Environment and Planning C: Government and Policy 2014, volume 32, pages 000 – 000

doi:10.1068/c1271r

Siting ‘scientific spaces’ in the US: the push and pull of regional development strategies and national innovation policies

Jennifer J ClarkSchool of Public Policy, Georgia Institute of Technology, 685 Cherry Street, Atlanta, GA 30332‑0345, USA; e‑mail: jennifer.clark@gatech.eduReceived 25 March 2012; in revised form 17 October 2013; published online 29 January 2014

Abstract. The term ‘science park’ evokes a “you know it when you see it” consensus among policy experts. Although the function of science parks is broadly understood as collaborative applied research between universities, industry, and governments, the physical and institutional form of these ‘cooperative research centers’ shows significant variation. In this paper I present a typology of such centers in the current US context and discuss how they are changing. Using evidence from one high-tech industry, I underscore the agglomerated nature of basic and applied science—a key argument in the rethinking of decisions around public investments in scientific spaces. Finally, I conclude that US innovation policy is shifting to a more explicitly metropolitan orientation, recognizing the importance of proximity and agglomeration. Implicit in this shift is a reevaluation of the geography of public investment in technology infrastructure as it pertains to cities and regions.

Keywords: technology policy, research centers, regional development, urban innovation

1 Introduction: the role of research centers in regional innovation policyThe term ‘research center’ evokes a “you know it when you see it” consensus among science and technology policy makers and regional development experts. However, what research centers actually are and what they really do diverges from this consensus. Research centers vary by organizational structure, governance, portfolio and scope, geography, and, of course, impact. Although the function of research centers is broadly understood as a space of collaborative, scientific research shared between university, industry, and government actors, the physical and institutional form of these ‘cooperative research centers’ (CRCs) varies significantly from country to country and even state to state and city to city.

In a broad theoretical sense CRCs function as containers for tripartite research activities, forming an institutional umbrella under which actors from universities, government, and industry collaborate (Etzkowitz and Leydesdorff, 1997). In their current form, US‑based CRCs serve several key functions. First, they invent things (principally materials and products). However, process innovations are far less frequent. Second, they train people. Usually, the people trained are elite graduate students and not incumbent workers engaged in current production work. This role separates CRCs from the workforce investment system and connects them closely with research universities. Third, CRCs acquire specialized equipment to support research programs. Fourth, they convene a set of research collaborators. And, finally, they generate revenue by charging ‘bench fees’ for access to the centers themselves and for participation in a research consortium (Clark, 2013).

CRCs in the US vary considerably in terms of governance and structure, ranging from national labs administered directly by federal agencies to an extensive network of university‑based ‘basic’ research centers (see table 1). Across forms, however, industry participants partner with research centers in order to access specialized research assets that

2 J J Clark

Tabl

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Siting ‘scientific spaces’ in the US 3

are significantly subsidized by the public sector through federal (and increasingly state and local) grants. This includes the ‘cheap talent’ provided by graduate students and postdoctoral fellows used to operate the facility. It also includes access to capital equipment that offsets the initial costs of acquiring specialized machines. CRCs allow firms to access the equipment on a user fee basis until and unless the research requiring these specialized assets becomes commercializable and ultimately profitable enough to acquire the equipment for production.

In short, CRCs function as institutional intermediaries subsidizing private sector research and development (R&D) and provide a national backdrop for less market‑oriented scientific discovery. At this functional level CRCs are central institutional actors in a broader policy debate at the intersection of science and technology policy and economic geography about (1) the spatial organization of R&D intermediaries—the location of individual research centers and the formation of distributed networks of CRCs and (2) the governance of sites within regional innovation systems. Ultimately, these research centers are critical intermediaries reproducing the specialized labor markets and providing the accessible R&D infrastructure that makes regions distinct in an increasingly flat world.

