NBER WORKING PAPER SERIES INNOVATION AND PRODUCTIVITY ACROSS FOUR EUROPEAN COUNTRIES. Rachel Griffith Elena Huergo Jacques Mairesse Bettina Peters

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1 NBER WORKING PAPER SERIES INNOVATION AND PRODUCTIVITY ACROSS FOUR EUROPEAN COUNTRIES Rachel Grffth Elena Huergo Jacques Maresse Bettna Peters Workng Paper NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambrdge, MA December 2006 We would lke to thank Laura Abramovsky, Manuel Arellano, Rupert Harrson, Jord Jaumandreu, Elzabeth Kremp, Perre Mohnen, Helen Smpson. The analyss contaned n ths paper was funded by the European Commsson, the ESRC and ESF under grant RES and the Advanced Insttute of Management Research (AIM). All errors and omssons reman the responsblty of the authors. The vews expressed heren are those of the author(s) and do not necessarly reflect the vews of the Natonal Bureau of Economc Research by Rachel Grffth, Elena Huergo, Jacques Maresse, and Bettna Peters. All rghts reserved. Short sectons of text, not to exceed two paragraphs, may be quoted wthout explct permsson provded that full credt, ncludng notce, s gven to the source.

2 Innovaton and Productvty across Four European Countres Rachel Grffth, Elena Huergo, Jacques Maresse, and Bettna Peters NBER Workng Paper No December 2006 JEL No. L1,L60,O31,O33,O47 ABSTRACT Ths paper compares the role nnovaton plays n productvty across the four European countres France, Germany, Span and the UK usng frm-level data from the nternatonally harmonzed Communty Innovaton Surveys (CIS3). Despte a consderable number of natonal frm-level studes analysng ths relatonshp, cross-country comparsons usng mcro data are stll rare. We apply a structural model that descrbes the lnk between R&D expendture, nnovaton output and productvty (CDM model). Our econometrc results suggest that overall the systems drvng nnovaton and productvty are remarkably smlar across these four countres, although we also fnd nterestng dfferences, partcularly n the varaton n productvty that s assocated wth more or less nnovatve actvtes. Rachel Grffth IFS 7 Rdgmont Street London WC1E 7AE UK rgrffth@fs.org.uk Elena Huergo Unversdad Complutense Facultad CC. Económcas y Empresarales Campus de Somosaguas Madrd - Span ehuergo@ccee.ucm.es Jacques Maresse INSEE, CREST 15, Boulevard Gabrel PERI MALAKOFF CEDEX FRANCE and NBER maresse@ensae.fr Bettna Peters ZEW Postfach D Mannhem Germany b.peters@zew.de

3 I Introducton The poor productvty performance of European countres relatve to the US has been an mportant focus for government polcy. Table 1 shows aggregate productvty levels and growth rates n But, t s not only that Europe s laggng behnd the US, but that there are also larger dfferences across European countres n labour productvty development, for nstance between Span at the lower and the UK at the upper bound. Emphass has been placed on the need for Europe to move nto the knowledge-based economy. Post-war growth n Europe, t s argued, was largely based on mtaton, drven by captal accumulaton, whle what s needed now s for European countres to shft towards growth based on nnovaton. 2 In fact, R&D ntensty n the four major EU countres (France, Germany, Span and the UK) les behnd US as can be seen n Table 1. [Table 1 here] What role does nnovaton play n productvty growth across European countres? There are two major challenges facng researchers tryng to answer ths queston - how do we measure nnovaton and can we get data that are comparable across countres? Commonly used measures of nnovaton are R&D expendtures or patent counts. Whle both have strengths as measures of nnovaton they also have weaknesses. R&D s a measure of nputs, and takes no account of the productvty and effectveness of effort. Patents are a crude measure of outputs, capturng only some sorts of nventon, and beng of very dfferng values. In ths paper, we use comparatve data across European countres at the frm level from the harmonzed Communty Innovaton Surveys (CIS data), whch provdes ndcators of nnovaton nput and outcomes. Our nterest focuses n comparng frm-level nnovaton and productvty behavour across countres, whch has been hampered n the past due to a lack of nternatonally comparable data, n partcular as concerns nnovaton. Precsely we estmate a structural model for 1 We report fgures for the end of the nnetes to 2000 n Table 1 because t s the tme span of the data we used n the econometrc part. More recent data stll confrms ths pattern across the countres. 2 See, for example, EU (2003), Aghon and Howtt (1998). 5

4 manufacturng frms n France, Germany, Span and the UK, that drectly lnks R&D to nnovaton outcomes and then lnks nnovaton to productvty. Ths allows us to dsentangle the contrbuton of R&D ntensty per se from the effectveness of nnovatve effort n leadng to productvty gans. 3 In summary, our results suggest that overall the systems drvng nnovaton and productvty are remarkably smlar across France, Germany, Span and the UK, although we also fnd nterestng dfferences, partcularly n the varaton n productvty that s assocated wth more or less nnovatve actvtes. The paper s organzed as follows. Secton II explans the framework and descrbes the data. Secton III presents the results and some robustness checks. Secton IV concludes. Appendx A provdes a formal descrpton of the econometrc model estmated, and Appendx B comments the data n more detal. II Model and Data II. Model In ths paper we apply a structural model whch has the followng basc form: frms decde how much effort to put nto nnovaton; knowledge s produced as a result of ths nvestment; output s produced usng knowledge (along wth other nputs). Ths model s formalsed n four equatons: () the frm s decson to engage n suffcent effort to result n observable research and development (R&D) nvestment, () the ntensty wth whch the frm undertakes R&D, or R&D nvestment functon, () the nnovaton or knowledge producton functon, where we allow knowledge to take two dfferent forms - process and product nnovatons, and (v) the output producton functon, where 3 The productvty of nnovatve effort s what Maresse and Mohnen n comparable work propose to call for short nnovatveness or nnovatvty. See Maresse and Mohnen (2002, 2005) and Mohnen et al. (2006). 6

