A REVIEW OF CORPORATE R&D INTENSITY DECOMPOSITION

Transcription

1 Internatonal Centre for Innovaton Technology and Educaton A REVIEW OF CORPORATE R&D INTENSITY DECOMPOSITION Author Petro Moncada-Paternò-Castello Unversté lbre de Bruxelles, Cte Solvay Brussels School of Economcs and Management Cte Workng Paper Cte - Internatonal Centre for Innovaton, Technology and Educaton Studes Unversté Lbre de Bruxelles CP114/05 50, avenue F.D. Roosevelt B-1050 Bruxelles Belgum 1

2 A revew of corporate R&D ntensty decomposton Petro Moncada-Paternò-Castello (*) Abstract Research and development (R&D) ndcators are ncreasngly used not only to facltate nternatonal comparsons, but also as targets for polces stmulatng research. An example of such an ndcator s R&D ntensty. The decomposton method of R&D ntensty was conceved wth the am of evaluatng aggregate R&D ntensty and explanng the dfferences n R&D ntensty between countres. For polcy purposes, t s partcularly mportant to determne whether the dfferences are ntrnsc (e.g. due to frms undernvestment n R&D) or structural (e.g. due to dfferences n the sectors that make up an economy). Despte ts mportance for analytcal purposes, the theoretcal and methodologcal framework enablng decomposton of corporate R&D ntensty has been elaborated only recently, and t s stll not commonly used n the lterature. Moreover, examnaton of the R&D ntensty of frms n dfferent ndustres and at dfferent layers of aggregaton leads to mxed results, the reasons for whch are not fully understood. Ths paper ams to revew the theoretcal and methodologcal frameworks of corporate R&D ntensty decomposton and how t s appled n the lterature n order to determne the polcy mplcatons of emprcal results that at frst sght may seem to be contradctory. More specfcally, ths paper surveys the lterature to determne () the theoretcal framework of determnants of corporate R&D ntensty, () the methodologes that have been put n place to decompose corporate R&D ntensty and the emprcal results reached and () the lkely reasons for the contrastng results. Fnally, the paper ponts out the possble polcy mplcatons and suggests some potental avenues for future research n ths area. JEL classfcaton: O30; O32, O38; O57; F23; R39 Keywords: corporate R&D ntensty gap; decomposton; lterature survey; R&D polcy Acknowledgements: The author s partcularly grateful to Mchele Cncera (Unversté Lbre de Bruxelles, Belgum) and Geomna Turlea (ndependent expert for the World Bank and the European Commsson, Romana) as well as to Sara Amoroso, Federco Bag, Andres Brandsma and Fernando Hervás Sorano (all from the European Commsson, Jont Research Centre) for ther hghly valuable and very much apprecated research and edtoral suggestons. Englsh language edtng of the document has been mplemented wth accuracy and dedcaton by Helen MacDonald, Clare Rose and Gllan Whytock (Prepress Projects Ltd, UK). Ths work has been realsed n the framework of IRIMA II, a jont JRC IPTS/DGRTD/A4 project of the European Commsson. Dsclamer: The vews expressed are purely those of the author and may not n any crcumstances be regarded as statng an offcal poston of the European Commsson. (*) Contact Informaton: European Commsson Jont Research Centre (JRC), Insttute for Prospectve Technologcal Studes (IPTS). Edf. Expo, Calle Inca Garclaso No 3, Sevlle, Span. Emal: petro.moncadapaterno-castello@ec.europa.eu 2

3 1. Introducton Research and development (R&D) ntensty ndcators are ncreasngly used not only to facltate nternatonal comparsons, but also as targets for polces stmulatng research. The two goals are, of course, ntmately lnked; t makes lttle sense to set a quanttatve polcy target unless t s known whether t s hgh or low compared wth economes at smlar stages of development. In fact, R&D expendtures have long been an mportant concern for nnovaton analysts, who have used them as proxes for nnovaton nputs and have consdered them to be a determnant of growth, productvty and compettveness. For ths reason, R&D ntensty targets are one of the man objectves of the European Unon s research and nnovaton polcy agenda, namely the Lsbon Strategy, devsed n 2000, and the related Barcelona Target, set n 2003 (whch states that the EU should spend 3% of gross domestc product (GDP) on R&D, two-thrds of whch should come from the prvate sector). A benchmarkng exercse performed at the tme revealed that the EU was not performng at the same level as ts man competng economes, notably the USA and Japan. In the EU, only 1.9% of GDP was beng nvested n R&D, compared wth 2.7% n Japan and 2.98% n the USA; n other words, there was an R&D ntensty gap (European Commsson, 2003). As a result, a target for EU R&D ntensty was set n an effort to close the gap (Sheehan and Wyckoff, 2003). More recently, the mportance of the Barcelona Target has been reterated and renforced n the Europe 2020 strategy, part of the EU flagshp ntatve (European Commsson, 2010a), whch supports an ncrease n prvate research and nnovaton nvestment and puts the emphass on the mportance of polces postvely affectng the demographcs (creaton and growth) of companes operatng n new/knowledge-ntensve ndustres. One of the approaches that has been developed and used by scholars and polcy analysts to nvestgate the EU R&D ntensty gap, and to determne the extent to whch t s attrbutable to dfferences n R&D nvestment between countres, sectors, or even frms, has been the decomposton of the R&D ntensty gap nto ts major economc determnants. Actually, the decomposton methodology for the R&D ntensty gap was orgnally conceved wth the am of evaluatng the extent to whch changes n aggregate R&D ntensty can be explaned by changes n ndustral structure (van Reenen, 1997). Despte the sgnfcance of the analytcal purpose, the theoretcal framework and the methodology needed to decompose countres R&D ntensty have been elaborated only recently, and are stll not extensvely used n lterature. Accordng to Becker and Hall (2013), the lterature on the determnants of R&D nvestment n ndustry sub-groups, as well as on sectoral decomposton of such determnants, s rather lmted. Yet the results of the decomposton studes of corporate R&D ntensty are often contradctory (Moncada-Paternò-Castello et al., 2010). The mcro macro statstcal ssue s a major topc for economc polcy research. In fact, the analyss of mcro-level statstcs allows the evaluaton of the characterstcs of an economc system at the most accurate (untary) scale. Aggregate mcro-level statstcs can, n turn, be partcularly useful for understandng ndustry and macro-level dynamcs, and thus are extremely valuable for polcy desgn, montorng and evaluaton. Despte ths, large-scale applcaton of aggregate mcro-level statstcs s stll lmted, especally n the feld of the knowledge economy, 3

