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Phone: +91 11 2676 4600 / 2689 1111; Fax: +91 11 2612 2448E-mail: info@isid.org.in; Website: http://isid.org.in

Institute for Studies in Industrial DevelopmentNew Delhi

225Working Paper

May 2020

R. Rijesh

Liberalisation, Structural Change and ProductivityGrowth in Indian Organised Manufacturing SectorAbout the Institute

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ISID

Working Paper

225

Liberalisation, Structural Change and

Productivity Growth in Indian Organised

Manufacturing Sector

R. Rijesh

Institute for Studies in Industrial Development

4, Institutional Area, Vasant Kunj Phase II, New Delhi - 110 070

Phone: +91 11 2676 4600 / 2689 1111; Fax: +91 11 2612 2448

E-mail: info@isid.org.in; Website: http://isid.org.in

May 2020

© Institute for Studies in Industrial Development, 2020

ISID Working Papers are meant to disseminate the tentative results and findings obtained

from the ongoing research activities at the Institute and to attract comments and

suggestions which may kindly be addressed to the author(s).

CONTENTS

Abstract 1

I Introduction 1

II Structural Change and Productivity 3

II.1 Accounting for Structural Change: Shift-Share Approach 5

III Structural Change and Productivity in Indian Manufacturing: Brief Review 6

IV Performance of the Indian Organised Manufacturing Sector (1991-2016) 9

IV.1 Industry Composition 10

IV.2 Growth Profile 12

a) Medium to High Technology (MHT) Manufacturing Sectors 14

b) Medium to Low Technology (MLT) Manufacturing Sector 15

c) Low Technology (LT) Manufacturing Sector 16

V Structural Change and Productivity Growth: Decomposition Analysis 17

VI Concluding Remarks 23

References 25

Appendix 28

Figure(s)

Figure 1 Manufacturing Sector in GDP (%) 1990-2011 9

Figure 2 Organised Manufacturing Sector Composition: By Technology Groups

(1991 & 206) 10

Figure 3 Annual Growth Rates of Registered Manufacturing (1991-2016) 12

Figure 4 Growth Performance of Organised Manufacturing Sector: 1991-2016 13

Figure 5 Growth Performance of MHT Industries: 1991-2016 14

Figure 6 Growth Performance of MLT Industries: 1991-2016 15

Figure 7 Growth Performance of LT Industries: 1991-2016 16

Figure 8 Growth of Labour Productivity in Indian Manufacturing: By Technology

(1991-2016) 17

Figure 9 Average Annual Growth Rates of Labour Productivity: By Technology 18

Figure 10 Change and Decomposition of Aggregate Manufacturing Productivity:

(1991 to 2016) 19

Figure 11 Productivity Decomposition in Indian Manufacturing Sector: 1991-2016 21

Table(s)

Table 1 Labour Productivity Decomposition: By Technology Groups (1991-2016) 20

Table 2 Productivity Decomposition across Major Sectors: 2000-07 & 2008-16 21

Table A1 Composition of Indian Organised Manufacturing Sector: By Technology

Intensity (1991 & 2016) 28

Table A2 Growth of Manufacturing Sector: Select Indicators (2-digit NIC) 1991-2016 29

Table A3 Industrial Structure and Productivity of Indian Manufacturing

at the 3-digit level (1991-2016) 30

Liberalisation, Structural Change and Productivity

Growth in Indian Organised Manufacturing Sector

R. Rijesh*

[Abstract: The present study examines the nature of structural change and productivity growth

dynamics in the Indian organised manufacturing since the beginning of economic liberalisation in the

early 1990s. We find significant changes in manufacturing as the specialization pattern moved towards

technology-intensive segments with considerable improvement in output, productivity, wages and

capital intensity over time. However, a corresponding growth in employment is not observed across many

industries. The decomposition of labour productivity at the sectoral level reveals the overwhelming

presence of within-sectoral technological change component of growth across different industries.

However, the detailed plant-level data shows the evidence of positive structural change, in terms of static

and dynamic shifts, among the medium technology-intensive sectors during the 2000s. The findings

point out the need to have comprehensive and strategic policy interventions to address the structural

rigidities and institutional bottlenecks in the manufacturing sector.]

Keywords: Structural change, Labour Productivity, Shift-Share Decomposition method, organised

manufacturing, Indian industry.

JEL Classification: D24, L6, O47, P23

I. Introduction

Historically, the development experiences of nations indicate a consistent positive

correlation between the rate of growth of industrialization and the pace of economic

growth (Sabillon, 2008). It is a stylized fact that changes in economic structure from

traditional agrarian activities to modern manufacturing activities generate a number of

direct and indirect benefits. For instance, the growth experiences of advanced nations

reveal that the shifting of economic resources towards manufacturing is associated with

substantial structural change bonuses in terms of improved output per worker (labour

productivity), technological change, learning effects, specialisation, and inter-sectoral

linkages than any other productive sectors in the economy (see Szirmai, 2013). Since

manufacturing activity is characterised by high productivity and technological dynamism,

the transition of economies from low income to high income requires the development of

* Dr. R. Rijesh, Assistant Professor, ISID. Paper prepared under the ISID’s ICSSR Research Program

on ‘Industrial, Trade and Investment Policies: Pathways to India’s Industrialisation’.

2

a well-functioning industrial base (see Weiss and Tribe, 2016). Naturally, the emerging

developing countries have adopted various policy instruments to orientate towards high-

income growth path through industrialisation.

Since the beginning of the Second Five-Year Plan during the late 1950s, the

policymakers in India recognised the need to build a strong industrial base in the country.

The successive policy deliberations, while maintaining the principle of self-sufficiency and

minimal external dependence, framed policies that aimed to develop a vibrant

manufacturing sector with heavy investment by the public sector. This form of inward-

looking development regime was successful in building a diversified industrial base over

time. However, the continued pursuit of blanket protective policies such as industrial

licencing, capacity caps, technology restrictions, tariffs, quota etc. created large economic

inefficiencies and cost burden1. Critics argued that the lack of market competition

increased economic cost through resource misallocation, inefficiency, poor productivity

and lack of external competitiveness.

The systematic economic reforms introduced in the early 1990s is aimed at

correcting these market distortions by creating a favourable environment for improving

production efficiency and competitiveness. This is expected to contribute significantly to

the overall growth and development of the economy through various direct and indirect

effects. In contrast to the earlier regime, the liberalisation initiatives endorse outward

orientation and increased openness in the market. The opening up of the economy is

expected to bring substantial economic gains through increased competition, technology

upgradation and economies of scale.

The industrial response to the reform process is well-documented in the literature.

Several studies have examined the performance of the industries across various

dimensions including output growth (Goldar, 2018; Nagaraj 2003 & 2017, Ahsan and

Mitra, (2016), productivity (Erumban et al, 2019; Rijesh 2016; Goldar, 2014) employment

(Thomas, 2014; Papola 2012) etc. In general, the emerging consensus is that there was

perceptible growth in the quality and variety of industrial goods, especially during the

mid-2000s. However, this was not accompanied by a significant improvement in the

relative size of the industrial sector in GDP, which has been stagnated around 14 to 15 per

cent (Nagaraj 2017).

The existing studies have reported significant changes in the composition of

industrial production and productivity since reforms. However, only a few have studied

the systematic link between the changes in industrial structure and productivity changes.

The selected studies that examined the aspect of structural change and productivity, for

example, Erumban et al, (2019); Ahsan and Mitra (2016); Aggarwal and Kumar (2015),

1 Several empirical studies have examined the cost implication of blanket import substitution regime

on Indian manufacturing. For further details, see Bhagwati and Srinivasan (1975) Chudnovsky, et al,

(1983) Ahluwalia (1985), Lall (1999) Kochhar et al (2006) and Panagariya (2008).

3

Cortuk, and Singh (2011), are largely based on aggregate sectoral level data covering a

limited period. Since the structural transformation can result from movement of factor

inputs, especially labour, from low productive manufacturing to higher value added

manufacturing and productivity pattern can significantly differ across industrial units,

there is a need to take into account the industrial heterogeneity at a more detailed level in

exploring the nature of structural change. The present study is an attempt to fill this

research gap in the literature. In particular, the study aims to analyse the pattern of

structural change in the organised manufacturing sector and the contribution of resource

reallocation across industries on aggregate manufacturing productivity since the onset of

economic liberalisation in the early 1990s.

The rest of the paper is organised as follows. Section 2 details the concept of

structural change and growth and the method to account these changes in manufacturing.

Section 3 provides a brief overview of the literature on the structural change and

productivity in Indian manufacturing sector. Section 4 presents the descriptive evidence

of the changing structure of manufacturing across different parameters during 1991-2016.

Section 5 details the empirical results of the decomposition exercise of labour productivity

to account the incidence of structural change in manufacturing. Section 6 concludes.

II. Structural Change and Productivity

In the course of economic development, one of the distinctive notable feature is the changes

in the relative pattern of productive structure of the economy. The increased rate of economic

growth has often accompanied by considerable changes in the composition of sectoral

output, employment, organisation and institutional dynamics. This is significant as these

changes can further impact the process of growth over time (Matsuyama, 2008).

