Background
Completing upper secondary education [
1] is known to be associated with higher work participation and reduced health-related absence in young adulthood [
2‐
7]. The associations remain after adjusting for known predictors of completing upper secondary education and relevant outcomes [
3,
4]. However, most studies focus on single outcomes, using cross-sectional designs or short follow-up time, for instance looking at subjects’ first period of long-term sick leave or time to disability. Such studies give results that are easy to communicate, but the choice of outcome measure, and the time of measurement, can often seem arbitrary. If detailed individual follow-up data is available over a longer period, we may consider several outcomes, which together constitute a continuous outcome process, where individuals move back and forth between different types of states over time. By utilizing the individuals’ full outcome trajectories, we can analyse how paths for completers and non-completers unfold over a longer period and estimate the time-varying effects of having completed upper secondary education on all outcomes together.
Dropout and health disparities are very closely linked [
2‐
4,
8], and some have argued that school dropout is essentially a public health issue [
8]. Non-completers may be at a disadvantage when trying to enter the job market, they might take jobs that are more taxing on health, they might have higher chances of being laid off, and the lack of a diploma may prevent them from entering colleges and universities. However, dropping out, or conversely, completing different types of education, do not happen by coincidence. Rather it is a process starting in early childhood, associated with early home environment, quality of caregiving, socioeconomic position (SEP), intelligence, behaviour problems, peer relations, and parent involvement [
9]. Students differ in background skills and several other traits. A study from 2012 examined 110 so-called dropout indicators and found that the three best predictors of completion were growth in mathematics test scores from grades 7-12, growth in GPA (Grade Point Average) from grades 9-12 and level of school engagement [
10]. In addition to academic skills, mental health problems are strongly associated with increased risk of dropout[
11‐
14]. Others have reported that completion rates to some degree are explained by labour market characteristics in the corresponding residential region [
7,
15].
Predictors of non-completion and completion may also be linked to the outcomes we consider, hence making them confounders. For instance, Plomin and Deary [
16] concluded that intelligence is one of the best predictors of important life outcomes, including occupation, mental and physical health. Other studies find a clear association between intelligence and adolescent SEP and adult educational attainment. In a paper from 2009, analysing a cohort of males born in Norway between 1967–1971 (n =160 914), years of education at age 28 was strongly associated with intelligence test score and parental education, while parental income had a smaller influence [
17]. Other studies suggest that disability is to some degree “inherited” from parents down to children [
4]. A naive unadjusted analysis is likely to yield a biased comparison of the exposures non-completion and completion. However, if sufficient covariate information is available, we can adjust for confounding variables such as these to reduce the bias.
Statistics Norway produces official statistics and collects detailed individual follow-up data on work participation, education and health-related absence for all Norwegian citizens. Recently, several papers have demonstrated that multi-state models are suitable for analysing data of this type [
18‐
24]. In the multi-state framework, hazard-based methods for survival data can be used to model transitions, for instance using Cox proportional hazards models, Aalen additive hazards models or the Nelson-Aalen estimator [
25]. For a detailed introduction to multi-state models for time-to-event outcomes, see for instance [
26‐
30].
The Norwegian education system consists of mandatory primary education [
1] that lasts for seven years (six at the time of our study population) followed by three years of mandatory lower secondary education. After graduating, usually the year individuals turn 16, students may enrol in upper secondary education or discontinue further education. More than 95% of the youths choose to continue. Upper secondary education in Norway consists of two distinct fields; general studies and vocational tracks. General studies are geared towards tertiary education [
1] in college or university; vocational tracks are geared towards specific trades. One may also obtain admission right to colleges and universities from a supplementary year after vocational tracks. Alternatively, one can obtain admission right at the age of 23 by having 5 years of education or a combination of specific types of experience (employment/volunteering/folk high school/military duty) and 1 year of upper secondary education. In other words, one does not need to have completed general studies for admittance into tertiary education. Normal time spent for general studies is 3 years. For vocational tracks, a duration of 3-5 years is normal, 2 years at school followed by two years as an apprentice is most common.
Previous studies on consequences of non-completion or completion have typically had a relatively short follow-up time, focused on isolated outcomes or not done separate analyses for vocational tracks and general studies [
2,
3,
5‐
7]. However, the two fields are arguably not comparable in terms of learning outcome and type of students, so the effects could be quite different [
31]. Thus, there is a need to evaluate long-term outcomes for both fields.
