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An Examination of the Geography of Innovation MARYANN P. FELDMAN (Economics and Management, Goucher College, 1021 Dulaney Valley Road, Baltimore, MD 21204, USA) This research examines spatial patterns of manufacturing product innovation and provides a model of factors contributing to geographic concentration of innovation. The model maintains that innovation is related to concentrations of innovative inputs, including: university R&D, industrial R&D, the presence of related industry and the presence of specialized business services. Empirical estimation reveals evidence of the importance of geographically mediated spillovers. 1. Introduction While there is general agreement that the rate of technical change is important in determining an economy's rate of growth, we have a limited understand- ing of the sources of technical progress and of why the pace of progress varies so significantly over time and space (Lucas, 1993). One finding of the new growth theories is that divergence in growth rates may be a result of '" increasing returns to knowledge (Romer, 1986). This is relevant for innova- tive activity because perhaps more than other economic activities, innovation '" depends on knowledge. The ability to produce commercially viable product ] e .innovation relies on scientific and technical knowledge coupled with knowl- i edge of the market. As a result, geographic concentrations of knowledge are N likely to create higher levels of innovation than would otherwise be achieved. ] o This paper provides an empirical investigation of the location of innova- > tion, and seeks to contribute to our knowledge of product innovation along .. j two dimensions. The first purpose is to develop a model of the spatial \) dimensions of innovation within a broader institutional understanding of the S innovation process. This conceptualization relates the location of innovation l to the presence of the various knowledge bases which contribute to the 1 process of commercializing technology. The second purpose is to test empiri- ..!S © Oxford University Press 1993 ------------ 451 at University of North Carolina at Chapel Hill on July 30, 2014 http://icc.oxfordjournals.org/ Downloaded from
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Innovation An Examination of the Geography of...An Examination of the Geography of Innovation MARYANN P. FELDMAN (Economics and Management, Goucher College, 1021 Dulaney Valley Road,

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  • An Examination of the Geography of Innovation

    MARYANN P. FELDMAN (Economics and Management, Goucher College, 1021 Dulaney Valley Road,

    Baltimore, MD 21204, USA)

    This research examines spatial patterns of manufacturing product innovation and provides a model of factors contributing to geographic concentration of innovation. The model maintains that innovation is related to concentrations of innovative inputs, including: university R&D, industrial R&D, the presence of related industry and the presence of specialized business services. Empirical estimation reveals evidence of the importance of geographically mediated spillovers.

    1. Introduction

    While there is general agreement that the rate of technical change is important in determining an economy's rate of growth, we have a limited understand-ing of the sources of technical progress and of why the pace of progress varies so significantly over time and space (Lucas, 1993). One finding of the new growth theories is that divergence in growth rates may be a result of

    '" increasing returns to knowledge (Romer, 1986). This is relevant for innova-~ tive activity because perhaps more than other economic activities, innovation '" depends on knowledge. The ability to produce commercially viable product ] e . innovation relies on scientific and technical knowledge coupled with knowl-i edge of the market. As a result, geographic concentrations of knowledge are N likely to create higher levels of innovation than would otherwise be achieved. ] o This paper provides an empirical investigation of the location of innova-> ~ tion, and seeks to contribute to our knowledge of product innovation along .. j two dimensions. The first purpose is to develop a model of the spatial \)

    ~ dimensions of innovation within a broader institutional understanding of the S innovation process. This conceptualization relates the location of innovation l to the presence of the various knowledge bases which contribute to the 1 process of commercializing technology. The second purpose is to test empiri--§ ..!S © Oxford University Press 1993 ------------ 451

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    cally this conceptualization introducing a measure of innovative output to the discussion of the location of innovative activity. The data are introduced and the spatial patterns of innovation are discussed. The estimation of an innovation production function reveals that innovation is found to cluster geographically in areas which contain concentrations of specialized resources which enhance and facilitate the innovation process.

    2 . Why Location Matters for Innovative Activity

    Innovation is expected to exhibit strong geographic clustering because the process of bringing new products to market relies on specific scientific and technical knowledge. Innovation has a cumulative character which builds on existing expertise. Early in the literature, Machlup (1962, pp. 161-163) pointed out that the process of solving a technical problem often raises new research questions and positions existing innovators to continue to innovate. An area with innovative activity will develop a set of specialized resources which provide comparative advantage for the next round of innovation. This process is defined by Arthur (1990) as self-reinforcing expertise and gives rise to the geographic clustering of innovative activity.

    The ability to produce innovative output is determined by a set of knowledge capabilities or inputs which are not confined to the organizational boundaries of the firm (Nelson and Winter, 1982). Knowledge inputs can be embodied in human, institutional and facility form, and will be relatively immobile (Tassey, 1991). As knowledge in a product category or industry develops, it becomes cumulative and non-transferable or place specific (SPRU, 1972; Thomas, 1985; Dosi, 1988a; Lundvall, 1988; Grossman and Helpman, 1989).

