Where are the jobs? Estimating skill-based employment linkages across sectors for the Indian economy: An input-output analysis

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Where Are the Jobs? Estimating Skill-based Employment Linkages across Sectors for the Indian Economy: An Input-Output Analysis Tulika Bhattacharya , Bornali Bhandari , Indrajit Bairagya PII: DOI: Reference:

S0954-349X(19)30139-0 https://doi.org/10.1016/j.strueco.2020.03.003 STRECO 913

To appear in:

Structural Change and Economic Dynamics

Received date: Revised date: Accepted date:

20 April 2019 3 February 2020 15 March 2020

Please cite this article as: Tulika Bhattacharya , Bornali Bhandari , Indrajit Bairagya , Where Are the Jobs? Estimating Skill-based Employment Linkages across Sectors for the Indian Economy: An Input-Output Analysis, Structural Change and Economic Dynamics (2020), doi: https://doi.org/10.1016/j.strueco.2020.03.003

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Highlights 

The paper estimates the direct as well as indirect employment creation by Indian economic sectors for 2004-05 and 2011-12, thus examining the persisting debate on the transformation of jobs in the Indian context across sectors.



There is a uniform shift in employment from low-skilled occupations to high-skilled occupations across the Indian economy as a whole.



However, this shift in employment is not uniform across all the sectors at the disaggregated level. For agriculture, manufacturing and non-manufacturing, skill sets have been upgraded mostly with regard to low-medium skilled employment overtime, whereas, for services, it is the medium-high and high skilled employment that is mostly created.



The linkage analysis clearly brings out the fact that a number of sectors like ‗agriculture‘, ‗wood products‘, ‗hotels & restaurants‘ are able to create a wide spectrum of skilled employment, both directly and indirectly, while some sectors are creating a specific type of employment.



The paper indicates where the jobs are and can be generated and at what level of skills, thus establishing a baseline for broadening our measures of skilling.

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Where Are the Jobs? Estimating Skill-based Employment Linkages across Sectors for the Indian Economy: An Input-Output Analysis

Tulika Bhattacharya Assistant Professor, Department of Economics, St. Joseph‘s College (Autonomous), Bangalore. Email: [email protected]

Bornali Bhandari Senior Fellow, National Council of Applied Economic Research, New Delhi. Email: [email protected] & Indrajit Bairagya Assistant Professor, Centre for Human Resource Development, Institute for Social and Economic Change, Bangalore. Email: [email protected]

Corresponding Author Tulika Bhattacharya Assistant Professor, Department of Economics, St. Joseph‘s College (Autonomous), Bangalore. Email: [email protected]

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Where Are the Jobs? Estimating Skill-based Employment Linkages across Sectors for the Indian Economy: An Input-Output Analysis

Abstract The objective of this paper is to identify the sectors of the Indian economy that are able to generate different types of skilled employment (both directly and indirectly) through an estimation of their employment linkage effects with respect to varying levels of skills, using the Input–Output technique. The contribution of this paper lies in its redefining ‘skill’ by combining three types of education - general, vocational and technical – for arriving at four types of skilled employment categories—low, low-medium, medium-high and high skilled. The paper incorporates these four types of skilled employment into an Input–Output framework, using the World Input–Output Database for estimating the forward and backward employment linkages with respect to four different skill types for India for 2004-05 and 2011-12. The estimation of these employment linkage effects is critical for identifying the key employment-generating sectors of the Indian economy characterized by varying levels of skill. Moreover, a comparison of these linkage effects for two time-periods at varying levels of skill across sectors helps understand changing nature of employment structure over time, and how it varies across sectors. Keywords: Skills; Employment linkages; Input-Output. JEL Classification: C67, J21, J23, J24.

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1. Introduction Going by Ministry of Finance‘s (2016) observation ―to exploit its demographic dividend, India must create millions of ―good‖—safe, productive, well-paying—jobs‖, what it entails is that job creation should also be well-supplemented by skilling and re-skilling techniques for the workforce entering the job market, if India were to fully reap the benefits of its demographic dividend. Keeping in view this aspect, the Government of India introduced the Skilling India initiative with the primary objective of imparting varied skills to the workforce in the country through the introduction of vocational training facilities and certificate courses, inter alia. Thus, the Skilling India initiative is largely focussed on tackling the issue from the supply side, that is, by addressing the question, ―What skilling facilities are available for making the workforce in India skilled?‖ However, it is also important to look at the skilling issue from the demand side, that is, to answer the following questions: Where are the jobs? Which are the sectors creating a demand for ‗skilled workforce‘? And at what level of skills has this demand been created? Therefore, apart from focussing on imparting different types of skill training, it is also equally important to identify the sectors wherein there is demand for different types of skilled employment. As a first step towards addressing this issue, the Ministry of Skill Development and Entrepreneurship [(MSDE) (2015)], in its national policy, identified 24 priority sectors, and subsequently projected the potential demand for skilled manpower in these sectors for the year 2022. However, the projections are completely based on the demand for workers directly from within the sectors, whereas, in an interdependent economy characterised by production and consumption linkages across sectors, it is important to identify both direct and indirect employment creation. More precisely, if the output of sector Y goes up, by implication, employment in the sector also enhances through direct effect. In addition, employment in the sectors that supply inputs to Y would also go up due to higher input demands; and the same process of employment generation, in turn, carries on for the sectors that supply inputs to the first set of input-supplying sectors and so on (Bhattacharya and Rajeev, 2014). Similar effects can be seen for sectors that demand the output of sector Y as their input and, therefore, the final picture needs to be assessed in view of all the linkage effects. In fact, the importance of employment linkages have also been documented by the National Skills Development Corporation (NSDC) and the IT-ITeS Sector Skills Council NASSCOM (2013) report, which specifically mentions that the Indian Information Technology (IT) industry created three million jobs directly and 9.5 million jobs indirectly in 2012–13, implying that the indirect effect may even have a more influence than 4

the direct one. However, the report does not distinguish between skills levels measured on any rigorous basis (economic definitions), but it does differentiate between the number of years of experience: entry (0–2 years), middle (2–12 years) and leaders (12+ years). The issue of estimating direct as well as indirect employment generation has gained attention over time among researchers. For instance, a study by Sarma and Ram (1989) estimated employment, income and output linkages for India‘s manufacturing industries, using Input-Output (I-O) table for the year 1979-80. To state some recent developments also in this field, Bhattacharya and Rajeev (2014) have used 2003-04 and 2007-08 I-O tables while identifying those sectors of the Indian economy with a potential to generate both direct and indirect employment. Not only with respect to India, but also estimation of direct and indirect employment through linkages is also done by scholars like Gorg and Ruane in 2000, incorporating employment aspect into an I-O framework for the Irish economy as part of testing the hypothesis of whether firm size matters for having high employment linkages or not. Similarly, using 199697 I-O tables, Valadkhani (2003) identified high employment generating industries for the Australian economy. Besides, using the 2007 I-O table, along with employment data for China for the years 2002-2009, Bin (2010) examined how direct as well as indirect employment creation influenced output as well as the export performance of the Chinese economy. Most importantly, the study concludes that the non-linear formulation of employment within an I-O framework reflects the actual economic situation of China in a better way in addition to highlighting the need for appropriate policies for sustaining the growth of the Chinese economy. Thus, an integration of a rigorous analysis of direct and indirect employment creation coupled with appropriate policies may help all stakeholders plan better for the future. However, it is important to mention that all the above papers have discussed the issue of employment creation (both direct and indirect) considering homogeneous sets of skill among workers. Given the heterogeneity of skill sets characterising the workforce, it becomes necessary to estimate direct and indirect employment generation of Indian economic sectors with respect to varying skill types. The present paper, which stems from this motivation, has two parts. First, the paper defines a skilled worker based on different forms of educational attainment, viz., through general education, along with other forms of technical and vocational education. This is a major point of departure from the existing studies that mostly consider only general education while

