Hitotsubashi University
Institute of Innovation ResearchComparing the Productivity Impacts of Knowledge
Spillovers from Network and Arm s Length
Industries:Findings from Business Groups in Korea
Keun LEE
Kineung CHOO
Minho Yoon

IIR Working Paper WP#13-15

Aug. 2013

Institute of Innovation Research
Hitotsubashi University
Tokyo, Japan
http://www.iir.hit-u.ac.jp

Comparing the Productivity Impacts of Knowledge Spillovers
from Network and Arm’s Length Industries:
Findings from Business Groups in Korea

Keun LEE,* Kineung CHOO,** and Minho Yoon+

This revision: August 2013
First draft: October 2012

* Corresponding author: Professor of Economics, Seoul National University,
Shillim-dong, Seoul 151-746, Korea
E-mail: kenneth@snu.ac.kr
Tel: 822-880-6367 Fax: 822-886-4231**Assistant Professor, Korea Naval Academy, Changwon, Korea
E-mail: choo21@snu.ac.kr+Assistant Professor, Kyung-book National University, Daegu, Korea
E-mail: racoon22@knu.ac.kr

The authors gratefully acknowledge the support provided by the National Research
Foundation of Korea (Grant no. NRF-2010-330-B00093). This paper has been presented at
the International Workshop on Innovations and IPRs, held in Institute for Innovation
Research, Hitotsubashi University, Japan, June 30, 2013. The revised version of this paper
has benefitted from the valuable comments by Ashish Arora, Mark Shankerman, Sadao
Nagaoka, and other participants.

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Abstract
Given the increasing significance of knowledge spillovers in innovation, this study
investigates and compares knowledge spillovers from arm’s length firms in the industries(market) with those from other sister firms in the same business group (network). By
dividing the knowledge pool into pools within and outside a sector to which a firm belongs,
we re-examine the ongoing debate on the relative size of the intra- versus inter-sector
spillovers, and address a new question on the relative size of spillovers from networks
compared with those from markets. We find that although both intra- and inter-sector
spillovers are significant, no evidence proves the dominance of either type of spillover,whether the spillover is from industries or from networks. More importantly, we find thatspillovers from networks are greater than those from industries regardless of whether thecomparison was made between intra- and intra-, inter- and inter-, intra- and inter, or interand intra-sector spillovers. Results imply that knowledge spillover is not automatic but
subject to limitations related to the tacitness of knowledge, and that certain types ofknowledge can be transferred only through direct interaction, which is more prevalent withina network organization such as a business group.
Keywords: knowledge spillover; network; business groups; productivity; inter-sector; intrasector; innovation
JEL Codes: D22, D23, D24, D83, D85, L25

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1. Introduction
Knowledge is a public good to a certain extent. Thus, an innovating firm cannot often
prevent other firms from using its new product or process inventions, and other firms mayachieve more innovations by exerting less effort (Jaffe, 1986; Medda and Piga, 2007). Other
companies can use the knowledge of a firm at lower or zero costs because such knowledge isoften shareable, inexhaustible, and reusable. Knowledge can spill over through various
routes, including upstream and downstream linkages, learning by doing and observing, themovement of workers involved in research and development (R&D) activities, and various
local networks between scientific and engineering personnel from different organizations(Hubert and Pain, 2001; Medda and Piga, 2007; Plunket, 2009; Dindaroglu, 2010;
Desrochers and Leppälä, 2010). Firms located in a spatial neighborhood (i.e., in the samecluster) or in a conceptual neighborhood (i.e., in the same industry or network) benefit fromthe knowledge-creation activities of other firms.
Given its existence in diverse contexts and channels, knowledge spillover has been thesubject of a large number of theoretical and empirical studies with difference foci. One of theearly theoretical studies conducted is that of d’Aspremont and Jacquemin (1988), whichfinds that the optimal amount of R&D in cooperative R&D in a duopoly will be larger withknowledge spillover than without it. This research has led to several extensions.1 Empiricalstudies on knowledge spillovers are well surveyed in the work of Griliches (1992), anddefining the spillover pool, both domestic and international, has been a key issue inempirical literature. Jaffe (1986) constructs the potential spillover pool of an industry as thesum of the innovative activities of other firms in the industry. Empirical research confirms
the effects of knowledge spillover from both domestic and international pools (Adams andJaffe, 1996; Geroski, 1995; Coe and Helpman, 1995).
By examining further details of a spillover, one finds diverse results from empiricalanalyses. For instance, Wakelin (2001) finds that companies in the innovation-using sectors
appear to benefit more from the R&D of other firms in the sector than from their own,
whereas innovation-producing sectors do not appear to benefit from their own R&D
expenditure or that of others. Given this situation, Kafouros and Buckley (2008) address anemerging issue, that is, the conditions under which firms benefit from spillovers. Although
Kafouros and Buckley (2008) search for such conditions, such as firm size, technological1

For instance, Suzumura (1992) extends the case to an oligopoly.
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opportunities, and competition, other reasons also exist. This study pays attention to theinherent tacitness of knowledge, which tends to restrict the degree of knowledge flows interms of its transferability to and learning by other firms. One argument is that spillover andtransfer among sister firms affiliated with the same business group or conglomerate may beless subject to such limitations. However, few studies analyze the spillover impact of aknowledge pool of a network consisting of affiliates in a business group, and compare themwith spillover among arm’s length firms in the industry.
Business groups, which consist of legally independent firms operating in multiple markets,
are a common phenomenon in emerging markets and in several developed economies. This
form of organization has attracted increasing interest in economic and business studies, as
reviewed by an article in the Journal of Economic Literature (Khanna and Yishay, 2007).
Earlier literature on business groups has focused on their emergence in an environment withmarket failures or institutional voids. Thus, a business group is perceived as anorganizational device for internalizing transactions that are too costly to happen in themarkets (Leff, 1978; Goto, 1982; Khanna and Palepu, 1997 and 2000). A resource-based
view of the business group has stressed developing and sharing certain capabilities amongfirms affiliated with the group (Kock and Guillen, 2001). Chang and Hong (2000) confirm
the positive profitability impacts of group-level resource variables, such as advertisement,
intra-group transactions, and R&D expenditure. Resource (including knowledge) sharing
among affiliates of a business group is more logical when certain resources are unavailable
in the markets, or when the benefits from such sharing within a network are greater than thebenefits from market transactions regardless of market failure (Cheong et al., 2010). Thus,
the purpose of this study is to compare the size of spillover impacts from within-group(network) with that from industries (arm’s length relationship).
This study focuses on the function of a business group as an effective organization for
expanding the knowledge resources of a firm, and thereby boosting its productivity. We
construct the knowledge pool of a business group (network) as an analogy to that of anindustry, and then compare the relative size of these kinds of spillovers, namely, spillovers
from networks and industries. We initially test the hypothesis that productivity impacts ofknowledge spillover pools from business groups may be greater than those from industry
spillover pools. Branstetter (2000) observes the vertical keiretsu of Japan as a valuable
economic institution that internalizes knowledge spillovers, and verifies spillover impacts
based on the weighted R&D expenditure of other firms as a knowledge pool available to a5

firm. However, Branstetter does not compare the relative size of the impacts from the
network (within-group) and from the industry (outside-group). Assessing the impact of intraand inter-sector spillovers both at the group and industry levels is the concern of the second
hypothesis of this study. This issue is becoming more relevant in the current era
characterized by an increasing trend of technology fusion, but is not addressed in literatureon business groups.
With regard to inter- versus intra-sector spillovers, a number of scholars suggest that theformer is more significant than the latter in explaining economic growth, social returns, or
productivity (Hubert and Pain, 2001; Harris and Robinson, 2004; Kafouros and Buckley,2008) for firms in highly competitive environments. Moreover, recent studies provide
limited evidence on intra-sector technology spillovers, whereas significant inter-sector
spillovers are often reported (van Stel and Nieuwenhuijsen, 2004; Javorcik, 2004; Bwalya,
2006; Kugler, 2006; Badinger and Egger, 2010). However, spillovers cannot be understoodin a one-dimensional manner. A number of studies combine the geographical dimension withthe sectoral dimension. Autant-Bernard (2011) analyzes the nature of intra- and inter-sector
knowledge spillovers both within regions and between regions, and compares the relative
size of spillover impacts. However, to our knowledge, no study has yet examined intraversus inter-sector spillovers among firms affiliated with a business group. Thus, we test ahypothesis that affiliates in a business group will obtain more spillovers from sister firms in
different sectors than from those in the same sector. This reasoning is consistent with theidea of technology fusion proposed by Kodama (1992) and Suzuki and Kodama (2004), thatis, persistent technological diversification is necessary for the survival and long-term growthof a technology-based firm.
In the present study, we use sales per employee as a dependent variable, which is adefinite and simple measure of productivity that is less subjected to data noise. Thus, thisvariable is used in existing literature, such as in the work of Kafouros and Buckley (2008).
Then, we regress this variable on the variables measuring the sizes of knowledge pools in
networks and arm’s length industries, controlling other factors. Robustness tests are also
conducted using total factor productivity (TFP).
The current study proceeds as follows. Section 2 reviews related literature and derives thetwo hypotheses on the relative sizes of knowledge spillovers, which an affiliate of a businessgroup benefits from. Section 3 establishes the phenomenon of knowledge spillover inbusiness groups within the context of Korean firms using patent citation data. Section 4

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describes the methodology and data used in this study, and then section 5 presents the main
results on the relationship between knowledge spillover and firm productivity. Section 6
concludes the study.

