The University and the Creative Economy Richard Florida, Brian Knudsen, and Kevin Stolarick



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Chapter 3

The University and the Creative Economy


Richard Florida, Brian Knudsen, and Kevin Stolarick*



Gary Gates contributed to an earlier version of this paper. Thanks to Patrick Adler and Andrew Bell for research assistance.

Introduction


Most who have commented on the university’s role in the economy believe the key lies in increasing its ability to transfer research to industry, generate new inventions and patents, and spin off its technology in the form of startup companies. As such, there has been a movement in the United States and around the world to make universities “engines of innovation” (Feller 1989; David 1997; Gibbons 2000) and to enhance their ability to commercialize their research. Universities have largely bought into this view because it makes their work more economically relevant and as a way to bolster their budgets. Unfortunately, not only does this view oversell the immediately commercial function of the university; it also misses the deeper and more fundamental contributions made by the university to innovation, the larger economy, and society as a whole.

We argue that the university’s increasing role in economic growth stems from deeper and more fundamental forces. The university’s role in these forces goes beyond technology to both talent and tolerance. To prove this point, our research utilizes Florida’s 3Ts theory of economic development, which specifies the role of the 3Ts of technology, talent, and tolerance in economic development. We recognize the ongoing, productive debate over the creative class approach (Kotkin and Siegel 2004; Malanga 2004; Peck 2005; see also responses by Florida 2004c; and Florida, Mellander, and Stolarick 2007), but note that that debate is outside the scope of his chapter. Our research simply uses the 3Ts theory as a broad and overarching framework to orient our detailed empirical investigation of the university’s role in economic development broadly.

This article provides a data-driven, empirical analysis of the university’s role in the “3T’s” of economic development, looking in detail at the effects of university R&D, technology transfer, students and faculty on regional technology, talent, and tolerance for all 331 U.S. metropolitan regions.

The findings show that the university plays an important role across all 3Ts. First, as major recipients of both public and private R&D funding, and as important hotbeds of invention and spin-off companies, universities are often at the cutting edge of technological innovation. Second, universities affect talent both directly and indirectly. They directly attract faculty, researchers, and students, while also acting as indirect magnets that encourage other highly educated, talented, and entrepreneurial people and firms to locate nearby, in part to draw on the universities’ many resources. Third, research universities help shape a regional environment open to new ideas and diversity. They attract students and faculty from a wide variety of racial and ethnic backgrounds, economic statuses, sexual orientations, and national origins. On the whole, university communities are generally more meritocratic and open to difference and eccentricity; they are places where talented people of all stripes interact in stimulating environments that encourage open thought, self-expression, new ideas, and experimentation.

The findings further suggest that the university’s role in the first T, technology, while important, has been overstressed. We find that the university’s even more powerful role across the two other axes of economic development—in generating, attracting, and mobilizing talent, and in establishing a tolerant social climate that is open, diverse, meritocratic, and proactively inclusive of new people and new ideas has been neglected.

We conclude that the university comprises a powerful creative hub in regional development. On its own, though, the university can be a necessary but insufficient component of successful regional economic development. To harness the university’s capability to generate innovation and prosperity, it must be integrated into the region’s broader creative ecosystem.



Theory and Concepts

Universities have long played an important role in research, development, and technology generation. Recently, they have been said to play a key role in regional development, too. Any discussion of the university’s role in innovation and economic development quickly circles back to the now classic cases of Stanford University and MIT, which played critical roles in the development of Silicon Valley and the greater Boston area and more recently around Austin, Texas, and the North Carolina Research Triangle. (The literature here is vast, but see in particular: Geiger 1986, 1993; Leslie 1990, 1993; Gibbons 2000.) From these cases, many have concluded that the university serves as a catalyst for economic development. Etzkowitz (1989) and Etzkowitz, Gebhardt, and Terra (2000) argue that the traditional university whose primary missions are research and teaching has been supplanted by an increasingly “entrepreneurial university,” which generates revenue and enhances its political viability through technology transfer, the commercial transfer of innovation, the generation of spin-off companies, and direct engagement in regional development. One Silicon Valley entrepreneur, when asked yet again for “the secret of Silicon Valley’s success,” summed up this perspective by simply responding: “Take one great research university. Add venture capital. Shake vigorously.”

There is a broader theoretical underpinning for the view of the university as an “engine of innovation.” According to the “linear model of innovation,” innovations flow from university science to commercial technology (Smith 1990). This model informs the view that new and better mechanisms can be deployed to make the transfer and commercialization more effective and efficient, increasing the output of university “products” that are of commercial value to the economy.

Solow (1957) argued that productivity growth was only partly attributable to the traditional explanatory factors, gains to capital and labor. The unexplained “residual” productivity growth, he surmised, must have been due to technological change, which he defined broadly. More recent studies suggest that universities have significant effects on both corporate innovation and regional economic development. Mansfield (1991) later found that investments in academic research yield significant returns to the economy and society.