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defining a skilled worker (Anderson et. al., 2001; Pertold-Gebicka, 2010; Amirapu and Subramanian, 2015). Second, the paper attempts to identify the sectors that can create different types of employment, both within itself as well as in other sectors (direct and indirect) with varying levels of skill. For this purpose, the paper uses the Input–Output framework for estimating their employment linkage effects with respect to different levels of skills. While measuring these employment linkage effects by varying skill types, the current paper also tries to consider the evolution process that is involved in the generation of skilled employment. In that respect, the paper compares the skilled employment generation by the sectors for two different time-periods (2004-05 and 2011-12), thus considering the structural change that may have evolved over time with a demand for different skilled workers. There is a persisting debate across different countries worldwide among researchers (namely, Katz and Autor, 1999; Autor, Katz and Kearney, 2006; Spitz-Oener, 2006; Goos and Manning, 2007; Fernandez-Macias, 2012 and others) for the last few years related to the structural change in employment and job polarization i.e., whether there is a uniform shift in employment noticed from low-skilled occupations to high-skilled occupations or the growth is taking place only for high-skilled and low-skilled employment with a declining trend in employment for the middle-skilled workforce. Therefore, considering the two time-periods (2004-05 and 2011-12) for analysis, the present paper even helps us understand the above debate on job polarisation in the context of the Indian economy through estimation of direct and indirect employment creation of the sectors by varying skill types. Defining ‘Skill’ Any discussion focusing on skill development has to fundamentally begin from what constitutes ‗skill‘ definitionally, given that it keeps changing in the existing literature because of its evolution as a concept as well as the availability of data. Several studies highlight (See Gujarati & Dars, 1972; Dunne et al., 1997; Hamermesh, 1996) that the difference between skilled and unskilled workers should be ascertained by taking into account the ‗production‘ and ‗non-production‘ worker share data. While production workers are mainly involved in manufacturing of raw materials required for creating a finished article (it‘s mainly a lower skilled occupation), non-production workers are highly skilled workers associated with their technical or managerial abilities. Categorisation of such nature has allowed researchers like Davis and Haltiwanger (1991), Berndt et al. (1992), among others, in distinguishing between unskilled and semi-skilled workers. Segregation of this nature also allows to better gauge the 6

impact each set of workers has on economic growth and also points out the exact categories of occupations in respect of which India is lagging behind. However, such a categorisation has its limitations. The production workers often miss out on capturing workers like floor supervisors and apprentices from the production process (Cheon, 1999). Getting occupation-wise data on non production workers is also difficult, besides creating problems of measurement. Hence, using a non-production/production worker proxy for skill is arduous and can be inaccurate. Several other indicators have also been used for indentifying a skilled worker. Wage rate differential has also been used in several studies for segregating skilled and unskilled workers (See Keane and Prasad, 1991; Anderson et. al., 2001), while others have also used occupational data. For instance, Reder (1955) makes a distinction between different types of workers based on their skill levels using their respective job classifications and further looks into their average wages. Orrenius and Zavodny (2006) also consider occupation as a proxy for skill. Balasubramanian (2016) has estimated the skill content of occupations for India, using the O-NET data to show that there is a robust and positive relationship skill content and employment growth regionally in India. Moreover, to define skill, occupational mix has been even used by OECD (1998) in its ISIC standards, wherein it has classified different types of skills (e.g., White-collar high-skill (WCHS); White-collar low-skill (WCLS); Blue-collar high-skill (BCHS); and Blue-collar low-skill (BCLS)) using different occupational standards. Based on these four types of skills, it has made nine groups of workers with different occupational standards. In addition, occupational standards have also been used by Autor et. al. (2003), Goos & Manning (2007) and Fernandez-Maciaz (2012) for defining skill by distinguishing routine vs. non-routine tasks and manual vs. cognitive tasks. Based on these classifications, Autor et. al. (2003) categorises workers under four characteristics: routine manual tasks (mostly involving traditional unskilled or semi-skilled tasks); routine cognitive tasks (mostly clerical jobs); nonroutine cognitive tasks (these tasks mostly include managerial and scientific tasks, which are typically service-oriented jobs); and non-routine manual tasks (especially low-skilled service sector-oriented jobs). Apart from occupational standards, educational attainment data is another popular way of classifying skills. Anderson et. al. (2001) argue that worker-education data is a better proxy for defining skilled people for NAFTA countries. However, Pertold-Gebicka (2010) shows that this requires a strong assumption that the employment structure of occupations reflects correctly their skill requirements. Kapoor (2014), in her study of job creation in Indian 7

organised manufacturing sector, defines skill intensity as the per centage of workers possessing secondary or higher education. Pertold-Gebicka (2010) also considers educational attainment of workers as a proxy for defining their skill levels. The study thus considers college graduates as high-skilled workers and high school graduates as less-skilled workers and then examines the skill intensity of different occupations. Similarly, Amirapu and Subramanian (2015) have also used educational attainment as an alternative measure of skill. But such a measure is only possible if the occupational structure accurately reflects the educational attainments required to execute the jobs (Gebicka, 2010). Given that the definition of skill has evolved over time and that it depends a lot on data availability, as a first step, the current paper has come up with a strategy of combining three forms of educational attainment - general, technical and vocational - as a measure of skill. The reasons for combining the three types of education (general, technical and vocational) are two-fold. First, the International Labour Office (2010), in its G-20 Training Strategy, stresses that education is one of the most important determinants of skilled people. The report argues that a proper facilitation of general education, in combination with technical and vocational education, increases the average productivity of workers and makes them skilled, thereby enhancing the overall productivity of the economy and also encouraging workers to optimise their working capacities. Thus, the report suggests that the creation of a workforce endowed with different levels of skills, including access to general as well as technical and vocational education, is one of the major ingredients that all G-20 economies should possess to ensure growth and development in the long-run. Second, the recent policy changes and the emphasis being placed on the ‗Skilling India‘ initiative and the evolution of the National Skills Quality Framework (NSQF) imply that skills may no longer be associated with general educational attainment alone (Ministry of Finance, 2016). Further, it is evident from the NSQF levels of different sector skills councils that apart from general education, it is also important to examine other factors such as the knowledge–skill ratio, and the experience levels of workers, while defining a skilled worker. Based on the current literature on the skills spectrum in the Indian context and the corresponding gaps identified in the literature, this paper thus attempts to define a skilled worker in a much broader sense by combining three types of education. Thus, four types of employment have been defined in this paper based on four skill levels —low-skilled, low8

medium-skilled, medium-high-skilled, and high-skilled, for arriving at a macro picture of the Indian economy, given the vast diversity existing across different sectors. The paper is organised as follows. Section 2 outlines the methodology for defining skilled employment, combining general, technical and vocational education. It also presents the methodology used for estimating the employment multiplier, along with employment linkage effects, for different skill types and describes the underlying data sources used for accomplishing this objective. Section 3 first presents the share of different types of employment in each sector of the Indian economy, and then highlights the employment linkage effects for different skill types, using Input–Output (I-O) tables. Section 4 concludes the paper.

2. Methodology and Data Sources

2.1.

Defining Skilled Employment Based on General, Technical and Vocational

Education This paper covers three types of educational attainment, viz., general, technical, and vocational, which are estimated from the Employment–Unemployment Survey conducted by the National Sample Survey Office in its 68th Round for the year 2011–12 (NSSO, 2014) and 61st Round for the year 2004-05 (NSSO, 2006). Based on a detailed educational classification presented in table A.1 in the appendix, we have defined four types of employment for the above years, viz., low-skilled, low-medium skilled, medium-high skilled and high-skilled (Box 1).

Box 1: Four Types of Employment Based on NSSO (2013) Education Codes •

Low-Skilled Employment: Not literate to below primary and no technical education and/or did not receive any vocational training. • Low-Medium Skilled Employment: Attained secondary education and no technical education and/or received vocational training. • Medium-High Skilled Employment: General education with higher secondary or diploma/ certificate course and/or technical education with diploma/certificate course below graduate level and/or received vocational training. • High-Skilled Employment: Graduate, post-graduate and above and/or technical education with diploma/certificate course for graduate and above level and/or received vocational training. Source: Authors‘ computations using 68th (2011–12) and 66th (2009-10) Employment–Unemployment Survey by National Sample Survey Office (NSSO, 2013; 2011).

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The process of combining a three-dimensional measure of skills was done in two steps. In the first step, we combined the general and technical education codes to arrive at four types of employment (Table 1 depicts the first step). In the second step, we considered a combination of general and technical education as a variable and related it to vocational education (Table 2). The second step provided the final skill variable, a combination of all the three forms of education - general, technical and vocational. Table 1: 1st step: combining General and Technical Education, 2011–12 Technical Education General Education Not literate Literate without formal schooling TLC Others Literate: below primary Primary Middle Secondary Higher secondary Diploma/certificate course Graduate Post-graduate & above

No technical education

Technical degree in different subjects

Diploma in different Diploma in different subjects (below subjects (above Missing graduate) graduate) Cases

Low-skill

Low-skill Cases do not exist

Low-medium skill

Lowmedium skill

Medium-high skill Cases do not exist

Mediumhigh skill

High-skill

Missing Missing Cases Low-skill Low-medium skill Medium-high skill High-skill cases Source: Authors‘ formulation using the 68th Round (2011–12) Employment–Unemployment Survey conducted by the National Sample Survey Office (NSSO, 2014).

Table 2: 2nd step: Combining General and Technical Education with Vocational Education, 2011–12 Combination of General and Technical education

Vocational education Non-formal Did not receive vocational any vocational training training Low-skill

Formal vocational training Missing cases Low skilled Low-medium skilled Low-medium skill Medium-high skilled Medium-high skill High skilled High-skill Missing cases Medium-high skill Low-medium skill Low-skill Missing cases Source: Authors‘ formulation using the 68th Round (2011–12) Employment–Unemployment Survey conducted by the National Sample Survey Office (NSSO, 2014).

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2.2.

