Dense Communication and R&D in Knowledge-based Industrial Clusters: Comparative Study of Small & Medium-sized Firms in Korea and China

Transcription

1 Dense Communcaton and R&D n Knowledge-based Industral Clusters: Comparatve Study of Small & Medum-szed Frms n Korea and Chna Nobuak Hamaguch Research Insttute for Economcs&Busness Admnstraton, Kobe Unversty and Yoshhro Kameyama The Internatonal Centre for the Study of East Asan Development Workng Paper Seres Vol October 2007 The vews expressed n ths publcaton are those of the author(s) and do not necessarly reflect those of the Insttute. No part of ths artcle may be used reproduced n any manner whatsoever wthout wrtten permsson except n the case of bref quotatons emboded n artcles and revews. For nformaton, please wrte to the Centre. The Internatonal Centre for the Study of East Asan Development, Ktakyushu

2 Dense Communcaton and R&D n Knowledge-based Industral Clusters: Comparatve Study of Small & Medum-szed Frms n Korea and Chna Nobuak Hamaguch and Yoshhro Kameyama Research Insttute for Economcs & Busness Admnstraton, Kobe Unversty The Internatonal Centre for the Study of East Asan Development (ICSEAD) Abstract Ths paper presents an analyss of the effects of dense communcaton of ndustry-unversty-government cooperaton on enhancng n-house (a company s own) R&D actvtes n Korean and Chnese knowledge-based ndustral clusters: the Seoul Dgtal Industral Complex, Daedeok Valley, and the Zhongguancun Scence Park. Our unque survey data enable us to examne frms communcaton behavors,.e., communcaton frequency, partcpants, and purposes, related to the choce of communcaton mode. Results of ths study demonstrate that agglomeraton mght mpart least two nfluences on an ndvdual frm: agglomeraton stmulates more n-house R&D through exchange of deas; and t reduces n-house R&D by promotng ts outsourcng. JEL classfcaton: O32, R11, O40 Keywords: agglomeraton, communcaton externaltes, ndustral cluster, Seoul Dgtal Industral Complex, Daedeok Valley, Zhongguancun Scence Park 2-1 Rokkoda Nada-ku, Kobe, , Japan. E-mal: hamaguch@reb.kobe-u.ac.jp Comments are welcome. Send your comments to kameyama@csead.or.jp. Address: 11-4 Otemach, Kokurakta-ku, Ktakyushu, , Japan. TEL: ; FAX:

3 1. Introducton Ths paper analyzes the effects of communcaton among local busness partners on the nnovaton efforts of small and medum-szed frms n scentfc-knowledge-based ndustral clusters n Korea and Chna. These two countres are known for ther strong performance n manufactured goods exports. Despte the ncreasngly hgh technologcal ntensty of ther exported products, ther nnovatve capablty has only rarely been studed emprcally. Studes of knowledge-based ndustral cluster have manly examned cases of developed countres; the related lterature on developng countres s thn. Amng at fllng such gaps, we conducted an emprcal study of three hgh-tech ndustral clusters: the Seoul Dgtal Industral Complex (SDIC), Daedeok Valley (DDV), and the Zhongguancun Scence Park (ZSP). These ndustral clusters are located wthn densely populated metropoltan areas of Korea and Chna. Our prme questons are how much and n what way nnovaton of small and medum-szed frms n these clusters can beneft from local nteracton. The selecton of these three clusters s justfed by the followng observaton. Scentfc-knowledge-based ndustres usually start n developed countres, but they have become partly dspersed to developng countres to acheve cost savngs. However, unlke assembly-type actvtes, because knowledge s strongly concentrated globally, the spread of such ndustres s constraned by the avalablty of qualfed human resources. Wthn ths scenaro, the three clusters of our study represent a small group of newly emergng clusters of knowledge-based ndustres. Observng the locaton pattern of knowledge-based frms, they are apparently created manly n large metropoltan areas. Locatonal advantages of urban centers nclude the exstence of larger markets for new technology products both for sellng products and for purchasng nputs, avalablty of a hghly educated workforce, and access to the scentfc knowledge created at unverstes. Informaton related to such advantages of markets, talent, and knowledge s frequently exchanged wthn metropoltan areas often through nformal and unplanned meetngs. Accessblty of such nformaton s mportant for nnovaton. Therefore, t s natural for frms to seek metropoltan locatons whch enable face-to-face communcatons wth key persons. For example, a telecommuncatons equpment sector executve n Bejng repled to our ntervew: It s only n Bejng that we can meet nformally wth hgh-rank offcals and unversty scentsts who are nvolved n the decson of the technologcal standard of the next generaton. By usng them, we try to take any nformaton whch mght help us to determne the strategy of our company. Certan types of tact knowledge of ths sort 1

