It seems as if every city wants to be the center of a tech cluster. When Amazon announced a couple of years back that it was taking bids from cities for the location of a second headquarters, 238 cities applied. But other than hoping Amazon falls into your lap, how could a city create conditions for a tech cluster to emerge? And given that most cities are not going to be come tech clusters, what\’s the next-best strategy? William R. Kerr and Frederic Robert-Nicoud discuss what we know about this subject \”Tech Clusters\” (Journal of Economic Perspectives, Summer 2020, 34:3. pp. 50-76).
Six cities appear to qualify under any aggregation scheme: San Francisco, Boston, Seattle, San Diego, Denver, and Austin all rank among top 15 locations for venture capital and for patents (scale) and hold shares for venture capital, patents, employment in R&D-intensive sectors, and employment in digital-connected occupations that exceed their population shares (density). They also pass our highly rigorous “sniff test”—that is, they just make sense. Washington, Minneapolis-St. Paul, and Raleigh-Durham would join the list if relaxing the expectation that the share of venture investment exceed population share (which is hard due to the very high concentration in San Francisco). New York and Los Angeles are more ambiguous: they hold large venture capital markets (and venture investors frequently declare them leading tech clusters), but their patents and employment shares in key industries and fields are somewhat less than their population shares. Were we to disaggregate these huge metro areas, we would likely identify a sub-region that would independently fit on this short list by still holding sufficient scale and yet having achieved a more recognizable density. Said differently, there is surely a part of New York and Los Angeles that would be stand-alone equal to or greater than Austin (for example, Egan et al. 2017). Chicago’s activity is mostly equal to its population share or less.
What about trying to create a new tech cluster? In looking at why current tech clusters are located where they are, it becomes clear that there is often a large amount of chance involved–that is, decisions made by a few key actors at key moments, sometimes for family reasons, about where an \”anchor firm\” for the tech cluster would end up being located. For example, before Bill Gates and Paul Allen moved to Seattle, they were living in Albuquerque where Microsoft was based. Kerr and Robert-Nicoud write:
In most accounts of the origin of tech clusters, such as Klepper’s (2010, 2016) comparisons of Detroit and Silicon Valley, emphasis is given to the initial placement of a few important firms and the spinoff companies they subsequently generate. This outsized influence for anchor firms generates ample room for random influences on the early location decisions vital to a future cluster. For example, William Shockley, who shared a Nobel Prize in Physics for his work on semiconductors and transistors, moved to the San Francisco area to be near his ailing mother. Later, the spinoffs from his firm Shockley Semiconductors included Intel and AMD. Similarly, Moretti (2012) describes how personal factors led Bill Gates and Paul Allen to move Microsoft from Albuquerque to Seattle, their hometown. At the time, Albuquerque was considered the better place to live, it was favored by most of Microsoft’s early employees and the location of many early clients. Yet proximity to family won out, and this decision has reverberated well beyond Microsoft’s direct employment. The agglomeration advantages sparked by Microsoft have attracted countless other tech firms to Seattle, including Jeff Bezos relocating from New York City to Seattle when he founded Amazon. Had Gates and Allen not moved home to Seattle, Albuquerque might be home to two of America’s three most valued companies in 2020.A similar and related randomness arises due to the often-serendipitous nature of breakthrough discoveries and their outsized subsequent importance. Zucker, Darby, and Brewer (1998) show that the location of biotech industry follows the positioning of star scientists in the nascent field, and the surging prominence of Toronto for artificial intelligence harkens back to the choice of some key early researchers to locate there, well before the field became so prominent. Duranton (2007) formalizes how
random breakthroughs could lead to shifts in the leadership of cities for a tech field
or industry, such as the migration of semiconductors from Boston to Silicon Valley. …
While random sparks play a role, the same breakthroughs often occur contemporaneously in two or more locations (Ganguli, Lin, and Reynolds 2019). Accordingly, a new line of work considers the factors that shape which location emerges as the winner. Duran and Nanda (2019), for example, study the widespread experimentation during the late 1890s and early 1900s as local automobile assemblers learned about the fit between this emerging industry and their city. Despite having fewer entrants initially, activity coalesced in smaller cities—Cleveland, Indianapolis, St. Louis, and Detroit—with Detroit being the ultimate winner by the late 1920s. The smaller city advantage may have been due to the higher physical proximity of relevant stakeholders, allowing for easier experimentation, prototyping, and circulation of ideas. So long as smaller cities had sufficient local input supplies, they may have provided more attention and financial support to the new technology compared to larger markets and fostered relational contracts.
The unique origin of each existing tech cluster suggests future efforts to seed from scratch are likely to be similarly frustrating. Instead, a better return is likely to come from efforts to reduce the local costs of experimentation with ideas (Kerr, Nanda, and Rhodes-Kropf 2014), alongside the provision of a good quality of life. There is also likely a role for cities that have developed a position in an emerging sector, even if by random accident due to family ties, to increase the odds they are favored in the shakeout process. Such support is more likely to work if it is broad-based to a sector and avoids attempting to “pick winners” by targeting individual companies. Other cities can take the strategy of increasing
their connectivity to leading centers via remote work. Tulsa Remote pays qualified workers with remote jobs $10,000 to move to Tulsa, Oklahoma, and similar programs are popping up elsewhere. Rather than seeking to “become the next Silicon Valley,” these efforts focus on connecting with the existing hotspots and being an attractive alternative with a lower cost of living.
Beyond anchor firms, universities also feature prominently in the history of tech clusters, both for the United States and globally (Markusen 1996; Dittmar and Meisenzahl 2020). Under the guidance of Fred Terman, Stanford University fostered a strong relationship with the growing tech community, such as the 1948 creation of the Stanford Industrial Park that would house 11,000 workers from leading tech firms by the 1960s. … Hausman (2012) documents how university innovation fosters local industry growth, and these spillovers can attenuate rapidly (see also Andersson, Quigley, and Wilhelmsson 2009; Kantor and Whalley 2014). … These historical examples are starting to provide insight that will advance our theory on tech clusters. Duranton and Puga (2001) model a system of cities
in which new industries are emerging in large and diverse “nursery” cities.