ARTICLE IN PRESSG ModelESPOL-2958; No. of Pages 12
Research Policy xxx (2014) xxx–xxx
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Research Policy
jo ur nal ho me page: www.elsev ier .com/ locate / respol
International) R&D collaboration and SMEs: The effectiveness ofargeted public R&D support schemes
anna Hottenrotta,b,c,1, Cindy Lopes-Bentob,c,d,∗
Düsseldorf Institute for Competition Economics (DICE), GermanyK.U.Leuven, Department of Managerial Economics, Strategy and Innovation, Leuven, BelgiumCentre for European Economic Research (ZEW), Mannheim, GermanyUniversity of Zurich, Zurich, Switzerland
r t i c l e i n f o
rticle history:eceived 21 February 2013eceived in revised form7 November 2013ccepted 12 January 2014vailable online xxx
EL classification:1430233138
a b s t r a c t
This study analyses the impact and effectiveness of targeted public support for R&D investment at thefirm level. We test whether the policy design aiming at incentivizing (international) collaboration andR&D in SMEs achieves input as well as output additionality. Our results show that the targeted publicsubsidies trigger R&D spending, especially so in internationally collaborating SMEs. We further evaluatethe different impact of privately financed and publicly induced R&D investment on innovation perfor-mance. The results confirm that the publicly induced R&D is productive as it translates into marketableproduct innovations. While both types of R&D investments trigger significant output effects, the effect ofpolicy-induced R&D investment on sales from market novelties is highest for international collaboratorsas well as for SMEs.
It is today widely acknowledged that private sector firms’nvestment in research and development (R&D) plays a crucialole, not only for the discovery of new technologies, but alsoor their diffusion. Market failures, however, impede firms fromnvesting the socially optimal amount in R&D, so that the private
Please cite this article in press as: Hottenrott, H., Lopes-Bento, C., (Itargeted public R&D support schemes. Res. Policy (2014), http://dx.do
evel of R&D-investment tends to be lower than socially desirableNelson, 1959; Arrow, 1962; Bloom et al., 2010). Consequently,ublic policies are designed such as to reduce the cost of private
∗ Corresponding author at: K.U.Leuven, Department of Managerial Economics,trategy and Innovation, Naamsestraat 69, 3000 Leuven, Belgium.el.: +32 16 37 61 97; fax: +32 16 32 67 32.
R&D in order to incentivize firms to pursue socially valuable R&Dprojects that would not be carried out otherwise.
The present study is concerned with one specific public policy,namely direct financial support for R&D. While the effectiveness ofsuch R&D subsidies has been studied at length before, the follow-ing analysis goes beyond the questions that are commonly raisedin this stream of literature. Whereas most studies are primarilyconcerned with whether a subsidy has a positive effect on inputand/or output additionality,2 our analysis evaluates firstly how thetreatment effect is affected by specific policy features that aim totarget particular recipient firms. By pursuing targeted R&D sup-port schemes governments aim to address those firms that have
nternational) R&D collaboration and SMEs: The effectiveness ofi.org/10.1016/j.respol.2014.01.004
the highest crowding-in potential, thereby reducing the likelihoodthat public money is wasted. However, the ex-post effectivenessof the design is not obvious ex-ante. Moreover, we analyze if the
2 See David et al. (2000) for an extensive review of concepts and insights from theliterature.
argeted innovation policy design fosters innovation performancen the recipient firms, that is, whether the publicly induced partf the R&D investment translates subsequently into product mar-et innovations. In particular, we study the case of the innovationolicy in place in Flanders (to be explained in detail in the follow-
ng section), the northern part of Belgium. This policy distinguishestself from other policies in that it incorporates special features formall- and medium-sized firms (SMEs) and for firms that engagen (international) R&D collaboration.
Our results indeed suggest higher input additionality for SMEsnd, in particular for internationally collaborating SMEs. Moreover,istinguishing between the privately financed and the publicly
nduced part of R&D investment, our findings point to significantlyigher output additionality for internationally collaborating firmss compared to nationally collaborating firms or non-collaboratorss well as for SMEs compared to large-sized firms. In other words,he results show that the subsidy-triggered R&D expenditures dondeed lead to product market innovations, especially so in theargeted groups.
The article proceeds as follows. Section 2 illustrates the Flem-sh policy design as well our research question. Section 3 brieflyeviews related literature. The empirical research strategy will beescribed in Section 4. Section 5 presents the data, Section 6 dis-usses the results and Section 7 concludes.
