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ENTREPRENEURSHIP,
INNOVATION AND
TECHNOLOGICAL CHANGE
Zoltan J. Acs and David B. Audretsch
ISSN 05-5
Zoltan J. Acs
University of Baltimore
1420 North Charles Street
Baltimore, MD 21201-5779
and
David B. Audretsch
School of Public and Environmental Affairs
Indiana University
1315 East Tenth Street, Room 201
Bloomington, IN 47405-1701
December 2005
ENTREPRENEURSHIP, INNOVATION AND TECHNOLOGICAL CHANGE
by Zoltan J. Acs and David B. Audretsch
This research was undertaken by the Institute for Development Strategies. The statements, findings,
conclusions, and recommendations are those of the authors and do not necessarily reflect the views
of the Institute for Development Strategies, or the Ameritech Foundation.
1
Entrepreneurship, Innovation and Technological Change
Zoltan J. Acs and David B. Audretsch
Prepared for Foundations and Trends in Entrepreneurship
October 2005
2
1. Introduction
One view of entrepreneurship and innovation is that they are virtually synonymous.
As Shane and Venkataraman (2000, 218) argue, the field of entrepreneurship is defined
by the study of “how, by whom and with what consequences opportunities to produce
future goods and services are discovered, evaluated and exploited.” This would suggest
that innovation and entrepreneurship are almost a tautology.
Instead, we take the position here that entrepreneurship has an organizational
component and involves the creation of new enterprises. This reflects the view of Gartner
and Carter (2003, 195), who posit that “Entrepreneurial behavior involves the activities of
individuals who are associated with creating new organizations rather than the activities
of individuals who are involved with maintaining or changing the operations of on-going
established organizations.” This view suggests that the relationship between
entrepreneurship, when viewed as the creation of new organizations, and innovative
activity, is anything but trivial. Rather, what distinguishes entrepreneurship from
innovation is the organizational context.
In fact, well into the 1970s, a conventional wisdom prevailed suggesting the
entrepreneurship, at least as represented by new ventures, had a competitive disadvantage
for undertaking innovative activity (Shane and Ulrich, 2004). This conventional wisdom
had been shaped largely by scholars such as Alfred Chandler (1977), Joseph Schumpeter
(1942) and John Kenneth Galbraith (1962) who had convinced a generation of scholars
and policy makers that innovation and technological change lie in the domain of large
corporations and that small business would fade away as the victim of its own
inefficiencies.
3
At the heart of this conventional wisdom was the belief that monolithic
enterprises exploiting market power were the driving engine of innovative activity.
Schumpeter had declared the debate closed, with his proclamation in 1942 (p. 106) that,
"What we have got to accept is that (the large-scale establishment) has come to be the
most powerful engine of progress." Galbraith (1956, p. 86) echoed Schumpeter's
sentiment, "There is no more pleasant fiction than that technological change is the
product of the matchless ingenuity of the small man forced by competition to employ his
wits to better his neighbor. Unhappily, it is a fiction."
At the same time, the conventional wisdom about new ventures and small firms
was that they were burdened with a size inherent handicap in terms of innovative activity.
Because they had a deficit of resources required to generate and commercialize ideas, this
conventional wisdom viewed small enterprises as being largely outside of the domain of
innovative activity and technological change. Thus, Even after David Birch (1982)
revealed the startling findings from his study that small firms provided the engine of job
creation for in the U.S., most scholars still assumed that, while new ventures and small
businesses may create the bulk of new jobs, innovation and technological change
remained beyond their sphere.
While this conventional wisdom about the singular role played by large
enterprises with market power prevailed during the first three decades subsequent to the
close of the Second World War II, more recently a wave of new studies has challenged
this conventional wisdom. Most importantly, these studies have identified a much wider
spectrum of enterprises contributing to innovative activity, and that, in particular, new
ventures and small entrepreneurial firms as well as large established incumbents play an
4
important role in the innovation and process of technological change (Acs and Audretsch,
1988).
Taken together, these studies comprise a new understanding about the about the
links between entrepreneurship, innovation and economic growth. The purpose of this
article is to weave together and interpret the disparate set of studies that, when taken
together, constitutes a new understanding about the role that entrepreneurship plays with
respect to technological change and innovation and to contrast it with the conventional
wisdom. This article begins with linking together the prevalent theory concerning
opportunity recognition and exploitation from the entrepreneurship literature to economic
theory, and in particular the most prevalent theory in economics about innovation and
technological change – the model of the knowledge production function. Just as the
conventional wisdom was shaped largely by the available empirical data and analyses, so
it is with the newer view. Thus, in the following section of this chapter, issues arising
when trying to measure innovative activity are discussed.
The debate and the evidence regarding the relationship between innovative
activity and organizational context is examined in the third section. In the fourth section,
the impact that the external industry context exerts on technological change is identified.
The role that the external knowledge context, or what has become known as knowledge
spillovers and geographic location plays in innovative activity is explained in the fifth
section. This leads to a re-interpretation of the role of entrepreneurship in innovative
activity and technology in the sixth section.
Finally, a summary and conclusions are provided in the last section. A key finding
is that the conventional wisdom regarding the process of innovation and technological
5
change is generally inconsistent with the new understanding about the role of
entrepreneurship in innovative activity. The empirical evidence strongly suggests that
new ventures and small entrepreneurial firms play a key role in generating innovations, at
least in certain industry and spatial contexts. While the conventional wisdom is derived
from the Schumpeterian Hypothesis and assumption that scale economies exist in R&D
effort, more recent theories and empirical evidence suggests that scale economies
bestowed through the geographic proximity facilitated by spatial clusters seems to be
more important than those for large enterprises in producing innovative output.
Entrepreneurship plays a crucial role in innovative activity by serving as the mechanism
by which knowledge spills over from the organization producing that knowledge, to the
(new) organization commercializing it.
2. Opportunity and Innovation
Contemporary theories of entrepreneurship generally focus on the decision-making
context of the individual. The recognition of opportunities and the decision to
commercialize them is the focal concern. This literature views opportunities as real and
independent of the entrepreneurs that perceive them. For example, Shane and
Venkataraman (2000), along with Casson (2003), define entrepreneurial opportunities as
the discovery of novel means-ends relationships, through which new goods, services,
resources and agency are created. However, the causes generating opportunities need to
be explained. As Companys writes, “By employing the opportunity construct, scholars
have made enormous contributions to the study of strategic management and
entrepreneurship. Unfortunately, the opportunity construct that scholars have used in their
research remains poorly understood. By explaining how scholars have addressed these
6
questions, one may be able to show the progress that has been made in explaining the
opportunity construct and the enormous work still left to do by scholars in this area.”
(Companys, 2005, p.4).
