Skills Training and Economic Growth Evidence from Russia Second World Congress of Comparative Economics «1917 –2017: Revolution and Evolution in Economic Development» Skills Training and Economic Growth Evidence from Russia Vasiliy A. Anikin Institute of Sociology Russian Academy of Science

Motivation Contradictory development of BRICS High rates of GDP pc growth (before 2012/2013) Low significance of human capital in productivity and growth (Timmer & Voskoboynikov, 2014) Critique of the knowledge economy theory (Green et al., 2016) Low incidence of training among the working population Russia representing a trend away from the knowledge economy

Formal training and GDP pc in Russia, 2001-2015 % $ PPP Source: Training data retrieved from the RLMS-HSE data, representative samples; % of working population GDP per capita data retrieved from the World Bank

Research questions How do individuals within a certain level of development represented by Russia build and maintain their human capital through acquisition of training? What are the factors that obstruct the development of human capital for Russian workers? And to what extent do individual patterns of human capital acquisition become stable over time?

Methodology (I) Heterogeneity bias FE models only estimate within effects: To avoid the problem of heterogeneity bias, all the higher-level variance, and with it any between effects, are controlled out using the higher-level entities themselves (Allison, 2009), included in the model as dummy variables To avoid having to estimate a parameter for each higher-level unit, the mean for higher level entity is taken away from both sides of regression equation

Methodology (II) An RE solution for heterogeneity bias (Bell and Jones, 2015) FE model as a constrained form of the RE model By using the RE configuration, we keep all the advantages associated with RE modeling Mundlak (1978): heterogeneity bias is the result of attempting to model two processes in one term Mundlak’s formulation simply adds one additional term in the model for each time- varying covariate that accounts for the between effect: that is, the higher-level mean.

Methodology (III) Time-variant variables The higher-level entity j’s mean = the time-invariant component of time-variant variables Estimate of the within effect The ‘contextual’ effect that explicitly models the difference between the within and between effects Source: Bell and Jones (2015); Snijders and Bosker (2012); Berlin et al. (1999)

Multilevel perspective of RE Individuals Occasion*Time

Data The Russian Longitudinal Monitoring Survey, RLMS-HSE Conducted by the National Research University Higher School of Economics (NRU-HSE) and ZAO “Demoscope” together with Carolina Population Center, University of North Carolina at Chapel Hill and the Institute of Sociology RAS Panel samples, 2001-2013 Working population Training variable has a binary outcome: COURSES FOR THE IMPROVEMENT OF PROFESSIONAL SKILLS OR ANY OTHER COURSES, LAST 12 MONTHS {Yes=1 / No=0}

Model Dynamic RE Probit, with additional higher-level mean of time-variant variables Unobserved random effects uj The outcomes are realizations of independent Bernoulli random variables Yij with probabilities depending on uj Inter-class correlation (ICC), to capture unobserved individual characteristics (the uj)

Determinants of training Within Between Experience of skills training in earlier periods 0.565*** Use of PC at the working place 0.234** Have subordinates 0.167** Economic growth 2001-2013 0.218*** Context Generic labour -0.0681* State ownership 0.261** Prompt salary payment 0.385* Non-formal contract -0.705*** Note: Controlled for a standard set of socio-demographic variables

Predicted probabilities, selected effects Previous experience of training Using of a PC at work

ICC Model Name ICC (=rho) Std. Err. LR test of H0: rho=0 Model 1: Mundlak Dynamic Probit RE 0.076 0.0335 Rejected (Xi2=5.99, p=0.007) Model 2: Dynamic Probit RE 0.099 0.0359 Rejected (Xi2=9.31, p=0.001) Model 3: Dynamic Probit RE 0.101 Rejected (Xi2=9.52, p=0.001) Model 4: Dynamic Logit FE n/a Model 5: Dynamic Logit RE 0.116 0.0367 Rejected (Xi2=12.03, p=0.000)

Findings Skills training was associated with prosperous years of economic growth Participation in training is a matter of individual strategies workers stick with being employed at good jobs Unobserved individual characteristics account for about 8% of workers’ propensity to undertake formal training during the years of economic prosperity Known structural context of training explains why workers one workers undertake training and others do not

Thank you! Vasiliy A. Anikin Moscow, 107065 Shabolovka, 26, building. 4, Room. 4331 vanikin@hse.ru Institute of Sociology Russian Academy of Science