1
CREATIVE CLUSTERING:
AGGLOMERATION EFFECTS IN INNOVATION
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A Thesis
Presented to
The Honors Tutorial College
Ohio University
______
In Partial Fulfillment
of the Requirements for Graduation
from the Honors Tutorial College
with the degree of
Bachelor of Arts in Political Science
______
by
Thomas Irwin
June 2012
2
This thesis has been approved by
The Honors Tutorial College and the Department of Political Science
Dr. James Mosher Professor, Political Science
Thesis Advisor
Dr. James Mosher
Honors Tutorial College, Director of Studie Political Science
Jeremy Webster Dean, Honors Tutorial College
3
Acknowledgments
As with any long project, this paper benefited from the influence of several people to whom I am indebted. First and foremost, my advisor Professor Jim
Mosher deserves thanks for his patient guidance and interested involvement with my work on this project. His thoughtful criticism, informed insight, and
genuine interest pushed me to make this paper better than it otherwise could
have been. I would also like to thank former Dean of the Honors Tutorial
College Ann Fidler, and Professor Julie White, in the Department of Political
Science, for their early encouragement and support of my cross‐disciplinary academic goals.
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Contents Acknowledgments ...... 3
Introduction ...... 5
Literature Review ...... 11
Theory ...... 22
Case Studies ...... 35
Financial Services ...... 35
Automotive Industry ...... 43
Silicon Valley/ Route 128 ...... 66
Analysis ...... 81
Financial Services ...... 81
Automotive Industry ...... 86
Silicon Valley / Route 128 ...... 90
Conclusion ...... 95
References ...... 106
5
Introduction
Innovation is a challenging, but extremely important subject to study. It is at best poorly understood, and it seems that for every idea that is developed there exist a thousand real‐world examples that defy the logic of the theory.
Nevertheless, innovation is recognized as a primary driver of real economic growth. Without innovation there is no Industrial Revolution, there is no
Internet Revolution and so there go several centuries of technological and
economic progress. It is therefore the goal of this project to attempt to shed some light on the subject, in a way that complements the existing body of scholarly work and allows us to make some claims about appropriate public policy to promote innovation.
We will be using a very particular definition of innovation throughout this discussion that bears mentioning because it encompasses more activities than we might think if we have in mind only the work that takes place in research labs. I adopt the convention of Chris Freeman and Luc Soete from their book The Economics of Industrial Innovation, in which they contrast their definition of innovation with what they call invention. Claiming to have adopted the notion from Schumpeter, they write that invention is “an idea, 6
sketch or model for a new or improved device, product, process, or system,” while innovation refers to the whole process of bringing a technology from the stage of idea to commercial availability (Freeman & Soete 1997). We adopt this definition because it is the economic impact of innovative activity that motivates this study in the first place. Technological progress has long been recognized to be a primary source of real economic growth, and therefore if we are concerned about economic growth and increasing the overall material standard of living, we must be concerned with innovation.
One of the most important characteristics of innovation, which once pointed out is obvious but whose importance is not always fully recognized, is that it depends on human action. It is beyond the scope of this project to address the issue of where innovative ideas themselves come from, but even if ideas come to us fully formed like manna from heaven, there is still a great
deal of work remaining for us to transform those ideas into real goods and
services. This is the issue that drives our interest in innovation. We must be
cognizant of the role that human action plays in innovative development, so
we may better understand how to improve innovative outcomes.
To narrow the focus of the project to the point where it could be
completed in a reasonable amount of time, it was decided to focus on 7
empirical case studies in innovative agglomeration. Agglomeration of industrial activity is a phenomenon observed in numerous industries, all over the globe. Cities and towns are obvious examples, and these clusters have been the subject of economic study for over a century. In recent years, interest in the location of economic activity has increased, with the emergence of the
“new economic geography” literature and other work. It is now believed that these clusters of activity provide a variety of economic benefits to the individuals and firms that live and work within them.
It was hypothesized at the outset of this project that benefits of agglomeration specific to innovation, and therefore the incentives to agglomerate innovative activity, were somewhat different from those affecting industrial activity generally. To test this idea, I focused on a descriptive analysis of three innovative agglomeration case studies. This analysis was intended to allow for the discussion of innovation across a range of sectors, and to permit a consideration of a wide range of factors in the locational decisions of innovative firms and people. It is hoped that this method of analysis will provide a starting place for developing a theory of innovative agglomeration that is grounded in the real world, in the same way that
Richard Nelson and others used comparative analysis to structure the 8
discussion of national institutions of innovation in National Systems of
Innovation (Nelson 1993).
The three case studies chosen represent a variety of strongly‐ agglomerated sectors that have many significant differences. This allows for a consideration of factors that transcend sectoral boundaries in promoting innovative agglomeration, and lets us be more general in our conclusions about innovation incentives. Innovation in the financial services industry in
New York, the automotive industry in Detroit, and the semiconductor and computer industries in Silicon Valley and Route 128 will be considered. These selections present us with geographic and industrial diversity.
The financial services industry was chosen in large part because it does not generate innovations that one typically considers “technological.” Usually when we are asked to picture technology, we think of computers, rockets, smart phones, and electric cars. History, however, shows us that process breakthroughs like the division of labor and the moving assembly line are no less important, particularly in their economic impact. Financial industry innovation typically does not generate tangible products, but it does create new ways of providing capital, investing money, generating liquidity, etc., all of which improve the efficiency and impact of the services that the industry 9
provides. By studying the unique innovation in this sector, we are able to make our analysis more general, and therefore more useful.
The automotive industry case study was selected for several reasons.
First, it is a very strong industrial agglomeration that has remained so over more than a century of existence. Second, the automotive industry is one of
the largest manufacturing sectors in the American economy, and we would
therefore expect that this industry would be strongly influenced by more
traditional agglomeration factors like material transportation costs, resource
access, and unskilled labor pooling. Considering this case study allows us to
address innovative agglomeration side by side with other potentially relevant
agglomeration factors.
The Silicon Valley / Route 128 case study was picked because it has
been very well documented in recent years, and is sometimes discussed as a
story of contrasting industrial cultures that led one cluster to success and the
other to failure1. Given that the two agglomerations were working in similar sectors, we expect that the contrast between the two regions’ fates will provide
1 AnnaLee Saxenian was particularly influential in making this case, and her book Regional Advantage provided a good starting point for the research into this case study. 10
insight into what agglomeration advantages set Silicon Valley apart and made
it better able to compete in the market.
Through the course of this project, and the case study analysis just described, it has become clear that innovation depends strongly on the ability to coordinate a multitude of resources and people to tackle problems that are often unpredictable. Thus, to successfully innovate requires that firms have access to a variety of knowledge and skills that they may not always be able to internalize. The extent to which a particular sector of the economy is subject to agglomerative pressure in innovation seems to depend on the degree to which its need for skills and expertise is broad, and the speed with which it must innovate to stay competitive. To put it simply, where innovation is interdisciplinary and happening quickly, we expect to see agglomeration.
11
Literature Review
We turn now to a review of major ideas in the innovation and
agglomeration literatures. In the course of conducting the literature review for
this project, several books and journal articles relating to the study of
agglomeration and the study of innovation were considered. In each case, it
has been only relatively recently that the topics have been given in‐depth
consideration in their own right. Certainly agglomeration and innovation have
both had a place in academic study before the late 20th century, but it was not
until this time that these subjects were given separate, sustained consideration
by a large body of scholars.
In their article “Agglomeration and the adjustment of the spatial
economy,” Combes, Duranton and Overman discuss the recent emergence of the “new economic geography”, written about extensively by Fujita,
Krugman, and Venables in The Spatial Economy, as well as the older urban systems theory, which both consider the spatial aspect of economic activity.
Being informed by these two frameworks, which share some common characteristics, the authors argue that the combination of increasing returns to certain kinds of economic activity and the difficulty of moving some sorts of 12
resources and work from one region to another “is the main driver of the clustering of economic activity and a key determinant of the way in which linkages between areas operate.” In other words, agglomeration occurs because there are economic benefits to being close to others that increase as the cluster grows, and because it is not always easy to pack everything up and move on to greener pastures (2005).
As part of their stylized model, the authors focus on labor market effects, treating land as perfectly immobile, and capital as perfectly mobile.
Drawing from both the new economic geography and the urban systems literature, the authors look at three primary microeconomic effects that tend to generate the aforementioned increasing returns: sharing, matching and learning. Sharing refers to the spreading of fixed costs over larger numbers of people, and clearly as the local market grows these costs become lower and lower for any individual. Matching refers to the ability to find quality matches between economic actors in various contexts, including employer/employee matches and consumer/product matches, with the assumption being that there are more opportunities for better matches in denser areas. Lastly, learning concerns the creation and spread of knowledge, and how more frequent, 13
closer interactions between individuals create better opportunities for this to occur.
While these factors generate increasing returns to density, they are opposed by a variety of increasing costs, that the authors bundle collectively
into cost of living. These include costs associated with longer commute times,
general congestion, the import of agricultural and other goods, and high
housing costs, among others. Both the new economic geography and urban
systems theory incorporate this tension into their models, and ultimately get
the same result, which is that there is some optimal point at which the
increasing costs of clustering overcome the increasing returns (Combes,
Duranton & Overman 2005).
Rosenthal and Strange, in “Geography, Industrial Organization, and
Agglomeration,” discuss the attenuation of agglomeration economies over relatively short distances, and also the effect of the structure of the industrial
system on agglomeration economies. They find that agglomeration economies,
the external benefits that accrue to firms as the result of being close to other
firms, tend to drop off very quickly in the first few miles from a cluster, but
they begin to fade more slowly as the distance increases. What this means is
that the greatest benefits come from being very close, rather than just being in 14
the general area. The authors speculate that this is likely because spontaneous contact and information exchange between workers will tend to become much
less common as distances increase beyond the range of walking (2003).
The study also concludes that agglomeration economies are stronger
where a cluster is made up of smaller firms, and weaker in the presence of larger ones. While the authors note that their evidence tends to support standing arguments about the advantages of “entrepreneurial industrial system[s],” in particular the work of AnnaLee Saxenian, they do not go further in establishing the underlying mechanisms responsible for this phenomenon
(Rosenthal and Strange 2003).
“Density and Creativity in U.S. Regions,” by Knudsen, Florida,
Stolarick and Gates, addresses specifically the role of density and the degree of its importance to innovation. As many others have done, the authors consider the phenomenon of knowledge spillovers from both private and public research and development (R&D) to be a key driver of agglomeration, pointing to previous research that tends to indicate that the inputs to R&D will tend to cluster to take advantage of these spillovers, and then other innovative firms and individuals will follow (2008). 15
The article focuses on the role of density in promoting face‐to‐face contact among innovators, arguing that it is this contact that allows the sorts of knowledge spillovers that improve innovative outcomes. The authors further contend that not just any sort of face‐to‐face interaction will suffice here, writing “Density is not subordinate—conceptually or empirically—to interaction.” They claim that it is the sort of spontaneous interaction that occurs when people are closely clustered together that promotes innovation, and that a low density region, even if it provided excellent transportation systems to bring people “closer,” cannot substitute for a high‐density agglomeration (Knudsen et al. 2008).
Michael Storper and Anthony Venables have written about the importance of face‐to‐face contact, and are cited by Knudsen, et al., in the article discussed above. As they write in the abstract to “Buzz: Face‐to‐face
Contact and the Urban Economy,” their argument is that face‐to‐face contact is the most important consequence of proximity, and that it must be the foundation for models of urban clustering. They go further, saying that face‐ to‐face contact is especially important “where information is imperfect, rapidly changing, and not easily codified, key features of many creative activities,” (2004). Storper and Venables identify four key functions of face‐to‐ 16
face interaction: communication, building trust and coordinating incentives
and goals, “screening and socializing,” and motivation. These properties are
what the article uses in explaining why face‐to‐face contact is considered to be so important.
As a communications technology, the authors claim several
advantages for face‐to‐face interaction, including the ability to make use of
visual cues, and claim that it enhances the transfer of what they call non‐
codifiable information, or information that relies on a shared background of
knowledge that is not easily transferred (Storper and Venables 2004).
