Harnessing Demographic Dividend Before it is Lost Forever in India

The Indian Journal of Labour Economics https://doi.org/10.1007/s41027-022-00422-5 ARTICLE Harnessing Demographic Dividend Before it is Lost Forever in India Jajati Keshari Parida1 · S. Madheswaran2 Accepted: 14 December 2022 © The Author(s), under exclusive licence to Indian Society of Labour Economics 2023 Abstract Based on the secondary data taken from Population Census, and the EmploymentUnemployment Surveys and Periodic Labour Force Survey of the National Sample Survey, it is found that Indian economy is passing through a critical phase of economic development in which it is likely to lose its demographic advantage. Because, in India while about 4.5 million people were leaving agriculture every year prior to the Covid-19 pandemic years, the non-farm sectors job was not growing adequately to accommodate the persons leaving agriculture, and the newly educated non-farm job seekers. As a result there was an upsurge in educated youth unemployment (18% and about 24 million) rate, and hence the discouraged youth labour force. On the other hand, an increase in the share (from 8.0 to 10.2%) and growth (3.0–5.1%) of elderly population put a question on the process of harnessing demographic dividend in India. Based on these findings it is argued that an integrated approach of development is necessary to boost the labour force participation of youth and overall population to boost the growth of per capita national state domestic product (NSDP) in Indian states. This could be achieved through the promotion of micro and small enterprises along with infrastructure development along with a systematic emigration and remittances policy. Keywords Demographic transition · Youth LFPR · Industrial development · Demographic dividend * Jajati Keshari Parida S. Madheswaran 1 School of Economics, University of Hyderabad, Gachibowli, Hyderabad 500046, India 2 Institute for Social and Economic Change (ISEC), Naagarabhaavi, Bengaluru, Karnataka 560072, India 13 Vol.:(0123456789) ISLE The Indian Journal of Labour Economics 1 Introduction The changing age structure of the population can have positive implication on the economic growth and overall welfare of a country (Lee and Mason 2006; Gribble and Bremner 2012). The demographic dividend can be defined as the process of acceleration of economic growth owning to the changes in the age structure of a country’s population with the transitions from high to low birth and death rates, resulting in a higher share of working age population. The Indian economy was passing through a phase demographic dividend, in which the share of working population was the highest (Aiyar and Mody 2011; Joe et al. 2018; Mehrotra 2015). But with falling total fertility1 rate, the share of elderly population has started rising (it increased from 8.2% to about 10.2% during 2011–12 and 2018–19). In this context, the decline of overall labour force participation rates (Kannan and Raveendran 2019; Parida 2015; Mehrotra and Parida 2021) and an upsurge in youth unemployment (Mehrotra and Parida 2019, 2021), have certainly put questions on the process of harnessing demographic dividend (which is going to disappear during post 2040, and India will become an ageing society for ever) in India. Since the structural transformation, which began during post 2004–05 seems to be stalled during 2017–18 (Mehrotra and Parida 2021), the rising enrolments in higher educational institutions (in general, technical and vocational education) in recent years (Kannan and Raveendran 2012; Mehrotra and Parida 2019) is likely to exacerbate the problem of educated youth unemployment and employability issues. Hence, the main objective of this paper is to examine the pattern of youth unemployment in India, and to identify the sectors in which educated youth could be accommodated. Moreover, this paper provides a discussion on effectiveness of the previous policy measures, and suggests measures to reduce the extent of youth unemployment in order to sustain the process of economic growth in India. This paper is organized in seven sections. Section provides the introduction. Section two provides the sources of data and outlines the econometrics methods. Section three provides regression results and discussion. Section four details the context of losing demographic dividend in India, while section five explains the broad sectoral youth employment trends, quality of jobs and the situation of rising unemployment and its implications on poverty in India. Section six provides a discussion on the effectiveness of past and present initiatives of the government. It also outlines a road map that could help in tackling the problem of rising youth unemployment so as to sustain economic growth in India. Finally, section seven concludes the paper. 1 As per the Census population projection (by Govt. of India), Total Fertility Rate (TFR) is expected to decline from 2.34 during 2011–2015 to 1.72 during 2031–2035 and it will continue to decline at this pace. 13 ISLE The Indian Journal of Labour Economics 2 Data and Methods This paper is based on secondary data. The major sources of secondary data includes: National Sample Survey Organisation (NSSO), the Periodic Labour Force Survey (PLFS) and Census of India. Employment indicators like workforce, labour force, unemployed, Not in Labour Force Education and Training (NLET), labour force participation rate and unemployment rates etc., are calculated using both NSS and PLFS data. NSS and PLFS estimates are adjusted further to the projected census population to obtain absolute numbers. Both employment and unemployment figures are computed using the Usual Principal and Subsidiary Status (UPSS) of persons. The sectoral employment, formal-informal employment etc., are computed using other information like National Industrial Classification (NIC) codes, enterprise type, number of workers in the enterprise, types of job contract, availability of social security benefits etc. The enterprise survey conducted during 2010–11 (67th round) and 2011–12 (73rd round) are also used to provide a broad picture of informality by estimating the number and share of un-registered and informal enterprises. To examine the impact of labour force participation on per capita gross state domestic product (GSDP) (proxy for the demographic dividend), we have estimated dynamic panel data regression models. Since, we have a short panel (number of cross sects. (27 states2) greater than number of years (15 years3), the system Generalized Method of Moments (GMM) method developed by Arellano and Bover (1995) and Blundell and Bond (1998) is preferred to pooled Ordinary Least Square (OLS) regression model. This method of estimation not only produces unbiased and consistent estimators in presence of potentially endogenous regressors, but it also overcomes the possible heteroscedasticity and autocorrelation problems in the data. According to Roodman (2009), the system GMM is an augmented version (...truncated)