The point at which students exit formal education matters, with completion of upper secondary education considered an essential prerequisite to enter the workforce (OECD, 2020). The strong relationship between an individual’s level of education and income potential has long been acknowledged (e.g. Houthakker, 1959), with earnings inequality increasing alongside increased inequalities in education (Chiswick, 1971). Furthermore, the effects of educational level are intergenerational, with effects of parental education observed in a range of child outcomes including education, health, income, and cognitive skills (e.g. Black et al., 2005). Given the negative effects of early exit from formal education, this study aims to investigate the possibilities for, and the predictors of, sub-optimal engagement with upper secondary education in Sweden.

The earliest point at which an individual can leave education is commonly at the end of lower secondary education. In Sweden, while attendance at upper secondary school is legally optional, only 5.09% of students do not enrol in upper secondary education (OECD, 2020) and graduation from upper secondary school is considered necessary for successful entry into the workforce. Less than 15% of students fail to complete upper secondary school (Andrei et al., 2011) and drop out is lower in girls than boys (World Bank, 2020). However, the historically low levels of school dropout obfuscates the risk of students engaging with the educational system in sub-optimal ways. 

Admittance to upper secondary school in Sweden is competitive, with students accepted into programmes based on their final compulsory school grades. The education is provided free of charge and is open for all students under 20 years old who have completed compulsory schooling to enrol in (Bäckman et al., 2011). The majority of participating students enter one of 18 national upper secondary programs (six academic and twelve vocational), with a minority attending introductory programs for students who do not qualify for the national programmes (Skolverket, 2021). All tracks are school-based. All levels of schooling are covered by nationally determined curricula in Sweden. This introduces expectations for a school’s mission and values and also outlines subject-specific syllabi guiding teachers’ planning and assessment (Skolverket, 1994, 2018). Evolving over several iterations, the current (2011) curriculum has a results-orientated focus, compared to the preceding (1994) curriculum’s open competence-orientated focus (Wahlström & Sundberg, 2015). 

Key socio-demographic characteristics that this study aims to integrate to its understanding of dropout include socioeconomic status and student migration background. This choice reflects the pedigree of a number of factors, such as socioeconomic status, gender, low parental education, and ethnic minority background as predictors of dropout (e.g. Jimerson et al., 2000; Rumberger, 1987). Studies of ILSA data have evidenced persistent relationships between student socioeconomic background and achievement in Sweden (Authors, 2021). Since 1990 the proportion of Sweden’s population born aboard has grown by 10% (SCB, 2021), a trend mirrored in the growth of students with an immigration background in Swedish schools (Skolverket, 2009). Prior research (e.g. Böhlmark, 2008) has demonstrated that students who immigrate to Sweden at a young age adjust well to Swedish school. However, Swedish students outperform students with a migration background in grade 9, and the gap between Swedish student’s performance and students who moved to Sweden in their teenage years is particularly pronounced (Skolverket, 2009). 

Against this background, the following research questions are considered:

1. Which sub-optimal educational pathways do students follow after graduating compulsory school?

2. Which socio-demographic characteristics predict following sub-optimal educational pathways for Swedish teenagers? 

3. Does the risk of sub-optimal post-secondary school engagement vary between the iterations of Sweden’s school curricula?

The study uses data from the Gothenburg Educational Longitudinal Database (GOLD). Compiled by Statistics Sweden and provided anonymised to the researchers, GOLD combines data from Statistics Sweden and multiple sources including the Central Student Aid Board, the National Archives, and the National Agency for Higher Education Services (University of Gothenburg, 2020). The analyses use data pertaining to students from the birth cohorts 1979-2000, who completed compulsory school between 1994 and 2015. The dataset is split into two groups reflecting which curriculum the students attended compulsory school and transitioned to gymnasium under, either the 1994 or the 2011 curriculum. Data from several variables is examined to create a hierarchy of educational pathways into which students are grouped. Multinomial logistic regression is used to investigate the risk of entering a sub-optimal educational pathway. As the outcome that we investigate in this analysis, educational pathway after compulsory school, is a categorical and nominal outcome, with the order in which the educational pathways are established hierarchically ordered in as far as students are required to meet an increasing number of conditions to qualify for group membership, this method offers space for promising results. Multinomial logistic regression models the log odds of the available outcomes as a linear combination of the predictor variables, enabling researchers to identify which indicators increase or decrease the risk of individuals deviating from a reference outcome, which we refer to as Perfect participation. The solution computed allows us to see which indicators predict a student being in one of the sub-optimal education pathways rather than being a perfect participant, and calculates the odds ratio for each predictor.