Examples of this type of information are the unwritten business practices that
structure activity within a firm, or the professional practices of a particular
discipline, which are typically learned or transmitted through the process of
socialization.
The building of trust and aligning of incentives is also claimed as an
advantage of face‐to‐face contact, drawing in part on the use of non‐verbal
cues once more and citing psychologist Albert Mehrabian in arguing that
these cues serve to either confirm or contradict the verbal message being
delivered, thus allowing us to more easily understand a speaker’s true
intentions. Another factor at work is that face‐to‐face contact frequently 17
requires some sort of sacrifice on the part of one or more of the parties involved, often in terms of time or money required to travel to the meeting place. This sacrifice serves to develop an implicit understanding among all parties of the importance of the communication involved (Storper and
Venables 2004).
The “screening and socializing” function concerns the development of social and professional networks to screen out potential collaborators and partners. Storper and Venables claim that face‐to‐face contact is usually required to develop and sustain the sort of informal networks that are able to serve this screening function. The motivation function has to do with the changes in behavior that occur when we know we and our work are on display for others to see. The complex of emotions that result from measuring oneself against others in the workplace, the authors argue, serves as an excellent motivator, and this motivation only really occurs in the presence of sustained face‐to‐face contact with others (2004).
As mentioned previously, there is a recognition in parts of the innovation literature that the nature of certain types of knowledge may serve as an obstacle to the transfer of that knowledge, and thus contribute to agglomeration incentives. Clive Lawson and Edward Lorenz discuss this 18
knowledge and collective learning in their analysis of what they call “regional innovative capacity,” (Lawson and Lorenz 1999). The paper centers on the idea that there is not just individual learning by the individual workers in firms, but also collective learning that builds competencies and capabilities, the terms used by the authors to denote the things that organizations have learned to do well. This collective learning builds a sort of tacit knowledge that is embodied in the structure and operating procedures of an organization, and this knowledge is often costly to transfer, as it depends on the structural context of both the firm and the local region to give it meaning. Lawson and
Lorenz draw on previous work in organizational theory to indicate that transfers of tacit knowledge are most easily done when personal interaction, or face‐to‐face contact is possible.
Lawson and Lorenz argue that the process of sharing and transferring tacit knowledge by individuals not only promotes collective and individual learning in a region, but also creates new knowledge. They write, “As we try to articulate what amounts to an intuition or rough idea about a new product or a technology, we are forced to clarify our ideas and to develop new and more adequate concepts or models about the technology we are trying to develop,” (1999). The authors argue that much of the sustained competitive 19
advantage in regional clusters is the result of interactions between individuals
with partially overlapping bodies of tacit and codifiable knowledge, and the
sharing and creation of knowledge that results.
Another important idea, briefly discussed by the authors, is Nelson and
Winter’s notion that there is a path dependency force at work in innovative
activity. The argument is that an organization adopts new knowledge
incrementally, adding onto the existing methods and knowledge of a firm
rather than completely overturning them. Thus, if a firm is to be able to absorb
new knowledge it usually requires existing familiarity with similar ideas, and
therefore the past performance and behavior of an organization determines
which opportunities for learning are available. Lawson and Lorenz use this
idea to point out the importance of knowledge diversity to innovative
capacity, since a wide knowledge base will allow firms to maintain more
opportunities for learning, and hence innovation (1999).
Leiponen and Helfat also discuss the advantages of breadth in the use
of knowledge sources and the search for what they call “innovation
objectives.” At the heart of their analysis is the unpredictability of innovative
activity, and breadth of objectives is used as a hedge against that
unpredictability. If we try more approaches, we have a better chance that at 20
least one of them will pan out into a useful product. With regards to
knowledge, a broader search allows for more possibilities for knowledge
complements, where knowledge from one source not only adds to a firm’s
existing pool but also improves the quality of knowledge that the firm already
possesses (2009).
Laursen and Salter also argue that breadth is important, and focus on the idea of “openness” in considering the role of breadth and depth of knowledge search in innovative firms. The path dependency argument arises here as well, though with a focus on the importance of building up relationships and expertise when trying to incorporate new knowledge into an organization. Laursen and Salter also mention that the source of the knowledge, as well as the content of the knowledge itself, plays a role in how firms are able to make use of it. The strategies and expertise required to interact with universities, for instance, are different from those required to share findings and learn from a private research lab. The difference between knowledge search in radical innovation and incremental innovation is also discussed, and the authors claim that the “newness” of knowledge producing radical innovation tends to put greater importance on search depth over breadth (2006). 21
Rogers Hollingsworth touches on the value of breadth in his article
“The Snare of Specialization,” which discusses the increasing trend towards
specialization, and what he refers to as the “fragmentation of knowledge.” His
argument is premised on the idea that over‐specialization at the expense of
breadth and interdisciplinary cooperation tends to generate knowledge and
ideas that are not especially helpful in addressing real‐world problems
(Hollingsworth 1984). Hollingsworth continues to develop and address these
ideas in a paper on cognitive complexity and major scientific discovery,
writing, “[In our study,] the discoveries were made by scientists who
internalized considerable scientific diversity, who tended to be boundary‐
crossers, and could communicate with scientists in multiple fields,”
(Hollingsworth 2007).
This literature review provides a grounding point from which to begin
talking about agglomeration effects in innovation. Clearly knowledge,
learning, and human interaction are given places of key importance in the
existing innovation scholarship, and it is my intent to elaborate on these ideas
to provide a somewhat different perspective on the interaction between
innovation and agglomeration.
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Theory
Despite a recent upsurge of interest in the subject, innovation is still a
poorly understood activity at best, and there remains a great deal of work to
be done to firmly establish the foundations for further research. It is far
beyond the scope of this project to develop a full, comprehensive theory of
innovation, but there are several ideas that are either neglected by the existing
innovation literature or bear elaboration and modification.
One idea that appears frequently in the innovation literature is the
distinction between radical and incremental innovation, a classification that
exists at the level of individual innovations. Robert Dewar and Jane Dutton
describe radical and incremental innovation in terms of the new knowledge
involved in their creation. Radical innovation, they write, is a “clear
departure” from technology in current use, while incremental innovation is
made up of “minor improvements or simple adjustments in current
technology,” (1986). They place innovations on a continuum of classification
with incremental at one end and radical at the other, with various technologies
spread somewhere in between. This distinction addresses the fact that the 23
scope of innovative activity varies greatly, and that some projects are more
conservative than others.
It is not always clear, though, whether this distinction is useful or even
very accurate. Consider, for example, the development of the personal
computer, or from more recent times, the iPhone. Without a doubt, both of
these products were radical innovations, in that they established completely
new product markets that dramatically changed customer preferences and
ways of doing business. On the other hand, there was very little technology in either of these devices that was truly new or unique. The first successful PC and the first successful smart phone were built of components that were largely already available at the time, with only relatively minor improvements
or modifications made before incorporating them into the larger device. What
we realize from this is that the radical vs. incremental distinction is not always helpful in understanding how innovation occurs, and that other complementary classifications may be necessary.
The examples above point the way to a new, complementary categorization: broad innovation versus narrow innovation. I believe that this
new distinction can be used to help illuminate some important concepts that are relevant to the discussion of agglomeration, and that are generally 24
neglected by the existing innovation literature. The iPhone and the PC do not fit neatly into the radical vs. incremental categorization, since they exhibit
characteristics of both, but these devices are quite clearly examples of broad
innovation that transcends traditional disciplinary boundaries, and requires
the integration of many different sets of knowledge and expertise.
The broad vs. narrow distinction rests on the fact that different innovations show different degrees of internal integration. That is, broad innovations are characterized by a large number of components that interact with one another to produce the effect of the product or process, while narrow innovations are those that are largely self‐contained, and are not primarily the result of integration. It is important to note that, in this context, component means simply a part of some larger system, and so components need not be physical parts of tangible goods. The term can also encompass things like marketing or business expertise. Consider once more the iPhone, whose components included the iTunes store and Apple’s strength in marketing.
These things were just as vital to the success of the iPhone as the microprocessor, antennae, and casing that make up the physical device itself.
The value of making this distinction between broad and narrow innovation lies in the importance of interdisciplinary breadth to developing 25
complex systems with a high degree of internal integration. As systems grow larger or more complex, and incorporate a larger number of components into their operation, it becomes increasingly likely that the knowledge and experience of a single person or a single field of study will be inadequate to solve the problems of integration. The Saturn V rocket that lifted the Apollo astronauts into space is an excellent example of a complex system innovation that would not have been possible without the interaction of a large number of scientists, engineers, and project managers with a variety of different skill sets.
The new categorization provides a framework within which to discuss
the importance of this interdisciplinary cooperation. Interdisciplinary
cooperation is not only helpful in the realization of some innovations, it is an
absolute precondition for them. Narrow innovation does not rely strongly on
this sort of cooperation, but in the case of broad innovation it is a limiting
factor in firms’ ability to successfully complete innovative projects. Without an
existing exposure to a wide base of knowledge, firms may overlook promising
opportunities that do not seem to have bearing on the particular specialties of
their workers. Additionally, even where opportunities are recognized, the
costs associated with coming up to speed with the current state of knowledge
may be prohibitively high, especially with respect to time. Because broad 26
innovations typically involve the integration of large numbers of different components, they are more prone to be affected by these issues.
Interdisciplinary interaction helps to avoid these problems by increasing the opportunities for mutual inspiration and learning. People working within a firm or industry with related, but not identical, skill sets are
likely to share some of the tacit knowledge internal to their respective disciplines or markets, while also having expertise that does not overlap. This generates opportunities for learning that do not incur the costs associated with learning from sources that are external to the firm or region.
The ability to draw on a broad range of knowledge sources is important also to the flexibility of firms. A key characteristic of innovation is that it changes existing markets, often in dramatic ways. The products and technologies available change what is bought and sold, and they also change the ways that we seek to and are able to interact. Thus, in the presence of innovation, markets are fluid and unpredictable. Sabel’s discussion of industrial districts and flexibly specialized firms indicates that in such markets, firms and regions that are able to adapt and change quickly and at low cost are likely to thrive, while firms with less flexibility will struggle to compete (Sabel 1994). The incorporation of interdisciplinary knowledge allows 27
firms to achieve a higher level of flexibility than would otherwise be possible
because it allows them to more rapidly shift into new markets and product
lines that demand different sets of knowledge. Firms that rely heavily on a
single specialty run the risk of being effectively locked into a particular
technology or set of business practices that may fall behind the current state of
the art.
I believe these factors help to offer more insight into why
agglomeration seems to be so advantageous to innovation within and between
firms. Face‐to‐face communication between individuals with partially
overlapping knowledge provides an excellent mechanism for sharing
information that leads to more innovation opportunities and better solutions,
and there is no better way to facilitate this communication than to bring
people into close proximity with one another. Likewise, face‐to‐face
communication allows coordination between individuals of different
expertise, and this provides opportunities for incorporating knowledge and
practices into an innovation through recruitment rather than sharing or
learning. As the number of skilled workers in a region increases, the
opportunities for spontaneous or planned interaction and coordination 28
increase as well, and so we expect more people to learn from and work with
one another (Storper and Venables 2004).
The importance of interaction across disciplines and professions has
been under‐emphasized. Learning certainly takes place between individuals
within the same profession, or in the same field of study, but the body of
knowledge that has potential for exchange in such circumstances is relatively
small. For individuals in different, but related, disciplines, the opportunities
for exchange of knowledge are much larger. Additionally, the need for
coordination between people with radically different skill sets deserves more
attention. This includes especially the interaction between technical innovators
and market innovators, without which the leap from idea to innovation would
be made much more difficult.
Another topic of key importance to innovative agglomeration is the
time‐sensitivity of innovation. More so than any other type of production or economic activity, innovation is subject to intense time pressures that have a strong impact on the shape of incentives. There is a price‐premium associated with being first to market with a new product. Innovators expect that the fruit of their labor will give them an edge in markets over their competitors if they are able to develop them faster than the competition. This creates 29
extraordinary incentives to reduce the time needed to innovate, and drives the cost of time upward.