2. Literature and Hypotheses: Knowledge Spillovers and Business Groups
Knowledge can be used at a lower marginal cost or without any cost because it is reusable
and non-exclusive. Consequently, knowledge accumulated by one firm can broaden thetechnology base of other firms without appropriate compensation for the former (Glaeser etal., 1992). Once developed by a firm, knowledge enhances the knowledge production ofother firms because new knowledge can be built upon existing knowledge without
exhaustion (Laursen and Meliciani, 2000). Knowledge has the inherent properties (at least in
a partial sense) of non-excludability and non-rivalry. Thus, the production function of a firm
depends on the level of knowledge available in the economy, as well as on its own inputs(Jaffe, 1986; Medda and Piga, 2007).2
According to the viewpoint of transaction cost economics, firms use internal capital orlabor markets to reduce transaction costs resulting from market imperfections. An internalmarket is less costly than an external one in the presence of asymmetric information. Firms
conduct their own R&D, and consequently, generate diverse technologies. Technology is oneof the input factors of a firm that can be traded in the markets. Nevertheless, the tacitness oftechnological knowledge makes it difficult to transfer completely through markets. Therefore,
a number of firms acquire technology from intra-firm markets. Conglomerates or businessgroups are good examples of internal markets. These organizations replace external markets,with trades or transfers occurring within the boundaries of related firms or business groups.
The economic logic behind business groups can be a starting point for an argument regarding
the positive benefits for group-affiliated firms resulting from the knowledge pool of themother group. A rapidly changing business environment tends to lead firms toward requiringand promoting more cross-fertilization between technological areas. If a firm truly enjoys
cross-fertilization among the technologies within its boundary, then spillovers will probably
occur across firms within a business group (widened boundary of a firm). Branstetter (2000)
argues that the vertical keiretsu of Japan is a valuable economic institution that internalizes
knowledge spillovers.
2

Although R&D spillovers spur the diffusion of new knowledge, they may also create disincentives for firms toundertake their own R&D investments (Bernstein, 2000).
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Diversified business groups are common in emerging and developed economies (Khannaand Yishay, 2007). Based on the transaction cost perspective, when the cost of acquiring
inputs through markets is extremely high, firms extend their boundaries and internalize
rather than externalize their functions. During the early period of industrialization, markets
for factors such as skilled labor, capital, or technology are nonexistent or incomplete. Under
the circumstance of market imperfections, business groups replace poorly performing or
nonexistent economic institutions such as banks and external labor markets (Ghemawat and
Kanna, 1998; Chang and Hong, 2000). For example, in Korean business groups or chaebols,
certain key activities (e.g., recruiting, training, and promoting employees; advertising; andR&D) have been conducted at the group level until recently. The labor mobility of scientists
and engineers is a conduit for knowledge spillovers among firms (Dindaroglu, 2010; Kim
and Marschke, 2005). The higher mobility of skilled labor among affiliates may facilitate thetransfer of knowledge better within the business group than within the market.
Based on the perspective of resource-based theory of the firm, business groups can beeffective organizations promoting the more efficient use of the knowledge resources of afirm. A resource-based view of a firm stresses sharing of resources among affiliates within
the group (Chang and Hong, 2000). In particular, reusable and inexhaustible resources, suchas technologies and managerial resources, are transferred to other affiliates to generate
efficiency-enhancing results. More efficient resource utilization is possible by pooling
resources at the business group level and sharing them among affiliates. Intra-grouptransactions among affiliates can offer lower prices because information asymmetry is lessnotable among firms affiliated with the same group. Obviously, the redistribution of profits
from good to bad performers by the group headquarters can incur costs that are unique tobusiness group affiliates (George et al., 2004).
Firms generally do not use technologies only for internal production purposes (Cesaroni,2004). A number of underutilized technologies may exist even in firms that are highly
diversified across product lines (Cheong et al., 2010). Cesaroni (2004) points out that thepossibility of recovering the costs of R&D activities through in-house exploitation is
drastically reduced when technological diversification is directed toward fields that are
marginal to core technological competencies, and if the firm is not facing a sufficiently large
demand. Nevertheless, the recovery of R&D costs at the group level is easier through intragroup technology transactions and transfers when confronted with an imperfect technology

8market. 3 The presence of business groups allows affiliates to more readily develop
technologies that are marginal to their core competencies. In a technologically diversified
group, the chances of finding affiliates within the group that are underutilizing theirtechnologies would be higher. In addition, the potential for knowledge cross-fertilization will
be better exploited within a business group than in a stand-alone firm. This reasoning is
logical because knowledge is not exhausted but improved by the learning process throughrepeated use. Moreover, intra-group transfers of technologies are more efficient and fasterthan those between unrelated firms (Granstrand, 1999).
Therefore, we propose that knowledge pools at the group level affect the innovations ofaffiliated firms and their productivities. Given that member firms of a business group areclosely interconnected, the influence of spillover pools from the network may be stronger
than that of broader spillover pools from the market. Furthermore, the existence of cognition
failure (Noteboom et al., 2007) resulting from the tacitness of knowledge will enableknowledge within a network to become more readily recognized, transferred, and utilized
than knowledge at arm’s length relations. That is, certain types of knowledge or the tacit
dimension of knowledge can be transferred only through direct interaction and experience,
which are more prevalent within a network organization, such as a business group. Thus, thefirst hypothesis of this study is that productivity impacts of spillovers from firms affiliatedwith the same business group (network) are greater than those of spillovers from other firms
in the industries (market). To our knowledge, no research, except that of Branstetter (2000),
focuses on spillovers from both an affiliated business group and an affiliated industry.
However, Branstetter (2000), who uses weighted R&D expenditure rather than patentapplications to measure knowledge pools, does not address the relative size of impacts
arising from different types of spillover pools, such as networks (within a group) andindustries (outside a group).
Our next hypothesis deals with comparing spillovers from the same and from different
sectors. This issue has not been addressed in literature on business groups, but is becoming
more significant, given the increasing trend toward technology fusion (Kodama, 1992). Ideas
from a certain sector can induce new ideas in another sector.4 This type of spillovers canemerge from the mobility of R&D employees, upstream (downstream) relationships with
3

Technology transfer is different from knowledge spillover, which R&D practitioners cannot appropriate.
However, the benefits from a business group do not only result from technology spillover per se but also fromthe speed and ease of technology transfer within the group. In fact, making a clear-cut distinction between theeffects of knowledge spillovers and transfers is difficult.
4
Los (2000) refers to this type of knowledge spillover as an “idea-creating” spillover.
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suppliers (customers), and public information contained in patents, scientific journals,
conferences, and so on (Los, 2000). In literature concerning the spillover effects ofknowledge, a number of studies have addressed the issue of the relative size of inter- andintra-sector spillovers (Bernstein, 1988; Rouvinen, 2002; Kafouros and Buckley, 2008).
However, no previous study has focused yet on the effects of inter- and intra-sector
spillovers within a business group, and has compared spillovers from affiliates in different
sectors and spillovers from affiliates in the same sector. Early research on spillovers has
mainly focused on intra-sector effects, such as positive externalities from the subsidiaries ofmultinational corporations (MNCs) to domestic firms in the same sector. By contrast, theresults of recent studies are not conclusive with regard to intra-sector spillovers (possibly
because of the competition effect). This outcome has caused researchers to switch theirattention from intra-sector to inter-sector spillovers (Sasidharan, 2006).
Table 1 summarizes studies on inter- or intra-sector spillovers from R&D or foreign direct
investment. Hubert and Pain (2001) argue that spillovers across sectors should be consideredin policy making as an additional channel of spillovers. Studies attempting to identify intrasector spillovers may underestimate externalities from inward investment by foreign-owned
companies (Hubert and Pain, 2001). Several recent studies suggest that inter-sector
spillovers are more prevalent than intra-sector spillovers in explaining economic growth orsocial returns (Javorcik, 2004; Harris and Robinson, 2004; van Stel and Nieuwenhuijsen,
2004; Kugler, 2006; Jordaan, 2008; Badinger and Egger, 2010). Moreover, Bwalya (2006)
finds limited evidence for intra-industry spillovers from foreign to local firms throughhorizontal channels, but cites significant evidence for inter-industry spillovers throughbackward and forward linkages. In this case, we can hypothesize that a group-affiliated firm
will obtain more spillovers from sister firms in different sectors than from those in the same
sector within the business group.
[Table 1]
The dominance of externalities across sectors over those within a sector can be explained
as follows. Inter-sector spillovers occur through backward and forward linkages between
buyers and sellers, whereas intra-sector spillovers occur through imitation, licensing,competition, or labor mobility (Harris and Robinson 2004). An innovative firm has anincentive to facilitate knowledge transfers to upstream or downstream firms, thus enablingrecipients to produce intermediate inputs or equipment more efficiently. Therefore, adverse
competition effects are more likely happening within a sector (Bwalya, 2006). Thus,
10knowledge spillovers within a sector may be counterbalanced by competition effects—asituation that leads to lack of intra-sector spillovers, according to several studies (Aitken andHarrison, 1999; Javorcik, 2004). By contrast, a greater potential for spillovers exists throughforward and backward linkages, given that supplier-buyer relationships have grown stronger
because of technological complexity (Kugler, 2006).
In summary, the discussion on the relative importance of intra- and inter-sector spillovers
should consider the possible different impacts of specialization and diversity on productivity(van Stel and Nieuwenhuijsen, 2004). Although specialization facilitates spillovers amongfirms in the same sector either through direct contacts and the mobility of skilled labor,
diversity fosters spillovers among firms in different sectors through cross-fertilization ofideas across different lines of work (van Stel and Nieuwenhuijsen, 2004; Plunket, 2009;
Desrochers and Leppälä, 2010). Several studies indicate that industries in a region growfaster when the region is less specialized (Jacobs, 1969; Glaeser et al., 1992). The
specialization viewpoint asserts that spillovers are more likely to occur among similar firms
sharing common knowledge. By contrast, the diversification viewpoint emphasizes thatcross-fertilization and complementarities among firms enhance knowledge spillovers(Autant-Bernard, 2011). Another stream of theoretical literature posits the idea oforganizational and cognitive distance (Noteboom et al., 2007), and find that such distancehas an inverted U shape with respect to the value of learning (Wuyts et al., 2005). Thisfinding implies that spillover or learning effects are highest when a certain (optimal) level of
distance exists.
Kodama (1992), who coined the term “technology fusion,” observed that between 1980
and 1986, the Japanese textile industry spent 70% of its total R&D outside its principal
products. In turn, the technologies developed by the sector gained potentials for other sectors.
New fibers, for example, have been used in making building materials and filtration systems
for kidney dialysis machines (Kodama, 1992). In Korea, Cheil Industries, the former leading
textile company in the country, has evolved into a major chemical company by applying its
fiber technologies to electronic materials and chemicals. Suzuki and Kodama (2004) argue
that persistent technological diversification through fusion across internal or external
technologies are necessary for the survival and long-term growth of technology-based firms.
Kugler (2006) suggests that generic technology, which can be deployed easily in production
across sectors, is more likely to be propagated and require fewer absorptive capacities thansector-specific technology.
The preceding discussion suggests that formulating a theoretical argument in favor of thedominance of either inter- or intra-sector spillovers will not be easy because it will dependon the relative importance of diversity compared with specialization. Thus, we do not assign
any weight to the technical distance of patents from diverse sectors.
In summary, we consider the four sources of knowledge spillover pools presented in Table11

2, and focus on the four main comparisons shown in Part 2 of the table, which can begenerated by combining intra- and inter-sector dimensions with the dimension of network
versus arm’s length industries. We also partly deal with two more possible comparisons, as
shown in Part 3 of the table. However, these possible comparisons are not the focus of thisstudy.
Table 2. Four sources of spillover pools and their comparison combinations.
Part 1. Four sources of spillover pools
Network

Arm’s Length Industry

Intrasector

A: Affiliated firms in the same
sector

C: Arm’s length firms in the same
sectorInterSector

B: Affiliated firms in a different
sector

D: Arm’s length firms in adifferent sector

Part 2.