Input-Output Framework for Different Types of Employment with Respect

to Different Types of Skills This section presents the steps employed in estimating the employment multiplier as well as employment linkage effects (both forward and backward) with respect to different skill types. We start with the assumption of a fixed employment coefficient i.e., with each output change, there will be an associated change in employment (Pradhan, Saluja and Singh, 2006; BulmerThomas, 1982). This constant return to scale assumption is considered standard in an I-O framework.1 Following this assumption, we get the fixed employment coefficients for each sector as follows: Ei = Li / Xi ----------------------------- (1), (i = 1,2,…,n), where, Li is the employment in sector ‗i‘, Xi is the gross output and Ei‘s are the fixed employment coefficients for i-th sector. In other words, Ei‘s are the labour requirements per unit of gross output across sectors, Xi. However, in the previous equation, L i is the total employment in sector ‗i‘, with labour considered homogeneous, while in reality, it is not. To capture the heterogeneity of labour force by skill types, the current paper considers different types of employment as defined in the previous sub-section. Following that, Li can be written as the summation of abovementioned four different types of employment, viz, low, low-medium, medium-high and high skilled. Thus,

Li = LSi + LMSi + MHSi + HSi

(2)

where, Li is the total employment in sector ‗i‘, LSi is low-skilled employment in sector ‗i‘, LMSi is the low-medium skilled employment in sector ‗i‘, MHSi is the medium-high skilled employment in sector ‗i‘ and HSi is the high skilled employment in sector ‗i‘.

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Any standard methodology is based on some theoretical assumptions which in a sense are also its limitations. Leontief type production function has been used in this Input-Output (I-O) framework. This means that in I-O framework, the intermediate inputs used in the production process are assumed to remain in fixed proportions regardless of scale of production, thus the inputs used is a constant ratio to the output produced. Although this assumption is somewhat restrictive in manner, sometimes it is justified by mentioning its simplicity followed by its advantage in solving the problems through this straightforward fixed proportion production function (Christ, 1955). 11

Based on these different types of skilled employment, we calculate the fixed employment coefficients with respect to each type of employment. Therefore, we write four types of fixed employment as follows: Ei1 = LSi / Xi ------- (3), (i = 1,2,…,n), Ei1 is the low skilled labour requirement per unit of gross output, Xi. Ei2 = LMSi / Xi ------- (4), (i = 1,2,…,n), Ei2 is the low-medium skilled labour requirement per unit of gross output, Xi. Ei3 = MHSi / Xi ------- (5), (i = 1,2,…,n), Ei3 is the medium-high skilled labour requirement per unit of gross output, Xi. Ei4 = HSi / Xi ------- (6), (i = 1,2,…,n), Ei4 is the high skilled labour requirement per unit of gross output, Xi.

Next, following the methodology given by Bulmer-Thomas (1982), we form the diagonalised matrices with the elements of the fixed employment coefficients (viz., from Equations 3, 4, 5 and 6) for each type of skill. Mathematically, they can be represented as follows:

LSi =

i1,

i2,

i3,

i4,

* Xi -----(3a)

LMSi =

i2

* Xi ----- (4a)

MHSi =

i3

* Xi ----- (5a)

HSi = where,

i1

i4

* Xi ----- (6a)

are the diagonalised matrices formed from the vector ‗E‘, whose

elements are defined by Equations 3, 4, 5, and 6, respectively. Further, these diagonalized matrices with fixed employment coefficients for all skill types have been juxtaposed into an I-O table for calculating the direct as well as indirect employment generation by the sectors. To accomplish that, the basic equation of output determination has been considered from the conventional I–O model, which is X = (I – A)-1 F --------- (7)

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where X is the vector of output, (I – A)-1 is the Leontief Inverse matrix, F is the vector comprising final demand, A is the technical coefficient matrix which implies the direct input requirement from ith sector in order to produce one unit of output in the jth sector. The equation thus represents the amount of output created directly as well as indirectly for oneunit change in final the demand.

Substituting this relation of X from (7) in Equations (3a), (4a), (5a) and (6a), we have the following labour equations with respect to each type of labour (viz., low-skilled, low-medium skilled, medium-high skilled and high-skilled):

LSi =

i1 *

(I-A)-1 F = K1F -------- (8a)

LMSi =

i2

* (I-A)-1 F = K2 F -------- (9a)

MHSi =

i3

* (I-A)-1 F = K3F -------- (10a)

HSi =

i4

* (I-A)-1 F = K4F -------- (11a)

where (I – A)-1 is the Leontief Inverse matrix, F is the vector comprising final demand, and K = [kij], the i, jth element of K, which measures employment created directly and indirectly in the ith sector when the jth final demand changes by one unit. Again, ∑ i kij gives the employment multiplier, thus measuring the total direct and indirect employment created throughout the economy when the jth sector final demand increases by one unit (BulmerThomas, 1982; Pradhan, Saluja and Singh, 2006). After estimating the employment multiplier ∑i kij, it is appropriate to calculate the indices for capturing the forward and backward linkages for employment with varying levels of skill which, in turn, helps identify a key employment-generating sector for all skill types.

These employment linkage indices with respect to each type of employment are as follows: Employment Backward Linkage with respect to low-skilled employment, (EBLLS) = [(1/n) ∑i k1ij] / [(1/n2) ∑i ∑j k1ij], (i, j = 1,2,…,n)

Employment Forward Linkage with respect to low-skilled employment, (EFLLS) = [(1/n) ∑j k1ij] / [(1/n2) ∑i ∑j k1ij], (i, j = 1,2,…,n)

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Employment Backward Linkage with respect to low-medium skilled employment (EBLLMS) = [(1/n) ∑i k2ij] / [(1/n2) ∑i ∑j k2ij], (i, j = 1,2,…,n) Employment Forward Linkage with respect to low-medium skilled employment (EFLLMS) = [(1/n) ∑j k2ij] / [(1/n2) ∑i ∑j k2ij], (i, j = 1,2,…,n) Employment Backward Linkage with respect to medium-high skilled employment (EBLMHS) = [(1/n) ∑i k3ij] / [(1/n2) ∑i ∑j k3ij], (i, j = 1,2,…,n) Employment Forward Linkage with respect to medium-high skilled employment (EFLMHS) = [(1/n) ∑j k3ij] / [(1/n2) ∑i ∑j k3ij], (i, j = 1,2,…,n) Employment Backward Linkage with respect to high-skilled employment (EBLHS) = [(1/n) ∑i k4ij] / [(1/n2) ∑i ∑j k4ij], (i, j = 1,2,…,n) Employment Forward Linkage with respect to high-skilled employment (EFLHS) = [(1/n) ∑j k4ij] / [(1/n2) ∑i ∑j k4ij], (i, j = 1,2,…,n) It is pertinent to note that for calculating the employment backward linkage coefficients for different skill types, the demand-driven Leontief input inverse matrix ((I – A)-1) is considered (Leontief, 1936; 1941), whereas the Ghoshian allocation coefficient matrix or the output inverse matrix ((I – B)-1)2 (Ghosh, 1958) is considered while calculating the employment forward linkage coefficients with respect to different skill types. Based on these linkage coefficients, we identify key employment-generating sectors of an economy (BulmerThomas, 1982) with respect to varying skill types that can create different types of employment, both directly and indirectly, through their strong linkage effects. 2.3 Data Sources The first important data source that we have used in this paper relates to the 68th and 61st rounds of Employment–Unemployment Surveys conducted by the National Sample Survey Office (NSSO) for the years 2011–12 (NSSO, 2014) and 2004-05 (NSSO, 2006), respectively. The NSSO provides large sample, unit-level data on the employment– unemployment situation for the Indian economy for the corresponding years. From the unitlevel data, we have used the usual principal as well as subsidiary status (UPSS) of the sample 2

B is the allocation coefficient matrix, where, bij= Xij / Xi -------- (i), where bij = allocation coefficient, Xij= ith commodity going to jth sector as inputs and Xi = ith sector‘s output. Thus, it is evident from Equation (i) that the amount of ith commodity going to jth sector in turn depends on ith sector‘s output only, that is, its own output production.

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observations as their employment status. The NSSO (2014; 2006) definition of ―usual activity status relates to the activity status of a person during the reference period of 365 days preceding the date of survey. The activity status on which a person spent relatively longer time (i.e. major time criterion) during the 365 days preceding the date of survey is considered as the usual principal activity status of the person.‖ Besides, ―a person whose usual principal status is determined on the basis of the major time criterion could have pursued some economic activity for a shorter time throughout the reference year of 365 days preceding the date of survey or for a minor period, which is not less than 30 days, during the reference year. The status in which such economic activity is pursued known as the usual subsidiary economic activity status of that person‖ (NSSO, 2014; 2006). The main focus of the NSSO is to estimate the number of persons getting employment in different sectors in order to arrive at the employment/unemployment rate. Thus, as per the NSSO, if a person belongs to both categories of principal and subsidiary status, he/she is counted only once (according to their principal status) in order to avoid the problem of double counting. On the other hand, in the present exercise, our primary objective is to measure different types of total employment generation capabilities (both through principal and subsidiary status) of the major sectors. Apart from the employment related information, NSSO also provides data on three types of educational attainment levels (general, technical and vocational) of workers, which have been used for formulating four types of skilled employment across various sectors of the Indian economy. In this context, it should be noted here that NSSO employment-unemployment 2004-05 survey is first of its kind to provide information on vocational education in addition to general and technical education. Moreover, NSSO continued the employmentunemployment survey for the Indian economy in the similar fashion till 2011-12. Thereafter, the government has introduced a new employment-unemployment survey for the year 201718 with the name as Periodic Labour Force Survey (PLFS) which is not comparable with the previous NSSO employment-unemployment surveys because of the different weights used in NSSO and PLFS (Ghosh, 2019; Kaushal, 2019). Therefore, in order to main the comparability, we have restricted our analysis between 2004-05 and 2011-12.