Consequently, the changes in the relative weight of significant component of an aggregate

indicator of the economy, which we refer as structural change, has been the central element

of economic development (Ishikawa, 1987). In a broader context, structural change signifies

the movement of resources from agriculture into non-agricultural activities, followed by

manufacturing industries and services. In economic history, the concept of structure denotes

the relative significance of economic sectors in terms of production and factor content

(Syrquin, 1988). In this respect, the process of industrialisation is often considered as central

to the process of structural change (see Syrquin, 1988; Weiss and Tribe 2016).

Industrialisation, especially specialisation in manufacturing activities, are crucial

for economic growth as manufacturing activities provide opportunities for significant

technical change and productivity growth as they offer higher scope for specialisation,

technological learning and product diversification than any other sector (UNCTAD, 2016;

Weiss and Tribe, 2016). This is even more relevant for low-income countries since the

relatively higher differences in productivity between manufacturing and non-

manufacturing in these economies induce the shift of labours towards manufacturing to

bring about structural change bonus in terms of higher labour productivity growth. This

4

growth-enhancing process of structural change in terms of better growth of income per-

capita and manufacturing specialisation is evident from the recent historical experiences

of China and East and South-East Asian NICs (See McMillan and Rodrik, 2011). Hence, a

robust manufacturing sector is an essential element for successful economic growth and

technological catch-up as it generates virtuous, cumulative linkages with rest of the

sectors, enhance technological progress and offers the strongest potential for higher

productivity gains in the economy (UNCTAD, 2016)2.

The reallocation of economic resources among different sectors, i.e., structural

change, is a prominent driving force of economic growth and productivity growth (see

Lewis 1954; Clark 1957; Kaldor 1966; Kuznets 1966; Denison 1967; Syrquin 1988; Lin 2009).

The literature linking the relationship between economic growth and structural change

assert that changes in economic structure result from preferential differences in the income

elasticity of demand, sectoral-specific productivity changes, user-producer spillovers and

differential propensities towards entrepreneurial discovery (see Pasinetti, 1981; Peneder,

2003; Dietrich, 2012). In the case of demand-side explanations, the increase in income per-

capita tends to change the structure of demand as per the operation of the Engels Law. The

dissimilar income elasticities across sectors lead to readjustments in the production process

which increases the speed of structural change. On the supply side, the rate of structural

change is influenced by the rate of sectoral productivity growth differentials. The changes

in average productivity growth encourage the restructuring of factors inputs that are

decisive for structural change (Dietrich, 2012). However, it is plausible that the direction

of causality can also reverse as changes in the structure of the economy can impact

aggregate growth due to the existence of productivity heterogeneity among sectors (see

Timmer and Szirmai, 2000; Fagerberg 2000; Dietrich, 2012).

Studies highlight that the inter-sectoral productivity differentials act as an

incentive for efficient resources allocation among sectors (Isaksson, 2010). The continued

shift of factor inputs from lower (agriculture) to higher (manufacturing) productive sectors

augment aggregate productivity and income per capita. This structural change bonus has

been a major source of growth in developing countries (see Lewis, 1954; Chenery et al.,

1986; Timmer and Szirmai, 2000; Szirmai, 2012). Conventionally, most empirical studies

have focussed on the movement of labours from agriculture to manufacturing and ignored

the structural dynamics within the manufacturing sector (Timmer and Szirmai, 2000). In

the course of economic development, industries may upgrade their economic activities

from a relatively low level of productivity to industries having higher productivity and

labour inputs move from less productive segments to higher productive branches. Over

time the performance of aggregate labour productivity can be affected by changes in the

2 The role of manufacturing for potential productivity gains and income growth through production,

investment, knowledge and income linkages has been well recognised in the development literature

(see Kaldor, 1966, UNCTAD 2016).

5

structure of different manufacturing industries. In case rapid structural change occurs,

labours can transfer from less productive manufacturing to more productive

manufacturing activities3 rather than acquiring efficiency gains resulting from the new and

improved method of production within individual industries (Fabricant 1942; Fagerberg

2000; Timmer and Szirmai 2000).

II.1 Accounting for Structural Change: Shift-Share Approach

To examine the contribution of structural change on labour productivity growth in the

Indian manufacturing sector, we use the conventional shift-share (S-S) decomposition

framework first introduced by Fabricant (1942) and used extensively in the literature (see

Syrquin 1984; Fagerberg 2000; Timmer and Szirmai 2000; McMillan and Rodrik, 2011; Vu,

2017). This conventional analysis proposes to decompose aggregate manufacturing

productivity growth into the effects of productivity growth within-sector (technological

progress) and effects due to structural change. The S-S decomposition analysis of

aggregate labour productivity of Indian manufacturing sector over the period [0, T] is

derived following Fagerberg (2000).

Let LP= Labour Productivity, O= Output (value added), L= Labour input. Then,

𝐿𝑃 =𝑂

𝐿=

∑ 𝑂𝑖𝑖

∑ 𝐿𝑖𝑖= ∑ ⌈

𝑂𝑖

𝐿𝑖

𝐿𝑖

∑ 𝐿𝑖⌉

𝑖

− − − − − (1)

i = manufacturing industry (i = 1…,n)

Labour productivity in manufacturing industry i is defined as

𝐿𝑃𝑖 =𝑂𝑖

𝐿𝑖⁄ − − − − − (2)

The share of manufacturing industry i in total employment is expressed as follows

𝑆𝑖 =𝐿𝑖

∑ 𝐿𝑖𝑖 − − − − − −(3)

By substituting, equation (2) and (3) into equation (1), we obtain

𝐿𝑃 = ∑⌈𝐿𝑃𝑖𝑆𝑖⌉

𝑖

− − − − − −(4)

3 In case demand rises relatively faster in sectors with low productivity growth, changes in the sectoral

structure can negatively affect the pace of economic growth. The intrinsic differences between

industries in their capacity to raise productivity shifts labours from progressive industries with

higher productivity growth to stagnant industries with low productivity growth (Peneder, 2003).

This negative feedback phenomena is known as Baumol’s cost disease which often result from

increased tertiarization in the economy (see Baumol, 1967).

6

Assuming ∆𝐿𝑃 = 𝐿𝑃1 − 𝐿𝑃0, ∆𝑆 = 𝑆1 − 𝑆0, we can derive the following decomposition

identity in level form using equation (4)

∆𝐿𝑃 = ∑[𝐿𝑃𝑖0∆𝑆𝑖 + ∆𝐿𝑃𝑖∆𝑆𝑖 + 𝑆𝑖𝑜∆𝐿𝑃𝑖]

𝑖

− − − − − − − (5)

In terms of growth rate, we can rewrite equation (5) as follows

𝐺𝑟𝑜𝑤𝑡ℎ (𝐿𝑃) =𝐿𝑃1 − 𝐿𝑃0

𝐿𝑃0

=∑ 𝐿𝑃𝑖0(𝑆𝑖1 − 𝑆𝑖0) + ∑ (𝐿𝑃𝑖1 − 𝐿𝑃𝑖0)(𝑆𝑖1 − 𝑆𝑖0)𝑖 + ∑ (𝐿𝑃𝑖1 − 𝐿𝑃𝑖0)𝑆𝑖0𝑖𝑖

𝐿𝑃0 − − − −(6)

Equation (6) indicates that the changes in aggregate productivity growth can be

decomposed into three effects, namely, static-shift effect, dynamic-shift effect and within-shift

effect. The first two effects represent structural change while the final effect signifies

technological progress in the sector (Aldrighi, and Colistete, 2015; Vu 2017).

The static industry effect is calculated as the sum of changes in labour allocation

across industries during the final year and initial year, weighted by the labour productivity

in the base year. The static effect is positive/negative when industries with higher

productivity attract more/less labour force and thereby increase/decrease the share in total

manufacturing employment (Peneder, 2003). The dynamic industry effect is the sum of

interaction between changes in labour productivity and changes in the labour share of

industries. It is assumed that when industries increase their labour productivity and

employment simultaneously, the combined effect would generate a positive contribution

to aggregate productivity growth (Peneder, 2003). The interaction effect reflects the

scenario when increased labour reallocation is directed towards industries with higher

growth in labour productivity4. The within industry effect correspond to changes in labour

productivity weighted by the initial share of labour employment in the industry. This effect

reflects that there is no structural change (allocation of labour across industries) and the

entire productivity changes reflect mainly pure technical change through learning by

doing, capital intensity, increased efficiency, average hours of work, and technological

advances (see Aldrighi, and Colistete, 2015).

III. Structural Change and Productivity in Indian Manufacturing: Brief Review

Several empirical studies have examined the changing nature of industrial structure since

economic reforms based on relatively simple measures of manufacturing composition.

Similarly, the rate of changes in industrial productivity has been an active area of research

in the past. Papola (2012) finds that despite a stable share of industrial share in GDP, the

manufacturing sector witnessed considerable structural change as the share of traditional

4 In case the interaction term is negative, this indicates that industries with higher productivity growth

cannot maintain their level of employment. This can result in falling share of employment in faster

productive industries which indicates the presence structural burden of structural change on

aggregate labour productivity as envisaged by Baumol (Peneder, 2003).

