Mobility and the changing structure of occupations: a cross-cohort comparison
Craig Holmes, Ken Mayhew and Felix Chow1
ESRC Centre on Skills, Knowledge and Organization Performance,
University of Oxford
Abstract
We look at the way occupational mobility may have been affected by technological progress and the
process of routinisation – the decline in the relative employment of middle wage routine occupations
(Autor, Levy and Murnane, 2003; Goos and Manning, 2007). We focus on two key questions:


Where do workers in routine occupations end up if they are displaced from their jobs by
routinisation, and does this differs between younger and older workers?
Do younger workers recognize that declining routine occupations may affect existing career paths
and choose different entry points into the labour market than older cohorts?
To compare older and younger workers, we will use panel data on occupations, education and training
from 1981 onwards, using the 1958 National Child Development Study (NCDS) and the 1970 British
Cohort Study (BCS). As well as comparing differences in initial occupational choices, this paper presents
a mobility analysis using logistical regressions. The effect of routinisation on job moves for each cohort is
separated out from other driving factors, such as career advancement or job mismatch.
Keywords: Class / occupational stratification; Labour markets; Transitions.
1
Correspondence email: craig.holmes@education.ox.ac.uk
1
Introduction
Over the previous three decades, technological progress has driven a shift in the occupational structure of
many countries, including the UK. Some jobs comprise of a number of tasks which could be replaced by
ICT capital. As the price of such technology falls, so too does employment in these jobs. These jobs are
referred to as routine in the sense that the tasks performed by workers in them tend to follow a series of
instructions, which could be replicated by an appropriately programmed machine. This process is often
referred to as routinisation (Autor, Levy and Murnane, 2003). Along with the related phenomena of
polarisation – which identified routine occupations as mostly middle-wage jobs – much of the related
discussion has been on the implications of these changes for wage inequality (Goos and Manning, 2007;
Autor, Katz and Kearney, 2006). However, changes in the occupational structure have potentially
important effects on mobility as well, but as yet these effects have not been rigorously analysed. With
improving upward mobility often mentioned as an ambition of successive governments, it is important to
establish what barriers exists in order to devise policies to overcome them.
There are numerous dimensions to the effect routinisation may have on mobility. In a recent paper
Holmes (2011a) examined the labour market outcomes of routine job employees displaced by
routinisation. A key question is whether these workers are able to move to well-paid non-routine jobs, and
if they are, what factors contribute to this upward mobility. Using data from the 1958 National Child
Development Study (NCDS), the paper presents a mobility analysis of these routine workers between
1981 and 2004, separating out the effect of routinisation on job moves from other driving factors, such as
career advancement or job mismatch. As expected, periods where the employment share of routine jobs
fell markedly across the entire economy were periods which witnessed increased the mobility of routine
workers towards both high and low wage non-routine jobs. The relationship between routinisation and
mobility is mediated through the qualification levels, specific skills and experience of workers.
We add to this analysis by comparing the experience of the NCDS cohort with a younger cohort taken
from the 1970 British Cohort Study. Members of this group entered the labour market near the end of the
1980s, after a decade of routinisation-driven changes to the structure of occupations. It is realistic to
expect that these changes may affect different cohorts in different ways. This paper presents some initial
findings from our analysis of two questions, through a comparison of the two cohorts. Firstly, we examine
where workers in routine occupations end up if they are displaced from their jobs by routinisation, and
whether this differs between younger and older workers. Secondly, we look at whether younger workers
recognize that declining routine occupations may affect existing career paths and choose different entry
points into the labour market than older cohorts.
The paper is arranged as follows. Section 2 provides a brief overview of an economic model of
occupational structure and mobility. This model is tested in the remainder of the paper. Section 3 sets out
the empirical strategy, including the methodology used in Holmes (2011a) to identify mobility effect of
routinisation, compared to other factors. Section 4 discusses the two survey from which the data is drawn.
Section 5 and 6 present the results of the two main analyses. Section 7 concludes, and points towards
further directions for this research.
2
The determinants of occupational mobility: a theory
Holmes (2011b) presents a model of the economy where production depends on the input of four types of
labour: professional, managerial, routine and service. This model will be briefly summarised in this
section, as it motivates the empirical strategy. Routine labour is a perfect substitute for capital in the
production process, so, as this technology becomes cheaper, it drives down routine employment and
wages. Employment in professional occupations requires the necessary accreditation or qualifications.
Managerial occupations and service occupations have no accreditation requirement, and represent high
skill and low skill non-routine occupations respectively. Labour market outcomes, and hence mobility, is
driven by the human capital of the individual, which comprises of a number of different elements,
including formal education, on-the-job experience and innate productive ability.
