Trade Liberalisation, Technology Import and Industrial ...

Trade Liberalisation, Technology Import

and Industrial Productivity:

Evidence from Indian Manufacturing Firms

R. Rjiesh

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: [email protected]; Website: http://isid.org.in

November 2018

ISID

Working Paper

203

© Institute for Studies in Industrial Development, 2018

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

1. Introduction 2

2. Technology Import and Productivity in Indian Manufacturing: Brief Review 4

3. Methodology and Data Sources 6

3.1 Methodology of the Study 6

3.2 Data Sources and Variable Construction 10

4. Descriptive Statistics 13

4.1 Overview of Sample Data 14

4.2 Technological Characteristics of Indian Manufacturing Firms 17

5. Estimation Results 19

5.1 Impact of Technology Import on Manufacturing Output 21

6. Concluding Remarks 24

References 26

Appendix 29

List of Figure(s)

Figure 1 Composition of Embodied and Disembodied Technology Import:

By Use‐based Classification 18

Figure 2 Technology Intensity in Indian Manufacturing: Select Indicators 19

List of Table(s)

Table 1 Distribution of Sample Manufacturing Firms: By Use‐based Classification 14

Table 2 Trend in Select firm‐level Characteristics: By Use‐based Classification 15

Table 3 Production Function Estimates for Indian Manufacturing Sector 20

Table 4 Production Function Estimates for Indian Manufacturing:

By Use‐Base Classification 21

Table 5 Productivity Determinant Model Estimation Results:

By Use‐based Classification 22

Table A1 Distribution of Sample firms Selected: By 2‐digit NIC 29

Table A2 Firms with consistent Sales and Technology Import for the Entire Period:

By Use‐Based Classification 30

Table A3 Technology Import by Top 10 firms 30

Table A4 Summary Statistics 31

Trade Liberalisation, Technology Import and

Industrial Productivity:

Evidence from Indian Manufacturing Firms

R. Rjiesh*

[Abstract: The present study is an attempt to examine the impact of technology import on productivity

of Indian manufacturing firms for the period of 1995‐2016. The study estimated an augmented Cobb‐

Douglas production function incorporating technology import, both embodied technology (capital goods

import) and disembodied technology (spending on royalties, technical fees etc.), as additional output

determinants. The production function is estimated using the semi‐parametric algorithm of Levinsohn

and Petrin (LP) that tries to correct the simultaneity bias resulting from input choices and productivity

shocks. In order to assess the robustness of the baseline model, a productivity determinant model is also

estimated using the standard panel estimation method. The empirical analysis is based on an unbalanced

sample of 4616 manufacturing firms, which are further segregated into firms that produce basic goods,

intermediate goods, capital goods and consumer goods, representing various use‐based categories of

manufacturing production. The descriptive statistics reveal the considerable expansion of manufacturing,

especially the trade linkage through import and export, during the sample period. However, some growth

deceleration is evident post‐crisis period of 2008. Most firms have found to occasionally engage in the

import of technology and the preferred mode of acquisition is embodied technology. Compared to the

internal technological effort (domestic R&D), the preference is towards acquiring technology from

abroad. The consumer goods and intermediate producing segments are the major consumers of foreign

technology inputs. The econometric results based on the direct estimation of the production function and

the productivity determinant model for the entire manufacturing, as well as use‐based categories largely

substantiates the conjecture that technology import has a positive and statistically significant impact on

Indian manufacturing output. This supports the predictions of new trade and growth theories that

international trade is a significant vehicle of technology transfer that provides dynamic productivity

benefits in manufacturing.]

Keywords: Technology Import, Productivity, LP, Indian Manufacturing, Panel Regression

JEL Classification: F14, D24, L6, O33

* Assistant Professor, ISID, New Delhi; E‐mail: [email protected]

Acknowledgement: The study is a part of the project sponsored by ICSSR. I duly acknowledge the

financial support and assistance by the ICSSR. I thank the institutional support of ISID during the

various stages of my research work. Some part of the work is also based on my Ph.D. for which I

am indebted to Prof. Alokesh Barua and Prof. K.L. Krishna. I thank the valuable comments and

suggestions that I received during the internal seminar held at ISID. However, any remaining

errors are solely my responsibility.

2

1. Introduction

Since the celebrated empirical work of Kaldor (1967), the critical role of industrialisation in

the process of economic growth is well recognised. It is argued that the allocation of

economic resources towards industrial activities often induces structural change bonuses

in terms of higher productivity, learning opportunities, technological progress and

spillover effect to the rest of the economy (Szirmai, 2013). Industrial growth arguably

involves considerable investment in production technology and innovation that not only

improve cost efficiency and quality, but also enable the less‐income economy to catch‐up

with the frontier economies of the world. The neo‐classical growth theories, especially

since the seminal contribution by Solow (1957), have clearly pointed out the significance

of the technical change in augmenting the growth process of the economy. The recent

growth theories, while relaxing the simplified notion of technology as an exogenous shock

or black box, regard it as an outcome of deliberate economic activity governed by economic

incentives. In this framework, an economy’s growth rate is determined not only by the rate

of capital accumulation, but also by the rate of investment in knowledge and skill intensive

innovations (see of Grossman and Helpman (1991); Rivera‐Batiz and Romer (1991) among

others).

However, it is well known that countries are not equally endowed with the stock of

knowledge, as the bulk of innovation activities are concentrated in the developed countries

(Rijesh, 2015). In general, the low‐income countries are characterised by structural

rigidities including market imperfections, distortions and lack of incentives to invest in

risk‐bearing innovative activities. Rather than investing in new technology generation, it

is more economical for them to access the pool of knowledge available in the rest of the

world and adapt them to the local manufacturing conditions. In this context, the new trade

and the endogenous growth theories argue that international trade can act as a vehicle of

knowledge transfer from rich countries to the poor countries (Zanello et al., 2016). In

particular, the trade‐induced dynamic effects on growth are channelled through the import

of technology‐intensive capital goods inputs which enhance the productivity and growth

of firms and industry over time. In this context, participation in international trade is

expected to benefit the low‐income countries to develop and modernise their

manufacturing sector.

The theoretical framework of Quah and Rauch (1990), Grossman and Helpman (1991) and

Rivera‐Batiz and Romer (1991) have shown that trade not only increases, but also sustains

the rate of economic growth through wider access to essential and efficient foreign inputs

like capital and intermediate goods. Since foreign capital goods are embodied with R&D

of the advanced countries, the efficiency of capital accumulation and productivity in

manufacturing improves, leading to further dynamic benefit for the entire economy (Lee,

1995; Herrerias and Orts, 2011)1. As mentioned earlier, the import of technology inputs

1 In a simple open economy model in the framework of endogenous growth theories, Rebelo (1991)

3

helps firms to reduce their marginal cost of production compared to autarky. As

production cost falls, firms will be able to devote a larger share of expenditure on

upgrading technology, and new product or process innovations thus increasing the

product scope (Damijan et al., 2014). Availability of new vintage and quality capital inputs

provide further dynamic learning effects (Bas, 2012). Generally foreign machines consist

of a bundle of ‘knowledge’ in the form of blueprints, installation support, quality control

software, and services of trained engineers and supervisors that help increase productive

efficiency in the short run and stronger domestic absorptive capacity in the long run (Mody

and Yilmaz, 2002)2.

Given the central role of technology import in industrial growth and development, it is

important to study its likely impact on improving manufacturing productivity, especially

in the less advanced countries of the world. In this context, the present study attempts to

examine the impact of foreign technology purchases on the productivity of the Indian

manufacturing firms in the recent period. The Indian case is particularly relevant, as the

economy has undergone some major changes in respect of policy in the recent decade. The

economic liberalisation policy reforms in the early 1990s have shifted the focus from highly

controlled, state‐led inward‐oriented development regime to a largely market‐oriented

outward‐oriented trade regime. Since the initiation of the comprehensive liberalisation

package, trade barriers have fallen drastically and economic integration has increased

considerably3. Due to the relaxation and lesser state‐level control over production matters,

it has become relatively easier for firms to access superior technology inputs from abroad.

For example, the share of capital goods imports in total merchandise imports—a proxy for

embodied technology—hovered around 16–23 per cent, which reflects the increased

demand for technology‐intensive inputs in the manufacturing sector. Since foreign

sourced factor inputs are embodied with better technology, knowledge bundle and

quality, incorporating or adapting them to the production process is expected to create

dynamic productivity benefits over time. In this context, the present study examines the

impact of technology import on the productivity of Indian manufacturing, using the

disaggregate data at the firm level. As a novel contribution, the present study not only

evaluates the impact of technology import on manufacturing, but also assess the impact

across different categories of manufacturing sector. In this regard, we categorised

manufacturing into four use‐based categories, namely basic goods, intermediate goods,

capital goods and consumer goods (both durable and non‐durable) producing sectors.

has shown that the growth rate is higher in a country that uses imported inputs relatively more

than domestically produced inputs for investment. 2 However, since the adoption of technology to domestic market is costly and market distortions

are prevalent in less income countries, the efficient diffusion of foreign inputs depends upon

factors such as the technological effort (Lall, 1992) and the absorptive capacity of the recipient firm

(Cohen and Levinthal, 1990). 3 For instance, the level of openness, measured as merchandise trade share of GDP has shot up from

13 per cent in 1991 to 42 per cent in 2013.

