EVALUATION OF THE IMPACT OF PROFILE DIRECTION TO THE INDICATORS OF THE ACTIVITIES OF RUSSIAN UNIVERSITIES

Since the Ministry of Education and Science of the Russian Federation began monitoring the university effectiveness, such a radical approach to cleaning up the higher education system from "useless educational institutions" has become a permanent subject of criticism in the scientific community. The main argument is that most of the monitoring indicators do not reflect the effectiveness of the institution in terms of the ratio of the output to the cost. Nevertheless, these indicators characterize different aspects of the activities of universities. And the results of monitoring allow to obtain statistical estimates of the degree of influence on these indicators of the specialization of the university (the structure of the student contingent in the areas of training). However, due to the specifics of the compositional data as covariates it is not possible to estimate the parameters of the regression model using the standard least squares technique. In addition, the original data are heterogeneous and contain outliers. To solve these problems, a number of robust principal component regressions are estimated. The estimation results are analyzed in terms of consistency. It is revealed that an increase in the share of mathematical, natural-science, information and engineering specialties in the structure of student training on average leads to an increase in the indicators of scientific activity, while for the economy and tourism, the opposite trend is characteristic. The larger share of the economy and tourism in the structure of specialties corresponds, on the average, to the smaller average score of the Unified State Examination and higher values of the indicator of financial and economic activity. Thus, the availability of these specialties in the structure of training is justified from the point of view of the financial status of universities.

α-преобразование, University, monitoring, indicator, compositional data, principal component regression, α-transformation, robust estimation

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