This paper summarizes the findings of HRM practices over the past decade and supplements them with the results of a recent survey of 200 CEOs of domestic industrial companies and CEOs of several Russian manufacturing subsidiaries of MNCs. We identify the resilient archetype of the Russian HRM system and variations inspired by the differences in strategic position and current economic performance of industrial companies
The present paper exploits new headwinds blowing against the post-transition of Russia. It is argued that the new goal for Russia is an effective transition to a post-industrial stage of development. The author considers five main frontiers that challenge the successful achievement of this goal: 1) over-education and low investment in training, 2) poverty of the working population, 3) high inequalities and unfair distribution of incomes, 4) low rates of growth in human development, and 5) neoliberalism and growing alienation in contemporary Russia
The literature tends to neglect the role of individuals in formal skills training in Russia during the period of economic growth between 2001 and 2014. The present study addresses this oversight. Although to a certain extent, studies have associated the prosperous years of recent economic growth in Russia with training, they have not considered that the Russian population insisted on better qualifications. The present study shows that such insistence came primarily from skilled non-manual workers who resided in cities, worked more than eight hours per day, had second jobs, and were in great demand by organizations. Drawing on the panel data of the Russian Longitudinal Monitoring Survey—Higher School of Economics, we argue that individual heterogeneity over the period of economic growth between 2001 and 2014 in Russia significantly contributed to the unobserved variation in training and cannot be ignored in applied socioeconomic studies of human capital. After accounting for important within- and between-person characteristics, we find that 26% of the variation in training during the studied years is attributable to the unobserved characteristics of individuals and 7% to jobs, whereas only 0.2% is accounted for by the time trend. We use multilevel longitudinal probit models with cross-classifications to partition the variation in skills training into individual and job-specific levels in the context of Russia’s recent economic growth. Our results show that in some knowledge-based societies, lifetime learning may slightly be a function of the years of economic prosperity and more likely based on the unobserved individual traits.