The Changing Demand for Skills in the UK

In this latest blog post, Andy Dickerson and Damon Morris changes in skill utilisation and returns to skills over time in the UK. 

‘Skills’ have long been a major policy priority, yet they are hard to measure. Skills are multi-dimensional, intangible and often unobservable. They are not well represented by individuals’ qualifications or by the occupational classification of the jobs they do. In the US information on skills is gathered from self-reported assessments by workers as well as from professional assessors. This Occupational Information Network, O*NET, system provides measures of skills, abilities, work activities, training, and job characteristics for almost 1,000 different US occupations. In our new paper, we show how these skill measures can be matched to UK data. We develop a database of comprehensive and detailed multi-dimensional occupational skills profiles for the UK which describe the utilisation of skills in the workplace.

We then utilise our occupational skills profiles to assess the changing demand for skills in the UK. We construct three indices of skills: analytical/cognitive skills; interpersonal skills; and physical/manual skills. We combine these with individual data on wages and employment from the Annual Surveys of Hours and Earnings (ASHE) and the Labour Force Survey (LFS) to produce a 4-digit SOC occupational-level panel dataset for 2002-2016. We use this dataset to examine the change in skills utilisation in employment over the period, and to estimate the wage returns to these skills. We argue that these two measures together provide a comprehensive picture of the changing demand for skills in the UK.

Changes in the utilisation of skills

Our results indicate strongly increasing use of both analytical skills and interpersonal skills, and declining use of physical skills over the period 2002-2016. Over the whole period, the index of analytical skills suggests that utilisation of this skill set grew by 10% over the period. The increase in interpersonal skills was more than double this (+23%), while utilisation of physical skills fell by 14%. These trends accord with our general understanding of the changing occupational structure of employment and the growth of services and the decline of manufacturing.

At the aggregate level, these trends are a consequence of a combination of both changing skills within (broader) occupations, and changes in the occupational structure of employment. Some evidence on where the changes are primarily situated can be obtained from undertaking a decomposition of the overall change in skills utilisation between 2002 and 2016 in each of the three skill measures. Specifically, we examine the extent to which the aggregate changes in each index of skills is a consequence of within-occupation or between-occupation changes.

Around 20-25% of the increase in analytical skills utilisation is between occupations, while the remaining 75-80% is within occupations. The within-occupation changes for interpersonal skills and physical skills are even greater. This decomposition suggests that the overall changes in skill utilisation are pervasive throughout employment and are affecting all occupations, rather than being concentrated in certain occupational groups. Thus, over the period 2002 to 2016, the UK labour market has seen a substantial increase in the utilisation in employment of analytic and, especially, interpersonal skills, and a decline in the use of physical skills in employment.

Change in the return to skills

We next turn to examine the returns to skills. We use a simple Mincerian log earnings function specification to estimate the conditional (wage) returns to skills and to compute the changing returns over time. The returns to our three measures of skills are illustrated in the below Figure, where we have standardised (mean 0, variance 1) the skills indices in order that comparisons between them can more easily be made.

Trends in the Returns to Skills 2002-2016

The dashed lines connect the year-by-year point estimates of the wage returns to analytic, interpersonal and physical skills. As can be clearly seen, the returns to analytic skills are strongly trended upwards over time. An alternative specification which interacts a linear time trend with the index of analytical skills is superimposed (together with its 95% confidence interval). The coefficient on the time trend for analytic skills shows that an occupation with a one standard deviation higher level of analytic skills will be associated with almost 2% higher wage growth relative to an occupation with an average level of analytic skills. Clearly, over the sample period, the returns to analytic skills have been not only positive and statistically significant but have been increasing strongly. It is important to note that this increase in returns has occurred while the utilisation of analytical skills has also been increasing.

The returns to interpersonal skills were clearly close to zero in the early part of the sample period, but have also been increasing over time. Again, this increasing return has occurred at the same time as the utilisation of interpersonal skills has been increasing sharply. We therefore conclude that the demand for both analytical and interpersonal skills is strongly increasing over the period of analysis.

Finally, the returns to physical skills are negative throughout the period but are fairly constant over time. In this case, the slope of the time trend is insignificantly different from zero. Recall that the utilisation of these skills has been falling sharply over the period. This suggests declining demand for these skills in employment over time, although this has been coupled with a corresponding reduction in supply.

The findings demonstrate the increasing importance of work-related skills for individuals’ earnings, over and above their educational qualifications and, in particular, for higher levels of analytical skills and interpersonal skills in the workplace. Our interpretation of the increased utilisation coupled with increasing returns to analytic and interpersonal skills is that the UK is experiencing significantly increased demand for these skills in the labour market. The policy implication is that analytical and interpersonal skills need to be developed within every type of education – whether so-called ‘vocational’ or ‘academic’. This is what the modern labour market requires.

Family Matters: how early disadvantage impacts employment outcomes of young people

Dr Stefan Speckesser, Dr Matthew Bursnall and Jamie Moore share the findings of a new report

A new NIESR and Impetus report on young people Not In Education, Employment and Training (NEET) reveals that young people with a disadvantaged family background [1] are 50% more likely to be NEET than better off peers irrespective of their education outcome.