Four main comparisons
Across sectors within a network

Spillover from A is larger orsmaller than B

Across boundaries within asectorSpillover from A is larger orsmaller than C

Across sectors within theindustry

Spillover from C is larger orsmaller than D

Across boundaries fromdifferent sectors

Spillover from B is larger orsmaller than DPart 3. Two more possible comparisons
Networked firms in the same
sector versus other firms in adifferent sector
Networked firms in a different
sector versus other firms in theSpillover from A is larger
or smaller than D
Spillover from B is larger orsmaller than Csame sector

3. Establishing the Case in the Context of Business Groups in Korea
Although the preceding section discusses the diverse impact of knowledge spillovers on
productivity, one may think that the phenomenon of knowledge spillover should beestablished first before its productivity impacts are measured. This process can be conducted12

in two ways. We initially discuss the case of knowledge sharing in the more specific context
of Korean business groups, and then, we provide technical evidence of the knowledgesharing phenomenon in Korean firms. In essence, the planned analysis requires data on firms
interacting within a network, and we find Korean firms affiliated with business groups to bea good choice. Korea is one of the few countries in the world where business groups are still
extensively spread as a key form of firms in the national economy. The data on Korean firms,
which clearly identify affiliates over time, are widely used in academic research published ininternational journals. Korea also fulfills another requirement, that is, the patent data for eachaffiliated firm should be available so that their knowledge spillover pools can be identified.
One institutional basis for knowledge spillover among affiliates in business groups isgroup-level or centralized personnel management, which includes hiring and training of new
staff by the group headquarters rather than by each affiliate (Chang 2003: 93-94; Chang andHong 2000). This practice has been in effect until the mid-1990s in the case of the oldest andlargest business groups, although certain business groups, such as STX, still maintain such asystem. For example, in the case of Samsung Group, one of the oldest business groups in
Korea, group-level hiring of workers and their centralized training started as early as 1957,
and had been maintained as late as 1994.5 Group-level management of human resources
includes, among others, group-level advertisement of recruitment and three to four weeks
training of newly hired employees, during which they train, eat, and rest together. After thiscommon training period, the newly recruited workers are assigned to different affiliates.
Their job applications are not for specific affiliates but are considered for the entire group,and thus, they should not have any complaint on which affiliate they will be assigned to in
their initial jobs.
Since the late 1990s, and particularly, the 2000s, typical business groups switched todecentralized staffing through which each affiliate announces job vacancies and hires its own
staff. However, even after this decentralization move, a group still maintains group-level
retraining sessions for newly promoted senior managers, and these officers, particularly the
directors, are still rotated among affiliates and are subjected to group-level personnel
management. Samsung maintains a group-level retraining organization called the Samsung
Human Capital Development Center at the outskirts of Seoul City. This organization is
responsible for training newly promoted senior managers from all affiliates (The LG Group5

Based on an interview (conducted on February 2013) with KT Chung, director general of theSamsung Economic Research Institute, who was formerly responsible for the personnel management
system of the Samsung Group, and also with Mr. CH Lee, a section chief of LG Academy.
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has a similar organization called the LG Academy). Similar to Samsung, each group has a
special personnel management committee at its headquarters to make decisions regarding job
rotation, promotion, and discipline of senior managers from all affiliates. This committee
communicates directly with the group chairman or the controlling families. Although thepractice of group-level or centralized recruitment of new staff has deceased, business groups
still tend to form and manage task forces for specific matter, including R&D projects, entry
into new business areas, and major overseas investments. These task forces are composed ofthe most talented or specialized employees from various affiliates.
The aforementioned facts suggest that the staff of Korean business groups tend to know
one another from the beginning of their employment, and cross paths throughout their career
regardless of their initial affiliation. They often work together and are rotated acrossboundaries of affiliates but within the boundary of the group. With regard to the spillover oftechnical knowledge, we have to examine the patent data of business groups. First, eachaffiliate applies and files patents in the name of each affiliate, which has been responsible forrelevant R&D projects. Korean business groups are different from American-styleconglomerates or MNCs which have clearly identified parent firm and affiliates. In Koreanbusiness groups, all affiliate firms are legally independent although they are under a common
controlling family. Moreover, although there are several core-business companies, no clear
distinction exists between parent and subsidiary firms because the equity ties among them
are typically not in the shape of a pyramid but of a matrix (or a circle) shape (Choo et al.
2009; Chang 2003: Ch. 6). Each affiliate in a business group has its own R&D unit although
in some groups, a group-level, independent R&D organization also exists, such as Samsung
Advanced Technology Institute (Chang 2003: 87). However, this R&D institute focuses onbasic or fundamental research, and is not concerned with short-term R&D applications,which is conducted by each affiliate for its own specific purposes.
Thus, for each R&D project, the affiliates that supervise a particular project are clearly
identified. Each affiliate files patents in its name, and is responsible for the R&D outcomesof its work. Table 3A shows that in the case of Samsung Group affiliates, they file patents intheir respective names; and then, the patents are distributed over several affiliates. One firm,
SEC, is dominant in terms of the number of patents. Given that R&D outcomes, and hence,
patents are among the main performance indicators, the chief executive officer (CEO) andsenior manager of each affiliate are keen on filing more patents in the name of theircompanies.

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[Table 3 ]A technical method for establishing patterns of spillover use within business groups,
compared with those of external spillovers is using patent citation data to estimate the degreeof intra-group citations or group-level localization of patent citations following the methodof Jaffe et al., (1993). Using patent citation data to estimate the degree of knowledge flow isreasonable because Jaffe et al. (2000) find through their survey of inventors that a significant
number of citations reflect actual knowledge flow, although not necessarily accurately. To
evaluate the geographic localization of patent citations for each country, Jaffe et al. (1993)
suggest an approach comparing the ‘probability of a patent matching the original patent bygeographic area, which is conditional on the original patent, with the probability of a matchthat is not conditioned by a citation link.’ The non-citation condition probability provides thebaseline or reference value, vis-à-vis the proportion of matching citations. The basic insightoffered by the approach proposed by Jaffe et al. (1993) is that the probability (or propensity)that the patents of country A will be cited by other patents can be compared to a similar
probability defined according to a reasonably comparable reference patent (called control
patent). In this study, we adopt a methodology similar to those of Jaffe et al. (1993) and Lee
and Yoon (2009) to measure the degree of localization at the business group level and not at
the country level.
In this case, we initially calculate the probability (A) that a patent by an affiliate (forexample, x) of a business group will cite a patent by a sister affiliate (for example, y) as
follows:
A=

n xyt
n xt

where

n xyt

is the number of citations made to the patents of an affiliate y by the patents ofanother affiliate x in year t (In the calculation, we use a summation for all the
combinations of x and y belonging to the same business group); andn xt

is the number of citations made the patents of affiliate x in t year (In thecalculation, we use a summation for all affiliates belonging to the same businessgroup).

15

Simply put, formula A computes for the ratio of the total number of all inter-affiliate
citations in each business group to all non-self citations made by all affiliates in a group. The
next step is to select a comparable (called control patent in the study of Jaffe) patent for eachoriginal (for example, x) patent by an affiliate of a business group. A control patent for eachpatent by an affiliate is selected randomly among a group of granted patents, which are of
the same class (out of the 417 United States (US) Patent and Trademark Officeclassifications) and applied in the same year as the paired patent for a business group. Then,
we calculate the probability (B) that a comparable (control) patent will cite a patent held byan affiliate (y in A, except x) in year t, such that:
B=

n cyt
n ct

,

where

n cyt is the number of citations made to the patents of an affiliate y by a comparable
patent c in t year (summation for all control patents; y can be any affiliate in agroup, except x with which c is matched); andn ct is the number of all non-self citations made by a comparable patent c in year t(summation for all control patents).
Simply put, formula B computes for the ratio of the total number of citations made bycontrol patents to patents held by all non-matching affiliates in each business group to all
non-self citations made by all control patents.
Our objective is to show that patents by affiliates in a business group have a higher
tendency to cite patents held by sister affiliates in the same business group than other patents.
Ideally, conducting analysis for more business groups will be better, however, we limit ouranalysis to an exemplary business group, that is, the Samsung Group. One reason for thisdecision is that the Korean patent system does not require patent applicants to report
citations when they file applications until recently, and thus, such data are not available.
Therefore, we have no choice but to use the US patents filed by Korean firms. However,even large business groups tended not to have a sufficiently large number of US patents until
recently. Table 3A shows that even for the Samsung Group, numerous affiliates have aconsiderably smaller number of US patents and a relatively large number of domestic(Korean) patents. Thus, we cannot repeat the same analysis for other business groups.

