Estimating the Determinants of Supply of Computing, Problem-Solving ...

Oxford University Press 2001 All rights reserved #

Oxford Economic Papers 3 (2001), 406–433

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Estimating the determinants of supply of computing, problem-solving, communication, social, and teamworking skills By Francis Green*, David Ashton{ and Alan Felstead{ * Department of Economics, Keynes College, University of Kent at Canterbury, Canterbury, Kent CT2 7NP, England. email: [email protected]; tel: +44 (1) 1227 827305; fax: +44 (0) 1227 827784 { Centre for Labour Market Studies, University of Leicester We investigate the sources of supply of several core skills, using an innovative approach to skills measurement that involves adapting a job analysis methodology and applying it in a survey context. We then estimate the determinants of skills supply using a production function model. The main findings are: (i) prior education and work experience have generally positive but diminishing marginal impacts on skills, consistent with the earnings function literature; (ii) off-the-job training is productive of most types of skill, while on-the-job training is effective for the generation of problemsolving and team-working skills. Both types of training are transferable from previous employers; (iii) more education enhances the development of computing skills at work, but with respect to other core skills, less educated workers make up for their lower education through more work-based learning; (iv) there is a strong association between the presence of some new or flexible organisation characteristics and both the level and growth of all types of skills. We argue overall that the contribution of work-based learning to skills development is more important than normally allowed for in the skills policy discourse.

1. Introduction For the last decade the policy discourse in the field of human resource development has come to accept that there is an increasing demand for higher skills in the modern workplace. In addition, a number of ‘core’ or ‘key’ generic skills have been identified by policy-makers as acquiring particular importance. In the United Kingdom, for example, the focus is on communication skills, the ‘application of number’, information technology skills, problem-solving skills, working with others, and improving one’s own learning and performance. Sociologists have pointed to workplace transformations requiring a range of new skills (e.g. Osterman, 1995), and economists have debated the relative importance of globalisation and of technological change as the underlying economic imperatives driving the rise in demand for skills (e.g. Freeman, 1995). Recent

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research is confirming, moreover, that changes in work organisation and associated skills are intimately linked to the new information technologies (Bresnahan et al., 1999). Despite the near consensus that there is a rising demand for skills, we remain a long way from understanding or agreement about the sources of generic skills. One perspective focuses on formal education or training institutions as the chief suppliers of skills. Conventional economics has typically emphasised this perspective, if only because years of schooling, formal qualifications, and length or frequency of formal training provide convenient measures of human capital investments. There is an understandable tendency for both policy-makers and researchers to slip into equating these indirect measures with the process of skill formation itself. A substantive literature is devoted to understanding the effectiveness of US educational institutions. That knowledge is relatively undeveloped elsewhere, but even in the US the studies in this literature do not address the impact of education on direct measures of skills used in the workplace. Outcome measures are typically some educational test, or indices of wages or occupational attainment. An alternative perspective on the source of skills emphasises work-based learning, to encompass not only training but also other forms of learning such as through job rotation. Underpinning this perspective is the idea that, if demand switches are the drivers of change, then unless one had an idealist faith in market forces, one would expect employers to take a leading role in generating the new skills that they themselves demand. Although the economics perspective has always recognised the role of learning by doing and of work experience in generating more skilled workers (Rosen, 1972), it has not been seen as central to any drive to raise skills across the workforce. The emphasis in several recent case studies suggests, however, that work-based learning is an especially important channel for skill acquisition (Eraut et al., 1998). But to what extent do case studies give a fair picture of aggregate developments? In this paper we investigate the sources of several generic skills that are deployed within British workplaces. We utilise an innovative methodology for the measurement of work skills, based on applying some principles of job analysis, drawn from occupational psychology, in the context of a nationally representative survey. The chief advantages of our quantitative approach are that it enables the effects of education on skills to be set alongside those of work-based learning, and that it allows us to derive implications that will be applicable across the whole survey population. The disadvantage by comparison with in-depth case study research, is that quantitative survey measures may not adequately capture learning processes or specific learning situations. This paper, in line with much of the economics of education literature, is premised on the assertion that less than perfect quantitative measures can be far better than none. In the next section we overview theories of the sources of work skills and highlight some studies which emphasise work-based learning. We follow with descriptions of our estimating model (Section 3) and the measurement methodology

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estimating the determinants of supply

(Section 4). Section 5 presents our detailed findings, and Section 6 summarises and concludes.

2. Skill sources There are multiple sources for the skills people acquire and use at work, including educational institutions, work itself, the family and other external institutions. Here we are primarily concerned with the first two of these. Typically, education is charged with the function of generating basic skills across the population, and advanced cognitive skills amongst a portion of the population. In some countries, for example Sweden, the school system is also expected to generate vocational skills. Schools and colleges may also be expected to produce the right social skills for the modern workplace (Soskice, 1993; Bowles and Gintis, 1976). A normal assumption in theory and empirical work is that education’s impact on skills is subject to the law of diminishing marginal returns. Training is also assumed to raise skills, and both studies of subjective perceptions (Felstead et al., 1997) and incorporation of training measures in earnings functions (Blundell et al., 1996) support this assumption. The contribution of the workplace is indirectly recognised in that workforce experience is found to have positive but diminishing returns in earnings functions, while firm tenure is sometimes included to proxy firm-specific skills. Although the conventional earnings function thus allows for multiple sources of skill supply, most academic and policy discourse has emphasised education and other formal training as the main (measurable) source of workforce skills. Educational achievements and qualifications are used as the main indicators of skills in the workforce. This equation of qualifications with skill is inevitably reflected in the political debate concerning the development of each nation’s human resources, which tends to focus almost exclusively on education and training. It may, however, be questioned as to whether an increasing supply of formal qualifications can match the kinds of skills which, if case studies are to be believed, are increasingly required in modern industries. This point may be illustrated by reference to three, now classic, studies. One of the earliest is Hirschhorn’s (1984) study of operators in continuous process plants. He argued that the integrated nature of the production process required operators (not just managers) to develop ‘synthetic reasoning’, that is, the ability to determine the kind of problem one faces and the information that is relevant. This type of diagnostic skill depended on ‘an ability to frame problems, infer causes from symptoms, and check the resulting hypotheses against one’s analytic knowledge’ (ibid, p. 90). He also argued that in such organisations operators require ‘fringe awareness’, whereby workers remain aware of their environment and of any anomalous events within it, enabling them to take corrective action while still focusing on their main objective. Subsequent to the work of Hirschhorn (1984), and in line with the findings of MacDuffie (1995), other studies of new forms of organising production in the automobile industry have reported similar skills being required. In an international

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study of the automobile industry Thompson et al. (1995) report that the new forms of production utilised in commercial vehicle manufacture are creating demands for ‘new skills’: . . . it is possible to identify common shifts in the nature of skills use across national boundaries. . . . On the one hand this means the ‘system skills’, including organisational and technological knowledge and abilities, are required; on the other hand, workers have to be able to work in teams, with an emphasis on behavioural or ‘extra-functional’ skills. (ibid, p. 738).

