Knowledge worker survey

Knowledge Workers and
Knowledge Work

A Knowledge Economy Programme Report

Prepared by Ian Brinkley, Rebecca Fauth, Michelle Mahdon and Sotiria Theodoropoulou

Contents

Acknowledgements 2
List of Figures and Tables 3
Executive summary 4
1. The knowledge economy and knowledge work: A review of the existing
definitions and measures 9
2. Redefining knowledge work and knowledge workers 19
3. Knowledge work across industries and regions 41
4. The changing nature of work roles and the returns to knowledge 49
5. The job characteristics of knowledge workers 54
6. Organisational culture in the knowledge economy: preferences
and reality 61
7. Conclusion and recommendations 68
Appendix A. Work-related tasks and activities by factor 76
Appendix B: Sample demographic and background characteristics 82
Appendix C: Description of organisational variables 83
Appendix D: Composition of workforce in the distribution and repairs and
in the hotels and restaurants sectors 84
References 85

Acknowledgements

This report has drawn on some of the initial research work and discussions from The Work
Foundation’s three year Knowledge Economy Programme, to be completed in April 2009.
However the views set out here are entirely those of The Work Foundation and do not represent
those of the sponsoring organisations.

We would like to thank Alana McVerry and Sezis Okut for their contributions to this paper.

2 Knowledge Workers and Knowledge Work

List of Figures and Tables

Figure 1: The 30-30-40 knowledge economy workforce 5
Figure 1.1: Growth of knowledge based service industries in Europe and UK 1970-2005 10
Figure 1.2: Shares of graduates and workers with only basic schooling in UK workforce, 1970-2006 14
Figure 2.1: What work tasks are most common across the workforce? 23
Figure 2.2: Number of different computer uses and how often computers are used each week 28
Figure 2.3: Share of workers that frequently perform at least one specialist computer task 29
Figure 2.4: Importance of ‘teach others’ task for different clusters 31
Figure 2.5: Perceived complexity of tasks performed by surveyed workers 32
Figure 2.6: The 30-30-40 knowledge workforce 34
Figure 2.7: Share of women in jobs by knowledge content 36
Figure 2.8: Share of jobs in the top three occupational groups by knowledge content 38
Figure 2.9: Share of graduates by knowledge intensity of the job 39
Figure 3.1: Share of jobs in knowledge industries by knowledge intensity 42
Figure 3.2: Composition of the knowledge-intensive services sector 43
Figure 3.3: Workforce composition in the health and welfare industry by worker cluster 44
Figure 3.4: Employment in knowledge intensive and more traditional services compared 45
Figure 3.5: Composition of the manufacturing sector 46
Figure 3.6: Regional composition of the workforce 47
Figure 4.1: Percentage earning more than median wages by worker cluster 53
Figure 5.1: Percentage of workers in the same job for more than 10 years by worker cluster 56
Figure 5.2: Percentage of workers working day shifts by worker cluster 57
Figure 5.3: Percentage of workers doing weekend work at least once/month by worker cluster 58
Figure 5.4: Percentage of workers with flexibility in choosing work schedule by worker cluster 59
Figure 6.1: Percentage prefer innovative firms by worker cluster 67

Table 2.1: Task factors with sample items 22
Table 2.2: Number of methods used to acquire new information and learn new tasks 30
Table 2.3: Prevalence of methods used for sharing and capturing knowledge 32
Table 3.1: Regional concentration of knowledge workers in the UK 47
Table 4.1: Job-skills/experience match by worker cluster 52
Table 4.2: Shares of women and men earning above the median wage 53
Table 6.1: Perceived organisational characteristics by worker cluster 63
Table 6.2: Preferred organisational characteristics 64

Knowledge Workers and Knowledge Work 3

Executive summary

The purpose of this report is to provide a portrait of work and the workforce in the knowledge
economy. We wanted to find out who the knowledge workers are, what they do in their
jobs, where they are employed and what employment structures, job characteristics and
organisational structures look like in the knowledge economy.

Knowledge work and knowledge workers are terms often used but seldom defined. When
knowledge work is defined it is usually by broad measures such by job title or by education
level. At best this gives us a partial and simplistic view of knowledge work in the UK.

This report takes a new approach. In a large and unique survey, we have asked people what
they actually do at work and how often they perform particular tasks. We have used that
information to assess the knowledge content of their jobs. The key test was the cognitive
complexity required for each task – the use of high level ‘tacit’ knowledge that resides in
people’s minds rather than being written down (or codified) in manuals, guides, lists and
procedures.

We then grouped the workforce into seven distinct clusters of jobs ranging from ‘expert thinkers,
innovators and leaders’ (the most knowledge intensive groups) to ‘assistants and clerks’ (the
least knowledge intensive)1. We describe the two highest knowledge groups as our ‘core’
knowledge worker.

With this measure we estimated that we have a 30-30-40 workforce – 30 per cent in jobs with
high knowledge content, 30 per cent in jobs with some knowledge content, and 40 per cent in
jobs with less knowledge content.

Within our 30 per cent ‘core’ knowledge worker group, the highest group of all (‘leaders and
innovators’) constituted just 11 per cent of the workforce. These high intensity knowledge jobs
combined high level cognitive activity with high level management tasks.

These high knowledge intensive jobs are, we suspect, what some of the more excitable
accounts of knowledge work we have in mind. The reality is that even after 40 years
uninterrupted growth in knowledge based industries and occupations, such jobs account for only
one in ten of those in work today.

1
These groupings are described in more detail on page 24


Executive summary

The 30-30-40 knowledge economy workforce

Many knowledge
tasks, 33%
Few knowledge
tasks, 40%

Some knowledge
tasks, 27%

We confirmed that knowledge work cannot be adequately described simply by looking at job
titles or education levels. About 20 per cent of people engaged in jobs with high knowledge
content – our core group of knowledge workers – were not graduates.

We also show that current job titles understate the knowledge content of jobs within some
sectors such as manufacturing. When jobs are classified by knowledge content, high tech
manufacturing has as many knowledge intensive jobs, proportionately, as high tech services.

Although our survey did not look in great detail into the geographical distribution of knowledge
workers, there were nevertheless indications that core knowledge workers tend to cluster in
urban areas, particularly in London, the South East and North of England and Scotland. This
is not a surprising finding given that face-to-face contact and the development of relationships
are important for exchanging information and especially tacit knowledge. Cities across the UK
– including Manchester, Leeds, Bristol and Edinburgh outside the South East – also provide


Executive summary

businesses with access to wider markets and to specialist skills. This result resonates with the
insights of our Ideopolis programme on the growing importance of cities in world economies.

Our results confirm high economic returns to knowledge – the vast majority of those in the most
knowledge intensive jobs enjoyed pay well above the median. But this was not true for those in
jobs with some knowledge content – such as care and welfare work.

The most knowledge intensive jobs were almost equally likely to be held by men and women,
but those jobs with some knowledge content – such as care and welfare workers, information
handlers, and sellers and servers – were overwhelmingly female. Woman have benefitted from
the growth of knowledge work, but the growth of more knowledge intensive work has not, of
itself, overcome the gender pay gap.

Some people have speculated that the growth of knowledge work is weakening the attachment
to permanent and long term employment relations. We find no evidence for this. Those in the
most knowledge intensive jobs are no more likely to be in temporary jobs than those in the least
knowledge intensive jobs and job tenures are also very similar.

Knowledge workers are not spear-heading radical changes in the way we work. As expected,
they do have more flexibility at work than those in less knowledge intensive jobs, but the
differences were not overwhelming. The reality is that less than 50 per cent of all workers
and less than 60 per cent of knowledge workers said they have some flexibility in their work
schedule, and only a very small minority said they can freely determine their own hours.

Perhaps not surprising, attachment to the standard nine to five day is still a central feature of
the labour market for both knowledge workers and non-knowledge workers alike. Knowledge
workers were far more likely to do occasional work at home, although over 60 per cent said they
did no home-working. Weekend working is relatively common across the workforce, but was
much less prevalent among knowledge workers.

We found two big labour market mismatches. The first was between the skills that people
said they had and the demands their current job made of them. The second was between the
organisational culture people perceived they actually worked in and the organisational culture
they would like to work in.


Executive summary

Significant minorities of workers reported their current jobs under-used their skills. The gap
was less marked for knowledge workers, but nonetheless significant. About 36 per cent of
knowledge workers said they were in jobs that under-used their skills compared with over 44 per
cent of those in jobs with some or little knowledge content.

Taken at face value, employers are not making the most of knowledge worker skills despite
such workers representing a substantial investment in human capital within the organisation.
However, these mismatches are even worse for jobs with low knowledge content – suggesting
a more general problem with labour utilisation rather than a particular difficulty with knowledge
work.

Some have expressed concern that the economy is producing too many graduates for the
available jobs that require graduate skills, forcing more graduates to accept lower pay jobs and
worsening the prospects for non-graduates.

We found mixed evidence. About 20 per cent of graduates were in low knowledge content
jobs. This is potentially worrying. However, the average job tenure for graduates in such jobs
was much lower than for non-graduates – suggesting graduates spend less time in these jobs.
Moreover, about 44 per cent of graduates in low knowledge content jobs reported that their job
duties corresponded well with their current skills.

Taken with the evidence on returns from knowledge and our previous work on labour market
polarisation2, the overall picture does not strongly support the idea that the UK is producing too
many graduates. The situation may be worse for those who entered the labour market more
recently, but we found little variation in these responses by age.

The vast majority of people in work think their organisation is characterised by formal rules
and policies, but very few say this is the sort of organisation they really want to work for. The
mismatch is even greater for knowledge workers: 65 per cent said their organisations were rule
and policy bound, but only 5 per cent expressed a preference for such organisations.

There is a much better match when it comes to characteristics such as loyalty and mutual trust
for both knowledge and non-knowledge workers. About 50 per cent of all workers said this
was a predominant characteristic of their organisation, and over 60 per cent said it was their
preferred organisational characteristic.

2
Fauth and Brinkley (2006) Polarisation and labour market efficiency, The Work Foundation


Executive summary

Knowledge workers are more likely to work for organisations that they think are innovative
or achievement orientated – not in itself a surprising result. What is surprising is that neither
feature seems to appeal to them very much. For example, 50 per cent of knowledge workers
said their organisation’s predominant feature was innovation, development and being at the
cutting edge, but only 24 per cent preferred this type of organisation.

Some of the differences in how people characterised their organisation can be partly explained
by whether the organisation was in a public based industry (education, health, public
administration) or in a private market based industry. But such differences between a public
and private based organisational culture did not explain preferences. It seems people reject
rule bound cultures and value loyalty and trust regardless of whether they work in the public or
private based sectors.

The gap between reality and organisational preference was wider in the public sector than in
the private sector. Public service workers were more likely to say they worked in a rules bound
organisation, which is predictable; but they also said they were less likely to be characterised by
mutual rust and loyalty than in the private sector.

These are the first set of findings from our knowledge working survey. We will be publishing
a second set of findings later in 2009 that look more closely at how knowledge work can be
regarded as ‘good work’ and how it relates to health and well-being at work.


1. The knowledge economy and knowledge work:
A review of the existing definitions and measures

Introduction The purpose of this report is to provide a portrait of the work and the workforce in the knowledge
economy. We want to find out who the knowledge workers are, what they do in their jobs, where
they are employed and what employment structures, job characteristics and organisational
structures look like in the knowledge economy.

The term ‘knowledge economy’ is often used but seldom defined. Essentially, it refers to a
transformed economy where investment in ‘knowledge based’ assets such as R&D, design,
software, and human and organisational capital has become the dominant form of investment
compared with investment in physical assets – machines, equipment, buildings and vehicles.
Thus, the term ‘knowledge economy’ captures the subsequently changed industrial structure,
ways of working, and the basis on which organisations compete and excel.

The presence and use of knowledge-based assets in the economy is of course not new –
knowledge based institutions such as universities go back centuries. However, in the late 1970s
and early 1980s three major economic and social forces combined to trigger the radical change
in economic structures that expanded the use of knowledge based assets and brought them to
the centre of economic activity across the OECD:

• The introduction of increasingly powerful and relatively cheap general purpose
information and communication technologies has not only been eliminating the physical
and geographical barriers of sharing information and ideas, but also expanding the
possibilities of generating new knowledge.
• Globalisation has been acting as an accelerator by opening up both markets of global
scale and an endless variety of niche markets as well as speeding up the spread and
adaption of new technologies and ideas.
• The rising standards of living in the advanced industrialised economies have, over the
years, created well-educated and demanding consumers with a voracious appetite
for the high value added services that the knowledge economy can characteristically
supply.

These changes are universal – they affect all industrial sectors, all sizes of firms, the public
sector as much as the private sector. And they are global – we have yet to find an advanced
industrial economy where these changes are not taking place.

The graphs below illustrate the growth of the knowledge economy in Europe by showing the
evolution of the share in value added, in the EU and the UK, of the sectors that the OECD and


The knowledge economy and knowledge work:

Eurostate commonly define as knowledge-based industries. These industries include high- to
medium-technology manufacturing and knowledge intensive services such as financial and
business services, telecommunications and health and education.3 The decline in manufacturing
is somewhat misleading, as we show in the report Manufacturing and the Knowledge Economy
(The Work Foundation, January 2009).

Figure 1.1: Growth of knowledge based service industries in the UK 1970-2005

50%
45%
40%
35%

30%
25%

20%

15%

10%
TOTAL MANUFACTURING
5% KE
Other services
0%
_1970 _1973 _1976 _1979 _1982 _1985 _1988 _1991 _1994 _1997 _2000 _2003

Source: The Work Foundation estimates from EU KLEMS database

Note: OECD definition – knowledge based services includes financial and business services,
communications, health and education services. Other services includes distribution, hospitality, public
administration, other services.

This change in industrial structure has also changed the structure of the workforce. The
interaction of technology with workers’ intellectual and human capital has, some argue, created
a new class of worker in today’s economy – the knowledge worker.

Peter Drucker, the management guru, is credited with popularising the term ‘knowledge worker’
as long ago as 1968 (Drucker 1968). Back then he argued, ‘Today the center is the knowledge
worker, the man or woman who applies to productive work ideas, concepts, and information
rather than manual skill or brawn…Where the farmer was the backbone of any economy a
century or two ago…knowledge is now the main cost, the main investment, and the main

3
It is interesting to note that knowledge-based industries in manufacturing are delineated by their high shares of sales
devoted to R&D, whereas knowledge-based industries in services are distinguished by their high levels of ICT usage
and graduate employment of graduates



product of the advanced economy and the livelihood of the largest group in the population’ (p.
264). Even in its nascent form, the very term ‘knowledge worker’ hints at a shift in nature of
some jobs where knowledge – not physical capital – is increasingly becoming the core currency
on the job market.

Forty years on, and we seem little closer to pinning down the terms ‘knowledge worker’ or
‘knowledge work.’ There are no official agreed definitions and no standardised measures.
As with the term ‘knowledge economy’, the term ‘knowledge worker’ is used frequently and
indiscriminately. It encompasses anybody from a relatively small number of professional and
technical specialists to a sizeable chunk of the workforce.

The following section reviews the diverse, but surprisingly sparse, literature on the definitions
and measurement of knowledge work and knowledge workers, including the definition used by
The Work Foundation thus far. In reviewing this literature, we highlight the important features
that a data-driven account of knowledge work and knowledge workers should reflect and the
shortcomings of previous attempts at providing such an account. Moreover, this review frames
our own method of deriving a better definition of knowledge work within the existing literature.
In later sections of this report we will use our newly developed definition of knowledge work
to explore the consequences of the knowledge economy in the structure of employment, job
characteristics organisational culture and good work.

