Factors Behind the Performance of Pharmaceutical Industries in India

8/20/2019 Factors Behind the Performance of Pharmaceutical Industries in India

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Economic & Political Weekly EPW december 27, 2014 vol xlix no 52 81

Factors behind the Performanceof Pharmaceutical Industries in India

Chiranjib Neogi, Atsuko Kamiike, Takahiro Sato

Changes in various policies related to trade and entry of

multinational companies in the Indian pharmaceutical

industry were initiated in the early 1970s. However, the

pace of growth of this industry has shown a remarkable

upswing only after 1991, and particularly after 2005. The

introduction of pharmaceutical product patents brought

new business opportunities. But the increase in

competitive pressure has possibly induced the exit of

small and inefficient firms and plants from the market.

Against this backdrop, it is necessary to assess the

performance of the pharmaceutical industry and find the

factors responsible for the variation in industry efficiency

and productivity. In this paper, stochastic frontier

analysis has been used to estimate the eff iciencies of

plants using unit-level panel data (2000-05). An analysis

of the forces of variation in the eff iciencies and

productivities of these industrial units has been

conducted. Plants with low efficiencies have not

survived – they have either merged with other plants or

been compelled to discontinue their operations.

Managerial skill and wage rates have had a significant

positive effect on the performance of these plants.

Some of the newly identified areas, with special facilities,

are conducive to better performance.

We gratefully acknowledge financial support from the collaborative

research projects, “The Internationalization of Japanese Firms and

Industrial Dynamics in India”, sponsored by Grant-in-Aids for Scientific

Research (B), “Long-Term Trends of India Vil lages”, sponsored by

Grant-in-Aids for Scientific Research (S), and “Constructing International

Research Network of Contemporary South Asian Studies” project, which

is a part of the Young Researcher Overseas Visits Program for

Accelerating Brain Circulation scheme by Japan Society for the

Promotion of Science. We are also grateful to the referee of this paper

for giving valuable comments and suggestions.

Chiranjib Neogi ([email protected]) is with the Economic Research Unit,

Indian Statistical Institute, Kolkata. Atsuko Kamiike (atsukokamiike@gmail.

com) is with the Department of Economics, Outemon Gakuin University,

Osaka. Takahiro Sato ([email protected]) is with the Research

Institute for Economics & Business Administration, Kobe University, Kobe.

1 Introduction

India’s industrial policy during the first phase after Inde-

pendence was based on strict regulatory control, enforced

through severe import restrictions alongside various per-

mit and licence requirements for the creation and expansion of

selected industries. There was no policy to encourage efficiency.

India’s industrial policy protected existing domestic firms

from foreign competition and, at the same time, lowered the

threat of competition from newly emerging firms at home. Theeconomic reforms initiated in the early 1990s, broadened in

scope gradually over the years that followed, have, however,

drastically changed the scenario. It is now becoming increas-

ingly important for an individual firm to improve its produc-

tive efficiency in order to survive in the face of ever increasing

competitive pressures.

In this paper, we examine the levels of technical efficiency

of individual plants from the Indian pharmaceutical industry

(NIC Code 2423), using unit-level data from the Annual Survey of

Industries ( ASI), covering the period 2000-01 through 2005-06.

This permits us to examine how the levels of technical efficiency

have changed over these years and to find the forces behind

the variation of efficiencies across plants. There are severalreasons why the pharmaceutical industry in India deserves

special attention in its recent past.

Foreign pharmaceutical companies dominated the Indian

pharmaceutical industry until the early years of the 1970s.

Prior to this, industrial policy was relatively favourable to-

wards foreign pharmaceutical companies. The regulation on

foreign capital in the pharmaceutical industry was liberal. Un-

der the Patents and Designs Act of 1911, which recognised

product patents for pharmaceuticals, foreign pharmaceutical

companies had been importing most of the drugs in bulk from

their parent companies abroad and selling the formulations.

The Patents and Designs Act of 1911 prevented Indian companies

from manufacturing new drugs. Thus, the foreign pharmaceu-

tical companies in India enjoyed a monopoly in the Indian

market. At that time, India was dependent on imports for most

essential drugs. Due to the lack of competition in the industry,

drug prices in India were very high and drugs were unafford-

able for a majority of the Indian population.

Several policy measures were taken in the 1970s that pro-

moted the development of the Indian pharmaceutical industry

and restricted the activities of foreign pharmaceutical companies.

The most important policies were as follows: (i) the enactment

of the Patent Act of 1970, (ii) the introduction of the Foreign


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december 27, 2014 vol xlix no 52 EPW Economic & Political Weekly82

Exchange Regulation Act of 1973 (FERA), and (iii) the

announcement of the Drug Policy of 1978.

In order to improve accessibility and affordability of essential

drugs in India, the Patent Act of 1970 was enacted, replacing

the Patents and Designs Act of 1911. The Patent Act of 1970

recognised only process patents and not product patents, and

reduced the patent period from 16 to seven years. Automatic

licences of rights could be issued three years after the grant of

patent. The Act allowed Indian pharmaceutical companies to

produce alternative processes for drugs that were not patented

in India. The Act encouraged reverse engineering and the

development of alternative processes for products patented in

other countries. Gradually, the market dominance of foreign

pharmaceutical companies had been reduced by the growth of

the indigenous pharmaceutical sector.

FERA was introduced to regulate foreign companies (foreign

capital) in India. FERA was to distinguish between companies with

foreign equity of more than 40% and those with foreign equity at

or below 40%, in order to restrict companies with foreign equity of

more than 40% to specific segments involving high technol-ogy. However, under FERA, foreign companies engaged in the

bulk manufacture of drugs involving high technology were al-

lowed to hold foreign equity above 40%. Under the circum-

stances, foreign pharmaceutical companies were still predom-

inant in the Indian pharmaceutical market until the mid-1970s.