Reports of such changes in skills have also been found in the service sector. For example Kelly (1989), in a study of US insurance companies, reported that where new technology was introduced in a manner which raised skills there was an increase in workers’ ‘contextual knowledge’. The latter comprised knowledge of the firm’s products, customers, processes, and procedures, together with the authority to make decisions and resolve problems. Contextual knowledge enabled workers to operate with a minimum of supervision and to integrate properly with other related tasks. Moreover, in addition to contextual knowledge, sales, clerical, and administrative jobs required mastery of other new skills not previously associated with such work. These were: social and communication skills required to meet and integrate the needs of customers, clients, marketing staff, and product designers; managerial skills related to planning, organising time effectively, thinking more comprehensively about the enterprise, and acting in a strategic manner; and general skills related to computer technology such as the ability to access larger networks, store and retrieve data, turn data into useful information, and use standard software packages. While this is by no means an exhaustive list of research, these studies do suggest that in some firms new organisational forms are creating a demand for new skills among broad sections of their workforce. The case studies relate to a variety of industries and countries. There is a good case, and some statistical evidence, that the new skills demanded are complementary to the introduction of information technology (Bresnahan, 1999). Collectively, they suggest three main areas in which, in addition to information technology skills, new skills are emerging, namely: the greater use of problem-solving skills and an ability to utilise them in the context of a wider knowledge of the organisation as a whole; teamworking skills, that is, the ability to operative collaboratively in pursuit of a common objective; and, thirdly, the ability to communicate effectively with colleagues and clients. Recognising the growing demand for such ‘key skills’, governments have been considering how the demand can be matched by changes in education. For example, the UK government is attempting to secure delivery of key skills within both the school and the university curriculum. It has introduced a separate Key Skills Qualification from September 2000, and has explicitly embedded the skills within other qualifications. Is it plausible, however, to expect such skills to be effectively transmitted through conventional educational or training institutions?

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A growing body of research from labour economists, sociologists, and management studies has recently provided an alternative perspective on the source of skills, stressing the indispensability of work-based learning. One of the first writers to theorise the importance of the workplace was Streeck (1989). Drawing on the experience of the dual system of youth training in Germany, Streeck argued that work-based learning was necessary to provide socialisation of workers and for the acquisition of important skills that were hard or impossible to codify (and hence teach in a classroom). Upon this premise, that education alone is not enough, Streeck argued the necessity for a regulated system of work-based learning to over-ride the market failures inherent in such a system. In parallel studies stemming from eastern production systems, Koike and Inoki (1990) explored the processes of skill formation in Japanese and other comparable organisations in Southeast Asia. In their analysis of the lifetime employment system, and in their comparisons with forms of on-the-job learning in other societies, Koike and Inoki highlight a number of features which they argue are responsible for the high levels of skill formation achieved by the Japanese. They argue that it is crucially important that workers are exposed to opportunities that enable them to acquire knowledge in breadth and in depth. By breadth, they mean that the worker has the opportunity to acquire knowledge about the organisation as a whole outside the confines of their immediate task. This is typically achieved by job rotation within the person’s own department or adjacent departments. By depth, they mean that workers acquire a thorough knowledge of the production process, the technology, and the finer details of how it operates in practice and over time. This may require a period of off-the-job reflection or study. Koike and Inoki also argue that it takes many years for a person to acquire all the requisite knowledge and the skills to utilise it. Hence the salience of a long-term employment system as the basis for a high level of skill formation. Also important is the system of support and reward for learning. Support comes in the form of help and feedback from colleagues and supervisors so that workers are told when mistakes are made and help can be given in correcting them before the person moves onto more complex tasks. If the process of learning and skill acquisition is to continue through time then such learning must be rewarded. Finally they highlight the importance of trust between superior and subordinate in making knowledge and information available to subordinates which they can use to learn. All these conditions they see as important in developing a high level of skill formation. Streeck’s and Koike and Inoki’s analyses fit into a theory of work-based learning that has also flowered in recent years. There is a tradition of theory which locates the learning process in a cultural context that stems from the initial work of Vygotsky. However, it was not until the work of Dreyfus and Dreyfus (1986) that we start to see a systematic attempt to develop a theory of workplace learning. Better known and more influential is the work of Lave and Wenger (1991) on the social practice theory of learning. Based on historical research into apprenticeships

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they introduced the concept of ‘situated learning’. Amongst other things, this focused attention on the ways in which skills and knowledge are acquired through active participation by the learner in communities of practice, that is in the process of interaction with others who have already mastered the trade. The learner internalises knowledge, which is ‘discovered’, ‘transmitted’ from others, or ‘experienced’ in interaction with others. To take just two recent examples, Stasz (1997) and Eraut et al. (1998) present evidence of such learning processes in the contemporary workplace. Engestro¨m (1987, 1993) has also made a significant contribution to our understanding of situated learning through the distinction between learning as ‘reproduction’, which merely reproduces existing skills, and learning as ‘expansion’, the learning required to cope with the challenges in changing work environments. The study of a sub-baccalaureate labour market in California by Stasz (1997) confirms, in particular, the central role of the workplace in acquiring the skills of teamworking, communication, and problem-solving. She found that such generic skills could only be learnt in the classroom, providing the classroom design reflects ‘authentic’ work situations. In practice, a large component of such skills are acquired in the workplace. Indeed, Stasz documents the different ways in which they are learnt which in all cases involved on-the-job learning. In some instances learning was supported by formal courses, in others by structured on-the-job training provided through the union, while in others it was by systematic informal training from dedicated workers with managerial support. Further evidence of the important role of work organisations in supplying new skills comes from the experience of German employers and the curriculum authority BIBB. Dybowski (1998) cites the examples of employers such as Carl Schenk AG, a machinery manufacturer and Mercedes Benz AG, both of whom were obliged to radically reorganise their approach to training on introducing higher levels of teamworking into their organisations. In the case of Carl Schenk, internal reorganisation involving the introduction of teamworking led to the subsequent reorganisation of training and the introduction of ‘learning and working islands’ through which to transmit the new skills. In the case of Mercedes Benz, systematic benchmarking revealed that the skills required for modern work processes (teamworking, problem solving, and communication) could not be acquired through simulated working and learning process in the classroom and training centre. They too introduced learning islands. Essentially, learning islands involve decentralising the learning away from the training centre to the point of production. This ensures that training is directly integrated in the production process but can still take place within a sheltered environment. Real production job orders are processed but sufficient time is allocated so that the team can, step by step, autonomously plan, execute and evaluate or improve its work. ‘The intention is to enable not only the individual but the team as a whole to autonomously organise its work processes and jointly reflect learning progress so that experience and skills can be acquired.’ (ibid, p. 129). The