. Our research question in light of Flemish innovationolicies
The general feature of the subsidy scheme of the agency fornnovation by Science and Technology in Flanders/Agentschap voornnovatie door Wetenschap en Technologie in Vlaanderen (IWT), ists bottom-up character: it is a permanently open and non-thematiccheme. In other words, any firm can submit an R&D project at anyime of the year.3 Upon evaluation, the firm will get informed abouthether or not the proposed project has been accredited for pub-
ic support. The subsidies are granted as matching grants, that is,he firm can apply with a specific project and in case of a success-ul refereeing process the government covers a certain share of theroposed total project cost, usually between 30 and 50%.
In Flanders, the government spent 628 million euros for a totalf 3019 projects between 2002 and 2008. The policies in place inlanders during that time period comprise special features tar-eting SMEs as well as collaborating firms. The percentage to beorne by the government will depend on these features. To sup-ort small and medium-sized firms in conducting R&D projects,he government covers a higher share of their total R&D projectosts. That is, SMEs receive an additional 10% of their total R&Droject costs. Likewise, in order to encourage firms to collaborate,n additional 10% of the total project costs can be obtained if therm collaborates with one or more partners for its R&D activities.his amount is again linked to firm size: If a firm qualifies as anME, it receives a 10% top-up for national or international collabora-ion. If a firm qualifies as large-sized firm, it receives the additional
Please cite this article in press as: Hottenrott, H., Lopes-Bento, C., (Itargeted public R&D support schemes. Res. Policy (2014), http://dx.do
0% if at least one of its partners is an SME or an internationalartner.4 The rationale of the former element of the current R&Dolicy is based on the argument that SMEs are more often finan-ially constrained than larger firms (see for instance Czarnitzki and
3 The scope of the IWT funding scheme is large, and also comprises fundingrograms for public research centers, universities and other institutes for higherducation. However, given that this study focuses on firms, we refrain from goingnto detail on any of their other funding schemes.
4 The background information is based on Larosse (2001) and the IWT 2012 infor-ation brochure, where further and more detailed information on the functioning
n the IWT can be found.
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Hottenrott, 2011). Yet, SMEs do contribute considerably to knowl-edge creation and technological progress as younger, smaller firmstend to engage in more basic and radical innovation projects (seee.g. Henderson and Clark, 1990; Henderson, 1993; Schneider andVeugelers, 2010; Haltiwanger et al., 2010). Furthermore, SMEs arean important source of job creation as they constitute the major-ity of firms in Flanders. Being aware of these aspects, the Flemishfunding agency grants a higher subsidy to SMEs in order to incen-tivize them to become active in R&D or to enable them to pursueR&D projects at the desired level and scope.
The rationale of the second policy element, i.e. granting highersubsidies to collaborating firms in order to increase incentivesfor such collaborations, is based on well-known arguments thatstress the value of collaborations not only for triggering additionalR&D spending, but also for enhancing R&D productivity (e.g. VanLeeuwen, 2002; Janz et al., 2004; Sakakibara, 1997; Hagedoorn andNarula, 1996; Hagedoorn, 2002; Belderbos et al., 2004; Cassimanand Veugelers, 2002, 2005). In the case of Flanders, the benefitsfrom collaboration, and in particular of the cross-border type, maybe particularly pronounced as in a small country the pool of knowl-edge a firm can dig in on national territory is usually limited. Firmsmay thus benefit from the larger pool of knowledge provided byinternational collaboration partners that facilitate spillovers froma richer pool of other R&D-active firms (Griliches, 1995). Moreover,international R&D collaboration promises additional gains throughdirect access to knowledge that is relevant for foreign markets.While off-shoring of own R&D abroad may be costly and subjectto a liability of foreigners (Sofka and Schmidt, 2009), collaborat-ing with partner firms that are already active in the target marketsmay therefore constitute a more cost-efficient way of doing R&Dinternationally. International collaboration may thus be particu-larly beneficial for firms active in global markets and firms that are“lonely riders” in their domestic markets. Moreover, SMEs may findcollaborations to be an appealing strategy for the internationaliza-tion of their (R&D) activities.
One concern with such grant-based direct support for R&D is ofcourse that firms could substitute private investment with publicmoney. Put differently, instead of topping-up the public supportby the amount foreseen by the policy, they may top it up by less,respectively not at all. Likewise, firms may use the public money tofinance a project that they planned to do anyways. In that situation,no additional R&D spending takes place. In this case we would facepartial crowding-out (respectively full crowding-out).5 By design-ing R&D support schemes in a way to best target firms with thehighest crowding-in potential, governments aim at reducing thelikelihood that public money is wasted. However, the ex-post effec-tiveness of the design is not obvious ex-ante.
