The Decision to Invest in R&D: A Panel Data Analysis for Spanish Manufacturing

Transcription

1 Manez Castllejo, et al., Internatonal Journal of Appled Economcs, 3(2), September 2006, The Decson to Invest n R&D: A Panel Data Analyss for Spansh Manufacturng Juan A. Máñez Castllejo *, María E. Rochna Barrachna, Amparo Sanchs Llops, and Juan A. Sanchs Llops Unversdad de Valenca and LINEEX Abstract Ths paper examnes the factors determnng frms probablty of nvestng n R&D actvtes usng a sample of Spansh manufacturng frms drawn from the Encuesta sobre Estrategas Empresarales, for the perod We estmate a panel data dscrete choce model that allows controllng for unobserved frm heterogenety, usng maxmum-lkelhood technques. Our results show that export ntensty, a sgnfcant market share, hghly qualfed workforce, frm sze, labor productvty, advertsng ntensty, and regonal and local technologcal spllovers have a postve and sgnfcant nfluence on the probablty of nvestng n R&D. In addton, ths probablty decreases wth frm age and foregn partcpaton n the captal of the frm. Keywords: frms R&D decson, panel data, multnomal dscrete choce model JEL Classfcaton: C23, L60, O33 1. Introducton Gven the mportance of R&D as a major factor drvng economc growth, understandng the determnants of frms R&D nvestments has receved much attenton among researchers and polcy makers (see, among others, the works of Cohen et al., 1987; Cohen and Levn, 1989; Levn et al., 1985; or the survey by Cohen, 1995). In ths paper we focus on the determnants of the decson to undertake R&D actvtes usng panel data technques. Most of exstng studes on ths ssue estmate ether cross-secton or pooled level panel data models. However, when analysng panel data, that s, when one has repeated observatons for a gven frm over tme, t s crucal, both n terms of consstency and/or effcency gans n the estmaton, to control for unobserved frm heterogenety that captures, among other thngs, for manageral ablty and nternal organsatonal capabltes to perform R&D. Therefore, t s mportant to estmate usng panel data methods. The am of ths paper s to provde emprcal evdence of the determnants of the R&D nvestment decson by Spansh manufacturng frms, estmatng a panel data dscrete choce model usng maxmum-lkelhood technques. In partcular, we evaluate the relevance of technologcal factors, such as local and regonal R&D spllovers, and also factors relatng to frm economc and technologcal opportuntes, frms success, market structure characterstcs, approprablty condtons, and other frm characterstcs. The data used s a panel of Spansh manufacturng frms drawn from the Encuesta sobre Estrategas Empresarales (ESEE, hereafter), for the perod

2 Manez Castllejo, et al., Internatonal Journal of Appled Economcs, 3(2), September 2006, Ths paper dffers from prevous emprcal work n at least two man ways. Frst, to the best of our knowledge, our work s the frst to analyse the yes/no decson to undertake R&D actvtes for Spansh manufacturng usng panel data estmaton methods and accountng for unobserved frm heterogenety. Secondly, nstead of usng the nformaton provded by an nnovaton survey, as most studes n the lterature, we analyse a representatve sample of Spansh manufacturng whch covers over 2000 frms over ten years and provdes detaled data at the frm level. Therefore, our approach s comprehensve n the sense that we take nto account a large number of factors that have been argued to affect the frm decson to undertake R&D actvtes. To antcpate our results, we fnd that export ntensty, a sgnfcant market share, hghly qualfed workforce, frm sze, labor productvty, advertsng ntensty, and regonal and local technologcal spllovers have a postve and sgnfcant nfluence on the probablty of nvestng n R&D. In addton, ths probablty decreases wth frm age and foregn partcpaton n the captal of the frm. The rest of the paper s organzed as follows. Next secton revses the exstng related lterature and dscusses the theoretcal background. The emprcal methodology and the data used are then presented, followed by the man results and conclusons. 2. Related lterature and theoretcal background The factors drvng frms decson to nvest n R&D actvtes have been under scrutny snce the poneerng work of Schumpeter (1942). There exsts a huge body of both theoretcal and emprcal lterature on the determnants of frms nnovatveness. However, there s not a unque theoretcal approach (see, among others, the works of Cohen et al., 1987; Cohen and Levn, 1989; Levn et al., 1985; or the survey by Cohen, 1995). Wthn the related lterature, a number of dfferent lnes of research and methodologcal approaches have been followed. Some studes have analyzed a research producton functon usng R&D expendtures as a measure of nputs and the number of patents as a measure of the output (see, e.g., Bound et al., 1984; Hall et al., 1986). Other studes have focused on the analyss of factors drvng the output sde of the nnovaton process, such as the number of product or process nnovatons (Baldwn et al., 2002; Cabagnols and Le Bas, 2002), or the number of patents ntroduced by frms (Pakes and Grlches, 1984). Fnally, other studes have analyzed the factors drvng the nput sde of the nnovaton process, such as the amount of R&D expendtures made by frms (Levn and Ress, 1984), or the decson of frms to undertake R&D actvtes (see, Cassman and Veugelers,1999, for Belgum; Beneto, 2003, Busom, 1993, and Gumbau, 1997, for Span; and Czarntzk and Kraft, 2004, for Germany, among others). However, these papers do not make use of panel data technques n estmaton. 1 In what follows we brefly dscuss the man factors that have been put forward by the related lterature as key determnants of the frms R&D nvestment decson. For the sake of the exposton, we classfy frm/market characterstcs nto the followng groups: economc opportuntes, technologcal opportuntes, frm success, market structure, approprablty condtons, R&D spllovers, and other frm characterstcs.

