EXTERNAL ENTRY AND THE EVOLUTION OF CLUSTERS:

A STUDY OF THE BIOTECHNOLOGY INDUSTRY IN CANADA

By

Dean A. Hennessy

A thesis submitted in conformity with the requirements

for the Degree of Doctor of Philosophy

Rotman School of Management

University of Toronto

Copyright by Dean A. Hennessy (2008)

EXTERNAL ENTRY AND THE EVOLUTION OF INDUSTRY CLUSTERS:

A STUDY OF THE BIOTECHNOLOGY INDUSTRY IN CANADA

Dean A. Hennessy

Doctor of Philosophy (2008)

Rotman School of Management

University of Toronto

ABSTRACT

Building on recent work in economic geography, evolutionary economics, and

international business, I examine how firms that enter from outside a region alter the knowledge

and opportunity structure for potential entrepreneurial entrants and indigenous incumbents in that

region. In particular, I examine the short and long run effects of both greenfield and acquisition

entry on entrepreneurial entry, as well as on the exit and growth of indigenous incumbents in

industry clusters. A comprehensive dataset of all firms in the Canadian biotech industry between

1976 and 2003 is used to study the dynamic effects within all regions that have experienced an

external entry. The results show a complex set of processes at work. Newer greenfield and

acquisition entrants have consistently opposing effects, with newer greenfields enhancing

entrepreneurial entry, but dampening growth and survival of indigenous incumbents in the longer

run. Older greenfields, those that have a long presence in a given region and are primarily

traditional pharmaceutical firms, have a similar effect to that of acquisitions. Moreover, the level

of agglomeration moderates the influence of ‘outsiders’ on the indigenous industry, especially in

the case of acquisitions. The results suggest that legal constraints on labor mobility barriers have

an important influence on the observed patterns. The overall patterns suggest that the search and

site selection of ‘outsiders’ is an important mechanism driving local industry evolution,

complementary to other traditional mechanisms.

Keywords: industry clusters; innovation; evolutionary economics; entrepreneurship; international business; economic geography.

ii

ACKNOWLEDGEMENTS

There are a number of people I wish to thank for their friendship, advice and support over

the years spent working on my dissertation. First, I thank my supervisor, Brian Silverman,

without whose support this would certainly have taken a different form. Brian is a consummate

teacher, researcher and gentleman – all qualities to which I aspire. I also owe a great deal to

Terry Amburgey for his guidance through the Ph.D. program – he has been a true mentor to me,

and to many others. I also thank David Wolfe who, as co-director of the Program in

Globalization and Regional Innovation Systems (PROGRIS) at the Munk Centre for

International Studies at the University of Toronto, provided the expertise on industry clusters on

my committee. David is a true leader and his enthusiasm is contagious. Finally, I wish to thank

David Audretsch (Max Planck Institute and Indiana University) for acting as my external

examiner. David’s expertise in many of the areas dealt with in the thesis provided an important

validity check on this study.

Other faculty members at the Rotman School of Management have also had an important

influence on my thinking – perhaps especially Joel Baum. Not only was Joel my first point of

contact in the Ph.D. program, but his work has also been an enormous influence on the way I

think about things. Likewise, it is evident the extent to which Olav Sorenson has influenced my

work. Other faculty members have been important influences and friends: Kim Bates, Kristina

Dahlin and Andreas Al-Laham, in particular. Kristina deserves special thanks for her helpful

advice along the way.

I also acknowledge the influence and friendship of a number of my colleagues in the

Ph.D. program. First, some co-authors – You-ta Chuang, Xuesong Geng, Stewart Melanson, and

Ranjita Singh, all of whom I have learned a great deal from. I am also grateful to Eytan Lasry

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and Stan Li for their friendship during this period, and for some lively discussions. Colleagues at

PROGRIS also provided a great deal of intellectual stimulation and support. Special thanks go to

Meric Gertler, the other co-director of PROGRIS, for his feedback on work in progress.

I would also like to acknowledge my new colleagues at Tilburg University in The

Netherlands, many of whom had an influence long before I first met them. Perhaps most notable

is Bart Nooteboom, whose influence on my thinking about industry evolution is clearly in

evidence in this dissertation. Others have also had a notable influence including Harry Barkema,

Jean-Francois Hennart, and Xavier Martin. Others in the wider academic community also

deserve a note of appreciation, including Rajshree Agarwal, Alfonso Gambardella, Steven

Klepper, Peter Murmann, and Sidney Winter. I also thank Xavier Castener for prodding me to

make the evolutionary connection more explicit.

This work is dedicated to my wife, Jennifer Leskiw, whose unstinting support made this

possible. This is certainly more so the case than is typical since she also gave up countless hours

in data coding and proofreading.

Dean A. Hennessy

University of Toronto

Toronto, Canada

iv

TABLE OF CONTENTS

Introduction……………………………………………………………………….page 1

Chapter 1 Industry and Firm Evolution……………………………………….page 10

Chapter 2 The Causes and Consequences of External Entry…..…………….. page 31

Chapter 3 Evolutionary Economic Geography..………………………………page 54

Chapter 4 Hypotheses…………………...…………………………………….page 75

Chapter 5 Data, Model and Methods………………………………………….page 92

Chapter 6 Analysis and Results……………………………………………….page 129

Chapter 7 Discussion and Conclusion………………………………………...page 173

References………………………………………………………………………....page 185

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LIST OF TABLES

Table 1 Variable Definition

Table 2a Table of Means and Standard Deviations

Table 2b Correlation Matrix

Table 3a Negative Binomial Models of Entrepreneurial Entry – External Entry

Table 3b Negative Binomial Models of Entrepreneurial Entry – External Presence

Table 3c Negative Binomial Models of Entrepreneurial Entry – External Presence

(supplemental)

Table 4a Hazard Rate Models of Indigenous Firm Exit – External Entry

Table 4b Hazard Rate Models of Indigenous Firm Exit – External Presence

Table 5a Growth Rate Models of Indigenous Firms – External Entry

Table 5b Growth Rate Models of Indigenous Firms – External Presence

Table 6 Summary of Hypotheses

Table 7 Summary of Results

Appendix 1 Summary of Biology and Biotechnology Milestones

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LIST OF FIGURES

Figure 1 Proportion of External firms to Total Firms in Selected Regions - A

Figure 2 Proportion of External firms to Total Firms in Selected Regions - B

vii

1

INTRODUCTION

There has been substantial interest in understanding the causes and consequences of the

geographic agglomeration of industry over the past decade, primarily from economic

geographers, but also increasingly from economists and management scholars (e.g. Krugman,

1991; Porter, 1998). While progress has been made in understanding some of the fundamental

determinants of geographic agglomeration, there remain substantial gaps in our understanding of

the dynamics of clustering. One key issue that has received very little attention is the interaction

between “insiders” and “outsiders”, meaning those that are indigenous to a region and those

coming from outside the region. Specifically, this study aims to better understand how external

entrants – firms that enter from outside a given region – affect the host region (i.e. the indigenous

local industry) once they enter.

How external entrants affect a region is an important question to answer for both

theoretical and practical reasons. From a theoretical perspective, the importance of insider-

outsider interactions is dramatically illustrated in history: in the 15th century, China experienced

a period of stagnation in science and technology after a prolonged period of innovation that put it

far ahead of Western civilization. This state of affairs has been ascribed to two things: first, rigid

internal bureaucracy and controls which impeded the generation of internal variety, and second,

a ban on foreign trade. Robbed of both sources of variety, innovation ceased (Lotman, 1990;

Mokyr, 1990). By contrast, “Italy as a crossroads was saturated by foreign influences …

[throughout its history]: Germans, Huns, Goths, Ostrogoths, Byzantines, Longobards, Franks,

Arabs, Normans, Maygyars…. Italy absorbed this flood… and [these] influences formed

themselves into a whole. The result was … a bursting of cultural activity unheard of in the

history of civilization: the Renaissance” (Lotman, 1990: 145).

2

This example illustrates the importance of both internal and external sources of

knowledge in innovation. The key assumption is that outsiders acted as a source of renewal by

transferring new knowledge and ideas, which when recombined with indigenous knowledge

created the conditions for cultural renewal. The effects of these kinds of interactions are also

evident throughout economic history – the rise and fall of countries and civilizations is

predicated on their ability to innovate. In almost all cases, successful and sustained innovation

occurs through interaction with outsiders – via trade or outright assimilation. There are a variety

of intellectual traditions that deal with different sides of this issue: from models of economic

growth and development, to trade, to technology and knowledge transfer, etc.1 They vary

substantially in terms of their underlying assumptions, and their emphasis on micro or macro

approaches, as well as the outcomes of interest.

An evolutionary approach to innovation and industry dynamics, which generally ignores

the external dimension, takes a somewhat different tack. As Schumpeter (1939: 70) noted: “The

essential point to grasp is that in dealing with capitalism we are dealing with an evolutionary

process.”2 He further noted: “[the] process of incessant rise and decay of firms and industries…

is the central – though much neglected – fact about the Capitalist System.” This emphasis on

economic process rather than equilibrium outcomes is central to evolutionary economic theory.

Industry evolves through the variation, selection and retention of firm routines and capabilities

1 Friedrich List was perhaps the first to study the issue of tech transfer (or rather acquisition) systematically in The National System of Political Economy (1841). Like Adam Smith before him, List observed that nations differed with respect to their technological competencies, and that idiosyncratic (national) knowledge, which was often considered strategic, could be acquired through reverse engineering or by training in foreign schools. List advocated not only the protection of infant domestic industries but also a wider range of measures aimed at facilitating industrialization and economic growth. Most of these policies focused on learning about new technology – often from foreign sources – and applying it to the domestic context, assuming that when (re)-combined with domestic knowledge, they would yield superior products or processes. 2 Note, however, that Schumpeter was not an evolutionary economist, according to Hodgson (1993), because of his commitment to equilibrium outcomes. Still, his ideas served as an important starting point for later theoretical development.

3

(Nelson & Winter, 1982). How new variety enters the industrial system is therefore key to

evolutionary processes. Much of the recent focus in evolutionary economics (and strategy) has

been on how new firms (spinoffs) generate variety through an inheritance mechanism by which

some capabilities (with variation) are transferred from the parent (incumbent) (Nelson & Winter,

1982; Winter, 1984). However, because industry evolution is generally taken to be fully

endogenous to firm routines and capabilities, there is little role for external variation to enter the

system.3 Another mechanism for industry evolution relates to the search processes of incumbent

firms, which can lead to geographic diversification. The emphasis in this area has been on the

evolution of a firm’s own capabilities. In particular, entry into other regions (i.e. location choice)

is often, though not always, motivated by firms seeking to fill gaps in their capabilities (Chang,

1996; Chung & Alcacer, 2002), thus the importance of location given firm characteristics (Chung

& Alcacer, 2002; Shaver, 1998).

Once they enter, however, outsiders also transform a host region by generating

externalities that would not otherwise be present. The effect of foreign firms on domestic

industry has been studied extensively in international business. This research has often shown

that the presence of foreign firms can have competitive (negative) effects by crowding out

domestic firms, as well as positive effects on domestic firms’ productivity through knowledge

spillovers and linkages with downstream suppliers, among other things (Blomstrom & Kokko,

1998). However, as yet, we know little about the conditions under which externalities are either

positive or negative. This is a key issue that has yet to be resolved. Furthermore, even at this

level of analysis little research has been conducted on understanding other important ways in

3 The key assumption then is that irrespective of where (and how) variation enters, it then diffuses efficiently through the system.

4

which foreign firms affect indigenous industry, such as through processes of entry, exit or

growth. Some of the research in this area is dynamic, but rarely is it evolutionary.

The example of the Italian Renaissance not only reveals the importance of outsiders in

innovation, but it is also pertinent because it resembles an innovation system. However, the

Italian Renaissance, like many other such phenomena, was a local affair – more properly the

domain of city-states rather than the territory that only much later became known as Italy. In

modern times, the nation does play an important role, which is emphasized in the National

Systems of Innovation literature (see Nelson, 1993). However, the related literature on Regional

Innovation Systems argues that knowledge tends to be local, at least insofar as it is tacit (Jaffe, et

al., 1993). Economic geographers also contend that inter-firm interactions tend to be stronger in

a more bounded geographic space, which implies that external entrants (either foreign or

domestic) should have a stronger effect on the host at the regional level than at the national level.

Many evolutionary concepts have been used extensively in economic geography. In fact,

there is a tradition in behavioral economic geography that has focused on the spatial evolution of

local industry (e.g. Pred, 1966), local here referring to a city-region. Until recently, this work had

dealt primarily with modeling locational dynamics, but over the past several years there has been

a shift in emphasis to the broader process of clustering. Some of this work, especially in the

innovation systems literature, addresses how new variety enters the region, although even in this

research stream, there is little accommodation for outsiders.4 This is surprising since one key

dynamic property of clusters that has often been highlighted is that once a region has reached

critical mass (usually of firms), it becomes a basin of attraction thereby drawing in more firms

from outside the region. As the international business literature has shown, the search processes

4 Entry processes have also been used to explain the agglomeration of industry in particular places since spinoffs tend to locate close to their parents (e.g. Klepper, 2002b; 2003).

5

of firms, leading to entry into other places, is likely to influence the regions they enter because of

the externalities they generate. In this sense, external entrants may alter the path of the regions

because of how they affect key regional and firm level processes, especially the components of

agglomeration: entrepreneurial entry, and the survival and growth of indigenous firms within the

region. Furthermore, these search (and site selection) processes act as linking mechanisms that

bind regions together into an industrial system. An evolutionary economic geography seeks to

explain the rise and fall of industry in particular places.5

Aside from the theoretical rationale for this study, there are also public policy

implications that make understanding the effects of outsiders important. First, policy makers are

often interested in attracting new firms (greenfield subsidiaries) to their region, and so it is of

practical concern to understand the effects of their entry and continuing presence on the local

industry. Second, there has been a substantial policy focus in recent years in attempting to

develop hi-tech clusters. Everyone it seems wants to become the next Silicon Valley – and a

great deal of money has poured into particular regions in building up institutional and

infrastructural supports, as well as through direct and indirect subsidies to firms.

Given this, it is also of practical concern to understand what happens when another class

of outsiders enters – i.e. when acquisitive entrants take over homegrown firms, which are

sometimes subsidized by public dollars. From an evolutionary perspective, this might be

regarded as the natural order of things. However, to many this is regarded as an appropriation of

homegrown technology, a sentiment captured in the following statement by the Advisory

Council on Science and Technology: “Canada should not continue to forego good opportunities

to generate jobs and social benefits for Canadians. Neither should we stand by while foreign

5 This is analogous to asking why did a renaissance occur (in some places) in Italy in the 15th century, whereas China stagnated at the same time.

6

firms strengthen their competitive standing and create good jobs outside the country as a result of

owning IP [intellectual property] paid for and created by Canadians.”6 These concerns are also

linked to a long tradition among Canadian economists on the effects that foreign entrants (e.g.

Orr, 1974), and more generally, foreign direct investment (FDI), has on Canadian domestic

industry (e.g. Safarian, 1966, 1973; Caves, 1974; Rugman, 1982; Baldwin, 1995).7

Empirical Setting

The focus of this study is on understanding how external entrants (greenfields and

acquisitions) affect three basic and inter-related components of agglomeration: entrepreneurial

entry, as well as the growth and survival of indigenous incumbents within knowledge intensive

industry clusters. These processes form the micro-foundations of agglomeration. Drawing on the

complementary work in evolutionary economics, economic geography, international business,

and other related fields, I argue that external entrants alter the knowledge and opportunity

structure for potential entrepreneurial entrants, as well as for indigenous incumbents. These

relate to the diversity that insiders can draw on, and the selection environment they are subject

to. However, because greenfields or acquisitions differ in their nature, the effects depend on the

mode of external entry – especially in the short run. In the longer run, they follow different

trajectories, but their effects should converge. External entrants include both foreign firms, and

domestic (multi-unit) firms from other regions in the country. The hypotheses are tested on a

comprehensive dataset of all biotechnology firms in different regions in Canada between 1976

and 2003.

6 In “Public Investments in University Research: Reaping the Benefit” (May, 1999). 7 This is a general concern in both small open economies such as Canada (e.g. Caves, Porter, Spence & Scott, 1980), and large ones such as the U.S.

7

The Canadian biotechnology industry provides a good vantage point to study these

processes, in part because of the relatively high proportion of external firms (primarily foreign)

that have entered by various modes. Another advantage of studying a relatively new industry is

the abundance of data available on its early history. This proves to be an important advantage

indeed because it provides an opportunity to examine how the cluster affects outsider influence.

An important limitation, however, is that these processes cannot be examined over a very long

period of time (in evolutionary terms). Studying a single industry has the advantage of providing

an implicit control for heterogeneity across industries. It also allows for an opportunity to

develop deeper insights into these dynamic processes, in part because more detailed data are

available than is possible to access consistently across industries.

This study is organized as follows: In the first three chapters, the relevant literature is

reviewed. Chapter 1 covers the evolutionary economics literature, as well as related work in

entrepreneurship. Chapter 2 investigates the relevant literature in international business and in

acquisitions, which provides much of the empirical evidence for the effects of external entrants

on other firms. Chapter 3 examines the relevant literature in economic geography. Although this

study has something to say about all these areas, the main contribution here is to the further

development of an evolutionary economic geography (Boschma & Frenken, 2006; Klepper,

2003). Part of the objective, therefore, is to provide a sharper theoretical lens through which to

better understand the micro-processes of agglomeration, with an emphasis on insider-outsider

interactions. From a broader perspective, rather than focusing on the generative mechanisms of

spinoff entry, the focus here is on the linking mechanisms across regions. These ideas are

elaborated further in Chapter 4 where the hypotheses to be tested are developed. This is followed

8

in Chapter 5 by a description of the data, model and methods used. The results are reported in

Chapter 6.

The results demonstrate a complex set of dynamics in relation to the effects that outsiders

have on insiders. Newer greenfields and acquisitions do not always influence indigenous

industry in the same way; in fact, they often have opposing effects. Not only do these different

types of outsiders have different effects, but also different classes of greenfields actually have

opposing effects. It was also found that the level of agglomeration of the region often moderates

these effects – but not always as expected. Furthermore, it is found that the factors that influence

entrepreneurial entry are not necessarily the same as those that influence indigenous incumbent

performance. The implications of this study in the context of evolutionary economic geography

are discussed in the conclusion. As Nelson (1995) states, evolutionary theory is still in its

infancy. Indeed, one direction, which is pursued here, is to develop a better understanding of the

mechanisms by which new variety is generated and selected, and the role that outsiders have in

this process.

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CHAPTER 1

INDUSTRY AND FIRM EVOLUTION

10

Introduction

Evolutionary thinking has a long history in economics and other related fields.8 Though

some strands of evolutionary economics go back to Malthus and even to Smith, the first formal

elements of an evolutionary program were articulated by Thorstein Veblen (1898) and later

elaborated by many of his students and followers. Many other prominent theorists since then

have also sought to understand industry dynamics from a somewhat evolutionary perspective –

including Marshall, Schumpeter, Hayek, and even Penrose. The common element in all these

accounts is the emphasis on economic process(es) rather than on equilibrium outcomes. Patterns

of economic change at the macro level are formed from various microprocesses, which are

manifested in the entry of new firms, as well as in the exit and growth of incumbents. The

following sections emphasize the importance of search and discovery (in the form of opportunity

recognition and exploitation) as a key part of the evolutionary process at different, but

interrelated levels of analysis. Outsiders, at the firm and industry levels, also have a role to play

in evolutionary processes – particularly in generating new variety, which creates new knowledge

and opportunities.

Search, Discovery and Evolution

Industry evolution is often characterized by long periods of continuity and relatively short

periods of (radical) structural change, during which time equilibria are ‘punctuated’ (Tushman &

Anderson, 1986; Gersick, 1991). The logic of punctuated equilibria is in accord with

Schumpeter’s “process of creative destruction”, which is driven by both technological and

organizational innovation. To Schumpeter (1950: 83), “The fundamental impulse that sets and

keeps the capitalist engine in motion comes from the new consumers’ goods, the new methods of 8 See Hodgson (1993), Metcalfe (1998) and Vromen (1995) for detailed accounts of this history.

11

production or transportation, the new markets, the new forms of industrial organization that

capitalist enterprise creates.” The early Schumpeter (1934[1909]) argued that the main source of

innovation in an industrial system was in entrepreneurial entry. Later, Schumpeter (1950[1942])

argued that new innovation was driven by the R&D conducted in large firms. Both create the

conditions for “creative destruction” because they introduce new innovations, which, under

certain conditions, challenge the dominance of (other) incumbents and upset the order of things.

This discrepancy of where the source of innovation resides is perhaps an artifact of the dominant

mode of innovation that Schumpeter observed at different points of time. Both are obviously

important sources of innovation, but probably at different stages in the industry life cycle.

This duality is recognized in later evolutionary theorizing. The model of industry

evolution developed by Nelson and Winter (1982), which is the foundation for one stream of

contemporary theory, assumes bounded rationality and routine behavior among heterogeneous

economic agents.9 Because individuals make decisions under conditions of uncertainty and are

boundedly rational, they ‘satisfice’ rather than optimize in their search for and selection of

solutions to problems (Simon, 1957). Industry evolves through the generation of new variety

(from incumbent search and new entry), which is the mechanism for change in the industrial

system, and through the selective retention of routines and capabilities, which is the mechanism

for continuity. Their model emphasizes how (technological) learning and market selection

among heterogeneous firms affect economic change and industrial dynamics. Industry structure

is endogenous to the process of innovative and imitative search by incumbents, as well as a

natural selection mechanism that determines the expansion/contraction of firms and their

9 The other main school – institutionalism – rejects mathematical formalism in favor of a more inductive case study approach (see Hodgson, 1993).

12

eventual exit.10 The search process, which is primarily driven by competition and operates

through investments in R&D and other capabilities, improves products or processes and tends to

lower costs, and those that have a cost advantage are more likely to survive and grow. New

entry, which is also a function of search by potential entrepreneurs and incumbents in other,

often related industries, also have an important influence on industry evolution by importing new

ideas and innovations, which increase variation and transform the knowledge base of the industry

(Winter, 1984).11 New entrants also tend to increase competition, thereby leading to incumbents’

search for new knowledge in order to remain competitive.

Search processes are key to discovery and innovation, in general.12 “Discovery goes

beyond search among existing options to include the creation of new options, and as Schumpeter

proposed, this creation is often destructive of existing resources and competencies.”

(Nooteboom, 2000: 17).13 Knight (1921) argued that discovery required real and radical

uncertainty. In other words, it goes beyond risk, which is associated with a known, closed set of

possible alternatives with a probability distribution attached to them. Although it is impossible to

know the future direction of innovation with any certainty, reasonable inferences about the

approach to take can often be drawn from experience, which suggests that variety generated

10 Industry structure has been characterized by a number of things including the number of firms, market concentration, size and age distributions of firms, etc. 11 Winter (1984) distinguished between two technological regimes: one that is more entrepreneurial, and the other more routinized, which correspond to the early Schumpeter (1934[1909]) and the later Schumpeter (1950[1942]) respectively. A technological regime is defined by the level of technological opportunity, appropriability conditions, cumulativeness of learning, and the nature of the underlying knowledge base (Dosi & Orsenigo, 1988; Malerba & Orsenigo, 1996). Malerba and Orsenigo (1996) argued that a widening pattern in technological innovation, associated with the former, is characterized by low concentration of innovative activities, small size of innovators, low stability in the ranking of innovators, and high rates of entry. A deepening pattern, associated with the latter, has the opposite tendencies. For routinized regimes in which learning is cumulative, barriers to entry are high since a high minimum level of technological competence is required. Potential entry and innovation is conditioned on the comprehensiveness of the technological regime and may determine whether entrepreneurs or established firms dominate in an industry (Winter, 1984). 12 The process of search and discovery is often described as one of abduction, or inference to the best explanation. Hodgson (1993) argues that abduction is the only true means to discover new knowledge. 13 This is closely tied to March’s (1991) distinction between exploitation of current competencies and the exploration for new ones, which is regarded as a firm level phenomenon.

13

through search is not entirely blind. Blind, random generation of variety involves much

duplication and failure, and is rather inefficient. But, as Nooteboom (2000) notes, “Only when

uncertainty is so radical as to preclude such inference and informed speculation, random

generation of variation and selection and reinforcement due to success is the only option left.”

These processes of search and discovery are examined in more detail at the individual

(entrepreneur), firm and industry levels.

Entrepreneurial Entry and Evolution

Opportunity Recognition and Exploitation

A great deal of recent attention has focused on how characteristics and traits are

transmitted from one generation of firms (parents) to the next (progeny) (e.g. Klepper, 2001).

This is doubtlessly a key issue in understanding continuity once a firm has entered, but a more

fundamental question is why does entry occur in the first place? Many of the early theories of

entrepreneurship were explicitly dynamic in the sense that they assumed that markets are in

disequilibrium, a condition that creates opportunities for individuals to exploit. In fact, the

Austrian School is premised on “the conviction that standard neoclassical microeconomics, for

which the Walrasian general equilibrium model…is the analytical core, fails to offer a satisfying

theoretical framework for understanding what happens in market economies” (Kirzner, 1997).

This underlying assumption features prominently in economic theories of entrepreneurship, but

the relative emphasis on disequilibria varies.

Schumpeter (1934) and Kirzner (1997) both developed frameworks based on a set of

assumptions about what causes disequilibria. Venkataraman (1997) describes these as two

different sorts of opportunities as the weak, Kirznerian opportunity, and the strong,

14

Schumpeterian opportunity. Kirznerian opportunities are based on differential access to

information, while Schumpeterian opportunities require new information. Since Kirznerian

opportunities tend to move the system towards equilibrium whereas Schumpeterian opportunities

disrupt the system, the nature of the opportunities created differs. Specifically, Schumpeterian

opportunities are riskier than Kirznerian since they deviate from existing knowledge. This

suggests that Schumpeterian opportunities are rarer and more valuable than Kirznerian

opportunities (Shane, 2003). These differences also imply that the process and requirements for

the discovery and exploitation of opportunities will differ as well.

More recently, an amended view, referred to as the “individual-opportunity nexus”,

argues that entrepreneurship involves a combination of both the presence of business

opportunities and the presence of entrepreneurial individuals acting upon them (Venkataraman,

1997). This requires a different definition for entrepreneurship compared to the “traditional”

entrepreneurial theories. The theory does not define entrepreneurship as self-employment or the

creation of new organizations, but as “the discovery, evaluation and exploitation of future goods

and services, through the introduction of new means, ends, or new means-ends relationships”

(Eckhardt & Shane, 2003: 336).14

Shane (2003) identified three major sources of entrepreneurial opportunities:

technological changes, political/regulatory changes and social/demographic changes. Each of

these changes creates different types of opportunities, and therefore the process of recognizing

and exploiting them also differs. Furthermore, entrepreneurial opportunities can be differentiated 14 Moreover, the creation of new means-ends frameworks is what differentiates entrepreneurial opportunities and profitable situations where optimizing occurs within already existing means-ends frameworks (Eckhardt & Shane, 2003). This implies that entrepreneurship focuses on the sources of opportunities and the process of recognizing and exploiting them (Shane & Venkataraman, 2000). Furthermore, it does not require new firm creation as a condition, since these activities can also be executed within existing firms or the opportunities can be sold to another organization (Shane & Venkataraman, 2000). Therefore, the individual-opportunity nexus clearly moves away from other theories of entrepreneurship.

15

by the forms they take, which include new products or services, new ways of organizing, new

raw materials, new markets and new production processes. Identifying these differences can also

increase our understanding of the entrepreneurial process (Eckhardt & Shane, 2003; Shane,

2003).

Given the existence of opportunities, the rather important questions remain: why do some

people discover specific entrepreneurial opportunities and others do not, and why, even after

having identified these opportunities, do some people act upon them and others do not? As

previously noted, many researchers have tackled these issues, but with ambiguous results.

However, Shane (2000) argues that prior knowledge plays a central role in the opportunity

recognition process, and consequently in the discovery of entrepreneurial opportunities. This

approach, which draws on research in social psychology to build a theoretical framework, aims

at capturing people’s willingness and ability to act. The framework he introduces draws on

Hayek’s (1945) conjecture that opportunity discovery is a function of the distribution of

information in society. It further assumes that given a specific change that creates new

opportunities, different individuals will discover different opportunities because they possess

dissimilar prior knowledge.

Following von Hippel (1988), Shane (2000) divides prior knowledge into three major

dimensions: prior knowledge of markets, prior knowledge of ways to serve the markets, and

prior knowledge of customer problems. Shane argues that these kinds of knowledge vary across

individuals since it is generated through their idiosyncratic life experiences, reflected in, for

example, education and work experience. At any given point in time, it will always be the case

that some individuals have knowledge about specific technological processes, market

characteristics, or about product features that others do not have (Venkataraman, 1997). This

16

idiosyncratic prior knowledge generates a so-called “knowledge-corridor” that allows the

individual to recognize specific opportunities, yet fail to recognize others (Venkataraman, 1997).

An implication of such a knowledge-corridor is that when information about some technological

change is globally distributed, only part of the population will be able to recognize a given

opportunity due to their own specific knowledge-corridor (Shane, 2000).15

Venkataraman (1997) argues that the reason, after having identified entrepreneurial

opportunities, some individuals act upon them and others do not is due to the combination of the

opportunity and the individual characteristics; in essence, the characteristics of an opportunity

influence the willingness of individuals to exploit them (Shane & Venkataraman, 2000). In

general, (potential) entrepreneurs are more willing to exploit a particular opportunity when the

expected benefits exceed the opportunity costs, taking account of liquidity (capital) constraints,

and a premium for risk bearing (Kirzner, 1973; Knight, 1921; Schumpeter, 1934). Prior research

has shown, for example, that opportunities are more likely to be exploited when expected

demand is high (Schmookler, 1966; Schumpeter, 1934), when industry profit margins are high

(Dunne, Roberts & Samuelson, 1988), when cost of capital is low (Shane, 1996), or when

learning from other entrants is possible (Aldrich & Wiedenmeyer, 1993).16 However, errors may

15 Shane’s (2000) evidence about prior knowledge is based on an empirical research obtained through in-depth case studies of entrepreneurs who exploit a MIT invention. His research showed that different opportunities were recognized by individual entrepreneurs within a range of different opportunities caused by the change. He also showed that the entrepreneurs were able to find opportunities even though they are not searching for them; and finally, he found evidence for the hypothesis that entrepreneurs find opportunities that relate to their prior knowledge. 16 Social and psychological factors also obviously influence the choice of whether or not to exploit an opportunity. These characteristics are typically divided into motivation, core-evaluation and cognition, and include, among other things, the willingness to bear risks, need for achievement, self-efficacy and tolerance for ambiguity (Shane & Venkataraman, 2000). Moreover, people who exploit opportunities typically perceive their chances of success to be much higher than they actually are and higher than those of others in their business (Cooper, Woo & Dunkelberg, 1988). Moreover, social ties also play a role in the process of deciding whether or not to exploit an opportunity. People who have stronger social ties to resource providers, which facilitate access to financial resources, are more likely to exploit an opportunity (Aldrich & Zimmer, 1986). Shane (2003) also examines age, education and personal motivation. All of these are linked to the literature on market structural barriers to entry (Caves & Porter, 1977), but tend to come about it from the other (individual) side.

17

be made in calculating expected profits, especially under conditions of uncertainty.

Occupational Choice

This still raises the question of what kind of prior knowledge (and experience) is most

relevant, and relatedly, where do specific opportunities come from? Research has shown that

individuals who have learned entrepreneurial behavior in previous employment are more likely

to exploit opportunities (Cooper, et al., 1988). This is because they believe they have a higher

probability of succeeding with this knowledge, and are therefore more confident to act upon their

findings. This is supported by Gompers, Lerner and Scharfstein (2005), who test two competing

views of how entrepreneurs are generated (in venture backed startups): first, that entrepreneurs

tend to come from young firms because employees learn critical skills in the early startup stage;

alternatively, that individuals become entrepreneurs when large bureaucratic firms fail to fund

their ideas. They found that entrepreneurial learning and networks developed in young startups

tended to be more important factors in entrepreneurial entry. Still, both should have a role to play

depending on the nature of the technology, and the stage of the industry life cycle. Also,

although the first might be regarded as being motivated by an active form of learning compared

to the second (in the sense that it is more intentional and less reactionary to organizational

conditions), both require the recognition of an opportunity, and this often entails the

appropriation of new knowledge that is not being exploited within an incumbent firm (Acs, et al.,

2005).17

17 Incumbents may not take advantage of opportunities because of blind spots and/or because they are locked-in to a particular path (thus creating externalities in the form of opportunities for potential entrepreneurs). These unexploited opportunities are likely to be exploited by employees since those closest to the process are best able to recognize the opportunity. This is what Acs, et al. (2005) refer to as the “spillover theory of entrepreneurship”. Note that in the case of science-based industries, this might also hold for public research organizations (PROs) (Audretsch & Lehmann, 2005). The effect may be more pronounced given the relatively fewer restrictions on commercializing innovations from PROs.

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Opportunity recognition and exploitation are closely connected to the issue of

occupational choice, of which entrepreneurial entry is but one option. A variety of factors alter

the threshold for entry.18 Many occupational choice models simply compare potential future

wages (Jovanovic, 1994), which fails to capture a range of other non-monetary incentives (and

preferences) that impact on the decision to enter.19 For instance, a key incentive for entry

includes a higher level of autonomy and control in self-employment. Moreover, at any given skill

level, there may be more options available in wage employment than is generally assumed. In

science-based industries, for instance, aside from starting up a new firm based on their own or

others’ inventions, research scientists may have the option to work in public research

organizations (PROs), or in private industry. Even within the public-private dichotomy, there are

a wide range of options. Also, skills are not always manifested in a single individual, thus a

founding team with the appropriate skill mix should lower the threshold for entry.

At the individual level, the literature suggests that getting to the point of startup is a

process that requires certain skills, knowledge and other resources. Aside from the requisite

education, the process of developing the skills, knowledge and other resources required to startup

a firm are learned in wage work (Cooper, et al., 1988; Gompers, et al., 2005; Jovanovic, 1994).

Thus, aside from other factors, experience may moderate risk tolerance. Even research that has

found that liquidity or capital constraints are binding on new entrepreneurs (Evans & Jovanovic,

1989), has also found that capital is highly correlated with other key variables, such as age, and

years of work experience, and this last factor in particular has a significantly positive effect on

18 Aside from the market structural factors already mentioned, these include: economies of scale, and technical barriers (including the intellectual property regime). The assumption is that these barriers increase over the industry life cycle. 19 Jovanovic (1994) predicted that the best potential entrepreneurs would end up in wage work when wages and perquisites were better. Note that the assumption of heterogeneity in workers’ and managers’ skills means that there are multiple overlapping alternative opportunity sets.

19

the likelihood of entry. 20 This correlation would be consistent with the notion that capital is

accumulated along the way to developing more experience. In essence, it is difficult to

distinguish between the two.21 Experience, skill development and knowledge accumulation are

processes that co-evolve with opportunity recognition and exploitation.22 But especially

important in the context of this study is the notion that opportunity recognition and exploitation

are also endogenous to the structuring of the industrial environment.

The Evolution of the Firm

While entrepreneurial entry is regarded as an important driver of industry evolution,

evolution also occurs at the firm level – upon entry into the industry. Life cycle models of firm

evolution suggest that there are stages in the growth of the firm, from emergence, to growth and

maturity (and eventual decline). There is, however, nothing inevitable about this. A firm may

languish in a low growth state or it may take off. Assuming that growth is even an objective,

whether and to what extent the firm grows depends on a number of factors, such as industry

context, demand, and firm specific capabilities. Penrose (1959: 7) noted long ago that there are a

variety of reasons why firms do not grow, including “inefficient management, ineffective capital

20 Evans and Jovanovic (1989) also noted that liquidity constraints are a central issue in the dispute between Knight and Schumpeter over the nature of entrepreneurship. Frank Knight (1921) argued that risk bearing is one of the essential characteristics of entrepreneurship since capital markets provide too little capital to entrepreneurs due to problems of moral hazard and adverse selection. Consequently, entrepreneurs must finance themselves and bear the risk of failure. Schumpeter (1934; 1950), on the other hand, argued that the functions of the entrepreneur and the capitalist are quite separate. Their findings were in favor of Knight – that liquidity constraints bind. 21 Nelson and Winter (1982) suggested that skills are the individual level equivalent of routines. Even though there are increasing barriers to entry, there is also a rising skill level in the industry, which may facilitate entry. The rising skill level may in part be due to a diffusion of knowledge through various institutions. The net effect of the two should determine the entry rate. For example, if barriers to entry rise more quickly than facilitators to entry, then the rate of entry declines. 22 This suggests two things: (1) the propensity to startup a new firm changes over the industry life cycle; and (2) individuals may develop greater absorptive capacity through skill development. Though absorptive capacity is usually considered a firm level construct, it is also applicable at this level. Greater absorptive capacity should translate into a greater ability to recognize opportunities.

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raising, lack of adaptability to changing circumstances, poor judgment leading to frequent and

costly mistakes, or simply bad luck due to circumstances beyond their control.”

Often upon entry, the entrepreneur has to transform her/himself into a manager in order

to facilitate growth. This is because growth requires some degree of bureaucratization and

structural differentiation, which leads to increasing complexity and sometimes requires that

professional managers replace the founders since the roles of the two are very different (Boeker

& Wiltbank, 2005; Eisenhardt & Schoonhoven, 1990). Research has found that new ventures are

less successful at growing past the founding stage if they leave the founding team unchanged

(Hambrick & Crozier, 1985). A number of factors aid in the transition from emergent startup to

growth stage company, which relate to the changing needs of the organization, to the ability and

desire of the founder to adapt to those changing needs, as well as to the ability of the founders to

prevent their own succession (Boeker & Wiltbank, 2005). A key role of strategy then is to

develop heuristics that address the challenges firms face at different stages in their development

(Aldrich, 1999).

Aside from the initial conditions at startup, such as founding team experience, external

conditions, such as the market stage, also have an important influence (Eisenhardt &

Schoonhoven, 1990). Opportunities and threats change over the firm and industry life cycles. In

developing a theory of firms that do grow, Penrose (1959) cautioned that “one can state the

necessary and sufficient conditions for successful growth, but how can one determine whether a

given firm meets these conditions?” A priori, this is indeterminate. However, a variety of internal

and external factors, which are primarily captured by capabilities (internal) and markets

(external), constrain the direction and rate of change of the firm. Penrose also indicated that

“Where there are opportunities for profitable investment, there are opportunities for the growth

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of the firm.”23 Although the search for such opportunities is generally intentional, trial-and-error

learning may lead to accidental discovery. Just as for potential entrepreneurs, intentional search

processes are based on the prior knowledge of the firm embedded in its accumulated and path

dependent routines and capabilities. It is in this sense that the firm acts as a focusing device for

opportunity recognition (Nooteboom, 2000). Whether the firm exploits those opportunities it

recognizes is at least partly dependent on its prior commitments (including sunk costs).

Nelson and Winter (1982: 19) argue that “Search and selection are simultaneous,

interacting aspects of the evolutionary process”. Search expands the opportunity set of firms, and

market signals provide selection feedback and direction of future search. In general, the state

space at a given point in time provides the platform for the next period which is given by a

probability distribution of possible outcomes – expansion, contraction, or status quo. Growth

(expansion) itself may also entail diversification (related or unrelated), which may take a variety

of forms – the development of a new plant, or the acquisition of a pre-existing one, for instance.