5 knowledge s an nput. The model s based on Crépon, Duguet and Maresse (1998), henceforth called CDM model. 4 Techncal detals on the model and ts estmaton are gven n Appendx A. In contrast to most prevous studes we estmate the CDM model not only for nnovatve but for all frms. That s, we estmate step () and () based on reported R&D fgures and use predcted values for all frms to proxy nnovaton effort n the knowledge producton functon. Ths model reflects the fact that all frms exert some nnovatve effort, but not all frms report ths effort. 5 For example, producton workers may well spend a small part of ther day consderng how the process they are workng on could be acheved more effcently. However, below a certan threshold, a frm wll not report ths effort as R&D. The output of ths nnovaton effort produces knowledge. As ndcated above, we allow knowledge output to take several forms, ncludng process and product nnovatons, and we assume that effort s a publc good wthn the frm, so t can be used to produce several outputs wthout depleton. We estmate the relatonshp between R&D nvestment and process and product nnovaton outputs usng data on frms that report both, and mpute knowledge output for all frms based on these estmates. The dea s that we beleve that a frm that reports zero R&D does not actually have zero knowledge output. Ths approach assumes that the process descrbng R&D nvestment and nnovaton outputs for non R&D reportng frms s the same as for reportng frms. 4 Other studes whch have used dfferent versons of the CDM model nclude Lööf and Heshmat (2002, 2006) for Swedsh manufacturng frms, Klomp and Van Leeuwen (2001) and Van Leeuwen and Klomp (2006) usng Dutch manufacturng data, Crscuolo and Haskell (2003) usng UK CIS1 and CIS2 data, Janz et al. (2004) usng German and Swedsh CIS3 data, Pars et al. (2005) usng data for Italan manufacturng frms, Maresse and Mohnen (2002, 2005) and Mohnen et al. (2006) usng French CIS1 and CIS3 data, Benavente (2006) for Chlean frms, Jefferson (2006) for a panel of Chnese frms, Peters (2006) for a panel of German frms. See also Hall and Maresse (2006). For a presentaton of the more tradtonal approach of estmatng R&D productvty and rate of returns drectly n terms of an extended producton functon, see, nter ala, Grlches (1979, 1994, 1996) and Grlches and Maresse (1998). 5 In addton to R&D expendtures strcto sensu (as defned by the Frascat manual, OECD 1963), the Communty Innovaton Surveys also nclude questons on several other specfc nnovaton expendtures,.e. expendtures related to the acquston of other external knowledge, to the acquston of machnery and equpment and tranng actvtes n the context of nnovatons, to market ntroducton of nnovatons, and to desgn and other preparaton actvtes for the producton and delvery of new products (Oslo manual, Eurostat and OECD, 1997). Many frms n the French CIS3 apparently dd not understand them well. We have preferred here to stay wth the more usual R&D varable, and not use 7

6 Fnally, n the model frms produce output usng a constant returns to scale Cobb-Douglas technology wth labour, captal and knowledge nputs. We choose ths structural model because t captures the man features of frm behavour, but s at the same tme parsmonous and emprcally tractable wth the data we have avalable. II. Data and Emprcal Implementaton In ths study we take advantage of the data from the thrd wave of the Communty Innovaton Surveys (CIS3). The CIS s a harmonsed survey that s carred out by natonal statstcal agences n all 25 EU Member States under the co-ordnaton of Eurostat. CIS3 was conducted n 2001 and provdes nformaton for the perod To fully explot the comparable nature of the nformaton we have had to assemble researchers from France, Germany, Span and the U.K who had access to the underlyng orgnal frm level data collected under CIS. 7 Great care has been taken to make these mcro data fully comparable across the four countres, and ths s one strength of our nvestgaton. For a more detaled descrpton of the data sets and ther comparablty across countres see Abramovsky et al (2004). As mentoned above, we beleve that all frms exert some nnovatve effort, so we use the whole sample of frms, and not only nnovatng frms. To explan whch frms report R&D we have to take the specfc characterstcs of the data set nto account. One dstnctve feature of the CIS questonnare n France, Germany and Span s that t asked all frms for a few general nformaton, such as the number of employees and the ndustry to whch the frm belongs, and whether they have (completed, ongong or abandoned) nnovaton actvtes or not. Only those frms wth nnovaton the Total nnovaton expendtures varable ncludng these other nnovaton expendtures. Our estmates usng ths more broadly defned varable, rather than R&D, are practcally unchanged for Germany and Span. 6 The survey was also carred out n Iceland and Norway as well as Turkey and Romana. Furthermore other non European countres have carred out surveys equvalent to CIS3. 7 Eurostat publshes results on a hghly aggregated sector level (manufacturng, wholesale trade, producer servces) for each country (Eurostat, 2004) and Eurostat's onlne data base, New Cronos, also only provdes nformaton at the sector level. Snce recently, Eurostat also proposes access to anonymsed mcro-aggregated CIS3 data. However, these data are currently avalable for only 12 out of the 25 EU countres, wth Germany, France and the UK not beng provded. 8