7

industries declined vis-à-vis modern industries. At the disaggregated level, agro and

material-based industries declined in importance while the share of capital-intensive

industries increased sharply during 1991-2007. Although there was an improvement in

labour productivity, the registered manufacturing witnessed an absolute decline in

employment during 1997-2007.

Thomas (2014) argued that post-1990s the rate of industrial growth has been highly

volatile. Based on ASI data, he found that the capital and skill intensive industries like

metals, automobiles and chemicals expanded rapidly compared to traditional segments

such as textiles, wood, food and beverages during 1991-2006. Since the traditional segment

absorbed a larger pool of labours, the declining trend limited the growth rate of

employment and wages. Nagaraj (2017) concur the relative stagnation of manufacturing

sector around 15 per cent of GDP but noted considerable product diversification and

improved quality and variety of domestic production during 1991-2014. Goldar (2018) also

report similar findings regarding the changes in the composition of industrial composition.

However, using double deflated gross value added (GVA), the author found a clear

upward trend in the share of manufacturing in aggregate GVA during 2003-2011.

There exist a large number of empirical studies on productivity dynamics of

Indian manufacturing, especially since economic reforms (for a comprehensive survey of

the literature, see Goldar, 2014). The seminal study by Ahluwalia (1991) reported a

turnaround of productivity trend since the initial liberalisation of the 1980s. However,

several subsequent studies questioned her findings on account of methodology and

variable construction (see Balakrishnan and Pushpangadan, 1994 & 1998; Trivedi, 2000).

Overall, the consensus is that the productivity growth of Indian manufacturing sector

during the 1990s was not substantial (Rijesh, 2016). In contrast, several studies during the

post-2000 period find a considerable increase in productivity (see Bollard et al 2013, Goldar,

2015 and Rijesh, 2016). For instance, using ASI unit-level data, Bollard et al (2013) showed

a sharp increase in productivity, measured by Total Factor Productivity (TFP), from 3.5 per

cent during 1980-1992 to 7.4 per cent during 1993-2007. In general, the majority of studies

have used both partial and total measures of productivity growth under the growth

accounting framework. Although studies have explored the nature of industrial

composition and productivity, not many have examined the link between the dynamics of

changes in the structure of industries on productivity growth during the reform period.

Only a handful of studies have examined this aspect in the context of India and also at the

aggregate level. The following discussion highlight some notable studies.

Cortuk and Singh (2011) examined the link between structural change and growth

in India from 1951 to 2007 at 1999-2000 prices data of GDP collected from the Reserve Bank

of India (RBI) database. For computing structural change, the study uses two different

indices computed annually, namely, (1) the Norm of Absolute Values (NAV)5 and (2) the

5 NAV measures aggregate shifts in sectoral shares in an economy and is calculated as the one-half of

the sum of the absolute value of sectoral share differences between the initial and final years of study.

8

Modified Lilien Index (MLI). The VAR analysis of structural change and growth and

structural break analysis revealed that 1988 is the period of a structural break in India. Post

this period, the VAR analysis revealed the significant positive impact of structural change

on growth. The Granger causality test using growth and MLI series indicate one-way

Granger causality from structural change to growth from 1988 to 2007.

Aggarwal and Kumar (2015) evaluated the pace of structural change in Indian

economy based on the norm of absolute values (NAV) indicator and the decomposition of

labour productivity following McMillan and Rodrik (2011) during the period 1973-74 to

2009-10. The relocation of labour from agriculture to more productive non-agricultural

sectors is found to be the prominent aspect of structural change during the 1970s and 1980s.

The most dramatic rate of growth in structural change occurred during the period from

2003-04 to 2009-10 which was mainly induced by the service sector. The failure to absorb

labours in productive activities is attributed to high capital intensive production structure,

high skill requirements and growing informality in manufacturing. Within manufacturing,

around three-quarter of value added is accounted by medium to low technology industries

in the recent period. The decomposition analysis further reveals marginal structural

changes as workers were increasingly absorbed in lower-productivity activities. The

reform period also witnessed the widening wage gap between low tech industries and

more advanced industries.

Ahsan and Mitra (2016) observed that within-sector productivity growth has been

significant for aggregate labour productivity growth in the Indian economy based on

McMillian and Rodrik (2011) decomposition method. Overall, there is evidence of the

movement of workers from sectors with low value added per worker to high value added

workers during the post-reform period of 1991-2004. During this period, structural change

contributed less than a fourth of overall labour productivity growth rate of 4 per cent.

Erumban et al (2019) examined the dynamics of structural change in the Indian

economy during 1981-2011 based on the 2015 version of India KLEMS database. The study

considered 27 industrial sectors including both formal (organised) and informal

(unorganised) sectors and used both labour productivity and total factor productivity

measures of productivity growth. The study observed an impressive growth of labour

productivity during 2003-2011 (7.5 per cent) accompanied by a positive impact of

structural change on aggregate labour productivity as workers moved from low

productivity industries to high productivity industries. However, the movement of

labours from less productive sectors to fast-growing sectors (dynamic productivity effects)

was not observed as there was lack of employment generation in high performing sectors.

In general, 50-80 per cent of aggregate productivity growth was contributed by within

industry productivity growth and the rest by structural change.

The MLI is computed as the standard deviation of the sectoral growth rates of employment from

initial and final period (see Dietrich, 2012 for further details).

9

IV. Performance of the Indian Organised Manufacturing Sector (1991-2016)

In this section, we attempt to provide a descriptive statistical account of the Indian

organised manufacturing sector. The primary data of industrial production and

performance parameters are collected from the Annual Survey of Industries (ASI),

published annually by the Central Statistical Office (CSO), Government of India. The ASI

is the principal source of registered manufacturing factories consists of census and sample

frame distributed across India. We collected the principal characteristics of industrial

statistics from the published reports of ASI Volume 1 at the sectoral level. Since the

National Industrial Classification (NIC) used to identify manufacturing activities have

undergone significant changes during the study period, a correspondence series was

prepared following the concordance scheme of Rijesh (2019). The detailed 3-digit level data

is collected from the concordance series provided by EPWRFITS, online database. The

factory level data at the unit level is procured from CSO and amassed at the 5-digit level

for the period 2000 to 2007 (NIC-98/04) and 2008 to 2016 (NIC-08). The price series at the

disaggregated level is compiled using the Wholesale Price Indices (WPI) from the Office

of Economic Advisor and Consumer Price Index (CPI) for industrial workers (Labour

Bureau), Government of India.

Figure 1: Manufacturing Sector in GDP (%) 1990-2011

Source: Authors compilation from National Account Statistics (CSO), various issues

A look at the share of the aggregate manufacturing, including both organised and

unorganised segments, in GDP indicates that the size has been relatively stable around 15

per cent to 17 per cent during 1990-91 to 2011-12 (See Figure 1). This supports the widely

held view of virtually stagnant share of the manufacturing sector in aggregate GDP since

liberalisation6. The continuing stagnancy of manufacturing indicates the inability of the

6 It has to be noted that the above conclusion is based on data at current prices. A recent article by

16.0215.17

17.03 16.14

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

1990-91 2000-01 2010-11 2011-12

Organised Sector Unorganised Sector Manufacturing Sector

10

industrial sector to have a dynamic impact on the economy. As evident from Figure 1,

more than 80 per cent of industrial output is generated in the organised sector. The sectoral

dynamics of the organised sector since economic reforms are explored in this section.

IV.1 Industry Composition

The objective of this sub-section is to study the structural changes in manufacturing

based on the compositional distribution of industries categorised at the 2-digit level of

broad classification. The detailed distribution of manufacturing sectors in terms of

production (GVA), labour, capital and wages across different technology-intensive

classification is given in Table A1 in the appendix. For brevity, the major trends across

technology groups are summarised in Figure 2.

Figure 2: Organised Manufacturing Sector Composition: By Technology Groups (1991 & 206) %

Note: MHT=Medium to High Technology, MLT=Medium to Low Technology, LT=Low Technology

Source: Authors compilation from Annual Survey of Industries (CSO), various issues

In terms of Gross Value Added (GVA), the size of manufacturing is dominated by

the Medium to high technology (MHT) segments for the entire period (41 per cent). On the

other hand, Medium to low technology (MLT) sector which had 28 per cent share in 1991,

improved its share to 35 per cent by 2016. In contrast, the share of Low Technology (LT)

has declined from 31 per cent to 24 per cent during the same period. This declining trend

is also visible across other parameters such as fixed capital, emoluments and employment

levels (see Figure 2). Although the technology-intensive (both MHT & MLT)

manufacturing occupied a larger portion of value added in manufacturing, the larger part

of the workforce is continued to be absorbed in the traditional segment (LT). In the case of

Goldar (2018) noted significant improvement in the share of manufacturing in aggregate GVA when

double-deflated GVA is used. For instance, the manufacturing share is reported to have increased

from 10 percent in 1980-81 to 22 percent in 2011-12 (see Goldar, 2018).

