Job Control, Job Demands and Job Satisfaction in Britain: Trends and Inter-Relationships

Job Control, Job Demands and Job Satisfaction in Britain: Trends and Inter-relationships

JOHN SUTHERLAND SCOTTISH CENTRE FOR EMPLOYMENT RESEARCH (SCER) DEPARTMENT OF HUMAN RESOURCE MANAGEMENT UNIVERSITY OF STRATHCLYDE, GLASGOW

(email: [email protected])

ABSTRACT

This paper has two aims. The first is to examine trends in job control, job demands and job satisfaction in Britain. The second is to examine the nature of the inter-relationships between these three concepts. Aggregate and disaggregated indicators of job control, job demands and job satisfaction are constructed using a series of data sets which have their origin in the Skills and Employment Surveys Series Dataset. Since 1992, survey year by survey year, job control has decreased, both for the aggregate indicator used and for each of the four disaggregated indicators identified. Job demands have increased, for the aggregate indicator used and two of its three disaggregated indicators. Relative to the reference year 2006, the aggregate indicator of job satisfaction is relatively higher in 1992 but relatively lower in 2012, and this too is the pattern for seven of the 12 disaggregated indicators reflecting different job aspects. For another five job aspects, predominantly reflecting extrinsic job aspects, levels of satisfaction are relatively lower in both 1992 and 2012. Job control and job demands are positively correlated, although the extent of this correlation has declined. This result provides empirical support for the contention that jobs have become more challenging and more demanding. Whereas job demands are negatively related to job satisfaction, job control is positively related to job satisfaction, providing empirical support for the argument that the loss of job control and the intensification of work lie behind the decline in job satisfaction.

KEYWORDS: Job control: Job demands: Job satisfaction: Skills and Employment Surveys Series Dataset

JEL CLASSIFICATION: J28 M51 M54

Job Control, Job Demands and Job Satisfaction in Britain: Trends and Inter- 1 relationships

1. Introduction The transformation of work with a consequential change in the job quality of employees is one of the principal features of the political economy of contemporary Western economies. Job quality is a contextualised phenomenon, consequently perceptions of it differ. Nonetheless different authors (e.g. Findlay et al, 2013: Green, 2006: Green et al, 2013: Osterman, 2013) produce common indicators of job quality. Two components are central to these indicators viz. the job and its working environment. The concepts of job control and job demands are central to the former and there is evidence which suggests that there has been a decrease in job control and an increase in job demands in recent decades in Britain. Further evidence also suggests that this reduction in job control and increase in job demands have been accompanied by decreasing job satisfaction. While international agencies, such as the European Commission (2001), were advocating the addition of worker well-being to the public policy agenda, therefore, economies were encountering what Clark (2005) identifies as “the phenomenon of good job-less growth” (p. 377).

This paper has two aims. The first is to examine trends in job control, job demands and job satisfaction in Britain using the Skills and Employment Surveys Series Dataset. For example, over time, has the influence workers have over their jobs increased or decreased? Have they had to work harder at these jobs? What has happened to their level of job satisfaction: has this increased or decreased? The second aim of the paper is to examine the nature of the inter-relationships between these three concepts. For example, are job control and job demands positively or negatively co- related? Does job satisfaction increase or decrease with increases or decreases in either job control or job demands?

The structure of the paper is as follows. Section 2 provides a context to the study, making use of some literature of relevance. Section 3 introduces and describes the data set used. It also outlines the model used in the empirical analysis. Sections 4 and 5 respectively examine job control and job demands; and section 6 analyses the nature of the inter-relationship between these two concepts. Section 7 examines job satisfaction; and section 8 analyses the nature of the inter- relationships between job satisfaction and job control and job demands. A final section concludes.

2. Context The principal factors which have affected the transformation of work are a combination of technology, management and markets. Although other factors are also of consequence, notably the neo-liberal policy regime prevailing and the demise of unions, nonetheless these three factors

1 The Skills and Employment Survey, 2012 was financially supported by the Economic and Social Research Council (ESRC), the UK Commission for Employment and Skills Strategic Partnership and the Wales Institute for Of Social and Economic Research, Data and Methods for a Welsh boost. The Skills Survey, 2006 was supported by 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 and the East Midlands Development Agency. The Skills Survey, 2001 was funded by the Department for Education and Skills. The Skills Survey, 1997 and the Social Change and Economic Life Initiative Surveys, 1986 -1987 were supported by the ESRC. Employment in Britain, 1992 was supported by the Leverhume Trust and an industrial consortium of funders.

2 comprise the essential components of most of the conceptual frameworks constructed and applied to describe, analyse and explain the diverse work outcomes from this transformation process.

Braverman (1974) and Kerr et al (1960) offer contrasting perspectives of the evolution of work consequential of technological change, manifest in particular in terms of job control and job demands. For Braverman, the ultimate consequence of technological change is the degradation of work. Technology fragments and simplifies work tasks; it de-skills work; it eliminates the scope for the worker to exercise any control over his/her work; and it intensifies work. In his analysis of the labour process, Braverman argues that the drive for efficient production is simultaneously a drive for managerial control. And managerial control is achieved “through monopolising judgement, knowledge and the conceptual side of work and, concomitantly, excluding workers from control and ownership of knowledge production as a whole process” (Smith, 2015, p. 224). Kerr and his associates, by contrast, offer a more optimistic perspective of the future of work. Rather than de- skilling, technology offers the prospect of the development of higher and more specialised skills, thereby liberating much of labour from tedious work. Rather than the diminution of job control, it offers workers the prospects of involvement in decision-making processes (Gallie, 2102), a process to be reflected, if decades later, in what becomes known as ‘high involvement work systems’ (Wood and Bryson, 2009).

Subsequent authors adopt a more nuanced approach to the impact of technological change. For example, for Autor, Levy and Murname (2003) technological change – especially Information and Communications Technology (ICT) - is skills biased and its impact varies across individuals and occupations. Distinguishing between routine and non-routine tasks, Autor, Levy and Murname demonstrate that whereas ICT substitutes for labour in the context of the former – therefore decreasing the demand for unskilled labour – it complements labour in the context of the latter – therefore increasing the demand for skilled labour. Issues pertaining to job control or job demands are not part of their problematic. To Green (2004), however, ICT increases the ability of management to control workflows and monitor the labour process. This improves efficiency at the workplace by increasing the ‘marginal productivity of effort’. Technological change, therefore, is ‘effort-biased’ (p. 715).

In most of the earlier conceptual frameworks which examine the impact of technological change on work and workers, technology is treated as if it is deterministic. Where ‘management’ is acknowledged, it plays no more than a mediating role. Indeed, Sissons and Purcell (2010) maintain that in the context of the actors in the industrial relations system, little attention is paid to ‘management’ until the 1980s. “It was unproblematic if not unimportant” (p. 83). Yet management is central to the system, determining how best to use knowledge, experience and entrepreneurial acumen to harness technology, putting it to use, for example, to change process and product, and to re-organise work.

Sissons and Purcell (2010) conceptualise the role of management in two ideal type models, both with implications for job control and job demands. The first model conceives management as a strategic actor, proactively adjusting to changes in circumstances, external and internal to the organisation, to attain the goals of the organisation. The management of human resources is central to this task. Contracts of employment, however, are imprecise and incomplete. Consequently,

3 management must manage the labour process to ensure organisational goals are met. Management’s ‘problem’, therefore, is how best to achieve this.

‘Taylorism’, ‘Scientific Management’ and ‘Fordism’ achieve this by means of bureaucratic control. Developments in cost accounting are integrated with standardised production technologies. Trust relations are low and management discipline strong. The working environment is associated with directed and regulated work with limited job control and extensive job demands (Green, 2006). However, the controls implemented are often inadequate to their tasks. Moreover, dysfunctional bureaucracy has its costs (Sewell and Wilkinson, 1992). Accordingly, rather than control and compliance, themes such as commitment and consent begin to enter the management lexicon, with the argument that management are more likely to succeed in their attempts to use labour more productively by seeking to engage with it rather than by trying to control it (Legge, 2005: Smith, 2015).

In principle, management may engage with labour at two levels in the organisation (Wall et al, 2004). At one level, and the level more pertinent to the context of this paper, engagement is about ‘role involvement’, where the focus is upon the employee’s job.2 It is associated with job redesign and job enrichment (Felstead et al, 2010: Wood et al, 2012). In essence, this is about ceding some control over jobs to workers on the assumption that enhancing labour’s scope to exercise discretion will improve their motivation, increase their well-being and, ultimately, result in increased workplace productivity and, thereby, organisational profitability (Boxall and Macky, 2014: Gallie et al, 2014). In addition to individual task discretion, ‘role involvement’ is associated also with work practices such as job rotation, functional flexibility, team working etc. (de Menezes and Wood, 2006). When combined with developments in technology, especially ICT, however, these new management practices not only change the way in which work is designed but also how it is organised and managed, for example in the management of the flow of work and work scheduling. By reducing the porosity of work, management systems decrease if not eliminate resting and waiting times (Elger, 1990). Moreover, these same systems facilitate the introduction of new apparatuses of control in the form of monitoring and surveillance systems (Gallie et al, 2012: Green, 2001: Rubery and Grimshaw, 2001).

Sissons and Purcell’s (2010) second ideal model of management presents management as being the agent of capital. In their strategic model, management is tasked with structuring and restructuring the organisation to meet the challenges presented by changes in consumer – hence product - demand and competition from other organisations. The economic context of the firm in the strategic model is product market competition. Now, Thompson (2011) argues, there has been a shift from managerial capitalism to financial capitalism, driven by the requirements of competition in capital markets which results in the subordination of product market competition.

‘Financialisation’ is a distinct form of capitalism which emphasises the salience of financial actors operating in financial markets motivated by exclusively financial objectives (Cushen and Thompson, 2016). The organisation is viewed as “an accidental bundle of liabilities and assets there

2 The second level is at the level of the organisation where the focus shifts from the narrow specifics of the job. At this level, by means of diverse direct and indirect voice mechanisms, employees have the opportunity to discuss if not necessarily influence strategic decisions of consequence to the organisation (Gallie, 2013: Willman et al, 2009: Wood and Bryson, 2009: Wood et al, 2012: Wood et al, 2015).

4 to be re-arranged to maximise shareholder value” (Blackburn, 2006, p. 43). Value is extracted by means of cost cutting strategies, principal among which is headcount reduction interventions implemented, inter alia, by means of redundancy, outsourcing, centralisation and supply chain harmonisation. Efficiency innovation, which eliminates jobs, takes precedence over market-creating innovation, which generates them (Cushen and Thompson, p. 357). At the level of the organisation, acquisition, merger and disposal are the dominant features of this system and, as a consequence, for large organisations, perpetual restructuring is the norm. At the level of the workplace, the process of permanent organisational restructuring causes both employment insecurity and role insecurity for employees and headcount reductions result in work intensification. Moreover, to the extent that the organisation and its workforce are effectively disposable, one casualty is the assumption of ‘mutual gains’ central to much of the high commitment/high involvement literature and the human capital narrative associated with the model of management as a strategic actor. This is Thompson’s, (2003) disconnected capitalism thesis.