Such ‘lumpy’ patterns of expansion also often belie the simplistic pattern of growth represented

in life cycle models (Penrose, 1959). Expansion/contraction (and exit) decisions are based on

their investment rules after observing market (profitability) signals (Chang, 1996; Nelson &

Winter, 1982), but subject to both internal and external constraints.24

Penrose also proposed that internal development has its own limits: “the growth of the

firm is limited by the growth of knowledge within it.” (Penrose, 1959: 27). Initially, the

23 Penrose (1959; 1952) was clearly against life cycle and ‘biological’ models of firm growth – life cycle models because they were too deterministic (and even tautological), and ‘biological’ models because the metaphor was overextended. However, her own theory of the growth of the firm is at times explicitly evolutionary because of the role she ascribes to disequilibrating forces in generating opportunities. Penrose was also a seminal influence in the development of Winter’s evolutionary theory (2007: personal communication). 24 Firms learn actively and passively, which in part depends on the industry context. Pakes and Ericson (1998), for instance, found that manufacturing was better explained by actively learning, whereas passive learning better characterized the retail sector. Exploration in this sense is purposive and therefore a more active form of learning, which is in essence a dynamic capability. Such intended actions do not preclude the possibility of ‘accidental’ discovery, but such random events do not constitute capabilities.

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emphasis in a new startup is likely to be on exploiting the resources and capabilities with which

it entered; later, the focus may shift to exploration. Thus, the search for new knowledge and

opportunities, which is usually driven by external stimuli (competition, shifts in technology or

markets, etc.), obviously does not stop upon entry. New knowledge can be generated not just

through R&D, but also other dynamic capabilities, which relax constraints and increase the

opportunity set of the firm. Dynamic capabilities, which refer to a “firm’s ability to integrate,

build, and reconfigure internal and external competencies to address rapidly changing

environments” (Teece, et al., 1997: 516), are “a learned and stable pattern of collective activity

through which the organization systematically generates and modifies its operating routines in

pursuit of improved effectiveness” (Zollo & Winter, 2002: 340).

The “knowledge evolution cycle”, which this characterizes, is a slight extension of the

standard variation-selection-retention model (Zollo & Winter, 2002). Variation is generated

through the search for solutions to address old problems in novel ways or to deal with relatively

new challenges. Often this occurs through scanning the environment for new ideas in the face of

current challenges. Subsequently, internal selection processes, based on prior experience, power

structures and legitimization processes, determine the efficacy of new ideas to enhance the

effectiveness of pre-existing firm routines and capabilities, or the opportunity to form new ones

(Nonaka, 1994). This is followed by a replication phase in which ideas are adapted to specific

contexts within and across the organization. Finally, retention is the enactment of routinization.

This is not necessarily a virtuous cycle – ideas may be misapplied, which diminish effectiveness.

The nature of knowledge changes over the cycle. Knowledge is at first tacit during the

phase of generative variation and then gradually becomes more explicit as it is selected, but then

becomes tacit again as it is applied to novel contexts within the firm. This cycle is also linked to

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March’s (1991) distinction between exploration and exploitation in that the phase of generative

variation is associated with exploration, and the replication phase with exploitation. The

exploitation phase can therefore prime exploration (Nooteboom, 2000; Zollo & Winter, 2002).25

Thus, rather than simply characterizing the relationship between the two as a tradeoff, it may

more properly be thought of as recursive and co-evolutionary (Zollo & Winter, 2002).

Exploration, which is often proxied by investments in R&D, has another important

benefit – it can also aid in the development of absorptive capacity, i.e. the ability to assimilate

new knowledge from outside sources (Cohen & Levinthal, 1989), which can facilitate the

recognition of opportunities.26 Investments in such capabilities are, however, a double-edged

sword since the knowledge generated from such activities can spillover to others, including to

potential entrepreneurial entrants. Alternatively, the firm can search for new knowledge

externally through direct linkages with other organizations, or it can acquire knowledge and

resources (including another firm) on the market. Just as for the potential entrepreneur,

opportunity recognition and exploitation for incumbents are generally seen to be endogenous to

their capabilities (and prior knowledge) and guide strategic choices (Penrose, 1959).

Of course, firms do not just grow, but they also fail. This is another form of selection –

of the whole firm (or unit) – at the market level. At any stage in a firm’s evolution, there are

substantial risks that firms must overcome. Stinchcombe’s (1965) “liability of newness”

describes four risks associated with new entrants: (1) the difficulties that new organizations

experience in reproducing roles, settling on operating procedures, creating a culture and learning

the skills; (2) the high costs (or inefficiency) of inventing roles and structuring relations; (3)

25 Nooteboom (2000) notes that the process of discovery, which is governed by a logic of abduction, is therefore a heuristic to move the firm from current to novel competencies, while surviving in the process. 26 A firm’s absorptive capacity may also be decisive in how open and flexible it is in the face of technological change.

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problems inherent in establishing relations with strangers (especially employees); (4) the

uncertainty associated with establishing ties to those who use the organization’s services. It is

precisely because of a lack of routine(s) that the young firm is at risk. However, as the firm

evolves, interlocking routines, bounded rationality, and historical commitments ultimately make

firm adaptation difficult and selection decisive (Nelson & Winter, 1982). Over time, firms

continue to be at risk of failure because they are likely to become misaligned with their

environment (liability of obsolescence), due to strong inertial pressures – i.e. because the firm

becomes locked-in to a particular path, making change difficult.27

All of this suggests that firms can adapt to external circumstances, and it is in this sense

that evolution can be “guided” by means of deliberate and strategic actions (Lovas & Ghoshal,

2000; Penrose, 1959). However, strategic actions occur within certain boundaries since search

parameters are guided by the evolutionary path of the firm, which is itself embedded in the

industry context. According to this view, managers can do little to improve the long-term

viability of their firms beyond making prudent investments in organizational capabilities and

R&D.28 Thus, the emphasis in many evolutionary models is on selection – particularly in larger

and older firms – which emphasizes that the environment is a hard constraint against which it is

difficult or impossible to change, rather than adaptation as the main determinant of firm

performance.29 However, both mechanisms for firm and industry change can work together

27 These liabilities are usually more closely associated with organizational ecology, but they are valid evolutionary explanations in general (Nickerson & Silverman, 2003). 28 By contrast, the positioning perspective emphasizes the adaptability of organizations. Though positioning scholars acknowledge that change can be slow and difficult, this perspective implicitly assumes that organizations are fairly flexible and open to redesign (Porter, 1980; 1985). 29 One extreme is often characterized by early versions of population ecology, which holds that industry-level change arises solely as a result of selection: individual organizations are largely inert so industries evolve only through organizational births and deaths (Hannan & Freeman, 1977), and organizational change is so hazardous that it is selected against (Hannan & Freeman, 1984). At the other extreme, many management scholars put great faith in adaptation: organizations can change and renew themselves, giving rise to industry change without entry and exit (Kanter, 1983; Tushman & O’Reilly, 1997).

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(Levinthal, 1997).30

Industry Evolution

Evolutionary thinking has a close affinity to the literature on industry life cycles since the

evolutionary processes underlying entry, exit and growth, aggregate to explain patterns of the

growth and decay of industries (Nelson & Winter, 2002). Early in the development of a new

industry, a new technology is introduced but there is considerable uncertainty about its efficacy,

its uses, and therefore even the markets. High numbers of new entrants enter to take advantage of

perceived opportunities, some of which are successful, some of which are not. Variety tends to

be at a maximum early in an industry’s history when issues of technology and design remain

unresolved. Over time, a dominant design emerges (Abernathy & Utterback, 1978), and an

industry path (or paths) is established. Because the knowledge inherent in these technologies is

cumulative, the industry becomes locked-in or path dependent. Firms not on these paths either

must switch, or fail. The move toward a dominant design therefore implies the destruction of

variety, and as variety diminishes, other forces come to dominate. Once a dominant design is in

place, the basis of competition shifts from product to process innovation. Firms that develop

lower cost processes within a technological trajectory can outcompete their rivals. Entry rates

slow during this period, in part because of rising barriers to entry, but exit tends to remain high.

If economies of scale are an important feature of the industry, which is generally assumed within

a technological trajectory, exits continue and industry concentration increases.

Nooteboom (2000) argues that this standard story is incomplete since the innovation

cycle is not really a cycle in that it moves from a period of ferment in novel combinations to

30 A dynamic view also suggests that the environment is not independent of the firm (Penrose, 1959: 42); in fact, their interaction might more properly be seen as a co-evolutionary process.

26

dominant design then stalls at the next innovation. Thus, there is a paradox in that dominant

designs precede innovation as much as follow it. An effective heuristic (at the firm level)

produces an alternating cycle of variety in content and variety in context. Once a dominant

design is established and the emphasis shifts to efficient production (exploitation), variety in

content closes down, but this provides a platform for the application of technologies to novel

contexts. This opening up of variety of context constitutes a phase of generalization, which then

provides the basis for new exploration by accumulating experience as inputs for the next novelty.

(It is in this sense that exploitation can prime the pump of exploration.) But in these new

contexts, there is a need to adapt the system to local (firm) conditions, which results in

differentiation and closes down the variety of context. This increases the variety of content, and

the cycle can begin again. Parallel practices are then tested against this and knowledge from

outside the firm can be adopted.

This more evolutionary approach is not deterministic; the cycle can stall at different

phases due, for instance, to organizational inertia following consolidation because of a lack of

incentives or opportunities for adaptation. It is also possible that there is such instability at the

stage of novel combinations, that the system gets stuck in an unresolved chaos of trials, errors,

and ongoing misfits that leads to a halt in industry development. The existence of a virtuous

cycle is therefore contingent on a variety of factors.31 Windows of opportunity also shift

advantage from incumbents to new entrants (and back again) over the cycle. Thus, new entrants

may enter later in the cycle with a new technology that challenges the dominance of

31 Discontinuities are both the cause and consequence of individual and firm level opportunities. They are the driving force of industry dynamics and require motive, opportunity and means (Nooteboom, 2000). Regarding motive, a crisis of misfits is necessary before one is willing to surrender the investments of past practices. Opportunity, on the other hand, arises from new variation of demand and other conditions (institutions, access to markets, etc.). Means arise from new varieties of resources (experience, knowledge, and other resources), which yield elements for novel combinations.

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incumbents.32

The search and discovery of opportunities is clearly an important issue at different (inter-

related) levels – individual, organizational, and industrial, and similar contingencies apply at all

levels, with respect to the path taken. At each level, a cycle may or may not be realized; on the

other hand, it may be possible to undergo continuous renewal. For instance, the logic of

abduction is a general heuristic to move from current to novel competencies, while surviving in

the process (Nooteboom, 2000). A general heuristic of search and discovery would therefore

claim to resolve some fundamental conflicts in the theory.33

The Role of Outsiders in Evolutionary Economics

At the industry level, outsiders tend to be either new entrepreneurial entrants, or firms

that are diversifying from other usually related industries. Recent work has examined the own

effects of another important class of outsider entrants – diversifiers from related industries (e.g.

Klepper, 2002a). These outsiders often challenge the prevailing structure, and sometimes the

technological trajectory of the industry. In both cases, entry is fully endogenous to the firms’

own capabilities.34 At the firm level, outsiders are entities outside of the firm. Thus, outsiders are

not only other organizations, but they can also be represented by new employees, in which case

employee turnover can act as a mechanism for change in firm routines, capabilities, and

32 New technologies can be “competence enhancing” or “competence destroying” for incumbents (Tushman & Anderson, 1990), depending on whether such technologies complement their pre-existing capabilities. The former tend to come from within the industry, the latter from outsiders – either new entrepreneurial entrants or diversifying entrants. 33 Similarly, the Kirznerian (incremental) and the Schumpeterian (radical) views of entrepreneurship are not necessarily at odds with one another, and may, in fact, be complementary if they operate at different times over the industry life cycle. 34 In reality, neither is necessarily an outsider – new entrepreneurs are likely to have experience working in an incumbent firm, and diversifying entrants may be highly related and therefore overlap to a significant extent.

28

knowledge.35

Outsider influence is therefore important in evolutionary economics for its transformative

role on industry structure, especially through its introduction of new variety in the form of new

routines and capabilities. The theory suggests that the effective absorption of new knowledge

from those outside the firm is a function of both novelty and comprehensibility (Cohen &

Levinthal, 1989; Nooteboom, 2000). Both cognitive distance and proximity are required for new

ideas to be effective in a new context.36 However, since the literature in evolutionary economics

has tended to treat a given industry as cohesive, geography has mattered little. Firms’

(geographic) place or position in relation to others is therefore not a defining characteristic of an

outsider. Evolutionary economics does treat mergers and acquisitions as a mechanism for

recombination at the firm level. Acquirers and targets have differing capabilities, which, when

combined with one another, generates new variety. This has implications for the newly combined

firm as well as the industry. However, geographic diversification in the form of greenfield entry

(into other places) is really not dealt with in the core of evolutionary theory, though it could be

regarded as a special case of diversification in general. In both cases, the own effects are

emphasized.

In geographic space, the concept of the outsider has a slightly broader sense since it often

assumes the importance of membership in a particular place.37 A key issue then is how to group

firms by geography (and technology) so that a meaningful distinction between insiders and

outsiders can be made. Furthermore, specific groupings (by geography or technology) have

35 Insider-outsider interactions can be destabilizing in the sense that too many outsiders, however defined here, may upset the order of things (at the firm or industry level). For instance, too much turnover within a firm may lead to the loss of capabilities. This partly depends on how tacit, or conversely, codified, the knowledge base is. 36 Search (and discovery), which involves scanning the environment (including other firms), tends to be “local” in cognitive distance. The tradeoff between cognitive distance and proximity suggests an optimal cognitive distance (Nooteboom, 2000). 37 The internal/external distinction is also somewhat more meaningful in geographic space.

29

implications for variety generation and selective retention. Outsiders, however defined,

undoubtedly have an effect on opportunity recognition and exploitation for both potential

entrepreneurs and incumbents, and therefore influence the evolution of industry.38 A substantial

body of research has explored the specific effects of outsider (i.e. external, usually foreign)

entrants on a host country (primarily in international business), on the one hand, and the effects

of acquisitions on the target (primarily in strategy), on the other. The next chapter reviews the

empirical research in more detail, some of which is evolutionary. Research in economic

geography, which is reviewed in Chapter 3, makes the distinction between insiders and outsiders

more explicit, at least in theory. These different though related streams of research suggest that

firms from enter a place from another place can generate both competitive and spillover effects.

38 Entry into other places by incumbents (i.e. geographic diversification) in the search, discovery, and exploitation of opportunities also plays an important role in the sense that it is search combined with entry (into new places) (and is therefore also search combined with selection).

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CHAPTER 2

THE CAUSES AND CONSEQUENCES OF

EXTERNAL ENTRY: THE EVIDENCE

31

Introduction

The evolutionary economics literature provides an overall framework for understanding

firm and industry dynamics. However, because it has not fully dealt with the external dimension,

we need to examine theoretical strands in other related fields to develop a better understanding of

the causal link between external entrants and the indigenous industry. For instance, research in

international business on multinational corporation (MNC) search and site selection (location

choice) investigates the motivations for and modes of entry, and also deals with the effects that

external firms (foreign in this case) have on the host country. Because this research generally

fails to distinguish between the effects that greenfield and acquisition entry have, the literature

on acquisitions in strategic management is also briefly reviewed. Another related literature on

knowledge flows between parent, subsidiary and host is also discussed.

MNC Search and Site Selection

Location Choice

Much of the research on the location choice has been in the context of the multinational

corporation. According to this literature, the search for new markets, resources, or knowledge

drives the entry of a multinational corporation (MNC) into other countries/regions (Dunning,

2000). Market-seeking is the most straightforward motive: rather than exporting, a foreign firm

sets up a new plant to supply and possibly to expand their local market, and possibly to minimize

transportation costs, bypass host country regulations, etc.39 Entry can also be motivated by

resource-seeking. If it is seeking basic resources that are homogeneous and non-distinctive, the

MNC may enter to be close to key inputs such as unskilled labor or materials. The MNC may be 39 Cost minimizing strategies can also be related to efficiency seeking.

32

seeking strategic resources, which are more location specific and embedded in firms and

institutions in the host region. These include local industrial networks (supporting industries and

institutions), skilled and professional workers and internal markets. Firms seeking new

knowledge, such as R&D capabilities, manufacturing technologies, marketing know-how,

managerial expertise, etc. (Chen, et al., 2004), enter to complement and enhance their already

existing capabilities.

Dunning (1993) argues that the extent of a firm’s international economic involvement,

which forms along a continuum from arm’s length transactions such as exporting, to contract

transactions such as licensing, to internal transactions within the same organization (a type of

FDI within the hierarchy), depends on the presence, or lack thereof, of three types of advantages:

Ownership specific advantages, location-specific advantages, and internalization advantages.

This is referred to as the OLI or eclectic theory of the MNC. Ownership-specific advantages are

the endogenous competitive advantages that a firm possesses relative to other firms. These

advantages manifest themselves as mobile, intangible assets that are exclusive to their owners.

Some examples include: human capital, product differentiation, brand image, product quality,

property rights such as patents, and technology. Intangibles most commonly associated with

MNCs are reputation and quality of branded products, the ability to tailor branded products to

local tastes, marketing expertise, production technology, customer service and know-how in

commodity trading.

Location specific advantages are generally considered exogenous, non-exclusive assets.

Location advantages associated with transacting in a particular environment include consumer

tastes, market structure, (number of firms, product differentiation, barriers to entry, etc), and

non-market (government) intervention such as those policies concerning tariff and non-tariff

33

barriers, and restrictions on FDI. Finally, internalization advantages are those generated from

administering international transactions within the same firm rather than using external markets

(Hymer, 1960). By internalizing activities within the firm and across countries, multinationals

are able to reduce transaction costs related to market imperfections. For example, by using

affiliates instead of exports to serve foreign markets, MNCs are able to avoid costs associated

with tariffs and exchange rates.

All three OLI advantages must be present for a firm to engage in FDI (Dunning, 1993).

Thus, a firm will invest in operations outside of its home market when it holds proprietary assets

that can be efficiently exploited internally within the firm. In other words, FDI is the matching of

internalizable firm-specific advantages with location specific advantages. For any given MNC,

ownership advantages can have more or less to do with transaction-cost minimizing than

intangible efficiency, and can also be more or less country specific than firm specific (Hennart,

1982).

Entry mode. The search criteria affect the choice of entry mode. There are basically two

ways to access higher level resources in another region: one is to acquire them; the other is to

interact, either directly or indirectly, with those who have them. Both the internalization and

transaction cost perspectives argue that entry mode is chosen to minimize the risks and costs

associated with accessing the market or internalizing the product or service. For instance, when

the tacitness of the firm’s own capabilities is relatively high, entry is likely to be by wholly

owned subsidiary (Kogut & Zander, 1993; Martin & Salomon, 2003). The choice between

greenfield or acquisition likely turns on the nature of the specific advantages that the firm has or

seeks.

34

Some work on location choice has suggested that firms that are knowledge seeking will

most likely enter by acquisition; however, when the gap between pre-entry capabilities and

market resource profile differs too much, firms are more likely to enter by greenfield to exploit

their own capabilities in another place (Hennart & Park, 1993). Entry may also be by greenfield

if the MNC seeks general resources that are embedded in inter-firm networks, or institutions. On

the other hand, firms that are seeking specific resources that reside in another firm will most

likely enter by acquisition.40 In general, MNCs leverage their knowledge base in entering into

other (geographic) markets and it is in this way that the scope of the (multinational) firm evolves

(Chang, 1996; Vermuelen & Barkema, 2001). Thus, entry mode choice and further investment

decisions are self-selected since they are endogenous to the firm’s own capabilities (Shaver,

1998). Also, when firms are knowledge seeking, they will sometimes enter other regions even if

from technologically advantaged regions in order to diversify their knowledge base (Cantwell &

Janne, 1999).41

Recent work has also reexamined the importance of location in the entry decision. A

reverse-internalization perspective has emerged to take account of the location advantages of

certain countries, due in part to differences in the technical bases of nations (Anand, et al., 2005).

Thus, the traditional view of a sectoral push, identified with traditional internalization theory, is

counter-balanced by the geographic pull of particular places (Anand & Kogut, 1997). Location

matters at a high level – due to differences in national policy, for instance (Nelson, 1993). Such

differences can also play an important role not just in the specific mode of entry, but also in the

40 There are very few cases of other modes of entry in the context of the Canadian biotechnology industry. However, the theory suggests that joint venture is preferred to acquisition when the costs of integration with a target are considered too great (Hennart & Reddy, 1997). Also, when in search of strategic resources embedded in the local environment, entry is more likely to be by joint venture. 41 More recently, there has been an emphasis on motive divorced from mode. Cantwell and Mudambi (2005), for example, distinguish between competence exploiting and competence creating subsidiaries.

35

subsidiary structure. For instance, Bonardi (2004) argues that when expanding abroad, firms in

regulated industries tend to follow a multi-domestic strategy, negotiating separately for each

market entry and arranging their operations as compartmentalized national organizations.

Although much of the work on the entry decision has been at the country level, some more recent

work has examined entry into particular regions, usually states (e.g., Chung & Alcacer, 2002;

Head, et al., 1995; Shaver & Flyer, 2000).

The Effects of Foreign Entry

But the question addressed here is: what happens once they enter? Research on this

question has focused on two areas: (1) what happens to the (multinational) firm; and, (2) what

happens to other firms in the region. Research on the first question has focused on the effects on

either the parent or the subsidiary, and sometimes the interaction between the two. Research on

the second question tends to focus on how externalities generated by multinationals by their

subsidiaries affect domestic firms. However, very little research has distinguished between the

effects of greenfields and that of acquisitions. Initially, they are likely to have very different

effects. However, over time the effects are likely to be contingent on the extent of integration

between parent and subsidiary, and subsidiary and local environment. In general, the subsidiary

becomes more integrated with the parent and/or the local environment. There is evidence that

upon entry into another region, firms also adapt to their local environments and begin to draw

more upon the local knowledge base over time (Birkinshaw & Hood, 2000; Frost, 2001), thus

becoming more embedded. Mode and motive for entry affect how a firm becomes embedded in a

region. This is probably especially so for greenfields since they often have no pre-existing ties in

the region. Increasingly, subsidiaries are seen to play a dual role of exploiting the existing

36

knowledge base of the MNC, and at the same time augmenting their knowledge through

innovation in the local context (Kuemmerle, 2002). There is, in effect, a sequential adaptation to

the local environment resulting in an on-going series of joint location-technology decisions

(Frost & Zhou, 2000). We will return to issues of integration after briefly discussing the

literature on externalities.

Externalities Generated by Foreign Firms

The consequences of foreign entry for the host country have been the subject of much

investigation in international business research. In general, foreign firms have been found to

influence the host country industry through both competition and spillovers (Caves, 1996). Even

if they enter another region to benefit from spillovers (i.e. are knowledge seeking), they also

often generate spillovers (see Blomstrom & Kokko, 1998, for a review). Much of the work done

in this area has focused on how foreign firms affect the productivity of host country firms (eg.

Chung, et al., 2003; Haskell, et al., 2002; Liu, et al., 2000).42 It has generally been shown that

foreign presence has a positive effect on domestic (labor) productivity due to various kinds of

positive externalities that result from their presence. These externalities are primarily based on

the assumption that foreign firms are more productive and have a lower cost structure than

domestic firms. The basic argument is that MNCs tend to be more efficient producers and

therefore force domestic firms to be more competitive. The literature on technological spillovers

suggests that MNCs have a higher level of technology and knowledge than domestic firms,

42 Also, the vast majority of these studies are of manufacturing industries which vary substantially in the degree to which spillovers matter (Blomstrom & Kokko, 1998). There are, however, important differences in the types of spillovers that are generated within hi-tech versus lo-tech sectors. (It seems that competitive effects tend to predominate in the latter.)

37

which have certain characteristics of public goods (Griliches, 1979). (Most of this work assumes

that foreign entrants are competing for domestic markets.)

Studies have shown that knowledge can be transferred (or spillover) between foreign and

domestic firms in a number of ways. One way is through demonstration effects – that is,

domestic producers observe and consequently adopt more efficient processes or new techniques

used by foreign firms that may improve their own productivity. The existence of demonstration

effects is based on the assumption of local search. This kind of diffusion of innovation occurs on

the assumption that product and process improvements are not only observable but also

replicable. The adoption of product or process innovations by domestic firms is therefore at least

partly a function of the codifiability of knowledge embodied in the innovations. These adoptions

may occur under conditions of incomplete information about the efficacy of the innovations in a

new context, and in fact may be adopted because MNCs are thought to be more efficient

producers.

Spillovers from foreign firms also include those generated through specialized training of

employees and subsequent labor mobility in the host country. This is especially the case when

greenfields import their own home country staff to run operations. Over time, the proportion of

local employees in managerial roles tends to increase. Models of occupational choice suggest

that as employees develop competence in progressively more senior roles in an industry, their

opportunity set increases, for a given set of alternative opportunities. Those who choose to take

advantage of other opportunities within the domestic industry take their knowledge and

capabilities with them. This can have an important effect in the longer run development of the

domestic industry especially in more technical industries in which the knowledge is highly tacit

38

and not easily transferred (Blomstrom & Kokko, 1998).43 However, managerial training is less

firm-specific than technical skills and more easily applied to other contexts.

Linkage effects can also result in spillovers to domestic firms. For instance, when foreign

firms enter a domestic market, they develop linkages with domestic suppliers and sub-

contractors, which lead to lower input prices, as well as the production of more complex goods.

Under the assumption that domestic firms tend to buy their inputs locally, and that inputs are

produced with increasing returns to scale, a final goods producer helps bring forth a greater

variety of specialized inputs, thus generating a positive externality to other final-good producers

(Rodriguez-Clare, 1996). A slight variation suggests that backward or forward linkages with

MNC affiliates may also force domestic suppliers to become more efficient if, for instance, lower

prices are negotiated on the input side. A good deal of literature has emphasized the effects of

backward linkages with suppliers and subcontractors. Aside from the benefit of lower costs,

foreign firms may force domestic suppliers to implement high quality standards, as well as

reliability and speed of product delivery. Domestic firms may then benefit from the same higher

standard of inputs.

Finally, “market access spillovers”, which refer to the capabilities that MNCs have in

exporting to other markets, may be important in some contexts. Foreign affiliates are connected

to an existing international network through their MNC. Domestic firms may be able to learn

how to access external markets and in particular how to manage the international marketing,

distribution and servicing of its products from these affiliates.

43 On the other hand, there is evidence of immobility of MNC employees in part because their opportunities are relatively greater than for strictly domestic firms. This would, of course, depend on the breadth and depth of the domestic industry.

39

The effect of spillovers depends fundamentally on the absorptive capacity of firms in the

host country.44 Research in international business has shown that the effect of spillovers from

foreign sources depends on the knowledge gap between foreign and domestic firms (e.g. Kokko,

1996). Foreign firms generate spillovers; however, as the gap widens, the ability of domestic

firms to absorb foreign knowledge is likely to increase up to a critical point, then fall as the

knowledge distance between firms becomes so great that domestic firms are unable to benefit.

Since more developed countries are often found to be positively influenced by foreign presence,

this effect is attributed to a higher level of absorptive capacity of firms in those countries.

While many of these positive externalities have been observed in a number of contexts,

we still have little understanding of the relative importance of each and how they apply in

specific contexts. Moreover, the relative importance of each may change over time. One obvious

problem is that many of these externalities are unobservable and therefore inferences have to be

made regarding their effect on domestic firms.

Other Outcomes of Foreign Entry

Despite the substantial research on host country productivity, there is still little research

on the effect of foreign firms on other kinds of outcomes for host country firms.45 A recent

theoretical contribution by Markusen and Venables (1999), who developed a model in which net

domestic entry is facilitated by backward linkages created or enhanced by foreign firms, has

generated more interest in the dynamic effects of foreign entry. They argue that positive linkages

44 Kokko (1996) argues that a greater technological distance between foreign and domestic firms inhibits the integration of foreign firms into the host economy thereby creating the conditions for foreign enclaves to develop. This may be less of a concern in the context studied here unless the distinctive capabilities of pharmaceutical and biotech firms are taken into account, especially in the early period. 45 Blomstrom and Sjoholm (1998) also point out that competitive pressures from FDI should be analyzed from the perspective of industry dynamics, not total factor productivity.

40

induce more entry of intermediate suppliers, which then leads to greater competition and variety

of inputs (due to differentiation) and in turn tends to reduce input costs, and raise profits. In the

short run, however, local firms’ profits will tend to diminish due to competition in the factor and

product markets.46 One empirical paper has found support for the positive effects of FDI on

domestic entry in the long run (Gorg & Strobl, 2002).

The emphasis on average productivity gains resulting from foreign entry seems to

confound two mechanisms through which this outcome operates: (1) by selecting out less

productive domestic firms, or (2) by learning (i.e. by means of knowledge transferred to and

received by domestic firms), or both. Thus, there is a reason to separate out these effects. Gorg

and Strobl (2003) show that the presence of foreign firms has a survival benefit for domestic

firms in the Irish economy in the long run – but only for firms in high tech sectors, which they

attribute to a variety of spillovers. Following Markusen and Venables (1999), they argue that

survival is determined by profit margins and since foreign firms lower the cost structure for all

producers, their entry also increases the probability of survival of domestic producers.

Unfortunately, their conclusions are limited by the fact that they fail to control for some

important firm level variation in their analysis.

Other studies focusing on productivity have found little evidence for spillovers.47 For

instance, Aitken and Harrison (1999) found no or negative evidence of spillovers in the context

of FDI in Venezuela, which, they argued was likely due to the fact that short and long run effects

are confounded. Spillovers take time to develop, and are therefore a longer run phenomenon,

whereas in the short run, negative competitive effects from foreign firms are likely to crowd out

46 The latter is assumed to be mediated by productivity gains since, ceteris paribus, higher productivity will reduce a host country firm’s average cost of production. 47 Note that in their meta-analysis of spillover studies, Gorg and Strobl (2001) argue that there is likely a publication bias in favor of finding evidence for spillovers.

41

domestic producers, primarily because foreign firms enter with a lower cost structure due to their

superior technology and can therefore undercut domestic producers. Although these arguments

are intended to apply to perfectly competitive output markets – i.e. where there is head-to-head

competition for markets, they are also supposed to apply to factor markets.

Crowding Out

Aitken and Harrison (1999) argue that one reason the evidence for spillovers is somewhat

ambiguous is because of a short run crowding out effect, which can occur when foreign firms

enter and steal market share from domestic producers. This may occur especially under

conditions in which foreign firms seek to expand into new markets. In fact, aside from Aitken

and Harrison (1999), there are a number of recent studies demonstrating a negative effect on

domestic production when foreign firms enter new (geographic) markets. This form of market

stealing is particularly effective when products are standardized and the MNC can compete on

cost, due in part to scale economies. Crowding out is implicit in many studies that examine the

effects of foreign firm competition on domestic firms’ output. However, very few studies have

examined other outcomes.

DeBacker and Sleuwaegen (2003), and Gorg and Strobl (2003), examined crowding out

of domestic firms in different contexts – Belgium and Ireland, respectively – and concluded that

the entry of foreign firms induced greater exit or lower entry rates of domestic firms in the short

run. They argue that MNCs negatively affect domestic firms’ survival prospects by lowering

output and price, forcing domestic firms to cut production, and therefore increase the average

cost of production; or through crowding out domestic rivals, by increasing wage rates in the

economy (DeBacker & Sleuwaegen, 2003). Moreover, if crowding out has an effect on domestic

42

productivity, it should also put those firms on the margins at greater risk of exit, the extent of

which depends on the amount of demand lost due to foreign competition. Although these are

thought to be short run effects, they may persist for some time. Kosova (2004) examined

crowding out of domestic firms in the Czech Republic and concluded that there tended to be a

gradual adjustment process rather than one of shock therapy following the entry of foreign firms.

But these few studies are really the extent of the literature on the crowding out effects of foreign

firms on domestic entry, exit or growth.

To address the ambiguity of short run and long run effects of foreign entry, Kosova

(2004) tested a model which includes both. She showed that there was a crowding out effect on

domestic firm growth and survival in the Czech Republic in the short run, but that there was no

‘dynamic crowding out’ effect due to the growth of foreign subsidiaries after they entered. In

other words, there was no evidence that foreign firms expanded in the Czech market at the

expense of the domestic firms in terms of their growth or survival. In fact, she found the

opposite, that the influence that foreign firms exert becomes positive in the longer run,

presumably due to various kinds of spillovers.48

In the theory, it appears that the effects on domestic industry – positive and negative – are

based on the entry and continuing presence of foreign subsidiaries in general. Most of the

empirical work fails to distinguish between firms that entered by greenfield or by acquisition.

Although many of the studies on the effects of foreign firms either examine greenfields only, or

pool the effects of foreign presence irrespective of the mode of entry, there are reasons to believe

that the externalities and outcomes generated by firms that enter by acquisition or by greenfield

48 There is, however, no resolution between the dynamic crowding out and the spillover hypotheses since a rather ad hoc rationale is given for the positive effect.

43

differ due to differences in the nature of the search (and site-selection) process. Some of these

differences are apparent when considering the separate literature on acquisitions.

Acquisitions

Entry by Acquisition

There is little research dealing with how acquisitions generate externalities and what their

effects might be. In order to understand the effects that acquisitive entrants have on other firms,

we need to know something about their effects on the target since externalities are flowed

through their new subsidiary. Some research has studied the effects of acquisitions on the

acquirer, but there is much less on the target itself. In fact, much of the research has examined

the post-acquisition performance of the combined firm, irrespective of location. This literature

also emphasizes a broader set of motives for entry by acquisition than that found in international

business.

Most (but not all) of the literature emphasizes the importance of acquisitions as a vehicle

for corporate profitability and growth. Acquisitions also help firms obtain resources that they

need to reconfigure their businesses or apply their existing resources to new uses. Thus,

acquisitions are a mechanism by which organizations change, reconfigure and redeploy their

resources and capabilities (Capron, 1999; Capron & Mitchell, 1998; Capron, et al., 1998).

Traditionally, there have been two main economic explanations for acquisitions: (1) market

power – i.e. as a means of reducing the intensity of competition and capturing rents; and (2)

efficiency – economies from larger scale operations in the combined firm lower costs. Thus, the

first operates on the revenue side, the second on the cost side. However, these explanations tend

to be static. Theories in strategic management, on the other hand, tend to focus on market failure

44

as a plausible alternative to understanding why acquisitions are used as a vehicle for entry,

especially in a dynamic environment. Strategy scholars have argued that imperfections in

markets for knowledge as well as other idiosyncratic resources create complications in the arm’s

length transfer of knowledge resources through markets. Market imperfections, which include

pricing problems and coordination difficulties, lead to resource transfer through acquisitions

because they provide the necessary long run interaction for effective learning and knowledge

exchange (Capron, et al., 1998).

Both evolutionary economics and the resource-based view treat the firm as a bundle of

resources (Nelson & Winter, 1982; Penrose, 1959), where intrinsic firm differences are the basis

for sustained competitive advantage. This suggests two things: first, that these capabilities are

path dependent and take time to develop because the inheritance of past routines tightly

constrains opportunities for learning so that a firm is most likely to develop skills in areas in

which it has prior experience (Teece, 1986). Second, firms must change to maintain competitive

advantage in changing environments, which they can do either by internally developing the

resources and capabilities that they require, or by acquiring another bundle of similar resources

on the market.49 However, because internal development of such resources and capabilities is

time consuming and costly, acquisitions are often preferred. It is for this reason that acquisitions

also tend to dominate simpler contractual exchanges when firms want to obtain tacit and difficult

to replicate resources embedded in another firm. Acquisitions tend to be more prevalent when

barriers to entry are high, such as in tech-intensive industries, primarily due to the path

dependent nature of the innovation process. This is especially the case with the introduction of

disruptive technologies, as occurred when the pharmaceutical industry was confronted with the

49 Or they can link directly with other firms, or be geographically proximate to others to benefit indirectly from spillovers.

45

introduction of new biotechnologies in the early 1970s. Acquiring another firm is, under these

conditions, the only way of gaining access to those capabilities.50

The Effects of Acquisitions on Target and Acquirer

There are a couple of possibilities regarding the effects of an acquisition on the target in

the near term. The first case is one in which specific resources are sought out in the target and

redeployed elsewhere. This often, but not always, results in closure. The second case is one in

which the resources of the parent are redeployed in the target to take advantage of the firm-

specific assets that creates value when recombined with the idiosyncratic resources of the target

(Capron, et al., 2002). Whether it is one or the other depends on the acquirer’s strategy, the

relative size of the target and acquirer, and what specific resources are sought out (Capron, 1999;

Capron, et al., 1998). In their study of the redeployment of resources between acquirer and

target, Capron, Dussauge, and Mitchell (1998) define these resources as R&D, manufacturing,

marketing, managerial, and financial, and found that R&D, manufacturing, and marketing tended

to be more subject to market failure than either managerial or financial resources. However,

managerial resources were usually required in combination with those resources for successful

transfer. Transfer of managerial resources was also almost always one-way – from parent to

target.

A target may be acquired for its technology – essentially because it is difficult or

impossible to develop otherwise, and/or because the bundle of resources it embodies is

50 Acquirers may go on a cross border shopping spree if they can acquire a target for a bargain, given favorable exchange rates.

46

undervalued (relative to the resources that can be combined with that of the acquirer).51 Research

has shown that acquisitions that are tech-related impact on the innovative activity of the acquirer

(Ahuja & Katilla, 2001). Non-technology related acquisitions may be motivated by the desire to

obtain access to distribution channels, gain entry into new (product) markets, or to obtain

financial synergies or market power (Capron, et al., 1998). One possible outcome of a tech-

related acquisition, especially if it is raided for its intellectual property and/or other portable

resources, is that its operations are discontinued. An acquisition of this sort is terminal in the

sense that the target ceases to exist (in pre-existing form). One possibility is that its facilities

would be moved closer to the parent.

A terminal acquisition may be indicative that the target is a distressed firm, in which case

the acquisition is made because the target’s fixed assets can be acquired cheaply and then

redeployed in another place (Anand, 2004; Anand & Singh, 1997). On the other hand, the target

may be acquired because it was successful rather than distressed. For instance, Shan and Song

(1997) found that targets tended to be more innovative than other firms in the biotech industry in

the U.S. This is partly backed up by Ruckman’s (2005) study of the U.S. pharmaceutical industry

in which she found that the R&D intensities of foreign acquirers tended to be lower than their

targets, suggesting technology sourcing as a motive.52 It is also the case that firms (potential

targets) seek out an acquirer for reasons other than distress, such as an exit strategy (e.g., in the

case of an entrepreneurial startup with venture capital involvement).

51 Karim & Mitchell (2000) distinguish between resource deepening and resource extension motives for acquisitions. With the former, the acquirer follows path dependent opportunities with respect to its own capabilities; the latter are used to make path-breaking opportunities. 52 Ruckman (2005) also found that there is no difference in the absolute difference in R&D intensities between target and acquirer if the acquirer is foreign or domestic. However, domestic acquirers tend to target firms that have a high R&D intensity when they themselves have a high R&D intensity, whereas foreign firms with lower R&D intensity tended to acquire firms with higher R&D intensity.

