a study of employment and skill utilisation in the UK

Department of Economics Faculty of Humanities and Social Sciences

Dissertation for the Degree of MRes Economics

The supply and demand of skill and labour: a study of employment and skill utilisation in the UK between 1993 and 2017 Luke Austin September 2018

Faculty of Humanities and Social Sciences Any student found to have cheated or plagiarised in assessment will be penalised. The Board of Examiners for Programmes will determine the nature and severity of the penalty but this may mean failure of the unit concerned or a part of the degree, with no provision for reassessment or retrieval of that failure. Proven cases of plagiarism or cheating can also lead to disciplinary proceedings as indicated in University Regulation 7.

I am aware of the guidelines on plagiarism: this coursework is the product of my own work. Name: Luke Austin Signed: ………………………………………………………………. Degree: MRes Economics Supervisor: Prof. Paul Gregg Word Count: 14,815

Date: 07/09/2018

This dissertation may be available for consultation within the University Library and may be photocopied or lent to other libraries for the purposes of consultation.

Copyright of this dissertation rests with the author. No quotation from the dissertation and no information derived from it may be published without the prior written permission of the author.

2

Contents Abstract ................................................................................................................................................... 5 1. Introduction ........................................................................................................................................ 5 2. Literature Review ................................................................................................................................ 6 2.1 Explanations for changing employment shares and job polarisation ........................................... 6 2.2 Studies of job polarisation in the UK............................................................................................. 9 2.3 Studying the supply and demand for skills ................................................................................. 10 2.4 Job quality and policy considerations ......................................................................................... 12 3. Methodology ..................................................................................................................................... 13 3.1 Measuring changes in employment shares of the wage distribution ........................................ 13 3.2 Shift-share analysis ..................................................................................................................... 13 3.3 Identifying skill utilisation using the UK Employer Skills Survey ................................................. 14 3.3.1 Taxonomies of skill ............................................................................................................... 14 3.3.2 Proxies for skill ..................................................................................................................... 15 3.4 Pseudo-cohort analysis ............................................................................................................... 17 4. Data ................................................................................................................................................... 18 4.1 Quarterly Labour Force Survey (1993-2017) .............................................................................. 18 4.1.1 Harmonising occupation classifications ............................................................................... 19 4.1.2 Data restrictions and summary statistics ............................................................................. 20 4.2 UK Employer Survey of Skills ....................................................................................................... 21 5. Analysis ............................................................................................................................................. 23 5.1 Changes in wage and employment shares ................................................................................. 23 5.2 Shift-share analysis of changes in employment share ................................................................ 27 5.3 Changes in the shares of occupational skill utilisation ............................................................... 29 5.4 Job quality and the skill utilisation of graduates ........................................................................ 33 5.5 Pseudo cohort analysis of wages and skill utilisation ................................................................. 35 6. Conclusion ......................................................................................................................................... 37 6.1 Discussion.................................................................................................................................... 37 6.2 Policy considerations and recommendations for further research ............................................ 39 7. References ........................................................................................................................................ 41 Appendix I: Ethics Form ........................................................................................................................ 45 Appendix II: Classification tasks from the British Employer Skill Survey .............................................. 49

3

List of Figures Figure 1: Development of key demographic groups between 1993 and 2017 .................................... 21 Figure 2: Median wage between 1993 and 2017 ................................................................................. 21 Figure 3: Changes in share of occupations in the UK (1993-2017) ....................................................... 21 Figure 4: Share of occupations in the UK (1993-2017) ......................................................................... 21 Figure 5: Box plot of skills measures (analytical, inter-personal, and physical) across the ISCO88 1digit level showing the distribution and variation of skill amongst occupational groups .................... 23 Figure 6: Percentage point changes in employment share by employment weighted wage deciles (1993-2017)........................................................................................................................................... 25 Figure 7: Share off employment by 1993 wage deciles (Top, Middle and Bottom) ............................. 25 Figure 8: Changing share of employment at the of 1-digit ISCO88 level (1993-2017) ......................... 25 Figure 9: Decomposition of shift-share changes of employment share of employment deciles ......... 29 Figure 10: Relationship between median wage and overall skill.......................................................... 31 Figure 11: Relationship between overall skill and change in employment share ................................ 31 Figure 12: Skill utilisation across occupations in the UK labour market between 1993 and 2017 ....... 34 Figure 13: Proportion of non-graduates in occupations requiring analytical, interpersonal and physical skills (1993-2017) .................................................................................................................... 35 Figure 14: Proportion of graduates in occupations requiring analytical, interpersonal and physical skills (1993-2017) .................................................................................................................................. 35 Figure 15: Graduate and Non-Graduate Cohorts: Wage Profiles ......................................................... 36 Figure 16: Graduate and Non-Graduate Cohorts: Proportion of workers in Analytical Occupations .. 37

List of Tables Table 1: Contribution of occupational groups to employment changes in different segments of the occupational wage distribution ............................................................................................................ 26 Table 2: Occupations by top ten growth (ISCO88 3-digit) .................................................................... 27 Table 3: Occupations by bottom ten growth (ISCO88 3-digit) .............................................................. 27 Table 4: Contributions of graduates and non-graduates to changes in employment shares .............. 29 Table 5: Skill utilisation across occupations (ISCO88 Major, 1- digit) ................................................... 31 Table 6: Skill utilisation of the top ten occupations by growth in employment share between 1993 and 2017 ............................................................................................................................................... 32 Table 7: Skill utilisation of the bottom ten occupations by growth in employment share between 1993 and 2017 ...................................................................................................................................... 32

4

The supply and demand of skill and labour: a study of employment and skill utilisation in the UK between 1993 and 2017 Abstract In this study I present evidence for the phenomenon of job polarisation in the UK between 1993 and 2017. I argue that the skilled-biased technological change (SBTC) and routine-biased technological change (RBTC) hypotheses do not fully explain the changes in employment share in the UK in this period. I analyse the changing employment shares of graduates and non-graduates using a shift-share analysis and a pseudo-cohort analysis. I show that graduates have been losing employment share in the top-paying occupations, as well as in high-skilled occupations, whilst overall share in these occupations has been increasing. I argue that this implies a declining demand for the skilled-labour of graduates, but not for skilled-labour in general. I also find that the occupations with the highest pay and growth in employment share are those with a high skill utilisation across all skill measures, including those usually classified as routine and non-routine.

1. Introduction Technological progress produces many benefits for the modern economy, but it also creates many puzzles. Labour market economists in particular have become increasingly interested in predicting the effect of technology on employment outcomes. Specifically, they seek to understand: the changing employment shares in developed economies; why some skill groups grow and others decline; the impact of technologies on job quality and wage inequality; and how the supply and demand for skills will change over time. Various theories were developed to explain the process of occupational upgrading in the US labour market during the 1990s (Oesch and Menés, 2011). A leading explanation, which became known as the skilled-biased technological change (SBTC) hypothesis, proposed that high-skilled occupations were growing at the expense of low-skilled occupations (Berman et al. 1998; Oesch and Menés, 2011). This view was challenged by empirical evidence in a number of developed economies that, rather than occupational upgrading, there was substantial growth in both very highpaid and very low-paid occupations, a process known as job polarisation (Autor et al., 2008; Goos and Manning, 2007). Alternative theories to SBTC have been proposed, such as the routine biased technological change (RBTC) hypothesis. These have been better suited to explain more recent data. However, there are still many questions to be answered regarding the fast-changing labour markets of modern economies. Will wage inequality and job quality continue to deteriorate? Will the trends of increased demand for skill continue, and will unskilled workers be pushed out of the labour market altogether? 5

How will the increased supply of graduates affect the demand for skill, and are graduates providing the kind of skill demanded by employers? I contribute to the literature by attempting to answer some of these questions. Specifically, I provide evidence for and against SBTC and RBTC, and find that overall, they fail to explain changes in employment share in the UK between 1993 and 2017. I find evidence for an increased demand for skilled labour, and yet declining prospects—at least in the near term, and from a relatively high base— for graduates entering the labour market, as demand for their labour fails to meet increased supply. In this study I broadly follow the methodologies of Goos and Manning (2007) and Salvatori (2018). The main differences are the data I use, and the definition and analysis of skill utilisation. Regarding data, I use Quarterly Labour Force Survey (QLFS) data between 1993-2017. This includes more recent data than Goos and Manning (2007) who used 1979-1999; and uses a more recent starting point than Salvatori (2018), who used 1979-2012. This has value both in terms of a robustness check, and because shift-share analysis is sensitive to the start and end points used. The skill analysis I use differs from both studies, as I use a more detailed concept of skill derived from the British Employer Skill Survey (ESS) dataset. I also build on their studies by analysing the demand for graduate labour using a pseudocohort analysis. 2. Literature Review In this section I first consider proposed explanations for changing employment shares in developed economies and the attempts to explain the occurrence of job polarisation. I provide a brief critique of the most well-known theories. I next review the literature on studies into job polarisation in the UK and observe the main trends. I then explore the ways in which the supply and demand for skill has been explored before considering the link with job quality. 2.1 Explanations for changing employment shares and job polarisation Explanations for the changing shares of employment have focused on the effects of technology and off-shoring (Salvatori, 2018; Autor 2013). Many of the studies in this area have argued that off-shoring is the dominant factor (Acemoglu and Autor, 2011; Autor and Dorn, 2013; Goos et al. 2014; Michaels et al. 2014); but there is also considerable support for technology as a key driver for the demand for labour, and especially certain types of labour and skills. A now classic example of the effect of technology on labour has been how occupations that have seen an increase in the use of computers experienced an overall increase in skill utilisation (Green et al., 2003; Elias and McKnight, 2001). Economists have attempted to predict the effect of technology on labour market outcomes as a means of explaining developing trends of wage inequality, falling job quality and changing dynamics of the

6

supply and demand for skills. Particularly, the skill-biased technological change (SBTC) hypothesis was developed to explain the deteriorating labour market outcomes of low-skilled and less-educated workers across many OECD economies (Katz and Autor, 1999; Goldin and Katz, 2008; Acemoglu and Autor, 2011). The SBTC hypothesis says that technological change favours skilled workers, and it predicts that the demand for skilled workers will rise relative to unskilled workers. This argument successfully explained much of the rising wage inequality in a number of countries (Katz and Autor, 1999). A key weakness of SBTC has been its failure to predict and explain the phenomenon of job polarisation: that is, the growth in the employment shares of both the highest-skilled (professional and managerial) and the lowest-skilled (personal services) occupations, but declining shares of employment in the middle (manufacturing and routine office) occupations. If the SBTC hypothesis was correct, then job polarisation would not occur (i.e. there should not be growth in low-skilled occupations relative to middle-skill jobs). The occurrence of job polarisation has been well-documented in many developed economies: US (Autor et al., 2006); Canada (Green and Sand, 2015); UK (Goos and Manning, 2007; Salvatori, 2015); Germany (Oesch and Menés, 2011; Spitz, 2006); Europe (Goos, Manning and Salomons, 2009). The findings of job polarisation led economists to seek more nuanced explanations of the effects of technology on labour. In response to this challenge, Autor, Levy and Murname (2003) proposed the routine-biased technological change (RBTC) hypothesis as an alternative. They argued that technology replaces human labour in routine tasks—tasks that are highly procedural or rule based, such as clerical and craft jobs—but cannot replace non-routine labour. Technology also complements workers in complex non-routine tasks (such as complex problem solving or analysis). Assuming that routine and non-routine tasks are imperfect substitutes, there should therefore be observable differences in the changes in the composition of tasks within jobs. Autor, Levy and Murname (2003) found that nonroutine occupations are situated at the lowest and highest ends of the wage distribution; whereas routine occupations were in the middle. RBTC was therefore able to explain the rise of high and low jobs at the expense of middling jobs. The RBTC hypothesis fits the data for the US in the 1990s, during which both job and wage polarisation was observed (Autor and Dorn, 2013); however, in the 2000s employment outcomes deteriorated as growth concentrated in low-skilled occupations and wage growth increased monotonically across the skill distribution (Mishel et al. 2013; Autor, 2014; Beaudry et al. 2016). Furthermore, wage polarisation has not generally been observed in other countries in which there has been job polarisation, such as

7

Germany (Dustman et al. 2009), Canada (Green and Sand, 2015) and the UK (Salvatori, 2018). There is still support for RBTC in the literature, but many researchers have again sought alternatives. Offshoring, as already mentioned, has also been identified as an important factor in the development of employment shares in the labour market (Blinder, 2007, 2009; Autor, 2013). The element that makes a task “offshorable” is that the task can be done remotely and does not depend on face-to-face interaction or physical contact (Blinder and Krueger, 2009; Blinder, 2009; Autor, 2013). Autor (2013) highlights that there can be considerable overlap between routine, abstract, manual and offshorable tasks. That is, many routine (easily codifiable tasks) can be automated or offshored and there are many non-routine tasks that are offshorable. He notes examples such as the provision of technical support and medical diagnosis. Goos and Manning (2007) suggest that those jobs that can be routinised are potentially the ones most likely to be shifted abroad. The overlap between routine and offshorable tasks highlights a potential problem of the relativity of their definitions. Routine tasks are defined as those that are easily codifiable and therefore automatable; and offshorable tasks are defined as those that can be done remotely. The problem is that the range of tasks that can be automated or performed remotely is constantly increasing. It also often happens that a task is “altered” so that it can be automated or done remotely. For example, the automation of check-out services involved facilitating customers to do parts of the task themselves, rather than fully automating the task previously done by checkout personnel. The problem can be stated differently: RBTC and offshorable explanation say that routine and offshorable jobs will lose share because they are automatable or can be done remotely; but they are not able to predict which jobs can be automated or done remotely in the future, or where in the wage distribution these jobs will be. Autor (2013) makes the important point that often what determines whether a task is automated or not is the cost of the task, and also the comparative advantage of allocating a task to a worker or a machine (or computer). With this in mind, it may turn out that the fact that many “routine” tasks have been automated may only reflect the prioritisation of certain tasks based on the cost-benefit implications of cost of labour and difficulty of automating. Ultimately it may be that the lowest paid occupations, which also happen to require the least skill, are not automated (and therefore do not lose employment share) simply because there is not enough benefit in terms of cost reduction. Rather, if cost-benefit is a driving factor of automation, it may be that the top occupations will the next target for automation. The implication is that, if the tasks identified as “routine” or “offshorable” are the ones disappearing, it may simply be coincidence that they are the ones in the middle of the wage distribution.