Economic and urban geographers are well acquainted with the extensive literature on territorial innovation systems and the emphasis on links between regional economies and embedded innovation capacities (Asheim and Isaksen, 2002). This literature developed from a series of discussions—notably, industry clusters and learning regions—highlighting the role of human and social capital and institutional infrastructure in producing and retaining territorially embedded capacities in enabling and emerging technologies in a complex and competitive global economy (Gertler and Wolfe, 2002). Although a series of policy frameworks followed from this extensive literature, the vast majority of science and technology policies have been slow to recognize geography as a critical variable in the analysis of innovation and technology transfer. As a consequence, US science and technology policy has rarely considered regional industrial specializations—even urban economies—in its locational criteria. In fact, the importance of spatial proximity to generating and sustaining innovative economic activity remains a subject of debate among leading economists with policy influence in the US. In a recent editorial in the New York Times, Christina Romer (2012), former Chair of President Obama’s Council of Economic Advisors, expressed skepticism about the benefits of economic clustering at all.

In spite of this lack of consensus on and recognition of the operational connection between innovation and regional economies, research centers continue to serve an expanding role as market intermediaries. Thus they bridge the gap between basic research financed and supported by public sector actors and the commercialization of innovations facilitated by private sector actors. However, the transition from basic to applied work and the hand‑off from public and academic researchers to private market actors has become increasingly muddled. While ‘muddling through’ has worked as a site‑by‑site policy approach in the past, the global recession has highlighted the need for a more efficient and generalizable approach to moving publicly funded research into private market commercialization.

In this paper I first discuss research centers in a comparative US context, illustrating the variation in forms and functions of these scientific spaces and how geography is evolving as key criterion in the next generation of innovation policies. Second, I look at one research‑intensive advanced manufacturing industry—optics and photonics—to describe the increasingly urban character of high‑tech industries and the persistent colocation of basic and applied research. And, finally, I suggest how and why regional development is increasingly important to discussions about the policy implications of research centers and the spatial organization of scientific research institutions in the US.

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2 Cooperative research centers in the US context: dominant innovation policy models and a typology of formsUnpacking the functions of the different types of US research centers requires an understanding of how they relate to broader models of innovation policy familiar in both industrialized and industrializing countries. There are three dominant policy models that incorporate cooperative research centers as intermediary institutions in national economic policies. The effectiveness of these models can and often is debated. Different models fit more easily at specific scales (regional versus national) or within specific national governance regimes (strong versus weak federalism). Further, these models are not mutually exclusive. They can be layered simultaneously or modified and tweaked in succession. There are also different levels of firm engagement (large and small firms), public sector participation (national and regional), and engagement by other stakeholders (such as universities and labor market intermediaries). Recent policy debates in the US highlight a transition from an established model focused on basic scientific research capacity to a model more integrated with production and regional development (Clark, 2012; 2013). As a consequence, it is important to understand how the current modes of US research centers fit these models—or, more often, do not.

The first distinct model is a firm networks approach: an explicit regional policy oriented towards an industry or technology cluster present in the region. This model is built on industrial districts and industry clusters frameworks. The rationale for this targeting is to support and sustain agglomeration economies. As such, the model is explicitly linked to the regional economy and the actors and firms present in the region. The Canadian model of regional development is a variation of this model (Bramwell et al, 2008; Doloreux et al, 2007; Salazar and Holbrook, 2007). Italian industrial districts follow this structure as well. Although the firm networks approach was popularized and adapted to the regional context in the mid‑1990s by Michael Porter’s work on industry clusters, the model is significantly older (Krugman, 1995; Marshall, 1920; Porter, 1990). The industrial districts research in Italy in the 1970s led to the development of regional‑scale industry approaches to providing resources and coordinating development and innovation efforts, particularly for small and medium‑sized firms (Cainelli and De Liso, 2005).

The second distinct model is a technology infrastructure approach that is a loosely organized innovation policy implemented through infrastructure investments or capacity‑building investments in research universities and/or cooperative research centers (Boardman et al, 2012). The selection of sites of investment is typically determined through a competitive proposal process that determines the location of investment. Although these selection processes are not immune from political considerations, merit criteria play a central role. As a consequence, places with existing infrastructure and capacity tend to do better than those building it. Similarly, places with existing political power tend to do better than those developing it. The current US system of innovation investment looks very much like this model with an emphasis on basic rather than applied research.