4

The rest of the paper is organised as follows. Section 2 provides a brief account of the

existing empirical studies on technology import and productivity in Indian

manufacturing. Section 3 describes the econometric methodology and data sources used

in the study. The descriptive statistical analysis of manufacturing firms’ performance and

technology characteristics are given in Section 4. Section 5 discusses the empirical results

and findings. Section 6 provides concluding remarks.

2. Technology Import and Productivity in Indian Manufacturing:

Brief Review

Since the beginning of economic planning, the importance of building a diverse industrial

base with advanced technology has been well recognised by the policymakers. However,

since the private sector’s R&D expenditure is extremely low in India, the preferred mode

of technology acquisition is from the external sources (Lall, 2001). One of the earlier studies

in this context is by Subrahmanian and Pillai (1976). The authors find that for the majority

of firms in the engineering, pharmaceuticals and dyestuff sectors, the varying degree of

foreign collaborations did not culminate in a better export performance. Kumar (1985), by

reviewing the literature on technology imports, highlights the excessive cost of such

arrangements in India. Katrak (1997) found that for 82 firms from the electrical and

electronics industries, the initial technological capability are weakly correlated to

technology import. Kumar and Siddharthan (1994) did not find any significant impact of

technology import on export performance across firms. However, most of these earlier

studies have overlooked the impact of technology import on productivity. Furthermore,

these studies cover a period of highly restrictive trade and industrial regime in which

severe restrictions on the external mode of technology purchases were the norm.

Basant and Fikkert (1996), using panel data on 787 Indian firms from 1974–75 to 1981–82,

studied the effect of foreign technology purchase, and domestic and international spillover

on Indian firms. The results indicate that when all of the industries are examined together,

there are high returns to expenditure on technology purchase and R&D. The study

suggested that India’s restriction on technology licensing agreements imposed a

substantial cost on the economy. Overall, the study finds that technology purchase by

Indian firms through licensing has a positive effect on productivity. Kathuria (2001) tested

whether the presence of foreign firms and disembodied technology imports results in

higher productivity growth for domestic firms. Using the stochastic frontier method for

368 medium‐ and large‐sized Indian manufacturing firms for the period 1975–1989, the

author found that for all manufacturing firms there was a positive effect of disembodied

technology import on productivity growth. In the case of non‐FDI firms, it was found that

embodied technology import improved productivity, whereas disembodied technology

import had a positive but not a statistically significant effect.

Hasan (2002) estimated production function using a panel data of 286 public limited

manufacturing firms from 1976–87 to determine the extent to which productivity has been

5

influenced by imported as well as domestic technologies. The imported technology was

measured through the import of capital goods (embodied technology) and supply of

design, spending on blueprints, technical assistance, royalty, etc., (disembodied

technology). The Instrumental Variable (IV) estimation of the production function reveals

a significant impact of embodied and disembodied technology purchases on firm

productivity. The disaggrete level analysis revealed that the import of disembodied

technology had a significant positive impact on productivity across technology‐intensive

sectors, like electrical & non‐electrical machinery, but not in non‐scientific firms. The

import of capital goods improved the productivity of scientific firms such as chemicals,

pharmaceuticals, and electrical and non‐electrical machinery.

Parameswaran (2009) examined the effect of international trade on R&D spillovers and

productivity of the manufacturing sector for the period 1989–2001. Using an unbalanced

panel of 2100 firms from PROWESS database, the study examined the impact of embodied

and disembodied purchase of technology on firm’s productivity. The contribution of

import of technology was assessed by estimating an augmented Cobb‐Douglas production

function using Levinsohn and Petrin (2003) methodology. The results suggested that the

effect of technology import (both embodied as well as disembodied technology) was

significant for manufacturing firms as a whole. The study finds that capital goods imports

were significant for technology‐intensive sectors, whereas the disembodied technology

was found to be significant for low technology industries. The study highlights that

technology import substitutes domestic R&D efforts. Rijesh (2015) examined the impact of

technology import on productivity performance of a large number of manufacturing firms

for the period 1995 to 2010. The LP estimates of production function for 5551 firms revealed

that technology import is a significant productivity determinant for overall manufacturing.

The technology‐intensive classification of firms revealed that the impact of capital goods

is much more pronounced across the medium technology intensive segments such as

machinery, chemicals, transport equipment, etc.

Goldberg et al. (2010a) found that trade reforms have substantially increased the access to

new types of intermediate inputs from abroad for 2927 firms during 1989–97. During the

period of liberalisation, two‐thirds of the increase in imported inputs occurred in products

that were not previously imported. Moreover, the authors observed substantial static gains

from trade through access to new imported inputs as these new varieties generated an

additional annual 4.7 per cent decline in the imported input price index which again

enhanced firms’ ability to manufacture new products. Similarly, Topalova and

Khandelwal (2011) revealed that a reduction in tariff protection improved firm‐level

productivity. Efficiency improvement results from increased competitive pressure from

lower output tariffs and access to superior foreign inputs made possible by lower input

tariffs. The study estimated industry level production function using LP methodology for

a sample of the firm during the period 1987–2001.

The brief review reveals that technology import has been a major instrument in enhancing

the productivity performance of manufacturing in India. Even though most results are

6

from a period when substantial restrictions were placed on firms’ choices on technology

acquisition, the findings of a statistically positive and significant impact is noteworthy.

Post‐reform studies have also pointed out the importance of imported technology, but

there is not much agreement on the relative merit of these purchases. That is, there are not

many evidences on the relative merit of embodied and disembodied technology import on

productivity. To the best of our knowledge, previous studies have not explored the impact

of technology import on different use‐based classifications of manufacturing. It is in this

context that the present study examines the impact of technology purchases from abroad

on firm‐level productivity of Indian manufacturing for the period 1995–2016. The

classification of firms across different use‐based product groups is expected to provide

insights into the relative impact of different modes of technology transfer in

manufacturing.

3. Methodology and Data Sources

3.1 Methodology of the Study

In this study, the impact of technology import from abroad on domestic manufacturing

output is examined through the estimation of production function at the firm level. As an

alternative framework, the study has also estimated a productivity determinant model,

popularly known as the two‐step procedure, incorporating technology import and domestic

technological capability.

(a) Production Function Estimation

The impact of technology import on manufacturing output is examined with the help of

firm level production function estimation using the semi‐parametric estimation algorithm

proposed by Levinsohn and Petrin (2003) or LP in short. The LP has emerged as the

standard procedure to derive productivity at the firm level in empirical literature,

especially for the Indian manufacturing sector4. The LP method is a semi‐parametric

estimation procedure that tackles the issue of endogeneity involved with input choices of

firms in the production procedure. Since the seminal contribution of Marschak and

Andrews (1944), it is well known that identification of productivity parameters from the

estimation of production function is problematic due to the potential correlation between

factor inputs and productivity. Most often, the firms input choices are determined by

characteristics of firm or efficiency. This brings the issue of simultaneity due to the

potential correlation between the choice of input mix and productivity shocks that are

unobservable to the econometricians but are known to the firm (De Loecker, 2007). This

4 Several recent empirical studies has used the Levinsohn and Petrin (2003) or LP methodology in

short, for estimating production function at the firm level. Some of the prominent studies being

Parameswaran (2009), Rijesh (2015), Thomas and Narayanan (2016) among others.

7

implies that firms’ choices of inputs are subject to the perception of its own productivity

performance (Rijesh, 2015). In such cases, the OLS estimates of the production function

would be biased because of the potential correlation between productivity and level of

inputs5. An innovative way to correct for the simultaneity bias is the semi‐parametric

method developed by Olley and Pakes (1996) and later modified by Levinsohn and Petrin

(2003).6

The LP method incorporates the unobservable effects on productivity from inputs and

produces reliable input coefficients. In order to address the endogeneity problem, the LP

methodology uses intermediate inputs such as raw material or energy as a proxy variable

which is assumed to have a monotonic relationship with the unobservable firm‐specific

productivity shock. The assumption of monotonicity allows inverting the intermediate

input demand function to unearth the unobservable productivity term as a non‐parametric

function of the intermediate input and capital. Thus, the insight of the LP method is that

the observable characteristics of the firm can be modelled as a monotonic function of the

productivity of the firm. Inverting such a function allows modelling the unobserved

component of firm productivity as a function of the observed variables. Therefore, in the

present study we follow the L‐P estimation procedure for estimating the impact of

technology import on manufacturing output. We use an augmented Cobb‐Douglas

production function that includes technology import purchases as additional factor

inputs.7 The details are as follows.