8

2.2 Studies of job polarisation in the UK In Goos and Manning’s (2007) cornerstone study of employment in the UK between 1975 and 1999, they found strong evidence of job polarisation using a shift-share analysis. They argued that SBTC was insufficient to explain the pattern of rising employment shares in the highest- and lowest-wage occupations, and they suggested that RBTC was a better explanation. Salvatori (2018) provides evidence on job polarisation in the UK based on a similar methodology as Goos and Manning (2007). He places particular emphasis on the contribution of different skill groups to changes in employment share. He used a detailed shift-share analysis of an extensive range of skill groups. He found that job polarisation has occurred in the UK in every decade between 1979 and 2012. The decline in middling occupations, he says, is explained by the decline of non-graduates and their shift down the wage distribution; whilst the top occupations absorbed most of this shift due to the rise in graduates. Interestingly he found that graduates, although mostly concentrated at the top, had also shifted down the wage distribution towards the middle. Ultimately, he argues that supply-side factors are more important for explaining the change in the distribution of occupational skill than technology. Bisello (2013) finds evidence for job polarisation in Britain between 1997 and 2006 using a task-based perspective based on data from the UK ESS. They examine the effects of computerisation on routine tasks, and they show that changes in employment are negatively related to the routine intensity of the occupation. They make an important contribution to the literature through their extensive use of the UK ESS and their development of flexible task measures based on the importance of certain tasks in each occupation. I develop a set of task measures in this study based on a similar approach (further details provided in Section III). Akcomak et al. (2013) also use the British ESS. They combine the ESS with employment data to analyse changes in employment between and within occupations in the period 1997 to 2006. They find that the within change has been substantial, and they argue that this supports SBTC. They also confirm the presence of job polarisation in the UK, and they cite this as evidence for SBTC and off-shoring, attributing more importance to the former. This study differs from the others due to its support for SBTC, particularly in light of its finding of job polarisation, which is typically used as evidence against SBTC. Holmes and Mayhew (2012) take a different approach to most other studies in analysing job polarisation in the UK. They find much less support for polarisation in the wage distribution as they do for changes in job titles: that is, they find that many jobs that are classified as high earning in a given starting period are actually earning middle or even low incomes in the end period. This suggests that there has been an inflation in job titles, rather than job polarisation. They highlight the role of education and union membership as important factors in suppressing relative wage growth in the middle. They argue that an increase in educational attainment explains the increase in wage growth 9

at the top, rather than a shift towards non-routine jobs, an argument supported elsewhere by evidence from the study by Salvatori (2018). Oesch and Menés (2010) analysed occupational changes in Britain, Germany, Spain and Switzerland between 1990 and 2008. They studied the data in relation to four themes: SBTC, RBTC, skill supply evolution and wage-setting institutions. They found significant occupational upgrading due to education, with the majority of growth concentrated at the top of the wage distribution, with declines in the middle relative to the bottom. They argue that this supports RBTC, but they suggest that differences at the country level in low-paid services are evidence for upgrading being channelled into a polarising pattern by wage-setting institutions. They also highlight that immigration in Britain and Spain has been crucial to meet the demand for low-skilled labour. The majority of the studies on polarisation in the UK labour market have found little to no support for SBTC—with the exception of Akcomak et al. (2013). There is mixed support for RBTC, with much of the literature highlighting its limitations and arguing for more nuanced theories that are better supported by the data, particularly explanations based on supply-side factors. Education (particularly graduates) are often cited as a key supply-side factor. There appears to be some sensitivity to the time periods used, as studies using a more recent data tend to emphasise within composition changes over between; and the arguments by Holmes and Mayhew (2012), that much of the evidence for job polarisation in the UK is based on incorrectly labelling job titles as high paying, is important. This finding ought to be studied further, although the problem with job titles can be counterbalanced in two ways: (i) by using more recent starting points, as this should “reset” the problem of an inaccurate occupational wage distribution; and (ii) using up-to-date skill surveys that focus on the changing underlying tasks of occupations. Studies based on longer data that use a longer timeframe would be more susceptible to the criticisms they highlight. 2.3 Studying the supply and demand for skills The literature on employment and job polarisation in the UK has suggested that supply-side changes and changes within skill groups has been a significant factor in the development of employment shares (see, for example, Salvatori, 2018). One of the most significant changes in the supply of skill in the labour market has been the substantial increase in graduates. Participation in higher education in the UK has expanded over the last two decades, to the point that one in three young people will pursue a higher education, up from one in eight (Elias and McKnight, 2001). The significant increase in the supply of graduates, who can be considered highly-skilled based on their level of education, is likely to have an impact on occupational outcomes in the labour market.

10

The effects of supply, or changes to the relative composition of the labour force, have been estimated in the literature using shift-share analysis to decompose the contributions of “employment cells” or “skill groups” into between and within components (see particularly Goos and Manning, 2007; Salvatori, 2018). The between component estimates the effect of an increase in the cell size of a particular skill group if the employment distribution within that cell stays constant. Conversely, the within component measures the effect of changes of employment shares across occupations within the skill group. Goos and Manning (2007) construct “counterfactuals” (comprable to the “between” effect) firstly, by dividing the workforce into two gender and twelve age cells to create 24 individual age-gender cells. They then perform the same analysis including four categories for education to generate 96 individual age-gender-education cells. They found that the age-gender counterfactual failed to predict job polarisation. Including education as a category significantly improved prediction power, as it explained the majority of the growth in “lovely” jobs, but it failed to predict the fall in “lousy” jobs. They therefore concluded that changes to the supply-side were unable to explain job polarisation. They did not include immigration as a category of this analysis as they argued that immigrants were an unimportant factor due to their small share of overall employment (8.9% of the labour force in 1999). Salvatori (2018) follows the methodology used by Goos and Manning (2007) closely. He includes immigration (whether foreign born worker or not) with education (university; GCSE, GCSE; and no qualifications), age (under 30, 31-50, and over 50) and gender to define 48 skill groups. Based on this he argues that changes in the composition of the workforce (the “between” component) have led to an increase in shares of employment at the top of the wage distribution, while the changing shares of skill groups across occupations (the “within” component) has led to growth at the bottom. Salvatori notes from this that the key variable is education, and in particular whether an individual is a graduate or not, with graduates making monotonically increasing positive contributions to the entire occupational distribution. He found that immigration did not substantially affect outcomes. On the demand side, there is evidence from a study by Beaudry, Green and Sand (2014, 2016) in the US that argues that there has been a reversal in the demand for skilled labour since 2000. Using a pseudo-cohort analysis, they found stagnating or decreasing returns to cognitive skills since 2000 for young workers aged between 25 and 35. They also found that high-skilled workers have displaced lower-educated workers in less-skilled jobs. In a different study, Deming (2017) found that there has been a significant increase in the demand for “social skills” in the US based on the increasing returns between 1980 and 2012. Deming also found that Math-intensive occupations, with a low social content, lost employment share; whereas occupations requiring both math and social skills grew the

11

most. The last finding reflects, not simply an increase in demand for skill, but an increased demand for a complex combination of skill. Finally, Dickerson and Morris (2017) derive measures of skill using the US O*NET combined with the UK SOC to estimate skill utilisation across occupations, and the returns to skill in the UK. They find significant growth in the use of analytical and inter-personal skills, and declining use of physical skills between 2002 and 2016, and they provide evidence that this is happening across all occupations. They also find positive and increasing returns to analytical skills, with lower, but increasing, returns for interpersonal skills. Significantly, they show that the returns to qualifications (as distinct from skills) are declining. They argue that the returns to physical skills are negative over the period. Based on their findings they argue that the demand for analytical and inter-personal skills in the UK labour market is strongly increasing. 2.4 Job quality and policy considerations Job polarisation and skill utilisation is part of a wider discussion about job quality and wage inequality. Skills, as a potential driver for labour market changes, are therefore a major policy priority in the UK (Dickerson and Morris, 2017). Job quality can be viewed in various ways, but the two most used proxies are: pay, where good jobs are those with high pay and bad jobs are those with low pay (see for example Goos and Manning, 2003); and skill utilisation, where high-skilled occupations are seen as desirable, and low-skilled occupations are seen as undesirable, in their own right, irrespective of pay (Green, 2013; Green et al., 2003). Regarding the use of skill utilisation as a proxy for job quality, it is also arguable that this is even more the case for graduates, since they are more likely to prefer high skilled occupations that are requisite with their high level of education (Beaudry et al, 2014). A link between job polarisation and wage inequality has been suggested by Goos, Manning and Salomons (2009). They posit that the rise in the share of income going to the highest earners in the US and the UK may have led to an increase in demand for low-skilled workers whose employment increasingly consists of providing services to the rich (Goos, Manning, and Salomons, 2009; Manning 2004; Francesca Mazzolari and Giuseppe Ragusa 2007). Holmes and Mayhew (2012) highlight the important of understanding the effects of occupational polarisation on earnings inequality in order to correctly identify the appropriate policy response. The question whether a general increase in demand for skills really implies that the skills gained from gaining qualifications will actually be utilised. Studies on overeducation (where a graduate has a job that does not require a degree) and overskilling (where a worker has more skills than required for the work) provide strong evidence for a wage penalty and lower job satisfaction for those workers affected (Chevalier, 2003; Dolton and Vignoles, 2000;

12

McGuinness and Sloane, 2011; Walker and Zhu, 2008, 2011). Holmes and Mayhew (2012) argue that policymakers should not rely on employers finding ways of utilising an increasingly skilled workforce. 3. Methodology In this study I use QLFS data to investigate the development of supply and demand for skilled labour by investigating the wages and changing proportions of workers in different occupations between 1993 and 2017. I use a shift-share analysis to calculate movements between and within wage deciles by skill group. I analyse skill utilisation in relation to changing job shares to investigate the demand for skill using three different skill measures (analytical, inter-personal and physical) derived from the ESS. Lastly, I use a pseudo-cohort analysis to analyse changing demand for graduates and their skills. The occupational classification used in the QLFS changes three times between 1993 and 2017, and so I bridge the classifications using a crosswalk. I use probabilistic matching with dual-coded datasets to match occupations from one classification to another. Ultimately, I convert all occupations to the ISCO88 occupational classification. ISCO88 is a widely used classification in the literature, so its use provides reasonable comparability with other studies. Further details of the crosswalk and occupational classifications are provided in Section 4. 3.1 Measuring changes in employment shares of the wage distribution I follow the approach developed by Goos and Manning (2007) to analyse changes in job quality and employment shares. First, I define job quality in terms of median wages: that is, a good job is one with a high wage, and a bad job is one with a low wage. There are other methods for defining job quality but using wages is a reliable and widely used method (Card and Lemiex, 1996; Juhn, Murphy, and Pierce, 1993). I use wage data from the QLFS by converting the hourly wage data into real values using the Consumer Price Index (CPI) based in 2015, and then take logs. Using the log of real hourly pay I rank occupations into deciles based on median wage in the year 1993. Lastly, I calculate the change of these occupations for each decile between 1993 and 2017. I also look at the top and bottom ten occupations for employment growth, as well as exploit the hierarchical nature of the ISCO88 classification to observe patterns at the ISCO88 major (1-digit) level. This analysis can provide evidence for or against increasing wage inequality and declining job quality by showing which jobs are gaining shares. 3.2 Shift-share analysis The changes in employment share can be further decomposed by using shift-share analysis to look at the effects of changing cell size of skills groups (the “between” component) and the changing

