Are the winners winning in the hourglass labour market?
Craig Holmes and Ken Mayhew
Paper submitted for 30th International Labour Process Conference, University of Stockholm,
March 27th-29th 2012
1
Introduction
The occupational composition of many national labour markets has changed significantly
over the past thirty years. The sort of jobs firms require and create depends on a number of
factors, but one of the key ones is the available level of technology. As the general level of
technology improves, firms are able to invest in more, better and cheaper capital, particularly
computerised machinery. Some jobs may be complementary to the introduction of new
technology, while others may be substitutable. For example, if an engineer is more productive
because she now works with an upgraded computer system or software design package, then
we would expect the total demand for engineers to increase as firms seek to capitalise on their
increased profitability. In contrast, an automated production process may replace part (or
indeed all) of a manned production line. These different changes can appear within a single
firm – the same automated production process that reduces employment of production line
workers may require firms to employ more highly skilled production supervisors to monitor
or program it.
One viewpoint is that jobs which are most complementary to technological improvements are
more highly skilled, while jobs which are substitutable with these advances are low skilled.
This is commonly referred to as skill-biased technical change. An alternative viewpoint is
that technology is related to particular tasks, rather than particular skills. Jobs which
predominantly performed routine tasks were replaced by new technology, while those which
performed non-routine tasks were not. Indeed in many cases, non-routine work is
complementary with new technology, though this is not always the case. Goos and Manning
(2007) showed that these non-routine jobs tended to be either high-wage jobs or low-wage,
jobs, while routine occupations were generally closer to the middle of the earnings
distribution. They characterised the UK labour market as increasingly polarised, with a
hollowing out of the middle and the development of an “hourglass” labour market. This has
been taken by policymakers as supportive of the view that the UK labour market continues to
provide an increasing number of good jobs. The UK government's recent strategy document
on improving social mobility states:
There is…evidence that the demand for skilled workers is currently outstripping
supply, which suggests there is 'room at the top' for highly qualified graduates from
all backgrounds. (HM Government, 2011, p. 11)
This, in turn, creates “winners” in the labour market by allowing for greater upward mobility
for existing workers and improving the labour market outcomes of new, more qualified
entrants.
A number of potential problems arise from this premise, which this paper explores. One
problem is that non-routine jobs at the bottom of the labour market have also been growing,
there is an increased likelihood that some of these existing workers or new, well-qualified
entrants will find themselves in lower wage work – that there is ‘room at the bottom’ as well.
This issue is not the main focus of this paper, although we briefly discuss our earlier research
on downward occupational mobility as a result of a decline in routine jobs. The main focus of
this paper is the earnings of the “winners”. In particular, it asks whether the growing number
of workers found in good, non-routine jobs have all benefitted in terms of higher earnings.
We present some descriptive statistics from earnings and wage distributions in these jobs, and
show where the growth has occurred within the pay spectrum.
Secondly, we look at issues related to wage mobility associated with the presumed ‘room at
the top’. Using data from the 1958 National Child Development Study (NCDS) we look at
whether there are improvements in earnings associated with transitions from routine
occupations to “good” non-routine occupations. Thirdly, we look at the labour market
outcomes for those entering the labour market after many routine jobs had disappeared. In the
absence of this change in the occupational structure, many of these new entrants would have
gone into routine jobs. Using data from the 1970 British Cohort Study (BCS), we look at
whether these workers were able to find work in “good” non-routine jobs.
2
Room at the bottom, too?
In some of our recent work on the mobility of routine workers, we find that the change in the
occupational structure has also increased mobility towards these jobs, regardless of
educational attainment (Holmes and Mayhew, 2011b). In light of this, we would argue that
policymakers do not spend enough time considering the implications of the ‘room at the
bottom’. For example, in a paper that provides a part of the conceptual framework for the
above strategy document, Crawford et al. (2011) raise the issue of the polarised (or
hourglass) labour market, but the implications they draw from it remains focused on the
'room at the top'. For those already in these low-wage jobs, the authors acknowledge that
“attempting to move individuals from the bottom to the middle of the skill/income
distribution may be harder, as there are fewer jobs in the middle” (pg. 9). The apparent
solution here is upskilling:
in the longer term increasing the supply of intermediate skilled workers may
encourage firms to up-skill [low-wage] jobs in order to employ such workers
(Crawford et al., 2011, pg 9)
Such policies have rarely been successful in the UK – over-education and underemployment
are much more commonly reported.
