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Knowledge Workers and

Knowledge Work

A Knowledge Economy Programme Report

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


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Knowledge Workers and Knowledge Work2

Contents

Acknowledgements

This report has drawn on some of the initial research work and discussions from The Work

Foundations 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.

Acknowledgements 2List of Figures and Tables 3

Executive summary 4

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

denitions and measures 9

2. Redening 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


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Knowledge Workers and Knowledge Work 3

List of Figures and Tables

Figure 1: The 30-30-40 knowledge economy workforce 5Figure 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 38Figure 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 exibility in choosing work schedule by worker cluster 59

Figure 6.1: Percentage prefer innovative rms 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 47Table 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


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Executive summary

The purpose of this report is to provide a portrait of work and the workforce in the knowledgeeconomy. We wanted to nd 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 dened. When

knowledge work is dened 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

peoples minds rather than being written down (or codied) 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


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We conrmed 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 classied 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 nding 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

The 30-30-40 knowledge economy workforce

Few knowledgetasks, 40%

Many knowledgetasks, 33%

Some knowledgetasks, 27%


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

Our results conrm 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 benetted 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 nd 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 exibility 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 exibility 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 ve 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 rst 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


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Signicant minorities of workers reported their current jobs under-used their skills. The gapwas less marked for knowledge workers, but nonetheless signicant. 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 difculty 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 ruleand 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 efciency, The Work Foundation

Executive summary


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Knowledge workers are more likely to work for organisations that they think are innovativeor 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 organisations 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 rst set of ndings from our knowledge working survey. We will be publishing

a second set of ndings 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.

Executive summary


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The purpose of this report is to provide a portrait of the work and the workforce in the knowledgeeconomy. We want to nd 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 dened. 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 rms, the public

sector as much as the private sector. And they are global we have yet to nd 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

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

Introduction


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Eurostate commonly dene as knowledge-based industries. These industries include high- tomedium-technology manufacturing and knowledge intensive services such as nancial 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).

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 todays 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 brawnWhere the farmer was the backbone of any economy a

century or two agoknowledge 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

The knowledge economy and knowledge work:

A review of the existing denitions and measures

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

50%

45%

40%35%

30%

25%

20%

15%

10%

5%

0%_1997

KE

TOTAL MANUFACTURING

Other services

Note: OECD denition knowledge based services includes nancial and business services,communications, health and education services. Other services includes distribution, hospitality, publicadministration, other services.

Source: The Work Foundation estimates from EU KLEMS database

_1970 _1973 _1976 _1979 _1982 _1985 _1988 _1991 _1994 _2003_2000


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Dening

knowledge and

knowledge

workers



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 ofcial agreed denitions 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 denitions

and measurement of knowledge work and knowledge workers, including the denition 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 reect and the

shortcomings of previous attempts at providing such an account. Moreover, this review frames

our own method of deriving a better denition of knowledge work within the existing literature.

In later sections of this report we will use our newly developed denition of knowledge work

to explore the consequences of the knowledge economy in the structure of employment, job

characteristics organisational culture and good work.

Denitions of knowledge

One of the central problems in dening knowledge work has been the difculty of dening

knowledge itself and distinguishing knowledge from information. Indeed, the terms information

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 Foundations 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 reect. 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.


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An alternative distinction is between tacit and codied knowledge (see Lundavall and Johnson1994 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, codied knowledge and information

are indistinguishable. The signicant difference is, therefore, between tacit knowledge and

information.

Conceptual denitions of knowledge work

Even with these distinctions in mind, knowledge work remains an elusive concept. Denitions

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 denitions 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 dened tasks (vs. clearly dened,

delineated tasks), exible 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 t 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 toolsoften 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




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that involve at least 50 per cent intellectual content (eg, analysis, decision making, creativity). Inturn, 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, ofces need to be cleaned and machinery serviced

and so on. This denition would also appear to rule out high-tech manufacturing, including some

of the most R&D intensive companies in the world.

Data-driven denitions of knowledge work

Moving on to more data-driven denitions 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 denitions 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 qualication 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 ofgraduates had increased to around 19 per cent, while the share of people with no qualications

had fallen to 12 per cent. The latest gures show that graduate employment accounted for just

under 23 per cent of workers in the UK.


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

70

60

50

40

30

20

10

0

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Degree holder

No qualication

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 dened 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 Banks 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. Higherlevel education is required for what the Bank calls, the production of new knowledge and

its adaptation to a particular economic setting(p. 5). The OECDs 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




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wages, higher level vocational skills undoubtedly offer an economic return even if it is not assignicant 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 scientic 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 denition 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 denitions of knowledge work

A nal approach to dening 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 classications

within the ofcial 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 denition, 42.5 per cent of the workforce would be classied as a knowledge worker in2007.