47

In the case of a continuing acquisition, there are a variety of possible changes (in the

target) resulting from an acquisition that affects its strategy, structure, and ultimately

performance, especially in an early stage venture. If the target is smaller than the acquirer, as is

typically the case, then there is a substantial change in the structural differentiation of the

organization, resulting in changes to the incentive system and level of bureaucratization (Stuart

& Sorenson, 2003). There is also the risk of a cultural mismatch between organizations

(Barkema, Bell & Pennings, 1996), which is due to “imprinting” (Stinchcombe, 1965), as well as

the path dependence of the separate entities.53 One consequence of this cultural mismatch is that

there is usually turnover in the target following an acquisition (Birch, 1987; Hambrick &

Cannella, 1993; Haveman, 1995). This is often indirectly due to the relatively low standing of

executives in the combined company (Hambrick & Cannella, 1993). Stuart and Sorenson (2003)

highlighted the importance of demographic differences between organizations, size in particular,

which exacerbate this cultural tension. Mergers between organizations of dissimilar size,

especially when the acquirer is the dominant firm, can lead to conflict precipitating senior level

turnover in the target. For instance, Mitton (1990: p. 347) notes that shortly after Eli Lilly

acquired Hybritech, a seven year old San Diego based biotech firm, in 1986, the chief executive

officer and the chief financial officer left because: “The culture of a large organization

descending on Hybritech did not suit their management style.” The two senior managers went on

to found a venture capital firm, which in turn provided financing for numerous new startups in

the region. Thus, the acquisition event triggered other entries indirectly (see Chapter 6 for other

examples in the Canadian biotech industry). On the other hand, turnover seems to happen even

53 Stinchcombe’s (1965) imprinting hypothesis posited that the time, place, and industry in which an organization is created shape many of the standard operating procedures, core values, and assumptions adopted by the newborn firm.

48

when the target and the acquirer are of similar sizes (Cartwright & Cooper, 1992), so the extent

of turnover may be more a matter of control rather than size.54

Consistent with the evidence on the contraction of target firms, the literature on post-

acquisition integration between parent and subsidiary shows that a target’s performance also

tends to decline in the post-acquisition period due to top management team turnover (Hambrick

& Cannella, 1993). Despite the evidence of a decline in acquired firms in terms of their size in

the short run, there is also evidence that merging firms capture synergies through asset

divestiture and resource redeployment (Chatterjee & Lubatkin, 1990). Thus, redeployment

constitutes a form of renewal and recombination in the acquired firm. However, Capron (1999)

found that target performance is contingent on how resources are reconfigured in the new firm;

and in fact, there is a significant risk of damaging acquisition performance in the process of

divesting and redeploying the target’s assets and resources. Nonetheless, she found that if done

right, an acquisition can refocus the firm. Furthermore, Capron, et al. (2002) showed that

acquirers receive abnormal returns when they transfer resources to the target, whereas acquirers

do not received abnormal returns when they receive resources from the target (because the

potential returns are bid away in the acquisition process).

The Link between Acquisitions and Entrepreneurship

A number of studies have made a connection between the acquisition event and the

entrepreneurial event. For instance, Brittain and Freeman (1986) found that recently acquired

semiconductor firms tend to spawn more startups as a result of the departure of senior level

54 Size may be one factor, but the overall issue is likely control. The process of acquisition may also lead to a dilution of holdings such that they lose relative control and even a sense of belonging. The other reason may be purely due to liquidity: executives in the target firm may receive payouts for at least some of their stake in the firm, which then provides them with other options.

49

employees in the target firm. Stuart and Sorenson (2003b) provide further evidence for the effect

of acquisitions on entrepreneurial entry in the U.S. biotech industry. Stuart and Sorenson (2003b:

180) argued that acquisitions, and initial public offerings (IPOs), constitute liquidity events

which “cause the equity positions of senior technologists and managers to become liquid at the

very time that the patterns of interaction, authority relations, and levels of autonomy in decision

making within their organizations change dramatically and when a salient mark of prior success,

leading a new venture to a liquidity event, boosts and propagates the reputations of senior staff

members at affected organizations.”55 An important contingency, however, is the strength of the

legal regime with respect to the enforcement of non-compete clauses in employment contracts.

Only in states where non-competes were weakly enforced was there a greater likelihood of new

entrepreneurial entry.

The effect of an acquisition on entrepreneurial entry is essentially a short run

phenomenon, however the effects may persist with the effective transfer of skills and

knowledge.56 Acquisition events may also have other outcomes that have not yet been explored –

such as on the growth and survival of indigenous incumbents. Also, assuming that the target is a

continuing entity, the evidence suggests a transfer of knowledge and other resources from the

parent to the newly acquired subsidiary. Over time, the newly recombined capabilities of the

subsidiary may spill over to other firms. These externalities should also impact indigenous firms

insofar as the knowledge and opportunity structure of the region is affected by the presence of

these outsiders. 55 Evans and Jovanovic (1989) found in their study of the National Longitudinal Survey that liquidity constraints limit new venture formation more than any other factor. IPOs make the holdings of senior employees who have a stake in the firm more liquid since their shares can trade freely on an exchange, thereby reducing their dependence on the organization. 56 The type of acquisition should also have an impact on the region. In particular, whether the acquisition is a tech acquisition, or if it is meant as a redeployment mechanism should matter in terms of what kinds of externalities result from this event. The effects will also depend on whether it is a terminal or continuing acquisition, as well as other factors that affect the knowledge transfer between parent and subsidiary.

50

Greenfield versus Acquisition Performance

Predictions about how greenfields and acquisitions are likely to affect other firms are also

contingent on how the subsidiary itself fares in the post-entry period. Mata and Portugal’s (2004)

study of FDI in Portugal compares the post-entry growth and survival outcomes of greenfield

and acquisition entrants. They show that the mode of entry (greenfield or acquisition) has an

impact on post-entry because of the differences in costs associated with each type of entry. On

the one hand, greenfields incur set up costs, as well as costs associated with learning about the

new environment. Greenfield entry may also induce rivalrous behavior because it adds capacity

to the market, whereas acquisition entry does not (Caves, 1996). The costs of acquisition, on the

other hand, include those associated with integration with the target (Mata & Portugal, 2004).

Greenfields tend to be smaller at the start compared to acquisition entrants, but those that survive

tend to grow faster than acquisitions. They also found that greenfield entry is more likely in

industries where scale economies and industry concentration are of lesser importance.

Mata and Portugal (2004) make a further distinction between the effect of foreign and

domestic entrants. They show that domestic firms are much more likely to exit than foreign ones.

Since these are de novo domestic entrants (i.e. small startups) as opposed to those that are

geographically diversifying, this should not be unexpected. They also find that the contrast

between acquisitions and greenfields is greater when differentiating between foreign and

domestic firms. Foreign acquisition entrants grow very little, greenfields grow very quickly, and

domestic firms are in between.57

57 These results have implications for the evolution of industry. It seems to suggest that the foreign share of employment will increase until foreigners dominate the domestic industry. However, this may be viewed as an evolutionary outcome in industries where multiunit firms are the norm since as they proliferate, they mix with domestic firms. Still, asymmetries may result which sometimes have political consequences.

51

Subsidiary Evolution

Although the issue of integration between parent and subsidiary has already been touched

upon, there is another related literature that deals not only with subsidiary-parent interaction, but

also subsidiary-local environment interaction, which often means country level, and how these

are likely to change over time. The literature on the transfer of knowledge between parent and

subsidiary has cited a variety of factors, such as cultural and geographical distance, that influence

the effectiveness of transfer. The extent of integration is also linked to the MNC’s international

strategy. Differences in strategy are reflected in different headquarters-subsidiary relationships

for acquisitions and greenfields. Some aspects of this relationship are also shown to change over

time, a process that is mediated by the MNC’s strategy (Harzig, 2001).

Kogut and Zander (1992) contend that the existence of the firm is predicated on its

knowledge transfer capabilities rather than the failure of markets or intermediate goods. The

difference in emphasis is important because it implies that knowledge flows from parent to

subsidiary are constrained by lack of integration between the acquirer and the target, at least in

the short run. In the longer run, however, there should be greater integration thereby enhancing

knowledge flows between parent and subsidiary. Bresman, Birkinshaw, and Nobel (1999) note

that a variety of factors contribute to knowledge transfer in acquisitions, but especially the nature

and frequency of communication, and the time elapsed since acquisition. The transfer of patents

is associated with the articulability of knowledge, size of the acquired unit, and the recency of

the acquisition. They also find, using case study data, that the immediate post-acquisition period

is characterized by one-way transfers of knowledge from the acquirer to the acquired, but over

time this gives way to high quality reciprocal knowledge transfer.

Greenfields, on the other hand, are already tightly coupled with the parent at inception,

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though they are not well integrated into their host environment initially. However, it has been

shown that new subsidiaries begin to draw more on the local knowledge base over time

(Birkinshaw & Hood, 2000; Frost, 2001), thus becoming increasingly more embedded. This is

consistent with Almeida’s (1996) finding that knowledge used in innovation by foreign

subsidiaries in U.S. regions is predominantly local. In fact, foreign firms use local knowledge

more than similar domestic firms. Foreign firms also contribute to local technological progress

since a significant proportion of the citations to their patents are local. Those that learn locally

also contribute locally, but it is apparent that externalities would be the result of novel

knowledge developed and subsequently imported from another context. How a subsidiary

interacts with its local environment is important to understand because it affects both the

subsidiary and indigenous firms (e.g. Chung, et al., 2003; Liu, et al., 2000; Markusen &

Venables, 1999). This is discussed in more detail in the next section dealing with the related

literature in economic geography.

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CHAPTER 3

EVOLUTIONARY ECONOMIC GEOGRAPHY

54

Introduction

The literature in economic geography has a close affinity to the international business

literature in the sense that both have emphasized the importance of competition and spillovers in

geographic space. One key difference is in the level of analysis, which has important

implications for how firms interact. Anther difference is the relatively greater attention devoted

to insider-outsider interaction in international business research. Although processes of entry,

exit and growth of firms have been dealt with in economic geography (e.g. Swann & Prevezer,

1996; Baptista & Swann, 1999), there is little if any work examining the effect of external

entrants on cluster evolution. It has generally been assumed that regions become basins of

attraction at some critical level of agglomeration thereby attracting the entry of more firms. As

yet, however, few if any studies of entry at this level of analysis have made a distinction between

those firms that are internally generated and those that are externally attracted. This is a

significant omission since, according to the theory and evidence in the literature previously cited,

external firms are likely to affect the structure of local industry by generating alternative

opportunities and introducing new knowledge and other externalities that affect the path of the

region.58

Early theories of why production concentrates in certain regions emphasized the

importance of transportation costs to their end markets as a key influence on location choice (e.g.

von Thünen, 1826). Most of these models focused primarily on the competitive effects of co-

location, emphasizing a similar kind of crowding out discussed in the international business

literature. More recently, the emphasis has shifted to the benefits of agglomeration, driven

primarily by an interest in understanding the phenomenon of clustering in more knowledge

58 In evolutionary theory, by contrast, all entry is considered insider or internal entry.

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intensive industries such as Silicon Valley. The change in emphasis has been partly motivated by

the persistence of clusters despite the fact that transportation costs have diminished over time.

The evidence shows that almost all industries are somewhat localized, but that different

factors – local industry-specific spillovers, natural advantages, and pure random chance –

contribute to geographic concentration (Ellison & Glaeser, 1997). The patterns of clustering

depend on the fundamental forces of agglomeration in a particular industry. Moreover, where

spillovers constitute the primary force, patterns of agglomeration also depend on what kinds of

spillovers are most important – imitation, spinoff, labor mobility, social networks, or

collaborative networks (Breschi & Lissoni, 2001). Not surprisingly, there is also substantial

variation across industries. Many types of services, especially retail, have a very limited

geographic range (excepting the chain form), whereas manufacturing tends to be more variable

because production does not have to be located where the end market is.

Many theoretical lenses and multiple methods have been brought to bear on

understanding the causes and consequences of clustering. Scholars from different disciplines

have also been interested in different aspects of the phenomenon. For instance, economic

geographers have tended to focus explaining the determinants of clustering (Dicken & Lloyd,

1990), whereas strategy scholars have been more interested in the consequences – the effect that

an agglomeration has on firm performance (e.g. Porter, 1990). Perhaps the two most influential

theoretical approaches in economic geography have been based on neoclassical (e.g. Krugman,

1991) and institutional theories. Evolutionary economic geography incorporates elements of

both.59

59 Acs and Varga (2002) distinguish between three literatures that deal with economic geography: the new economic geography (Krugman, 1991), new growth theory (Romer, 1990), and the new economics of innovation (Nelson, 1993). Another related area draws on Hirschman (1958) for inspiration in understanding regional development processes (e.g. Rodriguez-Claire, 1996; Markusen & Venables, 1999). The institutional approach has a more

56

The literature in economic geography and related fields is often dynamic, and some

explicitly evolutionary. In fact, there is also a tradition of evolutionary thinking in behavioral

economic geography going back at least to the 1960s (e.g. Pred, 1966). A number of key

assumptions in models of the spatial evolution of industry are held in common with evolutionary

economics. Firms are also assumed to differ in their abilities to obtain and process information

and are subject to imperfect information and uncertainty regarding future developments, which

inevitably leads to suboptimal location choice. While the emphasis in this earlier work was on

the dynamics associated with location choice, more recently, efforts to define an evolutionary

economic geography more formally have been advanced (e.g. Boschma & Frenken, 2006;

Klepper, 2003a). This chapter recasts some of this literature in a more evolutionary light and

considers the implications for insider-outsider interactions.

Evolutionary Economic Geography

An evolutionary application to economic geography relies on the same underlying

assumptions: bounded rationality, routine behavior, and heterogeneity among agents (Boshma &

Frenken, 2006). Evolutionary economics explains the changing distribution of routines and

capabilities as an outcome of search and selection. It is through these processes that a kind of

collective intelligence evolves in the sense that knowledge, embodied in skills, routines and

capabilities, tends to pool in specific places, particularly if tacit. This is a key condition for a

specialized knowledge base to develop in a region. Four key elements in (local) industry

evolution have been identified: chance, increasing returns, path dependency, and selection

inductive, case-based, orientation. The economic, in particular the neo-classical, and the institutional have been referred to as under and over-socialized accounts, respectively. Nelson and Winter (1982) argue that evolutionary economics represents a balance between formal modeling emphasized in neo-classical economics and “appreciative theorizing” emphasized in institutional economics. (See Boschma and Frenken (2006) for a more detailed discussion of the distinction between these theories.)

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(Boschma & Lambooy, 1999). The relative importance of each at any given time influences the

path the region takes. The local environment plays a role as well, though it tends to be

endogenous to the structuring of the local industry.

Chance and Spatial Evolution

Recent work in evolutionary economics has argued that firms emerge where they do

because of chance – i.e. because of the random distribution of entrepreneurs and resources. Over

time, as firms are generated in particular places, high quality firms generate more high quality

spinoffs, which are more likely to survive and prosper, and because spinoffs tend to locate close

to the parent, there is a natural explanation for why industries agglomerate where they do

(Klepper, 2002b; 2003).60 This is consistent with some accounts in economic geography, which

also often ascribes a strong role to chance – entrepreneurs are randomly distributed in space and

because spinoffs are randomly generated, chance (i.e. initial conditions) and small events early

on determine the long run spatial pattern of an industry (Arthur, 1987; Boschma & Lambooy,

1999). This emphasis on the emergent properties and the early generative phase of an industry

seems to suggest that this mechanism applies especially to indigenous firms.

Increasing Returns and Path Dependence

A key difference between evolutionary economics and economic geography is that the

latter ascribes a (stronger) role to locational advantages than does the former. Some of these

advantages may be due to (endogenous) agglomeration economies, which are positive

60 Just as with the non-spatial version of industry evolution, this account is fully endogenous to firm capabilities. For instance, Klepper (2002b) argued that heterogeneous firm capabilities, increasing returns associated with R&D, and a birth and inheritance process governing entry is sufficient to explain the evolution of the automobile industry centered on Detroit.

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externalities that generate increasing returns to firms in denser regions.61 Once a critical mass

develops, an element of necessity is added to the spatial distribution of firms through positive

feedback, which reinforces the advantages of particular regions (Arthur, 1990). How a region

attains critical mass, defined as the scale or volume at which processes become self-perpetuating

(Arthur, 1989), is a key issue that has received relatively little attention in the literature.62 Once

reached, the region becomes a basin of attraction, thereby drawing in more firms.63 However, as

already noted, the literature generally does not differentiate between insider entry (primarily new

startup) and outsider entry (those firms attracted to a region).

There are two key implications of the presence of agglomeration economies and the

increasing returns they generate: (1) they alter the costs/benefits to incumbents in those regions;

and (2) they alter the decision matrix for firms making entry/location decisions. The first relates

to the internal engine of (employment) growth in the region; the second relates to the entry of

both insiders and outsiders. These are, in fact, inter-related.

For incumbents, survival and growth are influenced through increasing returns generated

by agglomeration economies, normally characterized by the pooling of specialized intermediate

goods industries, the pooling of demand for specialized labor, and knowledge spillovers among

firms (Marshall, 1917). Although similar to the externalities emphasized in the international

61 Arthur (1987) argues that locational dynamics can be modeled as a Polya process in which increasing returns are generated from the increasing proportional presence of firms in particular regions. An increasing share of firms in one region therefore leads to a skewed distribution of firms which eventually locks out other regions. 62 The literature on complexity and self-organized criticality argues that the emergence of complex structure from simple local interactions is a spontaneous phenomenon (Arthur, 1989). This still begs the question of how this happens. At any rate, the initial conditions, i.e. the decisions of early entrants, may lead to a snowball effect such that the perceived benefits of a region become a self-fulfilling prophesy. Both Ellison and Glaeser (1997) and Brenner (2004) develop methodologies for detecting regions that are self-reinforcing. 63 How critical mass is generated is contingent on how it is defined in the industrial context. For instance, it has also been defined as a threshold value in total employment in a region. (If this is the case, the size distribution of firms, concentration, etc., may also be important issues.) The issue of critical mass also links to a fundamental problem in studies of industry clusters – that of identification and existence (Brenner, 2004). The very concept of a cluster assumes the existence of a critical mass, agglomeration economies, and increasing returns, which are of a self-reinforcing nature. However, while local self-augmenting processes are a necessary condition for the existence of local industrial clusters, they are not sufficient (Brenner, 2004).

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business literature, the emphasis in economic geography is on how positive externalities

reinforce the benefits of a region thereby increasing agglomeration due to positive feedback

(Arthur, 1990). A great deal of attention has focused on knowledge spillovers since they are

thought to drive patterns of hi-tech agglomeration.64 It is argued that such spillovers tend to be

local because proximity matters in facilitating the transfer and acquisition of tacit and complex

knowledge (Audretsch & Feldman, 1996; Jaffe, et al., 1993), and such knowledge transfers are

mediated through labor flows and firm level interactions, which tend to be local in many markets

(Almeida & Kogut, 1999).

Economic geographers contend that firms contribute to and benefit from a regional

system through the generation and sharing of tacit knowledge and the development of learning

capabilities that are essential to innovation. Such capabilities are sustained through localized

communities and networks of firms and supporting institutions that share a common knowledge

base and benefit from their shared access to a unique set of skills and resources. A common

regional culture developed through communities of practice also facilitates learning (Brown &

Deguid, 1991). Learning and interaction among organizations are cumulative and self-

reinforcing processes (Myrdal, 1957). Firms build up a common code of communication through

repeated interaction over time, and consequently develop an interdependent structure. Positive

feedback due to agglomeration economies can lower costs, including those associated with

search, and increase benefits for members of that agglomeration thereby influencing firm

performance.65

64 Dumais, et al. (2002) found that labor mix tended to account for agglomeration more than the other two. 65 See Sorenson and Baum (2003) for a review of the literature related to strategy.

60

The evidence suggests that agglomeration economies do matter in the location choice of

external firms (Head, et al., 1995; Chung & Alcacer, 2002).66 The location choice of firms is not,

in and of itself, random since it is to some extent endogenous to the firm’s capabilities (Shaver,

1998). This does not, however, preclude the possibility that errors may be made in location

choice because, as Pred (1966) noted, there is often considerable uncertainty in this decision due

to the fact that the relative benefits and costs of locations change over time.67 Also, location

choice itself is not a static decision, as the international business literature has pointed out.

Despite the sunk costs in entering – either in setting up a new plant or through the

acquisition of a pre-existing firm – external firms have the discretion to alter their commitments,

which can include scaling up, scaling down or outright divestiture. In the presence of

agglomeration economies, this would usually mean increasing their commitments (controlling

for alternative opportunities). Insofar as entry is motivated by competence exploitation, entry

into other regions can involve a replication of routines in different geographical contexts. This

especially applies to greenfield entry. Acquisitions involve recombination at both the firm and

industry levels.

Increasing returns also influences new entrepreneurial startup because even in cases in

which the initial location of firms is random, entrepreneurs make calculated decisions to startup a

firm (Delmar & Shane, 2003). The decision to startup a firm may be partly dependent on the

location of critical resources, such as social networks and capital (Stuart & Sorenson, 2003a),

66 Shaver and Flyer (2000) show that external firms may locate away from others to minimize spillovers. However, once the firm locates where it does, others may locate in close proximity. 67 Outsiders may also enter for reasons that are independent of agglomeration economies. These include herd behavior and imitation where firms follow others in under conditions of uncertainty; or they may enter to lower search costs, etc. (Maggione, 2002). Outsiders may gain advantages if there are economies associated with the prior entry of other outsiders, such as learning from their experience. In general, there are relative advantages and disadvantages associated with early or late entry. For instance, earlier entrants may have to contribute a higher share of building up the infrastructure than later entrants; whereas later entrants may lose out on opportunities, and be subject to more binding resource constraints. This suggests that there may be (optimal) windows of opportunity in entry.

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some of which are not randomly distributed because they accumulate in particular places over

time, possibly due to a co-evolutionary process. This also partly explains the local bias in new

venture formation: entrepreneurs acquire sponsorship and mobilize resources through their

established social networks (Shane & Stuart, 2002), which build up over time.

Selection

Selection refers both to the firm level selection of routines and capabilities, and to market

selection of firms (as bundles of capabilities). Evolutionary economics argues that search,

leading to the selection of routines and capabilities, is likely to be local in cognitive distance

(Nooteboom, 2000). However, the more tacit the knowledge, the more likely this is also

geographically local. If this is the case, then other organizations in the local environment are

likely to have a greater influence on the focal (indigenous) firm than those that are non-local,

especially in knowledge intensive industries. This is particularly the case for indigenous firms

since external firms also have access to knowledge and capabilities of the parent. Also, since

indigenous and non-indigenous firms likely differ with respect to their knowledge and

capabilities, then the latter may provide greater variety to select from, assuming some sort of

transfer mechanism.

In behavioral economic geography, the spatial evolution of industry is also at least partly

due to market selection. Firms may choose a location by chance or by intention – either way,

those that fall within the “spatial margins of profitability” have greater survival prospects than

those that do not (Pred, 1966). Another key difference between economic geography and

evolutionary economics is that the selection environments differ. In the latter, it is assumed to be

at the broad industry level. In the former, it is more typically the local level, though it may

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operate on different levels, depending on what is being selected. This difference matters if

selection is especially contingent on local environmental risk. More generally, external firms,

which are also multi-unit firms, are likely to be at lower overall risk by being geographically

diversified compared to single unit firms that are bound to one place. However, that does not

necessarily imply that they are less likely to exit within particular regions since the parent has the

discretion to withdraw in the face of changing circumstances and is therefore less subject to

inertia than purely indigenous firms. On the other hand, the local environmental risk that any

given subsidiary experiences, is mitigated by its access to the parental resources. Whatever the

outcome, it is for these reasons that foreign and domestic firms are sometimes thought to operate

in different selection environments, and may explain why exit rates between foreign and

domestic firms differ, as Mata and Portugal (2004) found. This would also be consistent with the

contention that it is the business unit that is the unit of selection rather than the firm as a whole

(Metcalfe, 1998).68

Indigenous firms within a given region should therefore be subject to a common selection

environment, and conversely firms in different regions are subject to different selection

environments. In fact, if the actual location is not so much a choice variable for entrepreneurs

because it is given in the sense that spinoffs are founded close to the parent, then selection effects

may be decisive for entrepreneurs since their survival (and growth) prospects are determined by

the munificence of the local environment.69 But this depends on how and to what extent the focal

68 It may be the case that domestic external firms have a greater opportunity for interaction with single unit indigenous firms due, for instance, to the existence of supra-regional (provincial or national) industry associations. Thus, even if two cross-border firms are closer geographically than two firms within a given country, national boundaries may matter in how some knowledge flows. On the other hand, factor markets tend to be more local. Moreover, knowledge developed from elsewhere in the same country may not have a different effect than that developed in another region in another country. 69 This may suggest that firms that are less fit will survive and prosper in such a milieu, whereas firms from other regions – ones that have survived without the benefit of agglomeration economies, may be more fit overall. This may ultimately have implications for competitive dynamics across regions.

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firm is bound to the local environment – for input markets, output markets, or both. Market

selection is therefore partly tied to increasing returns and agglomeration economies in the sense

that these mechanisms generate munificence.

Institutions and Localization Economies

Two other factors appear to be important influences in local industry evolution. First, are

the institutions; second, are the so-called urbanization economies. As already noted, institutions

feature prominently in both evolutionary economics and in some branches of economic

geography. Institutions can be formal or informal. Formal institutions usually refer to political

and legal systems; informal institutions, according to the old institutionalist tradition, generally

refer to habits, behavior and routines. Both of these are treated as endogenous to industry

evolution, though the latter more obviously so. In fact, institutions are often seen to be co-

evolving with industry and technology.

Economic geographers also contend that it is not just agglomeration economies, but also

urbanization economies that influence regional evolution. The former tend to be specific to the

industry domain, and therefore may involve non-specific, though strategic resources (at the local

industry level). The latter tend to be derived from generic resources (and are therefore less

endogenous), which might include transportation infrastructure, or even generic, though skilled,

local labor, and are broadly applicable across industries.70 Urbanization economies are also more

generally related to the diversity of the region: the greater the diversity, which may have no

specific relation to the focal industry, the greater the flexibility for actors to adapt under

changing industry conditions (Jacobs, 1969).

70 Generic resources may also become transformed into specific specialized resources over time (Martin & Sunley, 2006).

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Regional and Spatial Evolution

Intra-Regional Dynamics

All of these processes aggregate to influence the pattern of the evolution of industry in

regions. The literature on regional innovation systems often implicitly draws on a life cycle

approach to understanding intra-regional dynamics.71 Local systems simply mirror the global life

cycle, following a clear path from emergence to growth to maturity, and eventual decline.

However, just as at other levels of analysis, even if an industrial system begins to emerge, it may

or may not take off. If it does take off, then logistic (S-shaped) growth (in terms of the total

number of firms) in the region is generated by non-linear interactions, which generate positive

and negative feedback.72 Positive feedbacks tend to dominate early on (due to agglomeration

economies); later, negative feedbacks tend to dominate as the system approaches its carrying

capacity. In this context, negative feedbacks are due to congestion effects, a form of crowding

out. This bids up prices and lowers margins for incumbents, and consequently lowers entry and

raises exit rates. This pattern is consistent with a simple ecological system – when the state of the

system is small relative to the carrying capacity, which is assumed constant, the familiar logistic

growth pattern results.73

71 A variety of theories and models can and have been applied to understanding local industry dynamics. The regional innovation systems literature draws on a system dynamics approach, and also bears some resemblance to population ecology (since they draw on many of the same sources). These ideas have a clear relation with evolutionary economics. 72 Nonlinear first-order systems exhibit S-shaped growth because the dominant feedback loop shifts as the system evolves. Linear first-order systems can only exhibit three behaviors: exponential growth (when positive feedback dominates), exponential decay (when negative feedback dominates), and equilibrium (when the loops exactly offset one another). As the system approaches its carrying capacity, the positive loops driving growth weaken and the negative loops restraining growth strengthen until the system is dominated by negative feedback, and the population then smoothly approaches a stable equilibrium at the carrying capacity (Sterman, 2000). There are many variations on these simple models. 73 However, it may be possible to overshoot the optimum, which generate oscillations around the carrying capacity. An example might be in the footwear industry in which entry into particular regions continued despite high exit rates (Sorenson & Audia, 2000). A variety of other patterns can also be explained by second order processes (Sterman,

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There is, of course, nothing inevitable about any given path – there is substantial

variation in the configuration and composition of regions. Building on Winter (1984), Audretsch

and Fritsch (2002) identify four types of regional regimes: (1) the entrepreneurial regime,

characterized by high entry rates but low employment growth; (2) the routinized regime,

characterized by low startup rates and high growth; (3) the revolving door regime, in which

barriers to entry are low (and therefore entry is high), but does not lead to creative destruction;

and (4) the downsizing regime, characterized by low entry rates, in which incumbents produce

fewer innovations, leading to lower growth. Processes of entry, and incumbent exit and growth

therefore underpin these growth regimes.

A given region can also move from one regime to another over time. Regions are path

dependent due to cognitive (at the collective individual level) and technological commitments (at

the collective firm level) as well as irreversibilities in institutional and other extra-organizational

investments, all of which can lock-in a region and the firms therein into a particular path. Such

commitments may create opportunities, but they may also sow the seeds for the eventual demise

of a region when new technologies arise, because of the difficulty in adapting to changes. The

extent of lock-in, however, depends on the extent of specialization of the region. The computer

industry, for instance, has been shown to become strongly regionally specialized over time

(Beardsell & Henderson, 1999). Markusen (1985) also noted that there is a tendency for markets

to become oligopolistic and vertically integrated over time. This has two effects: first, it dampens

entrepreneurial activity, the flexibility of labor, and the availability of local networks; and

2000). For example, an oscillating system is generated by negative feedback with delays, due for instance to the time it takes for labor training, or supplier entry. Other systems, such as limit cycles, oscillate within a set range because of various nonlinearities. Limit cycles, of which chaotic systems are a form, are not self-perpetuating; they require an exogenous force to keep the system in motion. If perturbed by any kind of random shock, systems may oscillate around an equilibrium point in which case they may be locally stable. The pattern should also depend on the extent to which firms are dependent on the local resource base.

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second, it denies new firms access to local resource markets thus affecting their opportunities to

benefit from agglomeration economies. In essence, because this can lead to a trap of rigid

specialization, regions can become victims of their own success.

Inter-regional Dynamics

Industries vary substantially with respect to how geographically concentrated they are at

any given time. In technologically intensive industries, where it is assumed that spillovers are the

primary agglomeration force, it is often the case that production is concentrated in one or a few

places initially. The strong version of the increasing returns story suggests that a region with an

early initial lead may lock out other regions (Arthur, 1990). However, there are lots of

counterexamples of this particular pattern. Regional clusters often coexist over long periods, but

it is frequently the case that the region with the initial lead loses ground to other regions over

time. If regions become specialized, as the theory predicts, then lock-in to a technological path

creates the conditions for technological leapfrogging of regions (Amiti, 2001). This may be the

case if an emerging region arises specialized in a newer technology that will supplant the older.

It is, in essence, the tacit nature of knowledge – at the individual, firm and regional levels – that

creates the conditions for lock-in and regional specialization. This might also explain the

persistence of regional inequality in industrial development, suggesting that, although knowledge

inevitably diffuses across regions, absolute convergence is unusual, if not impossible.

Variation in clustering patterns across regions occurs because of differences in relative

entry and exit rates, as well as in the relative growth of incumbents. The theory suggests that

entry rates are at least in part due to the relative attractiveness of regions, which is due to

changes in the relative agglomeration economies (i.e. net of any diseconomies, such as

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congestion effects). This is particularly true in the entry decisions of external firms since they

may be able to reallocate resources across units, in different places. Despite how embedded they

may be in a region, external firms are still part of a larger corporate entity, which means they are

not as locked-in, despite their sunk costs and other location specific investments, as indigenous

firms. Factors of production – skilled labor and material suppliers – may also shift from one

location to another, subject to some inertia, as agglomeration economies change relative to other

regions. This is also somewhat true for other (indigenous) incumbents in a region; but since

inertia may exert a stronger effect, moves out are likely to be relatively uncommon, though still

possible. It may also be that a region that is lacking strong agglomeration economies relative to

another may provoke an indigenous firm to set up a subsidiary in that region. In general,

entrepreneurial entry, and growth and survival of indigenous incumbents should be influenced by

relative agglomeration economies. Thus, both internal and external forces influence the spatial

evolution of industry.

Public Policy

Despite the strong role sometimes ascribed to chance in the spatial evolution of industry,

strategy, in the form of public policy, often plays an important role in regional industrial

development, particularly early on. This is, of course, analogous to the role of strategy at the firm

level. Policies may be of comprehensive design intended to manage the regional path, such as

with science parks, whereas others may be designed to kick start the increasing returns

mechanisms (Arthur, 1990); others still may simply be ad hoc attempts to fine-tune an otherwise

spontaneously evolving system. The key choice variables for policy intervention include the

institutional arrangements – especially political and legal systems, as well as investments in

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infrastructure. Another key mechanism is in manipulating how new variety is introduced

through, for instance, R&D subsidies or tax incentives for both incumbents and new entrants.74

Drawing on the ecology literature, Maggione (2002) argues that, in broad terms, effective

policy interventions would include those that raise the carrying capacity or increase the growth

rate of the region. These are referred to as K-strategies and r-strategies, respectively.75 K-type

strategies focus on increasing the local endowment of resources (infrastructure, etc.), whereas r-

type policies are designed to increase the positive externalities, i.e. agglomeration economies due

to spillovers, which are endogenously generated by the location of new entrants in a region.76

The latter include startup incentives, subsidies, and information diffusion programs.77 Often,

these various policies in pure or mixed form are designed to move a region from one quadrant to

another (usually high growth) in the Audretsch and Fritsch (2002) typology. It may even be

possible to build flexibility into the local system by developing an appropriate infrastructure to

attract a more diverse set of organizations, thereby avoiding the trap of specialization.

Despite the obvious enthusiasm for facilitating the development of high tech clusters

through public policy interventions, fine-tuning the system for optimal outcomes can result in

unintended consequences because of non-linearities in the system. The impact of individual

policies is not additive – policies often interact with one another in such a way that they reinforce

and accelerate the effects of one another, or conversely cancel each other out (Brenner, 2004).

74 The use of tax or other incentives, generally used to entice external greenfields, may apply generally or be targeted to specific firms. Also, note that legal and political systems are often at a higher level, depending on how the region is defined. 75 Maggione (2002) draws on Hannan and Freeman (1989), in particular, in developing policy implications at the regional level. K-strategies tend to take longer to generate positive effects than r-strategies. 76 Maggione (2002) also identifies a γ-strategy, which is designed to build critical mass by taking an emerging cluster into the growth stage. This would appear to be a subset of an r-strategy, but the focus is more on kick starting the increasing returns mechanisms that ultimately lead to a self-sustaining cluster (Arthur, 1990). 77 An r-strategy tends to be a short run policy, whereas a K-strategy tends to be longer run because of the relative time it takes for these strategies to take effect. Given public budget constraints, there is also a tradeoff between investing in one strategy over that of another.

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Therefore, there is substantial complexity and causal ambiguity regarding the relationships

between policies and outcomes, and consequently assumptions about the relationships between

key variables can be mistaken. For instance, a strict entry selection policy should raise the

carrying capacity in the long run, but may lead to lower short run growth in the number of firms.

On the other hand, policies designed to produce new firms may be misguided if the new firms

are of poor quality.78 The generation of new firms is therefore a necessary but not a sufficient

condition for regional growth in the sense that promoting indiscriminate entry is costly in terms

of subsidies or tax expenditures that have to be traded off against alternative investments in

infrastructure or supporting institutions (Reynolds, 1999). Policies that facilitate the growth in

the size of incumbents might also constitute a trade off against one that focuses on the growth in

the number of firms in the cluster.

Insider-Outsider Interaction and Local Industry Evolution

All of this literature suggests that the distinction between insiders and outsiders is

important. If the proposition that skills, routines and capabilities are somewhat location-specific,

i.e. embedded in individuals, firms and institutions due to generative and selective mechanisms,

then there should be real differences between insiders and outsiders. Thus far, however, little

distinction has been made between them in studies of industry clusters; rather, the emphasis in

economic geography, broadly defined, has been on entry (or exit) in general. Insider and outsider

entry and exit clearly have different causes and effects and, as suggested above, the specific

policy mechanisms used to generate one differ substantially from the other.

78 Eliasson (2003) notes that post-entry, there are two types of errors in the selection process that must be avoided: losers must not be kept too long, and potential winners must not be lost.

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When outsiders enter, they can mix with insiders thus forming interdependencies, which

may be the basis for the co-evolution between the two. More than that, outsiders may become

intertwined with insiders to such an extent that they too become regarded as insiders. The

interaction of at first distinct populations might also be characterized as an evolving ecological

system.79 Jacobs (1969) argued that the most important spillovers come from outside the core

industry. If there is no internal generation of variety, it can only come from outside. Thus, the

external link can be a mechanism for renewal. The innovation systems literature suggests that

internal and external knowledge and other capabilities are complementary sources: even if

variety is generated internally, outsiders are likely to be attracted by that, and in turn introduce

new variation. The interaction between these two sources forms the basis of more novelty. But,

as previously noted regarding firm level absorptive capacity, “for the external source to maintain

novelty it is crucial to maintain distance.” (Nooteboom, 2000: 72). Distance has the merit of

novelty, but has limited comprehensibility. For exploitation, cognitive proximity is required, and

for exploration, cognitive distance. The effectiveness of knowledge acquisition/transfer is a

product of the interaction between novelty and comprehensibility (Nooteboom, 2000: 157-8).

When outsiders enter, they import new knowledge and capabilities that diffuse through

the local industry, and they generate new linkages, thereby altering the knowledge and

opportunity structure of the region and hence its trajectory. Routines, capabilities and skills

diffuse within a local structure and the region evolves through this interaction. Although most of

the evidence for the effects of outsiders relate to foreign firms on a host country, when applied to

79 Early ecologists, such as Hawley (1951), were much more sensitive to geographic or spatial differentiation than later ecologists. Later ecologists did make the distinction between central and peripheral organizations, but until very recently these were in abstract rather than physical space. The analogy to ecology is, however, somewhat limited in that the entities are essentially unitary. Here, entities may span multiple local ecological systems, which has implications for knowledge diffusion within and across regions, as well as for selection processes.

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the region the theory suggests that outsiders in general should generate similar externalities.80

The extent of interaction may also depend on a variety of other regional, subsidiary and parental

characteristics.

For the region, a broader dynamic is at work. As new knowledge is generated, a new

center consolidates and then a process of generalization leads to search at the periphery to meet

the challenges of differentiation and to utilize opportunities for exchange leading to novel

combinations (Nooteboom, 2000).81 Reciprocal search by outsiders often leads to their entry in

order to increase their proximity to insiders. As outsiders enter and become more embedded in

the region, greater interaction over time should yield convergence in practices, and possibly

performance. It also suggests that the liability of foreignness, insofar as it exists, will

disappear.82 This raises the key issue of definition – what is an insider, and what is an outsider?83

An insider may be defined by a clear identity, history, and a high degree of commitm

homogeneity with others in a particular place. This does not necessarily require that an entity be

born in a particular place; in fact an outsider may be closely identified with a region if it is highly

embedded with respect to its key stakeholders – employees, suppliers, and customers. Thus, even

if we can clearly identify outsiders, it is also likely that their status as outsiders, and hence their

identities, changes over time.

ent to and

80 There may be differences for domestic firms in one region entering other regions with respect to how broadly knowledge spills over in a national system, and in general how much interaction there is among firms at a level broader than the region. However, in general, domestic multiunit firms (which may also be multinational) likely have similar characteristics to foreign multinationals in terms of their ability to develop new linkages, as well as their ability to provide similar opportunities as (other) MNCs. (This is due in part to a more similar average size.) 81 Loman (1990) argued that in the cultural domain, there are stages in the assimilation of foreign knowledge. Over time, knowledge that enters from the periphery is entirely dissolved and becomes the new center. There is a parallel in the domain of innovation systems, but the analogy should not be taken too far since outsiders often continue to maintain a separate identity, particularly if they are a subsidiary of a large MNC. 82 In fact there may even be an advantage of foreignness due to the not invented here (NIH) syndrome (Cantwell & Iammarino, 2005). For instance, talent and suppliers may be more attracted to outsiders if they are seen to be more stable and creating a broader set of opportunities, thus creating a hierarchy of resources along quality lines. 83 These are not exactly mutually exclusive categories. Outsiders are also insiders elsewhere (i.e. in their home region), and insiders may be also be outsiders elsewhere if they are multiunit entities. There may even be degrees of being an outsider. Even outsiders from a broader geographic area may be more loosely identified with a region.