Defining Definitions of knowledge
knowledge and One of the central problems in defining knowledge work has been the difficulty of defining
knowledge knowledge itself and distinguishing knowledge from information. Indeed, the terms ‘information
workers worker’ and ‘knowledge worker’ can be used interchangeably. There is a vast literature in
which the concept of management of knowledge is hard to distinguish from the management
of information. For example, the general conclusion from one meta-analysis is that much of
what is described as knowledge management is really either management of information or a
description of organisational changes that improved information sharing (Wilson 2002).

We argued in The Work Foundation’s Knowledge Economy Programme interim report (Brinkley
2008) that what distinguishes knowledge from information is the way in which knowledge
empowers actors with the capacity for intellectual or physical activity. Knowledge is a matter of
cognitive capability and enables actors to do and reflect. Information, by contrast, is passive
and meaningless to those without suitable knowledge. Knowledge provides the means by which
information is interpreted and brought to life.



An alternative distinction is between ‘tacit’ and ‘codified’ knowledge (see Lundavall and Johnson
1994 and OECD 1996: 12). The latter can be written down, for example, in manuals, guides,
instructions and statements and is easily reproduced. Tacit knowledge, however, resides
with the individual in the form of expertise and experience that often cannot be written down
and is expensive to transfer to others. In many respects, codified knowledge and information
are indistinguishable. The significant difference is, therefore, between tacit knowledge and
information.

Conceptual definitions of knowledge work
Even with these distinctions in mind, knowledge work remains an elusive concept. Definitions
and descriptions of knowledge work have ranged from the theoretical to the anecdotal and are
very infrequently based on a robust assessment of data on workers and what they actually do.
When data are used, usually proxy measures for highly skilled labour are employed. Depending
what resource we look to for evidence, we might come away thinking that nearly everyone in the
workforce today is a knowledge worker or that almost no one is, with the exception of a select
few.

Several experts have outlined conceptual definitions of knowledge work. For example, Drucker
(1999) focused on the differences between ‘manual worker productivity’ and ‘knowledge worker
productivity.’ The key enablers of the latter include abstractly defined tasks (vs. clearly defined,
delineated tasks), flexible application of knowledge, workers’ autonomy, continuous innovation
and learning into job roles, assessment based on quality (not just quantity) of output and
perceiving workers as organisational assets. While this general outline is useful, Drucker did not
take the additional useful step of specifying the occupations that fit into the knowledge worker
category. One could argue that he simply outlined a more modern conception of a good job
where workers are viewed as more than what they produce.

Robert Reich (1992) was a bit more explicit in outlining what he terms as the ‘symbolic analysts’,
the workers who engage in non-standardised problem solving using a range of analytic tools
often abstract in nature. The keys to these workers’ success include creativity and innovation
and incorporate occupations ranging from lawyers to bankers to researchers to consultants.

Another US-based researcher took a fairly divisive stance on knowledge work by declaring
that, ‘all knowledge work is intellectual work. Thus, a job that is not intellectual enough will not
contribute to knowledge work. Such jobs should not be allowed in a knowledge organisation’
(Amar 2002). The paper argued further that knowledge organisations should only have jobs



that involve at least 50 per cent intellectual content (eg, analysis, decision making, creativity). In
turn, the author suggested that knowledge organisations should do away entirely with traditional
manual jobs that require only physical skills.

It is hard to know whether this should be taken literally or if the argument is that knowledge-
intensive tasks should be shared by all workers. After all, even in knowledge organisations,
knowledge workers need to be supported, offices need to be cleaned and machinery serviced
and so on. This definition would also appear to rule out high-tech manufacturing, including some
of the most R&D intensive companies in the world.

Data-driven definitions of knowledge work
Moving on to more data-driven definitions of knowledge work, some analysts have tried to
describe knowledge workers as all those who work in particular organisations or in particular
sectors or institutions – sometimes under the dubious impression that knowledge workers make
up the overwhelming majority of workers in such industries. However, in practice, organisations
in these industries need to deploy a wide range of complementary jobs with varying degrees of
intellectual content.

Another class of proxies that economists often use for distinguishing knowledge workers
is based on the investment expenditures in activities such as education and research and
development. In line with this approach, one of the definitions of knowledge workers that The
Work Foundation (TWF) has been using so far for their research is university graduates as a
proxy for highly-skilled workers and investment in human capital.

There has been a strong association between the rise of employment in knowledge intensive
industries and the employment of graduates in the workforce. There has also been a major shift
in the share of the workforce with some form of qualification across all sectors of the economy.
As Figure 1.2 below shows, in 1970, for example, less than 10 per cent of the workforce had a
degree and 60 per cent of people in work had had only basic schooling. By 2005 the share of
graduates had increased to around 19 per cent, while the share of people with no qualifications
had fallen to 12 per cent. The latest figures show that graduate employment accounted for just
under 23 per cent of workers in the UK.



Figure 1.2: Shares of graduates and workers with only basic schooling in UK workforce,
1970-2006

70
Degree holder
60 No qualification

50

40

30

20

10

0
70

72

74

76

78

80

82

84

86

88

90

92

94

96

98

00

02

04
19

19

19

19

19

19

19

19

19

19

19

19

19

19

19

20

20

20
Source: EU KLEMS Database

Economists often suggest that knowledge economies need to invest in skills at all levels – from
improving basic numeracy and literacy to expanding the share of young people entering the
university system, strengthening vocational skills, and promoting life-long learning. However, it
has typically only been investment in higher education that has defined knowledge work.

The premise underlying these measures of knowledge work is that in advanced industrialised
economies investment in higher education earns economic returns in the form of higher wages,
and hence knowledge workers are those with at least a graduate-level education.

The World Bank’s Knowledge Economy Index (KEI) uses the distinction between information
and knowledge to separate investment in basic education and higher education (Chen
and Dahlman 2005). Basic education is required to use and process information. Higher
level education is required for what the Bank calls, ‘the production of new knowledge and
its adaptation to a particular economic setting’ (p. 5). The OECD’s composite indicator of
knowledge investment similarly includes includes spending on higher education as a share of
GDP.4

However, it is less clear whether such distinctions can be easily made for vocational skills. The
evidence suggests that while lower level vocational skills may have relatively little impact on

4
OECD Science and Technology indicators. The other components are investment in ICT and R&D



wages, higher level vocational skills undoubtedly offer an economic return even if it is not as
significant as from higher education. And it would be hard to argue that the more sophisticated
vocational skills – for example, in diagnostic work – are not also engaged in the production and
adaptation of new knowledge.

Other proxies for knowledge work and workers have focused more narrowly on the link between
investment in scientific and technical skills and technological innovation. The narrowest
measure is the share of workers in R&D: typically, these more specialist types of knowledge
workers account for between 1 and 1.5 per cent of the workforce across the major OECD
economies even using the wider OECD definition that includes support technicians. A wider
measure is the share of workers with a science, technology, engineering or mathematics degree
(STEM graduates). Both can be used as a proxy for the ability of an economy to generate and
absorb technological innovations.5

Job-content definitions of knowledge work
A final approach to defining knowledge work has been to look at the sort of jobs that people do.
Here we see a very wide variety of examples. Suff and Reilly (2005) provide a useful summary
of some of the approaches adopted. Most studies give examples of managerial professional and
associate professional workers and often concentrate on particular groups. For example, a 2007
report on ‘enterprise knowledge workers’ was based on a sample survey of senior business
executives and managers (Economist Intelligence Unit 2007).

Broader measures of knowledge workers have been based on occupational classifications
within the official statistics. One of the more widely used measures adopted by The Work
Foundation has been to group together the three top occupational groups of managers,
professionals and associate professionals. These are jobs that, at least traditionally, require a
certain level of educational and/or vocational training and are the least likely to be affected by
technological advances and competition from low-wage manufacturing imports. Using this broad
stroke definition, 42.5 per cent of the workforce would be classified as a knowledge worker in
2007.

This broad classification has the virtue of providing readily available statistics on the extent and
growth of knowledge work. But it is also clear that some of the classifications do not work well.
The job title ‘manager’ is applied to a much wider range of jobs in the UK than elsewhere in
Europe, likely including many relatively low paid, basic supervisory roles (European Foundation
for the Improvement of Living and Working Conditions 2007). The category ‘managers,

5
Also referred to as HRST (human resources in science or technology)



legislators and senior officials’ accounts for about 15 per cent of the UK and the US work forces,
but less than 10 per cent in Germany, France, Italy and Spain, according to estimates by the
ILO (all figures 2007 or latest available). Moreover, other job categories are also likely to include
people undertaking similar tasks to those within the top three occupational groups.

More sophisticated approaches by researchers in Australia, the US and the UK have regrouped
the existing statistical occupational codes (Webster 1999; Autor, Levy, and Murnane 2003; Elias
and Purcell 2004).

The Australian research was primarily interested in trying to measure the production of
intangible ‘intellectual’ assets, and so regrouped occupations according to whether they were
associated with the production of such assets (Webster 1999). A further distinction was made
between workers that directly produce intangible assets for others including teachers, sales and
marketing workers, consultants, researchers and financial advisors. These workers also include
those who acquire and use skills, knowledge and talent to make a contribution to the goodwill or
efficiency of their firms including medical staff, scientists, managers and engineers.

The US researchers were interested in the impact of computerisation on the workforce (Autor,
Levy, and Murnane 2003). Notably, they wished to assess whether computers were more
substitutable for routine than non-routine forms of work. To do so, the researchers took the
existing statistical occupational codes and recategorised jobs into five groups based on the
degree of computer substitution and adherence to strict rules – both proxies for more routine
forms of work. The groups included:

1. Expert thinking: includes solving problems outside of rules based solutions, with
computers assisting but not substituting. As well as high level research and creative
work, this might also include the mechanic who is able to identify a solution to a
problem that computer based diagnostics could not.

2. Complex communication: includes interacting with other people to acquire or convey
information and persuading others of their implications, with computers assisting
but unlikely to replace – examples might include some managers, teachers and
salespeople.

3. Routine cognitive: includes mental tasks closely described by rules such as routine form
processing and filling, often vulnerable to computerisation.



4. Routine manual: includes physical tasks closely described by rules, such as assembly
line work and packaging, that may be replaced by machines.

5. Non-routine manual: includes physical tasks hard to define by rules because they
require fine optical or muscle control such as truck-driving and cleaning, and unlikely to
be either assisted or replaced by computers.

This delineation recognises the importance of workers’ inputs and serves as a useful guide
for understanding the types of job roles that are unaffected or even enhanced by mass
computerisation relative to the jobs that have become less relevant to the economy. From this,
we can argue that knowledge work goes beyond basic processing of information and cannot
be based on strict adherence to rules; in other words, it can be assisted and enhanced, but
not replaced, by computers. Thus, expert thinking, complex communication and analytical
reasoning – defined by the authors as making effective oral and written arguments – help define
knowledge work, as opposed to the routine cognitive along with routine and non-routine manual
categories.

Finally, UK research focuses on the links between occupations and graduate qualifications
(Elias and Purcell 2004). Over time, the researchers have assessed the average educational
attainment of workers in each of the minor occupational groups (ie, 371 occupations in total),
accounting for workers’ age given the increase in degree holders over time. Based on this
analysis, five umbrella groups of occupations based on educational qualifications were created:

1. Traditional graduate occupations: includes professions that historically have required an
undergraduate degree (eg, solicitors, scientists, doctors, teachers).

2. Modern graduate occupations: includes newer professions that graduates have been
entering since the 1960s (eg, chief executives, software professionals, writers).

3. New graduate occupations: includes occupations where entry-level has recently shifted
to incorporate degree holders (eg, marketing and sales managers, physiotherapists,
welfare officers, park rangers).

4. Niche graduate occupations: includes jobs where majority of entry-level workers are not
graduates, but there is a growing number of specialists who do come in with degrees
(eg, sports managers, hotel managers, nurses, retail managers).



5. Non-graduate jobs: includes professions where a graduate degree is not required and
most employees do not have degrees.

Similar to the US approach, this methodology directly incorporates the changing nature of the
labour market to analyse how occupations shift over time.

These three categorisations get us closer to what knowledge work might be, but they are still
constrained by the existing occupational codes. In all three studies, there was a strong overlap
between the sort of jobs that were classified as producing intellectual assets or associated with
expert thinking and complex communication skills or affiliated with graduate workers and the top
three occupational codes.

At one level this is reassuring: it suggests the top three occupational codes are capturing many
‘knowledge work’ jobs and so serve as a reasonable proxy. At the same time, it is important to
keep in mind that they are proxy measures nonetheless and hence only give us a partial picture
of knowledge work in today’s economy.

To sum up, what is missing from all of these attempts at defining knowledge work is a thorough
analysis of workers themselves and what they do at work. Moreover, different definitions provide
fairly divergent estimations of the size of the knowledge workforce in the UK. For example,
graduate employment in the UK in 2008 was just over 20 per cent of the workforce, while the
top three occupations (managers, professionals, associate professional and technical) account
for over 40 per cent. As we describe in more detail later in this report, the aim of the present
study is to focus directly on a large sample of UK workers to better understand the key tasks
and activities that make up their daily working life and develop a more robust measure of
knowledge work within the economy.


2. Redefining knowledge work and knowledge workers

This section develops our definition of knowledge work and knowledge workers. We do that in
three stages:

• First, we discuss the technical aspects of our survey and its analysis, and how we
reclassify the workforce into task-based ‘clusters’ on the basis of the distinguishing
features in the jobs they do.

• Secondly, we identify the different sorts of knowledge content within each of our
clusters, allowing us to identify these task-based characteristics that distinguish
knowledge work.

• Thirdly, we use our new definition of knowledge work to provide a cross-sectional
picture of the UK’s workforce today and how the new definition measures up against
previous definitions.
6
Research We performed our analysis in several steps. We started off by conducting a survey of, among
design6
others, the tasks that people employed in the knowledge economy frequently do at work. We
presented our survey respondents with a list of 186 tasks and asked them to rate how frequently
they perform each of them. We then analysed this survey information along two lines. On the
one hand, and to make our data more easily manageable, we identified groups of tasks, (eg
data analysis, administrative tasks, people management, maintenance moving and repairing)
that were frequently performed together by the same survey participants. On the other hand, we
identified groups of workers depending on how frequently they performed particular groups of
tasks.

In addition, our survey provided information on the use of technology, the methods of sharing
and acquiring knowledge and the complexity of the tasks that the participants perform at work.
The survey information allowed us to come up with a fresh taxonomy of both the types of tasks
that characterise work in the knowledge economy and the different groups of workers within the
labour force. In what follows, we present some important details on the methods we used and
then discuss our results regarding the definition of work in the knowledge economy.

6
Readers who are not interested in the specific technical details of our methodology can largely omit reading this sub-
section in full without losing track of our analysis


Redefining knowledge work and knowledge workers

Our survey
Our knowledge workers’ survey was designed in four phases.

First, we conducted an extensive literature review of existing sources on job and task analysis,
job content and job design. From this review, we compiled an initial list of approximately
125 work-related tasks or activities featuring manual tasks, cognitive tasks, social tasks and
technical tasks, to name a few.

Second, we conducted qualitative case studies of workers in two knowledge-based
organisations. For these case studies, we conducted focus groups and interviews with more
than 40 workers employed in a range of jobs within the organisations.

Third, we collated the evidence to finalise our list of tasks and activities for a pilot version of the
survey. The initial survey included 138 work-related tasks and activities as well as additional
items on workers’ background and job characteristics, features of job quality and working
conditions and work-related outcomes. The pilot survey was distributed to 200 workers who
participated in an online panel. Participants were required to work at least 20 hours per week in
one job, although they could have more than one job.

Finally, based on the evidence from the pilot study, we revised our survey further, incorporating
more work-related tasks and activities and deleting the tasks that did not appear to distinguish
workers. Our final survey comprised 186 work-related tasks and activities. The full list of the 186
work-related tasks and activities is provided in Appendix A.