3 Manez Castllejo, et al., Internatonal Journal of Appled Economcs, 3(2), September 2006, Economc opportuntes The ncentves to nvest n R&D depend on the economc opportuntes faced by frms, that s, the scope of future demand, the sze and growth of the market, and also the consumer s wllngness to pay for new or mproved products (Schmoockler, 1962). In order to measure frm economc opportuntes the followng factors may be consdered. Frst, the geographc extent of the frms man MARKET s a key determnant of the possbltes to explot the nnovaton results. Secondly, accordng to Spence (1975), frms producng a STANDARDISED PRODUCT may use R&D actvtes to dfferentate ther product and soften competton. Thrdly, t has been stressed the possble dscplne effect of EXPORT actvtes, snce exportng frms may need to nnovate n order to face the hgher compettve pressures of foregn markets (Klenschmdt and Cooper, 1990; Kotable, 1990). Related to ths dea, Cohen and Levnnthal (1989) argue that nternatonal markets are channels for technology transfers that ncrease frms technology and stmulate R&D. Fourthly, the degree of product DIVERSIFICATION s also assocated wth greater ncentves to nnovate snce the frm may spread ts R&D results among ts dfferent products (Chen, 1996). Fnally, frms accountng for a sgnfcant MARKET SHARE of ther market could experence hgher ncentves to perform nnovaton actvtes so as to mantan ther domnant poston. 2.2 Technologcal Opportuntes Technologcal opportuntes refer to the possblty of convertng research resources nto new products or better producton technques (Cohen and Levnthal, 1989; Lunn and Martn, 1986). The hypothess here s that the ncentves to nnovate ncrease wth technologcal opportuntes. Technologcal opportuntes are often measured usng both the ndustral sector n whch frms operate and frm labor qualty. The INDUSTRIAL SECTOR n whch frms operate s controlled for usng two-dgt NACE-93 classfcaton ndustry dummy varables. We expect that frms wthn hgh technologcal opportunty sectors wll have more ncentves to perform R&D actvtes. Further, the propensty of frms to undertake R&D actvtes s expected to rse wth frm LABOUR QUALITY, measured as the percentage of hghly qualfed employees (not ncludng R&D related workforce). A hgh degree of labor force qualfcaton may facltate the mplementaton and development of nnovaton actvtes (Bartel and Lchtenberg, 1987). 2.3 Frm Success A thrd group of factors nfluencng the R&D decson s assocated wth frm success. The usual clam here s that better performng frms are more prone to undertake R&D actvtes. Frm success may be measured usng frms age, sze and labor productvty. Frm AGE, measured as the number of years elapsed snce the frm was founded, captures frm experence and knowledge accumulaton, and t usually proxes for frms effcency dfferences. Accordng to learnng models (Ercson and Pakes, 1995; Jovanovc, 1982), frm AGE s a sgnal of effcency snce only effcent frms are able to grow and survve n the market. However, the product lfe cycle model of Klepper (1996) predcts an nverse relatonshp between age and the propensty to nnovate at the ndustry level. Accordng to ths model, older frms would face less technologcal opportuntes and therefore have lower ncentves to nvest n R&D. The assocaton between R&D nvestment and frm SIZE has attracted a lot of attenton n the lterature (Acs and Audretsch, 1987; Cohen and Levn, 1989 for a revew; Kamen and Schwartz 1982; Schumpeter, 1942). From a theoretcal pont of vew, a number of arguments support a postve assocaton between frm sze and R&D, such as economes of scale and scope, larger market sze, lower rsk, hgher approprablty possbltes, etc. The emprcal results n the lterature are mxed but n general they suggest that large frms tend to be more nnovatve,