13

occupational distribution of each cell (the “within” component). Following Salvatori (2018), who bases his method on the one used by Goos and Manning (2007), this can be expressed as follows: ∆𝑆𝑜𝑡 = ∑𝑔 ∆𝑆𝑔𝑡 𝜔𝑜𝑔 + ∑𝑔 ∆𝜔𝑜𝑔𝑡 𝑆𝑔𝑡

(1)

Where ∆𝑆𝑜𝑡 is the change in the employment share of decile o between t0 and t1; ∆𝑆𝑔𝑡 is the change in the employment share of skill group g; ωog is the average share of group g employed in occupation decile o; ∆ωogt is the change in the share of group g employed in occupation decile o; and 𝑆𝑔𝑡 is the average employment share of demographic group g between t0 and t1. The first term on the righthand-side of equation (1) is the “between” component, which accounts for the change in employment caused by changing shares of different skill groups whilst holding the distribution of the group across occupations constant. The second term is the “within” component, which shows how employment shares are affected by changes in the distribution of the skill groups across occupations whilst holding the relative size of the group constant. The skills groups that can be defined depend on the area of interest. As outlined in Section 2, Goos and Manning (2007) divided the workforce into gender-age cells to create 24 groups. They then add four categories for education to generate 96 individual age-gender-education groups. Salvatori (2018) defined 48 skill groups using age-gender-immigration-education cells. These studies both found that education, particularly graduates, was by far the strongest determining factor. I therefore focus on graduates and non-graduates, and include gender, to create four distinct skill groups. I experimented with additional categories, such as age and immigration, or with more nuanced education categories, but it did not substantially affect the findings for graduates. Using only four groups is preferable, as it simplifies the analysis. I use this analysis to show how the supply of graduates is affecting the wage distribution across occupations, and how graduates themselves are being integrated into the labour market. 3.3 Identifying skill utilisation using the UK Employer Skills Survey Having observed the changes in employment share I next analyse skill utilisation across occupations. There are two crucial issues when attempting to identify skills. The first is the definition of “skill” that is used to create a categorisation, or taxonomy, in order to group skill measures. The second issue is what proxies to use to measure skill. 3.3.1 Taxonomies of skill Autor (2013) has been particularly influential in relation to the categorisation of skill. He insists on the distinction between skills and tasks, where a task is something that produces output, whereas skill is a worker’s capability for that task. The literature has typically followed him in using a three-way taxonomy to codify task groups. There is some variation, but the rubric usually follows a cognitive14

routine distinction as follows: (i) routine cognitive and manual tasks (easily codifiable tasks); (ii) abstract analytical and managerial tasks (which require creativity, problem solving or persuasion); and (iii) non-routine manual tasks (physical flexibility, stamina, and visual recognition) (Autor et al, 2003; Autor, 2013). The cognitive-routine distinction is often used in the literature, but there are issues with this approach. The first issue is that advances in the use of technology, particularly algorithms, has led to a number of tasks being automated that were previously considered “cognitive” (Salvatori, 2018). This includes things such as medical diagnosis, the design of legal contracts and complex marketing and sales analysis. The second issue, and as already mentioned in section 2, there is often considerable overlap between the routine and offshorable category which potentially obscures results. To address these issues, I use an approach used by Dickerson and Morris (2017) and Bisello (2013) to modify the cognitive-routine categorisation based on a “Data-People-Things” taxonomy to construct three measures of skill: analytical, inter-personal, and physical. This approach does not rely on the automatability of the task, but instead focuses on the actual skill utilisation; and it allows for a more nuanced interpretation of skill. Although the physical measure roughly corresponds with “routine”, and the analytical and inter-personal measures correspond roughly with “cognitive”, there are important differences. As I will show, those occupations that are usually identified simply as cognitive appear as a combination of analytical, inter-personal and physical skills, rather than as the domination of any one skill. Furthermore, the inter-personal measure is also a potential means to explain why some occupations that are usually identified as routine have experienced growth in employment shares, where RBTC would expect them to lose share. 3.3.2 Proxies for skill Earnings, education (or qualifications) and occupation have been widely used as proxies for skill based on the established notion that productivity and earnings are a direct measure of a worker’s human capital (Autor, 2013); however, these methods have serious flaws (Green, 2006; Dickerson et al., 2012). The first, earnings, can be used to identify skills, but this may instead reflect the supply and demand dynamics for particular skills. That is, high wages could reflect high demand for certain skills, or it may reflect a skill shortage, rather than a particularly high level of skill (Elias and McKnight, 2001). Qualifications or educational attainment as a proxy, is objective and easily identifiable. For example, it is possible to rank occupations based on number of years of education to create a hierarchy based on the mean level of additional education (Elias and McKnight, 2001). However, this approach has the weakness that many workers that gain skills through experience will not have paper qualifications to show this; and further, there are occupations, such as bar work or serving in restaurants, that are

15

typical employment for students, and therefore ranking the skill content of these occupations based on education would lead to inaccuracy (Elias and McKnight, 2001). Qualifications therefore only have a loose link with specific job skills, and there are too many unobservable elements that can be caused by mismatch or overqualification (Dickerson and Morris, 2017). Lastly, occupations, are widely available in major surveys, and can be easily aggregated using the hierarchical structure of most occupation classifications; but the hierarchies do not accurately reflect skills, nor do they reflect the fact that the underlying skills of occupations change over time (Dickerson, 2017). This approach also often obscures overlaps between occupation categories and skill groups (Autor, 2013). To address the above issues, it is common to use a task model to study how changes in the supply of skill, technologies, trade and offshoring have affected the demand for labour, skill utilisation and real wages amongst skill groups (Autor, 2013). Using task descriptors is a powerful approach, as it provides a detailed statistical analysis of occupations with a reasonably large volume of data. Evidently, however, this approach ignores any heterogeneity amongst individual workers within an occupation, as it aggregates to the occupational level, even though there may be considerable differences amongst workers. Another issue is that the task content of occupations may change over time, whereas the use of descriptors is static. In practice, this is unlikely to be a problem if the timeframe of the study is not too long, or if the database of descriptors is updated regularly. I create reliable measures of skill directly using the British ESS. The ESS is a series of surveys conducted between 1986 and 2012 aimed at providing information about the tasks and skills being used in the British labour market. Bisello (2013) points out that the ESS is primarily geared towards research, unlike the US O*NET, which is more focused on administrative details. The ESS is therefore particularly suitable for deriving skill measures. The surveys cover a variety of topics, including wages, education and employment history. The section of interest asks 36 detailed questions about the nature and importance of a number of tasks and attributes relevant to an individual’s job. These range from basic, physical tasks to complex, cognitive tasks. For example, one question asks, “what is the importance of analysing complex problems in depth to your job?”. The respondent is asked whether the task is: essential, very important, important, not very important, or not at all important. I categorise each of these question into one of the three measures. See Appendix II for further details, and for the exact categorisation of each survey question, as well a comparison of the categorisation used by different studies.

16

Having categorised each question, I then generate a score for each skill based on whether the task is essential or very important to the job. The scores are standardised (i.e. I subtract the mean and divide by the standard deviation) and use a simple average to determine whether skill utilisation for that particular skill is above average or not. I also use the three score measures to calculate an overall skill utilisation measure. This is done at the ISCO88 unit level (4-digit) where possible, but where there is insufficient data, or the ESS does not cover certain occupations in enough detail, I approximate using 3- or 2-digit averages. I aggregate these scores at the ISCO88 1-digit level for analysis, and also look at those occupations that gained and lost the most employment share at the more detailed 3-digit level. I also consider the skill utilisation of non-graduates and graduates separately. This analysis shows which skills—or, more especially, what combination of skills—are prominent across the wage distribution, and how this might impact the growth and decline of certain occupations. 3.4 Pseudo-cohort analysis Having considered the changes in employment share, and the supply and demand for skill in the earlier part of the analysis, I then delve deeper into the demand for graduates utilising a pseudo-cohort technique. The method I use has been developed by Beaudry et al. (2014, 2016) to show that the demand for skilled labour has been declining in the US since 2000. Following their approach, I group cohorts based on potential experience (where potential experience is the age at which an individual finished education minus their age). Cohorts, c, are defined as follows: 𝑐 = 𝑦 − 𝑃𝐸𝑋𝑃

(2)

Where y is year and PEXP is potential experience. I differ from Beaudry et al. (2014) in that they calculate potential experience as age minus 23, on the assumption that 23 years is required to gain a college degree. This is unnecessary here, as the QLFS provides an exact age for when the individual finished education and is therefore more accurate. I use two-year groupings of cohorts to improve accuracy; and I use only the first five years of potential experience, as this is where the majority of movement occurs in wages and employment shares (Beaudry el al., 2014). I then use these cohorts to analyse changes in the wage profiles of non-graduates and graduates as a way of determining the respective demand for these skill groups. Falling wages of new-cohorts would be evidence for declining demand for graduates. It is also important to observe the start wage as the cohort enters the labour market, and the wage after five years. This is because a low initial wage in the first year of experience is less of a problem if the cohorts subsequently “catch-up” later on. Lastly, I use the same cohort analysis to observe the proportion of graduate cohorts in analytical occupations between 1993 and 2017. The argument is that graduates would expect to be in analytical or cognitive roles. A decline in the proportion of graduates in analytical roles would reflect two key 17

things: (i) a decline in demand for graduates in these highly skilled roles; and (ii) a decline in job quality for graduates. Both of these implications are significant for policy given the strong emphasis in the UK on increasing graduate participation. This analysis can also shed light on the difference between gaining qualifications and skills, and how employers differentiate between the two. 4. Data 4.1 Quarterly Labour Force Survey (1993-2017) For employment data I use over a hundred quarters of data from the QLFS from January-March 1993 to October-December 2017. The survey consists of a “main” case and a “boost” case. In the first, households are interviewed for five consecutive quarters; whereas in the second, the households are interviewed annually for four consecutive years. There are five different waves in each quarter. Waves 1 and 5 are from the main cases, and waves 2 to 4 are from boost cases. Earnings were only available in both wave 1 and wave 5 from 1997 onwards. Before 1997 they were only covered in wave 5. Earnings questions included people of working age (16-69), as well as those older than 70 years but that are still in work. The self-employed are not included. When using QLFS data it is important not to use all four quarters from any one year to calculate annual averages. To do so would risk double, triple or quadruple counting individuals who have appeared multiple times due to the wave structure. To avoid this issue, I use every quarter between 1993 and 2017 using only waves 1 and 5. This avoids any overlap of individuals in any one year, and also provides some genuine cohort characteristics as individuals in the main case will re-appear annually (i.e. without overlapping in any one year). There is a risk of non-random attrition bias when using QLFS data, as people with a high dropout rate are likely to have similar characteristics compared to those that remain throughout the survey and complete every wave (Peracchi and Welch, 1995; Paull, 2002). This problem could lead to the underor over-representation of certain groups. Furthermore, people that stay in the survey longer are likely to give responses that differ, leading to so-called “time-in-sample” bias, or “panel conditioning” (Paull, 2002). These issues are compounded by problems such as refusal to participate, non-contact, and the fact that only two-fifths of the sample are asked about earnings. To address these various forms of bias, the survey provides weights for each observation. The weights are estimated using sub-national population totals by age and sex, and they are calculated to show how representative the respondent is of the population. The QLFS provides two separate weights, one for earnings, and another weight for everything else. It is important to use the correct weight when analysing earnings as they are calibrated differently. Throughout this study I use the appropriate survey weights to address possible bias.