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fact the curriculum authority (BIBB) are examining ways of integrating the use of learning islands into the curriculum, suggests that these are not just isolated examples, but represent part of a wider movement in Germany to integrate the learning of these new skills into the process of production. Finally, the recent evidence of a strong association between computer usage and earnings, after controlling for education and many other variable inputs, is suggestive that information technology skills are being acquired at work (Autor et al., 1998; Green, 1998). Even where panel data suggests that some of the return to computing is really a return to otherwise unobserved ability, there is evidence of a significant impact of computer usage on wages growth (Entorf and Kramarz, 1997). Nevertheless, these innovations are far from ubiquitous, and indeed we are frequently reminded that managers often have the discretion and the opportunity to shape aspects of skill formation through the ways in which relationships within the organisation are structured. Thus, following the work of, amongst others, Eraut et al. (1998), we know that the development of problem-solving skills requires access to knowledge about the production process and about the organisation itself. This access is better facilitated through management systems that share and communicate the knowledge. The same is true of other new skills. Many activities involve a strong component of tacit knowledge which is picked up in everyday practice and skills that are developed in the context of day-to-day relationships at work. There must be the opportunity to practice these skills, which means that if employees are to solve problems they must have the authority to make appropriate decisions. Thus they also become more involved in the decisionmaking processes. In addition, employees require support, in the form of effective feedback from both the human resource professionals and, crucially, line management in developing these skills. It is therefore only in what are variously called ‘new’ or ‘transformed’ or ‘flexible’ workplaces where employees have the opportunity to practice and build these skills that we are likely to see their emergence (Osterman, 1994; OECD, 1999). Finally, while our argument so far has been to re-emphasise the role of workbased learning, in addition to formal education and training, in acquiring generic skills, it should be noted that the different supply sources can be either substitutes or complements. Previous studies have posed a degree of complementarity between education and subsequent learning (e.g. Koike and Inoki, 1990). The ability to learn is indeed sometimes seen as an important skill in itself. This complementarity is often argued to explain the greater access of educated workers to training, found in most studies of the determinants of the determinants of training (e.g. Blundell et al., 1996). Since many formal training courses are similar to education courses there is a certain paradox here. It is assumed (on the basis of countless earnings functions) that education’s impact on skill exhibits diminishing returns—that is, later education has less effect than early education. One might therefore expect an individual with more education to get less out of a formal education-like training

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course than a less educated individual. So, the observed positive correlation between prior education and training access might be due to something else— unobserved individual heterogeneity or the different, non-education-like, nature of some training courses that make them complementary with prior education.1 Alternatively, the observed link between education and access to training could be due to segmentation in the labour market rather than educated workers being more able to add to their skills.

2.1 Research hypotheses Given the preceding discussion, one of our objectives will be to investigate the extent to which a variety of indicators of schooling and of work-based learning are associated with the deployment of new skills in the workplace. In addition, we encapsulate the arguments drawn from the economic, sociological and psychological literatures on skill acquisition in ten specific hypotheses, as follows: H1: More education is associated with greater skills. This is an assumption of human capital theory, and also of much educational theory, but also a prediction of screening theory. H2: There are diminishing marginal returns to education in the production of skills. Together with H1, H2 underpins the earnings function literature. H3: Those with more education are better able to improve their skills as a result of taking better advantage of work-based learning opportunities. The counter hypothesis, consistent with diminishing marginal returns to learning opportunities, is that less educated workers can acquire new skills faster than more educated workers when later exposed to work-based learning. H4: Greater time spent learning how to do a job leads to greater skills. H5: Prior training participation engenders greater skills. H6: Where training is firm-specific, the effect on skills of training from a previous employer would be less than the effect of training from the current employer. It is occasionally argued that training on the job is more likely to be firm-sponsored, hence more likely to be firm-specific in content, than formal off-the-job training. Hence, a possible hypothesis is that on-the-job training with the current employer has more effect on skills than on-the-job training from a previous employer. H7: Greater work experience entails more on-the-job learning, hence greater skills. H8: There are diminishing marginal returns to work experience. H7 and H8, like H1 and H2, are assumed and supported in the earnings function literature. H9: Longer job tenure with an employer generates greater firm-specific skills, but should have no independent effect on generic, transferable, skills. H10: Using ‘new’ work organisation practices (such as quality circles), including ‘new’ human resource practices (such as appraisal systems and consultation .......................................................................................................................................................................... 1 Royalty (1996) has maintained that the observed relationship between education and training access is explicable in terms of differential turnover between high and low educational categories.

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procedures), leads firms either to engender new skills in their existing workforce, or to recruit people with the new skills they require. We now proceed to test these hypotheses.

3. A simple empirical model of skills production We have data on several types of skills used in jobs at two points in time, 1992 ðt ¼ 1Þ and 1997 ðt ¼ 2Þ (see next section for our measurement methodology). We assume a production function framework in which each skill depends upon prior inputs2 Sit ¼ 1 EDit þ 2 WBLt þ "i þ uit f or t ¼ 1; 2 ð1Þ where Sit represents the skill of individual job i, EDit is a vector of education inputs, WBLit a vector of indices of prior inputs of work-based learning, "i a fixed level of skill acquired independently of education or work (either genetic ability or some other external source such as leisure activities), and uit is a random error term, with Eðui1 Þ ¼ Eðui2 Þ ¼ 0. For EDit , we utilise a quadratic function of years of schooling SCHit . It is convenient to partition WBL into several elements. To capture conventional prior inputs of human capital formation, we utilise a conventional quadratic function of years of work experience ðWEXPÞ, dummy indicators of prior off- and on-the-job training participation with current and previous employers (OFFTRCUR, ONTRCUR, OFFTRPREV and ONTRPREV), an index of prior learning time ðLÞ, and the length of job tenure with the current employer ðÞ. To investigate H3 we also include an interaction between schooling and work experience. We also include a vector, Z, of organisation characteristics argued to be associated with the flexible organisation, and hence with newly demanded generic skills. In addition, it is arguable that these characteristics would have a greater impact on skills the longer an individual is exposed to them. Hence we also interact the characterstics with job tenure. Our main estimating equations for skill levels are therefore of the form Sit ¼ a0 þ a1 SCHit þ a2 SCHit2 þ a3 SCHit :WEXPit þ a4 Lit þ a5 OFFTRCURit þ a6 OFFTRPREVit þ a7 ONTRCUR þ a8 ONTRPREV þ a9 WEXPit þ a10 WEXPit2 þ a11 it þ a12 Zit þ a13 it :Zit

t ¼ 1; 2

ð2Þ

Our various hypotheses imply expected signs a1 > 0; a2 < 0; a3 > 0; a4 > 0; a5 > 0; a6 > 0; a7 >; a8 > 0; a9 > 0; a10 < 0; a11 ¼ 0; a12 > 0 and a13 > 0 Since we do not have full enough information on RHS variables to estimate (3) for t ¼ 1 (see below), we shall first estimate (3) using OLS just for t ¼ 2. There are several common econometric issues involved here. First, our sample includes only .......................................................................................................................................................................... 2 This approach follows any number of studies in the education production function literature. It is our output measures that are new. See Hanushek (1979).

