It is well established that a successful transition to the labor market has long-term social and economic implications for both individuals and society. However, the journey from school to the world of work is not straightforward and needs to be better understood. An evolving labor market adds to the complexity and nonlinearity of trajectories (Furlong, 2016), highlighting the importance of understanding transitions as a process.

Education and career pathways are extensively studied in the sociology of education and labor economics. Sociologists explore transitions between study and work within the framework of the life course perspective (Monaghan, 2020), paying special attention to the patterns of trajectories (Lorentzen et al, 2019) and transition regimes (Walther, 2006). Extensive literature in the sociology of education is devoted to the relationship between education and labor market outcomes and individual socioeconomic backgrounds. The latter is manifested by parental education, professional status, and income, as well as cultural capital. Also, socioeconomic status (SES) impacts educational and career aspirations–low SES students have lower aspirations than their counterparts from a more advantaged background (Kim, Klager, Schneider, 2019). In various countries, there is empirical evidence that low SES is a strong predictor for the lower levels of educational attainment and labor market outcomes (Walpole, 2003). In due turn, labor economists focus on the labor market outcomes of the trajectory, returns on investments in human capital, and its determinants. Drawing from the human capital theory, accumulation of more human capital–a higher level of educational attainment, stronger cognitive abilities, and noncognitive skills, combining studies and work– positively impact labor market outcomes (Nilsson, 2019) and could make school-to-work transition smother.

Typically, event history and regression analyses methods are used to examine educational pathways. However, such methods commonly examine only unique transitions rather than full sequences of steps in education and employment. With the growing popularity of longitudinal data, sequence analysis accompanied by logistic regression analysis has become the option to overcome this limitation and explore trajectories in their complexity. A number of international, especially European, empirical studies have incorporated sequence analysis in the investigations of youth transitions (see e.g., Brzinsky-Fay, 2007; Quintini & Manfredi, 2009; Lorentzen et al. 2019). However, only a few studies employing sequence analysis on representative samples specifically examine trajectories of university graduates (Duta, Wielgoszewska, Iannelli, 2021).

Using data from the Russian national cohort longitudinal study "Trajectories in Education and Career, this study aims to untangle the nine-year education-career pathways of 9th-grade students who have received higher education by the age of 25. In Russia, bachelor's and master's degrees were introduced in 2009, though master's programs have anchored and proliferated across universities only recently, in the mid-2010-s. Thus, the cohort participating in this longitudinal study is the one experiencing the newly established educational options. Our study aims to identify the different types of pathways followed by the Russian graduates in their journey from school to higher education and to work and explore the factors contributing to different pathways. We investigate pathways following the sociological approach and methodology of sequence analysis but considering the key findings of labor economists, thus embracing the framework of the socioeconomic background and educational inequalities and the human capital theory. We investigate how socioeconomic status and aspirations, as well as academic abilities and personal characteristics, and an extended set of socio-demographic factors shape students’ paths through postsecondary education and the world of work. By considering how different factors shape not just specific transitions but long-term sequences of educational-employment states, we broaden our understanding of who follows certain paths and why.

We use data from the nine waves (2012-2020) of the Russian national cohort longitudinal study "Trajectories in Education and Career" (TrEC). The study follows a nationally representative sample of 9th-grade students through high school and on to postsecondary attainment or work. We trace the nine-year trajectories of those who have received higher education by 2020. We used sequence analysis followed by cluster analysis (Brzinsky-Fay, 2014; Brzinsky-Faya, Solga, 2016; Monaghan, 2020) to derive the typology of HE graduates’ education & employment trajectories. A sequence consists of a series of states in which respondents are found at different points in their life course within an observation period, in our case between the ages of 15(16) and 24(25). The sequences were built based on the nine states: 1) studying at school; 2) studying at vocational school; 3) studying at the university; 4) combining university studies and work; 5) combining vocational school studies and work; 6) temporary employment; 7) permanent employment; 8) inactivity; 9) unemployment. The research sample of the graduates who have passed all the waves of the longitudinal study and hold a HE degree includes 1247 observations. Generating typologies of trajectories based on sequence analysis involves several stages. The first step involves generating the sequences of the nine states across nine years. Second, we compute the optimal matching distances between sequences using the TraMineR package in R. Third, we build a Ward hierarchical clustering of the sequences from the optimal matching distances. Then, we used a series of multinomial logistic regression models to estimate the probability of belonging to each cluster (pathway) for a given set of background characteristics. We built three models, starting with the baseline Model 1 which shows the statistical significance of the starting conditions (SES, aspirations, noncognitive characteristics). The next Model 2 includes the variables capturing academic abilities and measures of human capital while the final model (Model 3) includes all the variables together. Nagelkerke pseudo-R² value for the final model is quite high (0.52), meaning the logistic regression model fits the data. The obtained results are presented using relative risk ratios.

