Cooperative R&D and Firm Performance*

Transcription

1 Cooperatve R&D and Frm Performance* René Belderbos Katholeke Unverstet Leuven and Unverstet Maastrcht Martn Carree Unverstet Maastrcht Bors Lokshn Unverstet Maastrcht Correspondence: Department of Organzaton and Strategy Faculty of Economcs and Busness Admnstraton Unverstet Maastrcht PO Box 616, 6200 MD Maastrcht The Netherlands Phone: Fax: *The emprcal analyss for ths paper has been performed at CEREM/Statstcs Netherlands on the MICRONOOM database. The vews expressed n ths paper are those of the authors and do not necessarly reflect the polces of Statstcs Netherlands.

2 Cooperatve R&D and Frm Performance* JEL-codes: D24, O31, O32 Keywords: R&D, R&D cooperaton, spllovers, frm performance ABSTRACT We analyse the mpact of dfferent R&D cooperaton strateges on frm performance jontly wth the mpact of nnovaton expendtures and the effect of ncomng knowledge spllovers that are not due to collaboratve R&D, usng data on a large sample of nnovatng frms n two waves of the bannual Dutch Communty Innovaton Surveys. The results confrm a major heterogenety n the goals of R&D cooperaton, wth compettor and suppler cooperaton focused on ncremental nnovatons mprovng productvty growth, whle unversty cooperaton and agan compettor cooperaton are nstrumental n creatng radcal nnovatons, generatng sales of products and servces that are novel to the market. 1

3 1. Introducton The observed substantal ncrease n R&D allances n the late 1980s and throughout the 1990s, n partcular n sectors such as botechnology and nformaton technology (Hagedoorn, 2002; Tyler and Steensma, 1995) has provoked substantal academc and polcy nterest n the phenomenon. A large body of lterature n the management doman has been produced that dscusses varous motves that ncte frms to collaborate on R&D (e.g. Contractor and Lorange, 2002; Nooteboom, 1999). In parallel, a body of theoretcal lterature has been developed n ndustral organzaton focusng on the relatonshp between knowledge spllovers, R&D cooperaton, and R&D nvestment usng a game theoretcal perspectve. The latter lterature has been most concerned wth the potental mpact of R&D cooperaton and knowledge spllovers on R&D nvestment levels, and has largely been restrcted to the analyss of cooperaton wth drect compettors. A number of emprcal studes have explored the determnants of R&D cooperaton (e.g. Klenknecht and Renen, 1992; Frtsch and Lucas, 2001; Tether, 2002; Belderbos et al. 2003). A major fndng of recent contrbutons s that the goals and hence the determnants of R&D partnershps dffer dependng on the type of cooperaton and partner. Frtsch and Lucas (2001) fnd for German manufacturng frms that nnovatve effort drected at process mprovement s more lkely to nvolve collaborate wth supplers, whereas product nnovatons are assocated wth customer collaboraton. Tether (2002), usng UK data on nnovatng frms, fnds that cooperaton s mostly assocated wth frms pursung radcal rather than ncremental nnovatons. Belderbos et al. (2003) fnds substantal heterogenety n the determnants to establsh R&D collaboratons wth dfferent partners. Cooperaton wth a partner generally s more lkely to be chosen f the partner s an mportant source of knowledge for the nnovaton process, whle more basc knowledge sourced from unverstes and research nsttutes postvely mpacts all types of cooperaton. Collaboraton wth unverstes s more lkely to be chosen by more R&D ntensve frms n sectors that exhbt greater technologcal dynamsm. Surprsngly, the key queston whether collaboratve R&D has the expected postve mpact on frms (nnovaton) performance has remaned largely unexplored n both the ndustral organzaton as well n the management lterature (see e.g. Tether, 2002; Das and Teng, 2000). A number of papers have ncluded a cooperaton varable n emprcal models explanng 2

4 dfferences n frms nnovaton output (Janz et al., 2003; Van Leeuwen and Klomp, 2001; Klomp and van Leeuwen, 2001; Lööf and Heshmat, 2002; Monjon and Waelbroeck, 2003; Crscuolo and Haskell, 2003), but most of these studes have been prmarly concerned wth the mpact of R&D nvestments on performance and dd not examne systematcally dfferences n mpacts across cooperaton types. 2 Management studes have restrcted analyss to partcular performance ndcators and specfc ndustres, e.g. the relatonshp between allance networkng and patent output growth n the ASIC ndustry (Vanhaverbeke et al., 2004) or the effect of allances on hgh tech start-up frm performance n the botech ndustry (Baum et a. 2002). The contrbuton of ths paper s to examne n detal the effects of dfferent types of R&D cooperaton on frm performance. We consder the mpact of the four major types of partnerspecfc cooperaton strateges: cooperaton wth compettors, supplers, customers, and research nsttutes & unverstes. We analyse the effects of these R&D partnershps on two alternatve performance measures for a large sample of Dutch frms actve n manufacturng and servce ndustres: growth of value added per employee (labour productvty), and growth of sales per employee from products new to the market (whch we term nnovatve sales productvty ), and expect a heterogeneous mpact of the dfferent R&D partnershps. The analyss controls for the mpact on productvty of ncomng knowledge spllovers that are not due to R&D partnershps, as well as the effect of the frms total nnovaton expendtures. Usng data on a large sample of nnovatng frms n two waves of the bannual Dutch Communty Innovaton Surveys (1996, 1998) lnked to producton statstcs, we mtgate problems of smultanety and unobserved frmspecfc effects by examnng the mpact of R&D (collaboraton) n 1996 on subsequent productvty growth n The remander of the paper s organzed as follows: secton two provdes an overvew of the prevous theoretcal and emprcal lterature dscussng the mpact of R&D (cooperaton) and spllovers on frm performance. Secton three descrbes the emprcal model and data. Secton four dscusses the emprcal results and secton fve concludes. 2 Most of these studes use a smultaneous equatons approach poneered by Crépon et al (1998) n whch nnovatve sales levels n turn are allowed to mpact productvty or sales. In the current paper we allow R&D (cooperaton) to have a drect mpact on productvty. 3

