Friday, 7 September 2018

Does starting an apprenticeship boost young people’s earnings?

Chiara Cavaglia, Sandra McNally, Guglielmo Ventura

Apprenticeships feature in the vocational education systems of many countries, although their popularity varies widely. They are especially prevalent in countries like Austria, Germany and Switzerland and virtually absent in countries like Italy, Sweden and the United States, which rely more on classroom-based learning and put less emphasis on vocational education.

England is somewhere in between, with a high profile policy commitment to increase the number of apprentices in recent years. Much of the growth in apprenticeship provision has been taken up by older people (those aged over 25). A CVER study published today investigates whether and why the earnings benefits to completing an apprenticeship differ between younger and older people.

In related research , we investigate whether there is a return to starting an apprenticeship for young people. It relates to our previous work on this topic (see CVER Research Paper 009). In the new study, we only compare young people whose highest level of education is vocational (at Levels 2 or 3) – some of whom start an apprenticeship and some of whom do not.

We are interested in whether there is a payoff to starting an apprenticeship over and above leaving education with at most classroom-based vocational qualifications at the same level. This question is especially policy-relevant in the light of plans in England to increase the number of apprenticeships and to re-design post-16 vocational education with more of an explicit focus on apprenticeships.

We look at a range of employment outcomes for young people close to when they enter the labour market (at age 23) and after they have more labour market experience (at age 28). Compared with our previous work, we focus more on the causality question – that is, can apprenticeships be said to cause an increase in earnings? – and more explicitly on the gender gap in earnings between apprentices and non-apprentices.

We use linked education and labour market data from administrative data sources (Longitudinal Educational Outcomes) to undertake this analysis for the cohorts who finished their compulsory education between 2002/03 and 2007/08. For most of the analysis, we focus on the 2002/03 cohort, whom we can observe up to the age of 28 (in 2015).  We also make use of data from the Labour Force Survey to explore more fully the gender gap in earnings.

Using administrative data, we can control directly for many important observable characteristics that may influence both selection into apprenticeships and labour market outcomes. These include test scores at primary and secondary school, demographics and the secondary school attended. Although our set of controls is extensive and likely to absorb much of the pre-existing difference among those who start an apprenticeship and those who do not, we make use of other techniques (such as bounding and instrumental variables) to probe the question of causality.

Our results suggest a positive earnings differential from starting an apprenticeship in many contexts – and that this has a causal interpretation. But there is a huge range of estimates. For men, the differential is very high on average, especially for Advanced Apprenticeships. For women, the differential is roughly half the size and is especially modest for Advanced Apprenticeships by the age of 28.

For men, there is very high concentration in sectors where the return to an apprenticeship is high (such as Engineering) whereas women specialise in areas where the returns to having an apprenticeship are much lower (such as Child Development).

When we compare the earnings of men and women who did an apprenticeship, there is a large gap and much of this is attributable to the sector of vocational specialisation. But this is not the only reason. For example, among those who did an Advanced Apprenticeship, the gender earnings gap is still 13% (at age 23) even after including detailed controls for the apprenticeship sector, industry of work, etc.

Analysis of the Labour Force Survey suggests that this can partly be attributed to lower hours of work by women – but this is unlikely to be enough to explain the gap on its own. It is also interesting to note that the gender earnings gap between male and female non-apprentices (at vocational Level 3) is non-existent after including controls.

The results of our study should give cause for optimism that apprenticeships really do generate a positive return in the labour market for young people. Increasing opportunities for young people to access apprenticeships does seem to be a worthwhile policy, especially since these returns are experienced by individuals who leave school with low to medium qualifications.

But our research also illustrates huge variability in the returns to apprenticeships. A practical implication is that careers information to students should pay careful attention to the type of apprenticeships available rather than to encourage students to take any type of apprenticeship at all.

Do older apprentices get the same earnings boost as younger ones?

Steven McIntosh and Damon Morris

There has been a huge increase in the number of people over the age of 25 who are undertaking apprenticeships. Prior to 2007, there were essentially no apprentices in this age group in England; in 2016/17, nearly half of all apprenticeship starts were for such ‘older’ apprentices. A new CVER study  is the latest to show an earnings return to starting an apprenticeship for young people. In related research, we ask whether undertaking an apprenticeship at a later point in one’s career is associated with a similar earnings boost.

To answer this question, we analyse administrative data recording all apprenticeship starts in England between 2004 and 2013. These data are matched to tax records containing information on annual earnings, from which a measure of daily earnings can be derived.

We use such earnings information from the period up to three years before the start of each observed apprenticeship, and in the period up to three years after its completion. This allows us to look at the change in earnings following the completion of an apprenticeship, relative to earnings received by the same individual before their training.