The second most important data source for estimating direct and indirect employment generation by the sectors through their linkage effects is the Input-Output (I-O) table for India. In this paper, we have used I–O tables from two sources - Central Statistics Office

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(CSO) for the year 2003-04 (CSO, 2008) and the World Input-Output Database (WIOD) (Timmer, 2012) for India for the year 2011-12. 3

Finally, it is important to mention that we have incorporated four types of employment in 2011-12 and 2003-04 I-O tables. In order to incorporate those employment figures in the I–O table, it is important to match the sectors in the I-O table with those in the NSSO Employment–Unemployment Survey. For this purpose, we have used the National Industrial Classification (NIC-2008, 1998) codes published by CSO (2008; 1998) for classifying various sectors of the Indian economy. Also, since the paper compares the employment generation over two time-periods, the values of output and inter-industry transactions have been deflated using the implicit price deflator provided by National Accounts Statistics for the years 2009 and 2014 in the price and quantum indices.

3. Empirical Analysis

The empirical analysis starts with presenting the per centage share of four types of employment in the overall economy and also across sectors before exploring the employment forward and backward linkages for different skill levels.

3.1.

Per centage share of skilled employment in the overall economy

Based on the definitions of different types of skilled employment described in section 2, we have estimated the four types of employment (low-skilled, low-medium-skilled, mediumhigh-skilled and high-skilled) for the Indian economy for the years 2004-05 and 2011-12, with their respective per centage shares presented in figure 1.

Figure 1: Per centage Share of Different Types of Workforce in the Overall Indian Economy (2004-05 and 2011–12) 2004-05

2011-12

3

This is for the information that WIOD provides I-O tables for 40 different countries of the world, including India across 35 economic sectors for continuous years from 1995 to 2011. Moreover, WIOD constructed I-O tables for continuous years by extrapolating the intermittent national I-O tables for respective countries, along with information from other sources such as National Accounts Statistics (NAS) and International Trade Statistics (ITS). . 16

Source: Authors‘ computations using the 61st Round (2004-05) and 68th Round (2011–12) Employment– Unemployment Survey by the National Sample Survey Office (NSSO, 2006, 2014).

Figure-1 clearly brings out the fact that skill composition has changed over time for the Indian economy. More specifically, the per centage share of low-skilled workers has decreased from 49.3 per cent in 2004-05 to 37.1 per cent in 2011-12, while the major share is accounted for low-medium skilled workers, from 38.7 per cent in 2004-05 to 45.2 per cent in 2011-12. On the other side, the share of medium-high skilled workforce has increased from 5.6 per cent in 2004-05 to 8 per cent in 2011-12 (an increase by 2.4 per cent), while that of high-skilled workforce from 6.2 per cent to 9.7 per cent (an increase by 3.5 per cent). Thus, it becomes evident that the share of low-skilled workers has reduced over the years, while there is an upward trend in the low-medium, medium-high and high-skilled workers. This figure even helps us understand the changing nature of employment in the Indian context, though there seems to be no symmetric polarisation of the employment structure with respect to the study years (2004-05 and 2011-12) for the Indian economy. In fact, it shows a uniform shift in the employment pattern from low-skilled to high-skilled.

Further, in order to get an idea about the change in the per centage shares of the four types of employment across different sectors, we need to describe in detail the sectoral classification used in this analysis. We start with the more disaggregated sectors in the CSO 2003-04 and WIOD 2011-12 I-O table. The CSO provides an I-O table for 130 sectors of the Indian economy, while WIOD provides an Indian I-O table for 35 sectors. After consolidating similar sectors in the CSO and WIOD I-O tables, we have finally arrived at 23 sectors. These

17

23 sectors provide a complete macro picture of the Indian economy consisting of the primary sector, manufacturing sector, non-manufacturing sector and tertiary or services sector. More specifically, among these 23 sectors, ‗agriculture, hunting, forestry & fishing‘ appears under the primary sector; 13 sectors under the secondary or manufacturing sector (‗food, beverages and tobacco‘ to ‗other manufacturing‘); 3 sectors under the non-manufacturing (‗mining & quarrying‘, ‗construction‘ and ‗electricity, gas & water supply‘); and finally 6 sectors appear under the tertiary or services sector (‗transport‘, ‗communication‘, ‗trade‘, ‗hotels & restaurants‘, ‗financing, real estate & business activities‘ and ‗other services‘). The study follows a concordance table with a detailed division of sectors as well as the merging procedure of NIC codes of the sectors and the I-O sectors, as is done in a study by Bhattacharya et. al. (2018).

While classifying these sectors, we have also considered 24 priority sectors, as mentioned by the National Policy for Skill Development and Entrepreneurship, 2015 (Ministry of Skill Development and Entrepreneurship, 2015), wherein skilled employment has been projected for the year 2022. However, the issue of which sector is potentially important for creating what type of employment, both directly and indirectly, has not been examined. Our study attempts to fill this gap by way of measuring both direct and indirect employment generation by the sectors. To fulfil this objective, the current paper presents a concordance table (in Appendix Table A.2), with a matching of the sectors in WIOD (2011), the NIC codes (Central Statistics Office, 2008) and the National Policy for Skill Development and Entrepreneurship (Ministry of Skill Development and Entrepreneurship, 2015). This concordance table is critical and thus, used as a reference, while consolidating 23 sectors as part of estimating the shares of different types of employment and employment linkage effects for the Indian economy.

This section examines the share of each type of employment (by four skill-levels) across the above-defined 23 sectors for India. The analysis starts with a presentation of the shares of different types of employment in the broad sectors of the Indian economy (viz., agriculture, manufacturing, non-manufacturing and services) (figure 2), followed at the disaggregate level with these broad sectors for 2004-05 and 2011-12 (figures 3, 4 and 5). However, it should be noted here that this employment share corresponds to only the in-house employment creation within a given sector.

18

Figure 2: Per centage Share of Employment by Skill Type in the Broad Sectors of the Indian Economy (2004–05 and 2011–12)

Source: Authors‘ estimation using the NSSO (2004-05 & 2011–12) Employment–Unemployment Survey (NSSO, 2006, 2014).

Figure 2 shows most of the low-skilled workers were absorbed in the agriculture sector, followed by the non-manufacturing sector for both the study years. This is mainly because the Indian agriculture sector represents much of the disguised unemployment in the Indian economy. Again, the non-manufacturing sector accounts for a large number of low-skilled workers because of the presence of a high proportion of low-skilled informal workers in the construction sector. However, over time, the proportion of low-skilled workers has decreased in the agriculture sector from 61.7 per cent to 49.7 per cent and in the non-manufacturing from 48.9 per cent to 42.3 per cent, while low-medium skilled employment has increased from 33.8 to 43 per cent in agriculture and from 43.4 to 48.3 per cent in non-manufacturing. The manufacturing sector accounts for a highest share of low-medium skilled employment, with an increase by around 4 per cent in 2011-12 over 2004-05. Finally, the medium-high and high-skilled workers are mostly present in services sectors unlike agriculture, manufacturing and non-manufacturing and more interestingly, the share of high-skilled workers has increased considerably from 19.1 to 25.2 per cent in the services sector over the study years, while a decrease is observed in the share of low and low-medium workers. From the above discussion, it can also be inferred that for agriculture, manufacturing and non-manufacturing, skill sets have been upgraded mostly for low-medium skilled employment, whereas, for services, it is the medium-high and high-skilled workers who have gained importance over time. 19

The above trend in employment shift across skill levels in the broad economic sectors finds support in the findings of the existing literature. For instance, studies like Usami and Rawal (2018), Krishna et. al. (2016), Erumban et. al. (2019) pointed to a structural shift in the employment

generation

from

‗agriculture‘

to

non-manufacturing

‗construction‘,

manufacturing and services sectors in India. Moreover, Roy (2008), using NSS and Census data, observed a sharp decline in agricultural employment and new employment creation in several non-agricultural sectors like ‗construction‘, ‗trade‘, ‗transport related activities‘, ‗financial and business services‘ etc. Further, Usami and Rawal (2018) found mostly workers with low levels of education (low-skilled workers per se) absorbed in ‗agriculture‘ and ‗construction‘, while the educated and high-skilled workers in ‗services‘ sector. Thus, based on this observed trend in employment composition, the studies identified ‗construction‘ as the second largest low-skilled employment generating sector after ‗agriculture‘. A study by Fox and Gaal (2008), found low skilled jobs in the manufacturing sector eroded significantly at the global level, along with India witnessing an expansion of the non-manufacturing sector to low and low medium skilled jobs.

Figure 3: Share of Employment by Skill Type within the Manufacturing Sector (2004– 05 and 2011–12)

Source: Authors‘ estimation using the NSSO (2004-05 & 2011-12) Employment–Unemployment Survey (NSSO, 2006, 2014).

