New technology develops from complex interactions among numerous R&D labs located in particular places. The interactions among labs form R&D networks, which infl uence the rate of invention, the geography of spillovers (that is, the location of social benefi ts associated with a university’s research and patents), innovation and technology diffusion. This section provides a
brief description of the methodology for identifying and analyzing R&D networks using patents and patent citations.
R&D networks are constructed from interactions between R&D labs of particular organizations (for example, IBM or MIT) located in a specifi c region, working on a specifi c technology (for example, ‘Dynamic Informa- tion Storage and Retrieval’), in a particular time period. Interactions are identifi ed using the references on the front page of a patent, which serve the legal function of identifying ‘prior art’ upon which the current invention builds. These citations have been shown to capture knowledge fl ows among citing/cited organizations (Jaffe et al., 2000). Patent citations are interpreted as refl ecting communication. Communication takes many forms, which include reading papers, attendance at conferences, hiring consultants, word-of-mouth, analyses of patent data, hiring university graduates and personal communication.
R&D Networks: the Infl uence of a Region
Once nodes have been organized into networks, we measure the ‘system infl uence’ of each node. The estimate of each node’s system infl uence is based on the strength of its communication with other nodes, weighted or compounded by the strength of communication of the interacting nodes with the rest of the system. Once network node strengths are mapped, it is possible to accumulate the ‘system infl uence’ of each node (that is, a measure of the infl uence of a particular lab, technology or metropolitan region within the innovation system). A region’s infl uence within a specifi c technology, such as MEMS, refl ects interactions within the region’s R&D network as well as among MEMS R&D networks worldwide.
MEMS Patents
The data are drawn from the universe of patents granted by the US Patent Offi ce from 1963 to 1995. Information on patent citations begins in 1977.
Electronic data on assignee are available, beginning in 1969. We locate patents geographically, using the inventor’s address, which means that location in our analysis is the R&D lab’s location, not the headquarter (assignee) location. In addition to country and state, inventors have been sorted fi rst into counties and then into metropolitan areas.
We developed a core database of about 1200 MEMS patents, starting with a short list of key inventors and federally funded MEMS projects. Citations of these initial patents were used to identify additional MEMS candidate patents. Each candidate patent abstract and exemplary claim was read to ensure that the patent was a MEMS technology.9
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MEMS is Highly Geographically Concentrated
Our analysis of US regions shows that the development of MEMS is highly concentrated in a handful of places. The San Francisco Bay Area, Boston, Los Angeles, New York, Chicago and Dallas account for the bulk of the technology; Ohio is a minor player (Fogarty et al., 2002).
Figure 4.1 graphs the relative MEMS infl uence of US metropolitan regions. (Regional infl uence is calculated in two ways: as a source and as a destination for knowledge fl ows, both of which are communicated in patent citations. The top metropolitan regions are distinguished by being infl uential as sources of as well as learners from R&D networks.)
Figure 4.1 Regional distribution of MEMS technology, 1985–95
Following this fi gure are two additional charts: Figure 4.2 identifi es the top 25 most infl uential MEMS technologies (patent classes); Figure 4.3 lists the top 25 most infl uential organizations producing MEMS technolo- gies. Each was derived using the methodology described above. IBM was identifi ed as the most infl uential organization. One university (MIT) and one federal R&D lab (the DOE) rank within the top 25 as a source of MEMS technologies.
An important characteristic of enabling networks is location in a successful regional agglomeration supportive of new technology develop- ment. In a pre-competitive, incubation phase, geography plays a critical function: the accumulation of a critical mass of strong network connections 0 2 4 6 8 10 12 14 Source
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Figure 4.2 Fuzzy MEMS organization R&D networks
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that speed growth of the enabling technology. Because R&D labs have a specifi c location, an agglomeration of strong R&D networks serves a dual function; good regional sources of a technology are also good learners worldwide.
A high degree of geographic concentration of MEMS technology suggests the necessity for a region to invest suffi ciently to develop ‘critical mass’ to compete. As Figure 4.1 showed, not one of the top 20 MEMS regions is in Ohio. Only a handful of regions are real players (the top fi ve or six). The implication is that Ohio’s MEMS R&D network is weak, a condition that largely refl ects the absence of a large number of well-connected industry R&D labs specializing in the technologies critical to MEMS.
A BioMEMS Niche?
The high geographic concentration of MEMS technology suggests that, to be successful, states like Ohio would have to identify a niche within a range of technologies represented by MEMS. Ohio cannot be competitive for all categories of MEMS, certainly not without impossibly large sums of money and a whole new cast of industry R&D labs. Therefore, choosing to invest in MEMS does not make sense unless Ohio commits itself to building suffi cient scale to be competitive. One niche that has emerged is bioMEMS.
Figure 4.4 below depicts a hypothetical, stylized pattern refl ecting one possible outcome that keeps advantaged regions in the lead for many years.
The pattern indicated by MEMS as a whole, as well as other important technologies, suggests that markets tend to split regions into two groups:
advantaged (a net infl ow of ideas, talent, venture capital, investment) and disadvantaged (a net outfl ow of these key resources).
The fi gure shows a region’s share of a specifi c technology in a particular year (say 2000) as depending on its share of the technology in an earlier year (say 1995). The technology results from the interactions of R&D organizations (knowledge fl ows) in our regional innovation system and technology transfer mechanisms linked to the region’s university, Federal and industry R&D labs. Its shape and position refl ect both capabilities and connections. To the left (places with sparse local MEMS R&D networks), the market works against the region; to the right (places with dense local R&D networks), the market works for the region. Without intervention by the state, a region with a share of the technology equal to A will tend to lose share (from A to B, C, D); a region with a share equal to E will tend to gain share (from E to F, G, H).
If this condition characterizes MEMS, then to overcome the disadvantage associated with a position below critical mass, public investment must raise
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Figure 4.3 Fuzzy MEMs technology R&D networks
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the state’s MEMS’ capabilities from B to R (above critical mass) to produce permanent effects. Above the critical mass share, the market would tend to work to the region’s advantage, causing the fl ows to be reversed.
Developing a more informed policy requires learning more about the necessary scale for critical mass. The scale of investment necessary to achieve critical mass for bioMEMS is clearly less than what is necessary for all of MEMS. Because it represents a slice of MEMS, bioMEMS’ scale would lie closer to the ‘tipping point’ X.
One Approach to Identifying a Region’s Potential BioMEMS Infl uence One indication of Ohio’s prospects for bioMEMS comes from a comparison of metro regions’ ranks in two technologies: biomedical devices and MEMS.
These ranks include a separate analysis of R&D networks associated with biomedical devices using the same methodology as applied to MEMS (Fogarty et al., 2000). Remarkably all of the country’s top R&D centers are top-ranked in both technologies (the San Francisco Bay area, Los Angeles, Boston and New York–New Jersey). However, below these leaders, not one
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metropolitan region appears to play an infl uential role in both technolo- gies. Within the Midwest, Minneapolis is clearly the next-best region. It ranks third as a source of biomedical device technologies and fourteenth in MEMS. Detroit is the Great Lakes top MEMS region, ranking eighth nationally. However, because Detroit is absent from the list of infl uential sources of biomedical device technologies, and because of its poor per- formance creating and building new companies, it is unlikely to become competitive in bioMEMS.
How do Ohio’s metropolitan areas stack up? In MEMS, Cleveland ranked 25th in infl uence, while Columbus was well below, in 36th place. No other Ohio metropolitan area was ranked for the period we have analyzed (1985–95). For biomedical devices, Cincinnati (13th) and Cleveland (14th) were essentially tied. Columbus was ranked 17th.