Although the technology infrastructure approach is necessarily competitive and thus reinforces a mode of interjurisdictional competition that has been sharply criticized in recent years, it can be constructed to mitigate negative effects of that competition (Malecki, 2004). For example, the Fraunhofer Institute’s model in Germany allows for many sites of investment, with varied and specific portfolios based upon the embedded capacities of firms and institutions in an existing region. The role for firms in the Fraunhofer model is significant in terms of both engagement and funding. In many ways, the firms in the Fraunhofer model function as innovation intermediaries linking universities, government, and industry in an effort to move research into commercialization.

Siting ‘scientific spaces’ in the US 5

The third model is an industrial policy approach organized as a targeted national industrial policy that incorporates an innovation strategy. This approximates a traditional indus trial policy coordinated with a national science and technology strategy. In this model priorities are defined at the national scale, and locations for investment are selected in the course developing the industrial policy.

How these models apply in the US context is outlined in table 1. The table provides examples in the US of the dominant types of cooperative research centers and how they fit into this typology of national and regional approaches to the coordination of scientific research and economic development. It is immediately evident in the US case that there are several variations of research centers with distinctive approaches to scientific research and commercialization. And these different origins and goals contribute to a geography that is neither necessarily urban nor suburban. In fact, the common impression of suburban science is dependent on how one defines which research centers count and, to some extent, when. Scientific research in the US shows clear signs of becoming more and more urbanized as research centers locate closer to networks of firms (rather than a single corporate sponsor or partner) and closer to specialized labor markers (Christopherson and Clark, 2007a).

Although it may seem intuitive that regional production capacity and established industrial specializations should inform decision making about the placement of CRCs, this has not consistently been the case in the US. A model of applied scientific research integrating innovation and commercialization strategies with regional economic development objectives did emerge in the 1980s (Feller, 1997; Roessner, 1985). But in response to a set of studies identifying gaps between innovation and commercialization in the US, this model was never implemented in the US at the federal scale.

It is in this context that the cooperative research centers model of scientific production expanded in the US without an explicit connection between advancements in science and technology and the growth of the national economy (Atkinson‑Grosjean, 2002; Bluestone and Harrison, 1982; Bozeman and Boardman, 2004; Zysman and Tyson, 1983). Although the CRC model of scientific production has been deeply intertwined with national development priorities in most industrialized countries (Perry and May, 2007), in the US CRC models conceptualized at the federal level have not been consistently connected to economic development strategies at the subnational scale. CRCs focused on innovation and development (I+D centers) are usually state or regional initiatives, whereas federal CRCs focus on providing R&D capacities to a national network of firms and academics through targeted programs administered by federal agencies like the National Science Foundation (see table 1).

In the US the subnational CRCs, often called ‘Centers of Excellence’ or ‘Innovation Centers’, developed by state governments in the 1990s and 2000s represent a deviation from the model of scientific production sponsored directly by federal agencies. The College of Nanoscale Science and Engineering of the University at Albany–State University of New York is one key example of this emergent state‑level model (Kaplan, 2011; Seelye, 2012). For example, national labs tend to focus on a broad sector of the economy (eg, energy or defense) rather than specific industries (eg, automobiles or semiconductors) or a targeted technology (eg, biotechnology, optics, and photonics). However, this is slowly beginning to change. The Department of Energy’s National Nanotechnology Initiative looks more like the ‘distributed network model’ of federally funded scientific research epitomized by the Canadian National Centers of Excellence than like the historical research programs of the agency (Clark, 2010; Rank, 2002).

As the public sector’s role in science has shifted from (primarily) sponsoring basic research and generating demand to active collaboration in applied research, the distance between

6 J J Clark

scientific sites and places of production—both the institutional and physical distance—has become increasingly problematic for commercialization strategies based on regional innovation systems. For example, universities located in regions with the ‘fertile ground’ provided by strong entrepreneurship cultures, well‑developed firm networks and industry clusters, and/or established economic development institutions have seen greater success of technology transfer efforts than those without these key elements in place (Feldman, 2001; Feldman and Desrochers, 2003; O’Mara, 2005; Saxenian, 1994). All of these elements are key aspects of regional innovation systems.

