Vocational training in India: determinants of participation and effect on wages

Abstract

Background: India is at the cusp of a population change and is currently undergoing the phase of 'demographic dividend. ' This has thrown a challenge towards the policymakers to ensure that there are enough employment opportunities for the everincreasing labour force. One of the areas where improvement is urgently required in India is skill development. This paper attempts to identify the factors, which affect an individual's participation in vocational training using nationally representative National Sample Survey Office (NSSO) data. Further, we investigate the impact of vocational training on the wages of an individual at overall and sectoral levels. Methods: For addressing the first question of factors that are associated with participation in vocational training programmes, we will be applying logit and multi-nominal logit models. The explanatory variables will broadly be social and economic indicators of the individual as well as the individual's household characteristics. The second issue of identifying the effect formal vocational training on wages will be analysed through a multiple regression model. Results: We found that being an urban dweller increases the odds of participating in formal vocational training. Further, being male increases the odds of receiving formal vocational training. We found that having formal training increases the wage by 4.7% in the overall economy as compared to a person without any training. The effect is highest in the primary sector, where the individuals with vocational training had a wage increase of 36.9%. Workers with formal vocational training in the secondary sector had an increase in wages by 17.6%. Conclusions: The analysis done in the paper reveals that formal vocational training is associated with higher wages with the effect being the highest in the primary sector. Being male and urban dweller improved the odds of participating in formal vocational training. The model suggests that there exist good economic returns, which are associated with formal vocational training, and hence, it makes sense to invest resources in vocational training.

Training poverty in India: Reassessing the effect of social ascription

“…For instance, about 62 per cent of British workers receive training on an annual basis (ISSP Research Group, 2017). In India, the proportion of the population receiving any forms of skills training is considerably smaller, which was repeatedly confirmed by previous studies (Agrawal & Agrawal, 2017, Banerjee, 2016; Cabral & Dhar, 2019; Kumar et al, 2019). According to the recent microdata collected by the National Sample Survey Office, only 5 per cent of the adult population (of age 15 to 59 years) was receiving or received skills training in 2017–2018, while only 1.9 per cent of the population was receiving formal training (NSSO, 2020).…”

“…India has become a country where high‐qualified jobs in skills‐intensive enterprises coexist with a vast army of low‐paid, unskilled workers in agriculture and construction (Agrawal, 2012). From this perspective, disadvantaged ‘ascriptive inequalities’ (Grusky, 2001; Li & Wang, 2013; Linton, 1936; Nock & Rossi, 1978), associated with inherited statuses, such as lower castes, are still significant determinants of informal employment (Sheikh & Gaurav, 2020) and vocational training (Kumar et al, 2019). It is also believed that females are less likely to be formally trained than males.…”

“…It is also believed that females are less likely to be formally trained than males. Furthermore, individuals from Christianity and other religions are believed to have a higher probability than individuals belonging to the Hindu religion (Kumar et al, 2019).…”

Investigating social development inequality among steel industry workers in Pakistan: A contribution to social development policies

“…Skill level of workers also implies positive impact on socioeconomic score in labour market, there are evidences [ 39 ] which show that workers enriched with skills are in better position to bargain about salary as compared to those ones who are less skilled. having formal training increases the wage by 4.7% in the overall economy and the effect is highest in the primary sector [ 40 ]. This makes a clear difference in socioeconomic scores as it is reflected in our case in Fig 2 that skilled workers are at rich level as compared with unskilled or semiskilled workforce.…”

“…The low level (primary, middle and Matric) of education categories are positively correlated with SES but the high level (intermediate or above) is negatively correlated with middle and rich SES categories as comparison to base outcome category. Typically, the first line of emphasis is abilities, training and knowledge of specific skill sets in the sector of industries jobs, as discussed by [ 39 , 40 ]. On the other hand Table 4 reported the relative risk ratiothat in low education the chances of becoming poorer to wealthier are higher as compared to higher education among steel industry workers.…”