Technical innovation does not necessarily lead to work re-organisation and organisational change is not necessarily the consequence of technical change. Further, the introduction of new technologies and work re-organisation do not necessarily result in either a loss of job control on the part of the worker or an increase in job demands (Guest, 1990). Nonetheless, case studies of technical change present consistent evidence of both a decrease in job control and an increase in job demands for both manual and non-manual workers. Edwards and Whitson (1990) report that workers are working harder than before and that this is attributable to structural developments in the firms at which they work. Similar findings are reported by, inter alia, Batstone and Gourlay (1986); Lane (1998); Tomaney (1990); and Edwards et al (1998). Whereas these outcomes may be expected in classic ‘Taylorist’ or ‘Fordist’ regimes, similar outcomes of increases in work intensification are reported in case studies of organisations which redesigned jobs by introducing, for example, job rotation, functional flexibility and team-working, where these practices purported to enhance worker influence over their jobs (e.g. Elger, 1990; Garrahan and Stewart, 1991 and 1995; Turnbull, 1988). Nor do the outcomes change when the environment of the job changes, for example changing the workshop floor of the manufacturing plant for the call centre (Taylor and Bain, 1999; 2005). To the extent that job control and job demands are integral elements of the concept of job satisfaction, it is not surprising, therefore, that Rose (2005) comments upon the prevalence of a “despondency thesis” (p. 455) in the context of job satisfaction.

Despite its manifold merits, however, case study methodology does not lend itself to investigating trends over time in either job control or job demands (or job satisfaction). Further, it does not make possible an examination of the association between these concepts and the personal characteristics of workers and the workplaces at which they are employed. For this, survey methodology is required, preferably using repeated surveys where common questions are asked.

2. The Data Set The data sets used have their origin in the Skills and Employment Surveys Series Dataset, 1986, 1992, 1997, 2001, 2006 and 2012 (Felstead et al, 2014) (hereafter SES). The Skills Surveys is a series of surveys undertaken in 1997, 2001, 2006 and 2012 to investigate the employed workforce in Great Britain (and, from 2006, the United Kingdom). Although with a more specific skills focus, the series builds upon two previous, in some ways comparable, studies viz. the Social Change and Economic Life Initiative, 1986-7 and Employment in Britain, 1992. SES, therefore, pools data from six nationally

5 representative but independent cross section surveys where common questions are asked. Consequently, the problems of measuring concepts such as job control, job demands and job satisfaction are partially resolved by comparing responses to identical questions put to nationally representative samples at different points in time (Green 2004a: 2006: 2012).

Table 1 deconstructs the data set into its component parts. Which parts are used and which questions analysed are identified in subsequent sections of the paper as appropriate.

A conventional OLS model is used to examine each data set. Depending upon the investigation being undertaken, the dependent variable changes but always denotes an indicator of job control, job demands or job satisfaction. Unless otherwise specified, the set of independent variables is common throughout the estimations, and reflects the personal characteristics of an individual and the characteristics of the workplace at which he/she is employed.3 For the purpose of interpreting results from these estimations, given the particular nature of the construct of the dependent variables, throughout the focus is upon the qualitative rather than quantitative relationships which exist between the dependent and independent variables. In this respect, an independent variable with a positively signed coefficient indicates a positive relationship between this variable and the appropriate dependent variable (and conversely when the coefficient of an independent variable is negatively signed).

3. Job Control Job control may be interpreted and measured in several ways. One interpretation is that of autonomy, effectively total control over the job. More frequently, however, it is interpreted as either the amount of influence an individual has over his/her job or the extent of task discretion possessed. Hence, there is a plethora of potential indicators of what may be interpreted as job control. One consequence is that the use of different indicators in examinations of job control may result in different outcomes.

To examine trends in job control overtime, Gallie et al (2004; 2014) and Green (2006; 2008; 2012) construct ‘task discretion’ indices (i.e. reflecting the control an individual has over his/her immediate work tasks). Although examining sometimes different discrete time periods, constructing different indices and using different data sources, generally, the conclusion is that task discretion declines during the 1990s; levels off during the early 2000s; and there is “no significant change …. over the period 2006 – 2012” (Gallie et al, 2014, p. 213).

Gallie et al (2004) use the 1992 Employment in Britain Survey and the Skills Surveys of 1997 and 2001. They construct a composite task discretion index using responses to questions about the influence an individual has over ‘how hard to work’; ‘what tasks to do’; ‘how to do them’; and ‘quality standards’. They identify a decline in this index over the period examined. Green (2012) constructs a similar index using responses to the same questions from the Skills Surveys of 1997, 2001 and 2006. He finds a decline in the index for the period 1997 – 2001 but argues that this levels

3 The model assumes that the effect of each explanatory variable remains constant through time. Further, it is acknowledged that there may be unobserved individual specific and time invariant factors that affect the outcomes. Were these fixed effects to be present and to be correlated with the observed independent variables, there may be a bias in the reported estimates.

6 off during the period 2001 – 2006.4 Gallie et al (2014) use the (full) SES data set and make use of the same questions to produce their standard task discretion index, thereby extending their analyses of its trend to 2012.

An additional feature of some of these studies is an examination of the manner in which job control varies across the employed population, although the extent to which different authors attempt this is contingent upon the variables available within the data set and the statistical methodology used to analyse the data. From these studies, job control tends to vary with gender, with women having more control than men (Gallie et al, 2014: Lindley, 2016); qualification/skill, with those with higher qualifications/skills having more control than those with less/lower (Gallie et al, 2004: Green, 2008); occupation, with those in the higher occupations, such as managers, having more control than those in lower occupations, such as operatives,5 (Green, 2008: Gallie et al, 2014); contract of employment, with those holding part time or temporary contracts having less control than those who hold normal contracts of employment (Gallie et al (2004); unionisation, with unions being associated with less control (Gallie et al, 2004); and sector, where the scope for more job control is greater in the public sector (Gallie et al, 2004).

Two research questions motivate this section, therefore: Has the extent of job control changed over time? And to what extent does the degree of job control possessed vary between individuals?

Common questions which relate to job control are asked in four surveys in the original data set viz. 1992, 2001, 2006 and 2012. For the purpose of the analysis of job control, these four surveys are combined into a working data set.

Four questions capture the perspective of the experience of job control on the part of the worker: i. “How much influence do you personally have on how hard you work?” (where the name of the dependent variable in the subsequent regression is ‘howhard’); ii. “How much influence do you personally have on deciding what tasks you are to do?” (where the name of the dependent variable in the subsequent regression is ‘tasks’); iii. “How much influence do you personally have on deciding how you are to do the task?” (where the name of the dependent variable in the subsequent regression is ‘how’) ; and iv. “How much influence do you personally have on deciding the quality standards to which you work?” (where the name of the dependent variable in the subsequent regression is ‘quality’). The original Likert-scale responses to these questions are recoded and transformed into scalar variables where the values of the variables reflect degrees of control over the job. To illustrate: the response ‘a great deal’ is scored as ‘3’, whereas the response ‘none at all’ is scored as ‘0’. Subsequently, these scalar variables are aggregated to produce a composite measure of job control (where the name of this dependent variable in the subsequent regression is ‘influence’).

4 In contrast to these finding, Green (2008), using responses to a different set of questions from the Work and Employment Relations Surveys of 1998 and 2004, finds little change in the extent of job control as measured between the two years in question, illustrating the point that different outcomes may result when different indicators are used. 5 That said, one important finding of Gallie et al (2014) is that the extent of job control among those employed in Personal Services is increasing.

To examine whether job control has changed and to investigate the extent to which the experience of job control varies across individuals these transformed scalar variables, indicators of job control, are used as dependent variables in a set of OLS regressions. The independent variables used in the estimated model are identified in column 1 of Table 2 together with the footnote to this table.6 The results of these estimations are reported in Table 2.

With respect to changes in job control over time, for each of the five indicators constructed, all the year dummy variables (relative to the reference year of 1992) are negatively signed and statistically significant. Since 1992, survey year on survey year, therefore, the extent of job control possessed by workers has decreased, and progressively so for the aggregate variable ‘influence’ and the disaggregated component ‘quality’.

In the context of differences between individuals, job control is statistically significantly associated with an individual’s age; academic qualification level; occupational status; the size of the workplace at which he/she is based; and unionisation. Individuals aged 30 -39, 40 -49 and 50-60 have more control than individuals aged 20 -29, the reference category, for all indicators of job control. Individuals with qualifications, irrespective of the level of these qualifications, have more control than individuals with no qualifications, the reference category, again for each of the five indicators used. Individuals who are classified as managers or associate professional and technical have more control than individuals who are classified as administrative and secretarial workers, the reference category, irrespective of the indicator of job control used. Notably, individuals who are classified as professional have no more job control than those classified as administrative and secretarial with respect to how hard they work; the tasks they undertake; and the quality of their work, an outcome attributable, perhaps, to the nature of the work now done by professionals. In contrast, individuals who are classified as sales, operatives and elementary have less control over their jobs than individuals in the occupational reference category, irrespective of the indicator used. Individuals who are, effectively, self-employed have more control over their jobs than individuals who work at workplaces employing less than 25 (the reference category), no matter which indicator is used, an outcome perhaps to be expected. In contrast, individuals who are employed at relatively larger workplaces have less control over their jobs than those employed at the reference workplace size category, again irrespective of the indicator used. Individuals who are not union members (relative to those who are) and individuals who are not employed at workplaces at which unions are present (relative to those who are employed at workplaces which have a union presence) have more control over their jobs across all five indicators. Gender differences are observable only in the instance of control over the quality of work done, where women have more control than men.

There is, therefore, a consistency of outcome in the context of inter-individual differences in job control. Irrespective of the indicator used, the outcome is much the same across most of the independent variables. Moreover, there are some instances of progressive increases or decreases in the degree of job control possessed for individual categories of some variables. This is apparent for

6 Unfortunately, the nature of the pooled data set is such that the model estimated does not include variables of potential importance, notably employment status (there is no distinction between the self-employed and employees); employment contract (there is no distinction between whether the individual works part time or full time; or has a temporary or permanent contract of employment); and wages. One potential implication of the inability to identify the self-employed in the data set may be reflected in some of the results denoting workplace size, where ‘single employee’ is one of the size categories.

8 the age variables, where the extent of job control increases with age in the regressions of ‘intensification’, ‘tasks’, ‘how’ and ‘quality’ (but not ‘howhard’). It is also apparent for the variable denoting workplace size, although only in the regressions of ‘influence’, ‘howhard’ and ‘quality’, where the extent of job control decreases progressively as the size of the workplace at which the individual is employed increases.

