1. Skills Training in India: Market or Privilege?
14-th European Association for Comparative Economic Studies Conference
8-10 September 2016, Regensburg, Germany
Skills Training in India: Market or Privilege?
Vasiliy A. Anikin

2. Motivation Patchy socio-economic development of India:
In many aspects, India remains a pre-industrial society
Informal employment in non-agricultural sectors: 84%
Have no job-written contract: 71%
High socio-economic inequalities
Between and within-regional disparities
However
Increasing educational expenditures in tertiary education 35.9%
Decreasing age dependency ratio: 47.4%
Wide variety of training programs
High share of the rural population: 61%
Employment in agricultural sector: 51%
High role of ascription (gender, cast, etc.) in allocation of resources
Small contribution of human capital to GDP
Honorific assets are the principal assets:
prestige
good reputation
fame
difference and derogation
ethnic and religious purity
privileged lifestyle
Etc.

3. Data
National Sample Survey, NSS 68th Round Schedule 1.0: Employment and Unemployment, July 2011 – June 2012
Conducted by the Government of India, Data Processing Division, National Sample Survey Office.
The nationally representative sample, both households and individuals
Multi-structured data: states, districts, households, individuals
N of observations, selected = 157,748: 34.5%, individuals who have a job:
worked as regular salaried/ wage employee
worked as casual wage labour in both public and other types of works.
Q of interest: ‘whether they are receiving/ received any vocational training’

4. Formal and non-formal training
Receiving formal vocational training (1% of the adult population);
Received vocational training (10.8%, or 7.8% of the regular wage employees):
formal (2.6%),
non-formal (8.2%):
Hereditary (2.5%)
self-learning (1.8%)
learning on the job (3.4%)
Others (0.5%);
Did not receive any vocational training

5. Typical trainees in India, who are they?
Formal
Non-formal
Wage employees
Formally employed, with long-term job-written contracts
Having better employment conditions that provide:
Paid leave
Social benefits
Hereditary
Self-employed and helpers in hh enterprises
Realizing a right to be trained passed from parent to offspring (miras, e.g. barber mirarsdars in Bellary (Iyengar, 1933))
Self-learning and learning on the job
Unqualified (causal) labour
Wage employees without job-contracts
Disadvantaged employment relationships
Scheduled castes and downward classes

6. Occupational composition of formal training in India

7. Social groupings of formal training in India

8. Religion and formal training in India

9. Enterprise type and formal training in India

10. Employment and formal training in India

11. Geographical heterogeneity in India, Castes
Scheduled Castes
Scheduled Tribes
Source: Primary Census Abstract for Total population, Scheduled Castes and Scheduled Tribes, 2011
Office of the Registrar General & Census Commissioner, India

12. Predictors of formal skills training in India, assumed impacts
Ascription: Socio-demographics
Males (-)
Age, linear (+)
Urban area (+)
Marital status
Small size of household
Religion
Castes
Labour market
Overall market situation
Lower skilled occupations (+)
Job-specific
Full time work (+)
Regular payment (+)
Full-time job
Permanent nature of employment (+)
Organization-related level
Size, linear (+)
Location (?)
Enterprise uses electricity (+)
Private ownership (+)
Human capital
Technical education (+)
Schooling: further education (+)
Individual-specific
Christianity (+)
Scheduled caste (-)

13. Model Binary response:
Outcome: Whether received formal training treated as Binomial process
Link function: Logit
4-level cross-classified model with outlier and cross-class interactions:
Level-1 (i): individual ID, Ni = 36,430
Level-2 (j): households, Nj = 30,007
Level-3 (k): occupations, Nk = 112
Level-4 (l): states, Nl = 88
Model fit: Markov chain Monte Carlo (MCMC)
Univariate Metropolis Hastings sampling
Number of simulations is bound to 500 k,
Hierarchical centering at level 2 (Browne, Subramanian, Jones, & Goldstein, 2005)

14. 4-level cross-classified model of training
Fixed part
Yij ∼ Binomial (consij , πij)
Logit (πij) = ß0jkl cons + ß Xijkl
Random part
ß0jkl = ß0 + u0j + u0k + u0l

15. MCMC
A stochastic process in which future states are independent of past states given the present state (Lam, 2015)
Markov property: p(θ(t+1)|θ(1), θ(2),…, θ(t)) = p(θ(t+1)|θ(t))
Jumping rules are governed by a transition kernel
How it works: П(t) = П(t-1) x P = П(0) x Pt

21. 9% of probability of training is attributable to differences between states
7% - between occupations
60% - between households