This finding holds for all age groups and also has not changed over time. For the first time, our data allows us to create reliable NEET statistics for the 18-24 year-olds in local areas, which show that local employment gaps between disadvantaged young people and others differ greatly and are unrelated to overall NEET rates.

As with previous studies, our findings confirm much lower NEET rates for 18-24-year old people with good GCSEs, A-Levels or Level 3 vocational qualifications.

Local differences in this “employment gap” indicate that some local areas are more successfully tackling the negative effects of disadvantage, which are unrelated to education success, on young people’s school-to-work transitions.

Our study demonstrates the great potential of administrative data to learn more about the school-to-work transition of young people, which local areas are successful and where we can learn from good practice.

Findings in detail

We analyse linked education and labour market data for 3,486,000 young people, i.e. the full biographies of practically everybody leaving state secondary schools between 2007 and 2012 in England. People having a NEET experience are all people not observed in the data with education and employment activity for a period of three months at any point in time.

For the most recent point in time (March 2017), our results show that young people with low qualifications are twice as likely to be NEET than those with 5 GCSEs (29% compared to 15%). People with A-Levels of Level 3 vocational qualifications qualified experiencing the lowest NEET rates (8%).

Young people with a disadvantaged family background are 50% more likely to be NEET than better off peers. This is true at all levels of qualification (see Figure 1) and regardless of age. Also, it has not changed since 2010.

Figure 1: NEET rates in March 2017, by education level* at age 18

London has a much smaller gap between NEET rates of children from disadvantaged families and others (seven percentage points). A much wider gap is found for the North East: almost one third of all disadvantaged 18-24 year-olds are NEETs in this region, compared to 14% of their better off peers.

Figure 2: NEET rates in March 2017, by disadvantage* and region

Local area differences

Two maps of England’s Metropolitan and non-metropolitan counties with NEET rates and the gap between disadvantaged and non-disadvantaged young people show the complexity in local patterns of the youth employment gap (Figure 3).

The first one, showing NEETs as % of the population of young people in March 2017, shows the local areas by how large NEET rates are, in deciles, with the darkest ones showing where NEET rates are highest. The highest NEET rates are usually found in the metropolitan centres like the Inner London Boroughs, Merseyside, Greater Manchester, Newcastle and Sheffield. There are also relatively high rates in coastal areas like Blackpool, Brighton and East Sussex.

The second map shows the gap between NEET rates of young people from disadvantaged families and others, with the darker shades indicating larger gaps, i.e. more inequality. In combination, they show no consistent pattern. There are urban areas with larger NEET rates and smaller employment gaps, but also small gaps in some areas having comparatively low NEET rates like Leicestershire and York.

Figure 3: Maps of NEET rate and employment gap in small areas, March 2017

How to improve the situation

The main conclusion from this analysis is that improving education outcomes is a necessary, but not sufficient condition to lower the disproportionately higher NEET rates of disadvantaged young people. Better local support for them and investment in e.g. youth employability services and careers advice are also very relevant.

By showing regional and local differences in the employment gap, we find evidence that some local areas are more successfully tackling the negative effects of disadvantage, which are unrelated to education success, on young people’s school-to-work transitions. From this point of view, the analysis of large data offers a great potential to see where local actors can achieve better outcomes and to learn from good practice.

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[1] Based on Free School Meals eligibility in the final year of secondary school.

"Establishing the Employment Gap" was published on 24 April 2019. This blog originally appeared on the NIESR website https://www.niesr.ac.uk/blog/family-matters-how-early-disadvantage-impacts-employment-outcomes-young-people

Higher vocational education: An alternative to degrees?

A detailed picture of earnings effects of university degrees is emerging, but not much is known about the benefits of higher vocational education 

A recent report by the IFS provides evidence on the extensive earnings benefits for people graduating from university in England: At age 29, the average male university graduate earns 25% more than someone with similar background characteristics who did not go to university. For women, the earnings effect was found to be even higher, more than 50%. However, the report also found that some graduates, in particular male students achieving degrees in creative arts, English or philosophy, had lower earnings compared to similar A-Level students, who did not go to university.

Although there are clearly many more benefits from university studies than a “graduate wage premium”, adequate earnings benefits should result from achieving a degree in the presence of high costs and related individual debt. With life-course earnings remaining low for some graduates, not all debt will be repaid, which results in additional government spending due to loan write-offs. Higher vocational education offers an alternative choice of programmes of shorter duration, often offered by local colleges and resulting in lower debt (or if e.g. within an employer-funded apprenticeship no debt at all). 

In a new CVER study making use of linked data of hundreds of thousands English secondary school leavers, the National Institute of Economic and Social Research has now published the first ever comprehensive study, showing how many young people choose vocational education and how their earnings contrast with those of degree holders. It finds that earnings of degree holders in many subject areas are consistently higher by age 30 than those of people with higher vocational qualifications. However, we also find that people achieving Level 4-5 qualifications in STEM (Science, Technology, Engineering and Mathematics) subjects earn more than people with degrees from most universities, similar to earnings of graduates from prestigious Russell Group universities.