18

4.1.1 Harmonising occupation classifications The QLFS data uses three different occupation codes over the time period 1993 to 2017. The Standard Occupational Classification (SOC90) was adopted in 1992, with successors introduced in 2001 (SOC00) and 2011 (SOC10). The re-classification from SOC90 to SOC2000 was partly motivated by a Euro Stat exercise to harmonise European occupation classifications. It was found, for example, that the UK reported three times as many “corporate managers” as any other European country; and so, a review of the occupation classifications was implemented, which resulted in the SOC2000 coding (Elias and McKnight, 2001). A method to harmonise these classifications is required in order to investigate changes in occupations. This is not straightforward, as in many cases the correspondence is not 1-to-1. Even at the major (1digit) level the direct correspondence is always less than 80% (ONS, 2002). I use probabilistic matching to bridge the different classifications. Probabilistic matching uses dualcoded datasets to estimate the proportions of a given occupation that map to occupations in the other classification. Individual occupations can then be randomly allocated to a given occupation, thereby replicating the empirical distribution from the dual-coded datasets (Salvatori, 2018; Goos and Manning, 2007). One weakness of this approach is that it assumes the mapping is random, and that it is consistent across different time periods, but this may not be true. For example, there may be demographic effects that increase the likelihood of an occupation being reclassified, such as education or skills (Salvatori, 2018). This means that the demographic composition of the dual-coded dataset will be projected onto the proportions assigned to occupations over time; and the share of certain demographic groups in employment shares could be over- or under-represented. Salvatori (2018) attempted to solve this problem by conditioning the conversion on factors, such as education, age and gender; however, he found it did not have a substantial impact on the overall results. Given that the difference is negligible, I use unconditional conversion rather than conditional. I first convert SOC2010 to SOC2000, then SOC2000 to SOC90 using the following dual-coded datasets: the April-June 2000 of QLFS; January-March 2007 of QLFS; and December 1996-February 1997 of QLFS. I also use the ONS conversion tables for comparison and as a benchmark. I next convert SOC into ISCO88 using dual-coded Eurostat data. I also used conversion tables provided by the ONS1, and Gazeboom’s (2018) conversion tables, as benchmarks.

1

The ONS mappings of SOC90 to ISCO88COM and SOC2000 to ISCO88COM are available on request from the Occupation, Social & Country Classifications department ([email protected]).

19

4.1.2 Data restrictions and summary statistics The combined dataset of quarters between 1993 and 2017 has over 2.2 million observations. I restrict the dataset to those who are employed and aged between 18 and 65, which reduces the number of observations to 1.8 million. I then remove the highest and lowest percentile of wages to remove extremes, which leads overall observations to fall to 1.5 million, with between 30-70 thousand observations in each year. Figure 1 shows the development of key demographic groups within the QLFS dataset normalised to 100 in the year 1993. Graduates have grown substantially as a proportion of the workforce, from around 9% in 1993 to more than 21% in 2017. Most of this growth occurs in the 1990s, and has shown signs of stabilising, at least since 2012. The split between gender and employment has remained stable at around 50%. Male graduates increased their employment shares from 5% of total employment to 10%, and similarly female graduates increased from 4% to 11%. Immigrants enjoyed the largest increase in employment share, rising from just 3% in 1993 to 13% in 2017. There was barely any change in the share of employees aged under-30, whilst the mean age slightly increased from 38.1 years to 40.8 years. Figure 2 shows the development of the median hourly wage over the same period. There was rapid growth from the early 1990s until the financial crisis in 2008, after which the median wage declined, and has not shown signs of resuming growth. The success of the crosswalk can be seen in Figures 3 and 4, which show, respectively, the change in occupations at the one-digit ISCO88 level, between 1993 and 2017 (1993 = 100) and the changing share of occupations at the one-digit ISCO88 level over the same period. There are no sharp breaks between the changes in occupational classifications. Even with a simplistic analysis using Figures 3 and 4, there is a clear divergence between groups of occupations gaining employment share, and those losing employment share. Particularly noteworthy is the decline of clerks, which is almost symmetric with the rise of professionals, service workers and legislators and mangers.

20

Figure 1: Development of key demographic groups between 1993 and 2017

Figure 2: Median wage between 1993 and 2017

Notes: Graduates are defined as individuals with at least a degree from a higher education institution. Immigrants include all foreign-born workers. Each demographic group is normalised to 100 at the start date of 1993. Data is from Quarterly Labour Force Survey (QLFS) using quarters between 1993 and 2017.

Notes: Median wages calculated for all workers between 1993 and 2017 based on real wages (indexed to 2015 using Consumer Price Index). Data is from Quarterly Labour Force Survey (QLFS) using quarters between 1993 and 2017.

Figure 3: Changes in share of occupations in the UK (19932017)

Figure 4: Share of occupations in the UK (1993-2017)

Growth of employment shares of occupations since 1993

Employment shares of occupations between 1993 and 2017

160

25

140 20

120 100

15

80 10

60 40 1993

1997

2001

2005

Leg. & managers Technicians Service workers Plant & machine operators

2009

2013

5

2017

1993

Professionals Clerks Craft Elementary

Notes: Employment share is determined by the number of workers in an occupation divided by total number of workers. Employment shares are normalised to 100 in the base year 1993. Agricultural occupations have been excluded from this analysis due to the very small number of occupations.

1997

2001

Leg. & managers Technicians Service workers Plant & machine operators

2005

2009

2013

2017

Professionals Clerks Craft Elementary

Notes: Employment share is determined by the number of workers in an occupation divided by total number of workers. Agricultural occupations have been excluded due to the very small number of occupations.

4.2 UK Employer Survey of Skills The ESS is a biennial study of skills in the UK labour market, with an emphasis on how skill deficiencies impact business. It provides detailed information on the underlying task content of jobs. The survey covers all sectors of the UK economy, looking at businesses with at least two members of staff, so sole traders are excluded. The results are weighted using population estimates from the ONS. I use the sampling weights to adjust for bias and response rate throughout the analysis. I use survey data from 1986, 1992, 1997, 2001, 2006 and 2012. This provides a total of 25,826 observations, with roughly 21

4,300 observations per survey year. There is little point restricting the data based on age, as the dataset consists almost entirely of individuals aged between 18 and 60. The coverage of occupations of the ESS is quite good even at the most detailed 4-digit ISCO88 level, with 234 occupations covered out of the 327 distinct occupations in the QLFS. To calculate skill measures for the 93 occupations not covered at the 4-digit level I use a weighted mean value of the corresponding 3-digit score. At the 3-digit level, the survey covers 96 out of 104 occupations in the QLFS. The remaining 8 are covered by a mean value of the skill measure at the 2-digit occupational level. Figures 5 uses box plots to present a preliminary analysis of the skill distribution at the 1-digit ISCO88 level. Note that group 6, agricultural and fishery workers, is excluded from this analysis due to the very small number of individual occupations and very low share of employment (around 0.6% of total employment). The first three occupation groups (1-3) have a higher median analytical and interpersonal skill score than the other groups. There is considerable variation within the physical component amongst the first 3 groups reflected by the large range. The last three occupations groups (7-9) have a higher physical skill component than the others, and a lower analytical and inter-personal skill utilisation. Craft workers (group 7) show more variation in the analytical measure. The last two groups, clerks and service works (4 and 5), do not appear to excel at any particular skill, although service workers have a slightly higher median inter-personal score than the other groups. The outliers in the analytical and inter-personal measure perhaps suggests that these measures could be further broken down into more nuanced categories. I do not pursue this further as it is beyond the scope of this study; but it would be interesting to consider different categorisations, potentially using factor analysis (see for example: Green, 2012; Akcomak et al. 2013).

22

Figure 5: Box plot of skills measures (analytical, inter-personal, and physical) across the ISCO88 1-digit level showing the distribution and variation of skill amongst occupational groups

Notes: The analysis is done at the unit ISCO88 level using data from the British Employer Skill Survey (ESS) for periods between 1986 and 2012. Group 6 (agricultural and fishery workers) is excluded due to the very small number of occupations and small employment share. Survey weights have been used to correct for bias.

5. Analysis 5.1 Changes in wage and employment shares Following Goos and Manning (2007), I rank occupations into deciles according to their wage distribution in 1993 and observe the changes in the share of each these deciles. The results are shown in Figure 6, in which can be seen weak support for job polarisation. There is growth in high-paying occupations, small growth in low-paying occupations (albeit only in the lowest decile), and significant decline overall in most (though not all) middle occupations. The support is weak because when the deciles are grouped together into bottom (1st and 2nd deciles), middle (3rd to 8th deciles) and top (9th and 10th deciles) occupations, the implication is less polarisation, and more a pattern of upgrading, as top occupations grow at the expense of bottom and middling occupations. This shift from bottom and middle to top occupations is highlighted in Figure 7, in which each of these group is normalised to a starting value of 100 in 1993. The main trend is persistent and significant growth in top occupations, and consistent decline in middle occupations. The bottom occupations declined in much of the early 2000s, and only began increasing after 2005. This appears to be a long-term trend, unaffected by

23

economic cycles—although some of the growth in the bottom deciles after 2009 may be attributable to the recession. The implications for job quality are therefore positive, as most of the growth is away from poor jobs towards better ones. The results are similar to those obtained by Salvatori (2018), however, where I find that the bottom occupations marginally lost shares, he found that they marginally gained shares. The difference in result is likely due to the difference in timeframe, as he starts in 1979 and ends in 2012, whereas I start in 1993 and end in 2017. One possible interpretation of this is that job polarisation is not an on-going trend and may be limited to earlier periods. Another possibility is that the more recent start date that I use negates the effect highlighted by Holmes and Mayhew (2012), that the findings of job polarisation are caused by job title inflation. If this is the case, then using a more recent starting point would in principle reduce the appearance of job polarisation, as the wage distribution would be updated leading to a reduction in the inflation of job titles. The reality is most likely a combination of both effects: the extent of job polarisation has been slowing in the UK, whilst some of the observed job polarisation is explained by job title inflation. Figure 8 shows which of the ISCO88 major occupational groups gained and lost share between 1993 and 2017. The biggest gains were in legislators and managers (+5pp), professionals (+4pp) and service workers (+3pp) occupations; whereas the biggest losers were clerks (-4pp), craft (-4pp), machine operators (-3pp) and elementary occupations (-2pp). The contributions of these changes to the different groups of the wage distribution are summarised in Table 1. The last row in Table 1 shows a significant shift of employment share from middle occupations (-5pp) to the top occupations (+5pp). In particular, there is a shift of professionals from bottom (-0.1pp) and middle (-0.2) occupations, to the top (+4pp) occupations. Also notable is the shift downwards of clerks from middle (-6pp) to bottom (+3pp), with an overall loss of employment share of (-4pp). These movements in occupation shares along the wage distribution suggest pervasive trends affecting the entire workforce. To analyse this further I look at the top and bottom occupations for growth at the more detailed 3-digit level.

24

Figure 6: Percentage point changes in employment share by employment weighted wage deciles (1993-2017)

Percentage Point Change in Employment Share (1993-2017)

Changes in employment shares (1993-2017) 8 6 4 2 0 -2 -4 1

2

3

4

5

6

7

8

9

10

Employment weighted deciles over wage distribution (1993) Notes: Data from QLFS using all quarters from 1993 to 2017. Occupation coding converted to ISCO88. Employment deciles are based on rankings of median wages at the three-digit ISCO88 level in the year 1993.

Figure 7: Share off employment by 1993 wage deciles (Top, Middle and Bottom)

Share of employment by group of 1993 wage deciles 150 140 130 120 110 100 90 80 1993

1997

2001 BOTTOM (1, 2)

2005

2009

MIDDLE (3/8)

2013

2017

TOP (9, 10)

Notes: Data from QLFS between 1993 and 2017, normalised to 100 in 1993. Occupations ranked by median wage into deciles at 1993. Bottom (1st and 2nd decile), Middle (3rd to 8th deciles) and Top (9th and 10th). Occupations are converted to ISCO88.

Figure 8: Changing share of employment at the of 1-digit ISCO88 level (1993-2017)

Change in employment share (1993-2017) 6 4 2 0 -2 -4 Leg. & managers

Professionals

Technicians

Clerks

Service workers Agricultural

Craft

Plant & machine operators

Elementary

Notes: Categories are based on ISCO88 one-digit major level which are aggregated from an analysis at the three-digit level. Data is from QLFS. The change shown is the percentage point change between 1993 and 2017.

25

Table 1: Contribution of occupational groups to employment changes in different segments of the occupational wage distribution Bottom 2017-1993 1993 (pp Share change)

Middle 2017-1993 1993 (pp Share change)

0.00%

0.00

6.48%

2 PROFESSIONALS

0.22%

-0.08

3 TECHNICIANS AND ASSOCIATE PROFESSIONALS

0.00%

4 CLERKS

ISCO88 Major (1-digit) Occupations 1 LEGISLATORS, SENIOR OFFICIALS AND MANAGERS

5 SERVICE WORKERS AND SHOP AND MARKET SALES WORKERS 6 SKILLED AGRICULTURAL AND FISHERY WORKERS 7 CRAFT AND RELATED TRADES WORKERS 8 PLANT AND MACHINE OPERATORS AND ASSEMBLERS 9 ELEMENTARY OCCUPATIONS Total

Top

All

1993 Share

2017-1993 (pp change)

1993 Share

2017-1993 (pp change)

4.29

6.15%

0.59

12.63%

4.88

4.52%

-0.16

12.86%

4.31

17.60%

4.07

0.00

8.05%

0.51

0.47%

0.39

8.51%

0.91

1.21%

2.50

17.55%

-6.04

0.00%

0.00

18.75%

-3.54

12.16%

3.32

2.42%

-0.18

0.00%

0.00

14.59%

3.14

0.57%

0.01

0.06%

-0.06

0.00%

0.00

0.64%

-0.06

1.85%

-1.42

8.24%

-2.24

0.00%

0.00

10.09%

-3.65

1.11%

-0.31

7.53%

-2.60

0.00%

0.00

8.64%

-2.92

7.30%

-3.37

1.26%

1.08

0.00%

0.00

8.56%

-2.30

24.41%

-0.65

56.11%

-5.40

19.48%

5.30

100.00%

Notes: Data from QLFS between 1993 and 2017. Occupations were converted to ISCO88 and aggregated to the one-digit level. The columns may not sum perfectly due to rounding.