7 actvtes are requested to answer to a lot of addtonal questons, lke those on co-operatons, nformaton sources etc. R&D performers are by defnton frms wth nnovaton actvtes. Hence to explan of whether frms have R&D or not, we can only use lmted nformaton avalable for all frms. We use an ndcator of whether the nternatonal market s the frm s most mportant market to capture the exposton to nternatonal competton, ndcators of whether the frm receves publc fundng, and measures of approprablty condtons that the frm faces - the extent of legal and formal protecton of ntellectual property n the country. Effectve approprablty condtons are mportant n that they allow nnovators to receve the returns on ther nnovaton actvtes. As a result, they also ncrease the ncentves for and amount of nnovaton actvtes (Spence 1984). We also use sze and ndustry dummy varables, as well as a dummy varable for Eastern Germany n the German sample. Detaled varable defntons are gven n Appendx B. We use a larger set of varables to explan R&D ntensty measured as R&D expendtures per employee (n logs). Here we also consder demand condtons - whether envronmental, health and safety or other regulatory standards were an mportant reason for nnovatng - along wth an ndcator of whether the enterprse had some co-operatve arrangements on nnovaton actvtes durng , a set of categorcal varables reflectng dfferent sources of nformaton for nnovaton and ndcators for publc support for that frm at the local, natonal or EU level. As already stated, we dstngush two dfferent knds of nnovaton outcome: product and process nnovatons. Both are measured by a dummy varable ndcatng whether the frm has ntroduced at least one product and process nnovaton, respectvely. In addton to R&D ntensty we explan the nnovaton outcome by the same group of demand pull ndcators and approprablty condtons. Furthermore, we expect frms to be more successful n product nnovaton actvtes f they used customers or compettors as nformaton source and n process nnovaton actvtes f they used nformaton stemmng from ther supplers or compettors. We also nclude nvestment per employee n the producton of process nnovatons, because we want to allow for complementartes between process nnovaton and nvestment n captal that embodes new process technologes. We do not 9

8 nclude t n product nnovaton because we do not see evdence of such complementartes n that case. Productvty s measured as labour productvty (sales per employee, n logs) and depends on the knowledge measured n terms of product and process nnovaton outcomes. Snce we do not observe physcal captal n the data for all countres we proxy for t n the productvty equaton by nvestments n physcal captal. We allow all coeffcents to vary across countres (.e., we estmate our model separately for the four countres). In all equatons we control for unobserved ndustry characterstcs. We further control for frm sze n all equatons but the R&D ntensty equaton, R&D ntensty beng already mplctly scaled for sze. When ncluded n ths equaton, the sze ndcators are not sgnfcant, and our estmates n the other equatons of the model are not affected. Descrptve statstcs of the man varables n the model are reported for the four countres n Table 2. We restrct our analyss to frms wth at least 20 employees. 8 Some dfferences and smlartes are worth notng. [Table 2 here] The proportons of frms reportng that they are engaged n R&D and have process and product nnovatons are greater n France and Germany than n Span or the UK. French and German frms that do R&D also do so more ntensvely than ther Spansh and U.K counterparts,.e. R&D ntensty of R&D-performers s much hgher. Ths confrms the general pattern found at the aggregate level n Table 1. Note that n results not shown the performance n terms of shares of sales for new or substantally mproved products s about the same for the German, Spansh and UK product nnovatng frms, but hgher than for the French frms, whch are less successful n commercalzng ther nnovatve products. French frms draw on formal measures to protect returns from nnovaton 8 In Germany, the UK and Span, the CIS3 covers all enterprses wth 10 or more employees. In France, however, the target populaton for manufacturng ncludes frms wth 20 or more employees. We restrct ourselves to frms wth over 20 employees so we can compare the four countres. The number of frms by ndustry n each country s gven n the data Appendx Table B.1. 10

9 almost as often as German or UK frms, but less often on strategc measures. On the other hand, Spansh frms use both formal and strategc protecton measures much less frequently (though they have a hgh share of nnovatve sales). Average labour productvty n manufacturng s hghest n France, and s smlar n the UK and Germany, but lower n Span, whch s also what we see at the aggregate level n Table 1 n terms of GDP per employee. Investment per employee s hgher n Span than n the other three countres. Ths may be n part because the average sze of Spansh frms s smaller - average frm number of employees s only 74 for Span, as compared to 142 n France, 155 n Germany and 116 n the UK. Other notable dfferences are the followng: publc support for R&D s lower n the U.K; government s also less of a source of nformaton n the UK; unverstes are a greater source of nformaton n Germany; and Spansh and UK frms face less nternatonal competton n the sense that nternatonal markets are less often the frm s most mportant market. III Results We now turn to a dscusson of the estmates of the parameters of the structural model descrbed above and formally set out n Appendx A. Before nterpretng the results we pont to an mportant caveat of the study: we only have cross-secton data, and most of the factors we consder are smultaneously determned. Therefore, we need to take great care n nterpretng our results - all we are able to recover are correlatons, these are not necessarly causal relatonshps. III. R&D and R&D ntensty We start by consderng estmates of the determnants of whether frms undertake R&D, and f so how much R&D. The frst four columns (left hand panel) n Table 3 show estmates of the determnants of whether a frm engages n R&D contnuously over the perod , wth one column for each country. As dscussed above (n secton II.), we consder all frms as engagng n nnovatve actvty, but only some engagng n a suffcent amount for t to be reported as contnuous R&D. In the frst panel we estmate a dscrete (probt) model. The second four columns (rght hand 11

