Goal of research
The analysis of the principal participants of the educational market in Russia within the new institutional conditions.
We used theoretical and empirical (statistical) data analysis methods, which included premises and methods of the neoclassical and neoinstitutional economic theories, methods of the theory of human capital, game-theoretic models. In empirical research we broadly used statistical and econometric data analysis methods. The data we used was collected by vast sociological surveys.
Empirical base of research
We used the data of the Monitoring of Education Markets and Organizations, the longitudinal multipanel study “Trajectories in Education and Careers”, the international survey “Monitoring of the Labour Market for Highly Qualified R&D Personnel” and the bibliometric data of the scientific citation indexing service Web of Science.
Results of research
On the basis of the analysis of literature there were singled out the factors of productivity on an individual, organisational and institutional levels: degree of economic development of a country, political factors, religion. On the basis of the Russian data of the “Monitoring of the Labour Market for Highly Qualified R&D Personnel” international survey for the year of 2010 there was not establish a stable link between the reasons for mobility — dissatisfaction with a previous place of employment (voluntary mobility), change of an employment place initiated by an employer at a previous place, mobility for family or personal reasons not connected to work directly — and productivity of lecturers at Russian higher education institutions. The results vary depending on the way publication activity is measured: employees who changed a place of employment voluntarily are more productive and publish their works on an international level more; in the meantime, employees who changed place of work involuntarily are generally less productive and less likely to have publications on an international level.
The analysis of the longitudinal data for the period from 2010 to 2015 showed that the significant effect was reached quite quickly with the implementation of the “5–100” Programme: publication activity of employees of those higher education institutions that are the participants of the Programme grew faster than the activity of other Russian higher education institutions. More to this, there was observed a growth of a number of publications in well-cited journals per one employee in those higher education institutions that are the participants of the Programme.
On the basis of the data on Moscow schoolchildren that were admitted to higher education institutions we showed that, despite the fact that in most cases there was found a positive correlation between the individual Unified State Examination scores and the ranking of the quality of enrolment, the Unified State Examination scores were influenced by the family and school characteristics. Thus, inequality in terms of access to higher education may have its roots on those stages that precede admission to higher education institutions.
On the basis of the student survey we showed that students segregate themselves according to the level of academic achievements. In other words, academically successful students are prone to form connections with those who have similar academic level and to avoid the less successful peers. Such structures block dissemination of information within groups and may demotivate underachieving students, lower their productivity, and even lead to them being expelled. It was also shown that students are likely to choose friends with similar patterns of fast food consumption. It is noteworthy that patterns of healthy food consumption did not prove to be valuable predictors for a formation of friendship networks. These results may indicate that healthy food and fast food are the social patterns that are constructed in a significantly different way.
Level of implementation, recommendations on implementation or outcomes of the implementation of the results
The results of the studies can be used to form strategies of management and policies of employment for higher education institutions and to stimulate scientific activity of employees of higher education institutions. In addition to this, the results can be used to develop and implement financial support plans for higher education institutions’ candidates and students.