Innovation And Knowledge Diffusion In The Global Economy
Innovation and Knowledge Diffusion in the Global EconomyA thesis presentedbyJasjit SinghtoThe Department of Business Economicsin partial fulfillment of the requirementsfor the degree ofDoctor of Philosophyin the subject ofBusiness EconomicsHarvard UniversityCambridge, MassachusettsApril 2004
2004 – Jasjit SinghAll rights reserved.
Innovation and Knowledge Diffusion in the Global EconomyThesis Chair: Professor Tarun KhannaAuthor: Jasjit SinghAbstractThe first part of this dissertation studies two questions regarding the role ofmultinational firms (MNCs) in knowledge diffusion: (1) How actively do overseassubsidiaries of MNCs exchange knowledge with organizations from their host country?(2) To what extent do these subsidiaries facilitate bi-directional knowledge flow betweenthe MNC home base and the host country? These questions are analyzed using citationdata for over half a million patents from 4,400 firms and organizations from six countries.A novel regression framework using choice-based sampling is used to estimate theprobability of knowledge flow. The results suggest that there are significant bi-directionalknowledge flows between MNCs and their host countries, but MNCs contribute less tohost country knowledge than they gain from it. However, the exact pattern variessignificantly across countries and sectors, depending on the knowledge-intensity offoreign direct investment.The second part of this dissertation examines if collaborative networks amongindividuals explain two patterns of knowledge diffusion: (1) geographic localization ofknowledge flows, and (2) easier transmission of knowledge within firms than betweenfirms. Collaborative links among individuals are inferred using a “social proximitygraph” constructed from patent collaboration data for more than one million inventors.The existence of a direct or indirect collaborative tie is found to be associated with agreater probability of knowledge flow, with the probability increasing with the directnessiii
of the tie. Controlling for collaborative ties significantly reduces the estimated impact ofgeographic co-location and firm boundaries on the probability of knowledge flow. In fact,conditional on the existence of close collaborative ties, geographical co-location and firmboundaries have no additional effect on the probability of knowledge flow.The third part of this dissertation analyzes innovation in emerging and newlyindustrialized economies, with the emphasis being on Asian economies. In particular, Iuse patent data to study how the overall and sector-level innovative capabilities ofTaiwan, Korea, Hong Kong, Singapore, India and China have evolved over the past 30years. I also study the relative importance of foreign multinationals, business groups,individuals, domestic firms and research institutes in innovation, and the concentration ofinnovative activity.iv
AcknowledgementsI am extremely grateful to my thesis committee – Professors Tarun Khanna,Joshua Lerner and Richard Caves – for their constant guidance and support. I have alsobeen fortunate to get an opportunity to work closely with Professors Ken Corts, AnanthRaman and V.G. Narayanan, from whom I have learnt the nuts and bolts of research. I amalso thankful to Professors George Baker, Jerry Green and Lee Fleming for their constantencouragement and help over the years.It has been wonderful to be a part of the Boston academic community. I havelearnt a lot from the faculty and fellow students at Harvard, MIT and Boston University. Iam also grateful for detailed feedback and close mentoring from several people in thebroader academic community, who helped me immensely even though they barely knewme to start with and had little to gain in return. While space constraints keep me fromacknowledging them individually, I am indebted to each one of them!My parents Sarvajit Singh and Harmohinder Kaur have been my greatest sourceof strength. They inspired me to be an academic, and encouraged me to hang in thereeven on occasions when the journey looked rough. My wife Pia, little boy Pawan, and hissoon-to-be-born sibling (“B2B2”) have helped make my PhD dream a reality throughtheir endless love and support, and have brought a joyful balance to my life. I would alsolike to thank my mother-in-law Lisbeth, who helped us out when we were overwhelmedby the time pressures of having our first baby. And I am most fortunate to have a fatherin-law like Claes, who gave me confidence and even volunteered to proofread my thesis!v