Table 2 and 3 report which occupations have experience the largest gains and losses in employment shares, respectively. The occupations with the largest growth are generally those with the highest pay, as seen in columns 1 and 2 of Table 2. This is unsurprising since the top jobs have outgrown the bottom and middle jobs. The most notable exceptions are the 86% growth in employment share in personal care, and 19% growth in office clerks, which are relatively low-paid occupations. Closely aligned with personal care, although much better paid, there was also strong growth in health professions and social work. A first glance this suggests a bias towards occupations oriented around care. I return to this point later when discussing skill utilisation. I would expect to see more low-paying jobs in the top growing occupations if the RBTC hypothesis was correct, although the growth in care occupations is weak support, as this group of occupations is usually classified as non-routine. The jobs that lost the most share is shown in Table 3. The majority of these jobs are low paying, and many fit the typical routine characterisation such as secretaries (-3pp) and cashiers (-1pp); however, the list also includes manual, non-routine jobs such as cleaners (-2pp) and electrical mechanics (0.4pp). Interestingly, columns 1 and 2 show that the wages of these occupations grew strongly between 1993 and 2017, whilst at the same time they have lost employment share. A possible interpretation of this increase in wages is that those occupations that lost share due to the automation of its underlying tasks either experience an internal skill upgrading (such that they are now doing more non-routine tasks where the routine ones have disappeared through automation); or that those workers that remain in the occupation accumulate left-over tasks or new routine tasks, such that the

26

skill content does not change, but they simply have more tasks. A higher wage may therefore reflect either a task upgrading of the occupation or simply a greater expectation of workload2. Table 2: Occupations by top ten growth (ISCO88 3-digit)

Employment share in 1993 (3) 0.39% 0.70%

Change in employment (pp, 19932017) (4) 0.42 0.48

Percentage change in employment (5) 107.6% 68.6%

2.91

1.09%

0.61

55.4%

2.66 2.55 2.08 2.38 2.75

3.01 2.82 2.24 2.42 2.91

0.51% 3.91% 4.13% 0.64% 1.14%

0.64 0.66 0.79 0.92 0.97

125.0% 16.8% 19.1% 144.8% 85.0%

1.76 2.63

2.02 2.86

3.52% 5.13%

3.04 4.44

86.3% 86.6%

Median Wage in 1993 (1) 2.40 2.53

Median Wage in 2017 (2) 2.42 2.58

College, university and higher education teaching professionals Health professionals (except nursing) Production and operations managers Other office clerks Social work associate professionals Computing professionals

2.89

Personal care and related workers Other specialist managers

Occupation (ISCO88 subminor, 3-digit) Other teaching professionals Health associate professionals (except nursing)

Notes: Data from QLFS between 1993 and 2017. Occupations were converted to ISCO88. Median wage is based on the log of real hourly pay converted using the CPI based in 2015.

Table 3: Occupations by bottom ten growth (ISCO88 3-digit)

Occupation (ISCO88 sub-minor, 3-digit) Secretaries and keyboard-operating clerks Domestic and related helpers, cleaners and launderers Cashiers, tellers and related clerks Assemblers Library, mail and related clerks Machinery mechanics and fitters Textile, garment and related trades workers Metal moulders, welders, sheet-metal workers, structural-metal preparers, and related trades workers Manufacturing labourers Electrical and electronic equipment mechanics and fitters

Median Wage in 1993 (1) 2.14 1.66

Median Wage in 2017 (2) 2.30 1.94

Employment share in 1993 (3) 4.23% 4.59%

Change in employment (pp, 1993-2017) (4) -2.80 -1.52

Percentage change in employment (5) -66.1% -33.1%

2.03 1.97 2.12 2.24 1.69 2.28

2.09 2.06 2.37 2.50 1.77 2.32

2.64% 1.46% 2.29% 2.30% 1.09% 0.97%

-1.36 -0.90 -0.81 -0.81 -0.78 -0.57

-51.6% -61.4% -35.6% -35.4% -71.3% -58.8%

1.84 2.39

2.01 2.60

1.41% 1.65%

-0.43 -0.43

-30.5% -25.8%

Notes: Data from QLFS between 1993 and 2017. Occupations were converted to ISCO88. Median wage is based on the log of real hourly pay converted using the CPI based in 2015.

I have highlighted changes in wages and employment shares as they react to movements in demand. I next use a shift-share analysis to break down the changes in employment share. 5.2 Shift-share analysis of changes in employment share Figure 9 shows the employment share analysis shown in Figure 8 decomposed into the “between” and “within” components of four cells defined by education (whether graduate or not) and gender (male or female). The graph shows that the “within” changes dominate the changing shares of employment in each decile. This differs from the studies of Goos and Manning (2007) and Salvatori (2018), which

2

I do not explore this further here, but this could be tested by analysing in more depth the changing task content of low paying, routine occupations over time.

27

found a more substantial “between” component. Shift-share analysis is known to be sensitive to the start and end periods used (Salvatori, 2018), and so this difference in the result is not completely surprising. The time periods used in the other studies are characterised by significant growth in graduates and female participation in the workforce (i.e. relative changes in the size of the group cells, or between effect); but here, the dominant characteristic is the changing composition of employment shares within each group. I break these movements down even further in Table 4, which shows that graduates, both male and female, lost shares at the top of the wage distribution between 1993 and 2017. Overall, the contribution of graduates to growth in the top deciles was positive, but only because the relative share of graduates increased. The main contributor to the increase in the top wage deciles was due to the “within” increase of non-graduates, particularly female non-graduates. Graduates have gained share in the middle-paying jobs, which means they have been moving down the wage distribution. This suggests that the job quality of graduates is decreasing, whilst their relative share of employment continues to increase. Non-graduates lost shares in the between component due to their decreasing share of employment overall. Female non-graduates moved significantly into the top occupations (+3pp), and away from bottom (4pp) and middle (-5pp) ones. This suggests that the job quality of female non-graduates is increasing relative to other groups; which is unexpected, as it would seem more likely that female graduates would perform better on the basis that they are more skilled. If the SBTC hypothesis was correct, then graduates (high-skilled) should outperform non-graduates (low-skilled). There are two possible implications from the better performance of female non-graduates: (i) either growth in employment shares do not favour the skilled; or (ii) the skills from higher qualifications does not translate into better labour market outcomes. As I will show later when looking at skill utilisation, there is more support for the second interpretation. A notable feature of Table 4 is that male non-graduates are the only group that can be seen strongly polarising into bottom (+0.8pp) and top (+1pp) occupations, whilst also losing (considerable) share in middle occupations (-7pp). For all other groups, there is either a general shift upwards (as for female non-graduates) or downwards (as for graduates, both male and female). Why males would be performing worse than females is not immediately clear. It may be that, from a relative point of view, female workers are improving their employment outcomes, whilst, from an absolute perspective, male workers are not moving. The fact that male non-graduates are also more likely to be polarised perhaps suggests that they are not adapting as quickly to a fast-changing labour market, whereas females are reacting by upgrading.

28

Percentage Point Change between 1993-2017

Figure 9: Decomposition of shift-share changes of employment share of employment deciles

8

Shift-share analysis of changes of employment share by 1993 employment deciles

6 4 2 0 -2 -4 -6 1

2

3

4

5

Between

6

7

8

9

10

Within

Notes: Data from QLFS using all quarters from 1993 to 2017. Occupation coding converted to ISCO88. Employment deciles are based on rankings of median wages at the three-digit ISCO88 level in the year 1993. The between component corresponds to the change in employment share due to changing size of the skill group. The within component corresponds to the change in employment share due to changes in the occupational shares within the skill group. There are four skill groups: 2 by gender and 2 by education (i.e. graduate or non-graduate).

Table 4: Contributions of graduates and non-graduates to changes in employment shares All

Graduates Female

Non-Graduates Male

Female

Male

1993-2017

Total

Between

Within

Total

Between

Within

Total

Between

Within

Total

Between

Within

Total

Between

BOTTOM

-1.1

-1.8

0.7

0.8

0.4

0.4

0.4

0.2

0.2

-3.1

-1.6

-1.5

0.8

-0.7

1.6

MIDDLE

-6.2

-1.8

-4.4

3.0

2.0

1.1

2.3

1.2

1.1

-4.7

-2.1

-2.6

-6.8

-2.9

-3.9

TOP

6.3

2.6

3.6

1.0

2.5

-1.5

0.6

1.9

-1.3

3.4

-0.7

4.1

1.3

-1.0

2.3

Within

Notes: This table reports a breakdown of a shift-share analysis of four groups defined by 2 gender and 2 education (i.e. graduate or non-graduate) cells. Totals may not sum correctly due to rounding.

I further explore the trends for graduate employment when using a pseudo-cohort approach in the last part of the analysis. Before that I explore job quality from the point of view skill utilisation. 5.3 Changes in the shares of occupational skill utilisation In Table 5, I have aggregated the standardised scores for analytical, inter-personal and physical skills at the ISCO88 1-digit level. The highlighted cells in each column represent the occupation categories with a score above the unweighted average. Column 2 shows that the top three occupations by wage (column 1) all have an above average analytical skill requirement. This is similar to the finding of other studies that analytical—which roughly corresponds with non-routine or cognitive—roles are concentrated at the top of the wage distribution (Dickerson and Morris, 2017; Bisello, 2013; Akcomak et al., 2013). These same occupation groups also have an above average inter-personal skill requirement. The lowest paid occupation, elementary workers, does not have any above average skill requirement and lost employment share (-2pp). The next lowest paid occupation, service workers, has an above average inter-personal skill and gained substantial employment share (+3pp). The middle

29

occupations generally have an above average physical skill requirement, but low overall skill, and lost employment shares. Arguably what is most significant in Table 5 is the finding that the top paying occupations are not simply analytical, but, unlike other occupations, they require a combination of skills. This is confirmed by the overall skill score in column 5. This is further emphasised in Figure 10, which plots the standardised skill score for overall skill requirement of each occupation against its median wage. There is a clear positive relationship between overall skill utilisation and pay, with top paying occupations having much higher overall skill requirements than those in the middle and bottom. The higher skilled occupations also experienced the largest growth, as shown by column 6 of Table 5, and made clear in Figure 11. This provides direct support for SBTC since the highest skilled occupations also gained the most share. The service workers group is the one exception to this, as it is relatively low skilled occupation, but grew by 3.1pp between 1993 and 2017. If inter-personal skills are classified as “nonroutine” then the gain in employment share by service workers is evidence for RBTC, as RBTC predicts growth in non-routine occupations; however, it is not entirely clear that (and as required by RBTC) the growth of service workers is at the expense of any routine occupation—and if anything, service workers, which has the third lowest overall skill score (-0.6)—gained shares from occupations with a higher skill content, such as craft and trades workers or clerks. The distinguishing feature of service workers is the above average inter-personal skill requirement. The growth in employment share therefore appears to reflect a specific demand, rather than any technologically driven factors. Physical skills do not neatly translate into “routine” tasks, as they may include many non-routine elements, such as stamina or the technical use of hands. Figure 11 suggests that the loss of employment share of occupations dominated by physical skills appears to be more closely related to the low level of skill, rather than the particular type of skill. This interpretation is supported by Table 6, which shows the skill requirements of the top growing occupations. Column 5 shows that half of these occupations have an above-average physical skill requirement. In fact, many of the top growing occupations dominate in all three skill measures. This emphasises the complex role of skills in determining employment outcomes. All but two of the occupations in Table 6 require above average analytical skills, and all but three require above average inter-personal skills.