The Drug Policy of 1978 played a crucial role in lowering the

market dominance of foreign pharmaceutical companies. This

was the first comprehensive drug policy enacted in India. The

basic objective of the policy was to achieve self-sufficiency in the

production of drugs by promoting the development of the indige-

nous pharmaceutical sector. The policy emphasised the role of

research and development (R&D) and technology, and enhanced

the technological capabilities of the Indian pharmaceuticalindustry through the provision of R&D promotion measures.

Several measures to guide and control foreign companies with

75% share of the domestic market were implemented, to be

consistent with the basic objective of the Drug Policy of 1978.

The policy strengthened regulations on the foreign pharma-

ceutical companies with foreign equity of more than 40%. The

foreign pharmaceutical companies engaged in manufacturing

formulations only or bulk drugs not involving high technology

were required to reduce foreign equity to 40% or below. In

addition, under the policy, the Indian government gave pro-

duction licences to foreign pharmaceutical companies only if

they were involved in high technology bulk drugs and related

formulations, provided half of the bulk drug manufactured

was sold to other formulators. Most foreign companies had to

reduce their foreign equity to below 40% because they were

manufacturing only formulations or not manufacturing bulk

drugs involving high technology.

While these policy initiatives promoted the growth of the

indigenous pharmaceutical industry, they resulted in the decline

of the market dominance of foreign pharmaceutical companies.

The Indian pharmaceutical industry that relied on reverse

engineering and process innovation achieved self-sufficiency

in technology, and has been strengthening its export orientation

in the tide of economic liberalisation since the early 1980s. There

has been a steady growth of the Indian pharmaceutical industry

during the last three decades and it has emerged as one of the

leading global players in generics. It has also registered evolu-

tionary dynamics, driven by the survival, entry, and exit of plants.

The Indian pharmaceutical industry thus makes a good

case study for the process of “creative destruction”, which

Schumpeter (1942) proposed in order to explain the dynamics

of industry evolution. We will now review important shifts in

policies related to the pharmaceutical industry: (1) the liberali-

sation of foreign direct investment (FDI) regulation in the

pharmaceutical industry, (2) the introduction of pharmaceutical

product patents, and (3) the mandatory implementation of

good manufacturing practices (GMP).

Competitive Pressure

Since 1991, following the implementation of the policy of eco-

nomic reforms that substantially relaxed barriers to business and

trade, the entry of firms and plants into the Indian pharma-

ceutical industry increased progressively. In addition to theliberalisation of FDI regulation in the pharmaceutical sector

in 2002 that allowed FDI of up to 100% under the automatic

route, the introduction of pharmaceutical product patents

has also accelerated the inflow of foreign companies into

India, and several Indian companies have been taken over by

foreign companies.

The introduction of pharmaceutical product patents brings

new business opportunities to the Indian pharmaceutical

industry. The pharmaceutical outsourcing business has been

increasing in India. In the past, foreign pharmaceutical

companies hesitated to manufacture new drugs in India because

of the Patent Act of 1970, which did not recognise product

patents on pharmaceutical products. Recently, however, foreigncompanies have been increasingly outsourcing the manufac-

turing of their new drugs. The introduction of product patents

due to the amendment of the Patent Act of 1970 made it impos-

sible for Indian companies to manufacture patented drugs. The

incentive of Indian companies to misappropriate the know-how

gained from contractors (foreign companies) reduced. On the

one hand, for foreign companies, the amendment of the Patent

Act of 1970 that introduced product patents in India lowered

the risk of outsourcing to Indian companies. On the other,

multinational pharmaceutical companies were creating research

centres and manufacturing plants in India.

Recently, the contract research and manufacturing services

(CRAMS) business has been growing rapidly in India. Many

Indian companies entered the CRAMS business, and the

number of specialised CRAMS companies has increased. Foreign

pharmaceutical companies are increasingly outsourcing

manufacturing, drug discovery operations and clinical trials

to Indian companies.

The GMP, which is defined in Schedule M of the Drugs and

Cosmetics Rules of 1945, has become mandatory since 2005.

Many plants were not in a position to comply with the GMP and

these units have been closed. In addition to the increase in

competitive pressure, GMP compliance has possibly induced


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the exit of small and inefficient firms and plants from the

market. In addition, the degree of price control on drugs has

gradually reduced. All these factors contribute to an increase

in the competitive pressure on surviving firms and in the

number of firms entering and exiting.

The period after 1995 (i e, the post-TRIPS (Agreement on

Trade-Related Aspects of Intellectual Property Rights) period)

saw the strongest performance of the Indian pharmaceutical

industry on several fronts. The industry not only registered a

marked improvement in its production performance, but also

turned into a net foreign exchange earner during the recent

period. The Indian pharmaceutical industry, now a $19 billion

industry, has shown tremendous progress.

The Department of Pharmaceuticals has the following

“Vision” for the development of the Indian pharmaceutical in-

dustry (Department of Pharmaceuticals nd: 8):

• develop human resources for the pharmaceutical industryand drug research and development;

• promote public-private partnership for the development of

the pharmaceuticals industry;• promote Pharma Brand India through international cooper-ation;

• promote environmentally sustainable development of thepharmaceutical industry; and,

• enable availability, accessibility, and affordability of drugs.

For the achievement of these goals, it is necessary for the

Indian pharmaceutical industry to become globally competitive

through world-class manufacturing capabilities, with improved

quality and a higher efficiency of production, and there is a

need to stress on the up-gradation of R&D capabilities.