3
Wages at the top
Earnings is good, non-routine jobs should reflect both the increased demand generated by
technological progress, and increased supply resulting from an increasingly well-educated
workforce able to perform these jobs. The effects of these two changes are contradictory –
increased supply will lower wages, while increased demand raises them. It has been
commonplace for policymakers, in some cases supported by academic research, to point to
evidence of non-declining returns on higher education to argue that demand and supply for
workers in these jobs are moving together (e.g. BIS, 2010; Walker and Zhue, 2008; Machin
and Van Reenen, 2007), and that the growth of higher-skill jobs has benefitted those in those
jobs or moving into those jobs.
Recently, there has been concern that the polarisation effect identified by Goos and Manning
when looking at occupational titles is less apparent when looking at wage distributions
(Holmes and Mayhew, 2011a). This has been the result of a small number of high-skill jobs
experiencing very rapid wage growth over the past two decades, whilst many other upper-end
jobs have not. The result of this is that many of these good non-routine jobs earn wages much
closer to the middle than the top. Hence, as well as looking at average wages between
different groups of occupations, it is also important to look at the distribution of these
earnings.
Holmes and Mayhew (2012) decompose changes in the overall UK wage distribution
between 1987 and 2001 into effects caused by changes in the composition of a range of
factors that are related to earnings across the workforce (such as qualification levels,
occupational structure and union membership) and the way different worker characteristics
correspond to wages – what economists call rates of returns or wage premia. The wage
differentials between different occupations have changed, but these changes are not the same
across the distribution. The relative earnings of managerial workers, for example, have grown
particularly around the 75th percentile of the distribution, but much less elsewhere. Rates of
return to a degree also appear to have fluctuated at different points in the wage distribution.
The analysis shows that the graduate premium has grown sharply for the top 15% of earners,
but has remained relatively constant (or even declined) for everyone else.
This suggests that many of these apparently good jobs are far less appealing than their job
titles may suggest, as the types of jobs captured within an occupational definition widens.
This seems to reflect both the changing composition of the workforce and changes in the way
certain characteristics are linked to earnings. However, it is not a simple exercise to untangle
all of the potential interactions between composition and wage premia at the aggregate level –
for example an increase in demand for a range of high skill non-routine jobs alongside an
increase in supply of graduates from an increasingly heterogenous university sector is
unlikely to have a easy-to-quantify effect on total earnings distributions.
Table 3.1: Occupational employment shares, 1995-2008
Managerial
Professional
Intermediate
1995
13.9%
10.8%
9.6%
2002
13.3%
12.0%
14.0%
2008
15.6%
13.2%
14.4%
Source: LFS, own calculations
To illustrate some of these effects, this section presents data from the Labour Force Survey
on the distribution of weekly earnings in occupational categories associated with the idea of
high skill non-routine work – SOC major groups 1 (managerial occupations), 2 (professional
occupations) and 3 (intermediate and associate professional occupations)1. We focus on two
We use both SOC90 for the 1995 data, and SOC2000 for 2002 and 2008 data – we acknowledge the
differences between the two classifications, however, we believe these differences are less relevant for the top
occupations than those lower down, and where issues may arise the biases which result should only strengthen
our conclusions. For example the overclassification of supervisory positions as managerial occupations in
1
periods: 1995-2002 and 2002 to 2008 (prior to the onset of the recession). Table 3.1 shows
the changing employment share of workers in these occupations. The LFS provides wage
data on a subset of the overall sample – the above table gives employment share for this
subset. The patterns of growth are consistent with the overall sample.