4. Job Demands The concept of job demands has been defined and measured in various ways. Consequently, there is a plethora of potential indicators of the concept, always with the possibility that in investigations of job demands the outcomes may change with changes in the indicator used. The indicators which have been constructed and used have their origins in diverse questions posed in several surveys which relate to employment conditions which refer to ‘working hard’ (i.e. ‘work intensity’). Illustrative examples of these questions include: about ‘the speed of work’; and ‘the necessity to work to tight deadlines’ (to be found in the European Survey on Working Conditions of 1991 and 1996 and used by Green and McIntosh (2001)); about ‘working hard’; ‘working at high speed’; and ‘working under tension’ (to be found in all the Skills Surveys and their predecessors and used by Green (2001: 2004a: 2004b: 2006) and Gallie et al (2014); and about ‘working under a great deal of pressure’; working but ‘never seem(ing) to have enough time to get everything done’; and ‘having to work extra’ (to be found in the Eurobarometer series and used by Gallie (2005).

Although discrete indices are constructed, nevertheless, the trends suggest that job demands (or work intensification) increase from the early 1990s to the mid-1990s. Peaking at the mid-point of the decade, it levels off thereafter. However, after a decade of little change, there is a “resumption of work intensification” between 2006 and 2012 (Gallie et al, 2014, p 215).

In contrast to job control, there are few attempts made to examine inter-individual differences in job demands. One issue which is probed in some of the empirical studies, however, is the nature of the relationship between job demands and job control (eg. Gallie (2005); Green (2006). This is addressed in the subsequent section.

Two research questions motivate this section: Is there evidence that job demands increase over time? And to what extent do job demands vary between individuals in the workforce?

Common questions which relate to job demands are asked in four surveys in the original data set viz. 1992, 2001, 2006 and 2012. For the purpose of the analysis of job demands, these four surveys are combined into a working data set.

Three questions capture perceptions of the experience of job demands: i. “my job requires that I work very hard” (where the name of the dependent variable in the subsequent regression is ‘hard’); ii. “I work under a great deal of tension” (where the name of the dependent variable in the subsequent regression is ‘tension’); and iii. “I often have to work extra time, over and above the formal hours of my job, to get through the work or to help out” (where the name of the dependent variable in the subsequent regression is ‘extra’). Again, the original Likert-scale responses to these questions are transformed into scalar variables where the values of the variables reflect degrees of these manifestations of job demands. To illustrate: ‘strongly agreeing’ with the proposition that ‘my job requires that I work very hard’ is scored at ‘4’, whereas ‘strongly disagreeing’ is scored at ‘1’.

Subsequently, these scalar variables are aggregated to produce a composite measure of job demands (and the name of this variable in the subsequent regression is ‘intensification’).

To examine the extent to which job demands vary over time and between individuals, these transformed scalar variables are used as dependent variables in a set of OLS regressions. The independent variables used in the estimated model are as previously identified. The results of the four estimations are reported in Table 3.

Two conclusions may be made from an examination of these results. The first is that there is some evidence of increasing job demands for the years examined. Relative to the reference year of 1992, the three year dummies in the regressions of three of the four dependent variables are positively signed and statistically significant. Furthermore, in the specific context of an individual reporting that he/she has to work very hard in his/her job, the values of the relevant coefficients increase with successive survey years. In contrast, the coefficients of the year dummies for the regression denoting the extent to which the individual is required to work ‘extra’ are negatively signed.

The second conclusion is that there is considerable variation in the extent to which individuals experience job demands for the indicators constructed.

In the regression of the variable ‘intensification’ (i.e. the aggregate of the scalar scores of the three initial indicators of job demands) the dependent variable is statistically significantly associated with gender; age; academic qualification level; occupational classification; the size of the establishment at which the individual works; and unionisation. Intensification is positively related to being female; being aged 30 -39 and being 40 -49 (relative to being aged 20 -29, the reference category) (but not being aged 50 -60); and having academic qualifications (relative to having no academic qualifications, the reference category). Moreover, the extent of intensification increases progressively with the qualification level possessed. Intensification is greater for those in the higher occupational classifications (i.e. managers, professionals) (relative to those in the administrative and secretarial occupational grouping, the reference category). In contrast, intensification is less for those in the lower occupational classifications, notably those in the elementary occupations. Intensification is less for those employed in relatively larger establishments (relative to those employed at workplaces employing less than 25 employees, the reference category). Intensification is negatively related to being a union member. Paradoxically, it is positively related to being employed at a workplace at which there is no union present.

The dependent variable ‘intensification’ is the aggregation of the scalar scores of three distinct indicators of job demands. Outcome patterns similar to the aggregate variable are observed for the categorical variables denoting occupational classification and workplace size in the regressions of the variables ‘hard’; ‘tension’; and ‘extra’. Nonetheless, there are instances where different output patterns are observed. Although the dummy variable female is positively signed in the regressions of ‘hard’ and ‘tension’, it is negatively signed in the regression of ‘extra’, although the latter result is not statistically significant. The age category variables are not jointly statistically significant in the regression of ‘extra’, although they are in the regressions of ‘hard’ and ‘tension’. By way of contrast, the academic qualification level categorical variables are not jointly statistically significant in the regression of either ‘hard’ or ‘tension’, but they are in the regression of ‘extra’. As with the aggregated variable not being a union member is negatively signed in the regressions of

‘hard’ ‘tension’ and ‘extra’. However, the dummy variable denoting no union present at the workplace is negatively signed only in the regression of ‘tension’.

5. Job Control and Job Demands Green (2006) argues that one paradox of employment in many affluent economies is that work has become more challenging and demanding. Moreover, Gallie (2004) finds that “higher levels of job control … have a strong association with higher levels of work pressure” (p 361). Accordingly, the aim of this section is to examine the nature of the relationship between job control and job demands.

Questions pertaining to job control and job demands are available in the original data set for years 2001, 2006 and 2012. Data from these years form the working data set for the purpose of this investigation.

By way of a preliminary examination, pairwise correlations of the aggregate variables ‘influence’ and ‘intensification’ for each year in the working data set and all years combined are presented in Table 4. Although the value of each correlation coefficient is not particularly high, nonetheless all are positively signed and statistically significant at (p < 0.01). Notably, however, the value of the correlation coefficient does not increase over the survey years.

The variables ‘influence’ and ‘intensification’ are derived variables, however, obtained by aggregating the scores of the variables ‘howhard’, ‘tasks’, ‘how’ and ‘quality’ and ‘hard’, ‘tension’ and ‘extra’, respectively. Table 5 presents the pairwise correlations of these original variables, again for each year in the working data set and for all years. Three observations may be made from these tables. First, the values of the correlation coefficients are relatively higher when they involve correlations between variables within the aggregate variables ‘influence’ and ‘intensification’ rather than correlations between variables which cross the aggregate variables ‘influence’ and ‘intensification’. Secondly, the values of the correlation coefficients tend to be relatively higher for correlations between the variables associated with the aggregate variable ‘influence’ than they do for correlations between variables associated with the aggregate variable ‘intensification’, reflecting the results of the previous sets of regressions pertaining to job control and job demand. Thirdly, only three results produce outcomes where the correlation coefficients are negatively signed. All involve correlations associated with the variable ‘tension’; and two of these involve correlations between the variables ’tension’ and ‘howhard’. However, none of these results is statistically significant. There is, therefore, some provisional support for the hypothesis that job control and job demands are positively correlated, although the extent of this correlation is not great.

To explore the relationship further, the OLS regression of aggregate variable ‘influence’ is re- estimated twice for each year in the working data set and all years. In the first re-estimation the aggregate variable ‘intensification’ is added to the set of independent variables used in previous regressions. In the second re-estimation, ‘hard’, ‘tension’ and ‘extra’ – the original variables which denote constituent components of the aggregate variable ‘intensification’ - are incorporated instead as independent variables. These results are reported in Table 6.

The results of these regressions provide further evidence that job control and job demands are positively correlated. To adopt the terminology of Green (2006), jobs have become more challenging and more demanding. However, the extent of this correlation declines over time. For

11 example, in the three separate survey year regressions of ‘influence’ the value of the coefficient of the variable ‘intensification’ is less in 2012 than it is in 2001 and 2006. Further the coefficients of the year dummies in the estimation of ‘influence’ using the combined data set (cf. column 10 of Table 6) are negatively signed, although not statistically significant. When the derived variable ‘intensification’ is disaggregated into its three constituent parts, the variables ‘hard’ and ‘extra’ are similarly positively correlated in each of the regressions of ‘influence’. The extent of their correlation with the variable ‘influence’ changes over time, however, manifest both in the changing values of the respective coefficients in the three separate survey year estimations and the negative signs of the coefficients of the year dummy variables in the combined data set (cf. column 11 of Table 6). In contrast, the third constituent component of the aggregate variable ‘intensification’, ‘tension’ – denoting that an individual works under a great deal of tension – is negatively correlated in each of the regressions of ‘influence’. Although it is inappropriate to infer causation from correlation, as influence over the job increases, there appears to be a decrease in the tension associated with the job.

6. Job Satisfaction There is a presumption that job satisfaction is a convenient proxy for workers’ utility and hence their well-being. As such, this interpretation conforms more to the classical paradigm in economics than the neo-classical one (Spencer, 2008). Kalleberg (1997) contends that job satisfaction refers to an “overall affective orientation on the part of individuals towards work roles which they are presently occupying” (p. 126). The expectation is that job satisfaction differs between individuals where these differences are to be explained in terms of the personal characteristics of the individual and the characteristics of the job he/she holds. The problem for analysis, however, is that job characteristics are subjectively constructed not objectively determined. Individuals evaluate what purports to be similar objective job characteristics differently because what they seek to get from work (i.e. their job preferences) differ (Perales and Tomaszewski, 2015).

Rose (2003) argues that it is necessary to distinguish between ‘job’ and ‘work’. “The primary meaning of a job is that of a contractually held and rewarded post or appointment” (p. 505). Hence what should only be included in the calculus of job satisfaction are extrinsic job facets, such as hours, pay, etc.. Intrinsic job facets, such as ‘scope for initiative’, ‘relations with managers’ etc., “the qualitative experiences derived from the work performed in the post” (p. 507) Rose considers to be of secondary importance. To the extent that other researchers disagree with Rose’s working premise and, therefore proceed to examine job satisfaction differently, reported trends in job satisfaction are difficult to interpret, tied as they are to differences in conceptualisation and measurement. Two contrasting studies illustrate this point.

Green and Tsitsianis (2005) examine responses to the questions about job satisfaction in the General Household Survey, 1972 – 1983, (‘satisfaction with the present job’) and the British Household Panel Survey (BHPS), 1992 – 2002, (‘all things considered, how satisfied or dissatisfied are you with your present job?’). They find a small downward trend in job satisfaction for the period in question. Clark (2005) also uses data from the BHPS for the same period but examines responses to the full range of questions addressed in the survey e.g. satisfaction with ‘total pay’, ‘promotion prospects’, ‘scope to use initiative’, ‘the work itself’ etc.. He concurs that there has been a “secular decline in overall job satisfaction over the 1990s” (p. 387). Additionally, however, he argues that not only are there important differences in the domain satisfaction measures – whereas satisfaction

12 with pay increases over the period, satisfaction with the work itself decreases, for example – changes in job satisfaction have not been the same for all workers, with falls for, again for example, women and older workers.