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On the other hand, it is possible that external firms act as a source of renewal not just

because they import new knowledge, which is a somewhat static conception, but also because

they open new conduits to knowledge developed elsewhere, which may be a source of dynamic

renewal. Just as internal networks act as the conduits through which knowledge flows in a

regional system (Owen-Smith & Powell, 2004), outsiders act as a pipeline through which new

knowledge and capabilities flow (or spill over) into a region.84 It is in this way that outsiders

create a bridge across local innovation systems binding regions together into a global industrial

system.85

The effects of these interactions assume something about the structure of the region and

possibly about the order of entry. It is generally assumed that outsiders follow insiders into the

region, which may occur for at least a couple of reasons: first, because insiders bear the costs of

laying down the infrastructure; and second, and relatedly, because the assumption of knowledge

seeking is such that the region generally requires a critical mass (for generalized spillovers), or if

not so much that then at least the presence of pools of specialized labor and suppliers, as well as

other organizations with whom to partner directly. However, the opposite may also be the case –

an external enclave sometimes precedes the formation of an indigenous industry within a region,

in which case outsiders define the local industry and are, in effect, the insiders.86 The

international business literature suggests that positive externalities that outsiders generate may

84 Note that a fundamental question remains regarding the diffusion of knowledge: whether it is the network or the place that matters most. Systems of innovation (regions) are networks, which vary with respect to their structure. Regions might also be regarded as nodes in a broader industrial network. 85 Indigenous firms may also branch out to other regions, which also forms a bridge to other regions. (The strength of the flow from one direction to the other may matter in the differential effects that these links have to the focal region.) 86 The size distribution of firms may be an important issue in the ecology of a region since outsiders are often large multinational corporations. The question is whether they act as anchors for the region, thereby providing the basis for further attraction (Agrawal & Cockburn, 2003). A prior question is how did they evolve to a given (large) size within a particular region since they rarely start out that way.

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only be operative in the presence of an indigenous industry. However, it is possible that outsiders

could act as a generative mechanism for an indigenous industry by spinning off new firms.

The implications of insider-outsider interactions for the long-run evolution of the local

system are somewhat ambiguous.87 One key question has to do with which of the various

feedbacks generated by external firms, both positive and negative, dominate and when? And how

do some key contingencies affect outsider influence. For instance, the stage of development of

the region has an impact on relative attractiveness for both potential (indigenous) entrepreneurs

as well as outsiders, and possibly the effect they have. The time since entry of outsiders would

also seem to matter. Furthermore, and to reiterate a key issue here, how do different types of

outsiders influence the process? Not only is there little distinction in this literature between

insiders and outsiders, or internal and external firms, nor is there a distinction between different

types of outsiders.88 As the international business and acquisitions literatures suggest, greenfields

and acquisitions should differ with respect to the degree and kind of externalities that are

generated, primarily because of the extent of integration between parent and subsidiary, and

subsidiary and region. This will be investigated further in the next chapter.

87 Other questions emerge regarding the long-run mix of regions. Even if, as Mata and Portugal (2004) observe, foreign presence increases over time due to differential entry and exit rates with domestic firms, that does not necessarily mean that outsiders will eventually take over the local industry. Rather, as previously noted, when the multiunit firms proliferate, inter-regional interaction is an important feature of industry evolution. 88 Note that this distinction necessarily separates internal and external acquisitions. Internal acquisitions may be differently motivated than external acquisitions.

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CHAPTER 4

HYPOTHESES

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Introduction

The hypotheses developed here emphasize how external entrants’ search, leading to their

entry into other regions, acts as a key mechanism driving local industry evolution in the regions

they enter. In particular, the evolutionary path of a region is fundamentally influenced by how

external entrants affect the knowledge and opportunity structure of a region thereby influencing

the entry decisions of potential entrepreneurs, as well as affecting the survival and growth of

indigenous incumbents in those regions. These two influences are inter-related but differ: the

influence on regional knowledge is through various direct and indirect transfer mechanisms

(spillovers, labor migration, linkages, etc.), whereas the influence on opportunity tends to be

more about the absorption of resources (labor and materials).89

External entrants differ by motive for and mode of entry. Greenfield entrants should

differ from acquisition entrants with respect to the relative knowledge and opportunities they

bring to the region. Entry by acquisition into other regions is often a key way to source

technological advantage, and may also present opportunities to lock-up new technologies (Shan

& Song, 1997). This is especially the case under conditions in which intellectual property rights

(IPRs) are strong (Gans, et al., 2002). Greenfields, on the other hand, are generally thought to

rely more on their own knowledge base than do acquisitive entrants, and in this sense may

import more novel knowledge and capabilities (i.e. those that are not recombined with already

pre-existing capabilities in the region, as with acquisitions). Even when there is a close alignment

between motive and mode, subsidiaries evolve over time changing their original mandate.90

89 Though this is not strictly the case since linkage effects create opportunities indirectly by lowering costs and therefore barriers to entry. 90 As noted, in the prior literature, mode and motive are closely linked. Though greenfields are more associated with competence exploitation because they are thought to enter to exploit the knowledge base of the parent, acquisition entrants could also be thought of as competence-exploiting initially if they exploit the knowledge base of the target. In both cases, even if they start out being competence-exploiting they may become competence-creating (Mudambi & Cantwell, 2007). In other words, mode of entry is an initial condition, but motive evolves. Their evolution into a

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In both cases, how they affect the region should change over time due to their evolving

roles and interactions. The short-term is linked to the longer term by assumptions about how the

subsidiary interacts with the local community and with the parent, and how these interactions

(e.g. knowledge flows through linkages) change over time. The embeddedness of the subsidiary

will depend on a variety of factors, such as the relative autonomy of the unit, the alternative

opportunities of the parent, and the availability and quality of local resources. The longer run

effects will depend on the relative strength of positive and negative externalities.91 In the longer

run, greenfield or acquisition presence could lead to the generation of new knowledge, and new

linkages that have a positive effect. On the other hand, it is also possible that dynamic crowding

out will dominate the positive externalities generated by external firms. This ambiguity may be

resolved by accounting for some key contingencies such as the size of agglomeration, and the

time since entry. While the effects of external entrants on indigenous entry, exit and growth may

all move in the same direction, it is also possible that they do not. This is because different

evolutionary (and policy) mechanisms drive each. Because this model breaks down changes in

geographic agglomeration into its various component parts – entry, survival, and growth, it aims

to explain some important dynamic effects of external entrants on indigenous development (and

the regional path).92

competence-creating entity is a function of local and parental factors. The focus here is on mode of entry primarily because public policy mechanisms are often tied to that. 91 Different ways of measuring short run and long run effects are used. Short run effects are measured here as (1) a count of new external firms in a given period; (2) new external firm share of total firms in a given period; and (3) the new external firm share of employment in a given period to the total. Similarly, the long run is measured here as (1) total count of each type of external firm in a given period irrespective of when they entered; (2) total external firm share of total firms in region; and (3) total external firm employment share of total employment in region. 92 Note that the focus here is on knowledge intensive industries in open regions, i.e. production can be exported to other regions. This implies that competition, insofar as it exists, will tend to be over input rather than output markets. It is also assumed that the industry is growing overall, but there is variation in the stage of development of local industries in particular regions. This means that the hypotheses will tend to capture the local industry in either the emergent or growth stages, but not so much in the mature stage.

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The Effects of External Entrants on Entrepreneurial Entry

Whatever their mode of and motivation for entry, external entrants likely affect the

decision calculus of potential entrepreneurs because they alter the knowledge and opportunity

structure of a place. The literature previously cited emphasizes two opposing effects – one is a

crowding out (negative) effect due to increased competition, which is usually regarded as a short

run effect; the other, a positive effect due to knowledge spillovers, pooling of skilled labor,

linkage effects with suppliers and others, etc., occurs over the longer run. However, some of

these mechanisms may impact more on entry than others, and the relative influence of each

changes over time.

The effects on entrepreneurial entry should also depend on the mode of external entry. In

the short run, new greenfield entry may crowd out potential entrepreneurs because it increases

competition for scarce resources – particularly when setting up a new plant entails hiring new

people.93 The main argument in the international business literature for how FDI crowds out host

country firms revolves around the assumption that salaries in MNCs are generally higher than in

domestic firms (Blomstrom & Kokko, 1998). Not only is it likely that external firms are larger,

on average, and pay better than indigenous firms, it is also likely that external firms provide

more opportunities for skilled workers in terms of perquisites, research funding, career

enhancements, and diversity of experience. External firms can access that talent directly by

hiring those capabilities, i.e. from (star) scientists, or experienced managers; contracting may

also present alternative opportunities for potential entrants thereby dampening the likelihood of

93 On the other hand, it might be the case that there is no crowding out in the short run if greenfields enter on the competitive fringe – i.e. the relative scale of entry is small – so that they do not have an impact over scarce local resources. It is also possible that they do not hire locally initially.

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starting up a new firm. The strength of such an effect should depend on the scale of entry in any

given period.94

Entry by acquisition, on the other hand, should not affect local market structure in the

short-term because it is just a change in the owner, and therefore there is no obvious effect on

entrepreneurial entry. However, insofar as an acquisition constitutes a liquidity event, then it

releases resources into the local environment (Stuart & Sorenson, 2003b). This is because the

acquiring firm may purge the target of any redundancies in capabilities or resources, in which

case employees would be let go, including those with specialized knowledge and capabilities

such as the founders, thereby triggering new entrepreneurial events. Once again, the actual effect

of an acquisition is likely to depend on the scale of entry.95

The characteristics of external entrants change upon entry and such changes could be

expected to continue to alter the opportunity structure of the region over time. The positive

effects emphasized in prior work are primarily based on two arguments: a linkage effect and a

spillover effect. Since external (foreign) entry induces the entry of more suppliers, which

increases competition at that level, thereby lowering the cost structure of the focal industry, then

this lowers the barriers to entry for potential entrepreneurs (Markusen & Venables, 1999; Georg

& Strobl, 2003). Although the theory primarily focuses on vertical linkages, increasing interfirm

94 Note that there are also reasons to believe that the effect of domestic versus foreign external entrants would differ. In the analysis both types are pooled because of the lack of a sufficient number of domestic events to analyze separately. 95 It may be the case that the number of prior acquisitions of entrepreneurial ventures in the region affects entry if potential entrepreneurs think about the prospect of being acquired as a possible exit strategy. These two explanations differ in terms of the timing and nature of the opportunity, who acts upon it, and how that information is acted upon. The first deals with expectations of potential entrepreneurs not affiliated with the target; the second is due to the liquidity event related to the target. Note that Stuart and Sorenson (2003b) argue that only acquisitions by demographically dissimilar firms result in liquidity events. Biotech-biotech acquisitions they argue, ex post, are less likely to lead to such events because other biotech firms tend to be weaker acquirers. However, their argument suggests that there is nothing intrinsic about whether an acquisition is by another biotech firm or not, rather it is other characteristics such as size, and access to financial markets (due to its status as a public firm) that would seem to matter most. Whether the target is innovative or not should also matter. In the case where acquisitions act as a pure signal we should expect similar effects since the outcomes of acquisitions are generally observable.

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interactions may also generate other kinds of externalities or spillovers due to the introduction

and diffusion of novel capabilities. These are general and indirect effects of external presence on

potential entrepreneurs, irrespective of where they come from – indigenous or non-indigenous

incumbents, or from public research organizations. The greater the number of employees

working in external firms also increases the chances of spillover through direct or indirect

communication.

There may also be more direct effects such as when external firms provide the platform

for entrepreneurial startup. The presence of external firms influences potential entrepreneurs

through recruitment, and through turnover (selecting in and out at the firm level). Within external

firms, potential entrepreneurs may develop specific kinds of complementary skills and

capabilities working in external firms, for instance, in terms of product development, marketing,

financing, how to enter external markets, etc. This could facilitate entrepreneurial entry if the

acquisition of these skills, knowledge, and networks leads to a greater expectation of success

upon entry. This is also associated with the spillover theory of entrepreneurship, which argues

that employees are often in the best position to recognize, and exploit opportunities that are not

otherwise being captured by incumbents (Acs, et al., 2005).96 In this sense the presence of

outsiders facilitates opportunity recognition (and exploitation) for potential entrepreneurs. Both

greenfields and acquisitions should contribute to those effects in the longer run.97 To summarize,

the following are expected:

H1a: the greater the new greenfield entry in the region in a given period, the lower the number of entrepreneurial entrants (in the short run).

96 The direction of this effect is also consistent with the bureaucratic push argument – i.e. that employees leave to startup firms when they cannot find sponsorship for the idea within the firms. But these are decidedly different processes yielding possibly different results. 97 But because greenfields generate linkages and other externalities where none previously existed, it is expected that they would exert a stronger effect.

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H1b: the greater the new acquisition entry in the region in a given period, the greater the number of entrepreneurial entrants (in the short run). H1c: the greater the greenfield presence in the region, the greater the number of entrepreneurial entrants into a region in the longer run. H1d: the greater the acquisition presence in the region, the greater the number of entrepreneurial entrants into a region in the longer run.

The Effects of External Entrants on Indigenous Incumbents

Survival Effects

A critical question that links external entry to the evolution of local industry is: how does

the presence of external entrants affect the fates of indigenous incumbents – are they more or less

likely to survive, and grow? As with entrepreneurial entry, the answer to this question rests

primarily on two things: (1) the nature of the externalities; and (2) the nature of the interaction

between firms in regions, especially how external firms interact with indigenous firms in a host

region, as well as the nature of the interaction between subsidiary and parent. The emphasis in

prior research (at the country level) on productivity spillovers may actually masque two different

forces – one a selection effect, the other a growth effect for those firms that survive in the long

run.

Positive externalities tend to be stronger at the regional level since knowledge spillovers

in particular are likely to be more intense in a more bounded geographic space. The effects of

external entrants on the survival and growth of indigenous firms in the region will to a significant

extent be determined by the scale of external entry. In both cases, it takes time for the new

subsidiary to generate knowledge spillovers and linkages; in the short term, linkages to either the

external environment (for greenfields) or to the parent (for acquisitions) are weak. How changes

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in their presence influence the survival and growth of indigenous incumbents also likely changes

over time (i.e. these are longer run phenomena).

External entrants should affect the survival prospects of indigenous incumbents to the

extent that externalities result from their presence. The negative externalities exerted by

greenfields on indigenous incumbents in a region are likely to dominate the spillover effects in

the short run because they absorb resources that might otherwise be available – especially skilled

individuals in small startups – and should therefore have a crowding out effect not just on entry,

but also on the survival prospects of indigenous incumbents in the short-run. Just as with its

effect on entry, an acquisition, insofar as it constitutes a liquidity event, releases resources into

the community that would not otherwise be available. More skilled people may be available not

only to start up new firms but possibly also to move to other firms – especially to other startups

that are lacking in specific capabilities. This lowers the likelihood of exit for indigenous firms on

the margins.98

However, if an indigenous incumbent survives in the short-run, it may eventually benefit

from the positive externalities, more than offsetting competitive effects in the long run. The

spillover effects, as well as linkage effects, will depend on the extent to which the subsidiary is

tightly coupled with the parent since that affects how knowledge is transferred between parent

and subsidiary. Greenfields enter strongly integrated with the parent, but it may take some time

until they adapt to and become embedded in the local environment. Often, they draw more on the

local knowledge base over time – from other firms and institutions in the region – than that of the

parent (Frost, 2001). Greenfields enter with novel knowledge and capabilities that may diffuse to

other firms and individuals, through linkages, labor mobility, etc. Thus, their presence should

98 This might actually have a stronger effect than on entrepreneurial start up in the case where the target is of relatively low quality.

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have a positive effect on indigenous survival. It is also likely that after the initial liquidity event,

the acquired firm should also have a positive effect on survival in the longer term as the

subsidiary becomes better integrated with the parent and new capabilities are created and

transferred. It may also begin to build new linkages in the region. Thus, acquisition presence

should also positively affect indigenous survival.

Because greenfields enter with no prior linkages in the region, it is also likely that over

time they generate more linkages that did not previously exist, compared to acquisitions. This

will lead to the entry of new suppliers and lower the cost structure for incumbents over time

(Markusen & Venables, 1999). Greenfields also likely import more novel knowledge in the form

of routines and capabilities, compared to acquisitions, which diffuse into the region through

training and subsequent mobility of labor. Thus, it is expected that the longer run effects would

be stronger for greenfields than for acquisitions.

H2a: the greater the new greenfield entry in the region in a given period, the greater the exit rate of indigenous incumbents (in the short run). H2b: the greater the new acquisition entry in the region in a given period, the lower the exit rate of indigenous incumbents (in the short run). H2c: the greater the greenfield presence in the region, the lower the exit rate of indigenous incumbents in the longer run. H2d: the greater the acquisition presence in the region, the lower the exit rate of indigenous incumbents in the longer run.

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Growth Effects

Similar reasoning underlies the expected effect on the growth of indigenous incumbents,

measured here as the number of employees. Greenfields absorb resources, and acquisitions

release resources in the short run, which positively and negatively affect incumbent firm growth,

respectively. In particular, greenfields provide alternative opportunities not just for potential

entrepreneurs from public research organizations, but also for those in indigenous incumbent

firms, which should lower their growth in the short run. Liquidity events due to acquisition

should also influence the growth of indigenous incumbents in a way consistent with their effects

on exit – i.e. since skilled individuals have the option not only to start up new firms, but also to

go to incumbents this, by definition, increases the growth of incumbents.

Once again, the long-run effect on growth is linked to the positive externalities generated

by external firms. In both cases, it could be expected that the effects change over time –

greenfields become better connected to the community, and targets become better connected to

their parent. However, as before, since greenfields are expected to import more novel knowledge

from other regions and generate new linkages that did not previously exist, the effect of their

presence on the growth of indigenous incumbents should be stronger than for acquisitions in the

longer run.

H3a: the greater the new greenfield entry in the region in a given period, the lower the growth of indigenous incumbents (in the short run). H3b: the greater the new acquisition entry in the region in a given period, the greater the growth of indigenous incumbents (in the short run). H3c: the greater the greenfield presence in the region, the greater the growth of indigenous incumbents in the longer run. H3d: the greater the acquisition presence in the region, the greater the growth of indigenous incumbents in the longer run.

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Alternative Hypotheses of Entry, Survival and Growth

The above hypotheses suggest that a variety of positive externalities will override any

negative externalities in the long run. The primary arguments in favor of a positive effect in the

longer run are two-fold: (1) outsiders generate dynamic linkage effects which lower the barriers

to entry for potential entrants, and lower costs and increase opportunities for incumbents; and (2)

outsiders generate spillovers in the form of knowledge and other resources that are not otherwise

captured by incumbents, which provides opportunities for potential entrepreneurs, or for other

(indigenous) incumbents. In both cases, these take time to materialize. The alternative argument

is that the negative externalities will dominate over the longer run.

In terms of new entrepreneurial entry, the negative effects could outweigh the positive,

particularly if external firms’ ability to absorb potential entrepreneurs (thereby internalizing local

skills, knowledge and capabilities) exceeds its propensity to generate them. In fact, dynamic

crowding out due to the absorption of skilled labor and other resources is more likely a longer

run phenomenon if, as the evidence suggests, greenfields start small, and usually with

transplanted personnel, and then grow over time. In other words, it takes time to develop those

opportunities for skilled labor, which includes potential entrepreneurs, especially if the

subsidiary develops a competence-creating mandate. Other opportunities in the form of

consulting contracts for those working in public research organizations (PROs) may also

neutralize entrepreneurial startup. If this is the case, i.e. if attraction and absorption are the

stronger forces, then the longer run effect would be the opposite of that proposed above – that is,

negative. In fact, this might also be the case for acquisitions assuming that they too become

attractors of local resources in the longer run, i.e. after having gone through a period of

restructuring (the basis of the liquidity event).

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External firms do not always grow, even those that are innovative. However, even if that

is the case, they can be relatively more attractive than indigenous firms in terms of the

opportunities they provide.99 So, in general, external firms are likely be stronger competitors for

local talent, which should have two effects: one, the price of remaining labor inputs is bit up,

and/or two, the quality of the pool of skilled candidates available to take up employment with

indigenous incumbents declines. This is likely to be particularly constraining when the

indigenous industry is not well developed. These two effects should have a negative influence on

the survival and growth prospects of indigenous firms. It may also be the case that external firms

exert strong competitive pressures on suppliers. They may be in a stronger bargaining position

that locks out other, especially smaller firms. Moreover, this may be a longer run phenomenon

depending of the terms of supply contracts. Thus, it is possible that spillover effects could be

dominated by the crowding out effects in the longer run in general.100

H4a: the greater the greenfield presence in the region, the lower the number of entrepreneurial entrants into a region in the longer run. H4b: the greater the acquisition presence in the region, the lower the number of entrepreneurial entrants into a region in the longer run. H4c: the greater the greenfield presence in the region, the greater the exit rate of indigenous incumbents in the longer run. H4d: the greater the acquisition presence in the region, the greater the exit rate of indigenous incumbents in the longer run.

99 It is expected, however, that the effects would be stronger for more innovative firms. The growth of innovative external firms depends on the mandate of the subsidiary. For instance, those with a knowledge-creating mandate (i.e. primarily R&D) may maintain a more or less stable size with the purpose of transferring the knowledge and innovation therein to other units (in other locations) in the firm. 100 However, the crowding out of indigenous industry, even if dynamic, is only one side of the coin in the sense that it is strictly about the absorption of labor (and other scarce resources). The spillover argument, however, takes account of both sides, i.e. it is both the absorption and the release of skilled labor that is important. The two sides are difficult to disentangle, but in essence it may be the continuous processes of firm turnover and subsequent mobility that are key because they help generate a pool of skilled labor (and other scarce resources) which facilitates the diffusion of knowledge (novel routines/ capabilities) in the long run. Also, as indigenous firms grow and expand geographically they too provide opportunities. Still, as long as external firms are growing faster than indigenous firms, then they may dampen entry, growth and survival.

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H4e: the greater the acquisition presence in the region, the lower the growth of indigenous incumbents in the longer run. H4f: the greater the greenfield presence in the region, the lower the growth of indigenous incumbents in the longer run.

The Contingent Effects of Agglomeration on External Influence Since the positive and negative externalities generated by external firms tend to co-exist

to some extent, it is important to understand the conditions under which one or the other will

dominate.101 A key factor that may resolve some of the ambiguity in prior research regarding the

influence of external (foreign) firms on the indigenous industry is the size of the agglomeration.

Economic geographers contend that agglomeration economies – a greater pool of skilled labor,

and suppliers, as well as higher levels of spillovers – should positively influence local industry

development in general. It also means greater infrastructure development. All of these factors

lower costs and enhance learning for incumbents in the region.

Because positive externalities are thought to dominate when there is a critical mass of

firms in a region, then the presence of external firms should have an unambiguously positive

effect at higher levels of agglomeration, over-riding any possible crowding out effects (static or

dynamic) when this is the case.102 After all, a higher external presence does not necessarily

imply the existence of a critical mass to be able to generate agglomeration economies (nor does it

101 Also note that if there is no effect, that does not necessarily imply the absence of spillovers or crowding out, rather, it may mean that they offset one another. There is little rationale in the prior literature as to why one effect should dominate the other, nor do we understand the conditions under which either is likely to occur. 102 Note that there is a lot of overlap in the arguments for agglomeration economies and that for (external) economies associated with outsiders. There are some key differences here, however. First, the three primary mechanisms for generating positive feedback: knowledge spillovers, pooling of skilled labor, and the pooling of suppliers operate in general. The mechanisms associated with external presence, however, relate primarily to knowledge transfer (and competition) and how they influence the indigenous industry. In other words, the internal/external distinction assumes some sort of difference between the two groups in terms of capabilities and opportunities, which affect one another. (Note that this assumes that the region has not yet matured in which case negative externalities would be expected to dominate (once again).)

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imply the existence of much of an indigenous industry in order to receive any transfers).

However, it is likely that more skilled labor is absorbed by external firms than released at lo

levels of agglomeration because there are relatively fewer alternatives.

wer

ed

up

her

103 When released, skill

labor may have no indigenous industry to go to. (This may be an argument in favor of starting

a new firm because the options are: stay put, or start up a new firm. The problem is the costs are

often too high to startup a new firm early on.) In other words, it is not just the presence of

outsiders, but also the level of development of the local industry that should determine the effect.

Another basic rationale for this effect is related to the absorptive capacity of the

indigenous industry – the positive externalities generated by external firms need an environment

in which to take root because it also assumes that the indigenous industry is sufficiently

developed to be able to absorb spillovers. This argument is more obviously applicable to the

effects on indigenous survival and growth, but entrepreneurial entry should also be positively

influenced by the increasing positive influence of externalities.104 Insofar as dynamic crowding

out also occurs in acquisitions, these effects should be operative irrespective of the mode of entry

of external firms.105 Thus, even if crowding out does occur at lower levels of agglomeration,

then the net effect generated by external presence on the region will tend to be positive at hig

103 The evidence suggests that the propensity of external firms to generate new entrepreneurial startups might be overstated. After all, this is contrary to the evidence that new entrepreneurs are more likely to come from young startups rather than older incumbents (Gompers, et al., 2005). (The spillover theory of entrepreneurship relates to incumbents in general.) Also, it is well established that in the biotechnology industry new founders came primarily from the ranks of academic scientists early in the industry’s history (Zucker, Darby & Brewer, 1998), and that later on many founders tended to come from established firms (Stuart & Sorenson, 2003a). This suggests that most of the new firms are generated not by outsiders but by insiders. Still their influence is likely to operate through indirect mechanisms. 104 It may also be that the negative externalities become less negative – not so much because external firms become less attractive in absolute terms at higher levels of agglomeration (since dynamic crowding out primarily operates through the absorption of labor), but rather because they may become less attractive in relative terms due to the presence of more firms in general. 105 It is also likely that there will be more slack in the local environment at higher levels of agglomeration, implying that the short run crowding out at any given time would be counteracted above that level. However, this does not necessarily imply a positive short run effect. Also, assuming that the interaction between the local environment and the subsidiary is more operative in greenfield than in acquisition entrants, as previously suggested, then the effect may be stronger in the former.

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levels of agglomeration through their influence on the development of a skilled labor pool, in

generating linkages, and in the diffusion of external knowledge, in general.106

H5a: the effect of greenfield presence on entrepreneurial entry in the region will be positive at higher levels of agglomeration. H5b: the effect of acquisition presence on entrepreneurial entry in the region will be positive at higher levels of agglomeration. H5c: the effect of greenfield presence on the survival of indigenous firms in the region will be positive at higher levels of agglomeration. H5d: the effect of acquisition presence on the survival of indigenous firms in the region will be positive at higher levels of agglomeration. H5e: the effect of greenfield presence on the growth of indigenous firms in the region will be positive at higher levels of agglomeration. H5f: the effect of acquisition presence on the growth of indigenous firms in the region will be positive at higher levels of agglomeration.

The Evolution of the Cluster

The theory suggests that the time since a new subsidiary entered a region should impact

on a variety of spillover mechanisms, including the training and subsequent mobility of

employees, which helps generate a skilled labor pool, as well as direct and indirect linkages with

other organizations in the region. External presence should capture some of the long run effects,

but it is problematic, however measured, since it mixes entry cohorts, the relative weight and

effect of which likely change over time. If a new cohort is overweighted and tends to crowd out

indigenous industry, compared to later cohorts, then it may show negative or no results when in

106 The assumption is that this applies to the case in which congestion effects are not operative. In the Canadian biotechnology industry, it would be fair to say that this is the case. In other words, most regions will be in either an emergent or growth stage, but few, if any, would be in a mature stage. Though it is not really possible to test for congestion effects at higher levels of agglomeration, there might be negative effects at low levels of agglomeration. This is due to the real costs of building up the infrastructure. (This suggests a U-shaped effect associated with agglomeration – costs decline with increasing numbers of firms bearing the collective costs up to a point then increasing with congestion.)

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fact later cohorts show positive effects. Lagging external entry cohorts over a number of periods

should capture most of the temporal variation, which is done here. Time also plays a role in

terms of specific period and age effects (of the industry and the firm). These factors are

controlled for here as best possible.

It is difficult to speculate on the effects outsiders have on the macrostructure. If the

effects that external firms have on the indigenous industry are symmetrical in the sense that all

externalities operate in the same direction on entry, survival, and growth, then there are two

(pure) cases. Where positive externalities dominate, the indigenous industry will be ever

expanding; where negative externalities dominate, the indigenous industry will shrink (out of

existence). In the first case, external firms would limit the over-expansion of indigenous firms as

the relative external presence declines, ceteris paribus. In the second case, as the relative

external presence increases, positive externalities would come to dominate. But, of course, not

all things are constant – external entry (and expansion) is likely endogenous to indigenous entry

to some extent. Moreover, these pure cases may not exist – different mechanisms may dominate

different processes; and even if they do have the same sign, the relative magnitudes will

determine the configuration of the region.

Various feedbacks change over time. A significant issue then is how to distinguish

between these effects. Positive feedback from agglomeration can lead to reinforcing or balancing

effects (if the main effects of outsiders are otherwise negative). There are, of course, a number of

intermediate cases that might approximate the typology of Audretsch and Fritsch (2002). For

instance, if external firms crowd out entrepreneurial entrants over time by offering alternative

opportunities, but at the same time generating spillovers that facilitate the growth and survival of

indigenous incumbents, then the average size of incumbents will increase.

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Over time as outsiders become more embedded in the local environment, they may

continue to generate new knowledge by their links outside the cluster. On the other hand, it is

also likely that their status as outsiders changes over time, and the extent to which they become

insiders will determine the extent to which they no longer experience a liability of foreignness

(Hymer, 1960). Or, if they are already insiders, especially if their existence predated that of an

indigenous industry, then the assumption is that their knowledge has already diffused, or even if

it continues, it does so at a diminishing rate. This might be supported by the supposition that they

tend to draw more on the local knowledge base over time.107 Some of these speculations are

elaborated on following a discussion of the findings.

107 This suggests two opposing hypotheses that will be examined in future research: the first, that external presence acts as a source of continual renewal and therefore always has a positive effect; the second, that it increases the effect, but at a decreasing rate.

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CHAPTER 5

RESEARCH SETTING, DATA AND METHODS

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Biotechnology: A Brief Background

Definitions of Biotechnology

Narrow definitions often limit biotechnology to genetic engineering and recombinant

DNA technology (rDNA). However, biotechnology is really an umbrella term that encompasses

“the application of science and engineering in the direct or indirect use of living organisms or

parts or products of living organisms in their natural or unmodified form” (Canadian

Environmental Protection Act). Biotechnology relates to the use of microorganisms, such as

bacteria or yeasts, or biological substances, such as enzymes, to perform specific industrial or

manufacturing processes. Applications include the production of certain drugs, synthetic

hormones, and bulk foodstuffs as well as the bioconversion of organic waste and the use of

genetically altered bacteria in the cleanup of oil spills (American Heritage Dictionary, 2004).

Biotechnology is a group of techniques and technologies that apply principles of genetics,

immunology, and molecular, cellular, and structural biology to the discovery and development of

novel products (Audretsch, 2001). Biotechnologies have broad applications and encompass many

subsectors including therapeutics, diagnostics, vaccines, agriculture, forestry, aquaculture,

veterinary, food, neutraceuticals, etc. Agbiotech includes processes involving genetically

modified organisms (GMOs) for the purposes of growth and disease resistance, and pest

management. Biotechnology also has applications to environment, energy, and engineering, most

of which deal with bioremediation, or environmental cleanup. There may also be overlaps across

subsectors – for instance, veterinary applications may include R&D in vaccine production for

animals, which may also relate to vaccine production for humans. Proteomics is the

identification of proteins and the analysis of their function in cells. The area is recognized as the

next challenge in biomedicine: diseases in the body are recognizable through the make-up of

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their proteins. Recognizing this development is more difficult than pure gene analysis. While

only 35,000 human genes exist, it is estimated that more than a million proteins exist.

Therapeutics, which deals with the treatment of disease and includes drug development,

is the largest and perhaps broadest category. Drugs can be prescription or non-prescription and

production can occur in innovative or non-innovative firms. Over-the-counter (OTC)

medications include non-prescription drugs, self-care products, herbal remedies, natural health

products, personal care products, nutritional supplements and home diagnostics. Innovative firms

are usually those that spend substantial sums of money on R&D and patent their inventions on

the way to commercializing them. Non-innovative firms are more imitative, such as generic drug

producers which often focus on the production of off-patent drugs – i.e. those drugs developed

by innovators. In reality, these distinctions are blurred by the fact that some firms produce both

prescription and non-prescription therapeutic products. In some cases, biotech firms began as

contract research organizations (CROs) – in order to generate revenues, but had their own

research and development programs on the side. Many of the pioneers in biotech now refer to

themselves as pharmaceutical firms, further confounding easy distinctions.

A Brief History of Biotechnologies108

The word biotechnology can be traced to 1917, when it was used to refer to a large-scale

production of materials from microbes grown in vats. (See Table 5 for a summary of key events

in the history of biotechnology). Although biotechnology is thought of as a new industry, its

roots are traceable to over 6,000 years ago to when beer was first fermented. Indeed, early

biotechnology was almost exclusively focused on fermentation techniques to produce drinks,

food and fuel. Dairy farming was developed in the Middle East going back as far as 4000 BC. 108 Much of this history comes from the “About Biotechnology” and “Biotech Ontario” websites.

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Egyptians used yeasts to bake leavened bread and wine, and Egyptians, Sumerians and Chinese

developed techniques of fermentation, brewing and cheese-making by 2000 BC. By 1500 AD,

acidic cooking techniques led to sauerkraut and yogurt production – two examples of using

beneficial bacteria to flavor and preserve food, and the Aztecs made cakes from Spirulina algae.

A number of key events occurred in the early modern period to establish the science base

of the industry. Charles Darwin’s On the Origin of Species published in 1859, lay down some of

the intellectual foundations of contemporary biosciences. A couple of years after that, French

chemist Louis Pasteur developed pasteurization – a process for preserving food by heating it to

destroy harmful microbes. In 1865, Austrian botanist and monk Gregor Mendel described his

experiments of heredity, and in the process founding the field of genetics. In 1879, William

James Beal developed the first experimental hybrid corn. In 1910, American biologist Thomas

Hunt Morgan discovered that genes are located on chromosomes. In 1928, Fred Griffith, an

English medical officer, discovered genetic transformation – that genes can transfer from one

strain of bacteria to another. Canadian scientists also made significant contributions to the

emerging field. Most important was the 1921 discovery of insulin by Frederick Banting, Charles

Best, James Collip and John James Richard MacLeod at the University of Toronto, which

subsequently led to the development and use of insulin in the treatment of diabetes.109

Modern biotechnology or second generation biotechnology grew out of molecular

biology and genetic engineering which emerged after World War II. In its early stages, it

involved the integration of microbiology, biochemistry and chemical engineering for large-scale

fermentation, sewage treatment, and for applications in the chemical and pharmaceutical

industries. The term ‘genetic engineering’ was coined by Danish microbiologist A. Jost in a

109 Also of importance to the Canadian context was Canada's first diptheria antitoxin developed and produced at the University of Toronto Antitoxin Laboratories (later renamed Connaught Laboratories), in 1914.

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lecture on sexual reproduction in yeast, in 1941. In 1943, Oswald Avery, Colin MacLead and

Maclyn McCarty used bacteria to show that DNA carries the cell’s genetic information. James

Watson and Francis Crick described the double helix of DNA in 1953, using X-ray diffraction

patterns. Some Canadian contributions during this period included Olah Hornykiewicz’s

discovery in the 1960s that Parkinson’s disease patients had less dopamine in their brains, which

continued to contribute to the development of L-Dopa as a therapeutic agent. In 1961,

researchers in Toronto also discovered the hematopoietic stem cell.

In the early 1970s, Paul Berg, Stanley Cohen and Herbert Boyer introduced recombinant

DNA techniques by developing ways to cut and splice DNA. The 1973 breakthrough discovery

of recombinant DNA became the platform for research in cloning, genomics and proteomics. In

1975, scientists organized the Asilomar conference to discuss regulating recombinant DNA

experiments. Another significant advance was George Kohler and Cesar Milstein’s discovery in

1974 that fusing cells could generate monoclonal antibodies. The first genetically engineered

product – human insulin produced by Eli Lilly and Company using E. coli bacteria – was

approved for use in 1982. In 1984, Kary Mullis developed a polymerase chain reaction (PCR) to

mass-produce specific DNA fragments. The first release into the environment of a genetically

engineered plant (tobacco) occurred in 1986. The year following saw the first release of

genetically engineered microbes in field experiments. Some other Canadian achievements during

this period included the discovery of P-glycoprotein in 1974. In 1983, Toronto researchers led by

Tak Mak of the Ontario Cancer Institute discovered the T-cell receptor, which has been

described as the “holy grail” of immunology.

In 1990, the international Human Genome Project was launched. The goal of the project

was to identify and sequence all of the genes in the human genome. By 2001, due to resource and

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technological advances, the Human Genome Project accelerated and a map of the entire human

genome sequence with analysis was published. Scientists can now manipulate DNA, the

fundamental building block of life. Early results range from the manufacture of genetically

engineered drugs to the cloning of Dolly, the sheep. The completion of the initial sequencing and

draft of the human genome is expected to facilitate the identification of new kinds of drugs for

commercialization. In addition to improved drug therapies, disease diagnosis and treatment

benefit from genomics, allowing researchers to test individual genetic profiles against a group of

drugs available for a specific condition in order to identify the most effective treatment. A new

challenge has emerged in proteomics – to map all human proteins.

The Biopharmaceutical Industry

The Boyer and Cohen discovery led to the creation, in 1976, of Genentech, sometimes

considered to be the first biotech firm (McKelvey, 1996).110 (Genentech established a Canadian

subsidiary in 1990, at first settling in Hamilton, and later moving to Mississauga, part of the

Toronto CMA.) On the one hand, perhaps too much is made of the Genentech story because

various aspects of what is now regarded as biotech was already being done then. On the other

hand, there is little doubt that this event did provide the catalyst to commercialize inventions

made in the labs of public research organizations. At that time, pharmaceuticals was a mature

industry consolidating on a global basis – but the nature of competition changed with the

introduction of new technologies, especially rDNA (see Galambos & Sturchos, 1998; McKelvey,

1996). The new biotechnology constituted a disruptive technology – one that posed a threat to

the pharmaceutical industry.

110 An important part of this story is the role of Robert Swanson, a venture capitalist who was able to see the commercial possibilities of the Cohen-Boyer invention. It was Swanson who convinced Boyer to co-found Genentech in 1976, thus combining science with an entrepreneurial vision (McKelvey, 1996).