Figure 3.1, 3.2 and 3.3 shows the change in the percentage of workers earning below a certain
gross weekly wage (given along the x axis) in these occupations. These measures show were
work has shifted within the distribution – negative values indicate proportionally less
employment below a certain threshold, while positive values denote proportionately more
employment below this point.
Figure 3.1: Changing cumulative distribution of gross weekly earnings, managerial
occupations, 1995-2008
2000
1900
1800
1700
1600
1500
1400
1300
1200
1100
900
1000
800
700
600
500
400
300
200
0.0%
100
% change in employment share below threshold wage
5.0%
-5.0%
1995-2002
2002-2008
-10.0%
-15.0%
-20.0%
Threshold gross weekly earning, £
Across all three occupations, the period between 1995 and 2002 saw a large fall in the
proportion of jobs earning below median wages2, suggesting that many of the jobs in these
occupations, potentially including any new jobs created during this time period, were higher
wage. For managerial jobs and (to a lesser extent) professional occupations, we see quite a
long tail in this shift, with a noticeable increase in the employment share of jobs earning
SOC2000 would, on its own, result in a rising number of lower wage managerial occupations between 1995 and
2002 – this is not found to be the case.
2
The median gross weekly wage for these three SOC categories over the three time periods, in 1995 prices, are
£406.66, £412.81 and £310.87 respectively.
above £800 per week. In contrast, the growth in these occupations during the past decade has
been led to much less of an increase in higher wage employment. For all three occupations,
there has been a modest increase in employment at the bottom end of the spectrum in these
occupations, most noticeably in professional occupations. There is also a decline in
employment nearer the middle of the earnings spectrum for all occupations3, which implies a
growth in jobs at the highest end.
Figure 3.2: Changing cumulative distribution of gross weekly earnings, professional
occupations, 1995-2008
5.0%
-5.0%
1995-2002
2000
1900
1800
1700
1600
1500
1400
1300
1200
1100
900
1000
800
700
600
500
400
300
200
0.0%
100
% change in employment share below threshold wage
10.0%
2002-2008
-10.0%
-15.0%
-20.0%
Threshold gross weekly earning, £
We extend this analysis in two directions, firstly looking at differences by qualification level,
and then considering differences by sector of employment.
3.1
Graduates and non-graduates
Figures A.1 to A.6 break down these distribution changes into graduates – workers with a
qualification equivalent to NVQ level 4 or 5 – and non-graduates to see whether opportunities
to benefit from the growth in higher-wage non-routine work is particularly linked to the
increase in more qualified workers. As professional work has always been heavily dominated
by graduate workers, we focus on the managerial and intermediate occupations. For the
3
Increased employment below £300 per week, but decreased employment share below £400-600 per week
implies a decline in employment between £300 and £600, and a growth in employment at either side of this
range.
former, Figures A.1 and A.2 show that between 1995 and 2002, the increase in employment
at the very top end was limited – there is very little decline in employment share below
around £1000 per week for non-graduates, whereas a sizeable proportion of graduates are
now being found in jobs earning over £1400 per week.
Figure 3.3: Changing cumulative distribution of gross weekly earnings, intermediate
occupations, 1995-2008
2000
1900
1800
1700
1600
1500
1400
1300
1200
1100
900
1000
800
700
600
500
400
300
200
0.0%
100
% change in employment share below threshold wage
2.0%
-2.0%
-4.0%
-6.0%
-8.0%
1995-2002
2002-2008
-10.0%
-12.0%
-14.0%
-16.0%
Threshold gross weekly earning, £
There is much less observable difference since 2002 at the upper end – for both types of
workers, there is evidence of a decline in employment share between around £500 and £1200,
and growth above that. Similarly, at the bottom end, in both time period, the patterns of
change are broadly the same – between 1995 and 2002, a large proportion of both types of
workers have moved away from employment below median wage threshold, while after
2002, there was an increase in the proportion of all workers doing lower wage jobs.