16

Table 3B presents the results of estimating the degree of group-level localization in thepatent citations by the affiliates of Samsung Group. Part 1 of the table indicates the total
number of citations and the number of intra-group citations. From 1995 to 1997,
approximately 6.6% of the 22,211 citations are intra-group, and among these, the majority is
accounted for by self-citations of each affiliate. Thus, citations made to patents held by sister
affiliates in the Samsung Group account for only 2.1% of the total (Part 2 of the table). This
figure is compared to a probability that other independent and comparable patents (called
control patents by Jaffe) make citations to patents held by Samsung. Part 3 of the tableindicates that the probability is only 0.43%. Then, the t-statistics in Part 4 show that the gap(1.67%) is significantly different from zero, thus implying that the probability (propensity)that Samsung affiliates will cite a patent of a sister affiliate is significantly higher than theprobability that other patents will cite the patent of that affiliate.
In the following section, we now measure the productivity impact of such knowledge
spillover within a business group.

4. Data and Research Method

1) Data
The Korean firm data used in this study consist of distinctive data sets from two different
databases, namely, typical financial statement data and patent data of firms.
First, the financial data on Korean firms are obtained from Korean Information Service(KIS)-Value, the database of the KIS Company, which has been used in numerous empiricalstudies focusing on Korean firms (Chang and Hong, 2000; Choo et al., 2009; Cheong et al.,2010). The database covers all detailed items reported in financial statements, and includes
firms listed in the Korea Stock Exchange (KSE), the Korea Securities Dealers AutomatedQuotation (KOSDAQ, the Korean version of the NASDAQ Stock Market), and all externallyaudited firms. The firms belonging to business groups are then identified. Information on
business groups and their affiliates is obtained from the study of Choo et al. (2009). The
definition of group used in this study is broader than that of the typical top 30 chaebols
designated and monitored by the Korea Fair Trade Commission. The number of affiliates ofa business group varies each year depending on the exits from and entries to the group.
Therefore, the number of group affiliates in our sample is not fixed. For a group to be
included in the sample, it should have more than two affiliates each year during the seven-17year sampling period (1991 to 1997). In addition, an eligible group should have more than
two patent applications, and should remain in the KIS data set throughout the sampling
period. Otherwise, the group is excluded. Thus, the sample includes 79 groups and 417 firms.
The number of firm-year observations is 2,242. Table 3 presents the names of the groups
included in the sample and the number of affiliates of each group.
[Table 4]
The second data set consists of patent data of Korean firms. Patent data refer to the outputof the innovation activities of a firm and serve as proxy for its technological capabilities. The
current study uses patent applications filed with the Korean Intellectual Property Office(KIPO) from 1989 to 1997. Patent data can be downloaded from the Korea Intellectual
Property Rights Information Service (http://www.kipris.or.kr), a publicly accessible Webbased patent database supported by KIPO. The information contained in a patent application
includes the name of the applicants, renewal fee status, the final decision on patentability,
International Patent Classification codes, inventors, and abstracts. We have downloaded
approximately 10,000 text files and arranged them by variables using software such as SAS
and Ultraedit. We then match applicants in the patent data with company names in thefinancial data. Table 5 shows the trend of patent applications filed at the KIPO. The tableindicates that the technological capabilities of Korean firms, as represented by patent stocks,
have been accumulated rapidly. The number of patent applications by local firms exceededthat of foreign applications in 1992. In 1993, domestic corporations overtook foreign firms
in the number of applications. By 1997, the number of patent applications by domestic
corporations reached 67,346, a sevenfold increase from only 9,082 in 1990. The number ofapplications by domestic corporations decreased by approximately 35% in 1998 as a resultof the Asian financial crisis of 1997, and did not recover fully even in 2002.
[Table 5]2) Variable Construction
The annual number of patent applications is susceptible to noise; thus, this study uses
three year-cumulative sums of patent applications. Specifically, the study sums up thenumber of patents applied for in the periods T-2, T-1, and T to obtain a proxy for the18knowledge base of a firm for year T. The concerned explanatory variables, such as group andindustry patents, are then calculated based on these cumulative values. The firms and theirpatents are classified based on the standard industry classification (SIC) codes of theaffiliated firms, which indicates that variations are at the sectoral level of the firms, and not
at the underlying technological level.
In this study, we construct four variables representing the additional knowledge bases of a
firm aside from its own patents, namely, intra- and inter-sector spillover pools outside thebusiness group to which the firm belongs, and intra- and inter-sector spillover pools within
the group.
The inter-sector-within-group spillover pool of a firm is represented by the three-year
cumulative sum of the numbers of patents applied for by its sister firms in the same businessgroup but from different sectors. That is, the inter-sector-within-group spillover pool of firm
k is calculated by Group _ Spilloverint er   Pgj , where g and i denote the group and thej isector, respectively, with which firm k is affiliated, and Pgj denotes the three-year
cumulative sum of the numbers of patents applied for by sister firms belonging to businessgroup g and sector j. The intra-sector-within-group spillover pool of firm k isGroup _ Spilloverint ra  Pgi  pk . Pgi indicates the sum of the numbers of patent applications
by other sister firms in sector i and business group g to which firm k belongs. pk denotes
the number of patents applied for by firm k. Therefore, we simply sum up the numbers ofpatent applications by all sister firms in the sector under which firm k is classified, excluding
the patent applications of firm k. By contrast, the inter-sector spillover pool of a firm is
proxied for by the sum of the numbers of patents that other firms (excluding its sister firms)
in other sectors have applied for. The inter-sector spillover pool of firm k is calculated asfollows:
Industry _ Spilloverint er   Pj  Group _ Spilloverint er , where Pj denotes the sum ofj ithe numbers of patents that all other firms (including sister firms) in sector j have applied for,and i is the industry to which firm k belongs. The intra-sector spillover pool of firm k is
calculated by Industry _ Spilloverint ra  Pi  Group _ Spilloverint ra , where Pi denotes the sumof the numbers of patents applied for by all other firms (including its sister firms) in sector i.
We do not discount patents in different sectors in terms of technological distances.

19

Although several studies, such as that of Kafouros and Buckley (2008), adopt this type of
weighting method, we have decided not to do so because whether long- or short-distanceknowledge is more significant for a firm in raising productivity remains uncertain, as
discussed in the preceding section. Instead, we attempt to measure the relative size of the
impact itself.
We provide the following explanation for the actual variables used in the spillover
regressions. Hereafter, all variables related to the patent counts are constructed based on thethree-year cumulative sum as previously described.
Patent counts at the group level (group_patent): This number is the sum of patents

applied for by affiliates in a business group. This study uses group patents as a proxy forgroup-level knowledge stock that can spill over to affiliates. In calculating the knowledge
stock of a group that is available to a firm, we exclude the patents of the concerned firm
(hereafter referred to as firm k). To compare intra-industry spillover with inter-sector
spillover, we divide group patents into patents within the sector (group_patent(in)) withwhich firm k is affiliated and patents outside the sector (group_patent(out)). Thus,
group_patent(in) is the sum of the numbers of patents applied for by all other affiliates(excluding the firm itself) in the same business group and in the same sector that firm kbelongs

to.

That

is,

group_patent(out)isGroup _ Spilloverint er   Pgj ,

and

j i

group_patent(in) is Group _ Spilloverint ra  Pgi  pk .

Patent counts in the industries are also divided into two parts as follows.
Industry_patent(in) refers to the patents applied for by all other firms in the same sector

to which firm k belongs to, excluding those by firm k itself and other sister firms in the samebusiness group with which firm k is affiliated. Specifically, industry_patent(in) is expressed
as follows:
Industry _ Spilloverint ra  Pi  Group _ Spilloverint ra .
Industry_patent(out) refers to the patent applications by all firms in the other sectors,

excluding those by other affiliates in the same business group with which firm k is affiliated.
That is, industry_patent(out) denotes20Industry _ Spilloverint er   Pj  Group _ Spilloverint er .
j i

Age is the logarithm of the age of firm k. The age of firm k is defined as (one plus) the

number of years elapsed since the foundation of firm k.
Market share is the ratio of the gross output of firm k to the gross output of the sector.

The gross output of the sector is defined at the three-digit sectoral level.6
Export ratio7 is the export to sales ratio, i.e., the share of exported output.

Table 6A presents the descriptive statistics for variables used in knowledge spillover
estimations. This study uses labor productivity (defined as sales per employee) as adependent variable. The mean of sales per employee in the sample is 232 million Korean
won. The three-year cumulative sums of the numbers of patents within and beyond theindustry to which a representative firm k belongs are respectively 5,593 and 67,790, on
average. The average number of group patents within the sector with which firm k isaffiliated is 486. The number of group patents outside the sector to which a firm belongs isapproximately four times as large as the number within the same sector. Meanwhile, theinter-sector spillover pool is approximately 12 times larger than the intra-sector spillover
pool. The maximum value of the export ratio is 99.6%, which means that several firms are
fully oriented toward foreign markets. As shown in Table 6B, the correlations among
variables are not high. Therefore, multicollinearity is not an issue for our data.8[Table 6A] here
[Table 6B] here

3) Regression Models

The hypotheses investigated in this study are as follows. First, a group-affiliated firm
enjoys higher spillover effects from the knowledge pools of the business group to which the6

The current study adds the outputs of all externally audited firms within a sector in the KIS database to
calculate the sector output.
7
KIS export data have missing values. This study fills up the missing values with data obtained from theTS2000 of the Korea Listed Companies Association.
8
The correlation between the undivided group patents (group patent(in+out)) and group patent(out) is
significantly high. However, these variables are not used together.
21

firm belongs than from the knowledge pools of non-affiliated firms. Second, a groupaffiliated firm obtains more spillover benefits from sister (affiliated) firms in other sectors
than those in the same sector. To explore the impact of knowledge spillovers on firm
performance, we regress labor productivity and total factor productivity on the spillovermeasures from two distinct dimensions, namely, arm’s length industry (or market) andnetwork (business group), as well as other control variables. The coefficients of spillover
pools imply whether the performance of a firm is affected by the knowledge available in thegroup or sector to which the firm belongs, or in other sectors. We include export ratio,
market shares, and firm age as control variables, which are often referred to in literature as
determinants of productivity.
To test the hypotheses, we adopt the following regression models: (3), (4), and (5). We uselabor productivity as a productivity measure, which is defined as sales divided by the
number of employees (that is, sales per employee).