This fact has a positive effect on the incentives for agglomeration. The costs associated with living and doing business in a densely packed region, like commuting costs and high rents, may be small in comparison to the time savings that are generated by having a large supply of knowledge labor and other inputs close at hand. It may be important enough to bring in a new scientist or engineer today rather than three weeks from now that we are willing to put up with high wage, rent, and transportation costs in an urban or industrial region. This also helps to address questions that arise in the agglomeration literature about the relationships between the costs of clustering and the size and location of cities.
It is important to recognize that the time‐cost of innovation will vary from industry to industry or region to region, especially at the country level.
Consumer electronics companies are probably faced with greater time
pressures to innovate than tire‐makers or computer numerical control (CNC)
machine manufacturers, for example. It may also be that the pace of demand
for innovation varies on account of external demand shocks. Military
contractors, for instance, are probably under much greater pressure to 30
innovate, and much more willing to incur high‐costs to make time savings,
during wartime than in times of peace.
With these considerations in mind, I propose a 2x2 matrix for the purpose of classifying innovation at the sector level with breadth on one dimension and speed of innovation on the other:
Broad Narrow
Fast Consumer electronics Semiconductor fabrication
Slow Military aircraft CNC machines2
This matrix gives us a framework within which to discuss certain factors
relevant to innovation agglomeration across whole industries.
Breadth, as it is used here, refers to the extent to which a large variety of knowledge, skills, sets of practices, or other innovative resources, is needed
2 While CNC machines are systems that incorporate knowledge from several disciplines, leading us to think that they should perhaps belong in the broad category, innovation with respect to CNC machines is actually quite narrow. Because the technology is by now quite mature, the opportunities for innovation are limited, and occur along only one or two disciplinary paths. 31
to realize an innovation. Broad innovation requires the input of large numbers
of people, a diverse set of professional disciplines, and a wide range of
knowledge. Narrow innovation, on the other hand, needs only a relatively small number of people, the input of one or two key disciplines, and a more restricted body of knowledge.
The pace of innovation dimension addresses specifically the rate at which firms and people must innovate in a given sector to remain competitive.
For the sake of innovation analysis in this paper, this distinction is drawn only for the pace of innovative activity, not the rate of production or exchange. As the analysis of the financial services industry case study will show, the speed of innovation in a sector may not match the speed of other activities in the industry.
The dominant dimension of the matrix is the broad/narrow classification, as it encompasses the need for interdisciplinary cooperation.
The need for face‐to‐face, cross‐disciplinary interaction is the primary motivator for agglomeration of innovation, since there is no easier way to facilitate contact than to bring people close to one another. The speed dimension is has a weaker effect on agglomeration than breadth, but
nevertheless it plays a significant role. If innovation is occurring at a fast pace, 32
firms must be able to secure innovative resources, including human resources, quickly and at low cost. Combining these two dimensions, we expect agglomerative pressure to be strongest in sectors where innovation is broad and fast, and weakest in sectors where innovation is narrow and slow.
Considering the examples from the table above gives us a sense of how
to classify innovation in different sectors. Consumer electronics such as smart
phones and tablet computers incorporate a variety of different functionalities
and are marketed for a large number of different purposes. This requires
coordination of a large number of technical and non‐technical personnel,
incorporating many different skill sets. Additionally, innovation in this sector
occurs very quickly, especially given the strong consumer demand for the
latest and greatest devices.
Semiconductor fabrication firms, which supply sectors like the
computer and consumer electronics industries, must also innovate rapidly, in
part to match the frenetic pace of innovation of their customers’ industry. On
the other hand, their products are more well‐defined, encompass a narrower
set of technologies, and are sold in a fairly mature market. This means that the
range of expertise and knowledge typically involved in innovation in this
sector is comparatively narrow. 33
In contrast, innovation in military aircraft is, at least at present, slow
and broad. The pace of innovation in this sector is primarily a function of government demand, and since the end of the Cold War this demand has
dropped dramatically. The pressure to provide new ideas and new designs on a tight schedule simply is not there. Nevertheless, what innovation there is still requires a diverse set of expertise to accomplish, and so we classify innovation in military aircraft as broad and slow.
Innovation in CNC machines, given that it is a mature technology with a relatively small customer base, is slow and narrow. A small range of people with specific expertise are involved, and there is little reason for the makers of these machines to push strongly for a faster pace of innovation.
From these examples, we get a better sense of the differences between
the four types of innovation described by the breadth‐speed matrix. In
particular, we realize that broad innovation is strongly dependent on the
interaction of people and firms that can bring together different perspectives,
knowledge and practices. Without interaction across a wide set of experiences and competencies, broad innovation simply is not possible.
I believe that these interdisciplinary interactions are the driving force behind agglomeration of innovative firms and individuals, especially as the 34
technologies we use grow increasingly complex. Where breadth is required to
realize innovative success, there must be a way to bring together the different
sources of required knowledge and skills. Whether innovation takes place
within a large firm, where breadth and a variety of professions and fields of
study are internalized, or small firms embedded in multidisciplinary
knowledge networks where breadth is drawn from the outside, it is vital that
there be a critical mass of people with different sets of expertise.
Next I will consider several case studies that were conducted in order
to determine and illuminate factors relevant to innovative agglomeration.
These case studies provide empirical grounding for the theory elaborated
here, and the unique elements of each sector that will be discussed help us to
explore commonalities and differences in innovation in different contexts.
35
Case Studies
Financial Services
Innovation in the financial industry presents us with an interesting subject for discussion. One distinguishing characteristic of innovation in the financial services sector is that it is most frequently system innovation, requiring not only the development of ideas for new financial instruments, but also the collection of capital, market information, and customer contacts. The integration of these things results in the systems that represent true innovation in financial services. We therefore expect that the particular agglomerative forces affecting innovation in the financial industry will differ from industries that tend to generate a higher share of product innovations.
Additionally, the financial industry is subject to a great deal more regulation than most industries, at all levels of firm operation and all levels of government. The course and pace of financial industry innovation is dramatically affected by the state of the financial regulatory environment, and
so as we consider case studies in the financial industry we must be mindful of
how government policy shapes innovation in ways that might not be readily applicable to other industries. 36
We will first consider the case of the emergence of the high‐yield bond
market in the late 1970’s and early 80’s. The use of high‐yield “junk bonds” to finance leveraged buyouts and corporate takeovers will also be considered here because it demonstrates the often continuous nature of the innovative process. The term junk bond refers to any bond that is rated as lower than
investment grade by the major rating agencies. These “junk” ratings indicate
that the rating agencies believe there is a higher risk of default, and therefore
junk bonds usually offer higher yields to attract investors in spite of the higher risk. As Glenn Yago points out in his book Junk Bonds, an overwhelming
majority of large companies, if they were to issue bonds, would be rated
below investment grade (1991). The term therefore refers to a potentially
massive segment of the bond market.
As Yago writes, junk bonds “By definition...have existed ever since the
bond rating system began in 1909,” (1991, 18). The story of innovation we are
interested in, however, does not get its start until the 1940ʹs, when W.
Braddock Hickman released a study concluding that high‐yield junk bonds performed better in the long term than the investment grade securities, which
was later corroborated by T.R. Atkinson using data from the 1940ʹs through
the 1960ʹs (Yago 1991; Bruck 1988). These studies began the transition of junk 37
bonds from true junk status to the superstar financial instrument of the 1980ʹs.
The story next turns to Michael Milken and the investment bank for which he
worked, Drexel Burnham Lambert.
Milken had an intense curiosity about high‐yield bond trading, and at
Drexel he was, for the most part, encouraged in his development and
application of junk bond trading strategies. It was Milken who would bring
junk bonds into the mainstream and remove much of the stigma associated
with trading in bonds that were unable to achieve investment grade rating. To
do this, he needed access to capital with which he could test his ideas, as well
as clients to whom he could make his pitch. Both of these he found working at
Drexel, where he was afforded a degree of autonomy in pursuing his goals not
typically seen in established Wall Street firms (Bruck 1988).
This autonomy stands out as one of the most important features of
Milkenʹs innovative success in trading junk bonds, as it allowed him to freely
develop relationships with customers and co‐workers at Drexel, and pursue
trading strategies with fewer restrictions on the level of risk that could be
assumed. Milken eventually established a sort of firm within the firm at
Drexel that he used as the vehicle for his investment philosophy. Parallels can
be drawn between Milkenʹs early experiences at Drexel, when he was given 38
free rein to test his theories on the trading of junk bonds, and the culture of some of Silicon Valleyʹs innovative firms which were known for their informal work environments, lack of hierarchical structure and emphasis on results.
Milkenʹs trading unit grew by leaps and bounds year after year through the late 70ʹs and early 80ʹs and as his success became more widely known his client list became larger and larger. We are then brought to the next
innovation, the use of junk bonds to finance leveraged buyouts and corporate
takeovers. The precise credit for these innovations is difficult to place. Bruck
describes leveraged buyouts and hostile takeovers as flowing naturally from
the debt‐heavy strategies Milken was pursuing with his clients, the issuers of
most of the junk bonds (1988). Yago, on the other hand credits Drexelʹs
corporate finance department, based in New York rather than in Beverly Hills
with Milkenʹs unit, with the idea to finance takeovers with junk, and makes
the claim that Milken “initially opposed the idea of pursuing the takeover
market,” (1991, 27). In any case, the leap from large‐scale junk bonds trading
to financing leveraged buyouts and takeovers with junk was a significant
system innovation for Milken and Drexel. The company, and Milken’s unit in particular, coordinated the offering, sale, and marketing of the junk bond 39
financing, and also helped to find suitable targets and vehicles for takeover
(Bruck 1988).
The story of high‐yield bonds, leveraged buyouts, and hostile takeovers is
one that speaks to the innovative power of breaking or flouting industry norms, and the value of risk. Drexel’s fluid culture at the time when Michael
Milken’s junk bond unit was developed set free the creative and risk‐taking energies of the men and women responsible for Drexel’s rise in the 1980’s.
Sometimes, however, it is difficult to pinpoint cultural elements within firms, or individual personalities, as the source of particular innovations. In these cases, it is often found that structural or regulatory factors play a major role in promoting innovation, as the cases of high‐frequency trading and credit default swaps illustrate.
High‐frequency trading has in the past decade become one of the most important innovations of the US stock market. In the usage of the term here, high‐frequency trading will be taken to mean that trading which occurs on the millisecond scale, with multiple trades occurring every second. Such trading has been a very recent development, with the SEC’s Regulation ATS 3
3 Regulation ATS is the common name for the SEC regulations published as “Regulation of Exchanges and Alternative Trading Systems.” 40
promulgated in 1998 finally allowing electronic exchanges to perform many of
the same functions of traditional stock exchanges like the New York Stock
Exchange (Duhigg 2009). The origins of high‐frequency trading, however, go
further back and of course depend on the establishment of the first electronic
communications and trading networks.
Perez points to several of these early networks, and the regulations
restricting them, as important steps on the road to high‐frequency trades.
Instinet, established in 1969, was one of the first communications networks
that allowed for sale and purchase orders of stocks to be placed anonymously,
and in 1971 Nasdaq, the first electronic stock exchange, opened for trading.
The SEC ban on fixed minimum commissions paved the way for the high
trading volumes that would be essential to high‐frequency trading strategies ,
which often rely on differences of only pennies in the buy and sell prices of
stocks to turn a profit. With Regulation ATS in 1998, the electronic
communications and trading infrastructure that now forms the foundation on
which high‐frequency trading is based was fully established (Perez 2011).
High frequency traders currently tend to cluster for reasons of
information flow. In part this has to do with the communications time delays
introduced by distance from exchanges, which are of crucial importance to 41
high‐frequency trading algorithms often operating on the millisecond time
scale. This is the source of the phenomenon of co‐location in the high‐
frequency trading industry, where we see many firms paying high premiums
for server floor space in the buildings where the exchange servers are located.
Because there is a time‐lag associated with transmitting electronic signals over
longer distances, this allows these firms to make trades before more distant
rival firms are able to even place their orders (Schmerken 2009).
Credit default swaps are another recent, and influential, financial
service innovation. Credit default swaps are financial derivatives used to pass
on the risk of a financial instrument from one party to another. In a typical
credit‐default swap, the so‐called buyer of protection makes regular
payments, akin to insurance premiums, to the seller of protection, who is then
obligated to pay a pre‐determined amount to the buyer on the occurrence of a
particular financial event, usually default on the basis instrument (Zabel 2008).