    Commercialization requires firms to organize the diverse types of comple-mentary knowledge which facilitate the innovation process (Teece, 1986). Firms may be expected to internalize knowledge sources up to the point at which external transactions become advantageous. If knowledge sources are too costly, too specialized or otherwise constrained from becoming part of the firm, then external transaction may be preferred. Geographic concen-trations of resources, known as agglomeration economies, may be concep-tualized as similar to the economies of scope realized by complementary activities in large organizations. Proximity of complementary activities promotes information transfer which lower the cost and reduce the risks associated with innovation. In other words, innovative activity benefits from the presence of a variety of resource and knowledge inputs embedded in a socially-constructed and spatially-delimited setting.

    Dosi provides five 'stylized facts' or characteristics of the innovation

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    process which are instructive in considering why location may benefit inno-vative activity (Dosi, 1988a, pp. 222-223). The stylized facts are: the uncertainty of the innovation process, the reliance on university research, the complexity of the innovation process, the importance of learning by doing, and the cumulative character of innovative activity. Each of these characteris-tics is considered in turn.

    Innovation can be viewed as highly uncertain. This uncertainty extends beyond the lack of information about anticipated events to include the existence of previously undefined scientific and technical problems. One means to reduce uncertainty in the innovation process is to participate in information exchanges to keep a company at the cutting edge of a technology and to facilitate problem solving tasks. Innovative networks can be interpreted as the formation of research communities in which firms join to exploit new developments in an industry in a timely manner (Nelson, 1990). To the extent that location promotes timely information exchange, innovation will be enhanced. The cost of membership in innovative networks is a reciprocal sharing of information which creates a de facto market for these transactions. The importance of networking for innovation in specific industries within geographic areas has been documented by Saxanien (1990) for Silicon Valley and Powell (1989) for the biotechnology industryl.

    The uncertainty involved in using a new technology provides an incentive for firms to locate together (Lundvall, 1988, p. 355). When technology is standardized and reasonably stable, information exchange may be translated into standard codes. In this case, long distance transmission of information can take place at low costs. On the other hand, when technology is complex and evolving rapidly, long distance standardized transmission is not possible. Therefore, location close to the source of the technology allows firms to translate information into useable knowledge, creating an incentive for firms using complex and dynamic technologies to locate near knowledge sources.

    A geographic concentration of rival firms may provide a knowledge resource to reduce innovation's uncertainty. Von Hippel (1988) finds that reciprocal information trading between rival firms provides an important innovative input. Allen (1983) suggests evidence of the geographic nature of information trading among rival firms in the nineteenth century English steel industry. A geographic concentration of rival firms appears to facilitate networking and problem solving, and advance the state of knowledge in the industry (Porter, 1990). As the presence of an industry expands in a given location, firms can specialize in the production of complementary products, and provide expertise to enhance solution searches and reduce uncertainty.

    1 Freeman (1991) provides a review of studies on the importance of networking for innovarive activity.

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    Universities have become important to the innovation process. Universities emphasize the free exchange and flow of information and their existence in an area creates a sort of intellectual commons. In contrast to the notion that knowledge is a public good easily transferred, via publications, gaining commercial control over a new technology requires access to individuals who can turn information into knowledge (Nelson, 1989). An example of the importance of face-to-face interaction is provided by a survey of biotechnology researchers by Grefsheim et al. (1991). This work uncovered that the most important and timely information comes from personal communications which provide information far in advance of printed sources. Interviewed researchers also felt that the stylistic limitations of formal papers limited their substantive usefulness. Specifically, 'Formal papers do not contain the experimenter's strategies and perspectives, nor can they convey what the experimenter thinks the work means and how it dovetails with or contradicts other work' (Grefsheim et al., 1991, p. 41). While it cannot be disputed that academic conferences and long-distance consulting arrangements provide a means for information dissemination, such contact is less frequent, more costly and qualitatively different.

    The complexity of innovative activity increases the scope of the activities needed to complete the innovation process. To manage this complexity, innovators must conduct intricate search procedures across a variety of disciplines to find specific information. Within each discipline searched, the source of information will be highly specialized. The limited usefulness of this information on a day-to-day basis favors external transactions.

    The increased scope of innovative activity is suggested by the increased prominence of business services. These services provide information about consumer demand and help shepherd new product innovation through a maze of regulations and product specifications. The specialized services of patent attorneys, market research and feasibility studies, and commercial testing laboratories are beyond the means of all except the largest corpora-tions to internalize. Survey work by MacPherson (1991) found that the intensity of the usage of external producer services correlates highly with realized product innovations in medical and chemical firms. Most importantly, since producer services exist solely to supply information, these firms tend to locate near their clients (Coffey and Polese, 1987).

