The effects of vocational education on adult skills, employment and wages: What can we learn from PIAAC?

Vocational education and training (VET) are highly valued by many. For instance, VET is expected to play an important role in achieving two Europe 2020 headline targets set in the education field: (a) reduce the rate of early school leavers from education to <10%; (b) increase the share of 30–40 years old having completed tertiary or equivalent education to at least 40%.

In this paper we investigate the effects of VET on adult skills and labour market outcomes by using the PIAAC survey. Data comparability across countries, the breath of countries involved, and the almost unique presence of information on assessed skills, training, earnings and employment makes this survey especially valuable to study the different facets of VET as compared to more academic education.

Our approach is to consider the education careers available to individuals as alternative treatments in a multivalued treatment framework. Focusing mainly but not exclusively on upper secondary, post-secondary and tertiary education, we assume that individuals are exposed to four alternative treatments: (1) vocational education at the upper secondary or post-secondary level; (2) academic education at the upper secondary or post-secondary level; (3) vocational education at the tertiary level; (4) academic education at the tertiary level.

Our key assumption is that, after controlling for individual differences in parental education, country of birth and the number of books in the house, the assignment of individuals to these treatments can be considered as good as random. This assumption implies that our selection on observables is capable of controlling for the sources of self-selection into educational tracks. We discuss its plausibility in the context of the data being used. This is important for the interpretation of our results.

Only when this assumption holds we can interpret our estimates of the effects of alternative treatments as causal effects. When it does not, a more modest interpretation is in order, that views our findings as interesting correlations at best. In particular, if there are factors affecting selection into different curricula that we cannot control for with the data at hand, our estimates are affected by selection bias, which could amplify the estimate gap in labour market outcomes associated to alternative curricula. For instance, failure to properly control for unobserved ability could increase the estimated premium associated to academic curricula if individuals with higher ability are less likely to select into vocational tracks.

When we compare the labour market payoffs accruing to vocational and academic education at ISCED 3 and 4 (upper secondary and post-secondary education), we find that a vocational curriculum is associated to slightly lower hourly earnings, a higher probability of being currently employed, and a higher share of the completed working life spent in paid employment. The estimated differences are small: for earnings, the negative gap ranges between \(-\,1.3\%\) for males and \(-\,4.8\%\) for females; for the probability of employment and the share of time spent in paid employment, the estimated positive gaps are 2.2 and 3.3% points for males and 1.9 and 0.6% points for females.

The contrast between vocational and academic education is much sharper when we consider tertiary education (ISCED 5). In this case, the earnings gap between vocational and academic education at the time of the interview is as big as \(-\,19\%\) for males and \(-\,21.7\%\) for females. There is also a small negative gap in the probability of being currently employed. This gap should however be contrasted with the positive gap in the share of the working life spent in paid jobs, estimated at 6.9% points in the case of males and at 3.7% points in the case of females. Overall, the evidence we have on different ISCED levels is that vocational education does not perform as well as academic education when earnings are concerned, and performs slightly better than academic education when employability measures are considered.

Independently of the ISCED level, we find that individuals with vocational education have a higher likelihood of being NET (not employed and with no education or training in the past 12 months), report poorer health and have poorer civic behaviour than comparable individuals with academic education. There is also evidence that vocational education is associated to poorer labour market returns among older than younger cohorts. Whether these differences simply reflect cohort effects or also indicate the presence of age effects is impossible to tell with the data at hand, which are a cross section of individuals. This is an important issue that must be left to better data and further research.

When we consider the proficiency in foundation skills we find that those with a vocational education tend to be less effective than those with an academic education, for any ISCED level. This is true for both genders and, in spite of some heterogeneity, for all countries. The negative gap is larger for those with tertiary education and increases with the country-specific share of vocational students. In particular, we estimate that the negative percentage gap associated to vocational education at the secondary or post-secondary level ranges from \(-\,2.0\) to \(-\,2.2\%\) for literacy, from \(-\,1.9\) to \(-\,2.9\%\) for numeracy and from \(-\,1.8\) to \(-\,2.3\%\) for problem solving skills. In the case of tertiary education, the negative gap is larger and ranges from \(-\,5.7\) to \(-\,5.9\%\) for literacy, from \(-\,6.7\) to \(-\,7\%\) for numeracy and from \(-\,4.4\) to \(-\,4.7\%\) for problem solving skills.