3.2
Sectors
Figures B.1 to B.4 break down the change in managerial earnings by the four largest sectors
these workers are found. This shows that the pattern of change in the distribution was broadly
similar between 1995 and 2002 across sectors, although there are differences in terms of
increased employment at the top end of the spectrum. Due to increasingly small sample size,
we do not analyse the link between educational attainment, industry and occupation. That
said, the patterns of changing employment share between managers in real estate and
business activities, and managers in retail seem similar to the overall change for graduates
and non-graduates respectively, suggesting there may be selection (or self-selection) intro
different types of managerial work based on educational attainment. Since 2002, as the
number of managerial occupations has increased, these jobs have become increasing
heterogeneous across different industries. Retail and financial intermediation have seen
significant growth in lower wage managerial occupations since 2002, whereas manufacturing
and real estate, renting and business activities has predominantly seen a shift away from work
at the lowest end during this time period. Here there is less obvious similarity between
graduate and non-graduate earnings, suggesting something other than a division based on
educational attainment.
4
Room to move up?
The previous section has suggested that over the past two decades, the growth in good nonroutine jobs has created higher wage employment, although these gains are becoming
increasingly spread out with, and a number of these jobs are considerably lower wage than
before. One issue is whether these higher wage jobs are available to those already in the
workforce, which is considered in this section. Another issue is whether the growth in these
jobs creates better initial employment options for increasingly well qualified new labour
market entrants, which is examined in the following section.
Using data from the 1958 National Child Development Study (NCDS) we look at whether
there are improvements in earnings associated with transitions from routine occupations to
“good” non-routine occupations. Wage data is available at four points in time in our data –
1981, 1991, 1999 and 2004. We look at gross weekly earnings in high-skill non-routine
occupations at this time, comparing these earnings by the occupational group of employment
five years prior to these dates (so we exclude data from 1981 as the majority of the sample
were not in employment in 1976). The occupational groups we consider are professional,
managerial, intermediate, routine, and two low-wage non-routine categories – non-routine
manual and service (see Holmes, 2011, for more on the derivation of these categories in a
previous occupational mobility study). Figures 4.1 and 4.2 shows the distribution of weekly
earnings (in 1981 terms) for those in managerial occupations and intermediate occupations,
comparing workers by their occupational group five years previously. This would seem to
indicate that those transferring from routine occupations earn less than existing managers or
intermediate occupation workers.
Figure 4.1: Gross weekly earnings distribution, managerial occupations, by occupation of
origin
30.0%
Managerial
Employment share
25.0%
Routine
20.0%
15.0%
10.0%
5.0%
950 - 1000
900 - 950
850 - 900
800 - 850
750 - 800
700 - 750
650 - 700
600 - 650
550 - 600
500 - 550
450 - 500
400 - 450
350 - 400
300 - 350
250 - 300
200 - 250
150 - 200
100 - 150
0 - 50
50 - 100
0.0%
Gross weekly wage
Figure 4.2: Gross weekly earnings distribution, intermediate occupations, by occupation of
origin
35.0%
Employment share
30.0%
25.0%
Intermediate
Routine
20.0%
15.0%
10.0%
5.0%
950 - 1000
900 - 950
850 - 900
800 - 850
750 - 800
700 - 750
650 - 700
600 - 650
550 - 600
500 - 550
450 - 500
400 - 450
350 - 400
300 - 350
250 - 300
200 - 250
150 - 200
100 - 150
0 - 50
50 - 100
0.0%
Gross weekly wage
This could be explained purely by observable differences between workers who have been in
managerial occupations for longer period of time and those who had previously worked in
routine occupations but who have either progressed or been displaced from these jobs. For
instance, workers in routine jobs who move into managerial or intermediate occupations may
be, on average, less educated. Alternatively, it may be that there are unobservable (to the
econometrician) differences between these two categories of workers, leading to the creation
of different types of managerial occupations, with correspondingly different earnings profiles.
It may also be the case that the available progression routes between routine and higher skill
non-routine occupations lead to these kind of stratification, so that early employment
decisions matter.