The survey was sent out to 2,011 online panel respondents. All participants had to be working in
at least one job for a minimum of 20 hours per week for at least 3 months. Descriptive statistics
for the sample are found in Appendix B. With a few exceptions, our sample demographics were
comparable to those found in the 2007 Labour Force Survey (LFS) data. Our sample included
slightly more workers in the managers and senior officials along with administrative and
secretarial occupational categories than LFS estimates, and slightly fewer skilled tradespeople
and workers in elementary occupations. We captured a range of demographic and background
information about respondents as well as both general and specific characteristics of their jobs.
Appendix C provides a summary of these variables. The respondents indicated the frequency
with which they engaged in each of the tasks on a 4-point scale ranging from 1=never to
4=often.



Exploring work tasks in the knowledge economy: Factor analysis
To help make our data analysis more manageable, we ran an exploratory factor analysis (EFA).7
Our ultimate goal is to classify the respondents of our survey into groups depending on the
tasks they perform most frequently. Given that the list of tasks on whose frequency we asked
them to report was a long one, exploratory factor analysis helped us to shorten it by grouping
the tasks into 10 groups. For that purpose, this technique used the responses of our survey
participants on how frequently they perform each of the tasks to group these tasks into a few
distinct groups (‘factors’). The factors with sample tasks are detailed below with the figures in
brackets detailing the number of tasks from the original list that were included in the relevant
group (see Table 2.1 on the next page).

Each of the 10 factors was created by computing the mean of the relevant items. Figure 2.1
below displays the average factor scores across the full sample, that is, the average frequency
with which the tasks classified under each of the factors (groups) were performed in our
sample of workers. A score of one means the task is not very common across the sample –
either because it is rarely performed or because it tends to be confined to a specialist group of
workers. A score of four means it is very widely performed across the sample of workers. So for
example, people management tasks, data and analytical tasks, and administrative tasks are the
most frequently performed. In contrast, personal and domestic tasks, creative tasks and caring
tasks are the least frequently performed across the sample as a whole.

The high frequency of people management tasks and of data manipulation and analysis
underlines the emphasis of the knowledge economy in tacit knowledge that resides with
individuals and in information. The high prevalence of these tasks is consistent with the
importance of investment in both human capital and in software and computerised databases
in the UK economy.8 Data processing and analysis tasks are quite wide-ranging, spanning from
specialist analysis to mere data entering.

On the other hand, the relatively low incidence of care and creative tasks might seem surprising
given the large numbers employed in care-based industries and occupations and in the creative

7
In general, factor analysis is a statistical technique used to explain variability among a set of ‘observed’ variables (ie,
the 186 tasks in this case) through the creation of fewer ‘unobserved’ variables called factors or latent variables. By
finding the commonalities between different sets of items, we can effectively collapse our 186 individual items into a
more analysable set of factors. EFA was used in the first instance to get a sense of the number of factors comprised in
the 186 items as well as to identify the items that were poor factor indicators (ie, items that do not load on any factor or
load onto more than one factor). Confirmatory factor analysis (CFA) was subsequently used to validate the hypothesised
factor structure and our model exhibited adequate fit. The analysis suggested that 126 of the 186 tasks in our survey
could be collapsed into 10 distinct factors. The 60 excluded items tended to be very general types of tasks and activities
that most workers engaged in
8
HMT October 2007.Intangibles and Britain’s productivity performance



Table 2.1: Task factors with sample items

Factor Sample items
Data processing Compile data; Statistically analyse data; Identify patterns in data/information;
and analysis (9) Interpret charts/graphs; Enter data

Leadership & Make strategic decisions; Develop organisational vision; Identify issues that will
development (28) affect the long-term future of organisation; Foresee future business/financial
opportunities; Manage strategic relationships
Administrative Manage diaries; Order merchandise; Organise/send out mass mailings; Make and
tasks (10) confirm reservations; Sort post
Perceptual & Judge speed of moving objects; Visually identify objects; Judge which of several
precision tasks objects is closer or farther away; Judge distances; Know you location in relation to
(11) the environment or know where objects are in relation to you
Work with food, Clean/wash; Prepare/cook/bake food; Stock shelves with products/merchandise;
products or Gather and remove refuse; Serve food and beverage
merchandise (5)
People Assign people to tasks; Manage people; Teach others; Motivate others; Mentor
management (16) people in your organisation
Creative tasks Create artistic objects/works; Use devices that you draw with; Take ideas and turn
(10) them into new products; Take photographs; Engage in graphic design
Caring for others Provide care for others; Dispense medication; Diagnose and treat diseases,
(5) illnesses, injuries or mental dysfunctions; Expose self to disease and infections;
Administer first aid
Maintenance, Install objects/equipment; Use tools that perform precise operations; Use hand-
moving & powered saws and drills; Test, monitor or calibrate equipment; Take equipment
repairing (18) apart or assemble it
Personal, animal Excavate; Dig; Plant/maintain trees, shrubs, flowers, etc.; Feed/water/groom/
and home bathe/exercise animals; Sew/knit/weave
maintenance (14)

and cultural industries9. The former reflects the fact that care-related tasks are relatively
specialised, so are not frequently used at work outside the health and social care area. The
low incidence of creative tasks also reflects the fact that these tasks are relatively specialised.
Moreover, a common feature of sectors such as creative and cultural industries is that they
generate large numbers of jobs for people in non-creative roles, so even within these industries
the number of people working in specialised creative tasks may be relatively small.

About 17 per cent of the tasks originally included in the survey were excluded from the final
identification of group (factor) tasks. These excluded tasks are reported at the end of Appendix
A. In most cases, tasks were excluded from factor analysis, because they were too common

9
The Work Foundation, 2007 Staying Ahead: the economic performance of the UK’s creative industries



across these groups to be classified under a specific group or another. Notable examples fall
under various forms of communication, collaboration, advice giving and problem solving. In
other words, these tasks are so common they do not help us differentiate between workers who
can be described as knowledge workers and other groups in the workforce. However, there
were also tasks, most notably falling under ‘creative tasks’, that the survey participants hardly
reported to perform with any frequency.

Figure 2.1: What work tasks are most common across the workforce?
2.5

2.1
2.0 1.9
1.7
Mean frequency 1 to 4

1.5 1.4 1.4 1.4
1.3 1.3
1.2
1.1
1.0

0.5

0.0
t

g
is

s

ts

n

ip

g

e

tic
en

sk

iv
io

in

in
ys

uc

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em

is

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ov
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al

od

er

re

m
ec

C

m
an

in
ag

ad

do
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pr

pr
dm

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an

Le
&

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&
&

ai
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al
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ep
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le

si

on
k

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op

es

or

ep

rs
W
oc
Pe

Pe
rc
pr

Pe
a
at
D

Source: Knowledge Workers Survey, The Work Foundation, 2008

Note: 1 = least common, 4 = most common

Exploring the different types of workers in the knowledge economy: Cluster analysis
Having identified the broad types of tasks that workers in the knowledge economy perform, we
then proceeded to creating a new taxonomy of workers based on what they actually do in their
jobs on a day-to-day basis. Using the 10 task factors, we ran a cluster analysis, a technique
used to identify homogenous subgroups within our sample of UK workers. What the analysis
does is create groups or clusters of workers based on commonalties of task content and



frequency. Thus, our worker clusters are entirely based on workers’ reported tasks and activities
on the job.10

The novelty of our results is that our taxonomy cuts across classifications of workers according
to their educational attainment and occupation, that is, the proxies used in previous research for
identifying knowledge workers.

Based on the task factors, 1,744 of the 2,011 (87 per cent) workers in our sample best fit into
seven worker clusters. The analysis revealed that 267 workers reported very high frequencies
on each of the tasks (ie, 1-2 standard deviations above the mean) and were identified as
outliers. These workers were subsequently omitted from the analytic sample.11 The composition
of the seven clusters is detailed below. Appendix D presents the average factor scores within
each of the seven clusters.

The list below offers a snapshot of each of the seven cluster groups. We provide in parentheses
the share of workers in the sample that is classified under each cluster. We detail the most
common groups of tasks (as identified in our factor analysis) in each of the seven clusters as
well as the five specific tasks that workers engage in most frequently in their jobs. We list five
minor occupations that workers are classified in to give a sense of the occupational variability in
the worker clusters.

• Leaders and innovators (11 per cent)
◦ Frequently performed tasks: Data and analysis, leadership and development, people
management.
◦ Occasionally performed tasks: Administrative tasks, creative tasks.
◦ Specific tasks: Collaborate with people inside organisation on project/programme,
analyse information to address work-related problems, manage people, write reports,
provide consultation/advice to others.
◦ Example occupations: Production and functional managers, financial institution and
office managers, business and finance associate professionals.

10
We first ran a two-step cluster analysis to identify any outliers in the sample as well as to get an estimate of the
optimal number of clusters in the sample. Based on this initial analysis, we subsequently ran a k-means cluster analysis
specifying seven clusters. We also ran a latent class analysis and found that the seven cluster solution best fit the data.
The clusters used in the remainder of the report are based on the k-means analysis
11
We examined the individual background characteristics of this omitted group and found that the omitted group was
more likely to be male and more likely to have been at their current organisations for 20 years or more relative to the
average. No other significant differences were observed



• Experts and Analysts (22.1 per cent)
◦ Frequently performed tasks: Data and analysis, people management.
◦ Occasionally performed tasks: Leadership and development, administrative tasks.
◦ Specific tasks: Collaborate with people inside organisation on project/programme,
enter data, compile data, analyse information to address work-related problems, write
reports.
◦ Example occupations: ICT professionals, teaching professionals, managers and
proprietors in service industries, research professionals, customer service occupations.

• Information handlers (12.8 per cent)
◦ Frequently performed tasks: Administrative tasks.
◦ Occasionally performed tasks: People management, data and analysis.
◦ Specific tasks: File (physically/electronically), sort post, manage diaries, enter data,
handle complaints, settle disputes and resolve grievances.
◦ Example occupations: General administrative occupations, secretarial occupations,
financial institution and office managers, managers and proprietors in service industries,
financial administrative occupations.

• Care and welfare workers (7.5 per cent)
◦ Frequently performed tasks: Caring for others, people management, work with food,
products or merchandise.
◦ Occasionally performed tasks: Data and analysis, administrative tasks, perceptual
and precision tasks.
◦ Specific tasks: Provide care for others, administer first aid, clean/wash, dispense
medications, expose self to disease/infections, write reports.
◦ Example occupations: Care associate professionals, care services, childcare
services, social welfare associate professionals.

• Servers and sellers (7.0 per cent)
◦ Frequently performed tasks: Work with food, products or merchandise, people
management, administrative tasks.
◦ Occasionally performed tasks: Data and analysis, perceptual and precision tasks,
leadership and development.
◦ Specific tasks: Clean/wash, handle complaints, settle disputes and resolve
grievances, manage people, stock shelves with products or merchandise, order
merchandise.



◦ Example occupations: Managers in distribution, storage and retailing, managers
and proprietors in hospitality and leisure services, food preparation trades, elementary
personal services.

• Maintenance and logistics operators (11.3 per cent)
◦ Frequently performed tasks: Perceptual and precision tasks, maintenance, moving
and repairing.
◦ Occasionally performed tasks: People management, work with food, products or
merchandise, data and analysis, administrative tasks.
◦ Specific tasks: Visually identify objects, know location in relation to the environment
or know where objects are in relation to you, judge distances, lift heavy objects, load/
unload equipment/materials/luggage.
◦ Example occupations: Protective services, security occupations, transport drivers,
metal machining, fitting and instrument making trades, science and engineering
technicians, construction trades.

• Assistants and clerks (28.3 per cent)
◦ Occasionally performed tasks: People management, data and analysis, work with
food, products or merchandise, administrative tasks.
◦ Specific tasks: Handle complaints, settle disputes and resolve grievances, collaborate
with people inside organisation on project/programme, teach others, clean/wash, coach
or develop others, provide consultation/advice to others, motivate others.
◦ Example occupations: Customer service occupations, sales assistants and retail
cashiers.

The assistants and clerks cluster was the least well-defined group of workers as its members
tended to report engaging in all but the most general tasks relatively infrequently in their jobs.
We explored the specific occupations of this group to see if we had systematically omitted
relevant tasks and found this not to be the case.

To sum up, the results of our cluster analysis have allowed us to make a first attempt at
classifying workers in the knowledge economy on the basis of what they do. In what follows we
try to refine this classification in order to gain a better understanding of the cognitive complexity
of the tasks that workers belonging to different clusters perform most frequently and the sectors
in which they are employed.



Bright minds The next stage was to gauge the cognitive complexity of the tasks that workers in different
and powerful clusters mostly perform. This helped us distinguish, for example, between basic processing
machines for tasks such as data processing from higher level analytical tasks. We used three of their work
tasks of varying characteristics for which we got information through our survey:
complexity
• First, the extent to and ways in which workers in various clusters use (IT) technology.
• Secondly, the type of and variability in methods of sharing and capturing knowledge and
ideas when performing new tasks.
• Thirdly, the perception of workers about the complexity of the tasks that they have to
perform at work.

The assumptions that underlie the selection of these three criteria are that frequent and
specialist use of computing technology and frequent use of methods of sharing and garnering
new knowledge involving direct human interaction will characterise clusters of workers that
perform more tacit knowledge-intensive tasks. Similarly, the perceived complexity of tasks will
be higher for those clusters of workers that perform more tacit-knowledge-intensive work.

One of the hallmarks of the knowledge economy, and indeed one of its key enablers, is
the ubiquity of computing technology. In addition to facilitating work processing and email
communications, computers have sped up processing times for many work-related tasks,
thereby increasing workers’ efficiency or to engage in more difficult tasks that were not possible
previously.

We captured the importance of computing technology for the tasks that our survey respondents
perform by asking them two questions as part of our survey. First, we enquired how often
they use a computer at work. Across the full sample, workers reported using the computer 3-4
times per week on average. Secondly, we asked respondents to choose from a list of 12 tasks/
activities those that they do on their computer at work.

As seen in Figure 2.2 below, there was significant variation in the reported frequency of usage
and variability of activities performed on computers, suggesting varying degrees of importance
of information technology in workers’ jobs.

Those who used computers most frequently and for the greatest number of tasks were leaders
and innovators, experts and analysts and information handlers. They used computers daily in
their jobs, while performing an above average number of tasks on them. At the other extreme,



Figure 2.2: Number of different computer uses and how often computers are used each
week

8
Index frequency 1 to 5; number of uses 0 to 12

7.2 Number of computer uses Frequency of use each week
7
6.3
6 5.5
5.2
5.0 4.9 4.8
5
4.2 4.2
4 3.7 3.7 3.6 3.6 3.5
3.4
2.9
3

2

1

0
Innovators Experts Info All groups Assistants Servers Care and Operatives
handlers and clerks welfare
workers
Worker clusters

maintenance and logistics operators reported using computers once or twice per week to
perform around three tasks on average.

Tasks such as email, word processing, internet research, spreadsheet calculation, presentations
and managing diaries emerged as the most common work-related uses of computers across
worker clusters. Most of these tasks are relatively basic and likely follow an explicit set of rules.
Possible exceptions are internet research, spreadsheet calculation and presentations, which
can vary substantially on difficulty (eg, depending on whether a worker designs his/her own
presentation or types up someone else’s).

On the other hand, more specialist tasks such as statistics, system maintenance, graphic design
and software design are less common and likely to require expertise that is independent of the
technology itself. A recent study examining computer usage in the UK reported that only about
a quarter of workers used computers for complex or advanced tasks (Green et al. 2007). Our
estimates (shown in Figure 2.3 below) suggested the use of computers for specialist tasks
ranged from just 10 per cent in the case of care and welfare workers to 60 per cent for leaders
and innovators.



