Overseas R&D, knowledge sourcing, and patenting: an empirical study of Japanese R&D investment in the US

Research Policy 33 (2004) 807–828

Overseas R&D, knowledge sourcing, and patenting: an empirical study of Japanese R&D investment in the US夽 Tomoko Iwasa∗,1 , Hiroyuki Odagiri2 National Institute of Science and Technology Policy, Ministry of Culture, Sports, Education, Science and Technology, 1-3-2 Kasumigaseki, Chiyoda-ku, Tokyo 100-0013, Japan Received 12 July 2002; received in revised form 22 September 2003; accepted 14 January 2004 Available online 19 March 2004

Abstract This paper purports to study the contribution of R&D at home and abroad to the firm’s inventive activity, using a sample of 137 Japanese multinationals. The empirical analysis relates the number of inventions in Japan and that in the US, as measured by the number of patents issued by the USPTO, to the parent’s R&D, the US subsidiaries’ R&D, the presence of R&D in Europe, the firm’s experience in the US, entry mode, and industry dummies. In addition, to study the subsidiary’s role in sourcing local technological knowledge, we construct indices of local technological strength of the state in which the subsidiary is located. The results, most importantly, indicate that these indices positively contribute to inventions at home and in the US among Type R firms, whose R&D subsidiaries mainly aim to research, suggesting that knowledge sourcing is an important function of these subsidiaries and locational choice is important for this purpose. These results do not hold among Type S firms, whose R&D subsidiaries mainly aim to support local manufacturing and sales activities. © 2004 Elsevier B.V. All rights reserved. JEL classification: F23; O32 Keywords: Overseas R&D; Knowledge sourcing; Spillover; Absorptive capacity; Japanese firms

1. Introduction This paper purports to study the contribution of R&D activities of Japanese multinationals at home 夽

The comments of anonymous referees are greatly appreciated. An earlier version of this paper was presented at the Annual Conference of Japanese Economic Association at Otaru, Hokkaido, June 2002. ∗ Corresponding author. Tel.: +81-45-787-2425; fax: +81-45-787-2096. E-mail address: [email protected] (T. Iwasa). 1 Present affiliation: Faculty of Economic and Business Administration, Yokohama City University, 22-2 Seto, Knazawa-ku, Yokohama 236-0027, Japan. 2 Present affiliation: Graduate School of Economics, Hitotsubashi University, Kunitachi, Tokyo 186-8601, Japan.

and abroad on their inventive activities. A particular emphasis will be placed on the contribution of overseas R&D as a means of sourcing the best and newest scientific and technological knowledge at the global level. Conventionally, the main function of overseas R&D has been considered to be the support of local sales and manufacturing activities, that is, to adapt the technologies invented at home to local input conditions, demand conditions, and regulations. Now, however, technological knowledge sourcing (or, simply, sourcing) is emphasized in many high-technology industries as a motive for overseas R&D. This reflects the emerging consensus among firms that it is indispensable for them to establish an effective R&D system so

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that they can keep an access to any scientific discovery and technological innovation generated at globally dispersed facilities and, hopefully, use it as a seed for further enhancement of their own technological capabilities. Empirical studies on overseas R&D by Japanese firms have actually found sourcing as an important motivation for overseas R&D, particularly in the US and Europe (Odagiri and Yasuda, 1996; Florida and Kenney, 1994). Only a few studied the contribution of such sourcing, however. Branstetter (2000) showed that the number of citations to US-invented patents by the patent applications Japanese firms filed in the US is positively related to the number of their US subsidiaries.3 By contrast, using the data of Swedish MNEs and estimating a production function, Fors (1997) found that their home productivity is unrelated to their overseas R&D intensity. Thus, the results have been not only few but also mixed, warranting more studies. In this paper, we aim to measure the contribution of R&D activities of Japanese multinationals both at home and abroad (confined to the US in this paper), by relating their patenting activities in the US to their strategies and characteristics, including home and overseas R&D expenditures. In pursuing this study, we will introduce two innovations. The first is the hypothesis that technological knowledge sourcing should be more effective if the subsidiary performing R&D is located in a state more favourable for sourcing. Indices of local technological strength will be developed, which consist of the local technological knowledge stock and spillovers from nearby states. Second, we separate companies into two types, ‘research-oriented’ (Type R) and ‘local-support-oriented’ (Type S), depending on the function of their main R&D subsidiaries, assuming that they differ in the relative importance of technological knowledge sourcing in overseas R&D. We will estimate a modified Cobb–Douglas knowledge production function, one for the firm’s US R&D 3 Though not directly related to FDI, Branstetter made two more studies on the contribution of US sources on Japan’s innovation. Branstetter (2001) failed to find the contribution of foreign spillovers to Japanese firms’ patenting, whereas Branstetter and Nakamura (2003) found that the Japanese firm’s citation-adjusted patent output is positively related to its number of citations to US patents, which in turn is positively related to the number of the firm’s alliances with US firms.

activity and the other for the parent’s R&D activity in Japan, applying the negative binomial estimation method to a sample of 137 Japanese manufacturing firms. It is hypothesized that technological knowledge sourcing is a significant input in this production function among Type R firms but not necessarily among Type S firms. The results, we will show, support this hypothesis by indicating that the index of local technological strength has a significantly positive effect on the patents of both the Japanese parent and the US subsidiaries only among Type R firms. The remainder of the paper is organized as follows. Section 2 discusses the determinants of technological knowledge sourcing. Section 3 gives a brief discussion of the R&D activities in the US by Japanese firms. Sections 4 and 5 explain the variables used in this analysis and Section 6 explains the model. Section 7 discusses the distinction between Type R firms and Type S firms. The estimation results will be given in Section 8, followed by our conclusion in Section 9.

2. Sourcing of overseas technological knowledge The conceptual framework for our analysis is shown in Fig. 1. R&D activity of the firm, whether at home (Japan) or abroad (US), serves two purposes. The first, direct one is to make research on specific projects. It purports to invent new products or new processes, the outcome of which may be conveniently measured by the number of patents though, admittedly, there are inventions (e.g., knowhows and new designs) that cannot be patented and the value of patent varies widely across inventions. The second, indirect one is to increase the amount and diversity of knowledge accumulated within the firm, which enhances its absorptive capacity and the general level of the firm’s technological capabilities. This latter purpose is closely related to the firm’s sourcing activity because, with enhanced absorptive capacity, the firm can take more advantages of the technological knowledge stock present in the local areas of its overseas subsidiaries, called ‘local technological strength’. As a consequence, the level of the firm’s general technological capabilities is dependent on the R&D activity both at home and abroad as well as the local technological

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Fig. 1. R&D, sourcing, and invention in the Japanese parent and US subsidiaries: conceptual framework.

strength of the area in which the firm’s subsidiaries are located.4 As shown at the center of Fig. 1, the concept of sourcing plays a key role in our framework. Let us thus give some theoretical discussion on the role of sourcing in overseas R&D and the factors that determine sourcing. Conventionally, the main function of overseas R&D activities has been considered to be adaptation and demand-oriented, that is, to adapt the technologies generated within the home country to the local input conditions, regulations, or tastes of the host countries and support the local production and sales activities. In other words, their aim, presumably, was to seek a scope for further innovation through the exploitation of home technological knowledge bases (Kuemmerle, 1997).

4 Causality or simultaneity remains a big issue because, in studies of the determinants of overseas R&D such as Odagiri and Yasuda (1996), overseas R&D was assumed to be a function of the parent’s R&D expenditures. One may thus argue that firms with many inventions spend more for overseas R&D because of their higher capabilities, implying that the arrows in Fig. 1 may not be unidirectional. We admit that we have not resolved this simultaneity issue in this paper. Note, however, that in any estimation of a production function, a similar simultaneity issue arises because inputs (e.g., the amount of labour input to be employed) are endogenous in the sense that, provided the firms are rational, their amounts are determined as functions of the output: see, for instance, Intriligator et al. (1996), Chapter 8.

Increasingly, however, overseas R&D activities are motivated by supply-side factors. That is, they are made with the aim of contributing to company-wide innovation by taking advantages of local technological environment and local R&D resources, such as local science communities, technology-conscious local customers, and the local pool of scientists. In a study of electronics and pharmaceutical MNEs, Kuemmerle (1997) found that about 45% of foreign R&D sites are considered to be ‘home-base-augmenting sites’, which benefit from the local expertise and augment the home technological capability. As for Japanese firms, Granstrand (1999) argued that supply-side factors are crucial in motivating FDI in R&D. According to his questionnaire survey to 24 R&D-intensive Japanese multinationals, ‘creating access to foreign science and technology’ received a higher rating as the driving force than such market-oriented factors as ‘supporting local production’ and ‘supporting local customers and markets’. Usually, the type of knowledge to be sourced is general and basic in nature, not having a particular development purpose. Such knowledge may be tacit and can be gained more efficiently and accurately through direct and close interaction with local knowledge pools. By having an overseas R&D site, the firm would be able to attend conferences held by the local science community and have more opportunities to communicate with local researchers. They would also