Table 4.1: Ordered logit estimation of earnings distribution group
GENDER
NONWHITE
Occupation five
years before
PROFESSIONAL
MANAGERIAL
INTERMEDIATE
ROUTINE
MANUAL
SERVICE
UNEMP
NONEMP
AGE
Highest academic
and vocational
qualifications
VOC NVQ 0
ACAD NVQ 0
VOC NVQ 1
ACAD NVQ 1
VOC NVQ 3
ACAD NVQ 3
VOC NVQ 4
ACAD NVQ 4
VOC NVQ 5
ACAD NVQ 5
N
Managerial
-1.933 ***
-0.431 **
Intermediate
-2.142 ***
-0.025
0.246
Ref.
-0.290
-0.498
-1.212
-1.240
-1.941
-1.110
0.616
0.574
Ref.
-0.982
-0.586
-1.339
-1.495
-1.098
***
**
***
***
***
0.365 ***
0.179
-0.692
0.184
-0.483
-0.076
0.889
0.175
1.111
0.780
1.531
2550
***
***
***
***
***
***
**
***
***
***
***
***
0.394 ***
0.279
-0.164
0.158
-0.242
0.219
0.436
0.511
0.457
0.771
0.877
*
*
***
***
***
***
***
2313
To test this, we create a variable representing groups of earnings in Figures 4.1 and 4.2, with
Y = 1 indicating earnings between up to £50 per week, Y = 2 indicating earnings between
£50 and £100 per week, and so on up to Y = 20. We then estimate the following ordered logit
for managerial and intermediate occupations:
Pr (Y = y) =
ologit(age, gender, ethnicity, occupational group-5y, highest academic
qualification, highest vocational qualification)
(1)
Table 4.1 shows the results of this estimation. The main result shown in this table is that even
controlling for observable demographic and educational differences between workers, those
who transition into these jobs from routine work (as well as from service occupations and
from unemployment or non-employment) tend to experience less high wages in those
occupations. This can not be purely attributed to lower specific occupational skills developed
by doing either job for a longer period of time – the results show that those who move into
managerial or intermediate occupations from other higher-skill non-routine occupations do
not experience lower wages.
There are two additional aspects that are beyond the scope of this paper, but which need to be
addressed. Firstly, there is a question of whether these progression paths lead to earnings
gains – so far, we have showed that these transitions lead to lower than average earnings
relative to existing workers, but not whether these transitions are associated with upward
wage mobility. Secondly, we would like to know whether different sorts of transition are
associated with different outcomes. In previous work on occupational mobility using the
same NCDS data (Holmes 2011), the causes of transitions from routine occupations to nonroutine occupations was decomposed into career progression and displacement caused by a
change in the occupational structure. That research showed that some mobility in this cohort
was associated with the decline in the total number of routine jobs. As we have less
observations of earnings in these data, it is not possible to associate wage mobility with
changes in the occupational structure in the same way. However, one possible extension is to
use data on the causes of previous job exits to distinguish between more voluntary career
progression transitions and those caused by a decline in the total number of routine jobs.
5
A better start?
We now consider the change in the initial employment opportunities resulting from the
increase in non-routine work. In the absence of this change in the occupational structure,
many of these new entrants would have gone into routine jobs. Comparing data from the
NCDS with that from the 1970 British Cohort Study (BCS), we look at whether new entrants
into the labour market benefitted by being able to find work in better non-routine jobs, and
what role the increased educational attainment of the later cohort played in this.
Table 5.1 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 level4.
Table 5.1: Logit regression on occupational group of initial employment
GENDER
NON WHITE
BCS
Routine
-0.138 ***
-0.101
0.028
0.455 ***
-0.109 **
0.730 ***
0.253 **
0.495 ***
1.011 ***
-1.201 ***
-0.074
BCS * ACAD LVL3-5
0.012
BCS * VOC LVL3-5
-0.072
ACAD LVL3-5 * VOC LVL3-5
-0.226
BCS*ACAD LVL3-5 * VOC LVL3-5 0.466
0.018
-0.813 ***
-0.446 ***
0.444
0.098
0.254 **
0.368 ***
0.160
CONST
-2.493 ***
-0.406 ***
Highest
ACAD LVL3-5
qualifications VOC LVL 3-5
Interaction
terms
Managerial Intermediate
-0.451 ***
0.053
-0.255 **
-0.149
-2.238 ***
The estimation shows that, controlling for educational differences, 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. Again controlling for
educational differences, the table shows that younger BCS cohort is more likely to work in
intermediate occupations, but not any more likely to work in managerial occupations early in
the working life.