While the prevalent view in the entrepreneurship literature is that opportunities are
exogenous, the most prevalent theory of innovation in the economics literature suggests
that opportunities are, in fact, endogenous. The model of the knowledge production
function, formalized by Zvi Griliches (1979), assumes that firms exist exogenously and
then engage in the pursuit of new economic knowledge as an input into the process of
generating endogenous innovative activity. Thus, according to this strand of literature
opportunities are not exogenous. Rather, opportunities are created endogenously; they are
more prevalent in some industries than in others. They tend to be more common in high
tech industries, since most innovations take place in high technology opportunity
industries and not in low technology opportunity industries (Scherer, 1965; Geroski,
1989; Audretsch, 1995). The extent to which the results of innovation can be
appropriated by incumbent firms also varies among industries.
One way to reconcile the difference in the view of opportunities between the
literatures of entrepreneurship and the economics of innovation is the unit of analysis.
While the entrepreneurship literature focuses on the individual as the decision-making
unit of analysis, the literature on the economics of innovation focuses on the firm as the
decision-making unit of analysis.
The starting point for the most prevalent economic theory of innovation is at the
level of the firm (Baldwin and Scott, 1987), Cohen and Levin (1989), Scherer (1984 and
1992), and Dosi (1988). In such theories the firm is viewed as being exogenous and its
7
performance in generating technological change is endogenous (Scherer, 1984 and 1991,
Cohen and Klepper, 1991 and 1992, and Arrow 1962 and 1983). The most decisive input
in the knowledge production function is new economic knowledge. As Cohen and
Klepper conclude, the greatest source generating new economic knowledge is generally
considered to be R&D (Cohen and Klepper, 1991 and 1992).
Thus, while the entrepreneurship literature considers opportunities to exist
exogenously, in the economics literature they are systematically and endogenously
created through the purposeful investments in new knowledge. Of course, that the former
is focusing on the cognitive context of the individual while the latter is concerned with
the decision-making of the firm provides at least some reconciliation between these two
different views.
3. Measurement
Measurement of innovation and technological change have played a major role in the
analysis and understanding of the links between entrepreneurship and innovation. The
state of knowledge regarding innovation and technological change has generally been
shaped by the nature of the data which were available to scholars for analyses. Such data
have always been incomplete and, at best, represented only a proxy measure reflecting
some aspect of the process of technological change. Simon Kuznets observed in 1962 that
the greatest obstacle to understanding the economic role of technological change was a
clear inability of scholars to measure it. More recently, Cohen and Levin (1989) warned,
"A fundamental problem in the study of innovation and technical change in industry is
the absence of satisfactory measures of new knowledge and its contribution to
8
technological progress. There exists no measure of innovation that permits readily
interpretable cross-industry comparisons.”
Measures of technological change have typically involved one of the three major
aspects of the innovative process: (1) a measure of the inputs into the innovative process,
such as R&D expenditures, or else the share of the labor force accounted for by
employees involved in R&D activities; (2) an intermediate output, such as the number of
inventions which have been patented; or (3) a direct measure of innovative output.
These three levels of measuring technological change have not been developed
and analyzed simultaneously, but have evolved over time, roughly in the order of their
presentation. That is, the first attempts to quantify technological change at all generally
involved measuring some aspects of inputs into the innovative process (Scherer, 1965a;
1965b; 1967; Grabowski, 1968; Mueller, 1967; and Mansfield, 1968). Measures of R&D
inputs -- first in terms of employment and later in terms of expenditures -- were only
introduced on a meaningful basis enabling inter-industry and inter-firm comparisons in
the late 1950s and early 1960s.
A clear limitation in using R&D activity as a proxy measure for technological
change is that R&D reflects only the resources devoted to producing innovative output,
but not the amount of innovative activity actually realized. That is, R&D is an input and
not an output in the innovation process. In addition, Kleinknecht (1987 and 1989),
Kleinknecht and Verspagen (1989), and Kleinknecht et al. (1991) have systematically
shown that R&D measures incorporate only efforts made to generate innovative activity
that are undertaken within formal R&D budgets and within formal R&D laboratories.
9
They find that the extent of informal R&D is considerable, particularly in smaller enter-
prises.1 And, as Mansfield (1984) points out, not all efforts within a formal R&D
laboratory are directed towards generating innovative output in any case. Rather, other
types of output, such as imitation and technology transfer, are also common goals in
R&D laboratories.
As systematic data measuring the number of inventions patented were made
publicly available in the mid-1960s, many scholars interpreted this new measure not only
as being superior to R&D but also as reflecting innovative output. In fact, the use of
patented inventions is not a measure of innovative output, but is rather a type of
intermediate output measure. A patent reflects new technical knowledge, but it does not
indicate whether this knowledge has a positive economic value. Only those inventions
which have been successfully introduced in the market can claim that they are
innovations as well. While innovations and inventions are related, they are not identical.
The distinction is that an innovation is "...a process that begins with an invention,
proceeds with the development of the invention, and results in the introduction of a new
product, process or service to the marketplace" (Edwards and Gordon, 1984, p. 1).
Besides the fact that many, if not most, patented inventions do not result in an
innovation, a second important limitation of patent measures as an indicator of innovative
activity is that they do not capture all of the innovations actually made. In fact, many
inventions which result in innovations are not patented. The tendency of patented
inventions to result in innovations and of innovations to be the result of inventions which
1 Similar results emphasizing the importance of informal R&D have been found by
Santarelli and Sterlachinni (1990).
10
were patented combine into what F.M. Scherer (1983a) has termed as the propensity to
patent. It is the uncertainty about the stability of the propensity to patent across
enterprises and across industries that casts doubt upon the reliability of patent measures.2
According to Scherer (1983, pp. 107-108), "The quantity and quality of industry
patenting may depend upon chance, how readily a technology lends itself to patent
protection, and business decision-makers' varying perceptions of how much advantage
they will derive from patent rights. Not much of a systematic nature is known about these
phenomena, which can be characterized as differences in the propensity to patent."
Mansfield (1984, p. 462) has explained why the propensity to patent may vary so
much across markets: "The value and cost of individual patents vary enormously within
and across industries ... Many inventions are not patented. And in some industries, like
electronics, there is considerable speculation that the patent system is being bypassed to a
greater extent than in the past. Some types of technologies are more likely to be patented
than others." The implications are that comparisons between enterprises and across
industries may be misleading. According to Cohen and Levin (1989), "There are
significant problems with patent counts as a measure of innovation, some of which affect
both within-industry and between-industry comparisons."
Thus, even as new and superior sources of patent data have been introduced, such
as the new measure of patented inventions from the computerization by the U.S. Patent
Office (Hall et al., 1986; Jaffe, 1986; Pakes and Griliches, 1980 and 1984) as well as in
2 For example, Shepherd (1979, p. 40) has concluded that, "Patents are a notoriously
weak measure. Most of the eighty thousand patents issued each year are worthless and
are never used. Still others have negative social value. They are used as "blocking"
patents to stop innovation, or they simply are developed to keep competition out."