It should be noted, however, that physical proximity alone has not proved sufficient for successful technology transfer, as the uneven success of academic research parks demonstrates (Massey et al, 1992). Further, proximity is not an uncontested topic itself. Spatial proximity does not guarantee institutional or organizational compatibility (Boschma, 2005). In fact, a significant body of research in public administration and technology policy emphasizes that the organization, management, and the cultural character of university‑based CRCs matters to the productivity of researchers, technology transfer, and ultimately industrial performance (Bercovitz et al, 2001; Gray et al, 2001; Lin and Bozeman, 2006; Ponomariov and Boardman, 2008).

In the US the intimate relationship between publicly funded, university‑based CRCs and industry throughout the postwar era allowed many research universities to develop institutional forms far more integrated into their regional economies than the national labs (Noble, 1977; 1986). This experience differs from other industrialized countries, in large measure because of an existing capacity throughout a network of research universities. These universities were able to serve as the institutional home for basic and applied scientific research and establish productive partnerships with the R&D units of US corporations. In contrast, other industrialized countries developed distinct and new forms of CRCs in the second half of the 20th century. For example, Germany developed a set of new institutions through the Fraunhofer model. The celebrated models in Singapore (Singapore Science Parks) and Taiwan (Hsinchu Science and Technology Park) tie scientific research very closely to firms. In Canada the CRC system integrates a university‑based R&D system with a distributed network of commercialization centers.

In the US a research system embedded in universities produced institutional ‘facts on the ground’ that obviated the need for an entirely new policy model in the postwar period. This created an advantage in innovation capacity but also a path dependency resulting in a patchwork approach to scientific research funding and governance. That path dependency also reflects the limitations of research universities as economic actors in firm creation and particularly in transferring process innovations to existing firm networks. In other words, the existing US research center model is far better at research than development. In part, this institutional gap between innovation and production is simply a consequence of the focus of both university‑based CRCs and federal CRCs on promoting basic science. However, it also reinforces the impression that orphan intellectual property has difficulty moving into the production chain and out of the existing CRC system.

The failure to get innovation out of the lab and onto the assembly line is a central concern of policy makers, particularly in the context of a global recession. In June 2011 the Obama Administration launched the Advanced Manufacturing Partnership (AMP). The AMP recommended the introduction of a new, networked cooperative research center model: the National Network for Manufacturing Innovation. President Obama announced the implementation of the first pilot in Youngstown, Ohio focused on additive manufacturing in the fall of 2012. The goal is to implement fifteen such centers. And, unlike

Siting ‘scientific spaces’ in the US 7

previous initiatives, these research centers are explicitly required to engage firm networks of small and medium‑sized firms and regional development actors.

Recent initiatives in the United Kingdom by the Technology Strategy Board also recommended revamping CRC models, resulting in the introduction of the Technology Innovation Centers. The Canadian CRC system experienced similar adaptations in the 1990s and 2000s. In the US the challenge is that established CRCs receive high marks in evaluations of their design and development capabilities. It is the capacity for the deployment of innovations to commercializable applications that appears limited. It appears to be particularly limited in terms of engagement in process innovations relevant to small and medium‑sized manufacturers. And, it is this last category—moving innovations into small and medium‑sized firms—that relates most directly to the question of science and the city.

3 Science and the city: the changing geography of US scientific spacesAs previously noted, multiple models of cooperative research centers—initiated by a variety of actors at various scales—exist in tandem in the US (Forsyth, 2014). Some look like science centers, others like extensions of university campuses. Others still look like corporate R&D parks. The transitory nature of these institutional forms reflects, in part, the temporary, contingent, and competitive character of science and technology funding streams in the US. Federal funding for basic research flows from an array of agencies and competitive processes including the Departments of Energy, Commerce, and Defense as well as the National Institutes of Health and the National Science Foundation. Major ‘research center’ funding depends largely on annual congressional allocations to these agencies and their programs (an ‘ear‑marking’ process that has recently been rolled back). These grants often require sizable industry contributions. Many universities and federal research labs have long collaborated with large firms like Boeing or GE in order to facilitate these contributions.