The first credit‐default swaps were created by a team of bankers working for
J.P. Morgan who were looking for ways to free their firm from the burden of
risk and capital requirements associated with large loans to major
corporations. By buying credit default swaps, the bank could not only pass the
risk of default on the loans to a third party but also reduce its required capital 42
reserves. Thus, more capital could be made available for investment, allowing
higher profits, while both reducing risk exposure and unpredictability
(Guerrera 2011; Philips 2008).
One of the most important aspects of the emergence and spread of this
innovation is that a major part of the attractiveness of credit‐default swaps to
buyers and sellers was that it was traded “over the counter” and was therefore not regulated as heavily as exchange‐traded instruments. In this way, the regulatory environment created both opportunities and incentives for innovation. Capital reserve requirements gave banks strong incentives to make credit default swaps to move the risk of major corporate loans off their books, while the absence of specific regulations dealing with such swaps created the opportunity for change. 43
Automotive Industry
Agglomeration of the American automobile industry in southeastern
Michigan goes back to the turn of the 20th century, when the innovative center
of the industry shifted away from the northeastern United States. As
Rubenstein explains, the reasons that the industry relocated to Michigan had a
great deal to do with the existing industrial framework of the region.
Rubenstein identifies three major contributing or enabling industries to the
development of early automobile manufacturers: gasoline engines, carriage
makers, and bicycle manufacturers. Early clustering in the northeast, he
argues, was not only the result of the large concentration of customers in cities
like New York and Boston, but also the existing expertise of bicycle
manufacturers, which were already clustered in the region (1992).
The shift from the northeast US to southeastern Michigan did not take
long, however, as by 1904, 42 percent of all American automobiles were
produced within the state’s borders. Rubenstein attributes this in part to the
existing network of firms and skilled workers building gasoline engines and
traditional carriages. An example of the importance of this network of firms is
provided by the response to the Olds Motor Works fire of 1901, which 44
destroyed much of the productive capacity of the company. To compensate,
Olds began subcontracting large amounts of work to independent machine shops in Lansing in order to keep production stable (Rubenstein 1992).
These machine shops, and others like them throughout southeast
Michigan, pre‐dated the establishment of the automobile manufacturers, and provided much of the early production capacity and innovative expertise for
Michigan firms. Two of the early successful manufacturers, Ford and Olds, were themselves mechanically adept, and it is hardly accidental that this was the case. Other firms also got their start in the machine shops of southeast
Michigan, notably Dodge Brothers in Detroit, which exists today as part of
Chrysler. The shop had started in 1900 with the intention of doing machining work for other industries, but the company soon went to work as a contractor for Olds making parts for automobiles. Ultimately, the brothers used their expertise in making parts for Olds to start their own company making cars
(Rubenstein 1992). James Ward provides further evidence for the agglomeration benefits of the local machine shops, writing that the Packard
Motor Car Company’s decision to move to Detroit was based in large part on the presence of these machines shops and the city’s ready access to lumber
(Ward 1995). 45
Another important element of the automobile industry’s location in
southeast Michigan was the relatively free availability of capital there.
Southeast Michigan, and Detroit in particular, had a large number of wealthy
individuals looking for investment opportunities at the turn of the century.
These individuals, unlike conservative banks serving the firms of the
northeastern US, were willing to take on great risks to invest in automobile
manufacturing companies, and showed a tendency to become involved in the
management of the companies themselves. An example of this willingness to
invest in risky ventures is the Ford Motor Company. Henry Ford had in fact started two other companies for the purpose of making cars prior to the founding of the ultimately‐successful Ford Motor Company. One of these eventually became the Cadillac Automobile Company, while the other was simply a commercial failure (Rubenstein 1992).
The dramatic concentration of automotive production in southeast
Michigan in the first ten to fifteen years of the 20th century, Rubenstein argues,
was largely the result of the dominance of the Ford Motor Company in the
industry during that time period. Ford’s total US market share in 1913 came to
just over 46 percent, with other southeast Michigan firms responsible for
another 22 percent. No other company came close to Ford’s level of production 46
at this time, and it would be years before Ford’s dominance was ever seriously
challenged (Rubenstein 1992).
Interestingly, the development that ultimately shifted the production of
cars from southeast Michigan to other parts of the country was the
phenomenal success of Ford, which found itself unable to fill all of its orders
with the facilities on hand in 1912. Prior to this, discussions had taken place
within the company concerning the possibility of establishing branch
assembly plants throughout the continental US to better serve a greater
number of markets more cheaply. Parts were to be manufactured at the home
plants in Michigan, while final assembly of cars was to take place at branch
plants close to the final customers. The policies were finally implemented in
two phases, one between 1913 and 1917, and another from 1923 to 1932,
during which many existing plants were replaced with newer, more modern
facilities. It is important to note for the purpose of discussing innovation,
however, that the headquarters and design centers of the southeastern
Michigan firms remained primarily in Michigan (Rubenstein 1992).
Another local source of industrial expertise was the carriage‐making
industry in southeast Michigan. Several well‐established carriage producers
existed in Flint and Detroit at the turn of the century, including the Studebaker 47
Brothers company, and the Durant‐Dort Carriage Company founded by
William Durant, who would go on to found General Motors.
Durant entered the automobile industry by acquiring Buick in 1904,
which had changed hands several times after failing to become profitable.
With a successful carriage‐making company in place with infrastructure to
support it, he began converting his companyʹs production from carriages to
automobiles and the parts required to manufacture them. Durant continued
acquiring new firms as well, picking up the Olds Motor Works, the Oakland
Motor Car Company (later called Pontiac), as well as Cadillac, which were
reorganized under the holding company General Motors.
Durant was eventually pushed out of GM’s management in 1910 on
account of the massive debts he had incurred to finance the company’s string
of acquisitions. By the next year, Durant had established several new
companies, including the Chevrolet Motor Company, which he would
ultimately use to take back control of GM in 1916. GM would then expand
again, building several new plants in Flint and Detroit before Durant was
forced out once more in 1920. After this time, the DuPont company took
control of GM’s management on account of their ownership of more than a
quarter of existing GM stock (Rubenstein 1992). Rubenstein claims this to be 48
an important moment for the geography of the American auto industry,
saying “DuPont’s refusal to move GM production to the east in the early 1920’s
marked the last occasion that Michigan’s dominance of automotive production
was seriously challenged,” (1992, 77).
The story of Chrysler is perhaps a more interesting one for innovation
agglomeration because it offers insight into the choices of a firm entering the
business after a strong agglomeration had already been established. Walter
Chrysler had worked for Buick and eventually was appointed its president,
but he ultimately resigned and went to work for Willys‐Overland as its
executive vice‐president. During this time he gained control over the Maxwell‐
Chalmers Company, and established the Chrysler Motor Corporation, which
took over Maxwell in 1925.
One of the most interesting things about Chrysler’s establishment was
that it had roots outside of Detroit through its connections to Willys‐Overland,
which had the country’s largest automotive factory, located in Elizabeth, New
Jersey. Chrysler, however, chose to build itself around Maxwell‐Chalmers in
Detroit, and brought the engineering team of the New Jersey Willys plant to
Detroit to develop cars for Chrysler. The company then acquired the Dodge
Brothers company in 1928, which fully established them as a Detroit firm and 49
a force in automotive production. Its market share increased to 25 percent by
1933 and the company produced cars exclusively in Michigan until 1932 when
it purchased a plant in Los Angeles to serve the west coast market (Rubenstein
1992).
Southeast Michigan, and Detroit in particular, had become a center of
industrial activity decades before it began its rise as the center of the American
automobile industry. As Thomas Sugrue writes, Detroit had a “diverse, largely
regional manufacturing base,” which was aided by the availability of water
transport along the Detroit River and into the Great Lakes, with major
industries like food production and metal working established in the city
(Sugrue). Michigan as a whole had become wealthy on the backs of major resource‐based industries, specifically lumber, copper, and iron, and according to Rubenstein, Detroit “became the largest producer of iron ships and a national center for a variety of other iron products,” (1992, 39). Rubenstein argues that it was this wealth, and the desire to put it to use making greater wealth, that provided automakers in southeast Michigan an early advantage in obtaining capital. The Princes of Griswold Street, as the major Detroit financiers of the auto industry would become known, enabled the early 50
automakers like Ford, Durant, Buick and Olds to get their start (Rubenstein
1992).
Early experience with gasoline engines and other machinery in farming
and shipping applications also gave the Michigan firms an advantage, as they
had a pool of skilled labor to draw from when it came time to adapt the
gasoline engine to power motor vehicles. Detroitʹs shipyards had adopted
gasoline engines at an early stage because they offered significant power
advantages over other available motors at the time. Ransom Olds, of
Oldsmobile fame, gained much of his experience with gasoline engines in his
fatherʹs machine shop, building engines to power farm equipment (Rubenstein
1992). Henry Ford was another prominent automaker with significant
machine‐shop experience.
The story of the founding of Chrysler centers on the development of the
Chrysler six‐cylinder car by the engineering team led by Fred Zeder, Carl
Breer and Owen Skelton, who had begun working together at Studebaker
(Curcio 2000; Hyde 2003). Walter Chrysler, having taken over operations at
Willys‐Overland as executive vice president, brought Zeder, Breer and Skelton
on board to solve the design troubles plaguing the Willys models of the time. 51
This team was ultimately tasked with the creation of a new model car, named
the Chrysler, which was to be the flagship of the Willys brand.
The design and construction of this car would take place at the
company’s Elizabeth, New Jersey facility, at the time the largest automotive factory in the country (Rubenstein 1992). Chrysler relocated from his home in
Flint, Michigan to New York, to be closer to the operations of the company. In
New Jersey, Zeder, Breer and Skelton would work on the Chrysler, a car with a number of innovative design features (Curcio 2000; Hyde 2003).
At the same time as he was acting as the executive vice president to
Willys, Chrysler had taken charge of operations at Maxwell‐Chalmers, another struggling auto company, located in Detroit. Initially placed at the head of a reorganization committee in an advisory role by the financial backers of
Maxwell, Chrysler was eventually given greater control and a package of stock options that gave him a significant stake in the firm.
In 1921, Willys‐Overland went into receivership, and the Elizabeth property was sold at auction to William Durant, the founder of General
Motors who had been forced out of the company and started his own firm,
Durant Motors. The sale included the plans for the new Chrysler car, which was renamed the Flint, for the city of Flint, Michigan where it was eventually 52
produced. Nevertheless, Chrysler wanted a car from his engineering dream
team, who had started an independent engineering consultancy firm at
Chryslerʹs suggestion, known as ZSB for the initials of the three partners.
ZSB continued to work on designs for a new engine and automobile in
the Elizabeth plant, under a lease agreement with Durant Motors, the plant’s
new owner. These designs were owned by the ZSB firm itself, and offers were
made by other companies, notably Studebaker, for the purchase of the rights
to build the car. Having left Studebaker with some degree of ill will after a
dispute over design authority, and in light of their previous association with
Walter Chrysler at Willys‐Overland, the partners of ZSB decided the car
would best be built under Chrysler’s direction. At the time, Chrysler was still
acting as the head of the Maxwell‐Chalmers company, which was in the
process of phasing out Chalmers production due to lack of profitability. This
provided the opportunity for the new ZSB car to be produced in the newly‐
emptied Chalmers plant in Detroit (Curcio 2000; Hyde 2003).
Packard, one of the most successful luxury car manufacturers in the
world until its demise in the 1950’s, was another early car maker with its roots
outside of Michigan that eventually relocated to Detroit. The company was started in 1899 in Warren, Ohio with the introduction of the Model A by 53
brothers James Ward and William Doud Packard, and met with early success
on account of the car’s solid construction and performance. Having demonstrated their ability to build a reliable, capable automobile, the brothers
were able to attract the interest of Henry Joy, who invested $25,000 in the
company in 1900. Other wealthy Detroit businessmen would soon follow suit
with an additional $500,000 in capital, and plans for dramatic expansion were
drawn up in 1902 in which the company would relocate to Detroit to be closer
to its financiers and the city’s other industrial interests (Ward 1995).