4 for dfferent reasons but mostly because the avalable nformaton s lmted and nhomogeneous owng to measurement problems and some conceptual and methodologcal dfferences (De Panzza and De Prato, 2009; Bjørnskov and Foss, 2016) ( 1 ). The examnaton of frms R&D ntensty n ndustres and at dfferent layers of aggregaton leads to results that are mxed and not completely understood. For polcy purposes, t s partcularly mportant to determne whether the dfferences between countres/regons are ntrnsc, for example due to frms undernvestment n R&D (somethng that can be expected to be relatvely easly changed), or structural, for example attrbutable to the sector composton of an economy (change n whch s lkely to requre more effort and tme). Ths paper ams to contrbute to the lterature by offerng the frst survey of scentfc studes on the decomposton of corporate R&D ntensty. The man queston that ths paper ams to answer s whether the dfferences n corporate R&D ntensty arsng from decomposton studes at country level are sector specfc, frm specfc or data/methodologcal specfc. A second queston that ths research ams to answer s what the polcy sgnfcance s of emprcal results regardng corporate R&D that at frst sght may seem to be contradctory. Ths study wll () survey the lterature on the man determnants of corporate R&D ntensty and the methodologes used to decompose corporate R&D ntensty and ther man emprcal results (secton 2); () dscuss the man fndngs, ncludng the possble reasons for the contrastng results and the mplcatons for the qualty of the comparsons derved (secton 3); and () suggest the relevance of the fndngs for polces and some potental avenues for future research n ths area (secton 4). 2. Lterature revew: theoretcal and methodologcal frameworks emprcal results In ths secton, the theoretcal and emprcal lterature on the man determnants of corporate R&D s ntroduced. The central objectve of nvestgaton of ths paper s then tackled by elaboratng on the concept and purpose of decomposng corporate R&D ntensty, and presentng the emprcal decomposton results from the surveyed lterature on the subject Theoretcal framework of the determnants of corporate R&D ntensty Before presentng the lterature of the man determnants of corporate R&D ntensty, a general theoretcal framework of reference s provded. Economc theory ndcates that knowledge development (Schumpeter, 1949) and techncal change (Solow, 1957) are the major sources of productvty growth n the long term. R&D s a ( 1 ) Accordng to Bjørnskov and Foss (2016), other mcro macro problems are common to many economc studes. These nclude the use of detaled mcro-level data beyond case studes towards meso- and macro-statstcal nterests, the nteracton between macro-level nsttutons and polces and frm-level responses and, n partcular, the potentally complex nteractons between dfferent nsttutons and polces. 4

5 major source of techncal change (Romer, 1990; Guellec and van Pottelsberghe de la Pottere, 2001), and ths s recognsed as a key element for ncreasng the knowledge base and, wth t, the growth, productvty and compettveness of an economy (Mowery and Rosenberg, 1989; Cocca, 2008). In fact, most of the arguments n favour of polces targeted at rasng the level and effcency of R&D rely on the assumpton that there are close lnks between R&D nvestment and mcro- and macro-economc performance (Mtchell, 1999; Blbao-Osoro and Rodríguez-Pose, 2004; Grffth et al., 2004; Kafouros, 2008). The effects of mcro macro convergence of prvate and publc (socal) drvers n the mplementaton and promoton of corporate R&D actvtes are vsble the potental returns not only n productvty, but also n proftablty, sales, market captalsaton, employment growth, compettveness and socoeconomc welfare (see, for example, Morbey and Rethner, 1990; Grlches, 1994; Cncera et al., 2009a; Hall et al., 2010). As regards the frm-level dmenson, the theoretcal framework of determnants of corporate R&D ntensty s graphcally summarsed n Fgure 1, whch llustrates that the total corporate R&D ntensty of a gven economy (country) depends on both the structural (sector) composton effect and ntrnsc effect (Pakes and Schankerman, 1984; Erken, 2008; Matheu and van Pottelsberghe de la Pottere, 2010; Becker and Hall, 2013). We consder that the structural factors affectng an economy can be exogenous or endogenous. Endogenous factors are characterstcs typcal of a gven ndustry sector(s), whle exogenous factors are usually external to the sector(s) and the country s macro-economc system. Intrnsc factors are those that determne the characterstcs of the frm(s) and ts behavour, for example the frm s knowledge, fnancal capacty or strategy and ts R&D nvestment. Fgure 1. Theoretcal framework of determnants of corporate R&D ntensty Source: Author s elaboraton from Pakes and Schankerman (1984), Erken and van Es (2007), Matheu and van Pottelsberghe de la Pottere (2010) and Becker and Hall (2013). 5