4
We follow the best practice set out in Brambor, Clark and Golder (2006) and include all two-way and threeway 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 younger cohort benefits from being increasingly well educated – a greater proportion of
them go into good non-routine jobs as they are more qualified. What is surprising, however,
is that the better-educated workers in the younger BCS cohort are not relatively more likely
to find employment in good non-routine jobs – the interaction terms between higher
qualifications and cohort membership are either insignificant or negative in some cases.
Given the decline of routine occupations, the growth of managerial and intermediate
occupations, and the effects these patterns would be expected to have on future employment
prospect, career progression and earnings, we would expect sufficiently qualified new
entrants to be increasingly more likely to seek good non-routine jobs earlier in their careers5.
Understanding why early employment has not behaved in this way is a necessary extension of
this. One hypothesis that could be tested is that some routine jobs remain important gateways
into better employment later on, and that there are non-human capital barriers to entering
higher wage routine occupations for younger workers.
6
Concluding remarks
This paper has questioned an assumption underlying much of the UK government’s thinking
on the occupational structure of the labour market: that the growth in occupational groupings
associated with higher earnings will generally create “winners”, both in opportunities to
progress upwards in the labour market, and for increasingly well-qualified new entrants to
move into. We raise three concerns about this optimism. Firstly, that there is evidence that
earnings in these jobs has become increasingly heterogenous, with an increase in low pay in
these occupations, at the same time that the wages of a small proportion of these jobs has
accelerated away from the rest. Secondly, that upward mobility from declining routine
occupations to higher-skill jon-routine occupations is associated with a lower distribution of
earnings than those currently in these jobs, even after controlling for educational attainment.
Thirdly, comparing those joining the labour market in the early 1990s with those entering in
the late 1970s, newer entrants are more likely to start working lives in these growing
occupations. However, the incidence of this is not as great as we might expect – being more
educated in the later cohort is not associated with being more likely to go into managerial or
intermediate non-routine jobs, although it is not clear exactly what barriers to such entry are
causing this.
5
This motivation has been discussed in the literature, leading to the phenomena of routine jobs 'getting old'
(Autor and Dorn, 2009)
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opportunities using occupational age structure, American Economic Review, 99(2): 45-51.
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Brambor, Clark and Golder (2006), Understanding interaction models: improving empirical
analyses, Political Analysis, 14(1): 63-82.
Crawford, C, Johnson, P, Machin, S, and Vignoles, A, (2011), Social mobility: a literature
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A
Earning distributions: graduates vs. non-graduates
Figure A.1: Changing cumulative distribution of gross weekly earnings, graduates in
managerial occupations, 1995-2008
2000
1900
1800
1700
1600
1500
1400
1300
1200
1100
1000
900
800
700
600
500
400
300
200
0.0%
100
% change in employment share below threshold wage
5.0%
-5.0%
1995-2002
-10.0%
2002-2008
-15.0%
-20.0%
Threshold gross weekly earnings, £
Figure A.2: Changing cumulative distribution of gross weekly earnings, non-graduates in
managerial occupations, 1995-2008
-5.0%
1995-2002
-10.0%
2002-2008
-15.0%
-20.0%
Threshold gross weekly earning, £
2000
1900
1800
1700
1600
1500
1400
1300
1200
1100
1000
900
800
700