Studying the Changes

As one of the biggest emerging markets of the pharmaceuticalsector, India has to necessarily evaluate the performance of

the Indian pharmaceutical industry, especially after the enact-

ment of TRIPS. There are some studies on this industry that

are worth mentioning from the perspective of efficiency and

productivity, using establishment-level information.

One major study was done by the Institute of Economic

Growth (2010) on the effects of the new patent regime on

the pharmaceutical industry in India. In this study, the

authors’ argued that the introduction of TRIPS could result in a

reduction in the proportion of multinational pharmaceutical

companies in the Indian drug market and the prices charged

by foreign producers could go up – on average, by about 250%

– if the foreign firms had full freedom in pricing their prod-

ucts, with the government not resorting to compulsory licens-

ing. A major impact of the TRIPS agreement is the changing

strategy of the big pharmaceutical firms in India with respect

to not only the quantum of R&D expenditure, but also the di-

rection of their R&D. It has been argued that in the last decade,

domestic companies have started filing increasing number of

patents at home, as well as in international patent offices.

Saranga and Banker (2010) studied the productivity change

and factors driving this change in the Indian pharmaceutical

industry during 1994-2003. They have used a non-parametric

Data Envelopment Analysis (DEA) based methodology to esti-

mate productivity change, and decompose it into technical

and relative efficiency changes. They have found that higher

R&D investments and switching to higher value-added products

by a few innovative firms pushed the production frontier up-

wards, with increasing technical and productivity gains. The

higher technical and R&D capabilities, and wider new product

portfolios of multinational companies have also contributed to

the positive technical and productivity changes in the Indian

pharmaceutical industry.

In another study, Kiran and Mishra (2009) argued that during

the post-TRIPS period, the Indian pharmaceutical industry reg-

istered its strongest performance on several fronts. The industry

improved its production performance by a significant margin

and the pharmaceutical industry turned into a net foreign ex-

change earner during this period. Also, R&D expenses have in-

creased at a higher rate in the post-TRIPS period. Mazumdar

and Meenakshi (2009) examined technical efficiency using DEA,

and tried to analyse the effects of technological gaps and pro-

ductivity differences among different groups of industries. Theyargued that most firms failed to appropriate the benefits of

technological change, leading to a rise in inefficiency, and there

is a positive association between the size of firms and efficiency.

Mani (2006) argued that a TRIPS compliant patent regime does

not appear to have dampened the innovation capability of the

domestic pharmaceutical industry, and, on the contrary, firms

have both increased their research budgets and patenting.

However, there are some deficiencies in understanding the entire

sequence of conducting research, developing a molecule, and

introducing a new drug in the market. In fact, our study shows

that this is an area where public policy ought to be focused.

A comprehensive analysis of the Indian pharmaceutical

industry is found in Chaudhuri (2005). In it, he covered a widerange of problems relating to policies, patent laws, price

adjustment, etc, of the pharmaceutical industry in India in the

recent period.

There are some studies trying to find the factors responsible

for the variation of efficiency and productivity among firms.

However, few studies have analysed the performance of the

pharmaceutical industry using unit-level panel data. Hence,

there are good reasons to look into current performance

levels prevailing in the pharmaceutical sector in India using

such data as available in the ASI. An audit of the levels of tech-

nical efficiency, along with an analysis of the determinants of

efficiency is, therefore, of interest to both academicians and

policymakers. Some studies address the question of produc-

tivity and/or efficiency in the industry from the perspective

of technology. Fujimori et al (2010) estimated a stochastic

frontier production function, using data of small-scale Indian

pharmaceutical industries. In it, they use the data set of the

56th round (1999-2000) of the National Sample Survey (NSS)

manufacturing enterprise survey. The authors found that the

small-scale pharmaceutical industries (SSPI) have an ineffi-

ciency in their production activity. At the same time, they

showed evidence that an SSPI supporting policy improves the

technical efficiency to some extent.


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After the enactment of deregulation policies, the firms have

to find ways to survive in the market by increasing efficiency

and productivity. India is a typical example, where each state

has some special characteristics that influence the growth

and performance of industries in different ways. In addition,

the spread of types of industries in each state is not similar.

Each state has its own industrial policy, and though there is a

broad agreement in the policies of the states, their approaches

are not always the same. As a result, the growth and perform-

ance of industries in the states do not always move in the

same direction. Since the efficiency and productivity of the

industries also depend on the labour laws and the government’s

attitude towards implementation of labour laws, the liberal

states suffer from the inefficient use of labour in industries. In

addition, the performance of production units depends on

the organisation and ownership type. It is a common belief

that public sector enterprises, in general, are inefficient as

compared to private sector firms. However, inefficiencies are

not only confined to the public sector. Some recent studies

argue that inefficiency is an all-pervasive phenomenon, evenin developed countries where the role of public sector enter-

prises is very limited, and efforts should be made to increase

the efficiency of production units by the appropriate use of

inputs in the production process.

The main advantage of using plant level data is that the in-

formation loss will be much less as compared to that of aggre-

gate data. Since the information is available for each unit of

the industry and the information about the location and own-

ership type of each unit is also available, the analysis is done in

the following areas:

(1) The overall efficiency trend of the industry during the period.

(2) The state/location-specific analysis of efficiency.

(3) The ownership-type specific analysis of efficiency.(4) The size-specific analysis of efficiency of each group of plants.

(5) Efficiency of surviving and exiting plants.

(6) Forces behind the efficiency variation of production units.