11
Europe (Schwalbach and Zimmermann, 1991; Greif, 1989; and Greif and Potkowik,
1990), the reliability of these data as measures of innovative activity has been severely
challenged. For example, Pakes and Griliches (1980, p. 378) warn that "patents are a
flawed measure (of innovative output); particularly since not all new innovations are
patented and since patents differ greatly in their economic impact." And in addressing the
question, "Patents as indicators of what?", Griliches (1990, p. 1669) concludes that,
"Ideally, we might hope that patent statistics would provide a measure of the (innovative)
output ... The reality, however, is very far from it. The dream of getting hold of an output
indicator of inventive activity is one of the strong motivating forces for economic
research in this area."3
It was not before well into the 1970s that systematic attempts were made to
provide a direct measure of the innovative output. Thus, it should be emphasized that the
conventional wisdom regarding innovation and technological change was based primarily
upon the evidence derived from analyzing R&D data, which essentially measure inputs
into the process of technological change, and patented inventions, which are a measure of
intermediate output at best.
The first serious attempt to directly measure innovative output was by the
Gellman Research Associates (1976) for the National Science Foundation. Gellman
3 Chakrabarti and Halperin (1990) use a fairly standard source of data for U.S. patents
issued by the U.S. Office of Patents and Trademarks, the BRS/PATSEARCH online
database, to identify the number of inventions patented by over 470 enterprises between
1975 and 1986. Of particular interest is their comparison between the propensity of firms
to patent and company R&D expenditures, and a measure not often found in the
economics literature the number of published papers and publications contributed by
employees of each firm. Not only do they bring together data from a number of rich
sources, but they compare how the relationships between the various measures of
innovative activity vary across firm size.
12
identified 500 major innovations that were introduced into the market between 1953 and
1973 in the United States, the United Kingdom, Japan, West Germany, France, and
Canada. The data base was compiled by an international panel of experts, who identified
those innovations representing the "most significant new industrial products and
processes, in terms of their technological importance and economic and social impact"
(National Science Board, 1975, p. 100).
A second and comparable data base once again involved the Gellman Research
Associates (1982), this time for the U.S. Small Business Administration. In their second
study, Gellman compiled a total of 635 U.S. innovations, including 45 from the earlier
study for the National Science Foundation. The additional 590 innovations were selected
from fourteen industry trade journals for the period 1970-1979. About 43 percent of the
sample was selected from the award winning innovations described in the Industrial
Research & Development magazine.
The third data source that has attempted to directly measure innovation activity
was compiled at the Science Policy Research Unit (SPRU) at the University of Sussex in
the United Kingdom.4 The SPRU data consist of a survey of 4,378 innovations that were
identified over a period of fifteen years. The survey was compiled by writing to experts in
each industry and requesting them to identify "significant technical innovations that had
been successfully commercialized in the United Kingdom since 1945, and to name the
firm responsible" (Pavitt et al., 1987, p. 299).
4 The SPRU innovation data are explained in considerable detail in Pavitt et al. (1987),
Townsend et al. (1981), Robson and Townsend (1984), and Rothwell (1989).
13
The most recent and most ambitious major data base providing a direct measure
of innovative activity is the U.S. Small Business Administration's Innovation Data Base
(SBIDB). The data base consists of 8,074 innovations commercially introduced in the
U.S. in 1982. A private firm, The Futures Group, compiled the data and performed
quality-control analyses for the U.S. Small Business Administration by examining over
one hundred technology, engineering, and trade journals, spanning every industry in
manufacturing. From the sections in each trade journal listing innovations and new
products, a data base consisting of the innovations by four-digit standard industrial
classification (SIC) industries was formed.5 These data were implemented by Acs and
Audretsch (1987, 1988b, and 1990) to analyze the relationships between firm size and
technological change and market structure and technological change, where a direct
rather than indirect measure of innovative activity is used.
In their 1990 study (chapter two), Acs and Audretsch directly compare these four
data bases directly measuring innovative activity and find that they generally provide
similar qualitative results. For example, while the Gellman data base identified small
firms as contributing 2.45 times more innovations per employee than do large firms, the
U.S. Small Business Administration's Innovation Data Base finds that small firms
introduce 2.38 more innovations per employee than do their larger counterparts. In
general, these four data bases reveal similar patterns with respect to the distribution of
innovations across manufacturing industries and between large and small enterprises.
These similarities emerge, despite the obviously different methods used to compile the
data, especially in terms of sampling and standard of significance.
5 A detailed description of the U.S. Small Business Administration’s Innovation Data
Base can be found in chapter two of Acs and Audretsch (1990)
14
Just as for the more traditional measures of technological change, there are also
certain limitations associated with the direct measure of innovative activity. In fact, one
of the main qualifications is common among all three measures -- the implicit assumption
of homogeneity of units. That is, just as it is implicitly assumed that each dollar of R&D
makes the same contribution to technological change, and that each invention which is
patented is equally valuable, the output measure implicitly assumes that innovations are
of equal importance.6 As Cohen and Levin (1989) observe, "In most studies, process
innovation is not distinguished from product innovation; basic and applied research are
not distinguished from development." Thus, the increase in the firm's market value
resulting from each innovation, dollar expended on R&D, and patent, is implicitly
assumed to be homogeneous -- an assumption which clearly violates real world
observation.
In order to at least approximate the market value associated with innovative
activity, FitzRoy and Kraft (1990 and 1991) follow the example of Pakes (1985),
Connolly et al. (1986), and Connolly and Hirschey (1984). Based on data for 57 West
German firms in the metalworking sector, FitzRoy and Kraft (1990 and 1991) measure
innovation as the "proportion of sales consisting of products introduced within the last
five years." Presumably the greater the market value of a given product innovation, the
higher would be the proportion of sales accounted for by new products.
Similarly, Graf von der Schulenburg and Wagner (1991 and 1992) are able to
provide one of the first applications of a direct measure of innovative activity in West
6 It should be emphasized, however, that Acs and Audretsch (1990, chapter two) perform
a careful analysis of the significance of the innovations based on four broad categories
ranking the importance of each innovation.
15
Germany. Their measure is from the IFO Institute and is defined as the "percentage of
shipments of those products which were introduced recently into the market and are still
in the entry phase."7 Like the measure of innovative activity used by FitzRoy and Kraft
(1990 and 1991), the Graf von der Schulenburg and Wagner measure reflects the market
value of the innovation and therefore attempts to overcome one of the major weaknesses
in most of the other direct and indirect measures of innovative activity.
4. The Organizational Context
The knowledge production function has been found to hold most strongly at
broader levels of aggregation. The most innovative countries are those with the greatest
investments to R&D. Little innovative output is associated with less developed countries,
which are characterized by a paucity of production of new economic knowledge.