6 However, structural endogenous factors are also, at least to some extent, dependent on ntrnsc factors (Erken and van Es, 2007) ( 2 ). In other words, the sectoral structure of a country depends on not only, for example, hstorcal ndustral footprnts, but also (especally) on the country s aggregate capacty to be successful n technologcal development or n competton for technology markets and on ts collectve capacty for R&D-led growth. We should add that structural factors can nfluence frm-ntrnsc factors; for example, although frms access to government fundng for R&D depends on ther strategy and ther ablty (ntrnsc factors) to successfully obtan such fundng, t s condtonal on such publc ncentves beng avalable n the frst place (structural factor). The lterature attemptng to determne reasons for dfferences n R&D nvestment and ntensty between economes s extensve. In the followng sub-sectons, we report the man fndngs from ths lterature, focusng on only three man arguments: () productvty as one of key drvers that lnks structural and ntrnsc factors, () structural endogenous factors and () the ntrnsc factors determnng corporate R&D ntensty. ) Productvty as one of the man mcro macro drvers for corporate R&D actvty The lterature suggests that a vrtuous crcle exsts, whereby compettveness promotes R&D and technologcal development, leadng to productvty gans, whch n turn ncreases proftablty, whch then releases resources that can be used to nvest n (more) R&D. Essentally, n accordance wth endogenous Schumpeteran growth theory, productvty growth s postvely nfluenced by R&D expendture (Schumpeter, 1949; Grlches, 1994; Zacharads, 2003; Guellec and Sachwald, 2008). As Matheu and van Pottelsberghe de la Pottere (2010) put t, the underpnnng concept s that frms return on R&D nvestment can be acheved through a hgher level of productvty as a result of an accelerated rate of technologcal change. The ncreased effectveness (due to hgher productvty) of R&D nvestment (or effectve rate of return to R&D), together wth a hgher propensty to nvest n R&D, allows for greater compettveness of frms and of the economy as a whole. Therefore, heterogenety of both sectors and frms should be taken nto account as ths explans the substantal dfferences n the rate of productvty return to R&D nvestment (Cncera et al., 2009b, Ortega-Arglés et al., 2010; Montresor and Vezzan, 2015). In practce, mcro- and macro-productvty returns to R&D (lke the other drvers mentoned prevously, such as proftablty, growth, etc.) enable the possble convergence of objectves of the ntrnsc and structural factors. Unfortunately, the EU faces a productvty gap compared wth ts man compettors, and ths has wdened snce the fnancal economc crss that started n 2007, as can be seen n Fgure 1, whch shows productvty over the perod measured as GDP per capta. Fgure 1 shows average productvty among the EU-28 countres, and thus does not dsguses dssmlartes n the degree of development of dfferent EU countres. Ths dssmlarty can be seen n Fgure 2, whch reports productvty (as GDP per capta) and R&D efforts/ntenstes (R&D expendture as a percentage of GDP) n the dfferent EU-28 countres n ( 2 ) For more nformaton on ths relatonshp, see Erken and Donselaar (2006). 6

7 Fgure 1. GDP per capta n the EU-28 and selected countres n Source: author s own elaboraton of data from OECD (2015) ( 3 ). Note: data on the y-axs are n US dollars, constant prces, 2005 purchasng power partes. Fgure 2. GDP per capta and R&D ntensty n EU-28 countres n 2013 Source: author s own elaboraton of data from European Commsson (Eurostat) (2015) ( 4 ). Notes: EU-28 average productvty = 1 (ndex of reference); based on purchasng power standards per capta. The EU-28 GDP per capta n 2013 at current prces was EUR 26,600. The EU-28 R&D ntensty (gross domestc expendture on R&D as a proporton of GDP) was 1.98%. For a better graphcal representaton, data for Luxembourg (GDP per capta ndex = 2.57; R&D ntensty = 1.16%) are not plotted n the fgure. ( 3 ) Data extracted on 6 March 2015 from ( 4 ) Data extracted on 8 March 2016 from and 7

8 It should be remembered that a frm s R&D nvestment can be ether pro-cyclcal or countercyclcal and that R&D nvestment also depends on a frm s busness cycle and ther busness characterstcs (Vogt and Moncada-Paternò-Castello, 2009; Arvants and Woerter, 2014). Stephan (2004) stressed that the hgh-tech frms usually adjust ther R&D expendtures less to the busness cycle n contrast to low- and medum tech ones. These are mcro-level factors that, when analysed at the aggregate (macro-) level, could make between-country comparsons more dffcult. ) Sector composton (or structural) factors Industres are charactersed by, among other thngs, very dfferent levels of R&D nvestment relatve to ther output, and t should be noted that, n the absence of country-specfc dfferences, dfferences n aggregate R&D ntenstes between countres reflect the mx of ndustres n partcular countres (Moncada-Paternò-Castello and Smth, 2009). The advanced economes of the world may be smlar n terms of basc economc ndcators (e.g. ncome levels), but they dffer sgnfcantly n terms of ther technologcal specalsatons and hence ndustral structures. These ndustral structures, whch nfluence aggregate corporate R&D ntensty, can be affected by exogenous factors, such as the economc and fnancal shocks caused by global events, for example a fnancal downturn, a global ol crss or a war. The theoretcal bass for the effects of ndustry composton and sector characterstcs (.e. the endogenous structural effects) on the aggregate corporate R&D ntensty of a gven economy gves a clue as to why these nter-ndustry dfferences occur. Pakes and Schankerman (1984), based on the theoretcal work of other authors (e.g. Schumpeter, 1950; Grlches and Schmookler, 1963; Scherer, 1982), whle argung that the output of research actvtes (ndustral knowledge) exhbts unque economc characterstcs, developed a theoretcal model ndcatng that R&D ntensty depends on the combnaton of three factors: expected market sze and growth n demand, approprablty dfferences and technologcal opportuntes. Takng stock of ths theoretcal lterature and complementng t wth other studes (namely Erken and van Es, 2007; Matheu and van Pottelsberghe de la Pottere, 2010; Becker and Hall, 2013), we classfy the endogenous structural factors as market factors, technologcal opportuntes factors and ndustry and approprablty factors. These factors are nterlnked. a) Market factors refer, n partcular, to the compettveness level, the expected sze of the market and/or the demand nducement, the level of hgher educaton and the degree of labour market moblty (Pakes and Schankerman, 1984; Saxenan, 1996; Lundvall and Borras, 2005; van Pottelsberghe, 2008; Matheu and van Pottelsberghe, 2010; Aghon et al., 2014). b) Technologcal opportuntes are based the avalablty and the cost (effcency) of producng scentfc and techncal knowledge n dfferent areas or ndustral sectors. These factors also nclude the sze and the homogenety of the market for new technologes, for example the patent system (Foray and Lhullery, 2010; de Sant-Georges and van Pottelsberghe de la Pottere, 2013). These authors argue that the key functon of knowledge dssemnaton/adopton s to enable frms to rely on effcent R&D and nnovaton economc systems. 8