The tensions between the various streams of funding, industry participation requirements, and funding levels do not produce one static model of cooperative research centers. Instead, they produce dynamic spatial forms that can be simultaneously associated with the urban university campuses of major research institutions like Columbia, Harvard, the Georgia Institute of Technology, the Massachusetts Institute of Technology, and Carnegie Mellon as well as the sprawling, multi‑institutional, low‑density landscapes of California’s Silicon Valley, Northern Virginia’s ‘NetPlex’, or North Carolina’s Research Triangle.

In the US there is often a perception that science is conducted in bucolic suburban campuses away from central cities and their high‑rise office buildings (O’Mara, 2005). This perception is largely rooted in (1) the corporate campuses popularized in the postwar era; (2) the bucolic small‑town campuses of (usually) land‑grant research universities; and (3) the isolated siting of some national labs. Key examples of the corporate science park include the Palo Alto Research Center (formerly known as Xerox PARC and now known simply as PARC) in suburban Silicon Valley and Bell Labs (formerly of AT&T and now part of Alcatel‑Lucent) in suburban New Jersey. Of course, these decentralized suburban landscapes run counter to what we know about agglomeration economies and industry clusters. This contrast between perception and reality highlights how uncoordinated the siting of scientific infrastructure has been over the past century.

In the corporate context, the suburban siting of R&D facilities followed the relocation of administrative headquarters of large firms from central business districts to the outskirts of urban areas (Mozingo, 2011). William Whyte (1988) argued that this postwar spatial shift was largely motivated by the desire of corporate executives to work in closer proximity to the suburban setting in which they lived. Others argue that the suburbanization of science in the US follows a far more systemic trend (Mozingo, 2011).

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In these arguments, suburban science is a consequence of federal funding trends and land‑use decisions. In the first instance, the shift of postwar federal funding to newer Sunbelt cities moved scientific research out of urban cores in established industrial cities and into the newer locations of the South and Southwest. In the second instance, once this funding landed in the new Sunbelt cities, it followed the dominant, low‑density urban development patterns of that region (Clawson et al, 1973; Markusen, 1991; O’Mara, 2005). While this narrative accounts for a portion of the suburbanization trends in US science, it is an incomplete explanation. Even in the growing Sunbelt cities of the south and southwest, major university CRCs remained substantial urban sites of federally funded research (two major examples include the University of Texas in Austin and the Georgia Institute of Technology in Atlanta). These variations point to the need for a highly nuanced and contingent explanation for the spatial organization of scientific research.

Interestingly, even this corporate suburban orientation has begun to shift. When Alcatel‑Lucent took over AT&T in 2006, they decided to sell the 427 acre New Jersey campus in Holmdel designed by Eero Saarinen (in collaboration with Sasaki, Walker and Associates) and completed in 1962. However, Alcatel‑Lucent has retained the Murray Hill, NJ Bell Labs property located closer to New York City and area airports. Selling the Saarinen property in Holmdel as a corporate research park has proved so difficult that historic preservationists have identified the property as ‘threatened’ (National Trust for Historic Preservation, 2013). The contrast between the significant corporate investment in this research campus designed by one of the most celebrated modern architects of the period in the 1960s and the inability to even attract a new buyer for this landmark (if suburban) facility in the 2010s points to the rethinking of suburbanized science.

For universities, the spatial organization of scientific research is somewhat more ambiguous. A clear majority of the top‑twenty universities receiving federal funding for research are located in central cities (Haar, 2011). Of those not located in urban centers, most are located in prominent, low‑density metropolitan areas. The University of Michigan in Ann Arbor and the University of North Carolina and Duke University in the Research Triangle of North Carolina are emblematic examples. Cornell University is a notable exception in this category as the only campus consistently at the top of the list of research universities receiving significant levels of federal science funding located at a substantial distance from a major urban area.

The thirteen Department of Energy national research laboratories have reinforced perception of the suburban science park. Four of the labs are in the Silicon Valley and San Francisco area, two are in the greater Chicago area, and two are in the greater Albuquerque area. In aggregate, they present to an observer as suburban science parks. However, they were actually often sited at the periphery of urban areas that subsequently grew up around them. One of the most famous, Oak Ridge National Laboratories, is located on the western outskirts of Knoxville, Tennessee. Of the remaining labs, several are sited in much more remote areas including the Pacific Northwest National Laboratory in eastern Washington state.