Figure 2.3: Share of workers that frequently perform at least one specialist computer task

70.0%
60.4%
60.0%
51.8%
50.0%

40.0% 35.0%
30.4%
30.0%
23.7% 22.5% 22.2%
20.0%
9.9%
10.0%

0.0%
Innovators Experts Total Operatives Info Servers Assistants Care and
handlers and and clerks welfare
sellers workers

Worker clusters

The extent of use of information technology in combination with the extent to which it is used
for performing specialist tasks suggest a distinction between, on the one hand, leaders and
innovators and experts and analysts and, on the other hand, the rest of the worker clusters.
According to this criterion, the workers in the former three clusters seem to perform the more
tacit-knowledge-intensive tasks12 compared to the workers in the rest of the clusters. However,
this criterion is not sufficient for refining the clusters of workers in terms of the required level of
knowledge, as, by nature, the tasks of clusters such as carers focus more on work with humans
rather than information alone (as eg, in the case of information handlers).

Workers were also asked to identify the range of methods they use to share and capture
knowledge in two contexts:
1. When performing a new task at work;13
2. When sharing information with others.

These results, illustrated in Table 2.2 below, suggest that the leaders and innovators cluster
displayed the most versatility and variety in the methods used for that purpose. Experts and
analysts and, to a lesser extent, care and welfare workers also used a wide array of methods.
These findings confirm that the clusters of leaders and innovators and experts and analysts
include the workers that are most likely to frequently perform (tacit) knowledge intensive tasks,
while assistants and clerks and maintenance and logistics operators are the least likely.

12
In Section 1, we distinguished tacit knowledge from codified knowledge or information. The latter is easily reproduced
through eg manuals and guides. The former resides with the individual in the form of expertise and/or experience and
for that, it is more expensive to transfer across workers
13
Only 6 per cent of the sample reported not ever having to do new tasks on the job



Table 2.2: Number of methods used to acquire new information and learn new tasks

Task based groups Acquiring new information Learning new tasks
(0 to 9) (0 to 16)
Leaders and innovators 7.4 4.6
Experts and analysts 6.0 3.4
Information handlers 5.2 2.8
Assistants and clerks 4.9 2.7
Servers and sellers 4.6 2.7
Care and welfare 4.6 2.3
Maintenance and logistics 4.1 2.1
Average all groups 3.3 1.8

Source: Knowledge Worker Survey, The Work Foundation, 2008

Evidence that further supports this picture is provided by the average frequency with which
the task of ‘teach others’ has been reported across clusters (see Figure 2.4 below). The more
abstract and tacit the knowledge that workers use is, the more it has to be developed through
experience and human interaction, for which teaching is an important means. This task is part
of the ‘people management’ group of tasks that workers across all clusters (but assistants and
clerks) report relatively frequently. However, there is some variety in the average frequency with
which workers report ‘teach others’ as part of what they do. The reported frequency of this task
is relatively higher in clusters such as ‘leaders and innovators’, ‘experts and analysts’, ‘care and
welfare workers’.

Moreover, there are differences in the consistency with which this task is reported as a
frequently performed one14 across clusters with similar average frequency, suggesting for
example teaching others is more common within the experts and analysts cluster than it is
within servers and sellers.

More generally, the responses of our survey participants point to a high level of ‘tacit’ knowledge
within workplaces, ie of knowledge that resides with individuals. This finding underlines how
important social relations still are within the workplace for sharing and capturing knowledge, with
informal discussions with colleagues, supervisors and managers and less specific socialising
and conversing with others amongst the most frequent. Rather less frequent but still cited by

14
That is, there is variation in the standard deviation of the reported values



Figure 2.4: Importance of ‘teach others’ task for different clusters

Variability in frequency of ‘teaching others’ Mean reported frequency of ‘teaching others’
3.5
3.3
Index frequency 1 to 5; number of uses 0 to 12

3.0
2.8 2.8
2.6
2.5
2.3

2.0
2.0 1.8

1.5
1.08 1.04
1.0 0.91 0.91 0.88 0.89
0.85

0.5

0.0
Innovators Experts Servers and Care and Operatives Info Assistants
sellers welfare handlers and clerks
workers

Worker clusters

nearly 30 per cent of the sample were more informal debates and discussion through
‘brainstorm’ or ‘white board’ meetings.

That said, large numbers of workers also relied on more codified forms of knowledge such as
the internet/intranet and printed material such as procedural and technical manuals, and trade
magazines and journals.

Finally, we also asked the survey participants to identify how complex they perceive their
work tasks to be. Leaders and innovators and experts and analysts all reported higher than
sample average complexity in their tasks. The complexity of tasks performed by information
handlers and care and welfare workers was of average complexity, closely followed by the tasks
performed by sellers and servers and maintenance and logistics operators. At the other end,
assistants and clerks reported the lowest task complexity scores in the sample.



Table 2.3: Prevalence of methods used for sharing and capturing knowledge

Publish written material 15%
Attend induction meetings 18%
Attend events/trade shows 21%
Contact a chat/information exchange group 23%
Read professional journals/trade magazines 26%
Attend an external training session 26%
Hold ‘brainstorming’ or ‘whiteboard’ meetings 29%
Read technical material 34%
Talk to outside experts 34%
Use the intranet 36%
Attend an internal training session 42%
Read procedure manual 43%
Socialise/converse with others 44%
Ask supervisor/manager 60%
Use the internet 60%
Talk informally to colleagues 90%

Figure 2.5: Perceived complexity of tasks performed by surveyed workers

3.0
2.6
2.4
2.5
Job complexity 1 to 3

2.1
1.9
2.0 1.8 1.7
1.6
1.5 1.3

1.0

0.5

0.0
Innovators Experts Info Care and Servers Operatives Total Assistants
handlers welfare and and clerks
workers sellers

Worker clusters



To sum up, looking into the uses of IT, the methods of sharing and acquiring knowledge and the
perceived complexity of tasks performed by workers in the knowledge economy, we sketched
a more nuanced picture of how the worker clusters that we identified can be roughly ranked in
terms of the tacit-knowledge-intensity of the tasks that workers perform. We bring together our
insights in the following sub-section.

Towards a Our findings so far suggest that we can portray the composition of the knowledge economy
new definition workforce and the work that workers actually do in a 30-30-40 shape. Our classification
of knowledge suggests that around a third of the UK workforce can be regarded as the ‘core knowledge
work workers’, having to perform many knowledge tasks as part of their job. Another 30 per cent
performs only some knowledge tasks, less frequently and at lower levels than for our core
knowledge workers. So up to 60 per cent of people in work are doing jobs that require the use
of at least some tacit knowledge. However, there are also very large numbers of people – 40 per
cent of the workforce – whose jobs involve only a few tasks requiring tacit knowledge and who
rely largely on codified knowledge through manuals, rules and procedures.

More specifically,

• About a third of workers are in jobs requiring high knowledge content. This core group
of knowledge workers includes leaders and innovators who most frequently engage
in tasks requiring specialist, ie tacit in addition to codified knowledge. The workers in
this cluster accounted for 11 per cent of the sample. The remainder are experts and
analysts, who perform high-level knowledge, analytical tasks, but who do not regularly
engage in some of the other specialist knowledge tasks. Experts and analysts account
for another 22 per cent. These two groups of knowledge workers were 1.5 times more
likely to report regular use of specialist knowledge tasks in their jobs relative to the
other worker clusters.

• A further almost 30 per cent of workers engage in jobs with moderate knowledge
content – primarily codified knowledge – relating to the cluster specific tasks that define
these jobs (eg administrative tasks, caring for others and work with food, products or
merchandise) as well as the people management and communication tasks that are
shared by most workers. This group comprises the information handlers (13 per cent)
care and welfare workers (7 per cent) and servers and sellers (7 per cent).



• Finally, 40 per cent of workers engage in jobs with only few tacit knowledge tasks (eg
perceptual and precision tasks, maintenance, moving and repairing). As we noted
above, just over 10 per cent of these workers fall under the maintenance and logistics
operators cluster, which will include many skilled manual jobs. About 30 per cent
however falls under the assistants and clerks cluster and it is here where we are likely
to find many of the low quality, low pay jobs that characterise the bottom third of the
labour market.

Figure 2.6: The 30-30-40 knowledge workforce

Many knowledge
tasks, 33%
Few knowledge
tasks, 40%

Some knowledge
tasks, 27%

It should be emphasised at this point that the 40 per cent does not represent the ‘bargain
basement’ of the UK labour market, even though the assistants and clerks category is more
likely to include a high share of poor quality and low paid work. Our primary aim is to distinguish
knowledge work and knowledge workers on the basis of the extent of and frequency with
which they use tacit knowledge to perform their job tasks. Virtually all jobs involve some tacit
knowledge, but those workers that we have classified as ‘core’ knowledge workers performed
the most tacit knowledge tasks for their job and those in the 40 per cent performed the fewest



tacit knowledge tasks. We use the term ‘knowledge’ to mean explicitly ‘tacit’ knowledge rather
than codified knowledge.

What is more, some of the jobs in the 40 per cent category include skilled manual jobs which
might be low in tacit knowledge compared with others, but are undoubtedly rich in codified
knowledge. As we report later, this acquisition of skills and codified knowledge is reflected in
wages, which on average are higher than for some job groups with a higher tacit knowledge
content.

Moreover, it is likely that some jobs described as skilled manual by the occupation based codes
will be in the ‘core’ knowledge worker category because the individuals are undertaking a high
proportion of tacit knowledge tasks in their daily work. This was recognised in the research
by Autor et al. (2003) that we reported in Section 1, whereby mechanics who could diagnose
complex faults and find solutions outside the standard manuals fell into the ‘expert thinking’
category. It is also strongly implied in the analysis of the modern manufacturing workforce
included within the recent BERR Strategy Review and in the The Work Foundation report
Knowledge Economy and Manufacturing (Brinkley 2009).

The To sketch the knowledge economy workforce more accurately, we examine the general
demographics demographic and background characteristics of workers in our sample. These statistics and
of the figures allow us to put a face to the knowledge workforce.
knowledge
workforce: Earlier evidence from The Work Foundation suggests that the vast increases in female labour
gender and age force participation over the past decade have been one of the key drivers of the knowledge
economy15. Our results indicate that women indeed play a key role in the knowledge workforce.
Just over 40 per cent of all workers in the core knowledge intensive jobs were women. This is
however slightly less than the share of women in all jobs. Women were much more strongly
concentrated within the clusters of care and welfare workers, information handlers, and servers
and sellers. So while women are disproportionately concentrated in jobs involving some
knowledge tasks, they are under-represented within the ‘core’ knowledge workers category.

The picture in the work clusters with few knowledge tasks is more mixed. Women accounted
for just under 50 per cent of less knowledge intensive jobs, such as assistants and clerks, while
in contrast, the maintenance and logistics category comprised almost exclusively of men. The
latter jobs are most likely to require manual skills traditionally associated with male workers and
physical strength.

15
Brinkley (2008) How Knowledge is Reshaping the Economic Life of Nations ( (Knowledge Economy Interim Report)



Figure 2.7: Share of women in jobs by knowledge content

Many knowledge tasks Some knowledge tasks Few knowledge tasks
90%
79%
80% 75%

70%
58%
60%

50% 47%
44% 44%
40%

30%

20%
10%
10%

0%
Innovators Experts Care and Info Servers Assistants Operators
welfare handlers and and clerks
workers sellers

Worker clusters

Turning to the age characteristics of knowledge workers, we see that the core knowledge
workers are particularly concentrated in the 35-44 and 55+ (leaders and innovators) and the
25-34 (experts and analysts) age brackets. Information handlers are particularly common within
the youngest segment of our sample (18-24) as are servers and sellers. The latter cluster,
however, includes relatively many people aged 55 and above as well. Maintenance and logistics
operators tend to be mostly aged between 45 and 54 years.

Although our data only captures the current pattern of work across age groups rather than
over time, this picture does not necessarily imply that the younger the generations, the more
knowledge tasks their jobs involve. Assistants and clerks represent around a quarter of workers
within any given age-bracket whereas they also appear to be in relatively high concentration in
the 35-44 group, ie a group that also has relatively high numbers of leaders and innovators.



Comparison of A final test for the usefulness of our new definition of knowledge work and knowledge workers
new and old is its comparison with existing proxies. As mentioned previously, two of the key proxies used to
proxies for estimate the number of knowledge workers in the UK economy include:
knowledge
work 1. Workers employed in the top three Standard Occupational Classification (SOC)
categories including managers and senior officials, professional occupations and
associate professional and technical occupations.16
2. Workers with degrees.

While both of these operational definitions have some utility – and are likely to overlap with the
‘true’ estimate of knowledge workers in the economy – they are limited, primarily because they
attempt to force workers into predetermined categories.

In this section we detail how our seven worker clusters align with the major SOC codes as
well as educational attainment. We find that although there is a substantial overlap between
our definition of core knowledge workers and these proxies, our worker clusters suggest that
people outside the top three occupational classifications and people who are not graduates may
be holding jobs with many knowledge tasks and vice versa. If anything, this suggests that our
definition helps us understand work in the knowledge economy better.

A high share of our two core knowledge worker groups (leaders and innovators and experts
and analysts) – between 70 and 85 per cent, are in the top three occupational classifications.
However, significant numbers of these workers with many knowledge tasks are also found
outside the top three occupational groups, especially the more numerous experts and analysts
group.

Just under half of our middle knowledge task group was covered by the top three occupational
group categories. This group includes significant numbers of associate professional jobs that
fall within the standard occupational classification, but it is also clear that even more have been
classified to other occupational groups outside the top three.

Even more interestingly, however, between 20 to 25 per cent of people in clusters characterised
by few knowledge tasks are included within the top three occupational groups. Even though
these shares are low compared to the other worker clusters, it is important to note that the top
three occupational groups include workers whose jobs involve few tacit knowledge tasks.

16
The remaining six occupational categories include administrative and secretarial, skilled trades, personal services,
sales and customer service, process, plant and machine operatives and elementary



All in all, there is some correspondence between the occupational definition of knowledge
workers and our worker clusters, but the occupational definition likely inserts a false dichotomy
into the workforce that is not based on a detailed account of workers’ everyday tasks and
activities.

Figure 2.8: Share of jobs in the top three occupational groups by knowledge content

Many knowledge tasks Some knowledge tasks Few knowledge tasks Total
90% 84%
80%
72%
70%
60%
48%
50% 46% 45% 44%
40%
30% 26%
20%
20%
10%
0%
Innovators Experts Servers Care and Info Assistants Operatives Total
and welfare handlers and clerks
sellers workers

Worker clusters

Looking at the educational definition of knowledge workers, ie whether they are graduates, we
see that there was quite a bit of variability in educational attainment across the clusters. The
majority of both leaders and innovators and experts and analysts held degrees, compared
to only 13 per cent of maintenance and logistics operators. As seen in Figure 2.9, there are
significant numbers of degree holders in each of our clusters.

On average, 35 per cent of the sample had a degree, which is comparable to the UK average of
33 per cent (including both degree holders and degree equivalent qualifications).

What is notable is that significant numbers of people without a degree were engaged in jobs
with many knowledge tasks. For example, over a third of leaders and innovators and nearly half
of our experts and analysts group did not have a degree. The idea that such jobs can only be
done by graduates does not seem to hold water.



Figure 2.9: Share of graduates by knowledge intensity of the job

70% Many knowledge tasks Some knowledge tasks Few knowledge tasks Total
63%
60%
53%
50%
41%
40%
35%

30% 26%
21%
20%
13% 13%
10%

0%
Innovators Experts Care and Info Servers Assistants Operators All
welfare handlers and and clerks
workers sellers

Worker clusters

In the jobs with some knowledge tasks, the above average share of graduates or equivalent in
care and welfare occupations is not surprising, given the requirement and desirability for higher
level qualifications for many practitioners in this area. The same applies for the below average
share in the servers and sellers category, as these are not the sort of jobs we would typically
associate with graduate level qualifications.