In this study, effort has been made to understand the nature

of inefficiency in the pharmaceutical industry in India during

the recent period. The paper is based on unit or plant level

information on production of the pharmaceutical industry.

This is supposed to be the first attempt to measure technical

efficiency using plant level data of this industry.

The paper is organised as follows. Section 2 deals with data

and methodology. Empirical analyses are in Section 3 and

concluding remarks are in Section 4.

2 Method of Analysis

Efficiency Model

Theoretically, a competitive market in equilibrium cannot allow

any inefficiency of production units. In measuring efficiency, a

benchmark production function has to be constructed to judge

the performance of production units. This efficient production

function is called a frontier. The method of comparing the

observed performance of a production unit with the postulated

standard of perfect efficiency is the basic problem in measuring

efficiency. This is primarily a two-stage problem. First, an

ideal production frontier should be estimated with the ob-

served production information. Then, in the second stage, the

efficiency of production units is to be measured by the depar-

ture of the observed from the potential values.

If the objective of a producer is to minimise the wastage of

inputs, the performance of the production unit can be meas-

ured in terms of technical efficiency/inefficiency. On the other

hand, if the objective of a production unit is to minimise the

cost for a given level of output or the maximisation of profit by

allocating inputs and outputs, then the performance of a pro-

duction unit can be defined in terms of economic efficiency.

A stochastic frontier model is a major improvement over the

former models in that it makes a clear distinction between

white noise and inefficiency. Aigner, Lovell and Schmidt (1977)

proposed this stochastic model with the idea that the error

term is composed of two parts, and the form of the function is

y i = f(xi;β)e(v i–ui) [i = l,...,n].

The random error term vi has some symmetric distributionto capture the random effect of measurement errors and exog-

enous shocks, while ui is assumed to be a non-negative trunca-

tion of the N ( 0 , σ 2 ) distribution, providing the measurement of

technical efficiencies relating to the stochastic frontier. Now, a

simple OLS type estimate can provide a test for the presence of

technical efficiency in the data, that is, if ui= 0, then the varia-

tion in production from the frontier level is only due to the

random error or white noise. If it is assumed that technical

efficiency is present among the production units, then a

stochastic frontier approach to estimate the (in)efficiencies can

be obtained from the estimates of the parameters of the model.

In a panel estimation of efficiency, two aspects of efficiency

are of interest with respect to policy prescriptions regardingindustrial performance. The first aspect is to analyse the trend

or pattern of movement of efficiency over the period, and the

second is the identification of factors responsible for the varia-

tion of inefficiencies across time. The first aspect is important

in circumstances where policy interventions like deregulation,

introduction of reforms, new entry, etc, take place at particular

points in time. The second question encompasses the first, but

looking at the former is often done to get an aggregative idea.

Since the main motivation for efficiency analysis to policymakers

is to design policies to improve the performance of producers,

especially the inefficient ones, it is highly desirable to know

whether there are factors that can explain inefficiency.

Kumbhakar, Ghosh and McGukin (1991) and Reifschneider

and Stevenson (1991) first pointed out this inconsistency and

proposed a stochastic frontier model in which the inefficiency

effect (ui) is expressed as an explicit function of a vector of plant-

specific variables and a stochastic error term. Battese and Coeli

(1995) proposed a model that was equivalent to the Kumbhakar,

Ghosh and McGukin (1991) model, with the exception that when

allocative efficiency is imposed, the first-order profit maximising

condition is removed, and panel data is permitted. The Battese

and Coelli (1995) model specification may be expressed as

y 'it =β

0 +∑β

k χ '

kit+ (v

it – u

it), i =1, 2,…, N; t= 1, 2,…, T,


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where vit are random variables which are assumed to be iid,

following N(0,σ 2v ), and independent of u

it. u

it are non-negative

random variables and assumed to account for the technical

inefficiency in production, while being independently distri-

buted, as truncated at zero of the N(mit , σ 2

u ), distribution.

mit = z

itδ,

where zit is a vector of variables that influence the efficiency of

plants, and δ is the vector of parameters to be estimated. In

this model, all the parameters of the stochastic frontier func-

tion, as well as those of the inefficiency function, can be

estimated together by a single MLE procedure.

Data Set

Our empirical application is based on plant- or “factory”-level

data for the period from 2000-01 to 2005-06, which was

collected by the Central Statistical Office (CSO) of India in

the ASI. The primary unit of enumeration in the survey is

a factory in the case of the manufacturing industries, and

data are based on returns provided by factories. The presentstudy uses data on various plant-level production parameters,

such as output, sales, labour, employees, capital, materials,

and energy.

The ASI factory frame is classified into two sectors: the

“census” and the “sample” sectors. The sample sector consists of

small plants, employing 20-99 workers if not using electricity,

and 10-99 workers if using electricity. The census sector com-

prises relatively large plants. It covers all units having 100 or

more workers and some significant units, which, although

having fewer than 100 workers, contribute significantly to the

value of the manufacturing sector’s output. While the units in

the census sector were approached for data collection on a

complete enumeration basis every year, sample-sector units were covered based on well-designed sampling.

The present study focuses only on the census-sector data

for comparing the economic performances between continu-

ing plants, entrants and exiters. This is because comparative

analysis requires a consistent and exhaustive database to

distinguish between continuing plants, entrants and exiters.

A challenge was, however, posed by changes in the definition

of the census sector in the recent past. For the years 1997-2000,

the census sector was limited only to factories employing

200 or more workers. From 2000-01 onwards, factories em-

ploying 100 or more workers were under the census sector.

For consistency in the analysis, we exclude the years prior to

2000-01 from our analysis, and focus on the period from

2000-01 to 2005-06.