University research has also been found to make corporate innovation more efficient. Jaffe (1989) found that businesses located in close proximity to university research generate greater numbers of patents. Anselin, Vargas, and Acs (1997) found that university research tends to attract corporate research labs. A study of MIT by BankBoston (1997) found that MIT-related firms employed over a million people worldwide. However, these firms were highly geographically concentrated. The Cambridge Boston area was home to thirty-six percent of these companies, even though only nine percent of MIT graduates were originally from Massachusetts. Other New England areas were not nearly as successful at hosting MIT firms, even though many are located within a commuting distance of the MIT campus. Silicon Valley was a second major center for MIT-related firms. Other regions with significant concentrations of MIT-firm included Houston, Seattle, Minneapolis, and Dallas, along with several foreign regions. These findings suggest that some regions do better at commercializing university research than others.

Goldstein and Drucker (2006) examined the contribution of universities to economic development across U.S. regions, finding that universities tend to increase average annual earnings, with the most substantial effects occurring in small and medium-size regions.

The university as engine of innovation has been criticized as oversimplified for assuming a one-way path from university-based science and R&D, to commercial innovation and also for seeing the steps in the innovation process as discrete (see Florida and Cohen 1999). It has also been criticized for distorting the mission of the university. Robert Merton (1973) long ago contended that academic science should be an open project because it is firmly centered on the efficient creation of knowledge and movement of frontiers. Firms, on the other hand, seek scientific advance in order to increase profits and acquire intellectual property. Dasgupta and David (1994) have argued strongly for keeping academic science separate from industry. Close ties between industry and university might, they argue, draw academic scientists toward research enterprises with immediate short-term benefits to industry, but away from research with broader and long-term impacts to society and the economy. Conversely, Rosenberg and Nelson (1994) argue that university and industry research, basic science and applied science have always been intertwined, and that it is difficult to even discern the divide between science and technology.

Others argue that that regional differences, as well as university differences, are part of what accounts for differences in commercialization outcomes. Several studies identify the variables that allow firms and regions to better absorb research coming out of the university. Cohen and Levinthal’s (1990) concept of absorptive capacity suggests that successful commercialization requires absorptive capability on the part of regional firms. Smilor et al. (2007) identified three success factors across high-tech regions: strong political leadership, a provocative “incendiary” event, and a catalytic organization. Gunasekara (2004) suggests that elements inherent to a strong regional innovation system are likely to improve a region’s absorptive capacity.

Fogarty and Sinha (1999) have found a consistent geographical pattern in the flow of patented information from universities. Intellectual property migrates from universities in older industrial regions such as Detroit and Cleveland to high-technology regions such as the greater Boston, San Francisco Bay, and New York metropolitan areas. Although new knowledge is generated in many places, relatively few actually absorb and apply those ideas.

At the firm level, Kirchhoff et al. (2007) identify a positive relationship between a company’s R&D budget and its absorptive capacity. The R&D level trails only market size and the size of the foreign born population in terms of its influence on firm formation. Agrawal and Cockburn (2003) find a significant relationship between innovative firms and their proximity to so-called “anchor tenants.”

A substantial amount of literature seeks to reformulate and move beyond “university as engine” metaphor (Wolfe 2004; Huggins et al. 2008). The “triple helix school” (i.eEtzkowitz and Leydesdorff, 2000; Etzkowitz and Klofsten, 2005) suggests that ad hoc alliances between the public, private, and educational sectors preclude the discussion of a discrete university that is autonomous from industry, government, and institutions.

We contribute to these literatures by examining whether the university plays additional roles in regional economies—a role beyond technological development. On its own, a university may be a substantial regional resource, but its mere presence is not enough. The region must have the will and capacity to transform and capitalize on what the university produces. It requires a geographically defined ecosystem that can mobilize and harness creative energy. In order to be an effective contributor to regional creativity, innovation, and economic growth, the university must be seamlessly integrated into that broader creative ecosystem.

As noted earlier, we argue that the university’s increasing role in the innovation process and in economic growth stems from deeper and more fundamental forces. The changing role of the university is bound up with the broader shift from an older industrial economy to an emerging creative economy, which harnesses knowledge and creativity as sources of innovation and productivity growth (see Florida 2002, 2003, 2004a, 2004b, 2005). We argue that the university plays a role not just in technology, but in all three Ts of economic development: technology, talent, and tolerance.

It is important to note that Florida’s creativity theories have stimulated controversy and debate on the drivers and determinants of economic development (Kotkin and Siegel 2004; Malanga 2004; Peck 2005); Florida has responded in detail to these criticisms (2004c), clarifying and refining his theory, and providing additional empirical support for the 3Ts framework (Florida, Mellander, and Stolarick 2007). We recognize this debate and consider it to be useful and important, but its parameters are outside the scope of this study. In our research, we use the 3Ts as a logical guiding framework for an empirical investigation of the role of the broad, multidimensional impact of the university on economic development. Furthermore, the 3Ts enable us to contextualize the technological contributions of the university which are noted in the literature within the broader context of talent (human capital) and tolerance (or an open social and cultural climate) thus facilitating a more holistic approach to understanding the university’s role in economic development.

There is wide consensus among economists and other students of economic development about the primary factors that drive economic development. Solow (1957) found that technology is critically important. Today, drawing primarily upon the work of Lucas (1988), who in turn drew upon Jacobs (1961, 1969), the primary factor is seen to be human capital or what Florida refers to as talent. Drawing on Jacobs’s insights, Lucas declared the multiplier effects that stem from talent clustering to be the primary determinant of growth and he dubbed this multiplier effect “human capital externalities.” Places that bring together diverse talent accelerate the local rate of economic development.

