EMPLOYEE INVOLVEMENT, TECHNOLOGY AND EVOLUTION IN JOB SKILLS: A TASK-BASED ANALYSIS. FRANCIS GREEN

Transcription

1 1 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), 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 problemsolving 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.

2 2 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 nonroutine 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

3 3 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 3

4 4 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. 4

5 5 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 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 5

6 6 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 nonroutine 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 6

7 7 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 7

8 8 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 8

9 9 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 9

10 10 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). 10

11 11 example, the task of calculating is quite general and may easily be incorporated in nonroutine 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 11

12 12 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. 12

13 13 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 "nonroutine" (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. 13

14 14 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 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 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 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. 14

15 15 {{1}} Findings {{2}} The Changing Use of Generic Skills, 1997, 2001, and 2006 Altogether these data have two distinct advantages, compared with DOT- and ONETbased 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 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 higherlevel 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

16 16 {{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 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 ; 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 than over the period. 10 {{FIGURE 2 HERE}} {{TABLE 2 HERE}} 10 It is possible that the recall method might have inflated change in the earlier period. 16

17 17 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, withinindustry 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 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 11 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: S 0.5( S S )( E E ) 0.5( E E )( S S ), where S is the j j06 j97 j06 j97 j j06 j97 j06 j97 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. 17

18 18 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 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 , the continuation of a downward movement since at least The decline levelled off between 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 18

19 19 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 I concentrate henceforth on the Repetitive Physical Tasks index in order to highlight possible substitution by technology. 19

20 20 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. 20

21 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 rise by in the use of Influence Skills is associated with a rise in hourly pay of approximately 3.9% (0.229 x log points), while the rise by 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 21

22 22 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) S ijt E ijt D ijt L C L ijt H C H ijt t j OCC u j j ijt ijt 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 OCC j comprise 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 H L 0 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, 22

23 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 u ijt, 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 Aggregating over individuals within occupations, 23 L H H (2) S L jt E jt Djt C jt C jt t j u jt jt. 23

24 This procedure is similar to that followed by Spitz-Oener (2006), who used a firstdifference estimator. 16 Here, I prefer the fixed-effects estimator to provide more efficient estimates. Pseudo-panel fixed-effect estimation assumes that the 24 u jt 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 R 2 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. 24

25 25 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. 25

26 26 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 26

27 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 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

28 28 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 28

29 29 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. 29

30 30 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 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. 30

31 31 {{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 period. The communication and influencing activities covered in these scales can be regarded reasonably safely as utilizing mainly nonroutine skills. Computing activities complement these skill domains, even within highlydisaggregated 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 31

32 32 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 32

33 33 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. 33

34 34 References. Acemoglu, Daron and David Autor Skills, tasks and technologies: Implications for employment and earnings. In Orley Ashenfelter and David E. Card (Eds.), Handbook of Labor Economics, Vol. 4, Amsterdam and North Holland: Elsevier. Ashton, David and Johnny Sung Supporting Workplace Learning for High Performance Working. Geneva: International Labour Office. Abramovsky, Laura and Rachel Griffith ICT, Corporate Restructuring and Productivity, Institute for Fiscal Studies, mimeo. Appelbaum, Eileen, Thomas Bailey, Peter Berg and Arne L. Kalleberg Manufacturing Advantage: Why High-Performance Work Systems Pay Off. Ithaca and London: Cornell University Press. Ashton, David, Bryn Davies, Alan Felstead and Francis Green Work Skills In Britain. Oxford: Oxford and Warwick Universities. Autor, David H., Frank Levy and Richard J. Murnane. 2003a. Computer-Based Technological Change and Skill. In Eileen Appelbaum, Annette Bernhardt and Richard J. Murnane (Eds.) Low-Wage America. New York: Russell Sage Foundation. Autor, David H., Frank Levy and Richard J. Murnane. 2003b. The Skill Content of Recent Technological Change: An Empirical Exploration. Quarterly Journal of Economics 118 (4): Autor, David H., Lawrence F. Katz and Melissa S. Kearney The polarization of the US labor market. American Economic Review 96 (2): Autor, David and David Dorn Inequality and Specialization: The Growth of Low-Skill Service Jobs and the polarization of the U.S. Labor Market, MIT working paper. Autor, David H. and Michael Handel Putting Tasks to the Test: Human Capital, Job Tasks and Wages,.NBER Working Paper No , June. 34