Similarly, the most innovative industries, also tend to be characterized by considerable
investments in R&D and new economic knowledge. Not only are industries such as
computers, pharmaceuticals and instruments high in R&D inputs that generate new
economic knowledge, but also in terms of innovative outputs (Audretsch, 1995). By
contrast, industries with little R&D, such as wood products, textiles and paper, also tend
to produce only a negligible amount of innovative output. Thus, the knowledge
production model linking knowledge generating inputs to outputs certainly holds at the
more aggregated levels of economic activity.
7 The data based used by Graf von der Schulenburg and Wagner (1991) is the IFO-
Innovations-Test and is explained in greater detail in Oppenlander (1990), and Konig and
Zimmermann (1986).
16
Where the relationship becomes less compelling is at the disaggregated
microeconomic level of the enterprise, establishment, or even line of business. For
example, While Acs and Audretsch (1990) found that the simple correlation between
R&D inputs and innovative output was 0.84 for four-digit standard industrial
classification (SIC) manufacturing industries in the United States, it was only about half,
0.40 among the largest U.S. corporations.
The model of the knowledge production function becomes even less compelling
in view of the recent wave of studies revealing that small enterprises serve as the engine
of innovative activity in certain industries. These results are startling, because as Scherer
(1991) observes, the bulk of industrial R&D is undertaken in the largest corporations;
small enterprises account only for a minor share of R&D inputs.
At the heart of the conventional wisdom has been the belief that large enterprises
able to exploit at least some market power are the engine of technological change. This
view dates back at least to Schumpeter, who in Capitalism, Socialism and Democracy
(1942, p. 101) argued that, "The monopolist firm will generate a larger supply of
innovations because there are advantages which, though not strictly unattainable on the
competitive level of enterprise, are as a matter of fact secured only on the monopoly
level." The Schumpeterian thesis, then, is that large enterprises are uniquely endowed to
exploit innovative opportunities. That is, market dominance is a prerequisite to
undertaking the risks and uncertainties associated with innovation. It is the possibility of
acquiring quasi-rents that serves as the catalyst for large-firm innovation.
17
Five factors favoring the innovative advantage of large enterprises have been
identified in the literature. First is the argument that innovative activity requires a high
fixed cost. As Comanor (1967) observes, R&D typically involves a "lumpy" process that
yields scale economies. Similarly, Galbraith (1956, p. 87) argues, "Because development
is costly, it follows that it can be carried on only by a firm that has the resources which
are associated with considerable size."
Second, only firms that are large enough to attain at least temporary market power
will choose innovation as a means for maximization (Kamien and Schwartz, 1975). This
is because the ability of firms to appropriate the economic returns accruing from R&D
and other knowledge-generating investments is directly related to the extent of that
enterprise's market power (Cohen and Klepper, 1990 and 1991; Levin et al., 1985 and
1987; and Cohen et al., 1987). Third, R&D is a risky investment; small firms engaging in
R&D make themselves vulnerable by investing a large proportion of their resources in a
single project. However, their larger counterparts can reduce the risk accompanying
innovation through diversification into simultaneous research projects. The larger firm is
also more likely to find an economic application of the uncertain outcomes resulting from
innovative activity (Nelson, 1959).
Fourth, scale economies in production may also provide scope economies for
R&D. Scherer (1991) notes that economies of scale in promotion and in distribution
facilitate the penetration of new products, thus enabling larger firms to enjoy a greater
profit potential from innovation. Finally, an innovation yielding cost reductions of a
given percentage results in higher profit margins for larger firms than for smaller firms.
18
There is also substantial evidence that technological change -- or rather, one
aspect of technological change reflected by one of the three measures discussed in the
previous section, R&D -- is, in fact, positively related to firm size.8 The plethora of
empirical studies relating R&D to firm size is most thoroughly reviewed in Acs and
Audretsch (1990, chapter three), Baldwin and Scott (1987), and Cohen and Levin (1989).
The empirical evidence generally seems to confirm Scherer's (1982, pp. 234-235)
conclusion that the results "tilt on the side of supporting the Schumpeterian Hypothesis
that size is conducive to vigorous conduct of R&D".
In one of the most important studies, Scherer (1984) used the U.S. Federal Trade
Commission's Line of Business Data to estimate the elasticity of R&D spending with
respect to firm sales for 196 industries. He found evidence of increasing returns to scale
(an elasticity exceeding unity) for about twenty percent of the industries, constant returns
to scale for a little less than three-quarters of the industries, and diminishing returns (an
elasticity less than unity) in less than ten percent of the industries. These results were
consistent with the findings of Soete (1979) that R&D intensity increases along with firm
size, at least for a sample of the largest U.S. corporations.
While the Scherer (1984) and Soete (1979) studies were restricted to relatively
large enterprises, Bound et al. (1984) included a much wider spectrum of firm sizes in
their sample of 1,492 firms from the 1976 COMPUSTAT data. They found that R&D
8 Fisher and Temin (1973) demonstrated that the Schumpeterian Hypothesis could not be
substantiated unless it was established that the elasticity of innovative output with respect
to firm size exceeds one. They pointed out that if scale economies in R&D do exist, a
firm's size may grow faster than its R&D activities. Kohn and Scott (1982) later showed
that if the elasticity of R&D input with respect to firm size is greater than unity, then the
elasticity of R&D output with respect to firm size must also be greater than one
19
increases more than proportionately along with firm size for the smaller firms, but that a
fairly linear relationship exists for larger firms. Despite the somewhat more ambiguous
findings in still other studies (Comanor, 1967; Mansfield, 1981 and 1983; and Mansfield
et al., 1982), the empirical evidence seems to generally support the Schumpeterian
hypothesis that research effort is positively associated with firm size.
The studies relating patents to firm size are considerably less ambiguous. Here the
findings unequivocally suggest that "the evidence leans weakly against the
Schumpeterian conjecture that the largest sellers are especially fecund sources of
patented inventions" (Scherer, 1982, p. 235). In one of the most important studies,
Scherer (1965b) used the Fortune annual survey of the 500 largest U.S. industrial
corporations. He related the 1955 firm sales to the number of patents in 1959 for 448
firms. Scherer found that the number of patented inventions increases less than
proportionately along with firm size. Scherer's results were later confirmed by Bound et
al. (1984) in the study mentioned above. Basing their study on 2,852 companies and
4,553 patenting entities, they determined that the small firms (with less than $10 million
in sales) accounted for 4.3 percent of the sales from the entire sample, but 5.7 percent of
the patents.
Such results are not limited to the U.S. Schwalbach and Zimmermann (1991) find
that the propensity to patent is less for the largest firms in West Germany than for the
medium-sized enterprises included in their sample.
A number of explanations have emerged why smaller enterprises may, in fact,
tend to have an innovative advantage, at least in certain industries. Rothwell (1989)
20
suggests that the factors yielding small firms with the innovative advantage generally
emanate from the difference in management structures between large and small firms.