Florida’s 3T’s model is in line with the human capital theory of economic development. It agrees that human capital is the driving force in economic development, but it seeks to amend or supplement it. Foremost, it offers an alternative measure of human capital or talent, which has advantages both conceptually and practically. Most studies of human capital measure it as educational attainment.  Florida instead substitutes an occupational measure for the traditional attainment measure, for two primary reasons. First, attainment measures omit people who have been incredibly important to the economy, but who for one reason or another did not go to or finish college. Second, attainment measures do not allow regions to identify, quantify, or build strategy around specific types of human capital or talent. It is clear that nations and regions are specializing in particular kinds of economic activity, and occupational measures highlight this trend.

Additionally, Florida seeks an answer to the question of why some places are better able to develop, attract, and retain human capital/skills/creative capabilities. Recent work by Florida, Mellander, and Stolarick (2007) determined that the distribution of human capital or talent across regions is influenced by a university presence, available consumer service amenities, and regional tolerance. They also found that the creative class outperforms conventional educational attainment measures in accounting for regional labor productivity measured as wages and that tolerance is significantly associated with both human capital and the creative class as well as with wages and income.

Since Schumpeter (1962, 1982), economists have noted the role of the first T, technology, in economic growth (Romer 1986, 1990). More recently, there has been increased interest in the role of the second T, talent or human capital in economic growth (Lucas 1988). However, technology and talent have been mainly seen as stocks that accumulate in regions or nations. In reality, these stocks are accumulations of flows between these regions. The ability to capture these flows requires understanding the third T, tolerance, the openness of a place to new ideas and new people. Places increase their ability to capture these flows by being open to the widest range of people across categories of ethnicity, race, national origin, age, social class, and sexual orientation. The places that can attract the widest pool of creative talent—harnessing the creative contributions of the most diverse range of people—gain considerable economic advantage emerging as creativity magnets. They simultaneously catalyze talent from within and attract talent from the outside environment. With the rise of the Creative Economy, the university—as a center for research and technology generation, a hub for talent production and attraction, and a catalyst for establishing an open and tolerant regional milieu—becomes increasingly essential to both innovation and economic growth.


Data and Methods

To explore these issues, we conducted an empirical analysis of the university’s role in the 3T’s of economic development for all 331 U.S. metropolitan regions. Our university indicators include measures of students, faculty, research and development, technological innovation, and commercialization. The measures of students and faculty are from Integrated Post-Secondary Education Dataset (IPEDS) from the Department of Education; measures of research are from the National Science Foundation’s Science and Engineering data series; and measures of technology transfer (such as license income and startups) are from the annual survey of the Association of University Technology Managers (AUTM) and indicators.

The technology measures include indicators of high-tech industry from the Milken Institute and from the patent database of the U.S. Patent and Trademark Office. Talent measures include conventional measures of human capital based on educational attainment and measures of the creative class based on Florida (2002) and from the Bureau of Labor Statistics occupational data files. Tolerance measures are from the U.S. Census and include specific measures of integration (Integration Index), foreign-born people (Melting Pot Index), artistic communities (Bohemian Index), and the gay and lesbian population (Gay/Lesbian Index). (See Appendix A for a full description of all variables and data sources.)

We introduce a new measure of talent, the Brain Drain/Gain Index—a measure of the extent to which a region is gaining or losing college educated talent. We also introduce a new comparative measure of the university in the Creative Economy, the University-Creativity Index, a combined ranking of a region’s university strength and its creative class. We employ a variety of statistical methods and tests to shed additional light on the university’s role in the 3T’s of economic development.

Chapple et al. (2004) point out some of the limitations of using specific rankings to evaluate regions and find that older, more diversified economies are often penalized by specific technology ranking approaches. Their study takes issue with the Milken Institute measure of regional high-technology. We utilize the Milken Index measure in light of this critique. We note however, that criticism is primarily directed at understanding regional economies from an output-oriented, industry-based perspective. Both our measures and approach are much more holistic in nature and many of our measures are themselves composites of individual factors. Our diversified measures include industry, human capital, occupational, and numerous perspectives. We also appreciate the complex nature of these relationships and have created multi-dimensional measures to more fully reflect that complexity.

Technology

Technology is the first T. As noted above, various studies have found that universities play a significant role in regional technology. We begin with a listing of the top 25 regions in R&D intensity (measured as R&D spending per capita). One can already see a limit to the university as engine of innovation perspective. The top five regions are State College, PA (Penn State); Bryan-College Station, TX (Texas A&M); Iowa City, IA (University of Iowa); Rochester, MN (Mayo Clinic); and Lawrence, KS (University of Kansas). Rounding out the top 10 are Champaign-Urbana, IL (University of Illinois); Corvallis, OR (Oregon State University); Athens, GA (University of Georgia); and Lafayette, IN (Purdue University). In fact, the entire list is dominated by regions home to large state universities. Of leading high-tech centers, only Raleigh-Durham-Chapel Hill (15th) and Boston (19th) make the top twenty. Silicon Valley is conspicuously absent from the list.