Rather than examining trends in job satisfaction, more attention has been given to investigating the individual level correlates of job satisfaction. Irrespective of the diverse range of data sets used, most studies find that the characteristics of individuals, their jobs and their working environments are of consequence. For example, the level of job satisfaction is relatively higher for women (Clark, 1997); younger and older workers (Clark, 1996); those with opportunities for promotion (Clark, 1996); those who have received training (Jones et al, 2008); and those employed in small firms/establishments (Tansel and Gazioglu, 2014). Although there is controversy over the role of unions and union membership (Bryson et al, 2004: Guest and Conway, 2004), nonetheless there is consensus that job satisfaction is lower for those who are graduates (Clark and Oswald, 1996) and members of ethnic minorities (Perales and Tomaszewski, (forthcoming).

One additional feature of the examination of job satisfaction is its relationship with job control and job demands. For example, Green and Tsitsianis (2005) find that work intensity has a “strong negative and significant impact on job satisfaction” (p. 420). In contrast, task discretion has a strong positive impact and to the extent that this has declined it goes a “long way in unravelling the mystery of declining job satisfaction in Britain” (p. 422). The nature of the relationship between job satisfaction and job control and job demands is explored in the subsequent section.

Two research questions motivate this section: Has job satisfaction changed over time? And to what extent does job satisfaction vary between individuals?

Common questions which relate to job satisfaction are asked in three surveys in the original data set viz. 1992, 2006 and 2012. For the purpose of the analysis of job satisfaction, these three surveys are combined into a working data set.

12 job aspects are examined.7 Each aspect is prefaced with the following question: “How satisfied or dissatisfied are you with the following aspects of your own job?” The aspects in question are as follows: i. Your promotion prospects (where the name of the independent variable in the regression which follows is ‘prospects’); ii. Your pay (where the name of the independent variable in the regression which follows is ‘pay’); iii. Your job security (where the name of the independent variable in the regression which follows is ‘security’); iv. The opportunities to use your abilities (where the name of the independent variable in the regression which follows is ‘abilities’); v. Being able to use your own initiative (where the name of the independent variable in the regression which follows is ‘initiative’); vi. The hours you work (where the name of the independent variable in the regression which follows is ‘hours’); vii. Fringe benefits (where the name of the independent variable in the regression which follows is ‘fringes’); viii. The work itself (where the name of the independent variable in the regression which follows is ‘work’); ix. The amount of work (where the name of the independent variable in the regression which follows is ‘amount’); x. The variety in the work (where the name of the independent variable in the regression which follows is ‘variety’); xi. The training

7 The questionnaires actually refer to 14 aspects. Two aspects, however, relate specifically to ‘employees’ rather than ‘workers’ in general. Consequently, because these are irrelevant to the self-employed, a constituent part of the data sets examined, they are excluded from this analysis.

13 provided (where the name of the independent variable in the regression which follows is ‘training’); and xii. All in all, how satisfied are you with your job (where the name of the independent variable in the regression which follows is ‘allinall’).

The original Likert-scale responses to these questions are recoded and transformed into scalar variables where the values of the variables reflect degrees of satisfaction/dissatisfaction i.e. responses ‘completely satisfied’; ‘very satisfied’ and ‘fairly satisfied’ are scored positively, progressively; the response ‘neither satisfied nor dissatisfied’ is scored as ‘0’; and responses ‘fairly dissatisfied’; ‘very dissatisfied’ and ‘completely dissatisfied’ are scored negatively, and again progressively. Subsequently, these scalar variables are aggregated to produce a composite measure of job satisfaction (where the name of the independent variable in the regression which follows is ‘jobsat’).

These transformed scalar variables are used as dependent variables in a set of OLS regressions to investigate the extent to which job satisfaction, both in total and for its constituent aspects, changes over time and how it varies across individuals. The set of independent variables used in these estimations is as previously identified. These results are reported in Table 7.

With respect to the issue of change over time, in the regression of the variable ‘jobsat’ (denoting job satisfaction as a whole), relative to the reference year, 2006, whereas the 1992 year dummy is positively signed, the 2012 year dummy is negatively signed. Seven of the job aspects conform to this pattern viz. the regressions of satisfaction with ‘pay’; ‘the opportunities to use your abilities’; ‘being able to use your initiative’; ‘the work itself’; ‘the amount of work’; ‘the variety in the work’; and the job, ‘all in all’. For these variables, therefore, from the perspective of 2006, the level of satisfaction is higher in 1992 but lower in 2012. In the regressions of the other five job aspects, the coefficients of both year dummies are negatively signed. For satisfaction with ‘your promotion prospects’; ‘your job security’; ‘the hours you work’; the ‘fringe benefits’; and the ‘training received’ (predominantly extrinsic benefits from work), therefore, the level of satisfaction is higher in 2006 (a year of economic growth which followed years of economic expansion) than it is in either 1992 or 2012.

With respect to differences across individuals, job satisfaction as a whole (‘jobsat’) is statistically significantly associated with an individual’s gender; age category; level of academic qualifications; occupational classification; and the size category of the workplace at which he/she is employed. Job satisfaction is higher for females than males. Relative to the age reference category (20 -29), job satisfaction is higher for those in the relatively older age categories. Relative to those with no academic qualifications, job satisfaction is lower for those with academic qualifications. Job satisfaction is higher, indeed progressively so, for those in the higher occupational classifications e.g. managers, professionals and associate professional and technical, but lower for those in the lower occupational classifications e.g. operatives and elementary, where both outcomes are relative to those classified as administrative and secretarial, the relevant reference category. Relative to those employed in workplaces employing less than 25 people, the reference category, those employed at workplaces which employ a single person have higher levels of satisfaction. In contrast, those employed at relatively larger establishments have lower levels of job satisfaction.

The results of the regressions of job aspects ‘the opportunities to use your abilities’; ‘the work itself’; ‘the training you receive’; and the job ‘all in all’ conform exactly to this pattern. The job

14 aspect satisfied with ‘your job security’ is also statistically significantly associated with this same set of variables, however, relative to those in the 20 -29 age category, the reference category, individuals in the relatively older age categories report lower levels of satisfaction. Elsewhere, there is little pattern to be discerned in the results reported in Table 7. Gender is not statistically significant in the regressions of the variables ‘pay’; ‘hours’; ‘fringes and ‘amount’. The set of variables depicting age is not statistically significant in the regressions of the variables ‘pay’ and ‘fringes’. The set of variables depicting the level of academic qualifications held is not statistically significant is the regressions of ‘pay’; ‘initiative’; ‘fringes’ and ‘variety’. Finally, the set of variables depicting the size of the workplace at which the individual is employed is not statistically significant in the regressions of the variable ‘prospects’.

7. Job Satisfaction, Job Control and Job Demands Comment has been made already about the possible relationship between job satisfaction and job control and job demands. For example, Green and Tsitsianis (2005) find that work intensity has a “strong negative and significant impact on job satisfaction” (p. 420). In contrast, task discretion has a strong positive impact and to the extent that this has declined it goes a “long way in unravelling the mystery of declining job satisfaction in Britain” (p. 422). Echoing these sentiments, Green (2006) argues that “the deteriorations in autonomy and the intensification of work lie behind the decline in job satisfaction” (pp. xix). This section examines this hypothesis.

Data pertaining to job satisfaction, job control and job demands are available for two years, 2006 and 2012, in the original data set. Observations from these years constitute the working data set.

To examine the hypothesis, the OLS regression of ‘jobsat’ (i.e. aggregate scalar score for satisfaction of the 12 discrete job aspects) is re-estimated twice. The variables within the model estimated are as previously specified but the first re-estimation includes two additional independent variables viz. ‘intensification’ (denoting job demands) and ‘influence’ (denoting job controls), where these variables are the aggregate scalar scores of the variables ‘hard’, ‘tension’ and ‘extra’; and ‘howhard’, ‘tasks’, ‘how’ and ‘quality’, respectively. The results of this regression are reported in column 3 of Table 8. In the second re-estimation, the separate scores for the variables ‘hard’, ‘tension’ and ‘extra’; and ‘howhard’, ‘tasks’, ‘how’ and ‘quality’ replace the variables ‘intensification’ and ‘influence’, respectively, as independent variables in the estimation. These results are reported in column 4 of Table 8.

The first re-estimation provides empirical support for Green’s argument. In this re- estimation of ‘jobsat’, the variables ‘intensification’ and ‘influence’ are jointly statistically significant. Further, the respective signs of their individual coefficients conform to expectations i.e. ‘intensification’ is negatively signed whereas ‘influence’ is positively signed. The coefficient of the year dummy variable is negatively signed, an outcome compatible with the previous result pertaining to job satisfaction. However, note the impact the additional variables have on the variables in the original estimation of ‘jobsat’. In the re-estimation the signs of the coefficients of the variables rarely change, but most of their values do, sometimes quite markedly.

Further empirical support for Green’s argument is forthcoming from the second re- estimation. First, the variables ‘hard’, ‘tension’ and ‘extra’ – constituent parts of the variable ‘intensification’ (denoting job demands) – are jointly statistically significant. Secondly, the variables

‘howhard’, ‘tasks’, ‘how’ and ‘quality’ – constituent parts of the variable ‘influence’ (denoting job control) – are also jointly statistically significant. Finally, all seven variables are jointly statistically significant. Again, the coefficient of the year dummy variable is negative. And, again, the incorporation of these variables into the re-estimated model impacts on the values if not the signs of the coefficients of the variables in the original estimation. The four coefficients associated with job control i.e. ‘howhard’, ‘tasks’, ‘how’ and ‘quality’ are positively signed. However, only two of the three coefficients associated with job demands viz. ‘tension’ and ‘extra’ are negatively signed. The variable ‘hard’ - denoting that ‘my job requires that I work very hard’ – is positively signed. Having to work ‘hard’, therefore, is correlated with higher, not lower levels job satisfaction.

8. Conclusions This paper has investigated trends in job control, job demands and job satisfaction and has examined the nature of the inter-relationships between these concepts. This final section re-identifies the more salient empirical results and puts these into the context of previous studies of particular relevance.

Since 1992, survey year by survey year, the extent of job control has decreased, both for the aggregate indicator and for each of the four disaggregated indicators identified. Moreover, for the aggregate indicator, the magnitude of this decrease has increased survey year by survey year. This result contrasts with previous findings about this concept which had indicated some levelling off of the decrease during the early 2000s with no marked change being observed between 2006 -2102. Whereas these previous studies investigated job control over discrete sub periods, this study has capitalised upon a data set which covers a longer period and this may be one possible explanation for the different findings reported.

The extent of job control varies across the working population. This result confirms the findings of previous studies. Further, the variables which explain these variations are common between this and previous studies. For example, more control is associated with older workers; those with qualifications; and those in the higher occupational classifications. In contrast, less control is associated with those in the lower occupational classifications and those employed at larger workplaces. Where this study adds to the literature is the contention that these results about inter-individual differences in job control apply also when the aggregate indicator is disaggregated into its constituent parts, into influence over how hard to work, on deciding which tasks to do and on quality standards to which to work, for example.