30

Table 5: Skill utilisation across occupations (ISCO88 Major, 1- digit) Median Log Wage 2017 (1)

Employment share change (pp, 19932017)

Analytical (2)

Interpersonal (3)

Physical (4)

Overall Skill Score (5)

2.79

0.74

1.10

-0.21

1.01

4.88

2 PROFESSIONALS 3 TECHNICIANS AND ASSOCIATE PROFESSIONALS

2.84

0.84

0.58

-0.23

0.83

4.07

2.57

0.47

0.39

0.01

0.47

0.91

4 CLERKS 5 SERVICE WORKERS AND SHOP AND MARKET SALES WORKERS 6 SKILLED AGRICULTURAL AND FISHERY WORKERS

2.25

-0.17

-0.23

-0.38

-0.30

-3.54

2.07

-0.84

0.39

-0.81

-0.59

3.14

2.15

-0.51

-1.00

0.34

-0.62

-0.06

7 CRAFT AND RELATED TRADES WORKERS 8 PLANT AND MACHINE OPERATORS AND ASSEMBLERS

2.41

-0.35

-0.69

0.63

-0.16

-3.65

2.26

-0.78

-0.97

0.53

-0.76

-2.92

9 ELEMENTARY OCCUPATIONS

2.04

-0.93

-0.78

-0.52

-1.10

-2.30

ISCO88 Major (1-digit) Occupations 1 LEGISLATORS, SENIOR OFFICIALS AND MANAGERS

(6)

Notes: Median wages (column 2) calculated for all workers between 1993 and 2017 based on real wages (indexed to 2015 using Consumer Price Index). Data is from Quarterly Labour Force Survey (QLFS) using quarters between 1993 and 2017. Standardised skill scores for analytical, inter-personal and physical skills (columns 3-5) are calculated using data from British Employer Skills Survey (ESS). Cells highlighted in grey have scores greater than the unweighted average for that score. The overall skill score in column 6 combines the scores from the skill measures. Changes in employment share uses data from the QLFS.

Figure 11: Relationship between overall skill and change in employment share

2.9

6

2.8

5

Percentage Point Change in Employment Share (1993-2017)

Median Log of Real Hourly Pay

Figure 10: Relationship between median wage and overall skill

2.7 2.6 2.5 2.4 2.3 2.2 2.1 2.0 -2.00

-1.00 0.00 1.00 Standardised Skill Score

3 2 1 0 -1 -2 -3 -4 -5 -2.00

2.00

Notes: Median wage calculated using data from QLFS based on real hourly page indexed to 2015 using the CPI. The standardised score is calculated using data from the British Employer Skills Survey.

4

-1.00 0.00 1.00 Standardised Skill Score

2.00

Notes: The standardised score is calculated using data from the British Employer Skills Survey. The percentage point change in employment share is calculated from data using the Quarterly Labour Force Survey (QLFS) between 1993 and 2017.

It would be tempting to conclude that high-growth is linked with high-skills and cite this as evidence for SBTC; however, there are three important exceptions in Table 6: social work, personal care workers, and clerks. All three of these occupations have below average skill utilisation and yet grew significantly (145%, 86% and 19%, respectively). It might also be tempting to classify social work and personal care as non-routine and use this as evidence for RBTC; but office clerks are typically identified as routine occupations, so this is evidence against RBTC. Similarly, in Table 7, which shows the occupations that lost the most share of employment, there are two exceptions that are difficult to explain with SBTC and RBTC: electrical mechanics and machinery mechanics. Both of these 31

occupations have above average analytical and physical skill requirement and a positive overall skill score, but both lost employment share (-35% and -26%, respectively). The fact that high-skilled and non-routine occupations are losing employment share is evidence against SBTC and RBTC. Table 6: Skill utilisation of the top ten occupations by growth in employment share between 1993 and 2017 Change in employment (pp, 1993-2017) (1) 0.42

Percentage change in employment (2) 108%

Analytical Score (3) 0.68

InterPersonal Score (4) 0.99

Physical Score (5) 0.25

Skill Score (6) 1.05

Health associate professionals (except nursing) College, university and higher education teaching professionals Health professionals (except nursing)

0.48

69%

0.44

0.87

0.19

0.66

0.61

55%

0.89

1.62

-0.86

1.15

0.64

125%

0.67

1.12

1.28

1.23

Production and operations managers

0.66

17%

0.79

1.15

0.36

1.24

Other office clerks

0.79

19%

0.06

-0.53

-0.44

-0.17

Social work associate professionals

0.92

145%

-2.09

-0.25

-1.05

-1.96

Computing professionals

0.97

85%

0.91

-0.13

1.28

1.10

Personal care and related workers

3.04

86%

-0.63

0.29

-0.90

-0.56

Other specialist managers

4.44

87%

0.94

1.23

-0.92

1.00

Occupation (ISCO88 sub-minor, 3-digit) Other teaching professionals

Notes: Median wage and changes in employment share calculated using data from QLFS. The standardised skill scores are calculated using data from the British Employer Skills Survey. Cells highlighted in grey indicate that the score is above the unweighted average.

Table 7: Skill utilisation of the bottom ten occupations by growth in employment share between 1993 and 2017 Change in employment (pp, 1993-2017) (1) -2.80

Percentage change in employment (2) -66.1%

Analytical Score (3) -0.06

InterPersonal Score (4) -0.72

Physical Score (5) 0.16

Skill Score (6) -0.20

Domestic and related helpers, cleaners and launderers

-1.52

-33.1%

-1.19

-0.72

0.15

-1.10

Cashiers, tellers and related clerks

-1.36

-51.6%

-0.50

0.31

-0.25

-0.27

Assemblers

-0.90

-61.4%

-0.30

-0.74

0.50

-0.31

Library, mail and related clerks

-0.81

-35.6%

-0.30

-0.54

-0.19

-0.42

Machinery mechanics and fitters

-0.81

-35.4%

0.12

-0.41

0.78

0.23

Textile, garment and related trades workers

-0.78

-71.3%

-1.42

-0.99

0.83

-0.53

Metal moulders, welders, sheet-metal workers, structural-metal preparers, and related trades workers

-0.57

-58.8%

-0.22

-1.28

0.59

-0.19

Manufacturing labourers

-0.43

-30.5%

-1.04

-0.51

-0.14

-0.98

Electrical and electronic equipment mechanics and fitters

-0.43

-25.8%

0.64

-0.22

0.41

0.61

Occupation (ISCO88 sub-minor, 3-digit) Secretaries and keyboard-operating clerks

Notes: Median wage and changes in employment share calculated using data from QLFS. The standardised skill scores are calculated using data from the British Employer Skills Survey. Cells highlighted in grey indicate that the score is above the unweighted average.

32

It is also noteworthy that having a high inter-personal skill requirement does not in itself explain job growth, as cashiers and tellers have an above average inter-personal skill requirement and still lost 51.6% of its employment share. There may be a more specific demand for certain types of interpersonal skills that this measure does not capture, but it is beyond the scope of the current study to experiment with different skill categories. 5.4 Job quality and the skill utilisation of graduates I have shown that higher pay, and therefore better job quality, is strongly associated with higher skill utilisation, particularly analytical skills. However, it is also the case that occupations with a higher skill requirement, particularly analytical skills, can be considered as higher quality jobs in themselves. This assertion can be supported in a simple but crude way by using the job satisfaction data in the ESS. This shows that of workers employed in analytical roles (that is, where the analytical skill requirement is above the average), 74% are satisfied in their job, compared to 63% in roles that have no analytical skill requirement. This is only a rough measure, as it does not control for differences in pay, but an 11pp difference in job satisfaction is a large difference. If skill is accepted as an indicator of job quality, then increases in skill utilisation imply an increase in job quality; and thus Figure 11 can be interpreted as evidence for an improvement in job quality, as higher quality jobs are gaining employment share3. It might also be reasonably expected that graduates would benefit the most from this, given that they have higher educational attainment, and might therefore be assumed to have a higher level of skills, than non-graduates. To see if this is the case, I aggregated the score for skill utilisation reported in Tables 5-7 for each year, weighted by employment, and smoothed the annual figure using local regression (see Chambers et al., 1983; Cleveland, 1979). The result of this is shown in Figure 12 separately for all workers together, graduates, and non-graduates. Skill utilisation for the workforce as a whole has been rising since 1993. This is also true for non-graduates. Skill utilisation by graduates is much higher than the workforce as a whole, but there has been a strong trend downwards in overall skill utilisation since 1993, a trend which accelerated in the mid-2000s and appears to be continuing. The proportion of non-graduates and graduates in occupations with a high analytical, inter-personal or physical skill requirement can be seen in Figures 13 and 14, respectively. Figure 13 shows that nongraduates increased their share of analytical and inter-personal occupations; whilst Figure 14 shows that graduates steadily lost shares in analytical occupations throughout the 2000s, and roughly maintained the share in inter-personal occupations. Physical occupations declined for non-graduates

3

It is worth mentioning that a growth in occupational skill requirement may instead reflect a loss of job quality if work is simply becoming more demanding. I do not pursue this further, but I acknowledge it as a possibility.

33

but did not change significantly for graduates. The implication of Figures 12-14 is that the demand for highly skilled workers has been increasing (since non-graduates continue to increase their share of highly skilled jobs), but graduates are becoming less likely to obtain those jobs. This implies that, whilst the supply of graduates has been increasing, the demand for graduates has stalled, or even declined. To further emphasise this point, I analyse the wage profiles and share of analytical roles of graduates and non-graduates using a pseudo-cohort analysis. Figure 12: Skill utilisation across occupations in the UK labour market between 1993 and 2017

Notes: Skill utilisation is calculated based on data from the British Employer Skills Survey (ESS) and Quarterly Labour Force Survey (QLFS). A combined score of analytical, inter-personal and physical skills is standardised for all workers, graduates, and non-graduates, aggregated by year. The results are smoothed using local regression.

34

Figure 13: Proportion of non-graduates in occupations requiring analytical, interpersonal and physical skills (1993-2017)

Figure 14: Proportion of graduates in occupations requiring analytical, interpersonal and physical skills (1993-2017)

Proportion of Non-graduates in Occupations requiring above average skill utilisation

Proportion of Graduates in Occupations requiring above average skill utilisation

0.60

1.00 0.90

0.55

0.80 0.50

0.70

0.45

0.60 0.50

0.40

0.40

0.35

0.30 1993

1996

1999

ANALYTICAL

2002

2005

2008

PERSONAL

2011

2014

2017

1993

PHYSICAL

Notes: Data on skill utilisation is derived from the British Employer Skill Survey (ESS) combined with data from the Quarterly Labour Force Survey between 1993 and 2017. Above average is defined as being a score greater than the unweighted mean of the standardised skill score for each of the skill measures (analytical, personal, physical).

1996

1999

ANALYTICAL

2002

2005

2008

PERSONAL

2011

2014

2017

PHYSICAL

Notes: Data on skill utilisation is derived from the British Employer Skill Survey (ESS) combined with data from the Quarterly Labour Force Survey between 1993 and 2017. Above average is defined as being a score greater than the unweighted mean of the standardised skill score for each of the skill measures (analytical, personal, physical).

5.5 Pseudo cohort analysis of wages and skill utilisation I group cohorts of non-graduates and graduates separately based on their potential experience, using the first five years of their work life. Figure 15 shows the development of the median wage of each cohort of graduates and non-graduates. Looking first at non-graduates, they have experienced rapid increases in initial-wages and five-year wage-progression in the 1990s before a decline in the mid2000s. The recession led to some flattening of wage growth of non-graduate wages, and the cohorts appear to be continuing a downwards trend in initial wages and wage progression. The length of each line indicates the speed of wage progression, and so the shorter line of non-graduates compared to graduates means that they have less wage progression in the first five years of work than graduates. Non-graduates also saw wage progression reduce sharply after the recession. The graduate cohorts show much more rapid wage progression over the first five years of work, but they experienced lower wage growth compared to non-graduates overall; in fact, much of the development of graduate wages pre-crisis is sporadic, with no clear upwards trend. They were also affected by the recession but lost significantly more than non-graduates in terms of the fall from the peak wage after five years. The wage progression of graduates after five years shrank considerably during the recession (as shown by the significant shortening of the line for cohorts entering the labour market after 2008). The cohorts do not show signs of wages recovering, and in fact initial graduate wages appear to be continuing a downwards trend that started after the recession.

35

The wage profile of graduate cohorts suggests uncertain labour market outcomes for graduates. The lack of clear wage growth shows some ambivalence in terms of demand for graduate skills during a period where the wages of non-graduates were rising much more rapidly. Further evidence for weak demand for graduates is given by Figure 16, which shows the changing proportion of graduate and non-graduate cohorts in occupations with an above average analytical skill requirement (i.e. analytical occupations). This shows that the proportion of graduate cohorts in analytical occupations has been steadily declining since 2000. The pattern emerged long before the recession, and is therefore independent of the crisis, even if the recession compounded the trend further. The development of graduates can be contrasted with non-graduates, in which can be seen some moderate growth in the 1990s in analytical roles, before losing significant share in the mid-2000s, and then finally recovering to peak levels. The demand for analytical skills has therefore been increasing overall, whilst new graduate cohorts have become less and less likely to be obtain those roles. This together is evidence that the demand for new cohorts of graduates entering the labour market has been declining since the early 2000s. This result parallels the findings of Beaudry et al. (2014, 2016), who found that the demand for skilled labour has been declining in the US. However, I make the distinction here that the evidence suggests that it is the demand for the skilled labour of graduates specifically that is declining; whereas the demand for skilled labour in general (and particularly analytical skills) is increasing. Figure 15: Graduate and Non-Graduate Cohorts: Wage Profiles

Notes: Smoothed wage profiles of median-wage profiles: cohorts defined by potential experience. Each line corresponds to a separate cohort. Data is derived from the Quarterly Labour Force Survey.

