Occupational propensity for training in a late industrial society: evidence from Russia

Abstract

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 in the probability of training. The explanatory power of occupation‐specific determinants and skills polarization 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 contextualized 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 polarization, Russians are yet to live in a knowledge economy.

Advancing national human resource development research: suggestions for multilevel investigations

“…A more recent study (Anikin 2017) captured multiple levels by investigating occupational propensity for training within a societal and occupational structure context and considering labour market characteristics and job characteristics.…”

Training poverty in India: Reassessing the effect of social ascription

“…From the methodological perspective, this implies the existence of a complex structural inequality that is typically ignored in naïve modelling when we include the structural variables (e.g., occupations and states) as dummies or use nested hierarchical models. The between‐state variance is estimated at 1.236—that is 9 per cent of the variance of training due to differences between regions; between‐occupation variance is estimated at 0.969 (capturing approximately 7 per cent of the variation in the probability of training, the same as in Russia (Anikin, 2017)); between‐household variance is estimated at 8.468—that is 60 per cent of the variance of training due to differences between households dwelling in the same area. It is highly likely that heads of large households in the rural area are excluded from formal vocational training as compared to heads of small households residing in urban area even within the same district and holding similar occupation.…”

“…The Ministry of Statistics of India provides NCO‐2004 at three‐digit level—that is the structure of 112 occupations, which we apply without any additional adjustments, since NCO‐2004 was created as an adaptation of ISCO‐88 for the Indian labour market. Occupational diversity on the job market exists not only between occupations but also within them; differentials in wages within occupations may indicate this intra‐occupational diversity in industrial societies (Anikin, 2017; Coulombe & Tremblay, 2007; Gallie, 1991; Lambert & Bihagen, 2016). The intra‐occupational diversity is measured by the wage differences (in terms of standard deviations, see (Hox, 2010)) within occupations (see Appendices B and C).…”

Human Capitalin Valuable Orientations Of Students: Methodology, Methodology And Diagnostic Results

“…The scientific problem of organizing the diagnosis of awareness of the importance of the human capital in the orientations of students and young researchers of the SEC is as follows. Human capital is multidimensional (Anikin, 2017;Smyslov, 2007;Zgonnik, 2007). A generalization of scientific positions on the problem of diagnosing human capital allows us to talk about several methodological approaches, and hence several methodological strategies for obtaining and analyzing diagnostic information about human capital.…”