4 Manez Castllejo, et al., Internatonal Journal of Appled Economcs, 3(2), September 2006, although ths postve assocaton s not necessarly lnear. There are also reasons to expect frm LABOUR PRODUCTIVITY to ncrease the lkelhood of nvestng n R&D. More productve frms enjoy both better fnancal and economc condtons and so they may be more prone to undertake R&D actvtes. 2.4 Market Structure The decson to nvest n R&D wll be also nfluenced by market structure characterstcs, such as the degree of market concentraton and competton. There s no clear-cut relatonshp between R&D actvtes and market power. Accordng to Schumpeter (1942), ex ante market power generates fnancal means to nnovate and reduces rsk levels. However, followng Arrow (1962), the ncentves to nnovate are hgher n compettve markets because the expected ncremental rents from nnovatng are hgher as compared to monopoly condtons. In order to capture frms market competton, we consder the CONCENTRATION RATIO of the four largest frms n the relevant market 2 and account for the frm number of drect compettors (ncludng a set of three dummy varables capturng the NUMBER OF COMPETITORS faced by frms). 2.5 Approprablty Condtons Approprablty condtons refer to the extent to whch the results from nnovatve actvtes can be approprated by the frm or easly dffused wthn or across ndustres (Levn, 1988). The hgher the degree of approprablty of the returns to nnovaton, the hgher wll be the ncentves to nvest n R&D (Levn, 1988; Levn et al., 1987; Mansfeld, 1985). We measure APPROPRIABILITY condtons followng Beneto (2003), calculatng the rato between the total number of patents granted and the total number of frms that clam to have acheved nnovatons n the frms ndustral sector. 2.6 R&D Spllovers The lterature on nnovaton has stressed the mportance of spllovers n the decson to nvest n R&D. Interacton wth supplers, customers, publc assstance agences, ndustry assocatons, unverstes and the lke, can provde mssng external nputs nto the frm learnng process, such as staff tranng or consultng servces (Lundvall, 1988, 1992; Rothwell and Dodgson, 1991). There s also a number of papers suggestng that geographcal proxmty generates postve externaltes, market lnkages and possbltes for collaboraton that n turn foster technologcal mprovements and nnovatons. Canels (2000) stresses the mportance of local knowledge spllovers, ncludng quck dffuson of new technologes and knowledge through close nteracton wth other frms. Accordng to Grlches (1992), spllovers may be understood as deas borrowed by research teams of frm/ndustry from the results obtaned by frm/ndustry j. In ths lne, we consder three separate forms of R&D SPILLOVERS: ndustry-specfc, regon-specfc and local. Industry-specfc spllovers are captured by R&D actvtes undertaken by frms wthn the same ndustry but outsde the regon where the frm operates. Regon-specfc spllovers are captured by R&D actvtes performed by frms n the same regon but wthn dfferent ndustres, and local spllovers are captured by the R&D actvtes undertaken by frms n the same ndustry and regon n whch frms operate. 2.7 Other Frm Characterstcs Fnancal constrants, the degree of horzontal product dfferentaton, and frms ownershp have also been suggested n the lterature as nfluencng the decson to nvest n R&D actvtes. The

5 Manez Castllejo, et al., Internatonal Journal of Appled Economcs, 3(2), September 2006, amount of CASH FLOW may affect the nvestment spendng of fnancally constraned frms (Hall, 1990), and n partcular the decson to undertake R&D actvtes (see, e.g., Gerosk et al., 2002, and references theren). Frm ADVERTISING INTENSITY (as a measure of the degree of horzontal product dfferentaton) determnes the reputaton and mage of the frm, whch may be consdered as commercal resources or ntangble assets affectng the success or falure of nnovatons (Galende and de la Fuente, 2003). Therefore, we may expect that frms wth large budgets on advertsng may be more prone to nvest n R&D so as to mantan the value of these ntangble assets. Regardng frms ownershp, two key factors are FOREIGN CAPITAL PARTICIPATION and the legal structure of the frm, CORPORATE. On the one hand, foregn owned frms may be manly productve platforms for ther parent companes or, on the contrary, foregn ownershp may foster R&D actvtes. On the other hand, frms where management s separated from ownershp wll be less rsk averse (as compared to owned-managed frms) and thus more wllng to undertake rsky nvestments such as R&D actvtes (Love et al., 1996). Fnally, the foregn content of the frm physcal captal (FOREIGN PHYSICAL EQUIPMET) may be ndcatng whether foregn technology ncorporated n machnery s a substtute or complement to technology from R&D actvtes. Table 1 explans the defnton and constructon of all the varables ncluded n our emprcal model n order to control for all the factors dscussed above. Nomnal varables have been deflated usng specfc ndustry deflators accordng to the 20 sectors of the NACE-93 classfcaton. 3. Emprcal methodology and data We model the decson to nvest n R&D ( y t ) consderng that n each perod t a frm decdes to nvest n R&D f the ncrement to expected profts assocated wth nvestng s postve. To parameterse the reduced-form model that descrbes ths decson, we assume that varaton n proftablty (apart from unobserved components, ε t ) arses from two dfferent sources: macro condtons ( μ t ) and observable dfferences n frm and market characterstcs ( X t ), ncludng, among others, spllovers and ndustry dummes. Our emprcal estmatng equaton s defned by the followng process, y t 1 f μ + t βx + t ε t 0 = 1,..., n; t = 1,..., T = 0 otherwse. (1) We assume that ε t has two components, a permanent frm-specfc effect ( α ) and a transtory component ( ut ). If we assume jont normalty for εt = α + ut (t=1,,t ), ndependence of ε t from X and that ut (t=1,,t) are serally uncorrelated, a panel probt random effects maxmum lkelhood estmator may be mplemented. Snce ε1,..., ε T are ndependent gven the regressors and α, we can control for α by one-dmensonal ntegraton of the condtonal log-lkelhood functon wth respect to α.