Under the production function approach, it is assumed that firm’s output (Y) depends on

the use of traditional inputs such as capital (K) and labour (L) subject to the state of

technology (T). We consider T to be augmented by the adoption of foreign technology (FT)

purchases in the form of capital good equipment (embodied technology import, ETI) and

payment for licensing involving the purchase of design, blueprints and technical assistance

by paying royalties and technical fees (disembodied technology import, DETI). Assuming

a Cobb‐Douglas production function, we can write

𝑌 𝐴 𝐾 𝐿 𝑀 𝐸𝑇𝐼 𝐷𝐸𝑇𝐼 ‐‐‐ (1)

where, 𝑌 refers to output of the firm i in period t, 𝐴 is the efficiency level of firm i in

period t, 𝐾 , 𝐿 , 𝑀 , 𝐸𝑇𝐼 , 𝐷𝐸𝑇𝐼 are inputs of capital, labour and material,

embodied technology and disembodied technology, respectively. The natural log

of equation (1) gives a production function (equation 2) that is linear in logs of

output and inputs.

5 Another potential problem from OLS estimation of production function is the issue of selection

bias. This result from the decision of the firm to continue to serve the market based on its perceived

productivity or expected future profitability. 6 For a detailed outline on Semi‐parametric methods, see Yatchew (2003). 7 This is similar to the methodology adopted by Parameswaran (2009) and Rijesh (2015).

8

𝑦 𝑏 𝑏 𝑘 𝑏 𝑙 𝑏 𝑚 𝑏 𝑒𝑇𝐼 𝑏 𝑑𝑒𝑇𝐼 𝜀 ‐‐‐ (2)

where, 𝑦 denotes log of gross output of the firm, k is the log of capital stock, l is the log

of labour input, m is the log of raw material consumed, eT is the log of embodied

technology, deT is the log of disembodied technology and is the error term. Here,

ln 𝐴 𝑏 𝜀 ; where b measures mean efficiency level across firms and over

time and ε is the time‐producer‐specific deviation from that mean (Van Beveren,

2012). 𝜀 can be decomposed into 𝜀 𝜔 𝛾 where 𝜔 is the firm‐specific

productivity difference not captured by explanatory variables and 𝛾 is a pure

random variable capturing unexpected deviation from mean due to measurement

error, unexpected delays or other external disturbances (Van Beveren, 2012 and

Rijesh 2015). The firm‐specific shock (i.e., 𝜔 ) is a state variable that is observable

only to the firm manager and influences the input demand choices. Incorporating

them, we can re‐write equation (2) as

𝑦 𝑏 𝑏 𝑘 𝑏 𝑙 𝑏 𝑚 𝑏 𝑒𝑇𝐼 𝑏 𝑑𝑒𝑇𝐼 𝜔 𝛾 ‐‐‐ (3)

In order to control the variation in input that are correlated with the unobserved

productivity shock, the LP method uses raw material expenses of the firm as the control

variable. The assumption here is that firms’ material demand function is monotonically

increasing in its unobserved productivity, conditional on 𝑘 , a state variable. That is 𝑚𝑚 𝜔 𝑘 . Assuming monotonicity, the inverted raw material demand function can be

written as 𝜔 𝜔 𝑚 𝑘 , which shows that the unobservable productivity component

depends upon observable inputs and hence control for 𝜔 in the production function

estimation. Incorporating the productivity shock proxy, we can rewrite equation (3) as

𝑦 𝑏 𝑙 𝑏 𝑒𝑇𝐼 𝑏 𝑑𝑒𝑇𝐼 𝜃 𝑚 𝑘 𝛾 ‐‐‐ (4)

where, 𝜃 𝑚 𝑘 𝑏 𝑏 𝑘 𝑏 𝑚 𝜔 𝑚 𝑘 .

The estimation of production function takes place at two stages and the details are

elaborated in Levinsohn and Petrin (2003). The production function estimation, which is a

direct evidence of the impact of technology import on productivity, is carried out for 4616

manufacturing firms over 1995–2016 period. The disaggregate performance of firms is

analysed by estimating the production function across five use‐based classifications of

firms, namely basic goods, intermediate goods, capital goods, consumer durable goods,

and consumer non‐durable goods‐producing segments.

(b) Productivity Determinant Model

In the methodology outlined in the sub‐section 3.1(a), we propose to examine the impact

of technology import on output by estimating the augmented Cobb‐Douglas production

function. This is the direct method of assessing the impact of inputs on productivity. An

9

alternative method is to derive productivity residual from the standard production

function and model the determinants including technology import. Moreover, technology

accumulated from abroad also requires firms to have considerable internal technological

capability to assimilate, master and adapt the technology internally, which is often

reflected in their expenditure on R&D. In order to examine the impact of both external

technology purchases and domestic technological capability on the productivity of firms,

we model the determinant of productivity using the standard panel estimation

techniques8.

In this case, the first step is to estimate the firm‐level production function. We estimated

the Cobb‐Douglas production function (equation 5) using LP (2003) methodology.

𝑞 𝑏 𝑏 𝑘 𝑏 𝑙 𝑏 𝑚 𝜔 𝛾 ‐‐‐ (5)

This allows us to calculate productivity or Total Factor Productivity (TFP) of the firms by

taking the difference between actual and predicted values of manufacturing output (see

equation 6).

𝑇𝐹𝑃 𝑞 𝛽 𝑘 𝛽 𝑙 𝛽 𝑚 ‐‐‐ (6)

In the second step, productivity is modelled to be a function of technology and other

relevant factors. That is, we regress the firm’s productivity on the import of technology (TI,

which consists of ETI and DETI), Research and Development (RD) and exports (EXP). In

this case, the RD represents the domestic technological effort and capability of the firm

while the export variable represents the nature of outward orientation. Specifically, we

estimate the following equation,

𝑙𝑛𝑇𝐹𝑃 𝛼 𝛽 𝑙𝑛𝐸𝑇𝐼 𝛽 𝑙𝑛𝐷𝐸𝑇𝐼 𝛽 𝑙𝑛𝑅𝐷 𝛽 𝑙𝑛𝐸𝑋𝑃 𝜁 𝜏 𝜖 ‐‐‐ (7)

where, 𝑙𝑛𝑇𝐹𝑃 is the productivity of firm i in period t, ETI is embodied technology

import, DETI is disembodied technology import, RD is real expenditure on

Research & Development, EXP is real exports. 𝜁 and 𝜏 are coefficients of industry and year dummies. All variables are in natural logs. The data sources and variable

construction details are given in the sub‐section 3.2.

8 The two‐step procedure is the most popular method to assess the impact of technology or other

determinant on productivity. However, it has to be noted that this procedure often produces

inefficient and potentially biased coefficients because of the potential correlation between the

productivity determinant factors and production factor variables used to derive the residual in

the first stage of production function (see Harris, 2005). Therefore, the direct estimation of the

production function is relatively superior in exploring the impact of technology on productivity.

This is the main drawback of the two‐stage method explored in this section.

10

3.2 Data Sources and Variable Construction

The main source of data used for the empirical analysis is Prowess IQ version 1.81. Prowess

is an online client software with an interactive querying system maintained by the Centre

for Monitoring Indian Economy (CMIE) Pvt. Ltd. CMIE collects firm wise data from the

balance sheets and income statements of the company and covers the financial

performance of all listed (stock market) and a large number of unlisted companies of India.

The financial performance indicators are based on the audited Annual Financial Reports

of companies and information submitted to the Ministry of Company Affairs, company

filings with stock exchanges and prices of securities listed on the major stock exchanges.

Apart from the large cross‐section of manufacturing industries, the database covers

services, utilities and financial industries as well. The Prowess IQ covers 60–70 per cent of

the organised manufacturing sector, 75 per cent of corporate taxes, and 95 per cent of excise

duties collected by the Government of India (Goldberg et al., 2010b). Apart from

fundamental financial performance indicators, the Prowess database provides detailed

information on companies spending on technology from abroad such as import of capital

goods and foreign exchange spending on royalty, technical fees, etc., which is the main

rationale for using the database for our study.

From the Prowess database, we extracted selected Annual Financial Statement indicators

of companies such as sales, compensation to employees, gross fixed capital formation, raw

material expenditure, exports, imports, R&D expenditure, etc., belonging to the organised

manufacturing sector. We followed the National Industrial Classification (NIC9) scheme of

2008, according to which manufacturing belongs to NIC10 (food products) to NIC32 (other

manufacturing). All reported data were identified at the 5‐digit level of NIC for the period

1995 to 2016. The compiled data is cleaned before the final selection and the procedure

involves several steps. To begin with, we dropped 8592 firms that do not import

technology in the form of purchase of capital goods or payment of royalty, technical fees,

etc., for any of the reference period. For the remaining firms, we removed firms that do not

report sales (46 firms), gross fixed capital (3 firms), wages (6 firms), and material (45 firms).

For the remaining, we decide to remove firms with less than five years of sales data (1080

firms) during the study period. Finally, the sample data consisted of 4616 manufacturing

firms. The detailed list of manufacturing firms by 2‐digit NIC 2008 is provided in Table A1

in the Appendix. We can observe that the largest number of firms belong to the chemical

sector (556) while the smallest is in the printing and reproduction of recorded media (6).