10 panel) n Table 3 show the correspondng estmates of the determnants of how much frms nvest n R&D, condtonal on dong R&D. The numbers reported n these tables are margnal effects. All of the explanatory varables are dummy varables - they take the value 1 when the factor s mportant to the frm or s used by the frm (see precse defntons n the Data Appendx B) and the value zero f t s unmportant or not used. Therefore, the margnal effect s that of changng the dummy varable from 0 to 1. [Table 3 here] Consder the coeffcents on the Fundng varables. These tell us that French frms that receve fundng from natonal sources are 25% more lkely to report engagng n contnuous R&D than frms that receve no fundng from natonal sources - condtonal on the mean values of all of the other varables. In Germany frms that receve natonal fundng are 40.8% more lkely, n Span 27.3% more lkely, and n the UK 19% more lkely. The margnal effect of natonal fundng wll dffer across countres due to dfferences n the fundng system as well as potental dfferences n frm behavour. Overall, the margnal effects reported n Table 3 are surprsngly smlar across countres. They suggest as a whole that broadly comparable processes, n broadly comparable economc envronment, drve frms decsons to engage n R&D across the major European countres. Specfcally, what we see s that: frms that operate manly n nternatonal markets are more lkely to engage n R&D; and they engage n R&D more ntensvely (only sgnfcantly so n France and Span); frms n ndustres where greater use s made of formal or strategc methods to protect nnovatons are more lkely to nvest n R&D but gven the decson to nvest n R&D, protecton measures have almost no mpact on the amount of R&D. In Germany strategc methods are much more mportant than formal methods, whereas n other countres they are smlar. Note that the weakly negatve mpact coeffcent of strategc measures n Span stands out as an excepton. 12

11 recevng government fundng ncreases the probablty that a frm engages n R&D contnuously, natonal fundng has the largest mpact; recevng government fundng, however, has very lttle mpact on the ntensty of R&D, although natonal fundng does have some postve mpact n Germany and Span, but a negatve mpact n France and no mpact n the UK; larger frms are more lkely to engage n R&D; 9 frms n East Germany are more lkely to undertake R&D than those n West Germany. Ths result s also observed at the aggregate level, for nstance n 2000 the share of contnuous R&D performers n East Germany was 28 % as aganst 24% n West Germany (see Aschhoff et al., 2006). Ths s explaned by the exstence of specal support programmes for frms located n Eastern Germany to help them catch up on the productvty of West German frms. 10 III. Knowledge producton functons We next consder the estmates of the knowledge producton functons n Table 4. The frst four columns (left-hand panel) show them for process nnovaton, the four last columns (rght-hand panel) for product nnovaton. The numbers reported are agan margnal effects evaluated at the sample means. All of the explanatory varables except the frst two are dummy varables - they take the value 1 when the factor s mportant to the frm and the value zero f t s unmportant, and as before the margnal effects are those of changng ther value from 0 to 1. [Table 4 here] As expected, the margnal effects for R&D ntensty are both statstcally and economcally very sgnfcant. They show most clearly that greater R&D effort per employee leads to a hgher probablty of havng at least one process nnovaton and of havng at least one product nnovaton. The mpacts are agan remarkably smlar across the four countres, although lower for process 9 As already ndcated, we exclude the frm sze ndcators from the R&D ntensty equaton, but f we nclude them they are nsgnfcant. 13

12 nnovaton n the UK and hgher for product nnovaton n France. Furthermore, the margnal effects for physcal nvestment on process nnovaton are also qute sgnfcant for the four countres, wth the mpacts beng smlar although hgher n the UK. The ablty to protect an nnovaton through formal or strategc methods s less mportant for process nnovaton than t s for product nnovaton; although n contrast to the other three countres nether s mportant n the UK. As expected, supplers are an mportant source of nformaton for process nnovaton, whle customers have a consderable nfluence n stmulatng product nnovaton. Compettors are not such an mportant source of nformaton (n magntude as well as n sgnfcance) compared to supplers and customers. Envronmental regulatons are an mportant drver of process and product nnovaton n France, and process nnovaton n Germany. Standards have a negatve mpact on process nnovaton n France. There s a huge lterature dealng wth the relatonshp between frm sze and nnovaton actvtes. 11 Our estmates suggest that n all four countres larger frms are more lkely to be process nnovators (whch makes sense whenever process nnovaton entals a per unt cost reducton). Larger frms also appear more lkely to be product nnovators, especally n Span, but not n the UK. III. Output producton functons Fnally, we consder our estmates of the productvty equaton shown n Table 5. The coeffcents reported n ths table are elastctes or sem-elastctes, snce the dependent varable s the log of sales per employee. [Table 5 here] In contrast to the results for the R&D equatons and the knowledge producton equatons, the results for labour productvty are qute mxed across the four countres. We thus fnd that the 10 Detaled nformaton about the fundng polcy and ts effectveness n Eastern Germany s gven n Czarntzk and Lcht (2006). 11 Ths lterature has been ntated by J. Schumpeter who asserted that large frms n hghly concentrated markets have an advantage n nnovaton, or so called frst Schumpeter hypothess. For a survey of emprcal studes testng such hypothess, see, for example, Cohen and Klepper (1996). 14