There is evidence that the wage and employment returns to VET are higher in countries where the relative supply of VET graduates is lower. In these countries, skill performance by VET graduates is also better. However, in spite of the growing interest attracted by dual systems, which alternate school and work, we do not find systematic evidence that returns to VET are higher in the countries where vocational education systematically combine school and work.

The paper is organised as follows. Section 2 provides a review of the literature. Our empirical approach is discussed in Sect. 3. The data are briefly introduced in Sect. 4. Results for skills and competencies are presented in Sect. 5, and results for employment and earnings are discussed in Sect. 6. Conclusions follow.

Vocational education and training (VET) is frequently perceived as improving the opportunities of youths who lack the resources, skills or motivation to continue with higher education. Many have argued that VET provides useful skills to prepare these individuals for labour market entry and improve their chances of a successful professional career. In an influential paper, Ryan (2001), summarises the cross-country evidence, indicating that vocational programmes, and in particular apprenticeships, increase the chances of an early working life.

While there are virtually no studies investigating the effects of VET on skills, substantial research has been devoted in the past few decades to explore how VET affects labour market outcomes, including earnings and current employment status. This research has produced estimates of the economic returns to different curricula, focusing in particular on the comparison between academic education and VET. Data limitations have generally restricted cross-country comparative analysis, limiting the focus to national case studies.

U.S. based research includes Hotchkiss (1993), who studies the effects of vocational schooling on employment and wages for high-school graduates in 1980, finding no returns to vocational schooling even after controlling for training-related occupation choice. In contrast, Bishop and Mane (2004), find that students who take a certain percentage of vocational subjects in secondary school are more likely to earn higher wages and display higher participation rates compared with academic education students. Meer (2007), also find positive wage effects, but smaller than in Bishop and Mane.

In the UK, Dearden et al. (2002), find that academic education leads to higher returns, but also document that the majority of vocational education programs increase earnings relative to no vocational qualification, especially for low achieving school leavers. One exception is the NVQ2, a VET course often undertaken during employment, which seems to have a negative impact on the wages of individuals, even relative to those with no other qualification (Dearden et al. 2004). More recently, Bibby et al. (2014), compare returns to different curricula using matched administrative data and find that returns to workplace level VET are higher than those from classroom level VET.

Empirical research on the economic effects of VET is plagued by the difficult task of controlling for endogenous selection into different curricula in a credible way.¹ The few studies which have been able to use exogenous policy changes affecting the duration of academic education in vocational tracks tend to find no statistically significant effects of vocational education on wages or employment probabilities.

Oosterbeek and Webbink (2007), evaluate the effect of extending 3-years basic vocational programs with 1 year of academic education—a policy that took place in the Netherlands in 1975—on later wages. Adopting a difference-in-differences strategy, they investigate the effect of the change on wages 20 years later, but do not find any effect. Pischke and von Wachter (2008), exploit the gradual adoption of a 1 year increase in compulsory schooling in the lowest schooling track in Germany between the 1950 to the 1970s, investigating the changes on long-term wages, but also fail to find an effect. Hall (2012), assesses a policy change in Sweden in 1991 that increased the academic education content of vocational schooling at the upper secondary level. Exploiting random differences in time and the regional implementation of a policy pilot, she finds no effects of the policy on the wages earned up to 16 years after the beginning of secondary school.

Using a natural experiment, Malamud and Pop-Eleches (2010), find that VET graduates in Romania are significantly more likely to be employed as manual workers and craftsmen. However, there is no significant difference between VET and academic education in terms participation rates, unemployment rates, periods of non-employment, and family income. Finally, Fersterer et al. (2008), find that Austrian apprentices earn no significant wage premium when compared to other forms of school based education, such as those taking place in colleges or vocational schools.