4 would lead many frms to gather n specfc regons 1. For the dscusson n ths paper, an ndustral cluster s defned as an agglomeraton of numerous technologcally related frms. It can be sustaned when more frms enter (by startng up or by relocatng from elsewhere) than ext (by closng down or by relocatng elsewhere). To some extent, we can attrbute the growth of a cluster to government polces such as constructon of scence parks and provson of tax ncentves to foregn drect nvestment. However, the provson of such ncentves s not generally suffcent to sustan the cluster. Other factors affectng the behavor of ndvdual frm s acton of entry and ext must be addressed to explan the exstence of clusters. For the specfc case of knowledge-based ndustres, knowledge s an essental nput. Each frm seeks dfferentaton by ts unque deas resultng from efforts n research and development (R&D). The envronment n a cluster can offer both opportuntes and rsks for new start-ups to be establshed as sustanable busnesses. Although opportuntes mght stem from knowledge spllover through dense communcaton wth other agents, there are also rsks of mtaton and harsh competton that wll rapdly render ts technology obsolete. Our vewpont on the theoretcal ratonale of the formaton of knowledge-based ndustral clusters s descrbed n the next secton. 2. Theoretcal Background It s wdely accepted among economsts that frms beneft from knowledge spllover, whch gves rse to aggregate ncreasng returns to scale because of non-excludablty and non-rvalry of knowledge. Saxenan (1994) descrbed how people and frms are closely nterrelated wthn thrvng clusters such as those of Slcon Valley. Provded that the beneft from knowledge spllover s senstve to dstance, especally when the knowledge s tact, t s apparent as a source of agglomeraton economes. Notwthstandng, the stylzed models of new economc geography (Fujta, Krugman and Venables, 1999) have avoded ncorporaton of knowledge spllover because knowledge flows are nvsble; they leave no paper tral by whch they mght be measured and tracked, and there s nothng to prevent a theorst from assumng anythng about them that she lkes (Krugman, 1991, p.53). As Fujta and Thsse (2001) observed, precedent studes of knowledge spllover nclude the weakness of vague defntons of the sources of external economes, smply assumng that the ncreased number of locally partcpatng agents mght ncrease nteracton. They do not clearly 2

5 defne the underlyng mechansm of the local nteracton. Therefore, theoretcal analyss should open such a black box and explctly ncorporate nteracton of agents who engage n nnovaton. Consderaton of the nteracton over the geographc space naturally leads us to the queston of communcaton cost. Thus, anybody seekng a relevant spatal economc model of knowledge spllover must confront the challenge of addressng: not only that knowledge splls over but also why those spllovers decay as they move across geographc space (Audretsch and Feldman, 2004). In ths regard, the emprcal lterature provdes some clues to understandng the localzaton phenomenon of nnovaton. A poneer study by Jaffe, Trajtenberg, and Henderson (1993) consdered patent ctatons as a vsble paper tral of knowledge flows and found that patent ctatons s 5 10 tmes more lkely to occur wthn the same cty, suggestng the effects of proxmty. Audretsch and Feldman (1996) and Varga (1998) nvestgated the geography of nnovaton, specfcally addressng the role of unversty and R&D nsttutons. Audretsch and Feldman (1996) found evdence that, even after controllng the degree of geographcal concentraton of producton, knowledge created by unversty research engenders greater nnovaton of nearby frms. Results of ther study also suggest that the propensty of nnovatve actvtes to cluster s more attrbutable to knowledge spllovers than mere locatonal advantage n producton. Accordng to Varga (1998), unversty knowledge s transferred through R&D cooperaton between academa and ndustry, unversty semnars, scholarly publcatons, faculty consultng, ndustral assocates programs, ndustral parks, spn-offs (faculty and students), technology lcensng, the local labor market of scentsts and engneers, and local professonal assocatons of scentsts. Then Egeln, Gottschalk, and Rammer (2004) found that frms establshed as spn-offs from publc research nsttutons decde ther locaton as an optmzaton problem subject to the benefts of beng closer to ther parent nsttutons aganst some reasons to leave them. The former nclude contnung collaboraton n research, obtanng commercal contracts wth unverstes, and dependence on unversty s research nfrastructure, and exstng socal relatonshps. The latter nvolve proxmty of customers, opportuntes for cooperaton wth other nsttutons, and lower factor prces. Emprcal studes of ths type should confront the dffculty of lack of data and ambguous concepts of measurng nnovaton, knowledge, and proxmty. Jaffe, Trajtenberg, and Henderson (1993) have consdered that the output of nnovaton s represented by patents, whch s also convenent because patent data are easly accessble. Nevertheless, patents mght not be a perfectly good measure of nnovaton because all nnovatve outputs are not necessarly fled as patents. Alternatvely, Charlot, 3