This broad classication 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 classications 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)


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legislators and senior ofcials 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 gures 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 directlyproduce intangible assets for others including teachers, sales and

marketing workers, consultants, researchers and nancial advisors. These workers also include

those who acquire and use skills, knowledge and talent to make a contribution to the goodwill or

efciency of their rms 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 ve groups based on the

degree of computer substitution and adherence to strict rules both proxies for more routine

forms of work. The groups included:

Expert thinking1. : 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.

Complex communication2. : 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.

Routine cognitive3. : includes mental tasks closely described by rules such as routine form

processing and lling, often vulnerable to computerisation.




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Routine manual4. : includes physical tasks closely described by rules, such as assemblyline work and packaging, that may be replaced by machines.

Non-routine manual5. : includes physical tasks hard to dene by rules because they

require ne 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 dened by the authors as making effective oral and written arguments help dene

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 qualications

(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, ve umbrella groups of occupations based on educational qualications were created:

Traditional graduate occupations: includes professions that historically have required an1.

undergraduate degree (eg, solicitors, scientists, doctors, teachers).

Modern graduate occupations: includes newer professions that graduates have been2.

entering since the 1960s (eg, chief executives, software professionals, writers).

New graduate occupations: includes occupations where entry-level has recently shifted3.

to incorporate degree holders (eg, marketing and sales managers, physiotherapists,

welfare ofcers, park rangers).

Niche graduate occupations: includes jobs where majority of entry-level workers are not4.

graduates, but there is a growing number of specialists who do come in with degrees

(eg, sports managers, hotel managers, nurses, retail managers).


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Non-graduate jobs: includes professions where a graduate degree is not required and5.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 classied as producing intellectual assets or associated with

expert thinking and complex communication skills or afliated 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 todays economy.

To sum up, what is missing from all of these attempts at dening knowledge work is a thorough

analysis of workers themselves and what they do at work. Moreover, different denitions 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.




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This section develops our denition of knowledge work and knowledge workers. We do that inthree 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 denition of knowledge work to provide a cross-sectional

picture of the UKs workforce today and how the new denition measures up against

previous denitions.

6

We performed our analysis in several steps. We started off by conducting a survey of, among

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 identied 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

identied 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 thelabour force. In what follows, we present some important details on the methods we used and

then discuss our results regarding the denition of work in the knowledge economy.

6 Readers who are not interested in the specic technical details of our methodology can largely omit reading this sub-

section in full without losing track of our analysis

2. Redening knowledge work and knowledge workers

Research

design6


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Our surveyOur 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 nalise 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 nal 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 ofcials along with administrative andsecretarial 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 specic 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.

Redening knowledge work and knowledge workers


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Exploring work tasks in the knowledge economy: Factor analysisTo 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 gures 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 classied 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 conned 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 surprisinggiven 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

nding 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 rst 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). Conrmatory factor analysis (CFA) was subsequently used to validate the hypothesised

factor structure and our model exhibited adequate t. 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 in8 HMT October 2007.Intangibles and Britains productivity performance



22/88


Leadership &development (28)

Make strategic decisions; Develop organisational vision; Identify issues that willaffect the long-term future of organisation; Foresee future business/nancialopportunities; Manage strategic relationships

Administrativetasks (10)

Manage diaries; Order merchandise; Organise/send out mass mailings; Make andconrm reservations; Sort post

Perceptual &

precision tasks(11)

Judge speed of moving objects; Visually identify objects; Judge which of several

objects is closer or farther away; Judge distances; Know you location in relation tothe environment or know where objects are in relation to you

Work with food,products ormerchandise (5)

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

Peoplemanagement (16)

Assign people to tasks; Manage people; Teach others; Motivate others; Mentorpeople in your organisation

Creative tasks(10)

Create artistic objects/works; Use devices that you draw with; Take ideas and turnthem into new products; Take photographs; Engage in graphic design

Caring for others(5)

Provide care for others; Dispense medication; Diagnose and treat diseases,illnesses, injuries or mental dysfunctions; Expose self to disease and infections;Administer rst aid

Maintenance,moving &repairing (18)

Install objects/equipment; Use tools that perform precise operations; Use hand-powered saws and drills; Test, monitor or calibrate equipment; Take equipmentapart or assemble it

Personal, animaland homemaintenance (14)

Excavate; Dig; Plant/maintain trees, shrubs, owers, etc.; Feed/water/groom/bathe/exercise animals; Sew/knit/weave

and cultural industries9. The former reects 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 reects the fact that these tasks are relatively specialised.