9 c) Industral and approprablty factors nclude hstorcal track record, sector captal specfcty, ndustral market structure, the level of ndustry unversty collaboraton, creatve destructon and entrepreneural ablty (success) and the general nsttutonal framework (e.g. ndustral polcy, publc R&D expendtures and nfrastructures) n whch frms operate. Abundant emprcal studes (Cohen and Lorenz, 2000; Aghon, 2006; van Pottelsberghe, 2008; Veugelers, 2015) have dentfed varous other structural factors that contrbute to countres R&D levels. ) Intrnsc factors The theoretcal foundaton of corporate R&D ntensty dfferences, whch s determned by a frm s own levels of R&D nvestment and sales (ntrnsc effects), fnds a sold anchorage n the Schumpeteran arguments that R&D ntensty dfferences very much depend on the avalablty of nternal resources, access to external sources and hgh levels of product market competton on nnovaton (Aghon and Howtt, 2006). Becker and Hall (2013) suggest fve types of key ntrnsc determnants of prvate-sector R&D expendtures: frm-/ndustry-specfc economc and fnancal factors, product market competton, publc polces, locaton and endowment, and the presence of foregn R&D. (1) Frm-/ndustry-specfc factors The theoretcal explanaton underpnnng frms motvaton to nvest n R&D s centred on the expected (postve) return. Among the key nvestment sources and determnng factors of such nvestment are cash flow and sales, especally when frms have dffculty relyng on external funds; these arguments have a sold theoretcal Schumpeteran foundaton (Aghon and Howtt, 2006). We can therefore dstngush two man specfc factors: the benefts from R&D and the costs of R&D. As regards the former, there s a rch lterature ndcatng a postve correlaton between R&D nvestment and a company s sale growth (Morbey and Rethner, 1990), whle other studes have shown a strong lnk between R&D nvestment and productvty (see lterature cted n Productvty as one of the man mcro macro drvers for corporate R&D actvty ). More recent studes have found that the potental for ncreased proftablty as a result of R&D nvestment s a key factor determnng a frm s prvate R&D nvestment (Hall et al., 2010). As far as the cost of R&D s concerned, and n partcular the ablty of frms to access sources of fnance, demographcs play a relevant role, snce access to fnance probably depends upon a frm s age and sze. The emprcal results of the effect of cash flow on R&D nvestment are mxed. Most studes report a sgnfcant postve effect (e.g. Hall et al., 1998; Cohen, 2010; Cncera and Ravet, 2010), especally n the case of more technology-ntensve and/or smaller frms (Cncera et al., 2015), but some authors report nsgnfcant effects (e.g. Harhoff, 2000; Bond et al., 2003). However, the effect of sales on R&D nvestment s lkely to be postve (van Reenen, 2007; Borsova and Brown, 2013). Ortega-Arglés and Brandsma (2010), Cncera and Veugelers (2013) and Stanck and Bag (2015) found that the sze of R&D-ntensve frms plays a role n explanng the overall R&D ntensty gap between the EU and the USA. In both economes, R&D ntenstes tend to be hgher n smaller frms, but the effect s more sgnfcant n the USA than n the EU. One reason for the hgh R&D ntensty n the USA s the large number of small and medum-szed enterprses (SMEs) operatng n strongly performng R&D sectors, notably those concerned wth nformaton and communcatons technology (ICT). These results are, n part, confrmed by a recent study by Moncada-Paternò-Castello (2016b) showng 9

10 that the age dstrbuton of top R&D nvestors s strongly related to the sector (and technology) n whch these frms operate. In summary, age and sze wll affect the net prvate return to R&D but are not drvers of R&D per se. (2) Product market competton Ths has already been dentfed n Schumpeteran growth theory (Aghon and Howtt, 2006) as a factor havng possble mxed effects on R&D nvestment. In fact, a hgh level of market competton may undermne ncumbent frms ncentve to nnovate because these frms are less effcent n explotng nnovaton nvestment (Romer, 1994; Acs et al., 2009). In contrast, other streams of emprcal lterature (Gerosk, 1990; Damanpour, 2010; Ayyagar et al., 2012) have found market competton to a postve effect have on nnovaton, because frms use R&D as a strategc nvestment to combat or prevent competton. In addton, Aghon et al. (2002) found that the relatonshp between product market competton and nnovaton forms an nverted U- shape: the escape competton effect domnates at low ntal levels of competton, whereas the Schumpeteran effect domnates at hgher levels of competton. A possble explanaton of these controversal results s provded by Wu (2012) and Kubck et al. (2014). They argue that a low level of competton, attrbutable to a small number of large ncumbent frms and hgh barrers to access facng new entrants, provdes lttle ncentve to nvest n R&D. The greater the access and the less dfferentated the product, the greater s the ncentve to acheve an advantage through R&D. On the other hand, n hghly compettve markets, the tme for nnovaton s short and the potental gans from R&D could be small and hghly uncertan. The stuaton s dfferent for every country and sector, and such structural dfferences need to be taken nto account before assertng that any defcences n terms of R&D are ntrnsc to the country. (3) Access to publc polcy support Tax credts and drect subsdes for R&D have postve effects on frms R&D nvestment, but they also brng the threat of crowdng-out/substtuton effects (Bloom et al., 2002; Guellec and van Pottelsberghe de la Pottere, 2005; Hall et al., 2016). (4) Frm locaton Frm locaton s an mportant factor as a frm s R&D nvestment ncreases wth ts proxmty to unverstes and a sklled labour force (Vvarell, 2013; Capello, 2014; Amoroso et al., 2015). A pror, one would expect the economc structure of a partcular country to be less mportant to nvestment n R&D than ntrnsc qualtes such as natonal ncentves (e.g. taxes, grants). Yet the annual surveys of EU R&D Scoreboard companes, conducted snce 2005, clearly ndcate that, as reported n Moncada-Paternò-Castello et al. (2011) and Cncera et al. (2012), for these companes, the prncpal factors nfluencng R&D nvestment are, n order of mportance, (a) access to specalsed R&D knowledge, (b) the avalablty of researchers and (c) proxmty to other company actvtes (e.g. producton). In addton, the survey results show that top R&D nvestors man reasons for locatng R&D n Chna and Inda are market sze and growth, together wth the avalablty of R&D personnel. (Tübke et al., 2015). 10