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those employed. We do not therefore capture the skills of those who do not manage (or choose not) to gain employment. We are estimating the impact of schooling on skills conditional on gaining employment, and hence do not include the effect of schooling on the probability of being employed. Moreover, a selectivity problem arises because the probability of being employed (hence in the sample population) may depend on unobserved characteristics that are correlated with skills. There is no possibility in our data to control for this selectivity. The problem is common to thousands of conventional earnings function estimations, and selection bias is typically assumed to be not unacceptably high in such cases. Nevertheless, this qualification should be remembered. Second, a common issue in wage equations is that job tenure ðÞ is endogenous and the same problem arises here: higher skilled individuals may have more (or less) long-lasting jobs, so tenure, rather than capturing work-based learning with the current employer, might instead be determined by the skill level. We therefore exclude job tenure from some of the estimations. The third issue is that, if the fixed effect "i is correlated with any of the RHS variables, there arises the possibility of heterogeneity bias. This problem can be partly addressed, though at a cost, by estimating in first differences, and thus eliminating the fixed effect. The variable to be explained becomes not the level of skills but the skills value-added. The cost is in the loss of certain variables that are too unreliably measured in period 1 for inclusion in the estimation. We shall assume, for the present, that the organisational characteristics vector is reliably measured and the same in period 1 as in period 2; we relax this assumption below, in discussing the results.3 Since periods 1 and 2 differ by five years, we have Si ¼ Si2 Si1 ¼ 5a3 SCHi þ 5a9 þ 10a10 WEXPi2 25a10 þ 5a11 þ 5a13 Zi which is simplified as Si ¼ b0 þ b1 SCHi þ b2 WEXPi2 þ b3 Zi

ð3Þ

Equation (3) says that skill formation amongst those in work is affected by an employee’s prior schooling (the complementarity argument as in H3), by the extent of previous work experience (the effect of diminishing returns to work experience as in H8) and by organisational characteristics that promote learning at work. Our hypotheses imply expected signs: b1 > 0; b2 < 0, and b3 > 0. As well as the forced omission of certain inputs, which if not orthogonal to included inputs, could bias estimates, an additional econometric problem arises when, in some estimations, we need to restrict the sample to those who remained in the same job for at least five years. This restriction creates a further potential for selectivity bias, if omitted variables correlated with skill change also affect job duration. There is neither an obvious basis for believing this bias to be positive or negative nor, in our data, a prospect of separately identifying an equation estimating the probability of staying in a job. Our first difference estimates must .......................................................................................................................................................................... 3 We omit the training variables for the present.

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therefore be interpreted with caution, even though we have no reason to expect the bias to be unacceptably large.

4. Data We utilise data drawn from a British survey, known as the Skills Survey, which we carried out in the months of January to May 1997. Its aim was to investigate the concept of skill, its components and the implications for pay. The survey consists of data on individuals and their jobs. This includes conventional measures of skill (such as qualifications held by individuals), and detailed information on what people actually do in their jobs. The questionnaire used draws on the lessons of several disciplines such as economics which traditionally considers skill to be the stock of human capital acquired (e.g. Becker, 1964; Stevens, 1994), sociology which focuses on the complexity of jobs and on the autonomy individuals enjoy at work (Braverman, 1974), and occupational psychology which examines what tasks people do in their jobs and how effectively they carry them out (Ash, 1988; Primoff and Fine, 1988). Full details of the methodology utilised, together with descriptive statistics, are to be found in Ashton et al. (1999). In this paper we draw on a sub-set of the questions asked, focusing on types of skills that come closest to policymakers’ and researchers’ concepts of several generic skills that are said to be increasingly needed in modern workplaces: computing, problem-solving, teamworking, and communication skills.

4.1 Skills measures The survey utilised aspects of job-analysis methodology, adapted from common commercially-used procedures, to measure the skills utilised in respondents’ jobs. A section of the questionnaire was prefaced by the following: ‘You will be asked about different activities which may or may not be part of your job. At this stage we are only interested in finding out what types of activities your job involves and how important these are’. Respondents were asked: ‘in your job, how important is [a particular job activity]’. Examples of the activities included caring for others, dealing with people, using a computer, analysing complex problems and planning the activities of others. The questionnaire focused on 36 activities designed to cover the tasks carried out in a wide range of jobs. The response scale ranged from ‘essential’ to ‘not at all important’, with ‘very important’, ‘fairly important’, and ‘not very important’ in between. The rationale behind this approach to skills measurement is given in full in Ashton et al. (1999). Briefly, the idea is that individuals know a great deal about what they actually do at work, and that they are likely to give a more reliable report of what their job entails than of what their personal skills are. We then make the working assumption that respondents’ job duties are an unbiased measure of their skills. Some individuals will have more work skills than are indicated by their job duties, while others will have less and in effect be none too competent in their jobs. The assumption is of course open to question. Under-use of skills might average

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out as greater or less than the insufficiency of skills. If so, a weaker assumption sufficient to support this method is that the measurement error is not correlated with our explanatory variables. Similarly, individuals might still misrepresent the importance of their jobs. If that is the case, we rely on a subsidiary assumption that the extent of that misrepresentation is not correlated with variables of interest in our study. We believe, in any case, that the extent of any bias is minimised through our procedures. Since these job activities and the measurement scale adopted were drawn from the Job Analysis (JA) literature, we denote these questions as such in the subsequent discussion. For short-hand we shall refer to indices derived from these questions as skills, but it should be remembered that strictly they are indicators of what skills people exercise rather than of what they possess. Out of the 36 JA questions, those relating to computing, problem-solving, communication and social interaction, and teamworking are the focus of attention in this paper. Problem-Solving Skills are captured by four JA questions about the importance of spotting problems or faults, working out the cause of them, devising solutions to them, and analysing complex problems. For communication and social skills, we utilised two batches of questions. The first, concerning what we shall call Professional Communication Skills, comprises instructing, training, or teaching people, making speeches or presentations, and persuading or influencing others. The second, concerning what we shall call Social Skills, comprises ‘dealing with people’, ‘selling a product or service’, and ‘counselling, advising or caring for customers or clients’. Teamworking Skills are captured by two JA questions concerning ‘working with a team of people’ and ‘listening carefully to colleagues’ plus a question from a different section of the questionnaire as to the extent to which their employer has organised their work on the basis of teams. These various skill groups were chosen in part on the basis of their conformity with theories of skills demand developed in the case studies, but also on the basis of our earlier work involving all 36 JA questions (Ashton et al., 1999). There, we carried out a principal components analysis, which grouped the variables into eight components, four of which corresponded to the above categories. In the analysis here, for each of the four groups of questions we reduce the multiple measures to a single index of skill, using a new principal components analysis for each group. In every case, only one component had an eigenvalue of greater than one. The score for this component is calculated to give the index of skill for that group of variables. Computing Skills are captured by combining two questions. The first was a JA question concerning the importance of ‘computers or computerised equipment’ in the job. The second followed up those who were using computers, by asking about the level of sophistication of use. Four levels of sophistication were identified and specific examples were presented to respondents to aid in deciding where their use of computers should be categorised. The result is a five point index of computer skills as follows: 0 (zero use), 1 (straightforward, e.g. using a computer to print out invoices in a shop), 2 (moderate, e.g. wordprocessing, spreadsheets, e-mail), 3