13 elastctes of output wth respect to nvestment are close n France and Germany (0.13 n France and 0.11 n Germany) and sgnfcantly hgher than n Span and the UK (0.06 n both). Ths s on the low sde for Span and the UK, but n lne wth some prevous estmates n the lterature usng smlar data. 12 The process and product nnovaton mpact coeffcents appear even more dfferent n the four countres. The estmated coeffcent of process nnovaton n France s the only statstcally sgnfcant one, suggestng that process nnovaton s on average assocated wth a 6% ncrease n productvty. In the other countres there s surprsngly no such assocaton, whch could correspond to the fact that we are measurng revenue productvty (deflated by ndustry deflators, not by ndvdual frm deflators). 13 The estmated coeffcents of product nnovaton are sgnfcant n France, Span and the UK, but not for Germany, accountng for a 18% ncrease n productvty n Span, and a more modest one of about 6% n France and the UK. 14 IV Summary and Conclusons Ths paper nvestgates the drvers of nnovaton and how they feed through nto productvty at the frm-level for the four major European countres - France, Germany, Span, and the UK - usng data from the thrd wave of the nternatonally harmonzed Communty Innovaton Surveys. We estmate a structural model that descrbes the lnk between R&D expendtures, nnovaton output and productvty. Importantly, ths model allows for the fact that some frms may undertake nnovate efforts but do not report them as R&D. What do our results mply for polcy? In response to recent concerns about laggng productvty and poor nnovatve performance n Europe countres, the European Unon has set tself the ambtous target of ncreasng R&D expendtures to 3% of GDP by 2010 (ths s part of the "Lsbon Agenda"). 12 See, for example, Huergo and Moreno (2006) for Spansh manufacturng. 13 On ths ssue, see for example Maresse and Jaumandreu (2005). 15

14 Does the EU's poor performance le prmarly n low nvestment n R&D, or s the man problem that EU frms do not explot ther nnovatons well? Are the returns to nnovatve efforts and to nnovatons smlar across countres, or do they vary, and so does polcy need to have a dfferent focus n the dfferent countres? As we stressed n presentng our results, a major drawback of the CIS s that t s a seres of crosssectons, so we do not observe many of the same frms repeatedly over tme. Ths means that we need to take great care n nterpretng our results - all we are able to recover s correlatons, these are not necessarly causal relatonshps. Bearng that caveat n mnd, our results show some nterestng regulartes and some heterogenety between the countres. In terms of frms decson over whether or not to engage n formal R&D the determnants are remarkably smlar across countres. Ths suggests that broadly comparable processes drve frms decsons to engage n R&D across the major European countres. Government fundng plays an mportant role n all countres, wth natonal fundng havng the largest mpact. Frms that operate manly n nternatonal markets and larger frms are more lkely to engage n formal R&D, as are frms n ndustres where greater use s made of formal or strategc methods to protect nnovaton. Unsurprsngly, frms greater R&D effort per employee made them more lkely to be process or product nnovators. Furthermore, frms wth hgher nvestment per employee are also more lkely to be process nnovators. The ablty to protect an nnovaton through formal or strategc methods s less mportant for process nnovaton than t s for product nnovaton. Supplers are an mportant source of nformaton for process nnovaton, whle customers are sgnfcant n stmulatng product nnovaton. Compettors are not such an mportant source of nformaton (n magntude as well as n sgnfcance) compared to supplers and customers. 14 It must be noted that our poor estmates of the productvty mpacts of process and product nnovatons n Germany are not n lne wth more postve results found by Janz et al. (2004) for knowledge ntensve frms and by Peters (2006) for the more recent perod

15 In contrast to the large concdence found for the R&D equatons and the knowledge producton equatons, the results for labour productvty are qute mxed across the four European countres. Process nnovaton s only assocated wth hgher productvty n France, n the other countres there s no such connecton. Product nnovaton s assocated wth hgher productvty n France, Span and the UK, but not n Germany. 17

16 Appendx A: Econometrc Model Formally we can wrte our model as follows. Let = 1,, N ndex frms. The frst equaton accounts for frms nnovatve effort * r : r = z β + e, (1) * where we consder r * as an unobserved latent varable, and where z s a vector of determnants of nnovaton effort, β s a vector of parameters of nterest, and e an error term. We can measure (or proxy) frms nnovatve effort * r by ther R&D expendtures, denoted by r only f frms do (and/or report) such expendtures, and thus could only drectly estmate equaton (1) at the rsk of selecton bas. Instead we assume the followng selecton equaton descrbng whether a frm s dong (and/or reportng) R&D or not: rd * 1 f rd = w α + ε > c =, (2) * 0 f rd = w α + ε c where rd s the observed bnary endogenous varable equal to zero for non-r&d and one for R&D dong (and/or reportng) frms, * rd s a correspondng latent varable such that frms decde to do (and/or report) R&D f t s above a certan threshold level c, and where w s a vector of varables explanng the R&D decson, α a vector of parameters of nterest, and ε an error term. Condtonal on frm dong (and/or reportng) R&D actvtes, we can observe the amount of resources nvested n R&D, and wrte: r * r = z β + e f rd = 1 =. (3) 0 f rd = 0 18