5 2. Prevous Lterature In ths secton we wll brefly revew the theoretcal and emprcal lterature on R&D cooperaton, spllovers and productvty. Theoretcal models n the ndustral organzaton tradton have shown how knowledge spllovers ncrease the stock of effectve knowledge of frms and contrbute to proftablty ether by expandng demand or by reducng costs. Involuntary spllovers between competng frms provde an ncentve for R&D cooperaton as ths enables frms to overcome the dsncentve effect on R&D from the externalty that nvoluntary spllovers to rval frms consttute (e.g. Amr, 2000; De Bondt, 1996; Kamen et al., 1992; Suzumura, 1992; Leahy & Neary, 1997). Recent papers have also taken nto account that spllovers are not gven but that cooperaton allows frms to ncrease knowledge transfers voluntarly among the cooperatng partners (Katsoulacos & Ulph, 1998). Frms have ncentves to manage the flow of spllovers to and from compettors by attemptng to maxmze ncomng spllovers through R&D collaboraton whle at the same tme mnmzng outgong spllovers through nvestments n knowledge protecton (Cassman et al. 2002; Martn, 1999; Amr et al., 2003). The ndustral organzaton lterature has pad lttle attenton to collaboraton between frms that are not drectly competng on output markets. An excepton s Atallah (2002), who fnds that even wth small vertcal spllovers (knowledge spllovers between upstream and downstream ndustres) frms can proftably engage n vertcal R&D cooperaton, as there are fewer or no compettve consderatons nvolved wth potental leakage s of the fruts of larger R&D nvestments. The lterature n the management and technology polcy doman has examned broader motvatons for R&D cooperaton than nternalsng nvoluntary knowledge spllovers and has focused more on voluntary knowledge transfers. Explanatons for collaboratve R&D that have been extensvely dscussed revolve around such factors as sharng rsks and costs n the face of uncertan technologcal developments (Das and Teng, 2002; Tyler and Steensma, 1995), shortenng nnovaton cycles (Psano, 1990), the pursut of effcency gans such as economes of scope and scale or synergstc effects through effcent poolng of the frm resources (Kogut, 1988; Das and Teng, 2000), learnng through montorng technology and market developments (Hamel, 1991; Roberts and Berry, 1985), dealng wth regulatons and ndustry standards, and respondng to government subsdy polces (Benfratello and Sembenell, 2003; Nakamura, 2003). 4

6 Although t has been noted more generally that a substantal share of allances fal (Harrgan, 1986), R&D allances may be a source of compettve advantage and have long lastng effects on frm performance. Teece (1980) argues that organzatonal practces affect frms performance and can explan sustaned performance dfferences wthn ndustres due to slow dffuson of best practces and dffcultes n mtatng complex organzatonal capabltes. It has also been suggested that dfferent types of collaboraton may serve dfferent purposes, where the two man goals of nnovatve effort are cost reducton and market expanson. Collaboraton wth customers s mportant to reduce the rsk assocated wth market ntroductons of the nnovatons, as has been recognzed snce the semnal work of Von Hppel (1988). In partcular when products are novel and complex and hence requre adaptatons n use by customers, collaboraton may be essental to ensure market expanson (Thether, 2002). In contrast, cooperaton wth supplers s often related to the tendency to focus on core busness to reduce costs, wth outsourcng actvtes coupled wth cooperaton on nput qualty mprovements amed at further cost reductons. Cooperaton wth unverstes and research nsttutes s generally more amed at radcal breakthrough nnovatons that may open up entre new markets or market segments (Tether, 2002; Monjon and Waelbroeck, 2003). A number of emprcal studes have found a postve mpact of engagement n R&D cooperaton on nnovaton performance.e. sales of nnovatve products (Klomp and van Leeuwen, 2001; Janz et al., 2003; van Leeuwen, 2002; Lööf and Heshmat, 2002; Crscuolo and Haskell, 2003), patentng (Vanhaverbeke et al., 2004), and sales growth (Cncera et al. forthcomng). Some of these papers have also examned the effect of dfferent cooperaton types but wth ambguous results. Monjon and Waelbroeck (2003) regressed nnovatve sales levels of frms n a French CIS survey on a range of collaboraton and spllovers varables and found a mxture of negatve and postve mpacts of R&D cooperaton and spllovers. Cncera et al. (forthcomng) dstngushed between overseas and domestc collaboraton by Belgan frms and found a postve mpact on productvty of the latter but a counter-ntutve negatve mpact of the former. Lööf and Heshmat (2002) ncluded a selected group of cooperaton types n an nnovaton output equaton for Swedsh frms and found that cooperaton wth compettors and unverstes mpacted output levels postvely but cooperaton wth customers negatvely. These ambguous results can be partly attrbuted to the use of cross-secton data, whch does not allow takng nto account approprate lags wth whch cooperatve R&D mpacts nnovatve output and 5