    There is a certain element of serendipity in the search for relevant informa-tion. Shimshoni (1966) argues that the larger the number of skills and interests represented in a given geographical area, the greater the probability of encounters which may lead to fruitful information exchanges. Firms located in areas with a range of information sources to enhance the innovation process, will realize lower search costs in obtaining relevant information. ------------- 454 ------------

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    Some aspects of knowledge have a tacit nature which cannot be completely codified and transferred through blueprints and instructions. This knowl-edge is learned through practice and practical example (Nelson and Winter, 1982). Experimentation in the form of learning by, doing and learning by using play an important role in innovation. Such expertise can come from a variety of sources in related industries. It may be generated by product buyers as they provide information about their needs to enhance product design and development (Von Hippel, 1988). This expertise may be facili-tated by input suppliers who disseminate technical information which, in turn, facilitates new product innovation (Cohen et at., 1987). In addition, competitors, who face the same obstacles and bottlenecks, can be an impor-tant source of tacit information (Von Hippel, 1988; Porter, 1990).

    Recent work by Carlson and Jacobsson (1991) suggests that the market for new technologies is primarily regional. The development of technologically complex products requires close collaboration between suppliers and custom-ers. Until a product becomes standardized, constant specification and design changes make it too costly for suppliers to get involved with distant customers.

    The five characteristics presented here characterize innovation as a process reliant on timely flows of external information. The uncertainty of the innovation process suggests that one way in which firms can reduce uncer-tainty is by engaging in reciprocal sharing of information or networking with related firms. The prominence of university research argues for proximity to this innovative input to stay at the cutting edge of technology. The increased complexity of innovation suggests that other sources of information such as related industry presence and specialized business services are key to innova-tive success. These specialized information sources tend to locate near their client markets. Finally, the cumulative nature of innovative activity suggests that areas with demonstrated innovative success have assembled information to facilitate the next round of innovation. Firms located in areas with limited access to information inputs must rely on their own internal efforts and will face higher costs in acquiring information (Davelaar and Nijkamp, 1989; Brody and Florida, 1991). Firms may attempt to internalize these knowledge sources by hiring skilled individuals with relevant expertise but external transactions may be less costly. For the above reasons, innovation is expected to exhibit pronounced geographic clustering. This will be explored in the next section.

    3. Spatial Patterns of Innovation

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  • An Examination of the Geography of Innovation

    output2 . In the absense of direct measures of innovation, alternatives such as the amount of inputs used in the innovative process, for example, R&D expenditures (Malecki, 1985), or output measures such as the quantity of patented inventions Oaffe, 1989) have been used. R&D expenditures indicate resources budgeted towards producing innovative output, not the success of these efforts. The reliability of patent data has been questioned because not all patented inventions prove to be commercially viable and many successful innovations are not patented (Mansfield, 1991).

    This research uses a direct measure of innovative output to explore the location of innovative activity. In 1982, the Small Business Administration (SBA) conducted a census of innovation citations from over 100 scientific and trade journals. The SBA data captures innovations which, by nature of the citation, added new economically useful knowledge to a product category3. In contrast to patent data, which marks the certification of a new invention, innovation citations announce the market introduction of a commercially viable product4 • The SBA data contains a total of 4200 manufacturing product innovations with information on the location of the enterprise which introduced the innovations. Innovations are attributed to states in which the establishment responsible for the major development of the innovation was located6 .

    2 Griliches (1990, p. 1669) states that 'The dream of getting hold of an output indicator of inventive activity is one of the strong motivating forces for economic research in this area.'

    , This data was analyzed in considerable detail by Acs and Audretsch (1988, 1990). 4 The innovation citation data are highly correlated with other measures of innovative inputs. R&D

    expenditures are from the National Science Foundation as reported by Jaffe (1989). Patent counts by state are also from Jaffe (1989) and represent the average annual number of patents received in 29 states over an 8 year period. High-technology employment data are from the US Office of Technology Assessment (1984) for the year 1982.

    Correlations Among Measures of Innovative Activity

    Innovation PatentJ R&D Employment

    Innovation 1.0000 Patents 0.9344 1.0000 R&D 0.8551 0.8804 1.0000 Employment 0.9737 0.9888 0.7013 1.000

    5 Information as to the economic significance or the revenue generated by each innovation is not available. An example of the demands of this type of determination is ptovided by Trajtenberg (1990) in an extensive study of CT scanners.