For example, Scherer (1991) argues that the bureaucratic organization of large firms is
not conducive to undertaking risky R&D. The decision to innovate must survive layers of
bureaucratic resistance, where an inertia regarding risk results in a bias against
undertaking new projects. However, in the small firm the decision to innovate is made by
relatively few people.
Second, innovative activity may flourish the most in environments free of
bureaucratic constraints (Link and Bozeman, 1991). That is, a number of small-firm
ventures have benefited from the exodus of researchers who felt thwarted by the
managerial restraints in a larger firm. Finally, it has been argued that while the larger
firms reward the best researchers by promoting them out of research to management
positions, the smaller firms place innovative activity at the center of their competitive
strategy (Scherer, 1991).
Scherer (1988, pp. 4-5) has summarized the advantages small firms may have in
innovative activity: "Smaller enterprises make their impressive contributions to
innovation because of several advantages they possess compared to large-size corpora-
tions. One important strength is that they are less bureaucratic, without layers of
"abominable no-men" who block daring ventures in a more highly structured
organization. Second, and something that is often overlooked, many advances in
technology accumulate upon a myriad of detailed inventions involving individual
components, materials, and fabrication techniques. The sales possibilities for making
such narrow, detailed advances are often too modest to interest giant corporations. An
21
individual entrepreneur's juices will flow over a new product or process with sales
prospects in the millions of dollars per year, whereas few large corporations can work up
much excitement over such small fish, nor can they accommodate small ventures easily
into their organizational structures. Third, it is easier to sustain a fever pitch of
excitement in small organization, where the links between challenges, staff, and potential
rewards are tight. "All-nighters" through which tough technical problems are solved
expeditiously are common."
Two other ways that small enterprises can compensate for their lack of R&D is
through spillovers and spin-offs .Typically an employee from an established large
corporation, often a scientist or engineer working in a research laboratory, will have an
idea for an invention and ultimately for an innovation. Accompanying this potential
innovation is an expected net return from the new product. The inventor would expect to
be compensated for his/her potential innovation accordingly. If the company has a
different, presumably lower, valuation of the potential innovation, it may decide either
not to pursue its development, or that it merits a lower level of compensation than that
expected by the employee.
In either case, the employee will weigh the alternative of starting his/her own
firm. If the gap in the expected return accruing from the potential innovation between the
inventor and the corporate decision maker is sufficiently large, and if the cost of starting a
new firm is sufficiently low, the employee may decide to leave the large corporation and
establish a new enterprise. Since the knowledge was generated in the established
corporation, the new start-up is considered to be a spin-off from the existing firm. Such
start-ups typically do not have direct access to a large R&D laboratory. Rather, these
22
small firms succeed in exploiting the knowledge and experience accrued from the R&D
laboratories with their previous employers.
The research laboratories of universities provide a source of innovation-
generating knowledge that is available to private enterprises for commercial exploitation.
Jaffe (1989) and Acs, Audretsch, and Feldman (1992), for example, found that the
knowledge created in university laboratories "spills over" to contribute to the generation
of commercial innovations by private enterprises. Acs, Audretsch, and Feldman (1994)
found persuasive evidence that spillovers from university research contribute more to the
innovative activity of small firms than to the innovative activity of large corporations.
Similarly, Link and Rees (1990) surveyed 209 innovating firms to examine the
relationship between firm size and university research. They found that, in fact, large
firms are more active in university-based research. However, small- and medium-sized
enterprises apparently are better able to exploit their university-based associations and
generate innovations. Link and Rees (1990) conclude that, contrary to the conventional
wisdom, diseconomies of scale in producing innovations exist in large firms. They
attribute these diseconomies of scale to the "inherent bureaucratization process which
inhibits both innovative activity and the speed with which new inventions move through
the corporate system towards the market" (Link and Rees, 1990, p. 25).
Thus, just as there are persuasive theories defending the original Schumpeterian
Hypothesis that large corporations are a prerequisite for technological change, there are
also substantial theories predicting that small enterprises should have the innovative
advantage, at least in certain industries. As described above, the empirical evidence based
on the input measure of technological change, R&D, tilts decidedly in favor of the
23
Schumpeterian Hypothesis. However, as also described above, the empirical results are
somewhat more ambiguous for the measure of intermediate output -- the number of
patented inventions. It was not until direct measures of innovative output became
available that the full picture of the process of technological change could be obtained.
Using this new measure of innovative output from the U.S. Small Business
Administration's Innovation Data Base, Acs and Audretsch (1990) shows that, in fact, the
most innovative U.S. firms are large corporations. Further, the most innovative American
corporations also tended to have large R&D laboratories and be R&D intensive. At first
glance, these findings based on direct measures of innovative activity seems to confirm
the conventional wisdom. However, in the most innovative four-digit standard industrial
classification (SIC) industries, large firms, defined as enterprises with at least 500
employees, contributed more innovations in some instances, while in other industries
small firms produced more innovations. For example, in computers and process control
instruments small firms contributed the bulk of the innovations. By contrast in the
pharmaceutical preparation and aircraft industries the large firms were much more
innovative.
Probably their best measure of innovative activity is the total innovation rate,
which is defined as the total number of innovations per one thousand employees in each
industry. The large-firm innovation rate is defined as the number of innovations made by
firms with at least 500 employees, divided by the number of employees (thousands) in
large firms. The small-firm innovation rate is analogously defined as the number of
innovations contributed by firms with fewer than 500 employees, divided by the number
of employees (thousands) in small firms.
24
The innovation rates, or the number of innovations per thousand employees, have
the advantage in that they measure large- and small-firm innovative activity relative to
the presence of large and small firms in any given industry. That is, in making a direct
comparison between large- and small-firm innovative activity, the absolute number of
innovations contributed by large firms and small enterprises is somewhat misleading,
since these measures are not standardized by the relative presence of large and small
firms in each industry. When a direct comparison is made between the innovative activity
of large and small firms, the innovation rates are presumably a more reliable measure of
innovative intensity because they are weighted by the relative presence of small and large
enterprises in any given industry. Thus, while large firms in manufacturing introduced
2,445 innovations in 1982, and small firms contributed slightly fewer, 1,954, small-firm
employment was only half as great as large-firm employment, yielding an average small-
firm innovation rate in manufacturing of 0.309, compared to a large-firm innovation rate
of 0.202 (Acs and Audretsch, 1988 and 1990).
Recent studies in the United States include the work of Scott Schane using data on
new firm formation from the MIT technology commercialization database. Two studies,
on technological opportunity and new firm formation (Schane, 2001a) and technological
regimes and new firm formation (Shane, 2001b) provide additional evidence on the
importance of new firm formation and technological change.
The most important and careful study to date documenting the role of German
SMEs (enterprises with fewer than 500 employees) in innovative activity was undertaken
by a team of researchers at the Zentrum fuer Europaeische Wirtschaftsforschung (ZEW)
led by Dietmar Harhoff and Georg Licht (1996). They analyzed the findings made
25
possible by the Mannheim Innovation Data Base. This data base measures the extent of
innovative activity in German firms between 1990 and 1992. Harhoff and Licht (1996)
use the data base to identify that 12 percent of the research and development expenditures
in (West) German firms comes from SMEs (defined as having fewer than 500
employees).