Now, coming to the different types of employment within the manufacturing sector, we have found a substantial decreasing trend in low-skilled employment in all the sub-sectors, 20

excepting the ‗leather‘ sector. Moreover, we have found low-medium skilled as the majorly dominant employment type across sub-sectors within the manufacturing sector for the study years, 2004–05 and 2011-12. However, some manufacturing sub-sectors like ‗paper products‘, ‗chemicals‘, ‗machinery‘ and ‗transport equipment‘ show a concentration of highskilled employment with a substantial increase over the study years. Figure 4: Percentage Share of Employment by Skill Type within the NonManufacturing Sector (2004-05 and 2011–12)

Source: Author‘s estimation using the NSSO (2004-05 & 2011-12) Employment–Unemployment Survey (NSSO, 2006, 2014).

Within the non-manufacturing sector, the share of low-skilled workers is highest in the construction sector, followed by ‗mining and quarrying‘. This is because a large number of informal workers, mainly school dropouts, are engaged as construction workers. Although low-skilled workers account for a highest share in the ‗construction‘ and ‗mining and quarrying‘ sectors, their proportion shows a decrease between 2004-05 and 2011-12 in both the sub-sectors. However, the share of low-medium skilled employment has increased from 44 to 49 per cent in the construction sector over the study years, may be because of the presence of a large number of engineers and other vocationally trained and technically educated people in the construction utility projects and buildings, among other activities. Studies such as Fox and Gaal (2008), Aggarwal (2018), while arguing on similar lines, found a major increase in employment in the construction sector. In contrast, ‗electricity, gas and water supply‘ sector accounts for a higher proportion of high-skilled workforce, which shows an increase from 20 to 30 per cent between 2004-05 and 2011–12. Moreover, ‗mining and quarrying‘ sector also has witnessed a substantial increase in high-skilled employment between 2004-05 and 2011-12.

21

Figure 5: Share of Employment by Skill Type within the Services Sector (2004–05 and 2011–12)

Source: Authors‘ estimation using the NSSO (2004-05 & 2011-12) Employment–Unemployment Survey (NSSO, 2006, 2014).

Within the services sector, almost every sub-sector shows a decreasing trend in the share of low-skilled employment between 2004-05 and 2011-12. Moreover, some sub-sectors like ‗transport‘, ‗trade‘, ‗hotels‘ account for higher percentage of low-medium skilled employment, with a further increase over the study years. Further, some sectors like ‗communication‘, ‗financing‘ and ‗other services‘ have employed a larger proportion of highskilled employment, with a substantial increase witnessed between 2004-05 and 2011-12. This finding stands supported by another study conducted by Aggarwal (2018), which points out that service sector‘s employment generation is mainly confined to sub-sectors like ‗trade‘, ‗community and administrative services‘, etc.

3.2.

Employment Linkages with Respect to Different Skill Levels for India

The previous sub-section presented the share of different types of employment across sectors for the years 2004–05 and 2011–12. However, these figures provide only a picture of the concentration of employment within a sector. In an interdependent economy, it is also important to capture how one sector is able to generate employment in the other sectors through employment linkage effects.

22

In order to consider this aspect, this section uses the I-O table for India for the year 2003–04 (CSO, 2008) and 2011-12 Indian I-O table from the World Input-Output Database (Timmer, 2012), and then estimates the employment forward and backward linkages for different skill types across the 23 sectors for both these years by juxtaposing the employment numbers from the 61st and 68th Rounds of NSSO Employment–Unemployment Survey (NSSO, 2006; 2014). Tables 3 and 4 present the skill-based employment backward and forward linkages respectively across sectors for the Indian economy for the years 2004-05 and 2011-12 for four types of skill.

Table 3: Skill-Based Employment Backward Linkages across Sectors for India (2004-05 & 2011-12) Low Sectors

2004-05

2011-12

Low-Medium 2004-05

2011-12

Medium-High 2004-05

2011-12

High 2004-05

2011-12

Agriculture 4.61 6.20 2.69 3.96 1.75 2.63 0.97 Mining and Quarrying 0.32 0.44 0.26 0.39 0.35 0.43 0.32 Food, Beverages and 2.40 2.25 1.71 1.74 1.31 1.34 0.93 Tobacco Textiles 1.32 1.15 1.78 1.72 1.59 1.59 1.09 Wood and Wood 3.94 2.85 4.16 3.57 1.99 2.44 1.39 Products, Furniture and Fixtures Pulp, Paper, Printing 0.69 0.54 0.94 0.77 1.18 1.09 1.65 and Publishing Leather Products 1.04 1.69 1.36 1.51 1.21 1.40 1.44 Rubber and Plastics 0.72 0.59 0.73 0.75 0.80 0.80 0.86 Petroleum Products 0.28 0.20 0.24 0.29 0.33 0.41 0.34 Chemicals 0.53 0.33 0.53 0.45 0.59 0.58 0.74 Non-metallic Mineral 1.14 1.01 0.78 0.81 0.79 0.75 0.67 Products Metals 0.34 0.24 0.50 0.39 0.72 0.56 0.67 Machinery 0.30 0.20 0.46 0.35 0.73 0.54 0.85 Transport Equipment 0.26 0.17 0.44 0.31 0.76 0.58 0.99 Other Manufacturing 0.47 0.21 0.95 0.46 0.94 0.54 0.84 Construction 0.81 1.20 0.78 1.08 0.66 0.80 0.57 Electricity, Gas and 0.33 0.18 0.47 0.28 0.77 0.45 0.82 Water Supply Transport 0.53 0.32 0.69 0.46 0.88 0.64 0.74 Communication 0.11 0.10 0.40 0.22 1.11 0.66 1.28 Trade 0.47 0.49 0.88 0.89 1.31 1.34 1.17 Hotels and Restaurants 2.02 2.10 1.59 1.84 1.26 1.59 0.84 Financing, Real Estate 0.11 0.10 0.19 0.17 0.44 0.35 1.14 and Business Activities Other Services 0.28 0.42 0.46 0.60 1.51 1.51 2.69 . Sources: Authors‘ computation using the I-O table for India for 2003-04 (CSO, 2008) and 2011-12 (Timmer, 2012) along with 61st (2004-05) and 68th (2011-12) Rounds of Employment–Unemployment Survey conducted by the National Sample Survey Office (NSSO, 2006, 2014).

23

1.28 0.64 0.93 1.10 1.10

1.73 1.06 0.84 0.44 0.76 0.59 0.54 0.79 0.82 0.45 0.64 0.66 0.49 1.34 1.28 1.12 1.13

3.27

Table 4: Skill-Based Employment Forward Linkages across Sectors for India (2004-05 & 2011-12) Low Sectors

2004-05

2011-12

Low-Medium 2004-05

2011-12

Medium-High 2004-05

2011-12

High 2004-05

Agriculture 6.13 9.10 3.06 5.25 2.05 3.48 1.02 Mining and Quarrying 1.68 0.89 0.92 0.63 1.39 0.66 1.03 Food, Beverages and 0.40 0.45 0.34 0.44 0.27 0.32 0.18 Tobacco Textiles 0.65 0.66 1.15 1.32 1.01 1.19 0.50 Wood and Wood 7.83 4.70 8.14 6.36 3.53 4.14 2.25 Products, Furniture and Fixtures Pulp, Paper, Printing and 0.18 0.26 0.72 0.59 1.27 1.13 2.22 Publishing Leather Products 0.79 0.91 1.48 0.90 1.26 0.91 1.72 Rubber and Plastics 0.07 0.12 0.23 0.39 0.38 0.38 0.49 Petroleum Products 0.006 0.003 0.01 0.01 0.03 0.03 0.07 Chemicals 0.10 0.08 0.14 0.15 0.21 0.27 0.56 Non-Metallic Mineral 1.83 1.81 0.83 1.07 0.72 0.82 0.47 Products Metals 0.21 0.12 0.37 0.25 0.61 0.39 0.48 Machinery 0.04 0.02 0.12 0.09 0.31 0.20 0.39 Transport Equipment 0.024 0.002 0.12 0.07 0.33 0.28 0.50 Other Manufacturing 0.41 0.13 1.02 0.37 0.92 0.42 0.63 Construction 0.56 1.15 0.45 0.86 0.27 0.45 0.18 Electricity, Gas and Water 0.06 0.08 0.23 0.20 0.61 0.44 0.64 Supply Transport 0.42 0.29 0.60 0.49 0.84 0.73 0.50 Communication 0.07 0.05 0.59 0.26 2.12 1.20 2.43 Trade 0.79 0.99 1.38 1.69 2.26 2.61 1.90 Hotels and Restaurants 0.50 0.71 0.58 0.88 0.52 0.90 0.31 Financing, Real Estate and 0.04 0.03 0.15 0.13 0.56 0.39 1.75 Business Activities Other Services 0.21 0.45 0.36 0.61 1.53 1.66 2.76 Sources: Authors‘ computation using the I-O table for India for 2003-04 (CSO, 2008) and 2011-12 (Timmer, 2012) along with 61st (2004-05) and 68th (2011-12) Rounds of Employment–Unemployment Survey conducted by the National Sample Survey Office (NSSO, 2006, 2014).