    6 Since headquarters may announce new product innovations, the SBA data differentiates between the location of the innovating establishment and the location of the innovating entity (Edwards and Gordon, 1984). The parent company or headquarters is known as the entity. For example, Intel Corporation introduced a 16-Bit Micro-Controller (Model Number 8096). The major development was done by a division of Intel in Arizona. Intel is headquartered in California. In this case, the state of the establish-ment is Arizona and the state of the entity is California: the innovation was attributed to the state of Arizona. The site of the major development of the innovation is known as the establishment. The state identifier of the establishment is used to investigate the spatial patterns of innovation.

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    There are some limitations and potential sources of bias to note. The data are only available as a cross-section for the year 1982. The innovation citations which appear in publications may be biased towards unusual or specific items which the editors consider to be of special interest. Large firms with public relations departments may have greater rapport with journal editors resulting in an over representation of their innovative activity. This is perhaps offset by the fact that small firms aggressively pursue new product announcements as inexpensive advertising.

    States are not an entirely satisfactory unit of observation to use in this analysis. In contrast to the micro-economics literature in which the unit of analysis is generally accepted to be the firm, there is no such consensus in geography7. The analysis of geography is further handicapped by the lack of data on an ideal unit of observation. Theoretically, spatial processes occur within the boundaries of geographic areas characterized by functional link-ages and dependencies (Czmanski and Ablas, 1979). Geographic data, however, are collected and defined within the boundaries of political jurisdic-tions. While ideally we would like data at a sub-state level of aggregation, no such data currently exists.

    Table 1 presents the geographic distribution of innovations for the leading states. There is noticeable spatial concentration in the distribution of manu-facturing product innovation. Eleven states accounted for 81 % of the 4200 innovations. In order to normalize for differences in the state size, the number of innovations per 100 000 manufacturing employees is provided. New Jersey had the highest rate of innovation followed by Massachusetts and California. These states innovated more than twice the national rate of 20. 34 innovations per 100 000 manufacturing workers. Seven other states, New Hampshire, New York, Minnesota, Connecticut, Arizona, Colorado and Delaware were also more innovative than the national average.

    Table 2 presents the distribution of innovations by two-digit industries within states. The first row presents the national totals. For example, SIC 20: food and kindred products, accounted for 110 innovations introduced to the market in 1982. The second column presents the total number of innova-tions attributed to each state. Each cell in this Table corresponds to a state and industry combination. For example, Arizona was the source of 21 innovations for SIC 36: electronic and other electrical equipment and compo-nents.

    A plausible hypothesis about the distribution of innovations among states and industries is that the proportion of innovations attributed to each

    7 Agglomerations have been measured in rhe lirerarure using a variery of geographic unirs, ranging from rhe mulri-srare Manufacruring Belr (Norron and Rees, 1979) co rhe Merropoliran Sratisrical Area (Oakey, 1985).

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    TABLE 1. Which States are the Most Innovative?

    Innovations Innovations per 100000 State manufacturing workers

    New Jersey 426 52.33 Massachusetts 360 51.87 California 974 46.94 New Hampshire 33 30.84 New York 456 29.48 Minnesota 110 28.65 Connecticut 132 28.51 Arizona 41 27.70 Colorado 42 22.46 Delaware 15 21.13 National 4200 20.34 Rhode Island 24 18.46 Pennsylvania 245 18.28 Illinois 231 18.16 Texas 169 16.14 Wisconsin 86 15.61 Ohio 188 15.00 Florida 66 14.60

    SOURCE: Number of innovations is from the SBA innovation data. Number of manufacturing workers is from the 1982 CemUJ of Manufacturer! .

    industry and state is exactly determined by the probability of innovations attributed to the industry and to the state. For example, the proportion of innovations in electronics in California may be determined by the probability of an innovation occurring in the state of California and the probability of an innovation occurring in the electronics industry. [If s references the state and i references the industry, Psi = Ps. p;J. If this were true the concentration of innovations within a state and industry would simply reflect the propensity of the state and the industry to innovate with no interaction between the two influences. In such a case, the distribution of innovations would be deter-ministic and geographic factors would not affect the location of innovation. A chi-squared test of the independence of state location and two-digit indus-tries' innovations rejects this hypothesis at the 0.99 confidence level. The number of innovations in a state and industry are not determined strictly by the state and industry propensity to innovate.

