DP2015-14

Identifying High G rowth Firms in Indi a:
An Alternative Approach

Aradhna AGGARWAL
Takahiro SATO

March 28, 2015

Identifying High Growth Firms in India: An Alternative Approach
Aradhna Aggarwal and Takahiro Sato 1
Abstract
Over the past two decades, considerable interest has grown in high growth firms (HGFs).

However, the concept of HGFs still remains controversial. One of the most controversial
issues is size and age of these firms. The present study argues that the current literature on
HGFs may offer little help in addressing this issue given the constantly changing populationof HGFs. This study uses an alternative conceptual framework and proposes a concept of‘High Impact Group of Firms’ (HIGF). It explains the HIGFs in the framework of a new
stream of literature that focuses on business dynamics, productivity growth and industry
evolution, formulates testable hypotheses, and uses a novel methodology to identify it. The
empirical analysis is based on the plant level panel data of 22 manufacturing industries inIndian manufacturing during the period 2000-01 to 2005-06. Our empirical results revealthat much depends on the industry/sector specific characteristics.

JEL: L25, L26, O14, O33, O53

1

We gratefully acknowledge the financial support from the collaborative research project"The Internationalization of Japanese Firms and Industrial Dynamics in India" (Grant No.
25301022) sponsored by Grant-in-Aid for Scientific Research (B). We thank Tasukasa
Mizushima, Hideshi Esho and Etsuro Ishigami for their constant encouragement and Suling
Chien and Atsuko Kamiike for their research assistance.
Aradhna Aggarwal, the corresponding author is Professor in Indian Studies at Asia ResearchCentre, Department of International Economics and Management at Copenhagen Business
School and can be reached at aradhna.aggarwal@gmail.com
Takahiro Sato is Professor at the Research Institute of Economics and Business
Administration, Kobe University. His email is takahirodevelop@gmail.com
1

Identifying High Growth Firms in India: An Alternative Approach

1. Introduction

For decades, small businesses have been viewed as engines of growth by policy makers
across the world. This has resulted in various policy initiatives aimed at providing them withbusiness support to stimulate economic growth through them. These comprise of
infrastructure development, tax incentives, concessions, favourable regulations and otherspecial programmes. However, in his seminal work, Birch (1981, 1987) revealed that within
the small sector it is a small group of high-growth firms that accounts for disproportionately
large growth and employment as opposed to a large majority of enterprises that fail to grow.
He coined the term ‘gazelles’ for the former and “mice” for the latter. He also found that large
enterprises, ‘elephants’ in his analogy, make a negative contribution to net job creation. Since
this pioneer work of Birch, the concept of “high growth firms” (henceforth HGFs)/gazelles
has received considerable attention from policy makers and researchers. While few disputethe role of HGFs in employment and growth generation the likelihood of their being small
has been widely questioned in the literature. While some find that these are small firms andsmall establishments (Neumark et al 2011), others indicate that these firms are young but notnecessarily small (see inter alia Haltwinter et al 2012, Henrekson and Johansson, 2010).
Some go a step further and suggest that these firms are neither small nor young, rather theytend to be more matured and larger (Acs et al., 2008; Mason and Brown, 2010; Brown et al(2014). Some research summaries conclude that HGFs include all types of firms: established,
start-ups, small, and large ones (Audretsch, 2012; Nesta, 2009; Coad et al, 2014). These
contradictory observations send confusing signals to policy makers, who seek guidance inpicking winners with a focus on accelerating economic growth and job creation by promoting2them. A major reason for the controversy is ‘definitional ambiguity’ that surrounds theconcept of HGFs. There are usually two different approaches to define HGFs (see, Coad et al2014 for a recent literature survey). The first is to define them as the top X% of firms in a
population in terms of the selected growth indicators. The second defines them as firms
growing at or above a particular rate between a start and end year, or on annual basis over a
specific number of years 2. Both these approaches are subject to ambiguities that arise due tothe choice of growth indicators, measure of growth indicators (relative/absolute) 3, type ofgrowth (organic or through acquisition), and a threshold level of growth rate. The results arefound to be highly sensitive to these choices (Coad et al, 2014; Delmar et al., 2003; Daunfeldt
et al, 2012; Henrekson and Johansson, 2010; Halvarsson, 2013 for surveys). While analysing
the impact of the methodological choices on the identification of HGFs, Delmar et al(2003:100) conclude “what a high growth firm is, conceptually and operationally, is very
dependent on the growth measure used”. Another reason for the controversy regarding their
being small, new or matured firms, is that ‘high growth’ is not a characteristic of a specific
set of firms. Rather, it is a phase in the life cycle of a firm which may not be persistent overtime (Coad et al. 2014:99). Brown et al (2014: 5-6) view high growth phase as ‘episodic’ and‘a state that some firms undergo and temporarily experience’. This means that the populationand hence, characteristics of HGFs may constantly change. There are thus concerns that theconcept of HGFs as defined in the existing literature may have little value for public policy
making. An important policy question is how to best identify and target them.

