Overview

1.1 Motivation

The employability question is an urgent one because of a combination of demographic, macroeconomic and technological factors, and it has only been intensified by the 2020 Coronavirus pandemic. Demographic, because the ratio of India’s working-age population to non-working-age population is expected to peak in the 2030s (Mehrotra, 2014; Sharma, 2016). A large number of youth are expected to join the labour force, and if we do not productively employ them (NCAER, 2018), there is a real danger that the demographic opportunity may turn into a disaster instead of a dividend. Akin to the rest of the world (Hooley et al., 2013), India has experienced macroeconomic uncertainty since the recession of 2008 (Subramanian & Felman, 2019). Plus, jobless growth had characterised India’s growth story between 1977–1978 and 2011–2012 (Basu & Das, 2016). Mehrotra and Parida (2019) further document the falling employment between 2011–2012 and 2017–2018. The pandemic is forecast to have a negative impact on the Indian economic growth and employment (NCAER, 2020).

The fourth industrial revolution with its emphasis on technology has laid importance on higher-end cognitive skills linked to socio-emotional skills. ² India’s current rote-skills system poorly equips Indian graduates (Blom & Saeki, 2011) with the skills needed for complex problem-solving, critical thinking, creativity, people management, etc., necessary for this revolution (Gray, 2016). The Indian Skills Report 2020 reports that the employability of Indian graduates and postgraduates went up from 33.95% in 2014 to 47.4% in 2019 before declining to 46.2% in 2020 (Wheebox et al., 2020). ³ For 3 years between 2018 and 2020, this number has averaged around 46%.

In a departure from the global historical trend, India has transformed from an agricultural economy to a services economy, bypassing the interim industrial phase. In India, the growth of its services sector has outpaced the growth of the agriculture and industry sectors since the mid-1970s, though agricultural sector continues to be the largest employer and services the smallest. As Green (2013) succinctly said that mismatches lead to lower macroeconomic outcomes in terms of employment, productivity, etc.

1.2 What are Skills? What is the Skills Market?

We used the productive, expandable and social (PES) concept of skills defined by Green (2013, p. 10), that is:

Productive: using skills at work are productive of value;

Expandable: skills can be enhanced by training and development and;

Social: skills are socially determined.

While skills can be defined in various ways with different degrees of intensity, we use the NCAER (2018) definition of employability, as equal combinations of basic and higher-end cognitive skills, technical and vocational skills, and socio-behavioural skills. ⁴

Green (2013) identifies two markets for skills. The first one is where skills are formed with skilled workers, employers and external providers interacting in the skills formation market. The skilled workers are on the demand side, and the external providers and employers are on the supply side. The Commission of the European Communities (2000) (via Pilz & Wilmshöfer, 2015) has defined three types of learning, which are especially relevant in the context of a developing country: formal learning, non-formal learning and informal learning. ⁵

The second market is where skilled labour is deployed, and, here, employers demand skills, and skilled workers supply them. Generally, the demand for labour or skills is an indirect demand, that is, skills are used to produce a good or service.

In India, national comparable data on skills over a period of time are relatively limited but expanding. Three main sources are used by the various papers in this Special Issue. One is the National Statistical Office (the erstwhile National Sample Survey Office), which collects data on the education of households (GoI, Ministry of Statistics and Programme Implementation website). Education in India is trifurcated into general education (schools and colleges), technical education (engineering and other technical education) and vocational education (formal and other than formal means). These measures of education are used to develop various skill measures. The National Council of Education Research and Training conducts a National Achievement Survey Test for school students, studying in classes III, V and VII/VIII, to assess learning outcomes mainly in language and mathematics (GoI, Ministry of Education website). In addition, students in Class V are tested for environmental studies, and students in Class VIII are tested for science and social science. Private organisations like the Annual Status of Education Report (ASER) tests reading and numeracy skills of rural students in classes III, V and VIII (ASER Website, 2020), and tests the cognitive skills of chosen age groups.

The next section examines in detail the six papers in this volume. The last section presents the broad conclusions from the papers.

2.1 Manufacturing Versus Services

The first question we ask is whether a service-led economy is sustainable for India in the long run. It has also been a matter of policy debate (GoI, 2015, 2017; IMF, 2018), whether India should choose the path of manufacturing or services. This question is important because earlier empirical evidence indicates that the impact of structural change on the Indian economy has been limited (Aggarwal, 2014; Amirapu & Subramanian, 2015).

