Today, social mobility gradually becomes not just an advantage of a fair democratic system but also a prerequisite for development. Low upward social mobility, closely associated with inequality of opportunity, hinders national human capital accumulation, retards economic growth, and undermines social cohesion and engagement.

Education is a powerful “equalizer” of chances. Ensuring that individuals have equal opportunities to access quality education is a key goal of an effective social system. The real situation in education is somewhat different from the ideal. A lot of countries have witnessed a sharp increase in educational inequality in recent years [OECD 2018]. Russia, too, demonstrates significant sociodemographic disparities in student performance.

Questions about the role of the school in the formation of social inequality have been asked more than once in many research works [Blossfeld et al., 2016; Borman, Dowling, 2010]. However, separating the direct effect of the school from the individual characteristics of students is often quite difficult for the researcher. The school can do both reproduce in the learning process the existing social structure of society and strengthen or reduce social inequality.

Overall, about 41% of differences in student academic achievement can be attributed to school [Brunner et al., 2018]. The characteristic that has the strongest relationship with educational outcomes at the school level is socioeconomic composition [Coleman, 1966]. As an indicator of socio-economic composition, research uses the indicator of the individual socio-economic status of a student, aggregated at the school (or class) level [Perry, 2012].

The results of foreign studies assessing the effect of the socio-economic composition of schools on academic achievement are rather contradictory. While some studies confirm the presence of an effect, a number of studies proclaim the relationship as a statistical artifact. At the same time, almost all available studies use correlation designs with regression analysis or structural modeling, which does not allow us to speak about an independent effect of school composition. This study is aimed at assessing the effect of school socio-economic composition on student achievement separately from individual students’ characteristics.

The study uses data from the panel study Trajectories in Education and Careers (TrEC) . This project started in 2011, when eighth-graders from 210 schools in 42 regions of Russia participated in the Trends in International Mathematics and Science Study (TIMSS). The sample, composed of 4,893 respondents, was representative of Russian eighth-graders in 2011. The survey assessed student achievement in mathematics and science and also collected contextual data on school and family characteristics. At the end of the 9th grade, the same sample participated in the Programme for International Student Assessment (PISA), which measured literacy in mathematics, science, and reading. The original sample for the present study included 4,399 students who were respondents in both assessments. The final sample consists of those who did not change school in grades 8–9. At the first stage of data analysis, linear multilevel regression models were used to measure the compositional effect for the whole sample of schools. Two groups of models were constructed, one for mathematics scores and one for science scores in PISA-2012. A random intercept fixed slope model was applied to assess the compositional effect. Next, the propensity score matching (PSM) method was applied. The basic idea of the matching method consists in finding, for each observation in the treatment group (low-SEC school students), statistical “twins” in the control group (high-SEC school students), i.e. students who are as similar as possible in their observable characteristics. The method is used to balance the sample by partially solving the problem of self-selection into high- and low-SEC schools and to measure the achievement gap based on observations that only differ in the type of school. Performance disparities in the matched sample will show the compositional effect that is unrelated to the individual and school characteristics included in the model.

The following results were obtained in the work: • The socioeconomic composition of the school is one of the strongest factors in academic achievement compared to other individual and school characteristics; • The low socio-economic composition of the school has an independent negative effect (up to 0.25 standard deviation) on achievement in mathematics and science; • The results of previous studies, which did not use quasi-experimental methods, overestimated the effect of SEC by at least one third. The analysis shows that the SEC of a school is one of the independent factors in the formation of inequality in educational results in Russia. Compared to other individual and school characteristics, the link between the school's SEC and academic achievement in math and science is strongest. Even the level of knowledge in the past year is less related to the results when using regression analysis. In Russia, in recent years, the average level of segregation of schools by the socio-economic composition of students has been preserved [Kosaretsky, Frumin, 2019]. The concentration of pupils of the same status in schools can serve as a starting point for the negative effect of low school composition. At the same time, schools that previously dropped out of the topical agenda will be under attack: educational organizations in large cities, prosperous regions, and fairly well-resourced have a low composition as well. Point support for such educational organizations requires additional research into the causes of the effect and a detailed analysis of the components of the educational environment responsible for the reproduction of inequality through the socio-economic composition of the school.

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