Graduates were, however, also present in significant numbers in jobs involved few knowledge
tasks such as assistants and clerks, and operatives. Indeed, over a fifth in the low knowledge
content assistants and clerks category had a degree or the equivalent. This is shown in the
figure above. This is potentially worrying if a large share of graduates were going into such jobs
which are very unlikely to make much use of their qualifications. It would also question some
of our earlier findings that there was little evidence to support the view that there was an over-
supply of graduates in the economy.

There are a number of possible explanations for this phenomenon. It may be that graduates are
using these jobs as the first step to entering into the labour market before moving onto positions
more suitable for their skills, or combining jobs with further study for higher qualifications. There
is some indirect support for this suggestion from the job tenure data. On average, graduates in
these sorts of jobs have much shorter tenures than non–graduates. For example, 23 per cent of
graduates in the assistants and clerks category had been in the job with the same employer for
less than one year compared with 10 per cent of non-graduates.



Additionally, only around 44 per cent of graduates in jobs with only few tacit knowledge tasks
reported that there was a good match between their skills and the demands of their job. This
finding may further suggest that these graduates are only temporarily employed in positions with
few knowledge tasks.

Moreover, as our survey does not address the possibility that university degrees may not always
equip their holders with the skills that are useful in the labour market, there may be a mismatch
between the skills supplied and those demanded. Last but not least, It is also possible that
some well-educated migrants have taken less skilled work when they first arrive in the UK.

We had argued that although some graduates were going into occupations that traditionally had
not employed them in the past, the nature of some of these jobs had changed so that graduate
skills had become more relevant. As we show in the next section of this report, there is some
further evidence to support this suggestion.


3. Knowledge work across industries and regions

In the introduction to this report we showed that knowledge based industries17 had significantly
expanded their share of employment over the past 40 years, emerging as the biggest source of
employment creation in most of the advanced industrialised economies.

We used our classification of knowledge work to explore three related questions:

• Are the core knowledge workers concentrated in the knowledge based sectors the
way that more conventional measures suggest? Are they the only workers with high
concentration in these industries?
• What proportion of workers in the knowledge intensive sectors are core knowledge
workers?
• How many core knowledge workers are there in sectors such as less knowledge
intensive services and manufacturing?

First of all, just over half of our sample (all clusters) was employed in one of the knowledge-
based industries, a figure that is a little higher than national statistics showing the share of
the workforce employed in knowledge based industries indicate. Just under half of our survey
respondents were employed in knowledge intensive services, with around 4 per cent employed
in medium to high tech manufacturing industries.

Looking into where the core knowledge workers are concentrated, we see that about 60 per
cent of them were located within the OECD defined knowledge industries – confirming that
significant numbers of knowledge intensive jobs are spread across the rest of the economy. The
picture was more mixed for workers with only some knowledge tasks. Over 90 per cent of care
and welfare workers were located within the knowledge industries, which is hardly surprising.
However, those classified as information handlers and servers and sellers were more likely to
be found in the less knowledge intensive industries.

The operatives group (ie the maintenance and logistics operators) was under-represented
in the knowledge based industries, with just under a third of them employed there. However,
more surprising was that 40 per cent of the assistants and clerks group – those with the least
knowledge intensive jobs – were employed in knowledge based industries. It is highly likely that
the expansion of the knowledge based industries has also sustained demand for some fairly
basic jobs as well as for knowledge workers.

17
The OECD defines knowledge based industries as high to medium technology manufacturing, business and financial
services, telecommunications and health and education services. Manufacturing is classified by R&D to sales ratio,
while services are defined by the share of graduate labour and their use of ICT related technologies


Knowledge work across industries and regions

Figure 3.1: Share of jobs in knowledge industries by knowledge intensity

Many knowledge tasks Some knowledge tasks Few knowledge tasks

100%
91%
90%
80%
70% 64% 63%
60%
50% 45%
42%
40% 34% 34%
30%
20%
10%
0%
Experts Innovators Care and Info Sellers Assistants Operatives
welfare handlers and
servers

Worker clusters

Turning next to the composition of the workforce in knowledge-based industries, we see that
employment there is tilted in favour of core ‘knowledge workers’. However, these industries also
employ large numbers of people classified under the less ‘tacit knowledge-intensive’ clusters.
Thus, it seems that the expansion of the knowledge based industries benefits workers doing
less knowledge intensive jobs.

In knowledge intensive services about 40 per cent of workers were classified as core knowledge
workers and just under 40 per cent were classified as people performing some knowledge
tasks. Just over 20 per cent of these services’ workforce was employed in jobs with only a few
knowledge tasks.

Taken as a whole, high-tech, market and financial knowledge service firms primarily employed
knowledge workers – both leaders and innovators and experts and analysts – as well as
assistants and clerks and information handlers.

Figure 3.2 presents the composition of the workforce in different knowledge-intensive services
with the educational, care and cultural (ie public-based) services workforce depicted in the lower
bar and the high-tech, market and financial service workers in the upper one.



Figure 3.2: Composition of the knowledge-intensive services sector

Public-based
services sector 36% 48% 16%

Composition of
workforce

Private
services sector 45% 23% 32%

0% 20% 40% 60% 80% 100%
Workers with many knowledge
tasks
Workers with some knowledge Knowledge-intensive services sector
tasks
Workers with few knowledge
tasks

More specifically, as Figure 3.3. below suggests, within the health and welfare sector, 26 per
cent of the workforce belonged to the experts and analysts and leaders and innovators clusters,
whereas around 44 per cent were care and welfare workers. Given that highly specialised
medical professionals are classified under the former two clusters, this distribution suggests
that our cluster analysis classified workers with different knowledge-intensity in their work fairly
well.18

Moreover, we also found significant numbers of workers with many or some knowledge tasks in
the less knowledge based and technology intensive service and manufacturing industries (on
which more below). This confirms our view that the transformation towards a knowledge based
economy has been affecting a very wide range of industries and not just those classified as
knowledge intensive.

In more traditional services, about 24 per cent of workers were classified as performing many
knowledge tasks. However, these industries employed large numbers of people in jobs that
involved some knowledge tasks, accounting for just over 40 per cent of all jobs in traditional

18
The analysis of workforce composition of less knowledge-intensive sectors such as distribution and repairs and hotels
and restaurants suggested the same. In both cases assistants and clerks and servers and sellers, ie worker clusters
whose work involves the use of more codified than tacit-knowledge dominated the workforce. The respective graphs are
provided in Appendix E



Figure 3.3: Workforce composition in the health and welfare industry by worker cluster

11.2%
14.3%

2.7%

15.2%

Leaders & innovators
Experts & analysts
6.7% Information managers
Maintenance & logistics operators
Care & welfare workers
Servers & sellers
5.8% Assistants & clerks
43.9%

services. Workers with few knowledge tasks accounted for about a third of all employment,
mainly in the assistants and clerks category.

Figure 3.4 below compares the workforce composition of the two groups of industries by
knowledge intensity.

To sum up, first, growth in knowledge-intensive industries is likely to have significant effects on
aggregate employment performance as it creates jobs for both the core knowledge workers and
for workers with only some or a few knowledge tasks.

Secondly, the concentration of different types of knowledge workers across sectors suggests
that what is driving the knowledge economy is a diverse workforce making use of different
types and levels of knowledge, engaged in a variety of distinct tasks and employed in various
occupations. These complementarities between different types of workers acknowledge that a
well functioning economy is dependent upon all of its workers and not just the few who engage
in the highest level of specialist tasks.



Figure 3.4: Employment in knowledge intensive and more traditional services compared

Knowledge-
intensive services 36% 48% 16%

Service
industries

Other services 45% 23% 32%

0% 20% 40% 60% 80% 100%
Workers with many
knowledge tasks
Workers with some
Composition of workforce
knowledge tasks
Workers with few
knowledge tasks

Manufacturing The manufacturing workforce represents about 9 per cent of the total sample of our
in the respondents. Hence, it is no surprise that fewer workers across clusters, whether core
knowledge knowledge workers or not, are employed in that sector compared to services. Across the
economy manufacturing industries, about 31 per cent of workers were knowledge workers (ie, leaders
and innovators or problems solvers and analysts), a further 19 per cent were maintenance and
logistics operators and 10 per cent were information handlers.

However, this compositional pattern shifts when examining the formation of the medium- and
high-tech manufacturing workforce relative to employees in low-tech manufacturing firms.
Figure 3.5 on the next page portrays the composition of the workforce separately for the
knowledge-based (lower bar) and non-knowledge-based (upper bar) manufacturing sectors.

The workforce of medium-to-high tech manufacturing firms consisted to a larger extent
of knowledge workers, particularly experts and analysts, compared to more traditional
manufacturing firms. On the other hand, the more traditional firms were comprised of larger
proportions of assistants and clerks and information handlers relative to the medium- and high-
tech companies.



Figure 3.5: Composition of the manufacturing sector

Other
manufacturing 26% 34% 40%

Manufacturing
industries

Meduim-to high- 38% 29% 33%
tech manufacturing

0% 20% 40% 60% 80% 100%
Workers with many
knowledge tasks Composition of workforce
Workers with some
knowledge tasks
Workers with few
knowledge tasks

These figures suggest that employment creation in the medium- to high-tech manufacturing is
likely to be intensive in jobs for core knowledge workers in a way comparable to knowledge-
intensive services.

The location of The growth in knowledge-based industries reported over the past decade is reflected in all
the knowledge of our clusters, suggesting that these industries are a key part of the UK economy. However,
economy this trend is particularly true in Northern England and Scotland as well as the South West and
Wales. Indeed, in London, the South and East of England, there were more private than public
knowledge-intensive firms.

Although the composition of regional work forces has been quite similar, there have actually
been differences in the regional concentration of knowledge workers.

Although our survey did not look in great detail into the geographical distribution of knowledge
workers, there were nevertheless indications that core knowledge workers tend to cluster in
urban areas, particularly in London, the South East and North of England and Scotland. This
is not a surprising finding given that face-to-face contact and the development of relationships
are important for exchanging information and especially tacit knowledge. Cities across the UK
– including Manchester, Leeds, Bristol and Edinburgh outside the South East – also provide



businesses with access to wider markets and to specialist skills. This result resonates with the
insights of our Ideopolis programme on the growing importance of cities in world economies.

On the other hand, the South West and Wales region have a relatively high concentration of
workers with some knowledge tasks, while the North and Scotland have relatively more workers
with few knowledge tasks.

Table 3.1: Regional concentration of knowledge workers in the UK

Workers Workers Workers Share of
with many with some with few the national
knowledge knowledge knowledge workforce in
tasks tasks tasks the region
London SE 35.8% 33.7% 33.5% 34.1%
East
SW and 9.4% 12.6% 10.5% 10.6%
Wales
Midlands 16.6% 16.2% 16.1% 16.3%

North and 38.3% 39.4% 40.0% 39.0%
Scotland
Total 100% 100% 100% 100%


In terms of regional workforce composition, the proportion of knowledge workers was fairly
comparable across regions (33-35 per cent of regional work forces) with the exception of
the South West, Wales and the West, in which only 29 per cent of workers were leaders and
innovators or experts and analysts. This suggests that the potential of employment expansion in
different regions to create core knowledge jobs is relatively even. These findings are displayed
in Figure 3.6 below.

Looking specifically within the knowledge-based industries, regional differences in knowledge
work are starker. London, the South and East of England boast the highest relative percentage
of knowledge workers – including both leaders and innovators and experts and analysts – with
45 per cent of the workforce in specialist knowledge jobs. The percentages in other regions
range from 36 per cent in the South West, Wales and the West to 38 per cent in the North and
Scotland to 40 per cent in the Midlands.



Figure 3.6: Regional composition of the workforce

Northern England
33% 27% 41%
and Scotland

Midlands 34% 27% 39%

SW, Wales
and West 29% 32% 39%

London,
35% 26% 39%
SE & East

0% 20% 40% 60% 80% 100%
Workers with many
knowledge tasks
Workers with some Composition of workforce
knowledge tasks
Workers with few
knowledge tasks

All in all, knowledge workers seem to be relatively evenly distributed across regions with
perhaps the exception of the South West, Wales and the West.


4. The changing nature of work roles and the returns to knowledge

This section uses the our newly defined definition of knowledge workers and their responses to
our survey to understand whether there has been any change in the nature of work roles and
whether knowledge leads to higher returns to work.

The changing One of the questions pertaining to the consequences of the knowledge economy is whether
nature of work changes in technology and work organisation have altered the nature of some jobs within broad
roles occupational groups such as administrative and clerical. Our worker survey provides some
indirect evidence that the nature of work roles has been indeed changing.

About 13 per cent of our sample was classified as ‘information handlers’ and about 25 per cent
had a degree. This group of workers was uniquely defined by high frequencies of administrative
tasks such as organising travel, managing diaries, ordering merchandise and filing. These
administrative tasks filled the days of secretarial workers in the past, and arguably did not
require graduate level skills.

However, the information handlers of today also engage in tasks related to people management,
data and analysis and, to a lesser extent, leadership and development. The information
handlers – similar to other clusters – exhibit task overlaps with the core group of knowledge
workers, hence, the need for more highly qualified people to fill these positions. These roles
have been reinvented to incorporate available technology (which makes administrative tasks
less time consuming) and to provide high-level support for workers in knowledge-intensive firms.

What is a manager?
According to 2007 Labour Force Survey estimates, 15 per cent of the working population
is employed in managerial posts – the highest percentage for any of the nine occupational
groups. Among our sample, closer to 20 per cent were in management posts (using
formal occupational codes). Further, tasks related to people management tasks were the
most common activities workers engaged in across our sample. It seems everyone has
management responsibilities, which begs the question of whether the term manager is even
useful in mapping the workforce.

The term ‘manager,’ perhaps more so than any other occupational title, tells us very little
about the position that someone holds within an organisation, the tasks and activities that
make up their working life and the specialist knowledge required for the job. For example,
people who run a small store all the way up to those who oversee a multi-million pound
corporation would be classified as managers. These managers could be responsible for two
workers or 10,000.


The changing nature of work roles and the returns to knowledge

In the middle of the last century, Mills (1951) described a typology of managers that still
seems accurate today:

…managers are usually split into two types: those who have to do with business decisions
and those who have to do with the industrial run of the work. Both are further subdivided into
various grades of importance, often according to the number of people under them; both
have assigned duties and fixed requirements; both as groups have been rationalized (p. 82).

Our findings would support a further distinction between management and leadership. The
leaders and innovators were able to balance their heavy load of management tasks with
strategy, development, creativity, future planning and analytic tasks. Only 11 per cent of the
sample regularly engaged in leadership tasks in their jobs – clearly requiring a higher level
of specialisation than general managers.

Although we can distinguish managers from leaders, a few questions remain unresolved.
If almost all workers have people management responsibilities, do we simply have too
many managers in the UK? With so many people managing others, do staff have enough
autonomy at work? Should we loosen up management hierarchies so staff have more time
to specialise in tasks? Are career paths still based on the acquisition of management skills
rather than specialist knowledge skills?

This evidence speaks directly to one of the key debates, namely whether the transition to a
knowledge based economy has been leading to greater polarisation, with more good jobs at
the top of the labour market, more bad jobs at the bottom, and fewer jobs in the middle. One
argument in this debate is that the demand for jobs that require graduate level skills has been
lagging supply, so that some graduates are forced into less skilled and less well paid work. This
in turn reduces job opportunities for non-graduates, who would be forced into even less well
paid jobs or even out of the labour market altogether.

The facts that emerge from our survey do not support this view and dovetail with the insights of
our earlier research (Fauth and Brinkley 2007). We showed that over the past decade the share
of well paid and low paid jobs had stabilised. This was also true for jobs taken by graduates.
Moreover, aggregate wage data continued to show no significant narrowing of the wage gap
between graduates and non-graduates. Nor could we find any increase in the gap in labour
market outcomes between graduates and non-graduates, as measured by unemployment or
employment rates.