35 35 Ballantine, J. W. and Ronald F. Ferguson Plastic Manufacturers: How Competitive Strategies and Technology Decision Transformed Jobs and Increased Pay Disparity among Rank-and-File Workers, In Eileen Appelbaum, Annette Bernhardt and Richard J. Murnane (Eds.) Low-Wage America. New York: Russell Sage Foundation. Bartel, Ann P.; Casey Ichniowski and Kathryn Shaw "New Technology" And Its Impact on the Jobs of High School Educated Workers: A Look Deep inside Three Manufacturing Industries. In Eileen Appelbaum, Annette Bernhardt and Richard J. Murnane (Eds.) Low-Wage America. New York: Russell Sage Foundation. Berman, Eli; John Bound and Zvi Griliches Changes in the Demand for Skilled Labor within Us Manufacturing: Evidence from the Annual Survey of Manufactures. The Quarterly Journal of Economics, CIX(2, May): Bishop, Dan, Felstead, Alan, Fuller, Alison, Jewson, Nick, Kakavelakis, Konstantinos and Unwin, Lorna Constructing learning: adversarial and collaborative working in the British construction industry, Learning as Work Research Paper No 13, January, Cardiff: Cardiff School of Social Sciences, Cardiff University. Black, Sandra E. and Lisa M. Lynch How to Compete: The Impact of Workplace Practices and Information Technology on Productivity. Review of Economics and Statistics, 83(3): Black, Sandra. E. and Alexandra Spitz-Oener Explaining Women's Success: Technological Change and the Skill Content of Women's Work. Review of Economics and Statistics, 92(1), pp Bleakley, Hoyt and Aimee Chin Language Skills and Earnings: Evidence from Childhood Immigrants. Review of Economics and Statistics, 86(2):

36 36 Bresnahan, Timothy F., Erik Brynjolfsson and Lorin M. Hitt Information Technology, Workplace Organization and the Demand for Skilled Labor: Firm-Level Evidence. Quarterly Journal of Economics, 117(1): Brynjolfsson, Erik. and Lorin M. Hitt Beyond Computation: Information Technology, Organizational Transformation and Business Performance. Journal of Economic Perspectives 14 (4): Bryson, Alex, John Forth and Simon Kirby High-Involvement Management Practices, Trade Union Representation and Workplace Performance in Britain. Scottish Journal of Political Economy, 52(3): Cappelli, Peter and Nikolai Rogovsky New Work Systems and Skill Requirements. International Labour Review, 133(2): Caroli, Eva and John Van Reenen Skill-Biased Organizational Change? Evidence from a Panel of British and French Establishments. Quarterly Journal of Economics, 116(4): Cheng, Eddie W. L., Heng Li, Peter Love, and Zahir Irani A learning culture for strategic partnering in construction, Construction Innovation, 4: Darr, Asaf (2004). The Interdependence of Social and Technical Skills in the Sale of Emergent Technology. The Skills That Matter. Edited by Chris Warmhurst, Irena Grugulis and Ewart Keep. Basingstoke, Palgrave Macmillan. De Vilbiss, Carl E. and Leonard, Patricia Partnering is the foundation of a learning organization. Journal of Management in Engineering, 16 (4): Dickerson, Andrew and Francis Green The Growth and Valuation of Computing and Other Generic Skills. Oxford Economic Papers-New Series, 56(3): Dustmann, Christian, Johannes Ludsteck and Uta Schönberg Revisiting the German Wage Structure. Quarterly Journal of Economics, 124 (2):

37 37 Egger, Hartmut and Voler Grossman Non-Routine Tasks, Restructuring of Firms, and Wage Inequality Within and Between Skill-Groups. Journal of Economics 86 (3): Felstead, Alan and Duncan Gallie For Better or Worse? Non-Standard Jobs and High- Involvement Work Systems. International Journal of Human Resource Management, 15(7): Fernández-Macías, Enrique and John Hurley More and Better Jobs: Patterns of Employment Expansion in Europe. Dublin: European Foundation for the Improvement of Living and Working Conditions. Felstead, Alan, Duncan Gallie, Francis Green and Ying Zhou Skills at Work, 1986 to University of Oxford: SKOPE. Felstead, Alan and Francis Green Skills at Work in Scotland, 1997 to 2006, In. Glasgow: Scottish Enterprise. Gale, H.Frederick Jr., Timothy R. Wojan and Jennifer C. Olmsted Skills, Flexible Manufacturing Technology, and Work Organization. Industrial Relations, 41(1): Gallie, Duncan, Alan Felstead and Francis Green Changing Patterns of Task Discretion in Britain. Work Employment and Society, 18(2): Gera, Surendra, Wulong Gu and Zhengxi Lin Technology and the Demand for Skills in Canada: An Industry-Level Analysis. Canadian Journal of Economics, 34(1): Goos, Maarten and Alan Manning Lousy and Lovely Jobs: The Rising Polarization of Work in Britain. Review of Economics and Statistics, 89(1): Goos, Maarten, Alan Manning and Anna Salomons Job Polarization in Europe. American Economic Association, 99(2):