Table of ContentsChapter 1: Introduction . 1Chapter 2: Multinational Firms and Knowledge Diffusion: . 61. Introduction. 62. Hypotheses. 93. Data on Patent Citations and Multinational Ownership . 124. Preliminary Analysis. 175. Citation-Level Regression Methodology. 216. Results. 267. Further Issues in Using USPTO Patent Citations . 428. Discussion and Concluding Remarks . 44Appendix 2.1. A Note on Choice-Based Sampling and WESML . 47Chapter 3: Collaborative Networks as Determinants of Knowledge Diffusion Patterns. 511. Introduction. 512. Hypotheses. 543. Patent Data . 594. Empirical Methodology . 635. Results. 726. Limitations . 827. Conclusion . 84Chapter 4: Technological Dynamism in Asia. 871. Introduction. 872. Comparing innovation across countries: methodology. 913. Comparing innovation across countries: results . 924. Sector-level analysis of innovation: methodology. 965. Sector-level analysis of innovation: results . 1026. Comparing type of innovators: methodology . 1107. Comparing type of innovators: results. 1128. Concluding thoughts . 123References. 125vi
Chapter 1: INTRODUCTIONThis dissertation studies technological innovation and knowledge diffusion.Motivating my research is the belief that acquisition of knowledge and management ofinnovation are critical for economic success, both for firms and for regions. Therefore,better understanding of these phenomena would lead to better prescriptions for firms informulating their technology strategies, and for regions and countries in making policiesgoverning technology transfer, innovation, and both incoming and outgoing investment.The ease with which knowledge diffuses has important implications for growth(Grossman and Helpman, 1991). However, even though ideas are intangible in nature,empirical evidence shows that they do not flow freely across regional and firmboundaries. Two patterns of knowledge diffusion have been identified. First, knowledgeflows are geographically localized (Jaffe, Trajtenberg and Henderson, 1993). Second,knowledge flow is easier within firm boundaries than between firms (Kogut and Zander,1992). This dissertation studies two different aspects of these patterns. The first paperstudies how, because of easier flow of knowledge within firm boundaries, multinationalfirms (MNCs) can help overcome geographic constraints on knowledge flow and enableinternational diffusion of knowledge. The second paper studies how direct and indirectcollaborative links between individuals are a key mechanism giving rise to the aboveknowledge flow patterns in the first place.Governments around the world continue to spend huge resources to attract MNCs,at least partly in the hope of knowledge gains from them. However, literature on howforeign direct investment (FDI) contributes to knowledge diffusion still remains1
fragmented and inconclusive. My first paper (titled “Multinational Firms and KnowledgeDiffusion: Evidence Using Patent Citation Data”) extends existing research on role ofMNCs in knowledge diffusion. Related literature in international economics largelyemphasizes uni-directional knowledge flows from foreign MNCs to host countrydomestic firms. However, as the strategy and international business literature hasestablished, FDI can also be a channel through which domestic technology can fall intothe hands of foreign competitors. Therefore, except for countries that have little uniquetechnology of their own, it is important to consider bi-directional knowledge flows instudying net gains from FDI. The potential “leakage” of domestic knowledge throughFDI is a particularly real issue for technologically advanced countries, which are thefocus of my first paper.I find that knowledge flows from host countries to MNCs are about as intense asthose between domestic entities, showing that MNCs are able to tap into local sources ofknowledge just as much as the domestic entities are. On the other hand, knowledge flowsback from MNC subsidiaries to their host countries are weaker. In other words, on anaverage, MNCs do not seem to contribute as much to local knowledge as they gain fromit. However, this pattern differs across industries and countries depending on knowledgeintensity of local investment by foreign MNCs. I also find that subsidiaries of foreignMNCs, especially those from the same home country, are particularly good at learningfrom each other. Turning to cross-border knowledge flows, I find MNCs to be far betterthan markets at transferring knowledge across international borders, with knowledge flowbeing as intense from a foreign subsidiary to the MNC home base as from the home baseto the foreign subsidiary. I also find that greater overseas innovation by an MNC leads2
