Job Polarisation in India: Structural Causes and Policy Implications

Abstract

Automation impacts employment and wage levels at the micro-level, and the structure of employment-shift at the macro-level. Job polarisation is defined as the automation of 'middleskill' jobs that require routine cognitive and manual applications while high and low-skill occupations are preserved. This paper examines the nature of job polarisation in India during the period 1983-2012 when Indian manufacturing was being gradually automated. The research uses disaggregated data from National Sample Survey Office and examines supply-side factors such as nature of employment growth in manufacture and presence of educated labour force which have not been adequately analysed before. The study has three observations. First, only the increased demand for high-skilled workers in the formal sector is due to skill-bias of technology conforming to theoretical expectation. Second, the transition of agricultural labourers has been to low-skill manufacturing sectors such as construction and textiles signalling distress in traditional manufacturing to provide employment. Third, oversupply of secondary and tertiary educated labour force has squeezed out middle-skilled workers from middle-skill jobs to relatively low-skill manufacturing and service occupations, explaining the persistence of routine occupations. The study concludes that increased demand for high and low-skill jobs has co-existed with the persistence of middle-skill jobs in India.

The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment

“…The expression for V N (t) has an analogous interpretation, and is greater when the gap between the rental rate of capital and the cost of producing new tasks with labor ( w(τ )/γ(n(t )) ) is larger. 30 An equilibrium with endogenous technology is given by paths { K(t ), N(t ), I(t )} for capital and technology (starting from initial values…”

“…In this case, the present discounted values from innovation are negative. 30 There is an important difference between the value functions in (25) and (26) and those in models of directed technological change building on factor-augmenting technologies (such as in Acemoglu 1998Acemoglu , 2002. In the latter case, the direction of technological change is determined by the interplay of a market size effect favoring the more abundant factor and a price effect favoring the cheaper factor.…”

“…For reasons that will become evident, we modify the innovation possibilities frontier to (30) I ˙ (t ) = κ I ι(n(t )) S I (t ), and…”

Earnings inequality and the changing nature of work: Evidence from Labour Force Survey data of Bangladesh

“…In the context of regional experiences of structural transformation and RTI studies on India, Kuriakose and Iyer (2020) reflected that over-supply of the secondary-and tertiary-educated labour force has resulted in mid-skilled workers moving from mid-skilled jobs to relatively lowskilled manufacturing and service occupations in India, causing routine occupations to persist. This phenomenon has also led to job polarization and consequent wage polarization towards high-and low-skilled occupations at the expense of mid-skilled occupations.…”

Employability tautological trauma

“…One view, is that the job market will polarise, known as the displacement effect (Kuriakose and Iyer, 2020), into technical jobs at the one end and menial jobs at the other end with artificial intelligence replacing many routine tasks. It is therefore imperative that students acquire relevant skills, even for those jobs that do not currently exist (Römgens et al , 2020).…”