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The industry had experienced a series of decisive changes, starting with the development

and gradual acceptance of the germ theory of disease at the turn of the last century and

accelerating during the chemotherapeutic revolution of the 1930s and 1940s. Synthetic organic

chemistry and soil microbiology generated significant opportunities for pharmaceutical

innovation, but to take advantage of those situations, firms had to make strategic commitments to

entirely new capabilities in R&D, manufacturing and marketing. In the 1940s and 1950s,

advances in virology provided the basis for more targeted pharmaceutical R&D. In each of these

transitions there were winners and there were losers. The next, partially overlapping wave of

innovation was grounded in recombinant DNA and molecular genetics, and is sometimes

referred to as the “biotech revolution” (Galambos & Sturchos, 1998). One characteristic that

made this revolution different than those in the past was that the source of innovation was small

startups rather than large private research labs. This was primarily because these new

technologies were disruptive in nature such that the pre-existing players did not have the

capabilities to develop new products on the basis of these technologies.

Despite a common science base, the biotech industry developed somewhat independently

of the pharmaceutical industry at first. The emergence of the industry was also in part due to

changes in institutional norms related to the commercialization of publicly funded research. The

new biotechnologies developed out of a new set of technologies founded on basic science

conducted in PROs. The new dedicated biotechnology firms (DBFs) were highly specialized in

sets of niche technologies. At the same time, the pharmaceutical industry began to realize the

efficacy of some of these new technologies, but that they were lagging in terms of R&D.

Pharmaceutical firms essentially followed one of two strategies to regain their ground – they

tried to develop capabilities internally, and/or they developed alliances with, or acquired DBFs

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that had those capabilities (Galambos & Sturchos, 1998). 111 Once a new industry began to

emerge, the DBFs required other capabilities and resources that did not exist in the PROs. Over

time, there was a convergence in the technologies used by DBFs and pharmaceutical companies,

primarily because of their evolving mutually reinforcing relationship. The DBFs also needed the

pharmaceutical firms to access capital, and other capabilities such as marketing know-how, etc.

As a result of these interactions, pharmaceutical firms began to gain ground once again, in part

because they also had inherent advantages due to their economies of scope. Still,

interdependencies persisted primarily because the DBFs were more innovative.

Boundaries of the Industry

Over time, distinctions between pharmaceutical and biotechnology firms became

increasingly artificial, especially as the biotech industry matured and individual firms grew up to

resemble fully integrated pharmaceutical firms. Similarly, many firm characteristics such as size

or organizational structure became inadequate in distinguishing the two since structure itself may

have been a function of size, and/or perhaps time of entry. The older pharmaceutical firms were

more fully integrated, but some of the younger ones eventually evolved into that; on the other

hand, some could have grown up at a time when it was easier to access resources externally.

Consequently, they developed a more focused structure and relied more on external linkages for

other resources.

The background suggests the necessity of including pharmaceutical firms in a study of

the interaction between external and internal firms, if for no other reason than the fact that both

theory and anecdotal evidence suggest that the pharmaceutical firms, which are often external by

111 Galambos & Sturchos (1998) argue that U.S. anti-trust policies were at least partly responsible for the evolution of this networked structure because the pharmaceutical firms were somewhat restricted in acquiring other firms, they developed a cooperative linkage approach to knowledge acquisition.

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dint of the fact that they are typically multinational, generate some kinds of externalities. But it

also suggests a paradox – the pharmaceutical industry pre-dated that of biotech, and so there is

no clear causal relation between the externalities they are supposed to generate for DBFs. This

suggests that externalities are really potential forces that need a receiver in order to be absorbed.

In this sense, externalities could only be absorbed after an industry began to develop; they could

not act as a generative mechanism. In this case, it was the science base embedded in public

research organizations, combined with commercialization capabilities that acted as a generative

mechanism. But once formed, externalities could take hold.112

There were no clear demonstration effects of foreign (pharma) in terms of technology, or

in terms of organizational structure, that would have prompted entry of indigenous firms. This

was because the nature of the R&D in pharmaceutical firms was more scale dependent and

exploitative rather than exploratory in the early stages of the biotech revolution (Gambardella,

1995). (In fact, it may be the case that demonstration effects acted in reverse initially – DBFs

demonstrated the efficacy of the new technologies which few of the pharmaceutical firms had

capabilities in.) Early pioneers such as Genentech would have been more influential in terms of

their demonstration effects, not just in terms of entry, but other critical milestones such as going

public in 1980. Genentech was, as Galambos & Sturchos (1998) note, “the exception that was to

prove the rule.” In other words, the pharmaceutical firms were too different, at least initially, to

demonstrate anything of real relevance to DBFs.

However, once they entered, DBFs benefited from direct linkages with large

pharmaceutical companies, especially in terms of financial support as well as established

organizational capabilities in clinical research, regulatory affairs, manufacturing, and marketing.

112 This assumption would be consistent with Kokko’s (1996) contention that domestic firms can only benefit from foreign firms when the tech gap is narrow to be able to absorb the spillovers they generate.

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In return, the pharmaceutical firms received technological expertise and/or patents from the

smaller firms (Galambos & Sturchos, 1998). Pharmaceutical firms were also important to DBFs

later in the development of the biotech industry in terms of indirect linkages, and the training of

high level managers – but this latter externality in particular was likely not useful until the DBFs

grew to a certain size.

The Biotechnology Industry in Canada

The preceding discussion is important in the Canadian biotechnology industry because of

the dominance of large foreign multinational pharmaceutical firms. In general, the biotech

industry in Canada is an ideal setting in which to examine the effects of external entrants on

entrepreneurial entry, and on the survival and growth of indigenous incumbents because of the

relatively high degree of foreign ownership and control – currently a little over 30% in terms of

the number of firms overall.113 (See Figures 1 and 2 for proportions of external firms in selected

regions.) Canada has the second largest population of biotech firms in the world, next to the U.S.

Since the inception of the biotech industry in the late 1970s, over 600 firms have entered into

various subsectors in Canada, the large majority of which are in therapeutics and diagnostics. Of

these, almost 500 firms have been indigenous entrepreneurial entries, 102 of which exited by the

end of 2003.

Note that these entrepreneurial entrants are indigenous in the sense that they started up de

novo in a particular region in Canada. The pre-entry history of only about half of these firms is

known with any certainty. Of the ones that are known, about 159 of 229 (72%) had a public

research organization as a parent. Most of the remaining firms with known origins were second

113 The proportion is higher when all external entrants are included (i.e. including separate units), and much higher still when pharmaceutical firms are included, especially when employment is taken into account.

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generation or later startups which can trace their origins to a Canadian public research

organization. It is not clear whether this is truly representative of the overall population. The

unknown cases are either those that died some time ago, or those in sectors not related to human

health (which tended to publicize their affiliations much more than firms in other sectors).

Assuming this is somewhat representative, the relatively high proportion of first generation

indigenous firms of known origin gives a sense that this is still a relatively young industry.

External firms have tended to enter into regions and sectors in which there are large

agglomerations of firms. Since 1976 there have been 85 greenfield entries and 95 acquisition

entries, the large majority of which have been from foreign sources.114 Add to this the pre-

existing pharmaceutical firms, which count as 64 units across all regions at any given time (but

in far fewer firms), and that brings the total number of greenfields that entered at any time to

139. Taking these into account, employment by external (primarily foreign) controlled firms

accounts for the majority share in Canada. Firms that have expanded within Canada by creating

multiunit structures have tended to establish a presence (i.e. enter by greenfield) in other regions

in the country to develop other capabilities such as production and distribution. It is rarely the

case that a domestic firm has two or more R&D sites.

Roughly 20% of indigenous firms were acquired between 1976 and 2003. Of the 95

mergers or acquisitions, 13 were local, almost all of which were terminal upon being taken over

by another local indigenous firm. These are not included in the external measures. About 18% of

the 82 external acquisitions, 15 were also terminal upon acquisition, two thirds of which were 114 Note that it is impossible to be sure that every firm in the industry has been accounted for. Firms were tracked somewhat closely by private sources such as Contact Canada from 1989 onward. Government sources, such as Industry Canada, began tracking biotech firms in the mid-1990s. Another important source of information about biotech firms has been university-industry liaison offices, which only started tracking their spinoffs in the late 1990s. However, they also had an incentive to publicize their successes in spinning off firms and consequently many have developed comprehensive lists of spinoffs going back decades. Substantial efforts have been made through these and many other sources – especially news sources – to ensure all possible firms are included going back to the inception of the (modern) industry.

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Canadian acquirers. Another 11 did not survive past 2003. Of these 82, there is almost the same

number of Canadian and U.S. acquirers – 32 and 33 respectively; the remaining 17 were other

international, primarily European acquirers. Naturally, more acquisitions tended to occur in

regions with more potential targets, and therefore these are concentrated in later periods.

Note that there is a lot of variation in external presence across regions in Canada. Two

regions in particular – Montreal and Toronto – already had a strong external (in this case foreign)

presence from traditional pharmaceutical firms by the start of the study period. External entry

occurred much later in other regions, if it happened at all. Furthermore, there is substantial

variation in the distribution of types of external firms – greenfield or acquisition, as well as

origins – domestic versus foreign.

Also note that the data provide a unique opportunity to test the agglomeration hypotheses.

Since the data start at the beginning of the industry, it is possible to distinguish between early

and later stages of local industry development, and the effects outsiders have at different levels

of agglomeration. Prior studies examining the effects of foreign entry on domestic entry, exit or

growth, which is analogous to the question addressed here, presumably cannot test for this

because of their emphasis on mature industries (in many cases pooling industries), and/or

because of the relatively short panels used.

Regional Characteristics

Most biotechnology firms in Canada are concentrated in and around seventeen different

cities: Toronto, Montreal, and Vancouver have by far the greatest concentrations among these,

which together comprise just over half of all firms. As has been the case elsewhere, all levels of

government have been very active in promoting the growth of biotech clusters either through tax

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incentives, direct subsidies, or indirect subsidies through funding of R&D in the life and

biological sciences. Policy-makers have also been keen to replicate the success of other hi-tech

clusters such as Silicon Valley – especially in the biosciences where an opportunity has been

seen to exist. This is seen in various policy initiatives such as the National Biotechnology

Strategy, which was initiated in 1983 to provide the National Research Council (NRC) resources

to conduct research in various subfields of biotechnology. The Canadian Biotechnology Strategy,

launched in 1998, was an extension of the earlier program, but with a much expanded mandate

to: modernize the regulatory system, support cutting-edge R&D, increase access to investment

capital, strengthen Canada's intellectual capital, engage Canadians directly in shaping relevant

policies, create highly qualified human resource capacity, and update patent laws.

To support this mandate, five NRC research centres across the country were augmented:

The Institute for Marine Biosciences in Halifax (founded in 1952), The Biotechnology Research

Institute in Montreal (founded in 1987), The Institute for Biological Sciences in Ottawa (founded

in 1916), Institute for Biodiagnostics in Winnipeg (founded in 1992), and the Institute for Plant

Biotechnology in Saskatoon (founded in 1948). In addition to these research institutes, the NRC

has an Industrial Assistance Research Program (NRC-IRAP) to provide technological support to

small and medium sized enterprises to stimulate their innovation capabilities.115 One provincial

research institute – the Alberta Research Council (ARC), founded in 1936 in Edmonton, has also

played an important institutional role in that province since it began doing biotech research in the

early 1980s. Other related agencies include the Canadian Agri-Research Council (CARC), which

focuses on agricultural biotechnologies. In the late 1990s, regions with a ‘critical mass’ of firms

115 The NRC’s budget for biotech related research increased from $80M in 1998 to $130M Canadian in 2003 (Canada, 2004).

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also received government assistance in setting up business incubators designed to facilitate even

more entry, as well as the growth of (indigenous) incumbents.

In addition, the various research funding agencies, of which the Canadian Institutes of

Health Research (CIHR) and the Natural Sciences and Engineering Research Council (NSERC),

are the most important, provide financial support for basic and applied biotechnology related

research (Canada, 2004). Universities are, of course, also key institutions. Among the more

important are the University of Alberta (Edmonton), the University of British Columbia

(Vancouver), the University of Saskatchewan (Saskatoon), the University of Toronto, McGill

University (Montreal), and Queen’s University (Kingston).116 Many of these institutions have

played an important role in generating new knowledge – in fact, almost 60% of U.S. patents

granted in biotechnology related patent classes are owned and controlled by these organizations

(Canada, 2004b). By region (CMA), Toronto by far outstrips all others in terms of number of

patents granted; next in order, are Montreal, Vancouver, Ottawa, Edmonton, Calgary, Saskatoon,

Quebec, Hamilton, London, Victoria, Winnipeg, Kitchener, Sherbrooke, and Halifax.

Direct and indirect subsidies to firms have also played a role in both promoting new

startups and attracting new greenfields to particular regions. Most federal funding of R&D to

businesses operates through the Scientific Research and Experimental Development (SR&ED)

tax credit programs, which are considered among the most generous in the world (OECD, 2004).

By 1983, the value of the tax credits exceeded the value of R&D grants, at which time grants

constituted about 7% of Business related R&D (BERD), subsequently declining to 1.3% of

BERD by 2000. R&D tax credits to all manufacturers rose to an average of 18% of BERD by

1989. The average is much higher in more innovative industries. In addition to these tax credits,

there are a myriad of provincial programs supporting business-level R&D, primarily through tax 116 Saskatoon is a centre of agricultural biotechnology.

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credits (Czarnitzki, et al., 2004). The government of Quebec has been especially active in

providing tax incentives to firms (Beaulieu, et al., 2005), and in creating new startups, as well as

in attracting firms from other parts of the country and the world. Unfortunately data on R&D

related expenditures is not available going back beyond a couple of years.

Competition for biopharmaceuticals markets is normally global in scope, which is one of

the reasons non-U.S. firms will patent their inventions in key markets such as in the U.S.

Competition for key factors – skilled labor and local suppliers, in particular – tends to be more

local. On the other hand, this can be broader depending on the specificity of the skills and

resources required. The suppliers may be material suppliers of lab equipment, software for

analysis, or service organizations such as law firms with expertise in patent law. Perhaps more

important, however, are the contract research organizations (CROs) and contract manufacturing

organizations (CMOs). (A number of these indigenous firms were subsequently acquired by

foreign firms, which seems to reflect a global trend in the consolidation of this sub-sector of the

industry.) Service organizations such as CROs and CMOs may be particularly important in

lowering the threshold for entry since it diminishes the startup requirements – i.e. in their

presence, it is not necessary to operate a full service lab, or even to manufacture products that are

developed.

Venture capitalists have been instrumental in providing financial resources and in many

cases, managerial expertise especially to small startups. However, venture capitalists also tend to

specialize geographically as well as by industry (Powell, et al., 2004).117 They typically need to

be geographically proximate because they tend to be activist in their relationship with biotech

117 While it appears that patterns are similar in Canada, there are some important exceptions. The Canadian Medical Discoveries Corporation (CMDC), in London, Ontario, which has been instrumental in supporting a number of biotech startups throughout Canada, is not strictly local. In fact, geography is not a strong consideration in backing a young startup [conversation with Cal Stiller, Chair CMDC, November 2002].

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firms – often providing financial and managerial expertise that is frequently absent in young

startups. Also, VCs enter into an investment with the objective of exiting in the not too distant

future. Their exit strategy can be to take the firm public, or to make it an attractive target for

acquisition.

The pre-existing pharmaceutical base in Montreal and Toronto in particular allowed the

emerging biotech firms to develop critical linkages to ensure their survival and growth. The

importance of these primarily foreign firms is thought to be essential: they provided financing in

many cases (milestone payments); and they legitimized the startups especially in the early stages

of industry development because they could evaluate the efficacy of the new innovations. At the

same time, the pharmaceutical firms became stronger by absorbing new technology and ideas. In

turn, they may have generated spillovers because of their economies of scope, which allowed

them to combine capabilities across product areas. Of the three largest agglomerations,

Vancouver differs from the others in that it did not have a strong pharmaceutical base to start

with. The large pharmaceutical firms tended to follow rather than lead entry into that region.

Today many of the major pharmaceutical companies have a presence there, but they tend to be

sales, marketing and distribution facilities rather than production or R&D. On the other hand,

Vancouver has experienced a number of acquisition entries.

The Regulatory Environment

The regulatory regime has also had an important role in the development of the industry

in Canada. While governments at all levels have recently been proactive in developing the

industry, that was not always the case. In fact, the Eastman Commission of 1985 reported:

“Canada does not now possess either the scientific manpower or the physical infrastructure that

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would make it a major world centre for pharmaceutical research. Nor in the opinion of the

commission would it be wise for governments to seek to create such an environment in

competition with heavily supported long established centres in other countries.”118 Tariff policy

was designed to allow easy entry by multinationals into product areas that were not already well

served in the Canadian market. Thus, lower tariffs were levied on products in which there were

few alternatives compared to those in which there were many. This promoted investment,

primarily by U.S. firms, which first established sales branches in Canada for their pre-existing

lines. Later, many of these firms set up manufacturing facilities (Gordon & Fowler, 1981).

One of the oft-cited reasons for what was believed to be Canada’s slow start in

pharmaceuticals was due to the regulatory environment. In 1969, the decision was made to

provide compulsory licenses (CLs) on imported products, a decision that benefited the Canadian

generic drug sector. The policy was intended to lower prices and to benefit smaller Canadian

licensee firms, which imported the bulk raw material and manufactured the final drug in Canada.

Between 1970 and 1983, 181 CLs were issued on 58 drugs by 30 firms. In 1983, 66 licenses

were worked, which the firms sold under their brand name. Under a CL, the licensee had only to

establish bioequivalence and conduct purity tests, meaning that generic producers saved on the

enormous sunk costs of R&D and extensive clinical trials. It normally took 2-3 years to introduce

generic versions of the drug. Some domestic firms such as Novopharm and Apotex became very

strong in the field of generic drug manufacturing. For example, up to 1983, Apotex received 16

licenses out of the 181 issued and worked 13 of them.

Until the early 1990s, Canada was alone among developed nations in using CLs;

Canada’s trading partners did not favour their use and discouraged R&D investment in Canada.

118 The Royal Commission on the Pharmaceuticals Industry, was Chaired by Harry Eastman, an economics Professor at the University of Toronto.

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The Canada-U.S. Free Trade Agreement led to the first modification of compulsory licensing in

1987, through Bill C-22. The amendment provided protection for firms introducing new drugs

for a period of seven years. However, to get the CL, the licensee would have to manufacture the

necessary fine chemicals within Canada. If the chemicals were imported, the licensee could only

produce such drugs after 10 years of protection (Lexchin, 2003). Prior to Bill C-22, generics

were coming into the market at regular intervals. More pressure to conform to standards among

other OECD countries first through the North American Free Trade Agreement (NAFTA) and

later through the Trade-Related Intellectual Property (TRIPs) agreement led to an increase in the

patent term to 20 years and to the abolition of CLs (Bill C-91).119 (The Patented Medicines

Prices Review Board (PMPRB) was also established in 1987 to monitor the prices of

pharmaceutical drugs.) Henceforth, the Canadian system followed a first-to-file patent system,

similar to the European Union, but different than the U.S. first-to-invent system.

The Canada Health Act covers regulations for both prescription and non-prescription

drugs. The regulatory process for applications in human subjects is very costly and time

consuming, involving three phases of clinical trials during which time the firms must

demonstrate the efficacy of the drug and compliance with the regulations. Bringing an innovative

drug to market can take 10-12 years of R&D and can cost up to $200-$300 million Canadian. A

generic drug, by contrast, can take 2-3 years and cost $1-3 million Canadian.120

Over time, the R&D efforts of biopharmaceutical firms increased. In 1988, the

percentage of R&D spending to sales by all firms reporting revenues from patented drugs was

around 6.5%, this steadily increased throughout the 1990s, peaking in 1997, and levelling off to

119 An amendment to the Patent Act and the Food and Drug Act in 2003 allowed limited compulsory licensing to generics to produce medicines for the developing world. 120 The Proprietary Patented Medicines Act of 1909 was the first legislation to distinguish between prescription and non-prescription drugs. An amendment to the Act in 1919 was designed to ensure truth in advertising. A new Act in 1925 required that all products be labelled with information on ingredients.

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around 10% in 2003 (PMBRB, 2003).121 A good deal of this spending, however, has been on

production process improvements and applied R&D rather than on basic research. Despite the

apparent robustness of the industry in terms of the number of firms, some indicators suggest that

the indigenous Canadian industry is still more imitative than innovative (OECD, 2004).

A Brief History of the Canadian Biopharmaceuticals Industry

Historically, Canada was known as a country of branch plants. Canadian tariff policy was

primarily designed to protect Canadian industry from foreign competition, and was guided by a

fear of foreign domination, primarily U.S. Traditionally it was thought that branch plants

provided little benefit outside of the plant itself (see Safarian, 1973 for an exception). In most

cases, foreign pharmaceutical firms entered the Canadian market to get around tariffs, and were

simply market seeking – the R&D on new products was done elsewhere and produced and

distributed through branch plants in Canada.122 Many of the earliest pharmaceutical firms in

Canada were foreign – primarily from the U.S. In 1969, 57% of pharmaceutical firms were

Canadian; the remainder were primarily U.S. subsidiaries. However, the majority of the jobs

were in foreign subsidiaries (Gordon & Fowler, 1981). At the inception of what became known

as the biotechnology industry, the pharmaceutical industry was largely comprised of foreign

firms, which operated in many of the largest Canadian cities.123 Throughout the 1970s and 1980s

there was growing concern that Canadian tariff policy was hindering its competitiveness because

it raised costs and/or lowered quality of goods (although in the context of drugs, quality

standards were regulated by Health Canada). The Canada-U.S. Free Trade Agreement (1989)

121 In 2003, there were 87 firms, many foreign, that had revenues from patents. Other firms may have had patents but had no revenues. Also note that back in 1988, the pharmaceutical companies had committed to a 10% level in exchange for a change in the regulations leading to more patent protection.

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and the subsequent North American Free Trade Agreement (1994), in essence, put the debate to

rest.

Similar to how the modern biotech industry developed in other countries, public research

institutes and universities were instrumental in spinning off new (indigenous) DBFs and were

therefore central to the evolution of the industry. Perhaps the earliest example of this is the

University of Toronto’s Anti-Toxin Laboratories, founded in 1914, and privatized shortly after

its inception when Albert Gooderham provided money and facilities for the fledgling venture.

With new money, facilities, and a new name – Connaught Labs – the firm first focused on

developing antitoxins to combat the many medical scourges of the day, and later expanded into

other areas especially following the discovery of insulin by University of Toronto professors

Banting and Best, and their colleagues.124 In 1977, Connaught was the first company in the

world to produce the polio vaccine. The world-renowned labs were acquired by Institut Merie

of France in 1989; in 1996 it became a wholly owned subsidiary of Rhone-Poulenc (France), and

in 1999, when Rhone-Poulenc merged with Hoescht (Germany) to form Aventis, Connaught’

name was changed to Aventis Pasteur. It has since merged with Sanofi, to form Sanofi-Aventis.

It continues to have a large research presence in Toronto to this day, employing about one

thousand people.

ux

s

122 The early thinking on the costs and benefits of foreign multinationals was probably to some extent derived from direct observation. (See Morck, et al., 2005 for an historical account of Canadian tariff policy.) 123 The main pharmaceutical association in Canada is the Pharmaceutical Manufacturers Association of Canada (PMAC), now known as Rx&D, which was founded in 1914 and represents 62 companies, about 50 of which are subsidiaries of foreign multinationals. Canadian-owned generic companies formed the Canadian Drug Manufacturers Association in 1963, which now has 14 members. The Nonprescription Drug Manufacturers Association of Canada (NDMAC) represents about two dozen health care firms, some of which are divisions of the pharmaceutical companies. Biotech firms are represented in both, as well as by other associations. In general, there is a lot of overlapping membership. 124 The University of Toronto’s Anti-Toxin Laboratories was established by Gerry Fitzgerald, a professor of immunology, and later dean of the medical school. The facility was renamed Connaught in honor of Gooderham’s good friend, the Duke of Connaught. Fitzgerald was enormously influential in the early development of immunology, and its commercialization – though his primary emphasis was on public health (see Fitzgerald, 2004).

111

By the time the biotech revolution took hold, two things appeared to be happening: first,

the pharmaceutical firms were competing on two fronts – against the generic producers and the

new DBFs. Also, the generic producers began to gain ground, in part due to Canada’s CL policy.

Once they grew large enough and had enough resources behind them, they began investing in

more innovative activities. Aside from Connaught, the generic firms were essentially the only

Canadian firms in the industry in the late 1970s. Compulsory licensing opened a space for the

generics to enter – Apotex and Novopharm (now Viventia, owned by Teva Pharmaceuticals of

Israel) were the most important of these, both of which later moved into more innovative

activities. For example, Apotex was founded in Toronto in 1974, and is one of Canada’s oldest

domestic (generic) biopharmaceutical firms (that has not been taken over); it is also one of the

largest, with 5300 employees across 3 cities in Canada. Although it started out as a generic drug

producer, today it spends more than 20% of its revenues on R&D (company website), and now

has over 20 U.S. patents, all granted since 1995 (USPTO).125

The Case of Montreal

The case of Montreal is illustrative of one process by which the biopharmaceutical

industry evolved in Canada. The biopharmaceutical industry in Montreal is now home to over

274 firms and supporting organizations. It has around 80 indigenous biotech firms, and about 40

foreign firms, which entered by a variety of means. A good number of the latter are

pharmaceutical firms such as Merck, Wyeth, Abbott Labs, Aventis, Pfizer, etc. Some of the

larger indigenous biotech firms are generic pharmaceutical firms, but there are a large number of

DBFs, many of which are spinoffs from local universities. There are about 14 CROs, the largest

125 Remarkably, not only has Apotex not been taken over, but it is also still privately held by founder Barry Sherman.

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of which are Clinical Trials BioResearch (CTBR) and MDS, both with over a thousand

employees each.

The region also has a variety of important supporting institutions such as the NRC’s

Biotechnology Research Institute (BRI), which currently has about 800 employees. Other

research institutes include the Institut de Recherches Clinique de Montreal (IRCM) and the

Institut Armand Frappier (IAF). Over a hundred smaller institutes are closely affiliated with the

major research universities and hospitals. The university system, especially McGill University

and l’Universite de Montreal, also provides support in their various bioscience departments, labs,

and other facilities, through training of research scientists and in conducting basic research. In

addition to these functions, the universities, McGill in particular, have been successful in

spinning off a number of biotech firms.

A number of foreign pharmaceutical firms have a long history in the region. In 1976,

there were 12 foreign pharmaceutical firms in Montreal, some of which had roots in the region

going back to the late nineteenth century (e.g. Wyeth (1883)). Most of these traditional

pharmaceutical firms entered by greenfield; however, more recently, as the pool of potential

targets has increased, there have been more acquisition entries.126 Since 1976, 28 new

greenfields have entered, the majority of which were foreign, and there were 12 acquisition

entries. The few early domestic firms that existed tended to be OTC or generic producers, thou

there were a few important e

gh

xceptions.

126 These subsidiaries followed a typical locational pattern – locating first in the downtown core, and then migrating to the suburbs in their later stages of development as they grew in size and required more space. Presumably this was to minimize on costs due to congestion in the center. Also, since most of the early foreign firms were traditional branch plants simply producing and marketing standardized products, their human capital requirements would not have been as high as today’s more R&D intensive firms, making it more feasible to find workers in the suburbs. In 1991, all of the foreign subsidiaries were located in the outskirts of Montreal; subsequently, two thirds of new foreign entrants have located in the downtown core, in close proximity to many of the indigenous startups that were spun off of research institutes or other local firms.

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Today, employment in foreign owned biopharmaceutical firms accounts for almost 70%

of total employment in the industry in the region. These foreign firms have been a catalyst for

certain kinds of development in the region. Entrepreneurial entry resulting from acquisition may

be a second-order effect, i.e. due to turnover (Stuart & Sorenson, 2003); however, these

outcomes can often be traced. The indirect competitive or spillover effects (usually by

greenfields) are more difficult to trace than some of the direct effects. For instance, growth and

survival might only be traced through labor mobility; however, other externalities such as

demonstration effects are generally unobservable and can only be inferred by proximity in time

and place.

Pre-history of the Biotech Industry in Montreal127

The early history of the pharmaceutical industry seems to have been characterized by

moves that are consistent with economic explanations of market power (eliminating

competitors), and/or scale economies. In some cases, foreign multinationals acquired local

domestic firms. In other cases, early dynamics were dominated by events external to the region –

consolidation on a global level led to acquisitions and consequently rationalization at a local

level. The later period, subsequent to the “biotech revolution”, seemed to be motivated more by

knowledge seeking.

Merck’s acquisition of Charles E. Frosst & Co. in 1965 is one example of an acquisition

motivated by economic considerations – whether market power or scale economies, is difficult to

assess. The Canadian firm Charles E. Frosst & Co. was founded in 1899, when the Canadian

pharmaceutical industry was still in its infancy. From the start, Frosst was an innovative

127 Most of the history in this section was constructed through an extensive review of the news coverage in various news databases, supplemented with information from company websites and annual reports.

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company, introducing new products such as the numbered analgesics known as 217® and 222®

– products that are still used in Canada. During the mid-forties, Frosst pioneered nuclear

medicine in Canada by developing the country's first radioactive pharmaceutical products, for

sale both at home and abroad. In 1959, the company went public, and was the largest Canadian

pharmaceutical manufacturer. Merck (New York) had a branch in Montreal dating back to 1911,

at first as an importer and seller of pharmaceuticals and fine chemicals and, by 1930, as a

manufacturer.128 More importantly, Merck was following the same path of innovation and

discovery as Frosst. Merck was producing Vitamin B1 in 1940 and penicillin in the

Commonwealth’s first deep fermentation unit by 1944. Following that, the company's pursuit of

vitamin research led to discoveries in sulfa drugs, penicillins and corticosteroids. Its work in

nuclear medicine began with a request from the National Research Council for the production of

specialty compounds to be used as tracers in the study of chemicals and biological processes. In

1953, Merck merged with another drug manufacturer, Sharp & Dohme (UK), with Canadian

headquarters in Toronto, and became known as Merck Sharp & Dohme Canada Limited in 1961

(with headquarters in Montréal). In 1965, Merck acquired Charles E. Frosst & Co., and in 1968,

Merck Frosst Laboratories (now the Merck Frosst Centre for Therapeutics Research in Montreal)

was created to act as the service company to the two sales companies: Merck Sharp & Dohme

Canada Limited and Charles E. Frosst & Co. In 1982, the three companies were restructured

under the name Merck Frosst Canada Inc. and became a fully integrated pharmaceutical

company.129

Another early example of the influence of foreign multinationals was the founding, in

1925, of Ayerst, Harrison, & McKenna. Ayerst, initially a producer of cod liver oil, was founded

128 The Merck family has its roots in 1668 in Darmstadt, Germany when Frederic Jacob Merck opened an apothecary. These roots were transplanted to New Jersey by George Merck in 1891. 129 See the Merck Frosst website for more.

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by four former executives of foreign subsidiaries in Montreal. Later it began to develop insulin

with the assistance of James Collip, co-discoverer of insulin. In 1943, Ayerst was acquired by

American Home Products Corporation (AHPC), and remained one of the most innovative

pharmaceutical companies in Canada until the 1980s when it was acquired by Merck. The

acquisition resulted in a restructuring, which ultimately led to Merck moving much of the R&D

that was done in the Ayerst Labs to New Jersey. In this case, many of the research scientists

moved on to found Bio-Mega (1984), which quickly became a highly innovative biotech firm.

Bio-Mega was subsequently acquired by Boehringer-Ingelheim (Germany) in 1988.130 Like

Ayerst, out of which it was formed, Bio-Mega continued as a separate and highly autonomous

entity in the region, and remains one of the most innovative firms in Montreal as measured by

patents granted.

In closing the Ayerst labs, the Merck move also spawned another important player –

Biochem Pharma. Francesco Bellini, a former research scientist at Ayerst (and named on many

Ayerst patents), founded Biochem Pharma in 1986, and the firm took off on the strength of its

AIDS drug 3TC.131 BioChem was subsequently acquired by Shire Pharmaceuticals of Britain in

2001 for $5.9 billion dollars Canadian. Bellini subsequently left the firm that he founded and

rather than retiring, or starting up a new firm, he took over the helm of NeuroChem Pharma, a

young biotech firm in Montreal developing a drug to prevent Alzheimer’s. Bellini not only lent

his knowledge and money to the firm, but also his credibility and extensive network, thereby

130 It ran as a separate division until the 1980s when their R&D activities were integrated into Merck and moved to New Jersey. 131 After leaving BioMega, Bellini was first hired by Institut Armand Frappier (IAF) (affiliated with the Universite du Quebec) in 1984 to set up a new biochemicals branch; but when they failed to get funding for the expansion, Bellini talked the board into spinning off the branch as a public company under the Quebec Stock Savings Plan with 10 of their research scientists. This seems to illustrate entrepreneurial push due to bureaucratic inertia – in this case, IAF’s inability to help Bellini realize his objectives within that organization. Although the transition to entrepreneur for Bellini was not directly related to the closing of BioMega, it was undoubtedly indirectly responsible.

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attracting more resources. In the few years since he took over, NeuroChem has more than

doubled in size.132

The acquisition of BioChem Pharma by Shire Pharmaceuticals in 2001 had important

repercussions for the region.133 In this particular case, BioChem’s Vaccine division, which was

highly research-intensive with dozens of patents, was closed leaving 120 research scientists out

of work. (This despite the fact that a condition of the sale was that no one be laid off within a

certain period of time. Shire nonetheless proceeded to consolidate their R&D in their New Jersey

facility. Shire still maintains a presence in Montreal but now has a staff of about 120 out of over

300 who were there at the time of acquisition.) At least two new biotech ventures were spawned

by closing BioChem Vaccine. In 2003, Virochem was founded with 40 of BioChem Vaccine’s

former employees. In 2004, Adaltis was founded on BioChem’s diagnostics division. In both

cases, Bellini lent financial and other support to their founding. Those scientists who did not join

these new ventures found their way to other firms, including Neurochem; and at least one CRO

was established in the wake of the Biochem acquisition.134 Many other senior executives ended

up in other Montreal biotech firms – in Aegera Therapeutics, and Ecopia Biosciences, among

many others (Canadian Newswire, various dates).135

Not only did these events precipitate innovative entry, but they also created the

conditions for more linkages to develop in the region. Biochem Pharma relied extensively on

132 Bellini did start a new venture called Picchio Biopharma with Paul Desmarais of Power Corp., which was essentially a vehicle designed to finance promising ventures. In 2002, Picchio acquired a 33% stake of Neurochem. After Bellini took over the helm of Neurochem, not only did the size of the firm increase, but so too did its share price – more than doubling in a matter of months. This was at a time when average biotech stock prices dropped more than 50% (Yakabuski, “The many appetites of F. Bellini”, Report on Business Magazine, March 2003). 133 At the time of the acquisition, BioChem had about 1000 employees around the world, with close to half of those in Canada. BioChem Pharma also had two other divisions – BioChem Vaccines (1989), and BioChem Immunosystems (1992). 134 Bellini has vowed that he will not sell (Neurochem) out to foreign interests this time around (Yakabuski, 2003). 135 In 2004, the vaccines division of Biochem Shire was subsequently acquired by ID Biomedical of Vancouver, a rapidly growing vaccines developer. ID Biomedical was then acquired by GlaxoSmithKline, which had a very strong prior relationship in co-developing vaccines with Biochem Pharma, in 2005.

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linkages with other organizations – especially to local pharma and research institutes during its

growth phase – to gain access to new knowledge, and new markets. For instance, Biochem sold

the worldwide rights to 3TC to what was then Glaxo Wellcome (with a subsidiary in Montreal)

in order to develop and market the product more quickly and more broadly. In fact, Glaxo

initially took a minority stake in Biochem. (By comparison, Bio-Mega, perhaps because it was

acquired at an earlier stage in the development of the biotech industry, was basically self-reliant,

focusing on internal development. Furthermore it was not growth oriented – research rights for

the many patents it developed were licensed back to the parent or other production centers within

the MNC.)

There are many examples of how the mobility of highly skilled labor affected the local

industry – i.e. how foreign MNCs train local (senior) managers, which in the long run join

domestic firms. Another interesting example of knowledge flows between MNCs and domestic

firms is one in which the foreign manager comes to run the local affiliate and is subsequently

lured away by a domestic firm. This is what happened when Philippe Calais was transferred to

Montreal to run the Hoffmann-LaRoche subsidiary there. Within two years, Calais was lured by

Bellini to Virochem. His international marketing experience was cited as being crucial to the

firm’s growth prospects.136

Although this is not the research focus here, there are a number of cases in which events

outside the region – i.e. mergers and acquisitions among the multinationals – also had a

significant impact on the region. (It is also important to note because these changes show up in

the measure of external presence – external share of employment.) This was often the case when

two foreign multinationals with plants in the region merged, and re-organized their operations

often resulting in downsizing or outright divestiture. In other cases, rationalizations which 136 Cited in PR Newswire, January 6, 2003.

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sometimes resulted in divestitures were on a national or a broad regional level. The history of

Glaxo-SmithKline is illustrative. Burroughs-Wellcome had its Canadian headquarters in

Montreal since 1952; Glaxo’s Canadian operations were headquartered in Toronto, going back to

1902; after the merger in 1996, they became Glaxo-Wellcome, which then merged with

SmithKline (Montreal) to become Glaxo-SmithKline in 2000, with Canadian headquarters in

Toronto. (The Montreal facility was closed at first, but a new research facility was subsequently

established.) In some cases, external merger had little effect. Novartis Pharma was formed in

1996 from the merger of Ciba-Geigy and Sandoz, both Swiss-owned companies. Both had

facilities in Toronto and Montreal – Sandoz with its head office in Montreal, Ciba-Geigy with its

head office in Toronto. The head office of the combined firm is now in Montreal, with facilities

in both cities. Similarly, the merger between Bristol-Meyers and Squibb, both U.S. firms, had an

impact on different regions. E.R. Squibb & Sons of New York, first entered the Canadian market

in Toronto in 1925, and later entered St. Laurent (Montreal) in 1952. In 1989, Bristol-Meyers

(New York), with operations in both cities, merged with Squibb to form Bristol-Meyers Squibb

Canada. The Montreal facility is now the Canadian headquarters. In this case, not only was there

substantial downsizing in both locations, there was also collateral damage in Ottawa where

Bristol-Meyers previously had operations, which it closed as part of a rationalization process

upon merging with Squibb.

The decision to include pharmaceutical firms in this study therefore stems from two

related issues: first, because the knowledge base of both biotech and pharma is essentially the

same (or converged over time and are therefore not entirely separable); and second,

pharmaceutical firms constitute a large proportion of external firms (in this case primarily

foreign). The first point further illustrates the essentially artificial distinction between biotech

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and pharma in the later stage of development of the industry. Since pharmaceutical firms are

likely to be the purveyors of many of the externalities described earlier, excluding them may

result in drawing the wrong conclusions about the nature of insider-outsider interactions.

Today, foreign multinationals not only account for a significant amount of own R&D

spending, but they also account for other direct funding of basic research through R&D

agreements with public research organizations – usually in university departments. (A high

proportion of the R&D spending is, however, on clinical trials rather than on basic or applied

research.) In the absence of these funds, the opportunities to research and develop some products

would not exist. These research institutes have also generated new knowledge, which essentially

becomes a public good, as well as spinoffs. Thus, foreign presence also has an indirect effect on

the industry through their interaction with public research organizations. (One example is Merck-

Frosst’s $5 million funding of the Research Institute (McGill) on respiratory illness which was at

least partly responsible for the creation of Phage Tech in 1997.) Direct relations between internal

and external firms in the form of strategic alliances dealing with R&D, marketing, etc., were also

very important for many of the small firms. Later stage examples of external greenfield entry

include AstraZeneca, which built a state-of-the-art research facility in 1997. As is typical, they

entered relatively small, but now employ over 120 researchers. (Zeneca BioProducts and Astra

(Sweden) merged in 1997 to form AstraZeneca.) Both had facilities in the Toronto area, and in

the case of Astra, going back to 1957.

