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EMPLOYEE INVOLVEMENT, TECHNOLOGY AND EVOLUTION IN JOB SKILLS: A

TASK-BASED ANALYSIS.

FRANCIS GREEN

This is the accepted version of:

Green, F. (2012). "Employee Involvement, Technology and Evolution in Job Skills: A Task-Based

Analysis", Industrial and Labor Relations Review, 65 (1), 35-66.

The author is Professor of Labour Economics and Skills Development at the LLAKES Centre, Institute

of Education, London University.

He acknowledges support from several agencies that contributed to survey costs: The Economic and

Social Research Council, the Department for Education and Skills, the Department of Trade and

Industry, the Learning and Skills Council, the Sector Skills Development Agency, Scottish Enterprise,

Futureskills Wales, Highlands and Islands Enterprise, East Midlands Development Agency, and the

Department for Employment and Learning, Northern Ireland. He thanks David Ashton, Jagjit Chadha,

Alan Felstead and Maarten Goos for their comments on earlier drafts of the paper. Copies of the

programs used to generate the analyses are available by writing to the author at f.green@ioe.ac.uk. All

the data are in the public domain at the UK Data Archive.

{{Abstract}}

The author investigates the evolution of job skill distribution, using task data derived from the

U.K. Skills Surveys of 1997, 2001, and 2006, and the 1992 Employment Survey in Britain. He

determines the extent to which employee involvement in the workplace and computer technologies

promote the use of higher order cognitive and interactive skills. He finds that literacy, other

communication tasks and self-planning skills have grown especially fast. Numerical and problem-

solving skills have also become more important, but repetitive physical skills have largely remained

unchanged. He finds that employee involvement and computer technologies privilege the use of greater

generic skills, but substitute for repetitive physical tasks. However, the classification of all tasks as either

routine or non-routine is found to be problematic. Finally the author finds a strong connection between

the rising use of more academic skills and the education level required for entry into the labor market.

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It is widely accepted that technological change in the modern workplace is broadly

skill-biased.1 Upon closer inspection of the fundamental role of computerized technologies

(Brynjolfsson and Hitt 2000; Bresnahan et al. 2002), however, researchers have advanced a

“nuanced” theory of skill-biased technical change in which computerization substitutes for

labor in carrying out routine tasks yet complements higher educated labor in performing non-

routine interactive tasks. Since routine tasks have historically been concentrated around the

middle of the occupation and wage spectra, the consequences include a relative increase of

high-skilled jobs accompanied by a relatively smaller rise of jobs that use low-rewarded but

hard-to-displace skills, and a relative diminution of medium-skilled jobs—a “polarization” of

the labor market (Autor et al. 2003a; Goos and Manning 2007). The significance of such

findings stems not only from the backward connection between job tasks and education (e.g.,

Stasz et al. 1998; Levy and Murnane 2004), but also from the forward link with labor market

rewards (e.g., Murnane et al. 1995).

From the perspective of this nuanced theory of technological change, it is the

technology that drives skill and employment change, with any organizational shifts following

from the application of the new technology. In this paper, I argue that one feature of

organizational change—increased employee involvement—is an additional and independent

factor explaining the use of skills. I hypothesize that employee involvement promotes the use

of problem-solving, self-planning, and communication skills, including literacy (both reading

and writing). This trend, which is not necessarily in response to technological change, can

thus also be expected to alter the pattern of skills required to perform a job.

Using task data for Britain, I investigate and attempt to account for the changing job

skill distribution. Tasks are units of output-producing activity, and a generic skill is defined as

the capability to perform a range of similar tasks across a variety of occupations. The task

1 E.g., see Berman et al. 1994; Machin and Van Reenen 1998; Gera et al. 2001.

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data yield evidence on the requirements for generic skills. I use exploratory factor analysis to

combine similar tasks in order to generate generic skill indices in eight domains. To motivate

initial interest in the data, I first show that these indices have an association with pay. I then

document the growth in the use of several cognitive and interactive skills as well as the

comparatively stable use of physical skills in British jobs. The fastest growing skills are in the

domains of literacy and influence (the ability to perform a package of high-level

communication tasks).

This evolution of skills appears to be only partially accounted for by changes in the

occupational structure. I expect to find some consistency with similar patterns that have

emerged elsewhere in that evidence exists for the role that computers play in complementing

interactive skills regarding various forms of communication. I hope to discern the extent to

which computers are negatively or positively associated with the relative importance of

repetitive physical skills; moreover, I expect to clarify how tasks or skills regarded as routine

or non-routine are characterized. Going beyond the technology and following previous

qualitative research, I investigate whether organizational changes requiring greater employee

involvement also explain some of the skill requirements. Having developed a composite index

derived from indicators of the use of several communication mechanisms and the importance

of team-work, I explore how employee involvement in the workplace is associated with

several skill domains. Beyond the changes in occupational structure, how do both technology

and employee involvement contribute to changes in job skills?

{{1}} Technical Change, Organizational Change, and Generic Skills

In the next section I review briefly the nuanced theory of skill-biased technical change

and the evidence in its favor and propose that, as an explanation for changing skills, it must be

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augmented by an account of the independent effect of management’s introduction of

employee involvement mechanisms.

{{2}} The ALM Model

Autor, Levy, and Murnane (2003a) (hereafter ALM) distinguished between routine and

non-routine tasks and proposed that computers substitute for workers carrying out routine

manual or routine cognitive tasks yet are complements for workers involved in non-routine

analytic and interactive tasks. Their research is important because it provides a theoretical

framework for understanding how computers affect jobs and thus explains why new computer

technologies are indeed skill-biased. According to the ALM model, firms react to an

exogenous fall in the price of computers by changing the task mix towards those demanding

more non-routine activities, requiring both expert thinking (managing and solving analytical

problems) and complex communication skills (Levy and Murnane 2004). This switch in

comparative advantage increases the demand for college-educated workers, because they have

the knowledge and ability to carry out non-routine analytical and interactive tasks. ALM

provide some strong supportive evidence using data on job tasks derived from the Dictionary

of Occupational Titles. In U.S. workplaces, the prevalence of non-routine analytic and

interactive tasks has increased while that of routine cognitive and manual tasks has decreased.

Both the timing and the distribution among industries and occupations of these changes are

accounted for by computer capital investments.2 Autor and Dorn (2010) added support to this

notion in their examination of spatial variation within the United States in the rise of service

employment, whereas Spitz-Oener (2006), in her analysis of job requirements in German

2 This evidence contrasts with that of an earlier study (Howell and Wolff 1991) that also found, also

using DOT data, that new technologies shifted occupational structures so as to demand higher cognitive

skills, but had mixed effects on the demand for interactive skills.

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occupations, reported that computer capital substitutes for certain routine tasks while

complementing both non-routine analytic and interactive tasks.

The ALM model is significant insofar as it explains the partial polarization of labor

markets. In the U.K., high-wage occupations expanded most rapidly between 1979 and 1999,

consistent with skill-biased technical change. Growing second fastest were the lowest paying

jobs in services, leading to a declining middle: the so-called “hour-glass economy” of

burgeoning low-quality jobs (Goos and Manning 2007). A similar picture, with reductions in

mid-level jobs, occurred in the United States (Wright and Dwyer 2003; Autor et al. 2006;

Autor and Dorn 2010; Acemoglu and Autor 2011) and Germany (Dustmann et al. 2009; Black

and Spitz-Oener 2010) and more generally in Europe (Manning et al. 2009; Michaels et al.

2010). Fernandez-Macias, and Hurley (2008), however, found that the pattern of jobs growth

was more varied across EU countries between 1995 and 2006. According to Goos et al.

(2010), the routinization hypothesis is the most important factor accounting for job

polarization in Western European countries, followed by the decreasing prevalence in Europe

of offshorable jobs. Case study work has uncovered similar changes in routine tasks as new

technology is introduced, but at the same time it has revealed that demand has risen for certain

generic skills (Bartel et al. 2003; Autor et al. 2003b; Ballantine and Ferguson 2003). Among

rank-and-file jobs, communication skills are found to complement the introduction of new

technologies as are problem-solving skills and a facility for learning new tasks. Common are

reports indicating an increase in demands for reading and math skills, necessary for work with

newly automated equipment. Changes in skills demand are, however, quite heterogeneous

even within narrowly defined industries, reflecting varying managerial strategies and product

complexity (Bartel et al. 2003; Mason 2004).

{{2}} The Role of Employee Involvement

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The ALM model is deterministic in that it takes the driving force to be exogenous

reductions in the price of computers; the effect is presumed to be both direct and indirect.

Computer price reductions alter the needed skill mix by directly displacing routine tasks, and

they may allow for certain organizational changes to become profitable—such as the

delayering of management hierarchies—because they complement the new technologies

(Bresnahan et al. 2002). Those new forms of work organization may then involve more non-

routine tasks. Thus, in the ALM model new management practices and organizational forms

can play a partial mediating role, though the overall account of tasks is provided by the

available technology.

Yet, one can reasonably question whether the role of organizational change in

understanding skill change should be limited solely to that of an intermediary in a chain of

causation from technology to skill demand. Rather than assuming that organizational changes

are rational profit-maximizing responses to technology, it may be preferable to consider that

some organizational changes partly reflect independent thinking, the outcome of new

management knowledge, and associated tools and technologies. In case studies, researchers

found that new technology can help to facilitate, and interacts with, organizational change;

however, those same studies found that the new technology is usually neither a necessary nor

a deciding factor (Sung and Ashton 2005). The impact of innovative management practices on

productivity is enhanced in some cases when interacted with new technology, but occurs

independently in other cases. New management science needs to be learned, is unlikely to be

accepted immediately, is of heterogeneous value even in narrowly defined industries, and

encounters both real and ideological barriers that raise switching costs; hence it is introduced

at a varying pace across firms and industries (Pil and MacDuffie 1996; Ichniowski and Shaw

2003). Organizational practices also vary because there may be more than one locally optimal

way of organizing work. In cases when the effects of new working practices are mutually

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reinforcing, marginal innovations, by piecemeal introduction of individual practices, may fail

to reveal gains; thus, evolutionary learning is blocked. By this same logic, writers sometimes

distinguish the “high-trust” from the “low-trust” road, each of which is a stable local

equilibrium.

