Link: https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.34.3.50
Defining Tech Clusters
- “Clusters” traditionally indicate an important overall scale of local activity, complemented by spatial density and linkages amongst local firms.
US Tech Clusters
- Should Wall Street and the surrounding area of lower Manhattan be considered a tech cluster? After all, Goldman Sachs in 2020 employs more engineers than the total combined workforces of LinkedIn and Twitter.
- Maybe the Wall Street of the 1980s was not a tech cluster, but the Wall Street of 2030 might be. Perhaps Wall Street is evolving from being a cluster specialized in a sector — financial services — into a cluster specializing in a function — (fin)tech.
- Ranking of US metropolitan areas in terms of venture capital investment in descending rank - the format is city (% of VC investment): San Francisco (48.1%), New York (15.3%), Boston (10.5%), Los Angeles (6.5%), Seattle (2.1%).
- Patenting and especially venture capital investment are underrepresented outside of leading tech centers. Looking across the metro areas listed in Table 1, shares for venture capital and patents have a 0.98 correlation.
- There are challenges of defining tech clusters using the scale and density of local tech activity. 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).
Global Tech Clusters
- Marshall (1890) described three forces of what we now call agglomeration economies: knowledge spillovers, labor market pooling, and customer-supplier interactions. Economic research over the last two decades has shown all three forces, along with natural advantages of areas for certain industries (like harbors or coal mines), are important for explaining industrial clusters.
Preconditions and Dynamics of Tech Clusters
- An emerging frontier of research focuses on whether tech clusters can be created, and the necessary preconditions in doing so, with a persistent meta-finding that it is very difficult to predict where leading clusters will take root. Krugman (1991) emphasizes the role of historical accidents in explaining where clusters form and how local efforts to “become the next Silicon Valley” have a poor track record.
- When Silicon Valley went through its inflection point, many other cities would have looked much better prepared in terms of industry composition and talent base to be the next leading center. Indeed, accounts of the formation of Silicon Valley like Saxenian (1994) emphasize how the region’s “blank slate” allowed for new forms of work to emerge, versus some preexisting factor that destined the region for success. Being a “blank slate” may have worked for Silicon Valley, but it is not a strategy that consistently guarantees success!
- Random influences on the early location decisions are also vital to a future cluster. 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.