Preliminary results suggest that students can be grouped into four educational pathways, Early dropouts, Upper secondary school drop outs, Delayed upper secondary school graduates, and Perfect participants on the basis of their history of engagement with the upper secondary school system following graduation from compulsory school. A child’s immigration background is a strong predictor of them following a sub-optimal pathway. Interestingly, late arrival to Sweden (between ages 13 and 16) is the strongest predictor of dropping out of or delaying graduation from upper secondary school, but arriving in Sweden between the ages of 7 and 12 students predicts early dropout from education (i.e. immediately after compulsory school) the strongest. Attending a school with a high proportion of students born outside of Sweden is also a strong predictor of entering a sub-optimal pathway. Low levels of parental education significantly predict entry to all sub-optimal educational pathways. The risk of sub-optimal post-secondary school engagement, which is established by examining odds ratios, shows some variation between the iterations of Sweden’s school curricula. The odds ratios of students being early drops outs are higher for students under curriculum 1994 than curriculum 2011, but this is reversed for students dropping out of or delaying graduation from upper secondary school. These preliminary results suggest that investigating the determinates of taking a sub-optimal education pathway in Sweden over two decades is a promising line of enquiry, which is particularly pertinent given the implementation of two national curricula in the time period, and the aforementioned shift in demographic trends.

Andrei, T., Teodorescu, D., & Oancea, B. (2011). Characteristics and causes of school dropout in the countries of the European Union. Procedia-social and behavioral sciences, 28, 328-332. Authors. (2021). Bäckman, O., Jakobsen, V., Lorentzen, T., Österbacka, E., & Dahl, E. (2011). Dropping out in Scandinavia. Social exclusion and labour market attachment among upper secondary school dropouts in Denmark, Finland, Norway and Sweden (Arbetsrapport 2011: 8) Stockholm: Institutet för Framtidsstudier. Black, S. E., Devereux, P. J., & Salvanes, K. G. (2005). Why the apple doesn't fall far: Understanding intergenerational transmission of human capital. American economic review, 95(1), 437-449. Böhlmark, A. (2008). Age at immigration and school performance: A siblings analysis using Swedish register data. Labour economics, 15(6), 1366-1387. Chiswick, B. R. (1971). Earnings Inequality and Economic Development. The Quarterly journal of economics, 85(1), 21-39. https://doi.org/10.2307/1881838 University of Gothenburg. (2020). GOLD: Gothenburg Educational Longitudinal Database. https://www.gu.se/pedagogik-specialpedagogik/gold Houthakker, H. S. (1959). Education and Income. The Review of Economics and Statistics, 41(1), 24-28. https://doi.org/10.2307/1925454 Jimerson, S., Egeland, B., Sroufe, L. A., & Carlson, B. (2000). A Prospective Longitudinal Study of High School Dropouts Examining Multiple Predictors Across Development. Journal of School Psychology, 38(6), 525-549. https://doi.org/https://doi.org/10.1016/S0022-4405(00)00051-0 OECD. (2020). Education at a Glance 2020: OECD Indicator. Rumberger, R. W. (1987). High School Dropouts: A Review of Issues and Evidence. Review of Educational Research, 57(2), 101-121. https://doi.org/10.3102/00346543057002101 SCB. (2021). Folkmängd efter födelseland 1900-2020. Retrieved from https://scb.se/hitta-statistik/statistik-efter-amne/befolkning/befolkningens-sammansattning/befolkningsstatistik/ Skolverket. (1994). Läroplan för det obligatoriska skolväsendet, förskoleklassen och fritidshemmet Lpo 94. https://www.skolverket.se/download/18.6bfaca41169863e6a6549cc/1553957866629/pdf1069.pdf Skolverket. (2009). Vad påverkar resultaten i svensk grundskola? Kunskapsöversikt om betydelsen av olika faktorer. https://www.skolverket.se/publikationer?id=2260 Skolverket. (2018). Läroplan för grundsärskolan (reviderad 2018). https://www.skolverket.se/publikationer?id=3976 Skolverket. (2021). Gymnasieprogrammen. https://www.skolverket.se/undervisning/gymnasieskolan/laroplan-program-och-amnen-i-gymnasieskolan/gymnasieprogrammen Wahlström, N., & Sundberg, D. (2015). En teoribaserad utvärdering av läroplanen Lgr 11. World Bank. (2020). Education Statistics - All Indicators. https://databank.worldbank.org/source/education-statistics-%5e-all-indicators#