Job demands has increased survey year by survey year since 1992, for the aggregate indicator used in the investigation. There was no evidence of any levelling off during the period 1995 -2005 as had been reported in previous studies. Moreover, job demands increased for three of its four disaggregated indicators, an issue not addressed in previous studies of job demands. Further, previous studies had not examined the possibility of variations in job demands between individuals. Consequently, the result that there are some notable inter-individual differences in the extent of job demands for some of the disaggregated indicators identified is another novel feature of this investigation. For example, women, relative to men, are in jobs which require them to work very hard and under a great deal of tension, but they are not in jobs which require them to work beyond their normal hours of work. In contrast, those with academic qualifications, relative to those who have no qualifications, are in jobs which require them to work beyond their normal hours of work,

16 but they are not in jobs which require them either to work very hard or to work under a great deal of tension.

Job control and job demands are positively correlated, although the extent of this correlation has declined. This result confirms that of Gallie (2004) and provides support for Green’s (2006) argument that over time jobs have become more challenging and more demanding. A novel feature of this investigation is the examination of the relationship between the aggregate indicator of job control and the three disaggregated indicators of job demands. One result is particularly notable in this context. In contrast to the results for the other two disaggregated indicators, the disaggregated indicator reflecting that an individual works under a great deal of tension is negatively correlated with job control. Although it is inappropriate to infer causation from correlation, this result implies that as control over the job increases, there may be a decrease in the tension associated with the job.

Principally because of substantial differences in meaning and measurement, reported trends in job satisfaction in previous studies are difficult to interpret and compare. However, in their different ways, Green and Tsitsianis (2005) and Clark (2005) concur that there had been a secular decline in job satisfaction during the 1990s, although Clark also states that changes in job satisfaction had not been the same for all workers. In this paper, seen from the reference year of 2006, a year of economic growth and expansion, job satisfaction in terms of the aggregate indicator used was relatively higher in 1992 but relatively lower in 2012. Moreover, seven of the disaggregated indicators of job satisfaction reflecting different job aspects conformed to this pattern, where the results for 2012 no doubt reflect some of the workplace consequences of the recession. In contrast, for what are predominantly extrinsic job aspects (such as satisfaction with job security, hours worked, fringe benefits) satisfaction levels in both 1992 and 2012 were lower than they were in 2006, where these results may well reflect the pecuniary-type benefits of the economic growth experienced in 2006.

The results of the examination of inter-individual differences in job satisfaction reported in this paper confirm the results from previous studies in that differences are explained by the characteristics of individuals, the jobs they hold and their working environments. For example, for the aggregate indicator of job satisfaction, the level of satisfaction is higher for women (relative to men) and older workers (relative to younger workers). In contrast, it is lower for those with academic qualifications (relative to those with no qualifications) and those employed in relatively larger places of work. Few of the disaggregated indicators of job satisfaction, however, conform to this pattern. Although no equivalent alternative pattern is discernible in these results, the argument is that, again, what prevails at the aggregate level is not necessarily reflected at the disaggregated levels.

The relationship between job satisfaction and job demands and job control was examined for the years 2006 and 2012. The variable ‘intensification’ (denoting the aggregate indicator of job demands) was negatively related to the aggregate indicator of job satisfaction, whereas the variable ‘influence’ (denoting the aggregate indicator of job control) was positively related. This provides support for Green’s (2006) argument that the loss of job control and the intensification of work lie behind the decline in job satisfaction. That said when the three disaggregated indicators of ‘intensification’ were examined rather than the aggregate indicator, the independent variable ‘hard’

- denoting that ‘my job requires that I work very hard’ – was positively correlated with job satisfaction. Having to work ‘hard’, therefore, is correlated with higher, not lower levels job satisfaction.

Examining job control, job demands and job satisfaction is fraught with problems of meaning and measurement. Job control, for example, is frequently interpreted as ‘influence over the job’, ‘discretion over the immediate work task’ and ‘autonomy’ (responsible or otherwise). Too often these phrases are used as if they are interchangeable rather than describing different discrete levels of control an individual has over his/her job. This is one explanation why reputable surveys adhere rigidly to their preferred terminology, in the way, for example, the Skill Surveys examine the ‘influence’ an individual has. Measuring extensive work effort is relatively easy to the extent that it may be measured in terms of the number of hours worked over contracted hours, for example. Measuring intensive work effort is more problematical because this entails analysing individual subjective evaluations of personal work situations. Moreover, jobs are multi-faceted and these different facets may make different demands on the same individual (and provide different levels of satisfaction). Attempting to accommodate this merely compounds the difficulties the researcher confronts. Constructing an (usually unweighted) aggregate index from these several facets is one expedient solution frequently adopted. However, one important finding in this study is that what is observed in the aggregate is not necessarily observed when the aggregate indicator is deconstructed into its constituent parts. And, sometimes, that outcome for the disaggregated indicator is worthy of note.

To examine trends over time in job control, job demands and job satisfaction requires identical questions to be put to representative samples of some well-identified population at regular time intervals. The SES captures the ‘identical questions’ component of this requirement (to some extent). Its component surveys are more irregular than regular, however (perhaps with the exception of the more recent years). However, they are not panel surveys but surveys of different samples if from the same nationally representative targeted population. The several single equation OLS analysis of the pooled data sets undertaken in the paper have succeeded in revealing important relationships about and between three concepts in question. Nonetheless, the potential issue of unobserved heterogeneity and its implications for the detailed statistical results cannot be ignored.

These limitations aside, what this investigation has demonstrated is the disconcerting nature of the trends in job control, job demands and job satisfaction, as interpreted and measured, and the particular nature of the inter-relationships between these concepts. Such are the nature of these trends and inter-relationships the paper provides some support for Clark’s (2005) contention of ‘good job-less growth’, at least in Britain; and, from a public policy perspective, identifies legitimate concern about the consequences of the transformation of work.

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Table 1. Number of Observations in the Pooled Data Set and their Origin Year Frequency Percent Origin 1986 3,844 16.11 Social Change and Economic Life Initiative surveys 1992 3,212 13.46 Employment in Britain survey 1997 2,394 10.03 Skills Survey 2001 4,367 18.30 Skills Survey 2006 7,281 30.52 Skills Survey 2012 2,762 11.58 Skills Survey Total 23,860 100.00

Table 2. OLS Regression Results: Dependent Variables: ‘howhard’; ‘tasks’; ‘how’; ’quality’ and ‘influence’ Coefficient Coefficient Coefficient Coefficient Coefficient (robust SE) (robust SE) (robust SE) (robust SE) (robust SE) ‘howhard’ ‘tasks’ ‘how’ ‘quality’ ‘influence’ Female (=1) .0133 .0106 -.0141 .0406 ** .0504 (.0165) (.0202) (.0182) (.0200) (.0559) Age Categories 20 -29 (reference category) 30 -39 .0555 *** .1302 *** .1562 *** .1284 *** .4747 *** (.0187) (.0250) (.0223) (.0234) (.0649) 40 -49 .0419 ** .1658 *** .1886 *** .1596 *** .5578 *** (.0195) (.0257) (.0227) (.0238) (.0670) 50 -60 .0416 * .1917 *** .2146 *** .1744 *** .6227 *** (.0215) (.0273) (.0240) (.0255) (.0719) Highest Academic Qualification Categories None (reference category) Level 1 .0230 .0784 * .0917 ** .1481 *** .3439 *** (.0302) (.0409) (.0358) (.0347) (.1075) Level 2 .1101 *** .1576 *** .1598 *** .1552 *** .5820 *** (.0237) (.0316) (.0272) (.0283) (.0856) Level 3 .0795 *** .1130 *** .1449 *** .1042 *** .4451 *** (.0250) (.0331) (.0280) (.0293) (.0879) Level 4 .0644 ** .1615 *** .1744 *** .0995 *** .5011 *** (.0264) (.0343) (.0287) (.0310) (.0912) Not union member (=1) .0518 *** .0796 *** .0718 *** .0489 ** .2534 *** (.0180) (.0230) (.0196) (.0214) (.0615) Standard Occupational .1758 *** .4109 *** .3172 *** .2563 *** 1.1592 *** Classification (.0250) (.0309) (.0269) (.0295) (.0868) Managers Professionals .0216 .0502 .1392 *** .0256 .2369 ** (.0298) (.0399) (.0346) (.0367) (.1046) Associate professional and .0679 *** .0810 ** .1493 *** .1019 *** .4019 *** technical (.0243) (.0321) (.0276) (.0321) (.0881) Admin and secretarial (reference category) Skilled trades .0137 -.1774 *** .0734 ** .1943 *** .1024 (.0285) (.0395) (.0327) (.0338) (.1021) Personal services .0357 -.1209 *** .0023 .0952 *** .0131 (.0302) (.0407) (.0357) (.0365) (.1078) Sales -.0819 ** -.2200 *** -.1796 *** -.1016 ** -.5810 *** (.0372) (.0459) (.0385) (.0402) (.1246) Operatives -.1310 *** -.4878 *** -.2681 *** -.1483 *** -1.0342 *** (.0362) (.0424) (.0386) (.0412) (.1185) Elementary -.1442 *** -.3556 *** -.2258 *** -.1760 *** -.9029 *** (.0299) (.0395) (.0356) (.0366) (.1097)

Table 2. cont. ‘howhard’ ‘tasks’ ‘how’ ‘quality’ ‘influence’ Workplace Size Categories .2492 *** .4237 *** .2954 *** .2687 *** 1.2386 *** Single employee (.0239) (.0341) (.0290) (.0283) (.0900) Less than 25 employees (reference category) 25 -99 employees -.0506 *** -.0953 *** -.0559 *** -.0464 ** -.2512 *** (.0169) (.0225) (.0192) (.0207) (.0599) 100 -499 employees -.0514 *** -.1356 *** -.0924 *** -.0696 *** -.3519 *** (.0191) (.0241) (.0208) (.0228) (.0642) 500 or more employees -.0670 *** -.1250 *** -.0888 *** -.0740 *** -.3571 *** (.0221) (.0272) (.0250) (.0266) (.0746) No unions present at workplace .0092 .0782 *** .0623 *** .0774 *** .2244 *** (=1) (.0188) (.0231) (.0203) (.0218) (.0630) Year (1992) (Reference category) Year (1997) = 1 -.1762 *** -.2173 *** -.2708 *** -.4472 *** -1.1131 *** (.0389) (.0479) (.0446) (.0431) (.1322) Year (2001) = 1 -.3479 *** -.3420 *** -.3987 *** -.3993 *** -1.4894 *** (.0378) (.0459) (.0420) (.0398) (.1263) Year (2006) = 1 -.3374 *** -.3685 *** -.4170 *** -.4410 *** -1.5630 *** (.0389) (.0461) (.0423) (.0398) (.1283) Year (2012) = 1 -.3658 *** -.3327 *** -.4459 *** -.4439 *** -1.5899 *** (.0399) (.0483) (.0456) (.0422) (.1311) Constant 2.4216 *** 1.5693 *** 1.9002 *** 2.0600 *** 7.9588 *** (.0973) (.1238) (.1107) (.1138) (.3324)

