Figure 5 - uploaded by Francis Kuriakose
Content may be subject to copyright.
Source publication
Automation impacts wage levels at the micro-level and the structure of employment at the macro-level. Job polarisation is defined as the automation of ‘middle-skilled’ jobs that require routine cognitive and manual applications, whilst high- and low-skilled occupations are preserved. This paper examines the nature of job polarisation in India durin...
Citations
... table 1 indicates, the recovery of economic growth in terms of gross domestic product per capita to the levels of the pre-pandemic period can be achieved only by the financial year 2023, if the trend of partial unlock continues without further crisis in India. Coming on the heels of job polarization, the loss of middle-skilled work, automation and other digital transformations, the outlook for India's workers remain bleak(Kuriakose & Iyer, 2020). The existing employment opportunities in the informal economy continues to be precarious. ...
... 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 (R€ omgens et al., 2020). ...
... When analysing the top ten universities in terms of the number of graduates employed the strongest links related to the employer reputation, as opposed to overall reputation, links into industry, and emphasis on career development. An argument emerges that says governments should spend their funds on those universities that provide relevant skills (Blustein et al., 2020;Kuriakose and Iyer, 2020;R€ omgens et al., 2020;Hewitt, 2018) through close collaboration with industry and a career development focus (Lowden et al., 2011;Zacharewicz et al., 2019). We conclude that employability is not only driven by personal and cultural drivers but is influenced by institutional factors as well. ...
Purpose
This paper explores the institutional and economic drivers of employability, as existing literature focuses on the individual and skills aspects, of employability. Tertiary institutions, possessing a strong academic reputation and standing amongst potential employers, will achieve high graduate employability, however when measured, this is not the case.
Design/methodology/approach
This exploratory study builds on Santos' career boundary theory, recognising organisational boundaries; those related to the labour market, personal-aspects and finally, cultural boundaries (Santos, 2020). 37 Universities that provided their employability rate, within 12 months of graduation for 2020, are analysed. The Quacquarelli Symonds (QS) Ranking, measures drivers in terms of institutional reputation through survey responses, and partnerships with employers via research and placement data.
Findings
The regression explained 19% of the variation between the number of graduates being employed and the institutional and economic drivers. Universities in the same economic context, do not have the same number of employed students. Equally, those universities with the most favourable academic reputation, do not have the most employed student rate.
Research limitations/implications
Only 37 universities provided all their employability data, thus, research with a larger sample will have to be conducted, but equally more needs to be done to establish why the smaller universities are unable to submit all the required data.
Originality/value
An exploratory understanding of the institutional and economic drivers of employability, is provided.
... Job polarisation indicates a drop in demand for mid-skill jobs, with a simultaneous increase in demand for high-and low-skill jobs, which results in the 'hollowing out' of mid-skill jobs (Jaimovich & Siu, 2018). Job polarisation linked to automation has been empirically observed in the USA (Acemoglu and Autor, 2011;Jaimovich and Siu, 2018); UK, Sweden, and other European Countries (Petropoulos, 2018), and even in developing countries such as Brazil and Colombia (Kuriakose & Iyer, 2018). ...
... On the other hand, automation augments the functioning of nonroutine jobs that comprise low-and high-skill jobs. Source: (Kuriakose & Iyer, 2018) The twin effect of automation-that is, substituting routine tasks while simultaneously augmenting non-routine tasks-results in the hollowing out of mid-skill jobs and leads to job polarisation (Acemoglu & Autor, 2011). Sarkar (2018) uses National Sample Survey Office (NSSO) employment data covering 270 occupations in urban India from 1983-84 to 2011-12 to arrive at key observations. ...
... In India, nature of job polarisation observed has been different-routine jobs have managed to persist at a higher level than expected (Kuriakose and Iyer, 2018;Vashisht and Dubey, 2018). Kuriakose and Iyer (2018) argue that routine task-intensive jobs have persisted in India for two reasons, neither of which are directly related to automation or technology. ...
