Nov 7, 2018 - Human capital is called for and paid for in confined lacunas of the labour market: ⢠Educated professionals residing in urban areas of wealthy ...
Kyoto International Workshop Advanced Study on Transition Economics and Comparative Economics 7 November, 2018
Human capital in Russia: Investments and Returns Vasiliy A. Anikin v a ni k i n@ hs e . r u
Institute of Sociology Russian Academy of Science
Human development in Russia compared to other countries 0,98
HDI, 1990-2017
0,88 0,78 0,68 0,58 0,48 0,38
1990
Source: OECD (2016)
2000
Russia South Korea China
2010
2017
Singapore Japan India
Problem • Years of schooling and further education are not considered by people as “investments”, most Russians simply follow a social norm of getting (higher) education. • There is a big difference between simple accumulation of educational resources and purposeful investments in these recourses in order to convert them into capital. That is why, we need various instruments that will indicate that people purposefully contribute their time and money to acquire new knowledge and develop their skills.
What is already known? • Russia is a country of high human potential, but not a country of human capital
• Weak macroeconomic effect, as education contributes slightly to the GDP (Kapeliushnokov, 2008; Voskoboynikov and Timmer, 2013) • Weak microeconomic effect, as Russian economy is framed by the institutions that maintain ‘bad jobs’ which do not require human capital investments (Gimpelson, 2016; Gimpelson and Kapeliushnikov, 2013) such as training (Anikin, 2017) • In most cases, returns are bounded to the jobs, not human capital characteristics – a true challenge to the human capital theory
• Human capital is called for and paid for in confined lacunas of the labour market: • Educated professionals residing in urban areas of wealthy regions (Oschepkov, 2010; Lukyanova, 2010); contrary to this, manual workers are less likely to invest their time and money in human capital and are less paid off for their education
• In Russia, wages are severely affected by non-monetary inequalities due to labour market disparities between regions and different types of settlements.
Formal training in Russia, 2001-2015 %
$ PPP
9
26 000
8
22 000
7
18 000
6
14 000
5
10 000
4 6 000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Formal training, last 12 months Source: Author’s calculations. Training data retrieved from the RLMS-HSE data, representative samples; % of working population. GDP per capita data retrieved from the World Bank
What is unknown and what we expect? • Since the previous estimates are done on the basis of formal characteristics of human capital (educational attainments, years of schooling, and tenure), we still are not sure about the elasticity of wages regarding various forms of individuals’ investments in human capital. • • • •
People may invest their free time (informal investments) Formal investments for the sake of better job or higher salary Monetary (and supposedly expensive) investments in education and new specialty Investment in language and cognitive skills (foreign lang. and computer skills)
• What are the returns to cultural capital (conditions of socialization), different kinds of higher education and some cognitive skills, as compared to other socio-economic determinants? • We also do not know the impact of various qualification ranks to the salary of manual workers • We do expect training to be more significant for the middle-skill non-manual occupations (Lerman, 2017) • We do not expect high returns to some cognitive skills: • ‘Returns to skills are systematically lower in countries … stricter employment protection, and larger public-sector shares’ (Hanushek, Schwerdt, Wiederhold, and Woessmann, 2015)
Estimation • We expand the basic Mincer (1974) equation: Ln[Wage] = Ln[Wage0]+ ρ*Schooling + β1*Tenure + β2*Tenure2 + ε • By adding up more specific information about both formal and informal investments in human capital and specific skills: • Monetary and non-monetary (own money and time) • Language and cognitive skills (foreign lang. and computer skills) • Qualification ranks (applicable for manual workers)
• We then estimate the equation to managers, professionals, clerks (middle-skilled non-manual occupations), and manual workers Source: Mincer (1974)