Alternatively, the selected sample firms were further categorised into use‐based

classification which is given in Table 1 in section 4. We can observe that the consumer good

producing firms have the larger presence in the sample whereas the basic good firms have

the lowest presence. Based on the sample data, the real variables required for the empirical

analysis is constructed and the details are as follows.

9 NIC is based on ISIC nomenclature of the UN. The NIC‐2008 follows the broad structure of ISIC

rev4.

11

Output (Y): We have taken the sales of goods by firms to represent the output variable.

The reported nominal sales figures were deflated by the Wholesale Price Index (WPI) of

manufacturing product group at the disaggregate level to derive the value of output in

2004–05 prices. In case where the appropriate deflators are not available, the price index

corresponding to the nearest product group is utilized. The WPI index is available from

the office of Economic Advisor, Government of India website. For the entire period of

study, there were two WPI series i.e., WPI at 1993–94 prices and WPI at 2004–05 base prices.

We selected the common product groups among them and spliced the series to derive the

WPI at the base year 2004–05=100. As far as possible, we selected the relevant WPI at the

lowest levels of disaggregation to avoid any plausible bias in the estimation.10

Labour Input (L): Since the database does not report direct employment figures, we define

labour input in value terms, i.e. compensation to employees at 2004–05 prices. This figure

includes all cash payments in kind made by a company to its employees. We use the

Consumer Price Index (CPI) for industrial workers to deflate the nominal values of wages

at 2004–05 prices. This practice has been common among several past studies on

productivity in Indian manufacturing. For instance, see Srivastava (1996), Kambhampati

(2003), Mazumdar et al. (2009), Kim and Saravanakumar (2012), Rijesh (2015) among

others11.

Capital Input (K): In this study, Gross Fixed Capital Assets (GFA) expressed in 2004–05

measures the capital input. The GFA includes both tangible assets such as land, building,

plant, and machinery, and intangible assets such as goodwill assets, software, etc. Since

the reported GFA is the book value of the asset at current prices, we use the Perpetual

Inventory Method (PIM) to estimate the stock of capital at the constant price for each firm

in the sample. The PIM revalues the book value of capital, which is given at historical cost

to a replacement value for a given base year. The procedure is based on the methodology

proposed by Srivastava (1996) and later adopted by many studies including Balakrishnan

et al. (2000), Parameswaran (2009), Rijesh (2015), Thomas and Narayanan (2016), among

others. This method revalues the capital given at historical cost to a base year. The

calculation of capital stock is briefly explained below.12

The PIM method requires that the base year capital stock (𝐾 has to be revalued at the

replacement cost. The reported GFA in the database is converted to the replacement of cost

of the base year capital (i.e. GFA in the year 1995) using the Revaluation factor (𝑅 ). The

𝑅 is estimated through the procedure outlined in Srivastava (1996). It is assumed that the

life of plant and machinery is 20 years. For firms incorporated before 1975–76, it is assumed

that the earliest vintage of the machine in their capital mix dates back to the year of

10 In the absence of price data at the firm level, researches usually use the price series at the industry

level. If the firm price series are correlated with input choices of firms, the use of industry‐level

price will bias the input coefficients. For further discussion, see Van Beveren (2012). 11 A plausible issue with this measure is the potential bias in estimation where workers shares the

benefits of productivity (Sivadasan, 2006). 12 For detailed explanation, see Srivastava (1996).

12

incorporation. For all firms, we assume that the price of capital () has changed at a constant rate from 1975 or the year of incorporation, whichever is later, up to 1995. That is:

𝜋 1 ‐‐‐ (8)

In addition, we assume that investment has increased at a uniform rate for all firms.

𝑔 1 ‐‐‐ (9)

To derive 𝜋, we use the capital formation price series derived from the Gross Fixed Capital

Formation series, current and constant price for the organised manufacturing collected

from NAS, published by the CSO. The rate of growth of investment 𝑔 is approximated

by the rate of growth of gross fixed capital formation in manufacturing at 2004–05 prices.13

Assuming that the capital stock of the earliest vintage is t period old, the 𝑅 is derived

using the formula (10)

𝑅 ‐‐‐ (10)

The replacement cost of GFA at firm level is derived by multiplying the balance sheet value

(historical cost) of GFA in 1995 by 𝑅 for each firm in the sample. The capital stocks for the

subsequent time period were calculated by adding the gross investment (GI) to the earlier

stock. GI is obtained from the relation

𝐺𝐼 𝐺𝐹𝐴 𝐺𝐹𝐴 ‐‐‐ (11)

The real capital stocks (K) is obtained by deflating the revalued GFA by the capital good

price series (at 2004–05 prices) constructed using gross capital formation series (current

and constant) obtained from the National Accounts Statistics.

Material Input (M): The raw material expenses valued at 2004–05 prices represent the

material input variable. This includes the value of raw materials, power and fuel

consumptions. Raw materials are the sum of expenses on raw materials, stores, spares and

tools while power and fuel consumption expenditure includes expenses on power, fuel

and water. The nominal value of the raw material cost was deflated using raw material

price indices, base 2004–05 = 100. Since the price indices for raw materials are not readily

available, we constructed it using weights obtained from Input‐Output Transaction Matrix

Table, published by the CSO and the product price indices from the Index of Wholesale

Prices in India, base 2004–05 = 100. The price series is a weighted index of the WPI of major

13 See Rijesh, (2015) for similar exposition.

13

input groups, where the weights for the respective industries are calculated from the

absorption matrix of input‐output transactions.14

Technology Import Input (TI): For a less developed country such as India, a major means

of acquiring technology is through imports. The import of technology mainly consists of

two activities. One is through the purchase of machinery and equipment embodying better

technology and the second is the royalties, licensing, and technical fees paid by the firms

for foreign technology. The former reflects technology that is embodied in capital goods and

the latter indicates the disembodied form of technological flows from abroad. Using the

Prowess IQ database, we collected each company’s import of capital goods and payment

towards royalties, technical fees, etc. The nominal values of import of embodied

technology were deflated by the Unit Value Index (UVI) of machinery & equipment

import, base 2004–05=100. The disembodied technology purchases in real term are

constructed using the R&D deflator of the US, with the assumption that the US has been a

major provider of technical assistance to the Indian firms.15 The deflator is constructed

using the current and constant series of R&D for the manufacturing sector of US and

adjusted for the Rupee‐Dollar exchange rates. The total import of technology, both

embodied and disembodied technology, were deflated using the average price series of

R&D deflator and capital good import price, base 2004–05=100.

Research and Development (RD)

The expenditure on Research and Development (R&D), both capital and current account,

is taken to construct the real R&D expenditure (RD) expressed in 2004–05 prices. The

nominal values were deflated using R&D deflator which is constructed by taking the

average of capital good prices and consumer price index of industrial workers. The data is

collected from NAS and the Ministry of Labour & Employment, Government of India. The

RD measures the internal technological capability of the firm in the sample.

Real Exports (EXP)

The database provides information regarding firms selling goods abroad. The nominal

value of exports is deflated with the Unit Value Index (UVI) of exports at the 2‐digit level.

This provides real export (EXP) of manufacturing firms at 2004–05 prices.

4. Descriptive Statistics

In this section, we provide the basic characteristics of the sample data using standard

statistical tools and methods.

14 The construction of the price index follows the method outlined in Rijesh (2016). 15 This is similar to Basant and Fikkert (1996), Hasan (2002), Parameswaran (2009), and Rijesh (2015).

14

4.1 Overview of Sample Data

The firm wise data that we collected from the PROWESS IQ database were identified

across 5‐digit National Industrial Classification (NIC) 2008 registry, prepared by CSO,

Government of India. The NIC is the standard classification for industry product groups

in India which adopts framework of ISIC of the UN. The disaggregate industry

classification of firms products allowed us to categorise firms according to the specific

use for which the products are developed in the market. We adopted the Index of

Industrial Production (IIP) 2011–12 classification that clubs NIC‐2008 product groups

into six use‐based categories, namely Primary goods, Infrastructure/construction goods,

Intermediate goods, Capital goods, Consumer durables, and Consumer non‐durables.

Using the earlier IIP series of 2004–05, we standardised the use‐based product groups

into five main heads, namely Basic Goods (BAG), Intermediate Goods (IMG), Capital

Goods (CAG), Consumer Durable Goods (CDG), and Consumer Non‐Durable Goods

(CNDG).

The BG includes raw industrial products such as starch, coal, kerosene, basic chemicals,

fertilizers, metals, iron & steel, and aluminium. The IMG covers a wide range like textile

yarns, jute, tanned leathers, wood materials, refined petroleum products, plastic products

in primary forms, synthetic rubber, chemicals, insulated wires, accumulators & cells, and

railway instruments. The CAG is a durable producer good used to produce other goods

and includes a variety of machinery equipment and tools such as lathes, drills, agriculture

machinery, textile machinery, cement machinery, electric motors, and transport

equipment. The consumer goods industry, which consists of both CDG and CNDG,

includes final consumption (end) based products of food, beverages, leather, rubber,

electrical and non‐electrical appliances, automobiles, and transport vehicles. The

distribution of firms according to the use‐based classification is given in Table 1.