The present study addresses this question. It offers an alternative perspective on HGFs and2

According to OECD (2007), for instance, “all enterprises with average annualized growth
greater than twenty percent per annum, over a three-year period, and with ten or more
employees at the beginning of the observation period are high growth firms”.
3
Small firms are found to be overrepresented among HGFs when growth is measured inrelative terms, but, large firms are more likely to be HGFs when growth is measured inabsolute terms (Delmar et al., 2003).
3

uses a novel methodology to establish their likelihood of being small, young or establishedfirms. Instead of focusing on ‘individual HGFs’, we identify ‘population/groups of firms’
which might have a high potential of propelling productivity growth. Further, our criterion of
selection is not based on a threshold level of performance of firms or a group thereof. Rather,
we identify high potential or high impact group/s of firms on the basis of their contribution tomacroeconomic growth potential in terms of productivity growth 4. We draw on the Industrialorganisation (IO) literature on ‘business dynamics, firms’ heterogeneity, and macro levelproductivity growth’ to explain the phenomenon of ‘high impact group of firms’ (HIGFs)
within the overall framework of business and productivity dynamics. The argument runs like
this: the business landscape is not static. It is constantly undergoing changes due to a processof churning of firms in which a large number of new firms appear each year; while manyothers go out of business. These business dynamics are closely associated withmacroeconomic productivity dynamics due to considerable heterogeneity across new entrants,
incumbents and exitors in terms of their productivity. More efficient firms grow rapidly to
crowd out the less efficient ones pushing up the aggregate productivity growth levels to drive
industrial evolution and economic growth. The research question is: Which set of firms ismore likely to account for most of the productivity growth? Are they new firms? Are theysmall incumbent firms (gazelles)? Or, are they matured established firms? Theoretical
literature is ambiguous and does not rule out any possibility. Following the literature on‘firms level heterogeneity and macro level productivity dynamics’ we use the typology thatcharacterises firms by their stage of growth, and formulate testable hypotheses. These
hypotheses are tested using the decomposition methodology of aggregate productivity growth
pioneered by Baily et al (1992) and subsequently developed by important contributions ofseveral scholars (Griliches and Raves, 1995; Foster et al, 2001; Balwin and Gu, 2003; Olley
4

Earlier Acs et al (2008) also referred to HGFs as high impact firms but this was because
they defined them in addition to revenue growth by employment growth as well.
4

Pakes, 1996; and Melitz and Polanec, 2009). These methodologies use the plant level data to
avoid the problem posed by multi-plant firms and break down aggregate productivity growth
into the contribution of entering, incumbent, and exiting plants with direct implications forfirm level analysis. We extend these methodologies by adding more categories of firms forthe analysis and use the hitherto unexplored plant level data on Indian manufacturing for theperiod from 2000-01 to 2005-06. This is a unique dataset that provides us an opportunity to
distinguish between rapidly growing small firms (gazelles), new entrants and large firms. The
analysis focuses only on the manufacturing sector but will have implications for other sectors
as well. The manufacturing sector itself is highly diverse and comprises of a range ofindustries which may be grouped into various subsectors. In the literature, different
typologies have been used to classify the sector. We identify three subsectors for the analysis
based on their factor intensity. These are: labour intensive, resource intensive and technology
intensive subsectors. We expect that they will differ in terms of business and productivity
dynamics.

The study makes three important contributions to the existing literature. One, we propose thenotion of ‘high impact group of firms’ (HIGFs) as an alternative to HGFs. While the current
studies identify individual HGFs on the basis of their performance over a given period oftime and establish their importance by analysing their contribution to macroeconomic growth,
we identify a high impact group of firms on the basis of its contribution to themacroeconomic growth potential of an economy. Two, we explain the likelihood of HIGFs
being small, new or matured, within the theoretical framework of IO literature. The existing
HGF literature is essentially built upon the insights provided by empirical observations.
There is little theoretical support for these observations. We draw on the theories of firms’
heterogeneity, productivity dynamics and industry evolution to explain the phenomenon of
HIGFs. Finally, we extend the existing literature by bringing emerging economies within its
5fold. While focusing essentially on OECD countries, the existing literature places too muchemphasis on job creation. However, from the perspective of emerging economies, emphasisshould be placed not on increasing employment levels per se but on increasing high quality
productive employment. This view emphasises that job creation, linked to increased
productivity is the central mechanism which can translate economic growth into increased
incomes and improved social well-being. Even in the OECD country setting, many
researchers have recently questioned the exclusive focus on employment growth (Aiginger,
2006, 2007; Bravo-Biosca, 2010: 16) with little attention paid to the quality of employment.
Our approach of focusing on the role of HIGFs in accelerating macro productivity dynamics
is consistent with this view.

India presents a good case study for this analysis because it is an emerging economy that hasrelaxed its policy related entry barriers substantially over the past two and a half decades.
Since 1991, it has undergone comprehensive reforms in industry, trade, financial and fiscal
sectors to improve efficiency, productivity and international competitiveness of the industry
and to impart dynamism to the overall growth process. Evidence suggests that these reforms
have had a significant impact on business dynamics in the industrial sector in terms of thenumber of entry, and growth in assets, sales and profits (Mody et al, 2010; Aghion, 2005;
Alfaro and Chari, 2009; Bertrand et al, 2002; Rodrik and Subramanian, 2004; Kohli, 2006;
Panagariya, 2008; Bhaumik et al, 2009). This study exploits these dynamics to gain anunderstanding of high impact firms in the context of a developing country.

The rest of the paper comprises of six sections. The following section sets out the theoreticalunderpinnings of the analysis and formulates testable hypotheses. Section 3 discusses themethodology for the empirical analysis while Section 4 describes the data. Section 5
highlights some of the discernible patterns of business dynamics in Indian manufacturing to6set the stage for discussing empirical results in Section 6. Finally, section 7 concludes theanalysis.