Number of observations 17176 17178 17179 17172 17153 F (41, 17134) (41, (41, 17137) (41, 17130) (41, 17111) = 20.73 17136) = 40.38 = 28.03 = 56.41 = 56.61 Prob > F 0.0000 0.0000 0.0000 0.0000 0.0000 R-squared 0.0635 0.1448 0.1235 0.0811 0.1570

Wald test statistics for the F(3, 17134) F(3, 17136) F(3, 17137) F(3, 17130) F(3, 17111) age category variables = 2.93 = 18.43 = 29.66 = 18.30 = 29.90 Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = 0.0323 0.0000 0.0000 0.0000 0.0000 Wald test statistics for the F(4, 17134) F(4, 17136) F(4, 17137) F(4, 17130) F(4, 17111) education category variables = 6.85 = 7.40 = 10.77 = 8.46 = 11.94 Prob > F= Prob > F = Prob > F = Prob > F = Prob > F = 0.0000 0.0000 0.0000 0.0000 0.0000 Wald test statistics for the F(8, 1734) F(8, 17136) F(8, 13137) F(8, 17130) F(8, 17111) SOC category variables = 22.79 = 103.33 = 64.11 = 35.86 = 89.18 Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = 0.0000 0.0000 0.0000 0.0000 0.0000 Wald test statistics for the F(4, 17134) F(4, 17136) F(4, 17137) F(4, 17130) F(4, 17111) workplace size category = 37.31 = 61.51 = 39.46 = 32.26 = 70.66 variables Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = 0.0000 0.0000 0.0000 0.0000 0.0000 Wald test statistics for the F(13, 17134) F(13, F(13, F(13, 17130) F(13, SIC category variables = 3.05 17136) 13137) = 4.45 17111) Prob > F = = 7.50 = 4.56 Prob > F = = 6.69 0.0002 Prob > F = Prob > F = 0.0000 Prob > F = 0.0000 0.0000 0.0000

Footnotes to the above and all the subsequent regression results:

The model estimated also contains variables denoting ethnicity (via a dummy variable): the log of the number of hours the respondent usually worked: and the SIC of the activity undertaken at the workplace (via 13 dummy variables).

*, **and *** statistically significant at (p < 0.1), (p < 0.05) and (p < 0.01), respectively.

Table 3. OLS Regression Results: Dependent Variables: ‘hard’; ‘tension’; ‘extra’; and ‘intensification’ Coefficient Coefficient Coefficient Coefficient (robust SE) (robust SE) (robust SE) (robust SE) ‘hard’ ‘tension’ ‘extra’ ‘intensification’ Female (=1) .1525 *** .0361 * -.0296 .1570 *** (.0164) (.0200) (.0258) (.0464) Age Categories 20 -29 (reference category) 30 -39 .0130 .0516 ** .0586 * .1246 ** (.0203) (.0242) (.0325) (.0578) 40 -49 .0088 .0793 *** .0469 .1383 ** (.0206) (.0245) (.0334) (.0584) 50 -60 -.0434 * .0290 .0041 -.0024 (.0223) (.0259) (.0351) (.0612) Highest Academic Qualification Categories None (reference category) Level 1 0.0099 .0098 .0734 .0752 (.0316) (.0352) (.0486) (.0798) Level 2 -.0104 .0538 * .1053 *** .1510 ** (.0236) (.0286) (.0384) (.0645) Level 3 -.0325 .0223 .1679 *** .1632 ** (.0257) (.0302) (.0401) (.0688) Level 4 -.0099 .0205 .2999 *** .3155 *** (.0267) (.0319) (.0412) (.0722) Not union member (=1) -.0534 ** -.1003 *** -.0583 ** -.2061 *** (.0181) (.0216) (.0290) (.0507) Standard Occupational Classification .1755 *** .1155 *** .5612 *** .8520 *** Managers (.0241) (.0315) (.0413) (.0715) Professionals .1668 *** .0700 *** .4470 *** .6953 *** (.0332) (.0364) (.0521) (.0954) Associate professional and technical .1230 *** .1097 *** .2909 *** .5219 *** (.0246) (.0312) (.0420) (.0722) Admin and secretarial (reference category) Skilled trades .0570 * -.1673 *** .0852 * -.0241 (.0291) (.0359) (.0489) (.0824) Personal services .0814 *** -.0415 .0690 .1075 (.0304) (.0407) (.0537) (.0886) Sales -.0344 -.0466 -.0200 -.1026 (.0323) (.0407) (.0537) (.0912) Operatives -.0258 -.0995 *** -.0023 -.1244 (.0317) (.0380) (.0505) (.0853) Elementary -.0030 -.2226 *** -.0899 * -.3131 *** (.0316) (.0364) (.0475) (.0838)

Table 3. cont. ‘hard’ ‘tension’ ‘extra’ ‘intensification’ Workplace Size Categories -.0534 -.1392 *** .2018 *** .0147 Single employee (.0337) (.0388) (.0480) (.0851) Less than 25 employees (reference category) 25 -99 employees -.0206 .0261 -.0546 * -.0495 (.0170) (.0221) (.0291) (.0500) 100 -499 employees -.0447 ** .0091 -.0660 ** -.1004 * (.0188) (.0235) (.0309) (.0537) 500 or more employees -.0902 *** .0030 -.1393 *** -.2241 *** (.0218) (.0266) (.0359) (.0640) No unions present at workplace (=1) .0318 * -.0452 ** .1050 *** .0880 * (.0187) (.0221) (.0300) (.0533) Year (1992) (reference category) Year (2001) = 1 .1425 *** .1793 *** -.1245 ** .1900 ** (.0309) (.0408) (0506) (.0850) Year (2006) = 1 .1636 *** .1680 *** -.1851 *** .1394 * (.0308) (.0403) (.0499) (.0839) Year (2012) = 1 .2148 *** .2047 *** -.1352 ** .2771 *** (.0319) (.0431) (.0524) (.0885) Constant 2.2610 *** 1.1224 *** .4406 *** 3.8314 *** (.0994) (.1175) (.1584) (.2723) Number of observations 15011 14990 15003 14962 F (40, 14970) (40, 14949) (40, 14962) (40, 14921) = 27.79 = 28.64 = 67.62 = 65.49 Prob > F 0.0000 0.0000 0.0000 0.0000 R-squared 0.0891 0.0895 0.1720 0.1823

Wald test statistics for the F(3, 14970) F(3, 14949) F(3, 14962) F(3, 14921) age category variables = 4.00 = 4.10 = 1.93 = 4.34 Prob > F = Prob > F = Prob > F = Prob > F = 0.0074 0.0065 0.1221 0.0046 Wald test statistics for the F(4, 14970) F(4, 14949) F(4, 14962) F(4, 14921) education category variables = 0.60 =1.16 = 16.58 = 5.57 Prob > F = Prob > F = Prob > F = Prob > F = 0.6611 0.3280 0.0000 0.0002 Wald test statistics for the F(8, 14970) F(8, 14949) F(8, 14962) F(8, 14921) SOC category variables = 12.10 = 16.70 = 42.80 = 39.50 Prob > F = Prob > F = Prob > F = Prob > F = 0.0000 0.0000 0.0000 0.0000 Wald test statistics for the F(4, 14970) F(4, 14949) F(4, 14962) F(4, 14921) workplace size category variables = 4.75 = 4.28 = 10.66 = 3.42 Prob > F = Prob > F = Prob > F = Prob > F = 0.0008 0.0018 0.0000 0.0084 Wald test statistics for the F(13, 14970) F(13, F(13, 14962) F(13, 14921) SIC category variables = 7.90 14949) = 9.46 = 9.51 Prob > F = = 4.84 Prob > F = Prob > F = 0.0000 Prob > F = 0.0000 0.0000 0.0000

Table 4. Pair-wise Correlation Coefficients: ‘Influence’ and ‘Intensification’, by survey year and for all years combined Year Correlation Coefficient 2001 0.1922 2006 0.2018 2012 0.1489 All years 0.1876

Table 5. Pairwise Correlations ‘howhard’; ‘tasks’; ‘how’; ‘quality’; ‘hard’; ‘tension’; and ‘extra’, by year and for all years combined

Table 5a. 2001 ‘howhard’ ‘tasks’ ‘how’ ‘quality’ ‘hard’ ‘tension’ ‘extra’ ‘howhard’ 1.0000 ‘tasks’ 0.4071 1.0000 ‘how’ 0.4029 0.6226 1.0000 ‘quality’ 0.4027 0.4019 0.5088 1.0000 ‘hard’ 0.1027 0.1397 0.1118 0.0796 1.0000 ‘tension’ -0.0039 * 0.0404 * 0.0426 0.0201 * 0.3689 1.0000 ‘extra’ 0.1371 0.2083 0.2076 0.1347 0.3271 0.2950 1.0000

Footnote to Table 5:

*Identifies not statistically significant at (p < 0.05)

Table 5b. 2006 ‘howhard’ ‘tasks’ ‘how’ ‘quality’ ‘hard’ ‘tension’ ‘extra’ ‘howhard’ 1.0000 ‘tasks’ 0.4594 1.0000 ‘how’ 0.4540 0.5981 1.0000 ‘quality’ 0.3907 0.4128 0.5054 1.0000 ‘hard’ 0.1276 0.1524 0.1258 0.1065 1.0000 ‘tension’ 0.0123 * 0.0750 0.0636 0.0361 0.3679 1.0000 ‘extra’ 0.1366 0.2085 0.1881 0.1083 0.3350 0.2983 1.0000

Table 5c. 2012 ‘howhard’ ‘tasks’ ‘how’ ‘quality’ ‘hard’ ‘tension’ ‘extra’ ‘howhard’ 1.0000 ‘tasks’ 0.4297 1.0000 ‘how’ 0.4006 0.6113 1.0000 ‘quality’ 0.3847 0.4486 0.5225 1.0000 ‘hard’ 0.0594 0.1691 0.1247 0.0889 1.0000 ‘tension’ -0.0254 * 0.0474 0.0016 * -0.0131 * 0.3751 1.0000 ‘extra’ 0.0487 0.1941 0.1768 0.1033 0.3638 0.3203 1.0000

Table 5d. All Years ‘howhard’ ‘tasks’ ‘how’ ‘quality’ ‘hard’ ‘tension’ ‘extra’ ‘howhard’ 1.0000 ‘tasks’ 0.4356 1.0000 ‘how’ 0.4264 0.6089 1.0000 ‘quality’ 0.3930 0.4160 0.5099 1.0000 ‘hard’ 0.1051 0.1521 0.1207 0.0939 1.0000 ‘tension’ -0.0010 * 0.0579 0.0439 0.0205 0.3694 1.0000 ‘extra’ 0.1181 0.2057 0.1920 0.1157 0.3383 0.3019 1.0000

Table 6. Regression Results: Dependent Variable: influence Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient (Robust SE) (Robust SE) (Robust SE) (Robust SE) (Robust SE) (Robust SE) (Robust SE) (Robust SE) (Robust SE) (Robust SE) 2001 2001 2006 2006 2012 2012 All Years All Years All Years All Years ‘intensification’ .1245 *** .1256 *** .0902 *** .1196 *** .1198 *** ‘hard’ .2459 *** .4080 *** .3429 *** .3401 *** .3434 *** ‘tension’ -.1644 *** -.1285 *** -.2378 *** -.1589 *** -.1590 *** ‘extra’ .2659 *** .1553 *** .1947 *** .2018 *** .2009 *** Year 2001 (reference year) Year 2006 -.0614 -.0641 Year2012 -.1117 -.1200

Number of observations 3980 3980 5747 5747 2395 2395 12122 12122 12122 12122 F (38, 3941) (40, 3939) (38, 5708) (40, 5706) (37, 2357) (39, 2355) (38, 12083) (40, 12081) (40, 12081) (42, 12079) = 21.40 = 21.45 = 33.02 = 29.12 = 8.45 = 8.31 = 48.19 = 47.03 = 45.91 = 44.91 Prob > F 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 R-squared 0.1687 0.1774 0.1833 0.1905 0.1472 0.1580 0.1626 0.1704 0.1628 0.1706

Footnotes to Table 6:

Additionally, each regression contained all the variables identified in the results and footnotes associated with Table 2.