One common organizational change is the increase in employee involvement in

companies—meaning workers are becoming better informed about their employer,

participating in discussions about immediate production issues or wider organizational

matters, working as members of teams, participating in profit-sharing reward schemes or

similar performance-based incentives, being trained to perform jobs designed for greater

autonomy, and being a part of other, associated delayering of management functions.

Different studies list varying ranges of practices, but typically organizational practices such as

these are referred to as “high-involvement work practices,” “high-performance work

systems,” “high-commitment practices,” or simply “new workplace practices” (e.g., see

Bryson et al. 2005; Black and Lynch 2001; Appelbaum et al. 2000; Ichniowski and Shaw

2003). These practices have implications for skills utilization that run parallel with the

demands of computer technologies. First, compared with organizations that operate more on

Taylorist command-and-control principles, the high-involvement environment is one in which

management exercises less direct control over employees’ work. There is thus more need for

workers to think proactively and more scope for problem-solving. Second, there is likely to be

an increased need for interaction skills in order to function well in high-involvement corporate

environments. In situations that require employees to work together more, to cooperate with

colleagues, to exchange information and express opinions, and to learn and adopt at least

some of the organization’s common values and attitudes, communication activities acquire a

greater range and importance in their jobs. Since a good deal of communication is also

through the medium of the written word (on paper or on screen), employee involvement also

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increases the importance of literacy. In an environment of relatively increased participation,

there is also more call for the higher forms of communication that are entailed in facilitating

learning and inducing others to follow desired courses of action. As a shorthand, I refer to

these arguments as the Human Resource Management (HRM) model of task determination,

which I cast as additional to, rather than subsumed within, the ALM model.

Several studies have found complementarities between organizational changes and

changes in overall skills demand (e.g. Brynjolfsson and Hitt 2000; Caroli and Van Reenen

2001; Greenan 2003; Piva et al. 2005). Relatedly, studies link high-involvement work

practices explicitly with training (e.g., Cappelli and Rogovsky 1994; Osterman 1995;

Whitfield 2000). Case-study evidence also exists of an association between high-involvement

work practices and changing demands for several generic skills. Examples include Thompson

et al. (1995), Kelley (1989), and Ashton and Sung (2002), the emphasis of which was

typically on the use of communication, team-work, and problem-solving skills. Sometimes

this association is revealed through the difficulties of introducing new work practices in which

these skills are scarce in the workforce (Bishop et al. 2008; Cheng et al. 2004; De Vilbiss and

Leonard 2000). As for formal quantitative evidence, Gale et al. (2002) found a link between

indicators of certain “new” organizational practices in manufacturing industries and

management-perceived changes over three years in certain skill requirements (basic math,

basic reading, interpersonal communication, problem-solving, computing); and Felstead and

Gallie (2004) found that high-involvement work practices tend to be associated at one point in

time with high uses of several generic skills. The evidence I present below comes closest in

spirit to these studies but uses comprehensive indicators of the changing use of a wide range

of skills, covers a much longer period, and is applied to the entire labor force.

To summarize this part of the discussion, because schemes to induce greater employee

involvement may be introduced independently following normal variations in strategies as

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management philosophies and employee relations evolve, employee involvement should be

regarded as a potential independent and additional source of change in the tasks carried out,

not just as one channel for the impact of new technologies.

{{1}} Data and Measurement

{{2}} The Skills Surveys

To understand and account for the growth of generic skills, I needed data on skills being

used in jobs, which has heretofore been scarce but is now available in Britain over a

reasonable time span. With collaborators, I have since 1997 collected job skill data using the

“job requirements approach,” essentially an adaptation of occupational psychologists’

methods in the context of a socioeconomic survey.3 The idea is to collect information on the

tasks that are being done in jobs, where the same tasks might be done to greater or lesser

degrees, or at differing levels, across the whole spectrum of jobs. Tasks are then grouped into

domains that correspond to a common typology of skill domains. Some occupation-specific

skills cannot be captured in this way, but in principle all generic skills can be. The method

underlies the computation of skill requirements attached to occupations used in the U.S.

Dictionary of Occupational Titles (DOT), and the subsequent ONET system, which provides

career advice to students and human resource (HR) professionals, and the similarly-motivated

Quality and Careers Surveys in Germany. Though these data have been used by researchers as

a by-product of the original objectives of data collection, the data I use here were gathered

solely for research.4 They come from the three UK Skills Surveys of 1997, 2001, and 2006,

3 Collaborators were Alan Felstead, Duncan Gallie, David Ashton, Bryn Davies, and Ying Zhou.

4 Similar motives underpin the recent survey of “Skills, Technology, and Managerial Practices”

(STAMP) in the United States (Handel 2008). There is now a growing movement to integrate job

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supplemented for background purposes by the 1992 Employment in Britain Survey. All these

surveys used random probability sampling methods, and with the adjustment of sampling and

response weights, they yield nationally representative samples of the employed population in

Britain aged 20 to 60. Details are given in Felstead et al. (2007) and in Felstead and Green

(2008). The three Skills Surveys netted samples of, respectively, 2,467, 4,470, and 7,780

workers, with gross response rates of 67%, 66%, and 62%. Sample sizes for the analysis

below are reduced through the exclusion of Northern Ireland and Highlands and Islands

respondents (both regions only covered in 2006), through the focus on employees, and

because of a very small number of respondents with missing occupation data.

{{2}} Classifying Tasks

A lynchpin in the empirical testing of the ALM model is the identification of

programmable tasks from descriptions and classifications. However, in existing studies the

ability to make this classification is not obviously comprehensive, and the validity of the

categorization is not always clear-cut. Sensitive to this concern, Autor et al. (2003a: 1306)

used an alternative specification drawing on a wider range of DOT variables using principal

components analysis and found that their main findings held, though with some alteration:

unsurprisingly, they found that “variable choice does matter.”

To illustrate the potential for misclassification, observe that routine cognitive tasks

comprise, according to Spitz-Oener (2006: 243), “calculating, bookkeeping, correcting

texts/data, and measuring length/weight/temperature.” Probably many of these tasks are truly

routine (hence programmable) as classified, but the attribution is by no means certain. For

requirements methods into skills research in a number of other nations, including Northern Ireland,

Spain, Italy, Singapore, and across almost all OECD countries through the forthcoming Programme for

International Assessment of Adult Competences (Green and Keese 2011).

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example, the task of “calculating” is quite general and may easily be incorporated in non-

routine processes. In Autor et al.’s (2003a: 1323) analysis, “adds and subtracts 2-digit

numbers” is a task included in the GED Math score, which is classified as non-routine

analytic. Conversely, the activity of selling is not classified in Spitz-Oener’s study as routine,

yet can be, and frequently is, partly automated—one has only to think of the spread of Internet

sales. There will also be many more cognitive functions that have been automated than are

contained in the above list.

In fact, the classification of tasks into distinct domains is by no means an exact science

(Handel 2008; Ashton et al. 1999). It involves grouping multiple job tasks that are linked in

theory and go together in practice (as derived from exploratory factor analysis or some other

data reduction technique). Often, a plausible classification is made into cognitive, physical,

and interactive task domains with corresponding skill domains. More disaggregated domains

follow, reflecting the concerns that have surfaced as a result of specific debates, such as those

over literacy or problem-solving activities within the cognitive domain, or over

communication skills, “emotional labor,” or “caring labor” in the interactive domain. Yet we

lack a comprehensive and consensual typology of job functions, especially in the realm of

interpersonal tasks. Moreover, many work actions are indivisible, involving tasks from

multiple domains, making their allocation problematic. Giving a lecture requires both

cognitive skills and an act of communication; lifting a hospital patient entails both caring and

physical skills. According to Darr (2004), selling and technical functions are also increasingly

becoming combined in some sectors. Often, therefore, a judgment must be made as to how

tasks are to be classified. Discerning whether tasks ought to be categorized as programmable

is especially problematic. Reflecting this, the activities included in the analyses of the

changing distribution of routine/non-routine work are only subsets of the totality of tasks

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measured in job requirements data. Occupation-specific tasks, which also vary in their degree

of programmability, are also excluded.5

At least two concerns arise, then, concerning task classification. First, the potential

exists for misclassification; second, it is difficult to capture the extent to which tasks are

routine since this element can only be identified in some cases. The element of imprecision in

the extent to which industries’ and occupations’ programmable-task-intensity is accurately

recorded might matter less if the measurement error that such misclassification gives rise to

were random (for then the direction of biases could be predicted), but the assumption of

randomness would be a strong one.

In light of the above, the empirical analysis that follows uses a reasonably general

categorization of generic skills as proposed in occupational psychology, corresponding to the

performance of a range of cognitive or intellectual tasks, interpersonal tasks, and physical

tasks. The classification is made using task data on individual jobs, specifically 35 items that

began with the stem: “We are interested in finding out what activities your job involves and

how important these are . . . . ” To illustrate, one item refers to “making speeches and

presentations.” Respondents rate the importance of this activity on a 5-point scale ranging

from “essential” (scored 4) to “not at all important/not applicable” (scored 0). All items used

the same scale. The precise scales to be constructed reflect the covariance of items in the data

themselves as well as theory. I examined the covariance of the responses to these items using

exploratory factor analysis. This yielded eight factors that were easily interpreted as indices

for the use of the following skills domains: Literacy, Numeracy, External Communication,

Influencing, Self-planning, Problem-Solving, Physical Skills, and Checking Skills. Table 1

details under each sub-heading the items upon which each factor loaded strongly. To obtain

5 The ONET content model, the successor to the U.S. DOT system, incorporates occupation-specific

skill requirements. They are not classified according to whether they correspond to routine tasks.