productivity    1  firm _ patent   2  industry _ patent (in) 3 * industry _ patent (out )    Z  i  uit, (3)where Z is the vector of other control variables, and firm_patent is the number of patents by
a concerned firm itself.

productivi ty    1  firm _ patent   2  industry _ patent (in)  3  industry _ patent (out )   4  group _ patent (in  out )    Z  i  uit, (4)productivi ty    1  firm _ patent   2  industry _ patent (in )  3  industry _ patent (out )   5  group _ patent (in ). (5)  6  group _ patent (out )    Z   i  uit
5. Regression Results: From Spillovers to Productivity

Using the previously described models, we first conduct the Hausman test to check thevalidity of the random effect models. Then, we report the result that is supported by the
Hausman test for each specification. In the results, group patent is a proxy for the spillover
pool from the business group, and industry patent is a proxy for the spillover pool from otherfirms in the same sector or in other sectors. Thus, the significant and positive coefficients of22

these variables confirm the spillover effects on an affiliated firm from the business groups orarm’s length firms in an industry (from the same sector or other sectors).
Table 7 presents the baseline results using the firms’ own patents, intra-sector, and intersector knowledge pools of a firm. Based on the baseline regressions, we reexamine theongoing debate on the relative size of intra- versus inter-sector spillovers. Models 2 and 3
respectively demonstrate the results that intra-sector and inter-sector spillovers are existing
and are both significant. Among the control variables, export ratio and market share exhibit
significant positive effects, whereas age has a negative and insignificant effect throughout
the models. When we place both variables in Model 4, the sizes of the two coefficients do
not significantly differ. A test on the significance of the gap (reported in Table 10 along withthe other test results) reveals that the two coefficients are not significantly different. That is,
the results provide no evidence regarding the dominance of either intra- or inter-sector
spillovers. Such results are different from those of existing literature which find dominanceof either intra-sector (Bernstein, 1988; Rouvinen, 2002) or inter-sector spillovers (Harris andRobinson, 2004; van Stel and Nieuwenhuijsen, 2004; Javorcik, 2004; Bwalya, 2006; Kugler,
2006; Badinger and Egger, 2010; Autant-Bernard, 2011), but are in agreement with theperspective of Hubert and Pain (2001) and Medda and Piga (2007).
[Table 7]
Table 8 reports the results that compare the impact of within-group (network-based)knowledge pools and that of outside-group (industry-based) knowledge pools. In the
regression shown in Table 8, we use the variable “group_patent (in+out),” which is the sumof the numbers of patents by all affiliates within a business group, excluding the patents ofthe concerned firm k. This measure (coefficient) of the size of group-level knowledge
spillovers is compared with those of intra- and inter-sector knowledge spillovers from an
arm’s length industry (Models 1, 2, and 4, respectively), and their combination (Model 3).
The results of the four models all indicate that the coefficients of the within-group (network)
spillovers are greater than the spillovers from industry (either from the same or different
sector) and the sum of the intra- and inter-sector spillovers from the industry. The F-test
results in Table 10 confirm that the differences are significant. Therefore, we can conclude
that the impact of network-based spillovers is more powerful than that of arm’s length
relationship-based spillovers. This finding implies that group-affiliated firms obtain23

additional benefits from their sister firms in terms of spillovers. Model 5 in Table 8 aims tocheck the possibility of non-linearity by adding the square term of the group patent pool. As
the table shows, the result is insignificant.
[Table 8]
Table 9 indicates the classification of network-based knowledge pools into those within
and outside the sectors to which a firm belongs, namely, spillovers from affiliates in the same
sector and those from affiliates in different sectors. We attempt several combinations tocheck for robustness. In Model 1, we investigate spillovers in the same sector, and compare
the impact of sister firms and other firms in the same industry. The spillovers from the
network are greater than those from the market (non-affiliated), which is consistent with theresults in Table 8. In Model 2, we focus on spillovers from other sectors and compare theimpact of sister firms and other firms in other industries. Again, the spillovers from the
network are greater than those from the industry (non-affiliated firms).
Model 3 is a variation of Model 1, whereas Model 4 is a variation of Model 2. The results
are consistent in that a stronger impact is observed from the network than from the arm’s
length industry regardless of the dimension of the sector (intra- or inter-). The final test is
conducted on all intra- and inter-sector spillover variables, as reported in Model 5. Allvariables remain positive and significant, and the results are also consistent with regard to
the relative impact of the network versus the industry. That is, spillovers from networks are
greater than those from the industry in both intra- and inter-sector dimensions. The size ofthe spillover from the knowledge pool of a firm (own patents accumulated over time) tends
to be similar to that of the spillover from the knowledge pools of affiliated firms in the same
sector.
[Table 9]
Using the four coefficients of Model 5 in Table 9, we conduct significance tests on various
pairwise combinations of these coefficients. The test results in Table 10 suggest that network
spillovers tend to be larger than those from industries in all four possible combinations(intra- versus intra-, inter- versus inter-, intra- versus inter, and inter- versus intra-). Allcomparisons are significant at the 5% level, except inter- versus inter-, which is marginallysignificant at 12%. Thus, by considering all the results from Tables 8 and 9, we can conclude24

that spillovers from the network (within-group) are greater than those from the arm’s length
industries (outside-group).
Another consistent result is the relative size of intra- versus inter-sector spillovers.
Significance tests in Table 10 regarding the relative size of the inter- versus intra-sector
spillover from the industry (row 1 in columns 1, 2, and 3 of Table 10, using the coefficientsfrom Tables 7, 8, and 9) consistently indicate no difference in size. Furthermore, thesignificance test on the relative size of the inter- versus intra-spillover from the network(from affiliated firms) also indicates no significant difference (Row 8 in Table 10). That is,no difference exists between inter- and intra-sector spillovers at both network and arm’s
length industry dimensions, whereas literature tends to compare spillovers only at the single
dimension of the arm’s length industry. This finding strongly supports the argument that
intra- and inter- industry spillovers are both significant (Hubert and Pain, 2001; Medda andPiga, 2007).
The following discussion is on the magnitude of the spillover estimated from theregressions. Based on the coefficients from Model 5 in Table 9, an additional patent applied
for by sister firms in the same sector to which a firm belongs results in an increase of
approximately 6.58 US dollars (USD) in sales per employee for the firm affiliated with thesame business group. By contrast, a firm will experience an increase of only 1.12 USD inlabor productivity from one patent of an unrelated firm in the same sector.9 Meanwhile, one
more patent of affiliated firms in other sectors tends to increase sales per employee by 3.99
USD, whereas an additional patent in any unrelated firm in other sectors produces anincrease of only 1.89 USD in the labor productivity of the concerned firm. Given that a firmin the sample hires an average of 1,753 employees, one more patent from sister firms in thesame group of spillover pools results in a respective increase of 11,534 USD and 6,986 USD
in sales for an average firm affiliated with a business group from intra- and inter-sector
spillovers. From market spillover pools, an increase of 3,320 USD in sales is accrued to a
firm from the inter-sector spillover, and an increase of 1,960 USD is accrued from the intrasector spillover. By contrast, adding another patent in the knowledge pool of a firm results in
an increase of 7.08 USD in labor productivity, and an increase of 12,417 USD in the sales ofthe firm.
However, the absolute sizes of industry spillover pools significantly outweigh those of
9

The coefficients of spillover variables represent the impacts per patent in million Korean won; thus, weconvert Korean won to US dollar by using the average exchange rate for 2010 (KRW/USD = 1,156.26). The
constructed patent variables are based on the three-year cumulative sum of the numbers of patents. Therefore,
the real impact of one patent is approximately three times as large as the value of each coefficient.
25

group spillover pools, as shown in Table 4. For example, the knowledge pool of sister firms
in other sectors has only 1,974 patents, whereas the knowledge pool (market pool) of nonrelated firms in other sectors has 68,243 patents. The absolute size of the total effects ofspillover pools at a given time is larger in the market than in the network (or affiliates).
However, market spillover pools are not under the control of a firm. Thus, firms should pay
more attention to the level and growth of spillover pools from the network. Moreover, mosteconomic decisions are made based on the marginal effect rather than the total (or average)
effect comparison.
[Tables 10, 11A, and 11B]
The following discussion presents the result of several robustness tests. The test is
conducted using TFP rather than labor productivity as a dependent variable, and by
conducting cross-section estimations rather than panel estimations. The results are reported
in Table 11A (estimations) and Table 11B (tests of the differences in the coefficients). The
results are basically consistent between Table 10 and 11. An additional insight from thecross-section results is that the coefficient of the intra-sectoral knowledge pool of other firms
in the same sector is negative, which is logical because it clearly captures competition effects
among firms in the same industry, which cannot be demonstrated by panel regressions.
One of the initial premises of this study is that knowledge spillover is not automatic but issubjected to limitations related to the tacitness of knowledge, thus giving rise to theadvantage of business groups as they promote direct and indirect interactions among staff
members across firm boundaries but within the boundary of business groups. Although wearrive at such an interpretation, we have not actually proven the importance of the tacitnessof knowledge as an intervening factor. To address this issue, affiliated firms and their sectors
are classified into either more tacit knowledge- or explicit (codified) knowledge-oriented
sectors, and, simultaneously, knowledge pools are also classified into pools involving either
more tacit or more explicit knowledge sectors. Thus, we have created four combinations
using two types of knowledge pool sectors and two types of destination sectors. Then, we
test whether a group affiliate belonging to a tacit (or codified) knowledge-oriented sector
performs better in absorbing tacit (or codified) knowledge than codified (or tacit) knowledge.
In measuring the tacitness of knowledge across sectors, we have followed the method ofJung and Lee (2010), which use the number of patents per unit of R&D expenditure of a26

sector as a measure of inverse tacitness (explicitness) of the knowledge of a sector. The
intuition is that codified (explicit) knowledge is easier to patent because it is easy to describe,and that companies in a sector that mostly uses explicit knowledge choose the patenting
system as a defense mechanism, whereas companies in a sector that relies more on tacitknowledge tend to choose secrecy over patents (Nuria and Nieto–Antolín, 2007). Thus, for agiven amount of R&D activities, a sector involving more explicit knowledge will generate
more patents than other sectors. Actually, we find a systemic variation of this ratio oversectors similar to that presented in Jung and Lee (2010). For instance, the electronics sector
has the highest ratio of 17.1 patents per billion won, whereas the apparel sector has thelowest ratio of 0.6 patents per billion won. Thus, we have classified the sectors into twogroups: the first involves more tacit knowledge and the other involves more explicit
knowledge. Firms are classified accordingly in terms of their sectoral affiliations.
Table 12 shows the results of the additional regressions. In Model 1, we compare only twogroups, tacit versus explicit knowledge sectors. The coefficient of the knowledge pool fromaffiliated firms is shown to be larger in the tacit group. In Model 2, we test the two-by-two
combinations. The impact on productivity in affiliated firms is found to be highest whenfirms in the tacit knowledge-oriented sector try to absorb tacit knowledge, compared withother combinations, such as tacit for explicit, explicit for tacit, or explicit for explicit.
Obviously, these results should be considered with caution because the classifications ofsectors are all relative, whereas firms in any sector involve both tacit and codified
knowledge in different degrees. Model 3 uses a dummy for sectors that are more oriented
toward tacit knowledge, as well as two types of knowledge pools and their interactions, toindicate that the tacit knowledge sector dummy is positive and significant, and that theinteraction term of the tacit dummy and tacit knowledge pool is positive and significant,whereas interaction with the explicit knowledge pool is not significant. These results imply alarger impact of spillovers in cases involving more tacit than codified knowledge.
[Table 12]