In order to gain some novel insights on the ex-post effective-ness, we estimate in a first step whether we find evidence on inputadditionality. In a second step, we estimate whether the additionalR&D induced by the public policy – controlling for other perfor-mance drivers – leads to higher innovation performance. Indeed,even if we were to find positive treatment effects and significanteffects of specific policy features, it is a priori not clear whether theundertaken projects induced by public support only have an impacton input additionality or whether they also impact output addi-tionality, as measured for instance by product innovations. Projectevaluation by funding agencies is generally not only concerned withthe financial criteria of a submitted project, but also the social and
nternational) R&D collaboration and SMEs: The effectiveness ofi.org/10.1016/j.respol.2014.01.004
economic return for the region (Larosse, 2004). In other words, thegovernment also finances, or even favors, projects of more radical orbasic research nature, generally linked to higher risks and financial
5 See for instance Czarnitzki and Lopes-Bento (2013) for a more detailed overviewon subsidy effects on input and output additionality.
˛ = E(R&D |S = 1, X = x) − E(R&D |S = 0, X = x) (3)
The construction of the control group depends on the algo-rithm chosen to conduct the matching. In the present analysis, we
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onstraints. If such policy is efficient, the likelihood of the selectedrojects to result in product innovations that can be labeled as mar-et novelties should be quite high, given that the latter are generallyriven by more radical R&D as opposed to incremental innovationsesulting more often in products that are new to the firm, but noto the market. In this case, one could expect to see a positive signif-cant effect of induced R&D investment on firms’ sales from marketovelties. On the other hand, however, it is not obvious to whichxtent the risk of failure might be higher when undertaking riskierr more basic projects. In other words, if the government were tonance too many too risky projects or R&D that is too far from thearket, one would not find a positive impact of publicly induced
&D on market novelties, even if there is evidence of additional R&Driggered by the subsidy. Given these opposing arguments, it is notlear a priori what to expect with respect to the output additionalityffect of the innovation policy in place.
. Related literature
The impact of public policies on firms’ innovative behavior hasttracted a lot of interest in the literature and most studies findvidence for crowding-in effects. Studies tackling this researchuestion used different types of estimation methods to take thendogeneity of receiving a subsidy into account, but with the excep-ion of (Wallsten, 2000) who find dollar per dollar crowding-out, allonclude that direct subsidies stimulate private R&D funding irre-pective of the used method (see Czarnitzki and Lopes-Bento, 2013or a recent review). This is also the case in previous studies forlanders by Aerts and Czarnitzki (2006), Aerts and Schmidt (2008)r Czarnitzki and Lopes-Bento (2012, 2013) who find that publicupport stimulates private R&D investment. While these studiesocus on additionality at the firm level, other studies have beennterested in the expected welfare effects of targeted R&D grants.akalo et al. (2013), for instance, use structural modeling to modelhe grant application and R&D investment decisions of the firms asell as the funding decision of the government agency. They find
hat the social rate of return on targeted subsidies is 30 to 50%,trongly suggesting that the benefits exceed the opportunity costf public funds.
A separate stream of research has expressed an increasednterest in the impact of collaboration on innovation perfor-
ance. Empirical findings generally confirm the expected positiveelationship between R&D collaboration on innovation perfor-ance (Van Leeuwen, 2002; Janz et al., 2004; Sakakibara, 1997;agedoorn and Narula, 1996; Hagedoorn, 2002; Belderbos et al.,004; Cassiman and Veugelers, 2002, 2005).
Subsidized collaborative R&D has received less attention inhe empirical literature so far. Exceptions are Sakakibara (2001)nd Branstetter and Sakakibara (2002) who analyzed Japaneseovernment-sponsored R&D consortia. Both studies found evi-ence that participating firms have higher R&D expenditures asell as more patents. Further, Czarnitzki et al. (2007) apply aatching estimator in a multiple treatment setting, analyze the
ffects of R&D collaboration and public R&D funding on R&D perales and patent outcomes for Germany and Finland and find thatollaboration has positive effects. Likewise, only a few studies haveistinguished the productivity effects of privately respectivelyublicly funded R&D. To the best of our knowledge, even thoughadsen et al. (2008) suggest that input and output additionality
re interrelated, the only exceptions are Czarnitzki and Hussinger2004) and Czarnitzki and Licht (2006) who find a positive impact
Please cite this article in press as: Hottenrott, H., Lopes-Bento, C., (Itargeted public R&D support schemes. Res. Policy (2014), http://dx.do
f publicly induced R&D investment on German firms’ patent activ-ty and Hussinger (2008) who analyses the effects on new productales for German firms. The present study is thus not only amonghe first to combine evaluation studies with insights from research
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on collaborative R&D, but also the first to study multiple-targetinnovation policy features in terms of input and output additionality.