*** and ** statistically significant at (P< 0.01) and (p < 0.05), respectively.

Table 7. OLS Regression Results: Dependent Variables: ‘jobsat’; ‘prospects’; ‘pay’; ‘security’; ‘abilities’; ‘initiative’; and ‘hours’ Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient (robust SE) (robust SE) (robust SE) (robust SE) (robust SE) (robust SE) (robust SE) ‘jobsat’ ‘prospects’ ‘pay’ ‘security’ ‘abilities’ ‘initiative’ ‘hours’ Female (=1) .8923 *** .1129 *** .0633 .1766 *** .1081 *** .0866 ** .0485 (.3074) (.0416) (.0414) (.0399) (.0383) (.0358) (.0380) Age Categories 20 -29 (reference category) 30 -39 .0422 -.1100 ** .0249 -.1461 *** .0939 ** .1465 *** -.0141 (.3599) (.0505) (.0509) (.0492) (.0432) (.0400) (.0451) 40 -49 .4052 -.1055 ** .0003 -.1987 *** .1693 *** .1957 *** .0276 (.3637) (.0519) (.0507) (.0496) (.0437) (.0407) (.0445) 50 -60 1.3513 *** -.0126 .0923 * -.1675 *** .2518 *** .2627 *** .1254 *** (.3904) (.0548) (.0544) (.0567) (.0445) (.0427) (.0497) Highest Academic Qualification Categories None (reference category) Level 1 -.5563 .0314 .0158 -.2232 *** -.0691 .0409 -.0691 (.5676) (.0779) (.0795) (.0702) (.0630) (.0620) (.0680) Level 2 -.6484 -.0421 -.0071 -.1555 *** -.0736 .0245 -.0527 (.4658) (.0632) (.0662) (.0561) (.0506) (.0506) (.0583) Level 3 -.3597 -.0063 .0684 -.1570 *** -.0934 * .0140 -.0690 (.4733) (.0650) (.0692) (.0572) (.0541) (.0526) (.0602) Level 4 -1.8303 *** -.1618 ** .0076 -.3427 *** -.1974 *** -.0604 -.1792 *** (.4961) (.0659) (.0715) (.0639) (.0547) (.0543) (.0648) Not union member (=1) .3699 .0034 -.0447 .0102 -.0129 .0286 .1379 *** (.3332) (.0468) (.0455) (.0456) (.0386) (.0381) (.0396)

Table 7. cont. ‘jobsat’ ‘prospects’ ‘pay’ ‘security’ ‘abilities’ ‘initiative’ ‘hours’ Standard Occupational Classification 2.4940 *** .4105 *** .2796 *** -.0185 .3343 *** .3171 *** -.2691 *** Managers (.4645) (.0676) (.0660) (.0665) (.0515) (.0491) (.0576) Professionals 1.9073 *** .3544 *** .2439 *** .2044 *** .3661 *** .2389 *** -.3562 *** (.5347) (.0756) (.0706) (.0778) (.0626) (.0605) (.0634) Associate professional and technical 1.1891 ** .1501 ** .0575 .0660 .2537 *** .1664 *** -.1839 *** (.4854) (.0679) (.0650) (.0654) (.0542) (.0520) (.0574) Admin and secretarial (reference category) Skilled trades -.9217 -.1243 -.2007 *** -.1928 ** .0969 .0194 -.2087 *** (.5791) (.0788) (.0771) (.0795) (.0637) (.0605) (.0665) Personal services .3568 -.0997 -.3133 *** -.0095 .0045 -.0522 -.0784 (.5681) (.0832) (.0889) (.0784) (.0683) (.0608) (.0667) Sales -.9044 .0444 -.0401 -.1051 -.1170 -.2128 *** -.2588 *** (.6356) (.0912) (.0857) (.0771) (.0724) (.0698) (.0700) Operatives -2.4399 *** -.1963 ** -.2326 *** -.0565 -.2826 *** -.3302 *** -.3262 *** (.6227) (.0824) (.0796) (.0906) (.0783) (.0721) (.0698) Elementary -3.1232 *** -.2945 *** -.2025 ** -.1026 -.4666 *** -.4417 *** -.3346 *** (.5622) (.0780) (.0792) (.0697) (.0681) (.0645) (.0659) Workplace Size Categories 1.6891 *** .1278 * .1336 -.1287 * .1553 ** .3300 *** .1513 ** Single employee (.5446) (.0726) (.0841) (.0739) (.0631) (.0557) (.0638) Less than 25 employees (reference category) 25 -99 employees -.6095 * .0212 -.0015 .0465 -.0847 ** -.1007 *** -.0517 (.3367) (.0473) (.0463) (.0418) (.0391) (.0364) (.0411) 100 -499 employees -1.0382 *** -.0415 .0043 -.0681 -.1662 *** -.1790 *** -.0574 (.3642) (.0512) (.0499) (.0486) (.0444) (.0423) (.0440) 500 or more employees -.2228 .0563 .1267 ** -.0780 -.1388 *** -.2030 *** .0286 (.4035) (.0556) (.0551) (.0566) (.0522) (.0478) (.0477) No unions present at workplace (=1) .0576 0.0104 -.0095 .1469 *** .0592 .1050 ** -.1026 ** (.3540) (.0478) (.0455) (.0433) (.0417) (.0412)

Table 7. cont. ‘jobsat’ ‘prospects’ ‘pay’ ‘security’ ‘abilities’ ‘initiative’ ‘hours’ Year (1992) = 1 .5552 -.0243 .2081 ** -.1075 .0453 .1682 ** -.0420 (.5825) (.0796) (.0802) (.0782) (.0731) (.0672) (.0693) Year 2006 (reference category) Year (2012) = 1 -1.2719 *** -.1104 *** -.0855 ** -.2032 *** -.0955 *** -.1570 *** -.0576 (.3097) (.0422) (.0432) (.0401) (.0360) (.0341) (.0370) Constant 16.9774 *** .3534 1.0373 *** 1.4072 *** .9556 *** 1.1173 *** 3.9271 *** (.1.9551) (.2578) (.2566) (.2351) (.2799) (.2224) (.2271) Number of observations 10823 10944 11013 11011 11014 11016 11018 F (39, 10783) (39, 10904) (39, 10973) (39, 10971) (39, 10974) (39, 10976) (39, 10978) = 8.54 = 7.30 = 6.49 = 8.21 = 13.83 = 17.46 = 16.67 Prob > F 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 R-squared 0.0477 0.0346 0.0306 0.0439 0.0789 0.0890 0.0818

Wald test statistics for the F(3, 10783) F(3, 10904) F(3, 10973) F(3, 10971) F(3, 10974) F(3, 10976) F(3, 10978) age category variables = 6.11 = 3.02 = 1.64 = 5.45 = 12.21 = 13.17 = 4.31 Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = 0.0004 0.0286 0.1773 0.0010 0.0000 0.0000 0.0048 Wald test statistics for the F(4, 10783) F(4, 10904) F(4, 10973) F(4, 10971) F(4, 10974) F(4, 10976) F(4, 10978) education category variables = 5.60 = 3.57 = 0.66 = 7.89 = 3.84 = 1.56 = 2.86) Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = 0.0002 0.0064 0.6173 0.0000 0.0041 0.1820 0.0219 Wald test statistics for the F(8, 10783) F(8, 10904) F(8, 10973) F(8, 10971) F(8, 10974) F(8, 10976) F(8, 10978) SOC category variables = 18.70 = 17.59 = 12.38 + 3.31 = 27.80 = 26.76 = 7.13 Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = 0.0000 0.0000 0.0000 0.0009 0.0000 0.0000 0.0000 Wald test statistics for the F(4, 10783) F(4, 10904) F(4, 10973) F(4, 10971) F(4, 10974) F(4, 10976) F(4, 10978) workplace size category variables = 6.40 = 1.71 = 2.52 = 2.79 = 6.57 = 19.13 = 3.33 Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = 0.0000 0.1454 0.0390 0.0250 0.0000 0.0000 0.0099 Wald test statistics for the F(13, 10783) F(13, 10904) F(13, 10973) F(13, 10971) F(13, 10974) F(13, 10976) F(13, 10978) SIC category variables = 2.64 = 3.39 = 5.05 = 6.17 = 4.33 = 3.93 = 3.87 Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = 0.0011 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Table 7. cont. OLS Regression Results: Dependent Variables: ‘fringes’; ‘work’; ‘amount’; ‘variety’; ‘training’; ‘allinall’ Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient (robust SE) (robust SE) (robust SE) (robust SE) (robust SE) (robust SE) ‘fringes’ ‘work’ ‘amount’ ‘variety’ ‘training’ ‘allinall’ Female (=1) -.0530 .1056 *** -.0589 .1198 *** .0901 ** .0968 *** (.0430) (.0323) (.0363) (.0360) (.0394) (.0350) Age Categories 20 -29 (reference category) 30 -39 -.0665 .0578 -.0391 .1650 *** -.1332 *** .0420 (.0502) (.0386) (.0429) (.0420) (.0482) (.0430) 40 -49 -.0670 .1574 *** .0138 .2242 *** -.0958 ** .0844 ** (.0497) (.0378) (.0436) (.0428) (.0485) (.0429) 50 -60 -.0239 .2362 *** .1068 ** .3523 *** -.0396 .1633 *** (.0547) (.0402) (.0457) (.0437) (.0513) (.0459) Highest Academic Qualification Categories None (reference category) Level 1 .0542 -.0940 * -.0324 -.0039 -.1223 -.1096 (.0839) (.0570) (.0659) (.0664) (.0764) (.0679) Level 2 -.0017 -.0689 -.0538 -.0390 -.0838 -.1414 *** (.0720) (.0452) (.0515) (.0517) (.0610) (.0539) Level 3 .0597 -.0800 * -.0025 .0273 -.0216 -.1466 *** (.0730) (.0475) (.0528) (.0527) (.0636) (.0557) Level 4 -.0515 -.2057 *** -.1813 *** -.0207 -.1647 ** -.3206 *** (.0763) (.0507) (.0555) (.0541) (.0654) (.0584)