    Another hypothesis about the location of innovation is that the geographic distribution of innovation in an industry is determined by the location of that industry. Innovation may be geographically concentrated because indus-tries are geographically concentrated due to considerations of cost factors (Isard, 1956). The location of industries may determine the location of product innovation. In this case, the number of innovations in an industry

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    TABLE 2. Number of Innovations by State and Industry

    Selected Two-Digit SIC Code InduJtries

    State Total 20 28 34 35 36 37 38

    National total 4200 110 292 201 1392 840 66 1042 Arizona 41 0 0 2 8 21 0 9 California 974 8 18 26 398 288 18 187 Connecticut 132 3 11 5 47 24 3 33 Georgia 53 12 2 3 12 8 1 11 Illinois 231 5 15 18 81 31 1 56 Mass. 360 13 22 15 102 0 3 120 Michigan 112 1 11 7 29 9 21 0 Minnesota 110 6 5 8 38 14 0 28 New Jersey 426 6 75 19 114 58 0 131 New York 456 20 39 20 111 0 2 147 Ohio 188 3 16 21 51 27 3 44 Pennsylvania 245 14 27 16 78 30 0 66 Texas 169 4 14 10 58 35 2 37 Wisconsin 86 2 6 28 17 29

    SIC 20: food and kindred products; SIC 28: chemicals and allied products; SIC 34: fabricated metal products; SIC 35: industrial and commercial machinery and computer equipment; SIC 36: electronic and other electrical equipment; SIC 37: transportation equipment; SIC 38: measuring. analyzing and controlling instruments.

    and state would be highly correlated with industrial presence. The correlation of the count of innovation and total state manufacturing value added was 0.236. For two digit industries and states, the correlation between innovation and value added was 0.4202. Although innovation is positively correlated with industry presence, the relationship is far from deterministic.

    The geographic concentration of innovation is even more pronounced when greater industry detail is considered. Table 3 presents the distribution of innovation by state for a subset of the most innovative three digit industries. The industries presented in this Table are ranked by the number of innovations attributed to the product category. For example, there were 954 product innovations in computers (SIC 357) and 668 product innova-tions in measuring and controlling instruments (SIC 382). Column 3 lists the number of innovations attributed to each state.

    For each three digit industry, the two states which accounted for the highest number of innovations is listed. For example, the state of California provided 365 innovations for SIC 357: on average, one innovation in the computing machinery industry in California for every day of the year. This was 38.3 % of the innovations in the computing machinery industry as indicated by column 4. The state of California had a grand total of 974 innovations and column 5 reports that the computing machinery industry

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    TABLE 3. Distribution of Three Digit Industry by State

    (1) (2) ( 3) (4) (5 ) (6) % of % of Location

    Product State Count innovations state quotient

    Computers SIC 357 n = 954 California 365 38.3 37.6 167.84 Massachusetts 82 8.6 22.8 100.44

    Measuring and controlling instruments SIC 382 n = 668 California 134 29.1 13.8 126.42 Massachusetts 94 14.1 26.1 164.15

    Communication equipment SIC 366 n = 376 California 116 30.9 11.9 132.22 New York 45 12.0 9.9 110.00

    Electrical components SIC 367 n = 261 California 128 49.0 13.2 211.29 Massachusetts 26 10.0 7.2 116.13

    Medical instruments SIC 384 n = 228 New Jersey 57 25.0 13.5 248.15 New York 51 22.4 11.2 207.41

    General industrial machinery and equipment SIC 356 n = 164 Pennsylvania 25 15.2 10.2 261.54 New Jersey 18 11.0 4.2 107.69

    Drugs SIC 283 n = 133 New Jersey 52 39.1 12.3 381.25 New York 18 13.5 4.0 121.88

    represented 37.6 % of the state innovations. We also calculated innovation location quotients for these industries. The innovation quotients were calcu-lated as the percentage of innovation in a state accounted for by an industry divided by the percentage of national innovations accounted for by that industry. The ratio is then multiplied by 100. An innovation quotient of 100 indicates that the innovations are equally represented in the state and national economies. An innovation quotient greater than 100 indicates that the region can be regarded as relatively specialized in that activity. For the 13 most innovative three digit industries, the average innovation quotient was 239.5. This indicates significant regional specialization of innovative activity for these industries.

    The analysis presented here demonstrates that product innovations exhibit a strong tendency to cluster geographically and this tendency is more pronounced when individual industries are considered. Geographic cluster-ing of innovation has been noted in the 19th century examples of cotton textiles in Lowell, Massachusetts, machinery in Connecticut, and paper in

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    western Massachusetts as well as the more contemporary examples of auto-mobiles in Detroit, semi-conductors and personal computers in Silicon Valley and aeronautical products in Los Angeles (Hall and Markusen, 1985; Rosenberg, 1962; Scott, 1989). The data presented here suggests that geo-graphic clustering is a more general phenomenon. Theoretical models indi-cate that the geographic clustering of innovative activity is due to increasing returns to non-transferable activities (Arthur, 1990; Grossman and Help-man, 1989). Based on the innovation location quotients, it appears that certain states have developed a comparative advantage for innovation in specific industries. The next section will consider the empirical estimation of innovative production functions which link the location of innovative output to the presence of specialized resources to facilitate the innovation process.