We compare this to the before-after change in earnings of a control group of similar people who also started an apprenticeship, but for some reason did not complete. The change in the earnings of this latter group can be taken as an indication of the change in earnings that the treated group of completers would have received had they not undertaken their apprenticeship.

We divide the observed apprentices into two age groups; those aged 19-24 and those aged 25 and over (apprentices below the age of 19 typically do not have prior earnings information with which to perform the analysis and so are excluded). Figure 1 shows the average earnings in each year for those who undertook an Intermediate (Level 2) Apprenticeship, separately by age group. ‘Year 0’ in these charts represents when the apprenticeship was actually undertaken.

The left-hand diagram shows that for both male and female apprentices in the 19-24 age group, earnings are significantly higher after an apprenticeship than before. But this is true whether the individual completes their apprenticeship (solid lines) or does not complete (dashed lines). The additional value of completing the apprenticeship is measured by the extra change in earnings of the completers, compared with the change in earnings of those who fail to complete. This can be seen as the widening gap between the solid and dashed lines, around the time of the apprenticeship.

For the older age group of apprentices, the right-hand diagram shows much flatter earnings profiles over time, as we would expect since earnings profiles typically become flatter with age. It is still possible to see a widening gap between the solid and dashed lines, showing that, as for the younger apprentices, completing an apprenticeship leads to a larger change in earnings over time than not completing.

Figure 1: Log Daily Earnings of Intermediate Apprentices

Figure 2 shows similar earnings profiles to Figure 1, but this time for Advanced (Level 3) Apprenticeships. We observe very similar patterns. For both age groups and for both genders, we can see a widening of the earnings gap between completers and non-completers after the apprenticeship, demonstrating the value of these apprenticeships in the labour market.

Figure 2: Log Daily Earnings of Advanced Apprentices

Using regression analysis, we can quantify by how much more earnings increase for the treated apprenticeship completers than for the control group of non-completers, while holding constant other factors that could influence earnings, such as age on completion of the apprenticeship, ethnicity and duration of apprenticeship. The results are consistent with the impression given in Figures 1 and 2. For both men and women and at both apprenticeship levels, there is a positive boost to earnings, with the effect of completing an apprenticeship on earnings being two to three times larger for the 19-24 age group than for the older group.

Having established that older apprentices benefit from lower earnings differentials than younger apprentices, the key question is why. We ask whether it is due to older apprentices earning a lower differential than younger apprentices for the same type of apprenticeship, or whether it is due to older apprentices choosing to do apprenticeships in the ‘wrong’ areas where the differentials are lower.
The first point to note is that individuals in different age groups choose to do apprenticeships in different areas. Figure 3 shows the proportion of younger and older apprentices within each apprenticeship framework.

There is a clear difference in these proportions across frameworks. Those to the left-hand side of the diagram (Automotive, Construction, Electro-technical, Plumbing, Hair and Beauty, and Engineering) are dominated by younger apprentices. At the other end are frameworks where the majority of apprentices are aged 25 and older, such as Business Administration, and Health and Social Care.

Figure 3: Age Group Percent by Framework (Ranked in ascending order of age 25+ proportion)

Next, looking within sectors, we find that for men at Level 2, and for women at both levels, there are a number of sectors where apprentices in the younger age group receive a higher earnings differential than older apprentices in the same sector. This seems to be particularly the case in non-manual business service sectors such as Business Administration, Accountancy, and IT.

For these groups, our results show that these lower differentials for older apprentices are the main cause of the lower overall differential for this older age group. Why they receive such lower differentials within these sectors is an important question. Surveys of apprentices at this time[1] point to the possibility of lower quality apprenticeships for older apprentices, since they are more likely to be existing employees before their apprenticeship (and so more likely to be engaging in ‘top-up’ training) and to have shorter apprenticeships on average. They are also less likely to receive formal training with a training provider and to see their apprenticeship as essential to their job.

For men at Level 3, the results are different: the main cause of lower differentials for older apprentices here is that they tend to undertake apprenticeships in sectors where the earnings differentials available are much smaller, such as in Business Administration, whereas the largest earnings differentials available are in Construction, Electro-technical, and Manufacturing.

While older apprentices may not want to move into such sectors at this stage of their careers, for the skill needs of the economy it is important that overall there are sufficient opportunities available in such higher value sectors, and that the apprenticeship scheme does not become dominated by older apprentices in the lower value frameworks.  

[1] BIS (2013). ‘Apprenticeship Evaluation: Learners.’ Department for Business, Innovation and Skills Research Paper 124.