not just to direct learning by its foreign subsidiaries, but also to increase in its homebase’s absorptive capacity for foreign knowledge.While the study summarized above focuses on measurement of knowledge flows,the second paper (titled “Collaborative Networks as Determinants of KnowledgeDiffusion Patterns”) digs deeper into the mechanisms behind such knowledge flows.Numerous factors, including informal networks, institutions, norms, language, culture,incentives, and other formal and informal mechanisms might affect the ease with whichknowledge diffuses. However, this paper explores the extent to which the observedknowledge diffusion patterns can be accounted for simply by the fact that people withinthe same region or firm have close collaborative links that might facilitate flow ofcomplex knowledge. In particular, I analyze if collaborative ties between inventors helpaccount for the effect of geographic co-location and firm boundaries on the probability ofknowledge flow between individual inventors of U.S. patents.I allow for the possibility that direct and indirect ties could matter to a differentextent. For example, if an individual X has a direct collaborative relationship withindividual Y, and Y has a direct tie with Z, Z might learn indirectly about X’s workthrough his tie with Y. To measure the directness of collaborative ties among over amillion inventors in the U.S. patent database, I construct a “social proximity graph” basedon information about the team of inventors for each individual patent. This graph allowsme to derive a measure of “social distance” between inventors. This data is then used toexplore the extent to which collaborative links are important for knowledge diffusion.Collaborative ties are found to be crucial for knowledge flow, with the probability of3
knowledge diffusion between two teams of inventors being inversely related to the“social distance” between them.Even more interestingly, I find that collaborative networks are useful inexplaining why knowledge flows tend to be concentrated within firms and regions. Theeffect of being in the same region or the same firm on probability of knowledge flow fallssignificantly once collaborative networks are accounted for. In fact, conditional onhaving close collaborative ties, geographical co-location and firm boundaries have littleeffect on probability of knowledge flow. In contrast, for patent pairs with only indirectcollaborative ties or no collaborative ties at all, geographic co-location and firmboundaries continue to be associated with greater probability of knowledge flow, possiblybecause of other kinds of formal and informal mechanisms influencing intra-regional andintra-firm knowledge flow.The first two papers described above also make important methodologicalcontribution to the literature on knowledge diffusion. While patent citations are animperfect measure of knowledge diffusion, they are widely used in research as a way todirectly capture micro-level knowledge flow. Following this literature, the papersdiscussed above also use patent citations to measure micro-level knowledge flows.However, the methodology used here is entirely new. Jaffe, Trajtenberg and Henderson(1993) pioneered a widely-used statistical technique that tries to correct for factors otherthan knowledge spillovers that might determine distribution of technological activity, andhence the pattern of patent citations. However, Thompson and Fox-Kean (2004) haveshown that existing application of this technique often leads to over-estimation ofknowledge flows. To address this, I propose a novel citation-level regression approach4
that estimates the probability of micro-level knowledge flow between innovating teamsusing a novel regression framework based on choice-based sampling (Manski andLerman, 1977). As described in detail later, the resulting weighted maximum likelihoodapproach helps address some methodological concerns regarding existing use of citationsfor measuring knowledge diffusion.The third paper in this dissertation, titled “Technological Dynamism in Asia”(joint work with Ishtiaq P. Mahmood), compares the extent and composition ofinnovation in six Asian economies – Korea, Taiwan, Hong Kong, Singapore, India andChina. Using patent data from the past three decades, it shows how Korea and Taiwanhave transitioned to a level and quality of innovation comparable with world leaders,while Singapore and Hong Kong have only recently started to move in that direction. Thefindings suggest that the “Asian Tigers”, often studied as a homogenous bunch, actuallydiffer substantially in the extent to which, and the mechanisms through which, innovationis responsible for economic growth in recent decades.5