2. Business dynamics, firms’ heterogeneity and productivity growth: A Theoreticalframework

The literature on industry dynamics can be traced back to Gibrat’s law of proportional effect(Gibrat, 1931). According to Gibrat, there is no systematic relationship between firm growthand firm size. This means that firms grow at the same average proportionate rate irrespectiveof their size. He argues that new opportunities arise in each period and the probability withwhich a firm exploits them is proportional to its size. This law is not found valid by moststudies that followed. But, it generated considerable literature on ‘firm growth’ (Sutton,1997). From the perspective of the present study, two strands of literature are of particularinterest that link firm dynamics with business dynamics and macroeconomic productivity
growth. These are: selection- based and innovation (entrepreneurship) - based theories of firm
growth. The former can be attributed to Jovanovic (1982) while the latter is rooted inSchumpeter (1934, 1942). In his theory of firm selection and industry evolution, Jovanovic(1982) postulates that heterogeneous firms enter the industry without knowing what theirrelative efficiency is. Once they enter the market, they learn about their ability to manage thefirm, from noisy information based on the distribution of their realized profits. If they findthat their ability exceeds their initial expectations, they expand the scale of their businessotherwise they contract it and may even exit. Thus, the Jovanovic’s model is a theory of noisy
selection, where efficient firms self-select them to grow rapidly, and inefficient firms decline
and fail. There is a threshold rule for exit decisions: firms exit if they believe to be below the
cut-off. As a result, bigger firms are less likely to exit. The central hypothesis of this theory isthat the young and smaller incumbents are likely to grow faster than the entrants or larger and7matured firms but they are also more likely to exit.

This theory has been enriched by a series of contributions with alternative assumptions (seeSutton, 1997). In one of the influential studies, Pakes and Ericson (1989) waived theJavonvic’s assumption that firms learn passively about investment and innovations. They
assumed that firms actively invest in exploring profit opportunities in an evolving market
place. They find that success is not guaranteed by this proactive approach of investing inlearning. If firms survive they grow and enter in larger classes.

Another strand of literature which is rooted in the Schumpeter’s creative destruction process(Schumpeter 1934, 1942) argues that entry by innovative entrepreneurs is the force thatdrives industry’s productivity and destroys the value of established companies that haveenjoyed some degree of monopoly power derived from previous technological, organizational,
regulatory, and economic paradigms. This line of argument has been carried forward in a new
stream of literature (inter alia, Caballero and Hammour, 1994; Campbell, 1998) which argues
that new technology is embodied in a more recent vintage capital. This is because unlike
incumbents, new firms do not have to incur the costs of upgrading their capital. New
companies are essentially created to exploit a new technological/marketing opportunity that is
not detected or met by other firms and they can better harness new technologies. As a result,they are more productive. Their entry drives productivity growth at the aggregate level anddisplaces outpaced establishments. Melitz (2003) assumed monopolistic competition in a
general equilibrium setting under the conditions of uncertainty to analyse productivity effectsof firms’ dynamics under international trade. His analysis indicates that an open economy
induces entry by only those firms that have productivity greater than the threshold level. This
forces the least productive firms to exit. Overall, this stream of literature proposes that new
entrants are likely to be more efficient than the incumbent firms.
8

From the perspective of the literature on Strategic Management, however, growth is a pathdependent process and implies a gradual accumulation of new resources and capabilities overa long time (Penrose, 1959; Hall, 1993; Mosakowski, 1993). Using the theory of business andproductivity dynamics, Cooper, Haltiwanger and Power (1999) argue that when new firms
enter with new technology, existing firms also adopt new technology by retooling. This inturn may drive the productivity growth of incumbents also. Incumbents are thus likely togrow rapidly in the face of new entry. Most existing studies also show that the establishedand matured firms are instrumental in driving the aggregated productivity growth (Aggarwal
and Sato, 2011, for a literature review). Aghion et al. (2009) however find that the impact ofentry on productivity dynamics of incumbent firms depends on the industry leveltechnological advancement. If the incumbent is close to the technology frontier, it will alsoinnovate more to grow fast and escape entry. This is more likely to happen in technologicallylaggard industries. But, if the incumbent is farther from the frontier, he cannot compete withthe entrant and will have to contract or even exit. Thus, in technological advanced industries,
new firms are more likely to drive productivity than in technologically laggard industries.
While supporting this line of argument, the proponents of ‘product life cycle’ approach (Gortand Klepper, 1982; Cohen and Klepper, 1996; Geroski, 1995) argue that a young industry is
characterised by high entry rates as new firms propose a large number of new product designs.
As the industry matures, firms stop competing in terms of product design and start competingin terms of prices and costs. Entrants may be at a severe disadvantage in the latter type ofcompetition and consequently their impact on industry structure and performance decreases.
New entrants can thus be considered to be important agents of change in a high tech young
industry. They can crowd-out less competitive incumbent firms through increased
competition and /or stimulate incumbent firms to innovate themselves. We thus have four
competing hypotheses:9H1: Smaller and younger firms are more likely to form a high impact group of firms (HIGFs)
than their new and established counterparts.
H2: New firms are more likely to be the drivers of industry productivity and are more likely
to constitute a HIGFs .
H3: Incumbents are more likely to be a HIGF and drive aggregate productivity
H4: Technological characteristics and maturity of the industry matter. Incumbents are more
likely to constitute a HIGFs in technologically laggard firms while new entrants are expected
to propel productivity growth in high tech industries.

In what follows, we test these hypotheses using non-parametric productivity decomposition
methods.