7 performance (cf. Cncera et al., forthcomng), as well as unobserved fxed frm trats that mpact both frms ncentves to cooperate and ther nnovatve output (Van Leeuwen and Klomp, 2001). Both problems can be addressed by usng panel data on nnovatve performance and cooperaton strateges, as s the emprcal approach of ths paper. There s a large body of emprcal lterature examnng the sources of productvty growth and n partcular the role of nter-frm knowledge spllovers (e.g. Adams and Jaffe, 1996; Branstetter, 2001; Coe and Helpman, 1995; Basant and Flkkert, 1996). These studes have generally confrmed an mportant role of nferred spllovers on productvty growth. Smlarly, emprcal studes have documented the postve mpact of own R&D on productvty at the frm level (e.g. Grllches and Maresse, 1984; Lchtenberg and Segel, 1991; Hall and Maresse, 1995). A related lterature has been concerned wth the role of foregn multnatonal enterprses (MNEs) n productvty performance (Grffth, 1999; Harrs and Robnson, 2003). In these studes MNEs are generally found to be more productve than ther local ndustry compettors, whch s attrbuted to MNEs effcent explotaton of frm-specfc assets allowng for multplant economes of scale (e.g. Pfaffermayr, 1999) and the nternatonal transfer of accumulated tact and specalzed knowledge on producton (Atkn and Harrson, 1999). In summary, the lterature suggests that an analyss of dfferent types of cooperaton strateges should take nto account the dfferent possble ams of (collaboratve) R&D efforts. Productvty ncreases may be more reflectve of ncremental nnovatons and affected by collaboratve R&D amed at cost reductons, whle sales expanson through nnovatve products s lkely to be related to more basc R&D efforts and clent collaboratons. We explore ths by examnng n the emprcal analyss the effect of cooperaton on two dfferent types of productvty performance: labour productvty growth and the growth n sales of nnovatve products that are new to the market per employee ( nnovatve sales productvty ). Further, the lterature suggest that an analyss of the performance effect of cooperaton should control for the postve mpact of ncomng knowledge spllovers, as well as R&D expendtures, whle the exstence of multnatonal group lnkages should also be taken nto account. 6

8 3. Emprcal model, data and descrptve statstcs The goal of the emprcal analyss s to determne how dfferent types of R&D collaboraton affect frms productvty growth. To examne ths effect the analyss should control for the mpact of the frms R&D expendtures efforts as well as ncomng knowledge flows that are not due to cooperaton. A growth n productvty performance specfcaton has as major advantage that results are not based from smultanety bas and unobserved fxed frm attrbutes explanng both productvty levels and nnovaton efforts (e.g. Van Leeuwen and Klomp, 2001). We estmate the followng productvty (prodv) equaton ( s frm ndex): log( prodv ) = α + βx + ζ 1Comp _ coop + ζ 2Cust _ coop + ζ 3Supp _ coop + (1) ζ + γ 1Comp _ spl + γ 2Cust _ spl + γ 3Supp _ spl + γ Unv _ spl + Innnt + 4Unv _ coop 4 δ + θ log( prodv) + ε The dependent varable, log( prodv) = log( prodvt+ 1) log( prodvt ), s the growth n productvty measured as ether value added per employee or sales generated by new to the market products per employee, respectvely. Labour productvty growth wll be most affected by cost reducng nnovaton, whle nnovatve sales productvty growth s mostly affected by demand expanson orented nnovaton. Dfferences n the mpact of cooperatve R&D on the two performance measures can demonstrate the varety n purposes of dfferent collaboratve strateges. The model ncludes four dummes for cooperaton types wth dfferent possble partners: compettors, customers, supplers, and unverstes & research nsttutes (henceforward for convenence labelled unverstes ). 3 The same partners are dentfed as potental sources of ncomng knowledge spllovers. The model also controls for the frm s expendtures on R&D and other nnovaton actvtes (Innnt, nnovaton ntensty). The lagged log(prod) term s the level term of the dependent varable taken n the base year (1996). Frms that are hghly productve and at the fronter of productvty may be less lkely to be able record strong growth rates n 3 We do not nclude cooperaton wth consultants n the emprcal analyss because of ts heterogeneous character and doubts whether lnkages wth consultants are genune R&D efforts rather than market transactons. 7