Table 2 ranks the top 25 regions across the country in terms of licensing income per faculty and university-generated spin-off companies. Two regions generate more than $40,000 per faculty in licensing income—Rochester, MN, and Tallahassee, FL. These are also not regions that top the list of high-tech industrial centers. Two others, Santa Cruz and Santa Barbara, CA, generate more than $20,000, while seven others generate more than $10,000 in licensing income. San Jose, Boston, and Seattle, three noted high-tech industry centers, make this list, though a wide variety of other types of regions are on it, including a lot of classic college towns.

The ability of universities to generate new startup companies has frequently been noted as a key spur to regional growth of high-tech industry. The roles played by Stanford University in the Silicon Valley and of MIT in the growth of the greater Boston-Route 128 corridor are legendary. When considering number of startup companies per faculty member Rochester, MN, ranks first. This set of cities is followed by Galveston, TX, Charlottesville, VA, Birmingham, AL, and Salt Lake City. None of these cities is known as being a hotbed of entrepreneurial activity. However, the top ten is rounded out with Boston, the Research Triangle area, Madison, WI, Athens, GA, and Mobile, AL. Again, major state university centers also do rather well.


We conducted a variety of statistical analyses to better gauge the relationship between university technology and regional high-technology. In particular, we looked at the relationship between university technology outputs and the Milken Institute’s commonly used measures of high-technology industry. The main findings are as follows. There is a considerable overall relationship between university technology and regional high-technology industry. The correlations between university technology outcomes (invention disclosures, patent applications, licensing income, startups), and regional innovation and high-tech industry are consistently positive and significant. It should be noted that license income correlations are considerably stronger for the 49 large regions (those with populations of more than one million) than for all 107 regions for which data are available, but the rest of the correlations show no such large city bias. This finding confounds research by Matthiessen and Schwarz (1999), which suggests that successful commercialization is associated with large agglomerations.

The relationship between university technology and regional innovation is complex, however. There are some regions where university technology has a strong effect on regional innovation and high-tech industry, and others where it does not. Figure 1 is a two-by-two matrix that we use to illustrate the pattern of relationships between university technology to regional innovation. It compares regions with high and low scores on the Milken Institute’s Tech-Pole Index (a measure of high-tech industry concentration) to the level of university innovation (measured as university patenting in the region). Its quadrants identity four types of regions.



Strong university innovation does not necessarily translate into strong local high-tech industry. An apt, if oversimplified, metaphor for this dynamic is the university as the transmitter and the region as the receiver. In a few, highly selective cases the university sends out a strong signal which is picked up well by the region. However, this is far from the norm. In a large number of cases, the university may be sending out a strong signal—it is carrying out a lot of technical R&D and producing patents—but the region’s receiver is switched off and unable to take in the signal the university sends out. As numerous studies suggest, these signals can be and are frequently picked up by other regions outside the local region (Bathelt et al. 2004, Saxenian 2002, BankBoston 1997). This circumstance allows regions, where the signal coming from local universities is weak, but the ability to pick up and absorb signals from outside is strong to receive signals. The extent to which regions exhibit the capacity to absorb ideas and knowledge into their economies is indicative of the presence of a local ecosystem of creativity, places that, with their universities, create an environment amenable to the attraction of both new ideas and creative and knowledgeable people.

As Jane Jacobs (1961, also see Ellerman 2004) pointed out, it might be best to see the university in biological terms, where the talent and technology being produced by a university are "seeds." These seeds can land close to the parent plant; they can be carried by animals to other (generally nearby) locations; or they can be carried by the winds around the globe. However, like all seeds, just landing somewhere is not enough—if the soil is not fertile, if there is not enough water or light, or if there is too much, the seed will not sprout. Further, the seed might sprout but then not grow very much or be stunted. If the conditions are not right, many seeds will not sprout and will instead be carried on the next breeze or passing animal to better locations.

Additionally, like several plants, the university can change its surrounding ecosystem to make conditions more favorable for its seeds to take root. It can also create an environment in which more and different types of species—ideas and people—can combine, compete, reproduce, and evolve. However, if the ecosystem is not receptive, those seeds will only grow in more amenable regions.



Talent

Talent is the second T. Lucas (1988) long ago argued that economic growth stems from clusters of talented people and high human capital. Glaeser (2000a, 2000b, Berry and Glaeser 2005) finds a close association between human capital and economic growth. He shows that firms locate not to gain advantages from linked networks of customers and suppliers, as many economists have argued, but to take advantage of common labor pools of talented workers. Glendon (1998) found that human capital levels in cities in the early twentieth century provided a strong predictor for city growth over the course of the entire century. Wolfe (2004) notes the university’s role in talent generation and attraction. In their study of the economic effects of universities, Goldstein and Drucker (2006: 34–35) found that universities effect economic growth more through the production of human capital than from research and development. Universities are themselves generators of human capital. They attract and produce two primary types of talent—students and faculty. Regions that can retain these locally produced goods gain competitive advantage. Students represent the core production of universities. However, faculty members are important talent in their own right. In addition to teaching students and doing research, star faculty attract other faculty who in turn attract students. Star faculty can and often do have a magnetic effect in the attraction of people and even companies.

Table 4 lists the top 25 regions by student and faculty concentration. The list here is dominated by college towns. The top five large regions in terms of student concentration are Austin, the Research Triangle, San Francisco, San Diego, and San Jose, but that none of these regions ranks higher than 50th in terms of student concentration.