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have opportunities for direct interaction through collaborative research with local researchers, monitoring of contracted-out research, and hiring of local scientists as consultants. They will be able to learn about potential technological seeds more quickly and find more opportunities for licensing new technologies. In addition and possibly more importantly, they will be able to hire better scientists and engineers from local schools or firms. Through these interactions by overseas R&D sites, the firm will be able to source technological knowledge from local knowledge pools and enhance its general technological capabilities. This is the very concept of ‘technological knowledge sourcing’.5 The extent that the firm can gain from technological knowledge sourcing is determined by the interaction of several factors. Firstly, the firm needs an absorptive capacity, such as the capacity to scan, evaluate, and assimilate the technological knowledge to be sourced. Such a capacity, as Cohen and Levinthal (1990, p. 128) argued, is “a function of the firm’s level of prior related knowledge”, which is accumulated through R&D. Also, transmitting complicated and advanced technological information within the firm, particularly between the parent and subsidiaries, is a demanding task (Von Hippel, 1994). The firm as a group has to possess adequate knowledge-transferring mechanism across distant sites and the parent firm has to possess sufficiently high absorptive capacity to understand and utilize the sourced knowledge. Thus, for successful technological knowledge sourcing to take place, both the parent and the subsidiaries need to have a sufficient absorptive capacity, as shown in the vertical arrows from “US absorptive capacity” and “Japan absorptive capacity” in Fig. 1. Secondly, unless a sufficient stock of knowledge is present in the local area, sourcing is of little value. Local technological knowledge is developed through an effective interaction of research inputs at the host sites, such as scientists, engineers, and technicians with particular knowledge and skills, and research or5 Local technological strength of the firm’s locations in Japan may also have to be considered. However, with Japan being a small country about the size of just California and with most company laboratories located along the Tokaido Bullet Train Line from the Tokyo Metropolitan area (including Tsukuba) to Kansai area (including Osaka, Kyoto, and Kobe), the local difference within Japan is assumed negligible for our purpose.

ganizations, such as universities, research institutions, and company laboratories. As a result, innovative activities, particularly those of knowledge-intensive industries, tend to cluster at the ‘centres of excellence’ (COEs) 6 where ‘knowledge generating inputs’ are abundant (Audretsch and Feldman, 1996). Exchange of complementary knowledge is likely to be easier within such COEs (Jacobs, 1970). Firms locating there would be able to lower search costs and increase the probability to discover valuable information and, as a result, would have more opportunities for collaborative research, research alliances, and licensing. Jaffe (1989), for instance, found that geographic regions with greater amounts of knowledge-generating inputs tend to have a larger number of patent issuance. This benefit may or may not be confined to particular industries; for instance, Jaffe et al. (1993) found that firms can receive spillovers from wide technological areas regardless of the industry they belong. Thirdly, the level of local knowledge pools is also enhanced by the inward spillover of knowledge from geographically close areas. The nearer the originator of knowledge is from the firm’s R&D unit, the more will the firm be able to receive spillovers.7 Limitation on spillover caused by distance is also observed at an international level (Branstetter, 2001). This limitation, however, may be overcome when the firm establishes R&D sites abroad near the knowledge-intensive location, as shown by the empirical studies using the US patent citation data (Branstetter, 2000; Frost, 2001). Thus, according to Florida’s (1997) survey study, sourcing-related motivations, such as “to gain access to technical talent” and “to develop links to US scientific and technical community,” are positively associated with the R&D spending of foreign-affiliated laboratories in the US. This association was most 6 COE is defined as “a pre-eminent research base that promotes highly creative scientific research at the most advanced level in the world” (Science Council, 1995). Typical examples are the Boston Area and Bay Area in the US, Cambridge in the UK, and Tsukuba in Japan, where agglomeration of university(ies), national research institute(s), and research laboratories of private firms exists. 7 The benefit of geographical proximity may vary across industries. Adams and Jaffe (1996) observed that R&D output at a distant place could be beneficial in pharmaceuticals but may be of little value in chemicals. Such inter-industry differences could not be incorporated in our indices, however.

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prominent among biotechnology firms. In electronics firms, a more support-oriented motivation of “to customize products for US market” was also positively associated as well as “to gain access to technical talent,” whereas in automotives and in chemical and materials, none of these motivations was significantly associated. A survey study for Japanese firms by Goto and Nagata (1997) also found a positive correlation between the establishment of R&D facility in the US and the information access to university/research institute and competing firms in the US. In consequence, the extent that the firm can source from technological knowledge stock abroad depends on three factors, the level of absorptive capacity, both at the level of the parent firm and at the level of overseas R&D-performing subsidiaries; the level of local technological knowledge; and geographical proximity. In the following sections, we will present a model that takes into account all these factors. However, we first give a brief description of the R&D activity of Japanese firms in the US.

3. Data on Japanese R&D in the US The strength of Japanese manufacturing firms, it has been considered, owes much to a close interaction between their R&D division and manufacturing division including suppliers (Odagiri, 1992). To maintain this interaction, their internationalisation used to depend on an export-led strategy and, as a result, the start of their full-fledged overseas production was delayed. The internationalisation of R&D activities also started late. With the data of patenting activities of 569 MNEs in the US in 1985–1990, Patel (1995) have shown that the ratio of overseas innovation by Japanese firm was only 1.0% while that of the US was 7.8%. Even in the electronics industry, supposedly one of the most internationalized industries in Japan, this ratio was only 2.2% in 1990–1993 (Belderbos, 2001). These facts notwithstanding, Japanese firms’ foreign investment in R&D has increased from the latter half of the 1980s, following the increase in overseas production and sales activities. According to the Survey of Overseas Business Activities (SOBA) conducted by the Ministry of International Trade and

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Industry (MITI; now renamed the Ministry of Economy, Trade and Industry or METI) of Japan, overseas R&D expenditures as a percentage to industry R&D expenditures within Japan was only 0.8% in 1986 but rose to 3% in 1998.8 We will hereafter concentrate on the overseas R&D activities of Japanese firms undertaken in the US because the US possesses both the largest market in the world and rich technological knowledge pools. Besides, to Japan, the US is nearer than Europe. As a result, the US is the largest recipient of Japanese foreign direct investment (FDI) and their overseas R&D investment with 53% of their total overseas R&D expenditures. Japanese affiliates are also one of the main investors in R&D among foreign affiliates in the US (US Bureau of Economic Analysis, 1998). In the following empirical analyses, we will use unpublished individual company data for 1998 from SOBA. SOBA is the only available data source on the R&D expenditures made by foreign subsidiaries of Japanese companies in all industries except finance, insurance, and real estate.9 A questionnaire on the activities of parent firms and their foreign affiliates was sent to 3841 parent firms, which include both manufacturing and non-manufacturing firms. For the 1998 (fiscal year) data, the response rate was 56%. Among the 1347 manufacturing firms who responded, 700 had subsidiaries in the US, 172 undertook R&D activities (i.e., reported positive R&D expenditures at one or more of the subsidiaries) in the US, and their total R&D expenditures were 156,590 million yen. Since SOBA does not have data on the R&D expenditures of the parent firms, we have supplemented the data by using the Basic Survey of Japanese Business Structure and Activities (BSA) of MITI. Because the US subsidiaries’ R&D is also a flow variable, the treatment on the two R&D variables is symmetric. This treatment is appropriate because the dependent

8 The data on R&D expenditures within Japan is from the Survey of Research and Development (Statistics Bureau, Ministry of Public Management, Home Affairs, Posts and Telecommunications). 9 The definition of a foreign affiliate is a “foreign company in which a Japanese company owns not less than 10% of the stocks”. They consist of subsidiaries and grandchild companies, namely, subsidiaries of subsidiaries.

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variable, the number of patent grants during a fixed time period, is also a flow variable.10 Consequently, our sample consists of parent manufacturing firms that reported in SOBA to have one or more subsidiaries with positive R&D expenditures in the US, and also reported R&D expenditures in BSA, which numbers 137.11 The average number of subsidiaries in the US per firm of our sample firms is 4.3 (see Appendix A, Table A.1). Among these, on average, 1.5 reported to have made positive R&D expenditures. These subsidiaries will be hereafter called R&D subsidiaries.12 One hundred (73%) of the sample firms had only one R&D subsidiary, 23 (18%) had two R&D subsidiaries, and the remaining 13 firms had three or four R&D subsidiaries except two which had 9 and 11 such subsidiaries each.

4. Variables As shown in Fig. 1, we aim to study direct and indirect contributions of R&D (both at home and in the US) to patenting by Japanese parents and by the US subsidiaries. The direct contribution is that of own R&D, that is, contribution of parents’ R&D to their patented inventions or contribution of US subsidiaries’ R&D to their patented inventions. The indirect contribution is made by the R&D of both the Japanese parent and its US subsidiaries to either patenting through their raising the absorptive capacity of the entire firm with which it can take advantages of local technologi10

As discussed by Griliches and his followers (see his survey, Griliches, 1995, or Griliches, 1998) and generalized by Fors (1997) to the case with both home and foreign R&D variables, the use of flow R&D variables is appropriate if the dependent variable is a change variable (e.g., output change or productivity change) and the obsolescence of knowledge stock can be ignored. Assuming zero obsolescence, we may interpret our dependent variable as a change in the stock of patents. In our earlier version with a slightly different data set (Iwasa and Odagiri, 2002), we used the financial statement data and the stock variable: the main results are unaffected by this choice between the two data sources. For the detail of these datasets, see Appendix A. 11 A few firms were eliminated because they were not listed in the Toyo Keizai data to be explained later. 12 Thus, an R&D subsidiary refers to a subsidiary performing R&D and not necessarily a subsidiary established for the purpose of R&D. For instance, it may be an R&D department attached to plants.

cal strength and enhance its general technological capabilities. These capabilities, we expect, contribute to the patenting activity of both the parent firm and the US subsidiaries. Hence, the dependent variables in the following analysis are the numbers of patent grants based on the inventions made by the parent firm and by its US subsidiaries. A good question is whether we should use the data of the firm’s patents applied (or granted) in Japan or in the US. It may appear straightforward that, at least for patenting by the Japanese parent, we should use the Japanese patent data. Nevertheless, we use the American patent data because, firstly, patents applied in Japan may include trivial inventions because of the first-to-file rule,13 and secondly, identifying the location (i.e., Japan or the US) of the inventors is cumbersome with the Japanese patent data.14 Thus, our measure of R&D output is the number of patents granted by the US Patent and Trademark Office (USPTO) until the end of February 2003 that had been filed during January 1998–August 2002, because we are interested in the outcome of R&D activities in FY1998.15 Each patent lists the name(s) of the inventor(s) and the country (or state in the US) the inventor(s) resides. From this information, we constructed two variables. JPAT is the number of patents of which the assignees include the Japanese parent firm and all 13 In Japan, priority is determined on a first-to-file basis and, after application, the applicant can wait for up to 7 years (3 years since 2001) before requesting substantive examination. As a result, many firms apply even slight inventions for patents in order to preempt rivals, with many of them being eventually withdrawn without the patent office’s examination; hence, the number of patent applications in Japan is large and may not be a good indicator of the technological capability of the firm. The number of patents granted may be a more appropriate measure; however, the lag between application and granting can be substantial and R&D in 1998 may not have yielded many patent grants yet. 14 One can identify the location of the invention by making use of the information on the address and name of the inventor(s) listed in each patent. With Japanese patent data, however, it is prohibitively time-consuming to do so because of the large number of patents (say, 10 times larger than that of the US-issued patents) and downloading each patent data file from the JPO (Japan Patent Office) site takes more time because it contains graphic information as well. 15 To confine our analyses to inventions made after 1998, we excluded patents that claimed their priority on the basis of the foreign patent application before the end of 1997. We included utility and plant patents in the dataset but not design patents.