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Table 1 Skill indices by occupational group Problemsolving Managers Professionals Associate professionals & technicians Clerical & secretarial Craft & related Personal & protective service Sales occupations Plant & machine operatives Other occupations

Teamworking

Professional communication

Social

Computing

0.66 0.82 0.65

0.19 0.36 0.34

0.83 10.33 0.67

0.68 0.39 0.33

1.70 2.11 1.88

70.13 0.52 70.52 70.95 70.51 71.64

0.12 70.16 0.21 70.31 70.30 70.81

70.42 70.35 70.14 70.41 70.79 71.20

70.10 70.59 0.32 0.99 71.07 70.89

1.88 0.87 0.63 0.90 0.65 0.18

Note: See text for skill definitions. With the exception of computing skills, each skill index is standardised to have an average of zero across the whole sample.

(complex, e.g. computer aided design, statistical analysis packages), and 4 (advanced—e.g. using syntax for programming). The average scores on the skill indices are shown in Table 1, for each of the major occupational groups. The table shows a substantial degree of content validity to our methodology, in that occupations normally classified as having high skills when defined broadly (for example in terms of qualifications) also tend to score highly on our indices of skills. Other analyses showing positive returns to several skills indices similarly measured also lend support to the view that the methodology is capturing objective labour market processes (Green, 1998). Respondents were also asked about several activities involved in the jobs they had been doing five years previously, including identical questions concerning the skills under consideration here. To capture the change over the previous five years, for computing skills we subtracted the previous score (calculated just as above) from the present score. For all other skills, we subtracted the previous score on the importance scale from the present score. To simplify presentation, and because there was no difference in the pattern for the different types of skill, we present only results for an aggregate measure of skill change, being simply the total change across 12 activities. Finally, an additional measure of skill change, available in the data, is the perceived change in total skill. Respondents who had also been in work five years previously were asked whether there had been ‘a significant increase, a significant decrease, or no change, in the level of skill you use in your job?’, and responses were coded as 0 (skills decrease), 1 (no change), and 2 (skills increase).

4.2 Other variables Training participation was measured according to whether respondents had received past off-the-job and/or on-the-job training for their current type of

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work, classified according as to whether the training was received mainly during their current or a previous job. We measured learning time from a question which asked respondents how long it took them after starting their type of job to ‘learn to do it well’; we inputted dummy variables for learning times of ‘more than two years’, and ‘less than one month’. Other control variables were calculated in conventional ways. As noted above, there are reasons to expect organisations to differ in the extent to which they encourage and require the deployment of generic skills. Although many management studies discuss workplace transformations, there remains little agreement as to how these should be measured, and or as to how to indicate their skill implications. Our data set differs from those used by many other researchers. This has advantages and disadvantages worthy of note. On the plus side, we are able to get a more accurate measure of whether certain work practices are felt by individual employees on the shopfloor rather than relying on management’s estimates of whom they affect (cf. Osterman, 1995). Another advantage is that we hear directly from employees themselves about the skills their jobs demand rather than relying on possibly distant and misinformed managerial views of what particular jobs entail (cf. Burchell et al., 1994). However, while our unit of observation may reduce measurement error in these ways, there are disadvantages with which we also have to contend. Pitching our questions at individual employees inevitably limits the organisational information we are usefully able to collect. For example, it is normally inappropriate to ask employees about strategic management policies. Respondents were asked a total of six questions which, in combination and singularly, provide pointers as to the style of work organisation adopted. These include whether: respondents belong to a Quality Circle (QC); their organisation is committed or recognised as an Investor in People (IIP);4 there is a formal appraisal system at their workplace (APPRAISE); management organises meetings to inform the workforce of organisational developments (INFORM); management holds meetings where workers can express their views and opinions (CONSULT); and respondents have made more than one suggestion to improve work performance over the last 12 months (SUGGEST). We held that each of these characteristics capture, if imperfectly, an aspect of how the organisation is configured to encourage the sharing of knowledge and the development and utilisation of generic skills in the workplace. The sum of the number of characteristics present in the workplace (ORG_CH) provides an approximate summary measure of the extent to which it is a flexible organisation associated with higher levels of generic skills.5 The individual elements may, instead, be entered individually as separable skills determinants. .......................................................................................................................................................................... 4 Investors In People is a UK government kite mark that recognises approved human resource practices. 5 As a shorthand, we refer to organisations higher up the ORG_CH index as more ‘flexible’ organisations, in contrast to traditional organisations run on Tayloristic lines which have few or none of these characteristics. However, we do not necessarily imply that all these characteristics are on the increase or that Tayloristic organisations are necessarily being replaced.

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5. Findings 5.1 Determinants of skill levels Tables 2a to 2e give the results of the estimation of the determinants of skill level for each of our five types of new skill. In each table, column (1) shows the simple impact of education on skills, without controlling for any other factors. Column (2) introduces most variables from the levels equation (3), including the summary measure of organisational characteristics, ORG_CH. Column (3) introduces job tenure and its interaction with work organisation. Finally column (4) enters each component of ORG_CH separately. The results from column (1) show that, for every skill type, education raises skill, but is subject to diminishing returns, consistent with hypotheses H1 and H2. In all cases only a very small proportion of the variance of new skills is explained by education alone. After controlling for other inputs, however, the impact of education is in all cases reduced. It retains a quadratic effect, but is significant only in the cases of Social Skills and Computing Skills. A striking finding is that in respect of none of the estimations of skill levels is H3 supported. While the coefficient a^3 on the interaction between schooling and work experience is positive in most cases, it is always insignificant. Thus, there is no support for the proposition that better educated people acquire more skills simply through work experience, once one controls for training and other inputs.6 We return to this issue below. Hypothesis H4, however, is confirmed in respect of every type of skill: they are each positively related to the amount of prior learning time. In every case, those with low learning time had less skills than those with middling learning time (between one month and two years). Those with high learning time (more than two years) had also acquired significantly more problem-solving and professional communication skills than those with middling learning time. It could be argued that high learning time is itself an indication that high skill levels are being used in the job, and hence that in showing links between learning time and particular kinds of skills all we are doing is to confirm that each kind of skill is positively correlated with the aggregate. Nevertheless, longer learning time could in principle be needed to acquire certain kinds of skills but not others. Our analysis confirms that all the skills examined here are indeed linked to greater learning time.7 Hypothesis H5 is also strongly confirmed. Off-the-job training raises all types of skill except teamworking. On-the-job training is a source of both teamworking and problem-solving skills. There is no tendency for on-the-job training with the current employer to have a greater impact than on-the-job training with a previous .......................................................................................................................................................................... 6 In other estimations not shown here, there is also no evidence of education positively interacting with training: the lower educated worker benefits at least as much as the more educated from training. 7 As a check on the robustness of our findings, we excluded the learning time variables from the estimations. This did not alter the pattern of the other results.