Another well‐known independent car manufacturer, Studebaker, gives
a different side of the agglomeration picture. Studebaker was one of the few
companies with long term success in the automobile industry that did not
ultimately locate in Michigan. Based instead in South Bend, Indiana, where
the family had settled in the 1840’s, Studebaker was originally a wagon and
carriage manufacturer, and grew to be among the most successful in that
industry (Critchlow 1996). South Bend’s location in the Midwest near Lake
Michigan made it an attractive location for manufacturers. The St. Joseph
River connected the city with Lake Michigan, and several railroads also ran
through the town, providing vital transportation links to suppliers and
customers. 54
Studebaker began a slow shift into the automobile industry in 1902, and
jumped headlong into the field in 1908 with the acquisition of EMF, another
early manufacturer with operations primarily in Detroit. The EMF acquisition
improved Studebaker’s production capacity, and gave it a solid engineering
team, vital to the company’s success in an era when the pace of innovation was
unusually fast. In 1925, a new plant in South Bend was built as an attempt to consolidate manufacturing and engineering operations in one location. By this time, as Critchlow writes, Detroit was already “…the center of auto manufacturing in the nation,” (1996) and the consolidation of operations in
South Bend, rather than a shift to Detroit centered around the facilities that had been originally obtained in the EMF purchase, marks a key decision for the company’s future.
As noted above, Studebaker had solid engineering talent in the late teens and early twenties. Zeder, Skelton and Breer contributed engineering ability that kept Studebaker in the running as a major manufacturer. After the departure of this trio, however, Studebaker was left without strong engineering ability for much of the decade, and Critchlow argues that the team’s design of the Studebaker Six prior to leaving for Willys‐Overland was 55
the primary reason that Studebaker was able to keep its market share in their
absence (1996).
The company would rebuild its engineering department in later years,
and this would lead to the introduction of the groundbreaking Champion
model in 1939, the sales of which would move the company to the position of
largest independent manufacturer outside of the Big Three (Ford, GM and
Chrysler). As time went on, however, Studebaker would become increasingly
weak relative to the Big Three in terms of capable engineers, the core of the
innovative ability of any auto maker. After merging with Packard in 1954, the
new Studebaker‐Packard had forty engineers in its employ, compared to
Ford’s 13,000, with Studebaker engineering salaries between 25 and 50% less
than those paid by the Big Three. This, caused perhaps in part by the relative
isolation of the company in South Bend, would make it difficult to bring in
engineering talent to keep the company apace with innovation in the industry
(Critchlow 1996).
Clearly there are many forces at work in the agglomeration of
automobile production in Detroit. Early agglomeration centered on the
existence of complementary industries and the availability of capital. The
great value of the existing machine shops and gasoline engine makers in 56
Detroit was not that their products could immediately be used in the manufacture of cars, or even that their productive capacity could rapidly be transferred to making components for cars, which was indeed a significant
advantage. The most important thing was that the people making them had the technical understanding necessary to adapt existing technology to the
needs of automobiles. What we see in the early concentration of automotive
production in Detroit is that the most innovative, reliable, powerful,
comfortable, and affordable vehicles were consistently made by the Michigan
firms. The industrial foundations of Detroit gave its automakers experience
that gave them an advantage in efficient design and production.
Moreover, once these firms had become successful, their success would
continue to attract the best and brightest minds from the relevant technical
fields. Certainly by 1930, there were few places for a budding engineer with
an interest in cars to locate other than Detroit. Additionally, the availability of
industry‐specific capital resources in the area would continue to make
collocation desirable. In the case of Chrysler, it appears that a combination of
these two factors were at work. Chrysler already had a star engineering team
working in New Jersey, but the level of support they could be offered could not be matched outside of Detroit. 57
Perhaps the biggest factor in Chrysler’s location to Detroit was the
availability of the Chalmers plant, sitting nearly idle before the beginning of
Chrysler production. The Maxwell‐Chalmers company was near failure at the
time of the Chrysler takeover, and thus its capital assets became cheap,
relative to other available options. It is not only the success of companies in an
agglomeration, therefore, that makes innovation possible, or that makes
agglomeration attractive. When firms in the same sector co‐locate, the failure
of one firm may become a boon to the next as its physical capital becomes
available to other firms cheaply. In new, innovative industries this can be particularly important, since it has the effect of reducing the costs of entry for
startup firms, and the costs of expansion for existing, successful firms.
The Packard case illustrates the role of cheap capital in agglomeration of innovators. Innovative firms virtually by definition are capital‐hungry, and so it is vital to the success of any innovative enterprise to be able to easily gain
access to it. It is advantageous for firms to be physically close to the source of
their capital resources, in case they should ever be in the position of needing
to secure more. It is also the case, though, that once investors in a particular
city or area have warmed to the idea of an industry, other innovators in the
field may find it easier to secure funds from those same investors. This was 58
almost certainly the case in Detroit, where a massive amount of investment
was made in the auto industry at the turn of the century once several early successful firms had been established.
Studebaker is an interesting example to study because it shows both sides of the agglomeration picture. On the one hand, Studebaker probably benefited from its location in the Great Lakes region, especially since South
Bend was less than 10 miles from the Michigan border. Many of the same transportation links that made Detroit advantageous for the manufacture of cars, especially in the early stages, were also available to Studebaker in
Indiana. The company also had an advantage in the early stages of the industry on account of its strong carriage‐making expertise.
What the company lacked was a city with a strong network of skilled labor, and the industrial experience of Detroit’s manufacturing industries. At the turn of the century, Detroit’s population was approaching 300,000 people, with a metropolitan area population in excess of a half million. South Bend, by contrast, had fewer than 40,000 and did not have nearly the industrial diversity that Detroit could offer (Census 1998). Having been previously successful in the carriage industry, and able to draw on its own financial resources to fund its automotive enterprise, Studebaker might have survived 59
as a major player in the modern automobile industry if it had also had access
to the human capital of the Detroit firms. A lack of engineering talent in the
1930’s and 1940’s would put Studebaker behind the Detroit manufacturers in
innovative ability, and ultimately left them unable to compete.
The story of the Zeder, Skelton, Breer engineering team is interesting
because it provides a clear example of a team that functioned as a whole
greater than the sum of its parts (Breer 1995). Zeder and Breer met in the
apprenticeship program of the Allis‐Chalmers Manufacturing Company, a two‐year program that recruited graduates from what were the top mechanical engineering programs of the day. The two engineers became
friends during their apprenticeship, and this connection ultimately led Zeder
to hire Breer into Studebaker, where he had become chief engineer at the
Detroit facility. Zeder had previously hired Skelton into Studebaker from the
Packard Motor Car Company, also located in Detroit.
Interestingly, the association for which the ZSB team would become most famous, that is their work with Walter Chrysler, can also be traced back
to the Allis‐Chalmers apprenticeship program. In 1919, Don Devor was the
head of production at Willys‐Overland, but in 1909 he had been an
apprenticeship classmate of Breer and Zeder. This connection prompted Devor 60
to suggest to Chrysler, who we remember was executive vice‐president of
Willys‐Overland at the time, that he bring the ZSB team on board to improve
the quality of engineering at the Elizabeth, New Jersey plant (Breer 1995).
The experience of the ZSB team at Studebaker in Detroit provides
several examples of the benefits of innovative agglomeration. One
illuminating example has to do with the efforts to improve on existing
Studebaker designs, which had fallen behind in performance and reliability on
account of the companyʹs lack of engineering expertise prior to the
establishment of the engineering department under Zeder. The Studebaker
engine under production at the time Breer joined the company suffered from
frequent bearing failures. The supplier for the bearings, the General
Aluminum and Brass Company, was located near the Detroit facility, and
Breer and his team visited their labs in an effort to determine the source of
manufacturing errors. During their visit, Breerʹs team discovered their
supplierʹs facilities and laboratory practices to be in poor shape, which, as
Breer writes “[forced] the supplier to recognize the importance of proper
laboratory technique which proved of great value to their future,” (Breer
1995). 61
We see here the dissemination of best practices through interactions
among people in innovative industries. Some of the advantages of the formal
engineering training of the ZSB team were brought to the labs of the General
Aluminum and Brass Company, effectively for free. These practices could
obviously be applied to the work that the company did for any customer, and
thus the exchange of information provided positive externalities for all
members of the agglomeration.
Breer later makes the claim that research into engine noise and
machining techniques by the Studebaker engineering group “forced the
development of better production machines,” (1995, 40). This points out a
unique element of innovation in the agglomeration story. Innovative firms, by
definition, are creating new products and new ways of doing things. They will
therefore be likely to require their suppliers to make technological advances as
well, to provide for the needs of the new products. Thus, the innovation itself
provides the impetus for modernization and innovation in other firms,
particularly local suppliers. This makes the local area more attractive for
future innovators, because the cutting edge is already in place there on the side of suppliers. 62
Another example Breer discusses has to do with research into what are
called universal joints, which are used to transmit rotary motion along a shaft
capable of bending. The old Studebaker designs used a “Spicer” universal
joint, which in the design of their car had several performance problems. In
researching the solution to these issues, the engineers at Studebaker were
presented with a novel design by a man named Flick, who had a small
machine shop in the area. The development of this design for commercial
purposes, Breer writes, “resulted in the build‐up of an important supplier
company—the Universal Products Company,” and we see again the influence
of the existing machine expertise in Detroit on the ability of car makers to
innovate there (Breer 1995).
Early carburetor design is yet another area in which Breer discusses a
great deal of collaboration. A carburetor is a device used to feed fuel and air to
an engine, and is designed so that different amounts of fuel and air are drawn
in depending on the position of the throttle or accelerator. Modern cars almost
exclusively use fuel injectors, which serve the same purpose but function
differently, but fuel injector technology was at this early time in the industry
still in the developmental stages. Improvements in carburetor design were of vital importance to early automakers because they held the potential to 63
increase both the power and fuel efficiency of their cars’ engines. More
consistent and finely tuned carburetors wasted less fuel and provided a more
uniform burn.
As Breer writes, “Carburetors, in those early days, were worked out
and adapted to car requirements by trial and error.” It was difficult, therefore, to reliably test and tune carburetor designs in a laboratory setting. Enter F.H.
Ball and F.A. Ball, a father‐son team that had established themselves as successful high speed steam engine manufacturers in New Jersey. Ball and Ball would eventually locate to Detroit and establish a lab there where they designed and tested new carburetors. Breer worked with them while at
Studebaker in an effort to improve the Studebaker car’s performance, and was impressed with their laboratory facilities and testing practices. The Ball team studied carburetor performance based on the fuel‐to‐air mixture ratio, one of the most important parameters to ensuring complete fuel burn, and this was a practice ahead of its time in the early automotive industry. Ultimately it would be adopted by Breer and the engineers working with him at Studebaker, after several personal visits and consultations by Breer at the Ball lab (Breer 1995).
Once ZSB had returned to Detroit under Maxwell‐Chalmers, soon to be
Chrysler, instances of innovative collaboration with local and regional 64
innovators and suppliers continued. A notable example is the development of improved cam tappets, an integral part of the engine needed to prevent bending forces on certain components. These tappets needed to be especially hard to prevent damage to their surfaces over the life of an engine, and the steels used by Chrysler left little room for improvement in this regard.
According to Breer, “One day a Mr. Wilcox of the Wilcox‐Rich Company at
Saginaw, Michigan, came in with an experimental tappet design that looked very attractive,” (Breer 1995). The tappet used two different metals in a two‐ component design that provided much‐improved hardness at the point of contact, while not sacrificing the advantages of the original steel of the part.
While the new design had advantages, early production tests showed that the delivered parts were failing prior to their expected lifetimes, and there was discussion of abandoning the design altogether. At this point, a meeting with the production team, the engineers and the supplier Wilcox was called, where it was determined that the design would have to be dropped unless a
Chrysler engineer and a handful of assistants would be allowed to work in the
Saginaw plant to get the bugs out. This was allowed, and generated a superior product with none of the failure problems present in the first production runs
(Breer 1995). 65
These cases and examples provide insight into how innovation in the automotive industry played into incentives for agglomeration. We turn next to
consideration of semiconductor and computer industry innovative
agglomeration, and will return to analysis of the automotive industry in a later
section. 66
Silicon Valley/ Route 128
Analysis of innovation in the semiconductor and computer sectors is focused on Silicon Valley and Route 128. Silicon Valley has become the best
known of the two agglomerations, primarily because it continues to dominate
the semiconductor and computer industries in the United States, while Route
128 and the major minicomputer firms that called it home have since failed and fallen by the wayside.
