Friday, 13 March 2020

Training grants: a useful policy to address skills and productivity gaps?

As work changes, firm-provided training may become more relevant for good economic and social outcomes. However, so far there is little or no causal evidence about the effects of training on firms. Pedro Martins looks at the effects of a training grants programme in Portuguese firms.

As most academics, I am fortunate to be able to update my own skills on a regular basis. For instance, when I attend a research seminar, I learn from colleagues that are pushing the knowledge frontier in their specific fields. Some of their insights will sooner or later also feature in my own teaching and research, thus increasing my performance and that of my institution.

However, workers from other sectors typically have far fewer opportunities to increase their skills on a regular basis. Recent research by the European Investment Bank indicates that, on average, workers in Europe spend less than 0.5% of their working time on training activities. In the current context of major changes in labour markets – including artificial intelligence and automation and perhaps even coronavirus – this training figure seems too low.

Economics has long predicted some degree of under-provision of training in labour markets. First, training is expensive for firms, as it typically entails significant direct and indirect costs. Second, employers know they will lose their investments in training if employees subsequently leave. It will be even worse if workers are poached by competitors. Moreover, even leaving aside the issues above, firms may struggle to estimate the effects of training on their performance (sales, profits, etc), which will again detract them from upskilling their workers.

The context above points to an important market failure in training. This context may also explain in part the disappointing economic performance of many European countries over the last years. While labour markets have become more efficient, incentives for on-the-job training may paradoxically have declined, as workers move more easily to other firms. However, public policy may play a role in alleviating the under-provision of training. Specifically, governments can subsidise training in the workplace in order to bring its private net benefit more in line to its social value.

The new working paper featured in this blog and recently presented at a CVER seminar (‘Employee training and firm performance: Quasi-experimental evidence from the European Social Fund’) contributes empirical evidence to this question. The research evaluates the effects of a €200-million training grants scheme supported by the European Union on different dimensions of recipient firms.

The study draws on the difference-in-differences counterfactual evaluation methodology, comparing the outcomes of about 3,500 firms that applied and received a training grant (of about €30,000) and around 6,000 firms that also applied but had their application rejected. Using rich micro data from Portugal, the country where the scheme was introduced, firms can be compared over several years both before and after their participation in the training grants scheme.

The results indicate that the scheme had significant positive effects on training take up, both in terms of training hours and expenditure. For instance, training increased by about 50 hours per worker per year in the firms that received the grant, compared to firms that had their applications rejected. Deadweight – funding training that would be carried out even without the funding – appears to be very limited, in contrast to the findings of an earlier study of a programme in the UK (link).

Moreover, the additional training conducted by firms led to a number of important outcomes that the study can trace, including increased sales, value added, employment, productivity, and exports. These effects tend to be of at least 5% and, in some cases, 10% or more.

For instance, the figure below presents the average difference in total sales between firms that received the grant and those that did not. (Periods -9 to -1 refer to the years before the grant was awarded; while period 0, the comparison year, is when the firm applied for the grant; period 1 is when the firm conducted the training; and periods 2 to 10 refer to the years after the training was conducted.)  The results indicate that total sales are 5% higher in the firm in year 2 and 10% higher in year 5. However, there were no differences between firms before the grant was awarded, which is reassuring as to the counterfactual nature of the study.

The employment results are also interesting as they come from both fewer separations and increased hirings. Firms that increase their training activities in the context of the grant appear to want to expand their workforce but also to retain the workers they already employ. Moreover, the employment effects are stronger when the scheme ran in periods of recession, suggesting that training grants can also act as an active/passive labour market policy, with a positive ‘lock-in’ impact.

In conclusion, there is a case to be made for workplaces to become a little more similar to universities. On-the-job learning can make firms (much) more productive - but that may require a bigger role from governments. Training grants may be a promising tool in this regard.

Wednesday, 11 December 2019

Can the manifesto pledges plug the skills gap?

The parties are offering plenty of promises on improving technical and vocational skills, but, says Sandra McNally (CVER Director), there are significant gaps in their thinking.

Improving technical and vocational skills is a key aspect of improving productivity and social mobility in Britain. The relatively high number of people with poor basic skills and low number of people with high-level vocational skills are long-standing national challenges and have been highlighted in reports by the OECD, the government and academics. In light of this, key priorities of the incoming government should be:

  • To raise attainment and improve educational trajectories for “the forgotten third” who do not get good GCSEs year-on-year and many of whom never achieve a good upper secondary education.
  • To address the shortage of higher-level technical education (at Levels 4 and 5) that was highlighted by the Augar review. 
  • To increase the ability for adults to upskill or reskill later in life. 

