Stalled Structural Change Brings an Employment Crisis in India

The Indian Journal of Labour Economics https://doi.org/10.1007/s41027-021-00317-x ARTICLE Stalled Structural Change Brings an Employment Crisis in India Santosh Mehrotra1,2 · Jajati K. Parida3 Accepted: 27 April 2021 © Indian Society of Labour Economics 2021 Abstract Using national-level employment data, this paper explores both the supply and demand-side factors responsible for stalling India’s structural transformation on the employment side. We have found that although the overall LF participation has consistently been declining, the size of open unemployment and discouraged LF are rising at an unprecedented pace. This employment crisis arose because of the stalled structural transformation owing to the lack of effective demand for skilled workers in the non-farm sectors. This crisis is not only reflected in stagnant real wages, but it also adversely affected GDP growth and the incidence of poverty. Hence, unless measures are taken quickly, India’s demographic dividend, which ends in 2040, is under severe threat. Keywords Structural Transformation · Youth Unemployment · Discouraged Labour Force · Demographic Dividend 1 Introduction The Indian economy passed through a phase of structural transformation and became a lower middle-income country with substantial reduction in income poverty (see Chauhan et al. 2016) during 2004–2005 to 2011–2012. Over this period, a significant decline in the overall labour force participation rate (LFPR) in India also occurs, slower for males but much faster for women. This was caused by an * Jajati K. Parida Santosh Mehrotra 1 Centre for Development, University of Bath, Bath, UK 2 Jawaharlal Nehru University, Mehrauli, New Delhi, India 3 Department of Economic Studies, School of Social Sciences, Central University of Punjab, Ghudda Village, Bathinda, Punjab 151401, India 13 Vol.:(0123456789) ISLE The Indian Journal of Labour Economics upsurge in enrolment of boys and girls in the secondary and higher levels of education (Rangarajan et al. 2011; Hirway 2012; Thomas 2012; Kannan and Raveendran 2012; Mehrotra et al. 2014; Sudarshan and Bhattacharya 2009) along with a decline of the agricultural workforce due to mechanization (Himanshu 2011; Mehrotra et al. 2014) and rising cost of cultivation (Narayanamoorthy 2013). A review of past studies conducted in various countries of the world suggests that the overall LFPR of countries tends to fall over the initial period of economic development to reach a minimum and then starts rising as the country develops further (see Durand 2015; Bardhan 1979; Mincer 1985; Psacharopoulos and Tzannatos 1989; Schultz 1990). In other words, the LFPR shows a U-shape as countries progress from low to higher levels of economic development. It happens because, over the initial phase of structural transformation as women move out1 of agriculture and allied sectors because of a relatively stronger negative income effect than the positive substitution effect of the rising real wage, the overall female LFPR starts falling; however, it moves upward again as women acquire appropriate skills and return to the LF at an advanced stage of development to participate in non-agricultural jobs (Fatima and Sultana 2009; Klasen and Pieters 2015; Luci, 2009; Mehrotra and Parida 2017). Therefore, it was believed that in India the overall LFPR could also start increasing as educated youth (girls and boys) would begin to join the labour force (LF). But in contrast, it continued to decline post 2011–2012, despite the increasing size of both the educated LF and working age population (see Kannan and Raveendran 2019; Kapoor 2015; Kapoor and Krishnapriya 2019; Mehrotra and Parida 2019). This is perhaps because of a combination of factors: poor education level; poor competencies because of low-quality skilling; and also high level of skill mismatches in the labour market (see Ajithkumar 2016; Agrawal and Agrawal 2017; Hajela 2012; NSDC 2013; Mitra 2018; Mehrotra et al. 2015; Singh et al. 2020; World Bank 2008). This skill issue has implications for the phenomenon of rising educated youth unemployment (see Ahmed 2016; Mehrotra and Parida 2019). From the above review of studies, it could be hypothesized that both a set of supply- and demandside factors were driving the structural transformation in employment. Hence, this paper tries: to identify these factors, by examining the recent trends and pattern of LF participation, sectoral employment patterns and the nature and structure of current open unemployment, and to suggest timely measures to resume this structural transformation over the long run. This paper is organized as follows: Section two provides the sources of data and outlines the regression estimation methods of our study. Section three, which presents the paper’s findings, is organized into three subsections. The first subsection not only explains the broad trends and patterns of LF participation, but also highlights the situation of rising open unemployment and discouraged labour force. Subsection two analyses the existing demand-side crisis by explaining the sectoral employment trends. Subsection three goes deeper and explores the supply-side 1 Since women are active participants in the LF through their roles as contributing family workers on family farms for which they do not get any monetary remuneration. 13 ISLE The Indian Journal of Labour Economics factors that determine the labour force participation behaviour of both men and women in India. Section four concludes the paper along with a discussion on policy options. 2 Data and methods This paper is based on the unit-level data of National Sample Survey (NSS), collected through both “Employment and Unemployment Survey (EUS)” and Periodic Labour Force Surveys (PLFS2) stretching the period 2004–2005 to 2018–2019. The employment and unemployment status of individuals is obtained by considering their UPSS status. The sectoral employment is estimated based on the National Industrial Classification (NIC) 1998 and 2008 codes after due concordance. To obtain absolute numbers, the NSS estimates are adjusted3 to the projected census population. To explore the individual- and household-level factors that determine the LF participation (LFP) decision of men and women, at the micro-level, we have estimated their LF participation functions. Since the dependent variable in both the cases is dichotomous (which assumes value 1 for LF participation and zero otherwise) and we have a very large sample, probit regression is an appropriate choice. Both simple and instrumental variable (IV) probit regressions models are used. While the simple probit is based on the assumption that the latent variable LFP (is positive for labour force participation and zero otherwise) depends on exogenously determined explanatory variables, the IV-probit regression provides robust estimates in the presence of potential endogenous regressors. In this case, we expect that monthly per capita (...truncated)