The relationship between VET and the economic outcomes included in the PIAAC dataset is illustrated in Fig. 1. For the purposes of this paper, we consider initial education and training, whether vocational or academic, as the key input, denoted as VET, training and skills as intermediary inputs, denoted as X, and employment and wages as outcomes Y. The figure shows that the relationship between these variables is complex: we would expect, for instance, skills to influence wages and employment, but also that having a job helps maintain and accumulate skills. Similarly, individuals who have received more training may have higher wages, and better pay may induce workers to invest in further training.

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In this paper, we do not try to distinguish between direct and indirect effects, as Eq. (1) does, and focus instead on estimating the marginal effects \(\alpha _1\) and \(\gamma _1\)-using Eqs. (2) and (3). Including intermediary inputs such as skills or training in the earnings or employment equation is problematic from an empirical viewpoint because skills and training are endogenous variables. These inputs depend on initial education but also reflect individual decisions taken after the end of education. Furthermore, since intermediary inputs and outcomes refer to the same time period, simultaneity issues also arise. To address these issues, we would need additional sources of exogenous variation that we cannot find in the data at hand. We therefore take a more modest route and focus only on the gross (or total) effect of vocational education on labour market outcomes.²

How can evaluate the causal effect of VET on earnings, employment and skill proficiency? Answering this question is complicated because both education type and length, the two key attributes of any education career, are not randomly assigned to individuals, but rather are decided by students, parents and teachers.³ As suggested by standard economic theory, individuals decide the type and the quantity of education by maximizing their utility across a number of alternatives. Their optimal choice depends on preferences, cognitive abilities and attitudes, parental background and the perceived economic benefits and costs of each education career. Benefits include expected labour market returns, and costs include both the direct costs of education and the opportunity costs of delaying access to the labour market.

For given expected returns and costs of education, individuals with a preference for practical activities are more likely to opt for vocational education and individuals more attracted by abstract reasoning choose an academic track. Accordingly, while the average levels of manual dexterity and comprehension of practical problems tend to be higher among those who choose a vocational track, the average level of cognitive abilities tends to be higher among those who choose general or academic education. The asymmetric distribution of these individual characteristics is thus the result of the self-sorting of individuals across alternatives.

While preferences, abilities and attitudes determine education choices to a large extent, they also have a direct and autonomous effect on the outcomes we want to analyse in this paper, which include earnings, employment and skills. Therefore, the evaluation of the differential effect of vocational and academic education on these outcomes cannot rely on the simple comparison of average outcomes across individuals with different education careers.

Ideally, the causal effect of vocational education on outcomes can be identified if the individuals were randomly assigned to vocational or academic education. While random allocation is unfeasible in the real word, exogenous events (such as reforms of the school system) can produce situations equivalent to random allocation. A particularly suitable reform of vocational education has been exploited for instance by Malamud and Pop-Eleches (2010), who have found that in Romania the causal differential effect of vocational education is close to zero and that the entire difference between raw average outcomes is driven by self-sorting.

The economics of education literature has adopted several empirical strategies to identify the causal effect of vocational education, for instance by exploiting reforms which have altered either the length of compulsory education or the length of the vocational track. These exogenous changes have been used either as instruments in instrumental variable estimators or as discontinuities in regression discontinuity designs, generally in single-country settings.