17 Assumng that the error terms e and ε are bvarate normal wth zero mean, varances σ = 1 and 2 σ e and correlaton coeffcent ρ eε, we estmate the system of equatons (2) and (3) as a generalzed Tobt model by maxmum lkelhood (usng STATA and Heckman procedure to choose ntal values for the parameters). 2 ε The next equatons n our model are the knowledge or nnovaton producton functons: g = r γ + x δ + u, (4) * where g s knowledge proxed by both the product and process nnovaton ndcators, and where the latent nnovaton effort, r *, enters as explanatory varable, x s a vector of other determnants of knowledge producton, (γ,δ) a vector of parameters of nterest and u an error term. We estmate the knowledge producton equaton (4) as two separate probt equatons for the process and product nnovaton ndcators, by maxmum lkelhood n STATA. For the frms nnovatve effort r * we take the predcted value from the estmated generalzed Tobt equatons (2) and (3). That s, we estmate (4) for the sample of all frms, not only for the sub-sample of those reportng R&D expendtures. By usng ts predcted value, we also nstrument the nnovatve effort and take care that t s possbly endogenous to the knowledge producton functon. In other words, t seems lkely that characterstcs of frms unobservable to us (and thus omtted) can make them both ncrease ther nnovatve effort and also ther nnovatvty (.e. ther productvty n producng nnovatons). Ths would mean that the γ parameters n (4) would be based upward (because r * and u would be postvely correlated). The selecton and nnovatve effort equatons correct for ths (as long as w and z are ndependent of u ). * r 19

18 Fnally, frms produce output usng constant returns to scale Cobb-Douglas technology wth labour, captal and knowledge nputs so that, y = π k + π g + v, (5) 1 2 where output y s labour productvty (log of output per worker), k s the log of physcal captal per worker (proxed by physcal nvestment per worker), and g s knowledge nput proxed by the product and process nnovaton ndcators. In our applcaton we also nclude addtonal controls n equaton (5). We take care of the endogenety of g n ths equaton by usng n the estmaton the predcted values from the knowledge producton functon equatons (4). In summary, our model conssts of the four equatons shown n equatons (2), (3), (4) and (5). Snce we assume a recursve model structure and do not allow for feedback effects, we follow a three-step estmaton procedure. In the frst step we estmate the generalzed Tobt model (eqn. 2 and 3). In the second step, we separately estmate the two knowledge producton functons for product and process nnovatons as two probt equatons usng the predcted value of nnovatve effort from the frst step (to take care of both selectvty and endogenety of r * n eqn 4). In the last step, we estmate the productvty equaton usng the predcted values from the second step (to take care of the endogenety of g n eqn 5). 20

19 Appendx B: Varable Defntons Knowledge/Innovaton: Contnuous R&D engagement: Dummy varable whch takes the value 1 f the enterprse reports contnuous engagement n ntramural R&D actvtes durng the perod R&D ntensty: R&D expendtures per employee n 2000 (n logs). Process nnovaton: Dummy varable whch takes the value 1 f the enterprse reports havng ntroduced new or sgnfcantly mproved producton processes durng Product nnovaton: Dummy varable whch takes the value 1 f the enterprse reports havng ntroduced new or sgnfcantly mproved products durng (new to the market or only new to the frm). Share of sales wth new products: Share of turnover n 2000 due to new or sgnfcantly mproved products ntroduced durng Labour productvty: Sales per employee n 2000 (n logs). Investment ntensty: Gross nvestments n tangble goods n 2000, per employee (n logs). Publc Support: Local fundng: Dummy varable whch takes the value 1 f the enterprse receved local or regonal fundng for nnovaton projects durng Natonal fundng: Dummy varable whch takes the value 1 f the enterprse receved central government fundng for nnovaton projects durng EU fundng: Dummy varable whch takes the value 1 f the enterprse receved EU fundng for nnovaton projects durng

20 Demand Pull: Regulaton and standards: Two varables whch measure the share of frms for whch regulaton or standards were of hgh/medum and low mportance for nnovaton durng at the 2-dgt ndustry level, respectvely [reference: no mportance]. Envronmental, health and safety aspects: Two varables whch measure the share of frms for whch mproved envronmental or health and safety aspects were of hgh/medum and low mportance for nnovaton durng at the 2-dgt ndustry level, respectvely [reference: no mportance]. Sources of nformaton: Internal sources wthn the enterprse: Dummy varable whch takes the value 1 f nformaton from nternal sources wthn the enterprse were of hgh mportance durng Internal sources wthn the group: Dummy varable whch takes the value 1 f nformaton from nternal sources wthn the enterprse group were of hgh mportance durng Unverstes as source of nformaton: Dummy varable whch takes the value 1 f nformaton from unverstes or other hgher educaton nsttutes were of hgh mportance durng Government as source of nformaton: Dummy varable whch takes the value 1 f nformaton from government or prvate non-proft research nsttutes were of hgh mportance durng Supplers as source of nformaton: Dummy varable whch takes the value 1 f nformaton from supplers were of hgh mportance durng Compettors as source of nformaton: Dummy varable whch takes the value 1 f nformaton from compettors and other enterprses from the same ndustry were of hgh mportance durng Customers as source of nformaton: Dummy varable whch takes the value 1 f nformaton from costumers or clents were of hgh mportance durng