Before proceeding with an interpretation of employment linkages from the tables 3 and 4, it is to be noted here that those sectors possessing more than unitary employment backward linkage with a specific type of skill are capable of creating more than one unit (above average) of that type of employment in other sectors through demanding inputs from them when the final demand within the first set of sector increases by unity. In contrast, those sectors that have more than unitary employment forward linkage with a specific skill are capable of creating above average of that type of employment within-sector itself when the final demand from the rest of the economy increases by unity through supplying their own

24

2011-12 1.57 1.09 0.26 0.65 1.36

2.38 0.65 0.47 0.03 0.57 0.42 0.26 0.43 0.42 0.23 0.27 0.74 0.35 2.64 2.37 0.68 1.53 3.62

output to other sectors4. Thus, these skill-based employment linkages can capture both within (direct) and outside (indirect) employment generation capabilities of the sectors, which are not captured by considering only the employment share (as discussed in sub-section 3.1). Based on the above interpretation, tables 3 and 4 show that for the ‗agriculture‘ sector, the employment forward and backward linkages with respect to mostly all the skills are greater than unity for both the years 2004-05 and 2011-12, implying that the ‗agriculture‘ sector is capable of generating these four types of employment both within itself and in the other sectors indirectly through generating demand for other sectors‘ outputs as well as supplying its output to the other sectors. This result stresses the fact that agriculture continues to be the major contributor to employment generation, mainly due to the dynamics of the rural agricultural sector with its strong linkages with rural non-farm sector that creates gainful and productive employment and earning opportunities (Misra, 2013, Binswanger-Mkhize and D‘Souza, 2012). Also, it is found that though the agricultural sector remains characterised by low-technology low-skill-intensive employment, the advancements in technology enable the agricultural sector to adopt new machineries, modern agricultural inputs and equipment that further accelerate the growth of other non-farm activities with employment for varying skill sets (Pal and Biswas, 2009). Within the manufacturing sector, the sub-sector ‗wood products‘ is found to have all the four types of employment linkages with greater than unity for both the years; hence, this sector is able to create all these four types of employment both within itself and in the other sectors. Further, two of the most important manufacturing sectors, viz., ‗food, beverages & tobacco‘ and ‗textiles‘, have above unitary employment backward linkages with respect to low, lowmedium and medium-high skilled for both the years. Thus, these two sectors are able to create a substantial size of low- and low-medium skilled employment indirectly in other sectors when the final demand of these sectors increases by unity. Besides creating indirect employment, ‗textiles‘ generates low-medium and medium-high skilled employment within itself as well (due to the above unitary employment forward linkages for both the years) by supplying their output to other sectors to meet the final demand from the rest of the economy (from Table 5). Finally, within the services sector, the sub-sector ‗hotels &restaurants‘ is able to create a sufficient indirect employment for both the years with varied levels of skill (Table 4

It is to be noted that the employment linkages represent employment generation by the sectors due to per unit change in the final demand. Specifically, the employment linkage coefficients have been estimated incorporating the employment coefficients (employment per unit of output) with varying levels of skill into the I-O tables of the respective years and discussed in detailed in the methodology section.

25

4). However, the sectors that are mostly important for creating medium-high and high-skilled employment for both the years include ‗financing, real estate & business activities‘, ‗communication‘ and ‗other services‘. Thus, evidence also indicates that in respect of both manufacturing and services sectors of the Indian economy, we still need a combination of skills. Sectors such as ‗textiles‘, ‗food and beverages‘ and ‗hotels and restaurants‘ may generate employment for a spectrum of skills in India; in contrast, the ‗construction‘ sector mainly generates employment for the low-skilled. This kind of a comparison between manufacturing and service sectors in terms of their job creation has also been carried out by Gala et. al. (2017). Using World Input-Output Database for several countries over time, coupled with employment data for these countries, the authors have tried to examine which kind of jobs have a long-term impact on influencing economic complexity before finally concluding that sophisticated service jobs, especially finance- and business-related, are more important than manufacturing jobs. The paper, in this respect, also points out that the importance of manufacturing vs sophisticated service sectors in the creation of jobs varies across developed and less-developed countries. Besides, this structural shift in employment from manufacturing to services sector has also been studied by Hu (2018) based on demand for and supply of intermediate inputs among these sectors.

Apart from the above interpretations regarding the nature of linkage effects across different sectors, the above two tables (4 and 5) also throw up some interesting facts regarding the changing nature of linkage effects between 2004-05 and 2011-12, which may have an impact on the change in demand for different types of skilled workers across these sectors. For instance, if we compare the backward linkage effects (or, indirect employment creation) of the sectors for 2004-05 and 2011-12, we find that employment backward linkage of ‗agriculture‘ has increased for all types of skill (significantly increased for low-skill) in 201112 relative to 2004-05. However, sectors like ‗food, beverages & tobacco‘, and ‗textiles‘ show a decrease in employment backward linkage for low-skill, while an increase in employment backward linkage for low-medium skill in 2011-12 over 2004-05, implying that there is a shift in these two sectors‘ indirect job creation towards low-medium skilled people from low-skilled people. In contrast, sectors like ‗wood products‘, ‗trade‘, ‗financing, real estate and business activities‘, ‗other services‘ show a fall in the employment backward linkages for low- and low-medium skill types, but an increase for medium-high and high skill types. This implies that these sectors‘ contribution to medium-high and high-skilled

26

employment creation has increased more as compared to low- and low-medium skilled employment creation.

3.3.

Key Employment-Generating Sectors with Respect to Different Skill Levels

for India

Based on the above employment linkage effects with varying levels of skill, we can identify the key employment-generating sectors for the Indian economy with their corresponding types of employment. To begin with, the paper follows the approach provided by Diamond (1975) in Bulmer-Thomas (1982), which says that a key employment-generating sector is the one that has high employment linkages, or more precisely, one which has both employment forward and backward linkage coefficients greater than unity with varying skill types. Based on this criterion, the key employment-generating sectors for varying skill levels for India have been identified for the year 2011-12 and presented in Table 5.

Table 5: Key Employment-Generating Sectors for Different Skill Levels for India for 2011-12. Key Employment Generating Sectors Low-Skill

Low-Medium Skill

Medium-High Skill

High-Skill

Agriculture Wood and Wood Products, Furniture and Fixtures Construction Non-Metallic Mineral Products

Agriculture Wood and Wood Products, Furniture and Fixtures Textiles

Agriculture Wood and Wood Products, Furniture and Fixtures Textiles Other Services

Other Services Pulp, Paper, Printing and Publishing Communication Agriculture

Trade Pulp, Paper, Printing and Publishing

Trade Financing, Real Estate and Business Activities Wood and Wood Products, Furniture and Fixtures Note: Sectors with both employment backward and forward linkage coefficients greater than Unity are considered as key sectors. Sources: Authors‘ computation using World Input-Output tables for India for the year 2011 using World InputOutput Database (Timmer, 2012) and 68th Round (2011–12) Employment–Unemployment Survey conducted by the National Sample Survey Office (NSSO, 2014).

Based on the criterion stated above, Table 6 lists a number of sectors that create outward and within-sector employment having both backward and forward employment linkages greater than unity for 2011-12. For instance, ‗agriculture‘, being the major employer, has created both direct and indirect employment for low, low-medium and medium-high skill in 2011-12. 27

Apart from ‗agriculture‘, another important sector is ‗construction‘ that generates a major chunk of low-skilled employment, both directly and indirectly. Moreover, ‗textiles‘ is found to be the key employment-generating sector for low-medium skilled category, while a number of service sectors like ‗trade‘, ‗other services‘, ‗financing, real estate & business activities‘ are mostly known for creating both direct and indirect jobs for medium-high and high-skilled categories. In contrast, within the manufacturing sector, ‗wood products‘ is able to create a wide spectrum of skills, thus creating low, low-medium and medium-high skilled employment, directly as well as indirectly. However, a sector like ‗paper, printing and publishing‘ within manufacturing creates mostly medium-high and high skilled employment.

3.4.

Number of Direct and Indirect Jobs Created across Sectors

Now based on different skill-based employment linkages presented in tables 4 and 5, we calculate the absolute number of jobs (in million) being created within-sector (Table A.3) and indirectly, that is, across other sectors (Table A.4) for the years 2004-05 and 2011-12. However, they cannot be summed up due to the problem of double counting. In order to show how important is the estimation of indirect employment, along with direct employment, based on the two tables (A.3 and A.4), figure 6 illustrate some of the select sectors important for creating within-sector jobs, while some are important for creating outside sector jobs. 5

Figure 6: Number of Direct and Indirect Jobs Created across Select Sectors for 2004-05 and 2011-12 (in millions) 2004-05

5

These sectors have chosen purely on a random basis with an objective of showing the importance of sectors creating direct vs. indirect jobs. However, one can see the dynamics of all the 23 sectors as well in the detailed tables A.3 and A.4 presented in the appendix.

28

2011-12

Source: Authors‘ estimation.