AnnaLee Saxenian’s discussion of the industrial systems of the Route
128 and Silicon Valley technology agglomerations offers helpful insights into
the benefits of agglomeration for innovative people and firms, as well as the forces at work to promote the early stages of agglomeration. The analysis is of
particular interest because it provides a glimpse into the workings of two
specialized industrial regions, operating in very similar sectors at the same
time.
Both Route 128 and Silicon Valley had their origins as distinct
industrial regions in the military research funded by the federal government
during World War II, but as Saxenian points out, the industrial makeup of the two regions were radically different prior to the war, and the differences in the 67
regions’ starting points dramatically shaped their postwar development.
Massachusetts, and the Boston area in particular, had a strong industrial and technological base already in place (Saxenian 1994). The presence of MIT, and its explicit support of research for commercial applications, helped to build a
connectedness between the academic and corporate worlds that lent the
region’s industry a strong technological bent.
MIT was placed at the center of war‐time research when Vannevar
Bush, an MIT professor, was appointed director of the Office of Scientific
Research and Development in 1941. Millions of dollars were funneled to MIT
and its laboratories for research into advanced technologies like radar and
navigation systems in the 1940’s, more than any other university or institution.
This massive investment in MIT and labs in the Boston area brought with it an
influx of highly educated and trained engineers, physicists and technicians.
Local high‐tech firms also benefitted from the flood of federal dollars, with the
fledgling Raytheon increasing its sales from $3 million at the war’s beginning
to $173 million in 1945 (Saxenian 1994).
This explosion of investment had a profound impact on the direction of the region in the postwar period. In addition to providing direct funding and sales for local companies, helping them to grow rapidly during the period, the 68
military investment in World War II created what Saxenian calls “…an
intellectual and technological labor pool unsurpassed in the nation, if not in
the world,” (Saxenian 1994). This labor pool would provide the region with
the human capital it would need to become a leader in electronics and
computers.
Silicon Valley’s early development, while boosted by wartime research
funding to Stanford, was not supported by military investment to nearly the
degree that industry in Boston was. Most wartime research funding, as well as
orders for military electronics, did not go to the firms on the West Coast. In
large part this was the result of the small size and little‐known reputations of
most electronics companies in the Santa Clara Valley prior to the war
(Saxenian 1994). So, while the war promoted the growth of the region, it did
not fully establish the area as a strong industrial agglomeration of any sort,
much less semiconductors or electronics.
Saxenian’s discussion of the differences between the Silicon Valley and
Route 128 agglomerations has several primary themes. First is that the
relationship between the local universities and industry in each region was
very different. Silicon Valley was in very close geographic proximity to both
Stanford and Berkeley, each of which would end up graduating similar 69
numbers of doctoral students in engineering to MIT, giving an immediate
recruitment pool nearly double MIT’s by the mid‐1970’s. Stanford, in particular, played an important part in the development of the region through its deliberately close ties to local high‐tech industry. Through several innovative industrial cooperation programs, including the establishment of the Stanford Industrial Park and the Honors Cooperative Program, which allowed engineers working in local industries to take graduate courses on a part‐time basis, Stanford was able to create and maintain strong ties to the region’s high‐tech firms (Saxenian 1994).
MIT, on the other hand, kept companies in the Boston area, especially startups, at a greater distance. Geographically, the companies of Route 128 were not in such close proximity as the Silicon Valley firms were to Stanford, originally. More importantly, however, MIT and its faculty had much more distant relations with local firms than were the norm in Silicon Valley. MIT explicitly supported research toward commercial applications by its faculty,
but this research was not coordinated or shared with Route 128 firms as well.
MIT did not offer a program similar to the Honors Cooperative Program, and
the fees charged for access to its research and other resources were not within
the reach of many small startups (Saxenian 1994). 70
Another difference Saxenian points to is the relationships between firms in the two regions. Silicon Valley has long been known for its high level of both competition and cooperation. This culture had several underlying
causes. The common backgrounds and geographic proximity of the engineers
and scientists working in the region, the high degree of mixing between their professional and personal lives, and the specialized nature of most firms, all
contributed to an industrial system in which a large number of firms
competed fiercely, while nevertheless building a culture that emphasized
unity of purpose. Saxenian quotes one Silicon Valley entrepreneur as saying
“’There are a lot of people who come to work in the morning believing that
they work for Silicon Valley,’” (Saxenian 1994).
Silicon Valley firms were also marked by a high rate of turnover. It was
not uncommon for employees to change jobs after only a few years, or to go
on to start their own firms after getting experience working for an existing
company. According to Saxenian,
“During the 1970’s, average annual employee turnover exceeded 35 percent in local electronics firms and was as high as 59 percent in small firms...[an] anthropologist studying the career paths of the region’s computer professionals concluded that job tenures in Silicon Valley averaged two years,” (1994).
71
This high turnover had two effects. First, it was a source of technology
and information sharing among firms in the region, as expertise moved with people from company to company. This could help keep companies on the cutting edge without having to reinvent the wheel. The high turnover also freed up innovative ideas from languishing in management structures unwilling to support them. If an engineer’s current company was not willing to give his idea a try, he could pitch it to his bosses in a new job, or try his hand at starting a company of his own (Saxenian 1994).
The typical Route 128 experience was radically different from the
Silicon Valley culture just described. Route 128 firms tended to embrace vertical integration as a corporate strategy, emphasize traditional corporate hierarchy, and maintain tighter control over the flow of proprietary information. These practices had the effect of containing innovative ideas and people, and funneling them through distinct channels of authority.
The strategy of vertical integration in technology firms was a reapplication of a tried and true business strategy. Control the factors of production throughout the whole process, mass produce, and realize economies of scale. This strategy works well when making and selling a product that does not change very much from year to year, but in a highly 72
innovative environment, it means that one must keep pace with the state of
the art in all levels of the production process or risk falling behind in
consumer demand for one’s products. This strategy therefore tends to result in
a few large companies taking a dominant position, rather than having several
smaller companies each compete for parts of the process. In computers, for
example, Digital Equipment Corporation (DEC) and Data General, two major
Route 128 minicomputer manufacturers in the 1970’s and 80’s, were
responsible for making nearly every individual component that went into
their end products, minicomputers (Saxenian 1994).
The problems this presented for innovation were magnified by the
centralized and closed nature of most of the Route 128 firms. For instance,
while the internal structure of DEC was actually quite fluid at some levels,
upper management retained control over most important matters, and few
ideas were coming into the firm from the outside, or going from DEC to the
outside, except in the form of finished product (Saxenian 1994). This meant
that unless DEC could attract cutting‐edge talent, fund cutting‐edge research,
and consistently select the best cutting‐edge ideas, it was prone to falling
behind in the marketplace as better products emerged. 73
Tracy Kidder’s The Soul of a New Machine tells the story of the
development of a new Data General minicomputer in the early 1980’s. This
account gives insight into some of the problems encountered by the Route 128
firms. Among the most interesting of these is the degree of
compartmentalization that seemed to exist within the company, which acted
as a barrier to more rapid innovation. At the time the new computer that is the
subject of the book was being developed, a completely different machine
serving effectively the same function was being developed by another Data
General group located in North Carolina, which had very little contact with
the Massachusetts team (Kidder 1981). Effectively this meant that any
breakthroughs made by either team would either not be shared with the other,
or if they eventually were then it would be at a much slower rate than would
be possible under an organizational structure that permitted and encouraged
horizontal communication and collaboration.
Silicon Valley companies, on the other hand, tended not to have this
problem, since they were frequently organized in such a way that horizontal
communication between company divisions was standard business procedure.
Leslie Berlin’s description of Fairchild Semiconductor from its founding by the
Traitorous Eight, the exiles from Shockley Semiconductor, through Robert 74
Noyce’s departure to found Intel, gives the picture of a company that thrived on account of its open nature and ability to foster collaboration between divisions (2005). Because information and knowledge sharing is so vital to innovation and maintaining a quick innovative pace, such a structure has distinct advantages for companies that are dependent on advancing technology to increase their profits.
Other, related problems with agglomerations of vertically integrated firms exist. One of the most significant problems has to do with the effects of vertical integration on the market for local suppliers. The vertically integrated computer firms of Route 128, for example, internalized the production of nearly all the components that went into the computers themselves, including machining and fabricating the computers’ housings (Saxenian 1994). This meant that there were very few independent suppliers of a wide range of inputs to computer production, creating a business environment that was disadvantageous to small startups.
Another effect that this starvation of the independent supplier market
had was to force vertically integrated companies to innovate with respect to every aspect of their machines. To make a successful minicomputer, the vertically integrated companies would have to be innovating with regard to 75
the design of processors, memory, display devices, input devices, instruction
set, etc. This dramatically increases the overhead of putting a computer to
market, compared to an industrial system where firms are more specialized,
and are free to license their technologies to a large number of other producers.
To illustrate, consider a case where only two major computer
manufacturers exist, both vertically integrated and both using proprietary technology. If one of the companies makes a breakthrough in processor design and the other leaps ahead with regard to memory, the market will be presented with a computer with an advanced processor and obsolete memory, and a computer with outstanding memory but a processor incapable of taking full advantage of it. The advantage of a network system is that many of the breakthroughs that occur will take place in small, specialized companies that are dependent on technology licensing, or other similar agreements, to realize their profits. Thus, if our example above took place in a network‐based industrial system, a computer manufacturer would approach an independent supplier of memory, and an independent supplier of processors, and use the best of both worlds in the new machine. 76
To further elaborate on some of the ideas discussed above, and to
provide some better connection with real‐world examples, several specific
innovations will now be discussed.
The story of the Mac computer’s development is one of internal
politicking, external inspiration, and centralized control (Linzmayer 2004). In
many ways, the development of the Mac runs counter to the ideas that
Saxenian develops as giving Silicon Valley companies an innovative edge. In
parts, it seems very much like a DEC or DG story, with top down, centralized
decision‐making, exclusive pricing, and proprietary development. So what
makes it successful, and what sets it apart?
An important point is that Apple was able to draw on the innovative
talent of the area, in particular the Xerox PARC, the font of many
groundbreaking ideas that would make their way into the ill‐fated Lisa, and
the revolutionary Macintosh. In a deal worked out between Apple and Xerox, several Apple employees were given access to the PARC, where Steve Jobs and several Apple engineers were inspired by the user interface improvements that had been developed there. These would be key to the ultimate success of the Macintosh, and would be the Lisaʹs sole redeeming qualities (Linzmayer
2004). 77
In considering the centralized control that characterized Apple, we are
forced to ask, why is this not a problem for innovation in the way that it was
for DEC and Data General? The answer lies in the fact that Apple was not monolithic in the way that DEC and Data General were. Apple, even while its products were in some ways highly proprietary, incorporated components and innovations from other companies, and its innovation was really about combining the innovations of other companies in new, interesting ways.
Apple’s innovation was specialized, and in the early days this specialization was purely in the form of personal computers. Apple could innovate with regards to their products, without having to innovate with respect to every individual component. This left them much better prepared to keep pace with technological change than, say, IBM, DEC or Data General.
This also brings us back to the distinction between radical and incremental innovation. Apple did not make huge strides in any of the particular components that went into its computers. They didnʹt make the processors, they ended up not making the disk drives, and they didnʹt make the memory. What they made was an integration of a variety of existing components made by other companies, and a set of software that unified the whole package in a way that embraced ease of use. Apple’s contribution to the 78
end product was the unique integration of a set of components at the leading edge of technology.
The iPhone happened in much this way. Apple had originally entered into a partnership to create a phone with music capabilities with Motorola, which would eventually result in the ROKR, a lackluster product that never caught on with consumers. After this failure Apple then decided to develop its phone entirely in‐house, demanding autonomy from its wireless carrier partner Cingular. The iPhone, though, was really an innovation because it presented a unique combination of several ideas that had already been floating around, or technologies that had already been developed. Apple’s principal technological contribution was the software to integrate all the functions of the phone together, particularly the touchscreen interface, which
Apple’s engineers had experience with from their prior work on tablets
(Vogelstein 2008).