11 (5) Presence of foregn R&D Studes of the role of foregn R&D as drver of domestc R&D nvestment show mxed results. For example, Gorg and Greenaway (2003), Moncada-Paternò-Castello et al. (2011) and D Agostno and Santangelo (2012) have suggested that domestc R&D and nnovatve actvty can be augmented by competton because ths leads to knowledge spllovers from foregn frms. However, the same authors argue that greater competton reduces the propensty of domestc frms to nvest n R&D nvestment because return on nvestment, n terms of proftablty, s expected to be lower. Therefore, both the ntrnsc and structural components of corporate R&D ntensty n a gven economy are determned by a number of factors that could be macro or mcro n nature or orgn. In fact, t should not be forgotten that structural dfferences are the result of decsons by ndvdual frms over a long perod of tme. Ther performance and strategy may be nfluenced by government polcy, but the focus on ntrnsc factors may also remove the man mpedments to corporate R&D Concept and purpose of decomposng corporate R&D ntensty The lterature comparng prvate-sector R&D ntensty n competng economes n dfferent countres or regons of the world (e.g. EU vs. USA), and the varous factors that nfluence t, s extensve. Much of the scentfc effort devoted to studyng ths phenomenon seems to address one man ssue: whether the R&D ntensty dfferences between countres are the result of companes dfferent behavour n R&D (ntrnsc effect) or are manly due to the structure of the economy (structural effect). In other words, the queston s: are dfferences n overall R&D ntensty due to dfferences n the nvestment behavour of the companes wthn a partcular country, compared wth smlarly postoned companes n other countres, or do they smply reflect dfferences n the structure of the economy that cannot be remeded n the short term? Thus, the methodology for decomposng the R&D ntensty gap has been conceved to evaluate the extent to whch changes n aggregate R&D ntensty can be explaned by a change n ndustral structure or by a change n R&D ntensty of a gven ndustry, and also for benchmarkng purposes. In one of the semnal works to analyse corporate R&D ntensty, van Reenen (1997) defned decomposton as a straghtforward accountng exercse. Box 1 reports three examples of basc equatons frequently used n the decomposton of corporate R&D ntensty, whle Table A1 n the Annex reports the full lst and the further detals of man methodologcal approach ncludng man formulas and data sources used, the countes/regons compared, and the man results of 15 recent studes on the decomposton of prvate R&D ntensty. 11

12 Box 1. Examples of basc equatons frequently used when decomposng R&D ntensty r total = r s + r s where R&D ntensty r s the proporton of value added devoted to R&D (R&D /VA I) and s s each ndustry s share of total value added (VA / VA ) for = N ndustres. The bars denote a tme mean (average over T years). The values are changes over tme (for T years) Source: van Reenen (1997) RDI X - RDI Y RDI Z, ( PX, PY, ) P X, ( RDI X, RDI y, ) where RDI represents the extent of prvate R&D ntensty (R&D/VA) and P s the share of the value added, ndcates the sector, X stands for the country/regon X and Y represents the countres/regons wth whch country X s compared. Source: Erken and van Es (2007) RDI y RDI o = RDI (w y w o ) + w (RDI y RDI o ) where RDI s R&D ntensty, defned as R&D nvestments dvded by net sales. Superscrpts y and o denote, respectvely, "yolles" (young leadng nnovators) and "olles" (old leadng nnovators), subscrpt denotes ndustry, w y denotes the share of the sector accounted for by the total number of young frms and w o denotes the share of the sector account for by total number of old frms. Source: Cncera and Veugelers (2013) 2.3 Decomposton of corporate R&D ntensty: emprcal fndngs The dvergent fndngs n the lterature concernng the causes of the R&D ntensty gap between EU and US companes suggest that cauton should be exercsed when drawng general conclusons based on ndvdual studes (Moncada-Paternò-Castello, 2010). There s one group of researchers (e.g. Dos, 1997; Panta, 2005) who are more nclned to consder that the EU R&D defct s generally the result of companes undernvestment n R&D (ntrnsc effect). For example, more recently, Erken and van Es (2007) examned the dfferences n busness R&D between 14 EU countres and the USA n 36 sectors over a 17- year perod usng OECD-STAN ( 5 ) and ANBERD ( 6 ) data. They concluded that the contrbuton of sector composton to the R&D fundng gap between the EU and the USA was very low, whereas the ntrnsc effect was undoubtedly responsble for the prvate R&D gap. They also argued that, f only manufacturng sectors are taken nto account, corporate R&D ntensty does not dffer much between the USA and the EU. They suggest that the R&D gap s due manly to nsttutonal dfferences, ncludng, for example, a lower level of government support for research actvtes n the EU. ( 5 ) OECD stands for Organsaton for Economc Co-operaton and Development; STAN stands for STructural ANalyss Database. ( 6 ) ANBERD stands for Analytcal Busness Enterprse Research and Development database. 12

13 In contrast, other researchers have concluded that the gap s manly due to the structure of the economy (.e. sectoral composton or structural effect). Ths s true of one of the frst emprcal studes (Scherer, 1967), whch demonstrated that most R&D ntensty can be explaned by ndustry fxed effects. Later work by Cohen et al. (1987) showed that the sector n whch frms operate accounts for half of the R&D ntensty dfferences across frms. More recently, Ab Iorwerth (2005) undertook a detaled decomposton ( 7 ) of dfferences between Canadan and US R&D ntenstes across ndustres. He used the OECD-STAN database for ndustral analyss and the OECD Research and Development Expendture n Industry database and found that Canada s low aggregate R&D performance hdes hgh research ntenstes n some researchntensve ndustres. Nonetheless, the results also ndcated that the smaller relatve sze of these ndustres together wth the low R&D ntenstes n motor vehcle and servce ndustres accounted for the weak aggregate performance n Canada compared wth the USA. Lkewse, for the EU versus US comparson, Cupagea and Moncada-Paternò-Castello (2006), O Sullvan (2007) and Guellec and Sachwald (2008) suggest that the European prvate R&D nvestment defct s manly due to a sectoral composton effect. These authors found that the R&D ntensty dfference could be attrbuted to the fact that the ICT sector s smaller n the EU than n the USA. In fact, n the EU the ICT sector accounts for a relatvely much smaller proporton of overall busness expendture on R&D than t does n the USA. Ths concluson confrms the fndngs of GFII (2007), of the European Commsson (2007, 2008), and of Moncada-Paternò-Castello et al. (2010) and Cncera and Veugelers (2013), who based ther analyses on samples from the EU R&D Scoreboard data. Moncada-Paternò- Castello et al. (2010) found that the structural effect accounted for 85% of the gap between the EU and the USA, wth only 15% beng attrbutable to the ntrnsc effect ( 8 ). Moncada-Paternò-Castello et al. (2010) also analysed the dstrbuton of R&D among the top R&D-ntensve frms and found that n the EU R&D nvestment s concentrated n a relatvely smaller number of frms operatng n sectors that are generally of lower R&D ntensty than the USA. Cncera and Veugelers (2013) nvestgated the role of the older and younger frms n the corporate R&D ntensty gap between the EU and the USA and found that 55% of the EU gap s accounted for by greater R&D ntensty n younger US frms, and ths s almost entrely due to the dfferent sectoral composton n the two economes. Furthermore, Stančk and Bag (2015), who used EU R&D Scoreboard data ( ) to decompose the R&D ntensty gap, found that R&D ntensty s lower n the EU than n the USA, Japan or the Asan Tger countres, but hgher than n the BRIC countres (Brazl, Russa, Inda, Chna). The authors concluded that the former fndng can be attrbuted to structural effects, whereas the latter s the consequence of both hgher R&D ntensty wthn sectors and sectoral composton. Focusng on the R&D ntensty gap between the EU and the USA and usng frmlevel data, These authors also found that there s strong between-sector varaton and some evdence of wthn-sector varaton, although not always n favour of the USA. ( 7 ) He used the Bennet decomposton followng Dewert (2005). ( 8 ) A complete dscusson of these aspects s offered by Moncada-Paternò-Castello (2016a). 13