Table 1

Distribution of Sample Manufacturing Firms: By Use‐based Classification

Use‐based classification Number of firms (%)

Basic goods 144 (3.1)

Intermediate goods 1746 (37.8)

Capital goods 793 (17.2)

Consumer durables 869 (18.8)

Consumer non‐durables 1064 (23.1)

Manufacturing Sector 4616 (100.0)

Source: Author classification of Prowess data (CMIE).

We can notice that a large majority of firms engage in producing consumer goods that

include both durable and non‐durable products (42%) followed by intermediate goods

(38%), capital goods (17%) and basic goods (3%). In terms of broad sectors, the largest

amounts of firms (556) were under chemicals and chemical product groups (NIC code 20).

However, we do find that not all firms consistently engaged in production activities and

15

purchase of technology during the entire period of study. For instance, only 1005 firms

were reported to have consistent sales throughout the study period. Moreover, only 194

firms were engaged in technology purchases for the entire period from 1995 to 2016 (See

Table A2 in the appendix for the detailed list based on use‐based distribution of firms in

the sample).

In Table 2, we provide the growth trend among select firm‐level characteristics for the

entire period of study (1995–2016) as well as for two sub‐periods (1995–2007 and 2008–

2016). The first and second periods correspond to the pre‐ and post‐global financial crisis,

respectively. As before, the entire manufacturing is classified across five use‐based

classifications. The selected firm‐level characteristics are sales, wages, capital, exports,

imports, embodied technology import, disembodied technology import, technology

import, and domestic R&D expenditure. All variables are deflated by appropriate price

indices having 2004–05 as base year.

Table 2

Trend in Select firm‐level Characteristics: By Use‐based Classification (%)

BEC Variables 1995–2007 2008–2016 1995–2016

Basic goods

Sales 8.1 (6.4) 7.4 (5.6) 7.8 (6.1)

Wages 3.3 (6.2) 6.0 (6.0) 4.4 (6.1)

Capital 9.2 (9.4) 10.2 (8.8) 9.6 (9.2)

Exports 12.9 (2.1) 4.3 (1.3) 9.2 (1.8)

Imports 7.4 (6.5) 18.9 (4.1) 12.3 (5.5)

Embodied Technology import 51.5 (3.9) ‐8.6 (3.9) 25.7 (3.9)

Disembodied Technology import 15.1 (7.1) 25.1 (3.0) 19.4 (5.4)

Import of Technology 31.5 (4.5) ‐2.1 (3.8) 17.1 (4.2)

R&D Expenditure 5.5 (3.3) 10.7 (1.1) 7.7 (2.4)

Intermediate goods

Sales 8.7 (56.7) 4.1 (55.3) 6.7 (56.2)

Wages 4.8 (42.8) 2.6 (37.1) 3.8 (40.5)

Capital 10.9 (61.5) 11.2 (58.5) 11.0 (60.3)

Exports 16.8 (53.7) 4.8 (62.4) 11.7 (57.2)

Imports 12.4 (7.6) 0.9 (6.9) 7.5 (7.3)

Embodied Technology import 6.5 (56.8) 4.2 (55.3) 5.5 (56.2)

Disembodied Technology import 8.3 (45.2) ‐2.5 (13.0) 3.7 (32.0)

Import of Technology 8.9 (55.0) 4.8 (47.8) 7.1 (52.1)

R&D Expenditure 14.5 (20.7) 9.6 (12.1) 12.4 (17.2)

Capital goods Sales 12.4 (11.5) 6.8 (12.4) 10.0 (11.9)

Wages 4.7 (22.5) 3.9 (22.4) 4.3 (22.5)

Capital 10.6 (8.8) 13.8 (10.3) 11.9 (9.4)

Exports 10.2 (9.5) 1.4 (7.2) 6.4 (8.6)

Imports 15.1 (9.5) 4.5 (8.1) 10.5 (8.9)

16

BEC Variables 1995–2007 2008–2016 1995–2016



Import of Technology 15.4 (11.2) ‐2.6 (14.8) 7.7 (12.6)

R&D Expenditure 19.0 (27.9) 10.0 (33.9) 15.2 (30.3)

Consumer durables

Sales 14.7 (11.1) 10.9 (12.5) 13.1 (11.7)

Wages 7.4 (11.8) 6.8 (14.0) 7.2 (12.7)

Capital 12.0 (9.4) 11.6 (9.9) 11.9 (9.6)

Exports 12.7 (12.7) 6.0 (11.8) 9.8 (12.3)

Imports 12.4 (6.7) 7.2 (6.2) 10.2 (6.5)




R&D Expenditure 12.0 (16.7) 14.5 (12.9) 13.1 (15.2)

Consumer non‐durables

Sales 9.3 (14.2) 9.1 (14.1) 9.2 (14.2)

Wages 7.8 (16.6) 6.8 (20.4) 7.4 (18.2)

Capital 12.9 (10.8) 12.5 (12.5) 12.7 (11.5)

Exports 16.4 (22.0) 6.0 (17.4) 11.9 (20.1)

Imports 14.8 (69.8) 4.7 (74.7) 10.4 (71.8)

Embodied Technology import 8.8 (8.8) 2.0 (10.5) 5.9 (9.5)



R&D Expenditure 21.0 (31.3) 19.6 (39.9) 20.4 (34.9)

Manufacturing

Sales 9.6 (100.0) 6.1 (100.0) 8.1 (100.0)

Wages 5.4 (100.0) 4.5 (100.0) 5.0 (100.0)

Capital 10.9 (100.0) 11.5 (100.0) 11.2 (100.0)

Exports 14.8 (100.0) 4.6 (100.0) 10.4 (100.0)

Imports 12.0 (100.0) 4.4 (100.0) 8.7 (100.0)




R&D Expenditure 16.1 (100.0) 12.0 (100.0) 14.3 (100.0)

Note: Figures adjacent to the parenthesis are the average annual growth rates (%). The figure in

parenthesis is the relative average share in total manufacturing during a particular period (%).

Source: Author calculation from Prowess IQ (CMIE database).

For the aggregate manufacturing sector, all variables witnessed growth deceleration

during the post‐financial crisis period, except capital and disembodied technology

imports. The largest declining trend is observed among machinery equipment imports,

which fell from 6 per cent growth during the first period to negative 0.3 per cent during

the second period. The domestic production, measured in terms of firm’s sales, grew at the

17

rate of 8 per cent per annum during the entire period, which is marginally lower during

the first period (9.6%) relative to the second (6.1%) period. In the case of basic production

inputs, the capital stock registered an impressive double‐digit growth rate of 11 per cent

whereas the growth rates of wages are considerably less (5 per cent per annum) for the

entire period. As expected, the shrink in global economic activity during the post‐financial

crisis period is found to have negatively impacted the manufacturing trade of Indian firms.

For instance, both exports and imports, which registered double‐digit growth rates during

1995–2007 period (15 and 12 per cent, respectively), witnessed a sharp deceleration during

the 2008–2016 period (5 per cent and 4 per cent, respectively). Moreover, this pattern is

visible among technology import, too, especially in the case of embodied technology.

However, for disembodied technology, we did not find growth deceleration for the entire

period. In addition, we find double‐digit growth in domestic R&D expenditure (14 per cent

per annum), which reflects the growing technological effort by the Indian manufacturing

in the recent period.

In terms of manufacturing production structure, the largest production share is accounted

for by the intermediate industrial firms (56%) while the smallest share is by the basic goods

producing firms (6%). However, in terms of growth rates, consumer durables (13%) and

capital goods (10%) producing firms have registered impressive performance during the

entire period of study. Although the intermediate goods producing firms represent the

largest share in manufacturing, the growth in real wages is found to be the lowest (4%)

compared to other use‐based segments. Similar to what we observed at the aggregate level,

there is a perceptible fall in sales volume and wages during the post‐2008 period, except

for the real capital stock. The increased specialisation in intermediate goods production is

also reflected in the international exchange of goods as the majority of firms were found to

be exporting (57%) to the world market. On the other side, the major importing products

were found to be largely consumption‐oriented goods (78%). Relatively, the export growth

rates of all use based producing firms were higher than import growth rates, especially

during the first period of study.

4.2 Technological Characteristics of Indian Manufacturing Firms

For examining the technology profile of the Indian manufacturing firms, we have taken

the following variables, namely technology import in terms of equipment and machinery

purchases from abroad (embodied technology import, ETI) and foreign exchange spent

on royalty, technical fees, etc. (disembodied technology import, DETI) and domestic

R&D expenditure that reflect the internal technological capability of the firm. In the

following sub‐section, we provide descriptive statistical evidence from the data sample.

