Abstract
Drawing on recent research emphasizing the role played by social and collaboration networks in driving the spatial diffusion of scientific and technological knowledge, this chapter presents new evidence on the structural properties of knowledge networks in 331 US cities based on European Patent Office data for the period 1990–2004. Interestingly, and differently from previous studies, the chapter not only looks at cities’ internal network topological structure, but also at the embeddedness of metropolitan inventors within the broader US-wide collaboration network. To this end, it proposes new indicators aimed to capture US cities’ propensity to engage not only in local, but also in global knowledge exchanges. In particular, the chapter proposes a classification of US cities according to these dimensions and examines the evolution of metropolitan co-invention networks structural properties in a diachronic perspective. These trends are finally associated to cities’ inventive and economic performance.
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Notes
- 1.
Our definition of internal (i.e. within cities) vs. external (across cities) ties is based on inventors’ address as available from patent data, regardless they work in the same area or not.
- 2.
The reference year is the priority year, i.e., the first date at which the patent was applied for anywhere in the world, as it is the closest to the actual time of the invention.
- 3.
MSAs are defined according to the June 2003 definition of MSAs (http://www.census.gov/population/www/metroareas/metrodef.html). In absence of information on the MSA, information on the state and the zip code were used to assign an inventor to the corresponding MSA using ZIPList5, a commercial database listing every active ZIP code currently defined by the U.S. Postal Service (http://www.zipinfo.com/products/z5cbsa/z5cbsa.htm). For each ZIP code the database identifies the MSA in which the ZIP code lies.
- 4.
As in Fleming et al. (2007) and Lobo and Strumsky (2008), the metropolitan (also termed as internal or local) co-invention network is composed of the subset of nodes located in a given city and the ties among them; its structural properties therefore determines a city’s network structure. Differently, the external network is composed of the links connecting nodes residing in different cities.
- 5.
This is the largest group of connected nodes in a network; more formally, it is the largest sub-graph that contains the largest number of nodes.
- 6.
The reciprocal of an infinite distance, i.e., when two inventors in the network are not reachable, is set at 0.
- 7.
The choice of the median value for the creation of the classification is preferable to the use of average values as both internal and external reach show a very skewed distribution as summary statistics in Table 16.1 show.
- 8.
Because both internal and external reach are scale variant, we cannot exclude that this result can be influenced by an increase of the external network greater than the increase of the average internal network.
- 9.
Source: US Bureau of Economic Analysis (BEA) Regional Economic Accounts (http://www.bea.gov/regional/reis/).
- 10.
Source: http://www.bea.gov/iTable/iTable.cfm?reqid=70&step=1&isuri=1&acrdn=5#reqid=70&step=26&isuri=1&7023=7&7024=Non-Industry&7001=720&7090=70&7029=20&7031=5&7025=5&7022=20 (for regional per capita income data) and ftp://ftp.bls.gov/pub/special.requests/cpi/cpiai.txt (for CPI data).
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Breschi, S., Lenzi, C. (2013). Local Buzz Versus Global Pipelines and the Inventive Productivity of US Cities. In: Scherngell, T. (eds) The Geography of Networks and R&D Collaborations. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-02699-2_16
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