6 and Duranton (2004) prefer to measure the hgher productvty resultng from externaltes by earned wages, whereas Anseln, Varga, and Acs (2000) measure nnovaton by the number of new product announcements n trade and techncal journals. In turn, knowledge s treated as a sort of frm captal stock to produce nnovaton. Its measurement s also a subject of debate. Contnung efforts are beng made to construct a meanngful ndex syntheszng R&D nvestment, employment of knowledgeable talent, and stock valuatons reflectng deprecaton of past accumulatons. Regardng proxmty, the concepts of dstance, travelng tme, and the use of telecommuncatons must be consdered (because face-to-face communcaton and telecommuncatons are sometmes mutually complementary, rather than substtutve). Regardng the data, we lttle expected that any readly avalable dataset would meet our necessty to specfy the tral of actual nteracton nfluencng productvty and nnovaton. Many researchers rely on small-sample survey data, e.g., Adams (2002), Charlot and Duanton (2004, 2006), and Arta, Fujta, and Kameyama (2006). The data analyzed n ths paper were also obtaned from a questonnare survey admnstered to frm managers, whch was conducted n the three clusters n March Aprl 2005 as a part of the research project of the Internatonal Center for the Study of East Asan Development (herenafter ICSEAD Survey). Before descrbng detaled nformaton of the survey data n Secton 4, n the next secton, we provde a bref profle of each ndustral cluster from whch the data were collected. 3. Development of ndustry clusters n metropoltan areas: Seoul, Daejeon and Bejng 3.1. Seoul Dgtal Industral Complex The Seoul Dgtal Industral Complex (SDIC) s located n a southwest area of Seoul (Fgure 1). The locaton was prevously known as the Guro Industral Complex. As Korea s frst ndustral complex, t led exports of tradtonal manufactured products such as textles and garments durng the 1970s and 1980s. However, structural changes of Korean ndustry paved the way to closure of labor-ntensve factores n ths area. In the late 1990s, the Korea Industral Complex Corporaton (KICOX) transformed the rusty factory dstrct nto a hgh-rse ntellgent offce complex desgned for venture companes related to nformaton and communcaton technology (ICT). As of December 2006, the total offce space s almost two mllon square meters, accommodatng sx thousand frms and more than 80 thousand workers 2. 4

7 Fgure 1: Seoul: Establshment of IT ndustry locatons Perhaps the so-called Teheran Valley s better known as the ICT-related ndustral cluster of Seoul. Development of the Teheran Valley started at Teheran Street n the busy busness center of Gangnam Dstrct (Fgure 1). That development was prompted by the 1997 fnancal crss, whch prompted massve layoffs of engneers from large busness groups (Chaebol) and drastcally lessened job opportuntes for students, some of whom resorted to establshment of venture companes. Snce then, the Teheran Valley grew very rapdly, supported by the boom of the venture captal stocks lsted on the KOSDAQ n However, because of the offce rent hke and ICT bubble collapse and, more mportantly, because of the lack of compettve technologcal sophstcaton, numerous ICT-related venture companes n the Teheran Valley had ether faled or relocated (Sohn and Kenney, 2007). Some moved to SDIC and Daedeok Valley, attracted by the beneft of government support measures. The Teheran Valley s ncreasngly occuped by dgtal content frms, whch requre frequent nteracton wth ther customers concentrated n the Gangnam Dstrct. 5

8 3.2. Daedeok Valley The Daedeok Valley (DDV) s an ndustral complex of the central regon of Korea. It s located n the Yuson Dstrct of Daejeon Metropoltan Area, whch s 150 km south of Seoul (Fgure 2). Daejeon s the ffth largest urbanzed area of Korea, postoned at the juncton of two super express ralways (KTX) departng from the southern ctes of Busan and Mokpo. Followng the establshment of the Daedeok Research Complex 3 n 1973, the transfer of natonal scentfc research nsttutons such as the Electroncs and Telecommuncaton Research Insttute (ETRI) and the Korea Advanced Insttute of Scence and Technology (KAIST) has strengthened DDV s functon as a basc research hub. Later, research nsttutes of Korean prvate busness groups were establshed, especally after the Daejeon Expo n Establshment of venture companes has mushroomed snce the 1997 fnancal crss, when numerous researchers were dsmssed from local laboratores. The Korean government launched the DDV development plan n 2000 to support hgh-tech venture companes n the 56 mllon square meters of the developed area. As of 2006, the area s sx unverstes, along wth 824 hgh-tech companes and 63 research nsttutes, employ approxmately 12 thousand researchers wth master s and doctoral degrees: they number about 10% of all research workers n Korea 4. Throughout that expanson, along wth the avalablty of such rch scentfc human resources and sophstcated physcal nfrastructure, wdely varous support nstruments for venture startups provded by the local publc agences have also served an mportant role. In an earler evaluaton, Shn (2001) presented the crtcsm that the research park wth DDV has created few networks among research nsttutes. Yusuf (2003) found that DDV has not developed nto a full-fledged nnovatve cluster because of the lack of the followng aspects: cultural and socal amenty, easy access to fnancal and commercal center, start-up culture and dversfed producer servces to support t, and market realtes whch stmulate appled and development research. More recently, Sung, Gbson, and Kang (2003) presented ther concluson that although the venture entrepreneurs of DDV thnk they dd t all, through provson of low-cost space and nformaton sharng; scence parks and ncubators mght have contrbuted to ther success to a greater degree than the entrepreneurs apprecate. 6

9 Fgure 2: Map of Daedeok Valley 3.3. Zhongguancun Scence Park The core of the Zhongguancun Scence Park (ZSP) s n Hadan Dstrct n the northeastern corner of Bejng, where more than a dozen hghly recognzed academc nsttutons, ncludng Tsnghua Unversty, Pekng Unversty, and the Chnese Academy of Scences (CAS) are located (Fgure 3). In the early 1980s, computer related prvate busnesses (retal, parts and components, mantenance) emerged around unverstes to form the Electroncs Street. In 1988, the Bejng muncpal government announced the creaton of the Hgh-Technology Industry Development Expermental Zone, whch became the frst scence park of Chna. The current structure of ZSP was establshed n 1999 as a result of the unfcaton of fve scence parks n Bejng under the centralzed management of the Zhongguancun Scence Park Management Commttee. 7