21 Approprablty condtons: Formal protecton: Dummy varable whch takes the value 1 f the enterprse used desgn pattern, trademarks or copyrght to protect nventons or nnovatons durng Strategc protecton: Dummy varable whch takes the value 1 f the enterprse used complexty of desgn, secrecy or lead-tme advantage on compettors to protect nventons or nnovatons durng Cooperaton: Dummy varable whch takes the value 1 f the enterprse had some co-operatve arrangements on nnovaton actvtes durng Other: Internatonal competton: Dummy varable whch takes the value 1 f the enterprse s most sgnfcant market s nternatonal. Sze: Set of sze dummy varables accordng to the frm s number of employees n Categores are 20-49, 50-99, , , >1000 employees. Industry: Set of ndustry dummes accordng to the frm s man busness actvty durng the perod Classfcaton: see Table B.1. East Germany (for Germany only): Dummy varable whch takes the value 1 f the enterprse s located n Eastern Germany. 23

22 Table B.1: Number of frms by ndustry and correspondng proportons n total manufacturng for the four country samples France Germany Span UK Industry Nace % % % % Textle Wood/paper Chemcals Plastc/rubber Non-metallc Bass metals Machnery Electrcal Vehcles Nec All frms Notes: Data are from the CIS 3 n each country. The ndustry defnton s based on the classfcaton system NACE Rev.1 (Nomenclature générale des actvtés économques dans les Communautés Européennes) as publshed by Eurostat (1992), usng 2-dgt levels. 24

23 Table 1: Aggregate productvty and R&D ntensty across countres USA FRA GER SPA UK GDP per capta n 2000 (n US $) R&D ntensty n Average annual growth rate n labour productvty Average annual growth rate n mult factor productvty n.a. 0.9 Notes: The averages are calculated as the geometrc mean. The R&D ntensty s the gross domestc expendtures on R&D as a percentage of GDP. The growth of labour productvty s obtaned by dvdng the growth of value added at constant prces by the growth of the labour force. The rate of mult factor productvty growth s the part of GDP growth whch s not explaned by the weghted average of the rates of growth of captal and labour nputs. The data are from OECD (2005a, b). 25

24 Table 2: Means of varables across countres France Germany Span UK Knowledge/Innovaton: Contnuous R&D engagement R&D per employee (for frms wth contnuous R&D engagement) Innovator (product and/or process nnovaton) Process nnovaton Product nnovaton Share of sales wth new products (for frms wth product nnovaton) Labour productvty Investment per employee a Publc Support: Local fundng Natonal fundng EU fundng Demand Pull: Envronmental, health and safety aspects: low mportance Envronmental, health and safety aspects: medum or hgh mportance Regulatons and standards: low mportance Regulatons and standards: medum or hgh mportance Sources of nformaton: Internal sources wthn the enterprse Internal sources wthn the group Unverstes as source of nformaton Government as source of nformaton Supplers as source of nformaton Compettors as source of nformaton Customers as source of nformaton Observatons

25 Table 2 (contnued): Means of varables across countres France Germany Span UK Approprablty condtons: Formal protecton Strategc protecton Cooperaton Other: Internatonal competton Sze: < Sze: Sze: Sze: Sze> East German (only n Germany) Observatons Notes: Data are from the Communty Innovaton Survey, Wave 3 n each country. All varables cover the years , wth the excepton of R&D per employee, labour productvty and nvestment per employee (related to the year 2000) and sze (related to the number of employees n the year 1998). All values are n thousands of Euros, exchange rate for the UK s Euros per pound sterlng. 27

26 Table 3 (left panel): R&D equatons: selecton equaton Dep. Var. Engage n R&D contnuously (0/1) (1) (2) (3) (4) Sample France Germany Span UK Observatons Internatonal *** *** *** *** competton (0.019) (0.035) (0.018) (0.027) Cooperaton Appr. Cond.: Formal protecton *** * *** *** (0.021) (0.037) (0.027) (0.025) Strategc *** *** *** *** protecton (0.022) (0.035) (0.029) (0.023) Fundng: Local * *** *** (0.049) (0.055) (0.023) (0.048) Natonal *** *** *** *** (0.031) (0.047) (0.030) (0.063) EU *** ** ** (0.058) (0.081) (0.048) (0.071) Sze: Sze: *** *** *** (0.028) (0.051) (0.020) (0.025) Sze: *** *** *** *** (0.028) (0.051) (0.025) (0.030) Sze: *** *** *** *** (0.028) (0.047) (0.029) (0.031) Sze: > *** *** *** *** (0.040) (0.083) (0.058) (0.093) East Germany * - - (0.042) W_demand pull W_sources W_ndustry Rho Log-Lkelhood Notes: Standard errors n parentheses are robust. Reported are margnal effects (at the sample means) for the probablty of dong R&D contnuously and for the expected value of the R&D ntensty condtonal on dong R&D, respectvely. Industry dummes are ncluded n both equatons; demand pull varables and sources of nformaton are ncluded n the R&D ntensty. Correspondng margnal effects are not shown, but W reports the p-value of a test of the jont sgnfcance of the defned varables. 28