Production of students is only a small part of the overall regional talent story. It is important to examine the larger role of the university in the region’s overall talent or human capital system. To get a first glimpse of this lay-out, we look at the correlations between the talent produced by the university and the region’s overall talent base. Table 5 shows the correlations between university strength and talent.

There is a positive and significant correlation between both students and faculty and regional talent, measured by the percentage of the working age population with a college degree. A positive but less strong relationship is also found between students and faculty and the creative and super-creative classes. Here, it is important to note that university faculty are members of both the creative and super-creative class and when faculty are removed from those categories the correlation disappears. While there is a strong tie between regional talent and technology outcomes, the relationships between university talent and regional technology outcomes are mixed. The relationship is much stronger for students than for faculty. Students are significantly associated with the regions’ patents per capita (0.490), patent growth (0.473), and high-technology industry (using the Milken Institute Tech-Pole Measure, 0.431). The correlation coefficient is not a sensitive enough to isolate “star faculty.” Subsequent research should examine the degree to which an elite group of faculty members might serve as magnets for talent. Our research does not mean to suggest a firm boundary between the university’s technology generating and talent attraction roles. In fact, when universities attract talent to the region they are assisting in the commercialization of new discoveries. Technological knowledge is not completely codifiable. Many crucial forms of knowledge (skills, practices, memories) are embedded within individuals and their social networks (Wolfe 2004; Pavitt 1991). Attraction and retention of talent can also be seen as attraction and retention of technological knowledge. Furthermore, the ability of regions to retain human capital from local universities can be seen as a key indicator of absorptive capacity. We now look specifically at the issue of talent retention and attraction, using a new indicator developed for this purpose.


Brain Drain or Gain


There has been mounting concern in the United States and elsewhere over the so-calledbrain drain,” the movement of talented, high human capital people from one region to another, as seen from the losing region’s perspective. Low retention rates of local graduates is troubling to parents and economic developers alike, and many regions are trying to figure out ways to keep graduates from leaving or to lure them back when they get older.

However, focusing only on retention misses a crucial part of the picture. A region that retains many of its own graduates but fails to attract degree-holders from other regions will most likely fall behind. The availability of a strong pool of local talent can trump both physical resources and cost in attracting corporations and growing regional economies. Talented people are a very mobile means of production. Students often leave regions after their four years are up; and young, highly educated people are the most mobile of virtually any demographic group. Some regions produce talent and export it, while others are talent importers.

To get at this issue, we developed an index that quantifies the combined retention and attraction rates of university-educated talent. We call it the Brain Drain/Gain Index (BDGI). This measure makes no distinction between graduates retained and those drawn from other regions. It just computes the net result: the relative gain (or drain) of people progressing from students to degree-holding workers.

The BDGI for a region is calculated as the percent of the population age 25 and over with bachelor’s degree or above, divided by the percent of the population ages 18 to 34 currently in college or university (postsecondary school). A region with a BDGI above 1.0 is a brain gain region, a net recipient of highly educated talent. A region with a BDGI below 1.0 is a brain drain region, a net breeder or donor of university talent. It retains proportionately fewer degree-holders than degree-earners.1 We consider the BDGI to be the best available simple and easily tractable indicator of a region’s combined talent attraction and retention capability. Table 6 shows the 25 regions on the BDGI along with the percentage of the total population in college, percentage of 18 to 34 year-olds in college and percentage of those 25 and above with a college degree or above.

The Brain Drain/Gain Index is not a perfect measure. It does not capture the actual “flows” of college-educated persons, whether recently graduated or in mid-career, to/from a region, nor does it measure whether previous generations of college graduates have been retained. Instead, it measures the current “state” of education utilization and production across the region. It is designed to determine if a region is producing people with college degrees at the same rate in which it is using them. It could be considered a “temporal quotient” in that it compares percentages across two different life-stages for a region in the same way that a location quotient compares a regional-special concentration to a national one. The numerator for the BDGI is the standard and widely accepted measure for human capital, but it does include retired individuals. While retired people can have a college degree that they essentially are no longer putting to effective use, there is no clear cut-off age. In addition, many retired individuals continue to make significant regional social and economic contributions. The measure as currently constructed is straightforward and can be easily calculated at almost any geographic level using readily available Census data.

The most striking finding of our geographic data is that just 10 percent of all 331 U.S. metro regions are net attractors of talent. Of all regions, only 10 boast BDGI scores of 1.25 or above. Another 5 score over 1.20, and 8 more over 1.15. Only 23 regions nationwide do better than 1.15. Especially notable here are San Francisco, San Jose, Washington, DC, and Santa Fe, in that a large part of the population is college educated (more than 30 percent) and many employees have a college degree (more than 40 percent). We should also note that six regions score high on both the BDGI and our overall measure of university strength: Austin, Boston, Raleigh-Durham, San Francisco, San Jose, and Portland, ME. Our findings support the work of Stephan et al. (2004), who have previously commented on the geographic dimensions of the “brain drain.” Their study of PhD students revealed significant hemorrhaging from the Midwest toward the Pacific and Northeast.


To get at the relationship between talent and regional growth, we estimated correlations between the BDGI and a variety of regional outcome measures: patent growth, high-tech industry, population growth, job growth, and income growth (see Table 7). The correlations are uniformly high. The BDGI is related to key regional outcomes, especially employment growth and high-technology industry, but also regional innovation, population growth, and income growth.