3. Methodology

Empirically, the micro dynamics of macro productivity growth are captured by productivity
decomposition methodologies which decompose macroeconomic productivity growth into thecontribution of incumbents, entrants and exiting firms. The contribution of incumbent firms isfurther broken down into two components: pure productivity effects and reallocation ofshares from more productive to less productive firms. Baily et al. (1992) was the first study to
propose this type of decomposition methodology. It was followed by a number of alternative
decompositions. We propose to use this methodology for our analysis. For ensuring
robustness of our conclusions we use three main variants of this methodology in our analysis.
These are: Griliches and Regev (1995), Foster, Haltiwanger and Krizan (2001), and Dynamic
Olly and Pakes by Melitz and Polanec (2009); henceforth, GR, FHK and DOP respectively.
Considering the problems involved in the classification of multi plant firms by stage of10

growth, the methodology is applied to plant level data with direct implications for firms. It
must be noted that we shall be using ‘plants’ and ‘firms’ interchangeably in the rest of theanalysis.

Foster, Haltiwanger and Krizan (2001) defined aggregate productivity as the output-weighted(θit) average of the productivity of individual plants. The linear aggregation of productivity
implies a geometric average of productivity levels:n

At = � θf,t Af,tf

Af,t is the productivity for factory f in t, and θf,t is the output-share-weight for factory f in t.

Using this, they proposed the following methodology, to decompose aggregate productivity
growth:ΔAFHK= � θf,t−1 ΔAf,ttf∈S+ � Δθf,t �Af,t−1 − At−1 � + � Δθf,t ΔAf,t
f∈S

f∈S

+ � θf,t �Af,t − At−1 � + � θf,t−1 �At−1 − Af,t−1 �
f∈N

f∈X

In the above equation S, N, and X, represent the sets of incumbents, entering, and exiting
factories respectively, during the periods from t-1 to t; and a delta the change. The first termmeasures the contribution of the incumbent firms on productivity changes, weighted by the
initial year share. The second term which sums changes in shares using a plant’s productivity
as weight captures the reallocation eﬀect amongst established firms. The third term is thecovariance effect, which will be positive if firms increasing their productivity also capture
more market share over time. The last two terms capture productivity growth accounted for11

by new and exiting plants respectively. All productivity changes are expressed as diﬀerencesfrom aggregate productivity in t − 1. In this methodology referred to as FHK, an entering
plant contributes positively only if it has higher productivity than the initial average and an
exiting/new plant contributes positively only if it exhibits productivity lower/higher than theinitial average.

An alternative methodology is provided by Griliches and Regev (1995). Their methodology,
referred to as GR is as under.

����� ��ΔAGR
t = � θf ΔAf,t + � Δθf �Af,t − A� � θf,t �Af,t − A�
f∈S

f∈S

��
+ � θf,t−1 �Af,t−1 − A

f∈N

f∈X

In this formula a bar over a variable indicates the average of the variable over the base andend years. All productivity terms (except for within-effects) are expressed as average
productivity of two years. Thus, the average aggregate productivity level between the two
periods is used as the reference productivity level in this methodology unlike the FHK whichuses productivity of the initial period as the reference. The first term represents thecontribution of the established and matured firms, where firm level productivity changes areweighted by their average market share across periods. The second term measures theimportance of reallocation, which reflects firm level changes in market shares, weighted by
the deviation of productivity from the industry mean. This will be positive if firms with highproductivity capture more market share whilst low productivity firms shrink. The third termmeasures the contribution of entering firms, which is the sum of the deviations of entrant’s
productivity from the industry mean, weighted by market shares. Analogously, the fourthterm measures the contribution of exiting firms.
12

A third methodology is offered by Melitz and Polanec (2009) who extended thedecomposition methodology of Olley and Pakes (1996) and named it ‘Dynamic Olley andPakes’ (hereafter DOP). It is given by,

= ΔA�
ΔADOP
S,t + Δcov(θS,t , AS,t ) + θN,t �AN,t − AS,t � − θX,t−1 (AX,t−1 − AS,t−1 )t�t = 1 ∑nt Af,t and θ�t = 1 ∑nt θf,t .
where A
i=1
i=1
n
n
t

t

In the above, θg,t and Ag,t represent the aggregate market share and aggregate productivityof group g in period t. The contribution of incumbent firms is simply the aggregate
productivity that would have been observed absent entry and exit. The set of incumbent firms
is used as a benchmark for estimating how the group of entrants (or exitors) affects theaggregate productivity change.

Mathematically, the three methodologies may yield very different results depending on
features of firm dynamics in the data. In an industry where the productivity of incumbent
firms is growing, FHK decomposition yields lower contribution of exiting firms than the
DOP, whereas the opposite holds for the GR decomposition. Further, both FHK and GR
decompositions yield smaller contribution of established plants and larger contribution of
new plants as compared with DOP. Finally, the contribution of established firms are inflated
in FHK and GR due to the use of weights in measuring these effects, which according to
Melitz and Polanec (2009) captures a part of reallocation effect. According to Mason (2014)
however, the reallocation based contribution in DOP is highly inflated at the cost of theestablished firms’ contribution. The former ‘does not just capture resource reallocation but
also moves in line with changes in productivity performance at firm level even when noreallocation of resources has occurred (for example, when rapid productivity growth takes
13

place within large firms).