But none of the manifestos acknowledge any problem with a “forgotten third” of young people. The source of the problem is partly structural, partly a question of resources, with spending per student between 2010–11 and 2018–19 falling by 12 per cent in real terms in 16–18 colleges, and by 23 per cent in school sixth forms. The Conservative manifesto makes no promise to increase baseline funding beyond existing commitments. Both the Labour Party and Liberal Democrats make large spending commitments to FE in general with the Labour Party making specific mention of aligning the base rate of per pupil funding in post-16 provision with Key Stage 4. The Conservative manifesto does make a significant commitment to increase capital expenditure in Further Education Colleges. While this addresses one of the issues addressed in the Augar review, investment in buildings will not improve student outcomes if there isn’t also investment in their teachers (who are paid considerably less than teachers in schools).

The manifestos do not acknowledge that there is a particular problem with the lack of high-level vocational education in England vis-à-vis higher education. In England, only 4 per cent of 25 year-olds hold a Level 4 or 5 qualification as their highest level, compared to nearly 30 per cent for both Level 3 and Level 6. In contrast, in Germany, Level 4 and 5 makes up 20 per cent of all higher education enrolments.

The main Conservative pledge relevant to this is the establishment of 20 Institutes of Technology with a focus on STEM skills. The Liberal Democrats also promise some institutional reform with the establishment of national centres of expertise for key sectors, such as renewable energy, to develop high-level vocational skills. However, they go further in explicitly acknowledging a “skills gap” and committing to address this by expanding higher vocational training, without however, stating how they would go about this.

Labour promises a free lifelong learning entitlement for everyone, including training up to Level 3 and 6 years of training at Levels 4-6. To the extent that this removes some of the distortions in the financing of the post-18 education system (as well-documented in the Augar review), this would help to address the problem of the lack of higher-level vocational education. But it would be an expensive way of doing so, with the taxpayer (most of whom does not have Level 4-6 education) having to pay the full cost. Moreover, people who are educated up to Level 4-6 have a high private return from this investment compared to people with a lower level of education.

The party manifestos all have something to say about apprenticeships. They all acknowledge problems with how the apprenticeship levy is working. The Conservative manifesto states that they would look into the working of the Levy and see how it can be improved. Both Labour and the Liberal Democrats are far more explicit. They both commit to expand the use of the levy to other forms of training. While this seems like a sensible idea, only two per cent of employers actually pay the levy. All political parties could do with a few more ideas on how to incentivise the other 98 per cent of employers to invest in the training of their staff. The apprenticeship levy is not sufficient for this. The Conservative manifesto has ideas about how to expand R&D credits and it is a pity this does not extend to human capital.

With regard to lifelong learning, Labour and the Liberal Democrats make commitments that are universal whereas the Conservatives’ commitment is more targeted to specific groups through a National Skills Fund (which does not have much detail). Labour’s commitment is to a free lifelong learning entitlement (discussed above) whereas the key Liberal Democrat commitment is the introduction of “Skills Wallets” worth £10,000 for every adult to spend on approved education and skills courses, with the first £4,000 at age 25, £3,000 at age 40 and £3,000 at age 55. This idea has similarities to the “individual learning accounts” that were introduced in 2000 but abandoned a year later because of fraud.

Although the idea of investment throughout life is sensible (and does need to be facilitated), it would be important to ensure that similar mistakes are not repeated. But a more substantive issue is where employer investment appears in this framework. As a major beneficiary of adult training, there needs to be a mechanism for co-investment. This may also help to ensure that the training undertaken meets the needs of the labour market.

Any incoming government needs to be held to account on the extent to which their promises actually address national priorities and whether we see an improvement. The extent to which this is possible depends on the success of their overall economic strategy as well as to the success of specific measures relating to education and skills.

This article was originally published by King's College London's Policy Institute:

Friday, 27 September 2019

Is there a socio-economic gap in students’ academic match?

Young people from less well-off backgrounds are more likely to pursue lower ranked upper-secondary qualifications than their prior attainment would suggest that they can achieve.

Recent research from Konstantina Maragkou (University of Sheffield and CVER) examines whether socio-economic inequalities exist in the academic match of students in upper-secondary education.  Academic match occurs when student quality matches the quality of the qualification that they take, i.e. students are taking appropriate qualifications for their capabilities and prior attainment. The study uncovers a significant socio-economic gap in academic match among English students in upper-secondary post-compulsory education. Students from lower socio-economic backgrounds achieve less highly ranked qualifications compared to their similarly attaining but more advantaged peers. We show that this is associated with a wage penalty in the labour market.