10 Fgure 3: Map of Bejng Accordng to Tan (2006), the restructurng of research nsttutons and unverstes n response to state budget cuts and the mplementaton of new government programs to encourage commercal applcaton of scentfc results, such as the Torch Program, have formed a favorable envronment for Chnese hgh-tech development and have encouraged state-owned nsttutes to set up venture companes. Some spn-off companes have receved nvestment from unverstes. Among such companes are today s start-up hgh-tech conglomerates of Chna such as Unsplendor and Dongfang from Tsnghua Unversty, Founder from Pekng Unversty, and Lenovo and Stone, whch orgnated from CAS. Many of these companes retan majorty captal partcpaton of the orgnal unverstes and research nsttutes. Hgh-tech venture companes have also grown n number. They have arsen not only from the local communty of Zhongguancun, but have been formed by former students who studed or worked abroad and were subsequently encouraged to return and establsh ther own busnesses. Frms regstered n ZSP have been supported by a seres 8

11 of benefts such as smplfcaton of the frm establshment procedure, access to the venture captal fund, tax reducton, and cvl regstraton wth permanent resdent status. The scene of the Zhongguancun area has changed dramatcally from a suburban rural appearance to crowded skyscrapers n a decade or so. Although Chnese unverstes, especally those elte schools n ZSP, have acheved extensve commercalzaton, Chen and Kenney (2007) express concerns that some professors are so engrossed n ther commercal actvtes and students are beng used as cheap labor wth lttle attenton to research qualty or pedagogy. Consequently, the nvolvement of unversty admnstrators n the daly operaton of commercal enterprses mght skew unversty decson-makng regardng research fundng, faculty hrng and promoton. 4. ICSEAD Survey n SDIC, DDV and ZSP 4.1. Data Descrpton In admnsterng the ICSEAD Survey to frms of the three ndustral clusters, we restrcted the respondents to those of frms related to ICT and asked dentcal questons to facltate comparatve study of the responses. Man actvtes of our sample ICT-related frms are machnery and equpment (hardware), electronc parts and devces and package software (ncludng nformaton processng) n SDIC and DDV, and electronc parts and devces, package software and nformaton processng n ZSP. The questonnare was desgned to elct responses descrbng the manner, purpose, frequency of frms contact wth busness partners such as supplers, customers, research nsttutes (as scence research functons of unverstes), hgher educaton (as educatonal functons of unverstes whch refer to job search assstance offces of educatonal nsttutons), busness support agences, and fnancal nsttutons (ncludng nvestors and banks), and how geographcal dstance affects such nteractons. We selected frms regstered by local busness support agences, namely, the Korea Industral Complex Corporaton (KICOX), the Daedeok Valley Venture Assocaton (DVVA), and the Zhongguancun Scence Park Management Commttee. In addton, the survey was conducted ether by telephone or on drect vsts 5. The effectve reples were 50 each n SDIC and DDV, and 207 n ZSP. Table 1 shows summary statstcs. The average age of frms n the three ndustry clusters s matched at about sx years. It seems that these frms have arsen from the boom of venture busness n Korea and Chna durng the late 1990s and early 2000s. 9

12 The average number of employees s 23 n SDIC, 30 n DDV, and 46 n ZSP. These frms are categorzed as small and medum-szed accordng to the defntons used n each country. They appear to be hghly orented to R&D, such that about 40% of all employees are research staff engagng n R&D, wth a hgh rato of R&D expendtures aganst the total sales revenue n each cluster: 20% n SDIC, 22% n DDV, and 26% n ZSP. Patent rghts feld were 8 n SDIC, 11 n DDV, and 4 n ZSP. The low ncdence of patents fled n ZSP s attrbutable to the fact that patentng has come to be wdely used only recently n Chna n reacton to ncreased competton wth foregn frms and amendments of the legal system (Hu and Jefferson, 2006). The number of product tems, servng as a proxy for the level of product dfferentaton and responsveness to the market, s 23 n SDIC, 11 n DDV, and 12 n ZSP. Greater product varety n SDIC compared to DDV s an ndcaton of ts strong orentaton to the market takng locatonal advantage of ts locaton n Seoul, whereas DDV s characterzed by greater producton of patents based on the proxmty of scentfc research centers. 10

13 Table 1: Summary statstcs for sample frms n three clusters Note: Some frms n the ZSP answered these fgures not by sngle-unt establshment level but at the all busness establshment level. Those fgures of sample frms were not ncluded n ths table Characterstcs of communcaton behavor Table 2 presents the dstrbuton of answers to the queston related to the frequency of contacts wth major busness counterparts at the R&D stage 6. Not surprsngly, t s common among the three clusters that most frms contact ther supplers and customers wth hgher frequency shown as 1 3 tmes a month and once a week or more. Especally, more than half of the frms n SDIC gave the latter 11