Moreover, a common feature of sectors such as creative and cultural industries is that theygenerate 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 nal

identication 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 UKs creative industries

Table 2.1: Task factors with sample items

Factor Sample items

Data processingand analysis (9)

Compile data; Statistically analyse data; Identify patterns in data/information;Interpret charts/graphs; Enter data



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across these groups to be classied under a specic group or another. Notable examples fallunder 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.

Exploring the different types of workers in the knowledge economy: Cluster analysis

Having identied 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

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

2.5

2.0

1.5

1.0

0.5

0.0

Meanfrequency1to4

People

mana

geme

nt

Data

proc

essin

g&analy

sis

Admi

ntasks

Workw

ithprod

ucts

Percep

tion&

precisi

on

Lead

ership

Caring

Repair

&mo

ving

Creative

Person

al&do

mestic

2.11.9

1.7

1.4 1.4 1.41.3 1.3

1.21.1

Note: 1 = least common, 4 = most common

Source: Knowledge Workers Survey, The Work Foundation, 2008



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

The novelty of our results is that our taxonomy cuts across classications 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 t 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 identied 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 classied under each cluster. We detail the most

common groups of tasks (as identied in our factor analysis) in each of the seven clusters as

well as the ve specic tasks that workers engage in most frequently in their jobs. We list ve

minor occupations that workers are classied 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, peoplemanagement.

Occasionally performed tasks: Administrative tasks, creative tasks.Specic 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, nancial institution andofce managers, business and nance associate professionals.

10 We rst 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 t the data.

The clusters used in the remainder of the report are based on the k-means analysis11 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 signicant differences were observed



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Experts and Analysts (22.1 per cent)Frequently performed tasks: Data and analysis, people management.Occasionally performed tasks: Leadership and development, administrative tasks.Specic 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 andproprietors 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.Specic tasks: File (physically/electronically), sort post, manage diaries, enter data,handle complaints, settle disputes and resolve grievances.

Example occupations: General administrative occupations, secretarial occupations,nancial institution and ofce managers, managers and proprietors in service industries,

nancial 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, perceptualand precision tasks.

Specic tasks: Provide care for others, administer rst aid, clean/wash, dispensemedications, expose self to disease/infections, write reports.

Example occupations: Care associate professionals, care services, childcareservices, social welfare associate professionals.

Servers and sellers (7.0 per cent)Frequently performed tasks: Work with food, products or merchandise, peoplemanagement, administrative tasks.

Occasionally performed tasks: Data and analysis, perceptual and precision tasks,leadership and development.

Specic tasks: Clean/wash, handle complaints, settle disputes and resolvegrievances, manage people, stock shelves with products or merchandise, order

merchandise.



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Example occupations: Managers in distribution, storage and retailing, managersand 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, movingand repairing.

Occasionally performed tasks: People management, work with food, products ormerchandise, data and analysis, administrative tasks.

Specic tasks: Visually identify objects, know location in relation to the environmentor 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, tting 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 withfood, products or merchandise, administrative tasks.

Specic tasks: Handle complaints, settle disputes and resolve grievances, collaboratewith 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 retailcashiers.

The assistants and clerks cluster was the least well-dened 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 specic 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 rst attempt at

classifying workers in the knowledge economy on the basis of what they do. In what follows we

try to rene this classication 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.



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Bright mindsand powerful

machines for

tasks of varying

complexity

The next stage was to gauge the cognitive complexity of the tasks that workers in differentclusters mostly perform. This helped us distinguish, for example, between basic processing

tasks such as data processing from higher level analytical tasks. We used three of their work

characteristics for which we got information through our survey:

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

specialistuse 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 efciency or to engage in more difcult 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 signicant 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,



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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 difculty (eg, depending on whether a worker designs his/her own

presentation or types up someone elses).

On the other hand, more specialist tasks such as statistics, system maintenance, graphic designand 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 2.2: Number of different computer uses and how often computers are used eachweek

8

7

6

5

4

3

2

1

0Index

frequency

1to5;num

bero

fuses

0to12

Innovators Experts Infohandlers

All groups Assistantsand clerks

Servers Care andwelfareworkers

Operatives

Number of computer uses Frequency of use each week7.2

5.0

6.3

4.9

5.5

4.85.2

4.2 4.2

3.7 3.7 3.6 3.6 3.5 3.4

2.9

Worker clusters



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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 sufcient for rening 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:

When performing a new task at work;1. 13

When sharing information with others.2.