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indices for further analysis, I generated average scores from the responses to the component

items.6

Though it is fairly clear that certain of these domains can be safely classified as "non-

routine" (e.g. Influencing), it is hard to identify a priori which are unquestionably "routine."

Physical activities might appear to come close, but repetitive physical activities are more

likely to fall into this category. I therefore combined the Physical Skills index with an index

of how often the job involved short repetitive tasks, ranging from 0 ("never") to 4 ("always"),

to generate the Repetitive Physical Skills index (normalized to the same 0 to 4 range).7

{{TABLE 1 HERE}}

{{2}} Measures of Employee Involvement, Task Discretion, and Computerization

To capture key aspects of employee involvement, consistent data for all three years

were collected for whether the company holds meetings in which people are informed about

what is happening in the organization, and similarly whether there are meetings in which

individuals may express views to management. Data were also collected on whether

individuals participated in each of three schemes or policies: a suggestion scheme, an

appraisal system, and an improvement group or “quality circle.” Respondents indicated

whether working with a team of people was important, and I computed a teamwork dummy

variable for those who reported that this was “essential.”8 These variables are well correlated

in the data and are typically seen as indicators of individuals’ participation in the wider

6 An alternative is to compute factor scores and use these in subsequent analyses, the method used in a

study using the earlier data (Dickerson and Green 2004); I now prefer the scales developed here

because they provide figures that are more transparent and more easily interpretable, but the findings

are not particularly sensitive to the method used. Where I use title case this refers to the constructed

index. 7 Since the repetitiveness question applies to the whole job, even this index does not ideally capture

"routine." 8 This does not capture the nature of the team, such as the extent to which it is self-managed, data for

which is not available for all three years.

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organization. The responses are combined in a single index, hereinafter termed “Employee

Involvement,” ranging from 0 to 1, which scales with a Cronbach’s alpha coefficient of

internal consistency reliability of 0.70.

Also relevant to the HRM model as an indicator of involvement is the extent to which a

worker is afforded control over his or her immediate job tasks: workers who have little

influence, as in command and control systems of work organization, have little direct

involvement in decision-making about their own jobs. Conversely, designing jobs to have

high levels of autonomy is typically seen as part of a high-involvement work system.

Respondents were asked how much influence they personally had over four aspects of their

work tasks: how hard to work, what tasks to do, how to do them, and the quality standards to

which they worked. To each item they could respond on a 4-point scale ranging from “none at

all” to “a great deal” (of influence). The average scores across the four items form a “Task

Discretion Index” ranging from 0 to 3, with an internal consistency coefficient of 0.77. In the

analysis, the impact of the Employee Involvement Index is considered alongside the Task

Discretion Index, the former capturing organizational participation and the latter direct

employee involvement with work tasks.

To capture the use of computerized technology in individuals’ jobs, I distinguish

between types of computer use explicitly ranked in levels of complexity. Computer users

were asked directly about the level of their computer use, with several anchored examples for

the levels in a 4-point scale. I aggregate the two lower points to capture “low-level” computer

use (e.g., involving email use, word processing, and their equivalents), and the two upper

points to capture “high level” computing (e.g., using statistical packages, programming, and

their equivalents). The reference category is those not using computers at all. Cognitive

interviews at the pilot stage verified that these categories and examples were well understood

by respondents.

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{{1}} Findings

{{2}} The Changing Use of Generic Skills, 1997, 2001, and 2006

Altogether these data have two distinct advantages, compared with DOT- and ONET-

based analyses, for examining changes in job tasks. First, they give consistent worker-level

data on tasks at three separate times so that any changes that did occur do not have to be

inferred from changes in the shares of occupations. Second, they provide information about

key shifts in work organization and in computer technology as those workers experienced

them.

The aim of the empirical investigation was to examine the applicability of the ALM

model in British workplaces and to extend our understanding of skill requirements by

examining the role of employer involvement. To provide context for this study, I provide

some key facts about the changing use of generic skills in Britain. There has been a

substantive growth in certain types of skills over a 14-year period. First, Figure 1 presents a

picture of the change in use of four generic skills from 1992 to 1997. The changes are

computed from a series of questions in the 1997 Skills Survey, which asked respondents to

consider the tasks they had been doing in the job they had held five years previously. The

response points and scales were constructed in identical ways to the method used for the

current job, so the change could be computed as the difference between the skills used at the

two time points. Three skills surfaced in this time. Influence Skills, used in several higher-

level communication activities (see Table 1), rose the most. External Communication and

Problem-solving Skills also rose while the use of Physical Skills declined.9

9 Since no retrospective measure of repetitiveness is available in the 1997 data, there is no measure of

the change in Repetitive Physical Skills over 1992–1997.

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{{FIGURE 1 HERE}}

The retrospective method of measuring growth in the use of skills has its drawbacks. It

depends on respondents’ recall, excludes the experiences of those not employed five years

previously, and conflates lifecycle with trend effects. Using the more satisfactory method of

comparing nationally representative cross-sections, Figure 2 and Table 2 show that

Communication Skills were also among those that rose rapidly in the subsequent 1997–2006

period, the main focus of this study. Table 1 also reports how the detailed activities that form

the scales changed. Influence, Literacy and Self-Planning Skills were the fastest rising generic

skills requirements. The largest increases in the detailed activities (as measured by the

proportions answering at the top of the scale) are found for the items “organising your own

time”; “writing short documents, e.g., letters or memos”; and “listening carefully to

colleagues.” Taking Figures 1 and 2 together, note that Influence Skills have been the fastest

growing domain of job skill for a sustained period of 14 years. There were also more modest,

but statistically significant, rises in the use of Numeracy, External Communication, Problem-

Solving, and Checking Skills over the period 1997–2006; however, there were no significant

changes in the deployment of Physical Skills or the related index of Repetitive Physical Skills.

It is also worth noting that there is no evidence that the pace of growth of skills use

accelerated; in fact, the changes appear to have been greater over the 1992–1997 than over the

1997–2006 period.10

{{FIGURE 2 HERE}}

{{TABLE 2 HERE}}

10

It is possible that the recall method might have inflated change in the earlier period.

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Table 2 also reports bivariate correlations of each of the skill scales with the level of

required education required for each job.11

Notably, it is the skills whose use is growing the

fastest—Literacy, Influence, and Self-Planning—which are most closely related to the

required education level.

To what extent are these changes in job skills merely the consequence of shifts in

demand and hence in industrial structure, rather than changes in the skills being deployed

within each industry? One might, for example, suggest that Literacy Skills were becoming

more important because of the expanding service sector. Table 2 reports the outcome from

decomposing the changes, giving the percentage of each skill change that is associated with

skill changes within industries, if there had been no changes in 2-digit industry employment

shares from their 1997 levels. As the data demonstrate, the within-industry changes form

more than four-fifths of the total skill change in every case; indeed, in some cases, within-

industry change accounts for more than 100% of the change.12

{{2}} Changes in Employee Involvement, Task Discretion and Computer Use

Changes in the Employee Involvement Index, the Task Discretion Index, and for the

computer skills measures—the posited determinants of skills use—are shown in Table 3.

There has been a modest increase in the Employee Involvement Index between 1997 and

2006. A closer examination of its constituents reveals that all increased, though some only

very modestly, with the largest increase occurring in the use of quality improvement circles

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The level of required education in the job was obtained from a question asked of respondents: “If

someone were to apply today, what qualifications, if any, would someone need to get the type of job

you have now?” with the answers recoded from the stated qualifications to six levels ranging from 0

(none) to 5 (bachelor’s degree or above). 12

The decomposition identity is:

06 97 06 97 06 97 06 970.5( )( ) 0.5( )( )j j j j j j j j j jS S S E E E E S S , where S is the

average skill in industry j, E is the share of industry j in aggregate employment. The first term gives the

change that would occur solely through “between” changes in employment shares; the second gives the

skill change attributable to skill changes “within” industries if employment shares remained constant.

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by 11 percentage points in the period. For context, the table also includes 1992 figures when

available (derived from the earlier Employment in Britain survey (Gallie et al. 1998). What is

evident is that in every case the 1997–2006 rise is a continuation of a longer trend. This

pattern of moderately upward-trending participation indicators also occurs in other data

sources, notably the WIRS/WERS series (Kersley et al. 2006). By contrast, there was a

decline in the Task Discretion Index over 1997–2001, the continuation of a downward

movement since at least 1992. The decline levelled off between 2001–2006. This fall in the

extent to which employees reported personal influence over their job has been documented

elsewhere (Gallie et al. 2004), though not fully accounted for. Set against the rising Employee

Involvement Index, this presents a somewhat more complex picture of changing work

organization in British workplaces than is found purely on the basis of establishment-level

data.

{{TABLE 3 HERE}}

Meanwhile, the level of computer use expanded quite considerably over the period.

Between 1997 and 2001, computer use expanded by nearly eight percentage points, with most

of the expansion at the lower end; in the subsequent five years, there was no further overall

expansion in computer use, but there was a four-point increase in the proportions using

computers at the higher level of sophistication. This upward trend is entirely consistent with

on-going large investments in computer capital over the period (reaching 2% of GDP in 2002

according to Abramovsky and Griffith (2007)).

{{2}} Tasks and Wages

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Before proceeding with an investigation of the factors associated with task distribution

and growth, I first examine how much the variation in the skills use indicators matters in

terms of pay.13

In so doing, I provide some further evidence concerning whether the data on

task variations, within occupations as well as between them, are of interest as indicators of

genuine differences between jobs.