36

Figure 16: Graduate and Non-Graduate Cohorts: Proportion of workers in Analytical Occupations

Notes: Smoothed employment share profiles of the proportion of workers in analytical occupations: cohorts defined by potential experience. Each line corresponds to a separate cohort. An analytical occupation is one that has an above average analytical requirement based on an unweighted mean of standardised scores derived from the British Employer Skill Survey. This data is then applied to the Quarterly Labour Force Survey.

6. Conclusion 6.1 Discussion This study has attempted to answer questions about the supply and demand for skill in the UK economy. Overall, I found positive implications for job quality as workers have exhibited a pattern of upgrading from bottom and middle occupation towards the top, rather than polarisation. I found some evidence that job polarisation has been weakening, but this could be attributable to the problem of job-title inflation highlighted by Holmes and Mayhew (2013). I examined the growth and decline of occupations. I have suggested that the increase in wages of those occupations that have lost employment shares could mean that these occupations experience an internal skill upgrading, or that routine occupations that lose employment share experience an increase in workload of similar tasks to compensate for lack of skill. I found that female non-graduates have performed particularly well, whilst male non-graduates showed a clear pattern of job polarisation. The reason for this is not immediately apparent, but it could be because females are responding better to the challenges of the modern labour market. At the same time, graduates have been found to be contributing to rises in the top occupations as their relative share of employment increases; however, they are also moving down the wage distribution 37

due to occupational changes within the graduate skill group. This is consistent with the argument for a slowdown in the demand for analytical or cognitive skills (Beaudry et al., 2014, 2016), however I find that this is specific to graduates rather than to skilled labour in general I have shown that there is a clear link between employment share growth and an occupation having a complex combination of multiple skills, rather than any particular skill. Conversely, those occupations that lost the most share tend to be low-skilled and to be dominated by a physical skill requirement, though not always. Also, whilst it is generally the case that high-skill workers are gaining shares, and low skill workers are losing shares, there are a number of counter-examples. I have argued that it is not clear whether occupations with a high skill content are growing in share simply because they are high-skilled, or whether the skill demands of occupations are increasing because the tasks of less skilled occupations (which are disappearing) are being absorbed by the high-skilled occupations. It is possible that with automation of many tasks, the “left-over” tasks are absorbed into other, more skilled occupations. These occupations that absorb tasks are likely to become better paid due to the resulting higher skill requirements, but then the low-skilled occupations that survive are paid even less than before. The findings partially support SBTC and RBTC, however the evidence for job polarisation (albeit weaker than reported elsewhere) presents difficulties for the SBTC explanation. The general increase in highskill labour at the expense of low-skill labour supports the SBTC hypothesis, although the growth in certain low-skill occupations (such as clerks and service workers, who also increased in wages) does not support STBC. Furthermore, there were a number of counter examples that cannot be explained by either SBTC or RBTC, particularly the growth of low-skilled, routine employment or the decline of high-skilled, non-routine employment. Overall, whilst SBTC and RBTC can be useful as a starting point for explaining changes in employment share, neither hypothesis provides a completely satisfactory explanation of the data. I have shown that the demand for skill has been increasing, particularly the demand for analytical skills, and that this reflects an overall increase in job quality. However, graduates newly entering the labour market since the early 2000s have experienced poorer employment outcomes, with declines in wage profiles and falling participation in analytical roles. I argue that this reflects deteriorating demand for graduates, rather than a decline in the demand for high-skilled labour. The reason for a decline in graduate employment prospects could be for a number of reasons. One possibility is simply an oversupply of graduates, rather than insufficient demand. This explanation is unlikely since there is clear evidence for strong demand for skills generally. Another explanation is that the qualifications graduates obtain do not translate into a high level of skills. It is possible that 38

graduates have become too generic, possessing too many skills that employers do not actually need, and at the same time they command higher wages. If this is the case, and non-graduates are able to provide those skills, then employers would naturally turn to the cheaper option. This is a plausible explanation and fits the data. Related to this, another possible interpretation is that the increase in supply of graduates has been coupled with a decrease in the quality of graduates. It is important to emphasise that not all graduates are the same. A declining demand for graduates does not necessarily mean for all graduates. Studies have shown that the returns to degree depend significantly on degree type, the type of institution studied at and educational outcome (see, for example, Walker and Zhu, 2008, 2011). Further work is needed to see whether the findings in this study reflect a decline in demand for graduates in general, or whether it reflects a compounding of a weak demand for certain types of graduates. In this study I only used four skill groups based on education-gender cells. I decided against using additional groups for ease of analysis, but I could have included immigration, although this was not a key factor in the analysis. A particular weakness of this study was that I did not analyse wage inequality in any detail. For example, log differentials could have been used to consider changes in wage inequality. There was a need for more nuanced skill categories in this study. Particularly, it would be beneficial to find a way to differentiate between, or better explain why, some inter-personal occupations gained employment shares, and some lost employment shares. 6.2 Policy considerations and recommendations for further research With so much uncertainty in the labour market, more needs to be done to improve communication between employers and workers. This is particularly the case for graduates who must decide whether to invest a substantial amount of money in their own labour capital, and how best to allocate their time and resources in gaining skills. Efforts such as the ESS are an important contribution to this, but employers could be encouraged to take a more active role in determining the skill profiles of the labour force through communicating their needs more clearly. With this end in mind, a beneficial first step would be more involvement of employers with the development of university courses. The problem of the declining prospects for new cohorts of graduates raises the question of whether so many people should be going to university. Although the evidence strongly suggests there is still a premium to earning a degree, the job quality implications of obtaining a job with a low skill requirement should not be ignored. More information on the employment outcomes of graduates— both in terms of the wage premium, but also occupational skill outcomes—so that they make better decisions when choosing courses or institutions, or whether deciding to go to university at all, would be beneficial. Lastly, given the fast-changing labour market in modern economies, it may be that the

39

answer is on-going skill acquisition throughout working life. The more options for gaining skills— particularly the skills employers actually want—the better. Further research in this area could investigate in more detail the effects of technology and job polarisation on wage inequality and job quality. This should include more nuanced skill categorisation, potentially using factor analysis. The shift-share analysis could also be expanded to include more skill groups, such as immigrants or age cells. It would also be helpful to find ways to further investigate the problem of job-title inflation as a robustness check for the findings. Lastly, further study could differentiate between different types of graduates by controlling for key factors, such as institution, degree type and education outcome.

40

7. References Akcomak, Semih, Suzanne Kok, and Hugo Rojas-Romagosa. 2013. “The Effects of Technology and Offshoring on Changes in Employment and Task-Content of Occupations.” CPB Discussion Paper 233. CPB Netherlands Bureau for Economic Policy Analysis. http://ideas.repec.org/p/cpb/discus/233.html. Last accessed 06/09/2018. Autor, David H, Katz, Lawrence F, & Kearney, Melissa S. 2006. The Polarization of the US Labor Market. The American Economic Review. 189–194. Autor, David H. 2013. “The ‘Task Approach’ to Labor Markets: An Overview.” Journal for Labour Market Research. 46(3): 185–99. Autor, David H., and David Dorn. 2013. “The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market.” American Economic Review. 103(5): 1553–97. Autor, D.H., Levy, F. and Murnane, R.J. 2003. The Skill Content of Recent Technological Change: An Empirical Exploration", The Quartely Journal of Economics, 118, pp. 1279-1333. Autor, David H. 2014. “Skills, Education, and the Rise of Earnings Inequality among the ‘Other 99 Percent.’” Science. 344(6186): 843–51. Acemoglu, Daron, and David H. Autor. 2011. “Skills, Tasks and Technologies: Implications for Employment and Earnings.” In Handbook of Labor Economics. Vol. 4B, edited by David Card and Orley Ashenfelter, 1043–171. Amsterdam: Elsevier B.V. Berman, E., Bound, J. and Machin, S. 1998. ‘Implications of Skill-biased Technological Change: International Evidence’. Quarterly Journal of Economics. 113, 1245–1279. Beaudry, P., Green, D. and Sand, B., 2014. The Declining Fortunes of the Young Since 2000. American Economic Review. 104(5), pp.381-386. Beaudry, P., Green, D. and Sand, B., 2016. The Great Reversal in the Demand for Skill and Cognitive Tasks. Journal of Labor Economics. 34(S1), pp.S199-S247. Bisello, M., 2013. "Job polarization in Britain from a task-based perspective. Evidence from the UK Skills Surveys," Discussion Papers 2013/160. Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy. https://ideas.repec.org/p/pie/dsedps/2013-160.html. Last accessed 06/09/2018. Blinder, A. S. 2007. “How Many U.S. Jobs Might Be Offshorable?". CEPS Working Paper, No. 142, March. Blinder, A. S. 2009. “How Many U.S. Jobs Might Be Offshorable?”, World Economics 10(2): 41–78. Blinder, A. S. and Krueger, A. B. 2009. “Alternative Measures of Offshorability: A Survey Approach”, NBER Working Paper 15287. Card, D., and Lemieux, T., 1996 “Wage Dispersion, Returns to Skill, and Black-White Wage Differentials,” Journal of Econometrics. LXXIV (1996), 319–361.

41

Chevalier, A., 2003. Measuring Over-education. Economica. 70(279), pp.509-531. Chambers, J. M., W. S. Cleveland, B. Kleiner, and P. A. Tukey. 1983. Graphical Methods for Data Analysis. Belmont, CA: Wadsworth. Cleveland, W. S. 1979. Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association. 74: 829–836. Deming, D., 2017. The Growing Importance of Social Skills in the Labor Market. The Quarterly Journal of Economics, 132(4), pp.1593-1640. Dolton, P. and Vignoles, A., 2000. The incidence and effects of overeducation in the U.K. graduate labour market. Economics of Education Review, 19(2), pp.179-198. Dustmann, Christian, Johannes Ludsteck and Uta Schoenberg (2009), “Revisiting the German wage structure”, Quarterly Journal of Economics. 114(May), 843–882. Dickerson, Andy and Damon Morris (2017), “Creating a Database of Occupational Skills: Technical Report”, CVER mimeo, September 2017. Dickerson, Andy, Rob Wilson, Genna Kik and Debra Dhillon (2012), Developing Occupational Skills Profiles for the UK: A Feasibility Study, Evidence Report 44 February 2012, UK Commission for Employment and Skills. Dickerson, A., and Morris D., 2017. THE CHANGING DEMAND FOR SKILLS IN THE UK. Department of Economics and CVER University of Sheffield, November 2017. http://conference.iza.org/conference_files/Statistic_2018/morris_d8617.pdf. Last accessed 06/09/2018. Elias, P. and McKnight, A., 2001. Skill measurement in official statistics: recent developments in the UK and the rest of Europe. Oxford Economic Papers, 53(3), pp.508-540. Katz, Lawrence F., and David H. Autor, “Changes in the Wage Structure and Earnings Inequality” in Orley Ashenfelter and David Card (Eds.), Handbook of Labor Economics, vol. 3A (pp. 1463–1555), (Amsterdam: North-Holland, 1999). Katz, L.F., Goldin, C.: The Race Between Education and Technology. Harvard University Press, Cambridge (2008). Ganzeboom, Harry B.G.; Treiman, Donald J., “International Stratification and Mobility File: Conversion Tools.” Amsterdam: Department of Social Research Methodology, http://www.harryganzeboom.nl/ismf/index.htm. Last accessed 06/09/2017. Goos M., Manning A. 2003. McJobs and MacJobs: The Growing Polarisation of Jobs in the UK. In: Dickens R., Gregg P., Wadsworth J. (eds) The Labour Market Under New Labour. Palgrave Macmillan, London. Goos, M. and Manning, A. 2007. “Lousy and lovely jobs: the rising polarization of work in Britain", The Review of Economics and Statistics, 89(1), pp.118-133. Goos M., Manning, A. and Salomons, A. (2009), “Job Polarization in Europe.", American Economic Review, 99(2), pp. 58-63. 42