Chapter 2: MULTINATIONAL FIRMS AND KNOWLEDGE DIFFUSION:Evidence Using Patent Citation Data1. IntroductionInnovation and knowledge diffusion play a critical role in economic growth,with growth rates being highly sensitive to how easily knowledge diffuses (Romer,1990; Grossman and Helpman, 1991; Eaton and Kortum, 1999). While economistsonce believed that ideas should be costless to transport, recent empirical literature hasestablished that knowledge spillovers are geographically localized (Jaffe, Trajtenbergand Henderson, 1993; Audretsch and Feldman, 1996; Branstetter, 2001; Keller, 2002).Foreign direct investment can play an important role in overcoming this geographicconstraint on the diffusion of knowledge (Caves, 1974; Aitken and Harrison, 1999;Branstetter, 2000).1 Governments around the world continue to spend huge resourcesto attract multinational firms (MNCs), at least partly in the hope of knowledge gainsfrom them. However, literature on how foreign direct investment (FDI) contributes toknowledge diffusion still remains fragmented and inconclusive.Existing literature largely emphasizes uni-directional knowledge flows fromforeign MNCs to host country domestic firms. However, while FDI can lead toknowledge flows for the domestic players, it can also be a channel through whichdomestic technology can fall into the hands of foreign competitors. Therefore, exceptfor countries that have little unique technology of their own, it is important to considerbi-directional knowledge flows in studying net gains from FDI. The potential1Multinational activity is not the only way in which global economic activity can contribute to knowledgediffusion. Trade can also play an important role (Coe and Helpman, 1995), but is not studied in this paper.6
“leakage” of domestic knowledge through FDI is a particularly real issue fortechnologically advanced countries, which are the focus of this paper. For example,Dalton and Shapiro (1995) say, “Rapid growth of foreign R&D in the US has led toconcerns about an erosion of US technology leadership Some observers havequestioned the quality of the research effort by foreign companies. They have arguedthat US research centers of foreign companies are merely ‘listening posts’ that focuson technology scanning.” A central goal of my paper is study the extent to which thisconcern is valid.It is hard to measure knowledge spillovers directly. Therefore, several studieshave tried to estimate the effect of FDI on productivity of domestic firms (Caves,1974; Aitken and Harrison, 1999). A challenge in doing so, however, has beenseparating knowledge spillover effects of FDI from its effect on competition (Caves,1996; Chung, 2001; Chung, Mitchell and Yeung, 2003). An alternate empiricalapproach, which I follow in this paper, is to measure knowledge diffusion using patentcitation data. While patent citations are an imperfect measure of knowledge diffusionand also make it hard to separate true externalities from intentional knowledge transfer(Peri, 2003), they are widely used in research as a way to directly capture micro-levelknowledge flows (Jaffe and Trajtenberg, 2002). I measure bi-directional knowledgeflows between MNC subsidiaries and domestic players, and also between MNC homebase and host countries, using data on citations made by over half a million patentsoriginating from 4,400 MNCs and domestic organizations in the US, Japan, Germany,France, UK and Canada. In its use of patent data in studying role of MNCs, the currentpaper builds upon Almeida (1996), Branstetter (2000) and Frost (2001), while placing7
much more emphasis on bi-directional knowledge flows, and looking at cross-
This dissertation studies technological innovation and knowledge diffusion. Motivating my research is the belief that acquisition of knowledge and management of innovation are critical for economic success, both for firms and for regions. Therefore, better understanding of these phenomena would lead to better prescriptions for firms in
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about distance education, and other factors affecting adoption and diffusion of distance education within the health education profession. Theoretical Framework . The diffusion of innovation theory explained how a new idea, product, or innovation disperses through society (Rogers, 1962). “Diffusion is a process in which an innovation is
Rogers, describes diffusion as a dynamic process by which an innovation is communicated through certain channels over time among the members of a social system. So there are four key elements of the diffusion of innovation process: 1. an idea or innovation; 2. channels of communication to spread knowledge of the innovation; 3.
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successfully increase the production of useful innovation. Nelson 9 and Pavitt and Walker) in their review and analysis of government policies and programs toward technological innovation, state that Federal innovation policy and prescription encourage innovation, not its adoption; knowledge transfer and utilization [diffusion] are