6 Manez Castllejo, et al., Internatonal Journal of Appled Economcs, 3(2), September 2006, The ncluson of μ t accounts for the nfluence of macro-level changes n R&D condtons and nsttutonal factors common across frms, such as R&D polcy varatons, the busness cycle, credt-market condtons and other tme-varyng factors. We also consder the role of observable frm/market characterstcs ( X t ) n the R&D nvestment decson and nclude those factors expected to affect the frms decson to perform R&D actvtes, as descrbed n the prevous secton and defned n Table 1. We use data drawn from the ESEE, a representatve annual survey of Spansh manufacturng frms carred out by the Mnstry of Industry snce It ncludes exhaustve nformaton at the frm level. 4 After cleansng the data, we end up wth an unbalanced panel of 907 operatng frms (8025 observatons) from 1990 to We classfy a frm n a gven perod as an R&D frm when t clams to undertake R&D actvtes. The proporton of R&D frms steadly ncreased along the perod from 32.1% to 36.3%. In the estmaton we lag all frm and market characterstcs one year to avod possble smultanety problems Estmatng Results In order to check whether the ndvdual unobservable frm effects should be treated as fxed or random, we estmate our model (equaton 1) both by condtonal logt and panel probt random effects estmators. We test one model aganst the other usng a Hausman test. The results of the Hausman test (see the bottom of Table 2) ndcate that we cannot reject the null hypothess that regressors are unrelated to α. Therefore, we present the results of the more effcent estmator under ths scenaro, the panel probt random effects maxmum lkelhood estmator. These results are reported n Table 2. Together wth the estmated coeffcents we also present margnal effects. Furthermore, gven that wth the random effects approach we may dentfy the rato ρ = V( α) V( α + u t ), we also present a test for the null of whether ρ=0. Gven that the 2 χ 1 statstc s , we reject the null and thus the model has to be estmated accountng for frm effects nstead of usng panel pooled level data technques. The estmaton results reported n Table 2 show that most of the factors consdered as determnants of the yes/no decson to undertake R&D actvtes perform as expected. Regardng tme dummy varables, we obtan that only the 1998 dummy s sgnfcant and that 2 the hypothess that tme dummes are jontly equal to zero s not rejected (the χ 9 statstc s and the correspondng p-value 0.24). Thus, R&D actvtes do not seem to be very senstve to changes n macro-condtons. In relaton to varables measurng economc opportuntes, we fnd that both EXPORT INTENSITY and havng a sgnfcant MARKET SHARE have a sgnfcant and postve effect on the probablty to nvest n R&D. However, the geographcal extent of the product market, the degree of product DIVERSIFICATION and producng a STANDARDISED PRODUCT do not sgnfcantly affect the probablty of performng R&D. In relaton to the varables capturng technologcal opportuntes, most of the INDUSTRY DUMMIES coeffcents are sgnfcant and postve. Further, these coeffcents are hgher for medum and hgh technologcal ntensty ndustres, as compared to the low technologcal ntensty ones. 6 The coeffcent of LABOUR QUALITY s postve and sgnfcant ndcatng that frms wth hghly qualfed workforce are more prone to perform R&D actvtes.

7 Manez Castllejo, et al., Internatonal Journal of Appled Economcs, 3(2), September 2006, Regardng our measures of frm success, we obtan a negatve and sgnfcant effect of AGE on the probablty of undertakng R&D. Ths result s consstent wth Huergo and Jaumandreu (2004), who also found that the oldest frms tend to show lower probabltes to nnovate. The coeffcent of age could be capturng the lfe cycle of the frm product, so that older frms would face less technologcal opportuntes and therefore have lower ncentves to nvest n R&D (Klepper, 1996). Further, we obtan a postve and non lnear effect of frm sze on the probablty of undertakng R&D actvtes, n lne wth exstng lterature (see, e.g., Beneto, 2003; Cassman and Veugelers, 1999; Kamen and Schwartz, 1982). Fnally, the coeffcent of LABOUR PRODUCTIVITY s also postve and sgnfcant. Concernng the varables capturng the market structure, we obtan that nether the CONCENTRATION RATIO nor the NUMBER OF COMPETITORS are sgnfcant. Our results also show that our measure of APPROPRIABILITY s not sgnfcant. 7 Regardng technologcal SPILLOVERS, we obtan that both regonal and local spllovers have a postve and sgnfcant mpact on the probablty of performng R&D actvtes. Ths may suggest that geographcal proxmty, accessblty, physcal concentraton and favorable externaltes facltate the flow of knowledge and stmulate R&D actvtes. Among other frm characterstcs, we obtan that the ADVERTISING INTENSITY of the frm affects postvely and sgnfcantly to the probablty to nvest n R&D. The coeffcent of FOREIGN CAPITAL PARTICIPATION s sgnfcant and negatve, ndcatng that frms partcpated by foregn captal n Span are manly productve platforms and that nnovatve actvtes are carred out by ther parent companes. Fnally, to evaluate the goodness of ft n our model, we compare actual and predcted patterns of partcpaton n R&D actvtes. Our model predcts properly 57% of the ndvdual patterns. In partcular, our model predcts better the patterns of those frms that ether always undertake R&D actvtes or never perform these actvtes (63.69% and 91.59%, respectvely). 5. Concludng remarks In ths paper we provde emprcal evdence on the R&D nvestment decson by Spansh manufacturng frms. The panel data nature of the data and the wde avalablty of nformaton at the frm level allow us to better dentfy the determnants of the frms decson to nvest n R&D and to control for frm unobservable heterogenety. We estmate a panel data dscrete choce model usng data drawn from the ESEE for the perod Our man results are the followng. Regardng frms economc opportuntes, we obtan that the probablty of undertakng R&D actvtes ncreases wth export ntensty and wth market share. In relaton to frm technologcal opportuntes, frms n hgh and medum technology ndustres and frms wth a hgh proporton of qualfed employees are also more prone to nvest n R&D. Regardng ndcators of frms success, both labor productvty and frm sze have a postve nfluence on the probablty to perform R&D. Regardng R&D spllovers, we fnd that both local and regonal spllovers are mportant drvers of R&D actvtes. Therefore, our results provde emprcal evdence n support of the mportance of technologcal externaltes as a complement to the nnovaton actvtes performed by frms. In partcular, our fndngs suggest that geographcal proxmty, by facltatng market lnkages and frms nteracton, generates postve externaltes that stmulate nnovatng actvtes. As for other factors, frms devotng resources to dfferentate ther product by means