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Table 2a Determinants of work–based skills: problem–solving

Education SCHOOLING SCHOOLING2

(1)

(2)

(3)

(4)

0.39 (0.11){ 70.0090 (0.0038){

0.09 (0.11) 70.0007 (0.0036)

0.05 (0.12) 0.0000 (0.0040)

0.00 (0.12) 0.0015 (0.0038)

Work-based learning SCHOOLING*WEXP High learning time

0.23 (0.07){ 70.59 (0.10){ 0.06 (0.09) 0.25 (0.11){ 0.10 (0.08) 0.19 (0.11)* 0.024 (0.012){ 70.00049 (0.00028)*

Low learning time Off-job training with current employer Off-job training with previous employer On-job training with current employer On-job training with previous employer Work experience (WEXP) WEXP2 Job tenure Organisation ORG_CH

0.25 (0.02){

ORG_CH*tenure

0.0009 (0.0010) 0.22 (0.07){ 70.59 (0.10){ 0.06 (0.09) 0.25 (0.11){ 0.10 (0.08) 0.20 (0.11)* 0.009 (0.025) 70.00040 (0.00031) 0.00098 (0.00094) 0.27 (0.03){ 70.00024 (0.00020)

QC INFORM CONSULT SUGGEST APPRAISE IIP CONTROLS R2

No 0.143

Yes 0.242

0.0012 (0.0013) 0.21 (0.07){ 70.55 (0.10){ 0.05 (0.08) 0.20 (0.11)* 0.11 (0.08) 0.22 (0.11)* 0.004 (0.024) 70.0004 (0.0003)

Yes 0.243

0.19 (0.07){ 0.41 (0.11){ 0.11 (0.10) 0.60 (0.09){ 0.16 (0.08){ 0.15 (0.07){ Yes 0.252

Estimates by OLS. Control variables were: gender, part-time status, establishment size, gender segregation of job type, private/public sector, union membership. All equations have sample size 2123 and include a constant term. Standard errors in parentheses, corrected for heteroscedasticity using White’s method; significance levels: * ¼ 90%, { ¼ 95%.

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Table 2b Determinants of work-based skills: teamworking


(1)

(2)

(3)

(4)

0.46 (0.11){ 70.0146 (0.0038){

0.06 (0.09) 70.0030 (0.0032)

0.03 (0.10) 70.0023 (0.0033)

0.03 (0.10) 70.0023 (0.0033)

0.09 (0.06) 70.22 (0.08){ 70.04 (0.06) 70.02 (0.09) 0.17 (0.06){ 0.16 (0.09)* 70.004 (0.010) 70.0001 (0.0002)

0.0010 (0.0011) 0.10 (0.06) 70.23 (0.08){ 70.04 (0.06) 70.03 (0.08) 0.18 (0.06){ 0.15 (0.09)* 70.017 (0.021) 70.0001 (0.0002) 70.00 (0.00)

0.0010 (0.0011) 0.08 (0.06) 70.22 (0.08){ 70.04 (0.06) 70.02 (0.09) 0.17 (0.06){ 0.17 (0.09)* 70.016 (0.021) 70.0001 (0.0002)

Work-based learning SCHOOLING*WEXP High learning time Low learning time Off-job training with current employer Off-job training with previous employer On-job training with current employer On-job training with previous employer Work experience (WEXP) WEXP2 Job tenure Organisation ORG_CH

0.28 (0.02){

ORG_CH*tenure

0.29 (0.02){ 0.0000 (0.0001)


No 0.017

Yes 0.212

Yes 0.213

0.28 (0.05){ 0.43 (0.09){ 0.23 (0.08){ 0.27 (0.06){ 0.31 (0.06){ 0.18 (0.06){ Yes 0.214


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Table 2c Determinants of work-based skills: professional communication


(1)

(2)

(3)

(4)

0.49 (0.10){ 70.0113 (0.0035){

0.14 (0.09) 70.0015 (0.0030)

0.09 (0.10) 70.0004 (0.0032)

0.04 (0.10) 0.0010 (0.0031)

0.39 (0.06){ 70.33 (0.07){ 0.28 (0.07){ 0.39 (0.09){ 0.02 (0.07) 0.03 (0.09) 0.017 (0.009)* 70.0003 (0.0002)

0.0013 (0.0013) 0.38 (0.06){ 70.34 (0.07){ 0.28 (0.07){ 0.39 (0.09){ 0.02 (0.07) 0.04 (0.09) 70.004 (0.02) 70.0002 (0.0002) 70.0001 (0.0006)

0.0017 (0.0012) 0.37 (0.06){ 70.28 (0.07){ 0.27 (0.07){ 0.35 (0.09){ 0.04 (0.07) 0.05 (0.09) 70.011 (0.022) 70.0001 (0.0002)


0.25 (0.02){

ORG_CH*tenure

0.25 (0.02){ 0.0001 (0.0001)


No 0.107

Yes 0.345

Yes 0.346

0.19 (0.06){ 0.31 (0.08){ 0.21 (0.08){ 0.58 (0.06){ 0.31 (0.06){ 0.01 (0.06) Yes 0.360


424


Table 2d Determinants of work-based skills: social


(1)

(2)

(3)

(4)

0.57 (0.11){ 70.0172 (0.0038){

0.27 (0.10){ 70.0082 (0.0035){

0.26 (0.11){ 70.0081 (0.004){

0.19 (0.11) 70.0060 (0.0038)

0.08 (0.07) 70.32 (0.08){ 0.29 (0.07){ 0.24 (0.10){ 70.012 (0.07) 70.01 (0.10) 0.006 (0.011) 70.0001 (0.0002)

0.0003 (0.0012) 0.09 (0.07) 70.34 (0.08){ 0.29 (0.07){ 0.22 (0.10){ 70.01 (0.07) 70.02 (0.10) 0.005 (0.023) 70.0001 (0.0003) 70.001 (0.001)

0.0006 (0.0012) 0.07 (0.07) 70.26 (0.08){ 0.27 (0.07){ 0.20 (0.10){ 0.00 (0.07) 0.00 (0.10) 70.003 (0.023) 70.0001 (0.0002)


0.18 (0.02){

ORG_CH*tenure

0.17 (0.03){ 0.0001 (0.0001)


No 0.031

Yes 0.160

Yes 0.162

70.05 (0.06) 0.20 (0.08){ 0.21 (0.08){ 0.54 (0.07){ 0.11 (0.07) 0.16 (0.06){ Yes 0.177


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Table 2e Determinants of work-based skills: computing skills


(1)

(2)

(3)