14 response 7. Smlarly, contact wth fnancal nsttutons s frequent n the three clusters. Regardng contact wth research nsttutes, frms n the ZSP mantan more frequent contact than ther Korean counterparts. Stll, frms that reported contact of at least once per month wth research nsttutes were more numerous n DDV than n SDIC. Ths dfference suggests that frms n SDIC are more market-orented and those n DDV are more research-orented. Contacts wth the source of hgher educaton and busness support agency are scarce n Korea and more frequent n ZSP. Table 2: Frequency of busness partner contact (%) Next, Table 3 reports whch of face-to-face contact or telecommuncatons (such as telephone, fax and e-mal) s preferred as the mode of communcaton wth busness counterparts n each cluster. In general, we consder that by preferrng the use of face-to-face communcaton, frms are exchangng more tact, ntangble, and complcated nformaton wth ther counterparts. In the three clusters, such relatonshps are more relevant n ther contact wth busness support agency and fnancal nsttuton, perhaps because ther communcatons are expected to nvolve subsdes and credt. The frms n the two Korean clusters value face-to-face contacts wth customers as well. It s also noteworthy that frms n DDV engage n more 12

15 face-to-face contact wth research nsttutes. Although Table 2 shows that frms n the three regons mantan frequent contacts wth customers and supplers equally, Table 3 shows that the relaton wth supplers reles less on face-to-face communcaton than n the case wth customers. That fact mght suggest that the relatonshp wth supplers s fundamentally at arms length, guded by prce, but human relatonshps are more mportant n sales. Takng the nformaton that frms n ZSP have a hgher ncdence of contact wth busness support agences suggests a strong nfluence of ncentves offered by publc polces n the regon. Table 3: Means of communcaton wth busness partners (%) We can characterze some aspects of the communcaton behavor of ICT-related small and medum-szed frms n the three clusters. In terms of the frequency of contact, we found hgh ntensty wth customers, supplers, and fnancal nsttutons n all three regons. Contact wth research nsttutes s most frequently done n ZSP, and also n DDV. The frms n ZSP contact busness support agences more frequently. Contact wth supplers does not generally nvolve face-to-face meetngs, but meetng face-to-face s consdered more effectve for communcatons wth customers n the two Korean clusters. Wth busness support agences and fnancal nsttutons, face-to-face communcaton s more valued n all three clusters. Communcaton wth research nsttutes s mostly undertaken n face-to-face meetngs n DDV, but frms n ZSP use telecommuncatons more because of the hgher frequency. Communcatons are done mostly wthn the same regon, but ZSP frms are more lkely to fnd ther supplers and customers outsde the cluster when drect contact s necessary. 13

16 5. Dense Communcaton and R&D Actvtes 5.1. Estmatons wth Two Hypotheses In ths secton, we explan the methodology of our emprcal analyss. Our research strategy reles on the followng two competng hypotheses. Hypothess 1 (H 1 ): Frms that use ndustry-unversty-government cooperaton report hgher n-house R&D actvtes (e.g. the number of researchers). Hypothess 2 (H 2 ): Frms that use ndustry-unversty-government cooperaton report lower n-house R&D actvtes. We can nfer that nteracton wthn the cluster enhances R&D actvty of each frm f the data support H 1. The hypothess s related to the nsght of Jacobs (1969) related to the role of the ctes n economc development where nnovaton of one frm becomes an nput for the others, leadng to mutually self-renforcng creaton of new opportuntes among nteractng frms. In other words, H 1 states that external knowledge s a complement rather than a substtute for n-house R&D. In contrast, we can pant a dfferent pcture of an ndustral cluster f the data support H 2. In ths case, the beneft of locatng n the cluster s the possblty of outsourcng. To our knowledge, the relatonshp between the outsourcng of nnovaton process and urbanzaton has not receved much attenton. A notable excepton s a study by Love and Roper (2001), whch found evdence of a substtutve relatonshp between n-house R&D and outsourcng actvty. To nvestgate these hypotheses, we consder the followng reduced form model. ln RES = α 0 + α1 ln Emp + α 2 ln Age1 + α 3 ln R & D + β 1DM1 + β 2DM 2 + β3dm 3 + β 4 DM 4 + β5dm 5 + β6dm 6 + µ (1) Theren, RES stands for the number of research staff employed by the company, R & D represents the frm s R&D expendture, Emp and Age1 respectvely express frm characterstcs of the employment sze and the years n operaton from establshment, and µ s the dsturbance term. The frst two terms are for control accordng to the sze and experence of frms, whch tends to ncrease R&D actvtes. Because R&D can be done ether n-house or by outsourcng, the sgn condton of the correlaton between RES and R & D s not obvous: frms mght ncrease R&D expendtures to contract outsde servces whle not ncreasng (or reducng) ther own R&D staff. We ntroduce dummy varables denoted as DM 1 through DM 6, where subscrpts respectvely correspond to counterparts: 1= supplers ; 2= customers ; 3= 14

