1086
1086
134684
134684
134684
21
Table 5: Coefficients of linear regression analysis of occupational status of job at career peak
(regression effects)
Labour market entry cohort (1950s=ref.)
1960s
1970s
Status father’s occupation (ISEI)
* 1960s
* 1970s
Level of education (Primary (lo)=ref.)
Lower secondary (lbo/mavo)
* 1960s
* 1970s
Higher secondary vocational (mbo)
* 1960s
* 1970s
Higher secondary general (havo/vwo)
* 1960s
* 1970s
Lower tertiary (hbo)
* 1960s
* 1970s
Higher tertiary (wo)
* 1960s
* 1970s
Status first occupation (ISEI)
* 1960s
* 1970s
Working experience
Working experience squared
Current aggregate unemployment rate
Constant
R-square
Number of men
* p<0.05, ** p<0.01
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
1.307
1.758
0.247**
0.805
-1.475
0.146**
1.386
-0.278
0.151**
0.500
-0.002
0.042
4.299
5.404
0.199**
-0.078
-0.073
3.609
2.140
0.078*
-0.005
-0.103
1.605
1.753
2.585*
4.070**
4.280**
3.281**
12.459** 12.994**
8.420**
15.135** 15.693**
8.718**
16.548** 16.977**
7.511**
0.654**
0.014
0.000
-0.210
32.884** 31.094** 28.487**
0.082
0.276
0.288
816
816
816
0.024*
-0.000
-0.027
7.757**
0.582
816
1.407
3.293*
0.660
-1.787
-1.229
1.483
4.392** 3.807*
-0.971
-1.421
-0.755
-0.032
10.123** 4.868
4.013
3.605
2.211
8.376
17.087** 10.064**
-0.364
-0.819
-8.491
-4.687
15.124** 4.818
3.071
2.956
-0.345
5.056
0.650**
-0.008
0.049
0.015
0.024*
0.000
-0.000
-0.161
-0.002
26.457** 6.137*
0.299
0.593
816
816
22
70
Occupational Status
60
50
40
30
20
0
60
120
180
LM Entry cohort 1950s
240
300
360
LM Entry cohort 1960s
LM Entry cohort 1970s
Figure 1: Average occupational status for different labour market entry cohorts by working experience
(in months)
70
Occupational Status
60
50
40
30
20
0
60
120
180
Primary and Lower Secondary
240
300
360
Higher Secondary
Tertiary
Figure 2: Average occupational status for different levels of education by working experience (in
months)
23
100
90
Percentage at max
80
70
60
50
40
30
20
10
0
0
60
120
180
LM Entry cohort 1950s
240
300
360
LM Entry cohort 1960s
LM Entry cohort 1970s
Figure 3: Percentage having reached career peak for different labour market entry cohorts by working
experience (in months)
100
90
Percentage at max
80
70
60
50
40
30
20
10
0
0
60
120
180
Primary and Lower Secondary
240
300
360
Higher Secondary
Tertiary
Figure 4: Percentage having reached career peak for different levels of education by working
experience (in months)
24