Workers differ in their levels of education attainment – they may be either university graduates, high
school graduates or unqualified. Individuals with higher attainment are able to work in the better jobs,
either because these jobs require higher levels of skills developed through more education, or because
education acts as a signal in a screening process where the most able invest in more education. University
graduates are able to supply their labour to either managerial or professional occupations. High school
graduates supply their labour to either managerial or routine occupations. Finally, the unqualified are able
to supply their labour to either routine or service occupations. Thus, obtaining higher qualifications can
facilitate upward progression.
Within each educational category, workers differ in their productivity in each occupation. This relative
productivity can reflect a number of different dimensions. Firstly, it can represent differences in general
cognitive abilities. For example, some people may be innately better problem solvers, making them more
productive managers, or possess better tool-handling skills, making them better process operatives (a
routine occupation). Secondly, individuals develop capabilities through experience which allows for
progression to better jobs, leading to the creation of a career. For example, working for a time in a
workshop is considered necessary experience for promotion to the position of shop foreman. This type of
mobility was introduced in a formal model by Sicherman and Galor (1990). It is included in the model by
allowing relative productivity in better occupations to increase as workers get older. Thirdly, individuals
develop specific skills from working in a particular occupation, where these skills are only useable within
that occupation. This is included in the model by allowing the relative productivity of the job currently
being performed to increase as the worker spends more time in it. Wages in each occupation are set per
effective unit of labour input, so more productive workers earn more for the same time input. In the
absence of routinisation (i.e. with stable relative wage rates) mobility would be in the form of career
progression, as individuals become relatively more productive in better occupations, and as a result may
move occupations as they get older2.
A change in the relative wages of the different occupations alters supply patterns and generates additional
mobility. For example, an exogenous fall in the wage of routine workers (caused, as in the case of
routinisation, by a fall in the price of ICT capital for which routine labour is substitutable) would lead
some unqualified workers to move to service occupations while some high school graduates would supply
their labour to managerial work3.
The model also makes predictions about the outcomes of different cohorts. For example, the younger
cohort may be more mobile – skill specificity make older workers less likely to move between
occupations and more likely to accept falling wages, whilst young workers are less keen to move into
those in decline. Moreover, they may be better placed in terms of transferable or generals skills to move
out of routine occupations into those better jobs. At the same time, general career-progressing experience
can increase the prospects of upward mobility following routinisation.
In addition, as a result of entering at a time where some of these occupational changes were starting to be
recognised, the entry points of younger workers might be different. They may have taken into account
changes in established career paths that older generations who entered prior to these changes were unable
to. If they anticipate a further decline in routine occupations, they are less likely to want to develop
specific skills in these occupations. These factors have been discussed by Autor and Dorn (2009), who
argue that as a result, routine jobs are getting 'older'.
2
Assuming that this effect outweighs the routine occupation specific skill effect.
This is the sort of occupational mobility that a part of the literature tends to focus on, which starts with the
assumption that changes in employer or job arise when there is a shift in an individual's information set or
preferences, or the external labour market environment, such that an alternative occupation yields a higher expected
lifetime utility.
3
3
Methodology
We run two analyses in this paper. The first analysis follows Holmes (2011a), which separates out the two
major reasons for occupational transitions identified in the model: career progression, and displacement
caused by routinisation. Simply looking at the occupational mobility of routine workers over time will
capture both. Ideally, we would compare against a counterfactual cohort, looking at the probability of
moving between different occupational categories for a workforce unaffected by routinisation, and one
that entered the labour market just as routine occupations began to decline. However, an appropriate early
cohort study does not exist in the UK.
The methodology in Holmes (2011) looks at transitions between occupational groups are examined across
a number of periods of time. The factors affecting transitions from routine occupations are estimated
using a binominal logistic regression, where the independent variables are education, specific experience,
age and two demographic variables (gender and race), running one regression for each final occupation
group. As a final explanatory variable, a measure of routinisation is included for each period of transition.
This final addition to the model can, at least to an extent, separate out occupation moves caused by
routinisation and those caused by general career progression.