Table 2 reveals that the major importer of technology from abroad belongs to the

intermediate goods sector (52%), followed by the consumer durable sector (22%) and

capital goods sector (13%). Although all these sectors have recorded double‐digit growth

rates during the first period, the growth rate fell across sectors during the second period.

The disaggregation of technology import into embodied and disembodied technology

18

purchases shows the significant presence of intermediate goods sector and consumer

durable producing sector in the sample. The relative comparison reveals that the preferred

mode of technology acquisition among use‐based firms is the capital goods imports from

abroad (see Figure 1). For instance, in the case of intermediate goods producing firms, the

bulk of foreign technology purchases (91%) is through capital goods imports. However, in

relative terms, the growth of disembodied technology import has been substantial across

the use‐based category of firms during this period (see Table 2).

Figure 1

Composition of Embodied and Disembodied Technology Import:

By Use‐based Classification (1995–2016)

Note: BAG=Basic Goods, CAG=Capital Goods, CDG=Consumer Durable Goods, CNDG=Consumer

Non‐Durable Goods, IMG=Intermediate Goods, MAN=Manufacturing.

Source: Authors calculation based on data collected from Prowess IQ, CMIE.

The global slowdown since the post‐2008 period seems to have adversely affected the

growth in embodied technology purchases by the consumer durables, capital goods and

basic goods sectors. All these segments have reported negative rates of growth during this

period. Overall, the technology import (both embodied technology and disembodied

technology) for the overall manufacturing sector have reported a growth rate of 6 per cent

per annum for the entire period of study. However, there is a large discontinuity in

technology purchases as only 194 firms have reported purchasing technology from abroad

continuously during the entire period of study (see Table A2 in the appendix for details).

Compared to the in‐house domestic technological effort, which is proxied by the firms’

expenditure on Research & Development (R&D), we find that firms devote a larger part of

their expenditure to acquiring technology from abroad (see Figure 2). As can be seen from

Figure 2, the technology intensity variable, measured in terms of firms’ sales revenue,

indicates that the share of technology import in total manufacturing is around 1.6 per cent,

77.1 76.7 74.7

84.1

91.2

84.1

22.9 23.3 25.3

15.9

8.8

15.9

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

BAG CAG CDG CNDG IMG MAN

Embodied Technology Import Disembodied Technology Import

19

which is relatively much larger than the share of domestic R&D of 0.4 per cent. It is striking

to note that, apart from capital goods and consumer non‐durables, the share of domestic

R&D is less than unity for the rest of the sector. The consumer‐durable industry groups

(3%) undertake the largest spending on the import of technology and as before, the mode

of preference is capital‐embodied technology from abroad. The analysis of the top 10 major

importers of technology during the last five years is found to be Reliance Industries Ltd

(12%), Maruti Suzuki India Ltd (8%), Tata Steel Ltd (3%), J S W Steel Ltd (3%) and Hyundai

Motor India Ltd (3%). These firms belong to the intermediate and consumer durable goods

segment (see Table A3 in the appendix for further details).

Figure 2

Technology Intensity in Indian Manufacturing: Select Indicators (%)

Source: Author’s calculation from Prowess IQ, CMIE.

5. Estimation Results

In this section, the econometric results of the production function estimates of

manufacturing firms discussed in the methodology section 3 are given. The direct

estimation of production function incorporating technology factors as factor inputs is

statistically more appropriate and widely preferred in the empirical studies on firm level

productivity in India16. For the aggregate manufacturing sector, the firm‐level estimates

16 As noted before, alternative strategy is to run a standard production function estimation using

labour and capital inputs and derive productivity residually. The resulting productivity can be

modelled as a function of a range of potential determinants including technology inputs. This

Two‐step procedure, however, often produces inefficient and potentially biased coefficients

(Harris, 2005). The bias arises from the potential correlation between productivity determinant

factors and production factor variables used to derive the residual. Some of the prominent Indian

1.2

1.6

2.7

1.0

1.6 1.6

0.1

1.1

0.5

1.1

0.1

0.4

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Basic goods Capital goods Consumer

durables

Consumer non‐

durables

Intermediate

goods

Manufacturing

Embodied Technology Disembodided Technology Technology Import R&D

20

are given in terms of OLS and LP. As discussed earlier, in the presence of simultaneity

between productivity shock and input choice decision by the firm, the OLS estimates are

expected to produce an upward bias in the labour coefficient and downward bias in the

capital coefficient. Therefore, consistent estimates are derived using LP, which is a semi‐

parametric method that tackles the problem of endogeneity. The summary statistics of the

selected empirical variables are given in Table A4 in the appendix. Compared to the

standard input variables, there is a high degree of dispersion among the technology import

variables in the sample. This is expected as not many firms have sourced technology from

abroad on a consistent basis during the study period.

Table 3

Production Function Estimates for Indian Manufacturing Sector (1995–2016) Dependent Variable: lny

Variables OLS LP

(1) (2) (1) (2)

lnl 0.594***

(0.006)

0.595***

(0.006)

0.312***

(0.009)

0.313***

(0.009)

lnk 0.367 ***

(0.005)

0.363 ***

(0.005)

0.477***

(0.021)

0.477***

(0.021)

lnTI 0.040 ***

(0.002)

0.020***

(0.002)

lneTI 0.026 ***

(0.002)

0.017***

(0.002)

lndeTI 0.041***

(0.003)

0.027 ***

(0.004)

Constant 2.106

(0.020)

2.106

(0.020)

𝑅 0.740 0.876

Observations 63090 63090 63090 63090

Note: (a) For OLS Huber‐White robust standard errors are given in parentheses. In LP, bootstrap

standard errors are given in parentheses. (b) *significant at 0.01 level for a two‐tailed test, **at

0.05 level for a two‐tailed test, ***at 0.1 level for a two‐tailed test. (d) lny= log of output, lnl=log

of labour, lnk=log of capital, lneTII=log of embodied technology import, lndeTI= log of

disembodied technology import, lnTI=log of technology import

In Table 3, the econometric result of the manufacturing sector is given. For both OLS and

LP estimation method, we have used two models. In the first model, the technology inputs

appear separately as embodied form and disembodied form. In the second case,

technology import is taken as the sum of both embodied and disembodied forms of

technology purchases. In all estimated models, the results reveal that the standard

studies that have employed direct estimation of production function incorporating technology

import inputs are Basant and Fikkert (1996), Hasan (2002), Parameswaran (2009), and Rijesh

(2015).

21

production function factors such as labour and capital have the expected positive sign and

are significant at 1 per cent level. Comparing the estimates of capital reveals that the OLS

produces substantially lower coefficient size in all cases. For instance, the capital estimates

using OLS is found to be 0.36 while LP results indicate an improvement of 0.48. This

suggests that the problem of simultaneity has been addressed by the LP estimates.

5.1 Impact of Technology Import on Manufacturing Output

The elasticity of output with respect to technology import variables is found to be positive

and statistically significant in the Indian manufacturing sector (see Table 3). The

production function estimates based on LP reveals that a 1 per cent increase in technology

import is associated with a 0.02 per cent increase in output, on average. The disaggregation

of technology import further confirms similar range for the elasticity coefficient. For

instance, a 1 per cent increase in the purchase of capital goods embodied foreign

technology increases manufacturing output by 0.02 per cent while a similar increase in

disembodied technology from abroad increases output by 0.03 per cent. Although the size

of the coefficient is rather small compared to other major inputs, the statistically significant

positive impact of technology import is rather noteworthy.

Table 4

Production Function Estimates for Indian Manufacturing: By Use‐Base Classification (1995–2016) Dependent Variable: lny

Variables Basic Goods Capital Goods

Intermediate

Goods Consumer Goods

(1) (2) (1) (2) (1) (2) (1) (2)

lnl 0.356 ***

(0.046)

0.355 ***

(0.045)

0.258***

(0.018)

0.254***

(0.020)

0.261***

(0.013)

0.265***

(0.013)

0.384***

(0.015)

0.384***

(0.015)

lnk 0.578 ***

(0.139)

0.575***

(0.157)

0.458***

(0.056)

0.468***

(0.052)

0.510***

(0.040)

0.510***

(0.040)

0.391***

(0.036)

0.389***

(0.035)

lnTI 0.020**

(0.008)

0.031***

(0.004)

0.020***

(0.002)

0.018***

(0.002)

lneTI 0.017**

(0.008)

0.027***

(0.004)

0.014***

(0.002)

0.016***

(0.002)

lndeTI 0.017

(0.013)

0.023***

(0.007)

0.036***

(0.007)

0.028***

(0.007)

Observations 2226 2226 10605 10605 24596 24596 25663 25663

Note: (a) All estimates are carried out using LP algorithm. The bootstrap standard errors are given

in parentheses. (b) *significant at 0.01 level for a two‐tailed test, **at 0.05 level for a two‐tailed

test, ***at 0.1 level for a two‐tailed test. (d) lny= log of output, lnl=log of labour, lnk=log of

capital, lneTI=log of embodied technology import, lndeTI= log of disembodied technology

import, lnTI=log of technology import.