Microsoft is a standout example of a computer software company that bucked the trend of agglomeration in Silicon Valley. The company had its origins in the programming done by friends Bill Gates and Paul Allen while the two were in high school in Seattle. Later, when the Altair 8800 was released, Gates and Allen went to work writing a version of the programming 79
language BASIC for the machine, and Microsoft was born. The company
located in Albuquerque, New Mexico because MITS, the company that made
the Altair for which their products were originally written, was located there
(Ichbiah & Knepper 2001).
Microsoft soon expanded into development for other platforms,
specifically the Intel 8080 and later the 8086. With expansion into these
platforms, Microsoft began to grow beyond its original scope, and started to
consider the possibility of relocation. Ichbiah and Knepper describe the
company’s motivation for moving its operations with a quote by Paul Allen, saying that the company was having trouble attracting talent to the
Albuquerque area, and that they thought that Seattle, the founders’ hometown, would be more attractive to potential recruits (2001).
Microsoft truly began to expand with the introduction of MS‐DOS, its first operating system, originally created for and released on the IBM PC in
1981. IBM had first heard of the company in their microcomputer market studies prior to developing the IBM PC. Microsoft had made a name for itself by writing software for the Intel 8080 and 8086 that would allow users to program the processors in the programming language BASIC, and IBM brought them on board to develop several other languages for their new PC. 80
Microsoft would go on to also develop several other business‐oriented programs for the PC, most notably Word, which has since become the world‐ standard word processor. Microsoft would then go on to develop more software for Apple’s Macintosh, greatly expanding their business as the Mac
took off (Ichbiah and Knepper 2001). 81
Analysis
Financial Services
Of all the case studies considered, the financial industry case study is
the one that lends itself least to the sort of categorization typically seen in the
innovation literature. Usually when innovation is discussed, we are asked to
consider the development of new products or processes, most often with some
sort of technical component in which innovation is dependent on the
overcoming of physical limitations, e.g. increasing the batch yield of a
chemical process by incorporating an additional mixing procedure. Financial
services innovation, on the other hand, occurs primarily with respect to
business practices that are subject to social constraints, rather than physical
ones. There is no doubt, though, that the financial industry is innovative, and
that it is constantly creating new knowledge and new products for sale to its customers.
Consider the case of Michael Milken and junk bonds. The sale of junk bonds itself was not an innovation by Milken and his group—these bonds had been sold for decades by companies with lower‐than‐investment grade ratings to finance various endeavors. Neither was Milken’s role to recognize that 82
diversified junk bond portfolios tended to outperform investment‐grade ones
in the long term: this had already been noted by Hickman and Atkinson in the
1940’s and 60’s, respectively. Milken’s innovation was to realize that this fact could be exploited to profit from the market’s misperception of risk, and apply this knowledge to build a market for junk bonds to finance leveraged buyouts.
Certainly this innovation caused dramatic changes in corporate finance for several decades.
As an innovation, Milken’s building of a market for junk bonds does not lend itself to classification as either radical or incremental, as it was radical
in its impact but incremental in the level of new knowledge required to realize
it. Even the matrix classification is somewhat tricky when we try to apply it to
this scenario. We might consider Milken’s innovation fast, given that he made
much more money in much less time than was typical for the industry. On the
other hand, the actual pace of knowledge creation and the building of the
leveraged buyout market was fairly slow, and took many years to fully
develop. It was the case, though, that building Milken’s market required
access to a broad spectrum of talent in the financial industry, including
corporate bond traders, experts in mergers and generally those with access to
capital. What Milken built was a system of corporate customers, investment 83
funds, savings banks and takeover orchestrators that made junk bonds more valuable than they otherwise had been. Milken’s junk bond trading can therefore be considered broad.
In this particular instance, that Milken started at Drexel in New York is worth noting. The fact that Milken’s innovation drew on a broad base of knowledge and capital resources made it unlikely to come from outside the established industry agglomeration on Wall Street, as it would have been difficult to develop the required financial backing, industry contacts and
information network elsewhere. Milken’s innovation was the whole system of
junk‐bond financing and leveraged buyouts, and this needed the broad range
of resources, capital and human, that New York had to offer. Nevertheless, because it had been well‐established in New York, Milken’s business
continued to do well and retain its innovative edge even after the move to the
West Coast. That his operation was built on broad‐knowledge innovation occurring at a slow pace allowed Milken to physically remove his unit from the existing agglomeration without sacrificing its innovative competitiveness.
We might consider such a situation to be lightly agglomerative, as there were clearly good reasons why the innovation occurred within an established Wall 84
Street firm, yet the pressures were not intense enough to make a move
elsewhere undesirable once the operation had gotten started.
High‐frequency trading, on the other hand, fits neatly into a narrow/fast classification. HFT relies strongly on complex, proprietary trading
algorithms used by trading houses in an attempt to outmaneuver competitors in a trading environment with time horizons of barely a millisecond. The development of these algorithms requires a deep understanding of trading fundamentals and the mathematics used to represent them, but the extent of the knowledge involved generally stops there. The agglomerative pressures for high‐frequency traders are relatively low as a result, and it is therefore
unsurprising that the need for co‐location with major exchanges to reduce
trading latency takes precedence over clustering with other traders, especially
in light of the secrecy surrounding their primary innovative product, the
trading algorithms.
Finance in general has many strong reasons for agglomeration, the
most important of which is probably access to capital and strategic
information. Innovation in finance is less about surmounting problems in the
physical world than it is building a market for a particular financial
instrument or investment strategy. This means that the importance of being 85
“in the know,” or having ready access to capital resources to make a market, is
extremely important. In this way, the innovation‐based agglomerative benefit has less to do with sharing ideas and new knowledge than it does sharing
contacts and resources.
There is the sense, though, that in spite of the fact that innovation in
financial services is strongly agglomerated in New York, innovation is not truly the driving force behind the agglomeration of the industry itself. The
speed of trading and transactions in finance, especially the recently‐developed
practice of high frequency trading, is very fast. Yet the actual pace of change with respect to the financial products purchased and sold is relatively slow.
Thus, while financial innovators do agglomerate, they do so because the industry itself already has reasons to cluster together.
86
Automotive Industry
Like the industries themselves, the innovative agglomeration of the
automotive industry in Detroit is very different from the agglomeration of the
financial industry in New York. This is primarily the result of differences in
the types of innovation carried out by each industry, and the kind of
knowledge required to realize these innovations. The design, construction and
sale of a new car is an undertaking that incorporates a large number workers
in a variety of different disciplines, including electrical and mechanical
engineers, machinists, welders, and other technical workers, as well as non‐
technical staff in various aspects of the business like marketing and sales. Cars
are complex systems that require hundreds of individual parts to work.
Therefore, it is best to think of automotive innovation as being broad systems
innovation.
The main reason for Detroitʹs emergence as the dominant automotive
agglomeration in the US was its existing broad base of industrial and
manufacturing knowledge. As Rubenstein points out, Detroit had significant
experience in both carriage‐building and gasoline engine manufacture before
the auto industry got its start there (1992). This, combined with a strong base 87
of machine shops and other factories, made Detroit a strong industrial
agglomeration before the automotive industry even began to emerge. The
advantages of having a broad base of industrial talent gave Detroit
automakers an early incentive to locate there, and as the industry met with
success the agglomeration became self‐sustaining and developed to serve the
car companies specifically.
It bears reiterating that the primary benefit of agglomeration had to do
with the innovative resources, primarily human resources, available in the
region. Both Ford and General Motors would eventually shift major parts of
the production operations to other cities, states, and regions within the United
States, but the innovative design and business operations would remain in
Detroit. Therefore, the reasons for maintaining the agglomeration had less to
do with the city’s access to physical resources than its large pool of diverse
knowledge.
Another significant factor in causing the auto industry to agglomerate
was that the rate of innovation in the industry, especially in the early years,
was very rapid. The invention of the automobile being so recent, car
companies in the early 1900’s through the 1920’s and 30’s had a myriad of
improvements great and small to make to their creations. Thus, the pressure to 88
agglomerate that existed as a result of the broad nature of automotive
innovation was intensified by the fact that innovation had to occur rapidly to
remain competitive. There was simply no time to wait around for the right
people and the right machines needed to get the newest, most advanced cars
in production, and the companies that operated where the necessary talent already existed performed better than their competitors.
Some of the most interesting aspects of the automotive industry case study are found in relation to the ZSB engineering team and their work with
Studebaker and Chrysler. While at Studebaker, there are numerous examples of the engineering group’s learning from and teaching their suppliers and partners in ways that improved innovative outcomes. Breer’s interactions with the General Aluminum and Brass Company, the Flick machine shop, the
Universal Products Company, and the Ball carburetor undoubtedly improved
Studebaker’s products and thus the company’s ability to compete in the automotive. These exchanges of knowledge were the result of face‐to‐face contact between people with different sets of expertise, and they improved the quality of innovation possible at Studebaker. It seems likely that the decision to consolidate engineering operations in South Bend in the 1920’s had a negative impact on Studebaker’s ability to compete, and cut the company off 89
from Detroit’s innovative expertise that otherwise might have extended the company’s life.
Detroit’s agglomeration can be explained by recognizing the effects of
breadth and speed in automotive innovation. Engineering and manufacturing
a successful new car requires a wide range of technical expertise, including
mechanical and electrical engineers. Integrating the engineering process with
the sale and marketing of these cars requires interaction and cooperation
between engineers, styling designers, business managers and marketing
professionals. The easiest way to allow this cooperation across disciplines in
the short time‐scales available for the development of a new automobile is
agglomeration. In the process of agglomerating we increase both the
frequency and quality of knowledge transfers, as well as stimulating mutual
inspiration.
90
Silicon Valley / Route 128
The Silicon Valley / Route 128 case study gives especially interesting
insight into the role of breadth in generating and sustaining agglomeration of
innovative activity. We have in this case the emergence of two distinct agglomerations, one which continues to thrive to this day and one that has since faded away. Drawing from Sabel’s study of industrial districts and flexible specialization (1994), we see that in Silicon Valley the looser and more flexible industrial structure of the region gave it a distinct advantage in responding to changing market conditions, specifically the decline of
American semiconductor production in the face of foreign competition, and the emergence of the personal computing market. This structure also encouraged face‐to‐face contact among people of different disciplines or experience, fostering learning and mutual inspiration in a more intense way than in other regions.
Route 128, on the other hand, was stifled by its vertical integration. The
Route 128 firms were generally of medium size, somewhere between a giant like IBM or AT&T and the small startups that were common in Silicon Valley.
This meant that on their own these firms could afford to bring together larger 91
numbers of knowledgable workers than an individual California startup, but
could not match the hiring resources of an IBM or the knowledge base available in the Silicon Valley region. Because the Route 128 firms were less collaborative and more secretive, they were largely dependent on their own knowledge resources, and this caused them to fall behind market trends when personal computers became more popular.
The systems nature of the innovations driving Silicon Valley’s success, particularly the personal computer and, more recently, devices like the iPhone, makes breadth especially important. The great number and diversity of input technologies needed to develop a device like a desktop computer or smart phone mean that to create a successful system, a large number of different sets of knowledge must be internalized to a firm or project team. We therefore see in the analysis of these case studies another of the critical points of the theory being developed, namely that the agglomeration incentives differ from sector to sector, in part based on the type of innovation that typically occurs within the given industry. The technology products developed in
Silicon Valley, especially integrated circuits and computers, are complex enough that concentrated effort by a large number of people in many different directions is required to generate significant innovation. As a result, 92
semiconductor and computer technology firms faced significant pressure to agglomerate, to take advantage of the benefits of the large, concentrated labor pool that emerged in northern California.
In each of these two agglomerations, Silicon Valley and Route 128, the pace of innovation was fast. Innovation was not only important to the semiconductor and computer industries, it defined them, and this meant that the pressure to out‐innovate the competition was very high. As in the auto industry, the computer and semiconductor firms needed fast access to people and knowledge that could be most easily obtained within a strong high‐tech innovative agglomeration like Silicon Valley. Again, Route 128 fell behind because its ready‐available knowledge base was not as strong. Knowledge tended not to spread as quickly among the Massachusetts firms, and so they fell behind the pace of innovation.
