Jajati Keshari Parida
Central University of Punjab, India
Scientific Tracks Abstracts: Bus Econ J
In India, the size of Not in Employment, Education and Training (NEET2 ) youth population increased massively in the recent years (Mehrotra & Parida, 2019, 2021). A country, which has just moved up in the income ladder3 from a low-income economy to a lower middle-income country (during 2007-08), and is passing through the phase of its demographic dividend, an increased level educated youth unemployment, and rise in NEET population should be the cause of utmost concerns, particularly, from the perspective of sustaining economic growth for achieving inclusive development. Furthermore, the rising volume of NEET population, may also lead to the emergence of situations like: growing demand for reservations of jobs (Mian & Sufi, 2012), increased level of social unrest, growth of criminality and insurgences (Bhattacharyya & Nair, 2019; Pervaiz et al., 2012) due to the poor psychological health conditions of the NEET population (Nardi et al., 2013; Rodwell et al., 2018 and Parola & Felaco, 2020). A review of international level studies (See BÄ?lan, 2015) in Romania; Cascioli, (2011), Batini et al., (2017), Gaspani, (2018) and Parola & Felaco, (2020) in Italy; Robson (2010) and Russell et al. (2011) in United Kingdom; Vancea & Utzet (2018) and Rodriguez-Modroño (2019) in Spain; Susanli (2016) in Turkey; Tamesberger & Bacher (2014) in Austria; Zudina (2018) in Russia; Bruno et al. (2014), Carcillo & Königs (2015) and Caroleo et al. (2020) in OECD countries; Holte (2018) in Norway; Kraak (2013) and Akinyemi & Mushunje (2017) in South Africa; Rodwell et al., (2018) and Mitrou et al., (2019) in Australia; Pattinasarany, (2019) in Indonesia; Chen, (2009) in Taiwan) reveals that the phenomenon of NEET population is rising in Europe, Africa, East Asia and Pacific regions. Most of these studies noted that NEET population increased during the post financial crisis (2007-08) periods, and it relatively higher among women, and among those who have just completed their basic education. However, in India there is dearth of studies apart from Mehrotra & Parida4 (2019, & 2021) which identifies the factors determining growth of NEET population. Indian economy has been passing through a phase of structural transformation in employment since 2004-05 (Behera & Tiwari, 2012 and Mehrotra et al., 2014). Due to growth of mechanization in agriculture about 4.5 million per annum workers are leaving agriculture, but the non-farm sector jobs are not growing adequately to accommodate these workers (Mehrotra & Parida, 2021). Moreover, the number of educated youth job seekers is already rising, which is reflected through an upsurge in their unemployment rate (Mitra & Verick, 2013; Bairagya, 2018; Mitra & Singh, 2019 and Mehrotra & Parida, 2019, 2021). The overall youth unemployment rate (open) increased almost three times, from about 6 per cent to about 18 percent during 2011-12 and 2017-18 (Mehrotra & Parida, 2019). There could be both structural as well as institutional (family) factors behind the growth of NEET population in India. Hence, the main objectives of this paper are: to find out the trends and composition of NEET population; and to explore both macro (aggregate level) and micro (individual and family characteristics) level factors responsible for the rise in NEET population in India. The major contribution of this study is to come out with appropriate policy measures that may help reduce the volume of NEET and educated youth unemployment in India. This study is based on secondary data. Both the Employment and Unemployment Survey (EUS) and the Periodic Labour Force Survey (PLFS) data of the National Sample Survey (NSS) were used to find out the trends and composition of NEET population. The EUS quinquennial rounds (stretching from the period 1983 to 2011-12) and PLFS (annual employment surveys conducted during 2017-18 and 2018-19) were used. Both individual and family level characteristics were also taken from the EUS and PLFS unit level data for the micro level estimation. On the other hand, information on macroeconomic indicators were compiled from other national level data sources including: Ministry of Agriculture, Reserve Bank of India (RBI) and Centre for Monitoring Indian Economy (CMIE) private limited. These indicators include: Net State Domestic Product (NSDP), Infrastructure development (Length of roads (in KMs), Number of branches of the Scheduled Commercial Banks, Number of Industries/Factories), and Mechanization in Agriculture (Number of Tractors sold in India) etc. Based on state-wise (29 states5 ) annual aggregated data (from the year 2005 to 2019), a balance panel data set is created for the macro level regression estimation. Using Instrumental Variable (IV) Probit and System GMM regression models, it is explored that a set of supply and demand side factors restricts the labour market trajectories of educated youth. The growing mechanization in agriculture, stagnant real wages, sluggish non-farm sector output growth, infrastructural backwardness and the existing social-cultural setup in which educated youth live together create a negative environment that compels them to remain out of the workforce for a longer period even after completion of their education/training. Based on these findings, it is argued that policies aiming development of infrastructure along with promotion of industry should be given top most priority, to boost non-farm sector output and employment growth for checking the upsurge in NEET population in India.
Business and Economics Journal received 5936 citations as per Google Scholar report
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