This paper traces the conceptual evolution of job polarisation owing to automation and investigates it in the Indian context. India has been plagued by jobless growth and has witnessed jobless recoveries after recession, and this proclivity of Indian industries to substitute capital for labour raises social and public policy concerns. The ongoing COVID-19 pandemic has only further polarised jobs by pushing salaried employees into informal or agriculture-related jobs, and although numerous sectors have roughly regained their pre-pandemic economic position, workers who lost their jobs have not re-entered the workforce, signalling a jobless recovery. Additionally, the pandemic is set to catalyse automation due to multifarious reasons. Taken together, if deliberate attempts are not made to facilitate labour force participation and devise social policy, millions of Indians who became jobless due to the pandemic may find that their jobs have been automated.
... 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. ...
With structural changes in production coupled with technological progress, there have been shifts in modes of production and patterns of employment, with important consequences on task composition of occupations. This paper has utilized different rounds of Labour Force Survey data of Bangladesh and combined it with occupation network data of the United States along with its country-specific database and analysed the role of such factors on labour market outcomes. Our analysis shows a fall in the average routine intensity of tasks with no evidence of job polarization. We find a decline in earnings inequality where the decomposition analysis shows that earnings structure effect rather than characteristics effect plays a key role, with routine-task intensity of jobs and education explaining the majority of differences in earnings. Our analysis suggests that investing in education should be the highest priority, with greater emphasis on skill-biased training programmes, particularly those involving cognitive skill.
... Beyond the current pandemic, any long-term view of labor has to take into account four main effects on work situated in structures of neoliberal globalization, if sustainable development is the main objective (Rosenberg, 2010). The first of these effects is job elimination through processes such as job-polarization that replaces middle-skilled routine worker with machines (Kuriakose & Iyer, 2020). The second effect is job substitution where labor intensive work is reconstituted into capital-intensive work due to disruptive structural changes such as platformization (Kuriakose & Iyer, 2021). ...
This article examines how COVID-19 impacts migrant workers and what can be done for their equitable transition after the pandemic is subdued. The immediate policy response to the pandemic was closing of national borders that resulted in a state of emergency on a global scale. The need for continuous and safe passage of goods, services, and workers was acknowledged by laws and policies that were an 'exception' to the rule, and deemed 'essential'. This approach resulted in five distinct types of impact on the migrant worker in the spheres of employment, health, movement, social protection, and opportunities. This study uses the framework of 'just' transition from sustainability discourse to imagine a labor-centered long-term policy for the migrant worker.
Technology is reshaping the occupational landscape. Technological adoption has accelerated in the last few years with the increasing use of robots and automation. However, only a few pieces of literature are available that discuss how robotization and automation would change the world of work. In India, hardly any empirical research discusses the risk of robots and automation in occupations. The article estimates the industrial robot density in the manufacturing sector in India for the period 2011–2012 to 2020–2021. It also observes the workforce distribution changes by industry and occupation for the same period. Further combining the O*Net data with the Employment and Unemployment Survey data and Periodic Labour Force Survey for 2011–2012 and 2020–2021, respectively, constructs the composite index of risk of automation (RoA). The RoA index scores reveal that between 2011–2012 and 2020–2021, the automation risk in all jobs in India has increased and the risk intensity varies across occupations and industries.
Caste-based wage discrimination can counteract the development process. This article uses two distinct estimation methods to examine earning gaps between forward castes also referred to as ‘general category’ workers and traditionally disadvantaged or ‘backward caste’ workers in the Indian labour market. First, we interpret the inequality indicator of the Theil index and decompose Theil to show within and between-group inequalities. Second, a Threefold Oaxaca Decomposition is employed to break earnings differentials into components of endowment, coefficient and interaction. Earning gaps are examined separately in urban and rural divisions. Within-group inequalities are found larger than between groups across variables, with a higher overall inequality for forward castes. Wage differentials are substantially greater for urban areas and favour FC. A high endowment implies pre-market discrimination in human capital investments such as nutrition and education. Policymakers should first invest in basic quality education and simultaneously expand postgraduate diploma opportunities, subsequently increasing participation in the labour force for traditionally disadvantaged in disciplines and occupations where forward castes have long dominated.
JEL Codes: J01, J08, J15, J30, J31, J71
In this two-part series, we discuss problems specific to digital platforms in India and the type of regulatory framework required to ensure labor rights. In the first part, we flag three main structural problems Indian platform workers face. The second part explores the role of institutions in creating a regulatory framework for digital platforms so that an expanded set of worker rights, including data rights, are available to platform workers.