Clearly, the three methodologies are expected to yield different results. Since the results are
sensitive to the choice of methodology we have used all three of them to ensure that ourconclusions are robust. Since our analysis required some additional categories of firms to be
included in these methodologies, we have extended the existing classification of firms by amore elaborate one which includes: large incumbents, new entrants, and small incumbents,
switching-in large incumbents, switching-out large incumbents 5 , and exiting firms. We
analyse the contribution of each set of business firms. The analysis is conducted for the total
manufacturing sector and three sub-sectors, namely, labour intensive, resource intensive, andtechnology intensive, separately. Finally, productivity is measured using both, labour
productivity (LP) and total-factor productivity (TFP) measures. The aggregate LP ismeasured as a weighted average of plant-level productivity. It is defined as
nt

nt

f

f

LPt ≡ � θf,t LPf,t = � θf,t (GVAf,t)Lf,tThe aggregate TFP is defined as
nt

nt

f

f

TFPt ≡ � θf,t TFPf,t = � θf,t (5

GVAf,t�)

K f,t α� Lf,t βSee, Table 2 for definition.
14

Where, θ is weight in terms of ‘output’ 6, GVA is ‘Real Gross Value Added’ which is
calculated using a double-deflation method. This means that both ‘gross value of output’ and‘inputs’ are deflated by their respective price indices 7. More specifically,

GVA= (gross value of output)/ (wholesale price index)-(total input)/ (input price index).

Further, L is man-hours of workers, and K is defined as the initial value of net fixed capitaldeflated by the implicit deflator of net capital stock in the organized manufacturing sector.
Finally, the parameters �α, and β denote the elasticity of production with respect toproduction factors. These are estimated using the semi-parametric technique proposed by
Levinsohn and Petrin (2003) based on a Cobb-Douglas production function.

4. DataOur empirical application is based on the plant or ‘‘factory’’ level data for the period 2000-01
to 2005-06, compiled by the Central Statistical Office of India in the Annual Survey ofIndustries (ASI). The ASI factory frame is classified into 2 sectors: the 'census sector' and the'sample sector'. The sample sector consists of small plants employing 20 to 99 workers if not
6

Different parameters have been used as weights in the existing literature. These are, forinstance, share of revenue, output, employment, value added, or costs. Of them, output (orrevenue) or employment are used most frequently. Following the traditional literature, wehave used ‘output’ weight in the present study.
7
The input price index is constructed as the weighted average of fuel price, material price,and other input prices. Fuel price, material price and other input prices are constructed using
wholesale prices, implicit deflator of national account statistics and weights from
input-output tables. The data sources we use for constructing input price index are: Reserve
Bank of India, Handbook of Monetary Statistics of India and Database on Indian Economy;Central Statistical Organisation, Input-Output Transaction Table.
15

using electricity and 10 to 99 workers if using electricity. The census sector comprises
relatively large plants. It covers all units having 100 or more workers and also somesignificant units which although having less than 100 workers, contribute significantly to thevalue of output. The composition and number of plants are summarized in Table 1.

16

Table 1: Composition of plants by year and sector
year

CensusSampleTotal

2000-2001

12,297

9,930

22,227

2001-2002

14,532

10,242

24,774

2002-2003

11,264

16,341

27,605

2003-2004

11,207

20,18531,392

2004-2005

14,122

12,611

26,733

2005-2006

17,826

1,748

19,574

Total

81,24871,057

152,305

Source: ASI plant level; data 2000-01 to 2005-06

We focus only on the census sector (100 or more workers) for the decomposition analysis
because the data on census plants is exhaustive. We observed this group consisted of five
types of plants by their stage of growth: large incumbents (CS), newly established large
plants (EN), small sized plants classified in the sample sector in 2000-01 but expanded andupgraded to qualify for the census sector by 2005-06 (ES), large survivor plants that changed
their industrial classification (SS and SO), and plants that stopped functioning (exiting plants
or EX). It is instructed to note that the ES category firms are essentially gazelles in Birch’s
analogy. In all, we define 6 categories of plants for our analysis. Their definition andnotations are provided in Table 2.

17

Table 2: Plant Status Variables
Status

Notation

Definition

Large incumbents

CS

Present in both period t and t-k in the census sector

ES

Present in t in the census sector and t-k in the sample(Elephants)
Expanding small
incumbents (Gazelles)

sectorNew entrants (Fawns)

EN

Present in t in the census sector, absent in t-k

Switching in incumbents

SS

Switching-in incumbents that are present in t in the Census sector inindustry i, absent in t-k in industry i, but present in t-k in industry j

Exitors

EX

Present in t-k in the census sector, drop out in t

Switching out incumbents

SO

Present in t-k in industry i, absent in t in industry i, but present inindustry j

Our decomposition analysis thus shows contributions of six groups of plants to aggregate
productivity of the manufacturing sector. For analyzing the impact of technological andindustrial specifics of the industry on productivity dynamics, we classified the census sector
plants into two digit industries based on the Standard ISIC (Rev 3) classification. Under thisclassification, the industry groups 15-22 and 36 comprise of labour intensive industries suchas food, textile, apparel, wood, and paper. These are matured and least technology intensive
industries. Industries ranging from 23 to 28 are resource based and comprise of mineral andmetal based industries. These are essentially medium tech industries requiring large
investment. Finally, the industry codes 24,29,30,31,32,33,34, and 35 pertain to technology
intensive industries; they are essentially driven by technology.