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the inventors reside in Japan. USPAT is the number of patents of which the assignees include either the Japanese parent or any of the US subsidiaries in our sample with positive R&D expenditures and all the inventors reside in the US. In addition, a few cases in which the inventors’ addresses include both Japan and the US were included in both JPAT and USPAT, because such cases are considered to be the most evident outcome of knowledge sourcing.16 The proportion of Japanese firms’ inventions abroad is small. Among the total of 12,920 patent grants by USPTO to inventions in Japan and/or the US, 92.7% were based on the innovative activities solely in Japan, while inventions abroad and joint inventions accounted for 6.1 and 1.3%, respectively. Thus, the ratio of patents based on overseas inventions is a mere 7.4% even when joint inventions are included (excluding the inventions in Europe, etc.). Still, this figure is higher than the 1.0% figure of Patel (1995) or the 2.2% figure of Belderbos (2001) mentioned in the previous section, reflecting, probably, the fact that our sample firms are relatively more internationalised and R&D-intensive. Also, since our data is for 1998–2003 whereas those of Patel and Belderbos, respectively, are for 1985–1990 and 1990–1993, further internationalisation of patenting activities may have occurred among Japanese firms. As mentioned earlier, we confine our analysis to the R&D-patent relationship in Japan and the US. Still, if the firm is making R&D in other areas, particularly Europe, then it is also expected to enhance the general technological capabilities of the firm, thus contributing to inventions in Japan or the US. Because nearly 60% (84 out of 137) of the sample firms reported to have made no R&D in Europe,17 we decided to use a dummy variable (EURO) rather than the R&D expenditures. EURO is equal to one if and only if the firm reported positive R&D expenditures in Europe. Other than the US and Europe, R&D expenditures by Japanese multinationals are very small and their purposes are mainly the support of local manufacturing; hence, they are ignored. 16 We thank an anonymous referee for suggesting this inclusion. Because of the small number of these cases, their inclusion or exclusion hardly affects the estimation results. 17 For Europe, we have aggregated R&D expenditures of affiliates located in 18 countries from EU, Scandinavia, and Switzerland.

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5. Indices of local technological strength As discussed repeatedly, we are most interested in the role of Japanese R&D subsidiaries in the US in technological knowledge sourcing and this sourcing, as discussed in Section 2, depends on the level of local technological knowledge and geographical proximity, in addition to the level of absorptive capacity. This section explains how we constructed the indices for local technological strength (LTS). Geographical units to be employed are the US states. Needless to say, state boundaries need not be economic or technological boundaries. Some clusters may extend beyond state boundaries, such as the New York/New Jersey area, and some big states may be separated into multiple clusters, for instance, the Bay area and Los Angeles/San Diego regions in California. However, aggregate patent data are available only at the state level and thus the analysis is made at the state level.18 We begin by measuring the knowledge stock of state k in technological field m, denoted as KSkm , by the stock of issued patents in field m of which the first inventor lives in state k. Technological fields are separated into 38 in the PATSIC data published by USPTO.19 The stock was calculated by the perpetual inventory method using the 1982–1998 patent data and a 10% depreciation rate. To measure the level of knowledge pools that can be accessed by a subsidiary located in a certain point within the state, KSkm has to be modified for two reasons. First, in a large state, the subsidiary will not be able to make a constant access to some of the knowledge created within the state because of the distance. Hence, knowledge stock has to be adjusted for the size of the state. Second, the inflow of knowledge stock from neighbouring states has to be taken into account. To make the first adjustment, KSkm is divided by a half of the radius of the state. The radius is calculated from the area of urban and built-up land of the state in 18 Many firms locate corporate headquarters in Delaware due to corporate laws, which allow substantial operations in other states or countries, and preferential tax system. Therefore, when the question on the ‘state where the subsidiary is located’ was replied as Delaware in SOBA, the real address of the subsidiary sought from the Toyo Keizai data was instead used. 19 A few fields, e.g., missiles, in which no Japanese firm is active were ignored.

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order to avoid underestimation of per-acre knowledge stock among larger states with proportionally smaller but concentrated urban land. To make the second adjustment, we need to add the knowledge stock of other states. However, because knowledge from more distant states is less likely to spill over, we assume that the amount of spillover is inversely proportional to the distance between the recipient state and the originating state. We thus calculate the total knowledge stock of state k in technological field m, TKSkm , as the sum of its own knowledge stock, adjusted for its size, and spillovers from other states as follows:20
KSm KSkm TKSkm = + (1) (1/2)R(k) D(k, ) =k

where R(k) is the urban radius of state k and D(k, ) is the distance between state k and state . This stock depends on state (k) and technological field (m) and we need to aggregate over these dimensions to get a firm-specific index of local technological strength. Let us begin with the latter. Suppose that the R&D subsidiary is making research in field m. It will undoubtedly gain from its state’s knowledge stock in field m. A good question is whether it will gain from that in field n (=m). In other words, will there be inter-field spillover of knowledge? We can separate three cases: (1) no spillover, (2) incomplete spillover, and (3) complete spillover. In the case of no spillover, TKSkm is the only knowledge stock available for field m. By normalizing it so that the sum across states is unity, we get an index of normalized knowledge stock, NKSkm , as follows: TKSkm NKS1km =   TKSm

(2)

where superscript 1 refers to the case of no spillover. In the third case of complete inter-field spillover, we can simply add TKSkn over n to get the level of knowledge stock for state k (and for field m although it is common across fields and subscript m can be omitted); hence, for all m, after normalization:  n TKSkn NKS3k ≡ NKS3km =   (3)  n TKSn 20

A.

For more detail on the calculation of this variable, see Appendix

where superscript 3 refers to the case of complete spillover. In an intermediate case, that is, the case of incomplete spillover, we need to calculate a weighted sum of NKSkn , with weights reflecting technological proximity between fields. Let us assume that if firms whose main business is in industry j spend a larger amount of R&D expenditures in field n than in field n , then n is technologically closer to j than n is. We then construct the weights using the table on “intramural expenditure on R&D (disbursement) by industry and product field” in the Report on the Survey of Research and Development (Statistics Bureau, Ministry of Public Management, Home Affairs, Posts and Telecommunications) of Japan. This table shows how much firms in industry j (=1, . . . , 22, excluding non-manufacturing industries) expend for R&D in field m (=1, . . . , 26), to be denoted as RDXjm . Thus, we first assign each subsidiary to one of the industries and aggregated the original 38 technological fields into 26. Then, for each industry, we put the fields in the order of RDXjm until the sum of RDXjm reaches 80% of the entire R&D expenditures of the industry. These fields, we assume, are ‘crucial’ for the industry. For example, for the ‘communication and electric equipment’ industry, the crucial fields are ‘communication and electronics equipment’ and ‘household electric appliances’ with relative importance of 83 and 17%, respectively. Although most of the industries diversify their crucial R&D activities over multiple fields, there are two exceptions, ‘drugs and medicines’ and ‘motor vehicles’, in which the firms expend more than 80% of their R&D expenditures on their own fields alone. Using the relative R&D expenditures across crucial technological fields as weights, we now calculate the normalized technological knowledge stock of state k in industry j as follows:  n∈Ωj (TKSkn × RDXjn )/  n∈Ωj RDXjn NKS2kj =   (4)  n∈Ωj (TKSn × RDXjn )/  n∈Ωj RDXjn where superscript 2 refers to the case of incomplete spillover and Ωj is the set of crucial technological fields for industry j. Table 1 shows the values of these three indices of normalized knowledge stock for selected states and

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Table 1 Normalized technological knowledge stock by state and industry: top five states (a) Case of complete inter-field spillover: calculation of TKS (all fields) and NKS3 State knowledge stock (total across technological fields) Ranking

State

Own (A)

Spillover from other states (B) (%)

1 2 3 4 5

1 2 3 4 5

2,167.09 1,388.28 1,147.77 855.69 952.16

11.0 7.1 5.8 4.4 4.8

173.78 652.89 741.56 928.33 693.31

0.7 2.7 3.0 3.8 2.8

2,340.87 2,041.17 1,889.33 1,784.02 1,645.47

5.3 4.6 4.3 4.0 3.7

Total

19,654.73

100.0

24,621.62

100.0

44,276.36

100.0

(c) Case of incomplete inter-field spillover: NKS2 for selected industries Drugs and medicines