The logistic regression (or logit model) is set up as follows. Let Yi be a dummy variable which takes value
of 1 if the individual i is in the given occupational group Y at the end of the period. Then, the logit model
specifies the functional form of the probability distribution over belonging to a certain occupation at the
end of the period t as:
Pr(𝑌𝑖𝑡 = 1|𝑋𝑖𝑡 , 𝑅) =
exp(𝛼+𝛽𝑋𝑖𝑡 +𝜃𝑅𝑡 )
1+exp(𝛼+𝛽𝑋𝑖𝑡 +𝜃𝑅𝑡 )
= 𝑙𝑜𝑔𝑖𝑡 −1 (𝛼 + 𝛽𝑋𝑖𝑡 + 𝜃𝑅𝑡 )
Rearranging gives the log odds ratio, z, as a linear function:
Pr(𝑌 =1|𝑋𝑖𝑡 ,𝑅)
)
𝑖𝑡 =1|𝑋𝑖𝑡 ,𝑅)
𝑧𝑖 = ln (1−Pr(𝑌𝑖𝑡
= 𝛼 + 𝛽𝑋𝑖𝑡 + 𝜃𝑅𝑡
(1)
where Xit is a vector of educational attainment, specific experience, age, cohort and other personal
characteristics of individual i (including any interaction terms), in period t, the period of transition, and R
is a measure of routinisation (or the change in the occupational structure) in that period. The time period
may enter the equation in a number of forms, but the baseline model includes it as a linear trend and is
interpreted as the accumulation of career-progressing experience.
The second analysis looks at early occupational choices. This assesses the hypothesis that, when faced
with the prospect of declining routine jobs, different cohorts may enter the labour market at different
ways, with less people moving into routine occupations in cohorts that are more able to anticipate the
declining occupations. We estimate a logit model for the probability that a worker is in a routine
occupation as a starting occupation. A baseline model predicts the probability conditional on qualification
levels personal characteristics, and the cohort. This model is extended to interact cohort with
qualifications, to see whether people of different education levels systematically start their working lives
in different places across the two age groups.
4
Data
These models are estimated using the National Child Development Study (NCDS) and the 1970 British
Cohort Study (BCS). The members of the NCDS study were all born in a single week in March 1958.
Data has been collected on these members in a series of waves. The most useful waves for assessing
labour market outcomes over a period where routinisation has taken place are between the fourth and
seventh waves, taken in 1981, 1991, 1999-2000 and 2004-5 respectively. The fourth wave is the first one
taken after the school leaving age (respondents were aged 23) and records early labour market experience.
The seventh wave was completed in 2004-5 (respondents were aged 46-47), and has the most recent data
on wages, employment and education. It is possible to construct an entire working life history over this
time period using responses from all four waves, including periods of employment, unemployment, selfemployment and non-participation for a number of reasons such as sickness or further education. As with
all longitudinal studies, there are missing data. The sample size is around 12,000 for the fourth wave, and
around 10,000 for the seventh wave. Employment data are available for a subset of these observations.
Just under 5000 individuals report employment data in both the fourth and seventh waves, though this
sample increases if we look at those who were non-employed or unemployment in one of the waves.
The BCS is a longitudinal study following 17,200 subjects born in England, Scotland, Wales and
Northern Ireland in a particular week in April, 1970. The cohort has been surveyed in series of waves
since the study's inception, and until now there have already been eight waves. As the focus of our study
is on labour market outcomes, we restrict our sample to the fifth wave onwards, when the subjects are at
the ages of twenty-six, twenty-nine, thirty-four, and thirty-eight. The fifth wave, taken in 1996, is the first
full wave taken after the school leaving age of the subjects4. The most recent wave is the eighth wave,
which was completed in 2008. There is missing data in our dataset. The fifth wave has a sample size of
9,000, and the eighth wave has data on around 8,900 individuals. Approximately 6,500 subjects are
reported in both the fifth and the eighth wave.
4
The forth wave was taken in 1986 when the subjects were sixteen. There was a sample-survey in 1992 where only
a sample of 1600 of the subjects were surveyed.
The data suggests six periods of transition for the first analysis: 1981-1986, 1986-1991, 1991-1995, 19951999, 1999-2004 and 2004-2008. For the NCDS, four of these periods coincided with the actual survey
years of the dataset (although we do not include the 2008 NCDS wave), and the remainder (1986 and
1995) fell in the mid-point between two surveys. For the BCS, we start with occupational data in 1995.
The remainder of the years coincided with the survey years. While it is possible to construct a full
occupational profile for all study members in all years, answers to questions about current jobs would
seem more reliable than answers to questions about past jobs.