The case of Microsoft provides an interesting counterpoint to the
Silicon Valley / Route 128 story. The company has obviously met with great success since its founding, and yet it never based its operations in Silicon
Valley. We notice, however, that in the early years of the company, its innovation was probably fairly narrow, and in many of its product lines this remains the case. The company therefore had less of an incentive to cluster 93
with others, since it only needed to bring together talent from one or two different technical disciplines. The company’s early partnership with IBM also
played a role, since it allowed for the integration of Microsoft’s narrow
technical innovation into broader systems like personal computers. Since IBM was by far the largest manufacturer of computers at the time, Microsoft was able to take advantage of the resources of the larger company, especially its marketing and sales expertise. The integration of Microsoft software into IBM
PCs allowed expansion of their market at a rate that would have been impossible had the small Washington‐based company been acting on its own, and allowed Microsoft to tap into a broad base of technical and business expertise that it would otherwise have been isolated from.
There are other factors that improved the chances for Microsoft’s success outside the major computer technology agglomerations. One is that network effects associated with Microsoft’s products gave it an early advantage in the market that sheltered it from competition. This explanation rests on the fact that in the early days of the computer industry, customers frequently became “locked in” to specific product families because if they switched software their old files would be incompatible and therefore useless, and if they used different software packages from those that their 94
collaborators were using they would be unable to share data back and forth.
Thus, once a given set of programs became popular, it would tend to maintain
its position in the market, even in the face of lower‐priced or higher‐quality
competition. This is especially true of productivity software and operating systems, which formed a bulk of the products developed in the early computer software industry.
95
Conclusion
As a topic of study, innovation is extraordinarily complex, and there are many different factors that play a role in the undertaking of innovation
The decision to focus on agglomeration in this project has helped narrow the scope to a point where some firm conclusions may be drawn that illuminate possibilities for public policy intervention.
The case studies we have discussed give us a sense of the complexity of the innovative process, and the difficulties facing any attempt to systematically analyze the subject. Clearly there are differences between sectors that drastically alter the picture of innovation for each industry.
Nevertheless, there are commonalities that cross sector boundaries, and the classification schemes outlined help give us a better sense of the similarities between different types of innovation. These similarities speak to the importance of agglomeration in the innovative process, and help guide us in
understanding the potential role for public policy in encouraging innovation.
The aspect of innovation most responsible for agglomeration is
innovative breadth. Attempts to innovate are strongly dependent on the
ability to access many sources of knowledge and expertise, and this plays a 96
strong role in the structuring of agglomeration incentives. There is no better
way to facilitate this access than to bring these sources—the people with
knowledge and skills‐‐close together. As Storper and Venables point out, face‐
to‐face contact is virtually unrivaled as a mechanism for communicating
complex ideas and coordinating unfamiliar tasks and interactions (2004).
Agglomeration drastically increases the possibilities for this sort of interaction
between innovators of diverse experience and skill, thus improving the
quality of knowledge transfers, while also allowing for greater coordination. It
is important, too, that agglomerations be large, so they may consistently
provide an appropriate mix of skills and knowledge to transform ideas into
innovations.
The speed of innovation in a particular sector also strongly affects the
incentives for agglomeration. Considering Silicon Valley and Route 128, both
of which were part of an industry where the pace of innovation was very fast,
we see that the ability to quickly acquire and make use of talented workers
was a determinant of success. Both of these agglomerations benefitted from
the close proximity of major research universities, and Silicon Valley took
further advantage from its firms’ ability to easily and rapidly recruit from
within the local labor market. These characteristics allowed firms within 97
Silicon Valley to more rapidly obtain the knowledge, skills, and talent they
needed once they realized they needed it, and made them more competitive in an environment that depends on speed for innovative success. Route 128’s
ultimate failure was a result of its inability to easily and quickly exchange ideas between its firms and individuals. While the cluster benefited from the presence of MIT through its large and steady supply of top engineering graduates, its firms were not in close enough contact with the university to result in a rapid exchange of ideas. Likewise, contact between individuals within Route 128 firms and between the firms themselves was not close enough to facilitate much cooperation or collaboration.
The conclusion from this analysis is that in sectors in which innovation is broad, as we have defined it, and fast, we expect to see agglomeration of innovative activity. To put it another way, scope and speed in innovation create clusters.
With this conclusion in mind, we turn at last to the public policy implications of innovative agglomeration. Clustering of innovators and innovative activity undoubtedly improves the quality and quantity of innovations produced, as well as increasing the wealth and economic strength of the surrounding region. We can therefore consider innovative 98
agglomeration as a social goal. There are several justifications for the use of
public policy to promote innovative agglomeration, and they each give
different insights into the sorts of policies that might be desirable to encourage
clusters to develop.
First, we recognize that there are positive externalities associated with
innovative agglomeration. This means that if a firm decides to locate in an
existing agglomeration, it does not receive all of the benefits from that choice.
Other firms in the agglomeration also benefit from the presence of the new
firm, yielding a net social benefit higher than that which the firm sees. This is
characteristic of public goods problems, and leads to the under‐provision of
agglomerations to the public if net individual benefit to the firm is lower than
net social benefit. This provides an opportunity for public policy to lower
agglomeration costs for firms, in order to bring firm net benefit and social net
benefit in line.
Firms and individuals also probably underestimate the innovative
advantages of agglomeration. The benefits of face‐to‐face contact are not
tangible or immediately obvious, and the value of exposure to and
internalization of breadth to both firms and individuals is not always apparent
either. Returning to Hollingsworthʹs treatments of specialization and cognitive 99
complexity (1984 & 2007), we see that innovators often face strong pressures to
specialize even though doing so is likely to limit their creative ability. Policy may therefore be built on the premise of trying to adjust firm‐level and
individual incentives toward agglomeration of breadth, to offset their misjudgment of agglomeration benefits.
Another possible justification for public policy intervention comes from the issue of coordination problems. As previously mentioned, there is some critical size below which the benefits to locating within a cluster are very small. Thus, while firms may understand that there are benefits to agglomeration, there may be no particular incentive to begin to cluster, given that these benefits are initially small and not all firms are agreed upon where to go. In the absence of coordinated agreement by firms to agglomerate in a particular area, other, non‐uniform costs and benefits associated with doing business in different areas may dominate firm location decisions. Once a cluster reaches the critical size, though, it becomes clear where the cluster agglomeration benefits are large enough that they dominate locational choices, and the cluster becomes self‐sustaining. The costs of trying to coordinate the location of dozens, or even hundreds of firms are likely to be prohibitively high. Policy may therefore aim to solve these coordination problems by 100
signaling where agglomeration should occur, potentially by providing the
push above critical size in the first place.
With respect to trying to promote agglomeration, one of the most
obvious things to say is that policy must be oriented to a regional scale. This is
not to say that the local level of government is the only one capable of making
effective policy with respect to innovation, but rather that policies that work at
a scale larger than a relatively small region are unlikely to have much effect on innovative agglomeration. Since the primary innovative advantage of clustering is the facilitation of face‐to‐face contact that crosses disciplinary boundaries, policies should be focused on the creation or improvement of opportunities for this sort of interaction across breadth to occur. Storper and
Venables point out that the “buzz” generated from face‐to‐face contact is a result of frequent and sustained contact, rather than intermittent interaction
(2004). Thus, to maintain the interdisciplinary “buzz,” policy should focus on promoting clusters in small local regions, rather than larger areas like states.
This leaves us with somewhat of a policy conundrum, however.
Agglomerations like Silicon Valley, Route 128, and even the “Detroit” auto industry, often transcend municipal boundaries, encompassing larger regions.
Thus, cities may have neither the resources nor the authority to address the 101
incentives problems. On the other hand, other local‐level units of government like counties, while adequate for the provision of public goods like roads and police protection, are probably poorly suited to developing and implementing the sort of policies needed to broadly alter locational incentives, which are sure to require large amounts of resources.
State government faces an entirely different set of problems. States are
probably able to secure the resources and broad authority required to address
firm incentives, but they may be so isolated from local political and economic
structures that they miss nuances in local circumstance. State governments
may also be subject to political attacks over regional favoritism that could
distort the range of acceptable policies for stimulating agglomeration. Citizens
of Cleveland, for instance, are not likely to be strong supporters of a state
policy to promote a technology cluster in Cincinnati. There are precedents,
however, for structuring regionally‐oriented policy making. Regional airport
authorities are good examples of successful public solutions to regional
problems.
An alternative policy path that combines the scope and power of state
government with strong ties to local and regional concerns is the state‐
sponsored research university. Such universities are able to draw on the 102
financial resources of the state, and because of their educational mission,
universities are conduits for state funding of local concerns that is partially insulated from criticisms of local favoritism. They are also capable of being much more local in their focus, as they become vital parts of the communities they inhabit, developing strong economic and social ties to their regions in the process of executing their educational and scholarly missions.
MIT and Stanford are both standout examples of research universities that are frequently credited with helping to establish local industrial and innovative agglomerations, as we saw in the Silicon Valley / Route 128 case study. Both of these universitiesʹ policies of encouraging local entrepreneurship and university partnerships have been recognized as having direct impact on the location choices of specific firms (Stanford and HP being a standout example). Furthermore, Saxenian argues that the extent of each university’s entrepreneurial ties had a significant impact on the fortunes of high‐tech industry in their local regions (1994). These two examples, and most especially Stanford, demonstrate the effectiveness of university policy in providing greater incentives for agglomeration, and could be used as models for state action through universities elsewhere. 103
Universities also have the advantage of being self‐contained
agglomerations in themselves. One of the main purposes of a university is to
bring together in one place a diverse group of scholars, researchers, and students for the purpose of exchanging ideas and information, and so a university therefore acts as a sort of ready‐made agglomeration of talent that can be used as a seed for developing the critical mass needed to make a cluster self‐sustaining.
Other policies present themselves for our consideration. Many
authorities have tried to establish clusters of industrial activity and innovation
in the past. At least a good many of these efforts have failed, if not an outright
majority. Declaring a “special economic zone,” and offering tax breaks or subsidies to particular industries are examples of policies that are ultimately doomed to failure. What they lack is a strong connection to the human inputs to innovation. Innovation demands the human element, and cannot be
accomplished without it. This is perhaps especially true for broad innovation,
where not only is the input of ideas and effort of people required, but also
their collaboration and cooperation. Thus, those policies that address only
purely economic concerns, while perhaps useful in aiding the agglomerative
process, are no substitute for a policy with a deep human connection. 104
This is the value of the sorts of interaction that took place at Stanford leading up to the emergence of Silicon Valley. Both within the university itself and between the university and the regional community in which it was imbedded, learning, cooperation and the building of relationships took place at a deep and meaningful level. According to Saxenian’s accounts, there was a strong sense of accomplishment and purpose that arose from these connections (1994), thereby creating a culture receptive to the broad interdisciplinary collaboration that is best able to innovate.
To be sure, my argument is not that universities are the only possible vehicles for effective policy, or that universities in and of themselves act as germs for the growth of innovative agglomeration. Certainly there are universities in which the culture is not outwardly oriented, where the economic and innovative concerns of the local region are neglected. And there may also be ways of building the infrastructure for a culture of interdisciplinary collaboration and innovation that are not centered on universities. Government research labs, properly oriented, may be able to serve the same function.
On the other hand, a research universityʹs mission is already imbued with the characteristics that promote a strong innovative culture: learning, 105
exploration, sharing, and collaboration. The fundamental justifications for bestowing on universities a new role as the nerve centers of regional innovation are already there. While we may wish for, and eventually need, other mechanisms for encouraging a culture of broad innovative cooperation, it seems a waste not to take advantage of one that lies ready for use.
In conclusion, I think it worth reiterating that innovation in the real world is complex, nuanced, and difficult to truly understand. The value of innovation to the economic progress of civilization is simply too great to not give the issue serious attention. We must not, however, be too quick to jump to conclusions about where innovation comes from, or how to make it happen.
It has been my intention to provide a perspective of different emphasis than those seen in the innovation literature to date, and I believe the focus on the concentration of breadth provides a valuable way of structuring our ideas about how to make our society a more innovative one. 106
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