Goos M., Manning, A. and Salomons, A. (2010), Explaining Job Polarization in Europe: The Roles of Technology, Globalization and Institutions", CEP Discussion Paper, No 1026, November. Goos, Maarten, Alan Manning, and Anna Salomons. 2014. “Explaining Job Polarization: Routine‑Biased Technological Change and Offshoring.” American Economic Review 104(8): 2509–26. Green, F. 2006. Demanding Work: The Paradox of Job Quality in the Affluent Society, Princeton. Princeton University Press. Green, F., Felstead, A., & Gallie, D., 2003. Computers and the changing skill-intensity of jobs, Applied Economics, 35:14. Green, D. and Sand, B., 2015. Has the Canadian labour market polarized?. Canadian Journal of Economics/Revue canadienne d'économique, 48(2), pp.612-646. Holmes, Craig, and Ken Mayhew. 2012. “The Changing Shape of the UK Job Market and Its Implications for the Bottom Half of Earners.” Resolution Foundation Working Paper. http://www.resolutionfoundation.org/publications/changing-shape-uk-job-market-anditsimplications-/. Last accessed 06/09/2018. Juhn, C., Murphy, K.,, and Pierce, B. 1993. “Wage Inequality and the Rise in Returns to Skill”. Journal of Political Economy CI (1993), 410–442. Manning, A., 2004. We Can Work It Out: The Impact of Technological Change on the Demand for LowSkill Workers. Scottish Journal of Political Economy, 51(5), pp.581-608. Michaels, Guy, Ashwini Natraj, and John Van Reenen. 2014. “Has ICT Polarized Skill Demand? Evidence from Eleven Countries over Twenty-Five Years.” Review of Economics and Statistics 96(1): 60– 77. Mishel, Lawrence, Heidi Shierholz, and John Schmitt. 2013. “Don’t Blame the Robots: Assessing the Job Polarization Explanation of Growing Wage Inequality.” Working Paper. Economic Policy Institute. https://www.epi.org/publication/technology-inequality-dont-blame-the-robots/. Last accessed 06/09/2018. Oesch, D., and Menés, J. 2011. Upgrading or polarization? Occupational change in Britain, Germany, Spain and Switzerland, 1990–2008, Socio-Economic Review, Volume 9, Issue 3, 1 July 2011, Pages 503–531. ONS. 2002. “Relationship between Standard Occupational Classification 2000 (SOC2000) and Standard Occupational Classification 1990 (SOC90).” http://www.ons.gov.uk/ons/guidemethod/classifications/archived-standardclassifications/standard-occupational-classification-2000/relationship-between-soc2000-andsoc90.zip. Last accessed 06/09/2018. Paull, G., 2002. Biases in the reporting of labour market dynamics. Working Paper Series. https://www.ifs.org.uk/wps/wp0210.pdf. Last accessed 06/07/2018. Peracchi, F. and Welch, F., 1995. How representative are matched cross-sections? Evidence from the Current Population Survey. Journal of Econometrics, 68(1), pp.153-179.

43

Salvatori, A., 2018. The anatomy of job polarisation in the UK. Journal for Labour Market Research, 52(1). Spitz‐Oener, A., 2006. Technical Change, Job Tasks, and Rising Educational Demands: Looking outside the Wage Structure. Journal of Labor Economics, 24(2), pp.235-270. Walker, I. and Zhu, Y., 2008. The College Wage Premium and the Expansion of Higher Education in the UK. Scandinavian Journal of Economics, 110(4), pp.695-709. Walker, I. and Zhu, Y., 2011. Differences by degree: Evidence of the net financial rates of return to undergraduate study for England and Wales. Economics of Education Review, 30(6), pp.11771186.

44

Appendix I: Ethics Form

Department of Economics Research Ethics Committee Section 1 – Full Ethics application form for UG & PGT/PGR students UG Student ☐ Student ☐ Project Title (max 15 words)

Masters Student ☒

PhD

Cohort Analysis of Graduate Occupations

Name of applicant/s Mr Luke Austin Email for applicant/s [email protected] Name & contact email for Prof. Paul Gregg ([email protected]) Bath supervisor Name of external n/a placement supervisor (if applicable) Contact email for external n/a supervisor Application Date 17/07/2018 Proposed start date 17/07/2018 Proposed end date 17/07/2018 Funding body (if relevant) n/a Will you be testing a hypothesis? Will this project be pre-registered? YES

YES NO

NO

Data Analysis: What method of data analysis have you planned for this project? Please tick: Quantitative √ Qualitative Mixed-Methods Previous ethical approval 1. Has this proposal had (or is it awaiting) ethical approval from anywhere else? YES: Approved ☐ YES: Awaiting Approval ☐ NO: Not applicable ☒ If you answered YES: Please state which ethics body you received or awaiting Ethical Approval from: __________N/A_____________________________________________________________ N.B. If you have received approval from any other body, please attach your ethics approval letter and the approved ethics application, as we will need to see these to grant approval. 1. NHS BASED PROJECTS If your project is based in the NHS, does it require: a. Research & Development approval?

45

YES: Approved ☐

YES: Awaiting Approval ☐

b. Full NHS/IRAS ethics approval? YES: Approved ☐ YES: Awaiting Approval

☐

NO: Not applicable ☒ NO: Not applicable ☒

You will need to attach a copy of your NHS ethics approval letter and one copy of the approved ethics application, as we will need to see these to grant approval. PLEASE GO STRAIGHT TO SECTION 3. SECONDARY DATA ANALYSIS 2. Does this proposal involve secondary data analysis? This is when you are analysing data that has already been collected by somebody else, i.e. you will have no part in collecting the original data. YES ☒ NO ☐ N.B. Please attach evidence of ethical approval for the original study. The Section 2 Project Description should detail what you intend to do with the data, not how the data were originally collected. It is important to note whether the data you are using have already been anonymised. Information Classification Scheme Confirm that you have completed the mandatory data management online training module ☐ What category of data will you be collecting? Internal Use

☐

Restricted

☐

Highly Restricted

☐

Section 2 – Description of Research DESCRIPTION OF RESEARCH Please provide all further information under the following headings in the boxes provided. Please refer to the guidance document for advice on how to fill in each section.

1. Research Question: How have graduate occupations developed from 1990s until present, and how have they been affected by crises. 2. Background and aims of the research (maximum 300 words) To analyse trends, and the effect of crises, on graduate occupations and earnings using a cohort analysis of Quarterly Labour Force Survey (QLFS). 3. Who will be recruited to participate in the research? n/a 4. How many participants will be recruited? n/a 5. How will participants be recruited? 46

n/a 6. Are there any potential participants who will be excluded? If so, what are the exclusion criteria? n/a 7. Where will the research take place? n/a 8. How will informed consent be obtained from all participants or their parents/guardians prior to individuals entering the study? n/a 9. Will the study actively involve deceiving the participants? n/a 10. Will participants be made aware they can drop out of the research study at any time without having to give a reason for doing so? n/a 11. Outline the design of the research study and list the procedures to which the participants will be subjected, how much time (roughly) it will take for participants to take part in the study, any questionnaires administered and any interview schedule. (maximum 300 words) n/a 12. Describe potential risks to participants (physical, psychological, legal, social) arising from these procedures. n/a 13. Describe potential risks to researcher/s and how these will be managed. n/a 14. How will participants be debriefed? n/a 15. How will confidentiality and security of personal data relating to your participants be maintained? [Please outline your data management plan here] n/a 16. Will the participants be audio-taped or video-taped? n/a 17. Is any reimbursement of expenses or other payment to be made to participants? n/a 18. Any other relevant information? n/a

Section 3 - Supporting documents & signature/s Checklist: have you attached? YES

NO

N/A

[ T [ y T p[ y eT p[ ay eT qTp ay uye qp op uae teq oa eau tq fq eo u

[ T [y Tp ye[ paT[ eqyT [ aup y T qoep y utae p oeq a e tfu q a eo u

[ T y[ T p [ ey T ap[ eyT q a[p y u Te q op ya tuep q eoae u ftq ao reu

Evidence of ethical approval from another body & approved application Approval letter from NHS Service Lead Information sheets Consent forms Debrief sheets 47

Interview schedules

[ [ [ T T T Questionnaire measures [ [ [ y y y T T T Recruitment advertising/flyers p p p [y y[ y[ e e Tep pT pT a a a ye ey ey your application Keep in mind that if any of the above information is missing, will be q q pqa ap ap returned to you without a decision. u u euq qe qe o o o Please submit your application and all associated documents to [email protected]. au ua ua t Please not that if your application involves primary data or is funded by ESRC,qt it will need tto oq oq o e e be considered by the Research Ethics Committee SSREC whouemeet on the following dates: u tu t t http://www.bath.ac.uk/statutory-bodies-committees/bodies-and-committees-senate/ssrecf f f oe eo eo committee/datesofmeetings.html . r r trf ft ft o o o e Please note that failure to include any relevant section or rsignature may eresult in your re r m m fm form being rejected. of of o t rt rtm mr m Signature of applicant Print Name Date h h h ot to to e e e m Luke Austin Luke Austin 17/07/2018 hm hm h d d tde et et By signing and submitting the form, you are agreeing with thehofollowing statement: o o hd dh d c c eco ‘I have discussed the ethical aspects of the proposed project oewith my supervisor(s) and/oroe u u u dc the other researchers involved in the project. I am also aware with the cd cd of and will comply m m om university policies for storage and processing of human uoparticipant data.’ uo u e ce cem mc m Print Name Date Signature of lead n n n ue eu eu researcher or supervisor t t t m nm nm n o o eot te te Paul Gregg 23/07/2018 r r r no on on t tt ttr rt for the application By signing you are agreeing that you take joint responsibility and rh h h ot to to conduct of the research. e e e rh hr hr s s tse et et u u u hs Print Name Date Signature of sh sh m m em undergraduate dissertation ue ue u m sm sm (academic) supervisor ms m m a a a u um mu By signing you are agreeing that you have read this proposalm and have ensured that all r r r m am am a relevant information is included. y y y mr rm rm o o aoy ya ya f f rfo or or a a a yf fy fy n n ona ao ao i i fin nf nf n n n ai ia ia t t ntn nn nn e e e it ti ti r r nre en en e e ter rt rt s s s ee ee 48 ee t t rts sr sr i i eit te te n n n si is is g g g

Appendix II: Classification tasks from the British Employer Skill Survey I use the following taxonomy derived directly from Dickerson and Morris (2017) to categories tasks from the UK Employer Skills Survey (ESS): Analytical skills (21 items): Reading Comprehension, Writing, Mathematics, Science, Critical Thinking, Active Learning, Learning Strategies, Monitoring, Coordination, Negotiation, Complex Problem Solving, Operations Analysis, Technology, Design, Programming, Troubleshooting, Judgment and Decision Making, Systems Analysis, Systems, Evaluation, Time Management, Management of Financial Resources, Management of Material Resources Interpersonal skills (7 items): Active Listening, Speaking, Social Perceptiveness, Persuasion, Instructing, Service Orientation, Management of Personnel Resources Physical skills (7 items): Equipment Selection, Installation, Operation Monitoring, Operation and Control, Equipment Maintenance, Repairing, Quality Control Analysis

Using this I categories 36 task measures from the ESS into either: analytic, inter-personal or physical skill. I compare the approach of Dickerson and Morris (2017) with the more simplistic approach of Akcomak et al., (2013) in the table below.

49

Skills and Employment Survey Series: Variable Availability, 1986-2012 Task Description 1 knowledge of how organisation works 2 using a computer/ pc/ other computerised equipment 3 working out cause of problems/ faults 4 planning own activities 5 planning the activities of others 6 organising own time 7 thinking ahead 8 reading written information (eg. forms, notices, signs) 9 reading short documents 10 reading long documents 11 writing materials such as forms, notices or signs 12 writing short documents 13 writing long documents 14 thinking of solutions to problems 15 analysing complex problems in depth 16 advanced mathematical/ statistical procedures 17 arithmetic (adding, subtracting, multiplying, dividing numbers) 18 arithmetic involving fractions (decimals, percentages, fractions) 19 specialist knowledge or understanding 20 knowledge of particular products or services 21 complexity of computer use in job 22 paying close attention to detail 23 persuading or influencing others 24 working with a team 25 counselling, advising or caring for customers or clients 26 teaching people (individuals or groups) 27 making speeches/ presentations 28 listening carefully to colleagues 29 dealing with people 30 selling a product or service 31 physical strength 32 physical stamina 33 skill or accuracy in using hands/fingers 34 knowledge of use or operation of tools 35 spotting problems or faults 36 checking things to ensure no errors 37 noticing when there is a mistake

50

AKR 2013 cognitive cognitive routine routine routine service service cognitive cognitive service service service routine routine routine

DM 2017 Analytic Analytic Analytic Analytic Analytic Analytic Analytic Analytic Analytic Analytic Analytic Analytic Analytic Analytic Analytic Analytic Analytic Analytic Analytic Analytic Analytic Personal Personal Personal Personal Personal Personal Personal Personal Personal Physical Physical Physical Physical Physical Physical Physical