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 restrict our sample to individuals aged 25–59, who are likely to have completed their education and to be only marginally affected by retirement. We use the IPWRA method to estimate average treatment effects for the four relevant treatments (vocational ISCED 3–4, academic ISCED 3–4, vocational ISCED 5 and academic ISCED 5). For most countries in our sample we use the publicly available hourly data on hourly wages. For the five countries where hourly wages are not publicly available, the relevant estimates are provided by the OECD.
Table 6 presents the estimated effects of education type and level on log hourly earnings, separately for males and females. The table shows average relative effects across countries, obtained by weighting each estimated country specific effect with the reciprocal of its variance, so that more precise estimates weigh more. Consider first education at ISCED level 3 or 4. Estimates in column (1) suggest that vocational education does not perform as well as academic education. The negative gap is smaller for men—<2% points and not statistically significant—than for women, who suffer a 4.8% negative and statistically significant gap.
At the tertiary level the negative gap widens, ranging from 19% for men to 21.7% for women results [see column (2) in the Table]. The second row in the table reports for both genders the differences in average treatment effects of vocational with respect to academic education for the sub-group of individuals aged 25–44. The third row reports instead the estimated differential ATE between the group aged 45–59 and the younger age group.
The large gap in hourly earnings at the tertiary level could be driven by the fact that college graduates are more likely to access top paying occupations. However, part of the gap could be generated by our failure to properly controls for differences in individual ability. If the ablest individuals sort into higher academic education, such failure bias the treatment effect of vocational education downward.
By adding the numbers in the second row to the numbers in the third row one can get the estimated differences in average treatment effects for the older age group. The table shows that the negative effect of vocational education is stronger among older males and females. To illustrate, the earnings of older male workers with vocational education at ISCED 3–4 are 5.5% lower than with academic education at ISCED 3–4. This negative gap is equal to 6.7% for older females.
The finding that the negative gap is generally larger for the older than for the younger age group does not necessarily imply that this gap widens with age, as we are comparing different cohorts rather than individuals at different points of their age profiles. Since it is impossible with the cross section data at hand to distinguish between age and cohort effects, the effects of age on estimated returns by education type cannot be discussed here.
We estimate country-specific average treatment effects between vocational and academic education, pool the estimated differences in returns for each ISCED level across genders and regress them on relative supply, the difference in the average years of education and a dummy indicating whether the vocational system in the country has programs that combine school and work, using as weights the reciprocal of the variance of estimated returns, so that more precise estimates receive a higher weight.Footnote 14
As shown in the first column of Table 7, there is some evidence that differences in returns correlate negatively with relative supply and positively with differences in the average length of education programs. There is also some evidence that wage returns associated to vocational education are relatively higher in countries with programs that combine school and work, although all these estimated effects are not statistically significant at conventional levels. We hasten to stress that this exercise does not uncover causal relationships and should therefore be interpreted with caution.
We have documented so far that vocational education-both at ISCED 3–4 and at ISCED 5 levels-makes a difference with respect to hourly earnings, and that this difference can be sizeable. Next, we document the effects that this type of education has on current employment status.
As in the case of earnings, we estimate average treatment effects for each of the four available treatments and compute average differences in employment probabilities between vocational and academic education at both ISCED levels. Our estimates are reported in Table 8, which is organized as Table 6. The numbers reported in the table should be interpreted as percentage differences between employment probabilities. We find that vocational education at ISCED 3–4 level increases employment probability compared to academic education at the same ISCED level. When compared to tertiary academic education, however, the advantage of vocational education (at the same ISCED level) disappears and turns into a disadvantage that is especially large among females (\(-\,2.7\%\) points).
Our estimates also reveal that almost any employment advantage for those with vocational education tends to disappear in the older age group. If one adds the second and the third row of the table-for each gender-the percentage gap in employment probabilities turns negative in all cases with the single exception of the comparison between vocational and academic ISCED 3–4 education for females.