9 productvty than frms that are followers. 4 In that case we expect θ to fall wthn the nterval [- 1,0]. If θ s zero, ths effect s absent and there s no gradual convergence between leadng frms and productvty laggards. If θ s 1, than a productvty lead n one perod s fully neutralzed n the next and passed productvty has no mpact on future productvty levels. 5 The X-vector conssts of other frm-level control varables, such as sze, dummes controllng for foregn and domestc groups, dummes for cost reducng and product mprovng objectves of nnovaton, and dummes for the ndustry of the frm at the two-dgt level. One worry s that our specfcaton does not allow separatng the effect of the ncomng spllovers from the effect of cooperaton: cooperaton can have a drect effect on productvty but wll at the same tme ncrease the reported ncomng spllovers from the collaboraton partner. In order to estmate the full mpact of cooperaton, we have to separate spllovers due to purposeful nformatonal exchanges that arse n formal cooperatve arrangements from spllovers that are not due to such cooperaton (e.g. arsng from market contacts wth supplers and customers). We extract the former spllovers due to collaboraton by takng as the spllover measure the resduals obtaned from regressng the spllover varables on the correspondng cooperaton varable and the set of ndustry dummes. (2) Comp _ spl = λ Comp _ coop + Z + η hor comp (3) (4) (5) Cust _ spl Supp _ spl Unv _ spl = λ Cust _ coop + Z + η cust sup p cust = λ Supp _ coop + Z + η = λ Unv _ coop + Z + η nst unv sup p The estmated resduals from these equatons place of the spllover varables comp ηˆ t through Comp _ spl through Unv _ spl unv ηˆ are then ncluded n n our specfcaton. The resduals are no longer systematcally related to frms R&D collaboratons and wll ndcate the mportance of true spllovers. 6 4 Snce the model ncludes a full set of ndustry dummes, ths varable can also be nterpreted as the effect of the productvty level of the frm relatve to the ndustry mean n To see ths, one can smply rewrte the relevant part of (1) as log( prodv t+ 1) =...(1 + θ )( prodv) t. 6 Whereas the four spllover sources ncluded n the model dentfy the source, there are a number of other types of ncomng spllovers n the CIS survey that dentfy the channel of the knowledge spllover (databases, trade fars, 8

10 Data and Varables The emprcal analyss uses data from two consecutve Communty Innovaton Surveys (CIS) from 1996 and 1998 and producton statstcs data for the same years, avalable at the Netherlands Bureau of Statstcs (CBS). It has been only recently that researchers have been able to utlze consecutve CIS surveys merged wth producton statstcs. 7 An advantage of the Dutch CIS surveys s that they have been held every other year rather than n four-year ntervals as has been customary n other EU countres. Ths allows us to more accurately examne performance changes over a sutable tme frame (two years). The producton statstcs database ncludes nformaton on output, employment, and value added. The CIS database contans nformaton concernng R&D and nnovaton actvtes of the frm, ncludng nnovaton expendtures, nnovaton n partnershp data and sources of knowledge used n the nnovaton process. The CIS and producton statstcs surveys are sent to all large frms and to a random sample of smaller frms n the Netherlands. To create a two-year (panel) data set, nnovatng frms n 1998 were matched wth nformaton on these frms n the 1996 survey: 2353 frms were ncluded as nnovatng frms n both surveys. We then lnked these frms va a unque d number to the producton statstcs data. The data are at the establshment level and nclude manufacturng as well as servce frms. Due to the mssng values for some of the explanatory varables the complete sample ncludes 2056 frms. The labour productvty growth varable s the growth n net value added per employee (drawn from the producton statstcs) between 1996 and The alternatve performance measure nnovaton sales productvty growth s the growth n the value of sales of product and servces that are new to the market per employee, between 1996 and Ths varable s drawn from the CIS surveys, n whch frms are asked to ndcate what percentage of sales has been due products or servces ntroduced n the passed two years that were new to the ndustry, not just patents). There s a clear and arguably substantal overlap n these measures (e.g. f nformaton from compettors s mportant, t may reach the frm through patents or trade shows) makng t problematc to nclude all types of spllovers avalable n the survey. We dd nclude a composte measure of all other spllover ratngs by the frm n the model, but ths varable proved nsgnfcant wth no mpact on the estmates of the four source-specfc spllover measures, and was omtted n the fnal specfcaton. The source-specfc spllovers are apparently able to capture the lon s share of the mpact of ncomng knowledge on productvty growth. 7 Other examples are Belderbos et al (2003) and Klomp and Van Leeuwen (2001). 9

11 novel to the frm. Frms that ncrease the performance on ths varable are lkely to be more productve n the pursut of more radcal nnovatons. Ths n turn s a prerequste for further frm growth (Klomp and Van Leeuwen, 2001). The cooperaton varables are taken from 1996 CIS survey and are dummy varables takng the value one f the frm ndcated that t was or had been engaged durng n actve R&D cooperaton wth compettors, supplers, customers, and unverstes or research nsttutes, respectvely. Hence we post that cooperatve R&D projects n have ther man mpact on productvty growth n the two-year perod Ths s a relatvely plausble assumpton, gven that most R&D cooperaton projects last between 6 months and 2 years and that R&D requres some tme to translate nto nnovatve output and productvty advances. But t s not ruled out that some cooperatve projects do have faster mpacts on productvty. If ths s the case, than early R&D projects (e.g. those started n or before 1994) may already have had ther mpact on 1996 R&D levels and show no further mpact n , n whch case the emprcal results wll underestmate the mpact of cooperaton. In order to address ths emprcally, we also test for the mpact of an alternatve cooperaton measure: whether a frm s a persstent R&D collaborator:.e. whether the frm s cooperatng wth the respectve type of partner both wthn the and the perod. If cooperatve projects have a more varable lagged mpact on productvty, the persstent cooperaton varables may show more robust results. The frm-specfc and type-specfc ncomng spllovers are drect measures of the mportance of sources of ncomng knowledge for the frms nnovaton process. The CIS survey asks frms to rate on a Lkert scale (1-5) the mportance of varous external sources of nformaton n terms of the effectveness n the frms nnovaton process n the past two years. Gven ths wordng of the queston on sources of ncomng knowledge,.e. ther effectve use the nnovaton actvtes, the answers are more an ndcator of ther contrbuton to nnovaton output than n ndcators of nnovaton nputs. Hence, such effectve spllovers n (the 1996 CIS survey) are more lkely to affect 1996 productvty levels than productvty growth. We therefore do not nclude the spllover measure for 1996 but the spllover measures from the 1998 survey (effectve spllovers durng ) as havng an mpact on productvty growth. As dscussed above, we do not use the scores as explanatory varables but clean the spllover measures from the mpact of cooperaton. We regress the 1998 spllover 10