600
500
400
300
200
0.0%
100
% change in employment share below threshold wage
5.0%
Figure A.3: Changing cumulative distribution of gross weekly earnings, graduates in
professional occupations, 1995-2008
5.0%
-5.0%
2000
1900
1800
1700
1600
1500
1400
1300
1200
1100
1000
900
800
700
600
500
400
300
200
0.0%
100
% change in employment share below threshold wage
10.0%
1995-2002
2002-2008
-10.0%
-15.0%
-20.0%
Threshold gross weekly earnings, £
Figure A.4: Changing cumulative distribution of gross weekly earnings, non-graduates in
professional occupations, 1995-2008
5.0%
-5.0%
1995-2002
-10.0%
2002-2008
-15.0%
-20.0%
-25.0%
Threshold gross weekly earning, £
2000
1900
1800
1700
1600
1500
1400
1300
1200
1100
1000
900
800
700
600
500
400
300
200
0.0%
100
% change in employment share below threshold wage
10.0%
Figure A.5: Changing cumulative distribution of gross weekly earnings, graduates in
intermediate occupations, 1995-2008
2.0%
2000
1900
1800
1700
1600
1500
1400
1300
1200
1100
-2.0%
1000
900
800
700
600
500
400
300
200
0.0%
100
% change in employment share below threshold wage
4.0%
-4.0%
-6.0%
1995-2002
-8.0%
2002-2008
-10.0%
-12.0%
-14.0%
-16.0%
-18.0%
Threshold gross weekly earnings, £
Figure A.6: Changing cumulative distribution of gross weekly earnings, non-graduates in
intermediate occupations, 1995-2008
2.0%
-4.0%
-6.0%
-8.0%
1995-2002
-10.0%
2002-2008
-12.0%
-14.0%
-16.0%
-18.0%
Threshold gross weekly earning, £
2000
1900
1800
1700
1600
1500
1400
1300
1200
1100
900
800
700
600
500
400
300
1000
-2.0%
200
0.0%
100
% change in employment share below threshold wage
4.0%
B
Earning distributions: sectorial analysis of managerial occupations
Figure B.1: Changing cumulative distribution of gross weekly earnings, managerial
occupations in manufacturing, 1995-2008
2000
1900
1800
1700
1600
1500
1400
1300
1200
1100
1000
900
800
700
600
500
400
300
200
0.0%
100
% change in employment share below threshold wage
5.0%
-5.0%
-10.0%
1995-2002
2002-2008
-15.0%
-20.0%
-25.0%
Threshold gross weekly wage
Figure B.2: Changing cumulative distribution of gross weekly earnings, managerial
occupations in retail and wholesale, 1995-2008
10.0%
5.0%
-5.0%
1995-2002
-10.0%
2002-2008
-15.0%
-20.0%
-25.0%
Threshold gross weekly wage
2000
1900
1800
1700
1600
1500
1400
1300
1200
1100
1000
900
800
700
600
500
400
300
200
0.0%
100
% change in employment share below threshold wage
15.0%
Figure B.3: Changing cumulative distribution of gross weekly earnings, managerial
occupations in real estate, renting and business activities, 1995-2008
5.0%
2000
1900
1800
1700
1600
1500
1400
1300
1200
1100
1000
900
800
700
600
500
400
300
200
0.0%
100
% change in employment share below threshold wage
10.0%
-5.0%
-10.0%
1995-2002
-15.0%
2002-2008
-20.0%
-25.0%
-30.0%
Threshold gross weekly wage
Figure B.3: Changing cumulative distribution of gross weekly earnings, managerial
occupations in financial intermediation, 1995-2008
-5.0%
-10.0%
1995-2002
2002-2008
-15.0%
-20.0%
-25.0%
Threshold gross weekly wage
2000
1900
1800
1700
1600
1500
1400
1300
1200
1100
1000
900
800
700
600
500
400
300
200
0.0%
100
% change in employment share below threshold wage
5.0%
C
Datasets
Our analysis of earning distributions comes from the Labour Force Survey. 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 for a subset of the
overall survey. The sample size is around 60,000 households, leading to approximately
120,000-130,000 individual observations.
Our analysis of mobility uses data from the National Child Development Study (NCDS). 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 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, self-employment 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.
To compare early employment patterns, we use the British Cohort Study (BCS). 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. The
fifth wave, taken in 1996, is the first full wave taken after the school leaving age of the
subjects, and has a sample size of around 9,000.