38 38 Goos, Maarten, Alan Manning and Anna Salomons Explaining Job Polarization in Europe: The Roles of Technology, Globalization and Institutions. London School of Economics, CEP Discussion Paper No Green, Francis Employee Involvement, Technology and Job Tasks. National Institute of Economic and Social Research, Discussion Paper No Greenan, Nathalie Organisational Change, Technology, Employment and Skills: An Empirical Study of French Manufacturing. Cambridge Journal of Economics, 27(2): Green, Francis and Mark Keese (Eds.) Job Tasks, Work Skills and the Labour Market. Paris: OECD. Handel, Michael J Measuring Job Content: Skills, Technology, and Management Practices. Institute for Research on Poverty, Discussion Paper Howell, David and Edward Wolff Trends in the Growth and Distribution of Skill in the U.S. Workplace, Industrial and Labor Relations Review, 44(3): Ichniowski, Casey and Kathryn. Shaw Beyond Incentive Pay: Insiders' Estimates of the Value of Complementary Human Resource Management Practices. Journal of Economic Perspectives, 17(1): Kelley, Maryellen R Alternative Forms of Work Organisation under Programmable Automation, In Stephen Wood (Ed.) The Transformation of Work? Skill, Flexibility and the Labour Process. London: Unwin Hyman. Kersley, Barbara, Carmen Alpin, John Forth, Alex Bryson, Helen Bewley, Gill Dix and Sarah Oxenbridge Inside the Workplace. Findings from the 2004 Workplace Employment Relations Survey. London: Department for Trade and Industry. Levy, Frank and Richard J. Murnane The New Division of Labor. New York and Oxford: Russell Sage Foundation and Princeton University Press. 38

39 39 Lindley, Jo The Gender Dimension of Technical Change and Job Polarisation. University of Surrey, Discussion Papers in Economics, 05/10. Machin, Stephen and John Van Reenen Technology and Changes in Skill Structure: Evidence from Seven Oecd Countries. Quarterly Journal of Economics, 113(4): Manning, Alan We Can Work It Out: The Impact of Technological Change on the Demand for Low-Skill Workers. Scottish Journal of Political Economy, 51(5): Mason, Geoffrey Enterprise Product Strategies and Employer Demand for Skills in Britain: Evidence from Employers Skill Surveys, SKOPE Working Paper No. 50, Centre on Skills, Knowledge and Organisational Performance, Universities of Oxford and Warwick. Michaels, Guy, Ashwini Natraj and John Van Reenen Has Ict Polarized Skill Demand? Evidence from Eleven Countries over 25 Years. CEP Discussion Paper No 987. Murnane, Richard J., John B. Willet and Frank Levy The Growing Importance of Cognitive Skills in Wage Determination. Review of Economics and Statistics, 77 (2): Osterman, Paul Skill, Training, and Work Organization in American Establishments. Industrial Relations, 34(2): Pil, Frits K. and John Paul MacDuffie The Adoption of High-Involvement Work Practices. Industrial Relations, 35(3): Piva, Mariacristina, Enrico Santarelli and Marco Vivarelli The Skill Bias Effect of Technological and Organisational Change: Evidence and Policy Implications. Research Policy, 34(2):

40 40 Rubery, Jill and Damian Grimshaw ICTs and Employment: The Problem of Job Quality. International Labour Review, 140(2): Spitz-Oener, Alexandra Technical Change, Job Tasks, and Rising Educational Demands: Looking Outside the Wage Structure. Journal of Labor Economics, 24(2): Stasz, Cathleen, Kimberly Ramsey, Rick Eden, Elan Melamid and Tessa Kaganoff Workplace Skills in Practice. Santa Monica: RAND. Sung, Johnny and David Ashton High Performance Work Practices: Linking Strategy and Skills to Performance Outcomes. London: DTI and CIPD. Thompson, Paul, Terry Wallace, Jōrg Flecker and Roland Ahlstrand It Ain't What You Do, It's the Way That You Do It: Production Organisation and Skill Utilisation in Commercial Vehicles. Work, Employment and Society, 9(4): Whitfield, Keith High-Performance Workplaces, Training, and the Distribution of Skills. Industrial Relations, 39(1): Wright, Erik Olin, and Rachel Dwyer The Patterns of Job Expansions in the United States: A Comparison of the 1960s and 1990s. Socio-Economic Review 1:

41 Physical Problem-solving External Communication Influence Change in Skills Indices, Figure 1 Changes in the Use of Generic Skills, Same Job Different Job Total Source: Skill Survey, 1997, calculated from retrospective questions. Base: is all workers who had also been in work in Each skills index ranges from 0 to 4. 41

42 42 Figure 2 Changes in the Use of Generic Skills, Source: Skill Surveys, 1997 and Each skills index ranges from 0 to 4; see Table 2 for standard deviations. 42