17 research nsttutes ; 4= hgher educaton ; 5= busness support agences ; and 6= fnancal nsttutons. We assgn 1 to each dummy varable f the answer s such that the frm would have face-to-face contact wth each counterpart more than once per month and 0 otherwse 8. These dummy varables represent frms dense communcaton wth respectve counterparts. Unverstes generally have multple functons as nsttutons of scentfc research and hgher educaton. In the case of ZSP, unverstes play an enhanced role as a sort of busness support agency by organzng scentfc parks and as fnancal nsttutons by nvestng n spn-off frms (Chen and Kenney, 2007). We assume that frms counterparts n unverstes are dfferent for each purpose. In eq. (2), we expect a postve sgn for β s f H 1 s supported. Alternatvely, the sgn should be negatve f H 2 holds. Takng eq. (1) as a benchmark case, we extend the model by ncorporatng the nteracton terms of the R&D expendture and dense communcaton dummes. Ths enables us to examne H 1 and H 2 more drectly by examnng the correlaton between the R&D expendture and the n-house R&D effort about frms whch mantan dense communcaton wth any busness counterpart descrbed above. Ths model s gven as ln RES = α ln Emp + 2 ln Age1 0 + α1 α + β 1( DM 1 ln R & D ) + β 2 ( DM 2 ln R & D + β 3( DM 3 ln R & D ) + β 4 ( DM 4 ln R & D ( 5 ln R & D ) + β6( DM 6 ln R & D + β 5 DM ) + µ (2) ) ) Further extenson s made by stpulatng that longer experence of a frm mght not affect n-house R&D efforts drectly, but t ndrectly nfluences them: frms wth longer experence have effectve human relatonshps through whch frms can draw more advantage of external knowledge from communcaton actvtes. Lettng a dummy varable YD 1 represent a more experenced frm, we assgn 1 f Age1 of a frm s above the average frm-age of the sample group of each cluster; we assgn 0 f t s below the average. Consequently, the model wll be the followng. ln RES = α ln Emp + 2 ln Age1 0 + α1 α + β 1( YD 1 DM1 ln R & D ) + β 2 ( YD1 DM 2 ln R & D ) + β 3( YD 1 DM 3 ln R & D ) + β 4 ( YD1 DM 4 ln R & D ) + β 5 ( YD 1 DM 5 ln R & D ) + β6( YD1 DM 6 ln R & D ) + µ (3) 15

18 A dummy varable YD 2 represents a less experenced frm, whch we assgn 1 f Age1 of a frm s below the average frm-age of the sample group of each cluster and otherwse 0. We substtute YD 2 for YD 1 to examne the effect of communcaton for younger frms Estmated results We estmate the three specfcatons for the three-cluster sample group usng the OLS estmatons wth robust standard errors. Estmaton results of eqs. (1) and (2) are reported n Tables 4(a) 4(c). Usng eq. (1), t s common to the three clusters that n-house R&D effort ( RES ) s postvely correlated wth R&D expendture ( R & D ) and frm sze measured by employment ( Emp ). The effects of the frm age ( Age 1) are postve and statstcally sgnfcant only for DDV frms. In other words, n SDIC and ZSP, frm age from ther establshed year mght not drectly contrbute to n-house R&D efforts. Regardng the communcaton dummes, panel (a) shows that SDIC frms exhbt an H 2 -type effect (low n-house R&D effort) n ther relatonshp wth fnancal nsttutons such as banks. In other words, f frms have dense communcaton wth local fnancal nsttutons, t s lkely that they can obtan nformaton related to the opportuntes for outsourcng a part of the R&D process. Smlarly, the panel (b) shows that the DDV frms have an H 2 -effect channel through customers and busness support agences, although communcaton wth hgher educaton apparently enhances n-house R&D (H 1 -effect). It s worth recallng that DDV s a government-sponsored scence park n whch local busness support agences have a strong role n promotng commercal use of the scentfc research. Panel (c) exhbts that ZSP frms gan an H 2 -effect through contact wth research nsttutes. The adjusted R2 of the eq. (1) s suffcently hgh that we are able to nfer that ths specfcaton of the model has reasonable explanatory power for the three clusters. Wth these results, we can dentfy n each hgh-tech cluster those sgnfcant nformaton channels through whch n-house R&D efforts are nfluenced and n whch drecton, whether ncreased or decreased (.e. outsourcng). For eq. (2), we test that the effects of dense communcaton related to counterparts are not drect but are nstead mparted through ther effects on R&D expendture. As presented n Table 4, ths proposton s unsupported, except for the nteracton between the communcaton wth busness support agences and R&D expendture n DDV. 16

19 Table 4: Regresson results related to dense communcaton and R&D Note: *, ** respectvely represent sgnfcance at the 10% and 5% level. 17