Gross value added (Y) in real terms is measured by the double

deflation method. Output is deflated by the corresponding

wholesale price index of drugs and medicine, while the inputs

are deflated by the aggregate price index, constructed as the

weighted average of fuel price, material price, and other input

prices. Fuel price, material price, and other prices are con-

structed using wholesale prices, implicit deflators of national

account statistics, and weights taken from the Input-Output (I-O)

tables. The data sources used for the construction of the input

price index are taken from the Handbook of Monetary Statistics

of India (RBI), Database on Indian Economy (CSO), Input-Output

Transactions Table, and National Account Statistics (CSO). Man-

hours of workers are used to measure labour input ( x 1) and

capital ( x 2) is defined as the initial value of net fixed capital,

deflated by the implicit deflator of net capital stock in the reg-

istered manufacturing sector. Implicit price deflators are con-

structed from the National Account Statistics of CSO. However,

it is well known that there is no universally accepted method

of measurement of capital stock and that measures are imper-

fect and subject to measurement error.

3 Analysis of Efficiency

Technical efficiencies are estimated for the panel data of plants

in the pharmaceutical industry in India over the years 2000-01

to 2005-06. Since we consider both the exiting and continuing

plants, the panel is unbalanced in this analysis. We have used

Coelli’s programme, FRONT4.1, for estimating the efficiencies,

and used the effect model of the programme to find the factors

behind the variation of efficiencies. Figure 1 shows the distri-bution of efficiencies of plants, which is positively skewed,

indicating concentration of plants at lower efficiency levels.

Size-Specific Analysis

It has been argued that the larger sized plants usually get the

advantage of economies of scale. We are trying to see if this

phenomenon is true for the efficiency of plants in the pharma-

ceutical industry, that is, the larger sized plants are more efficient

than the smaller sized plants. We have classified the plants

into three groups by their value of fixed capital stock (FCS).

Small size is defined by an FCS value below Rs 10 crore, medium

is from Rs 10-100 crore, and large is more than Rs 100 crore.

Mean = 0.2468

Std. Dev. = 0.21356

N = 1,927

efficiency0.00 0.20 0.40 0.60 0.80

0

50

100

150

200

250

y

Figure 1: Frequency Distribution of Efficiency


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Table 1 shows that the mean efficiency is higher for the large-

sized plants as compared to that of small- and medium-sized

plants, and that the mean efficiency of medium-sized plants is

higher than that of small-sized plants. However, the variation in

efficiency indicates that the small-sized plants are more homo-

geneous than the large-sized plants in terms of technical effi-

ciency. Thus, the large plants in the pharmaceutical industry

get the advantage of economies of scale.

Ownership-Specific Analysis

It is generally believed that manufacturing units in the private

sector are more efficient than public sector units. The units

producing pharmaceutical products are classified in terms ofownership and a comparison of their efficiencies are reported

in this section. There are six types of ownership, as defined by

CSO, namely, (i) wholly central government, (ii) wholly state

and local government, (iii) central and state government and/or

local government jointly, (iv) joint sector public, (v) joint sector

private, and (vi) wholly private. The number of wholly private

sector units in the pharmaceutical industry is much higher

than compared to other types of ownership. Table 2 reports

the values of mean efficiencies of units for different categories.

The average efficiency figures show that the efficiencies of

the private joint sector units is the highest, and slightly higher

when compared to those of the private sector units. Dispersion

of efficiency in the private sector is maximum among these

categories of ownership. However, a similar value of standard

deviation is observed in wholly central and joint sector private

units. The wholly state government units and the units belong-

ing to the public joint sector show poor performance in terms

of average efficiency. The distribution of average efficiency in

Table 3 suggests that most of the plants are concentrated at a

low efficiency value, irrespective of the ownership type. Values of

“joint sector public” plants show that 75% of the plants are at

the lowest efficiency level. Though we have observed that the

average efficiency of the “wholly private” plants is the highest

among the different ownership types, the distribution shows

that about 83% of the plants are at the lower ranges of efficiency.

However, only a small number of plants fall under the very high

efficiency category. Thus, the characteristics of private and public

plants are not very different in terms of efficiency distribution.

Trend of Efficiency

Table 4 describes the year-wise mean efficiency of plants and

their corresponding dispersion values. The number of plants

during this period are between 273 and 397, due to the entry

and exit of plants during each year. The figure of mean effi-

ciency indicates that there is a rising trend of efficiency from

2000-05, with a marginal fall in 2004. However, it is interest-ing to note that there is also a rising trend of variances during

this period that indicates the existence of both low- and high-

efficiency plants in the latter period.

The figures of year-wise efficiencies of the units in Table 5

show that units belonging to the private sector show a risingtrend throughout the period of study. However, the units be-

longing to other sectors show a mild downward trend during

the latter phase of our study. Thus, the above analysis reveals

that in terms of efficiency, the units that belong to the private

sector perform better than those in the state-managed sector.

Figure 2 (p 87) depicts the trends of efficiency of units in four dif-

ferent ownership categories during 2000-05. In this figure, we

have clubbed all the joint sector units into a single category.

Dynamics of Plants

The Indian pharmaceutical industry has seen steady growth

during the last three decades and has emerged as one of the

leading global partners in generic drugs. It has also registered

Table 1: Comparison of Average Efficiency (Over Time) among DifferentSizes of Pharmaceutical Plants during 2000-05

Average Capital N Mean Minimum Maximum Std Deviation

Small 439 .1382 .0038 .7164 .1267

Medium 273 .2958 .0012 .8794 .2182

Large 42 .4643 .1244 .9086 .2250

Total 754 .2134 .0012 .9086 .1963

Efficiencies are averaged over the period for each plant.Size is defined in terms of fixed capital.

