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

Wednesday, 6 March 2019

Devolution at any cost?

Since the introduction of the apprenticeships levy in April 2017, local leaders up and down the country have relentlessly called for it to be devolved. The rationale is that local leaders know better than the central government when it comes to understanding and meeting employers’ needs.

However, as with every new policy, there are key questions that need to be answered: 
  1. is the policy effective, i.e. likely to achieve the expected results? And 
  2. what's the best level to implement this policy? Is it better to implement it at the national level or are local authorities more likely to achieve a positive impact?

This new report, undertaken jointly by the Centre for Vocational Education Research and the What Works Centre for Local Economic Growth provides a real life example of how these questions play out in practice by looking at the impact of devolution of the Apprenticeships Grants for Employers (AGE) to the local level through City Deals.
The report first looks at whether the AGE was at all effective in driving apprenticeships numbers. It provides some evidence suggesting that the programme – at the national level – had a limited but positive effect in driving apprenticeships take-up for small and medium size businesses.
The bulk of the report considers whether the devolution of AGE to several local authorities had a more positive effect on take-up of apprenticeships in these areas. To do so, the report compared apprenticeships take-up in local authorities that have negotiated devolution of AGE in their City Deals with those who did not. The report finds that devolution had close to no effect on apprenticeships take-up and in some instances even had a negative effect.
The report offers one possible explanation. Most of the flexibilities granted through the City Deals were around eligibility of large employers, or firms taking more than one apprentice. Results for the national scheme suggest that it didn’t matter for large employers. The report also provides statistics showing  that most smaller firms don’t take on more than one apprentice. As such, the limited and even negative effects of devolving AGE might be the result of local authorities negotiating flexibilities on the wrong margins. This suggests that future efforts might be better targeted at other means for increasing the number of apprenticeships (our apprenticeship toolkit covers some of the possibilities).
The natural conclusion of this experiment is that devolution per se is no silver bullet in promoting local economic development. Before negotiating over powers, central and local government need first and foremost to have an excellent grasp of how a policy works in practice at the national level and in local area. Only in this way, can local policy-makers hope to use their closer position to employers to improve the effectiveness of local labour market policies.

Thursday, 7 February 2019

High performing but less academic students need more ways to get on

Dr Stefan Speckesser of NIESR and CVER discusses a comparison study of 16 to 18-year-old students following A-Level or vocational and technical routes. It shows us that Non-A-level students need more options for progression.

Continued education investment should be the first choice for young people showing the best performance and achievement in both A-Level and vocational and technical routes (“Non-A-Levels”) between the ages of 16 and 18. For these successful students, attending higher education yields significant returns to education investment, especially in terms of life-time earnings, as shown for example is a recent report by the IFS. However, my recent study with Matthew Bursnall and Andreina Naddeo,  based on school-level data and linked education records found large differences in patterns of progression to higher education, which are mainly resulting from the choice of A-Level and Non-A-Level routes in upper secondary education, the so called Key Stage 5 (KS5).

We followed 650,000 English secondary school leavers of the academic year 2009/10 through the education registers until 2012/13, and found very different patterns of education progression. While 56% of all A-Level students move on to higher education after Key Stage 5, only 17% of the Non-A-Level students do so. Students combining A-Levels with Non-A-Levels show similar transition patterns to those doing A-Levels only. The lowest rates were observed for students starting initially on the A-Level route before completing on Non-A-Level programmes. The gap is smaller for high-performance students (using the old-style “UCAS-Tariff”), but still about thirty percentage points (Figure 1).

Source: Linked education register data for England, secondary school leavers 2009/10 (650,000; 407,000 in KS5 2010/11-2011/12)
We also found large differences in the percentages of those starting higher education attending Top Third Universities (based on the “Complete University Guide” definition) depending on the KS5 routes chosen, again also when trying to distinguish low and high attainers in either route (Figure 2). Subjects studied also differ by KS5 routes, with some higher percentages of Non-A-Level students enrolled in Science, technology, engineering, and mathematics (“STEM”) subjects and business studies (Figure 3).
Source: Linked education register data for England, secondary school leavers 2009/10 (650,000; 162,000 attending university in 2012/13)

Source: Linked education register data for England, secondary school leavers 2009/10 (650,000; 162,000 attending university in 2012/13)
Options to improve education progression for Non-A-Level students
The higher market value of certain non A-level qualifications no doubt encourages some students to enter the job market early, but this does not entirely explain the gap in education progression for those with a background in vocational and technical education. And since such qualifications are more often taken by students from economically disadvantaged backgrounds, this lack of progression is likely to adversely affect social mobility.
How could the situation be improved?
  • Young people need to be well informed about how KS5 choices might affect long-term outcomes. A selection into Non-A-Level routes, while likely to have higher initial labour market value, may curtail options for educational progression.
  • A disadvantaged family background remains a barrier for young people to progress to A-Levels with long-term negative effects on social mobility. Improving targeted financial support for families would be another important mechanism to increase progression to A-Levels and higher education.
  • Students with successful achievement in Non-A-Level programmes would benefit from a tailored offer of higher education, which builds on their vocational and technical education and skills, for example Level 4 and Level 5 programmes. Such “higher vocational” education shows large effects on individual wages. Their positive earnings effects also imply improved workplace productivity, so there are effects beyond the individual for firms and for society at large.
  • Finally, the introduction of the Apprenticeship Levy resulted in new Apprenticeship Standards, many involving Higher Apprenticeships, which can create further opportunities for higher education at either Levels 4 and 5 or degree (or equivalent) based on employer funding. They should offer new opportunities to improve education progression for high performers in vocational and technical routes.
This post was originally published on the NISER blog site: https://www.niesr.ac.uk/blog