Harhoff and Licht show that the likelihood of a firm not innovating decreases
with firm size. For example, 52 percent of firms with fewer than 50 employees were not
innovative. By contrast, only 15 percent of the firms with at least 1,000 employees were
not innovative. More striking is that the smallest firms that do innovate have a greater
propensity to be innovative without undertaking formal research and development. While
only 3 percent of the largest corporations in Germany are innovative without undertaking
formal R&D, one-quarter of the innovative firms with fewer than 50 employees are
innovative without formal R&D.
The study also shows that even fewer SMEs in the five new German Laender are
innovative than is the case in West Germany. Over two-thirds of the smallest SMEs in
East Germany are not innovative, and they are less than half as likely to undertake R&D
as are their Western counterparts.
Systematic empirical evidence also suggests that the German Mittelstand is
confronted by considerable barriers to innovative activity. Beise and Licht (1996)
analyzed the Mannheimer Innovationspanel consisting of 43,300 innovating firms to
identify the main barriers to innovative activity confronting German small- and medium
sized enterprises. The major barrier to innovation listed in both 1992 and 1994 was too
26
high of a gestation period required for innovative activity. In 1994 nearly 60 percent of
German SMEs reported that too long of a high gestation period required to innovate was
a very important barrier to innovative activity. Other major barriers to innovative activity
include legal restrictions & restrictive government policies, too long of duration required
to obtain government approval for a new product, a shortage of finance capital, a lack of
competent employees, and too high of a risk.
Thus, there is considerable evidence suggesting that, in contrast to the findings for
R&D inputs and patented inventions, small enterprises apparently play an important
generating innovative activity, at least in certain industries. By relating the innovative
output of each firm to its size, it is also possible to shed new light on the Schumpeterian
Hypothesis. In their 1991a study, Acs and Audretsch find that there is no evidence that
increasing returns to R&D expenditures exist in producing innovative output. In fact,
with just several exceptions, diminishing returns to R&D are the rule. This study made it
possible to resolve the apparent paradox in the literature that R&D inputs increase at
more than a proportional rate along with firm size, while the generation of patented
inventions does not. That is, while larger firms are observed to undertake a greater effort
towards R&D, each additional dollar of R&D is found to yield less in terms of innovative
output.
5. The Industry Context
In comparison to the number of studies investigating the relationship between
firm size and technological change, those examining the relationship between innovation
and the external industry structure or environment are what Baldwin and Scott (1987, p.
27
89) term "miniscule" in number. In fact, the most comprehensive and insightful evidence
has been made possible by utilizing the Federal Trade Commission's Line of Business
Data. Using 236 manufacturing industry categories, which are defined at both the three-
and four-digit SIC level, Scherer (1983a) found that 1974 company R&D expenditures
divided by sales was positively related to the 1974 four-firm concentration ratio. Scherer
(1983a, p. 225) concluded that, "although one cannot be certain, it appears that the
advantages a high market share confers in appropriating R&D benefits provide the most
likely explanation of the observed R&D-concentrator associations."
Scott (1984) also used the FTC Line of Business Survey Data and found the U-
shaped relationship between market concentration and R&D. However, when he
controlled for the fixed effects for two-digit SIC industries, no significant relationship
could be found between concentration and R&D. These results are consistent with a
series of studies by Levin et al. (1985 and 1987), Levin and Reiss (1984), and Cohen et
al. (1987). Using data from a survey of R&D executives in 130 industries, which were
matched with FTC Line of Business Industry Groups, Cohen et al. (1987) and Levin et al.
(1987) found little support for the contention that industrial concentration is a significant
and systematic determinant of R&D effort.
While it has been hypothesized that firms in concentrated industries are better
able to capture the rents accruing from an innovation, and therefore have a greater
incentive to undertake innovative activity, there are other market structure variables that
also influence the ease with which economic rents can be appropriated. For example,
Comanor (1967) argued and found that, based on a measure of minimum efficient scale,
there is less R&D effort (average number of research personnel divided by total
28
employment) in industries with very low scale economies. However, he also found that in
industries with a high minimum efficient scale, R&D effort was also relatively low.
Comanor interpreted his results to suggest that, where entry barriers are relatively low,
there is little incentive to innovate, since the entry subsequent to innovation would
quickly erode any economic rents. At the same time, in industries with high entry
barriers, the absence of potential entry may reduce the incentives to innovate.
Because many studies have generally found positive relationships between market
concentration and R&D, and between the extent of barriers to entry and R&D, it would
seem that the conventional wisdom built around the Schumpeterian Hypothesis has been
confirmed. However, when the direct measure of innovative output is related to market
concentration, Acs and Audretsch (1988b and 1990) find a pointedly different
relationship emerges. In fact, there appears to be unequivocal evidence that concentration
exerts a negative influence on the number of innovations being made in an industry.
Acs and Audretsch (1987, 1988b, and 1990) found that not only does market
structure influence the total amount of innovative activity, but also the relative innovative
advantage between large and small enterprises. The differences between the innovation
rates of large and small firms examined in the previous section can generally be
explained by (1) the degree of capital intensity, (2) the extent to which an industry is
concentrated, (3) the total innovative intensity, and (4) the extent to which an industry is
comprised of small firms. In particular, the relative innovative advantage of large firms
tends to be promoted in industries that are capital-intensive, advertising intensive,
concentrated, and highly unionized. By contrast, in industries that are highly innovative
29
and composed predominantly of large firms, the relative innovative advantage is held by
small enterprises.
6.The Geographic Context
The evidence revealing small enterprises to be the engine of innovative activity in
certain industries, despite an obvious lack of form R&D activities, raises the question
about the source of knowledge inputs for small enterprises. The answer emerging from a
series of studies (Jaffe, 1990) is from other, third-party, firms or research institutions,
such as universities. Economic knowledge may spill over from the firm or research
institution creating it for application by other firms.
That knowledge spills over is barely dispute. However, the geographic range of such
knowledge spillovers is greatly contested. In disputing the importance of knowledge
externalities in explaining the geographic concentration of economic activity, Krugman
(1991) and others do not question the existence or importance of such knowledge
spillovers. In fact, they argue that such knowledge externalities are so important and
forceful that there is no compelling reason for a geographic boundary to limit the spatial
extent of the spillover. According to this line of thinking, the concern is not that
knowledge does not spill over but that it should stop spilling over just because it hits a
geographic border, such as a city limit, state line, or national boundary.