Methods and Measures

Firms, or rather establishments, were identified from a wide variety of sources starting

with the annual Contact Canada directories of biotechnology firms, and of pharmaceutical firms.

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Since the Contact Canada directories are self-reported, much of the information was verified or

augmented through Industry Canada’s own directory of biotechnology and pharmaceutical firms,

as well as through news directories such as Canadian Newsstand, Canadian Business and Current

Affairs (CBCA), and Lexis-Nexis (Canadian edition). News sources were particularly important

in clarifying ambiguities in the recorded histories. They were also important for collecting data

for some control variables such as alliances. Company websites, as well as Annual Reports, if

public, were also used. See Table 1 for a summary of the variable definitions and sources of

each.

An internal entrepreneurial entry is classified as such if it was either a spin-off from an

incumbent firm or a research institute, or a startup with no prior employment or financial

relationship to established firms or institutions in the industry (the latter is highly unusual). An

external firm can be either foreign or domestic, however the large majority of these firms are

foreign. The dataset also contains other types of firms, which are separately classified. The

distinction between units that are primarily R&D or manufacturing is also made.

Firms that rely on chemical engineering rather than life sciences, such as engineering and

environmental firms, are often included in biotech studies; however, because they rely on a

different set of capabilities and operate according to a different logic they are excluded from the

data. Many of these firms also claim a very long history, with little information about when they

actually began biotechnology, or even to what extent they use it. In other cases, they may be

chemical suppliers. Similarly, other related technologies such as medical devices, and

bioinformatics were not included.

Data on the number of employees in pharmaceutical firms (primarily multinational) and

by location was difficult to obtain because generally only Canada-wide numbers were reported in

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public sources. In many cases, even information on a firm’s activities by location was difficult to

obtain. In general, information on plants that conduct at least some R&D was more readily

available than on those that did not. The many mergers and acquisitions among the large

pharmaceutical companies compounded these difficulties making constructing an historical time

line on each company a complex and time-consuming task. In many cases, company data from

public sources had to be augmented with fragments of information from news sources. Where

activities and numbers by location were still not readily apparent, I followed up directly with the

companies to help fill in missing information. This still resulted in some missing data, which was

filled in through the impute function in Stata 8.0.

Regional level. The regional level is primarily defined as the census metropolitan area

(CMA) – those areas with a population of 100,000 or greater, as defined by Statistics Canada. In

some cases, biotech firms are present in Census Agglomerations (CAs), which are those regions

with populations of 10,000 or greater. Aside from the fact that regional data are aggregated to

this level, these are particularly useful classifications because they are based on commuting

patterns suggesting an increased likelihood of the effects occurring within that region. Also, note

that not all regions in which biotech firms are present have experienced an external entry. Thus,

we can compare the results for regions with and without external entrants.

Dependent variables. Firm, or rather enterprise-level data includes entry/exit by year and

type. Entrepreneurial entry is at the level of the region; survival and growth of indigenous firms

are at the firm level.137 The growth of the firm is measured as the growth in the number of

employees. This is used because it is often the case that either biotech firms do not have

revenues, or such data are not available because the firm is privately held.

137 Terminal acquisitions do constitute exits, but of a different sort hypothesized here since they may represent a success rather than a failure, especially in the case in which the target seeks out an acquirer as part of an exit strategy. Moves to other regions also constitute exits but for similar reasons are separately classified here.

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Independent variables. Three sets of measures for each of the external entry and

presence variables are used: (1) a simple count of external firms in the region; (2) the proportion

of the external firms to total firms region; and (3) the external share of total employment in the

region. The short run effects, greenfield and acquisition entry, are measured as each of these

variables lagged up to three periods. The longer run effects are measured using greenfield and

acquisition presence.138 The share of employment has been used in prior research as a proxy for

externalities, and is measured by pooling the share of employment in each firm type (greenfield

or acquisition) to the total employment in each year.139 The long run effects are tested by the

greenfield and acquisition presence lagged by one period. The longer lags of the external entry

variables might also be considered long run. In essence, the lagged external entry allows us to

examine the changing effects of cohorts of entrants. All models include traditional

pharmaceutical firms. An alternative measure of the composition of greenfields was also tested:

where traditional pharmaceutical firms that entered prior to 1976 are separated from those

greenfields that entered later (but with a count of the former included as a separate control).

Acquisition entry is determined by a majority stake (> 50%) in the target.140 Only cases

in which the target is a continuing entity are included in constructing the entry and presenc

variables. In the longer run, positive externalities would really only result from continuing

e

138 The share of external presence may be an inferior measure in the sense that it also captures the growth or divestitures of external firms, which might have counteracting effects – it could generate either negative or the positive effects due to the kinds of externalities hypothesized here. However, since the biotech industry is still in a growth phase, it is believed that the positive externalities will dominate. Insofar as growth does contain a competitive effect, the results reported here are conservative tests. An alternative measure might simply be the share of external firms in the region. Another alternative might simply be the time since the subsidiary entered. (Though independently neither of these would capture the changing role of subsidiaries in the region.) To capture the evolutionary component a bit more, time since entry could be interacted with the stock of external firms. These and other alternatives are examined in other related research. 139 Note that all incumbents in a region are at risk of acquisition at any time; in other words, no matter how an incumbent entered, and whether or not it was previously acquired puts it at risk of being acquired. In essence this suggests that the characteristics of incumbents is more likely to play a role in an acquisition than regional characteristics, which dominate greenfield and entrepreneurial entry decisions. 140 Some research in international business uses much lower thresholds, which may be appropriate only in the longer run since they do not constitute liquidity events, but possibly knowledge flows. However, this is not tested here.

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entities. Terminal acquisitions, on the other hand, are target entities that were discontinued,

moved or closed after being acquired. They are not included in these measures, nor are they

treated as indigenous exits here. Nonetheless, they do show up as a permanent reduction in the

level of external presence and therefore persist. This persistence was examined as alternative

measures of the acquisition variables. In other cases, there is little information on the target

following its acquisition, which may indicate a strong transfer of control to the new parent,

and/or it may indicate a change in organizational structure such that the subsidiary becomes a

shell. Alternative counts were constructed with and without these cases in order to test for

differences. This lack of information adds an additional complication since these cannot be

included in the aggregated count of employees of acquired firms.

The presence variables are also interacted with the level of agglomeration. The positive

externalities due to external presence should be reinforced at higher levels of agglomeration. Or,

if the main effect of external presence shows up as negative, it is expected that it would turn

positive at higher levels of agglomeration.

Firm level controls. Firm level controls are included in the exit and growth models to

eliminate some obvious alternative explanations. Expected returns to innovation, in the form of

patents issued (from the United States Patent and Trademark Office), should enhance survival

and growth prospects for a firm.141 Prior research has shown that a firm’s innovative activity in

the form of patents has an important influence on firm performance. Internal development, of

which patenting is an indicator, can either substitute or complement knowledge from external

sources (e.g., Gambardella, 1995). The number of new alliances in a given year (interfirm and

institutional) control for other direct knowledge sources that affect a firm’s performance. The

141 Note that most Canadian biotech firms patent in the U.S. first primarily because most tend to operate in a global market and therefore seek protection in the most important market.

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extensive literature on alliances has generally found a positive relationship between a firm’s

alliances and its performance – including survival and growth.

The firm’s status as either public or private has also been found to have an important

effect on performance outcomes in part because public firms often have access to resources that

private firms do not. This is coded as a dichotomous variable – a firm either is or is not public

(1/0). Controls for firm size (number of employees) and age (years) are included because of the

extensive evidence that these characteristics influence firm performance. Size of the firm is

really only used as a control in the exit models since the log-lin specification of the growth

models already includes size on the right hand side of the equation (see the section on Growth

Models).

A control is also included for whether the firm operates in the human sector or not (1/0).

This is because, as already noted, there are substantial differences between the human sector and

other sectors especially in terms of regulation – firms in the human sector normally have

considerable regulatory hurdles that non-human sector firms do not have. The main human

subsectors include therapeutics, diagnostics, and vaccines. The main non-human sectors include

agriculture, aquaculture, neutraceuticals, and other related sectors.

Regional level controls. Many of the local industry variables are constructed by

aggregating the firm level data to the regional level. Although data aggregated to the region level

are particularly important in the entry models since entry is at the level of the region, some of

these data are also important controls in the exit and growth models. These data include the

number of public research organizations (PROs) in the region in each year, and the number of

venture capital firms (from the Canadian Venture Capital Association (CVCA)) in the region

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each year, both of which have also been shown to influence firm performance.142 Only VC firms

that declare that they fund biotechnology are included – this is a relatively small subset of the

total of all VC firms.

Other controls include the human population of the region, and the number of doctoral

degrees granted each year in the life sciences from all degree granting institutions in the region.

A count of patents for firms and institutions aggregated to the regional level is also included as a

measure of the knowledge stock of the region, discounted by 30% per year to account for their

declining commercializeable value over time (see Blundell, et al., 1995). Prior research has

shown that prior period entries (of other indigenous firms) also influence entry next period due,

for instance, to a demonstration effect.143 Year was also included in most models.

Non-compete covenants. Acquirers often write clauses prohibiting the target’s senior

staff from competing directly with the new owners, if let go.144 Prior research has found that the

existence of such covenants has an important effect on founding events, and mobility in general.

States that had a strong legal regime with respect to the enforceability of covenants had a lower

probability of new foundings in the U.S. biotech industry (Stuart & Sorenson, 2003), and of

mobility in general (Marx, et al., 2007), compared to those that did not. In the Canadian context,

however, there is little variation across provinces since the law pertaining to competition is

national in scope. Canadian courts have refused to enforce restrictive covenants found to be too

long or too vague as to time, too broad as to geography, too broad as to scope of competitive

business, or purporting to apply to prospective customers of the employer. 142 Firm level venture capital (VC) financing was incomplete and is therefore not included in the survival and growth models. 143 Exits may open up a resource space for new indigenous entrants in the same way that liquidity events do. Prior exits are tested outside of the main models reported here. 144 Based on informal interviews with a number of executives, firms in the biopharmaceutical industry almost always have senior managers and scientists sign non-compete clauses prohibiting them from leaving to start up a new company (based on similar technology) or being lured away by a competitor. These restrictions are usually in effect for up to five years, which means there may be an impact on the results.

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Descriptive Statistics

Tables 2a and 2b show the descriptive statistics – means and variances for the variables

used in all models. The means, standard deviations, and univariate correlations are overall

statistics – i.e. capturing both within and between variation. As expected, almost all of the

regional level variables are highly correlated. In particular, the number of biotech firms is highly

correlated with regional population density, the number of PhD graduates in biosciences in the

region in the year, the number of PROs in the region, the number of biotech VCs in the region,

and innovation in the region. Some of these other variables are also highly correlated with one

another: the number of PROs with biotech VCs, for example. Also, the number of new PhDs in

the life sciences in the region is especially highly correlated with all of the regional variables –

greater than 0.54 in all cases.

Also as expected, the greenfield and acquisition entry and presence variables are

somewhat correlated with some of the other variables. The acquisition entry and greenfield

presence variables are especially correlated with the regional variables: the number of biotech

firms in the region, PROs in the region, the number of VCs in the region, and the level of

innovation in the region. By contrast the greenfield entry and acquisition presence variables are

much less correlated with other variables. Mean greenfield presence is 19.7%; the mean

acquisition presence is substantially lower – only 4%. The main reason for this relatively low

proportion is that acquisition entrants entered much later on average than greenfield entrants;

however, acquisition entrants tended to be larger at entry. Naturally, for both variables there is

substantial variation across regions, and across time within regions. Greenfield presence is

especially variable because it is very high in some regions initially and then tapers off, or in other

cases fluctuates, as an indigenous industry is generated and as new greenfields enter.

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Also note that the rates of growth of each class of firm by type of entry – indigenous,

greenfield, and acquisition – were calculated to determine how they match with prior research,

and whether they confirmed an assumption regarding the relative growth of greenfields and

acquisitions. It was found that greenfields grew faster on average (11%), compared to acquisition

entrants (8%). However, as expected, greenfields that entered prior to 1976 grew at a slower rate

on average (4.7%) compared to those that entered afterward (17.3%). This reflects the fact that

older greenfields were already of large size by the start of the study period. By comparison to

both groups, average indigenous growth for those that survived, was about 19.5%. It was also

found that growth rates of firms declined following a take over.

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CHAPTER 6

ANALYSIS AND RESULTS

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Introduction

Previous chapters argued that the entry and continuing presence of outsiders would

influence the evolution of the indigenous industry in three key ways – through entrepreneurial

entry, survival and growth. In this chapter, the models that test the hypotheses are introduced and

the results are examined (see Table 6 for a summary of the hypotheses). The main hypotheses

suggest that there should be a crowding out effect of new greenfield entry in the short run, after

which time positive externalities generated by labor mobility and linkage effects, in particular,

should take over. Acquisition entry, on the other hand, should exert positive effects in the short

run, assuming these constitute liquidity events. The continuing presence of acquired firms should

thereafter generate positive externalities similar to that of greenfields. However, because of the

ambiguity in the literature regarding the effects of outsiders, alternative hypotheses suggest the

opposite – that crowding out due to competition for scarce resources (especially labor) will

dominate in the longer run. The effects of greenfield and acquisitions presence at different levels

of agglomeration are then examined. These are tested in three sets of models of entry, exit, and

growth.

______________________________

Insert Table 6 about here

_____________________________

Models

Entry Models

Count models are used to predict the rate of entry into a region. Because entry is a count,

pooled cross-sectional data are used to estimate the number of entries expected to occur within

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an interval of time. The basic Poisson model is given by:

Pr(Yit) = expλ(xit)[λ(xit)y/y!]

where both the probability of a given number of events in a unit interval, Pr(Yit = y) and

the variance of the number of events in each internal equals the rate, λ(xit). Thus, the basic

Poisson model makes the strong assumption that there is no heterogeneity in the sample. With

count models, such as the Poisson, there is a problem of overdispersion due to the fact that

distributions of event count models tend to be skewed (because zero bounds the lower end of the

observed range). This can be compensated for using the negative binomial model (Cameron &

Trivedi, 1998). I also test for overdispersion using the negative binomial model against a

baseline Poisson (Cameron & Trivedi, 1998). The negative binomial is given by:

λit = exp(πitxit)εt

where the error term, εt, follows a gamma distribution. The presence of εt produces

overdispersion (Blundell, et al., 1995). The specification of overdispersion tested here takes the

form:

Var(Yt) = E(Yt)[1 + αE(Yt)]

Pooling repeated observations on the same firms likely violates the assumption of

independence across observations and results in the autocorrelation of the residuals over time.

This renders ordinary least squares (OLS) estimates inefficient and produces biased estimates

(Baltagi, 1995). Hausman tests are run to determine whether the random or fixed effects

specification is more appropriate (Baltagi, 1995). In general, a high value of the Hausman

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statistic would suggest that the fixed effects specification is preferred. Though there are

advantages to using a random effects specification, controlling for unobserved heterogeneity,

which can be done with a fixed effects specification, is desirable. On average, there are 20

region-year observations for each of the 28 regions that have experienced an entry, yielding a

total of 560 observations. Given the relatively short time horizon, tests of the lag of the external

presence variables are essentially restricted to no more than three periods.

Endogeneity may be a problem in general. In this particular case, external entry or

presence are not likely to be strictly exogenous in any of these models – especially in the models

of entrepreneurial entry. Thus all independent variables are lagged by one period in the models

of external presence. This ensures causal order, but it is also appropriate since the presence

measures are intended to capture longer run effects. Using lags, however, poses challenges for

external entry models – that of capturing the very short run adjustments within the period of

entry. These models therefore include current period external entry with three period lags of the

cohort.

The basic entry models are given by:

Pr(DeNentry)rt = b0 + b1grentryrt + b2acqentryrt + b3grentry rt-1 + b4acqentryrt-1 + b5grentryrt-2 + b6acqentryrt-2 + b7grentryrt-3 + b8acqentryrt-3 + controlst + e

Pr(DeNentry)rt = b0 + b1grpresrt-1 + b2acqpresrt-1 + controlst-1 + e

Pr(DeNentry)rt = b0 + b1grpresrt-1 + b2acqpresrt-1 + b3 N + b4grpresrt-1 x N + b5acqpresrt-1 x N + controlst-1 + e

Where deNentry is de novo (entrepreneurial) entry, acqentry is the acquisition entry in the

immediate period, grentry is greenfield entry in the immediate period, acqpres is acquisition

presence in the region, and grpres is greenfield presence in the region. There are three sets of

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measures for each of the prior period entry and the presence variables: (1) simple count of

external firms; (2) proportion of external firms to total firms; and (3) external share of total

employment. Each of these is lagged up to three periods in separate models (the external entry

variables are of particular interest). Controls (all at the regional level) include the human

population logged to normalize the distribution; number of new PhDs graduated in the life

sciences; number of research institutes; number of VCs in life sciences; number of biotech firms

in the region and at the national level. Here, r represents the region, and t the year. The

interaction tests the effect of external presence at different levels of agglomeration, measured as

the number of biotech firms in the region (N).

Exit Models

As a first step to the analysis, non-parametric (Kaplan-Meier) estimates of the survival

and hazard functions, with 95% confidence bands, were graphed. The survival graph is concave

to the origin. The cumulative hazard rate is accelerating in time:

Y(t) = Xt-1β + σε

where Y is the log of the time to exit, Xt-1 represents the covariates, β the parameter

estimates, σ is the scale parameter, and ε is the vector of errors from an assumed distribution.

In general, much of the strategy literature relies on the use of exponential distribution.

One rationale for that is that the baseline can be specified as a purely random process whereby

the event series is modeled as a stochastic point process (Amburgey, 1986). This is then defined

as a renewal process suggesting the use of an exponential distribution (Amburgey, et al., 1993).

The use of an exponential is often justified on the grounds that it is common in practice; but

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another reason is that it is easy to use and interpret. Nonetheless, specification tests starting with

the Cox proportional hazard rate assumption are conducted.

The exit rates are first estimated using a Cox proportional hazards model (Cox-PH),

which is a semi-parametric model. The advantage of using a non-parametric specification is that

it makes no assumptions about the distributional form of the data. The Cox partial likelihood

model is applicable to time varying covariates under certain conditions (Kalbfleisch & Prentice,

1982; Cox & Oakes, 1984). However, with this data there is no clear way to determine whether

or not the proportional hazards assumption is violated. Graphical methods are often used to

determine the validity of the Cox partial likelihood model. The test is done by graphing ln(-

ln(S(t;x)) versus t, for different values of x and checking if the lines in the plot are parallel.

However, because of the range of the data used, this does not work here. In general, another

reason for rejecting a Cox partial likelihood model is that it tests the order of events but not the

space in between those events. Thus, time dependence disappears and whatever information

comes from the inter-arrival times is discarded. Furthermore, Cox and Oakes (1984; p. 123)

argue that “the partial likelihood will have high asymptotic efficiency relative to the full

likelihood if the ratio of the between-group component to the within-group component is small.

This condition will usually hold unless any of the following holds: (i) the parameter beta is far

from zero; (ii) censoring is strongly dependent on Z(T); or (iii) there are strong time trends in the

covariates.” Both (ii) and (iii) are especially problematic in this context.

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Different parametric specifications of the full model were then tested – the exponential,

Weibull, lognormal, and log-logistic. Parameters were estimated by maximum likelihood

estimation using the Stata 8.0 program. The estimation procedure clusters observations by firm

to reduce the impact of unobserved firm-specific effects (White, 1982). The significance levels

of the parameters were evaluated by examination of t-ratios, whereas the goodness-of-fit of the

different models was evaluated by examination of likelihood ratio statistics.

The exponential model was determined to be the most appropriate based on how it

compared with other specifications (see Blossfeld & Rohwer (2002) for a discussion of

evaluation methods). The exit rates are estimated using a hazard rate model:

h(t, x, β) = h0(t)exp(xβ)

where h(t, x, β) is the rate at which firms exit at time t, conditional on the firm still

existing at time t-1; h0 is the baseline hazard function when all of the covariates are set to zero,

and x is a vector of plant and industry characteristics that impact on the firm’s hazard rate.

Indigenous firm exit is given by the following models:

Pr(Exit)irt = b0 + b1grentryrt + b2acqentryrt + b3grentry rt-1 + b4acqentryrt-1 + b5grentryrt-2 + b6acqentryrt-2 + b7grentryrt-3 + b8acqentryrt-3 + controlst

Pr(Exit)irt = b0 + b1grpresrt-1 + b2acqpresrt-1 + controlst-1 Pr(Exit)irt = b0 + b1grpresrt-1 + b2acqpresrt-1 + b3N + b4grentryrt-1 x N + b5acqentryrt-1 x N + controlst-1

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where the variables are defined as previously. The only difference is in the level of

analysis, and in the control variables. Once again, there are three sets of measures for each of the

prior period entry and the presence variables: simple count of external firms, proportion of

external firms to total firms, and external share of total employment. Organizational variables

include: size, age, number of patents, number of alliances, and whether or not it is public. Once

again, r represents the region, t the year, and i represents the firm. The interaction tests the effect

of external presence at different levels of agglomeration, measured as the number of biotech

firms in the region (N).

Growth Models

The models of growth of firm size, where size is measured by the number of employees,

are tested using alternative specifications. The models are of the form:

ln(growthit) = αln(growthit-1) + βX it-1 + e

where X is a vector of control and other variables of interest, as previously discussed. In

addition to the concern over autocorrelation, common to the entry models, is the issue of sample

selection bias due to attrition in the growth models. That is, if a firm fails, it leaves the sample

without its final performance changes represented in the data (Wooldridge, 2002). Thus, Lee’s

(1983) generalization of Heckman’s (1979) two-stage-least-squares (2SLS) procedure is tested to

correct for this possibility. This may be an important issue here since of the roughly 500

indigenous firms that entered, about one hundred failed and another 82 were acquired. In other

words, more than one third had some life-altering event occur.

Unobserved heterogeneity can be controlled for in one of two ways. First, by using a

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fixed effects specification (Baltagi, 1995; 195), where the between estimators (based on cross-

sectional component) yield long run estimates and the within estimators (based on time-series)

yield short run estimates. The Hausman test is also conducted to ensure that the fixed-effects is

preferred to the random-effects specification. An alternative specification is the first differencing

model using a heckman twostep. We can eliminate the individual effects by first differencing

(see Wooldridge, 2002). In contrast to the fixed-effects model, the first differencing model

assumes substantial serial dependence in uit. The relative efficiency of the fixed-effects and first

differencing methods depends on the appropriateness of their assumption concerning the serial

dependence of uit. All models are run using time dummies.

The employment growth of a firm can be represented by the model:

git = xitβ – ct+ uit

where ct represents the unobserved individual effects. These can be differenced out as

follows:

git – gi,t-1 = (xit – xi, t-1)β + uit – ui,t-1

Δgit = Δxit β + Δuit

Specifically, the firm level growth models are given by:

girt = b0 + b1 girt-1 + b2grentryrt + b3acqentryrt + b4grentry rt-1 + b5acqentryrt-1 + b6grentryrt-2 + b7acqentryrt-2 + b8grentryrt-3 + b9acqentryrt-3 + controlst-1 + e

girt = b0 + b1 girt-1 + b2grpresrt-1 + b3acqpresrt-1 + controlst-1 + e girt = b0 + b1 girt-1 + b2grpresrt-1 + b3acqpresrt-1 + b4N + b5grentryrt-1 x N + b6acqentryrt-1 x N + controlst-1 + e

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where girt = lnsizeirt - lnsizeirt-1 (size = #employees); the other variables are as previously

defined. The controls are the same as those in the exit models. Once again, there are three sets of

measures for each of the prior period entry and the presence variables: a simple count of external

firms, the external share of total firms, and the external share of total employment. Also as in the

entry and exit models, the interaction tests the effect of external presence at different levels of

agglomeration, measured as the number of biotech firms in the region (N).

Results

Entry Models

Tables 3a, 3b and 3c report the results of the entry rate models. A lagged dependent

variable was included in the presence models (Tables 3b and 3c) to determine if there was a

dynamic adjustment process. In most cases, the parameter estimates of lagged entry were

significant suggesting that current period entry is dependent on prior period entry. The other

control variables (Model 1) show that location specific characteristic such as human population,

do have a significant effect on entrepreneurial entry as has been found in other studies. Also, the

models show that the number of Ph.D. graduates in the biological and health sciences tends to

have a positive effect on entry, though this is never significant; and the human population has a

positive and significant effect when lagged by one period. The models also show that the number

of public research organizations (PROs) and the number of biotech VCs tend to positively affect

entry, though these results tend to be significant only within the period of entrepreneurial entry.

To conserve on degrees of freedom (and because year dummies did not work consistently across

all models), Year, denoting the calendar year, was included as a control instead. This was

positive and significant when lagged by one period. The number of firms at the national level,

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and the number of firms in the region, which is a measure of agglomeration tend to be significant

but negative (Table 3b). Running the models without lags (Table 3a) shows that they both tended

to be positive.

The results also show that those regions that have a higher level of innovation experience

lower entry rates. Most of the models show that this result is highly significant within the period

of entry, but lagged by one period, it is insignificant. The variable is constructed as the number

of patents granted in the biosciences in the region discounted by 30% each year. Variations on

this variable, including a simple cumulative count of patents granted as well as the number

granted in the prior period yielded similar results. This result is consistent with a number of

studies (e.g. Acs, et al., 2005; Gambardella, 1995). Acs, et al. (2005) argue that knowledge

exploitation (and internalization) by incumbents, of which patenting is an outcome, should

negatively influence new entrepreneurial entry. In this sense, controlling for other factors,

discounted patents in a region may represent a rising barrier to entry in that it indicates a higher

level of knowledge required to enter; a higher level of patenting may also impede entry into areas

that are already under patent protection. On the other hand, an insignificant result can be

explained by the fact that only some patents matter to entrepreneurial entrants. Specific firms

may need access to specific knowledge rather than to general knowledge, irrespective of where it

is located. It would be expected, however, that innovation in a region, as measured by patents,

would be a strong attractor to external entrants – though perhaps more to greenfields than to

acquisition entrants.

The average bank rate, a measure of the cost of capital, was also tested since it may

influence the startup decision if borrowing to finance startup operations is a consideration. The

results are often significant and negative, as expected – but, just as with some of the other

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variables, within the period of entrepreneurial entry only. However, since it really does not add

to model fit, and for purposes of comparison with other research, it is excluded from the reported

regressions.145 For some entrepreneurial startups, the cost of borrowing may not be that

important, particularly if they start up out of a public research organization that provides

substantial resources, including lab facilities and time, to facilitate their startup and growth. In

other cases, it may be that access to VC funding is more relevant (the number of biotech VCs in

the region tends to be significant and positive in the within period models). The latter variables

are retained for purposes of comparison with other studies. Still, this does provide evidence that

all these sources of capital and/or resources are important at the time of startup, though the extent

to which they substitute or complement one another is still unclear.146

The Effect of External Firms on Entrepreneurial Entries

Alternative variables were constructed to test the effect of external firms on indigenous

entrepreneurial entry. The first set of external variables was constructed using simple counts of

firms – greenfields and acquisitions. The second set was constructed as the proportion of external

firms to total firms. The problem with both of these sets of measures is that they understate the

relative importance of some large biopharmaceutical firms. The relative opportunities in some

145 Some other variables were also tested in models not reported here for comparison to other studies. For instance, the number of public firms was found to have a negative effect. One explanation for the result is that this too may be an indicator of a barrier to entry in that it represents a more advanced stage of development of the region and therefore requires a higher level of skill and resources to enter. Another explanation for the effect of the number of public firms is that more public firms may represent an alternative opportunity in terms of employment for potential entrepreneurs. The industrial organization literature uses average firm size as an indicator of a technical barrier to entry, which, incidentally, was found to lower the likelihood of entrepreneurial entry. The number of prior period IPOs was also tested. A priori, this was expected to be positive for two reasons: first, because it may act as a demonstration effect of one important objective for entrepreneurs – taking the firm public; second, it may act as a liquidity event (Stuart & Sorenson, 2003b). The results, however, were insignificant. These were also excluded from the results reported in Table 3 because they did not add significantly to model fit. 146 More investigation is required regarding the relative importance of each, especially debt financing (banks) versus equity financing (VCs), as inputs into the startup process compared to the signal they send about potential sources of future financing.

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large multinational corporations should vary monotonically with their size. To test this, the third

set of measures is based on the employment share of external firms to total firms in the region. In

each case, a variable for new external entry and one for total external presence was constructed.

One set of the presence models also separates greenfields that entered prior to 1976 from those

that entered later. Since the former are primarily traditional pharmaceutical firms their effect may

differ from that of the latter.

New greenfield entry. Models 2 onward of Table 3 introduce the independent variables

of interest. Models 2-10 examine new entry; and Models 11-19 examine the effects of total

presence. It is found that both new acquisition and greenfield entry do have an effect on

entrepreneurial entry, though not quite as hypothesized. New greenfield entrants have a very

short run dampening effect on entrepreneurial entry – only within the same period in which the

cohort entered. In all subsequent periods the sign turns positive. Using counts of new greenfields,

only the first period dampening effect is significant, but then turns positive, though not

significant, afterwards. A similar pattern occurs using the share of new greenfields to total firms

– it starts out negative, though insignificant, and then turns positive and significant by the second

lag. Thus, using this measure, we might say that it takes up to two years for the positive benefits

of greenfields to appear. Using new greenfield employment share shows that new greenfield

entry, although positive, does not significantly influence entrepreneurial entry.

In general, there is some though limited support for the hypothesis that new greenfields

crowd out entrepreneurial entry in the short run (H1a). This could be explained by the fact that

greenfields typically enter small and with a cadre of employees from elsewhere and scale up only

if successful in the region. If this is generally the case, then we can assume that employment

within new greenfields is not a particularly attractive alternative to starting up a firm. Still, the

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results suggest that they may compete for local resources in the very short run after which they

release resources that may be valuable to potential startups.

Acquisition entry. The results show that new acquisition entry tends to dampen

entrepreneurial entry in all periods after they enter. The effects are significant using all variables

within the period of entry, and are only persistently significant using new acquisitions counts.

Otherwise, the negative effect is significant in the second lag using the proportion of newly

acquired firms to the total. A non-positive result could be explained by the fact that owners or

senior managers of the target firm are often held to non-compete clauses in employment

contracts which prevent them from starting up a new firm in the near term in the same region.

However, a negative result may be explained by acquisitions absorbing resources required for

entrepreneurial startup, possibly because they need to hire more staff, including potential

entrepreneurs, to fill the vacuum left by the departure of key staff.

Greenfield presence. The presence of all greenfields tends to have a positive effect on

the rate of entry (H1c), which is consistent with the trend we found from the cohorts of new

greenfields (Table 3a). Using counts of all greenfields, the results show significant and positive

effects. However, using the proportion of greenfields to total firms and employment share of all

greenfields, the coefficients are insignificant. Note that since external (greenfield or acquisition)

presence pools these firms irrespective of when they entered, it is generally claimed this

constitutes a long run measure, irrespective of the lag. Some tests with longer lags showed

similar results. Pooling all greenfields irrespective of when they entered also poses another

problem – they may be of a different type.

Separating out the pre-1976 greenfields from those that entered in 1976 or later shows

that the newer greenfields continue to have a positive effect, but the older greenfields (primarily

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traditional pharmaceutical firms) consistently crowd out entrepreneurial entry (Table 3b, Models

2, 5 and 8). There may be a variety of reasons for this dichotomy. As stated in the alternative

hypotheses, it is plausible that more innovative greenfields act as a strong attractor for potential

entrepreneurs, thereby dampening entry in a region. In fact, the older pharmaceutical firms do

tend to be highly innovative, as gauged by their R&D spending and patent output, and provide a

wealth of opportunities for potential entrepreneurs.

Acquisition presence. Consistent with the effect of lagged cohorts of new acquisition

entry, most of the measures of acquisition presence showed negative results suggesting that

acquisition presence tended to dampen entrepreneurial entry, contrary to the baseline hypothesis

(H1d). The alternative hypothesis (H4b) argued that this would be a form of dynamic crowding

out. Continuing acquisitions may also be attractive especially if they engage in more innovative

activities. In fact, many targets are acquired because they are innovative. On average, they are

also larger, older and more stable than either indigenous firms or newer greenfields. They also

provide an external link to the parent that is often attractive if that provides potential

opportunities. Still, these results are only significant using counts of acquisitions, and so only

limited support is given to the alternative hypothesis.

Also note that when acquisition and greenfield shares are pooled into an undifferentiated

measure of external presence, there were no significant results. Because separating the

greenfields and acquisitions sometimes yielded significant and often opposing results, this

demonstrates the importance of separating the two in empirical studies. The fact that newer

(post-1976) greenfields and acquisition presence are consistently of the opposite sign is also

notable. It is also notable that the older greenfields have a similar effect as the acquisitions.

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Interaction effects. Finally, the effect of agglomeration is tested by interacting the

presence variables with the number of firms in the region. The negative main effect of greenfield

presence, which is really driven by the pre-1976 greenfields, is offset to some extent by the

positive interaction between greenfield presence and the level of agglomeration. The parameter

estimates suggest that, on average, positive externalities dominate the negative effects when

there are more than 11 firms (0.864/0.084) in the region (using the relative share of greenfield

employment). This is reinforced by the results of an alternative model (Table 3c) using

greenfield employment share with year dummies without a one period lag of the independent

variables. The parameter estimates of this model suggest that the positive externalities are

operative at slightly higher levels of agglomeration (about 20 firms). These results generally

support the hypothesis (5a) that greenfield presence has a positive effect at higher levels of

agglomeration.147 Note that this measure combines both the older and the newer greenfields.

Since the newer greenfields tended to enter at higher levels of agglomeration, this result might

primarily be driven by their presence, which would be consistent with the positive main effect of

newer greenfield presence in Table 3b.

The acquisition interactions, however, show the opposite – the primarily negative main

effects are generally reinforced at higher levels of agglomeration. These effects show up using

both firm share and employment share (Table 3b), though in neither case are both main and

interaction effects significant at conventional levels. In the alternative model (Table 3c), using

acquisition employment share with year dummies without a one period lag of the independent

variables both main and interaction effects are highly significant. This significance is surprising

and a bit difficult to interpret without testing subgroups. It is possible that different subgroups

147 But it is important to note that the effect is primarily driven by pre-1976 greenfields. In unreported regressions, separating these pharmaceutical firms from those that entered in 1976 or later shows no significant effect at higher levels of agglomeration for those greenfields that entered later.

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confound the results, but without further investigation it is impossible to know if some types of

firms, such as those that are strictly innovative, would behave differently. Some other possible

interpretations are discussed later.

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Insert Tables 3 about here

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Exit Models

Tables 4a and 4b show the results of the exit rate models of indigenous incumbents.

Model 1 shows the baseline model of the control variables. The reported results use exponential

hazard rate models (reporting parameter estimates rather than hazard rates). Both the number of

firms in the country and in the region tends to increase the hazard of exit. These effects are

especially significant in the lagged presence models (Table 4b). Also, the greater the human

population of the region, the lower risk of exit. Once again, this is only significant in the lagged

presence models. Other factors such as the number or PROs in the region and the number of

biotech VCs in the region do have an effect in some of the models, but paradoxically they tend to

induce exit. The innovativeness of the region, measured as the discounted stock of all patents

granted in the region, tended to enhance indigenous firm survival. This too is a bit surprising

since logically there should be little effect once a firm has entered, presumably with its own

intellectual property.

Some firm characteristics also had an influence on indigenous incumbents’ survival. The

age of the firm has a strongly significant inverted U-shaped effect on exit, suggesting the risk of

exit increases up to a critical point after which it decreases. The critical point here is about 11.4

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years (calculated from Model 1), which is approximately the time it takes to get a new product

through the drug approval process. This result is consistent with some other studies. Also

consistent with prior studies, the larger the firm’s size, the greater its survival advantage.

Surprisingly, however, neither the number of patents nor its status as a public firm has an effect

on a firm’s survival. The number of alliances, on the other hand, does tend to enhance survival,

at least within period (Table 4a). The few indigenous left-censored firms in the population also

had a lower risk of exit than those that entered after 1976. This is consistent with the effect of

age, since older firms are at lower risk of exit. Firms operating in the human sector were at

higher risk of exit than those operating in other sectors. This result is consistent with the fact that

human sector firms have greater regulatory obstacles to overcome than those in other sectors and

therefore are at greater risk at any stage along the way to commercialization.148

New greenfield entry. New greenfield entry was hypothesized (in the baseline

hypothesis) to increase the exit rate in the short run, but decrease it in the long run. Almost all of

the signs are positive, meaning an increase in the exit rate. Only in the third lag (Model 2), using

counts of new greenfields is there a negative effect on indigenous firm survival. None of the

longer lags are significant. None of the lags is significant using either the proportion of new

greenfields to total firms, or new greenfield employment to total employment. Thus, there is no

evidence of a short run crowding out effect (H2a) on indigenous firm survival.

New acquisition entry. According to the baseline hypotheses, new acquisition entry is

expected to lower the exit rate of indigenous incumbents. This is generally the case using all

148 Also, in ancillary analysis, those firms whose parent was a public research organization tended to be at greater risk of exit. This may be because other firms were, for the most part, second or third generation firms which had already developed a broader array of managerial capabilities that were subsequently transferred to the startups via the entrepreneur. By the same token, since PROs were not necessarily in the business of commercializing their research, this particular skill was not transferred to the entrepreneurial startups. (Also, a high proportion of these firms were in the human sector.) Only recently have the mandates of some PROs begun to include commercialization as an objective.

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measures, except within the period of acquisition entry using new firm share, which shows a

dampening effect on indigenous firm survival. All of the lags using all measures are negative,

suggesting a positive effect on survival, and this is significant in a number of cases. Generally

there is a positive survival effect that persists. These results generally support the baseline

hypothesis 2b. This is very likely driven by the departure of employees from the acquired firm,

for whatever reasons. Liquidity would not, strictly speaking, be the main driver of this result.

Greenfield presence. In the longer run, greenfield presence was expected to lower the

risk of exit for indigenous incumbents, according to the main hypotheses. Most of the main

effects of greenfield presence show that this is the case (Table 4b), but the results are only

significant using counts. Thus, there is some, though limited, support for the hypothesis that

greenfield presence has a positive influence on indigenous incumbent survival (H2c).

But once again, that is not the whole story. Separating the earlier greenfields – those that

entered prior to 1976, from those that entered in 1976 or after, reveals a clear distinction between

the two. The results show that the positive effect on survival is primarily driven by the older

greenfields. Greenfields that entered post-1976 had a dampening effect on indigenous incumbent

survival. This result is more in line with the alternative hypothesis (4a) that greenfields act as

attractors for local resources (skilled labor in particular) such that it crowds out indigenous firms,

in this case resulting in their eventual demise. The opposite effect of the pre-1976 greenfields

might be explained by the fact that they are primarily large pharmaceutical firms. As has already

been noted, these traditional pharmaceutical firms have historically been important to smaller

biotech firms for a variety of complementary capabilities such as marketing and sales, milestone

payments, etc. These might be direct or indirect effects.

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Acquisition presence. The acquisition presence also tends to lower the rate of exit (Table

4b). This is consistent with the results of the models in Table 4a which show that successive

cohorts of new acquisition entrants tended to lower exit. Using counts of the number of

acquisitions, two of three are negative but none is significant; using proportion of external

acquisitions to total firms, the results show a negative and significant effect in two of the three

models. Using the employment share of acquisitions, the negative effect is only significant in the

presence of the interaction effects. Thus, there is some, though once again limited, support for

the hypothesis that acquisition presence enhances indigenous firm survival (H2d). As with the

older greenfields, this could be explained by (externalized) complementarities.