4.1
Occupational mobility
Occupations of employment are measured using the narrowest available occupational coding. One
problem with doing this over a long period of time is that the system of coding occupations has changed
three times since 1980. The 1981 wave uses the KOS (Key Occupations for Statistical Purposes) system
of job title classification, which categorises occupations within the 18 CODOT (Classification of
Occupation and Directory of Occupational Titles) major groups, while the 1991 and 1999 surveys use
SOC90 and the 2004 wave uses the most recent SOC2000 classification. The SOC2000 coding system of
occupations has a four level classification system, from major group (first digit) to unit group (fourth
digit). To make data comparable between 1981 and 2004, a conversion system was derived between KOS
and SOC2000 codes, using the descriptions of occupations provided for each group. The conversion is not
always perfect (see Holmes, 2010, for a discussion). In some cases a category in SOC2000 could apply to
several categories under KOS (and vice versa) and subjective judgements have been made. In some cases,
observations have been dropped because it was not possible to place one KOS code into a single
SOC2000 code. In the NCDS, total exclusions on this basis account for 12.43% at the minor group (threedigit) level for the 1981 survey.
A similar conversion was created between SOC90 and SOC20005. These two classification systems had a
much more overlap in terms of the descriptions of each category. A conversion was made from each
SOC90 occupation to a 4-digit SOC2000 category, where descriptions were on a similar level of
aggregation. These were then reduced into 3-digit categories which are used in the analysis.
Each 3-digit category was assigned to one of the six occupational categories, two more than the four
categories discussed in the model, as shown in Table 3. This adds an intermediate occupation category the
two high skill non-routine occupations on the basis that such occupations (including associate
professionals and technicians) have different entry requirements than professionals and potentially
different employment trends than managerial occupations. Moreover, a small category for manual non5
Both conversions are available upon request.
routine occupations was added to distinguish this sort of low wage non-routine work from service
occupations.
The allocation between different occupational categories were based on the wages (using average values
from NCDS data in the 1981 and 2004 waves), description and change in employment share (using
Labour Force Survey data). Aside from a few obvious categories (such as those which are clearly
professional from the descriptions), any occupation which experienced a trend decline in employment
share is considered to be a routine occupation. Looking at the wages and descriptions is used as a
common sense check – all these occupations have middle range wages and their descriptions suggest the
work involves administrative or manual processes which could be replaced by computer technology6.
4.2
Educational attainment
Across the four waves of the NCDS used in this paper, there are numerous systems for recording
educational achievement, including detailed data on a wide range of vocational courses which have
declined in importance in recent years. As a way to bring all of this data together, the highest NVQ
equivalent level across time is recorded7. Each individual has two educational variables – a highest NVQ
level in academic courses and a highest NVQ level in vocational courses, with both ranging from 0-5. The
data are cleaned so that NVQ levels do not decline over time.
4.3
Experience and specific skills
General experience is captured by age, or period of transition, which ranged from 1 to 5. Specific
experience within a routine occupation is included below using a scale measure from 0 to 4, which counts
all the previous periods where a worker was in a routine occupation at both the beginning and end of it.
This is likely to be a conservative measure of experience in specific routine occupations. However, it is
significantly simpler than checking over all employment histories and counting the total number of years
in routine occupations.
Specific experience and the time period are positively correlated, as would be expected, with a correlation
coefficient of 0.672 in the NCDS study. Within each period of transition, there is sufficient variety in
existing routine experience that issues relating to multicollinearity would not be an immediate concern –
around 55% of the total variance of specific experience is not accounted for by age.
6
A small number of occupational categories have been updated between this paper and Holmes (2011a). This was
based on feedback about the classification initially used.
7
The full table of NVQ equivalent qualifications can be found here:
http://www.gos.gov.uk/497745/docs/379399/428699/469541/qualificationsguidance
4.4
Routinisation
The measure of routinisation comes from data from the Labour Force Survey (LFS). Data on employment
have been collected by the LFS since 1973, on a yearly basis until 1992, then on a quarterly basis. Wage
data have been available from the LFS since 1993. The sample is much larger than any UK cohort study,
as it looks at around 60,000 households, leading to approximately 120,000-130,000 individual
observations.
Using these data, the measure of routinisation in each of these time periods is the changes in employment
of routine occupations across the whole labour market. shows the change in the different occupation
groups over each period.
Routinisation had its largest effects in the late 1980 and the early 2000s, and had much less of an effect in
the 1990s. The process of routinisation is assumed to directly affect the employment share of routine
workers and is exogenous. It seems sensible to look at decline rates rather than absolute declines – a 2%
fall in employment share will have a much larger effect on occupational mobility if the initial
employment share was 4% than if it were 20%. Thus, the routinisation variable takes the following
values:
Year
1981-6
1986-91
1991-5
1995-9
1999-2004
2004-8
Period of transition, t, NCDS
1
2
3
4
5
-
Period of transition, t, BCS
-
-
-
1
2
3
Change in employment share
-4.40%
-6.07%
-4.27%
-1.62%
-8.56%
-2.31%
Rate of decline
7.37%
10.98%
8.67%
3.59%
19.76%
6.74%
Routinisation
0.0737
0.1098
0.0867
0.0359
0.1976
0.0674
Source: LFS, own calculations
5
Displacement
Table 4 shows the six estimated regressions for transitions, conditional on starting in a routine occupation.