8 Manez Castllejo, et al., Internatonal Journal of Appled Economcs, 3(2), September 2006, of advertsng expendtures are more lkely to nvest n R&D whereas those frms partcpated by foregn captal seem to be less prone to undertake nnovatve actvtes. Our fndngs make an mportant contrbuton to the understandng of the determnants of frms decson to undertake R&D actvtes and suggest possble R&D promoton polces. Frst, polces fosterng frms technologcal nteracton would allow frms to take advantage of technologcal spllovers and thus rase the probablty of nvestng n R&D. Secondly, polces encouragng frms to partcpate n the export market or to ncrease labor productvty and labour qualty would have a postve mpact on the probablty of performng R&D. Endnotes * Correspondng author: Juan A. Máñez Castllejo, Facultad de Economía, Departamento de Economía Aplcada II, Avda. de los Naranjos, s/n, Valenca, Span. E-mal: jamc@uv.es. We gratefully acknowledge fnancal support provded by the Spansh Mnstry of Scence and Technology (code SEJ ) and the Valencan Regonal Government (code GV05/183). We would also lke to thank Fundacón SEPI for provdng the data. 1. Martnez-Ros and Labeaga (2002) usng the ESEE, but only for the perod , analyze the determnants of nnovatons estmatng dscrete choce models for panel data. However, ther emphass s on the factors drvng product versus process nnovatons and ther complementartes, rather than on the determnants of the decson to undertake R&D actvtes per se. 2. We follow Beneto (2003) and Huergo (1998), who calculate market concentraton ratos as descrbed n Table The samplng procedure of the ESEE s the followng. Frms wth more than 200 employees are requested to partcpate and the partcpaton rate s approxmately 70% of the number of frms n the populaton. Frms between 10 to 200 employees are randomly sampled by ndustry and sze strata, holdng around a 5% of the populaton. Frms wth less than 10 employees are excluded from the survey. The nfluence of the samplng scheme n R&D probabltes has been taken nto account n the emprcal analyss by ncluson of a varable that controls for bg and small frms. Analogously, the spllover varables are bult up takng account of that samplng scheme. 4. We do not nclude frms that faled durng the sample perod as ncludng them would nvolve modellng the probablty of falng and would substantally complcate the analyss. 5. Thus, we cannot use n estmaton the frst year of the sample, The omtted ndustral sector n estmaton s Meat ndustry. We consder as low technologcal ndustres the followng: meat, beverages, textles, leather and shoes, wood, paper, prntng and prntng materal, non-metallc mner products, metallc products, furnture and other manufacturng goods. 7. Note, however, that we have also consdered other factors that may be also capturng approprablty condtons, such as the ndustral sector n whch frms operate (whch

9 Manez Castllejo, et al., Internatonal Journal of Appled Economcs, 3(2), September 2006, captures technologcal features and thus s expected to be closely related to the degree of appropraton of the nnovaton results).