In our view, the relationship between the BDGI and regional growth is a two-way street. High BDGI regions have thick and thriving labor markets that are able to capture and absorb growth. However, high BDGI regions also have higher talent levels, which in turn is associated with higher technology levels. In effect, the correlation results for the BDGI reflect a “virtuous circle” where higher levels of talent lead to more technology generation, innovation and entrepreneurship, leading over time to higher rates economic growth more job generation and in turn to higher rates of talent production, retention, and attraction.




Tolerance


Tolerance is the third T. Major research universities can do much to “seed” tolerance and diversity in a region. Nationwide, university towns tend to be among the most diverse regions. Tolerance means being open to different kinds of people and ideas—ideally being proactively inclusive—not just “tolerating” their presence but welcoming diverse people as neighbors and entertaining their views as valid and worthwhile.

A key mechanism by which universities—both singularly and in partnership with communities—help build ecosystems of innovation and contribute to talent retention and attraction is through the promotion of tolerance and diversity, which have been shown to be important factors in individuals’ location decisions.

Scholars such as Joel Mokyr (1990) and Simonton (1999) have found that societies through history tend to flourish when they are open and eclectic but stagnate during periods of insularity and orthodoxy. Florida and Gates (2001) find that openness and tolerance are associated with differential rates of regional innovation and high-tech industry in the United States. Florida (2002) has found that talented and creative people favor diversity and a wide variety of social and cultural options. Openness to ideas—to creativity—is paramount to both talent attraction and economic success. Talented and creative people vote with their feet—and they tend to move away from communities where their ideas and identities are not accepted. Indeed, regions with large numbers of high-tech engineers and entrepreneurs also tend to be havens for artists, musicians, and culturally creative people. Seattle, Austin, and Boston are cases in point. Some scholars (Gunasekara 2004, 2008; Cooke 2002; Cooke and Morgan 2000) have pointed out that openness to learning is a key feature of successful regional innovation system. Kirchhoff et al. (2007) find the size of the foreign-born population is the second largest influence on the creation of new firms in a location.

The university has long functioned as a hub for diversity and tolerance. Universities have been called “Ellis Islands” of our time, noting their ability to attract large numbers of foreign-born students. The Silicon Valley venture capitalist, John Doerr, has frequently remarked that the United States should “staple a green card” to the diplomas of foreign-born engineering and science students who contribute significantly to the nation’s innovative capability (Miller 2008).

Indeed, universities can serve as an incredibly productive refuge for minorities seeking education as a hedge against discrimination. Gay men and lesbians show higher than average education levels and are often disproportionately represented on college campuses and in college towns (Black, Gates, Sanders, and Taylor 2000). Lifelong learning provides older citizens with a way to actively engage in a community. In general, the universities and university communities have long been places that are open to free speech, self-expression, political activism, and a broad diversity of ideas.

The university itself becomes an “island” of tolerance or at least a “spike.” By its very nature, the university is more diverse, both faculty and students—generally more diverse than the surrounding community. The university’s diversity can be contagious with the university’s diversity extending beyond the campus to the surrounding community. Moreover, if conditions are right, it can spread well beyond the immediate neighborhood and even have a multiplicative impact on broader regional diversity. This extra-university diversity changes the nature of the surrounding community so that it can attract and retain more of the talent and technology that the university is producing.

Cooke (2005) sees the university as a knowledge “transceiver,” which links local actors with global knowledge sources, in addition to transmitting knowledge across a global pipeline. We would emphasize the role of foreign human capital in the “transceiving” process. Foreign students and faculty members do not simply augment the diversity of their regional environments; they act as links to knowledge and financial networks that are crucial in the dissemination of technological knowledge. Saxenian’s (2002) construct of “brain circulation” highlights the key role of the university in fostering links between local economies and offshore networks.

Until relatively recently, though, the university had been a very insular environment, often purposely and intentionally separating itself from the broader society. In a way, university communities provided a function sort of like the old bohemian communities of Greenwich Village where eccentricity and difference were readily accepted, even encouraged. With the rise of creativity as the primary driver of economic growth, the norms and values of these once limited and isolated “creative communities” become more widely generalized and diffused throughout greater segments of society.

We conducted statistical analyses to gauge the relationship between the university and regional tolerance. We employ various measures of tolerance including an overall Tolerance Index, which is composed in turn of separate measures of racial integration (Integration Index), foreign born population (Melting Pot Index), artistic and bohemian communities (Bohemian Index), and the gay and lesbian population (Gay/Lesbian Index).

Between the original and paperback editions of The Rise of the Creative Class, the tolerance measures were revised to reflect the need to have and engage minority populations. The addition was not simply adding percentage of the population that is African American to the metrics. Instead, a much more nuanced argument is made. An Integration Index was added to the measures (for a complete discussion see Appendix B of the paperback edition). The important point is that regional growth and talent attraction are not impacted simply because a region is diverse (racially, ethnically, other ways). As Jane Jacobs (1969) pointed out, the interactions generate innovation. Separate “islands” of diverse populations do not have the impact that interactions among those groups can create. Integration provides a much better measure of regional openness and acceptance than simple percentages.

We found a considerable correlation between tolerance and the log of students and faculty, as Table 8 shows. Tolerance increases with both overall population and number of faculty, but the strongest relationships are almost always with the number of students. This is true in all but one case, the Melting Pot Index, which is roughly the same for total population and number of students. While integration does decline as population increases, this relationship is not as strong in communities with larger university-based populations.