12 rates by source on 1996 cooperaton and a set of ndustry dummes, and take the resduals of these equatons as a the measure of spllovers that are not due to purposeful exchanges n formal R&D partnershps. 8 We also nclude a R&D nput measure n lne wth the prevous lterature that documented postve relatonshp between research ntensty and productvty. Our R&D measure s total nnovaton expendtures as percentage of sales. Such expendtures nclude, n addton to nternal R&D, expendtures on extramural R&D contracts pad to other frms and research centres, expendtures on lcenses and hence also controls for the mpact of the of external technology sourcng. 9 Innovaton ntensty s taken from the 1996 survey. Further control varables nclude a set of 2-dgt ndustry dummes (we dstngush 19 ndustres) and frm sze (the logarthm of the number of employees). We also allow for dfferent productvty growth performance between ndependent frms and frms that are part of a domestc group or a foregn MNE. Group frms may have hgher growth f they can draw on technology and organzatonal expertse from headquarters and other groups frms. The Dutch ndustral structure s characterzed by the presence of several very large multnatonal corporatons. The large Dutch companes Akzo/Nobel, DSM, Phlps, Shell and Unlever are domnatng n terms of R&D expendtures, and are mportant elements n the Dutch technologcal nfrastructure. These large companes are characterzed as the core of frm networks wth comparable research ntenstes (.e. Cowan and Jonard 1999; Verspagen, 2000) and greater densty of knowledge spllovers. Fnally, we nclude demand-pull and cost-push varables n the model as controls. The demand-pull varable s a sum of scores on mportance of objectves of nnovaton relatng to demand. Cost-push s the sum of scores on mportance of objectves relatng to cost reducton. If cost-reducton s a major motvaton for nnovatons efforts, t may be more lkely that R&D translates drectly nto mproved labour productvty. Demand expanson orentaton s most lkely to mpact on new product sales productvty. 8 In the model ncludng aggregate spllovers and cooperaton, we regress the average spllover rate on the sum of cooperaton dummes. 9 We were not able to further decompose nnovaton expendtures between ntramural R&D and other nnovaton expendtures, such as outsourced R&D, procurement of lcenses, and nvestment n equpment. 11

13 Descrptve Statstcs Descrptve statstcs and the dstrbuton of cases by ndustry are presented n Table 1. There are 630 frms wth R&D cooperaton of any type among the nnovatng frms n the combned sample. Suppler cooperaton s the most frequent, wth 375 frms ndcatng to be engaged n ths type of collaboraton, followed by customer cooperaton (353 frms), unversty cooperaton (280) and compettor cooperaton (226). Some 1426 frms reported to have none of the three lnks. The comparson across ndustres ndcates that lnkng wth compettors s comparatvely more frequent n servces, n partcular for customer and suppler lnks. Scencebased ndustres such as electroncs and chemcals, but also the food ndustry, report a hgher share of unversty cooperaton compared to other types of cooperaton. Table 2 provdes a contngency table dsplayng the means of the varables used n the model by type of cooperaton. Ths nformaton provdes some prelmnary evdence that there exst sgnfcant dfferences along several key parameters between frms havng an R&D cooperaton lnk and the non-collaboratng frms. Collaboratng frms report substantally greater ncomng spllovers of all three knds compared to non-cooperatng frms (the F-tests n column 7 shows that these dfferences are sgnfcant). Wth the excepton of compettor spllovers, source specfc spllovers are greatest for frms that cooperate wth the source, as expected (F-tests agan shows that these dfferences overall are sgnfcant). The most dramatc dfference s n unversty spllovers: frms that engage n R&D collaboratons wth unverstes or research nsttutes report to receve spllovers more than twce the magntude as spllovers beneftng non-collaboratng frms. These fgures are both ndcatve that the mportance for the frm s nnovaton process of knowledge comng from a specfc source s reason to engage n cooperaton (Belderbos et al, 2003), but also of subsequent purposeful ncreases n knowledge transfers wthn the collaboratve agreement. The table also shows that cooperatng frms tend to be larger and more R&D ntensve, are more often part of a domestc or foregn group, and report greater emphass on cost reducton and demand expanson. Fnally, collaboratng frms show hgher labour productvty levels and hgher nnovatve sales per employee, wth the latter hghest for frms cooperatng wth customers and unverstes. However, these smple mean comparsons cannot be taken as evdence of the mpact of cooperaton strategy on productvty, as ths requres controllng for ntal productvty levels, ndustry dfferences, and the jont mpact of the other 12

