This study aims to understand skills training and employment preferences of India’s rural youthm, as well as examines the social networks and work-related mobility of these youth to identify factors that contribute to labour market choices and preferences.
Youth employment schemes like Deen Dayal Upadhyay Grameen Kaushalya Yojana (DDUGKY) and Rural Self Employment Training Institutes (RSETI) are bridging the skill-gap among India’s rural youth and make them economically independent. As India’s workforce grapples with low rates of job creation, the need for having globally relevant skills and prospects for self-employment are underscored.
The aim of this study is to understand skills training and employment preferences of India’s rural youth. Additionally, the study also seeks to understand the social networks and work-related mobility of these youth to see if these factors play a role in determining labour market choices and preferences. To achieve this aim, this study will use a unique database that has been put together as part of the screening process of the DDUGKY scheme for candidate selection. ‘Kaushal Panjee’ (KP) is a particular skill register maintained by the MoRD for registering interested candidates for its skilling and livelihood enhancing initiatives – i.e. both DDUGKY and RSETI, on which candidates can register through the KP app or the KP website. This KP database comprises of candidates who have expressed interest in taking up training, not an assured sign-up for any programme.
This study will be conducted in Odisha. Data on actual livelihood outcomes will be collected through a mix of baseline data captured by the KP portal as well as additional variables captured through a telephone survey. This study proposes to estimate factors that predict self-employment versus wage-employment choices by means of a phone calling exercise to collect information on whether the interested candidates eventually sign up for training, and if they do, do they opt for wage- training or self-employment training. Econometric techniques as well as machine learning techniques will also be utilised to estimate predictors and analyse any differences or similarities in importance of predictors.
Findings from this study will help policy-makers understand the current characteristics, aspirations, behavioural pattern and migratory trend, particularly within the wage and self-employment sectors of the labour market. They will also contribute to the design and development of better outreach programmes and policies for citizens seeking employment in the future.