These regressions show, across the two cohorts, the same broad patterns found for just the single NCDS
cohort as estimated in Holmes (2011a). Some higher qualifications increase the likelihood of moving
from routine occupations to higher wage non-routine occupations, however, there in many cases it is not
possible to distinguish significant differences in the effect of vocational qualifications below level 4 and
academic qualifications at level 2 and 3. They show slightly more evidence that higher qualifications
reduce the likelihood of moving to low wage service work. Moreover, the gender effects are similar to
those estimated previously – women are significantly more likely to move from routine occupations to
service or intermediate occupations, whilst it is more probable that men move to the other occupations or
stay in routine jobs, everything else being equal. Routine occupation-specific experience and age reduce
mobility. Finally, routinisation is again found to be an important driver of mobility, with periods where
more routine jobs lost across the whole economy associated with a much higher probability of
transitioning to both high wage and low wage non-routine occupations for both cohorts.
The model includes a dummy for cohort (BCS = 1 if the individual was born in 1970), and an interaction
term between the measure of routinisation and cohort8. These two variables help identify differences in
patterns of mobility across the two cohorts, resulting from both career progression and routinisation. The
estimates show that the younger BCS cohort is generally more upwardly mobile and less downwardly
mobile, everything else being held constant (including the occupational structure, so that
ROUTINISATION = 0). In addition, the BCS cohort is better educated, and so there is not just the
relative effect (comparing like for like, the individuals in the BCS cohort are more mobile) but an
absolute effect as well (more individuals with mobility-enhancing characteristics in the BCS cohort to
begin with).
Conversely, the estimation also finds that, after controlling for age, specific experience and qualifications
(the human capital drivers of mobility) the BCS cohort is less likely to be displaced, and so the additional
mobility generated by routinisation is small for this cohort. To an extent, this is good for the younger
cohort, as they generally avoid being displaced to low wage service work. At the same time, however,
they do not move to high wage non-routine work to the same degree that the older cohort do.
Explaining this is a challenge – the younger cohort has been shown to be generally more mobile in terms
of upward transitions from routine occupations, so it is not clear why such workers are less likely to be
affected following a decline in these jobs. One possibility is that the younger cohort may achieve
systematically better outcomes in these routine occupations than the older cohort, even after controlling
for specific and general experience and qualifications. What the source of this advantage might be is
unclear in the present analysis. Secondly, this could be evidence that the growth in non-routine
occupations is biased in favour those with more general labour market experience, in the way predicted
by a career mobility model, but which is not captured by the age variable.
To illustrate the size of these mobility effects, Table 1 presents estimates of transition probabilities across
the two cohorts, in two cases: the counterfactual world where occupational structure remained constant,
and the world where 10% of routine occupations had been lost in a given period. The estimates are for a
8
For brevity, we omit including the Ai and Norton (2003) approach for correctly identifying significant marginal and interaction
effects in logit models with interaction terms (see Holmes, 2011, for full discussion of this issue). The estimates here are not
substantively affected by the concerns they raise.
white male in the second period of transition (28-33 for the NCDS cohort, 29-34 for the BCS cohort),
with one prior period of routine occupation experience. The table shows that in both worlds, the younger
cohort is more mobile – in the counterfactual world, 82% of younger cohort routine workers remain in
this group compared to 93% in the older cohort. However, following routinisation, this gap in mobility
declines (as shown by the interaction effect column). Most noticeably, following routinisisation, far more
older cohort workers move to higher wage non-routine occupations than before, while the estimated
mobility of the BCS sample increases by very little.
Table 1: Estimated probabilities of transition from routine occupations
0% ROUTINISATION
Marginal
NCDS
BCS
effect
10% ROUTINISATION
Marginal
NCDS
BCS
effect
PROFESSIONAL
1.21%
9.63%
8.42%
3.55%
8.92%
5.37%
-3.05%
MANAGERIAL
3.14%
12.75%
9.61%
6.27%
12.95%
6.68%
-2.93%
INTERMEDIATE
2.98%
5.64%
2.67%
5.42%
7.25%
1.82%
-0.84%
ROUTINE
93.21%
82.08%
-11.13%
85.53%
77.95%
-7.59%
3.54%
SERVICE
0.17%
0.05%
-0.12%
0.34%
0.08%
-0.26%
-0.14%
6
Interaction
effect
Entry
Given these differences in mobility patterns across cohorts out of routine occupations, an obvious
question is whether they affect where workers start their working lives. Table 5 shows the results of a
logit regression on occupation of employment at the beginning of the first period of the sample for each
cohort (i.e age 23 for the NCDS cohort, and 25 for the BCS cohort). To include interaction between
cohort and education (given that the precise role of different qualifications as mechanisms for entry into
the labour market may have changed over time), we reduce the latter down to two dummies – one for
academic qualifications at level 3 and above, and another for vocational qualifications at the same levels.