0.61 (0.07){ 70.0161 (0.0025){

0.41 (0.09){ 70.0101 (0.0031){

0.45 (0.10){ 70.0111 (0.0032){

0.42 (0.10){ 70.0101 (0.0033){

70.02 (0.06) 70.50 (0.07){ 0.14 (0.06){ 0.21 (0.09){ 0.09 (0.06) 0.01 (0.09) 0.017 (0.009)* 70.0003 (0.0002)

70.0011 (0.0011) 70.03 (0.06) 70.48 (0.07){ 0.13 (0.06){ 0.23 (0.09){ 0.08 (0.06) 0.02 (0.09) 0.03 (0.02) 70.0004 (0.0002)* 0.0009 (0.0007)

70.0007 (0.0011) 70.00 (0.06) 70.48 (0.07){ 0.14 (0.06){ 0.20 (0.09){ 0.07 (0.06) 0.00 (0.09) 0.029 (0.020) 70.0004 (0.0002)*


0.19 (0.02){

ORG_CH*Tenure

0.18 (0.02){ 0.0000 (0.0001)

QC INFORM CONSULT SUGGEST APPRAISE IIP CONTROLS Pseudo–R2

No 0.049

Yes 0.126

(4)

Yes 0.128

0.11 (0.06){ 0.27 (0.08){ 70.07 (0.07) 0.28 (0.06){ 0.46 (0.06){ 0.13 (0.05){ Yes 0.132

Estimates by Ordinal Probit. Control variables were: gender, part-time status, establishment size, gender segregation of job type, private/public sector, union membership. All equations have sample size 2090 and include a constant term. Standard errors in parentheses, corrected for heteroscedasticity using White’s method; significance levels: * ¼ 90%, { ¼ 95%.

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employer, thus rejecting H6. However, it is worth noting that we do not have separate measures of training intensity with current and previous employers. If previous employers had given more training per participant than the current one, we could be falsely rejecting H6. Hypothesis H7 is confirmed, in that work experience is positively associated with problem-solving, professional communication and computing skills. Only in the case of problem-solving skills are there diminishing returns (H8). For teamworking and social skills, however, greater work experience per se has no significant impact. In all cases, job tenure had no separate impact on work skills. It does not appear that any of the skills here measured are enhanced through longer experience of a particular employer. This result is consistent with H9 as long as all the skill types are interpreted as transferable. The estimations in columns (3) and (4) give strong support to the view that new or flexible work organisation characteristics are associated with all the skills indices. In columns (3), the summary indicator ORG_CH is strongly and significantly positively associated with skills. The separate associations of each characteristic with each skill are shown in columns (4). Remarkably, each characteristic has a positive association with most or all new skills. This association by no means establishes that the skills are directly taught by firms with these organisational characteristics. The skills might be called forth in a variety of ways through the implementation of these characteristics. It is noteworth that the estimations in columns (3) show no significant interaction between the characteristics and job tenure. One possibility is that the causal link is between organisational characteristics and the recruitment of skilled workers. Once recruited, the policies do not further change skills, hence explaining why longer-serving employees have no more benefit from the organisational characteristics than short-tenure employees. An alternative possibility is that the characteristics, once introduced in the firm, have a quick but limited impact on skills, so that longer exposure adds no further benefit (i.e. rapidly diminishing returns). We return to this issue shortly.

5.2 Determinants of skill changes Table 3 shows findings from estimating the skills production functions in first difference form, based on eq. (4). We present results separately for the sources of change in computing skills and for the change in all other skills.8 We also look at the perceived change in total skill. In all three cases we examine skills change for all respondents who were in work five years previously (85% of the sample) and who were therefore able to answer the retrospective questions (columns (1), (3), and (5)). We then restrict the sample further to those who stayed in the same job over the five years (columns (2), (4), and (6)). The advantage of the latter restriction is .......................................................................................................................................................................... 8 The justification for this distinction is the similar behaviour of the other skills determinants, while the determinants of computing skills change has a distinctive pattern.

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Table 3 Sources of skill change, 1992–1997 Change in Change in Perceived change computing skills other new skills{ in total skill ....................................... ....................................... ....................................... (1) (2) (3) (4) (5) (6) all in same job all in same job all in same job SCHOOLING Work experience

0.021 (0.010){ 70.004 (0.003)

ORG_CH Off-job training with current employer On-job training with current employer CONTROLS R2/pseudo–R2 n

70.016 (0.016) 70.002 (0.004) 0.11 (0.02){ 0.07 (0.09)

70.27 (0.08){ 70.178 (0.020){

0.16 (0.09)* Yes 0.001 2019

Yes 0.029 1042

70.33 (0.09){ 70.16 (0.02){ 0.62 (0.13){ 70.13 (0.53)

0.016 (0.011) 70.013 (0.003){

1.07 (0.52){ Yes 0.069 2070

Yes 0.107 1070

70.042 (0.017){ 70.017 (0.004){ 0.15 (0.02){ 0.19 (0.10){ 0.22 (0.10){

Yes 0.025 2073

Yes 0.077 1074

{ Total of problem-solving, professional communication, social and teamworking skills. Estimates by OLS for columns (1) to (4), and by ordinal probit for columns (5) and (6). Control variables were: whether part-time five years ago, whether part-time now and gender; plus, where the regression was restricted to those remaining in the same job, establishment size, segregation of job type, private/public sector, and union membership. Standard errors in parentheses, corrected for heteroscedasticity using White’s method; significance levels: * ¼ 90%, { ¼ 95%.

that we can estimate the impact of the organisational characteristics in that job on skills change. The disadvantage is that it further reduces the sample size, and introduces the potential for selectivity bias. In respect of computing skills, H3 is confirmed. Thus, other things equal, the more educated worker gains computer skills faster than the less educated worker. However, this finding is sensitive to the sample chosen. For those in the ‘stayer sample’, education has an insignificant impact on the acquisition of computing skills. These contrasting findings suggest that education’s impact on the ability to acquire skills is mediated by its impact on job mobility. That is, if education raises the ability to acquire computing skills over time, people raise their utilisation of computing skills by moving jobs.9 The schooling effect is also positive for perceived total skills change, though the impact just fails to reach statistical significance. However, for the stayer sample, education lowers the perceived total skills rise, as also for the ‘other new skills’ measure. For these other skills, education, far from raising the ability to learn them, .......................................................................................................................................................................... 9 The same pattern of findings is obtained if we redefine the dependent variable to be the change in the importance of computer usage at work. Results are available on request.