This study makes use of detailed individual-level linked administrative data (‘Longitudinal Educational Outcomes’ data) from schools, colleges and tax authorities in England for a single cohort of students who undertook their GCSE exams in 2006. Academic match is measured using a continuous variable defining undermatched, matched and overmatched students based on the distance between each student’s prior attainment in GCSE exams and the median prior GCSE attainment of the other students who achieved the same chosen academic or vocational qualification in upper-secondary education. The students who follow upper-secondary qualifications that are studied by similarly achieving peers are then considered as matched to their qualification.  The outcome of this analysis shows that students from socially disadvantaged backgrounds are more likely to be exposed to academic undermatch (that is, having higher prior attainment than the median on their chosen upper-secondary course), even compared to others within the same school.

The study considers students’ match in Level 1 to Level 3 vocational qualifications and Level 3 academic qualifications (A-levels and AS levels) taken in upper-secondary education between ages 16 and 19. Previous literature on rates of return has shown a positive average income return to qualifications at Level 3, whether vocational or academic. However, with regard to the students who leave the education system with lower level qualifications, there is more controversy over the extent to which these qualifications offer good opportunities for future employment and earnings. Qualification choice is therefore important with potentially long-lasting consequences.

Figure 1 displays the variation of the total GCSE point score of students achieving each upper-secondary qualification with the upper and lower lines of the box representing the value at which 75% and 25% of the sample scored below that GCSE point score respectively, the middle line representing the median GCSE point score and the top and bottom extending lines the range. It is evident that there are substantial differences in the ranking of each qualification, with the median scores of students studying for the most highly-ranked qualifications being considerably higher than those of the students studying for the lowest ranked ones. In addition, the difference between the median GCSE point score of students studying for vocational and academic qualifications is also notable.

Figure 1: Measure of qualification’s quality based on median standardised GCSE scores of students achieving that qualification

From the raw data, a significant socio-economic gap in the academic match of students in upper-secondary education is observed, as illustrated in Figure 2. Students from the lowest socio-economic group are less well matched to their chosen upper-secondary qualification compared to students from the highest socio-economic group (i.e. they achieve less in upper secondary education than their GCSE scores would suggest they are capable of).

Figure 2: Academic match of highest and lowest SES students

When keeping important background factors constant, including prior attainment at ages 11 and 14, demographic characteristics and secondary school attended, students from socially disadvantaged backgrounds are still more likely to be exposed to academic undermatch compared to their more advantaged peers. This gap is greater among the highest achieving students. This means that, compared to other young people from a more advantaged background with the same level of prior attainment and the same other background characteristics, those from a less well-off background are more likely to study for lower-ranked qualifications in upper-secondary education. Also, undermatched students are more likely to be found in schools with lower proportions of high achieving students and higher proportions of disadvantaged students, suggesting the importance of peer groups and school guidance on qualification choices. Among the highest achieving students, 80% of the identified socio-economic gap on academic match could be explained by such differences in the schools that those students from differing backgrounds had attended.

In addition, the study shows that a significant proportion of undermatched students are likely to be found in rural districts with higher rates of youth unemployment and higher proportions of residents with only low-level qualifications.

Does being undermatched matter?  The study also estimates wage equations with the indicators of whether young people are matched or not to their upper-secondary qualification. The results show that there is a positive relationship between being academically matched and labour market income returns. Non-university participating girls who are one standard deviation less undermatched earn 17% more at age 25 while non-university participating boys earn 5% more.

In summary, the study shows that there is a significant socio-economic gap in academic match among English students in upper-secondary post-compulsory education. Students from lower socio-economic backgrounds achieved less highly ranked qualifications compared to their similarly attaining but more advantaged peers. We show that this matters for labour market outcomes. Policy-makers should be focusing more on providing students with information related to the available upper-secondary courses that are suitable to each student’s ability credentials and future educational and occupational aspirations.

Tuesday, 25 June 2019

Changing Aspirations and Outcomes in Post-16 Education

In this latest blog post, Steven McIntosh of University of Sheffield discusses CVER contributions to the recent Augar Review of Post-18 Education, and the findings that came out of that research.

CVER have been contributing new research to the recently published Augar Review of post-18 education and funding (available here).  I supplied evidence to the Augar Review commissioners on the factors that influence aspirations and outcomes of young people in post-compulsory education. More details can be found in an accompanying CVER briefing note and full details of the research are published in a DfE research report.

The work considered two cohorts of young people who took their GCSEs almost a decade apart, in 2006 and 2015 respectively, using data from the Longitudinal Study of Young People in England. The aim was to see what influences young people’s aspirations and choices for their post-GCSE education, and whether such relationships have changed over time between the two cohorts. We might expect some such changes to be observed, given the policy initiatives enacted during this period, for example the tripling of university tuition fees to £9000 in 2012, and the promotion of apprenticeships with a target of 3 million new starts by the end of the decade.