4. Estimation strategy
4.1. Treatment effects analysis
The aim of the first part of the following analysis is to estimatethe treatment effect of a subsidy on an outcome variable of inter-est. In other words, we want to know if, and to which extent, thesubsidy impacts R&D investment. In order to do so, we test for theeffect of the subsidy receipt on the firms’ internal R&D spending byconducting a treatment analysis.
Econometric evaluation techniques have been developed toaddress the estimation of treatment effects when the availableobservations on individuals or firms are subject to a potential selec-tion bias (see Heckman et al., 1999; Imbens and Wooldridge, 2009,for surveys). This typically occurs when participants of a publicpolicy measure differ from non-participants in important charac-teristics. In the following we use propensity score matching, whichhas the advantage over selection models not to need assump-tions about functional forms and error term distributions.6 In otherwords, based on the probability of receiving a treatment (obtainedfrom a probit regression) conditional on a set of observable charac-teristics X, the propensity score is an index function summarizing ina single number (the score) the wide set of observable characteris-tics affecting the probability of receiving a treatment (i.e. a subsidyby the Flemish government). Matching on the propensity score hasthe advantage not to run into the “curse of dimensionality” since weuse only one single index as matching argument (see Rosenbaumand Rubin, 1983).
The fundamental evaluation question can be illustrated by anequation describing the average treatment effect on the treatedfirms:
˛TT = 1NT
NT∑t=1
(R&DTi −R&DC
i) (1)
where R&DTi
indicates the expenditure of treated firms and R&DCi
the counterfactual situation, i.e. the potential outcome whichwould have been realized if the treatment group (S = 1) had not
been treated. In other words, for the untreated firms, R&DCi
corre-sponds to their internal R&D expenditures. S ∈ {0, 1} indicates thereceipt of a subsidy and NT the number of treated firms.
For the matching estimator to be valid, we have to build on theconditional independence assumption introduced by Rubin (1977).That is, we have to observe all the important determinants drivingthe selection into program participation, namely the receipt of anIWT subsidy. In other words, after conditioning on X, the settingcomes close to an experimental setting, and we have no a priorijudgment about whether a firm receives or does not receive a treat-ment. Based on this assumption, we can estimate the counterfactualsituation by using a selected group of non-subsidized firms thathave similar characteristics in X:
E(R&DC |S = 1, X) = E(R&DC |S = 0, X) (2)
and the average treatment effect on the treated can be written as:
TT T C
nternational) R&D collaboration and SMEs: The effectiveness ofi.org/10.1016/j.respol.2014.01.004
6 See Hussinger (2008) for a detailed overview on these different estimationmethods.
onduct a variant of the nearest neighbor propensity score match-ng, namely caliper matching.7 Furthermore, we allow for twoather than just one nearest neighbor in our matching routine.8
he pairs are chosen based on the similarity in the estimated prob-bility of receiving a subsidy stemming from a probit estimation onhe dummy for the receipt of subsidies S.9
Finally, it is essential that there is enough overlap between theontrol and the treated group (common support). In practice, theamples of treated and controls are restricted to common support.
e thus calculate the minimum and the maximum of the propen-ity scores of the potential control group, and delete observationsn treated firms with probabilities larger than the maximum andmaller than the minimum in the potential control group.10
.2. Innovation performance analysis
We estimate whether the additional R&D induced by the pub-ic policy also leads to more R&D outcome. In other words, wenvestigate the effect of the “additionality” of an IWT subsidy onnnovation performance. Innovation performance is measured byhe firms’ success in bringing innovations to the market, i.e. by thehare of sales that can be attributed to products that were newo the market. Such market novelties are not only an indicatoror successful R&D outcome, but also reflect the radicalness of thenderlying R&D. Incremental R&D may rather result in product-ange innovations that may be new to the firm, but not new to thearket.Given that not every firm has market novelty sales, the outcome
ariable NOVEL is left censored. We therefore estimate Tobit modelso account for this censoring.11 Since the subsidies are matchingrants where the percentage of covered costs can vary, we split&D investment into the part that is induced by the policy and themount that a firm invested anyways from private resources asndicated in Eq. (1). In other words, we separate R&D expendituresnto two components: R&D expenditures which would have taken
lace even if the subsidy scheme was not in place (R&DC ) and thosexpenditures that were induced by the subsidy (˛TT).