In this paper we adopt a selection-on-observables strategy because (1) PIAAC has useful information to model selection into alternative education treatments; (2) our approach is multi-country and comparative; (3) suitable reforms have occurred only in a few countries, but often too early or too late to be useful,⁴ and their effects are difficult to use in a multi-country setup.⁵

In several countries, VET could be attended either at the secondary level, at the post-secondary or at the tertiary level. In the early tracking countries of Central Europe (Austria, Czech Republic and Germany), pre-vocational education is an option already at lower secondary school. Depending on the organization of education systems in each country, students are allowed to move from the academic to the vocational track and vice versa (bridging). For instance, students attending vocational education at the upper-secondary level can continue their education with a post-secondary or tertiary academic education. Generally, students are also allowed to change track before completing a given education level, possibly after passing a transfer exam. In spite of the possibility of bridging across tracks, the usual observed pattern involve initial academic education, which ends after ISCED 1 in the early tracking countries and could last until the completion of ISCED 3 in the late tracking countries, followed by a bifurcation between a vocational and an academic track, the length of witch mainly depends on the education system and on individual decisions on the optimal quantity of schooling.

In principle, students can leave education at any time after the end of compulsory education. In practice, there are three relevant exit points, at the end of compulsory education (that in many countries coincides with the completion of ISCED 2), at the end of upper-secondary education (or after one or two additional years of post-secondary non-tertiary education in the countries where this level is available) and at the end of tertiary education (ISCED 5). By combining education type and length, we define five possible education careers which fit with the schooling decisions of the vast majority of students: the early leavers, who abandon education at the end of compulsory school with at most a basic academic education (typically at ISCED 2); those who achieve an upper-secondary or post-secondary degree (ISCED 3 or 4), either vocational or academic; and those who continue studying and achieve a tertiary degree, either vocational (ISCED 5B) or academic (ISCED 5A).⁶

We consider the education careers available to individuals as alternative treatments in a multivalued treatment framework. We assume that individuals are exposed to four alternative treatments: (1) vocational education at the upper secondary or post-secondary level; (2) academic education at the upper secondary or post-secondary level; (3) vocational education at the tertiary level; (4) academic education at the tertiary level. We restrict our analysis to individuals who have attained at least upper secondary education because PIAAC only registers vocational education as the highest attained education at ISCED levels 3 or 4 (upper secondary or post-secondary). The excluded group, who who has completed at most ISCED 2 education, corresponds to close to 16% of the total.

Since only one treatment is observed for each individual, the remaining treatments are considered as counterfactuals. We use the inverse probability weighted regression adjusted (IPWRA) method to estimate average treatment effects (ATE), defined as the means of the difference between each treatment and the benchmark treatment. The IPWRA method yields unbiased treatment effects under the CIA (conditional independence assumption) hypothesis, which states that, conditional on a set of pre-determined variables, potential treatments are as good as randomly assigned to individuals. This assumption requires that the individual traits that have determined the choice of initial education are adequately captured by observed pre-determined variables, so that the residual variation in initial education, conditional on these characteristics, is either random or due to temporary circumstances or to factors that do not influence the outcomes of interest. We stress that we can give to estimated ATEs a causal interpretation only when the CIA hypothesis holds. A non-technical description of the methodology is provided in the “Appendix”.

Our sample consists of seventeen countries with reliable information on vocational education at ISCED levels 3–5. They are: Australia, Austria, Canada, Czech Republic, Estonia, Finland, France, Germany, Ireland, Japan, Korea, Netherlands, Norway, Poland, Spain, United Kingdom and United States.⁷ PIAAC includes individuals aged between 16 and 65. We restrict our sample by excluding those aged below 25, who are likely to be still in school. We also retain individuals aged 60–65 when considering proficiency in fundamental skills and skill use, but exclude them when examining the effects of VET on labour market outcomes, because of the selection problems associated to retirement.

Is the contribution of vocational education to adult proficiency in basic skills (literacy, numeracy and ICT skills) larger or smaller compared to that provided by academic education? This question is relevant because a proper command of basic skills is a necessary condition for an active and independent life, for updating and extending advanced skills and competences and for remaining competitive in the labour market, but also for being able to access administrative, financial and health services and to profit from the broad range of advantages offered by the new technologies. Literacy and numeracy are often described as “basic” skills, because they provide a “foundation” on which the development of other competencies rests (OECD 2013, p. 19).