In Table 4, we provide the econometric result for the disaggregate manufacturing based

on use‐based classification. All estimates are based on the LP algorithm. Similar to what

we have observed at the aggregate level, the traditional factor inputs are found to be

22

positive and statistically significant at 5 per cent level across all use‐based classifications.

In all cases, the import of technology is found to have a positive and statistically

significant (at 5 per cent level) impact on manufacturing output, except for basic goods.

In the case of basic goods, both embodied and disembodied technologies report a

positive impact on output but the statistical significance is apparent only in case of the

former. For the intermediate and consumer goods producing segment, the output

elasticity of disembodied technology is found to be marginally higher than the embodied

technology. However, for capital goods, the import of tools and equipment embodying

superior technology has a relatively larger impact on output. Overall, the range of output

elasticity with respect to technology import ranges from 0.01 to 0.04 (see Table 4).

Table 5

Productivity Determinant Model Estimation Results: By Use‐based Classification

Variables

Manufacturing

Sector Basic Goods Capital Goods Intermediate Goods Consumer Goods

(1) (2) (1) (2) (1) (2) (1) (2) (1) (2)

lnETI 0.016 **

(0.002)

0.014**

(0.001)

0.018*

(0.006)

‐0.019**

(0.056)

0.024**

(0.007)

0.013**

(0.003)

0.011**

(0.003)

0.017**

(0.001)

0.011**

(0.0054)

0.018**

(0.002)

lnDETI 0.026**

(0.005)

0.058**

(0.002)

‐0.024

(0.0175)

0.064**

(0.008)

0.028**

(0.009)

0.031**

(0.003)

0.011*

(0.006)

0.028**

(0.003)

0.028**

(0.008)

0.087**

(0.002)

lnRD 0.034**

(0.005)

0.061**

(0.001)

0.057**

(0.0240)

0.087**

(0.008)

0.022*

(0.011)

0.085**

(0.003)

0.028**

(0.008)

0.047**

(0.002)

0.029**

(0.009)

0.052**

(0.002)

lnEXP 0.086**

(0.004)

0.055**

(0.001)

0.020

(0.015)

0.023**

(0.006)

0.062**

(0.011)

0.037**

(0.003)

0.095**

(0.009)

0.086**

(0.002)

0.067**

(0.010)

0.051**

(0.002)

Constant 2.098**

(0.027)

2.162**

(0.005)

2.568**

(0.087)

2.308**

(0.057)

2.254**

(0.063)

2.18**

(0.030)

2.049**

(0.052)

1.950**

(0.026)

2.35**

(0.052)

2.303**

(0.026)

Industry

Dummy Yes Yes Yes Yes Yes

Year

Dummy Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

R2 0.167 0.283 0.300 0.242 0.167

F‐test 122.01 10.59

(0.000)

15.93

(0.000)

17.1

(0.000)

12.11

(0.000)

Wald chi2 20985.47

(0.000)

1509.54

(0.000)

5727.55

(0.000)

31237.95

(0.000)

8909.12

(0.000)

Observations 63090 2226 10605 24596 25663

Groups 4616 144 793 1746 1933

Note: (1) The estimated equation (1) is based on Fixed Effect estimator. The equation (2) is based on

Flexible Generalised Least Square method of estimation. (2)Huber‐White robust standard errors

are given in parentheses. (3) **at 0.05 level for a two‐tailed test, *significant at 0.1 level for a two‐

tailed test. (4) lnETI=log of embodied technology import, lnDETI=log of disembodied technology

import, lnRD=log of real Research and Development expenditure, lnEXP=log of real exports.

23

In Table 5, the result of productivity determinant model, which is explained in sub‐section

3.1(b), is given. In this specification, the productivity residual, which is obtained in the first

stage of estimation of a standard Cobb‐Douglas production function, is regressed on

technology import, domestic technological capability (proxied by real expenditure on

R&D) and exports. We have used Fixed Effects (FE) estimator (within estimator) that

control for unobserved firm‐specific heterogeneity that is assumed to be constant over

time. The FE model assumes that the firm‐specific effects are correlated with the

explanatory variables. We use the Huber‐White robust standard errors to control potential

heteroskedasticity in the sample disturbance term. In order to control for certain degree of

correlation between the residuals in the model, we also use Flexible Generalised Least

Square (FGLS) method where the structure of the error covariance matrix is unknown. The

result for the FE is given in column (1) and FGLS is given in column (2).

The alternative specification further confirms our earlier result that reveals a positive and

statistically significant impact of technology import on output.17 The coefficient of

embodied technology and disembodied technology is positive and statistically significant

using FE and FGLS among overall manufacturing as well as use‐based classification,

except basic goods. In the case of basic goods, the FE estimator reports a negative but

statistically insignificant coefficient of disembodied technology while FGLS reports a

negative and statistically significant result for embodied technology. For the rest of the

segments, the coefficients are rather small but positive and highly significant. The range of

ETI coefficient is between ‐0.02 to 0.02 while the coefficient for DETI is between ‐0.02 and

0.09. The low range of coefficient may be because of the lack of active sourcing of

technology from abroad by the firms. Interestingly, the coefficient of domestic

technological capability, which is proxied by R&D expenditure, turned out to be positive

and statistically significant across all categories and estimation techniques. The RD

coefficient ranges from 0.02 to 0.09 across specification. The result points out the significant

role of accumulating technological capability in improving manufacturing productivity.

The export performance indicator, which is taken as a control variable, is found to have a

significant positive impact on productivity across manufacturing.

The econometric results provide considerable evidence to support the conjecture that

since liberalisation, technology import has been an important element in manufacturing

productivity. Moreover, both embodied and disembodied technology serve as an

important factors in determining manufacturing firms output in India. This provides

empirical support to the recent trade and growth theories that argue that increased trade

openness provides opportunities to learn, adopt, and assimilate foreign knowledge in

domestic manufacturing, thus creating dynamic productivity gains over time. By

importing technology, firms are exposed to the technological knowledge of advanced

countries. The use of technology from abroad further enhance and supplement the

technological capability of domestic manufacturing when it enhances indigenous

17 Rijesh (2015) has obtained a similar result for the aggregate as well as technology‐intensive

classification of Indian manufacturing firms during the period of 1995‐2010.

24

technological effort in the long run. The positive impact of R&D on productivity further

substantiates the argument that there are significant gain from investments in R&D. This

will enhance the technological capability and absorptive capacity of the manufacturing

firms in the future. Since technology import is not a sizeable, a pro‐active, systematic

approach will provide further productivity gains in the future.

6. Concluding Remarks

The basic objective of the present study was to examine the impact of foreign technology

import on the productivity of Indian manufacturing firms since liberalisation. The impact

of technology purchases in terms of embodied technology (import of capital goods) and

disembodied technology (purchase of royalties, technical fees, consultation fees etc.) is

considered as a major source of technology diffusion in the emerging catch‐up economies

like India. Since major technological advancement is largely concentrated in a handful of

advanced countries, access to technology is a major factor for the developing economies

that are building their manufacturing capability to compete effectively in the world

market. Since the opening up of the Indian economy, it has become easier for firms to

expand their input choices and adopt wide‐ranging production technology from abroad.

The recent trade and growth theories have long argued that the purchases of technology

through trade often result in substantial productivity (dynamic) gains to the follower

country. In this context, the present study attempted to assess the dynamic benefits of

technology import in terms of productivity improvement in the case of Indian

manufacturing firms for the period 1995–2016.

The impact of technology import on production is examined by fitting an augmented

Cobb‐Douglas production function incorporating both embodied and disembodied forms

of technology inputs from abroad. The direct estimation of production function is carried

out using the semi‐parametric approach proposed by Levinsohn and Petrin (2003), which

controls the potential simultaneity between factor inputs and productivity shocks. The

empirical analysis is based on an unbalanced panel of 4616 manufacturing firms over the

period 1995–2016. The inter‐sectoral dynamics of the relationship is analysed by classifying

the selected sample firms across five use‐based categories, namely basic goods,

intermediate goods, capital goods and consumer durable and non‐durable goods. The

categorisation of firms according to these different uses of production is a novel

contribution to the empirical literature on Indian manufacturing. The firm‐level data is

collected from Prowess IQ (CMIE). For the econometric analysis, we constructed the real

values of output, capital, material, labour and technology import variables using

appropriate price deflators.

The descriptive statistics reveals that the structure of manufacturing is largely dominated

by the consumer goods and intermediate producing goods firms. Most of the industrial

segment has witnessed considerable trade integration, measured in terms of growth in

exports and outputs. However, since the global financial crisis of 2008, manufacturing

25

sector has witnessed considerable growth deceleration, which is reflected in the number of

performance indicators. In terms of technology, only a few firms have imported

technology for the entire study period. However, most of the firms are found to engage in

some mode of technology purchases throughout the study, especially in the form of import

of machinery and equipment embodying better technology. The largest recipient of

technology import is the consumer goods‐producing firms. Relative to the domestic

technological effort, as proxied by the R&D expenses, firms are found to spend much larger

amount on technology from abroad. Although the share of embodied technology import

is quantitatively larger, the disembodied technology import seems to have registered

better growth performance during the study period.