Table 4 displays the results of regressions in which the dependent variable is the log of

hourly wages. Using the pooled data, column (1) is a benchmark conventional regression

including only the year dummies, years of schooling, and a quadratic in work experience,

which explains 29% of the pay variation. Column (2) substitutes the set of task variables for

the human capital variables. The skill use indicators together account for 35% of the pay

variation. Positive coefficients are attached to Literacy, Numeracy, Influence, Self-Planning,

and Problem-Solving Skills. Though these positive estimates could be interpreted through a

range of labor market models, one way is to regard them as reflecting a combination of higher

pay within occupations and sorting into higher-paying occupations. Some skills have a

negative association with pay: External Communication, Checking, and Repetitive Physical.14

These negative estimates could be interpreted as reflecting the sorting of individuals with

abundant amounts of these skills into lower-paying occupations. As column (3) shows,

occupations on their own account for just over half (54%) of the variation in pay.

Nevertheless, even after controlling for those variations associated with occupation, there

remain robust associations between the tasks and pay (column (4)). Column (5) indicates that

even after including the human capital variables, there remains considerable explanatory

power in the task variables.

13

Recent reconfirmation of the links between skills and pay, after controlling for educational

attainment, include Bleakley and Chin 2004; Dickerson and Green 2004; Egger and Grossman 2005;

Dustmann et al. 2009; Lindley 2010; Acemoglu and Autor 2011. 14

I concentrate henceforth on the Repetitive Physical Tasks index in order to highlight possible

substitution by technology.

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There may be issues of omitted variable bias in these regressions: unobserved factors

could be correlated with both pay and the observed skills measures. The data in column (6)

are an attempt to control for any such omitted variables that could be considered fixed effects

within an occupation, using a panel of average values in the 4-digit occupation cells described

below. The column gives the fixed-effects panel estimates. This method relies on two

assumptions: first, the unobserved variables in each occupational cell are indeed time

invariant, and second, the "price" of each skill does not change.15

Overall, the table shows that

the positive point estimates of the values attached to both Influence Skills and Problem-

Solving Skills are even higher in this specification than in the individual-level OLS estimates,

whereas the coefficient attached to Repetitive Physical Skills remains negative; in all these

cases the coefficients are statistically significant. The coefficients on the other skills, however,

are statistically insignificant, a consequence of the much lower precision in the estimates.

{{TABLE 4 HERE}}

Because of sorting or other mechanisms, none of these estimates can be taken as the

"price" of skills. In a competitive labor market, individuals could be expected to choose an

occupation that affords them the maximum overall return from their skills; this sorting,

however, should not be expected to lead to each skill having the same price in different

occupations (Dickerson and Green 2004; Autor and Handel 2009), since it is the whole bundle

of task returns in each occupation that matters for each worker's choice. I estimated the

returns within each occupation, and interestingly the average estimates of the coefficients

across occupations (weighted by employment in each occupation)—displayed in column

15

In separate cross-sectional runs for each year (not shown), I found no evidence of significant changes

over time in the coefficient estimates attached to each skill.

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21

(7)—are relatively close to the whole-sample estimates in column (4). The coefficient

estimates vary considerably across occupations, however.

Even though one cannot attribute predictive effects from these estimates, it is worth

noting that their magnitudes, combined with observed trends, are high enough to be consistent

with the proposition that changing demands for generic skills may be economically

significant, if modest, sources of the changing pay distribution. Thus, the 1997–2006 rise by

0.225 in the use of Influence Skills is associated with a rise in hourly pay of approximately

3.9% (0.229 x 0.169 log points), while the rise by 0.100 in the use of Problem-Solving Skills

is associated with a pay hike of 1.5%.

{{2}} Modelling the Determinants of Skill Use

Given the additional reassurance that task-based measures of skills use—since they are

related to pay—are likely to capture real differences between jobs (and not simply noisy

reporting error), I now investigate their determinants. The overall pattern of change in skills

requirements appears to be prima facie broadly consistent with both the ALM model and the

HRM model. The rise in computer use would lead us, in the ALM model, to anticipate the

increase in Influence Skills and Self-planning Skills, which are largely non-routine.

Moreover, both the Literacy and External Communication domains are partly non-routine,

though it is impossible to say to what extent. None of the domains could be classified with

complete confidence as purely routine and therefore programmable. However, Repetitive

Physical Skills comes closest, and its constancy while other skills grew is consistent with

ALM. Two of the five individual items classified by Spitz-Oener (2006) as routine are

separately selected from the data, namely “Calculating,” which corresponds to MATHS1

and/or MATHS2, and “operating or controlling machines,” which corresponds to TOOLS (see

Table 1). The former pair were virtually static over the decade, and therefore declined relative

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to the other activities, while the latter experienced a statistically significant absolute decline.

Meanwhile, the rises in the Employee Involvement Index would, according to the HRM

model, also imply rising requirements for Communication Skills (of which Literacy Skills are

a part), Problem-solving Skills, and Self-planning Skills, though the latter expectation is

counterbalanced by the decline in the Task Discretion Index.

To investigate formally how skill use is associated with employee involvement and

computer use, I estimate models in which the skill requirement is related to relevant job

characteristics as follows:

(1)

where S is skill requirement, E is the Employee Involvement Index, D is Task Discretion, C is

computer use, t is time, u captures unobserved components possibly correlated with variables

of interest, ε is random error, and subscripts refer to individual i in occupation j at time t. The

OCCj comprise 350 4-digit occupation dummy variables.

In the ALM approach, occupational code is typically treated as a proxy for technology.

In addition, by including computing use as an indicator of technology at the job level,

Equation (1) also allows for the possibility that technology differs within occupations. The

hypothesis is that 0^^

LH for higher-level interactive and cognitive tasks, and the

opposite for tasks that are more likely to be routine and programmable.

The earlier discussion, however, implies that both technology and work organization

affect the tasks to be done (hence skills required). On one hand, organization variables may

capture part of the impact of technology on skill requirements, which is not otherwise

captured by the occupation dummies and the computer use variables. On the other hand,

ijt ijt j j j

ijt H H

ijt L L

ijt ijt ijt u OCC t C C D E S

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organization variables should also be included to reflect the HRM approach to skills use.

Presumably, employee involvement varies across occupations, so the occupation dummies

can also be viewed as capturing, in part, the effect of work organization. Also in the

framework of the HRM approach, the hypothesis is that ^

and ^

are each positive, especially

for the interactive and cognitive skills domains. Employee involvement and task discretion are

job-level or organization-level features, which can vary within occupations.

Since both the ALM and the HRM approaches will presumably help explain the

changing skill requirements, the time trend is also relevant. Compared with the raw trends,

inclusion of the occupational dummies and the other indicators should result in reductions in

^

for those skills expected to complement the technology and work organization indicators.

Conversely, in the case of Repetitive Physical Skills, the estimates of ^

are expected to be

greater (or less negative) compared with the raw trend, when technology is controlled for with

the occupation dummies and computing indicators.

Due to the presence of the uijt, OLS estimates may give biased estimates of the causal

effects. An unobserved variable that affects employee involvement, for example, might

independently affect the demand for skill. To help counteract this problem, in the absence of

individual-level panel data or suitable instruments, I also constructed a pseudo-panel in which

the unit is an occupation cell, consistently defined over the three cross-sections in 1997, 2001,

and 2006. Aggregating over individuals within occupations,

(2) jtjtjjtHH

jtLL

jtjtjt utCCDES .

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This procedure is similar to that followed by Spitz-Oener (2006), who used a first-

difference estimator.16

Here, I prefer the fixed-effects estimator to provide more efficient

estimates. Pseudo-panel fixed-effect estimation assumes that the jtu are time-invariant, that

is, that the unobserved variables that may be correlated with skills requirements average out

within occupations at the same level over successive waves. The validity of that assumption

depends on the presumed distribution of the unobserved variables. Furthermore, panel

estimates with but three waves to work with cannot convincingly aspire to tackle potential

dynamics, though there seems no reason to expect substantive lags in the impact of

organizations and technologies on skills use. A potentially more serious issue concerns

another conceivable source of bias: reverse causation might be postulated, with, for example,

generic skills determining employee involvement or computer use. I address this issue below.

The estimates are shown in Table 5. For each skill use domain, column (1) gives the

raw time changes using the pooled data, with no other variables added. The estimates on the

2006 year dummy are identical to the average changes pictured in Figure 2. In column (2), 4-

digit occupation dummies are included. Unsurprisingly, in every case occupation accounts for

a substantial proportion of the variance across jobs: the R2 range from 0.20 for Checking

Skills to 0.38 for Literacy Skills. In addition, occupation accounts for a proportion of the time

trend in most domains: the rises in Literacy, Numeracy, External Communication Skills,

Problem-Solving, Self-Planning, and Influencing Skills are all partially associated with the

changing occupation structure. In the case of Repetitive Physical Skills, however, their

importance increased within occupations (as shown by column (2)), but the effect of the

changing occupation structure meant that there was no significant overall trend (as in column

(1)). Thus, to the extent that the occupation structure captures the technological demands, its

impact on Repetitive Physical Skills is consistent with the ALM model, which predicts a

16

First-difference and fixed-effects panel methods give identical point estimates when there are just

two waves and estimates that are typically very close when there are more than two waves.

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decline in routine physical tasks associated with technology. In order to match similar

analyses for Germany (Spitz-Oener 2006) even more closely, I also constructed a measure of

"Repetitive Physical Skill Intensity" by dividing the Repetitive Physical Skill index by the

sum of all other skill indices. The results appear in Table 5h. As column (1) illustrates, the

intensity declines over time, reflecting the stability of the importance of repetitive physical

tasks alongside the growth of other tasks. Two-thirds (68%) of this fall is accounted for once

the occupation dummies are included (Column (2)).