14 varables n a multvarate analyss. The results of ths analyss, estmates of equaton 1-5, are dscussed below. 3. Emprcal Results Table 3 reports the results of all varants of equaton (1) wth the spllover measures nstrumented by the error term of equatons The auxlary regressons of spllover measures on the correspondng cooperaton dummes n the prevous perod (not reported here) showed that cooperaton s ndeed a hghly sgnfcant explanatory factor of the correspondng spllovers, but explanng overall about 10 percent of the varaton n spllover levels. For both dependent varables, labour productvty growth and nnovatve sales productvty growth, we frst estmate an equaton wth aggregated measures of cooperaton and spllovers. Results from the aggregated specfcaton for labour productvty growth (model 1) strongly confrm the contrbuton of R&D cooperaton to productvty growth. The cooperaton varable s hghly sgnfcant and postve. Takng the exponent of the coeffcent mnus one gves the proportonal ncrease n productvty compared wth non-cooperatng frms, whch amounts to a substantal 13 percent dfference n productvty growth. In addton to the aggregate cooperaton measure, the aggregate spllover measure and Innovaton ntensty are postve and sgnfcant. Productvty growth s also hgher for foregn MNE-owned afflates and (margnally) hgher for domestc group frms, whle frm sze and the drecton of nnovatve efforts (demand enhancng or cost savng) have no apprecable mpact. The lagged productvty varable s hghly sgnfcant and negatve, ndcatng that productvty leaders are less able to show further productvty growth. The estmated coeffcent ndcates that a 1 percent hgher past productvty s assocated wth a 0.48 (1 0.52) percent greater current productvty. If spllovers and cooperaton are dfferentated by type of partner and source n model (2), only compettor cooperaton s found to have an ndependent postve mpact on labour productvty growth. If the cooperaton dummy takes the value one for persstent collaborators (frms cooperatng both n the and the perod) both suppler and 10 We also estmated the models wth a robust regresson technque to correct for possble heteroscedastcty, but found no non-trval dfferences n standard errors wth the least squares estmaton. 13

15 compettor cooperaton are found to have postve and sgnfcant mpacts (model 3). In model (2), the ndvdual source-specfc spllovers are not sgnfcant, but n model (3), unversty spllovers do have a margnally sgnfcant and postve mpact. Models (4) (6) present the results f the dependent varable s the growth n frms productvty n generatng sales of nnovatve products new to the market per employee. In the aggregate specfcaton (model 4), agan cooperaton and spllovers are sgnfcant contrbutors to ths type of productvty growth, but n the dfferentated equatons (models 5 and 6) we see dfferent mpacts of the cooperaton types. Now t s unversty cooperaton that has a sgnfcant mpact on productvty growth and compettor cooperaton gets a margnally sgnfcant mpact for persstent collaborators (model 6). In addton, clear contrbutons are confrmed by spllovers (not due to cooperaton) from unverstes and from customers. Surprsngly, nnovaton ntensty has no sgnfcant mpact here, but larger frms are more successful n obtanng ths type of productvty growth. Afflates of foregn multnatonals agan are able to record systematcally hgher productvty growth (albet only margnally sgnfcant), but domestc group membershp has no effect. The cost and demand orentaton of nnovatve efforts matter strongly for productvty growth n the expected drecton. A demand orentaton s more lkely to translate nto growth n new product sales, but a cost orentaton has a negatve mpact. Frms that devote more R&D efforts to cost reducton are not able to devote as much attenton to market expanson and perform less n ths type of productvty growth. Lagged productvty has a sgnfcantly negatve mpact wth a coeffcent of 0.72, ndcatng that a 1 percent ncrease n past productvty s only assocated wth a 0,28 (1-0,72) percent ncrease n current productvty. Ths shows that a past leadng performance n nnovatve sales productvty s more dffcult to sustan than labour productvty. Overall, the results show that R&D cooperaton, nnovaton ntensty, and ncomng spllovers all have ndependent mpacts on productvty growth (wth the excepton of nnovaton ntensty n the nnovatve sales equatons). The results dverge once spllovers and cooperaton are dfferentated by source and partner. The drecton of ths dvergence corresponds to our prors concernng the purposes of dfferent types of collaboraton. R&D cooperaton wth supplers appears more of an ncremental nature focused on reducng nput costs and assembly processes, and therewth labour productvty. Cooperaton wth unverstes s more focused on radcal nnovaton and creatng new products, mprovng nnovatve sales productvty. 14

16 Compettor collaboraton s the only type of collaboraton that has multple purposes and mpacts, effectve n generatng both labour productvty ncreases (e.g. through cost sharng) and nnovatve sales productvty ncreases (e.g. enablng the start of nnovaton projects through rsk sharng and mprovng sales through the establshment of technologcal standards). Customer cooperaton, n contrast, s not found to have any dscernable mpact on productvty growth: apparently, the nformaton on customer demands and technologcal requrements s already effectvely captured by ncomng spllovers from customers through market transactons, and do not requre formal collaboratve R&D agreements. The role of unverstes n frms productvty performance also stands out, as t s the only source of knowledge that both provdes effectve publc spllovers (not due to collaboraton) and mproves frms nnovatve sales productvty through formal R&D cooperaton. 5. Conclusons Despte a growng lterature on R&D cooperaton n both the felds of management and ndustral economcs, surprsngly lttle evdence has emerged on the performance effect of R&D collaboraton. Ths paper analyses the mpact of R&D cooperaton on frm performance jontly wth the mpact of nnovaton expendtures and the effect of ncomng spllovers that are not due to formal collaboratve agreements. We dfferentate between the type of R&D partner (compettors, supplers, customers, and unverstes & research nsttutes) and consder two performance measures: labour productvty and productvty n nnovatve sales new to the market. Usng data on a large sample of Dutch nnovatng frms n two waves of Communty Innovaton Survey (1996, 1998), we mtgate problems of smultanety and unobserved frmspecfc effects by examnng the mpact of R&D (collaboraton) on productvty growth n We fnd that suppler and compettor cooperaton have a sgnfcant mpact on labour productvty growth, whle cooperaton unverstes & research nsttutes and agan compettor cooperaton postvely affects growth n sales per employee of products and servces new to the market. New product sales are furthermore stmulated by ncomng spllovers (not due to collaboraton) from customers and unverstes & research nsttutes. The results are senstve to the lag wth whch nnovaton strateges are allowed to mpact productvty growth. Generally, 15