    4. An Empirical Model of Innovation and Location

    Emphasis in the industrial organization literature has shifted towards under-standing the process of achieving a commercially viable innovation (Mowery and Rosenberg, 1989). Kline and Rosenberg (1987) suggest four key know-ledge bases or innovative inputs to the innovation process: university re-search, industrial R&D, related industry presence, and specialized business services. Innovative output, INNis> the count of innovation for an industry i, in a geographic area, s, is modeled as a function of four innovative inputs:

    INNis = f (UNIVIs' INDISI RELPRESIs' BSERVIs) (1)

    where UNIVIs represents university research at the level of the academic department. INDlJ represents industrial R&D expenditures. RELPRESIs represents related industry presence, including firms using related technolo-gies and downstream users of a technology who may disseminate information relevant to innovation. BSERVIs represents the presence of specialized busi-ness services related to innovative activity within the industry. The industry group, I, is used to measure the more encompassing technological area across which informational spillovers are expected to occur. Implicit in the innova-tion process is a time lag involved in translating an invention into a commercial product innovation. The exact time lapse between discovery and subsequent commercial product innovation is highly variable and difficult to specify. For example, Mansfield (1991) estimated that the average time lag between an academic research finding and the first related commercial introduction of a new product was, on average, 7 years, with a standard deviation of 2 years. To model the expenditures on the innovative inputs, we use the average annual expenditures in the 10 year period prior to the

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    introduction of the innovation. This measure attempts to capture the stock of knowledge embodied in a geographic area and provides a more appropriate measure of regional capacity than a single year's expenditures.

    The dependent variable in the innovation equation is the number of innovations for an industry, i, which originated in state, S8. The independent variables in the model are defined as follows. University R&D is expenditure for research performed by academic departments and is from the National Science Foundation's Survey of Science Resources. Related industry presence is measured as value added for the major sector of the industry under consideration. As an example, SIC 283, drugs, would benefit from the presence of related activity in the industrial group SIC 28, chemicals and allied products.

    There are a variety of business services which provide knowledge of the market and the commercialization process. For example, the services of patent attorneys may be a critical input to the innovative process. Unfortun-ately, data on the presence of this input do not exist. All legal services are grouped together in SIC 8111 without any finer detail. Of all the producer services available to support innovative activity, the only category which is specifically and solely targeted ro the introduction of new innovations is commercial testing laboratories, SIC 7397. This is used as a rough proxy measure for business services related to innovative activity. To measure BSERV.s> the annual average receipts of commercial testing laboratories are used.

    Industrial R&D is measured as expenditure for industrial R&D performed within companies as reported by the National Science Foundation's Science Resources Survey. These data do not include the cost of R&D contracted to outside organizations such as universities and colleges, nonprofit organiza-tions, research institutions and other companies. The data represent the 10 year average of R&D expenditures. NSF confidentiality requirements mean that data is unavailable for some locations. Industrial R&D expenditures are available for 29 states. The available state data contain over 92 % of the total innovations introduced in the US in 1982. The sample for estimation accounts for 78% of the US population and 81 % of the university research expenditures in 1982. Conversely, states for which R&D data is unavailable account for 325 innovations or 7.7 % of the total innovations. The estimation

    • The estimation is based on the innovation data for the 13 most innovative three-digit industries. These 13 industries account for 80% of the total innovations. There were a total of 95 three-digit industries which contain at least one innovation citation. When the data are stratified by state, a large number of zeto cells result. In order to proceed with the estimation, it is necessary to limit the sample. Each industry included in the estimation contains a minimum of 50 innovation citations for the year 1982. In the sample used for estimation, there are 140 state and industry observations, or 37 %, with zero innovations.

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    uses the resulting combination of 29 states and 13 industries which yields 377 observations.

    Using state level data to model a more local phenomenon may bias the parameter estimates. Two additional variables are added to control for aggregation bias. State population, POP.s> is included as a control for state size and to facilitate cross state comparisons. A geographic concentration variable, CONC.s> is added to control for within state variation. This variable measures the degree to which manufacturing activity is concentrated within states and is added to the equation to compensate for the use of states as the unit of observation. The numerator of the concentration index is the value of manufacturing shipments for the largest MSA in the state and the denomi-nator is the value of manufacturing shipments for the entire state from the Census of Manufacturers9 . Industry sales, SALES i . are included as a control for product demand which may affect the quantity of innovations generated within an industry.

