What factors best explain the low incidence of skills training in a late industrial society like Russia? This research undertakes a multilevel analysis of the role of occupational structure against the probability of training. The explanatory power of occupation-specific determinants and skills polarisation are evaluated, using a representative 2012 sample from the Russian Longitudinal Monitoring Survey. Applying a two-level Bayesian logistic regression model, we show that the incidence of training in Russia is significantly contextualised within the structure of occupations and the inequalities between them. The study shows that extremely high wage gaps within managerial class jobs can discourage training, an unusual finding. Markets accumulating interchangeable and disposable labour best explain the low incidence of training; workers within generic labour are less likely to develop their skills formally, except in urban markets. Although we did not find strong evidence of skills polarisation, Russians are yet to live in a knowledge economy.
The experience of developed countries – particularly member-states of the OECD – has shown that employers are actively investing in developing the human capital of their employees. According to research conducted by the World Bank, more than half of the companies in developed countries provide their employees with training in one form or another. There is, however, reason to believe that the situation is quite different in Russia. Some studies have shown that the level of investment in training in Russia is much lower. This difference can be explained by the fact that employers do not see the point in such investment because it is much easier to lure employees with the required qualifications than to train their own staff. Moreover, Russia faces a problem with high employee mobility, meaning that companies are not sure that they will get a return on their investment. Given these circumstances, the present study examines whether investments in human capital in Russia are profitable. It investigates the wage return to job-related training using a difference-in-differences estimator to control for unmeasured differences in ability and measured differences in past wages as a proxy for ability and motivation. Estimates use panel data from The Russia Longitudinal Monitoring Survey – Higher School of Economics from 2004 to 2011. As predicted, positive returns to training are identified and the returns increase absolutely with the level of past wages.