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1991 2016 1991 2016 1991 2016 1991 2016

GVA Capital Wages Employment

LT MLT MHT

11

MHT, both the share of wages and employment witnessed a marginal improvement while

the share of capital has declined over time. Overall, it is evident that a decline (increase) in

GVA is associated with a corresponding decline (increase) in wages and employment

across manufacturing sectors, except in the case of leather products and non-metallic

mineral producing sectors (see Table A1).

In the case of individual industrial sectors, the largest share of manufacturing

output in terms of GVA is by the chemical sector (18 per cent) in 2016 (see Table A1). The

chemical sector also accounted for the largest share in manufacturing wages (14 per cent).

In the recent period, the highest contributor of GVA is from the MHT manufacturing

sectors (41 per cent) followed by MLT (35 per cent) and LT (24 per cent) sectors. Although

the relative share of technology categories in the overall manufacturing has been stable

over time, it is evident that the share of LT has declined from 31 per cent to 24 per cent and

the share of MLT has increased from 28 per cent to 35 per cent over time. In the MHT

group, the value added of chemicals, medical optics and transport equipment has

improved while the electrical & non-electrical machinery sectors output has declined. As

noted before, the declining GVA is associated with a corresponding fall in wages and

employment levels in the respective industries under each technology category. In the case

of MLT, except for non-metallic minerals and basic metals, the rest of the sectors increased

their share of GVA from 1991 to 2016. For LT, a large number of sectors experienced a

reduction in value added (6 out of 9) which is also visible in their declining share in wages

and employment during this period.

In terms of wages, the largest contributing industries belong to the MHT sector

(both chemical and transport equipment accounting 14 per cent share) in 2016. This is in

contrast to the situation in the early 1990s where textiles, which is an LT sector, accounted

for the largest share (17 per cent). Since liberalisation, the wage share of MHT sectors has

expanded. Compared to LT, technology-intensive manufacturing (both MLT and MHT)

have shown a marked improvement in the composition of wages. The relative number of

workers employed in the traditional industries (share of LT sectors in total manufacturing

employment) has also fallen over time. For instance, in 1991, the largest contributor to

employment was in textiles (17 per cent) which has declined to 11 per cent by 2016. In the

latest period, the food and beverage sector (13 per cent), textiles (11 per cent) and chemicals

sectors (10 per cent) provide the largest employment.

The manufacturing sector composition at the 3-digit level of disaggregation

distributed across major industry groups and technology classification is given in Table

A3. The pattern of industrial composition witnessed at the sectoral level is more apparent

at the disaggregated level. For example, the largest contributors (top ten) of industrial GVA

for the entire period is found to be concentrated among high technology-intensive

industries like other chemicals (10 per cent), basic chemicals (7 per cent), office &

computing machinery (4 per cent), motor vehicles (3 per cent) and special-purpose

machinery (3 per cent). However, only three of these technology industries, namely other

12

chemicals, office accounting machinery and special-purpose machinery contributed

significantly to the total employment (see Table A3 in the appendix). Although high

technology-intensive industries expanded considerably, this has been confined to a limited

number of industries. For instance, out of 56 industries at the 3-digit level, only 13

technology-intensive industries (10 HT and 3 MT industries) increased their contribution

to overall GVA. In that too, the major improvement was concentrated among few

industries like refined petroleum (13 per cent), other chemicals (12 per cent) and motor

vehicles parts (4 per cent). In contrast, a large number of industries reported a declining

share of GVA from 1991 to 2016. In terms of employment, the low technology industries

like spinning & weaving (12 per cent), other food products (8 per cent), tobacco (5 per cent)

etc. contributed major share during the entire period of study (see Table A3 for further

details).

IV.2 Growth Profile

For a comprehensive assessment of the compositional shifts in manufacturing, it is

important to examine the rate of expansion of industrial activities over time. Figure 3

shows the year-to-year growth of aggregate manufacturing GVA, wages, capital and

labour during 1991-2016. In general, the trend across variables is largely cyclical. In the

initial period of liberalisation, especially post-1995, there is a noticeable declining trend

among the key performance indicators. Thereafter, there is a marked revival that sustained

up to 2008, which also marked the beginning of significant turmoil at the global financial

market. Post-2008, the rate of expansion was relatively modest although there is some sign

of recovery post-2012.

Figure 3: Annual Growth Rates of Registered Manufacturing (1991-2016) %

Source: Authors compilation from Annual Survey of Industries (CSO), various issues

-15.0

-10.0

-5.0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016

GVA Wages Capital Labour

13

We fitted a semi-log model to capture the overall rate of expansion of selected

variables from 1991 to 2016. This is carried out using the time series data of production

(both value added and gross output), fixed capital, wages, employment and productivity

(labour) derived at the 2-digit level of sectoral classification grouped across three

technology-intensive classes. During the entire period, the rate of expansion of

manufacturing output, expressed in terms of value added, is around 8.6 per cent per

annum (see Figure 4). In terms of gross output indicator, the rate of growth is marginally

higher at 9.4 per cent (see Table A2 in the appendix for detail). This increase in production,

however, is not apparent across other performance indicators. For instance, compared to

industrial production expansion, the rate of growth of the number of total persons

engaged, industrial wages and labour productivity is reported to be only 3 per cent, 5.6

per cent and 5.5 per cent per annum, respectively. In contrast, we can observe a markedly

higher (10.9 per cent) rate of expansion of capital input which indicates increased capital

formation and mechanisation of manufacturing activities post liberalisation.

Figure 4: Growth Performance of Organised Manufacturing Sector: 1991-2016 (%)

Note: MHT=Medium to High Technology, MLT=Medium to Low Technology, LT=Low Technology

Source: Authors compilation from Annual Survey of Industries (CSO), various issues

Figure 4 further provides the overall growth rate of technology-intensive

categories across these parameters. Comparatively, the high technology industries have

outperformed other segments in terms of growth in output and productivity. It is striking

to note that all parameters of industrial growth in low technology industries, except the

capital, has been significantly low during this entire period. As seen earlier, the higher

rates of growth in the capital are evident across all segments of manufacturing industries.

8.6

10.9

5.6

3.0

6.3

0.0

2.0

4.0

6.0

8.0

10.0

12.0

Manufacturing MHT MLT LT

GVA Capital Wages Employment LP

14

a) Medium to High Technology (MHT) Manufacturing Sectors

The performance of five high technology-intensive industries on selected parameters is given

in Figure 5. We can notice the growth of industrial production (GVA) in medical optics (13.7

per cent), transport equipment (10.9 per cent) and non-electrical machinery (9.4 per cent)

were relatively higher than the aggregate manufacturing GVA (8.6 per cent). The chemical

sector, which accounted for the largest contributor of GVA (share of 18 per cent), has also

registered an impressive rate of 7.3 per cent rates of increase in GVA during this period. The

output expansion has been accompanied by an improvement in the efficiency at which the

labour is used in the production process as indicated by a well-above-average rise in labour

productivity across different industries. For example, the productivity growth of all

technology-intensive industries is in the range of 7.1 to 7.6 per cent, which is relatively higher

than the all India average of 5.5 per cent rates of growth, except in chemicals.

Figure 5: Growth Performance of MHT Industries: 1991-2016 (%)

Source: Authors compilation from Annual Survey of Industries (CSO), various issues

Although the industrial production and productivity have improved

substantially over the years, it is striking to note that a corresponding rise in employment

and wages has not been materialised. The moderate rise in wages does indicate the

inability of the sector to reap the benefit of increased productivity over time. The

expansion of value added seems to be largely based on the growth of capital relative to

labour. For instance, the expansion of capital in all industries have been substantially

higher than that of total persons engaged in production.

As evident from the Figure, the rate of growth in employment is strikingly poor,

especially in the case of machinery products which has grown around 2 per cent per

annum. The increased capital accumulation, however, been able to improve the

0 10 20 30 40 50 60

Chemicals

Non-Electrical

Machinery

Electrical Machinery

Medical Optical

Transport Equipment

GVA Capital Wages Employment LP

15

efficiency of labour across industries. This increased capital deepening explains the

substantial rise in productivity across different industrial sectors.

b) Medium to Low Technology (MLT) Manufacturing Sector

Compared to aggregate manufacturing, three industries, namely rubber & plastics (10.5

per cent), fabricated metal products (9 per cent) and refined petroleum products (8.9 per

cent), in this segment witnessed significant GVA growth (see Figure 6). As noted earlier,

the expansion of value added is less than that of gross output which indicates the

increasing use of material inputs in manufacturing (see Table A2). As before, the

prevalence of capital-intensive mode of production is evident across medium-technology

intensive industries as the rates of growth in the capital is relatively higher than labour

inputs. This retells the pattern observed in the high technology sector discussed earlier.

The productive efficiency of labour, however, found to have increased moderately (in the

range of 4.1 per cent to 5.7 per cent).

Figure 6: Growth Performance of MLT Industries: 1991-2016 (%)

Source: Authors compilation from Annual Survey of Industries (CSO), various issues

In contrast, we find a marked improvement in the rate of growth of industrial

wages in this segment relative to other technology groups. The rate of increase in worker

wages in all medium-technology industries, which is in the range of 6 to 8 percentage, is

higher than aggregate manufacturing (5.6 per cent), except for basic metals (see Figure 6).