10 Manez Castllejo, et al., Internatonal Journal of Appled Economcs, 3(2), September 2006, References Acs, Z. and D. Audretsch Innovaton, Market Structure and Frms Sze, The Revew of Economcs and Statstcs, 69(4), Arrow, K Economc Welfare and the Allocaton of Resources for Inventons, n Nelson, R. (ed.), The Rate and Drecton of Inventve Actvty, Prncenton Unversty Press. Baldwn, J., P. Hanel and D. Sabourn Determnants of nnovatve actvty n Canadan manufacturng frms, n A. Klenknecht and P. Mohnen (eds.): Innovaton and Frm Performance. Econometrc Exploratons of Survey Data, Palgrave, London, chapter 5, Bartel, A. and F. Lchtenberg The Comparatve Advantage of Educated Workers n Implementng New Technology, The Revew of Economcs and Statstcs, 69(1), Beneto, P Choosng among Alternatve Technologcal Strateges: An Emprcal Analyss of Formal Sources of Innovaton, Research Polcy, 32(4), Bound, J., C. Cummns, Z., Grlches, B. H. Hall and A. Jaffe Who Does R&D and Who Patents? n Z. Grlches (ed.): R&D, Patents and Productvty, Chcago, Chcago Unversty Press and NBER. Bussom, I Los proyectos de I+D de las empresas: un análss empírco de algunas de sus característcas, Revsta Española de Economía, Monográfco: Investgacón y Desarrollo, Cabagnols, A. and C. Le Bas Dfferences n the Determnants of Product and Process Innovatons : the French Case, n A. Klenknecht and P. Mohnen (eds.): Innovaton and Frm Performance. Econometrc Exploratons of Survey Data, Palgrave, London, chapter 6, Canels, M. C. J Knowledge Spllovers and Economc Growth, Edward Elgar, Cheltenham. Cassman, B. and R. Veugelers Make and Buy n Innovaton Strateges: Evdence from Belgan Manufacturng Frms, Research Polcy, 28 (1), Chen, R Technologcal Expanson: the Interacton between Dversfcaton Strategy and Organzatonal Capablty, Journal of Management Studes, 33 (5), Cohen, W Emprcal Studes of Innovatve Actvty, n Stoneman, P. (ed.), Handbook of the economcs of nnovaton and technologcal change, Blackwell. Cohen, W., R. Levn and D. C. Mowery Frm Sze and R&D Intensty: A Reexamnaton, Journal of Industral Economcs, 35 (4), Cohen, W. and R. Levn Emprcal Studes of Innovaton and Market Structure, en Schmalensee, R., y Wllg, R., (ed.), Handbook of Industral Organzaton, vol. II, ch. 18, Elsever Scence Publshers, B.V.

11 Manez Castllejo, et al., Internatonal Journal of Appled Economcs, 3(2), September 2006, Cohen, W. and D. Levnthal Innovaton and Learnng: the Two Faces of R&D, The Economc Journal, 99(397), Czarntzk, D. and K. Kraft Management Control and Innovatve Actvty, Revew of Industral Organzaton, 24(1), Ercson, R. and A. Pakes ''Markov-Perfect Industry Dynamcs: A Framework for Emprcal Work,'' Revew of Economc Studes, 62(1), Galende, J. and J.M. de la Fuente Internal Factors Determnng a Frm s Innovatve Behavor, Research Polcy, 32(5), Gerosk, P., J. Van Reenen and C. Walters Innovatons, Patents and Cash Flow, n A. Klenknecht and P. Mohnen (eds.): Innovaton and Frm Performance. Econometrc Exploratons of Survey Data, Palgrave, London, chapter 2, Grlches, Z The Search for R&D Spllovers, Scandnavan Journal of Economcs, 94, supplement, S29-S47. Gumbau, M Análss mcroeconómco de los determnantes de la nnovacón: aplcacón a las empresas ndustrales españolas, Revsta Española de Economía, 14(1), Hall, B The Impact of Corporate Restructurng on Industral R&D, Brookng Papers on Economc Actvty: Mcroeconomcs, 1990(1), Hall, B. H., Z. Grlches and J. Hausman, Patents and R&D: Is There a Lag? Internatonal Economc Revew, 27(2), Huergo, E Colusón y efcenca: un contraste a partr de estmacones ntra e ntersectorales, Revsta de Economía Aplcada, 17(6), Huergo, E. and J. Jaumandreu How Does Probablty of Innovaton Change wth Frm Age? Small Busness Economcs, 22(3-4), Jovanovc, B Selecton and the Evoluton of Industry, Econometrca, 50(3), Kamen, M. and N. Schwartz Market Structure and Innovaton. Cambrdge: Cambrdge Unversty Press. Klenschmdt, E. J. and R. B. Cooper The Performance Impact of an Internatonal Orentaton on Product Innovaton, European Journal of Marketng, 22(1), Klepper, S Entry, Ext and Innovaton over the Product Lfe Cycle, Amercan Economc Revew, 86(3), Kotable, M The Relatonshp between Offshore Sourcng and Innovatveness of US Multnatonal Frms: An Emprcal Investgaton, Journal of Internatonal Busness Studes, 21(3),