Among those not currently employed, we can distinguish in these data between those who have been in education and training in the past 12 months and those who haven’t (NEET). Table 9 shows the percentage of NEET among those not currently employed by gender, age group and type of education. We find that, for each level of education, vocational education is associated to a higher probability of being NEET. This probability increases with age for all education types, possibly because of early retirement patterns and other labour force exits.
Current employment status only partly captures employability associated to the level and type of education, because individuals who are currently employed may have experienced previous periods of joblessness. We therefore supplement the existing evidence with information on the total number of years of work experience acquired during a working life, which is available in PIAAC only for thirteen countries in our sample (missing for Austria, Germany, Canada and the US). We divide years by age minus 14, assuming that most labour market history starts after that age. Table 10 presents our estimates for the full sample and for the two sub-samples of younger and older individuals.
Focusing on all age groups, our results clearly suggest that vocational education increases the amount of time spent at work relative to academic education. When we consider vocational and academic education at the same ISCED level, involving similar numbers of years spent at school, we find that the gap between vocational and academic education is 5.7 and 12.2% points for males and 0.8 and 7.3% points for females.
To some extent, these positive gaps may depend on the fact that, even controlling for ISCED level, those in academic school tend to stay longer in school, and therefore have less time to spend in the labour market. To check this, we have depurated county-specific ATEs from the effect due to the differential numbers of years of schooling between each treatment and counterfactual in the aggregation procedure. As shown in Table 11, the estimated gaps decline in most cases, especially among females. The positive premium associated to vocational education, however, remains, especially among males.
Our investigation of the labour market payoffs to vocational and academic education at ISCED 3–4 (upper secondary and post-secondary education) suggests that individuals with vocational education earn slightly lower hourly earnings and enjoy a mildly higher probability of being currently employed than their more academically oriented counterparts. The higher probability of current employment of the vocationally trained is reinforced by their higher share of completed working life spent in paid employment, even after controlling for the length of education programs.
When we consider vocational and academic education at the tertiary level (ISCED 5), we find evidence that individuals with the former type of education have significantly lower hourly earnings and a slightly lower probability of being current employed than those with a more academic education. Independently of the ISCED level, we find that vocational graduates who are not currently employed are more likely to be NEET. When we consider the time spent in a paid job since leaving school, individuals with tertiary vocational education retain a clear advantage with respect to those with a more academic education.
To obtain a partial indication of whether the effect of initial vocational education (IVET) on labour market outcomes is mediated by workers’ cognitive skills, we have conditioned for literacy and numeracy test scores, neglecting the fact that these are endogenous variables. The effect of IVET on earnings estimated using the IPWRA method becomes smaller in absolute value for both genders and levels of education (ISCED 3–4 and ISCED 5), but remains negative and always statistically significant, with the exception of males with ISCED 3–4. The effect of IVET on employment is virtually unchanged for both genders with ISCED 5, and increases for both genders with ISCED 3–4 level.
We also investigate whether investing in continuous vocational education and training, either on the job or off the job (CVET), mediates the effect of IVET on earnings and employment. By including CVET among the controls, we obtain that the estimated effects of IVET on earnings and employment remain constant for both genders and education levels. Only the effect of IVET on employment for females with ISCED 5 level becomes smaller in absolute level and statistically insignificant. We conclude that the effect of IVET on labour market outcomes is mediated by the effects of IVET on cognitive skills rather than on CVET.
Our results on the relative employability of individuals with vocational education clearly reflect the current economic situation, and the current balance of demand and supply of skills. But what about future demand developments? Is the number of jobs requiring vocational skills expected to decline or to increase in the future? Statistics Norway (2013), has produced projections up to 2030 on the expected labour demand by type of education. These projections show that the relative number of jobs requiring upper secondary vocational education are likely to grow faster than supply in the near future, especially in the health care industry-because of the progressive ageing of the population, with potentially positive developments for the earnings and employability of those having the required skills. Of course, if the demand for jobs requiring academic tertiary education grows even faster, thereby offering better earnings and employment prospects, enrolment in vocational education may decline even in the presence of favourable demand developments.