In this paper, we analyse the long-term effects of completing upper secondary education by the age of 23 on the outcomes work, unemployment, tertiary education, sick leave and disability pension over a twelve and a half years period. The use of exactly twelve and a half years of follow-up, was a consequence of the inclusion age and the maximum follow-up time for people in the birth year cohorts included in our data. In our analyses, we consider every individual’s outcomes of work, unemployment, education (tertiary), sick leave and disability continuously throughout the follow-up period. To make the comparison of completers and non-completers as unbiased as possible, we adjust for a wide set of baseline confounders. As analyses are done separately for general studies and vocational tracks, we illustrate how outcomes and effects of completion unfold over time within each field.
Discussion
Our results show that, in both fields of upper secondary education, completers do better on a whole range of outcomes compared to non-completers. However, the effects change over time. For general studies, the effect of completion on education and employment changes dramatically over time and appears to be quite complementary; a large proportion of completers goes on to further education early, before transferring to work. Vocational students have higher probability of early employment compared to students from general studies – this goes for both non-completers and completers. Despite the gains in early employment, vocational students have the lowest probability of work towards the end of follow-up and generally the highest probability of unemployment, sick leave and disability. The exception was when we considered low IQ and parental SEP, where vocational track students were better off compared to general studies. Across all groups, unemployment is most common the first 2-3 years. In this period, the absolute effect of completion on unemployment diminishes, while the relative effect remains more stable over long terms. All groups experience an increase in sick leave the first few years, but the effect of completion on sick leave gets smaller over time. In the probability difference plots (Fig.
6), we saw that non-completers are more likely to have transferred to disability as time passes compared to completers; still, on a relative scale, the effect of completion diminishes. A closer inspection reveals that, even though the rate into disability remains highest for non-completers, it is slightly declining, while it is slightly increasing for completers.
The results suggest that non-completers have an added disadvantage when applying for jobs at young age, but could also mean they are not as active in seeking jobs. Furthermore, non-completers seem to have less secure jobs later on. The effect of completing vocational tracks, with regards to work, does not appear to be significant before after a few years into follow-up, which could suggest that some non-completers leave vocational studies due to employment possibilities. Sick leave increases during follow-up, which is likely due to more people in employment having obtained the right to paid sick leave. Non-completers have more sick leave and disability compared to completers across all groups, which could mean their jobs are more demanding on health, but could also be due to differences in lifestyles and health habits. Among non-completers, a reduction in sick leave is observed towards end of follow-up, which could indicate that non-completers are “closing the gap” – in terms of either education or experience – leading to better jobs and health, but it could also mean that some of the least healthy have transferred to disability pension. Disability is by definition an absorbing state, so people cannot return once entered. That the rate into disability is slightly declining for non-completers, but slightly increasing for completers, suggests that among non-completers there are individuals with an elevated risk of disability, making them transfer quickly.
The gain in early employment was offset by less tertiary education and more unemployment, sick leave and disability for vocational tracks. This could imply that vocational students are easy to employ, but less adaptable to changes in the labour market and that typical vocational trades are more taxing on health. However, it could also be related to unmeasured factors making vocational and general students different in other ways. The absolute effects of completion on unemployment, sick leave and disability are somewhat larger for vocational tracks than for general studies, and because more people attend vocational tracks, and non-completion is more prevalent, non-completion here is a bigger public concern than in general studies. However, the relative effects of completion on these outcomes are larger for general studies, which suggests that non-completers here are at greater disadvantage relative to completers, compared to in vocational tracks. The contrasts between weighted and unweighted results could indicate that more of the associations between exposure and outcomes are explained by other covariates in general studies compared to vocational tracks.
There can be several reasons to why completion leads to differentiation on the labour market. The most obvious would be that non-completers, given their status, lack a diploma – a proof of completion. Often when applying for jobs, even jobs that require no formal skills, an employer will ask for applicants’ diploma from upper secondary education. Other than simply lacking a diploma, non-completers were in some way unable to complete the requirements for completion; this could mean that grades were too low, lack of attendance or missing compulsory work. Thus, completers may have required skills useful both in a professional or educational environment, but also at a personal level making them better equipped for getting and keeping a job. Furthermore, non-completion could be connected to a feeling of failure for the individuals concerned, which potentially could lead to lower self-esteem and stigmatizing. Completion can therefore be seen as a combination of factors in addition to having a diploma.