Table 2: Comparison of Average Efficiency (Over Time) of Pharmaceutica lPlants of Different Ownership Types during 2000- 05

Type of Ownership N Mean Minimum Maximum Std

Deviation

Wholly central government 8 .1883 .0048 .6106 .1966

Wholly state and/or local government 11 .1546 .0119 .6115 .1761

Central and state and/or

local government jointly 5 .1651 .0209 .2637 .0912

Joint sec tor public 8 .1748 .0138 .444 8 .1470

Joint sector private 6 .2347 .0434 .5449 .1952

Wholly private ownership 716 .2152 .0012 .9086 .1979

Efficiencies are averaged over the period for each plant.

Table 3: Distribution of Average Efficiency of Pharmace utical Plants ofDifferent Ownership Type

Efficie ncy Wholly Wholly Central Government Joint Joint Wholly Total

Class Central State and/or and State and/or Sector Sector Private

Government Local Local Government Public Private Ownership

Government Jointly

0.0-0.2 62.50 72.73 60.00 75.00 50.00 60.61 60.88

0.2001-0.4 25.00 18.18 40.00 12.50 33.33 23.04 23.08

0.4001-0.6 12.50 16.67 8.94 8.75

0.6001-0.8 12.50 9.09 6.28 6.23

0.8001-1 1.12 1.06

Table 4: Comparison of Efficiency of Pharmaceutical Plants during 2000-05

Year N Mean Minimum Maximum Std Deviation

2000 330 .1691 .0015 . 8517 .1699

2001 299 .1722 .0040 .8104 .1737

2002 273 .2185 .0072 .8500 .2071

2003 298 .3026 .0012 .8644 .2251

2004 330 .2998 .0172 .9158 .2127

2005 397 .3010 .0129 .9086 .2294

Table 5: Year-Specific Comparison of Average Efficiency of PharmaceuticalPlants of Different Ownership Type during 2000-05

Type of Ownership Years

2000 2001 2002 2003 2004 2005

Wholly central government .1762 .1625 .1824 .2125 .2745 .1946Wholly state and/or local government .0952 .1197 .0826 .1818 .1481 .1510

Central and state and/or local

government jointly .0654 .2189 .3084

Joint sector public .1571 .2366 .2912 .2137 .2013

Joint sector private .1177 .0545 .4740 .3078

Wholly private ownership .1711 .1755 .2231 .3072 .3024 .3034


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SPECIAL ARTICLE


evolutionary dynamics, driven by the survival, entry and exit

of plants. In this section, we try to understand if there is any

correspondence between this dynamic and the efficiency ofplants. Table 6 shows that the mean efficiency of the 357 dis-

continued plants during the total period of study is 0.1632,

while that of the 397 continuing plants is 0.2586. Naturally, we

can argue that a low level of efficiency is one of the causes of

the plants exiting from the industry. It is evident from Table 7

that this phenomenon is observed irrespective of type of own-

ership of plants. It has often been argued that the public sector

plants are better protected than the private sector plants, and

that the plants do not quit from the market or close down their

units even in the case of poor performance. However, this

story is not true in the pharmaceutical industry. It is observed

from Table 8 that a high percentage of public sector plants

discontinued their operations during the period of study.However, the figures show that the plants belonging to the

“central and state and/or local government joint” ownership

have a lower percentage of discontinued plants and thus get

protection. Finally, Table 9 shows

that the percentage of small-sized

plants that discontinued their

operation is much higher as com-

pared to the other two size

classes, namely, medium and large.

This indicates that small-sized

plants are more vulnerable than

compared to larger-sized plants and prone to exit the industry.

Location-Specific Analysis

We have already mentioned that the spread of the type of

industries in each state is not similar. Each state has its own

industrial policy, and though there is a broad agreement in the

policies of the states, their approaches are not always the

same. Recently, some state governments have taken measures

to promote industrial agglomeration. In April 2000, the Govern-

ment of India released the special economic zone (SEZ) policy,and the state governments then set to work on the building of

SEZs for pharmaceutical industries in the agglomerated areas.

Till date, 40 such pharmaceutical and biomedicine SEZs have

been approved in agglomerated areas.

Some industrial policies have been undertaken by the

Government of India for the development of backward regions

of the country, in terms of industrial development. Some

industries are identified as thrust industries for the develop-

ment of those regions. The pharmaceutical industry is consid-

ered a thrust industry, and some proposals to provide incen-

tives to this industry have been accepted in these industrial

policies. In these policies, fiscal incentives such as excise duty

exemption, exemption of income tax for companies, and capitalinvestment subsidies were granted to new and existing indus-

trial units for their substantial expansion. A specific industrial

policy has been announced for the states of Himachal Pradesh

and Uttarakhand as the “Himachal-Uttaranchal Policy”,

and it has promoted industry agglomerations in both states

(Kamiike et al 2012).

According to Kamiike et al (2012), considering the degree

and age of agglomeration of the Indian pharmaceutical indus-

try, the geographical locations are classified into four regions.

First, the agglomerated industrial area is classified into two

groups, the new or emerging area, and the established area,

based on the initial year of operation of the plants. According

to these criteria, Himachal Pradesh and Uttarakhand are con-

sidered as Area-1, which is the new area. The other agglomer-

ated areas are classified into three sub-areas, according to

their age of operations. These are Area-2 (Delhi, Haryana, and

Punjab), Area-3 (Gujarat, Maharashtra, Goa, Daman and Na-

gar Haveli, and Daman and Diu), and Area-4 (Andhra Pradesh,

Karnataka, Tamil Nadu, and Pondicherry). Finally, Area-5 is

classified as other states of non-agglomerated area.