In PIAAC, literacy is defined as the ability to understand, evaluate, use and engage with written texts to participate in society, to achieve one’s goals, and to develop one’s knowledge and potential. Literacy proficiency is assessed by asking subjects to analyse a variety of written texts. Numeracy is defined as the ability to access, use, interpret and communicate mathematical information and ideas in order to engage in and manage the mathematical demands of a range of situations in adult life. To assess numeracy proficiency, individuals are asked questions involving numerical computation, geometrical representation, data manipulation, represented either as texts or graphically.

Problem solving in technology-rich environments (simply problem solving or PSTRE hereafter) is defined as the ability to use digital technology, communication tools and networks to acquire and evaluate information, communicate with others and perform practical tasks. The assessment of problem solving proficiency focuses on the abilities to solve problems for personal, work and civic purposes by setting up appropriate goals and plans, and accessing and making use of information through computers and computer networks.

In this section, our sample includes individuals aged between 25 and 64. All seventeen countries assess literacy and numeracy proficiency. Problem solving, instead, is measured only in fifteen countries, with the exclusion of Cyprus, Italy, France and Spain.

Skill proficiency is measured over a continuum bounded between 0 and 500. Higher values represent higher proficiency levels. An individual assigned a given proficiency level P has a 67% probability of responding correctly to the questions designed to have a difficulty level equal to P, a much higher probability of responding correctly to question less difficult than P, and a much lower probability of responding correctly to questions more difficult than P.

Differently from literacy and numeracy, the PIAAC assessment of proficiency in problem solving in technology rich environments (PSTRE) has been possible only for the individuals with had at least basic competencies in computer use, being the test computed based. Problem solving has been assessed only for those who reported having some preliminary computer experience, agreed in performing a computer based assessment (CBA) of their skills and passed a preliminary evaluation of their ICT competences.⁸

Overall, the evaluation of problem solving proficiency has been possible for 81% of our sample. For the remaining 19%, PSTRE proficiency is missing. There is heterogeneity in the proportion of missing values in this test across countries and educational careers (though not between genders). Poland, Japan, Estonia, Ireland and Korea have the highest rates of missing data as opposed to the Netherlands, the U.S., Canada and Norway, where the proportion of missing data is smallest.

Missing values are more frequent among individuals with vocational education at ISCED 3–4, followed by individuals with academic education at ISCED 3–4. They are less frequent at ISCED 5. Overall, the differential missing rate between vocational and academic education is relatively small when taken at the same ISCED level, while we observe large differences across education levels. Major exceptions to this pattern are Poland, the Czech Republic and Finland at ISCED 3–4 and Japan and Estonia at ISCED 5.

Missing values are due both to lack of ICT competences (including lack of experience and failure in core ICT tests) and to the refusal to undertake a computer-based assessment. Refusal of CBA is more frequent than lack of ICT competences in many countries, but the relative importance of each explanation does not depend on the educational career. For this reason and given that the lack of ICT competence is likely to induce refusal of CBA, in the following analysis we will treat PSTRE proficiency as a single outcome, without distinguishing between the two sources.

In this section we examine the effects of vocational education on labour market outcomes. We compare four treatments, vocational and academic education at ISCED level 3–4 (upper secondary or post-secondary education), and vocational and academic education at ISCED 5 (tertiary education). Selected outcomes include the hourly wage and current employment status. Our sample consists of the seventeen countries for which we have data and where vocational education can be clearly identified.

Our measure of earnings is hourly earnings including bonuses for wage and salary earners. PIAAC data include also broader measures of earnings, which encompass the earnings of the self-employed. Given their low reliability, however, we ignore these measures in the current paper. Employment-inclusive of self-employment—is one among alternative current labour market statuses at the time of the survey, which include also unemployment and out of the labour force.

We have shown that vocational education does not perform as well as academic education both in labour market outcomes and in the level of basic skills, including literacy and numeracy. This is especially true for higher education. Only at the upper secondary or post-secondary level does vocational education perform slightly better than academic education in the probability of being currently employed as well as in the time spent in paid employment, although the differences we find are small.