Soumya Bhadury, Abhinav Narayanan and Bhanu Pratap in their paper titled ‘Structural Transformation of Jobs from Manufacturing to Services: Will it Work for India?’ carry forward the work on structural change. Using the KLEMS (Capital Labour Energy Materials Services model) India data set from 1991 to 2016–2017, the authors examine the dynamics of labour productivity associated with (a) a steady shift in the composition of the workforce towards the services sector and (b) investment in human capital proxied by changes in the labour quality index. The authors not only look at inter-sector changes but also at intra-sector changes. They systematically note the divergent trends between manufacturing and services. For manufacturing, they find that there is a positive correlation between labour quality and growth in employment, but there is a negative correlation for the services sector. However, labour quality is not correlated with productivity growth in the manufacturing sector, but it is positively correlated in the case of the services sector.

Using quantile regression methods, the authors find a gap in association between labour quality and labour productivity for both manufacturing and service firms. The gap is not uniform across sectors. The results for both sectors indicate the existence of frictions in the labour market, arising mostly out of acute shortages of skills in the educated (above higher secondary) and semi-educated (above primary to higher secondary) labour force, especially at the higher ends of the distributions. Labour quality is measured by general educational attainment in this article.

The association between labour quality and productivity is statistically insignificant for all quintiles of worker distribution. The distribution of casual workers does not matter for the association between quality and productivity for manufacturing. On the other hand, for services, the authors find that the association of quality with productivity is positive and significant for all quartiles of worker distribution; however, the degree of association decreases for industries with a greater fraction of casual workers.

The authors’ findings suggest that an increasing prevalence of informal job contracts in services could potentially weaken the positive relationship between labour quality and labour productivity. Additionally, they observe that India’s labour force seems to be rapidly shifting towards services sub-sectors with lower productivity. This is likely to obstruct any potential gains in productivity associated with an optimal relocation of factors of production. In sum, the empirical evidence from the paper suggests that a service-led economy may not be sustainable in India in the long-run unless and until supported by investments in improvement of labour quality. The policy implications are straightforward: what is needed is a combination of improving labour quality, that is, skills and providing social safety nets for workers to mitigate casualisation. It is not just about improving employment but also about improving the quality of employment in both sectors.

With the urgency of improving labour quality in the economy re-emphasised, the next set of papers look at what skills are needed for this.

2.2 Socio-emotional Skills

There is a very large empirical literature based on the ASER data, which has looked at the weak foundational skills of the Indian schooling system. ⁶ However, employability is not just about cognitive skills but also about socio-emotional skills (NCAER, 2018). The examination of these set of skills, so vital for work, has been relatively limited in the Indian literature. Therefore, we explore whether it is possible to acquire socio-emotional skills in the context of general education.

Renu Gupta in her paper titled ‘The Role of Pedagogy in Developing Life Skills’ explores the recent concerns expressed by the Indian industry about the ‘employability’ of school and university graduates. This article examines the role of pedagogy in developing life skills (or twenty-first-century skills) and how life skills can be incorporated in the school/university curriculum. Recent frameworks have incorporated life skills within the school curriculum by stressing the importance of inquiry and collaborative work through all subjects taught in school. This paper finds a similar emphasis in the National Curriculum Framework (NCF) in India, but classroom observations and textbook analyses show that learning objectives in schools are frequently incorrect or misaligned with the NCF vision. This is observed in a range of classes, from initial literacy to reading in the higher classes and mathematics. The author finds remarkable consistency in teaching practices: that (a) teacher talk dominates, and that (b) teachers rely on textbooks.

The paper briefly touches on how the beliefs of teachers affect their classroom practices. Teaching may be adversely affected both by teachers’ lack of knowledge of the subject and by how they transmit the information to learners in an age-appropriate manner. However, these are not the only constraints. Teachers may know their subject and know how to teach, but they also have to demonstrate that the job is done, in the sense that the curriculum is completed in time. Parental pressure also affects instruction in India.

The author recommends that attention be paid to the professionalisation of teachers, as only then can students acquire skills that are relevant for the twenty-first century, which is what employers want. Another recommendation is the use of technology in classrooms, but this would require teachers to use it ‘smartly’.