State

NKS1

State

NKS2

California Massachusetts Maryland New York New Jersey

0.0750 0.0677 0.0552 0.0451 0.0423

California Massachusetts Maryland New York New Jersey

0.0750 0.0677 0.0552 0.0451 0.0423

Office computing and accounting machines State

State

NKS2

1

California

0.0928

California

0.0682

2

New York

0.0588

New Jersey

0.0492

3 4 5

Massachusetts Connecticut New Jersey

0.0517 0.0439 0.0356

New York Massachusetts Connecticut

0.0464 0.0426 0.0383

Household appliances NKS1

State

NKS2

1

New Jersey

0.0544

California

0.0662

2 3 4 5

Massachusetts Connecticut New York South Carolina

0.0515 0.0484 0.0423 0.0410

New York New Jersey Massachusetts Illinois

0.0432 0.0410 0.0378 0.0356

Motor vehicles and other motor vehicle equipment Ranking 1 2 3 4 5

(%) 100

Crucial technological fields Communication and electronics equipment Household electric appliances Total

(%) 83 17 100

Electrical machinery, equipment and supplies

State

Ranking

Crucial technological fields Drugs and medicines

Communication and electronic equipment

NKS1

Ranking

(%) (NKS3 )

(%)

California New York New Jersey Connecticut Massachusetts

(b) Case of no inter-field spillover: NKS1 for selected fields Drugs and medicines Ranking

Total stock (TKS = A + B)

Crucial technological fields Communication and electronics equipment Other electric equipment Motor vehicles Total

(%) 60 25 15 100

Motor vehicles

State

NKS1

State

NKS2

Michigan Utah Illinois Indiana Ohio

0.1071 0.0433 0.0403 0.0391 0.0384

Michigan Utah Illinois Indiana Ohio

0.1071 0.0433 0.0403 0.0391 0.0384

Crucial technological fields Motor vehicles

(%) 100

816

T. Iwasa, H. Odagiri / Research Policy 33 (2004) 807–828

Table 1 (Continued ) Professional and scientific instruments

Precision instrument machinery

State

NKS1

State

NKS2

Crucial technological fields

(%)

1

New York

0.0646

California

0.0672

49

2 3 4 5

California Massachusetts Connecticut New Jersey

0.0587 0.0470 0.0465 0.0385

New York Massachusetts New Jersey Connecticut

0.0545 0.0432 0.0427 0.0411

Communication and electronics equipment Precision instruments General machinery Total

Ranking

43 8 100

Source: Census Bureau (2000), National Resource and Conservation Service (1997), US Department of Interior, USPTO, Statistics Bureau (1999).

selected fields/industries. Part (a) corresponds to the case of complete inter-field spillover; hence, we calculate only in terms of total figures across fields. The column labelled ‘own’ was calculated by the formula in the first term of the right-hand side of Eq. (1) and the column labelled ‘spillover’, the second term. The sum of these is the total knowledge stock of the state, TKSk , which is shown in the second column from the right. The normalized TKSk , namely, the proportion to the US total, is shown in the far-right column, which is NKS3k . In terms of own stock, large states, such as California (11.0%), New York (7.1%), New Jersey (5.8%), Illinois (4.9%), and Massachusetts (4.8%), tend to have large knowledge stocks. By contrast, spillovers are relatively large among smaller states, such as Rhode Island and Delaware (not shown in the table), which have as their neighbours those states with large own knowledge stocks. In these states, spillovers even exceed own stocks. As a result, total knowledge stock is more geographically dispersed than own knowledge stock, even though California (5.3%), New York (4.6%), and New Jersey (4.3%) still remain as the biggest three. Parts (b) and (c) of Table 1 show the normalised knowledge stocks for the cases of, respectively, no inter-field spillover and incomplete inter-field spillover. They now vary across fields and, to save space, Table 1 shows the figures only for the five fields/industries that attracted the largest proportion of R&D investments in the US by Japanese multinationals. The NKS1km of ‘drug and medicines’ in (b) and NKS2kj of ‘drug and medicines’ in (c) are the same, because this industry has only one crucial technological field. The same applies for those of ‘motor vehicles’.

We now construct a firm-level index of local technological sourcing. Cohen and Levinthal (1989, 1990) argued that absorptive capacity is a function of the firm’s prior related knowledge and Branstetter (2000) argued that technological proximity affects the extent of sourcing. Therefore, the scope of sourcing would be constrained by the location and technological field of the subsidiaries. If the firm has a single US R&D subsidiary in state k and technological field m or industry j, then, it would be able to source only from the knowledge stock of this state and this field/industry. When the firm has US R&D subsidiaries in more than two states or more than two fields, it would be natural to assume that the firm (i) can source from the states in proportion to the R&D expenditures the firm is making there. Hence, an index of local φ technological strength for firm i, LTSi , φ = 1 (the case of no inter-field spillover), 2 (the case of incomplete inter-field spillover), or 3 (the case of complete inter-field spillover), is now defined as follows: φ LTSi

  φ h (NKSkh × RDikh )  = k   h RDih

(5)

where RDikh is the R&D expenditures of firm is subsidiary in state k and in field/industry h (technological field m in Eqs. (2) and (3), or industry j in Eq. (4)). Note that, if the firm is expending for R&D only in state k and field/industry h, then R&D expenditures are zero for other states and other fields/industries; hence, φ φ simply, LTSi = NKSkh This is the index we intend to use in our estimation. It measures the strength of local technological knowledge stock, taking into considerations not only the technological level of the state in which the subsidiary

T. Iwasa, H. Odagiri / Research Policy 33 (2004) 807–828

is located but also spillovers from other states. Furthermore, in the case of LTS3i or LTS2i , spillovers across technological fields are taken into considerations. As written earlier, we hypothesize that the firm having its subsidiaries in states with larger local technological strength will have more opportunities for knowledge sourcing, thereby enriching the technological capabilities of the parent firm.

817

φ

EUROi and LTSi as input measures. However, because JPATi is zero for 17 sample firms, the logarithm cannot be taken.21 Besides, PATi is a count data. Hence, following Hausman et al. (1984), we adopt a negative binomial model in the estimation.22   ln JPATi = β0 + β1 ln JREi + β2 ln USREi ln USPATi φ

+ β3 EUROi + β4 ln LTSi 6. Model We have thus far identified four factors that are expected to enhance the technological capabilities of firm i: home R&D expenditures, JREi ; R&D expenditures in the US subsidiaries, USREi ; R&D dummy in Europe, EUROi ; and the index of local technological φ strength, LTSi . These are the inputs in knowledge production functions with the outputs being the number of patents from inventions (including Japan–US conventions) in Japan, JPATi and that in the US, USPATi . Starting with the pioneering works of Griliches and his collaborators (see, for instance, Griliches, 1979; Pakes and Griliches, 1984; Jaffe, 1986; and Griliches, 1998), many efforts have been made to estimate knowledge production functions. The number of patents has been used frequently as the output measure despite a number of shortcomings of patent data, such as the different value across patents, the presence of patenting to pre-empt rivals, and different propensity to patent across technological fields (Griliches, 1990). The input measures are R&D expenditures (or their stock), technological spillovers from other firms, other industries, other countries, or universities, and other R&D-related variables. The Cobb–Douglas log-linear functional form is commonly employed “as a first approximation to a potentially much more complex relationship” (Griliches, 1995, p. 55) and the positive contribution of R&D expenditures on the number of patents has been confirmed by many: In addition to aforementioned Pakes and Griliches (1984), see more recent Branstetter (2001) who analyzed both US and Japanese firm data, and Kondo (1999) who analyzed a Japanese industrial data. Following these studies and following our framework shown in Fig. 1, we estimate the following log-linear patent production functions with JPATi or USPATi as the output measure, and JREi , USREi ,

+ β5 ln EXPi + β6 ACQi
+ γs Dis + εi

(6)

s

where φ = 1 (no spillover across technological fields), 2 (incomplete spillover), or 3 (complete spillover), EXPi (in logarithm) and ACQi are two additional variables that indicate the subsidiaries’ characteristics, Di s are a series of industry dummies, and εi is the error term. Table 2 gives the list of variables, together with the data summary, and Appendix A gives the data source. Since the dependent variable is the number of patents in 1998–2002 while all the explanatory variables are measured for 1998, the lag of up to 5 years is assumed between the R&D-related variables and its outcome, patents. Given that Japanese firms tend to apply patents to USPTO claiming the priority based on applications to JPO, the lag between the R&D activities and patent application should be set relatively long. EXPi is a variable indicating the length of the firm’s experience in the US and is measured by the number of months between the establishment of the company’s first US subsidiary (for sales, manufacturing, or any other purpose) and March 1999, the end of the 1998 fiscal year. This variable is included because a number of studies have shown that an increase in international experience contributes positively to the likelihood of 21 One way to avoid this problem is to add one to the number of patents before taking the logarithm and then apply the ordinary least-squares method (Branstetter, 2001). Unreported results with φ this methodology indicated that the results on LTSi , the variables that we are most interested, are unchanged but with lower t-values. 22 Poisson regression model, we found, is inappropriate because χ2 in the likelihood-ratio tests of alpha=0 have indicated that the probability that we would observe these data conditional on alpha = 0 is virtually zero. Here, alpha is the overdispersion parameter and an increase in alpha indicates a conditional variance of y to be larger (Long, 1997).

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T. Iwasa, H. Odagiri / Research Policy 33 (2004) 807–828

Table 2 List of variables and data summary Variable

Description

JPAT

The number of patents based on innovations in Japan and applied between January 1998 and August 2002 The number of patents based on innovations in the US and applied between January 1998 and August 2002 R&D expenditures of the Japanese parents in 1998 (in million yen) RD expenditures of the US subsidiaries in 1998 (in million yen) A dummy variable that equals one if the subsidiary in Europe expend for R&D activities Index of local technology strength in 1998 (no inter-field spillover) Index of local technology strength in 1998 (incomplete inter-field spillover) Index of local technology strength in 1998 (complete inter-field spillover) The number of months since the establishment of the first US subsidiary A dummy variable that equals one if the US subsidiary (the one with the largest R&D expenditure if there were more than two subsidiaries) had been acquired (including capital participation)

USPAT JRE USRE EURO LTS1 LTS2 LTS3 EXP ACQ

Mean

S.D.