All in all, these findings lend some credence to the hypothesis that the nature of work roles
has been changing across the economy with perhaps the exception of the assistants and
clerks. The workforce as a whole is becoming more skilled, partially as a result of technological
advances, in terms of formal qualifications and acquired experience within jobs. The evidence
from our survey suggests that it is increased demand for rather than excess supply of graduates
that underlies the polarised employment growth across occupations.

The quality of Turning to the extent to which jobs in the knowledge economy adequately tap into workers’
skills match in skills set and experience, just under half of the respondents (48 per cent) indicated that their
the knowledge job duties correspond well with their extant skills. Table 4.1. shows the responses of the
economy survey participants by worker cluster. At first glance, there does not seem to be a relatively
straightforward manner in which the high knowledge content of jobs can be associated with
the good fit between workers skills and their job requirements. Experts and analysts were most
likely to report a good match while leaders and innovators were very close to the average in that
respect, below care and welfare workers and information managers.

Still one can notice that the worker clusters with the fewest knowledge tasks along with the
servers and sellers reported the weakest (below average) match between worker skills and job
requirements. In the case of assistants and clerks and maintenance and logistics operators,
this evidence probably suggests that jobs with few knowledge tasks do not require very job
specific skills. On the other hand, the low ranking of the servers and sellers in that respect could
probably be linked to the relatively high concentration of temporary, fixed-term employees in
that cluster. The fact that this is also the cluster with the higher share of workers perceiving
themselves as ‘overskilled’ for their job (55 per cent) further supports this suggestion.

More generally, the fact that more than 40 per cent of workers in our sample felt that their
skills were underutilised at work along with the fact that many employers claim that the supply
of workers does not have adequate or the right mix of skills and previous experience for the
existing vacancies suggests a substantial mismatch between labour demand and labour supply
in the knowledge economy.



Table 4.1: Job-skills/experience match by worker cluster

Work cluster Good match Underskilled Overskilled
Experts and analysts 54.4% 10.6% 35.0%
Care and welfare workers 51.9% 10.7% 37.4%
Information managers 50.2% 7.2% 42.6%
Leaders and innovators 49.0% 13.0% 38.0%
Total 48.0% 10.3% 41.6%
Assistants and clerks 47.5% 11.4% 41.2%
Servers and sellers 40.2% 4.9% 54.9%
Operatives 38.6% 8.6% 52.8%

Returns to Most surveys and the aggregate evidence confirm significant returns to education, ie well-
knowledge educated people earn more over their lifetime than less well educated people (Leitch 2006).
Can the same be said about knowledge? The answer is a partial yes. Figure 4.1 below suggests
that the returns to knowledge do not increase with the number of tacit knowledge tasks in one’s
job.

Those in the most knowledge intensive jobs earn significantly more than the median – 80 per
cent of workers were above the median 2007 wage measured by the Labour Force Survey.
These differentials suggest that there are strong returns to knowledge work.

For workers with some knowledge tasks, however, the reverse was the case. Here, only 34 per
cent earned more than the median. This was lower than for those with only few knowledge tasks
such as assistance and clerks and maintenance and logistics operators.

One possible reason for this pattern is that the operatives group includes some relatively well-
paid skilled manual jobs. But it may also be evidence of gender wage gaps. Indeed, our data
in Table 4.2 below suggest that in female-dominated clusters (see Figure 2.7 above) such as
information handlers, only about 25 per cent of women earn above the median wage, compared
to almost 50 per cent of men, while among the care and welfare workers, only about one-third of
women command high earnings compared to almost two-thirds of men in that cluster.



Figure 4.1: Percentage earning more than median wages by worker cluster

90.0%
77.4%
80.0%

70.0%

60.0%

50.0%
39.9%
40.0%
33.3%
30.0%

20.0%

10.0%

0.0%
Workers with many Workers with some Workers with few
knowledge tasks knowledge tasks knowledge tasks

Table 4.2: Shares of women and men earning above the median wage within female
dominated worker clusters

Information handlers Care & welfare workers
Women 25.6 34.7
Men 49.0 63.0
The share of women in these clusters is 75 and 80 per cent respectively.
The figures above show the shares of women earning above the median wage.


To sum up, the frequent use of tacit knowledge in one’s job tasks seems to increase the returns
to labour, although this effect still seems to be weaker for women than for men.


5. The job characteristics of knowledge workers

This section uses the results of our survey to sketch some of the general features of work in the
knowledge economy. Knowledge-based work has been heralded as facilitating new forms of
employment driven by enhanced bargaining power, new technologies, and generational attitude
changes to work. Knowledge work is perceived as moving away from traditional 9-5 office jobs
and towards less permanent, more flexible and less structured forms of employment. Other
commentators have suggested that knowledge workers would open up new forms of flexibility
– for example, through various forms of teleworking – so that knowledge workers would no
longer be bound by the traditional 9-5 office routine. Instead they can work wherever an internet
connection exists, either individually or in remote clusters. Such workers have been labelled
‘nomads’ (Kluth 2008).

These are all fascinating and beguiling possibilities, and for some individuals they are clearly a
reality. However, these assertions are often made without substantial empirical evidence to back
it up.

According to estimates from the Labour Force Survey (for a review see Brinkley 2008) portfolio
working – rare to start with – has fallen for knowledge workers over the past decade. So has
self-employment (a common trend over much of the OECD). Temporary employment remains
small and has not increased as a share of employment. Nor is there much to suggest that
knowledge workers are turning to any significant degree to the more unusual formal working
arrangements such as job shares or nine-day fortnights.

More traditional flexible working arrangements, such as part-time and flexitime, do seem to
attract knowledge workers more. However, we should be careful about assuming this means
knowledge workers either do not get or do not want new flexibility at work. Knowledge workers
may enjoy flexibility through informal work practices – for example, they typically have high
levels of autonomy in how they get their tasks done.

Given that our definition of knowledge workers cuts across occupational groups and only partly
overlaps with the top three of them, we use it here to examine whether these changes in job
characteristics have been occurring.

Job One of the most important questions is whether traditional employment relationships in
characteristics organisations are still relevant in the knowledge economy (for a review see Brinkley 2008).
and flexible One camp has argued that knowledge workers reject traditional employment relationships, in
work turn preferring ‘portfolio work’ (ie, holding several part-time jobs simultaneously) and favouring


The job characteristics of knowledge workers

more freestanding relationships as temporary employee, freelancers or self-employed workers.
Yet, others have suggested that new forms of working have developed within the modern
corporation. In this case, the more specialist and entrepreneurial knowledge workers are given
the freedom to experiment and develop new ideas. These ‘intrapreneurs’ combine the freedom
of self-employment with the security and resources of big companies.

To assess whether these changes have been taking place, we inquired about workers’ job
tenure and the length of contracts in our survey. We found that neither long-term (ie beyond 10
years) nor extremely short-term tenures dominate in our sample.19 Nearly a third of workers had
been in their jobs between 1-2 years, with another 40 per cent in their jobs between 2-10 years.
We found that in our sample about 20 per cent of workers had been in their jobs for 10 years or
more, which we have taken as one indicator of long tenure jobs.

The most striking result is that there only seems to be little association between the knowledge
content of a job and the average tenure in that job. For the most knowledge intensive jobs,
average tenures were in line with the overall average, average tenures for jobs with some
knowledge content were somewhat below the average, and jobs with little or no knowledge
content had above average tenures.20

More specifically, people in jobs with some knowledge content such as information handling
and serving and selling jobs had job tenures significantly below the average, with about 12 per
cent in jobs with more than 10 years tenure. A potential explanation for low tenure is age: about
25 per cent of job handlers were under 25 years old. In contrast, people in maintenance and
logistics jobs with little tacit knowledge content had job tenures above average, with nearly 30
per cent in jobs lasting 10 years or more.

Permanent job contracts were the most prevalent in our sample with an average of 86 per
cent of workers in our sample being on permanent contract. This estimate is lower than the UK
average of 94 per cent (LFS). There was variation across our clusters in that respect, ranging
from 77 per cent of information handlers to 90 per cent of leaders and innovators. However, we
did not observe any straightforward association between the knowledge intensity of tasks in
different clusters and share of workers holding permanent contracts.

19
Our sample excludes some part time workers, so we would expect tenures to be somewhat longer in our sample than
for the workforce as a whole
20
As our survey is cross-sectional, we have to allow for the fact that tenures tend to be counter-cyclical – they fall when
employment is growing and rise when employment is contracting. This is partly because new jobs, by definition, are of
shorter tenure than old jobs; and partly because people are more inclined and able to move between jobs when they
are plentiful. When our survey was conducted, the employment of knowledge workers defined by occupation had been
increasing strongly so we might expect tenures for knowledge workers on average to be falling slightly



Figure 5.1: Percentage of workers in the same job for more than 10 years by worker cluster


35%
91%
29%
30%

25%
64% 63% 21%
19% 19%
20% 18%
17%
15% 13% 42%
45%
12%
34% 34%
10%

5%

0%
Experts Innovators Care and Servers Information Operators Assistants Total
welfare and and clerks
sellers

Worker clusters

Working time To assess whether knowledge work has been moving away from traditional working-time
patterns, we examined three aspects: first, the working-hours patterns of workers in our sample;
secondly, whether they work typical ‘nine to five’ shifts; and thirdly, whether they work during
weekends.

Nearly three-quarters of the workers in our sample work a standard full-time workweek, with
an average of 40 hours per week21. Knowledge workers were on average more likely to work
long hours (in excess of 45) than the average sample worker and more likely to work long hours
than those in jobs with some knowledge content such as care workers, sellers and services and
information handlers. Among those doing the most knowledge intensive jobs, those classified
as leaders and innovators were significantly more likely to work long hours than experts and
analysts.

The only other group where long hour working was equally prevalent was maintenance and
logistics workers – that is, jobs often associated with extensive paid overtime.

However, the picture is different if we look just at very long hour working, in excess of 60 hours
a week. This is not a common feature for most workers, and people in knowledge intensive jobs

21
It should be noted here that due to the way we compiled our sample by excluding those working for less than 20 hours
per week, it is most likely that the share of part-timers in our sample is under-estimated



were less likely to work very long hours than the average. In contrast, very long working hours
was more likely for the maintenance and logistics group and also for servers and sellers.

Knowledge work has sometimes been associated with a move away from the typical ‘nine to
five’ day as new technologies and more flexible work organisation open up more options. We
found no linear association between knowledge work and less traditional ways of working.
Overall, about three quarters of respondents reported a regular nine to five working pattern, and
for those in the more knowledge intensive jobs this was, if anything, more common.

More irregular working is common in just two groups – carers and welfare workers and servers
and sellers – where only between 40 and 45 per cent report working other than a nine to
five day. This is not surprising given the nature of the industries such jobs are likely to be
concentrated in, with high levels of part time working and some 24 hour provision.

Figure 5.2: Percentage of workers working day shifts by worker cluster


100%
88%
90% 86% 84%
80% 74% 75%
69%
70%
61%
60%
50% 45%
40%
30%
20%
10%
0%
Experts Innovators Information Servers Care and Assistants Operators Total
and welfare and clerks
sellers

Worker clusters

Weekend working was common in the past in more traditional industries such as manufacturing
and mining, but has become more associated today with the growth of service industries such
as retailing and hospitality, the care industries, and some recreational and cultural services. But
in addition, advances in technology mean that workers in knowledge intensive jobs can often
work as easily at home as in the office and may be tempted (or required) to do some work at



weekends in order to cope with workloads or spread the burden more evenly across the whole
week.

Working during the weekend is fairly common across the workforce, with 48 per cent reporting
they did some weekend work at least once a month. We found that the most knowledge
intensive jobs were the least likely to report weekend working, especially among the experts
and analysts group, where just over 30 per cent reported weekend working. Even so, between
30 and 40 per cent said they did some weekend working at least once a month, so it is not that
unusual. However, these proportions are dwarfed by the shares of workers in less knowledge
intensive jobs such as servers and sellers and care and welfare workers and the maintenance
and logistics group, where between 70 and 80 per cent reported some weekend working.

Figure 5.3: Percentage of workers doing weekend work at least once/month by worker
cluster


80%
70%
70% 66% 65%

60%
48%
50% 46%
40% 41%
40%
31%
30%

20%

10%

0%
Innovators Experts Servers Care and Information Operators Assistants Total
and welfare and clerks
sellers

Worker clusters

Autonomy One might expect that thanks to developments in information and communication technology,
and choice knowledge workers have high levels of autonomy and choice over how they manage their
workloads. Taken to the extreme, this is the concept of the ‘intrapreneur’, who is said to have
virtually all the freedoms of someone working for themselves within a corporation, although as
we pointed out in the introduction there is little hard evidence for their existence.



To test this we asked our survey participants who sets their working time arrangements,
namely whether they could entirely set them themselves; whether they could adapt them within
certain limits (eg flexitime); whether they could choose among several fixed working schedules
which were determined by the company/organisation; or whether the company/organisation
determined these arrangements without providing any options to its workers. We also asked
them how often they work from home as an indication of flexibility over the location of work.

About half the sample reported some form of flexibility over how they did their work, that is,
either through a formal arrangement such as flexitime or self-determined hours. Those with
many knowledge tasks reported significant higher levels of flexibility, with between 55 and 60
per cent saying they had some choice over hours. Among workers reporting some degree of
flexibility, only 10 per cent of workers had complete flexibility over their schedules. Nearly 20 per
cent of workers in the information handlers cluster reported this high level of flexibility, which fits
with the findings from our qualitative work conducted at the start of this project.

In contrast, less than 40 per cent of those in few knowledge tasks reported having any flexibility
over setting their working arrangements. While these differences are significant they are not
overwhelming.

Moreover, over 40 per cent of those with many knowledge tasks reported little or no flexibility
over how they managed their work.

Figure 5.4: Percentage of workers with flexibility in choosing work schedule by worker
cluster


70%
61%
60% 56% 54%

50% 47% 47%
43%
40% 36% 37%

30%

20%

10%

0%
Innovators Experts Information Care and Servers Operators Assistants Total
sellers

Worker clusters



On the whole, extensive home working does not seem to be a key part of work in the knowledge
economy. That is, the flexibility of knowledge employees in choosing their location of work is not
as high as their flexibility in choosing their work schedules: less than a quarter of respondents
reported working at home at least once a month. Again, this flexibility increased with the amount
of knowledge tasks that workers in the various clusters perform frequently: approximately 40 per
cent of leaders and innovators enjoyed this type of flexibility, relative to only about 15 per cent of
maintenance and logistics operators and assistants and clerks, respectively.

Our findings show that those with more knowledge based jobs have greater flexibility than
those in less knowledge based jobs, at least as far as choice over hours is concerned and the
ability (whether willing or not) to work at home. However, it is also striking how far the standard
working day with relatively fixed working arrangements still predominates in today’s labour
market. Even amongst those involved in knowledge intensive jobs, a sizeable minority had little
choice over working arrangements and those who could really determine their own hours are a
small minority.


6. Organisational culture in the knowledge economy: preferences and reality

The large research gaps in understanding the key characteristics of knowledge workers
and knowledge work also exist at the firm level. While there is a vast literature looking at
management of knowledge workers, there is little in the way of hard evidence. Further, we need
a better sense not only of the predominant organisational cultures in the knowledge economy,
but also the degree to which these realities mirror workers’ preferences. In this section we
examine workers’ perceptions of their predominant organisation culture to assess the balance
between rule bound cultures and innovative cultures, organisations defined by trust and loyalty
versus those defined by achievement and competition.