20 Fnally, workng wth eq. (3), we obtan the estmaton result presented n Table 5. Here, the nteracton term s ( YD k DM ln R & D ), where k = 1, 2 and j = 1 6. In j other words, we examne whether the effects of H 1 or H 2 through R&D expendture mght depend on frm age. More concretely, wth YD 1 ( YD 2 ), the more (less) experenced a frm, the more (less) effectve s the nfluence of external knowledge on n-house R&D effort. Frst, results for SDIC n panel (a) show that more experenced frms wth dense communcaton wth supplers tend to rely more on R&D outsourcng, whereas frequent face-to-face contact wth research nsttutes and hgher educaton are assocated wth more actve n-house R&D. Because these effects were not dentfed by eq. (1) n Table 4, we mght conjecture that the know-who cultvated through experence s necessary to take advantage of external knowledge n a large metropoltan area such as Seoul. In contrast, n-house R&D of frms wth short experence s not nfluenced greatly by external knowledge. Next, the result for DDV shown n panel (b) shows that more experenced frms nteracton wth busness support agences s related wth outsourcng. Ths result closely resembles that obtaned n eq. (1) n Table 4 wthout a dstncton of frm age. Interestngly, usng YD 2, we can dentfy that less-experenced frms tend to ncrease n-house R&D by engagng n dense communcaton wth hgher educaton. In a scence cty lke DDV, the supply of human resources from hgher educaton provdes mportant support to the growth of start-up frms. Fnally, n panel (c), regardng ZSP frms, we can observe that frequent face-to-face contact wth supplers and fnancal nsttutons mght engender greater n-house R&D effort f frms are more experenced. It s noteworthy that these effects were not sgnfcant n eq. (1) of Table 4 and were observable only for more experenced frms. In contrast to DDV, communcaton effects specfc to less-experenced frms were not confrmed n ZSP despte ts envronmental smlarty: t s surrounded by many unverstes. 18

21 Table 5: Regresson results related to dense communcaton, R&D, and busness experence Note: *, ** respectvely represent sgnfcance at the 10% and 5% level. 19

22 The foregong analyss adds new nsghts to the exstng lterature. Frst, communcaton externaltes related to nnovaton wthn an ndustral cluster, especally those whch are based on scentfc knowledge, consttute a complex phenomenon takng effect n dfferent ways n dfferent contexts. The beneft of communcaton externalty mght appear ether n the greater possblty of outsourcng of the nnovaton process, resultng n specalzaton and reducton of fxed cost, or n stmulus to n-house R&D efforts enablng frms product dfferentaton and growth. Lookng nto the nteracton more closely, these two effects concde n the same cluster. Results show one effect related to dense communcaton wth partcular busness counterparts and another effect n connecton wth other counterparts. In some cases, the extent to whch frms enjoy benefts of externaltes depends on ther duraton of experence. Our results show that duraton of experence makes a greater dfference n SDIC, whch s located n a large metropoltan area, where know-who grants partcular value, whereas DDV provdes encouragement to R&D of less-experenced frms. The relatonshp wth research nsttutes s mportant n ZSP, ndependently of frm age. Smlarly to the case of SDIC, frms wth longer experence n ZSP are able to take advantage of communcaton externaltes. 6. Concludng Remarks Numerous emprcal studes have examned the nature of agglomeraton economes based on knowledge spllover and communcaton externaltes. Most of them smply subsumed that the extent of agglomeraton economes s assocated wth the sze of the nearby populaton because greater nteracton can be expected among a larger number of people. We examned the effects of dense communcaton, as one sort of communcaton externalty arsng from ndustry-unversty-government cooperaton, on enhancement n-house R&D actvtes n Korean and Chnese knowledge-based ndustral clusters: SDIC, DDV, and ZSP. The salent concluson of ths study s that more attenton must be devoted to the complexty of nteractons that occur wthn the ndustral clusters. Usng the unque dataset of the ICSEAD Survey, we were able to dentfy that an ndvdual frm mght receve at least two types of nfluence from agglomeraton: stmulaton of n-house R&D through exchange of deas; and reducton of R&D by facltatng R&D outsourcng. Such competng effects co-occur wthn an ndustral cluster dependng on whch busness counterparts receve close contact, and dependng on a frm s duraton of experence. More detaled knowledge of these ssues s expected 20

23 to contrbute to more concrete and effectve polcy recommendatons. Such vewponts are useful for sem-ndustralzed countres such as Korea and coastal regons of Chna, whch seek to develop a Slcon Valley model of a regonal nnovaton system to upgrade ther export producton. The fndngs of the present paper ndcate several drectons for future research. The frst drecton s to nvestgate the regonal specalzaton consttuted by dversfed agents. Gven the scale economy n R&D, as the ndustry-academa-government cooperaton wthn a cluster s deepened and the connectvty across agents becomes stronger, each consttuent of the cluster can be more specalzed by concentratng ts knowledge resource to more effcent actvtes. Moreover, whle some R&D actvtes are nternal to the cluster, others wll be carred out through outsourcng to other regons. The second drecton s to dscuss the urban polcy to provde nfrastructure and nsttutonal ground to enhance nnovatveness of frms. Snce the ICSEAD survey dataset already contans some nformaton regardng the two ssues, we hope that we wll be able to report the research result n future. Fnally, n order to address the exchange of knowledge, t s desrable to observe the nteracton among ndvduals (scentsts and engneers) rather than among organzatons. 1 For example, Lucas (1988) descrbed that the central role of ctes n economc lfe s of exactly the same character as external human captal. 2 Informaton s gven by the Guro Dstrct homepage ( 3 The Daedeok Research Complex was renamed Daedeok Innopols n Relevant nformaton s gven on the Daedeok Innopols homepage: ( 5 Sample frms were selected from the drectory owned by the agency of ndustral support and ndustral estate development of each ndustral cluster. The selecton was not random: the samplng was made by contactng frm managers one by one untl the number who agreed to partcpate was suffcent. 6 For the questonnare survey, we separated R&D nto three sub-stages, namely basc research, product development and marketng and commercalzaton. We asked a queston related to the contact wth busness counterparts for each stage, where as such demarcaton s made by respondents. However, n ths paper, we analyze only those data of the basc research stage, not only because t s the process durng whch knowledge nput matters the most, but also because, n later stages, frms communcaton actvty s strongly concentrated on customers and supplers, whereas contact wth academc research nsttutes becomes neglgble. 7 It s presumed that these relatonshps wth hgher frequency reflect the exstence of regular dealngs, for example, delver of materals and ntermedate goods and servces at regular ntervals. In ths case, t s presumed that t does not necessarly connect drectly wth nnovaton actvty even f there s contact wth these counterparts wth hgher frequency. 21