Min

Max

88.59

282.86

0

1968

6.92

25.30

0

201

18,569

41,017

4

329,409

1,080

3,705

1

30,513

0.39

0.49

0

1

0.0438

0.0250

0.0074

0.1071

0.0435

0.0238

0.0045

0.1071

0.0760

0.0610

0.0008

0.1681

250.6 0.25

122.7 0.43

11

538

0

1

No. of observations = 137.

dispersed R&D activities. Hewitt (1980) argued that the longer the overseas operation, the more ‘organizational learning’ would take place, and the advantage arising from overseas R&D is more likely to be appreciated. A longer foreign experience would also enable the firm to learn the availability of local technological resources. Moreover, the costs of coordinating and managing overseas R&D facilities tend to decrease over time (Granstrand et al., 1993). An industry-level analysis by Hewitt (1980) found a positive effect of experience on the overseas R&D ratio. So did the studies by Iwasa (1996) and Belderbos (2003) on the R&D determinants of Japanese manufacturing subsidiaries. It is therefore expected that a longer experience in the US, regardless of whether it is on sales or production, would allow the firm to establish a mechanism and capability to exploit the technological seeds and generate new knowledge from them. ACQi is a dummy variable indicating the mode of establishment of the R&D subsidiary (the one with the largest R&D expenditures if the company has more than two R&D subsidiaries). It equals one if the subsidiary was established either by acquisition or capital participation, and equals zero if the subsidiary was

newly established either by greenfield or joint venture (JV). Among our sample of 137 firms, 34 (25%) had their largest R&D subsidiaries with ACQi = 1, indicating that the majority of R&D subsidiaries were established on greenfield sites. Kogut and Chang (1991) argued that Japanese firms used JV as a means of sourcing the technological knowledge from the American partners. This story, however, does not hold in the present study because, in most of the JV cases, the partners were other Japanese firms or their foreign subsidiaries, which is why we have included JV in the same category as greenfield investment. Their argument is perhaps more applicable to the cases of acquisition because acquiring firms should then be able to get a full control of the technological assets and capabilities of the acquired firms. We would then expect a positive coefficient for ACQi . Belderbos (2001), for instance, showed that the ratio of acquired subsidiaries has a significantly positive influence on the number of US patents. However, in most of the acquisition cases, the major purpose was the acquisition of manufacturing or marketing capabilities and the argument of Granstrand et al. (1993) may apply that, if the local

T. Iwasa, H. Odagiri / Research Policy 33 (2004) 807–828

firms are acquired for non-technological purpose, the acquired R&D function could be a mere duplication of facilities already possessed by the acquirers and, hence, the R&D expenditures of the acquired subsidiary would be eventually decreased. During the period when this subsidiary’s R&D expenditure has not yet been fully adjusted, the research outcome (JPATi or USPATi ) may be fewer than is expected from the subsidiary’s R&D expenditure (USREi ). We would then expect ACQi to have a negative coefficient.

7. Type R firms and Type S firms So far we have discussed as if sourcing of local technological knowledge is an important motivation behind all the R&D subsidiaries, namely, all the subsidiaries of Japanese MNEs in the US who have been expending on R&D. This assumption holds well in the case of, say, Japanese pharmaceutical firms establishing laboratories near universities in California or Massachusetts, or Japanese electronics makers establishing subsidiaries near Silicon Valley. However, in the case of Japanese carmakers and the car-component suppliers establishing manufacturing bases in Midwest states, such as Indiana and Kentucky, which also expend on R&D, sourcing may not be the major purpose. Rather, their main purpose must be to support local production by designing production processes suitable for local conditions and giving technical advises to the manufacturing division and suppliers, or to support marketing and sales by adapting the products developed in Japan so that they suit local tastes and regulation. In short, their main function must be to ‘support’. Partly due to the lack of subsidiary level R&D data, previous empirical studies on the overseas R&D activities of Japanese firms have failed to make this distinction, treating ‘research’ and ‘local support’ as homogenous. However, the relative importance between these two activities must be a crucial factor for the firm’s overseas R&D strategy (von Zedtwitz and Gassman, 2002). By separating the two types, we will be able to measure more accurately the contribution of home R&D and overseas R&D activities. Let us call the first type of subsidiaries ‘researchoriented’ and the second, ‘support-oriented’. In

819

research-oriented subsidiaries, both the direct effect (shown by the arrow from ‘R&D at US subsidiaries’ to ‘Invention at US subsidiaries’ in Fig. 1) and the indirect effect (shown by the arrow from ‘R&D at US subsidiaries’ to ‘General technological capabilities’ via ‘US absorptive capacity’ in Fig. 1) are important and expected to contribute to patenting in the US and Japan as assumed in our model. In support-oriented subsidiaries, however, the second indirect effect may be unimportant. Their purpose, basically, is to adapt the superior technology at home for local conditions. They are often located in areas where labour, land, and other inputs are easily available and not where universities and other laboratories are clustered, that is, not where local technological strength is high. In consequence, the level of the firm’s technological capabilities and the level of the local technological strength of the state in which the subsidiaries are located may be correlated negatively rather than positively. With this presumption in mind, we separate the sample firms into two types, Type R and Type S, depending on the objective of their main R&D subsidiaries. When the “main businesses” of an R&D subsidiary include R&D, or its ‘purpose of the FDI’ includes ‘design and development of products for the world market’, then, the subsidiary is classified as research-oriented.23 If otherwise, the subsidiary is classified as support-oriented. When the firm performs R&D at a single subsidiary, the firm is classified as Type R if this subsidiary is research-oriented and Type S if it is support-oriented. 73% (100/137) of the sample firms were classified in this manner. When the firm have two or more R&D subsidiaries, its type was determined according to the R&D type of the subsidiary with the largest R&D expenditure. Table 3 shows that only 31 firms, namely, less than a quarter of the sample, are categorised as Type R and the majority belongs to Type S. By industry, Type S is dominant in material-related industries (glass, cement and ceramics, steel, tire and rubber) as well as food. Assembly-type industries, such as general machineries, electronics, transport machinery, and 23 The data source is Toyo Keizai, Kaigai Shinshutsu Kigyo Souran 1999, in which the firms are asked to select the purpose of FDI from 15 categories.

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T. Iwasa, H. Odagiri / Research Policy 33 (2004) 807–828

Table 3 Summary statistics and industrial classification of Type R firms and Type S firms Industry

Type R firms

Type S firms

977,149 642,183 143,752

397,852 271,285 75,665

Number of all subsidiaries in the US Number of R&D-performing subsidiaries in the US

5.81 2.13

3.85 1.31

Parent R&D expenditures (JRE) R&D expenditures in the US (USRE)

36,607 2,252

13,293 737

164

66

13

5

1 1 6 8 0 0 1 2 6 5 0 0 1

8 2 17 4 3 5 4 14 21 14 2 3 9

31

106

(A) Means Parent consolidated sales Parent unconsolidated sales Sales in the US

Number of patents based on innovations in Japan (JPAT) Number of patents based on innovations in the US (USPAT) (B) Number of firms by industry Food Textiles Chemicals Pharmaceuticals Glass, cement and ceramics Steel Non-ferrous metal/metal products General machinery Electronics Transport machinery Precision machinery Tire and rubber Other manufacturing Total

precision machinery, also have many Type S firms. The only industry in which Type R is dominant is pharmaceuticals in which eight out of the 12 firms are classified as Type R. Not surprisingly, Type R firms are larger, more R&D intensive, more active in their US operation than Type S firms. Geographically, subsidiaries of Type R firms are highly concentrated in California (36.9%), followed by Illinois, New Jersey, and Michigan, with these four states accounting for nearly 60% (not reported in the table). By comparison, the subsidiaries of Type S firms are more geographically dispersed across states with the share of California being a much lower 24.4% and the combined share of top four states (New York, Illinois, and New Jersey, in addition to California) being slightly less than 50%.

8. Results Estimation results of the US patent production function (with USPATi as the dependent variable) and Japan patent production function (with JPATi as the dependent variable), respectively, are summarized in Tables 4 and 5 (subscript i in variable names will be suppressed hereafter). Twelve industry dummy variables are included in the equations as well as a constant term. Since the dummy variable for electronics is suppressed, the coefficients for industry dummies indicate if the intercepts for these industries are different from that of electronics. There was no Type R firm in some industries, which explains why some industry dummies are absent in Eq. (4)–(7). The degrees of freedom are only 15 in Eqs. (4), (6), (7) and, to increase them, the equations were re-estimated after the dummy variables with insignificant coefficients were eliminated. The result is Eq. (5), shown only for the LTS1 equation to save space, and indicates that the results are hardly affected. 8.1. Contribution of R&D expenditures As expected, the estimated coefficients of USRE in the USPAT equations (in Table 4) and those of JRE in the JPAT equations (in Table 5) are all positive and highly significant, confirming the presence of direct contributions of R&D expenditures, that is, R&D in Japan to inventions in Japan, and R&D in the US to inventions in the US. The estimated coefficients are in the range of 0.27–0.42 in the US and 0.70–0.85 in Japan.24 In either country, they are smaller among Type S firms, presumably because many of the inventions by Type S firms and their support-oriented R&D subsidiaries relate to production knowhows or product designs and hence are not suitable for patenting. Indirect contributions, that is, that of R&D in Japan to inventions in the US and that of R&D in the US to inventions in Japan, are always positive as expected but statistically significant only among Type S firms. This result, particularly that in the Japan patent equation, is puzzling. First, why is the level of R&D in the US 24 The coefficients in negative binomial models with log-linear specification may be interpreted as elasticities (Wooldridge, 1998). Thus, the fact that the estimated coefficients are significantly less than unity suggests decreasing returns.

