Abstract
Innovation is expected to exhibit strong geographic clustering because new product commercialization relies on knowledge that is cumulative and place-specific. This Chapter explores spatial patterns of innovation using the innovation citation data from the United States Small Business Administration (SBA). The data are based on new product citations and capture innovation which, by nature of the citation, add new, economically-useful knowledge to a product category.
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Chapter Notes
The computerization of the U.S. Patent office provided Jaffe (1989) an opportunity to look at regional patenting activity. Also see Jaffe, Trajtenberg and Henderson (1993).
Branch plant locations are more likely to engage in process innovation (Howells 1990).
This data was analyzed in considerable detail by Acs and Audretsch (1988, 1990 ). The data are introduced on page 4. A more complete description of the data is provided in Appendix A.
Information as to the economic significance or the revenue generated by each innovations is not available. An example of the demands of this type of determination is provided by Trajtenberg (1990) in an extensive study of CT scanners.
Patent counts by state are from Jaffe (1989) and represent the average annual number of patents received in 29 states over an eight-year period. High-technology employment data are from the U.S. Office of Technology Assessment (1984) for the year 1982 for the ten states with the highest high-technology employment levels. R&D expenditures are from the National Science Foundation as reported by Jaffe (1989). An alternative employment-based measure of innovations is high-tech employment as a percentage of non-agricultural employment (Glasmeier, 1985 ). This measure had a correlation coefficient of. 4474 with innovations and. 3201 with patents.
Estimates of employment in high-technology industry used in this table are from Glasmeier (1985). This measure is used because the counts of high-technology employment used in table 3–1 are available for only ten states. This measure has a correlation coefficient of. 4474 with innovations and. 3201 with patents.
Patent counts are from Jaffe (1989) and represent the average annual corporate patenting activity for a state for the years 1972–1977, 1979 and 1981. Jaffe only provides data for 29 states, and the ranking in Table 3–2 reflects the relative position out of the 29 cases.
Zvi Griliches (1992) provides a review of empirical literature on R&D spill-overs.
All data sources and a detailed description of the data and measures can be found in Jaffe (1989). Jaffe uses an eight-year sample (1972–1977, 1979 and 1981) and the data presented in Table 3–8 represent the eight-year average.
The number of observations for patents, P;,, is the product of 5 technical areas, i,and 29 states, s. For comparability with Jaffe (1989), the number of innovation observations excludes twenty zero cases. The zero cases are included in Chapter 4 when these results are extended using a Tobit analysis.
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© 1994 Springer Science+Business Media Dordrecht
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Feldman, M.P. (1994). Spatial Patterns of Innovation. In: The Geography of Innovation. Economics of Science, Technology and Innovation, vol 2. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-3333-5_3
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DOI: https://doi.org/10.1007/978-94-017-3333-5_3
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-4363-4
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