Our measured confounders include detailed information on family background and cognitive ability (IQ). The individual health variables (BMI, military duty eligibility (mental and physical health check) and childhood chronic disease) are slightly more crude measures. An example of a potential unmeasured confounder would be that we have no clear indication of a person’s motivation to succeed in education and jobs. However, we have a young cohort, and have excluded those who did not complete conscript examinations or did not start upper secondary education. This removes individuals with the most severe health issues, making the study population more homogeneous, reducing the chance for residual confounding. Even though certain known confounders are not measured directly, the “sum” of the covariates we have adjusted for, may sufficiently reduce bias when comparing exposures. In the analyses, we only adjust for variables measured prior to the exposure. The only exception is military conscript data, which may be measured during upper secondary education (at age 18). However, regarding IQ scores, there are studies indicating that cognitive ability assessed by typical IQ tests show substantial stability from childhood to later life [
41,
42].
Direct comparisons with previous studies are difficult, as there are no similar types of analyses on this topic. Several studies have looked at the consequences of non-completion, for example [
2,
3,
5‐
7], but these are mostly cross-sectional studies, often considering only a few non-recurring outcomes. In addition, most do not analyse the two fields of education separately. Some of the broad implications in these studies are more or less the same as from our analyses; non-completers are at increased risk of receiving various types of medical- and non-medical benefits [
2,
3,
5,
7]. In the paper by de Ridder (2013) [
3], they studied the risk of long-term sickness absence and disability pension from age 24–29 after dropout from upper secondary education, while controlling for health variables, school problems and parental SEP. They found a crude risk difference for dropout of 21 pp, and an adjusted risk difference of 15 pp. The absolute numbers are incomparable to our results because of the time aspect, but the remaining risk difference (15/21≈0.7) is in line with the impact of weighting in our analyses. Falch (2010) [
7] studies how completion affects the probability of 1) being a job seeker, 2) receiving welfare benefits, 3) being in education and 4) going to jail, during autumn (September - December) 5 years after starting upper secondary education. They approach the issue of confounding in two ways. The first is by multiple regression controlling for factors affecting completion (grades in lower secondary education, gender, 1. or 2. generation immigrant, parental education, chronic disease in childhood, distance to schools, unemployment rate, regional factors, type of education). In their results, quantitative effects are reduced by 35–70 percent compared to a crude analysis, which is comparable to our results from the weighted analysis with remaining effects typically between 40–85 percent. The second method they use to account for covariates, is to compare “equal groups”, where completion or non-completion may have happened “by chance”. More specifically, they compare students that “barely” completed to students who were very close to completing. A weakness of the study is that they consider only a short time-window of 4 months. Our study is unique in that it includes multiple recurring end-points over a long follow-up period in a large cohort, allowing us to see time-varying effects, while at the same time adjusting for several important confounders including family background, prior health and cognitive ability.
In our analyses, we only looked at the effect of a baseline exposure, and from the current analyses it is difficult to assess the effect of state histories on future outcomes. Other interesting questions could, for instance, be how tertiary education early on, or a high number of sick leave days or long periods of unemployment, affect later state probabilities. Another interesting approach would be to study the trajectories of individuals that end up in certain states, e.g. disability. This would call for even more advanced methods. A possible extension for future research, is to expand the state-space of the multi-state model. For instance, we could include more than one form of sick leave, e.g. based on diagnoses. Similarly, we could separate between high and low income work, based on taxable income, which would let us study differences in occupational social position in addition to work-participation.
Note that there is a fairly high percentage starting in vocational tracks in Norway compared to other countries. There are also very few that choose not to enrol in upper secondary education studies, even among individuals planning careers in untrained professions. Also, there are high rates of sick leave and disability, and relatively low unemployment rates in Norway. Hence, all our results might not be generalizable to other countries. Many vocational tracks professions in Norway also require a diploma of craftsmanship, which is not the case for many other countries. Thus, negative employment outcomes for vocational track could potentially be more frequent compared to other countries. Moreover, contrary to many other countries, entry into tertiary education (universities/colleges) is not contingent upon having graduated from upper secondary education as there are many ways of obtaining admission rights.