Who studies for higher vocational education?

Higher vocational qualifications are only taken by a tiny fraction of the more than 620,000 people leaving secondary schools in anyone year compared to degrees, see Figure 1. People leave secondary schools aged 16 with General Certificates of Secondary Education (GCSE, a “Level 2” qualification in the English education classification) and until their early twenties gain higher qualifications, especially A-Levels to enter university or vocational qualifications at intermediate level. By age 29/30, just 1.5% (about 9,500) of all students achieved higher vocational qualifications (“Levels 4 and 5”) as their highest education outcome[1]. Furthermore, we observe that while tertiary education attainment increases over time, Level 4-5 vocational qualifications tend to be acquired relatively late compared to degrees.

Figure 1: Highest level of education achieved by age 29/30

Source: Linked education register data for England, cohort of secondary school leavers 2002/03

When looking into the highest level of qualification people had before achieving degrees or higher vocational education, we found a number of differences: The majority of young people achieving a degree previously had A-levels (about 77%), some had Level 3 vocational qualifications (about 5.4%) or some combination of both (6.6%). In contrast, relatively more people starting higher vocational education held Level 3 vocational qualifications previously (34% of the Level 4 and 35% of Level 5 achievers). More than a quarter (30%) of all Level 4 achievers had even no previous Level 3 qualification, which is normally associated with entry into tertiary education.

Looking into their subject choices (Figure 2), higher vocational qualifications show a very unbalanced gender composition, with science/technology/engineering and maths (STEM) and construction subjects taken overwhelmingly by male students, and health, education and other subject mainly by female students. Only in business studies and Arts, Languages and Humanities show a less unbalanced pattern, much closer to the composition of degree students by gender.

Figure 2: Share of females in Level 4-6 qualifications by subject

Source: NPD-ILR-HESA linked data. Acronyms: ALH: Arts, Languages and Humanities, STEM: Science, Technology, Engineering and Mathematics.

How do earnings compare?

With newly available Longitudinal Education Outcomes (LEO) data for England, we can describe annual earnings of people having made particular education choices until the age of 30 based on the earliest available cohort of students leaving secondary school in the summer of 2003. We will be developing the analysis further in a future study with colleagues at the Institute for Fiscal Studies and University of Cambridge.

Figure 3 shows the descriptive association between average earnings and qualification type. It depicts an early advantage (in monetary terms) of a vocational education compared to academic education, although this converges over time as more people with academic education enter the labour market and get work experience. The earnings path can be understood essentially as a trade-off between work experience and education investments. In this scenario, it is worth noting that only after a few years, the lines depicting earnings trajectories, intersect. The average earnings growth for those who attended Russell group institutions is particularly striking. For men, average earnings for vocational and academic (non-Russell group) graduates converge by the age of 30 whereas for women, earnings of the former group increase at a faster rate. The gender differences largely reflect different subject choices made by men and women, which we discussed below.

Figure 3: Earnings Trajectories*, by type of qualification during the period 2004-2017

* in £ at 2015 price levels (CPI adjustment), academic qualifications by Russell/Non-Russell

Figure 4 further explores median annual earnings by age 30 for people with specific subject choices and whether their education was at degree level (Level 6 from a Russell or Non-Russell university) or for higher vocational qualifications (Levels 4 or 5), with a remarkable outcome. From a descriptive point of view, we find that by the age of 30, those achieving higher vocational qualifications in STEM subjects are observed to earn above some degree holders[2]. This finding remains consistent when using more sophisticated statistical techniques.

  

Figure 4: Median annual earnings* at age 30, KS4 cohort of 2002/03

* in £ at 2015 price levels
Source: Linked education register data for England, cohort of secondary school leavers 2002/03. Acronyms: STEM: Science, Technology, Engineering and Mathematics

An alternative to degrees?

In an additional multivariate analysis, we control for secondary school performance, work experience and a number of further characteristics, but earnings differentials remain similar to the descriptives. Hence, there is some confirmation that higher vocational education in STEM subjects offer substantial benefits by age 30, in line with earlier studies[3]. While for many subject areas, earnings of degree holders are consistently higher by age 30 than for those with highest attainment at Level 4 or 5, higher vocational qualifications could be a useful alternative for people aiming for technical qualifications. With many higher apprenticeships involving some form of tertiary education below degree level funded by employers and lower costs due to shorter programmes and/or local provision by further education colleges, higher vocational education could indeed be a useful alternative to university to for professional roles.

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[1] In the context that some are still progressing at that age.

[2]This also applies to Construction. However, few people obtained these Level 4-5 qualifications.

[3] Hanushek, E., Schwerdt, G., Woessman, L. and Zhang, L. (2017). General Education, Vocational Education, and Labour market Outcomes over the Life-Cycle. Journal of Human Resources

Brunello, G., and Rocco, L. (2017). The labour market effects of academic and vocational education over the life cycle: Evidence from two British cohorts. Journal of Human Capital