In some cases, remote or less‑populated locations for government research labs are a function of the type of research performed. For safety or security reasons, some kinds of research are better sited some distance from a central city. The example of nuclear weapons research comes to mind. Further, with an entrenched sense of a bright‑line boundary between basic and applied research, the distant siting of publicly funded basic research facilities did not initially appear to have a downside. It is only with the increased emphasis on cooperative research and the role of research centers in a networked regional innovation system that remote siting seems to limit industry engagement, technology transfer, and regional development (Stepp et al, 2013).

Siting ‘scientific spaces’ in the US 9

The growing intensity of the debates about where research should be located is highlighted by the recent university competition in New York City for the opportunity to build the new ‘Science School in the City’ (Perez‑Pena, 2011). New York City considered proposals from teams of universities vying for the opportunity to build a ‘high‑tech graduate school’ on land made available on Roosevelt Island in Manhattan. The competition was won by a team led by Cornell University. Cornell, with a main campus located more than four hours from New York City, was particularly interested in an urban site that could facilitate technology transfer, venture capital investment, and start‑up firms through increased proximity to a dynamic, dense regional economy. While some economists still see ‘clustering’ as contested, economic geographers and increasingly policy professionals see the recognition of agglomeration economies as simply an acknowledgement of current practice (Castells and Hall, 1994; Glaeser, 2011). And, clearly, university leaders value an urban arena for scientific research. The Cornell plan is estimated to cost US $2 billion, with an initial, individual endowment gift of US $ 350 million from an alumnus. The operating budget for this campus will require ongoing support by sponsored research grants from a variety of federal agencies and corporate research partners. It is important to note that the reurbanization of scientific research is in many respects a return to a previous model rather than a wholly new one. For example, in the IBM case, their research center was originally located adjacent to and in partnership with Columbia University before the Westchester campus was built in the 1950s.

4 An industry case of divergent approaches to colocation: optics and photonicsIf we take one technology or industry category as an example, we can illustrate the relationship between scientific research and the city. The optics and photonics industry serves as an example of the broader point about regional innovation systems and the role of urban research centers. The photonics industry is a merger of a high‑tech‑enabling technology, photonics, and the continuing evolution of a traditional manufacturing industry, optics, and imaging. Photonics technologies are broadly understood as the science and engineering of generating and transmitting light (photons). The industry involves products and components associated with this science. While the current photonics industry does not include all optics manufacturing, it is inclusive of a wide range of end products and intermediate components including optoelectronics. Although the industry is R&D intensive, it also has an established manufacturing capacity.

Both in the US and internationally, the photonics industry tends to be highly agglomerated in city‑regions with preexisting optics and/or imaging production specializations as well as photonics research capacity (Christopherson and Clark, 2007b; Clark, 2013; Feldman and Lendel, 2010; Hendry and Brown, 2006; Hendry et al, 2000; Miao and Hall, 2014). These regions include Ottawa, Montreal, and Quebec City in Canada; Jena in Germany; and Rochester, New York and Tucson, Arizona in the US. This tendency to colocate in particular regional economies makes photonics an emblematic case of the industrial cluster model as it pertains to technology‑intensive industries (Karlsson, 2008). Evidence from this industry also provides indicators of the concentration of scientific research in urban areas. In addition, the photonics industry provides employment‑level evidence of the colocation of basic and applied research activities in those urban centers with industry agglomerations.

In 2011 the International Society for Optics and Photonics (SPIE) conducted the first of a series of surveys of its global membership aimed gaining a greater understanding of the characteristics of photonics professionals (education and training, industry segment, occupational distribution, salary, and other variables). Included here is an analysis of the geocoded portion of that data for the US extracted from the larger survey. In the SPIE salary survey dataset the US records were geocoded using the US Census Bureau’s 2010

10 J J Clark

metropolitan statistical areas (MSAs) boundaries. The US records represent 3363 of 7359 observations in the total survey, or 46% of all observations.