This reduces the number of interaction terms to a more reasonable level9.
The estimation shows that workers in the younger BCS cohort are less likely to start work in routine
occupations. This is expected, as there are far fewer routine occupations in 1995 than there are in 1981.
9
We follow the best practice set out in Brambor, Clark and Golder (2006) and include all two-way and three-way
interaction terms in the model, even where no significant effects are expected. Keeping a large number of
educational qualification dummies would necessitate fourth, fifth and higher interaction terms, which would
overcomplicate the analysis.
The size of this change is demonstrated in Table 2, which compares the routine occupation employment
share in each cohort at the same point in the lifecycle to that of the overall labour force. Encouragingly,
the predicted model compares closely to the actual initial employment share of routine workers in each
cohort, and shows that the proportion of a cohort initially working in a routine occupation fell by about
20%. This drop is not as large as might be expected – the routine occupation employment share across the
entire labour force fell by around 25% between 1981 and 1995.
Table 2: Probability of initial employment in routine occupations across cohorts
COHORT (predicted)
YEAR
COHORT
POPULATION (LFS data)
Pr(ROUTINE=1)
N
Pr(ROUTINE=1)
N
Pr(ROUTINE=1)
N
1981
50.3%
7430
51.9%
10188
59.7%
84471
1995
40.6%
5083
41.1%
5541
44.9%
60415
That the younger BCS cohort is not avoiding initial employment in declining routine occupations is just
one result that does not fit with the predictions of the theory. Faced with a declining number of routine
occupations, we would expect more educated workers in the later cohort to be employed in other higher
wage non-routine jobs. This should be captured by a negative interaction effect between cohort and
education.
However, the estimated regression shows no evidence of this. There is no significant
interaction effect between academic qualifications and cohort and positive effect of cohort on the effect of
vocational qualification levels, rather than a negative one.
Again, to given an idea of the magnitude of the effects, 25% of white male workers with higher academic
qualifications enter routine occupations in the NCDS cohort, compared to 18% in the BCS cohort. For
those without higher qualifications, the probabilities are 58% and 50% respectively. Thus, higher
academic qualification are associated with far fewer entrants into routine occupations, but this
relationship is not significantly different across the two cohorts.
7
Discussion and conclusion
This paper has analysed several aspects of occupational mobility by comparing the experiences two UK
cohort studies. We focus on entry early in working life into, and subsequent mobility out of, routine jobs.
This has demonstrated a number of unexpected results that suggest a more complicated pattern of
working life occupational mobility than suggested by the model discussed at the beginning of the paper.
Moreover, it to a more complex version of Autor and Dorn's notion that routine jobs are 'getting older'.
The younger cohort may generally be more mobile, have less specific skills and higher qualifications than
the older cohort, such that they are less likely to be found in routine occupations over time. However,
within this there are a number of issues. For example, while workers from the younger cohort are
generally more upwardly mobile than comparable older workers at the same stage of their careers,
routinisation-driven mobility tends to be larger for the latter group and not the former. Secondly, a smaller
proportion of the younger cohort enter a routine job early in their working life, as many of these jobs had
disappeared. However, there is no evidence that the younger cohort disproportionately avoided routine
occupations, even though occupation specific skills are likely to be less valuable in the future. In some
cases, there is evidence that higher skilled workers, who could have found employment in higher wage
non-routine occupations, were relatively more likely to be found in routine occupations, compared to the
older cohort.
Why this might be is a challenge for further research. Along side this analysis of occupational mobility, it
is also important to consider the mobility of wages. The literature on polarisation suggests that there has
been a growth in high wage and low wage occupations and a decline in the middle. However, recent work
(Holmes, 2010; Holmes and Mayhew, 2010) finds little evidence of that in the wage distributions, with
the middle of the distribution still accounting for the majority of jobs. As we differentiate between the
effects of routinisation on occupational and wage distributions, it is also sensible to differentiate between
occupational and wage mobility to see whether the job moves captured here translate into the expected
change in wages. So far, there is an implicit hierarchy of wages for each occupational group, so that we
refer to moves from routine occupations to service occupations as downward mobility and to intermediate
or managerial occupations as upward mobility. Differences in occupational wages across cohorts might
help explain differences in mobility patterns.