The econometric results provide considerable evidence to substantiate the argument that

technology import has a positive and statistically significant impact on Indian

manufacturing output. The LP estimates, both at the overall manufacturing and at the use‐

based categorisation of firms, do indicate that the relationship between the two is robustly

positive and significant for both embodied as well as disembodied technology import,

except in the case of basic goods producing firms. Although the relative size of the

technology import coefficient is not large (compared to the estimates of labour and capital),

the overwhelming evidence of a positive relationship does indicate that technology import

is a major source of productivity improvement in Indian manufacturing. The result is

further confirmed from an alternative specification where we modelled the determinant of

productivity by taking technology import, R&D and exports. Apart from observing a

significant and positive impact of technology import, the results further draw attention to

the importance of investment in building domestic technological capability through firm

R&D. Overall, the results indicate that, since liberalisation, technology purchased from

abroad has provided opportunities for dynamic productivity gains in Indian

manufacturing.

26

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29

Appendix

Table A1

Distribution of Sample firms Selected: By 2‐digit NIC

2‐digit Description Number of firms

10 Food Products 284

11 Beverages 98

12 Tobacco Products 14

13 Textiles 526

14 Wearing Apparel 125

15 Leather and Related Products 56

16 Wood and of Products of Wood and Cork 20

17 Paper and Paper Products 141

18 Printing and Reproduction of Recorded Media 6

19 Coke And Refined Petroleum Products 39

20 Chemicals And Chemical Products 556

21 Pharmaceuticals, Medicinal Chemical and Botanical Products 297

22 Rubber And Plastics Products 330

23 Other Non‐Metallic Mineral Products 191

24 Basic Metals 435

25 Fabricated Metal Products, Except Machinery and Equipment 163

26 computer, Electronic and Optical products 185

27 Electrical Equipment 268

28 Machinery and Equipment n.e.c. 365

29 Motor Vehicles, Trailers and Semi‐Trailers 349

30 Other Transport Equipment 48

31+32 Furniture+ Other Manufacturing 120

Manufacturing Sector 4616

Source: Author compilation from Prowess IQ database, CMIE

30

Table A2

Firms with consistent Sales and Technology Import for the entire period:

By Use‐Based Classification

Use Based

Classification

Number of Firms

Sales Technology

Import

Embodied

Technology

Import

Disembodied

Technology

Import

Basic goods 54 12 9 4

Capital goods 163 39 28 20

Consumer durables 169 48 32 16

Consumer non‐durables 202 30 24 5

Intermediate goods 417 65 52 11

Manufacturing Sector 1005 194 145 56

Source: Authors calculation from Prowess IQ, CMIE

Table A3

Technology Import by Top 10 firms

Company Name NIC Use base classification Technology Import

(₹ Billion) %

Reliance Industries Ltd. 19209 Intermediate goods 306.0 (12.1)

Maruti Suzuki India Ltd. 29101 Consumer durables 200.4 (8.0)

Tata Steel Ltd. 24105 Intermediate goods 83.6 (3.3)

J S W Steel Ltd. 24105 Intermediate goods 81.4 (3.2)

Hyundai Motor India Ltd. 29101 Consumer durables 71.1 (2.8)

Steel Authority Of India Ltd. 24105 Intermediate goods 52.3 (2.1)

Bhushan Steel Ltd. 24105 Intermediate goods 41.8 (1.7)

Hindalco Industries Ltd. 24201 Intermediate goods 40.3 (1.6)

Jindal Steel & Power Ltd. 24102 Intermediate goods 38.1 (1.5)

Indian Oil Corpn. Ltd. 19209 Intermediate goods 34.0 (1.3)

Note: The figures in brackets represent share of technology import by each firm in total technology

import purchases (both embodied and disembodied technology import).

Source: Authors calculation from Prowess IQ, CMIE

31

Table A4

Summary Statistics

Sectors Variables Obs Mean Std. Dev. Min Max Manufacturing

lny 63090 6.598 1.893 ‐2.780 14.914

lnl 63090 3.673 1.764 ‐3.199 11.041

lnk 63090 6.191 1.712 ‐2.547 14.557

lneTI 63090 1.053 1.877 ‐3.328 10.816

LndeTI 63090 0.317 1.095 ‐2.530 10.251

LnTI 63090 1.235 1.980 ‐3.007 11.138

Basic Goods

Lny 2226 7.747 1.912 ‐1.753 12.482

Lnl 2226 4.658 1.793 ‐2.948 8.614

Lnk 2226 7.489 1.972 ‐0.921 12.451

lneTI 2226 1.311 2.080 ‐3.319 8.420

LndeTI 2226 0.536 1.409 ‐2.469 7.920

LnTI 2226 1.628 2.203 ‐2.968 8.530

Intermediate

goods

Lny 24596 6.603 1.832 ‐2.780 14.914

Lnl 24596 3.521 1.717 ‐3.199 11.041

lnk 24596 6.369 1.708 ‐2.127 14.557

lneTI 24596 1.080 1.937 ‐3.328 10.816

lndeTI 24596 0.216 0.913 ‐2.485 9.094

lnTI 24596 1.211 2.003 ‐3.007 11.138

Capital Goods

lny 10605 6.683 1.801 ‐2.468 13.036

lnl 10605 4.008 1.705 ‐3.041 10.622

lnk 10605 6.043 1.591 ‐2.413 12.598

lneTI 10605 1.133 1.868 ‐3.328 9.117

LndeTI 10605 0.564 1.374 ‐2.530 8.583

LnTI 10605 1.424 2.016 ‐3.007 9.361

Consumer Goods

Lny 25663 6.459 1.949 ‐2.636 13.052

Lnl 25663 3.594 1.785 ‐3.199 9.180

Lnk 25663 5.969 1.670 ‐2.547 12.238

lneTI 25663 0.971 1.797 ‐3.328 10.289

lndeTI 25663 0.293 1.077 ‐2.530 10.251

lnTI 25663 1.146 1.911 ‐3.007 10.125

lny= log of output, lnl=log of labour, lnk=log of capital, lneTI=log of embodied technology import,

lndeTI= log of disembodied technology import, lnTI=log of technology import.

32

List of ISID Working Papers

202 Economic Reforms and Indian Pharmaceutical Industry: Implications for Industrial

Upgrading, Dinesh Kumar Abrol, Rollins John & Amitava Guha, March 2018

201 Dimensions of NPAs in Indian Scheduled Commercial Banks, Santosh Kumar Das &

Pradyuman Singh Rawat, March 2018

200 Dimensions of NPAs in Indian Scheduled Commercial Banks, Santosh Kumar Das &

Pradyuman Singh Rawat, March 2018

199 Intensity of Use of Land in Urban Residential Areas, Hariharan Ramachandran, August

2017

198 International Trade and Productivity Growth: Evidence from the Organised

Manufacturing Sector in India, R. Rijesh, February 2017

197 Demonetisation: Macroeconomic Implications for Indian Economy, Santosh Kumar

Das & Pradyuman Shankar Rawat, February 2017

196 Health in the Era of Neo‐Liberalism: A Journey from Stateʹs Provisioning to

Financialization to Achieve UHC, Shailender Kumar, December 2016

195 Contractionary Fiscal Policy and Public Investment: An Empirical Analysis of

Emerging Regional Growth Dynamics in India, Santosh Kumar Das, October 2016

194 Bottled Drinking Water Industry in India: An Economic Analysis, Swadhin Mondal,

September 2016

193 An Analysis of Foreign Acquisitions in India’s Manufacturing Sector, Beena Saraswathy,

August 2016

192 Impact of Trade Liberalisation on the Indian Electronics Industry: Some Aspects of the

Industrial Policy Dynamics of Global Value Chain Engagement, Smitha Francis, July 2016

191 Pharmaceuticals, Product Patent and TRIPS Implementation, Dinesh Abrol, Nidhi Singh, et.

al., March 2016

190 FDI, Technology Transfer and Payments for Know‐How: A Case Study of Automobile

Sector, Swati Verma & K.V.K. Ranganathan, March 2016

189 Post‐TRIPS Contribution of Domestic Firms to Pharmaceutical Innovation in India: An

Assessment, Dinesh Abrol & Nidhi Singh, March 2016

188 Impact of Mergers on Competition in the Indian Manufacturing: An Assessment, Beena

Saraswathy, March 2016

187 Trends in Foreign Investment in Healthcare Sector of India, Reji K. Joseph & K.V.K.

Ranganathan, February 2016

186 Industrial Finance in the Era of Financial Liberalisation in India: Exploring Some

Structural Issues, Santosh Kumar Das, December 2015

185 Private Sector in Healthcare Delivery Market in India: Structure, Growth and

Implications, Shailender Kumar, December 2015

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

http://isid.org.in/

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