In the remaining columns of the tables I investigate what variables account for the

distribution and trend in the skill use domains. As a benchmark for comparison with the

human capital literature, columns (3) and (4) show how far education and previous work

experience account for the allocation of persons to tasks. According to the data in column (3),

human capital is positively related to all the domains. Education is more important for the

academic or cognitive domains, such as Literacy, than for non-cognitive domains; it has a

negative association with jobs requiring Repetitive Physical tasks. According to the data in

column (4), which includes occupational dummies, in most cases those with more human

capital are allocated to more skilful tasks even within occupations. Column (3) also shows

that there is some selection into tasks by gender: males disproportionately into jobs requiring

Numeracy, Influencing, Problem-solving, Repetitive Physical and Checking tasks; females

into jobs requiring External Communication. Not all of these associations disappear once

occupations are taken into account, indicating that some sorting by gender occurs even within

occupations. None of these findings are surprising, though their conformity with expectations

adds some external validity to the job requirements data. The main conclusion to draw from

these data, however, is that, though a good proportion of task variation is captured by 4-digit

occupation, a substantial amount of within-occupation and across-time variation remains.

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Column (5) displays the indicators of technology and work organization. In all but one

domain, it is evident that, when there is higher Employee Involvement or Task Discretion or

level of computer use, there is a higher use of the skill; the exception is Repetitive Physical

Skills, for which all the coefficient estimates are negative. It is also noteworthy that the

technology and work organization variables account for a good proportion of the skills use

variance, much larger than that done by the traditional human capital variables (column (3)).

Column (6), which includes the occupation dummies, shows that work organization and

computing are also associated with variation in skills use within occupations. Though the

coefficient estimates are typically reduced, indicating that occupation dummies do capture

elements of computing use and work organization as expected, the estimates remain highly

significant in all domains. Moreover, the work organization and computing variables, together

with the occupation dummies, account for a high proportion of the time trend in most cases.

With Numeracy, External Communication, and Problem-Solving, there is no remaining

significant positive time trend once occupation, work organization, and computing variables

are included. With Repetitive Physical Skills Intensity, the negative time trend estimate comes

down to less than one-sixth (0.128/0.874 = 0.15) of its raw value, and is statistically

insignificant.

These cross-section OLS estimates cannot, however, be accepted as establishing that

technology and work organization have a causal impact on the various domains of skills use,

since it is possible that workplaces in which employee involvement or computer is high are

also high on other unobserved factors, such as a “long-termist” managerial culture or top-end

product specification, either of which could also affect the skills needed from workers.

Columns (7) and (8) display the fixed effects estimates that control for unobserved

heterogeneity at the occupation cell level. Column (7) includes just the raw time trend and

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thus picks up only the average skill changes within occupations.17

The skill rises within

occupations are again revealed to be lower than the raw trends indicate, consistent with the

hypothesis that technology and work organization have their predicted effects partly through

the changing allocation of occupations.

Column (8) shows that, even after within-occupation fixed effects are removed,

employee involvement has significant positive effects on Literacy, External Communication,

Influence Skills, Self-Planning Skills, Problem-Solving Skills, and Checking Skills. Task

Discretion also has a positive significant effect on Self-Planning Skills. Together these

findings show a strong consistency with what is expected in the HRM model of skill

requirements. Similarly, consistent with skill-biased technological change, the computer use

indicators are positively and significantly associated with Literacy, Numeracy, Influence, and

Self-Planning skills. Where significant, the work organization and technology effects on skill

requirements are of similar orders of magnitude. For example, a one-standard deviation

upward shift in the level of computer use raises the use of Influence Skill by 0.69 whereas a

one-standard-deviation rise in employee involvement and task discretion elevates the use of

Influence Skill by 0.43.18

In addition, the within-occupation changes in job skills, shown by the year dummy

coefficients, can be accounted for by changes in computing requirements and employee

involvement. Comparing the Column (7) and Column (8) estimates for the Year 2006

coefficient, it is evident that for Literacy, Numeracy, Influencing, Self-Planning, and

Checking the coefficients are reduced and become statistically insignificant.

The ALM model also predicts that the computer level would be negatively related to the

use of routine skills. In the case of Repetitive Physical Skills, the level of computer use has an

17

I excluded occupation cells that contained fewer than 20 observations. For this and other reasons, the

trends in column (7) are not precisely comparable to the trends indicated by Column (2). 18

Estimates from inputting each set of variables on its own without the other, shown in Green (2009),

are similar in magnitude, implying that the effects of the technology variables and the work

organization variables are orthogonal to each other.

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ambiguous relationship: low-level use engenders higher physical skills than either high-level

use or no use. However, it is evident that high-level computing is, as predicted, significantly

negatively related to the Repetitive Physical Task Intensity indicator, similar to the finding for

Germany.

None of these findings is immune from a possible alternative interpretation involving

reverse causation. Changes in skill requirements, driven by some external factor such as

market competition, could be interpreted as calling forth changes in technology or in work

organization within industry cells. There are no suitable instruments for the independent

variables, nor are there enough waves to explore dynamics in ways that might allow reverse

causation to be ruled out or in. My interpretation is that the computerization of jobs, and the

attachment of greater employee involvement to the ways in which they are carried out, do

quite plausibly determine the tasks that have to be done. It is reasonable, for example, to

theorize that employee involvement through meetings and suggestion schemes requires the

use of literacy and other communication skills. Nevertheless, one of the considerations an

employer might have to consider when deciding to try to involve employees more might be

the literacy level in the workforce, which itself would reflect the literacy required of the job.

{{TABLE 5 (all) HERE}}

{{2}} Robustness Checks

As a further check on the panel findings the following sensitivity analyses were

performed. First, even though I have weighted the occupation-panel findings according to the

number of observations in each cell, it is possible that remaining biases might arise from

smaller occupation cells where there might by chance be a disproportionate number of

respondents with above or below average values for unobserved variables. There is a trade-off

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between the minimum cell size and reducing the number of observations for analysis. In the

analyses shown, the minimum size of occupation cell is set to be at least 20 in every year,

yielding an average size at 45. Raising the minimum cell size reduces the number of

occupation cells, and hence the reported precision of the estimates. I tested the sensitivity of

the findings to increases in the cell size, with the result that an insignificantly different pattern

of coefficient estimates was revealed, but with larger reported standard errors.

Second, I re-constructed the pseudo-panel in other ways, based first on 2-digit

industries, then on 2-digit occupations, and finally on cells combining both industry and

occupation, each defined at the 1-digit level. This approach allowed me to examine the

sources of within-cell skill changes with alternative measures of the cell. The findings are

similar to those above, with both computerization and employee involvement contributing to

the explanation of job skill.19

Third, as an alternative to characterizing technology with two indicators for the level of

complexity of computer use, I utilized another pair of indicators that captured the

respondents’ reports of the importance of computer use in their jobs. The higher level

indicator was the proportion of employees for whom computer use was “very important” or

“essential” whereas the lower level was for those for whom computer use was “fairly

important” or “not very important,” the reference category being “not at all important/does

not apply,” Employing these indicators instead of the level dummies used in Table 5 yielded a

similar pattern of results, though in most instances with lower precision for the computing

variable.

{{2}} An implication: "Academic" Skills and Education

19

See Green (2009) for these results.

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The expanded prevalence of tasks requiring cognitive and interactive skills is expected

to underpin the rising demand for education. As Table 2 demonstrates, the skills whose use

has risen fastest have the closest individual-level correlation with required education levels.

Bivariate correlations, however, may reflect other influences. From the perspective of

understanding rising demand for education, it is important to ask whether the rising use of

generic skills is reflected in increased average education requirements in those industries. To

investigate this issue, I aggregated those skills indices presumably related to required

education—Literacy, Numeracy, Problem-Solving, Self-Planning, and Influencing. Together

these form an additive index, loosely termed for these purposes as “academic” skills use.20

Shown in Table 6 are regressions of this variable on the Required Education level.

Column (1) gives the raw trend in Required Education, indicating a rise between 1997

and 2001 but no significant change thereafter. Column (2) displays a crude estimate of the

role played by the growing use of academic skills in the workplace. The skills are highly

correlated with Required Education level, and their growing use across all occupations

accounts for more than half of the growth between 1997 and 2001 and virtually all the growth

between 1997 and 2006. A one-standard deviation (0.78) rise in academic skills use raises the

level of required education almost one whole level (0.98). Column (3) includes the 4-digit

occupation dummies and shows that the academic skills remain associated with required

education, and together all of the growth in required education is accounted for. Finally,

Column (4) reports the pseudo-panel estimate controlling for within-occupation fixed effects.

Even within detailed occupations, the academic skills are strongly related to the required

education. These findings confirm that changing skill requirements can provide a robust basis

for understanding changing education requirements.

20

All variables were standardised for this purpose. The index had a Cronbach’s alpha of internal

consistency of 0.84 in the pooled data. The overall pattern was not sensitive to alternative formulations

of this index.

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{{TABLE 6 HERE}}

{{1}} Conclusion

I have obtained new measures of job skills from custom surveys designed using “job

requirements analysis” embodying principles adapted from those of occupational psychology.

I have thus been able to provide a concise but reasonably comprehensive classification of the

generic skills used in jobs to track the changes that have occurred and to study the effects of

both computerization and organizational change.

The model of computerization’s effects on the labor market proposed by Autor et al.

(2003a) also has some considerable traction in Britain. Communication Skills and other

interactive activities, summarized in the index of Influence Skills, are among those that

increased quite rapidly over the 1997–2006 period. The communication and influencing

activities covered in these scales can be regarded reasonably safely as utilizing mainly non-

routine skills. Computing activities complement these skill domains, even within highly-

disaggregated occupations, and the rise in the use of computer technology explains part of this

increase in skill utilization. Compared with previous accounts of the growing use of generic

skills in the United States and Germany, the additional factor to note, especially relevant in

the context of education policy, is the prominence of the growth of Literacy Skills and Self-

Planning Skills, which are also accounted for in part by computer use.