One notable industrial sector in this segment that has increased their presence in the recent

period is the refined petroleum manufacturing, which has performed better in terms of

GVA, wages and labour productivity.

0 5 10 15 20 25 30 35 40 45

Refined

Petroleum

Rubber &

Plastic

Non-Metallic

Mineral

Basic Metal

Fabricated

Metal

GVA Capital Wages Employment LP

16

c) Low Technology (LT) Manufacturing Sector

The performance of LT industries is comparatively lower than the high technology-

intensive sectors (see Figure 7). Out of the nine industries, only three industries, namely

apparel, leather and furniture & recycling, witnessed a higher rate of growth in value

added compared to aggregate manufacturing. It is striking to note that all industries,

which are traditionally highly labour-intensive, has witnessed a considerable capital

deepening over time. In six industries (namely food & beverages, tobacco, textiles, wood,

paper and print & publishing) the growth of labour is less than 3 per cent per annum. This

poor performance is further reflected in the slow expansion of industrial wages across LT.

The improvement in labour productivity is largely propelled by highly capital-intensive

mode of production. Being a labour surplus economy, this pattern of growth trajectory

clearly shows the inadequacy of the LT sector to emerge as the leading sector to generate

and use the wide pool of productive (surplus) labours across traditional manufacturing

activities.

Figure 7: Growth Performance of LT Industries: 1991-2016 (%)

Source: Authors compilation from Annual Survey of Industries (CSO), various issues

The descriptive analysis of the growth pattern of industries clearly shows that the

expansion of Indian manufacturing industries since liberalisation is largely being

dominated by the growth of capital-intensive mode of production. The absorption of

productive labour in across different segments of manufacturing and especially towards

less innovation-based production activities has not been materialised over the years.

Although there is evidence of expansion in labour productivity over time, a corresponding

rise in wages across sectors is not visible.

-5 5 15 25 35 45 55

Food & beverages

Tobacco

Textiles

Apparel

Leather

Wood Products

Paper Products

Print & Publishing

Furniture; & Recycling

GVA Capital Wages Employment LP

17

V. Structural Change and Productivity Growth: Decomposition Analysis

The focus of this section is to discuss the impact of structural change on productivity

growth of Indian manufacturing using the shift-share decomposition framework outlined

in sub-section 2.1. In section 4, we provided a broad overview of labour productivity

growth across aggregate industrial sectors distributed across three technology-intensive

classifications. The descriptive analysis in this section is carried out at finer levels of

disaggregation of the 3-digit and 5-digit level of NIC classification.

As evident from Figure 8, the annual growth of productivity has been largely

cyclical across the manufacturing sector. During the initial period of economic

liberalisation, the largest spike in growth rates was witnessed in the low-technology sector.

However, since the beginning of 2000s, the productivity of LT sector decelerated

considerably. During this period, the medium-to-low-technology witnessed some

improvement, though, post-2008 the rate of growth has been negative for several years.

The growth rates of medium-high-technology industries, although fluctuating, were

relatively stable throughout. Overall, the labour productivity in aggregate manufacturing

is around 6 per cent per annum during 1991-2016 (see Figure 9). As evident in figure 9, the

average growth of productivity in technology-intensive industries (both MHT and MLT)

was relatively higher than low-technology groups during this period.

Figure 8: Growth of Labour Productivity in Indian Manufacturing: By Technology (1991-2016) %

Note: MHT=Medium to High Technology, MLT=Medium to Low Technology, LT=Low Technology

Source: Authors compilation from Annual Survey of Industries (CSO), various issues

For a better understanding of the productivity dynamics, we segregated the entire

study period into following three sub-periods, namely (1) 1991-2000, (2) 2001-2007 and (3)

2008-2016. The first period corresponds to the initial decade of economic liberalisation. The

second period represents the second wave of economic reforms lasting until the onset of

the global economic crisis in early 2008. The final period indicates the aftermath of the

-25.0

-15.0

-5.0

5.0

15.0

25.0

35.0

45.0

1992 1995 1998 2001 2004 2007 2010 2013 2016

Manufacturing LT MLT MHT

18

financial crisis period. The average growth rate of productivity across manufacturing

during these periods is given in figure 9. In the case of LT, there is a clear deterioration in

trend growth rates during the selected three periods. The highest improvement in

productivity in these traditional industries is witnessed during the first period (6 per

cent).Since then there is a marked decline which is noticeably evident in the post-global

crisis period. The detailed 3-digit level analysis revealed that only two industry group,

namely grain mill products, starches, animal feeds (code 153) and furniture (code 361)

witnessed a higher growth rate relative to the aggregate manufacturing sector (see Table

A3 in the appendix). The MLT sector performed comparatively better, especially during

the second period, growing at a rate of 13 per cent per annum.

However, post-2008, the rate of growth in productivity declined sharply to -0.8 per

cent. Only two industry group, namely rubber products (code 251) and glass product (code

261) reported a higher productivity growth rate than the aggregate manufacturing sector.

Finally, in the case of highly technology-intensive group MHT, the overall performance across

periods is highly remarkable. During the three periods, the rate of growth in productivity was

markedly higher than overall manufacturing and for the entire period around sixteen 3-digit

MHT industries were able to improve higher than the aggregate manufacturing sector (see

Table A3 in the appendix). This indicates that overall productivity growth is largely driven by

technology-intensive industries in India. Finally, the period 2001-07 marked notable

productivity improvement across manufacturing groups (see Figure 9).

Figure 9: Average Annual Growth Rates of Labour Productivity: By Technology (%)

Note: MHT=Medium to High Technology, MLT=Medium to Low Technology, LT=Low Technology

Source: Authors compilation from Annual Survey of Industries (CSO), various issues

The discussion so far has explored the changing composition of industrial production

and productivity dynamics under different industrial groups and technological parameters.

The economic restructuring occurred since the beginning of economic reforms is expected to

bring significant structural changes in the production, especially in terms of improving

4.6

6.2

7.46.3

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

LT MLT MHT Manufacturing

1991-2016 1991-00 2001-07 2008-16

19

production efficiency. A popular method, as explained in detail in the sub-section 2.1, is the

decomposition of productivity (labour) into three effects, namely (a) the contribution of labour

productivity within each sector/industry (within-growth effects), (b) the productivity effect

from labour reallocation due to the differences in productivity levels between

industries/sectors at the initial period (static-shift effects) and (c) the productivity effects from

labour relocation because of differences in productivity growth rates between

industries/sectors over a selected period (dynamic-shift effects). The combined effects of static-

shift effects and dynamic shift effects equal the total effects of labour employment reallocation

across manufacturing (see Foster-McGregor and Verspagen, 2016).

In Figure 10, we provide the decomposition of labour productivity changes of

aggregate manufacturing into its three components: (a) within-growth effects, (b) static-

shift effects and (c) dynamic-shift effects. The within-effects is positive (negative) when the

weighted growth in labour productivity in an industry is positive (negative). The static-

shift is positive (negative) when labour reallocates (shifts) from relatively less (more)

productive industries to more (less) productive industries over time7. The dynamic effect

is positive (negative) when labours move towards industries that experience positive

(negative) productivity growth8. The positive effect indicates that the fast-growing

industries, in terms of productivity growth, also improve their share of total employment

in manufacturing (Fagerberg, 2000).

Figure 10: Structural Change and Decomposition of Aggregate Manufacturing Productivity: (1991 to 2016)

Source: Authors calculation from ASI published data, CSO

7 This indicates that the share of high productivity industries in total employment increases faster than

that of industries with lower productivity. 8 The static-effect indicates whether labours are shifting towards industries with above-average

productivity while the dynamic-effects captures whether labour productivity growth is higher in

those industries that also improve their employment share over time (see Foster-McGregor and

Verspagen, 2016).

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

1991-92 1994-95 1997-98 2000-01 2003-04 2006-07 2009-10 2012-13 2015-16

Within-Growth Static-Shift Dynamic-Shift

20

At the aggregate level, it is evident that the larger contribution of productivity

growth has occurred because of within-growth mechanism rather than pure structural

change effects. During the reference period, the within-growth effects or technological

progress effect is largely positive, except for a few years. This is expected as usually labour

productivity in most industries tends to grow over time. In contrast, the structural change

effects, which includes both static-shift effects and dynamic-shift effects, are not substantial

in contributing to productivity growth. Relatively, the static-structural effect seems to have

higher prominence than dynamic structural effects as the latter seems to be largely negative

throughout the study. It is striking to note that during the fast-growing period of 2000-07,

the role of structural change components is not substantial.

Based on 3-digit level ASI data, we attempt to decompose the productivity of the

manufacturing sector across three technology industry groups during 1991-2016, 1991-

2000, 2001-2007 and 2008-2016 (see Table 1). For the entire period, the overwhelming part

of productivity growth is accounted by within individual industries (see Figure 11).

Among the technology groups, the same pattern is visible during this period. In the case

of LT, the structural components were found to be negative throughout. The only segment

where the reallocation of labour from low productive activities to high productive

activities seems to be prominent is the MLT sector. In this sector, even though there is

significant productivity growth within individual industries, there is some evidence to

suggest that structural change has impacted productivity improvement during the

reference period. In the case of MHT, apart from the significant rise in within-industry

growth, there is marginal evidence that industries which enhanced their productivity

growth also improved their labour share.