A recent body of empirical evidence clearly suggests that R&D and other sources
of knowledge not only generate externalities, but studies by Audretsch and Feldman
(1996), Jaffe (1989), Audretsch and Stephan (1996), Anselin, Varga and Acs (1997 and
2000), and Jaffe, Trajtenberg and Henderson (1993) suggest that such knowledge
30
spillovers tend to be geographically bounded within the region where the new economic
knowledge was created. That is, new economic knowledge may spill-over but the
geographic extent of such knowledge spillovers is limited.
Krugman [1991a, p. 53] has argued that economists should abandon any attempts at
measuring knowledge spillovers because "...knowledge flows are invisible, they leave no
paper trail by which they may be measured and tracked." But as Jaffe, Trajtenberg and
Henderson [1991, p. 578] point out, "knowledge flows do sometimes leave a paper trail"
-- in particular in the form of patented inventions and new product introductions.
Studies identifying the extent of knowledge spillovers are based on the knowledge
production function. Jaffe (1989modified the knowledge production function approach to
a model specified for spatial and product dimensions:
I IRD UR UR GC
s
i
s
i
s
i
s
i
s
i
=∗∗∗
∗
β
β
β
ε
12 3
( ) (1)
where I is innovative output, IRD is private corporate expenditures on R&D, UR
is the research expenditures undertaken at universities, and GC measures the geographic
coincidence of university and corporate research. The unit of observation for estimation
was at the spatial level, s, a state, and industry level, i. Estimation of equation (1)
essentially shifted the knowledge production function from the unit of observation of a
firm to that of a geographic unit.
Implicitly contained within the knowledge production function model is the
assumption that innovative activity should take place in those regions, s, where the direct
knowledge-generating inputs are the greatest, and where knowledge spillovers are the
31
most prevalent. Audretsch and Feldman (1996), Anselin, Acs and Varga (1997 and 2000)
and Audretsch and Stephan (1996) link the propensity for innovative activity to cluster
together to industry specific characteristics, most notably the relative importance of
knowledge spillovers.
Innovation and Small Firms, representing a decade of work on innovation and
technological change in the 1980s, (Acs and Audretsch, 1990) examined the question,
“Why should entrepreneurship emerge as a driving force of the U. S. economy precisely
when both technical change and globalization seem to play an unprecedented role in the
national welfare?” However, this book did not answer the question, “Why is innovation
important to national welfare?” Innovation and the Growth of Cities, representing a
decade of work in the 1990s on innovation and cities, (Acs, 2002) demonstrated that
innovation is the driving force of the growth of cities and regions. Innovation is not an
autonomous miracle: it emerges out of knowledge creation and adoption. However, this
book did not answer the question, “Why is entrepreneurship important for regional
growth?”
Acs and Armington (2004, 2006), Acs and Storey (2004) and Acs and Varga
(2005) bridges the gap between these related but disparate works above, suggesting that
variations in entrepreneurial activity, and agglomeration effects, could potentially be the
source of different efficiencies in knowledge spillovers and ultimately in economic
growth. In other words, they answer the question, “What is the role of entrepreneurial
activity and agglomeration effects in economic growth?” As early as 1976 The Economist
magazine wrote about the coming entrepreneurial revolution, and in 1985, then President
Reagan announced, "we are living in the age of the entrepreneur." David Hart at the
32
Kennedy School of Government at Harvard University, discussing the dot-com bubble in
the late 1990s wrote, “The Entrepreneurship fad rested on a foundation of fact. New
companies made a significant contribution to economic growth in the past decade, both
directly and by stimulating their more established competitors.” And, Edward Lazear at
Stanford University wrote, “The entrepreneur is the single most important player in a
modern economy” (Lazear, 2002, p.1).
The efficiency of transforming knowledge into economic applications is a crucial
factor in explaining macroeconmic growth. New growth theory treats this factor as
exogenous. The theory offers no insight into what role, if any, entrepreneurship and
agglomeration play in the spillover of tacit knowledge. The answer to this question can
be pursued through the lens of the “new economic geography” and the newest wave of
entrepreneurship research. We pursue a better understanding of both the relationship
between geography and technological change, and that between entrepreneurship and
technological change, because these lines of research may prove fruitful in better
explaining variations in economic growth. Thus, this book remains a solid economic
study for an economic audience, while offering a conceptual bridge, to the related non-
economics-based social science fields.
7.The Entrepreneurial Context
The model of the knowledge production function becomes even less compelling in view
of the evidence documented in Section 3 that entrepreneurial small firms are the engine
of innovative activity in some industries, which raises the question, "Where do new and
small firms get the innovation producing inputs, that is the knowledge?"
33
The appropriability problem, or the ability to capture the revenues accruing from
investments in new knowledge, confronting the individual may converge with that
confronting the firm. Economic agents can and do work for firms, and even if they do
not, they can potentially be employed by an incumbent firm. In fact, in a model of perfect
information with no agency costs, any positive economies of scale or scope will ensure
that the appropriability problems of the firm and individual converge. If an agent has an
idea for doing something different than is currently being practiced by the incumbent
enterprises -- both in terms of a new product or process and in terms of organization --
the idea, which can be termed as an innovation, will be presented to the incumbent
enterprise. Because of the assumption of perfect knowledge, both the firm and the agent
would agree upon the expected value of the innovation. But to the degree that any
economies of scale or scope exist, the expected value of implementing the innovation
within the incumbent enterprise will exceed that of taking the innovation outside of the
incumbent firm to start a new enterprise. Thus, the incumbent firm and the inventor of the
idea would be expected to reach a bargain splitting the value added to the firm
contributed by the innovation. The payment to the inventor -- either in terms of a higher
wage or some other means of remuneration -- would be bounded between the expected
value of the innovation if it implemented by the incumbent enterprise on the upper end,
and by the return that the agent could expect to earn if he used it to launch a new
enterprise on the lower end
A different model refocuses the unit of observation away from firms deciding
whether to increase their output from a level of zero to some positive amount in a new
industry, to individual agents in possession of new knowledge that, due to uncertainty,
34
may or may not have some positive economic value. It is the uncertainty inherent in new
economic knowledge, combined with asymmetries between the agent possessing that
knowledge and the decision making vertical hierarchy of the incumbent organization with
respect to its expected value that potentially leads to a gap between the valuation of that
knowledge.
Divergences in the expected value regarding new knowledge will, under certain
conditions, lead an agent to exercise what Albert O. Hirschman (1970) has termed as exit
rather than voice, and depart from an incumbent enterprise to launch a new firm. But who
is right, the departing agents or those agents remaining in the organizational decision
making hierarchy who, by assigning the new idea a relatively low value, have effectively
driven the agent with the potential innovation away? Ex post the answer may not be too
difficult. But given the uncertainty inherent in new knowledge, the answer is anything but
trivial a priori.