I have found, however, that with a reasonably full specification of tasks, in many cases

it is not possible to determine a priori which activities are routine and therefore

programmable. This fact makes it difficult to assess the full strength of the ALM model in

explaining the changing distribution of skills. A plausible exception is Repetitive Physical

Skills, which remained stable over the period. There was a small decline in the practice of

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certain tasks that could be regarded as more routine. Lower-level numerical activities rose, but

only quite modestly. Taken together, the stability of these activities means that they fell as a

proportion of the total tasks performed. Thus the index of Repetitive Physical Skill Intensity,

which captures the relative importance of Repetitive Physical Skills, declined. Much of that

decline is accounted for by the changes in the explanatory variables, including occupational

structure and computing use. Moreover, the within-occupation panel estimates showed the

expected negative relationship between high-level computing use and Repetitive Physical

Task Intensity.

Work organization indicators also offer a strong explanation for generic skills use. The

formal evidence for the importance of the Employee Involvement Index, and to a lesser extent

the Task Discretion Index, supports the findings of a number of recent case studies in

determining skills use broadly across the whole range of a country’s workforce. The rising

Employee Involvement index in particular helps to explain the increases in use of Literacy,

External Communication, Influencing, Self-Planning, Problem-Solving, and Checking. The

Task Discretion index is also a significant positive determinant of Problem-Solving Skills use,

a fact that makes the actual rises in this domain more noteworthy because they occurred

despite the decline in task discretion. All these effects are additional to the effects of

technology.

The implications of rising generic skills use for the labor market depend on the

availability of these skills in the labor force pool. Some of the skills are found to have a robust

association with pay, though the price of skills varies greatly between occupations, and it has

not been possible to identify a single price for each. The growth of generic skills usage is

especially relevant in the context of educational policy. My findings confirm that there is a

strong connection between the rising use of more academic skills and the required education

level for entry into jobs. Assuming the trend persists, the rapidly rising use of literacy

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suggests that there will be a continuing strong economic rationale for improvements in

literacy standards in schools. The increasing importance of other forms of communication

poses questions about whether such skills can be best provided in schools and universities or

at work, potentially the object of future research.

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Figure 1 Changes in the Use of Generic Skills, 1992-1997.

Source: Skill Survey, 1997, calculated from retrospective questions. Base: is all

workers who had also been in work in 1992.

Each skills index ranges from 0 to 4.

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

Ph

ysic

al

Pro

ble

m-s

olv

ing

Exte

rna

l C

om

mu

nic

atio

n

Influ

en

ce

Ch

an

ge

in

Sk

ills

In

dic

es

, 1

99

2-

1997

Same Job

Different Job

Total

42

42

Figure 2 Changes in the Use of Generic Skills, 1997-2006.

Source: Skill Surveys, 1997 and 2006.

Each skills index ranges from 0 to 4; see Table 2 for standard deviations.

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Table 1 The Evolution of Tasks in Britain, 1997 to 2006

\

Tasks By Domain Heading a

% for whom task is

“essential” b

Change in % for whom

task is “essential”.

2006 1997-2001 1997-2006

Literacy

Reading written information, e.g., forms, notices, or signs 53.48 0.89 4.59

Reading short documents e.g., letters, or memos 44.91 2.17 7.20

Reading long documents e.g., long reports, manuals, etc 28.42 2.41 6.25

Writing material such as forms, notices or signs 41.64 3.69 6.81

Writing short documents, e.g., letters or memos 35.18 4.00 8.05

Writing long documents with correct spelling/grammar 20.82 2.79 6.10

Numeracy

Adding, subtracting, multiplying or dividing numbers 33.69 0.41 -0.29

Calculations using decimals, percentages or fractions? 25.93 0.66 1.29

More advanced mathematical or statistical procedures 11.99 0.72 1.57

Communication: External

Knowledge of particular products or services 40.99 2.69 5.74

Selling a product or service. 21.18 -2.06 -2.81

Counseling, advising, or caring for customers or clients 39.24 1.45 2.96

Dealing with people 64.97 -0.21 4.81

Communication: Influencing Others

Instructing, training, or teaching people 30.45 2.53 5.00

Persuading or influencing others 21.46 1.11 4.95

Making speeches or presentations 11.02 2.20 3.9

7

Planning the activities of others 15.26 1.26 1.4

3

Listening carefully to colleagues 47.04 3.48

8.8

7

Self-Planning

Planning your own activities 37.83 3.52 6.05

Organizing your own time 44.43 4.98 8.89

Thinking ahead 44.01 3.33 6.26

Problem-Solving

Spotting problems or faults 43.45 -0.08 -3.14

Working out the cause of problems or faults 36.26 0.54 -1.02

Thinking of solutions to problems 38.10 0.70 2.20

44

44

Analyzing complex problems in depth 26.02 -1.14 6.49

Physical

Physical strength e.g., carry, push, or pull heavy objects 14.08 -1.73 -0.69

Work for long periods on physical activities 16.19 -0.67 1.05

Skill or accuracy in using your hands or fingers 22.53 3.33 -1.68

Use or operate tools, equipment or machinery 31.42 -0.07 -3.13

Checking

Noticing when there is a mistake 50.42 1.74 3.53

Checking things to ensure that there are no errors 48.30 0.21 2.54

Paying close attention to detail 61.36 -4.27 -4.26

Notes: a. Tasks are presented under the heading of the generic skills domains, determined by the factors on which they loaded

highly in the factor analysis. b. For each item, respondents were asked “In your job, how important is …[each task],” answering

according to a scale: “essential,” “very important,” “fairly important,” “not very important,” “not at all important/does not apply.. In some cases, fuller descriptions are provided to respondents, together with further examples. The table presents the proportion

answering at the top point of the scale.

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Table 2. Job Skill Indices, 1997–2006

Job Skill Index

α † 1997 2006 1997-

2006

s.d.

of pooled data

% within

2-digit Ind. 97-06

Correlation with

Required Education

Literacy 0.92 2.407 2.630 0.223** 1.11 81.2 0.478

Numeracy 0.88 1.775 1.892 0.118** 1.31 137.2 0.346

Communication: External 0.66 2.522 2.633 0.111** 0.97 88.3 0.164

Communication: Influencing 0.81 2.068 2.292 0.225** 0.95 89.7 0.443

Self-Planning 0.85 2.813 3.020 0.207** 0.97 87.6 0.414

Problem Solving 0.85 2.720 2.819 0.100** 0.97 110.9 0.347

Physical:

Repetitive Physical

0.79

na

1.913

2.09

1.889

2.11

-0.024

0.021

1.16

0.89

-158.1

362.0

-0.260

-0.352

Checking 0.79 3.268 3.328 0.06** 0.74 130.3 0.212

Notes: Double asterisks (**) indicate change in mean value of index is significantly different from zero (p>=95%, two-tailed test).

† Cronbach’s Internal Consistency Reliability Coefficient.

Weighted bivariate correlation coefficients of each skill with the job’s required education level scaled to six points: 0 = no required educational

qualifications in job; level 1 = below GCSE or equivalent. (GCSE are national exams normally taken at age 16); level 2 at GCSE or equivalent;

level 3 = A level or equivalent (normally obtained at age 18); level 4 = tertiary diplomas and qualifications below degree level; level 5 = bachelor’s degree or above.

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Table 3. Employee Involvement, Task Discretion and Computing in 1992, 1997, 2001, and 2006.

Variable 1992 1997 2001 2006

Involvement Scale a -- 0.577 0.584 0.644

Constituents

Participation in Quality Circle (%) 19.9 31.2 37.2 42.2

Participation in Appraisal (%) -- 57.9 62.7 71.1

Makes Production Suggestions (%) 67.4 72.5 69.3 74.7

Expression of Views (%) 62.5 66.5 65.5 70.9

Company Gives Information (%) 70.0 73.3 70.6 75.7

Teamwork (%) 45.0 45.8 52.9

Task Discretion Indexb 2.44 2.25 2.18 2.18

High-level Computingc (%)

--

16.3 18.8 22.7

Low-level Computingd (%)

--

55.3 60.6 56.8

a Scale averaged from the five constituent elements; Cronbach’s alpha = 0.701. b Scale averaged from the four sources of task discretion delineated in the text, Cronbach’s alpha = 0.771 c Level of computer use is, at least, complex (e.g. use of statistical packages). d Computers are used in either a moderate or a simple way, for example for emailing or word processing. The residual cateegory

is “computers not used.”