First, the survey analysis indicates that photonics employment is highly concentrated in urban areas. More than 98% of the US survey respondents reported that they lived in a US MSA. This is in contrast to the 85% of total US employment considered urban.(1) The higher concentration of photonics employment in urban areas is consistent with the higher proportion of college degree holders in urban areas (29.9% in 2009) as compared with 17.5% in rural areas. For scientific occupations, the higher education levels in urban labor markets are likely a critical locational factor.

Further analysis of the SPIE survey data looked at the regions (MSAs) with the highest number of respondents in order to identify ‘clusters’ of photonics activity. The top‑ten MSAs identified represent the regions with the highest number of respondents to the 2011 SPIE salary survey. Because the optics and photonics industry does not hold an exclusive category in the NAICS system (it does not have a clear category in the industrial classification system), measures of employment concentration or industry concentration cannot be determined conclusively by standard measures of industry concentration (for example, using location quotients of employment by industry counts). The regions identified through the survey were consistent with the clusters listed by trade and advocacy organizations familiar with the industry. The regions identified as the top photonics locations were Silicon Valley (San Jose), Los Angeles, Boston, Washington, DC, San Francisco, Rochester, New York City, Tucson, Orlando, and Dallas (see table 2).

A close look at the types of work conducted by photonics professionals revealed some interesting relationships between basic and applied research and the specialization of certain regions. First, the survey results indicate that regions do not share the same profiles. Different regions (and different urban labor markets) focus on different aspects of the photonics market and therefore have a different portfolio of dominant technology applications. This evidence of regional industry specialization is compelling, if not unexpected (Shearmur et al, 2007). It is consistent with the literature on regional industry and technology specializations (Noyelle and Stanback, 1983). However, the identification of application variations by region within the same industry is unusually nuanced.

Second, regions (and the US as a whole) consistently reported basic research in the top‑five applications in their regional research portfolios (all regions except Silicon Valley/San Jose)

(1) Using 2009 numbers from the US Department of Agriculture’s Economic Research Service (http://www.ers.usda.gov/statefacts/US.HTM) using the same distinction (MSA versus non‑MSA) to define urban and rural as we did in the analysis of the SPIE survey responses.

Table 2. Top‑ten US photonics regions [metropolitan statistical areas (MSAs)] by number of responses.

Rank MSA Responses

1 San Jose‑Sunnyvale‑Santa Clara, CA 2482 Los Angeles‑Long Beach‑Santa Ana, CA 2193 Boston‑Cambridge‑Quincy, MA‑NH 2164 Washington‑Arlington‑Alexandria, DC‑VA‑MD‑WV 2035 San Francisco‑Oakland‑Fremont, CA 1976 Rochester, NY 1267 New York‑Northern New Jersey‑Long Island, NY‑NJ‑PA 1188 Tucson, AZ 769 Orlando‑Kissimmee‑Sanford, FL 7510 Dallas‑Fort Worth‑Arlington, TX 62

Siting ‘scientific spaces’ in the US 11

(see figure 1). This evidence points to the finding that, in regions with high concentrations of industry activity, there is colocation of basic and applied research.

However, the urban concentration of optics and photonics work does not neatly graft onto the network of photonics‑oriented cooperative research centers. This is another key point illustrated by the example of the optics and photonics industry. The colocation of CRCs and firm networks reflects science and technology policy investments as well as economic development practice. Insofar as these are aligned, there is clear colocation. For the top‑ten photonics regions listed in table 2, there are some clear examples of CRCs connecting with areas of employment concentration in the industry. For example, one of the leading research centers in the US is located at the University of Central Florida at the College of Optics and Photonics. The region ranked ninth on the list of regions with high employment concentrations in the industry. The Rochester, New York area has several established research centers at the University of Rochester, the Rochester Institute of Technology, and collaborative relationships with the College of Nanoscale Science and Engineering of the University at Albany. Rochester also has a concentration of firms represented by the Rochester Regional Photonics Cluster, a firm network group that represents over 100 firms and ranks sixth on the top‑ten list. In addition, Tucson, Arizona, which ranks eighth on the list, is well known in the optics industry for its established firm network and the University of Arizona’s College of Optical Sciences.