This initial condition of not just uncertainty, but greater degree of uncertainty vis-à-
vis incumbent enterprises in the industry is captured in the theory of firm selection and
industry evolution proposed by Boyan Jovanovic (1982). The theory of firm selection is
particularly appealing in view of the rather startling size of most new firms. For example,
the mean size of more than 11,000 new-firm startups in the manufacturing sector in the
United States was found to be fewer than eight workers per firm.5 While the minimum
efficient scale (MES) varies substantially across industries, and even to some degree
across various product classes within any given industry, the observed size of most new
firms is sufficiently small to ensure that the bulk of new firms will be operating at a
35
suboptimal scale of output. Why would an entrepreneur start a new firm that would
immediately be confronted by scale disadvantages?
An implication of the theory of firm selection is that new firms may begin at a small,
even suboptimal, scale of output, and then if merited by subsequent performance expand.
Those new firms that are successful will grow, whereas those that are not successful will
remain small and may ultimately be forced to exit from the industry if they are operating
at a suboptimal scale of output.
An important finding of Audretsch 1995) verified in a systematic and
comprehensive series of studies contained in the reviews by Caves (1998), Sutton (1997)
and Geroski (1995) is that although entry may still occur in industries characterized by a
high degree of scale economies, the likelihood of survival is considerably less. People
will start new firms in an attempt to appropriate the expected value of their new ideas, or
potential innovations, particularly under the entrepreneurial regime. As entrepreneurs
gain experience in the market they learn in at least two ways. First, they discover whether
they possess the right stuff, in terms of producing goods and offering services for which
sufficient demand exists, as well as whether they can product that good more efficiently
than their rivals. Second, they learn whether they can adapt to market conditions as well
as to strategies engaged in by rival firms. In terms of the first type of learning,
entrepreneurs who discover that they have a viable firm will tend to expand and
ultimately survive. But what about those entrepreneurs who discover that they are either
not efficient or not offering a product for which their is a viable demand? The answer is,
It depends -- on the extent of scale economies as well as on conditions of demand. The
consequences of not being able to grow will depend, to a large degree, on the extent of
36
scale economies. Thus, in markets with only negligible scale economies, firms have a
considerably greater likelihood of survival. However, where scale economies play an
important role the consequences of not growing are substantially more severe, as
evidenced by a lower likelihood of survival.
What emerges from the new evolutionary theories and empirical evidence on the
role of small firms is that markets are in motion, with a lot of new firms entering the
industry and a lot of firms exiting out of the industry. The evolutionary view of the
process of industry evolution is that new firms typically start at a very small scale of
output. They are motivated by the desire to appropriate the expected value of new
economic knowledge. But, depending upon the extent of scale economies in the industry,
the firm may not be able to remain viable indefinitely at its startup size. Rather, if scale
economies are anything other than negligible, the new firm is likely to have to grow to
survival. The temporary survival of new firms is presumably supported through the
deployment of a strategy of compensating factor differentials that enables the firm to
discover whether or not it has a viable product.
The empirical evidence (Caves, 1998; Sutton, 1997 and Geroski, 1995) supports
such an evolutionary view of the role of new firms in manufacturing, because the post-
entry growth of firms that survive tends to be spurred by the extent to which there is a
gap between the MES level of output and the size of the firm. However, the likelihood of
any particular new firm surviving tends to decrease as this gap increases. Such new
suboptimal scale firms are apparently engaged in the selection process. Only those firms
offering a viable product that can be produced efficiently will grow and ultimately
approach or attain the MES level of output. The remainder will stagnate, and depending
37
upon the severity of the other selection mechanism -- the extent of scale economies --
may ultimately be forced to exit out of the industry. Thus, the persistence of an
asymmetric firm-size distribution biased towards small-scale enterprise reflects the
continuing process of the entry of new firms into industries and not necessarily the
permanence of such small and sub-optimal enterprises over the long run. Although the
skewed size distribution of firms persists with remarkable stability over long periods of
time, a constant set of small and suboptimal scale firms does not appear to be responsible
for this skewed distribution. Rather, by serving as agents of change, entrepreneurial firms
provide an essential source of new ideas and experimentation that otherwise would
remain untapped in the economy.
7. Conclusions
Neither the conventional view prevalent in the entrepreneurship literature that
opportunities are exogenously given nor the view in the economics literature that large
incumbent organizations have a competitive advantage in generating and
commercializing opportunities seems to be entirely correct. Just as the entrepreneurship
literature may have undervalued the role that the external environment plays in
generating opportunities, particularly in terms of creating entrepreneurial opportunities
through knowledge spillovers, the economics literature may have trivialized the decision
to invest in and generate new knowledge with commercializing that knowledge.
The conventional wisdom in the economics literature on innovation held that
small firms inherently have a deficit of knowledge assets, burdening them with a clear
38
and distinct disadvantage in generating innovative output. This view was certainly
consistent with the early interpretation of the knowledge production function. As
Chandler (1990) concluded, “to compete globally you have to be big.”
More recent scholarship has produced a revised view that identifies
entrepreneurial small firms as making a crucial contribution to innovative activity and
technological change. There are two hypotheses why scholarship about the role of small
firms has evolved so drastically within such a short period. This first is that, as explained
in this paper, the measurement of innovative output and technological change has greatly
improved. As long as the main instruments to measuring innovative activity were
restricted to inputs into the innovative process, such as expenditures on formal R&D,
many or even most of the innovative activities by smaller enterprises simply remained
hidden from the radar screen of researchers. With the development of measures focusing
on measures of innovative output, the vital contribution of small firms became prominent,
resulting in the emergence of not just the recognition that small firms provide an engine
of innovative activity, at least in some industry contexts, but also of new theories to
explain and understand how and why small firms access knowledge and new ideas. This
first hypothesis would suggest that, in fact, small firms have always made these types of
innovative contributions, but they remained hidden and mostly unobserved to scholars
and policy makers.
The alternative hypothesis is that, in fact, the new view towards the innovative
capacity of new ventures and small firms emerged not because of measurement
improvements, but because the economic and social environment actually changed in
such a way as to shift the innovative advantage more towards smaller enterprises. This
39
hypothesis would say that the conventional wisdom about the relative inability of small
firms to innovate was essentially correct – at least for a historical period of time. Rather,
the new view of entrepreneurship as an engine of innovative activity reflect changes in
technology, globalization and other factors that have fundamentally altered the
importance and process of innovation and technological change. As Jovanovic (2001, pp.
54-55) concludes, “The new economy is one in which technologies and products become
obsolete at a much faster rate than a few decades ago…It is clear that we are entering the
era of the young firm. The small firm will thus resume a role that, in its importance, is
greater than it has been at any time in the last seventy years or so.” As the external
context gains in importance to shaping the competitive advantage of the firm, particularly
in terms of accessing and absorbing external knowledge, the role of entrepreneurship in
generating innovative activity would be expected to continue to increase. In this view,
entrepreneurship and innovation are not intrinsically linked, but as a result of the
competitive advantage bestowed in new ventures in absorbing and accessing knowledge
spillovers, these two phenomena that seemed to have little to do with each other in the
conventional wisdom will become increasingly associated in the future.
40
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