There is also a significant, negative correlation between the integration index and logged populations of students and faculty. This finding is in line with those of Thomas and Darnton (2006). We suspect several causes. First, there may be a relationship between racial integration in a jurisdiction and the emphasis it puts on university funding. Student and faculty numbers are key indicators of a jurisdiction’s resource investment. Perhaps both of these variables can be associated with a more “liberal” or “progressive” political environment. Homogenous regions may also experience high integration scores because the index compares neighborhood diversity to total regional diversity. In other words, the negative integration correlation may also suggest a positive relationship between regional heterogeneity and economic growth.



Each of the tolerance measures was regressed against the logs of total population, total students and total faculty for all 331 metro regions. As Table 9 shows, students appear to play the key role here. The correlations for the total number of students are positive and highly significant for the overall Tolerance Index and the separate Melting Pot, Gay, and Bohemian Indexes. The correlations for both population and faculty are generally negative and significant. The negative coefficients for population suggest that the impact that the total number of students has on diversity declines with increasing population. In other words, the universities have a bigger and more pronounced effect on tolerance when they are located in smaller regions.



Universities are institutions that value diversity and whose effects on diversity and tolerance extend far beyond their classrooms and laboratories. This scenario is especially true in smaller regions where the universities play large and more significant roles in shaping regional norms and values. As with the dimensions we have examined up until now, a university’s tolerance “signals,, are subject to differential rates of absorption. We suspect that much of this has to do with, among other things, the level of cooperation/animosity between a university and its surroundings. In addition the quality of the “signal” itself can vary, as many universities must overcome hurdles before they are beacons of diversity and meritocracy. However, universities, in general do foster social environments of openness, self-expression and meritocratic norms, and help to establish the regional milieu required to attract and retain talent and spur growth in the Creative Economy.


The University-Creativity Index

In order to get at the broader relationship between the university and regional creativity, we constructed a University-Creativity Index or UCI. The index combines a measure of student concentration with the percent of a region’s work force in the creative class. In keeping with Chapple et al. (2004), this is a diversified measure, which is more likely to capture more of the big picture. We view this not as a measure of actual creative performance, but rather as a measure of how a region’s absorptive capacity is capitalizing on its university capabilities and how it combines them with other creative assets. In our view, a ranking in the top 50 means a region has considerable assets to work with and is well positioned to leverage those assets for improved innovative and economic performance. Table 10 shows regions on the University-Creativity Index for four regional size classes.

The top five large regions are all noted high-tech regions: San Jose, San Francisco, San Diego, Austin, and Boston. Rounding out the top 10 are Sacramento and Oakland (both in the San Francisco Bay Area), Seattle, Denver, Los Angeles, and Chicago. The rankings for small and medium-size regions, not surprisingly, are dominated by major state university centers, such as Lansing, MI (Michigan State); Ann Arbor, MI (University of Michigan); Madison, WI (University of Wisconsin); Provo, UT (University of Utah); Gainesville, FL (University of Florida); Bryan-College Station, TX (Texas A&M); and Corvallis, OR (University of Oregon), among many others. These findings suggest there is tremendous potential for harnessing university assets for regional economic growth in these communities. This trend is already occurring in some of these places, notably Madison’s recent ascendance as a center for high-technology industry and spin-off companies.

A wide variety of regions that are not usually seen as topping the lists of high-technology centers also do well on the UCI. These include: Albany and Syracuse, NY; Omaha and Lincoln, NE; Dayton, OH; Trenton, NJ; Des Moines, IA; Spokane, WA; Muncie, IN; and Portland, ME. Our sense is that there is considerable unrealized creative potential in these regions. Of older Industrial regions, only Chicago places in the top 50. Other older industrial regions with superb universities and colleges—like St. Louis, Baltimore, Philadelphia, and Pittsburgh—rank only between 50 and 100. It is our view that these regions suffer from a significant absorptive capacity deficit. Alongside efforts to improve university research and technology transfer, these regions need to work on their ability to absorb the significant signals their universities are sending out.

Our findings suggest that there are many considerable advantages for developing inter-regional partnerships between older industrial regions and their surrounding university centers. Two places that jump out from the data are Central Indiana and Greater Detroit. Indianapolis, for example, which ranks 239th on the UCI, is flanked by Bloomington and Lafayette, which rank 3rd and 10th, respectively. Detroit, which ranks 140th on the UCI, is flanked by Lansing and Ann Arbor, which rank 4th and 21st, respectively. In our view, the economic future of these regions lies less in their older commercial centers and downtowns (which are in part legacies of the industrial age) and much more in the major university centers that are on their peripheries. These places would benefit from broad interregional partnerships—and the development of “superregional” strategies that combine the size and scale of their older centers with the considerable 3T capabilities of their major research university communities.


Conclusion

This study has examined the role of the university in the 3Ts of economic growth— technology, talent, and tolerance—suggesting that the role of the university encompasses much more than the simple generation of technology. We examined these issues for all 331 metropolitan regions in the United States, analyzing the performance of universities in producing technology and talent and in shaping the tolerance of their regions. We introduced a new indicator for talent flows, the Brain Drain/Gain Index (BDGI), a measure of the extent to which a region is attracting and retaining college educated talent. We also introduced a new comparative measure of the university in the Creative Economy, the University-Creativity Index, a combined ranking of a region’s university and its overall strength in the Creative Economy. We have used statistical methods to further illuminate the university’s role in the 3Ts and hope to shed new light on its broad role in economic growth and development.