6. Conclusion

Given the increasing significance of knowledge spillovers and technological fusion ininnovations, this study investigates the productivity impacts of knowledge spillovers from27

firms in an arm’s length relationship (or from the industry in general), and from a network
consisting of other affiliates in the same business group. To shed light on the debate
regarding the relative sizes of intra- and inter-sector spillovers, we have also divided
knowledge pools into spillover pools within and outside a sector to which a firm belongs.
This structure enables us to compare the sizes of spillovers over two-by-two combinations(between the industry and the network, and between the inter-sector and intra-sector).
This study finds that both intra- and inter-spillovers are significant channels of knowledge
spillovers, but no evidence is found on the dominance of either intra- or inter-sector
spillovers, regardless of whether the spillover is from an arm’s length industry or from a
network. This finding should be considered as a new contribution because literature tends tofocus on spillovers from the arm’s length industry, in general.
Second, and more significantly, we find that spillovers from networks (which consist ofother affiliates) are greater than those from arm’s length industries (which consist of
unaffiliated firms in the industries) regardless of the comparison among intra- versus intra-,inter- versus inter-, intra- versus inter-, or inter- versus intra-sector spillovers.
Third, we have further classified firms and sectors into either more tacit knowledge- ormore explicit knowledge-oriented sectors, and knowledge pools into pools involving moretacit or more explicit knowledge sectors. We discover that the impact on productivity among
affiliated firms is highest when firms belonging to the tacit knowledge-oriented sector
attempt to absorb tacit knowledge compared with other combinations, such as tacit for
explicit, explicit for tacit, or explicit for explicit.
These results imply that knowledge spillover is not automatic but is subjected tolimitations related to the tacitness of knowledge, and that certain types of knowledge can betransferred only through direct interaction and experience, which are more prevalent within a
network organization, such as a business group. The knowledge of a firm is either available
to, or more effectively exploited by, the affiliates of a concerned business group. In thissense, a business group is an effective organization for internalizing knowledge spillovers orfor promoting more widespread knowledge diffusion among affiliates wherein the R&D
activities undertaken by one firm benefits other firms in the same business group. The results
are consistent with the finding on the resource-sharing advantage of business groups, asverified by Cheong et al. (2010) and Chang and Hong (2000). Given such benefit and allother things being equal, affiliated firms will find it more possible to expand theirtechnological capabilities and to achieve a higher level of productivity. Finally, we have28

observed that higher spillover effects may be more specific to an environment with lessinter-firm mobility of staff members, as experienced by Korea in the past, given that a higher
inter-firm mobility of employees can be a substitute for intra-group knowledge spillover.
Thus, future research topics may include the impact on intra-group knowledge spillover ofalternative spillover channels, such as an increase in inter-firm employee mobility and theinternational ties of group-affiliated firms.

29

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34

[Table 1] Literature Comparing intra-sector (intra-industry) spillovers
with inter-sector (inter-industry) spillovers
StudiesBernstein(1988)

Hubert andPain (2001)
Harris andRobinson(2004)

Results
Inter-industry spillover to the social return is virtually the sameand small for all industries / Differentials between social andprivate returns and between social returns across industries
depend on the extent of the intra-industry spillovers.
There is evidence of significant intra-industry and inter-industry
spillovers / Both intra- and inter industry effects are significant
and although inter-industry spillovers are marginally larger thanintra-industry spillovers, the hypothesis of common coefficientscannot be rejected.
Inter-industry spillovers are generally more prevalent than intraindustry spillovers.

Data

Types

CanadaR&D spillovers

UK

FDI spillovers

UK

FDI spillovers

No empirical evidence for a positive relationship between intravan Stel andRegional
industry spillovers and value added growth / evidence forNetherlands
Nieuwenhuijsen
spillovers
positive relationships between inter-industry spillovers and value(2004)
added growth.
No evidence of intra-industry spillovers / positive productivity
spillovers from FDI taking place through contacts between
Lithuania FDI spillovers
Javorcik (2004)
foreign affiliates and their local suppliers in upstream sectors(evidence of inter-industry spillovers).
There is little evidence in support of intra-industry spillovers, but
Zambia
FDI spillovers
Bwalya (2006)
significant inter-industry spillovers.
Kugler (2006) knowledge spills over between but not within industries.

Colombia

FDI spillovers

Firms also benefit from spillovers originating from their own
industries, as well as innovative upstream sectors(inter-industry
Italy
R&D spillovers
spillovers).
Both inter- and intra-industry spillovers are significant, but themagnitude of spillovers are higher for the R&D undertaken by
Kafouros andintra-industry firms. / For firms in the environments of highUK
R&D spillovers
Buckley(2008)
competition, Inter-industry spillovers outweigh intra-industry
spillovers.
The presence of FDI creates negative externalities within
Jordaan(2008) industries and positive externalities between industries through Mexico FDI spillovers
backward linkages.
Inter-industry spillovers dominate intra-industry spillovers, which
13 OECD
Badinger and
R&D spillovers
turn out much smaller and even insignificant in somecountries
Egger(2010)
specifications.
The largest direct and indirect effects are associated with
Regional
private R&D activity that spills across industry boundaries / But,
AutantFrance
spillovers
Bernard(2011) Inter-industry spillovers decrease more drastically with distancethan intra-industry spillovers.
Medda andPiga(2007)35

[Table 3A] Samsung Affiliates’ Patents filed in Korea (KR) and the US
Affiliates’
Names
Cheil Industries

Samsung Total

Samsung PetrochemicalSamsung Corning

Samsung Heavy Industries

Samsung SDICountry 1991 1992 1993 1994 1995

1996

1997 1991-94 1995-97

KR

50

60

62

52

57

82

90

224

229

US

3

3

1

1

3

3

0

8

6

KR

1

8

34

25

36

30

44

68

110

US

0

0

1

2

1

0

3

3

4

KR

1

4

4

0

3

1

0

9

4

US

0

0

0

1

0

0

0

1

0

KR

51

34

38

11

22

18

26

134

66

US

1

1

1

1

3

1

2

4

6

KR

33

56

72

145

188

178

180

306

546

US

7

7

1

21

28

15

11

36

54

KR

314

300

220

210

376

628

529

1,044

1,533

US

0

1

11

21

61

74

60

33

195

KR

3,127 3,335 2,676 2,467 10,078 12,149 16,180 11,605

38,407

US

366

381

394

455

613

1,320 1,008

1,596

2,941

KR

200

196

273

302

398

498

549

971

1,445

US

6

2

8

7

27

42

23

23

92

KR

32

84

110

115

236

391

574

341

1,201

US

0

4

9

17

19

42

26

30

87

KR

0

8

21

64

59

40

37

93

136

US

0

1

0

0

0

0

0

1

0

KR

0

0

2

4

6

13

16

6

35

US

0

0

0

0

0

1

0

0

1

Samsung Gwangju

KR

8

9

10

13

25

75

475

40

575

Electronics

US

0

0

0

0

1

2

19

0

22

KR

86

77

80

49

86

107

68

292

261

US

0

4

2

4

2

6

3

10

11

KR

67

91

112

54

21

30

0

324

30

US

2

1

3

9

3

2

0

15

5

KR

31

13

18

4

6

51

59

35

116

US

1

0

0

1

0

1

0

2

1

Samsung Electronics

Samsung Electro-Mechanics

Samsung Techwin

Samsung C&T

Samsung Engineering

CJ (Cheil Jedang)

Woongjin Chemical

HansolSource: the authors; See the text for data sources
Notes: Affiliates include those which were separated from Samsung to be acquired by other businessgroups, such as CJ (1995), Woongjin Chemical (1996), and Hansol (1992).