Table 7. cont. ‘fringes’ ‘work’ ‘amount’ ‘variety’ ‘training’ ‘allinall’ Not union member (=1) .0917 ** .0378 .1366 *** .0152 -.0904 ** .0543 (.0437) (.0342) (.0394) (.0374) (.0427) (.0381) Standard Occupational Classification .1727 *** .2914 *** .0711 .3939 *** .2503 *** .1904 *** Managers (.0661) (.0496) (.0573) (.0517) (.0625) (.0529) Professionals -.0958 .2643 *** -.0801 .3058 *** .2258 *** .1479 ** (.0689) (.0564) (.0663) (.0613) (.0697) (.0597) Associate professional and technical -.1277 ** .2234 *** .0193 .2956 *** .1996 *** .0527 (.0631) (.0513) (.0589) (.0540) (.0621) (.0564) Admin and secretarial (reference category) Skilled trades -.3814 *** .0396 .0007 .0630 -.0638 -.0731 (.0785) (.0587) (.0656) (.0620) (.0771) (.0641) Personal services -.1916 ** .2137 *** .2946 *** .1669 *** .1626 ** .1007 (.0833) (.0615) (.0677) (.0641) (.0773) (.0645) Sales -.1145 -.0789 .0541 -.0806 .0985 -.1662 ** (.0827) (.0667) (.0695) (.0749) (.0791) (.0708) Operatives -.5250 *** -.0735 -.0626 -.2496 *** -.1042 -.1209 * (.0853) (.0603) (.0683) (.0760) (.0848) (.0682) Elementary -.3326 *** -.1830 *** .0052 -.2994 *** -.2761 *** -.2330 *** (.0758 (.0607) (.0653) (.0669) (.0740) (.0653) Workplace Size Categories .2442 *** .2363 *** .0814 .1396 ** -.0871 .2630 *** Single employee (.0766) (.0553) (.0667) (.0580) (.0685) (.0581) Less than 25 employees (reference category) 25 -99 employees -.0064 -.1342 *** -.0802 ** -.1377 *** -.0308 -.1133 *** (.0466) (.0355) (.0393) (.0365) (.0441) (.0395) 100 -499 employees .0718 -.1628 *** -.1062 ** -.1754 *** -.0672 -.1910 *** (.0507) (.0373) (.0447) (.0438) (.0480) (.0431) 500 or more employees .2288 *** -.1390 *** .0017 -.1700 *** .0829 -.0892 ** (.0564) (.0413) (.0485) (.0470) (.0533) (.0442)

Table 7. cont. ‘fringes’ ‘work’ ‘amount’ ‘variety’ ‘training’ ‘allinall’ No unions present at workplace (=1) -.1590 *** .0549 .0810 ** .0282 -.1466 *** .0103 (.0451) (.0374) (.0411) (.0402) (.0453) (.0400) Year (1992) = 1 -.0488 .2017 *** .1041 .1254 * -.2104 *** .0747 (.0808) (.0374) (.0682) (.0643) (.0762) (.0643) Year 2006 (reference category) Year (2012) = 1 -.0648 -.1169 *** -.1150 *** -.1134 *** -.0281 -.1242 *** (.0409) (.0335) (.0376) (.0362) (.0389) (.0370) Constant 1.0297 *** 1.5789 *** 2.0270 *** .8515 *** 1.1261 *** 1.8381 *** (.2756) (.1777) (.2066) (.2371) (.2549) (.2068) Number of observations 10917 11019 11016 11017 10961 11013 F (39, 10877) (39, 10979) (39, 10976) (39, 10977) (39, 10921) (39, 10973) = 12.29 = 10.66 = 7.38 = 17.05 = 8.86 = 7.99 Prob > F 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 R-squared 0.0506 0.0614 0.0387 0.0927 0.0425 0.0439 Wald test statistics for the F(3, 10877) F(3, 10979) F(3, 10976) F(3, 10977) F(3, 10921) F(3, 10973) age category variables = 0.91 = 14.88 = 4.85 = 22.81 = 3.23 = 5.17 Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = 0.4375 0.0000 0.0022 0.0000 0.0215 0.0014 Wald test statistics for the F(4, 10877) F(4, 10979) F(4, 10976) F(4, 10977) F(4, 10921) F(4, 10973) education category variables = 1.50 = 5.11 = 5.27 = 0.70 = 3.07 = 9.38 Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = 0.1989 0.0004 0.0003 0.5923 0.0154 0.0000 Wald test statistics for the F(8, 10877) F(8, 10979) F(8, 10976) F(8, 10977) F(8, 10921) F(8, 10973) SOC category variables = 12.75 = 13.00 = 4.69 = 24.47 = 9.37 = 8.01 Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Wald test statistics for the F(4, 10877) F(4, 10979) F(4, 10976) F(4, 10977) F(4, 10921) F(4, 10973) workplace size category variables = 7.71 = 14.62 = 3.35 =9.18 = 2.67 = 14.31 Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = 0.0000 0.0000 0.0095 0.0000 0.0303 0.0000 Wald test statistics for the F(13, 10877) F(13, 10979) F(13, 10976) F(13, 10977) F(13, 10921) F(13, 10973) SIC category variables = 13.62 = 5.43 = 1.65 = 6.66 = 3.95 = 4.43 Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = Prob > F = 0.0000 0.0000 0.0636 0.0000 0.0000 0.0000

Table 8. OLS Regression Results: Dependent Variable: jobsat Coefficient Coefficient Coefficient (robust SE) (robust SE) (robust SE) Female (=1) .9631 *** .9945 *** .6723 * (.3587) (.3524) (.3440) Age Categories 20 -29 (reference category) 30 -39 .2206 -.4239 -.3232 (.4484) (.4375) (.4261) 40 -49 .3760 -.4080 -.2806 (.4453) (4347) (.4209) 50 -60 .7921 * .0650 .2904 (.4545) (.4423) (.4303) Highest Academic Qualification Categories None (reference category) Level 1 -1.2220 * -1.8747 *** -1.7559 *** (.6839) (.6726) (.6624) Level 2 -1.0369 * -1.8792 *** -1.6989 *** (.6068) (.6000) (.5900) Level 3 -1.1203 * -1.6764 *** -1.5534 *** (.5981) (.5926) (.5822) Level 4 -2.4652 *** -3.0442 *** -2.9074 *** (.6168) (.6066) (.5962) Not union member (=1) .2665 -.1796 -.1678 (.3841) (.3599) (.3521) Standard Occupational Classification 2.1061 *** 1.0456 * .8681 * Managers (.5544) (.5350) (.5258) Professionals 1.8452 *** 1.7878 *** 1.5954 *** (.6319) (.6023) (.5822) Associate professional and technical 1.1335 ** .9144 * .8746 * (.5736) (.5424) (.5262) Admin and secretarial (reference category) Skilled trades -.8582 -1.0551 -1.3097 * (.7090) (.6718) (.6673) Personal services -.2367 -.0830 -.3266 (.6449) (.6147) (.6006) Sales -1.4204 * -.7326 -.6571 (.8085) (.8223) (.8070) Operatives -2.7795 *** -1.4949 ** -1.5224 ** (.7674) (.7287) (.7093) Elementary -3.6413 *** -2.3181 *** -2.8561 *** (.6839) (.6794) (.6563) Workplace Size Categories 2.0883 *** .5294 .4713 Single employee (.5672) (.5370) (.5234) Less than 25 employees (reference category) 25 -99 employees -5516 -.2149 -.0851 (.3936) (.3821) (.3751) 100 -499 employees -1.4236 *** -.9650 ** -.8328 ** (.4325) (.4209) (.4116) 500 or more employees -.5224 -.1058 .2330 (.4898) (.4644) (.4527)

Table 8. cont. Coefficient Coefficient Coefficient (Robust SE) (Robust SE) (Robust SE) No unions present at workplace (=1) -.1220 -.2913 -.3085 (.4074) (.3843) (.3700)

Year 2006 (reference category) Year (2012) = 1 -1.2041 *** -1.1188 *** -1.1801 *** (.3093) (.3026) (2946) Intensification -.3077 *** (.0806) Influence 1.2053 *** (.0612) Hard 2.2950 *** (.2403) Tension -1.9697 *** (.1773) Extra -.4577 *** (.1359) Howhard 1.4898 *** (.2460) Tasks 1.2259 *** (.1961) How 1.3085 *** (.2340) Quality .6151 *** (.1819)

Constant 18.0159 11.3253 *** 7.1912 *** *** (2.2394) (2.2175) (2.2329)

Number of observations 8175 8138 8138 F (38, 8136) (40, 8097) (45, 8092) = 6.85 = 17.16 = 19.62 Prob > F 0.0000 0.0000 0.0000 R-squared 0.0526 0.1319 0.1618

Table 8. cont.

Wald test statistics for the F(3, 8136) F(3, 8097) F(3, 8092) age category variables = 1.27 = 1.00 = 1.34 Prob > F = Prob > F = Prob > F = 0.2840 0.3925 0.2587 Wald test statistics for the F(4, 8136) F(4, 8097) F(4, 8092) education category variables = 5.40 = 7.17 = 6.96) Prob > F = Prob > F = Prob > F = 0.0002 0.0000 0.0000 Wald test statistics for the F(8, 8136) F(8, 8097) F(8, 8092) SOC category variables = 14.41 = 6.14 = 7.09) Prob > F = Prob > F = Prob > F = 0.0000 0.0000 0.0000 Wald test statistics for the F(4, 8136) F(4, 8097) F(4, 8092) workplace size category variables = 8.49 = 2.22 = 2.24 Prob > F = Prob > F = Prob > F = 0.0000 0.0640 0.0621 Wald test statistics for the F(13, 8136) F(13, 8097) F(13, 8092) SIC category variables = 2.59 = 2.21 = 1.69 Prob > F = Prob > F = Prob > F = 0.0014 0.0071 0.0570 Wald test statistics for the joint F(2, 8097) Significance of ‘intensification’ and = 197.21 ‘influence’ Prob > F = 0.0000 Wald test statistics for the joint F(3, 8092) Significance of ‘hard’, ‘tension’ and = 64.61 ‘extra’ Prob > F = 0.0000 Wald test statistics for the joint F(4, 8092) Significance of ‘howhard’, ‘tasks’, ‘how’ = 92.01 And ‘quality’ Prob > F = 0.0000 Wald test statistics for the joint F(7, 8092) Significance of ‘hard’ ‘tension’, ‘extra’, = 81.54 ‘howhard’, ‘tasks’, ‘how’ and ‘quality’ Prob > F = 0.0000