Building up wage equation, beyond education and skills • Family background (cultural capital, or socialization conditions) [𝜓]: • Place of living at the childhood • Parents’ education
• Socio-demographics [𝜆]:
• Gender, household size, and residential area
• Job-specific indicators [𝜃]:
• Risks of long-run unemployment • Problems with work because of a crisis (self-assessed indicator) • Enterprise’s ownership and size
• Employment relationships [𝜉]: • • • •
Average number of working hours per week Influence at the working place Timely payoffs Official employment (by written agreement, or a formal order)
Data • The Monitoring survey conducted by the Institute of Sociology of the Russian Academy of Science in 2017 • Sample: 4000 individuals • The data represent Russian population by gender, type of settlement, occupation, and individual income • We consider only working population
Classical part of Mincer equation Managers Ln (Wage0) [𝛼] Schooling [ρ] Tenure [β1] Tenure squared [β2]
ρ β1 β2
Professionals
Clerks
8.408*** 9.460*** 9.376*** (0.628) (0.189) (0.213) 0.00517 -0.000119 0.0385*** (0.0289) (0.00881) (0.0114) 0.0560*** 0.00813 0.0150** (0.0159) (0.00573) (0.00613) -0.00122*** -0.000138 -0.000282** (0.000374) (0.000130) (0.000119) Elasticities [% change in wages per 1 unit change] 3.9 5.7 1.5 -0.1 -0.02
Note: Robust standard errors are in parenthesis, *** p < 0.01, ** p < 0.05, * p < 0.1. Elasticities are calculated by formula: [exp(B)-1]*100, where B – is a coefficient's estimate
Manual Workers 9.122*** (0.216) 0.0234** (0.00969) 0.0146** (0.00673) -0.000397*** (0.000150) 2. 4 1.5 -0.04
Foreign language and computer skills Managers Everyday use of PC [𝛾1]
Professionals
Clerks
0.0504 0.187*** -0.0847 (0.136) (0.0573) (0.0701) Everyday use of I-net [𝛾2] 0.152 -0.143*** 0.146* (0.126) (0.0525) (0.0773) Invested in computer skills [𝛾3] 0.0970 0.00479 0.0214 (0.116) (0.0477) (0.0557) Invested in language skills [𝛾4] 0.474*** 0.0811 0.0349 (0.147) (0.0565) (0.100) Elasticities [% change in wages per 1 unit change] 𝛾1 21 𝛾2 -15 16 𝛾3 𝛾4 61
Manual Workers -0.00808 (0.0431) 0.0760* (0.0443) -0.115* (0.0667) -0.00655 (0.175)
8 12
Note: Both formal and informal Investments in language and computer skills had occurred over a period 2014-2017
Monetary and non-monetary investments Managers
Professionals
Travelled abroad [𝛿1]
Clerks
0.214* 0.166*** -0.103 (0.110) (0.0407) (0.0817) Were retrained [𝛿2] 0.0231 0.176*** -0.109 (0.259) (0.0537) (0.106) Spent free time on education [𝛿3] -0.0527 -0.0347 0.245** (0.146) (0.0427) (0.0962) Did not invest in human capital [𝛿4] 0.209 -0.0875* -0.225*** (0.138) (0.0492) (0.0697) Elasticities [% change in wages per 1 unit change] 𝛿1 24 18 𝛿2 19 𝛿3 28 𝛿4 -9 -25 Note: All the listed investments had (not) occurred over a period 2014-2017
Manual Workers 0.0982 (0.0895) 0.0706 (0.0765) -0.00250 (0.0847) -0.0838 (0.0593)
Cultural capital Managers Lived in Moscow or St. Petersburg during their childhood [𝜓1]
Professionals
Clerks
0.371** 0.190** 0.277** (0.183) (0.0863) (0.110) Father’s educ: Higher + sth. else [𝜓2] -0.286 0.0889 -0.173** (0.207) (0.0647) (0.0824) Mother: Vocational training [𝜓3] 0.0997 0.116* 0.0179 (0.155) (0.0628) (0.0557) Mother: Higher + sth. else [𝜓4] 0.435** 0.0846 0.0270 (0.201) (0.0646) (0.0693) Elasticities [% change in wages per 1 unit change] 𝜓1 45 21 32 𝜓2 -19 𝜓3 12 𝜓4 54
Manual Workers -0.136 (0.212) 0.161* (0.0929) 0.0152 (0.0358) -0.0332 (0.0656)
Note: Reference categories: ’reside in their childhood settlement’; higher school or below, both for mother and father
Job-specific indicators (I) Coefficients Risks of LR unemployment [𝜃1] Ownership – privatised [𝜃3] Private, created in 1990-s [𝜃4] Other forms of ownership [𝜃5]
𝜃1 𝜃3 𝜃4 𝜃5
Managers
Professionals
-0.198* -0.130** (0.111) (0.0567) 0.200 0.0333 (0.156) (0.0434) 0.102 0.164*** (0.136) (0.0522) 0.110 0.0317 (0.152) (0.0709) Elasticities [% change in wages] -22 -14 18
Clerks -0.149*** (0.0445) 0.207*** (0.0516) 0.191*** (0.0557) 0.191** (0.0744)
Manual Workers -0.0301 (0.0358) 0.0574 (0.0417) 0.0781 (0.0517) -0.0204 (0.0596)
-16 23 21 21
Note: Reference categories: state-owned enterprises; Risks of LR unemployment = being unemployed for at least 3 months
Job-specific indicators (II) Coefficients Problems at work [𝜃2] Enterprise’s size – Small [𝜃6] Middle [𝜃7] Large [𝜃8]