12 Manez Castllejo, et al., Internatonal Journal of Appled Economcs, 3(2), September 2006, Levn, R Approprablty, R&D Spendng and Technologcal Performance, Amercan Economc Revew, 78(2), Levn, R. and Ress, P Tests of a Schumpeteran Model of R&D and Market Structure, n Z. Grlches (ed.), R&D, Patents and Productvty, Prnceton, N.J., Natonal Bureau of Economc Research Prnceton Unversty Press, Levn, R., W. Cohen, and D. C. Mowery, R&D Approprablty, Opportunty and Market Structure: New Evdence on Schumpeteran Hypothess, Amercan Economc Revew, Proceedngs, 75(2), Levn, R., A. Klevorck, R. Nelson, and S. Waters Appropratng the Returns from Industral Research and Development, Brookngs Papers on Economc Actvty, 1987(3), Love, J. H., B. Ashcroft, and S. Dunlop Corporate Structure, Ownershp and the Lkelhood of Innovaton, Appled Economcs, 28(6), Lundvall, B. A Innovaton as An Interactve Process: from User-Producer Interacton to the Natonal System of Innovaton, n: Dos, G., Freeman, C., Nelson, R., Slverberg G. and Soete, L. (eds.), Techncal Change and Economc Theory, Pnter, London, Lundvall, B. A User-producer relatonshps, natonal system of nnovaton and nternatonalsaton, n Lundvall, B.A. (eds.), Natonal System of Innovaton: Towards a Theory of Innovaton and Interactve Learnng, Pnter, London, Lunn, J. and S. Martn Market structure, frm structure and research and development, Quarterly Revew of Economcs and Busness, 26(1), Mansfeld, E How rapdly does new ndustry technology leak out? Journal of Industral Economcs, 34(2), Martínez-Ros, E. and J. M. Labeaga Modellng Innovaton Actvtes Usng Dscrete Choce Panel Data Models, n A. Klenknecht and P. Mohnen (eds.): Innovaton and Frm Performance. Econometrc Exploratons of Survey Data, Palgrave, London, chapter 7, Pakes, A. and Z. Grlches Patents and R&D at the Frm Level: A Frst Look, n Z. Grlches (ed.), R&D, Patents and Productvty, Chcago: Natonal Bureau of Economc Research, Unversty of Chcago Press. Rothwell, R. and M. Dogson External Lnkages and Innovaton n Small and Medum- Sze Enterprses, R&D Management, 21(2), Schmookler, J Economc Sources of Inventve Actvty, Journal of Economc Hystory, 22(1), Schumpeter, J Captalsm, Socalsm and Democracy. New York: Harper Brothers. Spence, A. M Monopoly, Qualty and Regulaton, Bell Journal of Economcs, 6(2),

13 Manez Castllejo, et al., Internatonal Journal of Appled Economcs, 3(2), September 2006, Table 1: Varables defnton. y t Dummy varable takng value 1 f frm undertakes R&D actvtes n year t, and 0 otherwse. Year dummes Dummy varables takng value 1 for the correspondng year, and zero otherwse. Regonal market Dummy varable takng value 1 f the geographc lmts of the frm man market are local or regonal, and 0 otherwse. Natonal Market Dummy varable takng value 1 f the geographc lmt of the frm man market s natonal, and 0 otherwse. Internatonal-Natonal market Dummy varable takng value 1 f the geographc lmts of the frm man market are foregn or both natonal and foregn, and 0 otherwse. Standardzed product Dummy varable that equals 1 f the frm asserts to offer a standardzed product (as opposed to specfcally desgned for the customers), and 0 otherwse. Export ntensty Exports to sales rato (n %). Dversfcaton Ths varable tres to approxmate the degree of dversfcaton of the frm lne of busness. The ESEE provdes nformaton about the man products offered by the frm that account for at least the 50% of ts total amount of sales. Usng such nformaton, ths varable has been defned as the number of products that account for at least ths percentage. Market share Dummy varable takng value 1 f the frm asserts to account for a sgnfcant market share n ts man market, and 0 otherwse. Industry dummes 20 ndustral sectors of the two dgt NACE-93 classfcaton. Labor qualty Rato of the number of hghly qualfed employees to total employment (R&D labor force not ncluded) (n %). Age Log of the number of years snce the frm was born. Sze1 Dummy varable that equals 1 f the number of employees of the frm s below or equal to 20, and 0 otherwse (R&D workforce not ncluded). Sze2 Dummy varable that equals 1 f the number of employees of the frm s above 20 and below or equal to 50, and 0 otherwse (R&D workforce not ncluded). Sze3 Dummy varable that equals 1 f the number of employees of the frm s above 50 and below or equal to 100, and 0 otherwse (R&D workforce not ncluded). Sze4 Dummy varable that equals 1 f the average number of employees of the frm s above 100 and below or equal to 200, and 0 otherwse (R&D workforce not ncluded). Sze5 Dummy varable that equals 1 f the number of employees of the frm s above 200 and below or equal to 500, and 0 otherwse (R&D workforce not ncluded). Sze6 Dummy varable that equals 1 f the number of employees of the frm s above 500, and 0 otherwse (R&D workforce not ncluded). Labor productvty Log of labor productvty. Concentraton rato, CR4 Ths s the four-frms concentraton rato,.e. the market share accounted for by the four largest frms n the prncpal market n whch frms compete. Ths varable shows a hgh degree of non response n the ESEE. For ths reason, an estmaton of ths varable has been carred out for those frms that do not respond. The four-frm concentraton rato s computed from the responses of frms n the same market, ths market beng dentfed takng nto account the ndustral sector (50 ndustres defned from the three-dgt NACE-93 classfcaton), the geographc lmts n whch they manly commercalze ther products (local, regonal, natonal or abroad) and the number of compettors they clam to have n ther man market.