For that purpose, we asked all respondents to rate their agreement to four statements
describing organisational culture (Cameron and Quinn 2006):

1. This organisation is characterised by loyalty and mutual trust. Commitment to this
organisation runs high.
2. This organisation is characterised by commitment to innovation and development.
There is an emphasis on being on the cutting edge.
3. This organisation is characterised by an emphasis on achievement and goal
accomplishment. Aggressiveness and winning are common themes.
4. This organisation is characterised by formal rules and policies. Maintaining a smooth-
running organisation is important.

Table 6.1 below illustrates the share of each worker cluster within the group of workers who
reported that each of the four qualities characterises their organisation and the respective
shares for private and public sectors.22

The responses of our survey participants suggest several notable points.

First, the most prevalent of the four characteristics of organisations, according to their
workers, is their adherence to formal rules and policies (almost 60 per cent of our respondents
reported it) while the least prevalent characteristics are the emphasis on achievement and
accomplishment and the commitment to innovation and development (around 37 per cent
reported both).

22
The industries that we defined as private sector include agriculture, hunting and forestry, fishing, mining and
quarrying, manufacturing, electricity and water supply, construction, distribution and repairs, hotels and restaurants,
transport, storage and communication, financial intermediation, real estate and business activities. The public sector
includes public administration, education, health and social work


Organisational culture in the knowledge economy: preferences and reality

Interestingly, these perceptions seem to vary substantially depending on whether the
respondent works in the private or the public sector. Public sector organisations are perceived to
be more bound by rules and formal procedures and less committed to achievement, innovation
and development than private sector organisations. Half of our respondents thought that their
organisation is characterised by loyalty and mutual trust and quite notably, this feature was
slightly more prevalent in the private sector than it is in the public.

Secondly, workers with many knowledge tasks in general are clearly more likely than any
other group of workers to perceive their organisations as being committed to innovation and
development and as emphasising achievement and accomplishment. However, there is again a
sizeable difference in this perception depending on whether these core knowledge workers are
employed in the private or the public sector, with the private sectors scoring higher.

To the extent that commitment to innovation and development and emphasis on achievement
and accomplishment provide incentives for the use and expansion of tacit knowledge, which
resides with the individual, these data suggest that public sector organisations in the UK are
probably less well positioned to exploit the benefits of the knowledge economy.

On the other hand, the extent to which these ‘core’ knowledge workers perceive their
organisation as being bound by formal rules and policies and characterised by loyalty and
mutual trust is quite similar to that of workers with only some knowledge tasks (eg information
handlers, care and welfare workers and servers and sellers).


Table 6.1: Perceived organisational characteristics by worker cluster

Loyalty Innovation Achievement Rules
Private Public Total Private Public Total Private Public Total Private Public Total
Leaders & 69.9% 54.1% 63.3% 58.3% 44.6% 52.5% 49.5% 47.3% 48.6% 59.2% 77.0% 66.7%
innovators
Experts & 55.7% 46.7% 51.9% 52.2% 39.3% 46.7% 49.8% 35.3% 43.6% 61.2% 67.3% 63.8%
analysts

Knowledge Workers and Knowledge Work
All core 60.5% 49.1% 55.7% 54.3% 41.1% 48.7% 49.7% 39.3% 45.3% 60.5% 70.5% 64.8%
knowledge
workers
Information 68.8% 47.5% 62.3% 40.6% 34.4% 38.7% 37.7% 31.1% 35.7% 52.2% 55.7% 53.3%
handlers
Care & 40.0% 46.4% 46.2% 27.6% 31.4% 28.3% 36.6% 34.3% 36.1% 52.4% 57.1% 53.3%
welfare
workers
Servers & 55.6% 57.9% 56.0% 40.0% 22.4% 23.1% 40.0% 26.4% 26.9% 60.0% 68.8% 68.5%
sellers
All workers 63.4% 47.8% 55.9% 42.4% 27.8% 35.4% 38.8% 28.8% 34.0% 54.0% 66.3% 59.9%
with some
knowledge
tasks
Maintenance 41.4% 31.4% 39.4% 45.7% 42.1% 45.0% 40.7% 36.8% 40.0% 56.8% 84.2% 62.0%
& logistics
operators
Assistants & 41.8% 37.0% 40.5% 33.0% 21.0% 29.7% 34.3% 21.0% 30.7% 50.9% 57.1% 52.6%
clerks
All workers 41.7% 35.7% 40.2% 31.3% 23.4% 29.3% 35.0% 24.0% 32.3% 51.4% 57.1% 52.8%
with few
knowledge
tasks
All workers 52.4% 45.1% 49.7% 40.9% 31.7% 37.5% 40.4% 31.6% 37.1% 54.8% 65.5% 58.8%

63

64
Table 6.2: Preferred organisational characteristics

Loyalty Innovation Achievement Rules
Private Public Total Private Public Total Private Public Total Private Public Total
Leaders & 62.1% 51.4% 57.6% 23.3% 29.7% 26.0% 11.7% 13.5% 12.4% 2.9% 5.4% 4.0%
innovators
Experts & 53.2% 57.3% 55.0% 23.4% 21.3% 22.5% 19.9% 13.3% 17.1% 3.5% 8.0% 5.4%
analysts
All core 56.2% 55.4% 55.9% 23.4% 24.1% 23.7% 17.1% 13.4% 15.5% 3.3% 7.1% 4.9%
knowledge
workers
Information 74.6% 78.7% 75.9% 9.4% 8.2% 9.0% 15.2% 6.6% 12.6% 0.7% 6.6% 2.5%
handlers
Care & 40.0% 63.2% 62.3% 40.0% 18.4% 19.2% 20.0% 8.8% 9.2% 0.0% 9.6% 9.2%
welfare
workers
Servers & 64.2% 63.2% 64.0% 13.6% 10.5% 13.0% 16.0% 26.3% 18.0% 6.2% 0.0% 5.0%
sellers
All workers 70.1% 67.8% 69.0% 11.6% 14.6% 13.1% 15.6% 9.8% 12.8% 2.7% 7.8% 5.1%
with some
knowledge
tasks
Maintenance 65.5% 57.1% 63.9% 13.1% 11.4% 12.8% 15.2% 20.0% 16.1% 6.2% 11.4% 7.2%
& logistics
operators
Assistants & 63.2% 66.4% 64.1% 63.2% 66.4% 64.1% 14.2% 16.0% 14.6% 5.3% 9.2% 6.4%

clerks
All workers 63.9% 64.3% 64.0% 16.0% 9.1% 14.3% 14.5% 16.9% 15.1% 5.6% 9.7% 6.6%
with few
knowledge
tasks
All workers 63.0% 62.1% 62.6% 17.3% 16.8% 17.1% 15.5% 13.0% 14.6% 4.2% 8.1% 5.7%

Knowledge Workers and Knowledge Work


Another interesting result is that private sector care and welfare workers perceive their
organisations being based on mutual trust and loyalty (46 per cent) and committed to innovation
and development (24 per cent) to a much smaller extent than their counterparts in the public
sector and when compared with other groups of workers with jobs of similar knowledge intensity,
such as information handlers and servers and sellers.

We also asked workers to indicate which of the four organisational characterisations they would
prefer their organisations they work at to demonstrate (see Table 6.2 above). For all workers,
regardless of the knowledge intensity of their work, the strong preference was for organisations
built on mutual trust and loyalty, while very few respondents stated that they preferred to work
for an organisation bound by rules and procedures. Unfortunately, this latter characteristic
is also the one that most workers perceive as prevalent in their organisations. Over 60 per
cent of knowledge workers said their organisation was characterised by rules and regulations
but less than 5 per cent said they preferred such organisations. In contrast to the perceived
organisational culture characteristics, our respondents do not seem to be as divided in their
preferences depending on whether they work in the private or public sector
.
On the other hand, only few workers overall seem to prefer innovation and development and
the emphasis on achievement and accomplishment. This is still significantly less than the 50 per
cent of knowledge workers who characterised their firm and organisation as innovative. Even for
the knowledge worker group we labelled as ‘leaders and innovators’ only 27 per cent expressed
a strong preference for innovative organisations.

Table 6.2 suggests that there are two sharp contrasts between the preferences of core
knowledge workers and those of the rest. Core knowledge workers prefer relatively more to
work for organisations that promote innovation and development (regardless of whether they
are located in the public or private sector) and relatively less to work for organisations that
emphasise loyalty and mutual trust.

Moreover, the core knowledge workers are the ones that prefer the most organisations that
promote achievement and accomplishment and the least organisations that are bound by formal
rules and policies. However, they more or less share these preferences with workers with only
few knowledge tasks and workers with only some knowledge tasks respectively.

While our results suggest some interesting insights, there are also some caveats that should be
taken into account when interpreting them. This is especially true for the findings that show that



on the one hand, UK organisations are not widely perceived to be committed to innovation and
development and on the other hand, that relatively few of our respondents would prefer to work
in organisations with this commitment. These caveats are associated with the way the questions
in survey were asked.

Our questions on the perceived and preferred organisational characteristics did not
uncover or specify the definition of innovation that each respondent may have had in mind.
Innovation, sometimes categorised as ‘soft innovation’ in areas such as management and
work organisation, marketing and design, is very often only incremental, ie consisting of small
changes in the way things are done and without involving new technologies. However, if the
predominant perception of innovation is one of exclusively radical and technologically based
advances, people may underestimate the innovative character of their own organisation.
It is possible, therefore, that our questions have led to responses that underestimate the
commitment to innovation and development of firms.

Similarly, if organisations are considered as innovative only when they are ‘at the cutting edge’,
then they can also be perceived as riskier and more likely to fold, a perception that could explain
the low preference that our survey respondents expressed, even the core knowledge workers,
for working in such organisations. Again, the way our question was posed may have prompted
responses that understate the preferences of workers for innovative organisations.

The continued importance of rules and procedures in UK organisations combined with the
adherence to traditional styles of working suggest that in many ways the knowledge economy
is more about the growth of knowledge-intensive industries as a result of technology and an
increasingly skilled workforce rather than a complete overhaul of the world of work.



Figure 6.1: Percentage prefer innovative firms by worker cluster


30%
27%

25% 23%

20% 19%
18%
15%
15% 14% 14%
11%
10%

5%

0%
Innovators Experts Care and Servers Information Operators Assistants Total
sellers

Worker clusters


7. Conclusion and recommendations

The purpose of this report is to provide a portrait of work and the workforce in the knowledge
economy. We wanted to find out who the knowledge workers are, what they do in their
jobs, where they are employed and what employment structures, job characteristics and
organisational structures look like in the knowledge economy.

Knowledge work and knowledge workers are terms often used but seldom defined. When
knowledge work is defined it is usually by broad measures such by job title or by education
level. At best this gives us a partial and simplistic view of knowledge work in the UK.

This report takes a new approach. In a large and unique survey we have asked people what
they actually do at work and how often they perform particular tasks. We have used that
information to assess the knowledge content of their jobs. The key test was the cognitive
complexity required for each task – the use of high level ‘tacit’ knowledge that resides in
people’s minds rather than being written down (or codified) in manuals, guides, lists and
procedures.

We then grouped the workforce into seven distinct clusters of jobs ranging from ‘expert thinkers,
innovators and leaders’ (the most knowledge intensive groups) to ‘assistants and clerks’ (the
least knowledge intensive)23. We describe the two highest knowledge groups as our ‘core’
knowledge worker.

With this measure we estimated that we have a 30-30-40 workforce – 30 per cent in jobs with
high knowledge content, 40 per cent in jobs with some knowledge content, and 40 per cent in
jobs with less knowledge content.

Within our 30 per cent ‘core’ knowledge worker group, the highest group of all (‘leaders and
innovators’) constituted just 11 per cent of the workforce. These high intensity knowledge jobs
combined high level cognitive activity with high level management tasks.

These high knowledge intensive jobs are, we suspect, what some of the more excitable
accounts of knowledge work have in mind. The reality is that even after 40 years uninterrupted
growth in knowledge based industries and occupations such jobs account for only one in ten of
those in work today.

23
These groupings are described in more detail on page 24


Conclusion and recommendations

The 30-30-40 knowledge economy workforce

Many knowledge
tasks, 33%
Few knowledge
tasks, 40%

Some knowledge
tasks, 27%

We confirmed that knowledge work cannot be adequately described simply by looking at job
titles or education levels. About 20 per cent of people engaged in jobs with high knowledge
content – our core group of knowledge workers – were not graduates.

However, about 20 per cent of graduates were in low knowledge content jobs. This is potentially
worrying. But the average job tenure for graduates in such jobs was much lower than for non-
graduates – suggesting graduates spend less time in these jobs. Moreover, about over 40 per
cent of graduates in low knowledge content jobs reported their job duties corresponded well with
their current skills.

We also show that current job titles understate the knowledge content of jobs within some
sectors such as manufacturing. When jobs are classified by knowledge content high tech
manufacturing has as many knowledge intensive jobs, proportionately, as high tech services.



The most knowledge intensive jobs were almost equally likely to be held by men and women,
but those jobs with some knowledge content – such as care and welfare workers, information
handlers, and sellers and servers – were overwhelmingly female. Woman have benefitted from
the growth of knowledge work, but the growth of more knowledge intensive work has not, of
itself, overcome the gender pay gap.

Organisational Knowledge work and knowledge workers are often seen as at the forefront of radical workplace
implications change. Under these types of scenarios, well-educated knowledge workers have been enabled
by the new information and communication technologies to participate in the global economy
and throw off the shackles of permanent long term relationships with the corporate world. This
we are told will become the labour market norm in the future and companies and organisations
must adjust their work practices and forge new employment relationships to cope.

We find no evidence for this. Those in the most knowledge intensive jobs are no more likely to
be in temporary jobs than those in the least knowledge intensive jobs and job tenures are also
very similar.

Knowledge workers are not spear-heading radical changes in the way we work. As expected,
they do have more flexibility at work than those in less knowledge intensive jobs, but the
differences were not overwhelming. The reality is that less than 50 per cent of all workers
and less than 60 per cent of knowledge workers say they have some flexibility in their work
schedule, and only a very small minority said they can freely determine their own hours.

Perhaps not surprising, attachment to the standard nine to five day is still a central feature of
the labour market for both knowledge workers and non-knowledge workers alike. Knowledge
workers were far more likely to do occasional work at home, although over 60 per cent said they
did no home-working. Weekend working is relatively common across the workforce, but was
much less prevalent among knowledge workers.

Knowledge workers enjoy more flexibility than others and have more opportunities to work at
home, but the overall sense is one of conservatism rather than radicalism when it comes to the
employment relationship. Knowledge workers appear to value long term relationships with their
employer and remain fairly attached to the standard working day.



One question that flows from this analysis is why knowledge work and the growth of the
knowledge based industries has not led to a greater revolution in workplace organisation. It
could be that offered the chance to become ‘intrapreneurs’ or ‘nomads’ most people prefer
to opt for more secure and traditional relationships with a bit more flexibility than had been
previously possible. But it could also be that many organisations and workplaces have not yet
caught up with the possibilities that better educated workers and new technologies offer for
increased flexible working.

Our survey did not directly test out which of these propositions are closer to the truth or whether
it is an amalgamation of the two. However, it is striking that in one key area – determination of
hours – how few knowledge workers had full control over the hours they worked and that a very
large minority said they had no control at all.

This impression of rigidity is supported by the huge discrepancy in our organisation culture
question between knowledge worker preferences and reality when it came to organisations
characterised by rules and procedures. The vast majority of people in work think their
organisation is characterised by formal rules and policies, but very few say this is the sort of
organisation they really want to work for. The mismatch is even greater for knowledge workers:
65 per cent said their organisations were rule and policy bound but only 5 per cent expressed a
preference for such organisations.

There is a much better match when it comes to characteristics such as loyalty and mutual trust
for both knowledge and non-knowledge workers. About 50 per cent of all workers said this
was a predominant characteristic of their organisation, and over 60 per cent said it was their
preferred organisational characteristic.