36

[Table 3B] Relative Propensity of Inter-affiliates Citations in Samsung' Patents:
Comparison using 'Control' Patents
1991

1992

1993

1994

1995

1996

1997

1991-94

1995-97

Total No. of Samsung' patents

386

405

431

540

761

1509

1155

1762

3425

Total No. of citations

1927

2262

2401

2908

4543

9705

7963

9498

22211

67

91

97

137

295

604

567

392

1466

3.48%

4.02%

4.04%

4.71%

6.49%

6.22%

7.12%

4.13%

6.60%

No. of intra-group citations% of Total citations (A1)

Part 1. Comparison with Citations by control patents% of citation to Samsung's
patents (C1)
Relative Propensity of
Intra-Group Citation(A1-C1)t-statistics of (A1 - C1)

0.11%

0.15%

0.25%

0.67%

0.70%

0.35%

0.28%

0.34%

0.41%

3.37%

3.87%

3.79%

4.05%

5.79%

5.88%

6.84%

3.79%

6.19%

7.66

8.71

9.07

9.75

14.34

21.00

20.37

17.55

32.60

Part 2. Inter-Affiliate Citations within Samsung (excluding self-citations)No. of self-citations in Samsung

52

66

76

107

176

439

406

301

1021

% of Total citations

2.70%

2.92%

3.17%

3.68%

3.87%

4.52%

5.10%

3.17%

4.60%

No. of inter-affiliate citations

15

25

21

30

119

165

161

91

445

% of total citations

0.80%

1.14%

0.90%

1.07%

2.72%

1.78%

2.13%

0.99%

2.10%

Part 3. Citations by control patents excluding citations to paired Samsung affiliates% of citations to Samsung affiliates(C2)

Part 4. Relative Propensity ofInter-affiliate Citation (A2-C2)
t-statistics of (A2 - C2)

0.11%

0.15%

0.26%

0.71%

0.74%

0.36%

0.30%

0.35%

0.43%

0.69%

0.98%

0.64%

0.36%

1.99%

1.42%

1.83%

0.64%

1.67%

3.02

3.85

2.85

1.44

6.83

8.54

8.98

5.20

14.01

Notes: See the text for explanations and Lee and Yoon (2010). Citations by control patents in part 1
and 3 can be regarded as upper bounds because all control patents are randomly selected among
Korean-held patents only which tended to have more citations to Samsung’s patents than non-Korean
held patents which had very low incidence of citations to Samsung’s patents. Thus, here ourestimates in part 4 of propensity of inter-affiliate citations, compared to that by control patens, are a‘conservative’ estimates or lower bounds.

37

[Table 4] List of business groups included in the sample (as of 1997)
group name
Samsung
HyundaiLG
Dongyang Chem.
HanwhaDaewooTongil
Lotte
Posco
Sinho
Daesang
Dongkuk Steel
Hyosung
Hwaseung
Kapul
Aekyung
SK
Ssangyong
Kia
Kolon
Daesung
KoryeoPacific Corp.
Yuhan
DaelimSamyang
Sungwoo
DoosanAnam
Jinro
Yoongpoong
Halla
Hanchang
SamwhaKangnamChongkeundang
HanglasDaewon Steel
Kumho
Hanil

no. of affiliates
manufacturing
2)and externallyallaudited firms
24
17
15
13
11
10
10
9
9
8
8
8
8
8
8
8
7
7
7
7
7
7
7
7
6
6
6
5
5
5
5
5
5
5
5
5
5
5
4
4

group name1)

no. of affiliates
manufacturing
2)and externallyallaudited firms
Seah
4
Daenong
4
DPI
4
Nongshim
4
Woonsan4
Dongsung Chem.
4
Hankook Elecronics
4
Samyang Food
4
Samlip Food
4
Byucksan3
Sambo Computer
3
Haitai3
Kangwon Ind.
3
Sungsin Cement
3
Samchully
3
Woongjin
3
Iljin
3
Daewong Pharm.
3
Kirin
3
Hwachun3
Ilshin3
Poongsan3
Dongoh3
Dongwon2
Taekwang
2
Sindonga
2
Hanil Cement
2
Kyesung2
Hite2
Taihan Electric Wire
2
Haesung2
Rocket2
Crown
2
Choongbang
2
Samick
2
Kohap
1
Sammi
1
Sindoh
1
Kookje Pharm.
1

60
52
43
18
32
28
14
29
14
31
26
16
16
13
12
11
42
26
26
23
16
12
11
9
19
7
6
24
19
19
18
16
9
8
8
6
5
4
26
11

1)

11
9
8
8
7
6
6
6
4
14
13
13
13
10
8
6
6
5
5
4
4
3
3
9
8
6
6
5
4
3
3
2
2
2
2
13
6
5
3

Notes:
1) The figure includes only the firms that are under obligation to receive external
auditing.
2) The number of affiliates for each group is counted using the list of business groups
and affiliates from the Maekyoung Business Yearbook. The firms free from theburden of external auditing are also included in the figure.
38

[Table 5] Patent applications by applicant type and by year in Korea
year

All

1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002

10,587
12,75917,062
20,051
23,31525,82028,132
31,07336,49145,71278,49990,326
92,73475,18880,642102,010
104,612106,136

KoreanForeign

2,703
3,641
4,871
5,696
7,021
9,082
13,253
15,952
21,45928,564
59,236
68,41367,34650,59655,97072,83173,71476,5707,884
9,118
12,191
14,355
16,294
16,73814,87915,121
15,032
17,14819,263
21,91325,38824,592
24,672
29,17930,89829,566Externallyaudited
978
1,610
2,312
3,288
4,880
5,955
9,210
11,426
15,25920,75749,48857,227
54,10534,55833,52836,096
39,68841,598Notes:
1) Source: The Korean Intellectual Property Office2) “Externally audited” (last column in the table) refers to the number of patents applied
for by externally audited firms, which have generally more than seven billion won in
total assets as of 2002. The number of patent applications in this column is obtained fromthe authors’ own calculations after firm-applicant matching.

39

[Table 6A] Variables used in Analysis: Descriptive Statistics
Variables
sales_per_employee(million won)firm patentindustry_patent(in)industry_patent(out)group_patent(in)group_patent(out)group_patent(in+out)
export_ratio(%)
market_share(%)
firm age

Mean
232
156
5,593
67,790
486
1,974
2,460
18.4
5.8
18.3

Std. Dev.
172
1,503
13,31040,885
2,764
6,156
6,873
27.5
12.0
13.3

Min
37
0
0
6,954
0
0
0
0.0
0.0
0.9

Max
1,682
39,326
88,690153,000
42,35845,78345,783
99.6
95.0
74.0

Obs
2,242
2,242
2,242
2,242
2,242
2,242
2,242
2,242
2,242
2,242

Notes:
1) Firm patents mean the number of patents applied for by each firm in the sample.
2) industry_patent(in) for each firm in this table and succeeding regression tables refers
to the patents applied for by all firms in the same sector, except those by itself (eachfirm observation in the sample) and other affiliates in the same business group.
3) industry_patent(out) for each firm refers to the patents applied for by all firms in theother sectors than the sector to which a firm belongs, excluding those patents byother affiliates in the same business group.
4) group_patent(in) for each firm is the number of patents applied for by all affiliates inthe same business group and the same sector, excluding those by itself (each firm
observation in the sample).
5) group_patent(out) for each firm is the number of patents applied for by all of its sisterfirms in other sectors.
6) All variables using the number of patents are based on the cumulative sum of patents
applied during the current year and the preceding two years, namely, years T, T-1,
and T-2.
[Table 6B] Variables used in Analysis: Correlations
sales_per_
employee
1. firm patent
0.03
2. industry_patent(in)-0.06
3. industry_patent(out)0.16
4. group_patent(in)-0.01
5. group_patent(out)0.17
6. group_patent(in+out)
0.15
7. export_ratio
0.10
8. market_share
0.09
ln(firm_age)
0.00
variables

1

2

3

4

5

6

7

8

0.18
-0.04
0.07
0.05
0.07
0.08
0.31
0.06

-0.07
0.29
0.02
0.13
0.19
-0.06
-0.08

-0.03
0.00
-0.02
-0.10
-0.08
-0.07

0.05
0.45
0.12
0.03
-0.01

0.92
0.09
0.23
-0.10

0.13
0.22
-0.10

0.17
0.14

0.24

40

[Table 7] Comparing the impact of the intra- and inter- sector spillovers.
dependent var :sales per employee
coef.
cons.
t(z)-value
coef.
firm patentindustry_
patent(in)industry_
patent(out)t(z)-value
coef.
t(z)-value

model 2
RE
230.73
7.35
***
28.99
1.65
*
15.68
7.17
***

coef.
t(z)-value
coef.

export ratiot(z)-value
coef.

market sharet(z)-value
coef.

firm aget(z)-value

industry dummies
withinR-sq.

model 1
RE
240.27
7.67
***
52.92
3.03
***

betweenoverall

Hausman test

model 3FE
134.30
17.62
***
39.19
2.41
**

7.90
20.54
***
0.28
2.25
**
6.60
5.83
***

model 4FE
133.12
17.48
***
29.08
1.76
*
6.33
3.02
***
7.62
19.25
***
0.28
2.30
**
6.53
5.79
***

0.39
3.07
***
2.40
3.70
***
-10.02
-1.41

0.39
3.16
***
2.56
3.96
***
-7.52
-1.05

yes
0.015
0.287
0.231

yes
0.042
0.286
0.233

no
0.201
0.016
0.022

no
0.205
0.013
0.020

3.84(0.28)

4.17(0.38)

36.02(0.00)

32.81(0.00)***Significant at 1% level; **Significant at 5% level; *Significant at 10% level

41

[Table 8] Comparison of the impact of the spillovers from the network and the industry
dependent var :sales per employee
coef.
cons.
t(z)-value
coef.
firm patentgroup_
patent(in+out)t(z)-value
coef.
t(z)-value

model 1
RE
222.20
7.18
***
23.28
1.35
35.11
8.27
***

model 2
FE
133.53
17.61
***
33.56
2.07
**
19.53
4.51
***

model 3FE
132.88
17.53
***
23.05
1.42

model 4FE
132.70
17.50
***
26.31
1.59

model 5FE
132.73
17.40
***
26.36
1.59

17.21
3.95
***

17.80
4.05
***

17.42
1.57

coef.
(group_2patent(in+out)) t(z)-value
industry_
patent(in)industry_
patent(out)industry_
patent(in+out)

coef.
t(z)-value

coef.

t(z)-valuet(z)-valuet(z)-value

industry dummies
withinR-sq.

betweenoverall

Hausman test

4.86
2.29
**
7.27
18.02
***

4.87
2.28
**
7.27
18.02
***

0.27
2.20
**
6.45
5.74
***

0.27
2.20
**
6.45
5.74
***

0.36
2.93
***
2.27
3.56
***
-4.84
-0.69

0.26
2.16
**
6.49
5.77
***

7.11
18.94
***
0.27
2.23
**
6.41
5.71
***

yes
0.070
0.310
0.258

no
0.210
0.021
0.027

no
0.212
0.016
0.024

no
0.212
0.018
0.025

no
0.212
0.018
0.025

4.67(0.46)

34.41(0.00)

28.85(0.00)

34.47(0.00)

35.30(0.00)t(z)-value

coef.
firm age

7.45
18.81
***

t(z)-value

coef.
market share

12.16
5.54
***

coef.

coef.
export ratio

0.09
0.04

*** Significant at 1% level; **Significant at 5% level; * Significant at 10% level42

[Table 9] Spillovers from the affiliates in the same and different sectors
dependent var :sales per employee
coef.
cons.
t(z)-value
coef.
firm patentindustry_
patent(in)industry_
patent(out)group_
patent(in)group_
patent(out)t(z)-value
coef.
t(z)-value

coef.
t(z)-valuet(z)-valuet(z)-value

industry dummies
R-sq.