Interaction effects. Finally, to test for the agglomeration effect, the presence variables

are interacted with the number of biotech firms in the region. The interaction effects show that

greenfield presence has a positive effect on exit (or a negative effect on survival) at higher levels

of agglomeration only using the count measure; otherwise the results are negative, but

insignificant. (Note that the total effect of the greenfield presence would turn negative effect

(meaning a positive effect for survival) at a level of agglomeration that is out of range of the

data. Also note that the result is driven by pre-1976 greenfields, in other words, mostly

traditional pharmaceutical firms.) The results also show that the positive effect that acquisitions

consistently have on survival across all presence measures actually reverses itself at higher levels

of agglomeration. This is especially the case in the firm share and employment share models,

which suggest that the negative effects come to dominate when there are around 25 firms in the

region. Thus, the hypotheses (5c and 5d) are not supported.

Once again, the results are surprising and a bit difficult to interpret. It may be the case

that the positive externalities are more likely to accrue to indigenous firms at lower levels of

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agglomeration because of exactly the opposite of what was proposed – that it is even easier to

interact with outsiders in a way that provides survival benefits. At higher levels the community

may be more diffuse, making it more difficult for indigenous firms to access critical resources –

at least in terms of the effects on survival.

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Insert Tables 4 about here

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Growth Models

Tables 5a and 5b shows the results of the growth models. A Heckman twostep selection

model was tested and generally found to be significant. However the parameter estimates did not

differ substantially from the fixed effects models. Once again, the attrition problem is important

to deal with not only because of the number of true indigenous exits, but also, and perhaps more

importantly, because of the number of acquisitions. In many cases, indigenous targets were on a

(high) growth path that made them attractive to outsiders. Model 1 reports the coefficient

estimates for the baseline. Many of the control variables are significant and have the expected

sign. The size of the firm (log of number of employees) has a negative and highly significant

effect on the growth of indigenous firms. This is often included as a simple test of Gibrat’s law

which holds that firm size is independent of firm growth. This clearly rejects that and instead

suggests mean reversion. The number of biotech firms in the country, the number of PROs, and

the number of VCs that fund biotech firms in the region, all have significant positive effects on

indigenous firm growth. Other regional characteristics such as human population were

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significant but negative. The number of biotech firms in the region was usually positive, though

almost always insignificant. Innovation in the region, measured as discounted patents, had mixed

effect on indigenous firm growth, though always insignificant.149

At the firm level, there were a couple of notable effects. The older the firm was, the lower

its rate of growth (this is a strictly linear effect). Both the number of alliances and the number of

patents consistently showed a positive effect on growth, but this was only consistently significant

in the former. The firm’s status as public often had a positive, though insignificant effect.

New greenfield entry. Models 2 to 4 of Table 5a add the three different measures for

greenfield and acquisition entry, each within the period of entry and over three lags. Using

counts, new greenfield entrants have a negative effect on indigenous firm growth in the first two

years, but the effect becomes positive thereafter. However, none of these results is significant.

Using the proportion of new greenfield entrant, the signs switch from positive to negative and

back again. Using the share of greenfield employment, we see that the effect starts off negative

but then turns positive in the first lag. However, once again, none of these results is significant.

Thus, hypothesis 3a is not supported since there is no evidence for any kind of effect of new

greenfield entrants on incumbent growth.

New acquisition entry. New acquisition entrants, on the other hand, have a consistently

positive effect on indigenous firm growth through the first two lags. These results are more or

less consistent whether using counts, proportion of external to total, or employment share of

external to total firms. The only exception is in the third lag using new acquisition counts which

shows a significant negative effect (Model 2). Overall this provides some support for hypothesis

3b, but only if the short run is taken as the first two periods.

149 Also note that in regressions not reported in the tables, it was found that indigenous firms that were acquired grew less in the post-acquisition period than before. This confirms an assumption as well as prior research.

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Greenfield presence. Table 5b shows the effects of the various measures of greenfield

and acquisitions presence on indigenous firm growth. Greenfield presence has a negative effect

in the count models, but is positive in the firm share and employment share models. Once again,

the pre-1976 greenfields were separated from those that entered later. Contrary to the entry and

exit models, this had remarkably little effect in the growth models. Not only do the signs switch

across models for the pre-1976 greenfields, but also none is significant. The post-1976

greenfields are consistently positive, but none is significant (Table 5b, Models 2, 5, and 8).

Therefore there is no support for the hypothesis that greenfields have an effect on indigenous

firm growth in the longer run (H3c), nor is the alternative supported.

Acquisition presence. Consistent with the generally positive effect of new acquisition

entry over successive lags, the total acquisition presence has a positive influence on growth. This

is true across all models, and is often significant, thus providing support for hypothesis 3d. It

appears that acquisition presence does generate positive externalities that facilitate the growth of

indigenous incumbents.

Interaction effects. The interaction effects show that the level of agglomeration has no

influence on the effect greenfield presence has on indigenous firms. Even separating the two

subgroups of greenfields (pre- and post-1976 entrants) and interacting each with the level of

agglomeration produced no results. The interaction with acquisition presence, on the other hand,

does have an influence, but once again in the opposite direction of that predicted. In other words,

the positive main effect is diminished at higher levels of agglomeration, though never dominated

by the negative interaction. Thus, neither H5e nor H5f are supported. Once again, we might

speculate that this is because it is more difficult for indigenous firms to access knowledge and

other resources from acquired firms at higher levels of agglomeration. (If some of the effects are

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due to direct interactions, it might also be because the acquired firms have more options when

there are more firms in the region.)

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Insert Tables 5 about here

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Summary and Interpretation

At least three things are surprising and interesting about these results and the overall

patterns they produce (see Table 7 for a summary). First is the fact that different classes of

outsiders often exhibit clear opposing effects on entrepreneurial entry, and incumbent growth

and survival. The effects of acquisition entrants and older (pre-1976) greenfields tend to move

together, and in opposition to the newer (post-1976) greenfield entrants. Second, the results

suggest a duality in the effects that different classes of outsiders have. This duality is particularly

evident in comparing entry and exit models – those that have a positive effect on entry (i.e.

newer greenfields) have a negative effect on survival and sometimes growth, and vice versa.

Neither greenfields nor acquisitions have unambiguously positive or negative effects. Because

most prior studies in international business have tended to pool all of these classes together into

an undifferentiated group of external (foreign) firms (often just as FDI investment), these

underlying differences have to this point been invisible. In fact, pooling external firms in this

way tended to produce no results at all using this data. This non-result is consistent with a

number of studies, as previously noted.

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Insert Table 7 about here

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A priori, it was expected that these different classes would move in the same direction. In

other words, the only difference between them would be the magnitude, not the sign. Moreover,

it might have been expected that their effects would eventually converge so that these differences

would become negligible over time. The fact that the signs also differ suggests that the nature of

the externalities also differ, and that different mechanisms operate on indigenous entry, survival

and growth.

Newer greenfields (post-1976), have a positive effect on entrepreneurial entry which may

at least partly be explained by new linkage effects and other resources that are important for new

startups; but once an indigenous firm starts up, newer greenfields presumably absorb resources –

especially in the form of skilled labor – that might otherwise be available for indigenous firms.

At least in the early stages, newer greenfields are not strong attractors for potential entrepreneurs

who are often research scientists with some form of intellectual property. On the contrary, since

these newer (post-1976) greenfields usually enter small and only with a sales, marketing, and

regulatory functions, they likely provide critical complementary resources for indigenous

startups, who typically require such capabilities to commercialize their innovations. Once an

indigenous firm starts up, however, newer greenfields are likely their nearest competitors for

those resources – in part because their requirements are similar. Thus, they are also more likely

to seek similar types of knowledge and resources, but they also likely look similar in the sense

that newer greenfields are also more typically (successful) biotech firms rather than more

traditional pharmaceutical firms, suggesting the difference matters. In general, then, this suggests

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a dynamic process whereby newer greenfields both generate and absorb resources critical to

indigenous firms.

The older greenfields (primarily traditional pharma) and the acquisitions, by contrast,

both crowd out potential entrepreneurial entry. This may be because they are both strong

attractors for potential entrepreneurs on an ongoing basis. This is probably more obviously the

case for the traditional pharmaceutical firms (and the results suggest this). However, continuing

acquisitions are those that have some commitment from the parent. They are typically larger and

older than newer greenfields, and may appear more stable. More critically, they likely provide

more opportunities than either indigenous incumbents or newer greenfields, depending on the

parent and the nature of the local subsidiary. It is also almost definitely the case that this effect is

at least partly due to the lucrative consulting contracts that these kinds of firms, and particularly

the more innovative among them, provide potential entrepreneurs – scientists in this case.

Both the older greenfields and acquisitions, however, generate positive externalities that

enhance indigenous firm survival prospects (and growth for acquisitions). These externalities

may be of a general nature – through indirect linkages, and possibly through direct relations not

captured in the alliance data. Labor migration from large firms (older greenfields and

acquisitions) to smaller indigenous firms certainly contributes to this effect as well, but it may be

infrequent for a couple of reasons. First, the larger firms typically pay better; second, they offer

more career opportunities and diversity of experience. Moving to a small indigenous firm can be

a career killer since it is seen as a move to a lower status firm.150 On the other hand, some

seasoned professionals and other entrepreneurially-minded individuals might seek the challenge,

particularly if they take an equity stake. Also, as discussed in more detail below, the nature of the

legal regime may actually reinforce this effect. 150 This assumption was confirmed in informal discussions with some managers of pharmaceutical firms.

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Agglomeration effects

The third surprising result is how these effects change at different levels of

agglomeration. Rather than having an unambiguously positive effect at higher levels of

agglomeration, which is really only the case with greenfield presence in a couple of the entry

models, we see that the opposite is often the case. In the case of greenfield presence, where there

is evidence for positive interaction effects in the entry models, we see that the negative main

effect is reversed at higher levels of agglomeration (Table 3c). In the case of acquisition

presence, we see that negative interaction effects either reinforce the already negative main

effect, or balance the positive main effect. The first case is in evidence in one of the entry models

(Table 3c); the second case in the exit and growth models.

It was suggested that this negative interaction might be because there are more (and

possibly more mature) indigenous firms that also contribute to the labor pool, which may suggest

that an increasing proportion of new talent comes from that pool rather than from outsiders. The

effect of outsiders then would be more diffuse. This suggests that time might also be a factor in

the sense that a maturing indigenous industry makes more of a relative contribution. Also, as

these outsiders become insiders their effects stabilize over time. Another possibility is that at

higher levels of agglomeration, there might more voluntary departures than would be the case at

lower levels because employees have more options, thus the average quality of departing

employees is lower than when departures are primarily key fiduciaries of an acquired firm

(which then characterizes the acquisition event as a liquidity event). In such cases, second order

events are not driven strictly by liquidity. This is an issue that obviously requires more

investigation.151

151 Also note that a reversal in sign would occur primarily three regions – Toronto, Montreal, and Vancouver, though others have begun to join the ranks of established clusters more recently.

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Temporal effects

One possible reason that a short run crowding out effect is not more in evidence

following new greenfield entry in the short run is that crowding out is contingent on the amount

of slack resources in the local environment. It may be that in a growth industry, such as the

biotech industry in Canada during this period, there is sufficient slack to mitigate the effect of

crowding out somewhat.152 In the case of indigenous firm survival, recall that the long run

effects were the opposite of the main prediction – i.e. there was crowding out; and there were no

significant effects on indigenous firm growth in the long run. In general, and irrespective of the

effect, it is likely the case that if new greenfields enter small, they may be on the competitive

fringe and their effects may not be felt for some time. This appears to be the case especially with

indigenous survival since the longer run presence variables were often significant when the

shorter run entry variables were not.

The main prediction for new acquisition entrants, on the other hand, was that they would

exhibit positive effects. In strict terms, liquidity events should trigger new entrepreneurial events.

The effects on entrepreneurial entry in the short run, however, tend to be negative and often

significant. Apparently, these acquisitions did not constitute liquidity events in terms of their

effect on entrepreneurial entry, quite the opposite in fact since there is an immediate and

persistent crowding out effect. Given the terms of some non-compete clauses in employment

contracts, a liquidity effect in the form of new entrepreneurial entry may not be visible within the

period of three years. Thus, if liquidity effects do occur they are more likely to show up in the

152 On the other hand, the short run crowding out is really only applicable in the entrepreneurial entry models since new greenfields only show positive long run effects here. In one of the short run models there is some evidence of crowding after which there is a fairly rapid turning point. In this case, the effects of linkages and skill development (and turnover) appear to occur within a span of a couple of years (using new external firm share) in a way that facilitates entrepreneurial entry. However, this might be over-interpreting the results given that there is a significant negative result in only one of the three measures.

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presence models in which case they offset the crowding out effect and may account for the lack

of significance in some of the models. Still, crowding out dominates from the very beginning

suggesting that in general, newly acquired firms absorb resources that might otherwise be

available for new entrepreneurs. It is possible that this crowding occurs as a result of a liquidity

event in the sense that once key employees are released, there is a need to fill the vacuum with

potential entrepreneurs (research scientists through direct or contract employment).

Contrary to the effect on entrepreneurial entry, there is evidence of a positive effect in the

survival and growth models. In the latter case this is very short run and is not persistent; in the

former this seems to take a few periods to show up (at least in some of the models), as would be

expected. Most of the growth effect is likely due to employee turnover at the time of the

acquisition announcement; the survival effect may be due not just to employee turnover – but to

especially high quality turnover. Rather than starting up another firm, some senior people are

likely to go to some indigenous firms, especially if they are held to non-compete contracts (and

can justify that the new employer is not a competitor).153 However, it is difficult to assess the

extent to which the positive effects are due to turnover rather than to other generalized spillovers.

Taking the interaction effects into consideration, however, complicates this interpretation

since at higher levels of agglomeration, acquisition presence has a negative effect on indigenous

firm survival. If noncompetes do play a role here, then this suggests that the long run spillover

benefits from that mobility is overwhelmed by negative externalities generated when the

community is larger. This implies competition for resources, which includes key staff. Of course, 153 The Canadian context might be regarded as a semi-strong legal regime with respect to non-competes. Individuals bound by the terms of noncompetes are generally restricted from exploiting knowledge gained with the former employer. From a legal perspective, it might be more difficult to detect exploiting knowledge gained within an incumbent firm compared to a new startup. Noncompete covenants also often explicitly specify competitors that employees cannot join after employment, usually for a period of up to five years. Thus, if more visible firms were seen as competitors, which might especially be those who are outsiders, a clearer prohibition would apply to taking employment with them. This might actually channel these individuals into smaller indigenous firms thereby improving their survival and growth prospects.

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noncompetes should also play a role for greenfields but it is even more difficult to assess their

effects since mobility is not associated with a clear event as is often the case with acquisitions.

Other effects

Not only do the effects of the variables of interest have opposing effects on indigenous

entry, and their subsequent survival and growth, but so too do some of the control variables. As

Stuart and Sorenson (2003a) note, and as discussed above, the factors that facilitate

entrepreneurship do not necessarily promote firm performance.154 For instance, the level of

innovation in the region tends to act as a barrier to entry, but once entered, it tends to enhance

indigenous incumbent survival. This is also reflected in a dichotomy in the literature. On the one

hand, the regional innovation system literature suggests that positive spillovers should result

from higher levels of innovation in the region; on the other hand, some of the industrial

organization literature emphasizes that higher levels of innovation should lower entry since it

represents a rising minimum condition for entry.

The number of biotech VCs in the region tends to have a positive influence on the

decision to enter (especially in the within period models), and in one model at least it has a

significantly positive effect on indigenous growth, but it tends to actually dampen indigenous

firm survival prospects. If anything this should be insignificant if indigenous firms can access

capital from other sources. There is no obvious reason for this except perhaps for the fact that

VC funding is often tied to an exit strategy (for the VC), which may impact firm survival (but

154 Note, however, that their performance measure is IPO rates rather than firm survival or growth. The two more conventional performance measures that are used here – growth and survival – suggest that this is also the case in the Canadian biotech industry.

158

that should be with a very long lag).155 The number of PROs in a region has a marginally

positive effect on entrepreneurial entry with a one period lag, at best. It could be that their effect

is picked up in other correlated variables. It could also be that the measure used here is not fine

grained enough.156 Paradoxically, the number of PROs tends to decrease the survival prospects

of firms, though not always significantly, but it has little effect on growth.

A slightly different pattern occurs with some of the other variables. The regional human

population consistently enhances entrepreneurial entry, and survival, but not growth. The number

of national biopharma firms tends to have a negative effect on both indigenous entry and

survival, but a positive effect on growth. The number of biotech firms in the region has an

inconsistent effect even within period. It may be that a different count – perhaps of DBFs only,

i.e. excluding the traditional pharamaceutical firms, would change the results.

In general then, the factors that affect entrepreneurial entry do not necessarily have the

same effect on firm performance. However, we might also ask: Do the same factors that

influence survival also influence growth? As is evident from the above discussion on the effects

of regional variables, the answer is not always. But here we can also compare the effects of firm

level variables. For example, the number of alliances a firm has, has a clearly positive effect on

its growth, but it has a positive effect on its survival within period only. Age has a U-shaped

effect on a firm’s survival, but it is linear (negative) in growth. The number of patents a firm is

granted in the period has little effect on survival or growth (though in the latter case there is

155 Note that Stuart and Sorenson (2003a) found that the number of VCs in a region lower the likelihood of biotech firms going public (in the U.S.), which may be due to competition for funding. If a firm’s status as public lowers the risk of exit, as was generally found to be the case here, then these results would be consistent. 156 The measure used here is very broad – for instance, universities and all of their individual units are counted as one. This measure understates the effects of multiple units – usually active within different subdisciplines – within large research-intensive institutions such as McGill University (Montreal), the University of Toronto, the University of British Columbia (Vancouver), or the University of Alberta (Edmonton). Just like biotech VCs, this tends to have a positive effect on entrepreneurial entry within period, however, only when the number of biotech VCs is removed. This might suggest a substitution effect between the two.

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evidence of a positive effect). Likewise, there seemed to be little difference between public and

private firms in terms of their effect in survival or growth rates.

Discussion and Limitations

Geographic and Industry Boundaries

As previously noted, the results may be sensitive to variable definition and construction.

The key issues relate to the geographic and industry definitions and boundaries that were used to

construct the variables. The first issue deals with the question: how much does geography really

matter? Recent research has called into question the importance of localization economies in

firm growth and survival.157 In this study, I have argued and shown empirically that outsiders

exert certain kinds of externalities that tend to be local – but how local is local? How would the

results change if the boundaries of the region were considered more broadly, or more narrowly

for that matter? The second issue has to do with how would the results change if the industry

were considered more narrowly – only as dedicated biotech firms, as some other studies have

done? These questions are addressed in turn.

Boundaries of the Region

The regional unit is the census metropolitan area (CMA), which may be too narrow for

some of the effects hypothesized here. For instance, it may be that the labor market for managers

is broader, perhaps even national or international in scope. This may be especially true at higher

managerial levels. Also, knowledge diffusion depends on the specificity of the knowledge

domain (or technology regime), which is not always local. In many cases this is likely to be

international in scope. Still, there is undoubtedly some friction in terms of the mobility of

157 See Thompson (2003) for a critique of the localization argument.

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individuals and ideas. Moreover, at least some of the knowledge is also likely to be local –

especially the general applied knowledge relating to input markets (suppliers labor).

Some regions are quite close together – Toronto, Montreal, Ottawa, and Kingston are all

within five hours of one another, for instance. In fact, some have referred to the region between

Toronto and London, Ontario, which are only about two hours apart, as a biotech corridor rather

than a cluster. Included in this area are firms in Guelph, and Waterloo. Thus, it is important to

enquire as to whether such distances are relevant or not. It is undoubtedly the case that an

entrepreneur is likely to start up in close proximity to where the parent is (either research

institute or incumbent firm). However, how close is close? For instance, it is not unusual for

people to commute long distances along the highway 401 corridor, often up to two hours one

way, in which case an individual lives in one region and works in another. Where are they likely

to start up a firm then? Likewise, they may take up a position in another firm in any number of

different regions in relatively close proximity to where they live. The other possibility is that

potential entrepreneurs and firms can rely on resources in proximate regions, even if they are

relatively inert. The question is how would an external firm influence the outcome – resources in

another region might facilitate entry, exit and growth in a given region depending on the type.158

On the other hand, some regions are pooled even though they might reasonably be

separated. Toronto and Mississauga, for instance, or Montreal and Laval. However, because they

are part of the same CMA they are kept together. This allows for the consistent use of regional

variables, such as population, as well as some of the aggregated variables, which are difficult or

impossible to separate out into one region or another. However, there is sometimes substantial

158 One approach would be to pool all firms and discount the distance between firms in different regions. Another approach would be to re-aggregate to a broader geographic area. Using a distance measure provides additional information about optimal distances. However, recall that the CMA/CA boundaries are determined by commuting patterns. Thus, the region, defined as such, should capture most of the effects.

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heterogeneity with respect to the specific local resources that firms rely on. In fact, many of the

largest firms are located in the periphery of large cities, in the suburbs, whereas the smallest

firms are often located in the center. More correctly, they are located in close proximity to the

research institutes out of which they were generated. Location choice in a local sense is at least

partly driven by cost – as firms grow, they need to expand their physical space and tend to

relocate where land is plentiful and rents are cheaper.

However, this kind of geographic differentiation also points to another set of processes at

work in a growing firm. First, the internal processes of the firm become transformed during the

transition from the entrepreneurial to the growth stage. Part of this is due to the shift from

product development to production, which can be much more routinized (and requiring lower

levels of skill). This means that the proportion of R&D staff to the total declines with the size of

the firm and non-R&D staff – those in sales and marketing, finance, and other operational areas –

will expand. Second, with growth, firms tend to expand their linkages beyond their local base.159

If an indigenous firm is generated by a research institute with whom they have a strong tie, that

invariably weakens in relative terms over time as the young firm begins to develop linkages with

other organizations. These two factors lead to spatial evolution within the local industry.

Boundaries of the Industry

The second issue deals with the boundaries of the industry. Most studies of the biotech

industry take dedicated biotech firms (DBFs) as their central focus. This is problematic for a

couple of reasons. First, many of the relevant dimensions of knowledge are not restricted to this

group. In fact, the differences between pharmaceutical firms and biotech firms are increasingly

159 Hennessy (2005) studies the dynamics of foreign firms’ embeddedness in regions and how that changes over time. How indigenous firms’ linkages evolve is left to future research.

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artificial. In one sense, there is an important distinction in terms of their structural differentiation

– pharmaceutical firms are often older, larger, and more fully integrated than biotech firms.

Many pharmaceutical firms are also multinational corporations. However, this may just reflect

the stage of development of the firm. Many firms that started off being what is referred to as

biotech are now regarded as fully integrated pharmaceutical firms (and hence at a later stage of

development). Still, in order to better understand the differences, core biotech firms and

pharmaceutical firms should be distinguished in the analysis because they may exert different

kinds of externalities. This was shown to be the case here.

Other Measurement Issues

Types of greenfields. Not only are the larger pharmaceutical firms structurally different

than early stage DBFs, but in most cases their longevity also means that they were de facto

insiders in the regions at the start of the study period. Their effects on the local industry differed

dramatically from that of newer greenfields. There may also be some other ways to parse up the

industry – sectorally, or in terms of the innovativeness of greenfields, for instance. In some

preliminary analysis, using a subset of human sector greenfields did not seem to have much of an

impact (probably because some regions are specialized and region fixed effects were used where

possible). In the second case, some preliminary analysis was done using only innovative

greenfields which suggested that their effects were stronger – i.e. more negative on

entrepreneurial entry and more positive on incumbent survival. The externalities that innovative

and non-innovative greenfields generate therefore differ.

The differences in the effects of different kinds of greenfields have important policy

implications since it suggests that not just any kind of greenfield should be actively recruited to

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come to a region. Obviously, the cost of subsidies should be traded off against the potential gain

(see Haskell, et al. (2002) for an analysis of the costs/benefits of such incentives). But it appears

that there are conditions under which net benefits accrue to a region. These issues require much

more serious investigation.

Types of acquisitions. Some targets are acquired because they are successes, others

because they are failures. Being able to differentiate these cases may be important if it is thought

that of the quality of resources released upon acquisition differ. But to complicate matters, the

relative success of the target to that point is not always tied to its ultimate fate. Still, the type of

acquisition classified in more conventional terms does matter in terms of the externalities

generated. Terminal acquisitions really constitute an absolute loss in the region in terms of

employment but it can benefit others, as has been shown here.160 Acquired firms that do remain

in the region for a longer period are also of different types: they may remain autonomous or they

may become tightly controlled by the new parent; some remain or become innovative and others

may be hollowed out. These different cases suggest different and sometimes conflicting

outcomes. Understanding the effects of different types of acquisition is a fundamental issue from

a policy perspective since it suggests that not all acquisitions should be treated the same. Some

may have detrimental effects, whereas others contribute to a process of creative destruction. But

it is interesting to note that in the industry context studied here, both positive and negative effects

co-exist in the aggregate.

It is also interesting to note that the negative rather than positive effects dominate in their

effect on entrepreneurial entry. In fact, Stuart and Sorenson (2003b) also found that grouping all

160 It may be that the positive effects due liquidity events are more likely with terminal acquisitions – i.e. where the IP is raided and taken out of the region, since it is a final event usually involving cash, and the non-compete covenants may be less restrictive, particularly if the acquirer is foreign. Furthermore these might be regarded as real successes if gauged by their IP (though possibly not by their financial viability).

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types of acquisitions together yielded no or negative results. When they separated those firms

that were acquired by other biotech firms from those acquired by non-biotech firms they found

opposing results. They argued, and found, that the latter type of acquisition was more likely to

lead to a liquidity event and therefore should have a positive effect on entrepreneurial entry.

They also suggested that the negative effect might be the result of poor quality acquisitions. This

may be possible in this context; however, that does not explain the positive effects that

acquisitions have in the aggregate on indigenous survival and growth. While some explanations

were given for why this might be the case, this is an important issue for future research.

Multiple acquisitions. An acquisition event is a repeatable event – a firm can be acquired

more than once. Here, a firm is counted as having been acquired only once if it was acquired at

any point in its history. However, it is possible that subsequent acquisitions matter. There are two

issues. First, do acquisitions subsequent to the first one constitute liquidity events of the sort

already discussed? The answer is probably no, in the sense that the owners are not entrepreneurs

and other senior managers who have cashed in. By definition, the owners are located elsewhere

(i.e. because we are only dealing with external acquisitions); and in the case of large

multinationals, ownership is probably quite diffuse anyway. On the other hand, the employment

data show that lay offs occur after just about all acquisitions, which may lead to a release of

managerial and other talent. If this is the case, it could be that subsequent acquisition events do

have an effect in the short run, particularly on indigenous growth and survival. These

fluctuations in employment data should be captured using employment share as the measure of

acquisition presence; however, using counts as the measure of acquisition presence may

understate the real effects reported.

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The second issue has to do with how new ownership affects knowledge flows. The theory

argues that the external link, i.e. with the parent, is a conduit for new knowledge irrespective of

where it comes from and when it occurred. It does not really differentiate between types of

knowledge. Thus, once again, the measures that rely on counts do not really capture this. On the

other hand, using employment share as the measure of acquisition presence does capture

fluctuations in employment and hence the effects of re-acquisitions. Thus, these two measures of

acquisition presence differ quantitatively and qualitatively.

Types of exits. There are also a variety of types of exit. An exit is defined here as an

outright failure of an indigenous firm. Terminal acquisitions are also a form of exit, but these are

treated differently here. There are also some quasi-exit states such as bankruptcy (of which there

were a couple of cases but these were not treated as an exit if they disappeared from a directory

and subsequently were found to have signs of life). Another important form of quasi-exit is a

divestiture. Divestitures are an important part of industry dynamics in the sense that they can

refocus the firm. Divestitures may represent a kind of exit – either from a product line or from a

geographic location. This is an important issue not just from the perspective of a focal

indigenous firm, but also when considering the effects of outsiders.

External restructuring. An external restructuring event may occur for one of a couple of

reasons: the first is an autonomous refocusing leading to divestitures in some regions; and the

second is in the case of a merger initiated between MNCs leading to the rationalization of their

subsidiaries around the world. The latter case is especially likely to occur when two merging

MNCs, both with subsidiaries in a particular region, combine their facilities. There are numerous

examples of these external events in the form of merger waves in the pharmaceutical industry.

This susceptibility of local industry to these external events is itself an interesting subject for

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future research. An acquired external firm may simply be gutted and closed as in the case where

the target is acquired simply for its intellectual property, or it may be moved after a period.

Often, as previously noted, vestiges of these closures remain behind.

Once again, if a divestiture is made for strategic reasons in some head office elsewhere,

then the resources released are not necessarily inferior. (Strategic redirections do not necessarily

mean that the worst are let go.) For example, when Merck consolidated its R&D facilities in New

Jersey, it had important consequences for its local subsidiary in Montreal (Ayerst) and for the

region. Although divestitures mean a loss of jobs, they can also benefit the local industry in that

they provide more slack human resources, some of which can be highly skilled. The negative

consequences for individuals are obvious; those for local industry are less apparent. In general,

these kinds of divestitures probably have a similar effect as acquisitions of indigenous firms

(though they do not constitute a liquidity event). In fact, some MNCs have established programs

to help employees transition to new employment or even to startup their own firms. Once again,

some of these effects are captured in changes in the number of external firms when there is

outright closure; or in the employment data when there is a downsizing. Still, future research

should explicitly distinguish between new additions and subtractions due to restructuring in order

to better understand the effects.

Inter-regional dynamics. Divestitures (or further investment) by outsiders are important

events for inter-regional dynamics. Some other events, such as moves into or out of the region by

single facility firms, are also an important part of regional dynamics. If the firm was domestic, a

move constituted a simultaneous exit from their former location and entry into the new location.

In other cases, firms founded outside of Canada entered by moving their whole operations into a

region in Canada. Since these were not new indigenous entrants, they were not counted as such.

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On the other hand, even though they were external greenfields, at least in one sense, they were

not external subsidiaries, and therefore they were not included in that category either. However,

they more properly belong to the latter category since they are, in a sense, outsiders. Such moves

were, however, included in the total count of firms in a region. There were only a small number

of such cases – less than a dozen, so these cases should have little effect on the results, no matter

how they were classified.

Temporal effects. Another key issue is how important are the short run effects compared

to the long run effects? New acquisitions tend to crowd out entrepreneurial entry but enhance

survival fairly early on, and these effects tend to persist over the long run. There is also some

weak evidence of a short run crowding out effect for new greenfields on entrepreneurial entry

then a turning point, and some weak evidence that new greenfields enhance survival by the third

period. Otherwise most of the evidence suggests that these are longer run (i.e. more than three

period) effects.161 (Note that the older greenfields exert strictly longer run effects.) What we

cannot determine here is to what extent the long run effects of acquisitions are actually driven by

the short run. In other words, are the effects truly persistent?

Despite the fact that the results are not symmetrical, this might suggest something about

how long it takes for external firms to become embedded. One interpretation is that since

acquisitions are already embedded in the region, their effects are immediate, whereas the effects

of new greenfields take longer because they start from nothing and gradually have to build up.

This is likely whether the effects are due to employee development and turnover, or dynamic

linkages, or whatever. Another related issue is the fact that the negative and positive effects (on

entry and survival in particular) appear simultaneous; however, there may be an order to how

161 Interacting cohorts with time would provide more information about long run convergence or divergence in effects of older greenfields versus acquisitions. This will be done in future research.

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outsiders affect these processes, which is difficult to determine. This remains an interesting area

for future research.

Parental influence. How do parental characteristics influence the effects of their

subsidiaries? There are a number of parental characteristics that could be modeled and tested

explicitly including the prior experience in the country and internationally, the number and types

of other subsidiaries, and size, which may be important just in terms of the resources available;

etc. A key issue here that partly subsumes the other issues has to do with whether the parent is

foreign or domestic since it was assumed that the difference does not matter that much. If

foreign, does it matter where it is from? Most of this is left to future research. However, some

preliminary analysis suggests that it matters somewhat whether external firms are foreign or

domestic.

The hypotheses suggest that foreign and domestic external entrants should have similar

short and long run effects on the indigenous industry. There is no reason to believe that there

would be any difference if, for instance, external domestic firms are also multinational.

Likewise, there is little reason to believe that multiunit domestic firms would not have similar

effects (own and other), assuming that they possess characteristics that are similar to the others.

On the other hand, some foreign and domestic firms may actually have similar characteristics

that would influence their ability to generate and transfer knowledge, or to create opportunities.

Thus, it may be that it is these similarities and differences that matter rather than whether or not

the firm is foreign or domestic. Testing the effects of domestic versus foreign acquisitions

showed that the former tended to be insignificant, while the latter retained a significant and

negative sign.

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Stages of development. There are two types of stages of development that may be

important to the generation and diffusion of externalities – at the local and national levels. The

stage of development of the local industry, which is emphasized here, reflects local variation in

the resource base. In the Canadian biotech industry there is variation in stages across regions:

some are just beginning to emerge, whereas others have taken off into the growth stage, some of

which are even showing signs of maturity. It was hypothesized that positive externalities are less

likely in the emergent stage of development, but more likely when the local industry is in the

growth stage. Interacting the various external measures with the number of biotech firms in the

region, we see that only greenfield presence has a positive effect on entrepreneurial entry at

higher levels of agglomeration.162 Otherwise the interaction effects are negative. It was

suggested that this might be because a more densely agglomerated region makes it harder for

indigenous entrepreneurs to gain access to knowledge and resources that are required. It might

also be because the stage of development that matters most is broader in scope – i.e. at the

nationa

e

e

l level.

The stage of development at the broad (national) industry level also established boundary

conditions for entry, and even survival upon entry. A key issue is that certain kinds of knowledg

establish the floor for entry with respect to the technological base. While some elements of th

knowledge base are likely to be local, some other key elements may not be if the industry is

science-based, since it likely diffuses through public institutions via at least partly codified

principles. Both the local industry and the broader industry may be in and emergent, growth or

mature stage, though possibly at different times. These stages will coincide for early regional

leaders, whereas they may differ for challenger regions that emerge later. In fact, there may be an

162 Another approach would be using a splitting rule, like Agrawal, et al. (2004), to study the effects at these different stages.

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interaction between the two – there are costs and benefits to entering both early and late

to the stage of development o

relative

f both local and broader industry. This interaction will be

investig

er key

s in

re

ions between firms within regions. As this study has shown, this

distinct

aps

ting

ated in future work.

Network structure. Implicit in this study is the assumption that knowledge (and oth

resources such as labor) flows imperfectly across regions, but more freely within regions.

However, the variation in the configuration of regions suggests that clusters are not just node

an industry network, but that they too have a network structure that can facilitate or impede

spillovers. This structure is introduced incidentally here by distinguishing between insiders and

outsiders.163 The assumption here is that this distinction is important in understanding the natu

of direct and indirect interact

ion is important.164

Some of the results suggest that the benefits of clustering might be overstated, or perh

that the costs are understated. The costs associated with a larger cluster include the fact that

relationships may become locked-in and the structure may become subject to inertia, sugges

that the knowledge and other resources become concentrated within cliques. Though some

general resources flow freely and are in the form of public goods, others do not. Even labor

mobility it inhibited – by the status hierarchy of firms and by what is considered legitimate

experience. As previously noted, a move from a large diversified MNC is generally not

considered advisable at an early stage in one’s career. Thus, certain kinds of knowledge and

other resources are more likely to circulate within cliques such as external/foreign enclaves. In

this sense small indigenous firms are peripheral irrespective of the fact that they are insiders.

163 Centrality is certainly an important issue for access to knowledge and other resources, which is tied to some key issues related to market structure, especially concentration and market power. However, a firm’s status as an insider or an outsider likely links to centrality. 164 Though it fails capture some other possibly important structural features of the local landscape (e.g. Owen-Smith & Powell, 2004).

171

This might explain why greenfields have little or no effect on indigenous firm growth. This

should be different for acquired firms, which by dint of having been indigenous were already

connected to the local community. This might partly account for the positive effects they have on

indigenous firm growth and survival. However, this effect is actually strongest at lower levels

agglomeration. As the agglomeration becomes larger, it was suggested that the structure may

make knowledge and resources more diffuse which means they have less impact on specific

(indigenous) firms. But it might also be because the firm takes on a different status as an outside

which may increase the options of those employees to circula

of

r

te among other external firms. (If

this is t

unity

d centrality of insiders

(indigenous incumbents), will change over time as they evolve.

he case, it is likely more a longer run phenomenon.)

New greenfields are probably much more like real outsiders. They tend to provide

complementary capabilities for new startups probably especially through skilled labor, after

which time they compete for similar resources. This may be explained by resource overlaps –

especially since they tend to start off at a lower level of embeddedness in the local comm

(than acquisitions), and tend to grow faster.165 Even the status an

165 On the other hand, as outsiders they may be able to connect to other outsiders, especially if they have contact with other outsiders in other markets.

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CHAPTER 7

DISCUSSION AND CONCLUSION

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The central focus of this study has been on the effects that external firms have on

entrepreneurial entry, as well as on indigenous incumbents’ survival and growth, after they enter

a given region. External entrants alter the knowledge and opportunity structure of regions, which

has implications not only for the evolution of industry in a region, but also for the industry as a

whole. The results reveal a rather complex set of processes at work. Newer greenfields

consistently have the opposite effect on the indigenous industry than both more established

greenfields (primarily traditional pharmaceutical firms) and acquisitions. Moreover, their effects

on entrepreneurial entry differ from their effects on survival and growth. The presence of newer

greenfields tends to enhance entrepreneurial entry, but dampens indigenous firm survival. The

opposite is the case for acquisitions and older greenfields. Thus, both positive and negative

externalities co-exist in different classes of outsiders. These effects are also sometimes

conditional on the size of the agglomeration economies – sometimes enhancing the effect of

external firms, but sometimes diminishing it.

This study enhances our understanding of the geography of organizational knowledge

from multiple related perspectives by examining the effects of the entry of external firms into

regions, which is often motivated by their search for new diverse knowledge. External entrants

introduce diversity into a region because they often enter with different sets of capabilities than

indigenous incumbents (which entered as spinoffs of either pre-existing incumbents, or research

institutes in this case). This paper therefore complements work that emphasizes the evolution of

local industry through inheritance processes (e.g. Klepper, 2001). It also links an evolutionary

theory of the MNC (Chang, 1996; Kogut & Zander, 1993), which focuses on intra-firm

knowledge transfer, with the international business literature that focuses on intra-industry

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spillovers and competition between foreign and domestic firms (DeBacker & Sleuwaegen, 2003;

Haskell, et al., 2002; Kosova, 2005; Liu, et al., 2000).

This latter work has an obvious affinity with economic geography as has been shown

here. The theory developed here does not necessarily explain the high degree of geographic

concentration of industry as occurred in the U.S. auto industry (Klepper, 2002b). In fact, it may

partly explain the opposite: that sources of knowledge and expertise can remain geographically

dispersed throughout different regions, especially when path dependence due to tacitness in the

knowledge base creates the conditions for locational inertia. However, because firms themselves

are not as locked-in as they are sometimes characterized, they often go in search of new forms of

knowledge, which transforms both themselves and the regions they enter. This not only suggests

a co-evolution between the parent firm, their subsidiaries and the regions they are a part of, but

also a co-evolution between different regions. External entry therefore binds firms in different

regions together into an industry system and acts as a conduit for flows of new knowledge,

resources and capabilities.166 However, such moves contribute not only to variety generation, but

also to selection due to competition for scarce resources. This latter element seems to have been

left in the background in recent studies.