Knowledge workers are more likely to work for organisations that they think are innovative
or achievement orientated – not in itself a surprising result. What is surprising is that neither
feature seems to appeal to them very much. For example, 50 per cent of knowledge workers
said their organisation’s predominant feature was innovation, development and being at the
cutting edge, but only 24 per cent preferred this type of organisation.

However, this was less true for knowledge workers than others – suggesting either they were
less constrained than other workers or had found a way round the rules.



What is surprising is that even knowledge workers did not show strong preferences for such
organisational characteristics – along with other workers their strongest preference was for
organisations built on mutual trust and loyalty. Our survey did not allow us to probe in more
detail why innovation and achievement did not rank more highly in people’s preferences. One
possibility is the balance between risk and reward for most people in the organisation – for
example, the financial rewards from an ‘achievement and success’ orientated organisation
might not be evenly shared. Another is that rules and regulations and trust and loyalty are seen
as affecting all people in an organisation whereas characteristics such as innovation are seen
as relevant only to some jobs.

There are some warning signs here for public sector organisations – they scored worse than
the private sector for being rule and regulation bound (for which there can be good reasons as
well as bad) but also were less likely to be perceived as organisations high in mutual trust and
loyalty. Regardless of where they worked (public based or private based industries) knowledge
and other workers expressed similar preferences.

We have to be careful about over-interpreting some of these results. For example, organisations
must have some rules and procedures – however irksome for the individual – in order to
function. In some areas they are essential for safety and probity and consistency in dealing
with clients, customers, and citizens. Similarly, simple questions over skill utilisation and job
demands do not tell us whether the mismatch is a serious one or could be addressed by minor
changes to the job. People may also be reluctant to admit that the demands of their jobs are too
much for them. Even so, the results here are consistent with some other survey findings.

Skills and the Taken at face value, employers are not making the most of knowledge worker skills despite
knowledge such workers representing a substantial investment in human capital within the organisation.
economy Our survey found a significant minority of knowledge workers said they had more skills
than their jobs demanded of them. However, the position was even worse for those in less
knowledge jobs – so organisations who employed knowledge workers appear to be doing
rather better at matching their talents to job demands than for other posts. This suggests a
more general problem around issues such as job design, career development, and progression
across the workforce as a whole.

All groups of workers reported their current jobs under used their skills. The gap was less
marked for knowledge workers, but nonetheless significant. About 36 per cent of knowledge



workers said they were in jobs that under used their skills compared with over 44 per cent of
those in jobs with some or little knowledge content.

Our results confirmed high economic returns to knowledge – the vast majority of those in the
most knowledge intensive jobs enjoyed pay well above the median. But this was not true for
those in jobs with some knowledge content – such as care and welfare work.

Some have expressed concern that the economy is producing too many graduates for the
available jobs that require graduate skills, forcing more graduates to accept lower pay jobs than
their education warrants and worsening the prospects for non-graduates.

Taken with the evidence on returns from knowledge and our previous work on labour market
polarisation24, the overall picture from our survey does not strongly support the idea that the UK
is producing too many graduates.

There is however undoubtedly problems for a minority of graduates in finding jobs that match
their skills. The situation may also be worse for those who entered the labour market more
recently, but we found little variation in the responses by age. Those under 25 with less
knowledge intensive jobs were no more likely to report their skills exceeded the demands of the
job than those over 25.

Implications This survey was conducted in 2007, so pre-dates the recession. The perceptions of workers in
of the some parts of the private sector may well be shifting, noticeably in parts of the financial services
recession industries.

One of the biggest tests of any organisation is how to retain the trust, loyalty and commitment
of the workforce at a time when some redundancies, cut-backs, and loss of earnings and
promotion prospects is unavoidable. The relatively close match between perceptions and reality
of organisational preferences in terms of mutual trust and loyalty that we saw pre- recession will
come under strain.

So far employers have proved reluctant to lay off large numbers of people, with reported use of
pay and hours flexibility and recruitment freezes as alternatives to redundancy. In the first six
months of this recession, employment has fallen by less than the first six months of the previous
recession. In addition, where cuts have been made they have fallen disproportionately on
agency labour – partly to protect the ‘core’ permanent workforce.

24 Fauth and Brinkley (2006) Polarisation and labour market efficiency, The Work Foundation



Our wider work on the knowledge economy in previous recessions for the UK, US, and the
EU shows that employment among knowledge based service industries and amongst workers
in knowledge intensive jobs has been much more stable than employment in the rest of the
economy. Moreover, employment expands in public based industries such as education and
health. We may therefore see an opening up of more a gap between those in knowledge
intensive jobs and those in less knowledge intensive jobs.

Across the economy as a whole, business investment in knowledge based intangible assets is
cut back less severely than investment in physical capital. The most resilient form of investment
is in human and organisational capital. We interpret this as organisations trying to make the
most of the surviving workforce, restructuring and rethinking business models.

This makes it more imperative for organisations to address the widespread problem of skills
underutilisation, but at the same-time the means to do so may become more constrained by the
tendency to cut spending on all but the most essential. Moreover, to the extent that mismatch in
skills and job demands is addressed it is more likely to be directed at those in more knowledge
based occupations. These jobs are more likely to survive employment cut-backs and we expect
a higher proportion of newly qualified graduates to compete for less knowledge intensive
positions.

This in turn will increase pressure on the government to cut back the rate of expansion in
higher and further education on the grounds that the UK has an oversupply of graduates. The
recession will indeed change the balance between demand and supply for knowledge intensive
labour – primarily by restricting new jobs for graduate-level entrants. As graduate unemployment
rises and more graduates take any job going, the further expansion of further and higher
education will look more questionable.

However, it is important that the cyclical effects are separated out from the longer run needs
of the knowledge based economy. The post recession economy will see continued growth in
knowledge intensive service industries25 and to reduce the supply of graduate labour now would
have significant repercussions for the ability of such industries to expand in the longer run.
Indeed, there is a strong case to bring forward expansion in higher and further education so that
young people who would otherwise be consigned to a very difficult and possibly fruitless search
for work have the opportunity to study instead.

25
UKCES, 2009, Working Futures 2007-2017



Next steps These are the first set of findings from our knowledge working survey. We will be publishing
a second set of findings later in 2009 that look more closely at how knowledge work can be
regarded as ‘good work’ and how it relates to health and well-being at work.


Appendix A. Work-related tasks and activities by factor

Data and analysis
Compile data
Analyse information to address work-related problems
Write reports
Translate/interpret the meaning of written material (ie, reports, chapters, articles, books) for others
Statistically analyse data
Identify patterns in data/information
Interpret charts or graphs
Enter data
Use a technical package on your computer

Leadership and development
Build the external profile of the organisation
Debate topical economic, political, social, business issues
Evaluate ideas
Serve on expert committees
Assess the quality of work of people outside of your organisation
Implement new programmes, systems or products
Manage projects
Predict/forecast future trends
Use logic to identify strengths and weaknesses of alternate solutions, conclusions or approaches
Review management procedures
Present new business ideas/opportunities
Create new processes or procedures
Manage financial risks
Coordinate personnel and financial resources for new projects
Develop proposals/grants
Approve invoices
Formulate policies
Make strategic decisions
Develop organisational vision
Appraise the value of property or objects
Contribute to the organisation’s strategic plan
Initiate large-scale organisational change
Identify issues that will affect the long-term future of the organisation
Make decisions on the basis of environmental conditions
Plan for the fiscal year
Foresee future business/financial opportunities
Manage strategic relationships
Research new business opportunities



Administrative tasks
Sell products
File (physically or electronically)
Sort post
Organise travel
Manage diaries/calendars
Inventory stock
Order merchandise
Organise/send out mass mailings
Make and confirm reservations
Collect payment

Perceptual and precision tasks
Judge speed of moving objects
Visually identify objects
Use depth perception (ie, as a necessary part of your job)
Organise/arrange objects according to a pattern, colour or other detail
Judge which of several objects is closer or farther away
Estimate the size of objects
Judge distances
Know your location in relation to the environment or know where objects are in relation to you
Detect differences among colours
Notice different sound patterns
Use navigation skills

Work with food, products or merchandise
Clean/wash
Prepare, cook or bake food
Stock shelves with products or merchandise
Gather and remove refuse
Serve food and beverage

People management
Handle complaints, settle disputes or resolve grievances
Assign people to tasks
Resolve personal conflicts
Collaborate with people inside of your organisation on a project/programme
Counsel others
Manage people



Interview people
Recruit personnel
Give formal briefings to others
Teach others
Coach or develop others
Provide consultation/advice to others
Conduct classes, workshops or demonstrations
Motivate others
Mentor people in your organisation
Assess the quality of work of people in of your organisation

Creative tasks
Create artistic objects/works
Take ideas and turn them into new products
Take photographs
Create technical plans or blueprints
Engage in graphic design
Perform artistically
Use devices that you draw with (eg, design software, paintbrushes)
Develop new technology
Film people and events
Write chapters, articles, books, etc. for publication

Caring for others
Provide care for others (eg, children)
Dispense medication
Diagnose and treat diseases, illnesses, injuries or mental dysfunctions
Expose self to disease and infections
Administer first aid

Maintenance, moving and repairing
Lift heavy objects (as necessary part of job, not including occasional moving, etc.)
Climb ladders, scaffolds or poles
Load/unload equipment, materials, luggage
Move equipment/supplies
Use heavy machinery
Use tools that perform precise operations (excluding computers and basic office equipment)
Use hand-powered saws and drills
Use scientific/laboratory equipment



Test, monitor or calibrate equipment
Take equipment apart or assemble it
Manoeuvre, navigate or drive vehicles or mechanised equipment (ie, forklifts, passenger vehicles,
aircrafts or watercrafts)
Install, maintain or repair electrical wiring
Repair or maintain equipment/vehicles
Control machines
Install objects/equipment
Generate/adapt equipment to serve user needs
Expose self to hazardous conditions (eg, extreme weather, contaminants)
Expose self to extremely loud noises

Personal, animal and home maintenance
Excavate
Weld
Dig
Decorate
Sew, knit or weave
Manage building/site
Issue licences/permits
Tattoo, brand, tag people/animals
Help customers try on or fit merchandise
Plant or maintain trees, shrubs, flowers, etc.
Feed, water, groom, bathe, exercise animals
Apply beauty treatments and therapies
Collect fares, tickets
Set type

Survey items that were cut
Communicate orally or in writing to people outside of your organisation
Circulate information to others
Draw upon personal contacts/networks for work-related matters
Speak a language other than English (ie, as a necessary part of your job not including casual
conversations)
Talk to media
Liaise with suppliers
Interact directly with customers/clients
Greet clients/customers
Answer telephones for others
Collaborate with people outside of your organisation on a project/programme



Mentor people outside of your organisation
Compile, administer or grade examinations/ tests
Walk/run as a critical part of job (excluding commuting, getting lunch, etc.)
Use physical strength
Arrange/pack objects or materials
Construct or repair houses, buildings or other structures (eg, highways)
Plant, grow or harvest food
Cut or trim objects, materials (including hair, nails)
Paint
Drill
Wrap food
Design, make, alter, fit or repair garments or textiles
Generate/develop new ideas for the organisation
Compose music
Pose for photographs
Play musical instruments
Review research/evidence to be used in an economic, political, academic or business-related debate
or argument
Follow blueprints or designs to specifications
Engage in taxonomic classification
Read and evaluate technical/academic papers and articles
Present research findings
Determine whether events or processes comply with laws, regulations or standards
Discriminate different tastes and/or smells
Enforce directives/rules/policies
Distribute/set-up equipment
Supervise operation of equipment
Order equipment
Use physical speed
Inspect the condition/quality of objects
Proofread
Resolve conflicting findings (from data, reports, etc.)
Use geometry
Use algebra
Write computer programmes
Make/collate photocopies
Physically train or exercise
Transport materials, goods
Transport people
Scan items



Make deliveries
Mix ingredients, solutions, chemicals or dyes
Develop laws and statutes
Market a product/idea
Monitor investments/markets
Plan/coordinate events
Control finances/budgets
Assemble, install or repair pipe systems
Engage in tasks that require extreme precision
Identify, pursue, and arrest suspects and perpetrators of criminal acts
Fundraise


Appendix B: Sample demographic and background characteristics

Background characteristics %/Mean
Gender (male) 51.3%
M(SD) Age 37.93 (10.29)
Ethnicity (White) 93.6%
Social grade (ABC1) 55.6%
Region
North 39.0%
Midlands 31.4%
South 29.6%
Educational attainment (degree) 34.2%
Age complete FT ed. (>16) 60.4%
Marital status (married/cohabitating) 64.3%
Income (% greater than median) 47.1%
Occupation
Manager and senior officials 19.3%
Professional occupations 13.1%
Associate professional and technical occupations 14.6%
Administrative and secretarial occupations 16.8%
Skilled trades occupations 5.8%
Personal service occupations 7.1%
Sales and customer service occupations 8.1%
Process, plant and machine operatives 7.5%
Elementary occupations 7.6%
Work in knowledge-intensive industry 52.5%


Appendix C: Description of organisational variables

Variable(s) Description/Categories
Firm Culture Agreement (1=strongly disagree, 5=strongly agree) with four
organisational descriptions: (1) loyalty and trust, (2) innovation
and development, (3) aggressiveness and (4) formal rules in their
organisation
Job skills match Whether their current job demands are matched to their skill sets or
(1) if they could cope with more demanding tasks or (2) need further
training to complete their tasks
Repetition/job complexity Whether (yes/no) jobs entail: (1) unforeseen problem solving, (2)
repetitive tasks, (3) complex tasks and (4)learning new things
Autonomy Agreement (1=strongly disagree, 5=strongly agree) that respondents
have the: (1) ability to make decisions on own at work, (2) freedom to
choose the methods of work and (3) freedom to choose pace of work
Job intensity Frequency (1=never, 5=everyday) with which respondents feel (1)
overworked, (2) overwhelmed by workload and (3) subject to conflicting
demands
Social capital Agreement (1=strongly disagree, 5=strongly agree) that respondents
are: (1) treated fairly, (2) had attentive co-workers and (3) had
supportive supervisors
Absenteeism/ Number of days unable to carry out work tasks or go to work due to
presenteeism care reasons in past four weeks
General care General perceptions of care (1=poor care, 5=excellent care)
Job satisfaction Satisfaction (1=very dissatisfied, 5=very satisfied) with five aspects of
work: (1) pay, (2) security, (3) the work itself, (4) sense of achievement
and (5) hours
Life satisfaction Agreement (1=strongly disagree, 5=strongly agree) that respondents
feel: (1) their life was close to their ideal, (2) happiness with lifestyle, (3)
general life satisfaction, (4) life achievement and (5) degree to which
they would change their lives if they could
Perceptions of job Whether respondents: (1) like their jobs and see themselves doing their
jobs in the future, (2) dislike their jobs but see themselves doing their
jobs in the future or (3) see their job as a way to pay the bills only
Work-personal life spill-over Agreement (1=strongly disagree, 5=strongly agree) that: (1) the
demands of work interfere with personal life, (2) there is a conflict
between work and personal responsibilities and (3) work duties cause
personal activities to be changed


Appendix D: Composition of workforce in the distribution and repairs and in
the hotels and restaurants sectors

Figure 1: Distribution of workforce within the distribution and repair sector

6.7%
Leaders & innovators
10.3%
34.4% Experts & analysers

Information managers

Maintenance &
logistics operators
23.1%
Servers & sellers

Assistants & clerks
14.9%
10.3%
0.5%

Figure 2: Distribution of workers clusters within the hotels and restaurants sector

6.0% Leaders & innovators
18.0% 6.0%
4.0%
Experts & analysers
2.0%

2.0% Information managers

Maintenance &
logistics operators


Servers & sellers

Assistants & clerks
62.0%


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First published: March 2009

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