For these smaller metropolitan areas, the colocation of CRCs and firm networks or employment concentrations underscores the relationship between R&D infrastructure, firm networks, and a specialized labor market. In other words, specialization rather than urbanization could be the driver of colocation. However, for the larger metropolitan areas on the top‑ten list, there is a more pronounced pattern of scientific urbanization rather than technology specialization emerging. For regions like San Francisco, Los Angeles, New York, and Boston, these spatial patterns point to the conclusion that basic and applied are geographically interrelated as well as largely urban activities (see table 3). This finding also reinforces the proposition that the city’s role as a container for scientific activities may in fact be increasing as the bright‑line boundaries between basic and applied research become more blurry and the relationship between technology and commercialization becomes a focus of public policy.

Earth sciences, environmental monitoring

0 5 10(%)

15 20 25

Defence, security, law enforcementBiomedical, medical imaging, health care

Semiconductor manufacturingBasic research, science

OtherAstronomy

Optics manufacturingLaser engineering and equipment

Optics design and engineering

Figure 1. Top‑ten photonics applications in the United States.

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Siting ‘scientific spaces’ in the US 13

5 The rise of the research center: policy implicationsIt is well understood that intermediaries and institutions play an essential role in regional policy (Bristow, 2005; Morgan, 2004). Further, governance and organization of these entities have direct bearing on whether a region develops an institutional infrastructure, set of networks, and policy practices that support the colocation of innovation and production functions or, alternatively, develops as a high‑tech hub or branch plant economy. At their best, research centers serve as the translational spaces where innovations become commercialized and begin production in the region. In other words, basic research drives commercial applications and, ultimately, production.

The recent policy emphasis on advanced manufacturing has renewed a long‑standing concern of industry researchers and policy makers about ‘pushing technology down the supply chain’. This concern expands the scope of technology transfer beyond a preoccupation with high‑tech start‑ups and spin‑offs. This focus also presents an organizational challenge to the established model of scientific research with clear geographic implications. Rather than emphasize the introduction of innovative new products, these new policy goals reimagine the translational relationship between research centers and regional economies. These goals articulate a model with a greatly expanded intermediary function requiring longer term firm and industry relationships and sustained engagement with regions. These changes are critical to achieve three clear goals: (1) push process innovations into incumbent firms, (2) expand regional labor market capacities, and (3) build institutional networks that exchange knowledge across institutional platforms and geographies.

Ultimately, these goals require modifying existing R&D institutions to provide technical assistance to small and medium‑sized enterprises (SMEs) for innovative production processes as well as designing and prototyping innovative products on an ongoing basis. These innovative processes include energy efficiency; life‑cycle product design; and adoption of better, greener, safer materials. CRCs with an intermediary function in other industrialized countries often provide SMEs with access to testing to meet international standards and certifications—environmental, labor, corporate codes of conduct, systems, and logistics. This sort of expanded intermediary function provides suppliers with increased credibility with end producers in a global supply chain. In addition, access to shared facilities and technical assistance provides SMEs with the ability to learn and scale up without substantial upfront investments in technologically advanced capital equipment.

The intentional integration into a workforce investment system—allowing for the broad training of workers on specialized equipment and with specialized production systems—is required to expand the capacities of local labor markets (Lowe, 2007; Osterman, 1999; Wolf‑Powers, 2001). This training component for incumbent workers has typically not been associated with CRCs. While university‑based CRCs have long served as training grounds for graduate and undergraduate students enrolled in research universities, these facilities have generally not served as resources available to a broader set of students and incumbent workers.

In the dynamic process of ‘siting science’, there are two key points that emerge from this analysis and the regional innovation systems work produced by geographers. First, science spaces appear to function better when they function as nodes within both a regional and a national innovation system. In other words, they are multiscalar institutional forms. And, second, science spaces appear to function best when they serve an intermediary function in addition to their central scientific functions. Location plays a key role in both arguments. And, as the optics and photonics evidence indicates, basic and applied research is already colocating in cities and so is the labor market that supports both innovation and production. Further, if this analysis is representative, and proximity and centrality matter to the successful development and deployment of scientific research, it becomes increasingly difficult to see a suburban future for US science.

14 J J Clark

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