My past several posts have looked at the density of key economic and demographic factors across America’s metropolitan regions. Today, I turn to the density of high-tech industry and of innovation. Long ago, the great economist Joseph Schumpeter highlighted the role of innovation in powering the rise of new industries, the creative destruction of existing ones, and the growth in prosperity of economies. Robert Solow won the Nobel prize for identifying the role of technology in economic growth and development. Paul Romer has shown how the accumulation of scientific and technical knowledge is the central force in endogenous economic growth. Michael Porter and AnnaLee Saxenian, among others, have shown how clusters of high-tech companies and other economic assets have propelled the rise of new firms like Intel in semiconductors, Apple in computing, Genetech in biotech, Google in search, and countless others that have introduced not just new innovations but whole new industries and epochs of regional growth.
Patents are the conventional measure of innovation. Despite their various weaknesses, patents represent a systematic, quantitative measure of innovation and are used by economists as the single dominant measure of innovation. But, as with other measures, economists tend to measure them on a per capita basis.
Innovation Density: Our measure of innovation density is patents per square kilometer. The map below shows the density of innovation based on this measure. The median density of innovation is .008 patents per square kilometer. The densest metros have more than .4 patents per square kilometer, while the least dense have fewer than .001.
The chart below shows the top 10 metros in terms of density of innovation. The density of innovation in these metros ranges from 25 to more than 100 times the national norm.
Source: USPTO 2008.
It’s not surprising that San Jose (Silicon Valley) tops the list with .831 patents per square kilometer or that nearby San Francisco is second with .446 patents per square kilometer. Los Angeles is third with .41 patents per square kilometer, followed by Trenton, Bridgeport-Stamford, Connecticut, Greater Boston, Boulder, Greater New York, Ann Arbor, and New Haven.
The density of patents is closely associated with key regional economic outcomes such as regional wages (.668), regional incomes (.588), and regional economic output (.459). (As usual, I point out that these correlations only suggestion associations between variables. They do not specify any causation or make any claims about the direction of causality. Other intervening variables may come into play).
The scattergraph above plots the relationship between innovation density and regional wages – a key indicator of regional wealth and productivity. The close adherence of the observations to the fitted line suggests a close association between the two. Anchorage, Washington, D.C., Boulder, Trenton, Fremont, Napa, and, of course, San Jose are all located above the line – showing even higher wages than their density of innovations would predict. Most of these places are among the most innovative in the country.
High-Tech Density: I now turn to the density of high-tech employment. Our measure of high-tech density is the number of high-tech workers per square kilometer.
The next map shows the density of high-tech employment for U.S. metros. The median density of high-tech employment across all U.S. metros is less than one (.901) high-tech worker per square kilometer. The densest metro has nearly 40 high-tech employees per square kilometer, while the least dense have fewer than .1 high-tech workers per square kilometer.
The next chart shows the top 10 metros in terms of density of high-tech workers. The density of high-tech workers in these metros ranges from 15 to 42 times the median for all metros.
Source: Bureau of Labor Statistics 2008.
Los Angeles tops the list with 39 high-tech workers per square kilometer. San Francisco is next with 30, followed by Trenton (29), San Jose (Silicon Valley) (24), New York (23), Bridgeport-Stamford (22), Greater Boston (21), Greater Washington, D.C. (19), Boulder (17), and New Haven (14).
The next map shows the density of high-tech employment compared to what we’d expect given their population density based on a residual analysis.
The next chart shows the 10 metros which have the highest density of high-tech employment compared to what their population density would predict.
Source: Bureau of Labor Statistics 2008.
Now San Jose (Silicon Valley) tops the list, followed by Boulder – which BusinessWeek recently named the best place for high-tech startups – and Greater San Francisco. Trenton is fourth followed by Greater Washington, D.C.; Greater Boston; Los Angeles; Huntsville, Alabama; Bridgeport-Stamford; and Seattle.
High density is closely correlated with regional wages (.604), regional income (.509), and regional economic output (.372). (As usual, I point out that these correlations are only a sign of associations between variables. They do not necessarily mean there is causation and I do not make any claims about direction of causality. And, of course, other intervening variables may come into play).
The scattergraph below plots the relationship between high-tech density and wages. San Jose, Washington, D.C., and San Francisco are all well above the line.
My next post sums up the key findings of our density analysis.