17 allowng for a more varable lag structure by examnng the mpact of cooperaton strateges that are sustaned over a 2-4 year perod demonstrated a substantally more robust mpact of cooperaton on productvty. The results confrm a major heterogenety n the ratonales and goals of R&D cooperaton, wth compettor and suppler cooperaton focused on ncremental nnovatons mprovng the productvty performance of frms, whle unversty cooperaton and agan compettor cooperaton are nstrumental n creatng and brngng to market radcal nnovatons, generatng sales of products that are novel to the market, and hence mprovng the growth performance of frms (Klomp and Van Leeuwen, 2001). The fndngs provde qualfed support for the noton that cooperatng frms are generally engaged n hgher level nnovatve actvtes (Thether, 2002). Ths holds unequvocally for frms collaboratng wth unverstes (e.g. to get access to basc research) and compettors (to allow R&D for rsky projects), but not for frms engaged n vertcal cooperaton wth supplers and customers. In case the latter types of cooperaton are also partly focused on more radcal nnovatons, than at least there s no evdence n our analyss that these efforts are effectve n mprovng frms performance n brngng novel products to the market. The fndngs go some way n explanng the varety of results obtaned n prevous emprcal work on the effect of cooperaton on nnovatve sales and productvty, where sngle performance measures were used and no (varaton n) lag structures could be examned because of the cross secton nature of the data (e.g. Heshmat and Lööf, 2002; Monjon and Waelbroeck, 2003). Snce dfferent R&D strateges can mpact performance wth dfferent lag structures, future research should explore the dfferent possble ntertemporal structure of the mpact of R&D strateges on nnovaton output and frm performance. The ncreasng avalablty of consecutve CIS surveys wll allow constructon of panel data sets to examne the effectveness of varous nnovaton strateges n more detal. 16

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22 Table 1. Dstrbuton of observatons across ndustres and cooperaton types NACE Sector No. of obs. n sample Share % Share noncoop No of obs Share f COcoop=1 No of obs Share f Scoop=1 No of obs Share f CUcoop=1 No of obs Share f Ucoop=1 No of obs 11,14 Mnng 6 0,3 0,4 5 0,0 0 0,0 0 0,0 0 0,4 1 15,16 Food 134 6,5 6,4 91 7,5 17 8,3 31 7, , Textle 51 2,5 2,7 38 2,7 6 2,4 9 2,0 7 2, Paper 59 2,9 2,7 38 3,5 8 4,3 16 3,4 12 4, Prntng, 69 3,4 3,9 56 1,3 3 2,1 8 1,1 4 0,7 2 publshng 23,24 Petroleum and 93 4,5 3,5 50 4,9 11 5,1 19 8,8 31 7,5 21 chemcals 25 Rubber and 77 3,8 3,4 48 4,4 10 4,3 16 5,7 20 3,2 9 plastc 27 Metallurgy 26 1,3 0,8 11 3,1 7 2,9 11 3,1 11 3, Metal products 153 7,4 7, ,6 15 8,3 31 8,2 29 7, Machnes, 172 8,4 9, ,0 9 5,1 19 6,2 22 5,0 14 equpment Electroncs 125 6,1 5,8 83 4,9 11 5,6 21 7,4 26 8, ,35 Cars and 84 4,1 3,9 55 6,6 15 4,5 17 4,3 15 5,4 15 Transport 20,26,36,37 Other ndustry 149 7,3 7, ,1 16 5,6 21 5,4 19 5, ,41 Utltes 23 1,1 0,8 11 3,1 7 1,6 6 1,1 4 2, Constructon 143 7,0 7, ,0 18 6,1 23 3,7 13 8, Hotel, caterng ,7 18, , , ,9 56 9, Transportaton, 86 4,2 4,6 66 3,1 7 3,5 13 4,0 14 1,1 3 storage Busness ,4 9, , , , ,9 36 servces 90,93 Envronmental, other servces 28 1,4 1,3 19 0,9 2 1,6 6 1,7 6 1,1 3 Total ,0 100, , , , ,0 280

23 Table 2. Descrptve statstcs for dfferent for frms engagng n dfferent types of cooperaton No coop Compettor Coop=1 Suppler Coop=1 Customer. Coop=1 Unversty Coop=1 Mean 1 Test 1 F-value Mean 2 Test 2 F-value (1) (2) (3) (4) (5) (6) (7) Compettor spllover ** Suppler spllover *** 9.84*** Customer spllover Unversty spllover *** 44.50*** *** *** Frm sze Innovaton ntensty Foregn MNE Domestc group Cost Push Demand Pull Log(value added per employee)98 Log(nnovatve sales per employee ) # observatons (new sales sample) 1426 (939) 226 (154) 375 (248) 353 (238) 280 (212) ** sgnfcant at 5%; *** sgnfcant at 1% 1. The test s the comparson of the receved ncomng spllovers between the groups of frms that reported engagement n compettor (suppler, customer, unversty) cooperaton vs-à-vs the establshments that have not reported such engagement. 2. The test s the comparson of the receved ncomng spllovers between the groups of frms that reported engagement n compettor (suppler, customer, unversty) vs-à-vs the establshments that have reported no cooperaton lnks at all. 1