Friday, 7 December 2018

New Causal Evidence Suggests Technical Education Positively Impacts Students

Shaun M. Dougherty of Peabody College, Vanderbilt University, on the positive effects of the US Career and Technical Education  (CTE) system

Vocational education and training (VET) has a long history in Britain and Europe, perhaps based on connections to the rise of guilds in the middle ages. Career and technical education (CTE, as VET is now called in the United States) has had a formal place in US education for at least the last 100 years. For much of this time CTE occupied a less favored status, though this has started to shift in recent years as policy makers have started to admit that, though postsecondary training will be almost universally necessary in the next half-century for well-compensated employment, pursuing a bachelor’s degree full time at age 18 may not be the best path for everyone. In fact, the policy shift has gone beyond just realizing the importance of having more postsecondary options. Changes in secondary education, and increased demand for CTE in high schools (upper secondary) education has increased the demand for applied learning, and exposure to skills valued by employers at earlier ages. 

The increased focus on and demand for CTE access has led to an unexpected phenomena in some places. Schools that are dedicated to preparing students in secondary education in CTE and that two decades ago were in disrepair or under-enrolled, now see more interest than they can accommodate. The sharp increase in demand for particular schools has created new opportunities to learn about the impacts of participating in these programs, and the students who apply and attend.

One of the main challenges in assessing the effects of CTE or VET programs is that students who select into them are, almost by definition, different from students who do not. As a result, these observable, or unobserved, differences render the two groups incomparable, and thus we cannot estimate the causal effect by comparing two clearly different groups. However, when there are a lot of students who want CTE and a limited supply, application and admissions processed – if they include a component of randomization (or pseudo-randomization) offer a rare opportunity to estimate effects.

In the US, most CTE happens as individual elective courses that are spread throughout a student’s school day. These classes take place amidst other core academic classes, and there is typically no overlap in content between academic and technical coursework. In addition, only about half of all high school students in the US deliberately take more than one CTE elective course in the same subject area (think information technology, health services, or plumbing). Other schools have students spend half a day in a traditional high school taking academic coursework, and then get bussed to a local technical center where they spend the second half of their day doing technical coursework. In both of these settings, there are rarely chances to observe levels of interest that exceed capacity, and so no causal estimates of the program effects.

However, in a handful of US states (Massachusetts, Connecticut, Rhode Island, New York, New Jersey), there are stand-alone CTE focused schools where all students participate in some form of CTE. In these schools, academic and technical coursework often overlap in content, and typically they spend 3 of their 4 years in high school in the same shop getting exposed to a consistent set of peers and instructors. When students are in 8th grade (just before high school at about age 13) students can apply to attend these schools. Often, in their first year of high school students can explore four to eight different programs before, at the end of the year, settling on a single program to pursue for the rest of high school (through age 18, on average).

As demand for CTE has grown in the US, it has been particularly strong in these stand-alone technical high schools in Massachusetts, Connecticut, and New York City. Early evidence from Massachusetts and Connecticut suggest that there is a large positive effect on high school completion among those who apply and get in, relative to those who apply and are not admitted. In fact, the graduation probabilities are about 10 percentage points higher among those who were just admitted, relative to those who just missed being admitted. In both of these states, students apply to attend these technical high schools, and in both contexts they are scored on application criteria that include grades, attendance, and discipline data from middle school (lower secondary). Students are then ranked on their score and admitted in descending order until schools run out of seats. This process creates a de-facto lottery around the cutoff score and allows for the estimation of these large effects. To put the effects in context, this is like taking a student whose baseline probability of graduating from high school was below average, and pushing them several percentage points above average. In Connecticut, there is also suggestive evidence that enrolling in college may also be higher among students who were just admitted to technical high schools, but limited sample sizes (so far) mean those estimates are less precise.

By no means are the large estimates of CTE participation impact in these specialized school representative of all high school CTE programs in the US. However, these effects do seem a compelling example of what might can possible among students who are interested in technical education and under conditions that immerse students in their environment. One concern that policy makers have about CTE is that it may sacrifice general skills given the focus on specific technical skill in instruction. What we found in Connecticut, and with earlier work in Massachusetts though, is that test scores were higher (Connecticut) or no different (Massachusetts) among students in technical schools compared to those who just missed getting in.

The results from US technical high schools (at least this subset we’ve studied) seem promising and perhaps an example of what to expect could happen in similar efforts in the UK. For instance, though University Technical Colleges were set up in a manner similar to the technical high schools in the US, similar positive effects have not yet been apparent. Of course, the technical high schools in the US did not always have such rosy records either, and since the US system has been growing and improving over the last 15 to 20 years, some time may be needed for comparable models just getting set up in Britain to show similar effects. 


Dougherty, S.M. (2018). The Effect of Career and Technical Education on Human Capital Accumulation: Causal Evidence from Massachusetts, Education Finance & Policy, 13(2), http://www.mitpressjournals.org/doi/abs/10.1162/EDFP_a_00224.