2.3 Foundational Digital Skills

This takes us to our next paper on foundational digital skills. Digital literacy to functional digital skills is a foundational cognitive skill, a key component of twenty-first century skills, and a basic requirement for employability. The NCF (2005) and the National Education Policy 2020 have both emphasised information and communications technology (ICT) education in schools (CBSE Website; GoI, 2020). This can range from a basic ability to use of digital devices and tools in daily life to programming, coding and computational thinking. Bornali Bhandari, Charu Jain and Ajaya K Sahu in their paper, ‘Are Secondary Schools Imparting Digital Skills? An Empirical Assessment’, explore acquiring of foundational digital skills in secondary schools. This is another arena that has not been rigorously examined in the empirical literature. In general, assessments of learning outcomes and skills data are limited for primary school students. However, secondary school is important because it is a stepping stone for the world of work and acts as a bridge between childhood and adulthood.

The objective in this paper is to document the employability of currently enrolled secondary and senior secondary students aged 14–21 years, specifically their functional digital skills in 2014 and 2017–2018. The NSSO (National Sample Survey Office) data enable us to see whether students possess functional digital skills, that is, basic ICT skills, but the quality of this or the depth of knowledge cannot be assessed. The summary statistics suggest that 42.7% of students knew how to operate a computer, and 46.8% knew how to browse the Internet in 2017–2018. However, despite the substantial improvements between 2014 (NSS Round 71; GoI, 2016) and 2017–2018 (NSS Round 75; GoI, 2019), there were differences across spatial (rural–urban, state), gender, household groups (expenditure quintiles, social group, occupational groups) and household ownership/access of digital devices like computers and Internet at home.

This article attempts to understand the factors that would explain the likelihood of urban secondary and senior secondary students acquiring functional digital skills. Access to devices is a critical constraint for students acquiring functional digital skills.

In addition to households’ access to digital infrastructure, the impact of school digital infrastructure is assessed. Specifically, the threshold impact assesses the odds that a student would have digital skills if they belonged to a state where the share of schools with functional computers was higher than the national median. The evidence suggests that there is a threshold effect. Plus, students in states with schools having functional computers above the national median overcome the barriers of access to lack of ownership of digital devices at home.

The policy implications are that state governments should set a target for equipping all government secondary and senior secondary schools with computers, and mandating these for all private schools. There should be an improvement in school digital infrastructure and a revised curriculum to address employability gaps. Further, the NSSO should include more probing questions to assess the access and quality of ICT skills.

2.4 Vocational Education

While the need for technical and vocational education was high until the 1960s, little attention was paid to its quality (Sen, 1989). Due to a fall in demand, it saw a further slide in quality from the mid-1960s onwards. The Eleventh Five Year Plan re-emphasised vocational education in order to meet the challenge of employability (GoI, 2008). It envisaged mass-scale skill development in different trades through special training modules delivered through Industrial Training Institutes, polytechnics, vocational schools, etc., focusing on underprivileged youth. It suggested bringing about a paradigm change through a National Skill Development Mission, which was launched on 15 July 2015.

In the past decade, India has developed a fairly complex ecosystem with institutions, curriculum, schemes, courses, etc: a whole new ministry called the Ministry of Skill Development and Entrepreneurship (MSDE) was created to encourage vocational education; a National Skill Policy was drafted twice, once in 2009 and then in 2015; the Apprentice Act was reformed; linkages between vocational education and general schooling have been established; and vocational education is being encouraged in classes 9–12.

Matthias Pilz and Imke Julia Regel in their paper titled ‘Vocational Education and Training in India: Prospects and Challenges from an Outside Perspective’ discuss the main pillars of the Indian vocational education and training (VET) system and addresses policies and initiatives to restructure and upgrade formal VET in India.

The authors point out that there exists a clear distinction between VET in India. Vocational education is part of the higher education system that falls under the Ministry of Education (the erstwhile Ministry of Human Resource and Development), and it includes vocational courses at the secondary or upper-secondary schools as well as to colleges offering vocational diplomas.

In contrast, vocational training is the responsibility of the MSDE and various authorities and agencies at the national and state levels. The states manage the everyday operations of the vocational institutions and are responsible for the implementation of quality improvement and assurance schemes, among other things. The complexity of educational governance in India is increased by the large number of other ministries involved in vocational training programmes and initiatives in one way or the other, sometimes ineffectively in parallel schemes. There are also several apprenticeship schemes, the success of which is still relatively limited.

Technical education is distinguished from vocational education and comes under the Ministry of Education; there is even a line called technical vocational education, offering diplomas in engineering.