The turning point in the observed trajectories is continuing education after graduation. More than one-third of the graduates are pursuing or have already pursued a master's degree by the age of 25. Many of them combine study and work thus accumulating both the general and specific human capital and enhancing labor market outcomes in the long run. However, prolonged education pathways are very diverse and include linear transitions from bachelor to master's and reverse ones (from university to employment and back to education), as well as a delayed entry to the labor market after graduation. The conventional linear trajectory from university to permanent employment has become increasingly rare. Most graduates experience a combination of study & work, some experience episodes of part-time work and other precarious positions in the labor market. The special attention attracts a precarious or nonlinear trajectory, which includes graduates with the longest experience in a precarious position (part-time work, inactivity), while the significant predictor of following this path is the lower academic ability (the TIMSS eighth grade mathematics test score). Socioeconomic background, as well as academic abilities, are shaping the education pathways of graduates. High academic achievement is a strong predictor of prolonged education. The probability to follow the pathway with post-bachelor education is statistically significantly higher among high achieving students. As for the socioeconomic background, parental educational aspirations rather than parental education shape the educational choices of graduates. In terms of career pathways, the factors that influenced specific patterns in the duration of work experience include parental educational aspirations and non-cognitive characteristics. The probability to have more work experience, including the time spent combining study & work, is statistically significantly higher among graduates whose parents have higher educational aspirations. Also, the probability to follow the delayed path is statistically significantly higher among graduates who score lower in openness to experience.

Brzinsky-Fay, C. (2007) ‘Lost in transition? Labour market sequences of school-leavers in Europe’. European Sociological Review, vol.23, no.4, pp.409—22. Brzinsky-Fay, C. (2014) The measurement of school-to-work transitions as processes: about events and sequences. European Societies, Vol. 16(2). P. 213-232. Brzinsky-Fay, C., Solga, H. (2016) Compressed, postponed, or disadvantaged? School-to-work-transition patterns and early occupational attainment in West Germany. Research in Social Stratification and Mobility, Vol. 46. P. 21-36. Duta, A., Wielgoszewska, B., & Iannelli, C. (2021) Different degrees of career success: Social origin and graduates’ education and labour market trajectories. Advances in Life Course Research, Vol. 47. https://doi.org/10.1016/j.alcr.2020.100376 Furlong, A. (2016) The changing landscape of youth and young adulthood. Routledge handbook of youth and young adulthood. P. 19—27. Kim, S., Klager, C., Schneider, B. (2019) The effects of alignment of educational expectations and occupational aspirations on labor market outcomes: Evidence from NLSY79. The Journal of Higher Education, Vol. 90. No.6. P. 992-1015. Lorentzen, T., Bäckman, O., Ilmakunnas, I., Kauppinen, T. (2019) Pathways to adulthood: Sequences in the school-to-work transition in Finland, Norway and Sweden. Social Indicators Research, Vol. 141. No. 3. P. 1285-1305. Monaghan, D. B. (2020) College-going trajectories across early adulthood: An inquiry using sequence analysis // The Journal of Higher Education, Vol. 91. No. 3. P. 402-432. Quintini, G & Manfredi, T. (2009) Going separate ways? school-to-work transitions in the United States and Europe, OECD, Paris. Nilsson, B. (2019) The school-to-work transition in developing countries. The Journal of Development Studies, 55(5), 745-764. Walther, A. (2006) Regimes of youth transitions: Choice, flexibility and security in young people’s experiences across different European contexts. Young. Vol. 14, No. 2. P. 119-139. Walpole M. (2003) Social mobility and college: Low SES students’ experiences and outcomes of college. The Review of Higher Education, vol. 27, no. 1. P. 45-73.