5. Business dynamism in India: 2000-1 to 2005-06

Table 1 above shows that the number of census sector plants increased across all industries
without any exception over this period. Overall, the number of plants in our dataset increased
from 12,297 in 2000-01 to 17,826 in 2005-06 i.e. by 45 per cent. Our analysis indicates thatof the total plants in 2000-01, a mere 5802 (32.5 percent) plants emerged continuing

18

survivors (CS) in 2005-06; the rest are either newly entering plants (EN) or expanding small
incumbents (ES). Figure 1 presents a comprehensive picture of plant dynamics during
2000-01 to 2005-06 at two digit level. A closer examination of the trends in CS firms in
Figure 1 reveals that the share of large incumbents has been lower in relatively higher
technology and capital intensive industries as compared with that in traditional industries.
Overall, 50 to 60 percent of the plants functional in 2000-01 stopped functioning or reduced
the scale of their operation such that they were reclassified in the sample sector by 2005-06.
Of the 12297 plants operational in 2000-01, only 5982 (48.6 percent) survived by 2005-06
while the rest 6315 (over 51.4 percent) exited. The distribution of these plants is fairly
uniform across industries. However, some of the traditional industries such as tobacco,apparel, leather and wood industries witnessed a rather high rate of firms’ exit ranging from60 to 70 percent. On the other hand, technology intensive electronics machinery, instruments,
motor vehicle and transport equipment industries had an exit rate ranging between 50 to 60
percent. Petroleum and coke, chemicals and transport (including auto) industries haveexperienced the lowest exit rates but it also varied between 40 to 50 percent. The destructionof plants was more than offset by new entrants resulting in a restructuring process. This ismanifested in the patterns of net entry. Net entry rates have been positive in all the industries
at two digit level. Further, the average entry rate was as high as 45% for technology driven
industries against 29% for traditional industries. Resource intensive industries lay between
the two at 40%. Apparently, the creative destruction process (net entry) accelerates gradually
as we move from the traditional to technology intensive industries (Figure 1).

19

Figure 1: Plant dynamics in Indian manufacturing during 2000-2006 (%)
70

Resource Intensive

Labour Intensive

Technology Intensive

60
50
40
Net Entry
Large incumbents

30
20
10
0
15 16 17 18 19 20 21 36 23 25 26 27 28 24 29 30 31 32 33 34 35
Survival rate oflarge
incumbents

Entry rate

(1)

(2)

Expansion
rate of small
fast growing
plants(3)

Switching-in
rate

Exit rate

Switchingrate

(4)

(5)

(6)

Labour Intensive

36.5

21.0

42.0

0.5

39.1

0.6

Resource Intensive

27.3

27.2

44.4

1.1

32.8

1.1

Tech-intensive

29.8

19.6

48.8

1.8

31.1

1.6

Note: (1): The ratio of survivors to total plants in 2005-06, (2): The ratio of new entrants to total plans;(3): Fast
expanding small plants to total plants; (4):The ratio of switching-in to total plants; (5) and (6): The ratio of
exiting and switching-out plants to total plants

The upshot is that significant business dynamics have been taking place in the economy
across all sectors at the plant level. As a result of which the manufacturing sector has
undergone tremendous transformation during this period. The change is effected in allindustries. But the process of creative destruction seems to be rather intense in modern
sectors which are technology and capital intensive. On the other hand, some of the traditional
industries such as wood and leather also seem to have undergone significant dynamics at theplant level. A pertinent question is how different plants broken down by stage of growth
influenced productivity growth at the macro level.

206. Business dynamics and productivity growth
Productivity growth
Figure 2 presents the labour and total factor productivity growth rates across two digit levelindustries in Indian manufacturing over the period from 2000-01 to 2005-06. It shows that
productivity has increased across almost all sectors over this period with publishing andprinting, and petroleum and coke being the only two exceptions. As expected, there is aremarkable variation or dispersion in the aggregate productivity growth across industries. The
cross industry differences in productivity stem essentially from a different combination ofskills required in them, the ratio of capital to labour, business dynamics, technologicalprogress, and exposure to international trade. Our data indicates that typically, high-techindustries have experienced relatively higher rates of productivity growth than their low(labour intensive) - and medium (resource intensive)-tech counterparts irrespective of themeasure. The strongest productivity growth has come about in the office computing
machinery industry. Productivity in this sector grew over 34 times over this period and could
not be shown in the figure due to scale issues. Further, it may be seen that the patterns oflabour and total factor productivity growth are quite consistent. Interestingly, labour
productivity has grown a little faster than TFP in the labour intensive sector. Apparently, there
has been downsizing of employment or capital deepening in this sector which improves its
performance. In tech intensive sectors, on the other hand, TFP growth has typically outpaced
LP growth with the only exception of the automobile and other transport sectors. The latter
have been the fastest growing sectors in the economy experiencing rapid expansion inphysical investment. Capital deepening appears to be the major factor underlying strong
performance in labour productivity relative to TFP growth in these sectors.

21

Figure 2: labour productivity and TFP growth across two digit level industries: 2000-01
to 2005-06
1000

Labour Intensive

Resource
intensive

Technology intensive

800

600

400

200

0
15 16 17 18 19 20 21 22 36 25 26 27 28 24 29 31 32 33 34 35-200

TFP growthlabourproductivity_growth

Decomposition of aggregate productivity growth
Table 3 presents the decomposition results for Indian manufacturing based on the FHK, GR
and DOP methodologies. The top panel in the table is based on the TFP measure, and thebottom one is based on the labour productivity measure. As expected, the results are atvariance across the productivity measure and methodology used. Typically, however, thebulk of productivity growth of the census sector in Indian manufacturing over 2001-06 is
accounted for by large incumbents. This group of firms can explain up to 82% of the overall
growth in productivity depending upon the measure and methodology. Their productivity
contributions are more prominent for TFP than LP indicating that much of the productivity
growth of incumbents was driven by improved efficiency in the use of resources andtechnological changes (Disney et al 2003). Our results that the majority of economic growthcomes from incumbents (or elephants) are in tune with Acs et al (2008) and Brown et al.
22