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appears to be a hindrance. A possible interpretation is that the less educated worker can substitute work-based learning for schooling and converge to a similar level of skill to that of a more educated worker. Taken together with the lack of support for H3 in the levels equation, the evidence on H3 is decidedly mixed. The hypothesis of diminishing returns to work experience, H8, is confirmed with respect to the change in other new skills, and in perceived total skills. In other words, younger workers with less work experience are likely to acquire more work skills over a five year span than older workers. The impact of greater work experience on the change in computing skills is also negative, though the coefficient is not quite significant. Supporting H10, flexible organisational characteristics are positively associated with skills acquisition in respect of all types of skills. There are at least two ways to interpret this finding. One possibility, consistent with the findings from the levels equation, is that at least some of the characteristics were introduced within the previous five years, and that these had a rapid positive impact on job skills. Note, however, that the skill change regression findings cannot be explained in terms simply of a putative impact of organisational characteristics on recruitment, since the stayer sample are by definition not recent recruits. An alternative interpretation of the finding from the skills change regressions might arise if it is supposed that the characteristics have been in situ for all of the previous five years. With that assumption, the presence of flexible organisation characteristics enables job skills to be expanded over time. However, this interpretation would be in conflict with the finding from the levels equations (above), where no interaction between organisational characteristics and job tenure was found. The contrast in findings could be a consequence of the first differences estimation method here which eliminates fixed effects. Finally, the reported results also included the measures of participation in training with the current employer. Although we have no information as to when the training took place, we assumed there was a positive probability that it had taken place during the previous five years.10 We find that on-the-job training is significantly positively related to the acquisition of skills, as expected. However, off-the-job training only registers a significant positive impact on the ‘perceived skills change’ variable.

5.3 The importance of education and work-based learning In addition to evaluating particular hypotheses about inputs in the production of skills, it will be useful also to have some measure of the overall importance of education and of work-based learning in the generation of skills. There is only a limited sense in which we can do this because of the interactions between the various inputs. Nevertheless, one measure of the importance of a set of inputs is .......................................................................................................................................................................... 10 The findings on the other variables were not sensitive to the exclusion or inclusion of the training variables.

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Table 4 Proportions of skill variance associated with skill sources, by skill type

Education Organisational characteristics Other work-based learning indicators

Problemsolving

Teamworking

Professional communication

Social

Computing

4.4 13.7

1.7 19.0

10.7 21.6

3.1 7.8

4.9 5.8

14.3

8.2

20.0

6.0

5.5

R2 values for inclusion of each type of skill source when entered without the other sources.

the proportion of total variance explained by that set of inputs. We show these in the matrix in Table 4. Each element is the R2 value obtained by inputting just one set of inputs. It is notable that, for every skill, the education variables account for a smaller proportion of the variance than either the organisational characteristics or the other work-based learning variables. In most cases, the single summary measure of the organisational characteristics (ORG_CH) accounts on its own for the largest proportion of skills variance. Given the crudeness of the measure itself, which only approximately captures the character of a firm’s organisational and human resource policies, the power of the index is remarkable.

6. Conclusion This paper is the first quantitative study of which we are aware that relates inputs from multiple sources of skill supply directly to the stocks of particular acquired skills being utilised in the workplace. The method is enabled by the adaptation of a new methodology for the measurement of a range of skills in a social survey context, derived from job analysis principles. The study has a parallel in current research that is linking educational inputs to skills measured through the International Adult Literacy Survey project (OECD, 1995, 1997). While that project has the benefit of an objective measure of certain numeracy and literacy skills, the advantage of the approach adopted here is that we are able to look at a much broader range of skills, and can consider a rich set of variables capturing inputs into the skill formation process.11 A disadvantage of our approach is that we are measuring the skills that workers report that they used, rather than what they possess. Our results could be biased if the degree to which people’s skills are either deficient or under-utilised is correlated with our explanatory variables. Our broad findings are consistent with expectations, in that both education and work-based learning indicators are positively and significantly related to the stock of utilised skills. Schooling has the expected effect, exhibiting positive and dimin.......................................................................................................................................................................... 11 A further practical advantage is the lower survey costs, compared to those necessary to carry out objective tests and at the same time secure adequate response rates.

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ishing returns. Off-the-job training is productive of most types of skill, while onthe-job training is effective for the generation of problem-solving and team-working skills. Both types of training appear to be transferable from previous employers, a result that is consistent with the perceptions of nine out of ten employees receiving training in Britain (Felstead et al., 1997). Work experience and, in particular, learning time are also positively associated with most skill types. We did not, however, find consistent support for the proposition that more educated workers acquire extra skills faster than less educated workers. Indeed, with the exception of computing skills, there is some indication that less educated workers were making up for their lower education through more work-based learning. This finding suggests a need for further research into the factors underlying the observed complementarity between education achievement and training access. Our most remarkable finding, however, is the strong association between the presence of some ‘new’ or ‘flexible’ organisation characteristics and both the level and growth of all types of skills. An appealing interpretation of this finding is that the adoption of these characteristics is associated with a workplace transformation that entails the deployment of new skills. However, our data do not directly measure such transformations, and our findings do not allow us to distinguish between whether the organisations help to generate the needed skills (and if so how rapidly) or whether they merely utilise latent, hitherto unutilised skills, in the workforce. Nevertheless, the fact that the characteristics are linked to skills growth amongst workers that had stayed for at least five years in the job, suggests that the association is not simply a result of flexible workplaces recruiting more highly skilled workers. Further research to clarify these processes in a dynamic setting will be useful. Two important qualifications may be noted. First, although we have framed our study in a production function model, this approach by no means rules out that a screening process is underpinning some of the findings. Just as more able persons may be those who did more education, so they may have been more likely to receive training and to have been selected for employment in ‘flexible’ workplace organisations. Second, we have in this study examined only those ‘core’ skills that are the focus of sociological research and policy-makers concerns. Many of these skills would be regarded as ‘soft’ skills, and arguably might be expected to be acquired outside the context of formal education. The fact that education is found to play a somewhat small role in the production of some of these skills should therefore not be used to diminish the wider role of education. The implication is that schools, colleges, and universities may do better concentrating on the transmission of academic and technical skills with a high element of propositional and specialist knowledge. An implication for policy is that incentives and encouragement need to be given to employers to pay more attention to providing the opportunities for employees to develop these crucial skills in the workplace. Only a half of employers in our survey had more than three out of the six characteristics that were associated with

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high generic skills. Production in many sectors of the economy is still largely organised on Tayloristic lines. The majority of employees do not have the opportunity to develop key skills at work even if they wanted to. At the same time, our results suggest important limitations to the idea that individuals can be made largely responsible for the development of their own skills. Without the opportunity provided by a suitable workplace to acquire problem solving, teamworking, and to a lesser extent communication skills, education and formal training away from the workplace may not be an adequate substitute even for highly-motivated learners. It would therefore make good sense for public policy measures to play a more proactive role in encouraging employers to adopt practices that actively foster the development and utilisation of greater skills.

Acknowledgement We gratefully acknowledge funding from the UK’s Economic and Social Research Council. This paper is a revised version of a report prepared for the Skills Unit of the UK Department for Education and Employment. The research is part of a larger project entitled ‘Learning, Skills and Economic Rewards’, which in turn is part of the programme of research into the ‘Learning Society’. We thank Bryn Davies for his invaluable help at the design stage of the questionnaire.

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