𝜃2 𝜃6 𝜃7 𝜃8
Managers
Professionals
-0.0864 -0.0245 (0.108) (0.0357) 0.647*** 0.357*** (0.216) (0.0737) -0.224 0.446*** (0.239) (0.0828) 0.233 0.339*** (0.172) (0.0814) Elasticities [% change in wages] 91
Note: Reference categories: micro-size enterprises
43 56 40
Clerks -0.0902* (0.0495) 0.150*** (0.0542) 0.199*** (0.0728) 0.101 (0.110) -9 16 22
Manual Workers -0.0647* (0.0346) 0.268*** (0.0785) 0.279*** (0.0809) 0.309*** (0.0877) -7 31 32 36
Employment relationships Managers High power at work [𝜉1]
Professionals
Clerks
0.519*** 0.195 0.0202 (0.177) (0.136) (0.0883) Middle power [𝜉2] 0.571*** 0.330*** 0.0888 (0.205) (0.110) (0.0602) Timely payoffs [𝜉3] 0.107 0.164*** -0.00651 (0.114) (0.0517) (0.0464) Working hours per week [𝜉4] 0.00499* 0.00506*** 0.00170 (0.00289) (0.00155) (0.00203) Elasticities [% change in wages per 1 unit change] 𝜉1 68 𝜉2 77 39 𝜉3 18 𝜉4* 5 5,1
Manual Workers 0.522** (0.238) 0.259** (0.108) 0.117** (0.0473) -0.000650 (0.00200) 69 30
Note: High power at work – a capacity to influence the decisions in organization at the CEO level; middle power – at the level of an employee's department; no influence at all – a reference category; *fitted values for w. hours – 10 units
Qualification ranks of manual workers Coefficients High-qualified – 5th rank and higher Semi-qualified – 3-4th rank Significant interaction terms High-qualified * region capitals Semi-qualified * region capitals Semi-qualified * district capitals
Elasticities [% change in wages]
0.166*** (0.0621) -0.0515 (0.0786)
18
0.222** (0.0920) 0.310*** (0.112) 0.213** (0.104)
25
Note: Low qualification rank (1-2) and rural area – are selected as reference categories
36 24
Demographics Coefficients Females [𝜆1] Household size [𝜆2]
𝜆1 𝜆2
Managers
Professionals
Clerks
Manual Workers
-0.387*** (0.105) 0.0206 (0.0388)
-0.263*** (0.0348) -0.0335** (0.0134)
-0.333*** (0.0453) -0.0533*** (0.0131)
-0.202*** (0.0390) -0.00229 (0.0125)
-40 -5.4
-22
Elasticities [% change in wages] -47 -30 -3.4
Residential area Moscow / St. Petersburg [𝜆3] Regional capitals [𝜆4] District capitals [𝜆5]
𝜆3 𝜆4 𝜆5
Managers
Professionals
Clerks
0.538*** (0.182) 0.0873 (0.120) -0.0131 (0.0931)
0.521*** (0.0508) 0.0285 (0.0433) -0.0613 (0.0453)
0.769*** (0.0554) 0.172*** (0.0536) 0.0743* (0.0435)
Elasticities [% change in wages per 1 unit change] 71 68 116 19 8
Note: Reference category: rural area
Manual Workers 0.872*** (0.121) -0.0564 (0.0758) -0.00912 (0.0730)
139
Principal findings • The basic version of Mincer equation ‘works’ for the middle-skill nonmanual occupations and manual workers, partially works for managers (experience), and is not relevant for professionals • Investments of free time are significant only for the middle-skill nonmanual occupation, and are associated with an 27 percent wage increase • Managers receive the largest returns on investments to foreign language skills, as it leads to an 61 percent wage increase; professionals – everyday use of PC, 21% • The largest returns workers obtain not to human capital, but – residential disparities, quality of jobs and employment relationships
Conclusion • Russia has become a knowledge-based society, but it has yet to build a knowledge-based economy. • The relative returns to human capital even for professionals are lesser than the relative contribution of non-educational factors. • Generally speaking, jobs are more important than human capital. • The returns are visible only to the very specific investments in human capital, like learning foreign language (managers), retraining (professionals), training (clerks), or qualification ranks (manual workers). • For the exception of middle-skill occupations, for most of the occupations, the penalties for not investing in human capital are either slight, or do not exist at all.
For citation: • Anikin V. A. Human Capital in Post-Crisis Russia: Status and Impact // Journal of Institutional Studies, 2018, Vol. 10 (no. 2), pp. 90-117 • DOI: 10.17835/2076-6297.2018.10.2.090-117
The presentation is a part of the project “Social policy, social stratification, components of the population wellbeing and manifestations of inequality in Russia: analysis of interrelations at various stages of the life cycle” carried out within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) in 2018