Table 1: Labour Productivity Decomposition: By Technology Groups (1991-2016)

Productivity Changes Period Overall LT MLT MHT

Static-Shift 1991-2016 0.168 -0.025 0.143 0.050

Dynamic-Shift -0.160 -0.162 0.263 -0.261

Within-Growth 4.202 0.999 0.931 2.272

Static-Shift 1991-2000 0.042 0.030 0.016 -0.004

Dynamic-Shift -0.043 0.009 -0.011 -0.041

Within-Growth 1.157 0.310 0.344 0.503

Static-Shift 2001-2007 0.041 -0.030 0.118 -0.047

Dynamic-Shift 0.091 -0.016 0.132 -0.024

Within-Growth 1.724 0.263 0.811 0.650

Static-Shift 2008-2016 0.164 -0.063 0.077 0.150

Dynamic-Shift 0.041 -0.038 -0.001 0.080

Within-Growth 1.071 0.397 0.020 0.653

Source: Authors calculation from ASI published data, CSO.

21

Figure 11: Productivity Decomposition in Indian Manufacturing Sector: 1991-2016

Source: Authors calculation from ASI published data, CSO

Table 2: Productivity Decomposition across Major Sectors: 2000-07 & 2008-16

Sectors

2000-2007 2008-2016

Static-

shift

Dynamic-

shift

Within-

growth

Static-

shift

Dynamic-

shift

Within-

growth Technology

Food & beverages -0.016 0.055 0.065 0.000 0.003 0.107

LT

Tobacco -0.006 0.006 -0.013 -0.016 0.000 -0.004

Textiles -0.020 -0.017 0.082 -0.032 -0.005 0.117

Apparel 0.027 0.001 0.001 0.004 0.009 0.018

Leather 0.005 0.002 0.004 0.011 -0.002 0.016

Wood 0.000 0.002 0.003 0.003 -0.002 0.008

Paper 0.005 0.007 -0.004 -0.012 -0.003 0.023

Print & publishing 0.001 0.005 0.018 0.000 0.001 0.004

Furniture; & recycling 0.013 0.012 0.009 0.160 -0.097 0.073

Refined Petroleum 0.067 0.113 0.267 0.261 0.002 -0.126

ML

T Rubber & plastic 0.032 0.005 0.011 0.039 0.007 0.050

Non-metallic mineral -0.001 0.027 0.128 0.020 0.005 -0.046

Basic metals -0.019 0.030 0.041 -0.077 -0.011 0.020

Fabricated metals 0.028 0.031 0.028 -0.011 0.011 0.005

Chemicals -0.097 0.133 0.097 0.145 0.015 0.210

MH

T Non-electrical machinery 0.005 0.031 0.050 -0.068 -0.016 0.154

Electrical machinery 0.009 0.042 0.103 -0.005 0.023 0.044

Medical optics 0.002 0.005 0.015 -0.003 0.003 0.022

Transport equipments -0.034 0.032 0.022 0.111 0.090 0.126

Note: The final list of 5-digit level industries selected for the analysis are 476 (2000-07) and 586 (2008-16).

Source: Authors calculation based on 5-digit level ASI data compiled at the factory (unit)-level.

-0.2

0

0.2

0.4

0.6

0.8

1

Overall LT MLT MHT

Static-Shift Dynamic-Shift Within-Growth

22

The period-wise breakup reveals that the within-growth effect has been positive

throughout technology industry groups and sub-periods. For LT, the 1990s marked

significant structural change effect on productivity as the static-effect and dynamic-effect

remained positive. However, the subsequent period indicates evidence of the structural

burden phenomena as the expansion in productivity was not accompanied by an

improvement in employment. The negative sign implies that industries with higher

productivity could not attract labourers or the fast-growing industries failed to maintain

their level of employment. In the case MLT, the evidence of structural-bonus is prominent

as the static-shift effect is found to be positive during all three sub-periods. This is

noticeable during 2000-07 where both components of structural change are positive. This

indicates that labour reallocation across productive sectors has been an important source

of aggregate MLT productivity growth since economic reforms. Finally, in the case of

MHT, we do not observe any substantial improvement in labour reallocation towards

more productive activities or growing productive industries. Incidentally, the structural

change effect is found to be positive during the period of the global financial crisis (2008-

2016). Overall, consistent with the results reported in the existing literature, the structural

components appear to be largely dominated by the within-productivity growth effect in

Indian manufacturing.

Finally, we also carry out the decomposition analysis using the factory level (unit-

level) data of ASI which were aggregated at the 5-digit level. The productivity decomposition

of 5-digit level industries is aggregated across 19 industrial sectors identified at the 2-digit level

of industrial classification. Because of data complexity, the analysis is restricted to two periods,

i.e., 2000-07 and 2008-2016 (see Table 2). The decomposition analysis at finer disaggregated

level substantiates the earlier findings of an overwhelming effect of within-industrial

productivity growth during these two periods.

However, in contrast to the aggregate analysis, we do find evidence of structural

change effects on aggregate productivity among technology-intensive industries. During

the 2000-07 period, seven out of nineteen industries reported positive values for static and

dynamic shift components of structural change. Among them all of the technology-

intensive sectors shown positive dynamic shift which indicates that labours have moved

towards industries that experienced considerable productivity growth. Similarly, the fast-

growing industries have been able to maintain a larger labour share during this period.

However, post-2008 the phenomena of labour reallocation could not sustain as static and

dynamic components reported negative values for several segments. As noted at the 3-

digit level, the major contribution of productivity growth during this period continues to

be stemmed from within-industry across technology groups. Overall, the following three

industry groups, namely apparel, print & publishing, refined petroleum and rubber &

plastic, reported positive structural change effect on aggregate productivity throughout

2000-2016.

23

VI. Concluding Remarks

The purpose of the present study was to examine the magnitude of structural change in

the organised manufacturing sector of India since liberalisation. Since the nature of

structural change involves the reallocation of productive resources across the

manufacturing sector, the continual shift of factor inputs from lower to higher productivity

sectors within manufacturing is expected to raise aggregate industrial productivity. The

paper attempted to study the role of structural change in aggregate manufacturing

productivity using the shift-share decomposition methodology. The study uses the

organised manufacturing sector data from the Annual Survey of Industries (ASI) from 1991

to 2016 at a broad level (2-digit) as well as the disaggregated level (3-digit and 5-digit level).

The compilation at 5-digit of disaggregation was based on unit-level data compiled for the

period 2000-07 and 2008-2016.

The descriptive statistics revealed that since the onset of economic reforms the

share of technology-intensive manufacturing sectors such as chemicals, refined petroleum,

metals and machinery production in terms of output (value-added) and wages have

expanded relative to tradition-low technology manufacturing like food, beverages, wood,

paper etc. However, the low-technology sectors continue to contribute significantly to the

overall manufacturing employment (around 54 per cent in 2016). The technology-intensive

industries also reported better growth in output, wages, capital and productivity than

traditional sectors. Notably, the MLT industries like refined petroleum, rubber & plastic,

metal products, electrical equipment registered higher growth in employment. In all

industries, the output growth was accompanied by a significant increase in capital input

use which has helped raise labour productivity over time. On average, the labour

productivity increased around 6 per cent per annum which was relatively higher among

the technology-intensive industries (i.e., MLT and MHT). In contrast, there was a large

deceleration of productivity in many LT industries.

The significant changes in the composition of manufacturing production,

particularly from the substantial presence of traditional sectors in the initial reform period

to the gradual dominance of medium-technology industries, suggests continued shift of

labours across industries. The decomposition exercise of labour productivity indicates that

a large part of productivity gains is explained by within- growth component. In

comparison, the structural change components, although positive, are not substantial in

magnitude. In the case of technology-wise distribution of industries, the structural change

factors were negative for several industries mainly belonging to the LT sectors, especially

post-2000. The evidence of labours shifting towards relatively higher productivity

industries is found among relatively higher technology industries during the same period.

This was substantiated by the findings based on unit-level, which reported evidence of a

static and dynamic shift of workers towards highly productive industries, especially

belongs to the MLT sector during 2000-2007. The positive structural change effects,

24

although considerably less than the within-shift effects, indicates that the reallocation

process has positively contributed to the aggregate productivity.

The evidence of the significant presence of within-growth effect on productivity

is consistent with the existing literature (see Erumban et al, 2019; McMillan and Rodrik,

2011; Peneder, 2003; Fagerberg, 2000, Timmer, M. and A. Szirmai, 2000). However, in

contrast to Erumban et al, (2019) we do find evidence of both static as well as a dynamic

form of structural change operating at highly disaggregated level industries. The

positive role of structural change, although relatively modest in magnitude, is indicative

of the potential opportunities that need to be exploited for reaping dynamic gains in

production. To facilitate pro-growth resource allocation, policy efforts should be

directed towards improving factor market rigidities, human capital development,

infrastructural bottlenecks, rationalizing informality and easing supply-side bottlenecks

in the Indian manufacturing sector.