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47

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Table 4. Job Skills and Pay

Variables (1) (2) (3) (4) (5) (6) (7)

Year=2001 0.0996*** 0.100*** 0.0865*** 0.0923*** 0.0846*** 0.0943*** 0.074

[0.0129] [0.0122] [0.0114] [0.0109] [0.0105] [0.0217] {0.020}

Year=2006 0.148*** 0.162*** 0.166*** 0.163*** 0.150*** 0.156*** 0.134

[0.0121] [0.0115] [0.0108] [0.0104] [0.0101] [0.0221] {0.017}

Literacy 0.0640*** 0.0266*** 0.0263*** 0.0458 0.021

[0.00549] [0.00511] [0.00490] [0.0735] {0.012}

Numeracy 0.0345*** 0.00656* 0.00300 -0.0422 0.010

[0.00368] [0.00352] [0.00338] [0.0432] {0.010}

Influence 0.128*** 0.0771*** 0.0686*** 0.169** 0.073

[0.00658] [0.00614] [0.00591] [0.0803] {0.018}

Self-Planning 0.0357*** 0.0187*** 0.0159*** -0.0646 0.009

[0.00583] [0.00516] [0.00495] [0.0619] {0.017}

External Communication -

0.0824***

-

0.0345***

-

0.0318***

-0.0151

-0.037

[0.00512] [0.00512] [0.00491] [0.0594] {0.015}

Problem-Solving 0.0875*** 0.0307*** 0.0250*** 0.144** 0.024

[0.00634] [0.00570] [0.00547] [0.0619] {0.014}

Checking -

0.0293***

-

0.0324***

-

0.0249***

-0.0277

-0.024

[0.00742] [0.00655] [0.00629] [0.0779] {0.015}

Repetitive Physical -0.171*** -

0.0940***

-

0.0825***

-0.168***

-0.080

[0.00489] [0.00485] [0.00467] [0.0525] {0.013}

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49

Years of Schooling 0.0881*** 0.0310***

[0.00174] [0.00164]

Work Experience 0.0430*** 0.0249***

[0.00150] [0.00119]

(Work Experience)2/100 -

0.0749***

-

0.0418***

[0.00326] [0.00256]

Female -0.248*** -0.116***

[0.00876] [0.00881]

Occupation NO NO YES YES YES -

Observations 10325 10325 10325 10325 10325 169

R2 0.288 0.357 0.539 0.578 0.612 0.987

F(HC and gender) 966.7 223.3

F(Skills) 673.9 114.5 94.01 4.941

F(Occ dummies) 32.78 15.24 11.30

Notes: The dependent variable is the log of hourly pay. In column (7) are employment-weighted means of the within-occupation coefficient estimates, while the figures in curly brackets are the standard errors of the means computed from the spot estimates.

*Statistically significant at the .10 level; **at the .05 level; ***at the .01 level.

Table 5. Estimates of the Determinants of Generic Skills Use

a. Literacy

Variable (1) (2) (3) (4) (5) (6) (7) (8)

Year=2001 0.137*** 0.0683*** 0.108*** 0.0727*** 0.0780*** 0.0687*** 0.0544 0.0521

[0.0305] [0.0265] [0.0292] [0.0264] [0.0247] [0.0243] [0.0440] [0.0479]

Year=2006 0.228*** 0.165*** 0.147*** 0.170*** 0.0972*** 0.105*** 0.167*** 0.0848

[0.0283] [0.0251] [0.0274] [0.0252] [0.0231] [0.0231] [0.0423] [0.0532]

Years of Schooling 0.114*** 0.0125***

[0.00389] [0.00403]

Work experience 0.0462*** 0.0196***

[0.00340] [0.00299]

(Work experience)2/100 -0.0905*** -0.0461***

[0.00737] [0.00642]

Female -0.0110 -0.0467**

[0.0198] [0.0219]

Emp. Involvement 1.138*** 0.881*** 0.870***

[0.0314] [0.0307] [0.323]

Task Discretion 0.303*** 0.229*** 0.170

[0.0131] [0.0129] [0.137]

Low Computing 0.764*** 0.468*** 0.269

[0.0225] [0.0256] [0.259]

High Computing 0.999*** 0.668*** 1.021***

[0.0277] [0.0324] [0.341]

Occupation NO YES NO YES NO YES - -

Observations 11482 11482 11482 11482 11482 11482 169 169

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51

R2 0.006 0.376 0.089 0.380 0.350 0.480 0.974 0.980

F(Year dummies) 33.49 26.64 14.37 27.29 8.933 10.52 12.17 1.289

F(Occ dummies) 19.25 15.22 8.081

F(HC and gender) 261.7 17.40

F(EI&TD) 1107 658.9 4.671

F(computing) 750.0 223.3 5.585

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b. Numeracy

Variable (1) (2) (3) (4) (5) (6) (7) (8)

Year=2001 0.146*** 0.0637* 0.129*** 0.0676** 0.0719** 0.0480 0.102* 0.0126

[0.0360] [0.0326] [0.0352] [0.0325] [0.0321] [0.0311] [0.0575] [0.0625]

Year=2006 0.119*** 0.0820*** 0.0703** 0.0845*** 0.00677 0.0212 0.129** -0.0266

[0.0335] [0.0309] [0.0330] [0.0310] [0.0300] [0.0296] [0.0552] [0.0695]

Years of Schooling 0.0769*** 0.0143***

[0.00468] [0.00495]

Work experience 0.0213*** 0.0114***

[0.00410] [0.00367]

(Work experience)2/100 -0.0497*** -0.0312***

[0.00887] [0.00788]

Female -0.366*** -0.242***

[0.0238] [0.0269]

Emp. Involvement 0.222*** 0.235*** 0.392

[0.0408] [0.0394] [0.422]

Task Discretion 0.252*** 0.195*** -0.0865

[0.0171] [0.0165] [0.179]

Low Computing 0.902*** 0.599*** 1.012***

[0.0292] [0.0329] [0.339]

High Computing 1.589*** 1.159*** 1.989***

[0.0360] [0.0416] [0.446]

Occupation NO YES NO YES NO YES - -

Observations 11482 11482 11482 11482 11482 11482 169 169

R2 0.002 0.322 0.052 0.330 0.213 0.386 0.965 0.973

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53

F(Year dummies) 8.754 3.529 6.857 3.738 4.125 1.348 2.715 0.356

F(Occ dummies) 15.36 13.46 9.158

F(HC and gender) 151.3 30.75

F(EI&TD) 142.3 100.6 0.517

F(computing) 981.1 392.7 9.978

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54

c. External Communication

Variable (1) (2) (3) (4) (5) (6) (7) (8)

Year=2001 0.0400 -0.00256 0.0339 -0.000851 0.0214 0.0120 -0.0402 -0.0218

[0.0267] [0.0243] [0.0265] [0.0243] [0.0243] [0.0229] [0.0485] [0.0553]

Year=2006 0.116*** 0.0505** 0.0950*** 0.0593** 0.0617*** 0.0276 0.0305 -0.0296

[0.0248] [0.0230] [0.0248] [0.0232] [0.0227] [0.0218] [0.0466] [0.0614]

Years of Schooling 0.0326*** -0.00420

[0.00352] [0.00371]

Work experience 0.0192*** 0.0134***

[0.00308] [0.00275]

(Work experience)2/100 -0.0425*** -0.0285***

[0.00668] [0.00591]

Female 0.134*** -0.0288

[0.0179] [0.0202]

Emp. Involvement 0.735*** 0.683*** 0.986***

[0.0308] [0.0290] [0.373]

Task Discretion 0.303*** 0.248*** 0.192

[0.0129] [0.0121] [0.158]

Low Computing 0.407*** 0.277*** 0.0698

[0.0221] [0.0242] [0.300]

High Computing 0.318*** 0.280*** 0.582

[0.0272] [0.0306] [0.394]

Occupation NO YES NO YES NO YES - -

Observations 11482 11482 11482 11482 11482 11482 169 169

R2 0.002 0.311 0.019 0.313 0.180 0.393 0.949 0.956

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55

F(Year dummies) 13.71 5.344 8.997 6.884 4.621 0.955 2.501 0.117

F(Occ dummies) 14.57 13.89 11.38

F(HC and gender) 49.11 7.324

F(EI&TD) 681.3 568.4 4.491

F(computing) 169.9 66.10 1.719

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d. Influencing

(1) (2) (3) (4) (5) (6) (7) (8)

Year=2001 0.111*** 0.0306 0.0863*** 0.0354 0.0892*** 0.0495** 0.0464 0.0339

[0.0260] [0.0228] [0.0247] [0.0227] [0.0205] [0.0199] [0.0463] [0.0498]

Year=2006 0.229*** 0.128*** 0.160*** 0.134*** 0.130*** 0.0831*** 0.152*** 0.0508

[0.0242] [0.0216] [0.0232] [0.0217] [0.0192] [0.0189] [0.0445] [0.0553]

Years of Schooling 0.102*** 0.0141***

[0.00329] [0.00346]

Work experience 0.0470*** 0.0205***

[0.00288] [0.00257]

(Work experience)2/100 -0.0955*** -0.0493***

[0.00624] [0.00551]

Female -0.0719*** -0.138***

[0.0167] [0.0188]

Emp. Involvement 1.356*** 1.083*** 1.111***

[0.0261] [0.0252] [0.336]

Task Discretion 0.359*** 0.276*** 0.158

[0.0109] [0.0105] [0.143]

Low Computing 0.342*** 0.262*** 0.507*

[0.0187] [0.0210] [0.270]

High Computing 0.429*** 0.365*** 0.982***

[0.0230] [0.0266] [0.355]

Occupation NO YES NO YES NO YES - -

Observations 11482 11482 11482 11482 11482 11482 169 169

R2 0.009 0.367 0.108 0.376 0.388 0.524 0.964 0.973

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F(Year dummies) 49.47 26.46 25.29 27.57 23.12 10.11 9.420 0.423

F(Occ dummies) 18.35 13.93 9.225

F(HC and gender) 319.1 40.73

F(EI&TD) 2272 1468 6.393

F(computing) 207.7 101.0 3.836

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e. Self-Planning

(1) (2) (3) (4) (5) (6) (7) (8)

Year=2001 0.159*** 0.0761*** 0.135*** 0.0779*** 0.156*** 0.116*** 0.107** 0.106**

[0.0265] [0.0247] [0.0256] [0.0246] [0.0221] [0.0223] [0.0494] [0.0489]

Year=2006 0.216*** 0.116*** 0.150*** 0.113*** 0.173*** 0.125*** 0.141*** 0.0793

[0.0247] [0.0234] [0.0240] [0.0235] [0.0207] [0.0212] [0.0474] [0.0543]

Years of Schooling 0.0915*** 0.0193***

[0.00341] [0.00375]

Work experience 0.0372*** 0.0178***

[0.00298] [0.00279]