The mean efficiencies of pharmaceutical plants of different

regions are presented in Table 10 (p 88). It is observed that the

efficiency of the newly agglomerated area is the highest among

0.05

0.1

0.15

0.2

0.25

0.3

0.35

2000 2001 2002 2003 2004 2005

Central Government

State Government

Joint Sector

Private

Figure 2: Comparison of Average Efficiency of Pharmaceutical Plants ofDifferent Ownership Type during 2000-05

Table 6: Comparison of Average Efficiency (Over Time) of Continuing andDiscontinued Pharmaceutical Plants during 2000-05

Plant Type N Mean Minimum Maximum Std Deviation

Discontinued 357 .1632 .0012 .8517 .1785

Continuing 397 .2586 .0129 .9086 .2009


Table 7: Comparison of Average Efficiency of Continuing and DiscontinuedPharmaceutical Plants of Different Ownership Type during 2000-05

Type of Ownership Discontinued Continuing

Wholly central government 0.1900 0.1900

Wholly state and/or local government 0.1039 0.1341

Central and state and/or local government jointly 0.0209 0.2124Joint sector public 0.1918 0.2524

Joint sector private 0.0545 0.3018

Wholly private ownership 0.1825 0.2790

Table 8: Percentage Distribution of Continuing and DiscontinuedPharmaceutical Plants of Different Ownership Type during 2000-05

Type of Wholly Wholly Central and Joint Joint Wholly Total

Ownership Central State State Sector Sector Private

Government and/or and/or Local Public Private Ownership

Local Government

Government Jointly

Discontinuing 75.00 45.45 20.00 87.50 16.67 47.07 47.35

Continuing 25.00 54.55 80.00 12.50 83.33 52.93 52.65

Table 9: Percentage Distributionof Continuing and DiscontinuedPlants in Different Size Groups

Discontinued Continuing

Small 53.53 46.47

Medium 37.73 62.27Large 45.24 54.76

Total 47.35 52.62


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the five regions. It indicates that the new industrial policy for

the region helps the pharmaceutical companies to perform

better than compared to the other regions. The efficiencies of

the non-agglomerated area and of Area-2 (Delhi, Haryana,

and Punjab) are very low, while the efficiencies of plants in the

other two regions, which are comparatively matured agglom-

erated regions, are moderately high.

The distribution of efficiency in different regions is

described in Table 11. The percentage figures indicate that

only 31.82% of plants of Area-1 fall in the lowest category ofefficiency, while the percentage of plants in that category

from the non-agglomerated area (Area-5) is 72.03%. The

second highest percentage of plants in this category is

70.69%, and belongs to Area-2. The distribution generally

suggests that the matured agglomerated areas and the non-

agglomerated area are highly skewed in terms of the values of

efficiency. Table 12 shows the distribution of continuing and

discontinued plants in different regions. It is found that the

percentage of exiting plants is lowest in Area-1, while it is

highest in Area-2. The percentage of discontinued plants in

other areas is almost the same, around 47%. All these results

indicate a favourable effect of policies taken for the promo-

tion of industrial growth and efficiency in the industriallybackward regions.

Factors Responsible for Variation of Efficiency

The above findings have emboldened us to find out the

variables that are responsible for the variation of the technical

efficiency of plants in the pharmaceutical industry, using

some rigorous econometric

methods. As pointed out,

we have used the method

of Kumbhakar, Ghosh and

McGukin (1991) for our

analysis in this section.

We have used the Effect

Model for estimating the

stochastic frontier and the

estimates of the parameters

considered for the analy-

sis. Table 13 shows that the

coefficients of labour and

capital of the stochastic

frontier production func-

tion are highly significant.

The coefficients, and the

corresponding t-statistics of

the explanatory variables,

are presented in Table 14.This analysis explains the

inefficiency of the plants,

and all the variables are

found to be highly signifi-

cant, except one dummy

variable. We have consid-

ered the following effect

variables for the analysis:

(i) Continuity Dummy = 0 for exiting or entering plants, = 1

for continuing plants

(ii) Skill = Employees other than workers/All employees

(iii) Wage Rate = Total emoluments/Number of employees

(iv) Size Dummy-1 = 1 for medium sized, =0 otherwise(v) Size Dummy-2 = 1 for large sized, =0 otherwise

(vi) Ownership Dummy =1 for wholly private, = 0 otherwise

(vii) Location Dummy 1= 1 for Area-1, = 0 otherwise

(viii) Location Dummy 2= 1 for Area-2, = 0 otherwise

(ix) Location Dummy 3= 1 for Area-3, = 0 otherwise

(x) Location Dummy 4= 1 for Area-4, = 0 otherwise

(xi) Time points (1, 2…, 6)

It is found from Table 14 that the continuity dummy is nega-

tive and highly significant. This indicates that the continuing

plants have a positive impact on the variation of efficiency.

Similarly, the coefficients of skill and wage rate indicate a posi-

tive influence on the technical efficiency of plants. This result

is very natural, since most of the pharmaceutical plants are

capital-intensive and the efficiency of these plants depends on

the handling of the machines by skilled workers and on the

efficiency of the managerial staff.