25

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28

Appendix Tables

Table A1: Composition of Indian Organised Manufacturing Sector: By Technology Intensity

(1991 & 2016) share in %

Technology Classification/Sectors 2-digit

(NIC-2004)

GVA Capital Wages Employment

1991 2016 1991 2016 1991 2016 1991 2016

Low Technology Manufacturing 31.1 23.8 22.5 19.3 37.2 31.1 49.2 45.2

Food & beverages 15 10.3 9.4 7.6 8.2 10.7 9.5 16.1 13.1

Tobacco 16 2.0 1.3 0.3 0.3 1.9 0.8 6.5 3.3

Textiles 17 11.2 5.1 9.3 5.7 16.7 7.5 17.4 11.1

Apparel 18 1.6 2.4 0.4 1.0 1.1 5.1 1.8 8.1

Leather 19 1.0 0.9 0.7 0.4 1.2 1.8 1.6 2.8

Wood 20 0.4 0.4 0.2 0.2 0.4 0.4 0.8 0.7

Paper 21 2.4 1.3 3.0 2.1 2.2 1.6 2.1 1.8

Print & publishing 22 1.7 0.9 0.8 0.6 2.4 1.3 2.1 1.1

Furniture; & recycling 36+37 0.6 2.2 0.2 0.8 0.7 3.0 0.8 3.2

Medium to Low Technology Manufacturing 27.5 34.9 43.9 51.7 21.3 24.8 21.8 24.4

Refined Petroleum 23 3.6 13.6 4.2 14.9 1.5 3.0 0.8 1.1

Rubber & plastic 25 3.2 4.2 2.7 3.3 2.5 4.0 2.6 4.6

Non-metallic mineral 26 7.2 5.3 7.4 7.1 4.6 4.4 6.5 7.1

Basic metals 27 10.4 8.6 28.0 23.8 9.3 8.9 8.5 6.7

Fabricated metals 28 3.1 3.2 1.6 2.5 3.4 4.5 3.4 4.9

Medium to High Technology Manufacturing 41.4 41.2 33.7 29.0 41.5 44.1 29.0 30.4

Chemicals 24 16.3 18.0 18.6 14.4 12.1 14.0 8.6 10.2

Non-electrical machinery 29+30 8.3 6.4 4.7 3.3 9.3 9.2 6.8 5.7

Electrical machinery 31+32 8.0 4.0 4.3 2.1 7.8 4.8 5.1 3.8

Medical optics 33 0.8 2.1 0.7 0.7 0.9 2.3 0.7 1.5

Transport equipments 34+35 8.0 10.7 5.4 8.5 11.5 13.8 7.7 9.2

Manufacturing Sector All 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Source: Author calculation based on Annual Survey of Industries (Vol 1), various issues

29

Table A2: Growth of Manufacturing Sector: Select Indicators (2-digit NIC) 1991-2016 (%)

Sectors

2-

digit

(NIC-

2004)

Gross

Output

Gross

Value

Added

Fixed

Capital Wages Employment Productivity

Low Technology Manufacturing

Food & beverages Sector 15 7.9 6.7 10.6 4.6 1.7 5.0

Tobacco Sector 16 2.3 3.6 9.3 1.4 -0.5 4.1

Textile Sector 17 7.6 6.2 7.9 1.7 0.8 5.4

Apparel Sector 18 10.3 9.7 13.3 11.2 8.3 1.4

Leather & Leather Products 19 8.0 8.0 8.9 7.3 5.2 2.8

Wood & Wood Products 20 8.3 6.2 10.4 5.6 1.4 4.7

Paper & Paper Products 21 7.4 5.9 8.4 4.1 2.2 3.7

Print & Publishing Products 22 3.3 2.6 9.5 2.6 0.4 2.2

Furniture; & Recycling 36+37 16.6 12.5 15.4 10.9 8.0 4.5

Medium to Low Technology Manufacturing

Refined Petroleum Sector 23 9.4 8.9 15.0 8.0 3.2 5.7

Rubber & Plastic Products 25 11.0 10.5 10.0 7.0 5.0 5.5

Non-Metallic Mineral Products 26 8.4 8.4 10.5 6.0 3.9 4.5

Basic Metal Products 27 8.3 6.4 10.3 5.1 2.3 4.1

Fabricated Metal Products 28 9.4 9.0 12.2 7.2 4.9 4.1

Medium to High Technology Manufacturing

Chemicals & Chemical Products 24 7.8 7.3 7.8 5.6 3.0 4.3

Non-Electrical Machinery Products 29+30 9.7 9.4 9.1 5.5 1.8 7.6

Electrical Machinery Products 31+32 10.3 8.5 7.1 3.2 1.4 7.1

Medical Optical Products 33 15.3 13.7 12.8 10.4 6.5 7.1

Transport Equipment Products 34+35 12.5 10.9 12.4 5.8 3.4 7.5

Manufacturing Sector 9.4 8.6 10.9 5.6 3.0 6.3

Note: The growth rate is derived from the log-lin specification of the time series data.

Source: Author calculation based on Annual Survey of Industries (Vol 1), various issues and Price

Statistics from the Office of Economic Advisor, Ministry of Commerce

30

Table A3: Industrial Structure and Productivity of Indian Manufacturing at the 3-digit level

(1991-2016) %

SN. Tech-

nology

3-Digit Description Gross Value Added Employment LP

Growth

1991 2016 1991-2016 1991-2016 1991-

2016

1 MHT 242 Other chemical products 8.89 12.11 9.71 (9.7) 6.67 (4.1) 5.4

2 MLT 271 Basic Iron & Steel 7.01 5.67 9.51 (7.0) 5.30 (1.8) 5.2

3 MLT 232 refined petroleum products 3.13 13.35 8.52 (11.2) 0.59 (6.5) 4.4

4 MHT 241+233 Basic Chemicals + Processing of

nuclear fuel

7.47 5.65 7.33 (7.2) 2.50 (2.2) 4.9

5 LT 171 Spinning, weaving & finishing of

textiles

10.38 3.97 6.53 (6.0) 12.39 (0.3) 5.8

6 MLT 269 Non-metallic mineral products

n.e.c.

6.47 4.84 4.87 (7.2) 5.92 (3.8) 3.3

7 LT 154 Other food products 5.31 3.89 3.82 (6.4) 7.57 (0.9) 5.4

8 MHT 291+300 Office, accounting & computing

machinery

4.12 3.85 3.68 (10.3) 3.05 (3.4) 6.7

9 MHT 341 Motor vehicles 4.35 3.77 3.42 (9.7) 1.55 (0.3) 9.3

10 MHT 292 Special purpose machinery 3.67 2.70 2.87 (9.3) 2.72 (1.4) 7.8

11 LT 272 Basic precious & non-ferrous

metals

2.15 1.88 2.39 (7.4) 0.93 (2.3) 4.9

12 MHT 343 Parts and accessories for motor

vehicles & engines

0.00 4.10 2.26 (13.8) 2.51 (8.6) 4.8

13 MHT 359 Transport equipment n.e.c. 1.57 2.24 2.02 (10.9) 1.64 (3.8) 6.8

14 MLT 252 Plastic products 1.35 2.61 1.85 (13.2) 2.20 (7.1) 5.7

15 MHT 311 Electric motors, generators &

transformers

2.54 1.48 1.84 (9.7) 1.35 (2.1) 7.4

16 LT 181 Wearing apparel, except fur

apparel

1.56 1.66 1.76 (9.4) 4.50 (7.5) 1.8

17 LT 210 Paper & paper product 2.41 1.29 1.67 (5.6) 2.08 (2.2) 3.3

18 LT 160 Tobacco products 1.98 1.31 1.65 (3.8) 5.09 (0.1) 3.7

19 MLT 289 Other fabricated metal products,

service activities

1.37 1.84 1.62 (9.5) 2.45 (5.7) 3.7

20 LT 153 Grain mill products, starches &

animal feeds

1.41 1.79 1.62 (8.8) 3.76 (2.5) 6.2

21 MLT 251 Rubber products 1.90 1.63 1.58 (9.5) 1.44 (3.3) 6.1

22 LT 151 Production, processing &

preservation of meat, fruit

vegetables oils etc.