14 Several studes carred out n the last decades ndcate that economc and technologcal specalsaton s one of the man factors underpnnng the EU R&D nvestment gap. For example, some have nvestgated the reasons for the commonly observed pattern that R&D nvestments n Europe as a whole are generally lower than n the USA. Although Pavtt and Soete (1982) found that one of the man factors underpnnng ths phenomenon was the hgh degree of nternatonal specalsaton n ndvdual EU Member States, a more recent study found that technologcal capabltes n the EU showed a tendency towards convergence between 1998 and 2008 compared wth the USA (Fagerberg et al., 2014). These results ndcate that socal capabltes, such as a well-developed publc knowledge nfrastructure, condton the growth of technologcal capabltes. Moncada-Paternò-Castello (2016b) complemented these fndng by suggestng that the EU frms are less able than USA companes to create hgh-tech sectors or jon them quckly, and, therefore, to fully explot the growth opportuntes offered by frst mover advantages. Van Ark et al. (2003) observed that, n the USA, expendture on R&D outsde the manufacturng sector has been ncreasng snce the md-1990s and now accounts for about one-thrd of total R&D expendture, up from less than one-ffth n So, although the manufacturng sector stll accounts for the majorty of R&D expendture, ts share s declnng. These authors also note that growth n servces R&D has been slower n Europe than n the USA, and has stll not reached 20% of total R&D. At least part of ths gap s probably explcable by the fact that ICT dffuson has been slower n Europe than the USA. Matheu and Van Pottelsberghe de la Pottere (2010) lmt ther analyss of R&D ntensty to 20 manufacturng sectors, concludng that BERD ( 9 ) ntensty s manly drven by the degree of specalsaton n R&D-ntensve ndustres. Ths fndng supports the argument that a sectoral composton effect s the cause of the low EU R&D ntensty. Ths study focused on 10 European Member States and consdered a range of data that covered the perod from 1991 to The fndngs suggest that specalsaton n sectors of hgh R&D ntensty s the reason why R&D ntensty s hgher n some of the EU Member States than n others. More recently, Renstaller and Unterlass (2012), usng BERD panel data, analysed the development of R&D ntensty n the EU-27 countres and some other relevant non-eu countres over the perod They found that changes n aggregate BERD fgures were drven by structural changes and by changes wthn same sector wth rather dfferent speed of changes dependng on countres and sectors. Gumbau-Albert and Maudos (2013) used the EU-KLEMS 10 database to calculate R&D captal stock (rather than R&D expendtures) wth the am of nvestgatng dfferences n the technologcal captal ntensty of varous ndustres n the EU-11 countres and the USA. They found a technologcal gap n favour of the USA untl the md-1990s because of the greater accumulaton of technologcal captal n most of the productve sectors consdered. However, from 1995 onwards a change n productve specalsaton occurred: a sgnfcant drop n the ( 9 ) BERD stands for Busness Enterprse Expendture on R&D. ( 10 ) EU KLEMS stands for EU level analyss of captal (K), labour (L), energy (E), materals (M) and servce (S) nputs 14

15 relatve mportance of lower technology-ntensve ndustres n the EU-11 economy was accompaned by a sgnfcant drop n the relatve mportance of some medum technologyntensve ndustres n the USA, leadng to a reducton n the technologcal gap between the EU and the USA. Gumbau-Albert and Maudos (2013) also found that dfferences n the productve structure of European countres explan most of the dfferences n technologcal captal ntensty. Another recent decomposton analyss (Foster-McGregor et al., 2013) found that dfferences n the R&D ntensty (defned as the expendture of manufacturng frms on R&D relatve to manufacturng value added) of manufacturng frms n seven EU Member States and the USA and Japan are manly drven by the ntensty effect. Industry structure (composton effect) plays a role n some EU Member States but s never the prmary factor. However, the authors suggest that the relatve mportance of the composton effect and the ntensty effect n a decomposton exercse depends on the level of aggregaton of the ndustres, and they recognse that a more detaled ndustry breakdown would assgn greater mportance to the composton effect, assumng that companes n the same sub-sector are closer n terms of R&D ntensty. Other studes fnd some clear path of mxed (ntrnsc together wth structural) effects. A recent study by Beltz et al. (2015) based on OECD data at two-dgt level analysed the dfference between prvate-sector R&D ntensty n Germany and a selecton of OECD countres. These authors found that the structural effect and the behavoural (ntrnsc) effect play more or less equally mportant roles n explanng the dfferences between Germany and other OECD countres wth regard to prvate-sector R&D ntensty. Furthermore, they found that, although Germany often suffers from the behavoural effect, at the same tme t usually benefts from the structural effect; both effects are strongly drven by a few partcularly research-ntensve ndustres. Another nterestng paper comes from Lndmark et al. (2010), who compared two data sets EU R&D Scoreboard mcro-data and BERD statstcs to decompose EU and US R&D ntenstes. They concluded that about half of the overall R&D gap between the EU and the USA les n the ICT sector. In turn, ths ICT R&D gap has two facets. BERD data suggest that the gap s largely ntrnsc: R&D ntensty s lower n the EU than n the USA n several subsectors, even though ICT sector sze and composton are qute smlar. In contrast, company data from the EU R&D Scoreboard suggest that the gap s nstead structural: the sector sze and composton of sub-sectors dffer greatly, whereas R&D ntensty s smlar ( 11 ). In ths context, t should be emphassed that the hgh-tech sectors are mportant not only because companes n them nvest at a hgher R&D ntensty but also because, n such sectors, the lnk between R&D and productvty s greater and more sgnfcant (Ortega-Arglés and Brandsma, 2010). Nonetheless, Janger et al. (2011) decomposng R&D ntensty at EU country level, found that some countres specalse n knowledge-ntensve structures, but some other countres, despte focusng on less knowledge-ntensve structures, present hgh R&D ntenstes. Table A1 n the Annex summarses the results of most recent studes on the decomposton of prvate R&D ntensty. ( 11 ) Another reason could be that the top R&D nvestors are just more smlar, even f they are n dfferent subsector classfcatons. 15