The main aim of this study has been to further develop evolutionary economic geography

theory – in fact, this is the first study to explicitly examine the interaction between insiders and

outsiders in industry clusters. The empirical results demonstrate a complexity in the effects

outsiders have that has until now been unexamined. The international business literature has

typically pooled all foreign firms together to examine their effects on domestic industry. The

only study to examine the effect of FDI on entry and exit at the industry level simultaneously

166 Since the parent is affected by the role of any given subsidiary, the interaction between the two then feeds back to their future entry decisions regarding mode and location choice.

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found positive (spillover effects) for both (Debacker & Sleuwaegen, 2003). The results show that

doing so here would have been very misleading. This is the first study to separate these effects;

and it is the first to actually show opposing effects of new greenfields and acquisitions on

indigenous industry. It is also the first to show that outsiders can exhibit both positive and

negative effects on the indigenous industry, maybe simultaneously, suggesting that the

interaction between different classes of firms is an important driver of local industry evolution.

Furthermore, where positive effects occur, it would suggest that some of these interactions may

be key drivers of agglomeration economies.

The results of this study also tie into the “spillover theory of entrepreneurship” (Acs, et

al., 2005), and to the recent literature on institutional constraints in the form of non-compete

clauses in employment contracts (Stuart & Sorenson, 2003b; Marx, et al., 2007). The results

suggest that if non-competes impede entrepreneurial entry, as they most certainly do in the

Canadian context, then there are likely other effects in the form of enhanced growth and survival

prospects for indigenous incumbents. Spillovers resulting in new startups are contingent on legal

(and other institutional) constraints, which tend to be more operative in high-tech contexts

because of greater appropriation concerns (and because the primary mechanism driving spillover

is usually employee turnover). On the other hand, in this context and in other science-based

industries, perhaps the key generative mechanism is public research organizations, which tend to

have fewer appropriation concerns. Quite the contrary, since the emphasis more recently has

been on commercializing innovations developed in PROs through the creation of new firms.167

Thus, in science-based industries, PROs often substitute for incumbent firms as a generative

mechanism in the presence of regulatory constraints. However, there is surely a difference

167 This mechanism, particularly as it pertains to the supply of ‘star’ scientists is well-documented (Audretsch & Stephan, 1996; Zucker, et al., 1998).

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between the two sources in terms of the nature of the entrepreneurs that are generated. This ties

in to a broader set of public policy issues.

Public Policy Issues

This study raises a number of public policy issues that are of both theoretical and

practical interest. The overall effects suggest that acquisitions are not always bad, and

greenfields not always good (or vice versa as is sometimes supposed). For instance, policy

mechanisms designed to attract and retain new greenfields will have a mixed effect – enhancing

new entrepreneurial entry, but then dampening the survival of indigenous incumbents once they

enter. But on average, older greenfields will do exactly the opposite. On the other hand, policies

designed to inhibit takeovers for whatever reason – because they constitute a loss of a locally

developed firm and/or an appropriation of government subsidized R&D, or for anti-competitive

reasons – may be misguided. Although acquisitions do dampen entrepreneurial entry, they can

also be of benefit by enhancing the survival and growth of indigenous incumbents. The effects of

both often depend on the level of agglomeration.

The policy implications depend fundamentally on the overall objectives. Should it be

growth in the number of firms, or growth in employment, or perhaps more vaguely, self-

sustainability? If the objective is to develop a cluster irrespective of the composition, then this

suggests a set of policies designed to maximize the sum of all types of firms (and minimizing

exits). If the policy objective is employment growth, then the focus should not just be on

generating and attracting firms, but also on incumbent growth. And, if the quality of the jobs is

an important consideration then that might suggest another set of policies. But in all cases, the

mix of insiders and outsiders probably matters because of their differing effects and

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interactions.168 For instance, policies designed to attract outsiders will certainly have an impact

on insiders (and maybe vice versa), because as this study has shown, they do interact. In some

cases these are complementary objectives, in other cases, conflicting. However, in policy circles

there seems to be little attention paid to the interaction between the different mechanisms driving

cluster development.

Much of the policy focus in Canada and elsewhere seems to be on generating indigenous

startups and facilitating their growth in business incubators and other institutional support

centers.169 If generating more indigenous firms is the objective, then doing things to facilitate

entry would be desirable. Doing so may also be attractive to outsiders, particularly to potential

acquirers. In another sense, this kind of policy (or set of policies) might be a complementary to

ones that protect incumbents (and inhibit entry).170

There are two key legal/regulatory mechanisms that impact on the industry: (1)

competition policy; and (2) non-compete clauses in employment contracts. The two are linked in

the sense that non-competes are often interpreted as anti-competitive (at least in states where

they are weakly enforced). These two mechanisms are more closely associated with acquisition

policy, in general.171 However, the latter in particular has implications for greenfields as well

since the strength (and enforceability) of such clauses is tied to intra-regional mobility in general.

Not only do non-competes inhibit spillover in the form of new entrepreneurial entry, but they

168 Another consideration in managing the mix is that a high outsider presence may make a region more susceptible to restructuring, and therefore potentially unstable. 169 Note that there is a substantial debate about the efficacy of incubators – especially over whether they address a fundamental market failure in the provision of resources for startups or crowd out already existing services. 170 A former director of the MaRS project, a high profile incubator in Toronto, has even been suggested that new biotech startups should act as feeders to established firms. This suggests a kind of regional ‘seeding’ strategy in the sense that new innovations are captured in the form of new startups, and nurtured in incubators which provide complementary resources for their development. In turn, these new startups act as a resource to established firms – either through direct linkages (and contracting relationships) and possibly representing an acquisition opportunity. This is a distinctly different mechanism for capture and transfer than through mobility. 171 Note that there may be implications for competition policy in terms of the regulation of mergers and acquisitions. But it is difficult to say anything about that without further analysis on different types of mergers and acquisitions.

178

also restrict the diffusion of specific knowledge transferred through mobility. This might actually

have a stronger effect on older greenfields (and acquisitions) than on newer greenfields, which

seems to be suggested in the results. Still, insofar as mobility does occur, this should not inhibit

the transfer of skills that might be applied productively to cognate areas. There are two sides to

the regulation issue: on the one hand, incumbents clearly need to protect themselves from

unwanted spillovers, especially in the form of leakages to existing competitors or new entry by

former employees; on the other, it might generally be desirable to lower barriers to entry.172

These are competing objectives since in the absence of a strong enforcement regime, incumbents

probably have less incentive to innovate for fear of appropriation.173

Another key question is how do such constraints influence the evolution of the industry?

In the Canadian context, it appears that the industry is still relatively young in the sense that most

of the firms are first generation spinoffs from public research organizations, a state of affairs that

might be perpetuated by the legal regime and the policies supporting the generation of startups

from research institutes. Also, such policies may influence the location decisions of outsiders. On

the other hand, large outsiders appear to act as a resource to small indigenous firms, ensuring

their survival and growth (at least for acquisitions in the latter case). However, this might be of

concern if there is a negative impact on the quality of the technologies being commercialized,

which might be the case if, for example, there is a greater probability of producing higher quality

innovations from firms that are spunoff from incumbent firms than enriched by labor flows. All

172 The success of life sciences clusters in California, for instance, is often attributed to a weak legal regime (Marx, et al., 2007). 173 However, this does not appear to have impeded innovation in places like Silicon Valley.

179

things equal though, this might suggest alternative pathways to long run sustainability of

regions.174 But these are both issues that require some investigation.

Another important policy lever is through R&D funding, which typically comes in the

form of tax credits to firms and direct subsidies to research institutes. The use of R&D tax credits

at both the national and provincial levels is the most popular incentive scheme for increasing

innovation, and it appears to be nondiscriminatory in the sense that it is open to all types of

firms. This kind of policy can lower barriers to entry for insiders, and attract and retain outsiders

(and influence their commitments to a region). But insofar as such mechanisms have differential

effects on different classes of firms, they do discriminate. For instance, outsiders may have the

discretion to establish a specialized R&D unit from which to benefit from the incentives. Still,

this should benefit the region to some extent – presumably in terms of spillovers to indigenous

incumbents, though not so much in the form of new firms, as suggested above. Similarly,

building infrastructure and supporting institutions act as a general resource to firms irrespective

of their origins. Although generalized spillovers certainly occur, there are often specific

relationships that develop between different types of firms and public research organizations.

Often a mutual interdependence forms between large outsiders and PROs through funding and

research relationships. And, as noted above, the PROs often play an important role in

commercialization which links back to a specific effect on indigenous entry. So the relationship

between PROs and outsiders may be an indirect route by which new knowledge is generated and

subsequently commercialized through entrepreneurial spillover.175

174 Route 128 close to Boston would be the counter example to Silicon Valley in the sense that it is governed by a stronger legal regime. 175 In general, the relative benefits of various cluster policies should be weighed against the costs. In fact, there appears to be some evidence in other contexts that the public costs of cluster development can outweigh the benefits so the choice of mechanisms is important. This appears to be the case in the German biotech industry, for instance (Economist, October 2007).

180

Some of these policy issues are connected to other more general issues related to the

dynamic interaction between different classes of firms. The first issue is endogeneity between the

entry and continuing presence of outsiders, and that of insiders. Greater numbers of indigenous

firms, which operate through entry and exit, provide more potential targets and should therefore

lead to more entry by acquisition. A dynamic indigenous industry, signaled by more

entrepreneurial entry, should also attract new knowledge-seeking greenfields. Second,

endogeneity implies indirect effects between greenfields and acquisitions. For instance, newer

greenfield presence enhances entrepreneurial entry, thereby increasing the number of potential

targets and therefore new acquisition entry in the longer run; but the opposite is the case for

survival. There are also feedbacks from the effect external entrants have on levels of

entrepreneurial entry, and indigenous survival and growth, which operate through changes in the

presence variables, thereby affecting entrepreneurial entry in subsequent periods.

The third issue is the potential for direct interactions between different classes of

outsiders. In fact, outsiders in a region also likely influence the location choice of other potential

external entrants. The fourth issue is that as the local industry matures, some firms that are not

selected out will grow thereby creating alternative opportunities for potential entrepreneurs. In

this particular case, acquisitions were found to enhance indigenous growth, which should act as a

dampening mechanism on new entrepreneurial entry. This would be consistent with the direct

effect acquisitions have on entrepreneurial entry. Moreover, the trade-off between growth and

entry might apply more generally to both external and internal firms. Since the usual explanation

181

for declining entry rates over time is that of increasing barriers, this internalizing of skills and

capabilities is, in essence, an alternative explanation for what retards the entry process.176

Future Research

Given that evolutionary economic geography is in an early stage of development, and in

particular the strand dealing with insider-outsider interaction, this research also points to a

number of other avenues for further investigation. At a very general level, there is a need for

further refinement of the theory in order to clarify the conditions under which positive and

negative effects occur. As already noted, one way to do that is to develop a better understanding

of the role of specific subgroups of external firms in their interaction with the local industry, and

how they change over time. To this end, future work should also examine the assumptions about

the process of how different types of outsiders become embedded in local networks more

explicitly. For instance, how do subsidiaries’ linkages develop, with whom are they most likely

to interact, and how does their entry affect the (re)-structuring of the local industry networks and

the firms therein? Another key issue regarding time is in determining whether the dual effects

outsiders were shown to have on indigenous entry and survival/growth are simultaneous or

whether there is a temporal ordering of these effects. In addition, some of the key policy issues

noted above should be investigated further.

It is also important to examine the role of agglomeration (dis)economies more carefully.

In particular, there is a substantial overlap in the effects that outsiders and the agglomeration

more generally are thought to have which requires more clarification. It is also important to

examine the role of the stage of agglomeration at which external firms enter since their effects

176 This might suggest a dynamic tension between the forces of attraction and repulsion in larger firms, which both dampen and enhance entry. However, it was argued that the positive effect of newer greenfields on entry is probably not due to repulsion in the form hypothesized by the spillover theory of entrepreneurship.

182

may vary by the level of agglomeration, in part because the timing of external entry may matter

in terms of how they become embedded. A related issue to address is that spatial evolution

requires more attention to relative influences – location choice, and further investment (or

divestment), is fundamentally dependent on the nature of the alternatives. This is also true for

indigenous firms that branch out to other regions (which also has implications for their home

region that should be investigated). A more generic future research issue is that it is necessary to

study other industry contexts to ensure generalizability. These results may be more applicable to

knowledge intensive industries particularly if some of the spillover and competitive mechanisms

discussed here have stronger effects in such industries. Also, a replication of this study in other

longer-lived industries is desirable. It is necessary to understand these inter-relationships better

in order to move this area of inquiry forward.

Finally, we might also reasonably ask whether this line of enquiry is the right tack to

take. The international business literature suggests that studying insider-outsider interactions is

an important research orientation; however, the economic geography literature might suggest

otherwise. If outsiders become insiders eventually and it is really cluster dynamics in general that

matter, then there is little reason to make the distinction. The counterargument is that, to this

point, there has been an implicit assumption in economic geography that insiders and outsiders

differ – especially because the mechanisms of (internal) generation and (external) attraction

differ. Moreover, it has been shown here that there is a clear distinction between the two and that

the one does influence the other. Despite the questions still remaining, this study represents a

modest step toward developing another side of evolutionary economic geography, proposed by

Klepper (2003). Also, given the very little work in this area, this study represents a step toward a

183

fuller integration of ideas related to the evolution of local industry, and in particular, how

outsiders influence this process.

184

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205

Figures 1 and 2: Proportion of External Firms to Total Firms in Selected Regions

% External Firms/Total

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

Year

Perc

ent Toronto

MontrealVancouverOttawaSaskatoon

% External Firms/Total

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

Year

Perc

ent

EdmontonCalgaryWinnipegHalifaxVictoriaTotal

206

Table 1

Variable Definition

Variable Primary Data Source

Definition

Region Census Metropolitan Area (CMA) defined by Statistics Canada #biotech firms in country

Aggregate of frrm count

The number of biotech firms in the country, aggregated from the firms level data

#biotech firms in region

Aggregate of firm count

The number of biotech firms in the CMA, aggregated from the firms level data

ln(Population of region)

Statistics Canada

The natural log of the human population in the region

Ph.D.s graduated in bioscience in region

Statistics Canada – USIS File

A count of Ph.D. graduates in all life sciences by university by year, aggregated to the region level.

#Research institutes in region

Contact Canada/ Industry sources

A count of the number of research institutes in the region

#VCs in region

CVCA

A count of the number of venture capital firms in the region that finance bioscience firms

#IPOs

Contact Canada

The number of initial public offerings in the region

Innovation in region

USPTO

The number of U.S. patents issued in the biosciences in the region discounted by 30% per year

#biotech firms in country

Aggregated from firm data

A count of the number of all biotech firms in Canada

Public

Contact Canada

If the firm is public it is coded “1”, else it is “0”

#Patents

USPTO

A count of U.S. patents granted to each firm in each year

#Alliances

Lexis/Nexis, company websites, industry sources

Count of new alliances that the firm has of any type in a given year

Age (years)

Constructed from firm data (Contact Canada

Age in years since the firm entered the region

Size (ln(#employees in firm))

Contact Canada

The natural log of the number of employees in the firm in a given year

Greenfield entry

Contact Canada, etc.

Counts, firm share, and employment share of all greenfields that entered the region in a given year

Greenfield presence Contact Canada, etc Counts, firm share, and employment share of all greenfields in the region, irrespective of when they entered

Acquisition entry Contact Canada, etc

Counts, firm share, and employment share of all acquisitions that entered the region in a given year

Acquisition presence Contact Canada, etc Counts, firm share, and employment share of all acquired firms s in the region, irrespective of when they entered

207

Table 2a

Descriptive Statistics: Means, Standard Deviations, and Correlations

Variable Mean Standard Deviation

1. # new entrepreneurial entries (t) 0.096 0.294

2. indigenous exit 0.020 0.141

3. Indigenous firm size (employment logged)

2.377 1.261

4. # firms country 348.824 137.531

5. # firms in region 43.517 42.059

6. log of local human population 14.435 1.974

7. # new PhDs in Life Sciences in region

72.999 58.923

8. # PROs in region 2.163 1.418

9. #VCs in region 3.223 4.512

10. Innovation in region 156.108 229.860

11. Greenfield entry 0.011 0.042

12. Acquisition entry 0.010 0.027

13. Greenfield presence 0.197 0.202

14. Acquisition presence 0.040 0.064

15. Firm age 7.459 8.318

16. Public 0.144 0.351

17. #Patents 0.152 0.804

18. #Alliances 0.069 0.366

208

Table 2b

Descriptive Statistics: Correlations

Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1. # new entrepreneurial entries (t)

1.000

2. indigenous exit -0.042* 1.000

3. Indigenous firm size (employment logged)

-0.258* -0.045* 1.000

4. #biotech firms country -0.093* 0.101* 0.128* 1.000

5. #biotech firms in region -0.027 0.043* 0.174* 0.437* 1.000

6. log of local human population

0.026 0.009 0.081* 0.133 0.724* 1.000

7. # PhDs in Life Sciences in region

-0.012 0.039* 0.163* 0.415* 0.953* 0.771* 1.000

8. #PROs in region 0.011 0.025 0.148* 0.248* 0.862* 0.712* 0.858* 1.000

9. #VCs in region -0.027 0.027 0.129* 0.273* 0.700* 0.531* 0.573* 0.652* 1.000

10. Innovation in region -0.034* 0.047* 0.169* 0.470* 0.899* 0.541* 0.845* 0.747* 0.693* 1.000

11. Greenfield entry -0.004 0.006 0.085* 0.040* 0.434* 0.363* 0.425* 0.478* 0.326* 0.318 1.000

12. Acquisition entry -0.055* 0.056* 0.127* 0.406* 0.634* 0.392* 0.553* 0.486* 0.563* 0.662* 0.172* 1.000

13. Greenfield presence 0.028* -0.007 0.162* -0.156* 0.453* 0.560* 0.478* 0.642* 0.396* 0.428* 0.383* 0.211* 1.000

14. Acquisition presence -0.043* 0.053* 0.053* 0.340* 0.171* 0.165* 0.156* 0.231* 0.229* 0.271* 0.022 0.331* 0.121* 1.000

15. Firm age -0.291* 0.017 0.421* 0.033* -0.001 -0.036* -0.021 -0.019 0.021 -0.006 0.008 0.008 0.068* 0.005 1.000

16. Public -0.116* -0.007 0.361* 0.151* 0.205* 0.175* 0.206* 0.183* 0.183* 0.177* 0.082* 0.154* 0.082* 0.098* 0.197* 1.000

17. #Patents -0.060* 0.017 0.238* 0.042* 0.120* 0.110* 0.111* 0.108* 0.129* 0.112* 0.052* 0.105* 0.056* 0.077* 0.057* 0.182* 1.000

18. #Alliances -0.041* -0.023 0.173* 0.130* 0.135* 0.081* 0.137* 0.095* 0.070* 0.141* 0.035* 0.117* 0.019 0.029* 0.027 0.217* 0.222*

Level of significance: * p < 0.05

Table 3a

Negative Binomial Models of Entrepreneurial Entry – New External Entry

Variables Model 1 Model 2 Model 3 Model 4 Controls: New External

Count New External Firm Share

New External Employment Share

Constant

-84.744*

-42.099

-51.770

-91.540

ln(Population of region)

0.317

0.465

0.450

0.407

PhDs graduated in bioscience in regi

0.003

0.001

0.003

0.003

#Biotech firms in country

0.000

-0.000

-0.000

0.003*

#Biotech firms in region

0.012

0.019*

0.019*

0.007

#PROs in region

0.051

0.095

0.095

0.002

#Biotech VCs in region

0.137*

0.143*

0.143+

0.135*

Innovation in region

-0.011**

-0.007+

-0.009+

-0.011**

Year

0.040

0.039

0.004

0.031

Independents Variables:

Greenfield entry t -0.101* -0.871 -0.507

Greenfield entry-1 0.036

1.153

1.208

Greenfield entryt-2 0.023

2.735**

0.810

Greenfield entryt-3 0.000

0.839

0.810

Acquisition entry t

-0.231** -4.362+

-1.292+

Acquisition entry t-1 -0.200* -0.193 -1.464

Acquisition entryt-2 -0.273** -6.551* -1.000

Acquisition entryt-3 -0.208* -2.637 -1.352

Fixed effects Yes Yes Yes Yes Log-likelihood -456.8 -430.5 -417.1 -400.3 χ –squared 78.9*** 97.4*** 84.1*** 84.8*** Level of significance: + p<0.10 ; ** p<0.05; ** p<0.01; *** p<0.001

Number of observations = 560, decreasing in each lag.

210

Table 3b

Negative Binomial Models of Entrepreneurial Entry – External Presence

Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 External Counts External Firm Share External Employment Share Controls : Constant

-270. 732***

-235.164***

-282.573***

-213.130***

-139.442***

-250.214**

-216.283***

-186.091*

-158.113**

Prior period entry

0.079**

0.084**

0.073**

0.089***

0.084***

0.072**

0.100***

0.093**

0.019

ln(Population of region)

0.491*

1.038*

0.778*

0.602***

0.965**

0.558*

0.393*

0.709**

3.261**

Ph.D.s graduated in bioscience in reg

0.002

0.001

0.002

0.006

0.002

0.001

0.005

0.003

-0.001

#Biotech firms in country

-0.003**

-0.003*

-0.030**

-0.002

-0.001

-0.013*

-0.003

-0.003

-0.001

#Biotech firms in region (N)

-0.013

-0.014+

-0.016**

-0.016

-0.010

-0.013*

-0.010

-0.024*

-0.036**

#PROs in region

0.003

0.047

0.061

0.199

0.056

0.032

0.067

0.253

0.299+

#Biotech VCs in region

0.043

0.021

0.044

0.124

-0.056

0.070

0.119

0.111

0.142*

Innovation in region

0.000

-0.001

-0.002

-0.007*

-0.005

-0.000

-0.007*

-0.008*

-0.006

Year

0.134***

0.110**

-0.005***

0.104**

0.065+

0.123**

0.112**

0.090***

0.060***

#Pharma (pre-1976)

-0.165

-1.492**

-1.201**

Independents Variables:

Greenfield presence 0.084** 0.025 -0.606 -0.864* 0.046 -0.248

Greenfield presence (post-1976) 0.079**

1.435**

0.059

Greenfield presence X N

0.001+

0.084**

0.069***

Acquisition presence -0.179** -0.159* -0.306** -1.137 -1.107 -0.634

-0.345

-0.199

0.270

Acquisition presence X N -0.001 -0.147** -0.117***

Fixed effects? yes yes yes yes yes yes yes yes yes Log-likelihood -439.8 -438.1 -437.6 -446.3 -438.1 -437.0 -444.5 -443.9 -433.7 χ –squared 106.7*** 101.4*** 82.2*** 106.7*** 97.2*** 91.9*** 101.1*** 92.5*** 123.0*** Level of significance: + p<0.10 ; ** p<0.05; ** p<0.01; *** p<0.001

Number of observations = 540; all independent variables are lagged one period.

211

Table 3c

Negative Binomial Models of Entrepreneurial Entry – External Presence (with year dummies)

Variables Model 1 Model 2 External Employment ShareControls: Constant

-62.145***

-8.955***

ln(Population of region)

3.609***

0.357***

Ph.D.s graduated in bioscience in region

-0.010

-0.003

#Biotech firms in country

0.004**

0.002

#Biotech firms in region (N)

-0.011

-0.009

#PROs in region

0.076

0.219

#Biotech VCs in region

0.056

0.036

Innovation in region

-0.001

-0.003

Independents Variables:

Greenfield presence -0.306 -0.973**

Greenfield presence X N 0.049**

Acquisition presence -2.101*** -1.366**

Acquisition presence X N -0.083** Region Fixed effects Yes Yes Year Fixed effects Yes Yes Log-likelihood -400.1 -393.8 χ –squared 122.7*** 92.5*** Number of observations 540 540

212

Table 4a Exit Rate Models of Indigenous Firm Exit – New External Entry

Variables Model 1 Model 2 Model 3 Model 4 Controls: New Count New Firm

Share New Employment Share

Constant -137.819* -322.550*** -176.540*** -146.308***

ln(Population in region) -0.275 -0.322* -0.263 -0.214

PhDs graduated in biosciences in region 0.006 0.004 0.003 0.006

Biotech firms in country 0.009* 0.055 0.009+ 0.010*

#Biotech firms in region 0.004 0.013 0.003 0.003

#PROs in region 0.255 0.249 0.281 0.251

#Biotech VCs in region 0.034 0.021 0.026** 0.023

Innovation in region -0.006 -0.001 -0.005 -0.005

Size (ln(#employees in firm)) -0.557*** -0.553*** -0.562*** -0.557***

Age 0.248*** 0.254*** 0.251*** 0.257***

Age squared -0.010*** -0.010*** -0.010*** -0.010***

Left censored 2.300* 2.347* 2.288** 2.298**

#Patents -0.330 0.177 0.194 -0.194

#Alliances -1.570+ -1.153+ -1.156+ -1.580+

Public -0.298 -0.205 -0.209 -0.226

Sector (Human=1) 0.110 0.105 0.114 0.107

Year 0.063 0.156 0.082 0.066 Independents Variables:

Greenfield entryt -0.050 3.306 0.796

Greenfield entry t-1 0.015 1.949 0.587

Greenfield entry t-2 0.051 1.665 0.529

Greenfield entry t-3 0.226** 3.898 0.529

Acquisition entry t -0.154 5.389+ 0.895

Acquisition entry t-1 -0.307* -4.996 -4.448**

Acquisition entry-2 -0.280+ -3.031 -2.841

Acquisition entry-3 0.192 -6.261 -5.601* Log-likelihood -391.9 -377.9 -378.4 -379.1 LR χ-squared 116.2 121.0 120.0 117.9 Level of significance: +p<0.10; * p<0.05; ** p<0.01; *** p<0.001

Number of observations = 4,369. The results are from an exponential model.

213

Table 4b Exit Rate Models of Indigenous Firm Exit – External Presence

Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model 10 Controls: External Counts External Firm Share External Employment Share

Constant -71.097 -64.975 -72.735 -11.129*** -104.555 -96.199* -163.778** -8.165** -113.000 -236.399

ln(population in region) -0.619**

-0.884**

-0.586**

-0.390

-0.747**

-0.651**

-0.825**

-0.665**

-0.866**

-0.674*

PhDs graduated in the biosciences in region

-0.014

-0.010

-0.179**

-0.006

-0.012

-0.019**

-0.007

-0.013

-0.018*

-0.002

#Biotech firms in country

0.014**

0.013*

0.010

0.040*

0.013*

0.010*

0.018**

-0.015***

0.008

0.019**

#Biotech firms in region (N)

0.037**

0.047**

0.046**

-0.002

0.035*

0.041**

0.033*

0.034**

0.060***

0.020

#PROs in region

0.133

0.336

0.334

0.324

0.270

0.624**

0.368

0.210

0.623*

0.322

#Biotech VCs in region

0.097

0.109+

-0.047

0.286***

0.113*

0.085

0.115+

0.099

0.135**

-0.094

Innovation in region

-0.011*

-0.010

-0.008

-0.032***

-0.011+

-0.007

-0.019*

-0.011+

-0.013*

-0.009+

Size (ln(#employees in firm)) -0.069

0.054

0.014

0.016

0.076

0.018*

0.045

-0.070

0.004

-0.020

Age 1.330***

1.339***

1.380***

1.358***

1.328***

1.388***

1.345***

1.325***

1.375***

1.344***

Age squared -0.062***

-0.063***

-0.066***

-0.063***

-0.062***

-0.066***

-0.063***

-0.062***

-0.065***

-0.062***

Left censored 0.807

0.381

2.484

1.015

1.472

3.271**

0.117

0.774

1.670

-0.125

Public

-0.427

-0.499

0.526

-0.445*

-0.450

-0.531*

-0.505

-0.450

-0.537

-0.526

#Alliances

-0.428

-0.451

-0.459

-0.467

-0.441

-0.483

-0.449

-0.438

-0.459

-0.476

#Patents

-0.400

-0.415

-0.425

-0.406

-0.419

-0.452

-0.432

-0.416

-0.399

-0.458

Sector (Human=1) -0.241

0.168

0.270

-0.013

-0.230*

-0.232*

0.169

0.250

0.216

0.118

Year -0.031

0.477

0.276

0.023

-0.087

0.062

-0.085

-0.036

0.054

-0.123

#Pharma (pre-1976)

-0.283*** -1.553** -0.166** Independents Variables:

Greenfield presence

-0.055** -0.203** -1.079 0.008 -0.393 -0.019

Greenfield presence (post-1976)

0.191** 3.597**

3.167***

Greenfield presence X N

0.002** -0.033 -0.024

Acquisition presence

0.059 -0.052 -0.308 -2.276 -7.079** -6.937** -1.216 -3.230 -6.215**

Acquisition presence X N

0.004*** 0.273* 0.333** Log-likelihood 96.0 98.3 102.6 108.7 97.1 102.5 99.5 96.7 106.8 106.5 LR χ-squared 625.5*** 694.53*** 638.5*** 651.0*** 679.7*** 638.5*** 632.2*** 627.0*** 647.1*** 646.5*** Level of significance: +p<0.10; * p<0.05; ** p<0.01; *** p<0.001

Number of observations = 4,286 all independent variables are lagged one period. The results are from an exponential model reporting parameter estimates rather than the hazard rate.

214

Table 5a

Growth Rate Models of Indigenous Firms – New External Entry

Variables Model 1 Model 2 Model 3 Model 4 Controls: New Count New Firm Share New Employment

Share

Constant -0.254 -0.286 0.355 -0.016

Growth (Δln(#employees in firm)) 0.178***

0.178***

0.121***

0.122***

Size (ln(#employees in firm))

-0.002*

-0.002*

-0.191*

-0.192*

ln(Population of region)

-0.015

0.0004

0.004

0.010

#PhDs graduate in life sciences

0.0007

-0.0007

-0.0007

-0.001

#Biotech firms in country

0.002***

0.001*

0.001*

0.001***

#Biotech firms in region

0.0003

-0.002

0.0004

-0.003

#PROs in region

0.010

0.024

0.006

0.039*

#Biotech VCs in region

0.008

0.004

0.008

0.005

Innovation in region -0.0000

0.0003

-0.0000

-0.0002

Age -0.021***

-0.024***

-0.024***

-0.021***

Public

0.004

-0.019

-0.019

0.011

#Alliances

0.029**

0.034**

0.033***

0.035**

#Patents

0.011

0.012*

0.012+

0.012

Independents Variables:

Greenfield entry t -0.0004 0.026 -0.012

Greenfield entry t-1 -0.001 -0.018 0.101

Greenfield entry t-2 0.0006 -0.062 0.004

Greenfield entry t-3 0.009 0.179 0.050

Acquisition entry t 0.007 0.064* 0.721**

Acquisition entry t-1 0.003 -0.079 0.093

Acquisition entry t-2 0.009 0.076 0.025

Acquisition entry t-3 -0.022** -0.058 0.052 Chi-squared 11460.1*** 13324.1*** 11662.8*** 12793.5*** Time dummies Yes Yes Yes Yes Level of significance: + p<0.10; * p<0.05; ** p<0.01; *** p<0.001

Number of observations = 3,665. Growth is measured as the first difference of the log of the number of employees.

215

Table 5b

Growth Rate Models of Indigenous Firms - Presence

Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9

Controls: External Counts External Firm Share External Employment Share Constant

0.135

0.170

0.472*

0.355*

0.345

0.355*

0.475

0.135

0.714

Growth (Δln(#employees in firm))

0.122***

0.122***

0.123***

0.127***

0.127**

0.127***

0.128***

0.128**

0.128**

Size (ln(#employees in firm))

-0.199*

-0.199*

-0.201+

-0.198*

-0.201*

-0.200

-0.199*

-0.199*

-0.198*

ln(Population of region)

-0.005

-0.009

-0.029

-0.008

-0.021

-0.038

-0.006

-0.006

-0.052

PhDs in biosciences

-0.001

-0.001

-0.001

-0.008

0.001

-0.0004

-0.001

-0.001

-0.001

#Biotech firms in country

0.002***

0.002***

0.002***

0.001***

0.001***

0.002***

0.001***

0.001***

0.002***

#Biotech firms in region (N)

-0.0001

-0.0003

0.002

0.001*

0.000

0.001

0.0003

0.0003

0.0002

#PROs in region

0.012

0.012

0.007

0.027**

0.005

0.017

0.012

0.012

0.023

#Biotech VCs in region

0.009

0.008

-0.002

0.027**

0.007

0.010

0.008

0.008

0.008

Innovation in region 0.0002

0.0002

0.0005

0.009

-0.000

-0.000

-0.000

-0.000

0.000

Age -0.021***

-0.021**

-0.020***

-0.004***

-0.022***

-0.020***

-0.021***

-0.021***

-0.021***

Public

0.004

0.004

0.003

-0.019

0.005

0.012

0.002

0.002

0.002

#Alliances

0.029*

0.029***

0.031**

0.046***

0.029*

0.030**

0.029*

0.029*

0.030**

#Patents

0.011

0.011

0.010

0.002

0.011

0.012+

0.010

0.010

0.010

#Greenfields (Pre-1976)

0.001

-0.002

-0.002

Independents Variables:

Greenfield presence -0.002 -0.002 0.091+ 0.105 0.039 0.034

Greenfield presence (post-1976) 0.001 0.200 0.038

Greenfield presence X N -0.000 -0.003 -0.088

Acquisition presence 0.004 0.004 0.047** 0.161+ 0.137+ 0.641+ 0.168+ 0.176* 0.281**

Acquisition presence X N -0.0004** 0.006 -0.007* Chi-squared 15172.4*** 15096.8*** 15096.8*** 13571.2*** 13687.3*** 13577.5*** 11231.1*** 11983.6*** 10334.6*** Time dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Level of significance: + p<0.10; * p<0.05; ** p<0.01; *** p<0.001

Number of observations = 3,665. All independent variables are lagged one period. Growth is measured as the first difference of the log of the number of employees.

216

Table 6: Summary of Hypotheses

Variables

Entrepreneurial Entry

Indigenous Survival

Indigenous Growth

Main arguments

New greenfield entry

-

-

-

Crowding out due to competition for scarce resources upon entry

New acquisition entry

+

+

+

Liquidity effects, if operative, should have a positive influence on entrepreneurial entry, survival and growth.

Greenfield presence

+

+

+

Spillovers primarily generated through labor mobility and linkage effects will dominate.

Alternative: Greenfield presence

-

-

-

Negative externalities generated through competition for scarce resources (especially labor and supplier inputs) will dominate.

Acquisition presence

+

+

+

Spillovers primarily generated through labor mobility and linkage effects will dominate.

Alternative: Acquisition presence

-

-

-

Negative externalities generated through competition for scarce resources (especially labor and supplier in puts) will dominate.

Greenfield presence X N

+

+

+

Positive externalities should be accelerated at higher levels of agglomeration.

Acquisition presence X N

+

+

+

Positive externalities should be accelerated at higher levels of agglomeration.

Table 7: Summary of Results

Variables

Entrepreneurial Entry

Indigenous Survival

Indigenous Growth

Comments

New greenfield entry

No effect

No effect

No effect

No real evidence of crowding out

New acquisition entry

-

+

mixed

Liquidity effects appear to influence survival of indigenous firms. Negative effects on entrepreneurial entry likely due to the hiring and contracting of new scientific staff.

Greenfield presence

mixed

mixed

No effect

There is no clear evidence of either crowding out or spillover effects.

Older greenfield presence

-

+

No effect

There is an unambiguous crowding out effect on entrepreneurial entry and a spillover effect on survival.

Newer greenfield presence

+

-

No effect

There is a spillover effect on entrepreneurial entry and a crowding out effect on survival.

Acquisition presence

-

+

+

There is a crowding out effect on entrepreneurial entry and a spillover effect on survival and growth.

Greenfield presence X N

+

No effect

No effect

Higher levels of agglomeration general positive externalities, for entrepreneurial entry.

Acquisition presence X N

-

-

-

Higher levels of agglomeration general negative externalities, probably in part because the environment is more diffuse.

218

Appendix 1: Biology and Biotechnology Milestones

4000 BC Classical biotechnology: Dairy farming develops in the Middle East; Egyptians use

yeasts to bake leavened bread and to make wine.

2000 BC Egyptians, Sumerians and Chinese develop techniques of fermentation, brewing and

cheese-making.

1500 Acidic cooking techniques lead to sauerkraut and yogurt - two examples of using

beneficial bacteria to flavor and preserve food. Aztecs make cakes from Spirulina algae.

1859 On the Origin of Species - English naturalist Charles Darwin's theory of evolution - is

published in London.

1861 French chemist Louis Pasteur develops pasteurization - preserving food by heating it to

destroy harmful microbes.

1865 Austrian botanist and monk Gregor Mendel describes his experiments in heredity,

founding the field of genetics.

1879 William James Beal develops the first experimental hybrid corn.

1910 American biologist Thomas Hunt Morgan discovers that genes are located on

chromosomes.

1914 Gerry FitzGerald's development and production of Canada's first diptheria antitoxin

lead to the establishment of the University of Toronto Antitoxin Laboratories, later renamed the Connaught Laboratories. The labs now serve as a division of Aventis Pasteur the world's largest producer of vaccines.

1921 discovery of insulin at the University of Toronto by Banting, Best, Collip and MacLeod.

1922 development and use of insulin in the treatment of diabetes.

1928

F. Griffith discovers “genetic transformation” - genes can transfer from one strain of bacteria to another.

Modern biotechnology or second generation biotechnology grew out of molecular biology and genetic engineering and emerged after World War II. It involved the integration of microbiology, biochemistry and chemical engineering for large-scale fermentation, sewage treatment, and for applications in the chemical and pharmaceutical industries, is in its early stages.

1941 Danish microbiologist A. Jost coins the term genetic engineering in a lecture on sexual

reproduction in yeast.

1943 Oswald Avery, Colin MacLead and Maclyn McCarty use bacteria to show that DNA carries the cell's genetic information.

1953 James Watson and Francis Crick describe the double helix of DNA, using x-ray diffraction

patterns of Rosalind Franklin and Maurice Wilkins.

219

220

1960's Olah Hornykiewicz, who originally discovered that Parkinson's disease patients had less

dopamine in their brains, continued to contribute to the development of L-Dopa as a therapeutic agent while working in Toronto.

1961 Discovery of the hematopoietic stem cell by researchers in Toronto.

Early

1970's Paul Berg, Stanley Cohen and Herbert Boyer develop ways to cut and splice DNA, introducing recombinant DNA techniques.

1973 The breakthrough discovery of recombinant DNA which became the platform for research

in cloning, genomics and proteomics.

1974 Discovery of P-glycoprotein by Toronto researchers.

1975 Scientists organize the Asilomar conference to discuss regulating recombinant DNA experiments. George Kohler and Cesar Milstein show that fusing cells can generate monoclonal antibodies.

1982 First genetically engineered product - human insulin produced by Eli Lilly and Company

using E. coli bacteria - is approved for use by diabetics.

1983 Discovery by Toronto researchers of the T-cell receptor, described as the "holy grail" of immunology.

1984 Kary Mullis develops polymerase chain reaction (PCR) to mass-produce specific DNA

fragments.

1986 First release into the environment of a genetically engineered plant (tobacco).

1987 First release of genetically engineered microbes in field experiments.

1990 The international Human Genome Project, a 13-year effort, is launched. The goals of the project were to identify and sequence all of the genes in the human genome.

2001 The Human Genome Project accelerated and a map of the entire human genome

sequence with analysis was published, ahead of schedule.