Взаимосвязь между отношением к риску, успеваемостью студентов и вероятностью отчисления из вуза
Regression analysis is used to explore the relationship between students’ risk attitudes and academic performance indicators: current academic achievement and the likelihood of dropping out. Using empirical data on students of a highly selective Russian university, we reveal a considerable positive correlation between risk acceptance and the likelihood of being expelled. We believe that conventional student integration and drop-out models could also consider such individual student characteristic as risk attitude. Normally, it did not use to be regarded as a factor influencing the likelihood of student departure. Risk attitude as an individual student characteristic can be involved in the process of academic integration, affecting its progress. More risk-averse students remain underintegrated in the academic environment, which is fraught with dropping out.
Period of training in a higher school is a threshold on the professional life way, therefore, it reflects the willingness to vigorously respond to all the vicissitudes of life and desire for constant self-improvement. Data collection is produced at the Higher School of Economics, Department of Management. We assumed that students with high levels of emotional intelligence must have a high rank.
The paper contains empirical estimates of how behavioral factor (an attitude towards risk), rationality and uncertainty influence on investment decisions (capital investment) of Russian companies. The research is guided by the models of Sandmo (1971), Bo and Sterken (2007). We have tested a hypothesis, that risk preferring companies tend to grow investment, while risk averse companies are more likely to decrease the number of investment projects under uncertainty. The following rational variables, explaining investment policy, are included into the model: sales growth, market power, return on equity, debt to equity ratio, current liquidity. Since the time span of the research includes both the crisis period (years 2008, 2009) and the period before the crisis (2004-2007) we have also estimated the time effect on the companies’ investments.
The following estimators have been used to get the results: ordinary least squares; fixed effects model; random effects model; panel data models with binary variables controlling time effects; Hausman-Teylor’s model, generalized method of moments.