A second area of future research would be to consider more closely to issue of career paths. One possible
explanation of the patterns of mobility found in this paper is that a number of routine occupations act as
staging posts, which are attached to mechanisms allowing for career progression into higher wage nonroutine occupations. Hence, workers in the younger cohort take routine jobs early in the working life on
the expectation that they will likely progress to growing high wage non-routine occupations in the future.
However, this points to non human capital barriers to mobility and entry in the higher wage occupations
which would need to be identified.
References
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Autor, D, and Dorn, D, (2009), This job is “getting old”: measuring changes in job opportunities using
occupational age structure, American Economic Review, 99(2).
Autor, D, Levy, F, and Murnane, R, (2003), The skill content of recent technological change: an empirical
exploration, Quarterly Journal of Economics, 118(4).
Brambor, Clark and Golder (2006), Understanding interaction models: improving empirical analyses,
Political Analysis, 14(1).
Goos, M, and Manning, A, (2007), Lousy jobs and lovely jobs: the rising polarization of work in Britain,
The Review of Economics and Statistics, 89(1).
Holmes, C, (2010), Job polarisation in the UK: An assessment using longitudinal data, SKOPE Research
Paper No. 90, Cardiff: SKOPE
―, (2011a), The route out of the routine: where do the displaced routine workers go?, SKOPE Research
Paper No. 100, Cardiff: SKOPE
―, (2011b) A model of occupational choice, unpublished note.
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Cardiff: SKOPE
Sicherman, N, and Galor, O, (1990), A theory of career mobility, Journal of Political Economy, 98(1).
Table 3: Occupational groups using SOC 3-digit categories
PROFESSIONAL
MANAGERIAL
INTERMEDIATE
ROUTINE
Business and
statistical
professionals
Health professionals
Legal professionals
Information and
communication
technology
professionals
Public service
professionals
Architects, town
planners, surveyors
Science professionals
Engineering
professionals
Teaching
professionals
Librarians and
related professionals
Therapists
Functional managers
Production managers
Protective service
officers
Corporate managers
and senior officials
Financial institution
and office managers
Managers in
distribution, storage
and retailing
Managers and
proprietors in
hospitality and leisure
services
Managers and
proprietors in other
service industries
Transport associate
professionals
Protective service
occupations
Artistic and literary
occupations
Business and finance
associate
professionals
Sales and related
associate
professionals
Public service and
other associate
professionals
Social welfare
associate
professionals
Science and
engineering
technicians
Sports and fitness
occupations
Health associate
professionals
Design associate
professionals
Media associate
professionals
Administrative
occupations:
government and
related organisations
Leisure and travel
service occupations
Sales related
occupations
Draughtspersons and
building inspectors
Administrative
occupations: finance
Administrative
occupations: records
Administrative
occupations:
communications
Secretarial and related
occupations
Electrical trades
Printing trades
Metal machining, fitting
and instrument making
trades
Metal forming, welding
and related trades
Building trades
Textiles and garments
trades
Vehicle trades