T. Iwasa, H. Odagiri / Research Policy 33 (2004) 807–828

821

Table 4 Estimated US patent production function: negative binomial model, dependent variable = USPAT All firms

Type R firms

(1) ln JRE ln USRE EURO ln LTS1 ln LTS2 ln LTS3 ln EXP ACQ Food Textile Glass Steel Metal Chemical Pharmaceutical Machinery Transport Precision Rubber Other manufacturing Constant Log likelihood No. of observations

(2) 0.5083 0.3162 0.4596 0.1769

∗∗∗

(3) ∗∗∗

(4.05) (3.82)∗∗∗ (2.10)∗∗ (0.85)

∗∗∗

0.5143 (4.12) 0.3225 (3.90)∗∗∗ 0.4430 (2.04)∗∗

0.4882 (4.02) 0.3244 (3.82)∗∗∗ 0.4535 (2.08)∗∗

(5) 0.2332 0.3048 0.6399 1.6316

(0.83) (2.08)∗∗ (1.51) (3.22)∗∗∗

0.3371 0.2663 1.0801 1.5400

(1.15) (1.66)∗ (2.88)∗∗∗ (2.95)∗∗∗

0.2246 0.3980 0.8469 −19.4449

(0.55) (0.87) (1.14) (−12.16)∗∗∗

0.0304 (0.08) 0.0630 (0.13)

1.4341 −0.8818 −1.8426 −1.0850 −1.5226

(2.07)∗∗ (−1.32) (−3.29)∗∗∗ (−1.12) (−2.06)∗∗

0.3459 (1.54) −0.1538 0.4228 −0.8078 −20.4455 −0.7506 −0.2393 −0.8039 −0.7168 −1.4963 −0.5437 −0.9555 −1.9715 −2.0451 −0.3093 −3.5600 −267.58 137

(−0.57) (1.61) (−1.58) (−24.85)∗∗∗ (−1.06) (−0.42) (−1.63) (−2.23)∗∗ (−2.32)∗∗ (−1.18) (−2.16)∗∗ (−3.25)∗∗∗ (−2.54)∗∗ (−0.62) (−2.20)∗∗

−0.1545 0.4117 −0.8496 −20.5768 −0.6083 −0.1616 −0.7132 −0.6776 −1.5814 −0.5131 −1.0213 −1.9998 −2.0095 −0.2202 −3.0979 −266.94 137

(−0.59) (1.61) (−1.70)∗ (−24.62)∗∗∗ (−0.81) (−0.28) (−1.43) (−2.10)∗∗ (−2.43)∗∗ (−1.14) (−2.35)∗∗ (−3.61)∗∗∗ (−2.47)∗∗ (−0.44) (−1.93)∗

Type R firms (6) ln JRE ln USRE EURO ln LTS1 ln LTS2 ln LTS3 ln EXP ACQ Food Textile Glass Steel Metal Chemical Pharmaceutical Machinery Transport Precision Rubber Other manufacturing Constant Log likelihood No. of observations

(4)

(0.94) (−0.56) (1.61) (−1.86)∗ (−25.45)∗∗∗ (−1.01) (−0.58) (−1.43) (−2.09)∗∗ (−2.59)∗∗∗ (−1.17) (−1.80)∗ (−3.49)∗∗∗ (−2.55)∗∗ (−0.71) (−2.39)∗∗

(8) 0.0511 (0.14) 0.3223 (1.20) 0.9511 (1.78)∗

(9) 0.5605 0.2872 0.3066 0.0823

(4.44)∗∗∗ (3.00)∗∗∗ (1.16) (0.36)

1.9149 (3.77)∗∗∗

1.4455 (2.78)∗∗∗ −1.5402 (−2.93)∗∗∗ −1.2700 (−1.84)∗ 2.3143 (7.33)∗∗∗ 0.8655 (0.28) −75.47 31

(10) 0.5629 (4.46)∗∗∗ 0.2909 (3.02)∗∗∗ 0.3076 (1.18)

0.5459 (4.32)∗∗∗ 0.2965 (2.95)∗∗∗ 0.2919 (1.11)

0.1535 (0.61)

0.1911 0.4444 0.6351 −18.8096

(0.45) (1.04) (0.99) (−12.36)∗∗∗

0.5365 0.5264 1.0085 −0.5956 −20.3535

1.8742 −0.5016 −1.8764 −0.6643 −1.3894

(2.58)∗∗∗ (−0.82) (−3.82)∗∗∗ (−0.77) (−2.09)∗∗

0.6573 −0.9735 −1.6474 −1.6233 −0.4287

2.1720 (3.28)∗∗∗ 1.1678 (0.50) −72.30 31

1.8191 (2.78)∗∗∗ 1.3538 (0.54) −73.71 31

−21.1998 (−17.03)∗∗∗

Type S firms (7)

0.2597 (0.95) 0.4216 (2.89)∗∗∗ 0.6891 (1.74)∗

0.1507 −0.1438 0.3975 −0.9006 −20.3810 −0.7181 −0.3042 −0.7173 −0.6824 −1.5358 −0.5320 −0.8327 −2.0044 −2.1040 −0.3416 −3.6260 −267.25 137

(1.54) (0.99) (2.13)∗∗ (−0.79) (−11.78)∗∗∗

(0.89) (−1.56) (−2.99)∗∗∗ (−1.70)∗ (−0.52)

1.1360 (1.70)∗ −2.5287 (−1.13) −76.53 31

−0.4717 0.4975 −1.5552 −18.4938 −1.0697 −0.5017 −1.4582 −0.9670 −0.9362 −0.7113 −1.2335 −2.0947 −2.1778 −1.2189 −2.2433 −183.95 106

(−1.63) (1.71)∗ (−2.48)∗∗ (−19.37)∗∗∗ (−1.56) (−0.92) (−2.45)∗∗ (−2.46)∗∗ (−0.78) (−1.62) (−2.09)∗∗ (−3.33)∗∗∗ (−2.65)∗∗∗ (−2.53)∗∗ (−1.19)

−0.4666 0.4849 −1.5644 −18.9948 −1.0176 −0.4732 −1.4468 −0.9446 −0.9738 −0.6948 −1.2512 −2.1062 −2.1622 −1.1698 −2.0807 −183.86 106

(−1.63) (1.66)∗ (−2.51)∗∗ (−19.62)∗∗∗ (−1.48) (−0.87) (−2.48)∗∗ (−2.40)∗∗ (−0.80) (−1.60) (−2.16)∗∗ (−3.47)∗∗∗ (−2.63)∗∗∗ (−2.41)∗∗ (−1.11)

Note: In parentheses are z statistics. They are calculated based on robust standard errors to correct for heteroskedasticity. ∗ The level of statistical significance is 10%. ∗∗ The level of statistical significance is 5%. ∗∗∗ The level of statistical significance is 1%.

0.0913 −0.4544 0.4569 −1.5608 −18.7891 −1.0069 −0.5218 −1.4174 −0.9458 −1.0565 −0.6922 −1.1834 −2.1063 −2.2007 −1.1817 −2.2529 −183.82 106

(0.54) (−1.61) (1.58) (−2.50)∗∗ (−19.51)∗∗∗ (−1.45) (−1.01) (−2.43)∗∗ (−2.44)∗∗ (−0.90) (−1.62) (−1.99)∗∗ (−3.51)∗∗∗ (−2.64)∗∗∗ (−2.39)∗∗ (−1.30)

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Table 5 Estimated Japan patent production function: negative binomial model, dependent variable = JPAT All firms

Type R firms

(1) ln JRE ln USRE EURO ln LTS1 ln LTS2 ln LTS3 ln EXP ACQ Food Textile Glass Steel Metal Chemical Pharmaceutical Machinery Transport Precision Rubber Other manufacturing Constant Log likelihood No. of observations

(2) 0.7195 0.2125 0.5789 0.2539

∗∗∗

(3) ∗∗∗

(10.26) (4.05)∗∗∗ (2.87)∗∗∗ (1.87)∗

∗∗∗

0.7192 (10.44) 0.2120 (4.05)∗∗∗ 0.5840 (2.90)∗∗∗

0.7144 (10.72) 0.2133 (4.09)∗∗∗ 0.5797 (2.87)∗∗∗

(5) ∗∗∗

0.8490 0.0429 −0.0527 0.8643

(3.78) (0.20) (−0.10) (2.48)∗∗

0.0255 −0.2704 −2.5760 −0.9524

(0.06) (−0.66) (−3.56)∗∗∗ (−0.96)

0.5427 −0.2710 −2.1999 −1.3337 −0.4579

(0.95) (−0.56) (−3.34)∗∗∗ (−1.36) (−0.78)

0.7664 0.1841 0.3408 0.7663

(3.47)∗∗∗ (1.05) (0.97) (3.28)∗∗∗

0.1608 (1.10) −0.1330 −0.0210 −0.7897 0.8447 1.0125 0.2732 0.8956 0.2346 −2.3485 0.3567 0.2899 1.2688 0.9610 0.7325 −2.7923 −577.36 137

(−0.97) (−0.10) (−2.32)∗∗ (2.47)∗∗ (4.12)∗∗∗ (0.87) (2.20)∗∗ (0.79) (−6.26)∗∗∗ (1.17) (0.91) (6.42)∗∗∗ (1.51) (1.74)∗ (−3.33)∗∗∗

−0.1211 −0.0196 −0.8125 0.8018 0.8722 0.2084 0.8824 0.2125 −2.3337 0.3037 0.3061 1.1845 0.9530 0.6925 −3.1351 −578.05 137