(2014) in the HGF literature. Notably, this finding is rather common in productivity
decomposition analyses across the developed and developing countries (see Aggarwal andSato, 2011; Kocsis et al, 2009; UNIDO, 2009 for literature review). The strong performanceof incumbents is explained by their tangible and intangible resource base in terms of wide
human capital base, learning, dynamic capabilities, and resources for research anddevelopment. Another reason that underlies their superior performance is a constant threat ofentry facing them. They have to adopt the escape entry route to survive the threat. They enjoy
the relative advantages of size and experience and hence manage to out-compete other groups.
Our findings of the contribution of fast growing small incumbents (gazelles) are somewhat
confusing. While these plants explain 61% to 71% of the aggregate TFP growth, their labour
productivity effects remain rather small. Is it that they compete essentially on the basis oftechnology and efficient use of resources? To answer this question we combine these resultswith the productivity contributions of the exiting firms which are also quite at variance acrossproductivity measures.

It may be seen that the plants that exit have higher than the industry

average TFP but they are essentially labour inefficient. Could it be that the plants that exit arecharacterized by old capital? Jovanovic and Tse (2006: 3) provide evidence that ‘inefficient
producers may decide to operate in an industry until they reach the optimal date of theircapital replacement. They tend to exit the industry when their capital comes up for
replacement because they may not be able to finance the new plant and equipment’. This
introduces upward bias in their TFP calculations because they end up with little expansion incapital. In our case, these may thus be inefficient firms with old capital. Coming back to thecontribution of small incumbents to productivity, it could be that the exceptional TFP
performance combined with low labour productivity of small fast expanding firms in this
sector has essentially been due to their not expanding the capital base. New entrants are also
found to have a little impact. These results are consistent with Jovanovic (1982). Finally,
evidence of industrial structuring from low to high productivity firms is not robust. The
23

results from DOP methodology attribute most productivity growth to restructuring. This
could be because, as mentioned above, the DOP approach has drawbacks with regard to
productivity allocation within and between established firms.
Overall, most of the changes in aggregate productivity come from the productivity growth oflarge incumbents. This means that the large incumbents in particular large and matured ones
are more likely to reach the HIGF status with productivity improvements (Du and Timouri,2015). Aggregate figures however mask the sectoral variations.

Table 3: Decomposition of TFP and LP growth over 2000-1 to 2005-06 (%)
CS

RE

EN

ES

SS

EX

SO

Total

Total Factor Productivity
FHK

82.2

-12.1

4.1

73.0

-8.6

-38.0

-0.6

100

GR

81.1

-5.4

1.0

61.6

-11.7

-26.9

0.3

100

DP

29.4

69.9

1.1

61.9

-11.7

-49.2

-1.4

100

Labour Productivity
FHK

57.9

20.7

1.5

14.1

8.1

1.7

0.6

100

GR

69.5

15.0

-1.7

2.7

5.0

9.2

0.2

100

DOP

4.7

116.2

-5.9

-12.8

0.8

-2.3

-0.7

100

Source: Computed by authors based on the ASI Plant level data

Decomposition by subsector

Tables 4-6 present industry-wise decomposition results based on the DOP, FHK and GR
methodologies across 21 two-digit level industries over 2000-01 to 2005-06. We provide only
the weighted averages in the text to avoid an excessive dose of numbers.

Labour intensive: Table 4 presents the productivity decomposition results for thelabour-intensive sector. We find that the homogenous characteristics of the industry produce24results which are quite similar across productivity measures and methodologies. An important
finding is that the fast expanding small incumbents (or gazelles) are the driving force of
productivity growth in this sector. Unlike at the aggregate level, the contribution of large
incumbents to productivity growth in particular TFP remains rather small in the labour
intensive sector. This is in contradiction with the predictions of Aghion et al (2009) and
Geroski (1995) that incumbents in technologically laggard and matured industries may
respond to business dynamics by moving to technology frontiers to counter competition fromnew/expanding entrants. It may be seen that they do resort to downsizing of
employment/capital deepening to improve their labour productivity but their contribution
even to LP remains relatively small. Entry of new firms pulls down productivity growth atleast in the short period. This holds true, irrespective of the productivity measure andmethodology. The exit effects are however ambiguous. But, the plants that contract arenecessarily less efficient than the industry average. Finally, there is some evidence of internal
restructuring if labour productivity growth is used as a measure of productivity. But firms
with greater TFP are not likely to increase their market share.

Table 4: Weighted average of low tech decompositions: 2000-01 to 2005-06 (%)
CS

RE

EN

ES

SS

EX

SO

Total

Total Factor Productivity
FHK

9.19

0.05

-0.02

93.35

5.87

-8.48

0.04

100

GR

8.54

6.37

-2.49

55.99

4.51

26.98

0.09

100

DOP

6.91

13.0

-0.51

146.5

4.23

-70.11

0.04

100

Labour Productivity
FHK

17.3

13.4

5.50

61.10

5.79

-3.32

0.26

100

GR

25.7

12.6

-0.03

33.38

4.82

22.67

0.83

100

DOP

19.1

47.5

-9.07

74.58

4.79

-37.150.28

100Overall, gazelles contribute 80% to 90% of TFP and 40-70% of labour productivity in the25

labour intensive sector. This means that this group of firms is most likely to be the HIGFs andhas the potential of nourishing HGFs.

