1. 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

2. 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

3. 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

4. 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?

5. 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

6. 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.

7. 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)

8. Multilevel perspective of RE
Individuals
Occasion*Time

9. 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,
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}

10. 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)

11. 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
0.218***
Context
Generic labour *
State ownership
0.261**
Prompt salary payment
0.385*
Non-formal contract
-0.705***
Note: Controlled for a standard set of socio-demographic variables

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

13. 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)

14. 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

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