(−0.88) (−0.09) (−2.32)∗∗ (2.39)∗∗ (3.51)∗∗∗ (0.70) (2.14)∗∗ (0.72) (−6.06)∗∗∗ (0.99) (0.95) (6.44)∗∗∗ (1.47) (1.61) (−3.74)∗∗∗

Type R firms (6) ln JRE ln USRE EURO ln LTS1 ln LTS2 ln LTS3 ln EXP ACQ Food Textile Glass Steel Metal Chemical Pharmaceutical Machinery Transport Precision Rubber Other manufacturing Constant Log likelihood No. of observations

(4)

(0.67) (−0.93) (−0.03) (−2.29)∗∗ (2.17)∗∗ (3.08)∗∗∗ (0.59) (2.14)∗∗ (0.66) (−5.80)∗∗∗ (0.91) (1.23) (6.28)∗∗∗ (1.38) (1.53) (−4.28)∗∗∗

(8)

0.8199 (3.23)∗∗∗ 0.0240 (0.08) −0.0285 (−0.05)

0.7085 0.2776 0.6872 0.1604

(9.84)∗∗∗ (4.85)∗∗∗ (2.99)∗∗∗ (1.10)

0.8853 (2.41)∗∗

−2.2106 (−3.36)∗∗∗

−0.5342 (−1.03) −1.2867 (−0.64) −138.03 31

−0.8824 (−0.45) −139.00 31

(9)

(10) 0.7056 (9.94)∗∗∗ 0.2772 (4.86)∗∗∗ 0.6884 (2.98)∗∗∗

0.6968 (9.90)∗∗∗ 0.2929 (5.17)∗∗∗ 0.6865 (2.92)∗∗∗

0.0749 (0.47)

0.0002 −0.1859 −2.7715 −0.7684

(0.00) (−0.43) (−3.71)∗∗∗ (−0.80)

0.0324 0.0606 0.0041 −3.1670 −1.0184

0.5995 −0.1362 −2.1969 −1.3471 −0.3602

(0.95) (−0.28) (−3.16)∗∗∗ (−1.47) (−0.65)

−0.3164 −0.3737 −2.2073 −1.6804 −0.0532

−0.4656 (−0.85) −1.5892 (−0.74) −138.19 31

−0.2121 (−0.53) −0.3867 (−1.12) −2.0822 (−4.68)∗∗∗

Type S firms (7)

0.8599 (3.88)∗∗∗ 0.0933 (0.44) −0.0206 (−0.04)

0.0715 −0.1288 −0.0071 −0.8218 0.8417 0.8350 0.1691 0.9017 0.1955 −2.3194 0.2772 0.4093 1.1708 0.9074 0.6529 −3.3761 −578.20 137

(0.11) (0.09) (0.01) (−2.97)∗∗∗ (−0.72)

(−0.38) (−0.79) (−2.62)∗∗∗ (−1.34) (−0.09)

−1.0814 (−2.09)∗∗ −3.6036 (−1.37) −139.75 31

0.0000 −0.0458 −0.4937 1.3689 1.0473 0.3792 1.2107 0.3383 −3.2983 0.4593 0.3177 1.3050 0.9026 1.0221 −4.1339 −432.78 106

(0.00) (−0.21) (−1.62) (4.77)∗∗∗ (4.11)∗∗∗ (1.16) (2.61)∗∗∗ (0.93) (−7.53)∗∗∗ (1.48) (0.82) (6.62)∗∗∗ (1.42) (2.28)∗∗ (−4.80)∗∗∗

0.0150 −0.0465 −0.5086 1.3165 0.9358 0.3294 1.2129 0.3093 −3.3233 0.4186 0.3290 1.2463 0.8979 0.9905 −4.4523 −433.05 106

(0.12) (−0.21) (−1.66)∗ (4.78)∗∗∗ (3.79)∗∗∗ (1.04) (2.58)∗∗∗ (0.85) (−7.56)∗∗∗ (1.33) (0.84) (6.57)∗∗∗ (1.40) 2.18)∗∗ (−5.08)∗∗∗

Notes: In parentheses are z statistics. They are calculated based on robust standard errors to correct for heteroskedasticity. ∗ The level of statistical significance is 10%. ∗∗ The level of statistical significance is 5%. ∗∗∗ The level of statistical significance is 1%.

0.1339 0.0073 −0.0862 −0.5148 1.5129 1.0659 0.3299 1.2684 0.3884 −3.4165 0.4551 0.4199 1.3081 0.8452 1.0115 −4.2747 −432.49 106

(1.11) (0.06) (−0.40) (−1.63) (3.97)∗∗∗ (3.89)∗∗∗ (1.07) (2.78)∗∗∗ (1.03) (−7.66)∗∗∗ (1.45) (1.03) (6.00)∗∗∗ (1.30) (2.26)∗∗ (−5.31)∗∗∗

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subsidiaries of Type R firms unrelated to inventions in Japan if, as will be presently discussed, the presence of these subsidiaries is actually contributing in terms of sourcing? Is it because their US laboratories pursue specialized research themes, distinct from those in the Japanese laboratories? Or is it because, for effective sourcing, the first-hand information on “who is doing what, and where they are” is sufficient and the scale of the subsidiary’s R&D does not matter? There are in fact cases in which, with such information, the parent firm seeks access to research resources through licensing or collaborative research, which occurs commonly in the case of high-tech industries, such as biotechnology, since cutting-edge scientific knowledge tends to be transferred through market exchange mechanism rather than knowledge spillovers (Zucker et al., 1998). If this conjecture is correct, then the result suggests that, if the purpose of an overseas R&D subsidiary is knowledge sourcing alone, the firm should locate it in a state with high local technological strength but its size does not matter. Yet, as shown in Table 4, large R&D expenditures in the US contribute to the subsidiary’s own inventions, giving the firm an incentive to maintain a large R&D unit there. Second, why does R&D in the US of Type S firms contribute to inventions in Japan if its purpose is to transfer the superior Japanese technology to the US market or plants? Probably, the activity of ‘technology transfer’ and ‘local support’ is not a simple matter. In adapting the technology to local taste or local factor conditions, the scientists and engineers may have to come up with new ideas and these ideas, in turn, may be utilized by the headquarters R&D teams.25 In fact, such ability to learn from the experience in overseas R&D may have been the source of international competitiveness of these firms, for instance, those in the automobile industry. The dummy variable indicating the presence of an R&D subsidiary in Europe, EURO, has expected positive coefficients. They are significant among Type S firms in Japan patent production function, again suggesting the presence of learning from overseas experience, and among Type R firms in the US patent 25 The post-war experience in Japan illustrates that, when new technologies were imported, many Japanese firms had to adapt them to the Japanese conditions and, during this process, made many innovations of their own. See Odagiri and Goto (1996).

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production function. In other words, among Type R firms, R&D in the US and Europe contribute to inventions in the US but not in Japan. It may be that the firms who have established a Japan–US–Europe triad R&D network are the firms experienced in overseas R&D management, thus succeeding in raising the efficiency of the US R&D subsidiaries. 8.2. Local technological strength All the coefficients of LTSφ , φ = 1, 2, 3 are positive and (except LTS3 ) significant among Type R firms in either Japan or the US. Local technological strength of the state in which the US R&D subsidiary is located contributes to the invention efficiency of both Type R Japanese parents and their US subsidiaries, as hypothesized. Among Type S firms, however, this tendency is insignificant. For them, the location of subsidiaries does not matter though, as shown above, the R&D expenditures do matter. As presumed in the previous section, these firms have established US subsidiaries to exploit their own technological advantages. These advantages are transferred to the R&D divisions of their US subsidiaries, contributing to their inventions irrespective of the subsidiaries’ location. In Type R firms, by contrast, they establish their R&D subsidiaries in the US to exploit the technological seeds available in the US, in the hope of making use of them in the US R&D subsidiaries and also bringing them back to the Japanese headquarters laboratory. This result clearly supports our main hypothesis on the importance of technological knowledge sourcing as a motivation of overseas R&D investment among Type R firms. We can also compare the explanatory power of the three variables on local technological strength, LTSφ , φ = 1, 2, 3. Among Type R firms, in either USPAT or JPAT equation, LTS1 and LTS2 are estimated to have positive coefficients that are significant at least at the 5% level, whereas the estimated coefficient of LTS3 is insignificant even at the 10% level. Hence, spillovers across technological fields are inferred to be incomplete. In terms of both the estimated coefficients and their z-values, LTS1 and LTS2 are comparable, suggesting that sourcing is made within own technological fields and also possibly with spillovers from technologically related fields. Recall that LTS2 was calculated using the proportion of industry’s R&D expenditure to each

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technological field as the weight. Hence, the result that this variable has a significant coefficient but LTS3 (an un-weighted sum of knowledge stock of all the fields) does not suggest that even a Type R firm cannot source outside knowledge of a certain field unless it has made R&D investment in the field. Among Type S firms, none of the LTSφ variables is significant in either equation; hence, the null hypothesis is accepted that, with or without inter-field spillover, the location of the R&D subsidiaries does not matter. 8.3. Experience since the entry, mode of entry, and industrial differences The coefficients of EXP are insignificant, suggesting that the length of US experience, mostly on sales and/or manufacturing, appears irrelevant to the firm’s productivity in research. The coefficients of ACQ are positive in the US patent production function and weakly significant among Type S firms (in Eqs. (8) and (9)) and Type R firms (in Eq. (7)) Table 4. They are negative and insignificant in the Japan patent production function regardless of the type of firms. Similar to the finding of Belderbos (2001), acquired R&D subsidiaries appear to be more productive in invention. Such a higher productivity may owe to the technological capabilities these subsidiaries had already accumulated by the time of acquisition. These capabilities, however, are not useful in the Japanese parent’s sourcing from the US knowledge stock. Communication between a parent laboratory and its acquired R&D subsidiary may be less dense than that between a parent laboratory and its newly established subsidiary, making it more difficult for the parent to take advantages of the capabilities accumulated in the acquired subsidiary. The results for industry dummies agree with the inter-industry pattern of the propensity to patent. As is well known, the number of patents is small among pharmaceutical firms, even though the value of each patent is large.26 This fact is consistent with the robust negative coefficients in pharmaceuticals. 26 See Cohen et al. (2000) and Goto and Nagata (1996) for the fact that patents as a means of appropriation are most effective in the pharmaceutical industry. Also see Haneda and Odagiri (1998) for the fact that the contribution of patents on the market value of the firm is highest among pharmaceutical firms.