It is evident from figure 6 that within manufacturing sector, sub-sectors like ‗food, beverages & tobacco‘, ‗textiles‘ and within services, ‗hotels & restaurants‘ are creating more outside sector jobs than within-sector jobs for both the years 2004-05 and 2011-12. In sharp contrast, one of the most important non-manufacturing sectors, viz, ‗construction‘, and another subsector within services, viz., ‗trade‘ are creating more within-sector employment than indirect employment for both the years. Even within both direct and indirect employment creation, the skill composition has changed across these sectors between 2004-05 and 2011-12 due to a change in the demand for different types of skilled employment. For instance, though ‗food, beverages & tobacco‘ has created more indirect (outside) jobs for both the years, the number of low-skilled jobs that this sector is creating indirectly has decreased from 20.8 to 15.3 million over 2004-05 and 2011-12, while the number of low-medium skilled jobs has increased from 11.6 to 14.3 million. For ‗textiles‘ and ‗hotels & restaurants‘ also, the number

29

of indirect low-skilled jobs has reduced in 2011-12, while that for low-medium and mediumhigh skill has increased. Interestingly, a sector like ‗construction‘ shows a significant increase in the creation of direct jobs for low and low-medium skill in 2011-12 as against 2004-05.

4. Conclusion The paper begins with examining the persisting debate on the transformation of jobs in the Indian context through an estimation of changes in the percentage shares of four different types of skilled employment between 2004-05 and 2011-12. The results do not indicate any symmetric polarisation pattern of the transformation of the overall employment structure over the study years for the Indian economy. In fact, the results show a uniform shift in the employment pattern from low-skilled occupations to high-skilled occupations for the Indian economy as a whole. However, this employment shift is not uniform across the sectors at the disaggregated level. The results show that for agriculture, manufacturing and nonmanufacturing, skill sets have been upgraded mostly with respect to low-medium skilled employment between 2004-05 and 2011-12, whereas for services it is the medium-high and high-skilled employment that has been mostly created between 2004-05 and 2011-12. Again, within manufacturing, the dominant employment pattern that has gained importance over the study period is the low-medium skilled employment. However, within services, the dominant employment shift is towards high-skilled employment.

Apart from presenting the employment share across sectors, the paper also identifies the key employment generating sectors that have the potential to create both direct and indirect employment at different skill levels through employment linkage effects. A linkage-based analysis also shows that a number of sectors present in the Indian economy are creating a wide spectrum of skill employment. For instance, sectors like ‗agriculture‘, ‗wood products‘, ‗hotels & restaurants‘ etc. are creating different types of employment, both directly and indirectly, while sectors like ‗food, beverages & tobacco‘, ‗textiles‘ are mostly creating lowmedium skilled employment in other sectors through high employment backward linkages. In contrast, services such as ‗communication‘, ‗trade‘, ‗financing, real estate & business activities‘ and ‗other services‘ are especially creating direct and indirect medium-high and high-skilled employment.

30

An employment linkage analysis also shows a change in the demand for different types of skills across these sectors over time. For instance, sectors like ‗food, beverages & tobacco‘, and ‗textiles‘ are creating demand for more low-medium skilled people than for low-skilled people over time, thus representing upgradation of skills in these sectors over time. In contrast, the contribution of sectors like ‗wood products‘, ‗trade‘, ‗financing, real estate & business activities‘, ‗other services‘ towards medium-high and high-skilled employment is more than low- and low-medium skilled, indicating a clear upward shift towards skilled jobs in these sectors.

Also, the analysis points to a distinction between manufacturing and services sectors in terms of their employment creation, which can help policymakers understand the type of employment created by various sectors, both directly and indirectly, through their linkage effects, thereby enabling them to devise appropriate policies for each sector. Precisely, the paper indicates in which sectors are the jobs generated and at what level of skills and thus it establishes a baseline for broadening our measures of skilling. Moreover, given that the analysis is presented for two time-periods as part of capturing the structural change in the job market, it helps policy-makers understand the change in demand for different skill levels over time and how that varies across different sectors of the Indian economy, which is again critical to devising appropriate policies. Overall, the study is expected to motivate policymakers towards boosting some key employment generating sectors in order to enhance different types of employment, thus proposing a way forward to join in the ‗Skill India Mission‘. Based on the above empirical analysis, the suggested employment promotional policies are as follows: 

Since the findings of the paper point to changes in the demand for different types of skills across sectors over time, there is a need for sector-specific policies. For instance, sub-sectors within manufacturing like ‗food, beverages & tobacco‘, ‗textiles‘, etc. show an increase in the demand for low-medium skilled employment, while sectors like ‗wood products‘, ‗trade‘, ‗financing, real estate and business activities‘ indicate an increase in the demand for medium high skilled and high skilled employment opportunities. Thus, skill upgradation is needed as per the requirements of specific sectors. This further necessitates the implementation of some sectorspecific policies for employment generation with a special focus on human capital

31

promotion along with labour quality improvements, innovations in diverse fields of technology and skill upgradation across sectors (Aggarwal, 2018). 

Being the major contributor to employment generation with varying skill sets (directly as well as indirectly), agriculture needs a special attention through introduction of diverse technological innovations and enhancement of its linkages with the rural nonfarm activities.



The findings also point to an upward mobility of employment across skills in the different sectors. As the skill upgradation and change in the occupational structure are a dynamic process, sustained policy efforts should be directed towards reducing the mismatch between the skills that workers acquire and the availability of suitable employment opportunities across different sectors. Therefore, policies with a greater scope for technology-aided skilling and re-skilling of both the new and existing workforce, and complementarity between employment generation policies and skill development of the workforce are indeed crucial for generating sufficient jobs for a large labour-abundant economy like India.

Author Statement Tulika Bhattacharya: Conceptualization; Data curation; Formal analysis; Methodology; Visualization; Roles/Writing - original draft Bornali Bhandari: Conceptualization; Project administration; Resources; Supervision Indrajit Bairagya: Formal analysis; Writing - review & editing.

Acknowledgement The present paper is a substantially extended version (with additional data, analysis and inferences) of the working paper that was developed as a part of the larger research project ―Skilling India: No Time to Lose‖ at National Council of Applied Economic Research (NCAER), New Delhi. The authors are thankful to both NCAER and J.P. Morgan for their support. The authors are also grateful to Professor Biswanath Goldar and Professor Arup Mitra and two anonymous referees for their valuable comments and suggestions on initial draft of the paper. Thanks are due to Mr. R. H. Itagi for his help and suggestions. The usual disclaimers apply. Funding

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The present paper is a substantially extended version (with additional data, analysis and inferences) of the working paper that was developed as a part of the larger research project ―Skilling India: No Time to Lose‖ at National Council of Applied Economic Research (NCAER), New Delhi. Financial support to the above larger project by NCAER and J.P. Morgan is gratefully acknowledged by the authors.

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Appendices Appendix A.1: Education Codes General Education Codes: 01- Not literate 02- Literate without formal schooling (EGS/NFEC/AEC) 03- TLC 04- Others 05- Literate: below primary 06- Primary 07- Middle 08- Secondary 10 - Higher secondary 11- Diploma/certificate course 12- Graduate 13- Post-graduate and above Technical Education Codes: 01- No technical education 02- Technical degree in agriculture/engineering/technology/medicine, etc. 03- Diploma/certificate (below graduate level) in agriculture 04- Diploma/certificate (below graduate level) in engineering/technology 05- Diploma/certificate (below graduate level) in medicine 06- Diploma/certificate (below graduate level) in crafts 07- Diploma/certificate (below graduate level) in other subjects 08- Diploma/certificate (graduate and above level) in agriculture 09- Diploma/certificate (graduate and above level) in engineering/technology 10- Diploma/certificate (graduate and above level) in medicine 11- Diploma/certificate (graduate and above level) in crafts 12- Diploma/certificate (graduate and above level) in other subjects Vocational Education Codes: 01- Yes: receiving formal vocational training 02- Received vocational training: formal 03- Received vocational training: non-formal: hereditary 04- Received vocational training: non-formal: self-learning 05- Received vocational training: non-formal: learning on the job 06- Received vocational training: non-formal: others 07- Did not receive any vocational training Source: 68th (2011-12) Employment–Unemployment Survey conducted by the National Sample Survey Office (NSSO, 2014). 36

Table A.2: Concordance Table of Sectors in WIOD (2011), NIC (2008) and National Policy for Skill Development and Entrepreneurship (2015) 24 Sectors Listed by the National Policy for Skill Development and Entrepreneurship, 2015 Agriculture

Food processing

Textile and Clothing, Handlooms Furniture and Furnishing

Leather and leather goods

Pharma

Auto and Auto Components Gems and Jewellery, Handicrafts Construction

35 Sectors in World I-O Table

AtB: Agriculture, Hunting, Forestry and Fishing C: Mining and Quarrying

15t16: Food, Beverages and Tobacco 17t18: Textiles and Textile Products 20: Wood and Products of Wood and Cork 21t22: Pulp, Paper, Printing and Publishing 19: Leather, Leather and Footwear 25: Rubber and Plastics 23: Coke, refined Petroleum and Nuclear Fuel 24: Chemicals and Chemical Products 26: Other NonMetallic Mineral 27t28: Basic Metals and Fabricated Metal 29: Machinery, nec 30t33: Electrical and Optical Equipment 34t35: Transport Equipment 36t37: Manufacturing, nec; Recycling F: Construction

E: Electricity, Gas and Water Supply

23 Sectors in the Consolidated World I-O Table

Division of Sectors

Primary Sector 1.

