Sweden’s self-image as a leader in education was rocked in the early 2010s by the so-called PISA-shock, in which this formerly high-flying education system saw its performance in international assessments dramatically decline. This period of decline dominated the public and political discourse around education and reform in Sweden (e.g. Lundahl & Serder, 2020) for the rest of the decade. Over time, Sweden’s performance in Mathematics has recovered, as evidenced in numerous international assessments, including most recently TIMSS 2019 and PISA 2018 (e.g. Mullis et al., 2020; OECD, 2019a). Nevertheless, the improvement in the overall achievement of Swedish youth somewhat masks a persistent achievement gap which has been observed within the Swedish school system since the early 2000s, with growing variation in performance between schools in student grades (Skolverket, 2005, 2020). The achievement gap noted in domestic data has also been recorded in international data, with the achievement gap widening (Chmielewski, 2019) and Sweden’s decline in socioeconomic equality of outcomes the most severe among peer nations (Hanushek et al., 2014).

Socioeconomic status is a well-established predictor of educational outcomes (e.g. Sirin, 2005), and previous research using TIMSS data has confirmed this relationship in relation to mathematics outcomes for Swedish youth over multiple cycles of TIMSS between 2003 and 2015 (Authors, 2021). A longstanding strand of scholarship suggests that in addition to predicting achievement, socioeconomic background indicates varied opportunity to learn (OTL) course material, which in turn predicts test performance (e.g. Eggen et al., 1987). While this pattern of relationships has been consistently evidenced in the English-speaking world (e.g. Authors, 2021; Schmidt et al.,2013), in the Swedish context inequalities of opportunities have been inconsistently observed, appearing only among the 2003 and 2015 TIMSS cohorts (Authors, 2021).

A possible explanation for the lack of observable social reproduction through the delivery of the curriculum lies in the nature of the Swedish school system. A distinctive feature of the Swedish education system is its retention of the comprehensive model in which students are offered equal learning opportunities in integrated school settings (Arnesen & Lundahl, 2006) with limited within-school streaming when compared with other highly developed economies (Chmielewski, 2014). Of much interest to policy makers and researchers, reforms to the Swedish education system enacted in the 1990s introduced school choice and created a market for education (see Björklund et al., 2005). While admissions guidance prohibits cream-skimming (Põder et al., 2017), the exercise of school choice is socially segregated (Teske & Schneider, 2001) and the subsequent composition of schools can be interpreted as reflecting segregation beyond an expected neighbourhood effect (Böhlmark et al., 2016). Despite the observed social segregation between schools, analysis of international data suggests that the comprehensive school system in Sweden is still intact, with students of varying abilities attending the same schools, and that variation in performance between school is low when compared to other economies (OECD, 2019b).

Against this background, the following research questions are considered:

1. Are between school socioeconomic inequalities in mathematics outcomes and opportunity to learn mathematics observable among eighth graders in Sweden?
2. Do the relationships between socioeconomic status, opportunity to learn, and achievement very between high-, neutral-, and low-SES schools?

The study uses Swedish data from the grade 8 sample of the Trends in Mathematics and Science Study (TIMSS) 2019. The focus of the study is between school variation in the relation between inequalities in opportunity to learn (expressed as content coverage) and mathematics outcomes, and thus data from the student, teacher, and school questionnaire is used. Socioeconomic status and opportunity to learn are both conceived as unobserved phenomena and are thus modelled as latent factors. Socioeconomic status is indicated by the number of books in the home, the highest reported parental education level, and the sum of five home possession items. OTL is indicated by manifest variables in which teacher responses to items regarding when content is introduced is summed to create indicators of content coverage in each of the four sub-domains of mathematics (number, algebra, geometry, and data). Structural equation modelling is used in the study to model the relations between SES, OTL, and achievement in mathematics. Complex survey data such as the TIMSS 2019 dataset favours a multilevel approach to modelling, as it allows the variance in the dependent variable, in this case achievement, to be split across individual and school levels and provides model estimates at both levels. A two-level model is specified with individual achievement regressed on SES at the student level, and a trio of relations – achievement is regressed on SES and OTL, and OTL is regressed on SES – are specified at the school level. As the focus of the study is between school differences, data from the student and teacher questionnaires is aggregated to school level to build the between level of the model. The modelling process features two stages to reflect the research questions. In the first stage, model one – the basic model – is run to identify whether socioeconomic inequalities in outcomes and opportunities can be identified for the sample as a whole. In the second stage, model two separates schools into three groups with each school classified as high-, neutral-, or low-SES, with the goal of establishing whether patterns of inequalities differ between different school profiles.

Preliminary results suggest that for the Swedish TIMSS 2019 grade eight cohort as a whole, SES remains a strong predictor of achievement at the individual and school levels, in line with earlier research. However, evidence of socioeconomic inequalities in OTL were not observed. When the cohort was categorised as high-, neutral-, and low-SES schools, patterns of inequalities differed between groups, with the most notable results seen in the neutral-SES group. For this group, SES at the school level was a very strong predictor of achievement, and OTL was a significant predictor of achievement, which was not replicated in the other two groups. In Swedish, the neutral-SES schools could be described as lagom, a concept which roughly translates to ‘not too much, not too little’ or ‘just the right amount’. It is therefore highly relevant to stakeholders in the educational project that it is in these schools with a balanced socioeconomic intake that the Swedish system goes beyond its’ comprehensive character and appears to act in a compensatory manner in terms of mathematics provision.

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