16 3. Dscusson The lterature survey reported above descrbes clearly contradctory results. But why are the analyses of the EU R&D gap reported n the lterature controversal? The contradctory fndngs regardng the causes of the R&D ntensty gap between companes n the EU and the USA or other competng countres suggest that some methodologcal problems make t dffcult to converge on generally accepted measures of structural and ntrnsc effects. The decomposton of the R&D defct nto these two components has been shown to be hghly senstve to the level of data specfctes. More mportantly, n the case of studes consderng both manufacturng and servce sectors, the results lack robustness because of the wdely recognsed problems n comparng servce sector R&D data between, for example, the USA and the EU, whch are subject to very dfferent statstcal norms (Erken and van Es, 2007; Duchêne et al., 2010). Therefore, the results of dfferent studes seem to be hghly senstve to the level of detal at whch ndustres are compared (Jaumotte and Pan, 2005), on whether or not servce sectors are taken nto consderaton together wth manufacturng (Erken and van Es, 2007) and on the data used and methodologes adopted (Panta, 2005). Ths would suggest that t s worthwhle explorng a dfferent methodologcal approach: perhaps the parwse comparson of company performances n dfferent countres s the way to go. Moncada-Paternò-Castello et al. (2010) argue that the conclusons of these studes cannot necessarly be appled to all countres and all economes because of the possble heterogenety n R&D ntenstes and ndustral structures: the ntrnsc effect may domnate n some countres whereas the structural one domnates n others. Accordng to Lndmark et al. (2010), one factor that could explan the contradctory decomposton results s nternatonal flows of R&D and value added: companes tend to allocate a larger share of ther value added and a smaller share of R&D outsde ther home markets. In sub-sectors wth a large number of large US companes, these flows are unbalanced, and (BERD) R&D ntenstes are thus hgher n the USA than n Europe, all else beng equal. Smlar results were obtaned by Hernandez et al. (2013), who nvestgated the EU-US R&D gap by analysng BERD statstcs (natonal ntramural busness expendtures n R&D) and EU R&D Scoreboard data. They found that, based on EU R&D Scoreboard data, the R&D ntensty performance of ndvdual EU-based companes s smlar to that of ther US counterparts because of the constrants mposed by global competton. However, accordng to the natonal statstcs, ndustral actvtes located wthn the boundares of the EU are much less R&D ntensve than those located wthn the boundares of the USA, especally n key hgh-tech sectors (e.g. ICT). The EU R&D Scoreboard data capture the R&D nvested by EU or US companes all over the world, whereas BERD statstcs relate to natonal and foregn companes that perform R&D n the boundares of the gven terrtory (see Box 2 for more nformaton on such dfferences). Therefore, the authors argue, the ndustral (producton and R&D) actvtes of foregn-controlled companes play a pvotal role n the dscrepant results obtaned usng these two datasets. There are other cases of dscrepances n the calculaton of busness R&D ntenstes dependng on the approach adopted. For example, followng one of the frst examples by the French Mnstry for educaton and research (Le Ru, 2012), t s only n recent edtons of the Scence, 16

17 Technology and Industry Scoreboard (OECD, 2015) that the OECD has recognsed the role of structural dfferences between countres n the calculaton and comparson of ther R&D ntenstes, and overcome t by adjustng the R&D ntensty usng the OECD ndustral structure the sectoral share of OECD value added for the gven year (2013) as adjusted, common weghts across all countres. Instead, the unadjusted measure of BERD ntensty s an average based on each country s actual sector shares. The dfferent results between the two measurements of R&D ntensty are shown n Fgure 3. Fgure 3: Busness R&D ntensty (%) n OECD countres adjusted for ndustral structure, ,0 4,5 4,0 3,5 3,0 2,5 2,0 1,5 1,0 0,5 0,0 Unadjusted busness R&D ntensty Adjusted busness R&D ntensty Source: OECD Scence, Technology and Industry Scoreboard 2015 (OECD, 2015). As mentoned earler, one of the most mportant causes of ths apparent dscrepancy n corporate R&D ntensty decomposton, accordng to the lterature, s the nature of the data used, and especally the way n whch data are collected. To gve more explcatve nsdes on data dfferences, Table 1 summarses the statstcal features of data sources most frequently used n used n analyses of EU corporate R&D ntensty decomposton. There are several studes that exhaustvely dscuss the detaled statstcal dfferences between data, rangng from the defntons of R&D to the methodologes to collect the nformaton. As ths survey artcle s focused on the decomposton of R&D, we remt to such lterature for both the statstcal explanaton of dfferent data and the estmaton of the extent to whch these dfferences affect the qualty of any comparson. Examples of authors who have nvestgated the statstcal characterstcs n depth nclude Pot et al. (2007), who compared BERD and the Communty Innovaton Survey (CIS), and O Mahony and Tmmer (2009), who compared the EU-KLEMS wth the CIS. Azagra Caro and Grablowtz (2008) nvestgated the dfferences between BERD and the EU R&D Scoreboard, whle Cozza (2010) complemented natonal statstcal data on busness R&D wth EU R&D Scoreboard data. 17