6.

Leather products

8.

Rubber and plastics Coke, Refined Petroleum and Nuclear Fuel

C

15t16 17t18

Textiles Wood and Wood Products, Furniture and Fixtures Pulp, Paper, Printing and Publishing

7.

9.

AtB

NIC2008 Codes 01+02+0 3

Agriculture

Nonmanufactu 2. Mining and ring Quarrying Sector Manufactu 3. Food, Beverages ring and Tobacco Sector 4. 5.

Code of Sectors to Merge

05+06+0 7+08+09

10+11+1 2 13+14 16+31

20

21t22 19 25

17+18+5 8+59 15 22 19

23 24

10. Chemicals 11. Other NonMetallic Mineral Products

20+21 23

26 24+25 27t28

12. Metals 13. Machinery

29+30t33

14. Transport Equipment

34t35

26+27+2 8

29+30 32+33

15. Manufacturing, nec; Recycling

16. Construction 17. Electricity, Gas and water supply

36t37 Nonmanufactu ring Sector

F

E

41+42+4 3

35+36+3 7 37

24 Sectors Listed by the National Policy for Skill Development and Entrepreneurship, 2015 Transportation

60: Inland Transport 61: Water Transport 62: Air Transport 63: Other Supporting and Auxiliary Transport Activities; Activities of Travel Agencies 64: Post and Telecommunication s 50: Sale, Maintenance and Repair of Motor Vehicles and Motor Cycles; Retail Sale of Fuel 51: Wholesale Trade and Commission Trade, Except of Motor Vehicles and Motorcycles

Telecommunications

Retail

Tourism, Hospitality and Travel, Logistics BFSI Building, Construction and Real Estate Electronic & IT hardware

IT and ITES, Education/skill development Healthcare Security,

Media

35 Sectors in World I-O Table

and

52: Retail Trade, Except of Motor Vehicles and Motorcycles; Repair of Household Goods H: Hotels and Restaurants J: Financial Intermediation 70: Real Estate Activities 71t74: Renting of Machinery and Equipment and Other Business Activities L: Public Administration and Defence; Compulsory Social Security

M: Education N: Health and Social Work O: Other

23 Sectors in the Consolidated World I-O Table

Division of Sectors

Tertiary/S ervices sector

18. Transport 19. Post and Telecommunicati ons

Code of Sectors to Merge 60+61+62 +63

64

NIC2008 Codes 49+50+5 1+52

53+60+6 1+63 45+46+4 7

20. Trade

21. Hotels restaurants

50+51+52

and

22. Financing, Real Estate and Business Activities

H

J+70+71t7 4

L+M+N+ O+P

55+56 62+64+6 5+66+68 +69+70+ 71+73+7 4+75+77 +78+79+ 80+81+8 2 38+39+7 2+84+85 +86+87+ 88+90+9 1+92+93 +94+95+ 96+97

23. Other Services

38

24 Sectors Listed by the National Policy for Skill Development and Entrepreneurship, 2015 Entertainment, Beauty and Wellness, Building Hardware Domestic Help

35 Sectors in World I-O Table

23 Sectors in the Consolidated World I-O Table

Division of Sectors

Code of Sectors to Merge

NIC2008 Codes

Community, Social and Personal Services P: Private Households with Employed Persons Sources: Authors‘ estimation using WIOD (2011), Timmer (2012), NIC (2008), and National Policy for Skill Development and Entrepreneurship (2015).

Table A.3: Creation of Direct Vs. Indirect Jobs Across Sectors (in millions), 2004-05 Sectors

Low-Skilled Employment Within

Outside

Low-Medium Skilled Employment Within

Outside

Medium-High Skilled Employment Within

Outside

High-Skilled Employment Within

147.3 39.8 80.7 18.4 6.9 1.7 3.7 Agriculture 1.3 2.8 0.8 1.8 0.1 0.3 0.1 Mining and Quarrying 20.8 4.1 11.6 0.4 1.3 0.3 Food, Beverages and 4.3 Tobacco 4.6 11.4 8.9 12.1 1.0 1.6 0.5 Textiles 2.6 34.0 3.0 28.4 0.2 2.0 0.1 Wood and Wood Products, Furniture and Fixtures 5.9 0.7 6.4 0.2 1.2 0.3 Pulp, Paper, Printing 0.2 and Publishing 0.3 9.0 0.6 9.2 0.1 1.2 0.1 Leather Products 0.1 6.2 0.4 5.0 0.1 0.8 0.1 Rubber and Plastics 0.0 2.4 0.0 1.6 0.0 0.3 0.0 Petroleum Products 0.5 4.5 0.7 3.6 0.1 0.6 0.4 Chemicals 2.5 9.8 1.2 5.4 0.1 0.8 0.1 Non-Metallic Mineral Products 0.9 2.9 1.7 3.4 0.3 0.7 0.3 Metals 0.3 2.6 0.9 3.2 0.3 0.7 0.4 Machinery 0.1 2.3 0.4 3.0 0.2 0.8 0.2 Transport Equipment 0.6 4.0 1.8 6.5 0.2 0.9 0.2 Other Manufacturing, nec 11.4 7.0 10.0 5.3 0.8 0.6 0.6 Construction 0.1 2.9 0.5 3.2 0.2 0.8 0.2 Electricity, Gas and Water Supply 4.5 4.6 7.3 4.7 1.3 0.9 0.8 Transport 0.1 1.0 0.8 2.8 0.4 1.1 0.4 Communication 10.0 4.1 19.0 6.0 4.0 1.3 3.6 Trade 2.0 17.5 2.6 10.8 0.3 1.2 0.2 Hotels and Restaurants 0.4 0.9 1.7 1.3 0.8 0.4 2.7 Financing, Real Estate and Business Activities 4.8 2.4 8.9 3.1 4.8 1.5 9.6 Other Services Sources: Authors‘ computation using the I-O table for India for 2003-04 (CSO, 2008) and 61st (2004-05) and 68th (2011-12) Rounds of Employment–Unemployment Survey conducted by the National Sample Survey Office (NSSO, 2006,).

Outside 1.1 0.4 1.0 1.2 1.5

1.8 1.6 0.9 0.4 0.8 0.7 0.7 0.9 1.1 0.9 0.6 0.9 0.8 1.4 1.3 0.9 1.2

2.9

39

Table A.4: Creation of Direct Vs. Indirect Jobs Across Sectors (in millions), 2011-12 Sectors

Low-Skilled Employment

Low-Medium Skilled Employment

Medium-High Skilled Employment

High-Skilled Employment

Within Outside Within Outside Within Outside Within Outside 102.2 42.0 88.3 32.7 10.1 3.9 5.1 2.3 Agriculture 0.9 3.0 0.9 3.2 0.2 0.6 0.3 1.1 Mining and Quarrying 15.3 5.2 14.3 0.7 2.0 0.6 1.7 Food, Beverages and 3.6 Tobacco 3.5 7.8 10.6 14.2 1.6 2.3 1.0 1.9 Textiles 1.7 19.4 3.4 29.5 0.4 3.6 0.1 2.0 Wood and Wood Products, Furniture and Fixtures 3.7 0.6 6.3 0.2 1.6 0.5 3.1 Pulp, Paper, Printing 0.2 and Publishing 0.4 11.5 0.6 12.5 0.1 2.1 0.1 1.9 Leather Products 0.1 4.0 0.6 6.2 0.1 1.2 0.1 1.5 Rubber and Plastics 0.0 1.4 0.1 2.4 0.0 0.6 0.0 0.8 Petroleum Products 0.3 2.3 0.7 3.7 0.2 0.9 0.5 1.4 Chemicals 2.1 6.9 1.9 6.7 0.2 1.1 0.1 1.1 Non-Metallic Mineral Products 0.7 1.6 2.2 3.2 0.6 0.8 0.4 1.0 Metals 0.1 1.4 0.9 2.9 0.3 0.8 0.8 1.4 Machinery 0.0 1.2 0.5 2.5 0.3 0.9 0.5 1.5 Transport Equipment 0.6 1.4 2.4 3.8 0.5 0.8 0.3 0.8 Other Manufacturing, nec 19.5 8.2 21.8 8.9 2.0 1.2 1.3 1.1 Construction 0.2 1.2 0.6 2.3 0.2 0.7 0.4 1.2 Electricity, Gas and Water Supply 3.6 2.2 9.3 3.8 2.4 0.9 1.3 0.9 Transport 0.1 0.7 0.5 1.8 0.4 1.0 1.0 2.4 Communication 8.0 3.3 20.3 7.3 5.4 2.0 5.5 2.3 Trade 2.0 14.3 3.7 15.2 0.6 2.3 0.6 2.0 Hotels and Restaurants 0.5 0.7 2.7 1.4 1.4 0.5 6.4 2.0 Financing, Real Estate and Business Activities 6.0 2.9 12.1 5.0 5.7 2.2 13.8 5.8 Other Services Sources: Authors‘ computation using the I-O table for India for 2011-12 (Timmer, 2012) and 68th (2011-12) Rounds of Employment–Unemployment Survey conducted by the National Sample Survey Office (NSSO, 2014).

40