1.78 1.36 1.57 (6.6) 1.96 (2.6) 3.9

23 MLT 281 Structural metal products, tanks,

reservoirs & steam generators

1.69 1.35 1.44 (7.3) 1.71 (2.8) 4.4

24 LT 155 Beverages 1.14 1.34 1.35 (8.5) 1.03 (4.0) 4.2

31

SN. Tech-

nology

3-Digit Description Gross Value Added Employment LP

Growth

1991 2016 1991-2016 1991-2016 1991-

2016

25 MHT 312+313 electricity distribution and control

apparatus + Insulated wire & cable

1.81 0.72 1.27 (8.0) 0.98 (1.1) 6.8

26 LT 369 Manufacturing n.e.c. 0.57 1.38 1.08 (13.3) 1.43 (7.6) 5.3

27 MLT 273 Casting of metals 1.22 1.01 0.95 (7.1) 1.54 (2.5) 4.5

28 LT 152 Dairy product 0.63 0.96 0.85 (9.6) 1.02 (4.3) 5.0

29 MHT 243 Man-made fibers -0.11 0.24 0.84 (-3.3) 0.30 (0.7) -1.1

30 MHT 331+333 Medical appliances, instruments,

appliances +Watches & clocks

0.81 0.93 0.79 (10.0) 0.78 (3.6) 6.3

31 LT 221 Publishing 1.13 0.20 0.75 (-1.6) 0.65 (-5.2) 3.8

32 MHT 323 Television & radio receivers &

associated goods

0.86 0.29 0.75 (7.3) 0.38 (-2.2) 9.8

33 LT 172 Other textiles 0.43 1.14 0.72 (14.6) 1.51 (9.1) 5.1

34 MHT 322 Television & radio transmitters for

line telephony & telegraphy

1.53 0.74 0.66 (8.9) 0.47 (-1.0) 10.0

35 LT 222 Printing & service activities related

to printing

0.53 0.88 0.64 (7.6) 0.93 (4.3) 3.1

36 LT 173 Knitted & crocheted fabrics and

articles

0.37 0.76 0.57 (13.4) 1.45 (10.2) 3.0

37 MHT 321 Electronic valves & tubes & other

electronic components

0.39 0.44 0.56 (12.6) 0.56 (5.0) 7.3

38 LT 192 Footwear 0.55 0.62 0.51 (10.5) 1.29 (5.7) 4.5

39 MHT 352 Railway & tramway locomotives &

rolling stock

1.56 0.20 0.50 (0.8) 0.97 (-7.2) 8.6

40 MLT 261 Glass and glass products 0.74 0.45 0.47 (6.3) 0.61 (0.0) 6.3

41 MHT 293 Domestic appliances, n.e.c. 0.47 0.64 0.47 (12.1) 0.44 (1.6) 10.3

42 MLT 231 Coke oven products 0.50 0.22 0.42 (1.5) 0.33 (-1.1) 2.6

43 MHT 314 Accumulators, primary cells &

primary batteries

0.34 0.51 0.39 (13.9) 0.27 (5.6) 7.9

44 LT 191 Tanning and dressing of leather,

luggage handbags, saddlery &

harness

0.50 0.27 0.28 (7.2) 0.68 (3.9) 3.2

45 MHT 319 Other electrical equipment n.e.c. 0.15 0.40 0.25 (16.5) 0.31 (7.0) 8.9

46 MHT 315 Electric lamps & lighting

equipment

0.38 0.24 0.24 (10.1) 0.34 (3.6) 6.2

47 LT 202 Products of wood, cork, straw &

plaiting materials

0.27 0.34 0.24 (5.5) 0.55 (3.3) 2.1

48 LT 361 Furniture 0.07 0.25 0.23 (15.1) 0.36 (6.7) 7.9

49 MHT 351 Building & repair of ships & boats 0.18 0.04 0.23 (2.4) 0.29 (-0.6) 3.0

32

SN. Tech-

nology

3-Digit Description Gross Value Added Employment LP

Growth

1991 2016 1991-2016 1991-2016 1991-

2016

50 MHT 342 Bodies (coach work) for motor

vehicles; trailers and semi-trailers

0.15 0.22 0.17 (12.1) 0.31 (5.8) 6.0

51 MHT 353 Aircraft & spacecraft 0.20 0.13 0.13 (7.5) 0.08 (1.8) 5.6

52 MHT 332 Optical instruments &

photographic equipment

0.03 0.01 0.06 (5.5) 0.05 (-0.9) 6.5

53 LT 201 Saw milling & planning of wood 0.08 0.02 0.03 (-1.5) 0.14 (-3.7) 2.3

54 LT 371+372 Recycling of metal waste & scrap +

non-metal waste & scrap

0.00 0.07 0.02 (30.5) 0.04 (24.1) 5.2

55 LT 223 Reproduction of recorded media 0.00 0.00 0.02 (-11.0) 0.01 (-1.8) -9.3

56 LT 182 Dressing & dyeing of fur; articles

of fur

0.02 0.00 0.01 (-4.5) 0.02 (-5.5) 1.0

Manufacturing Sector 100.00 100.00 100.00 (9.1) 100.00 (2.8) 6.3

Note: Industrial composition is measured in terms of individual industries share of value added

and employment in total manufacturing (%). Figures are sorted in terms of major industries in

terms of average GVA during 1991-2016 (%); LP correspond to Labour Productivity and the

growth rates are based on CAGR; Figures in parenthesis of GVA and employment are growth

rates (CAGR) in percentages; LT= Low Technology, MLT: Medium to Low Technology, MHT:

Medium to High Technology.

Source: Author calculation based on Annual Survey of Industries (Vol 1), various issues & EPWRF

Time Series data.

33

List of ISID Working Papers

224 Foreign Direct Investment and Innovation activities in Indian Manufacturing

Industries, Sanjaya Kumar Malik, April 2020

223 Is Domestic Value Addition a Source of Export Sophistication? A Case Study India,

Anjali Tandon, April 2020

222 Occupational and Employment Mobility among Migrant Workers: A Case Study of

Slums of NCT of Delhi, Ajit Kumar Jha & Arvind Pandey, March 2020

220 Special Economic Zones: Location and Land Utilisation, Surya Tewari, March 2020

220 Import Intensity of India’s Manufactured Exports – An Industry Level Analysis,

Mahua Paul & Ramaa Arun Kumar, February 2020

219 Industrial Structure, Financial Liberalisation and Industrial Finance in India, Santosh

Kumar Das, January 2020

218 India and Industry 4.0, T.P. Bhat, January 2020

217 Review of Industrial and Development Corridors in India, H. Ramachandran, December

2019

216 Economic Reforms and Market Competition in India: An Assessment, Beena

Saraswathy, December 2019

215 Financial Risk Protection from Government-Funded Health Insurance Schemes in

India, Shailender Kumar, November 2019

214 Outward FDI from India: Review of Policy and Emerging Trends, Reji K. Joseph,

November 2019

213 Structural Asymmetry in Global Production Network: An Empirical Exploration,

Satyaki Roy, October 2019

212 Assessing Factor Proportions in Tradable Sectors of the Indian Economy, Anjali

Tandon, October 2019

211 Promoting Access to Healthcare in India: Role of Health Insurance and Hospital

Network, Shailender Kumar, August 2019

210 Voyeurism, Misogyny and Objectification of Women: Critical Appraisal of the

Historicity of the State of Women in Society and its possible impact on advertising,

Jaishri Jethwaney, August 2019

209 FDI in R&D in India: An Analysis of Recent Trends, Reji K. Joseph, Biswajit Dhar, Akoijam

Amitkumar Singh, June 2019

208 Penetration and Coverage of Government-Funded Health Insurance Schemes in

India, Shailender Kumar, May 2019

* Most of the working papers are downloadable from the institute’s website: http://isidev.nic.in/ or

http://isid.org.in/

Institute for Studies in Industrial Development4, Institutional Area, Vasant Kunj Phase II, New Delhi - 110 070

Phone: +91 11 2676 4600 / 2689 1111; Fax: +91 11 2612 2448E-mail: info@isid.org.in; Website: http://isid.org.in

Institute for Studies in Industrial DevelopmentNew Delhi

225Working Paper

May 2020

R. Rijesh

Liberalisation, Structural Change and ProductivityGrowth in Indian Organised Manufacturing SectorAbout the Institute

The Institute for Studies in Industrial Development (ISID), successor to the Corporate Studies Group (CSG), is a national-level policy research organization in the public domain and is affiliated to the Indian Council of Social Science Research (ICSSR). Developing on the initial strength of studying India’s industrial regulations, ISID has gained varied expertise in the analysis of the issues thrown up by the changing policy environment. The Institute’s research and academic activities are organized under the following broad thematic areas:

Industrialization: Industrial policy, manufacturing sector, MSMEs, technology development, production networks, industrial clusters/corridors, SEZs, land acquisition, natural resources, regional development, entrepreneurship, sustainability, etc.

Internationalization: Cross-border flows of capital flows, FDI, technology transfer, IPRs, balance of payments, trade and investment agreements, etc.

Corporate Sector: Ownership and control, finance and governance, financial institutions, company law, securities legislation, regulatory bodies, M&As, business groups, public enterprises, public-private partnership, business ethics, CSR, etc.

Labour and Employment: Employment growth and structural transformation; labour force; skill development; quality of employment, labour flexibility; differentiations and disparities; informal sector and un-organised workers; etc.

Public Health: Social, cultural and economic determinants of health; structure of health systems; research and capacity building in the areas of pharmaceuticals, medical devices and healthcare sectors; IPRs and other areas of industry-health interface, etc.

Media & Communication: Studies in the area of media, communication and advertising.

ISID has being maintaining databases on corporate and industrial sectors in particular and other areas of developmental and social and economic issues in general. Its Online Reference Services includes On-Line Index (OLI) of 252 Indian Social Science Journals as well as 18 Daily English Newspapers Press Clippings Archive on diverse social science subjects which are widely acclaimed as valuable sources of information for researchers studying India's socio-economic development.