Resource intensive: Contrary to the labour intensive sector, productivity growth in the
resource intensive sector is driven essentially by internal dynamics of large incumbents. It
may be noted that industries in this sector are essentially heavy and strategic in nature, andhad been assigned top priority in the state-led heavy industrialisation-regime adopted by thegovernment of India in 1956. Since then, substantial capacity has been built in theseindustries in India. Considering the fact that these industries are capital and scale intensive,the incumbent firms continue to build on their competitive advantages to improve their labour
and total factor productivity and push aggregate productivity growth up in this sector.
Inefficient firms in the liberalised era exited but their effect remains rather small, perhaps dueto their small presence. The small incumbents or new entrants (fawns) remained laggard incomparison to the existing matured and well established large incumbents. Clearly, given the
sector specifics, the ‘high growth incumbents’ are more likely to form HIGF in this sector andneed special focus from the macroeconomic growth perspective. Gazelles are not likely to
add substantially to the growth potential of the economy.

26

Table 5: Weighted average of Resource intensive sector decompositions: 2000-01 to
2005-06 (%)
Diversifying
Allocative

Small

Established

New

Total
Existing

Contracting

firms

firms

large

effeiciency

incumbents
incumbents

Total Factor Productivity

FHK

79.07

-0.16

4.57

14.99

-0.24

-0.24

2.00

100

GR

81.61

4.24

0.30

1.39

-0.79

8.81

4.43

100

DOP

40.02

87.16

-6.58

-20.96

-1.70

0.06

2.01

100

Labour Productivity

FHK

97.26

-9.90

0.11

1.25

13.35

-2.19

0.12

100

GR

91.33

7.75

-0.37

-0.52

1.57

0.03

0.22

100

DOP

23.48

98.56

-1.10

-3.26

-14.39

-3.32

0.03

100

Technology driven sector: This sector presents a classical case in which irrespective of themethodology and measure, fast growing small incumbents (or gazelles) emerge as the major
contributors to productivity growth and their share remains almost the same irrespective of
the productivity measure. According to the GR methodology, their contributions aresupplemented by the exit of inefficient firms. The contribution of large incumbents tomacroeconomic productivity remains small throughout and new entrants appear to pull
productivity down (except in FHK). Thus the gazelles’ role in enhancing the growth potentialof the economy appears to be most unambiguous in the high tech sectors and these is most
likely to be the HIGFs in the economy which needs to be targeted with the utmost urgency. It
may be noted that the business dynamics are also found to be the most prominent in this
sector.

27

Table 6: Weighted average of technology intensive sector decompositions: 2000-01 to
2005-06 (%)
CS

RE

EN

ES

SS

EX

Total

SO

Total factor productivity
FHK

13.14

-3.13

2.8

85.98

0.05

1.06

0.1

100

GR

12.22

5.23

-8.68

59.13

-0.64

31.02

1.73

100

DOP

7.27

12.61

-0.4

77.52

-0.13

2.93

0.2

100

Labour productivity
FHK

15.6

1.18

2.93

79.68

0.16

0.53

-0.09

100

GR

16.12

7.79

-8.06

54.63

-0.63

28.84

1.32

100

DOP

6.91

22.8

-0.21

69.19

-0.45

1.79

-0.03

100

7. Conclusion
Over the past two decades, considerable interest has grown in HGFs. Initially, it emanatedfrom the policy perspective due to the major role that these firms are shown to play in job
creation. Since 2010, there has been a rapid proliferation in research studies on HGFs.
However, the concept of HGFs still remains controversial. Much of this controversy can be
attributed to the ambiguity that surrounds the concept of HGFs. One of the most controversial
issues is size and age of these firms. There have been several empirical attempts to establish
whether these are young, small or matured firms. But findings are inconclusive. This is not
unexpected as ‘high growth’ is not a sustainable phenomenon. Rather, it is a temporary phaseor an episode which can come about in any firm. We therefore propose a concept of highimpact group of firms and explain it within the framework of overall business andmacroeconomic- productivity dynamics. While doing so, we attempt to identify it byanalysing the contribution of firms to macroeconomic productivity growth by stage of their
growth.

Our empirical study reveals that much depends on the industry/sector specificcharacteristics. The business dynamism is most strongly linked with firm and productivity
dynamism in the technology driven sector. Even if new firms (fawns) remain inconsequential
28

in productivity growth, fast expanding small firms or gazelles make a substantial contribution
to macroeconomic productivity growth and hence are the HIGFs. In the labour intensive
sector also gazelles display significant contribution to productivity. They are supported bylarge incumbent also in pushing macroeconomic productivity growth. In the scale intensive
heavy industries however large incumbents (or elephants) are likely to account for a majorityof productivity growth. Thus new and small firms are not likely to contribute significantly tomacro productivity dynamics in any subsector. In traditional and high tech sectors, therefore,
rapidly growing small firms should be the major targets for policy makers while in scaleintensive industries, established large firms need to be promoted to grow faster. Productivity
contribution of fawns remains rather small. It is well documented in the literature also (Bailyet al. 1992; Bahk and Gort, 1993; Jovanovic and Tse, 2006). Clearly, the policy makers haveto reorient their attention to the small number of high-potential firms in the small sector inboth labour-intensive and technology-intensive sectors in particular in the latter. However, in
the resource intensive sectors large matured firms need to be supported by suitably crafted
initiatives.

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