24 8 As descrbed n Note 6, t s assumed that frequent contact once a week and more s too hgh for R&D actvtes. For that reason, we adopt the frequency of more than once per month as dummy varables n our estmatons. Moreover, we were unable to obtan any statstcally sgnfcant result usng the frequency of once per week or hgher frequency as dummy varables for confrmaton. 22

25 References: Adams, J. D. (2002), Comparatve Localzaton of Academc and Industral Spllovers, Journal of Economc Geography, 2, Anseln, L., A. Varga and Z. Acs (2000), Geographc and Sectoral Characterstcs of Academc Knowledge Externaltes, Papers n Regonal Scence, 79, Arta, T., M. Fujta and Y. Kameyama (2006), Effects of Regonal Cooperaton among Small and Medum-Szed Frms on Ther Growth n Japanese Industral Clusters Revew of Urban & Regonal Development Studes, 18 (3), Audretsch, D. B. and M. P. Feldman (1996a), R&D Spllovers and the Geography of Innovaton and Producton, Amercan Economc Revew, 86 (3), Audretsch, D. B. and M. P. Feldman (1996b) Innovatve Clusters and the Industry Lfe Cycle, Revew of Industral Organzaton, 11, Audretsch, D. B. and M. P. Feldman (2004), Knowledge Spllovers and the Geography of Innovaton, n J. V. Henderson and J.-F. Thsse (eds.) Handbook of Regonal and Urban Economcs, Vol. 4, Amsterdam: Elsever: pp Charlot, S. and G. Duranton (2004), Communcaton Externaltes n Ctes, Journal of Urban Economcs, 56, Charlot, S. and G. Duranton (2006), Ctes and Workplace Communcaton: Some Quanttatve French Evdence, Urban Studes, 43 (8), Chen, K. and M. Kenney (2007), Unversty / Research Insttutes and Regonal Innovaton Systems: The Case of Bejng and Shenzhen, World Development, 35 (6), Egeln, J., S. Gottschalk, and C. Rammer (2004), Locaton Decsons of Spn-offs from Publc Research Insttutons, Industry and Innovaton, 11 (3), Feldman, M. P. and R. Florda (1994), The Geographc Source of Innovaton; Technologcal Infrastructure and Product Innovaton n the Unted State, Annals of Assocaton of Amercan Geographers, 84, Krugman, P. (1991), Geography and Trade, Cambrdge-Massachusetts: MIT Press. Fujta, M. and J.-F. Thsse (2001), Economcs of Agglomeraton: Ctes, Industral Locaton and Regonal Growth, New York: Cambrdge Unversty Press. Fujta, M., P. Krugman and A. J. Venables (1999), The Spatal Economy: Ctes, Regons and Internatonal Trade, Cambrdge, MA: The MIT Press. Hu, A. G. and G. H. Jefferson (2006), A Great Wall of Patents: What s behnd Chna s Recent Patent Exploson? ( access date: September 7,

26 Jaffe, A. B., M. Trajtenberg and R. Henderson (1993), Geographc Localzaton of Knowledge Spllovers as Evdenced by Patent Ctatons, Quarterly Journal of Economcs, 63, Love, J. H. and S. Roper (2001), Outsourcng n the Innovaton Process: Locatonal and Strategc Determnants, Papers n Regonal Scence, 80, Lucas, R. E., Jr. (1988), On the Mechancs of Economc Development, Journal of Monetary Economcs, 22, Shn, D. H. (2001), An Alternatve Approach to Developng Scence Parks: A Case Study from Korea, Papers n Regonal Scence, 80, Sohn, D.W. and M. Kenney (2007), Unversty, Clusters, and Innovaton Systems: The Case of Korea, World Development, 35, Sung, T. K., D. V. Gbson and B. S. Kang (2003), Characterstcs of Technology Transfer n Busness Ventures: The Case of Daejeon, Korea, Technologcal Forecastng & Socal Change, 70, Tan, J. (2006), Growth of Industry Clusters and Innovaton: Lessons from Bejng Zhongguancun Scence Park, Journal of Busness Venturng, 21, Saxenan, A. (1994), Regonal Advantage: Culture and Competton n Slcon Valley and Route 128, Harvard Unv. Press, Cambrdge, MA. Varga, A. (1998), Unversty Research and Regonal Innovaton: A Spatal Econometrc Analyss of Academc Technology Transfers, Boston: Kluwer Academc Publshers. Yusuf, S. (2003), Innovatve East Asa: The Future of Growth, Washngton, D.C.: World Bank. 24