18 Table 1. Summary of descrpton of data sources most frequently used n EU corporate R&D ntensty decomposton. Characterstc EU R&D Scoreboard BERD ANBERD CIS EU-KLEMS Monetary flows All R&D fnanced by a partcular company from ts own funds, regardless of where that R&D actvty s performed All R&D expendtures by those parts of companes located wthn the EU, regardless of where the funds for that R&D actvty come from As BERD database but for mssng data ncludes a number of estmatons As BERD database, plus ncludes a number of estmatons R&D nvestments are consdered as captal stock (and not as expendture) and are ncorporated n Gross Fxed Captal Formaton ( a ); R&D s specfcally consdered to be the producton of an asset Sample Top R&D-nvestng companes Statstcal Unt Companes: subsdares counted wthn the Data collecton frameworks Geographcal area Data category Economc sectors consoldated group; R&D systematcally attrbuted to the regstered offces Internatonal Accountng Standard (IAS) 38 World Audted company account data companes above a mnmum R&D threshold Internatonal Classfcaton Benchmark (ICB) A stratfed sample, coverng all large companes and a representatve sample of smaller companes wth no sze threshold Busness enterprses subsdares are counted separately; R&D s attrbuted to headquarters or regstered offces. Statstcs for enterprses are compled at natonal level and for local unts at regonal statstcs level (NUTS 2 level) Completes BERD wth nformaton from natonal statstcal offces and wth estmatons and sector reclassfcatons for nternatonally comparable data As BERD, but confned to selected ndustres and to frms wth 10 employees or more As BERD Busness establshments. The survey s carred out at the enterprse level. Frms that organse busness actvtes nto separate unts can be sampled several tmes Lke ANBERD (STAN), ths uses addtonal sources such as natonal accounts, ndustry surveys, labour force surveys and captal formaton surveys At detaled ndustry level per country but also provdes hgherlevel aggregates (e.g. total economy, total market, servces and total goods producton) Frascat Manual Frascat Manual Oslo Manual System of Natonal Accounts (2008 SNA) EU Member States and canddate Countres, EFTA Countres, Russan Federaton, Chna, Japan, Unted States R&D statstcs va surveys of sampled companes sent by natonal statstcal offces Statstcal classfcaton of economc actvtes (NACE) revson 2 34 OECD countres and sx non-member economes (Chna, Romana, Russa Federaton, Sngapore, South Afrca, Tape) R&D statstcs obtaned from surveys of sample companes plus a number of estmatons Internatonal Standard Industral Classfcaton (ISIC) revson 4 EU-28 Member States Innovaton statstcs surveys of sampled frms sent by natonal statstcal offces Statstcal classfcaton of economc actvtes (NACE) revson 2 25 EU countres, as well as Australa, Japan and the US Extends ANBERD (STAN) wth data from natonal accounts Internatonal Standard Industral Classfcaton (ISIC) revson 4 (a) A flow value, defned as the total value of a producer s acqustons, less dsposals of fxed assets. Source: Own elaboraton from OECD (2002, 2012), European Commsson (1997, 2007, 2008, 2016a, 2016b), Azagra Caro et al. (2008) and O Mahony and Tmmer (2009) 18

19 These studes suggest that nternatonal comparson at sector and mcro-level s not always possble ( 12 ) because of often deep methodologcal dfferences, but dfferent sources frequently brng an extremely useful complementarty of nformaton. In fact, there are recent promsng works that not only try to use dfferent sources of prvate sector R&D data, but also combne them wth addtonal datasets to brng a prevously mssng dmenson to the economc and polcy analyses of nnovaton. To gve a few examples, Derns et al. (2015) combned EU R&D Scoreboard wth patent data to dsentangle the technologcal profle of frms R&D nvestment, whle Alstadsæter et al. (2015) looked nto the effects of top corporate R&D ncome taxaton from the tax advantage of patent boxes. Amoroso et al. (2015) combned EU R&D Scoreboard data wth the fd Markets database ( 13 ) to assess the ablty of labour markets to attract knowledge-ntensve and manufacturng greenfeld FDI. Other authors combned mcro-data from BERD (among others) wth those from the EU s R&D Framework Programme to dsentangle the delocalsaton patterns n unversty ndustry nteracton (Azagra Caro et al., 2013), whereas Crac et al. (2015) matched ANBERD 14 data wth patent data from the Worldwde Patent Statstcal Database (PATSTAT) and the OECD Patent Qualty Indcators databases to estmate the nnovaton mpact of the vertcal ntegraton of knowledge-ntensve busness servces (KIBS) nto manufacturng ndustres. It worth mentonng that there are new, ambtous and promsng nsttutonal ntatves, such as the framework regulaton ntegratng busness statstcs FRBS (European Commsson, 2016a) whch am to harmonse statstcs, establshng a common legal framework for the systematc collecton, complaton and dssemnaton of European busness statstcs. A few more ponts about the use the R&D ntensty as a statstcal ndcator need to be made. As we have seen, aggregate R&D ntensty ndcator s affected not only by the ndustral structure, but also by characterstcs (demographcs, busness cycle) of the pool of frms that make up that structure, and by other structural factors and ntrnsc factors, as seen n secton 2.1. It s worth rememberng that, despte polcy targets and the related soco-economc objectves, companes should not be tempted to overnvest n R&D, that s to nvest more than ther man compettors. Indvdual companes may lose compettveness f they nvest below the sector average, but t s by no means clear that there are postve returns for any nvestment above the sector average, especally n the short term. The defnton of R&D ntensty as an ndcator of country or company performance s another mportant aspect to menton. Frst of all, the numerators and denomnators could be dfferent n nature. For example, the numerator s ether frms R&D nvestment or busness enterprse ( 12 ) They argue that, for example, the dstncton between natonal and foregn nvestment wthn the extramural R&D category, or of the actual R&D expendtures of multnatonals nvestment, would allow for a much better demographc dstrbuton of data. ( 13 ) fd Markets s an on-lne database mantaned by fd Intellgence, a dvson of the Fnancal Tmes Ltd. fd Intellgence collects avalable nformaton on nvestments snce 2003 and montors cross-border nvestments coverng all sectors and countres worldwde, relyng on company data and meda sources. ( 14 ) Is the " Analytcal Busness Enterprse Research and Development" database of the OECD 19