The same is true for JPAT in food processing, probably because some of the companies classified here have been diversifying into pharmaceuticals and other biotechnology-related fields. For many of the other industries and particularly among Type S firms, the dummies have negative coefficients in US patent production functions and positive coefficients in Japan patent production functions, with varying degrees of statistical significance. Recall that the electronics industry is taken as the norm. Therefore, the result indicates that, relatively to these industries, Type S electronics firms are more active in patenting of US inventions. In view of the globally intensive technological race common in this industry, both the propensity to invent abroad and the propensity to patent out of such invention must be higher.

9. Conclusion Using the data of 137 Japanese manufacturing firms, this paper investigated the contribution of home R&D and overseas R&D on the firm’s invention activity at home and in the US, as measured by the numbers of patents granted by the USPTO. A particular emphasis was placed on the technological knowledge sourcing activity. To measure the availability of knowledge to be sourced, we constructed indices of local technological strength that consist of withinstate knowledge stock and inter-state spillovers, and confirmed that locating US subsidiaries in states with high local technological strength significantly contributes to the firm’s invention among Type R firms, of which the main business of the subsidiaries is research, but not among Type S firms, of which the main purpose of the R&D subsidiary is to support the local manufacturing and sales activities. In addition, we found that, even in Type R firms, spillover across technological fields is incomplete. These findings indicate that technological knowledge sourcing is an important purpose of overseas R&D among Type R firms and that locating R&D subsidiaries at a ‘right’ place is indispensable for effective sourcing. Such sourcing is expected to take place through diverse channels including contacts with other researchers in universities and other firms, recruitment of local researchers, conferences, licensing, technology alliances, and collaborative research. Some of

T. Iwasa, H. Odagiri / Research Policy 33 (2004) 807–828

them may be achieved more effectively with nearby R&D units but some need not be so. Therefore, in order to have a more precise understanding of knowledge sourcing on a global basis, we will need more information on these diverse forms of knowledge transfer and how the overseas R&D units are (and can be) involved in them, which is also closely related to the issue of ‘R&D boundaries of the firm’ that discusses whether a certain knowledge should be created within the firm or procured from outside by such means as alliances, collaborations, and licensing (Pisano, 1990; Odagiri, 2003). A very broad and certainly very important issue is before us and our result, it is hoped, gives a first step, if a small one, towards this direction.

Acknowledgements We are grateful to Professors K. Fukao, H. Horaguchi, and other participants of the Conference for

825

constructive comments and K. Nakamura for research assistance. Appendix A. Data A.1. Sample firms To investigate if our sample firms are representative, we compare in Appendix A, Table A.1 the industry distribution of our sample firms to that of all the SOBA manufacturing firms with one or more subsidiaries in the US. Both the mean number of subsidiaries in the US and unconsolidated parent sales are larger among the sample firms, suggesting that these firms, which consist of firms making R&D expenditures in the US, tend to be larger and more internationalised. Not surprisingly, they are relatively concentrated in R&D-intensive industries, such as pharmaceuticals, although this tendency does not apply to some of

Table A.1 Sample firms and all manufacturing firms with subsidiaries in the US: a comparison Our sample

Industrial composition (%)

All manufacturing with subsidiaries in the US

Industrial composition (%)

Number of subsidiaries Mean S.D. Min Max

(A) 4.292 4.334 1 29

(B) 2.309 2.789 1 29

Parent sales (unconsolidated) Mean S.D. Min Max

355210.8 619103.2 4367 4597561

202567.7 547563.9 103 7525555

Number of parent firms by industry Food Textiles Chemicals Pharmaceuticals Glass, cement and ceramics Steel Non ferrous metal/metal products General machinery Electronics Transport machinery Precision machinery Tire and rubber Other manufacturing

9 3 23 12 3 5 5 16 27 19 2 3 10

(A) 6.6 2.2 16.8 8.8 2.2 3.6 3.6 11.7 19.7 13.9 1.5 2.2 7.3

43 13 81 24 15 20 51 103 123 108 26 10 83

(B) 6.1 1.9 11.6 3.4 2.1 2.9 7.3 14.7 17.6 15.4 3.7 1.4 11.9

137

100

700

100.0

Total

Source: Calculated by the author from the Survey of Overseas Business Activities.

(A)/(B)

1.86

1.75

1.07 1.18 1.45 2.55 1.02 1.28 0.50 0.79 1.12 0.90 0.39 1.53 0.62

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other supposedly R&D-intensive industries, such as transport machinery and precision machinery. A.2. Calculation of knowledge stock The knowledge stock of state k in technological field m, KSkm (t), is calculated according to the following formula (see, e.g., Goto and Suzuki, 1989), where, to indicate year, t is added in the parenthesis. KSkm (t) = Pkm (t) + (1 − δ)KSkm (t − 1)

(A1)

where δ is the rate of depreciation or obsolescence and was set equal to 0.1, following Griliches (1998). Pkw (t) is the number of patents issued in year t to state k in technological field m. The location of a patent is determined by the address of the first inventor. Because, until 1995, US patents expired after 17 years from grants, we ignored all patents issued before 1981 and calculated the stock in 1998, KSkm (1998), with the data of Pkw (1982), . . . , Pkw (1998) according to Eq. (A1).27 As stated in the text, KSkm (1998 to indicate the year of observation is hereafter suppressed) was then divided by a half of the radius of the state.28 The radius was calculated from the area of urban and built-up land of the state, which was obtained from U.S. Census Bureau, Statistical Abstract of the United States, 2000, adjusted to the unit of kilometre squared. The original source is US Department of Agriculture, National Resource and Conservation Service, and Iowa State University, Statistical Laboratory, 1997 National Resources Inventory, issued in December 1999. To calculate inter-state spillovers, the knowledge stock of each of other states was divided by the distance between this state (i.e., the originating state) and the recipient state. The distance between states is calculated with the Spherical Distance Method and the data source is Geographic Names Information 27

The patent is effective for 20 years after application if this is longer than 17 years after grant. Because the average time interval between application and grant was 2 years, it may be more accurate to calculate the stock with the 18 years (1981–1998) data. However, owing to the 10% depreciation and the fact that (1 − 0.1)18 ≈ 0.15, the addition of the1981 data hardly affects the result. 28 This methodology follows that of Hama (1958). For its application in the context of multinationals’ choice of location, see Fukao and Yue (1997).

System (GNIS) of the National Mapping Information of the US Geological Survey, the US Department of the Interior. The total knowledge stock of state k and field m, TKSmk , was then calculated as the sum of these two parts according to Eq. (1) of the text. Indices of local technological strength for firm i, φ LTSi , φ= 1, 2, 3 and other variables can be now calculated easily following the definitions in the text and Table A.2. The correlation coefficients between the variables are shown in the Appendix A, Table A.2. A.3. List of the data source Ministry of International Trade and Industry (MITI; now renamed the Ministry of Economy, Trade and Industry or METI). • The Survey of Overseas Business Activities (SOBA), which consists of two types of surveys: Basic Survey of Overseas Business Activities (every 3 years), and the Survey of Trends in Business Activities of Foreign Affiliates (other years). The present study mainly used The 29th Survey of Trends in Business Activities of Foreign Affiliates, 1999, which covers the FY1998 data. • The Basic Survey of Japanese Business Structure and Activities (BSA), 1999. Toyo Keizai Inc.: • Kaigai Shinshutsu Kigyo Souran, 1999. Japan Economic Research Institute: • Corporate Finance Database (compiled by the Development Bank of Japan). Statistics Bureau & Statistics Center, Ministry of Public Management, Home Affairs, Posts and Telecommunications: • Survey of Research and Development, 1999. US Patent and Trademark Office: • PATSIC file, Information Products Division, Technology Assessment and Forecast (TAF) Branch. • Patent full-text databases of issued patents. US Bureau of Economic Analysis: • Foreign direct investment in the United States • Gross state products.

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Table A.2 The means and correlation coefficients Variables

Correlation coefficients Means

JPAT USPAT ln JRE ln USRE EURO ln LTS1 ln LTS2 ln LTS3 ln EXP ACQ

88.591 6.920 8.557 4.981 0.387 −3.304 −3.297 −3.022 5.332 0.248

JPAT

USPAT

ln JRE

ln USRE

EURO

ln LTS1

ln LTS2

ln LTS3

ln EXP

ACQ

1 0.7425 0.4377 0.3362 0.2598 0.1233 0.0379 0.0932 0.2071 0.1294

1 0.3903 0.3736 0.2416 0.1309 0.0714 0.116 0.1549 0.1165

1 0.5331 0.401 0.1458 0.1541 0.2412 0.3215 0.0436

1 0.2609 0.2076 0.1746 0.1012 0.0917 0.1714

1 0.0274 0.0067 0.09 0.247 0.0988

1 0.9509 0.5204 0.0725 0.0366

1 0.6179 0.008 0.0613

1 0.0184 0.1487

1 0.0352

1

No. of observations = 137.

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