Our findings suggest that the role of the university goes far beyond the “engine of innovation” perspective. Universities contribute much more than simply pumping out commercial technology or generating startup companies. In fact, we believe that the university’s role in the first T, technology, while important, has been overemphasized to date, and that experts and policy-makers have somewhat neglected the university’s even more powerful roles in the two other Ts—in generating, attracting, and mobilizing talent and in establishing a tolerant and diverse social climate.

Future research should attempt to parse out how much of the statistical relationships are due solely to the presence of the university, whose employment of highly educated and often non-white individuals factors directly into the dependent variables in question. This scenario is particularly problematic for “college towns” where the university represents the bulk of the local economy. Testing separately for the presence (or absence) of knowledge-based activity and talent outside the university can be completed to investigate the extent to which the university generates a “spillover” effect into the regional economy. This more specific investigation was beyond the scope of this chapter, which is focused more broadly on the regional impact of the university across all regions, both those with and without a major university presence.

In short, the university comprises a potential—and, in some places, actual—creative hub that sits at the center of regional development. It is a catalyst for stimulating the spillover of technology, talent, and tolerance into the community.

First, in terms of technology: as major recipients of both public and private research and development funding and as sources of innovations and spin-off companies, universities are often at the cutting edge of technological innovation. However, university invention does not necessarily translate into regional high-tech industry and economic growth. In fact, we found that many regions have universities at the cutting edge of technology, but this does not develop into local regional growth. While universities comprise an important precondition for regional innovations, to be effective, they must be embedded in a broader regional ecosystem that can absorb their research and inventions and turn them into commercial innovations, industrial development, and long-term growth.

Second, universities play a powerful role in generating, attracting, and retaining talent. On the one hand, they directly attract top faculty, researchers, and students. On the other hand, they can also act as magnets for other talent, attracting talented people, research laboratories, and even companies to locate near them to access their research and amenities.

Third, universities and colleges have a significant effect on the third T, tolerance, shaping regional environments that are open to new ideas and diversity. Universities are the “Ellis Islands” of the creative age, attracting students and faculty from a wide variety of racial and ethnic backgrounds, income levels, sexual orientations, and national origins. University communities and college towns are places that are open to new ideas, cultivate freedom of expression, and are accepting of differences, eccentricity, and diversity. These norms and values play an increasingly important role in attracting talent and in generating the new ideas, innovations and entrepreneurial enterprises that lead to economic growth.

Our findings also indicate the simultaneity of university-economy relationships. Studies of technology note that a region’s “absorptive capacity” affects its ability to capitalize on technological research. We suggest that a region’s ability to absorb human capital is also important to regional retention of non-codified knowledge. We likewise suggest that a more open social and cultural climate also works to bolster greater regional absorptive capacity.

In order to be an effective contributor to regional creativity, innovation, and economic growth, the university must be integrated into the region’s broader creative ecosystem. On its own, a university’s actions are limited. In this sense, universities are necessary but insufficient conditions for regional innovation and growth. To be successful and prosperous, regions need absorptive capacity—the ability to absorb the science, innovation, and technologies that universities create. Universities and regions need to work together to build greater connective tissue across all 3Ts of economic development. The regions and universities that are able to synergistically and simultaneously bolster their capabilities in technology, talent, and tolerance will realize considerable advantage in generating innovations, attracting and retaining talent, and in creating sustained prosperity and rising living standards for all their people. Most of all, we encourage future research that probes the non-technological dimensions of the university in economy and society.


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Tables and Figures
Table 1

University R&D, Inventions and Patent Applications


Rank

Regions

R&D per Capita

Invention Disclosures per Faculty

Patent Applications per Faculty

1

State College

$3242.97

0.104

0.149

2

Bryan-College Station

2606.49

0.085

0.057

3

Iowa City

2259.52

0.081

0.081

4

Rochester MN

2146.82

1.434

0.717

5

Lawrence KS

1932.31

0.054

0.012

6

Champaign-Urbana

1913.54

0.062

0.031

7

Bloomington IN

1858.77

0.047

0.042

8

Corvallis

1775.23

0.044

0.034

9

Athens

1684.50

0.041

0.041

10

Lafayette IN

1440.97

0.076

0.046

11

Gainesville

1352.11

0.099

0.080

12

Charlottesville

1312.92

0.116

0.137

13

Madison

1299.71

0.194

0.109

14

Ann Arbor

863.43

0.054

0.045

15

Raleigh-Durham

805.49

0.143

0.093

16

Auburn

769.90

0.020

0.019

17

Columbia MO

695.05

0.024

0.012

18

Fort Collins

609.12

0.049

0.029

19

Boston

591.68

0.103

0.098

20

Bangor

583.29

0.005

0.003

21

Santa Barbara

582.07

0.079

0.069

22

Lincoln

543.46

0.013

0.016

23

Santa Cruz

510.79

0.095

0.083

24

Lansing

508.64

0.044

0.032

25

Baltimore

489.39

0.096

0.089

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