(Work experience)2/100 -0.0715*** -0.0384***

[0.00647] [0.00598]

Female -0.0288* -0.0168

[0.0173] [0.0204]

Emp. Involvement 0.695*** 0.542*** 0.757**

[0.0281] [0.0283] [0.330]

Task Discretion 0.560*** 0.463*** 0.458***

[0.0118] [0.0118] [0.140]

Low Computing 0.336*** 0.237*** 0.843***

[0.0201] [0.0235] [0.265]

High Computing 0.486*** 0.343*** 1.308***

[0.0248] [0.0298] [0.348]

Occupation NO YES NO YES NO YES - -

Observations 11482 11482 11482 11482 11482 11482 169 169

R2 0.007 0.288 0.076 0.292 0.314 0.422 0.948 0.966

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F(Year dummies) 38.25 12.57 20.43 11.66 36.72 18.32 4.467 2.411

F(Occ dummies) 12.83 9.903 6.047

F(HC and gender) 216.4 16.66

F(EI&TD) 1702 1087 8.595

F(computing) 206.0 69.11 7.354

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f. Problem-Solving

(1) (2) (3) (4) (5) (6) (7) (8)

Year=2001 0.0844*** 0.0438* 0.0732*** 0.0468* 0.0544** 0.0568** 0.0550 0.0596

[0.0267] [0.0252] [0.0260] [0.0252] [0.0232] [0.0234] [0.0477] [0.0534]

Year=2006 0.106*** 0.0673*** 0.0785*** 0.0760*** 0.0239 0.0328 0.0465 -0.0275

[0.0248] [0.0239] [0.0244] [0.0240] [0.0217] [0.0223] [0.0458] [0.0593]

Years of Schooling 0.0537*** 0.00294

[0.00346] [0.00384]

Work experience 0.0333*** 0.0183***

[0.00303] [0.00285]

(Work experience)2/100 -0.0710*** -0.0410***

[0.00657] [0.00612]

Female -0.280*** -0.104***

[0.0176] [0.0209]

Emp. Involvement 0.721*** 0.653*** 0.867**

[0.0295] [0.0297] [0.360]

Task Discretion 0.317*** 0.284*** 0.183

[0.0124] [0.0124] [0.153]

Low Computing 0.422*** 0.339*** 0.193

[0.0211] [0.0247] [0.289]

High Computing 0.779*** 0.576*** 0.924**

[0.0260] [0.0313] [0.380]

Occupation NO YES NO YES NO YES - -

Observations 11482 11482 11482 11482 11482 11482 169 169

R2 0.002 0.259 0.053 0.264 0.248 0.365 0.946 0.955

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F(Year dummies) 9.238 4.062 5.521 5.175 2.987 3.013 0.682 2.633

F(Occ dummies) 11.29 9.277 6.007

F(HC and gender) 156.7 16.93

F(EI&TD) 764.5 591.4 3.854

F(computing) 448.3 169.4 4.000

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g. Repetitive Physical

(1) (2) (3) (4) (5) (6) (7) (8)

Year=2001 0.0397 0.111*** 0.0693*** 0.114*** 0.0735*** 0.111*** 0.129*** 0.0799

[0.0246] [0.0219] [0.0235] [0.0218] [0.0236] [0.0220] [0.0434] [0.0501]

Year=2006 0.0332 0.101*** 0.118*** 0.122*** 0.0827*** 0.0989*** 0.117*** 0.126**

[0.0229] [0.0208] [0.0220] [0.0208] [0.0220] [0.0209] [0.0416] [0.0556]

Years of Schooling -0.103*** -0.0392***

[0.00312] [0.00332]

Work experience -0.0221*** -0.0109***

[0.00273] [0.00247]

(Work experience)2/100 0.0355*** 0.0194***

[0.00593] [0.00530]

Female -0.0590*** 0.00406

[0.0159] [0.0181]

Emp. Involvement -0.0845*** 0.0935*** -0.738**

[0.0299] [0.0278] [0.338]

Task Discretion -0.112*** -0.0280** -0.173

[0.0125] [0.0116] [0.143]

Low Computing -0.421*** -0.0435* 0.469*

[0.0214] [0.0232] [0.271]

High Computing -0.708*** -0.0981*** -0.0108

[0.0264] [0.0294] [0.357]

Occupation NO YES NO YES NO YES - -

Observations 11482 11482 11482 11482 11482 11482 169 169

R2 0.000 0.342 0.093 0.351 0.090 0.343 0.949 0.955

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F(Year dummies) 1.412 14.42 14.79 18.34 7.332 14.01 4.760 2.565

F(Occ dummies) 16.86 12.90 12.53

F(HC and gender) 293.1 38.06

F(EI&TD) 49.85 7.491 3.326

F(computing) 364.5 5.913 2.996

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h. Repetitive Physical Skill Intensity {{AU: is this Kappa intentional? Also in the Notes section?}}

Variable (1) (2) (3) (4) (5) (6) (7) (8)

Year=2001 -0.612*** 5.61e-05 -0.419** -0.0221 -0.246 -0.0628 -0.0774 -0.416

[0.188] [0.156] [0.178] [0.155] [0.152] [0.146] [0.360] [0.366]

Year=2006 -0.874*** -0.276* -0.303* -0.252* -0.195 -0.128 -0.354 0.215

[0.175] [0.148] [0.167] [0.148] [0.142] [0.139] [0.346] [0.407]

Years of Schooling -0.785*** -0.168***

[0.0237] [0.0237]

Work experience -0.259*** -0.129***

[0.0208] [0.0176]

(Work experience)2/100 0.523*** 0.298***

[0.0450] [0.0377]

Female 0.0674 0.330**

[0.121] [0.129]

Emp. Involvement -4.555*** -3.188*** -11.82***

[0.194] [0.185] [2.473]

Task Discretion -2.492*** -1.855*** -2.176**

[0.0810] [0.0774] [1.050]

Low Computing -5.498*** -2.689*** 1.041

[0.139] [0.154] [1.987]

High Computing -7.294*** -3.327*** -4.388*

[0.171] [0.195] [2.611]

Occupation NO YES NO YES NO YES - -

Observations 11482 11482 11482 11482 11482 11482 169 169

R2 0.002 0.431 0.105 0.437 0.349 0.504 0.961 0.973

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F(Year dummies) 12.53 3.641 2.797 2.662 1.366 0.477 0.953 2.911

F(Occ dummies) 24.43 19.15 10.13

F(HC and gender) 330.7 32.71

F(EI&TD) 908.4 508.4 14.39

F(computing) 1039 172.1 4.245

This dependent variable is the Repetitive Physical Task index divided by the sum of all other task indices.

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i. Checking

Variable (1) (2) (3) (4) (5) (6) (7) (8)

Year=2001 0.0322 0.0278 0.0293 0.0295 0.00690 0.0336* 0.0448 0.0562

[0.0204] [0.0201] [0.0204] [0.0201] [0.0191] [0.0194] [0.0401] [0.0447]

Year=2006 0.0644*** 0.0731*** 0.0550*** 0.0772*** 0.0143 0.0526*** 0.0845** 0.0255

[0.0190] [0.0190] [0.0191] [0.0192] [0.0178] [0.0185] [0.0385] [0.0496]

Years of Schooling 0.0182*** -0.00207

[0.00271] [0.00306]

Work experience 0.0111*** 0.00532**

[0.00237] [0.00227]

(Work experience)2/100 -0.0258*** -0.0124**

[0.00513] [0.00488]

Female -0.0245* 0.0585***

[0.0138] [0.0167]

Emp. Involvement 0.355*** 0.373*** 0.920***

[0.0242] [0.0246] [0.302]

Task Discretion 0.143*** 0.154*** 0.181

[0.0101] [0.0103] [0.128]

Low Computing 0.345*** 0.211*** 0.185

[0.0174] [0.0205] [0.242]

High Computing 0.485*** 0.319*** 0.555*

[0.0214] [0.0260] [0.318]

Occupation NO YES NO YES NO YES - -

Observations 11482 11482 11482 11482 11482 11482 169 169

R2 0.001 0.197 0.009 0.198 0.131 0.253 0.920 0.934

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F(Year dummies) 6.313 9.369 4.413 10.24 0.355 4.158 2.850 1.019

F(Occ dummies) 7.903 7.673 5.331

F(HC and gender) 21.65 5.195

F(EI&TD) 251.2 266.5 6.012

F(computing) 280.7 77.46 1.721

Notes: Columns (1) to (6) are OLS estimates using pooled-years individual-level data. Columns (7) and (8) gives fixed-effects (FE) estimates using the 4-digit occupation panel. The minimum occupation cell size is set to 20; average cell size per cohort is: 38 in 1997, 42 in 2001 and 52 in 2006; the estimates are weighted by cell size. For tables a-i: Standard errors are in parentheses.

*Statistically significant at the .10 level; **at the .05 level; ***at the .01 level.

Table 6. Generic Skills as Determinants of the Required Education Level

Variable (1) (2) (3) (4)

Year=2001 0.267*** 0.111** 0.0275 -0.0207

[0.0510] [0.0434] [0.0378] [0.0655]

Year=2006 0.220*** 0.00385 -0.0485 -0.116*

[0.0473] [0.0404] [0.0357] [0.0663]

"Academic" skills 1.263*** 0.488*** 0.938***

[0.0191] [0.0200] [0.208]

Occupation NO NO YES -

Observations 11378 11378 11378 169

R2 0.003 0.279 0.546 0.986

Notes: The Required Education Level is the level currently required to obtain the job, as reported by the job-holder; levels as defined in Table 2. “Academic” skills are the sum of Literacy, Numeracy, Influencing, Problem-Solving, and Self-Planning. Column (4) is a fixed-effects estimation using the 4-digit occupation panel, see notes to Table 5. Standard errors are in parentheses.

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