Should then vocational education be reduced in favour of more academic curricula? One might say that the answer is partially provided by the recent evolution of vocational curricula in many countries, which are characterized by increasing emphasis on more academic education. Our own view is more nuanced.

First of all, this paper provides substantial information on the benefits of vocational and academic education, but has very little to say on costs. Yet, individual decisions to undertake vocational or academic education depend both on benefits and on costs. Although academic education provides higher earnings, higher skills proficiency and better non-monetary outcomes, it could also have higher costs for some individuals, who may find that a vocational curriculum, if available, is privately optimum.

Beside direct and opportunity costs, an important component of the private cost of education is the disutility of the effort required to complete it, which depends on several factors, including the composition of the ability portfolio an individual is endowed with. On the one hand, individuals with higher cognitive ability, who often come from better educated families and environments, are advantaged in acquiring academic education and are likely to have a lower cost of effort when engaging in this type of education. On the other hand, individuals with a higher propensity to perform manual activities and with higher dexterity, possibly coming from a less privileged environment, may find it less costly to complete a vocational curriculum. When both benefits and costs are taken into account, vocational education can be for many the education choice that provides the highest net expected benefit. The availability of this track is also an opportunity that keeps some individuals in school, and reduces the risk of early school leaving.

There is scope for public intervention when the assignment of individuals to different ability levels is determined at least in part by parental background, especially when this background operates as a constraint that precludes choices. Carneiro and Heckman (2002) propose the distinction between short-term and long-term constraints to educational choice, with the former due to lack of economic resources and the latter induced by disadvantaged parental education. When the choice between vocational and academic education is affected by the presence of long-term constraints, public intervention may be desirable early on in an individual life, so as to compensate differences in the environment that affect the development of cognitive and non-cognitive abilities.

There may also be externalities at work. Our own evidence in Sect. 4 suggests that individuals with a more academic education are more likely to engage in voluntary activities and to trust others. These activities spill-over positively on others and on society at large, but the associated benefits are often ignored by singles when deciding which type of educational curriculum to pursue. The presence of these positive externalities suggests that the degree of investment in academic education could be “too low”.

Although the presence of long term liquidity constraints and of positive externalities associated to academic education may suggest that the degree of investment in this type of education is lower than the social optimum, we do not recommend that vocational education should be drastically reduced or even eliminated from upper secondary curricula. This is because individuals may differ in their portfolio of skills (practical vs academic) even after the differences in long term constraints have been taken care of. If this is the case, the availability of the vocational option may reduce early school leaving by keeping more practically oriented individuals in school.¹⁵

Rather the cutting back on vocational studies, perhaps a more promising policy avenue is that of making vocational education as effective as possible, by increasing its expected benefits. Will the recent evolution of education policy promoted by the EU, which includes the emphasis on facilitating transitions between vocational and academic tracks, a higher flexibility in the design of curricula and the promotion of a better integration between vocational education at school and training at work, help in this direction? In our empirical research we have asked whether the labour market outcomes associated to vocational education are more favourable in the countries where the combination of school and work programs is more advanced. However, we have found no systematic evidence that this is the case.

While we think that the empirical evidence based on PIAAC is interesting, there are drawbacks that should be mentioned: because of the nature of the data and of the comparative perspective, our empirical strategy relies on selection-on-observables. This strategy is based on assumptions that cannot be tested, and that can be rightly questioned. Perhaps a more promising empirical strategy would be that of exploiting natural experiments. When this is done, results are less negative for vocational education than the ones we find. Notice however that the results using natural experiments do not necessarily contradict what we have found in this report, as they refer to particular sub-samples of the population (LATE effects) rather than to average treatment effects applicable to the entire population, as our results do. Needless to say, further research is required to provide firmer conclusions.

While the evaluation of the relative benefits of vocational and academic education that can inform the policy maker and the public is certainly valuable, further research is also needed to assess what policy interventions are more effective at improving the market value of vocational education. A great help to evaluation would be that when new policies are implemented they are also accompanied by pilot experiments designed to provide data for subsequent evaluations.