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
8.5
26000
8
7.5
21000
7
6.5
16000
6
5.5
11000
5
4.5
4
2001
2002
2003
2004
2005
2006
2007
Formal training, last 12 months
2008
2009
2010
2011
2012
2013
2014
6000
2015
GDP per capita, PPP (current international $)
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
Estimate of the
within effect
The higher-level entity j’s mean =
the time-invariant component of time-variant
variables
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 timevariant 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
Experience of skills training in earlier periods
Use of PC at the working place
Have subordinates
Economic growth 2001-2013
Context
Within
0.565***
0.234**
Between
0.167**
0.218***
Generic labour
State ownership
Prompt salary payment
-0.0681*
0.261**
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
0.0359
Rejected (Xi2=9.52, p=0.001)
Model 4: Dynamic Logit FE
n/a
n/a
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
[email protected]
Institute of Sociology
Russian Academy of Science