    The model is estimated for two variations of the industrial R&D variable to consider the empirical question of the technological area across which 'spill-overs' associated with industrial R&D occur. The first variation measures total industrial R&D for the state, IND. s:

    log(INNs) = 13 1log(UNIV/s) + I3Jog(lND.J + 133log(RELPRES 1s ) + 134 log(BSERV.J + 135CONC.s + 136log(POP.s)

    (2)

    The second variation restricts industrial R&D to a given set of related industries within the state, IND/s:

    log(lNNis ) = 13 1log(UNIV1J + 132log(lND1s) + 133 log(RELPRES1J + 134log(BSERV.J + 135 CONC .s + 136log(POP s )

    (3)

    The second specification requires detail on industrial R&D expenditures at the two digit industry level. Unfortunately, before 1987, NSF only provides data on total industrial R&D expenditures at the state level and to proceed it is necessary to estimate industry level expenditures. Data from the 1987 National Science Foundation, Survey of Industrial Research and Development was used to allocate the average state R&D expenditures to two digit industries within a state, IND1s' Expenditures are classified under the industry of the

    9 For the estimation of the innovation equation the log of the geographic concentration variable is not taken. There is no strong a priori functional specification and the estimation of the innovation equation with a log transformation of this variable yielded similar results.

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    performing company. The allocation method described above assumes that the percentage of industrial R&D originating within an industry and state has remained relatively stable. There is no evidence that the within state allocation of R&D expenditures has changed. Malecki and Bradbury (1991) found that the geographic location of corporate R&D facilities in the US has remained consistent since 1970. Glasmeier (1988) noted a movement of scientists and engineers to the South and West during this time period, attributing this migration to the establishment of technological branch plants which locate R&D activity nearer the locus of production. Howells (1990, p. 138) found that these units are more oriented towards process innovation and incremental improvements and are fundamentally different from central R&D laboratories likely to be involved with product innova-tion. Nationally, the percentage of R&D expenditure allocated to the two digit industries considered here was relatively stable from 1978 to 1987 and the within state R&D expenditures allocation is assumed to be similarly stable.

    There are several estimation issues to consider. The dependent variable, the number of innovations by state and industry, is a censored dependent variable. The number of innovations will either be zero or some positive integer. Cases with no innovations provide information about how innova-tive locations differ from non-innovative locations. For these reasons, the Tobit model is used. An additional econometric concern always suspect with geographic cross-sectional data is multicolinearity. With state data, it is highly likely that the independent variables may be affected by some com-mon trend or underlying state characteristics. 10 Multicollinearity may result in less statistically significant coefficient estimates than expected (see Table 4).

    IND., IND[s

    UNIV I ,

    RELPRESS Is BSERV.,

    TABLE 4. Correlation Matrix for Innovative Inputs

    IND., IND[s

    1.00 n.a. 1.00 0.68 0.51 0.63 0.58 0.73 0.53

    UNIVIs

    1.00 0.39 0.56

    RELPRESIs BSERV"

    1.00 0.53 1.00

    NOTE: Reporred correlarions are for rhe log values of each of rhe variables. n.a. indicares rhe variables do nor appear in rhe model rogerher.

    10 No conclusive presence of sparial-correlarion was found and no correcrion was made. Geographic fixed effecr models were esrimared and rhe resulrs were robusr. Addirionally, Breusch-Pagan resrs revealed no hereroscedasriciry in rhe innovarion equarion specificarion.

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    5. Empirical Results

    The empirical results confirm that innovative activity within states is related to the presence of a broader technological infrastrucrure within the state. Table 5 presents the results of the maximum likelihood estimation of the Tobit specification of the innovation equation. The innovative inputs of university R&D, related industry presence, and the presence of specialized business services, are positive and statistically significant. Results are pre-sented for two variations of the industrial R&D variable: model A uses total industrial R&D expenditures, IND.,; model B uses estimates of R&D for the two digit industry, IND[so This allows an empirical testing of the extent of the technological neighborhood across ~hich R&D spill-overs occur. For example, do R&D spill-overs occur within a narrow band of related in-dustries or does R&D spill-over cross the entire spectrum of manufacturing industries? A comparison of the two models suggests that model A, using total industrial R&D expenditures, provides a better model of the innovation equation than the alternative model B. In as far as any inferences can be drawn, model A exhibits a better statistical fit. Total industrial R&D expenditures has a larger and more statistically significant coefficient than

    TABLE 5. Tobit Estimation of Innovation Equation

    Variable Model A Model B

    Log(IND.s) 0.190a (0.054)

    Log(lND js ) 0.049" (0.034)

    Log(lNIVjs) 0.123a 0.176a (0.044) (0.045)

    Log(RELP RES Is) 0.296a 0.35S" (0.046) (0.050)

    Log(BSERV.S> O.llsa 0.212a (0.057) (0.057)

    Log(POP.s) 0.103a 0.131 a (0.030) (0.033)

    Log(SALES j,) -0.230 -0.260 (0.117) (0. 12S)

    CONC.s 1.020a 1. 113" (0.195) (0.214)