30.37
3.22
***
16.20
3.16
***
0.26
2.15
**
6.41
5.68
***

t(z)-value

coef.
firm age

7.61
19.26
***

coef.

t(z)-value

0.40
3.18
***
2.60
4.02
***
-7.76
-1.09

within

yes
0.048

no
0.206

between

0.286

0.021

overall

0.235

0.027

5.01(0.42)

38.88(0.00)

Hausman test

model 3
RE
221.40
7.16
***
22.44
1.30

model 4FE
132.91
17.51
***
33.85
2.09
**

7.46
18.85
***
31.01
3.54
***
15.43
3.02
***
0.27
2.20
**
6.61
5.86
***

model 5FE
132.39
17.44
***
27.30
1.65
*
4.31
1.96
**
7.30
18.04
***
25.36
2.75
***
15.36
3.01
***
0.27
2.22
**
6.53
5.80
***

no
0.211
0.018
0.025

no
0.213
0.017
0.024

12.67
5.62
***

t(z)-value

coef.
market sharemodel 2
FE
134.48
17.69
***
35.46
2.18
**

coef.

coef.
export ratio

model 1
RE
231.35
7.36
***
30.31
1.73
*
13.54
5.94
***

26.94
2.89
***
37.50
7.67
***
0.36
2.91
***
2.23
3.50
***
-4.57
-0.65
yes
0.070
0.312
0.259
5.80(0.45)

43.81(0.00) 38.99(0.00)***Significant at 1% level; **Significant at 5% level; *Significant at 10% level

43

[Table 10] Test of the difference in the size of the estimated coefficients

Hypothesis tested

The model 4
in table 7
F value(Prob.>F)The model 4
in table 8
F value(Prob.>F)The model 5
in table 9
F value(Prob.>F)

industry patent(in)=industry patent(out)0.33(0.563)

1.17(0.280)

1.66(0.197)

industry patent(in) =group patent(in)4.34(0.038)**

industry patent(in)=group patent(out)3.93(0.048)**

industry patent(in)=group patent(in+out)

6.21(0.013)**industry patent(out)=group patent(in)3.83(0.050)**

industry patent(out)=group patent(out)2.39(0.122)+

industry patent(out)=group patent(in+out)

5.49(0.019)**0.87(0.350)

group patent(in)=group patent(out)industry patent(in+out)=grouppatent(in+out) (from model 3 in table8)

5.09(0.024)**

Notes:
1) H0: i   j . The null hypothesis is that the regressors, Xi and Xj, have the same size as
the coefficient.
( Rb  r )[ R( X X ) 1 R]1 ( Rb  r ) / q
The test statistic has the form,~ F (q, n  k ) , which hasee / (n  k )an F distribution under the null (see Johnston and Dinardo 1997).
2) Relative sizes among the estimated coefficients to be tested
A:

From

Model

5

in

Table

9:

group_patent(in)>group_patent(out)>industry_patent(out) > industry_patent(in)
B: From Model 4 in Table 8: group_patent(in+out) > industry_patent(out) >44

industry_patent(in)C: From Model 3 in Table 8: group_patent(in+out) > industry_patent(in+out)
3) **: Significant at 5% level: group_patent(in) > industry_patent(in)group_patent(in) > industry patent(out)group_patent(in+out) > industry_panten(in)group_patent(in+out) > industry_panten(out)group_patent(in+out) > industry_panten(in+out)
group_patent(out) > industry_panten(in)+: Significant at 15% level: group patent(out) > industry patent(out)Insignificant:industry patent(in)=industry patent(out)group patent(in)=group patent(out)45[Table 11A] Robustness Test using TFP in both Panel and Cross-section Estimation
dependent var :
TFP
coef.
cons.

t(z)-value
coef.

firm patentindustry_
patent(in)industry_
patent(out)group_
patent(in)group_
patent(out)t(z)-value
coef.
t(z)-value
coef.
t(z)-value
coef.
t(z)-value
coef.
t(z)-value

coef.
group_
patent(in+out) t(z)-value

panel regression

specification1 specification2 specification3 1995 + 1996 1996 + 1997
-0.38
-0.38
-0.38
-0.07
-0.74
-13.96-14.09-14.10
-0.44
-4.18
***
***
***
***
0.03
0.02
0.02
0.32
0.19
0.52
0.34
0.37
2.70
2.03
***
**
0.04
0.03
0.03
-0.11
-0.03
5.01
4.20
3.90
-7.04
-2.49
***
***
***
***
**
0.03
0.03
0.03
0.01
0.04
22.42
21.07
21.05
1.56
4.74
***
***
***
+
***
0.08
0.13
0.07
2.59
2.77
1.93
***
***
*
0.06
0.12
0.10
3.48
2.44
3.39
***
**
***
0.07
4.39
***

coef.
export ratiot(z)-value
coef.

market sharet(z)-value
withinR-sq.

cross-sectional regression

betweenoverall

0.001
1.87
*
0.02
6.01
***
0.262
0.066
0.100

0.001
1.76
*
0.02
5.96
***
0.027
0.071
0.107

0.001
1.78
*
0.02
5.99
***
0.270
0.070
0.107

0.003
2.90
***
0.01
1.93
*

0.003
2.74
***
0.01
2.19
**

0.201

0.203

Notes: 1) TFP estimates used here are from Choo (2007) and the residuals from the fixed effectmodels, which we believe better than those from either LP method that suffers from nonmonotonicity and perfect-collinearity or the GMM method that does not satisfy the criterion which a
GMM specification should meet to be selected. Actually, we have checked all the three methods, andsee Choo (2007) for more details.
2) Cross-section estimations using one year is subject to multi-collinearity problem owing to manyobservations with zero values for the number of patents, and thus we merged two most recent year
observations into one cross section observation taking averages of the two year values to get theseresults in the last two columns.
46

[Table 11B] Hypotheses Test using the Results in Table 11A
panel regression
TestedHypotheses / models

cross-sectional regression

specification1
F value(Prob.>F)

specification2
F value(Prob.>F)

specification3
F value(Prob.>F)

0.53(0.466)

0.03(0.856)

0.00(0.980)

69.01(0.000)*** 37.28(0.000)***industry patent(in) =group patent(in)2.28(0.132)+

19.86(0.000)***industry patent(in) =group patent(out)2.74(0.098)*

19.19(0.000)*** 18.48(0.000)***industry patent(out) =group patent(in)2.75(0.097)*

6.19(0.013)**0.84(0.361)

industry patent(out) =group patent(out)3.17(0.075)*

4.86(0.028)**

5.18(0.024)**0.32(0.574)

0.03(0.872)

0.55(0.461)

industry patent(in) =industry patent(out)industry patent(in) =group patent(in+out)

industry patent(out) =group patent(in+out)

1995 + 1996
F value(Prob.>F)

1996 + 1997
F value(Prob.>F)

6.20(0.013)**

3.99(0.046)**5.75(0.017)**

group patent(in) =group patent(out)Note: See the notes in table 10.

47

[Table 12] Results with Firms and Knowledge Pools divided into Tacit vs. Explicit
Knowledge Sectors
model 1 (LSDV)
dependent var :sales per employee
coef.
t(z)valuecoef.
firm patentt(z)value
coef.
industry patentt(z)(int+out)
value
coef.
group patentt(z)(in+out)
value
coef.
group patent(explicit
t(z)knowledge)
value
coef.
group patentt(z)(tacit knowledge)
value
coef.
tacit knowledget(z)sector (dummy)
value
coef.
tacit dummy*t(z)explicit
knowledge
value
coef.
tacit dummy*tacit
t(z)knowledge
value
coef.
export ratiot(z)valuecoef.
market sharet(z)value
No. of observations
cons.

2

adjusted R

model 2(LSDV)

diff.
diff.
diff.
explicit
tacit
explicittacit
knowledge knowledge test knowledge test knowledge test2
2
2
sectorsectorsectorsector
χ (1)
χ (1)
χ (1)

-155.40-1.28

639.5222.15-128.94-1.05***

model 3(LSDV)
all sectors

582.9318.04-443.42
-3.39

***

***

28.43
2.33

753.40
1.83

29.41
2.41

817.15
2.00

29.33
1.76

**

*

**

**

*

5.73
10.02

7.47
11.59

5.85
10.12

7.11
11.00

6.75
14.58

***

***

***

***

***

17.43
3.58

21.59
2.87

12.77
2.02

16.34
3.40
***

25.91
1.48
3.46
(0.22)********* 4.40
6.55-264.16 (0.01) 1061.88 (0.04)
-1.28
3.88

**

-242.01-0.87***

1031.96
7.71
***

9.79
1.09
1326.52
3.62
***

-0.09
-0.54
0.59
2.86
2.21

0.44
2.30
11.67
4.72

**

662
0.754

-0.11
-0.69

0.41
2.15

0.25
1.83

**

*

2.80
2.16

11.65
4.75

6.13
4.50

***

**

***

***

1,057
0.876

662
0.755

**

1,057
0.878

1,719
0.870

Notes: 1. The LSDV(Least Squares Dummy Variables) is equivalent to fixed effects as it is OLS
with individual dummies for all cross-sectional units. We use this because Stata package allows easy
test of the coefficient across samples.

48

2. The results of equality tests(χ2(1)) between the coefficients of the same type of knowledge poolacross 2 specifications in model 2 are as follows. χ2(1)=0.27(0.61) for the explicit knowledge pool;
χ2(1)= 6.84(0.01) for the tacit knowledge pool.

49