These results, illustrated in Table 2.2 below, suggest that the leaders and innovators clusterdisplayed 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 ndings conrm 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 codied 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 workers13 Only 6 per cent of the sample reported not ever having to do new tasks on the job

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

70.0%

60.0%

50.0%

40.0%

30.0%

20.0%

10.0%

0.0% Innovators Experts Infohandlers

Total Assistantsand clerks

Serversand

sellers

Care andwelfareworkers

Operatives

60.4%

51.8%

35.0%

30.4%

23.7% 22.5% 22.2%

9.9%

Worker clusters



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

Task based groups Acquiring new information(0 to 9)

Learning new tasks(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 nding 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 specic 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



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Figure 2.4: Importance of teach others task for different clusters

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0Index

frequency

1to5;num

bero

fuses

0to12


Assistantsand clerks

Servers andsellers


Operatives

Variability in frequency of teaching others Mean reported frequency of teaching others

0.850.91 1.08 1.04

0.88 0.890.91

3.3

2.8 2.8

2.6

2.3

2.0

1.8

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 codied 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 tasksperformed 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.



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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%

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

Figure 2.5: Perceived complexity of tasks performed by surveyed workers

3.0

2.5

2.0

1.5

1.0

0.5

0.0

Jobcompl

exity1to3


Total Assistantsand clerks

Serversand

sellers


Operatives

2.62.4

2.11.9

1.81.7

1.6

1.3

Worker clusters



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Towards a

new denition

of knowledge

work

To sum up, looking into the uses of IT, the methods of sharing and acquiring knowledge and theperceived complexity of tasks performed by workers in the knowledge economy, we sketched

a more nuanced picture of how the worker clusters that we identied 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.

Our ndings so far suggest that we can portray the composition of the knowledge economy

workforce and the work that workers actually do in a 30-30-40 shape. Our classication

suggests that around a third of the UK workforce can be regarded as the core knowledge

workers, having to perform manyknowledge 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

ofat least some tacit knowledge. However, there are also very large numbers of people 40 per

cent of the workforce whose jobs involve only a fewtasks requiring tacit knowledge and who

rely largely on codied knowledge through manuals, rules and procedures.

More specically,

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 codied 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, analyticaltasks, 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 knowledgecontent primarily codied knowledge relating to the cluster specic tasks that dene

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).



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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 classied as core knowledge workers performed

the most tacit knowledge tasks for their job and those in the 40 per cent performed the fewest

Finally, 40 per cent of workers engage in jobs with only few tacit knowledge tasks (egperceptual 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 manualjobs. About 30 per cent

however falls under the assistants and clerks cluster and it is here where we are likely

to nd 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

Few knowledgetasks, 40%

Many knowledgetasks, 33%

Some knowledgetasks, 27%



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tacit knowledge tasks. We use the term knowledge to mean explicitly tacit knowledge ratherthan codied 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 codied

knowledge. As we report later, this acquisition of skills and codied knowledge is reected 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 nd 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).

To sketch the knowledge economy workforce more accurately, we examine the general

demographic and background characteristics of workers in our sample. These statistics and

gures allow us to put a face to the knowledge workforce.

Earlier evidence from The Work Foundation suggests that the vast increases in female labour

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 someknowledge 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)

The

demographics

of the

knowledge

workforce:

gender and age



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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 workerswithin 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.

Figure 2.7: Share of women in jobs by knowledge content

Many knowledge tasks


Assistantsand clerks

Serversand

sellers


Operators

Worker clusters

Some knowledge tasks Few knowledge tasks

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

44% 44%

79%

75%

58%

47%

10%



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A nal test for the usefulness of our new denition of knowledge work and knowledge workersis its comparison with existing proxies. As mentioned previously, two of the key proxies used to

estimate the number of knowledge workers in the UK economy include:

Workers employed in the top three Standard Occupational Classication (SOC)1.

categories including managers and senior ofcials, professional occupations and

associate professional and technical occupations.16

Workers with degrees.2.

While both of these operational denitions 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 nd that although there is a substantial overlap between

our denition of core knowledge workers and these proxies, our worker clusters suggest that

people outside the top three occupational classications and people who are not graduates may

be holding jobs with many knowledge tasks and vice versa. If anything, this suggests that our

denition 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 classications.

However, signicant 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 signicant numbers of associate professional jobs that

fall within the standard occupational classication, but it is also clear that even more have beenclassied 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

Comparison ofnew and old

proxies for

knowledge

work



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All in all, there is some correspondence between the occupational denition of knowledgeworkers and our worker clusters, but the occupational denition likely inserts a false dichotomy

into the workforce that is not based on a detailed account of workers everyday tasks and

activities.

Looking at the educational denition 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

signicant 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

knowledge workers-march 2009

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