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    industrial R&D expenditures estimated at the industry level. The elasticity of innovative output with respect to total industrial R&D expenditures is three times the elasticity of industry specific R&D expenditures. The higher coefficient on total R&D expenditures may well reflect spill-overs across industries but still within state boundaries. Furthermore, research and development expenditures are usually undertaken by large integrated cor-porations with interests in several product categories. The lower coefficient for INDlJ may reflect the fact that industry assignment is based on the industry code of company performing the R&D, not on the industry towards which the research efforts and expenditures are directed. The results indicate that the presence of research expenditure undertaken by universities within the state also increase innovative output. Since the university research ex-penditure variable, UNIVIs> is measured at the departmental level, the results indicate that the strength of the research expenditure at the academic departments relevant to the innovative industry will have a statistically significant effect on innovative output within the state ll. The finding that university research expenditure translates into increased innovative activity in a state is supported by recent work by Jaffe (1989), Mansfield (1991), and Acs et al. (1992).

    Related industry presence, RELPRESIs> is statistically significantly associ-ated with innovative activity in a state. This indicates that experience with a technology as embodied in the manufacturing process, translates into in-creased innovative output in a state. Related industry presence was measured as value added in the larger industry group to which the innovation is attributed. This variable has the largest elasticity of innovative output with respect to an innovative input. This result supports the idea that learning by doing and learning by using are important inputs to the innovation process. The magnitude of this coefficient raises questions about the relationship of industry presence to the other innovative inputs. A strong industry presence in a region may reflect higher industrial and university R&D expenditures in related technical areas.

    The final innovative input is specialized business services, BSERV.s' Specialized business services ease the innovation process by providing infor-mation about diverse items such as next round capital financing or regula-tions regarding product testing and standardization. Their presence in an area may facilitate contractual arrangements to incorporate this knowledge

    II An alrernative specification of the innovation equation using tOtal university research expenditure, similar to the specification presented using two variations of industrial R&D, was estimated. Total state university research expenditure was statistically significantly related to innovations within the state, however, the overall fit of the model declined. Pragmatically, this is understandable because it is less plausible that basic research in biology would spill-over to innovations in an unrelared field such as electronics.

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    into the innovation process. Specialized business services are statistically significantly related to innovative output at the state level. This finding is consistent with MacPherson (1988) which suggests that localized external sources of specialized business services contribute to realized innovative output.

    Three control variables were added to the model: SALESi., POP.s and CONC. s • Industry sales, SALESi ., provides a control for the three digit industry to which the innovation belongs. As discussed earlier, all the industries used in the analysis are highly innovative opportunity industries. This variable provides a control for the total national industry sales which is related to the demand for the industry's products. This variable was not statistically significant. Population size, POP.s and the geographic co-incidence variable, CONC." are included to mitigate the effect of aggrega-tion bias caused by using states as the unit of observation. As discussed earlier, states are a less than satisfactory unit of observation. It is difficult to consider that economic spill-overs occur equally in California and in Rhode Island. For this reason, POP.s provides a control for the size of the state and facilitates cross state comparisons.

    The geographic coincidence index, CONC.s , provides a control for within state variation in the concentration of. manufacturing activity. We would expect that the more concentrated manufacturing activity is within a state, the more likely that innovative inputs would spill-over into increased in-novative output. The geographic coincidence variable is of the hypothesized sign and is statistically significant. This result confirms the view that within state concentrations of manufacturing activity are also important to innova-tive output.

    The results suggest that the clustering of product innovation at the state level is related to the presence of innovative inputs. These findings are consistent with the view of innovation as a process facilitated by diverse types of expertise and knowledge (Kline and Rosenberg, 1987).

    6. Summary and Discussion

    The evidence presented here suggests that the process of introducing new products to the market is facilitated by location. Product innovations exhibit a pronounced tendency to cluster in states which contain concentrations of innovative inputs.

    These states appear to have developed a comparative advantage for innova-tive industries which does not rest on natural resource endowments or the availability of low cost labor. The comparative advantage for innovative

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    activity is provided by the location of specialized knowledge resources which enhance the innovation process.

    By many indicators, US research capabilities are the envy of the world. This lead appears to be lost in the translation of scientific knowledge into new commercial innovations. American firms suffer from longer develop-ment cycles for new products and there is evidence that a wide variety of US industries have failed to commercialize new innovations effectively and rapidly 12. This evidence, coupled with America's declining trade perform-ance, has provoked wide debate over the need to find new ways to organize innovative activity. The results presented here suggest that geographic location and the geographic complementarity of innovative inputs may warrant further study and consideration.

    Acknowledgements

    The author would like to thank Zoltan Acs, David Audretsch, Wesley Cohen, Giovanni Dosi, Richard Florida, Michael Fritsch, Mark Kamlet, Edward Malecki, and Paula Stephan, the participants at the WZB seminar on the Geography of Innovation, and the three anonymous referees for comments. Cynthia Brandt provided research assistance.

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