Despite these policy changes, the results of our analysis (see Table 1 below) showed that following an academic path through A levels to university remained the most popular choice of young people, with around two-thirds in each cohort aspiring, at age 14, to follow this route post-GCSE. There was actually a small increase between cohorts in the proportion wanting to follow an academic route. There was also a small increase in vocational aspirations between cohorts. When aspirations were re-assessed in Year 11, just before taking GCSEs, the same patterns were observed, though the proportions aspiring to an academic route were lower than at age 14, perhaps as realism set in.

Table 1: Percentage Planning Type of Post-Compulsory Education, by Cohort and Sweep

Sweep 1 (Year 9, Age 14)
Sweep 3 (Year 11, Age 16)

Cohort 1
Cohort 2
Cohort 1
Cohort 2

What people aspire to is often what they end up doing. This was the case for around three-quarters of young people in both cohorts, irrespective of route aspired to. Those who did not fulfil their aspirations, such as those who wanted to do academic A levels but in the end chose the vocational route, were more likely to have lower achievement at GCSE. But even holding prior attainment constant, individuals from a more advantaged family background were more likely to see their aspirations fulfilled.  This is shown in Table 2 below. It is important young people from all social backgrounds should be given equal opportunity to reach their aspirations.  Advice and guidance could be important here in guiding young people towards the best options for them.

Table 2: Percentage who aspired to academic route in Year 11, who follow academic route in Year 12, by family background and prior attainment

Young person’s prior attainment
Highest Parental Education Level

No quals
Level 1/2
A Levels
Level 4
Cohort 1

7+ A*-C
5-6 A*-C
1-4 A*-C

Cohort 2

7+ A*-C
5-6 A*-C
1-4 A*-C

When we looked at the factors that influence such aspirations and outcomes, then in addition to family background and prior attainment, gender and ethnicity were important. Girls were more likely than boys to aspire to an academic rather than a vocational route, and the gender gap widened between the cohorts. Similarly, those from most ethnic minority groups were more likely to aspire to academic post-compulsory qualifications, holding other factors such as attainment and background constant, with the gap becoming wider for some groups (Mixed ethnicity and Bangladeshi). With respect to region, young people living in London were more likely to aspire to undertake A levels and to apply to university, with this gap increasing in the former case but narrowing in the latter case, between the two cohorts.

Focussing on vocational study, there were more young people taking Level 3 vocational qualifications amongst the more recent cohort. This was most notable amongst those with lower GCSE attainment (some A*-C GCSEs, but fewer than 5), but was actually observed at all levels of prior attainment. For example, amongst those young people with 5 or 6 GCSEs at Grades A*-C), 32% took a vocational Level 3 qualification in Year 12 in Cohort 2, compared to just 17% in Cohort 1. At the very highest level of GCSE attainment (7+ Grade A*-C GCSEs), vocational participation was lower, though even here we saw an increase between cohorts (from 6.5% to 9.5%).

As well as increased participation in vocational Level 3 qualifications in Year 12, the results also showed that members of Cohort 2 were more likely to progress from vocational Level 2 to vocational Level 3 between Years 12 and 13, compared to the earlier cohort. 47% of Cohort 2 members initially learning at vocational Level 2 progressed to vocational Level 3 in Year 13, compared to 30% in Cohort 1.

Looking at types of vocational qualifications, there has been a clear shift between cohorts towards BTEC qualifications, and away from NVQs, particularly at Level 2. BTEC qualifications were least popular amongst whites and people outside London, particularly in the east of the country from the North-East through Yorkshire to East Anglia. Amongst those to have a chosen the vocational route, there was also some evidence that apprenticeships were becoming more popular, though this was mainly just at Level 2. For those progressing straight to Level 3 after GCSEs, Advanced (Level 3) Apprenticeships were rarely taken, with a small increase in such participation between cohorts. Nevertheless, there is potential for more growth in this area, with more young people in Cohort 2 reporting that they discussed the possibility of doing an apprenticeship at age 15 at school or with family and friends, particularly amongst those who did not go on to apply to university. In Cohort 1, 28% of those who did not go on to apply to university had talked to someone about apprenticeships, compared to just 10% of those who did go on to apply. In Cohort 2, these numbers increased to 34% and 20% respectively.

Finally, another positive for vocational education was that there was only limited evidence of ‘churn’ (cycling between low level learning programmes and periods of low-skilled employment or unemployment) amongst low level vocational learners in the two cohorts. A majority of young people starting a vocational course in Year 12 remained in education throughout the whole of that year.

In summary, the academic route remains the dominant route for 16-18 year olds, who show a preference for following A levels and then university. Nevertheless, there are signs of development of vocational education for this age group, with more interest in apprenticeships, and more learning at vocational Level 3, including increased rates of progression from lower vocational levels. The challenge remains to make such routes of broader appeal and to ensure that coming from a disadvantaged background is not a barrier to realising aspirations.

Friday, 26 April 2019

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.

Thursday, 25 April 2019

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.

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

Wednesday, 3 April 2019

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.

[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