Skilled trades nec
Food preparation trades
Construction operatives
Mobile machine drivers
and operatives
Plant and machine
operatives
Process operatives
Transport drivers and
operatives
Assemblers and routine
operatives
Elementary
administration
occupations
Elementary process
plant occupations
Elementary goods
storage occupations
Elementary cleaning
occupations
Elementary personal
services occupations
Elementary agricultural
occupations
NONROUTINE
MANUAL
Healthcare
and related
personal
services
Childcare and
related
personal
services
Housekeeping
occupations
Sales
assistants and
retail cashiers
Hairdressers
and related
occupations
Personal
services
occupations
nec
Customer
service
occupations
Elementary
security
occupations
Elementary
sales
occupations
SERVICE
Elementary
construction
occupations
Construction trades
Agricultural trades
Table 4: Results of logit regressions for transitions from routine occupations across cohorts
GENDER
NONWHITE
BCS
ROUTINISATION
BCS*ROUTINISATION
ROUTINE EXP
AGE
VOC LVL 0
ACAD LVL 0
VOC LVL 1
ACAD LVL 1
VOC LVL 2
ACAD LVL 2
VOC LVL 4
ACAD LVL 4
VOC LVL 5
ACAD LVL 5
CONSTANT
PROFESSIONAL
MANAGERIAL
-0.4163 ***
-(3.96)
0.2342
(0.97)
2.1661 ***
(7.95)
11.0335 ***
(5.52)
-11.8695 ***
-(5.45)
-0.3933 ***
-(5.82)
-0.2778 ***
-(4.48)
-0.0719
-(0.49)
-1.5142 ***
-(5.38)
0.3614 *
(1.85)
-1.1372 ***
-(5.61)
-0.2384
-(1.11)
-0.6318 ***
-(3.86)
0.6512 ***
(3.59)
1.1406 ***
(6.78)
1.2044 ***
(5.54)
1.7489 ***
(5.41)
-3.4566 ***
-(14.30)
-0.1347 *
-(1.90)
-0.1777
-(0.87)
1.5046 ***
(8.73)
7.2344 ***
(6.09)
-7.0572 ***
-(5.25)
-0.2734 ***
-(6.31)
-0.1611 ***
-(4.06)
-0.1203
-(1.29)
-0.8399 ***
-(4.88)
-0.0433
-(0.33)
-0.5363 ***
-(3.88)
-0.4082 ***
-(3.04)
-0.0353
-(0.29)
0.1925
(1.39)
0.5544 ***
(3.94)
0.9097 ***
(5.35)
0.7490 **
(2.20)
-2.8319 ***
-(16.82)
INTERMEDIATE
0.3965 ***
(5.48)
0.1510
(0.81)
0.6675 ***
(3.63)
6.2533 ***
(4.66)
-3.5802 **
-(2.43)
-0.2255 ***
-(4.83)
-0.2596 ***
-(6.12)
-0.0541
-(0.56)
-1.1310 ***
-(6.32)
-0.1380
-(1.00)
-0.7484 ***
-(5.54)
-0.1536
-(1.18)
-0.2853 **
-(2.49)
0.3141 **
(2.21)
0.2672 *
(1.91)
0.0850
(0.37)
0.3033
(0.83)
-2.7395 ***
-(15.59)
ROUTINE
-0.3535 ***
-(8.33)
0.0621
(0.52)
-1.0976 ***
-(10.83)
-8.4230 ***
-(12.73)
5.8310 ***
(7.51)
0.3009 ***
(11.84)
0.1471 ***
(6.32)
-0.0593
-(1.01)
0.4801 ***
(5.04)
-0.0714
-(0.88)
0.3987 ***
(4.76)
0.1068
(1.38)
0.0639
(0.85)
-0.3575 ***
-(3.89)
-0.8201 ***
-(8.73)
-0.9807 ***
-(7.81)
-1.2644 ***
-(4.91)
2.0244 ***
(19.55)
SERVICE
2.0193 ***
(19.34)
-0.3873
-(1.48)
-1.1782 ***
-(5.96)
6.5980 ***
(6.26)
-2.5963 **
-(1.98)
-0.1532 ***
-(3.54)
0.0540
(1.29)
0.5285 ***
(4.01)
0.7703 ***
(3.73)
0.3213 **
(2.02)
0.7148 ***
(3.67)
0.4139 ***
(2.67)
0.7282 ***
(3.97)
0.2159
(0.96)
0.3429
(1.48)
0.3457
(1.03)
-0.8524
-(0.83)
-6.3050 ***
-(24.96)
MANUAL NON
ROUTINE
-2.1778 ***
-(10.21)
-0.0760
-(0.21)
3.8240 ***
(10.87)
6.1810 ***
(3.00)
-3.1571
-(1.37)
-0.2397 ***
-(3.24)
-0.0180
-(0.24)
-0.0532
-(0.32)
1.0699 ***
(3.01)
0.1990
(0.85)
0.6951 **
(2.03)
0.1032
(0.49)
0.4550
(1.35)
-1.1745 **
-(2.49)
-0.3223
-(0.71)
0.3602
(0.75)
0.1767
(0.17)
-4.9948 ***
-(12.48)
Table 5: Results of logit regression on initial occupation across cohorts
GENDER
NONWHITE
BCS
ACAD LVL 3-5
VOC LVL 3-5
BCS * ACAD LVL 3-5
BCS * VOC LVL 3-5
ACAD LVL 3-5 * VOC LVL 3-5
BCS * ACAD LVL 3-5 * VOC LVL 3-5
CONSTANT
ROUTINE
0.1022 ***
(2.71)
0.0843
(1.05)
-0.3195 ***
(6.71)
-1.4466 ***
(19.08)
-0.4736 ***
(7.24)
-0.0924
-(0.87)
0.4238 ***
(3.44)
0.5921 ***
(4.30)
0.1121
(0.40)
0.3225 ***
(9.19)