14 Manez Castllejo, et al., Internatonal Journal of Appled Economcs, 3(2), September 2006, Table 1: Varables defnton (contnued). Number of compettors 0-10 Number of compettors Dummy varable that equals 1 f the frm clams to have less than (or equal to) 10 compettors wth sgnfcant market share n ts man market, and 0 otherwse. Dummy varable that equals 1 f the frm clams to have more than 10 and less than (or equal to) 25 compettors wth sgnfcant market share n ts man market, and 0 otherwse. Number of compettors > 25 Dummy varable that equals 1 f the frm clams to have more than 25 compettors wth sgnfcant market share n ts man market, and 0 otherwse. Approprablty Industry-specfc spllovers Regon-specfc spllovers Local-spllovers Cash flow rato Rato of the total number of patents over the total number of frms that assert to have acheved nnovatons n the frm ndustral sector (50 sectors of the three dgt NACE-93 classfcaton) (n %). Percentage of frms that perform R&D n the same two dgt NACE-93 ndustry (20 ndustres) but outsde a gven regon. Percentage of frms that perform R&D n the same regon but outsde the correspondng two dgt NACE-93 ndustry (20 ndustres). Percentage of frms that perform R&D n the same regon and the same two dgt NACE-93 ndustry. Current revenues mnus current expendtures derved from the normal actvty of the frm, normalzed by sales (n %). Advertsng ntensty Advertsng expendture normalzed by sales (n %). Foregn captal partcpaton Dummy varable that takes value 1 f more than 25% of the frm shares are foregn owned, and 0 otherwse. Corporate Dummy varable that takes value 1 f the frm s a lmted lablty corporaton, and 0 otherwse. Foregn physcal equpment Frm average percentage of foregn physcal equpment.

15 Manez Castllejo, et al., Internatonal Journal of Appled Economcs, 3(2), September 2006, Table 2: Random effects multvarate probt model for the decson to undertake R&D actvtes. Coeffcents Std. Errors Margnal effects Std. Errors Year ( (0.030) Year (0.107) (0.025) Year (0.107) (0.025) Year (0.109) (0.022) Year (0.109) (0.025) Year (0.116) (0.025) Year ** (0.117) 0.066** (0.031) Year (0.119) (0.030) Year (0.121) (0.028) Economc opportuntes Natonal market (0.088) (0.019) Internatonal and Natonal market (0.107) (0.025) Standardzed product (0.081) (0.017) Export ntensty 0.182*** (0.038) 0.039*** (0.008) Dversfcaton (0.038) (0.008) Market share 0.224*** (0.068) 0.048*** (0.015) Technologcal opportuntes Food and tobacco 0.670*** (0.236) 0.185** (0.078) Beverages 1.707*** (0.339) 0.586*** (0.114) Textles 1.310*** (0.254) 0.416*** (0.095) Leather and shoes 1.619*** (0.277) 0.554*** (0.097) Wood (0.367) (0.121) Paper 0.849*** (0.281) 0.260*** (0.106) Prntng (0.301) (0.092) Chemcal products 2.083*** (0.366) 0.694*** (0.099) Rubber and plastc 2.191*** (0.305) 0.723*** (0.074) Non metallc mner 1.823*** (0.254) 0.610*** (0.082) Metallurgy 2.230*** (0.328) 0.735*** (0.073) Metallc products 1.719*** (0.280) 0.566*** (0.096) Machnery and mech. equp *** (0.328) 0.709*** (0.085) Offce machnes 1.363*** (0.476) 0.464** (0.183) Electronc 2.395*** (0.342) 0.767*** (0.072) Motors and cars 1.878*** (0.336) 0.635*** (0.104) Other transport materal 0.921*** (0.312) 0.287** (0.120) Furnture 0.921** (0.422) 0.285* (0.162) Other manufacturng goods 1.362*** (0.475) 0.460** (0.183) Labor qualty 0.021*** (0.007) 0.005*** (0.001) Frm success Age ** (0.057) ** (0.012) Sze *** (0.100) 0.080*** (0.026) Sze *** (0.141) 0.280*** (0.051) Sze *** (0.156) 0.590*** (0.051) Sze *** (0.158) 0.758*** (0.035) Sze *** (0.205) 0.883*** (0.019) Labor productvty 0.300*** (0.065) 0.064*** (0.014) Market structure Concentraton rato (CR4) (0.001) (0.000) Number compettors (0.076) (0.018) Number compettors > (0.096) (0.023) Approprablty condtons Approprablty (0.006) (0.001) R&D Spllovers Industry spllover (0.006) (0.001) Regonal spllover 0.020*** (0.004) 0.004*** (0.001) Local spllover 0.013*** (0.001) 0.003*** (0.000) Other frm characterstcs Cash flow rato (0.018) (0.004) Advertsng ntensty 0.047*** (0.009) 0.010*** (0.002) Foregn captal partcpaton *** (0.122) *** (0.016) Corporate (0.112) (0.024) Foregn physcal equpment (0.001) (0.000) Intercept *** (0.677) - - Log-lkelhood Number of frms 907 Number of observatons 8025 Hausman test: Corr( α, X ) = 0 (panel probt versus condtonal logt, that s, random effects versus fxed effects). 2 (36) χ = 36.00, p-value = Lkelhood rato test: ρ = Var( α )/ Var ( α + u ) = 0 (panel probt versus pooled level 2 data). χ (1) = , p-value = ***, **, *, ndcate sgnfcance at the 1%, 5% and 10%, respectvely. t