The coefficients of wage rates indicate that the higher the

wage rate, the higher will be the efficiency. The coefficients of

the size dummy indicate that there are economies of scale,

that is, large-sized plants are more efficient than smaller-sized

plants. The coefficients of the ownership dummy are also

highly significant, and it indicates that there is a positive

impact of private ownership on the variation of efficiency

Table 10: Comparison of Average Efficiency (Over Time) of PharmaceuticalPlants in Different Locations during 2000-05

Area N Mean Minimum Maximum Std

Deviation

Agglomerated-new (Area-1) 44 .3536 .0054 .9086 .2890

Agglomerated-establ ished-1 (Area-2) 58 .1643 .0120 .6106 .1582



Non-agglomerated (Area-5) 143 .1648 .0048 .8214 .1650

Total 754 .2134 .0012 .9086 .1963


Table 11: Distribution of Average Efficiency of Pharm aceutical Plants inDifferent Areas

Efficienc y Himachal Pradesh, Delhi, Maharashtra, Andhra Pradesh, Ot hers Total

Class Uttarakha nd Haryana, Gujarat, Karnataka, (Area-5)

(Area-1) Punjab D&D, D&NH Tamil Nadu

(Area-2) (Area-3) (Area-4)

0.0-0.2 31.82 70.69 56.79 63.24 72.03 60.88

0.2001-0.4 36.36 18.97 23.46 24.32 18.18 23.08

0.4001-0.6 6.82 6.90 11.11 8.11 5.59 8.75

0.6001-0.8 13.64 3.45 8.33 3.78 3.50 6.23

0.8001-1 11.36 0.31 0.54 0.70 1.06

Table 12: Percentage Distribution of Continuing and Discontinued Pla ntsin Different Areas

Area Discontinued Continuing

Agglomerated-new (Area-1) 34.09 65.91

Agglomerated-establ ished-1 (Area-2) 53.45 46.55



Non-agglomerated (Area-5) 46.15 53.85

Total 47.35 52.65

Table 13: Estimation of FrontierProduction Function (Effect Model) Dependent Variable = Log Value Added

Independent Variables Coefficient

Alpha 11.2657 (31.6266)

Log -capital 0.1903 (9.7673)

Log- labour 0.5037 (14.5953)

sigma-squared 0.8687 (22.2610)

gamma 0.7535 (20.3533)

No of observations 1,927

Figures in parentheses represent t-Statistics.

Table 14: Factors Responsible for theVariation in Inefficiency (Effect Model)

Dependent Variable = Inefficiency

Variables Coefficient

Intercept 5.3884

Continui ty dummy -0.1725** (-2.4572)

Skill -0.5887** (-2.1385)

Wage rate -0.0000 4** (-21.2168)

Size dummy-1 --1.7767** (10.9358)

Size dummy-2 -3.3800** (-14.5322)

Ownership dummy -0.4946** (-3.5990)

Locat ion dummy 1 -0.3155* (-1.6871)

Location dummy 2 0.4105** (3.2069)

Location dummy 3 0.0254 (0.3302)

Locat ion dummy 4 0.1814** (2.1832)

Time -0.1409** (-7.4075)

Log likelihood function -2419.59

No of observations 1,927

* Indicates significance at 10% level; ** indicate

significance at 5% level.


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SPECIAL ARTICLE


of plants. The sign of the coefficients of location dummies

implies that the efficiency of newly agglomerated areas (Area-1)

is significantly high when compared to locations in Area-5

(non-agglomerated areas). Coefficients of location dummy 2

and 4 indicate that efficiencies of Area-2 and Area-4 are

significantly lower when compared to Area-5. Coefficient of

location dummy 3 is not statistically significant. However,

these observations, except the first one, are not very promi-

nent in the analysis of mean efficiencies of different areas.

Finally, the coefficient of the time variable is significant and

it has a positive impact on efficiency, that is, efficiency is

increasing over time.

4 Some Concluding Remarks

It has been observed that the Indian pharmaceutical industry

was affected in a big way due to the implementation of the

policy of “Process-cum-Product Patents” during the last few

years. In addition, the policy of liberalisation of the Indian

economy in recent years created a dynamic environment for

the plants in the industry. The Indian pharmaceutical industryis now facing competition in the home market, as well as

threats from multinational pharmaceutical companies. Such

competition and threats result in mergers, acquisitions, and al-

liances for the survival of the plants.

In such a dynamic environment, it would be interesting to

examine whether there are any common factors in explaining

the performance, in terms of efficiency of plants, which influence

the survival and growth of a plant. From this analysis, we see that

the efficiencies of plants have increased over the years, but not

without fluctuation. However, the level and growth of efficien-

cies differ in considerable ways among the types of ownership

of plants. It is found that private players are doing significantly

better than compared to other types of ownership. A positive

association is found between the size of plants and their techni-

cal efficiencies. Therefore, we can conclude that economies of

scale are prevailing in the pharmaceutical industry of India.

Since the market of the pharmaceutical industry has become

more competitive, plants with low efficiencies cannot survive

and either merge with other plants, or are compelled to dis-

continue their operations. Managerial skill and wage rates

have a significant effect on the betterment of the performance

of these plants. Some of the newly identified areas, with spe-cial facilities, are found to be conducive to the better perform-

ance of the pharmaceutical industry.

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REVIEW OF RURAL AFFAIRS June 28, 2014

Emergent Ruralities: Revisiting Village Life and Agrarian Change in Haryana – Surinder S Jodhka

Vulnerability, Forced Migration and Trafficking in Children and Women:A Field View from the Plantation dustry in West Benga – Biswajit Ghosh

Link between Food Price Inflation and Rural Wage Dynamics – Atulan Guha, Ashutosh Kr Tripathi

Estimating Rural Housing Shortage – Arjun Kumar

Financial Literacy in Rural Banking: Proposal for an Alternative Approach – Sukanya Bose, Arvind Sardana

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Factors Behind the Performance of Pharmaceutical Industries in India

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