24 Table 3. Regresson Results: The mpact of (persstent) cooperaton on labour productvty growth and nnovatve sales productvty growth, Growth value added per employee (growth labour productvty) Growth new to the market sales per employee (growth nnovatve sales productvty) (1) (2) (3) + (4) (5) (6) + R&D Cooperaton *** (0.0225) Compettor ** Cooperaton (0.0364) Suppler Cooperaton (0.0308) Customer Cooperaton (0.0320) Unversty Cooperaton (0.0351) Incomng spllovers *** (0.0022) Compettor spllovers (0.0125) Suppler spllovers ) Customer spllovers (0.0115) Unversty spllovers (0.0195) Frm sze (0.0092) (0.0093) Innovaton ntensty *** *** (0.0009) (0.0009) Foregn MNE *** *** (0.0278) (0.0280) Domestc group * ** (0.0237) (0.0238) Cost push nnovaton (0.0061) (0.0061) Demand pull nnovaton (0.0069) (0.0070) Log(productvty) *** *** 1996 (0.0155) (0.0155) * (0.0604) ** (0.0444) (0.0456) (0.0472) (0.0078) (0.0076) (0.0072) * (0.0040) (0.0092) *** (0.0009) *** (0.0280) (0.0238) (0.0062) (0.0072) *** (0.0155) * (0.1055) *** (0.0101) * (0.0480) (0.6149) (0.1296) (0.1099) ** * (0.0340) *** (0.0270) (0.1698) (0.1469) (0.1479) ** (0.1614) (0.0571) (0.0562) *** (0.0523) *** (0.0849) ** (0.0483) (0.6207) * (0.1313) (0.1102) ** (0.0278) * (0.0343) *** (0.0272) * (0.2757) (0.1941) (0.1940) ** (0.2003) (0.0567) (0.0563) *** (0.0521) *** (0.0846) * (0.0477) (0.6185) * (0.1302) (0.1096) ** (0.0277) * (0.0340) *** (0.0270) Industry dummes Yes Yes Yes Yes Yes Yes R Number of obs * sgnfcant at 10%; ** sgnfcant at 5%; *** sgnfcant at 1%. Standard errors n parentheses + Cooperaton varables n columns (2) and (5) take a value of one f a frm ndcated that t was engaged n a partcular type of cooperaton n the 1996 survey (wthn the perod ). Cooperaton varables n columns (3) and (6) take a value of one f frms engaged n persstent cooperaton: cooperaton took place n two consecutve perods, and

25 Appendx A: Descrpton of varables # varable name Defnton 1 Compettor cooperaton 1 f the busness unt reported engagement n nnovaton n cooperaton strategy wth compettors, else zero 2 Suppler cooperaton 1 f the busness unt reported engagement n nnovaton n cooperaton strategy wth supplers, else zero 3 Customer cooperaton 1 f the busness unt reported engagement n nnovaton n cooperaton strategy wth customers, else zero 4 Unversty cooperaton 1 f the busness unt has reported engagement n nnovaton n cooperaton strategy wth unverstes, nnovaton centers, or research nsttutons, else zero 5 Compettor ncomng spllover Importance of compettors as source of knowledge for the frm s nnovaton process. Constructed as resdual from the auxlary regresson of compettor spllover taken from 1998 survey on a compettor cooperaton dummy taken from 1996 survey. 6 Suppler ncomng spllover Importance of supplers as source of knowledge for the frm s nnovaton process. Constructed as resdual from the auxlary regresson of suppler spllover taken from 1998 survey on a suppler cooperaton dummy taken from 1996 survey. 7 Customer ncomng spllover Importance of customers as source of knowledge for the frm s nnovaton process. Constructed as resdual from the auxlary regresson of customer spllover taken from 1998 survey on a suppler cooperaton dummy taken from 1996 survey. 8 Unversty ncomng spllover Average of mportance of unverstes, nnovaton centers, and research nsttutons as source of knowledge for the frm s nnovaton process. Constructed as resdual from the auxlary regresson of unversty spllover taken from 1998 survey on an unversty cooperaton dummy taken from 1996 survey. 9 Innovaton ntensty Total nnovaton expendtures/sales 10 Frm sze Logarthm of number of employees 11 Domestc group 1 f the busness unt s part of a domestc frm groupng, else 0 12 Foregn MNE 1 f the frm s afflate of a foregn MNE, else 0 13 Cost push Importance of cost-savng objectves for the frm s nnovatons Constructed as a sum of scores on 4 categores of objectves, relatng to processes, labour, materals, and energy. 14 Demand pull Importance of demand-enhancng objectves for the frm s nnovatons. Constructed as sum of scores on 2 categores of objectves, relatng to products qualty and new products and markets. 15 Productvty growth (value Growth n the net value added per employee = log (labour added) 16 Productvty growth (new sales) Note: all ndependent varables are for 1996 except for the spllover varables productvty 1998) log (labour productvty 1996) Growth n the value of sales new to the market per employee = log (1+ new sales/employees 1998) log (1+ new sales/employees 1996) 3