The challenge with this complex Technical and Vocational Education and Training (TVET) system is that it continues to suffer from poor quality. Vocational education is not aspirational partly because of its association with manual labour—in turn linked with caste—and partly because of its past performance. The integration of the informal sector and the formalisation of informal learning are major issues in India. It is necessary to recognise and upgrade the skills of people working in their respective sectors, which the Recognition of Prior Learning—a programme of the MSDE—tries to address. Further, the authors point out that the focus on short-term schemes and unclear structure of the framework for skill development is problematic. As in general education, an outdated and irrelevant curriculum (Pritchett & Beaty, 2012) coupled with poor quality of teachers continues to pose problems in vocational education. Further, the focus of TVET continues to be on theory versus hands-on proactive approach. While recent programmes have tried to address these issues, India still has a long way to go in turning around its vocational training programmes.

2.5 Technical Education, Vocational Training and Performance of Firms

Typically, the empirical literature clubs technical and vocational education to analyse them. However, an earlier paper in this issue (by Matthias Pilz and Imke Julia Regel) pointed out important differences between the two areas, mainly that they are managed by two different ministries leading to different certifications. Technical and vocational education should not be clubbed together in empirical analyses as they lead to different outcomes. For the first time, Seema Sangita’s paper, titled ‘Technical Education, Vocational Training and Performance of Firms’, distinguishes between academic, vocational and technical education, to analyse their differential impact on firms’ performance.

Her paper analyses the impact of technical education and vocational training on firms in the organised and unorganised manufacturing sector in India. For organised manufacturing, data from both 2012 and 2014 are used, while for unorganised manufacturing, 2015 data are used. The analysis analyses workers with a technical education, who have a degree or diploma at the below- or above-graduate levels in engineering, as well as those with vocational training in electrical and electronic engineering and the computer sciences. Ordinary least squares (OLS) and instrumental variable regression analysis are employed based on the Cobb–Douglas production function, enhanced to incorporate education and training.

The empirical analysis shows that vocational training and higher education in the general fields and in the technical fields lead to better performance by firms in both the organised and unorganised manufacturing sectors. These outcomes remain robust to the use of instrumental variable approach to address problems of endogeneity in the organised manufacturing sector. However, in the case of unorganised manufacturing, vocational training clearly retains its relevance. The empirical evidence also shows that higher education in general subjects seems to consistently benefit the organised manufacturing sector, while some levels of school education appear to benefit the unorganised sector.

Despite the poor quality of both technical and vocational education (as mentioned in the paper by Pilz and Regel), vocational training still has a positive impact on productivity of firms in the unorganised sector. An increase in the share of workforce with vocational training by 0.1% could increase the gross value added of firms in the organised sector by up to 6%–10%; the skills-starved unorganised sector experiences a much higher impact on firm performance—almost 30%, which is three to five times greater than the organised sector.

This suggests the underlying importance of vocational education as a major tool to tackle the issue of employability of the workforce, which could be absorbed in India’s largely unorganised Indian manufacturing sector. Of course, the analysis was conducted in the digital fields of specialisation where demand is very high.

2.6 Vocational Education and Female Labour Force Participation

The last paper in this volume also addresses the issue of employability—focusing on women. As mentioned earlier, India’s female labour force participation rate (FLFPR) is one of the lowest in the world, barely 21% for women aged 15 and above (as of 2019). The global average was 47.1% (World Bank Website, 2020). The literature has found that sticky cultural norms can explain the relatively low FLFPR in India (Gulati & Spencer, 2020). ‘Indian FLPR is lower than that of other countries with similar levels of educational attainment of women’ (Gulati & Spencer, 2020). In this context, the empirical result in this paper that vocational training can promote the FLFPR is a key contribution to both literature and policy. The objective of the paper ‘Does Vocational Training Promote Female Labour Force Participation? An Analysis for India’, by Indrajit Bairagya, Tulika Bhattacharya and Pragati Tiwari is to assess the impact of formal and informal vocational training on the FLFPR in India, based on the Periodic Labour Force Survey data for 2017–2018 (NSSO data), employing a trivariate probit model. The results show that participation in both formal and informal vocational training have a positive and statistically significant impact on female labour force participation across all specifications of the regression models, thus showing the robustness of the relationship. Most importantly, provision of vocational training helps break the traditional U-shaped relationship between female labour force participation and educational levels.