27 Table 3 (rght panel): R&D equatons: ntensty equaton Dep. Var. R&D ntensty (5) (6) (7) (8) Sample France Germany Span UK Observatons Internatonal 0.274*** * competton (0.075) Cooperaton (0.075) (0.126) *** (0.126) (0.076) ** (0.090) (0.120) ** * (0.111) Appr. Cond.: Formal protecton (0.072) Strategc protecton (0.074) (0.117) (0.137) (0.085) (0.121) * (0.077) (0.133) Fundng: Local (0.122) (0.152) Natonal ** (0.089) EU (0.139) *** (0.166) (0.084) (0.164) ** 0.293*** (0.138) (0.090) (0.236) (0.139) (0.251) Sze: Sze: Sze: Sze: Sze: > East Germany (0.139) W_demand pull W_sources W_ndustry Rho (0.050) (0.066) (0.056) (0.045) Log-Lkelhood Notes: Standard errors n parentheses are robust. Reported are margnal effects (at the sample means) for the probablty of dong R&D contnuously and for the expected value of the R&D ntensty condtonal on dong R&D, respectvely. Industry dummes are ncluded n both equatons; demand pull varables and sources of nformaton are ncluded n the R&D ntensty. Correspondng margnal effects are not shown, but W reports the p-value of a test of the jont sgnfcance of the defned varables. 29

28 Table 4 (left panel): Knowledge producton functon: Process Innovaton Dep. Var. Process Innovaton (0/1) (1) (2) (3) (4) Sample France Germany Span UK Observatons R&D ntensty 0.303*** 0.260*** 0.281*** 0.161*** (0.029) (0.036) (0.025) (0.034) Investment ntensty 0.023*** 0.022* 0.029*** 0.037*** (0.006) (0.012) (0.004) (0.007) Appr. Cond.: Formal protecton 0.035* (0.021) (0.038) Strategc protecton 0.075*** (0.025) (0.041) (0.031) (0.034) *** (0.032) ** (0.042) Sources: Supplers 0.243*** 0.331*** 0.405*** 0.407*** (0.034) (0.047) (0.028) (0.042) Compettors 0.062** *** (0.028) (0.053) (0.046) (0.049) Customers Demand Pull: Envron. Aspects low 0.717** 0.520** 0.916*** (0.332) (0.245) (0.254) (0.250) Envron. Aspects hgh 1.425*** 0.736*** (0.243) (0.237) (0.234) (0.197) Standards low * *** * (0.324) (0.279) (0.263) (0.248) Standards hgh *** (0.241) (0.227) (0.231) (0.224) Sze: Sze: (0.025) (0.049) (0.023) (0.029) Sze: ** 0.183*** ** (0.025) (0.047) (0.026) (0.034) Sze: *** 0.225*** 0.101*** 0.146*** (0.026) (0.048) (0.031) (0.035) Sze: > *** 0.238*** 0.255*** (0.042) (0.077) (0.081) (0.071) East Germany (0.040) - - W_ndustry Pseudo R Log-Lkelhood Notes: Standard errors n parentheses are robust. Numbers reported are the margnal effects (at the sample means) from a probt. W_ndustry reports the p-value of a test of the jont sgnfcance of the ndustry dummes. 30

29 Table 4 (rght panel): Knowledge producton functon: Product Innovaton Dep. Var. Product Innovaton (0/1) (5) (6) (7) (8) Sample France Germany Span UK Observatons R&D ntensty 0.440*** 0.273*** 0.296*** 0.273*** (0.038) (0.043) (0.026) (0.036) Investment ntensty Appr. Cond.: Formal protecton 0.134*** 0.087** 0.077** (0.025) (0.040) (0.034) (0.034) Strategc protecton 0.100*** 0.143*** 0.059* (0.030) (0.043) (0.034) (0.042) Sources: Supplers Compettors 0.125*** * (0.038) Customers (0.024) (0.066) *** (0.037) (0.048) *** (0.030) (0.056) *** *** (0.047) Demand Pull: Envron. Aspects low 1.290*** (0.467) (0.253) Envron. Aspects hgh 1.526*** (0.324) (0.255) Standards low (0.463) (0.295) Standards hgh *** (0.323) (0.244) (0.248) (0.254) (0.226) (0.203) (0.254) (0.251) 0.622*** (0.222) (0.229) Sze: Sze: *** * (0.028) (0.049) (0.022) (0.030) Sze: *** (0.028) (0.048) (0.026) (0.033) Sze: *** 0.147*** 0.097*** (0.030) (0.049) (0.031) (0.031) Sze: > *** (0.050) (0.090) (0.083) (0.061) East Germany (0.044) - - W_ndustry Pseudo R Log-Lkelhood Notes: Standard errors n parentheses are robust. Numbers reported are the margnal effects (at the sample means) from a probt. W_ndustry reports the p-value of a test of the jont sgnfcance of the ndustry dummes. 31

30 Table 5: Output Producton Functon Dep. Var. Labour productvty (1) (2) (3) (4) Sample France Germany Span UK Observatons Investment ntensty (0.010) *** (0.014) *** (0.006) *** 0.059*** (0.010) Process nnovaton 0.069** (0.033) (0.050) (0.043) (0.035) Product nnovaton 0.060*** (0.020) (0.034) 0.176*** 0.055** (0.034) (0.024) Sze: *** 0.083* 0.108** 0.070** (0.029) (0.049) (0.045) (0.035) Sze: ** 0.250*** 0.152*** 0.153*** (0.030) (0.053) (0.056) (0.041) Sze: *** 0.350*** 0.274*** (0.031) (0.055) (0.061) (0.046) Sze: > *** 0.455*** 0.510*** 0.268** (0.044) (0.083) (0.109) (0.118) East Germany *** - - (0.035) Constant 4.770*** 4.370*** 3.692*** 4.262*** (0.052) (0.093) (0.078) (0.063) R Observatons Notes: Standard errors n parentheses are robust. Numbers reported are coeffcents from an IV regresson. Industry dummes are ncluded n all regressons. 32

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