Using ˛TT, we estimate whether the acceleration in R&D inducedy the subsidy (provided that ˛TT > 0) also triggers an increase inutput additionality, as measured by sold market novelties. In ordero obtain the estimated treatment effect at the level of the indi-idual firm, we calculate the difference between the overall R&Dnvestment and the counterfactual R&D investment as follows:
TTi = R&Di −R&DC
i(5)
Please cite this article in press as: Hottenrott, H., Lopes-Bento, C., (Itargeted public R&D support schemes. Res. Policy (2014), http://dx.do
For non-subsidized firms R&DCi
is equal to their R&D intensitynd ˛TT
iis equal to 0.
7 Caliper matching aims at reducing the bias by avoiding to match treated firmsith control firms above a certain “distance”, i.e. those firms for which the value
f the matching argument Zj is far from Zi . It does so by imposing a predefinedhreshold ε, above which an observation is deleted from the potential control group.
ore precisely, ||Zj − Zi|| < ε for a match to be chosen (see Smith and Todd, 2005).8 The rationale of drawing two rather than just one nearest neighbor is to avoid
hat the results suffer from small sample sizes (we have 272 subsidized firms inur final sample, after the common support and caliper conditions). Despite theact that two neighbors sensibly increase the bias, all our covariates remain per-ectly balanced after the matching. Using two neighbors reduces the variance of thestimates, allowing for a smaller asymptotic mean squared error.9 The detailed matching protocol can be obtained from the authors upon request.
10 Even though we think that our set of covariates allows us to assume that selec-ion on unobservable effects is unlikely, we tested the robustness of our mainndings to estimating using IV regressions. This test confirmed that the results stillold even if we abandon the CIA. The results of the IV regressions are available uponequest from the authors.11 See pp. 525–535 in Wooldridge (2002) for technical details of the Tobit model.
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The Tobit model to be estimated can be written as:
NOVEL∗ = X ′ + ε, (6)
where NOVEL* is the unobserved latent variable. The observeddependent variable is equal to
NOVEL ={
NOVEL∗ if X ′ˇ+ > 0
0 otherwise(7)
where X represents a matrix of regressors, the parameters tobe estimated and ε the random error term. Since the standardTobit model requires the assumption of homoscedasticity in orderfor the estimates to be consistent (see Wooldridge, 2002, pp.533–535), we conducted several tests on heteroscedasticity usinga heteroscedastic specification in the Tobit model. The LikelihoodRatio tests confirm the presence of heteroscedasticity. Hence, weestimated the heteroscedasticity-robust model by a maximum like-lihood function in which we replace the homoscedastic standarderror term � with �i = � exp(Z′˛) in the likelihood function. Weincluded size class dummies based on the number of employ-ees and industry dummies to model group-wise multiplicativeheteroscedasticity.
Finally, given that the measures of R&D are estimated values forthe treated firms, ordinary standard errors would be biased down-wards and using them as covariates would induce measurementerror. Therefore, we conduct the procedure 200 times to obtainbootstrapped standard errors for the Tobit estimates. It should benoted that the entire estimation is bootstrapped 200 times, i.e.including the matching routine. In other words, the bootstrap takesthe sample as the population and the estimates of the sample astrue values for all the steps of our estimation. This procedure thusallows us to estimate how the sample mean of our actual samplevaries due to random sampling.
5. Data and variables
The data used in this study stem from the Community Inno-vation Survey (CIS) from the Belgian region of Flanders. Moreprecisely, they stem from three distinct waves of the CIS. First,the CIS4, covering the years 2002–2004, second the CIS5, covering2004–2006 and third the CIS6 that refers to the period 2006–2008.This data has been complemented by accounting data from theBelfirst data base issued by Bureau Van Dijk. Finally, information onR&D subsidies has been retrieved from the ICAROS database pro-vided by the Flemish agency for innovation and technology (IWT).The latter provides detailed information on the amounts of thegrants (and grant history) as well as on the duration of the fundedprojects.
After elimination of missing values, our final sample consistsof 1973 year-firm observations (referring to 1593 different firms)and comprises innovative as well as non-innovative firms, cover-ing manufacturing as well as business related services sectors.12
Tables A.1 and A.2 in Appendix A show the industry structure as
nternational) R&D collaboration and SMEs: The effectiveness ofi.org/10.1016/j.respol.2014.01.004
well as the firm size distribution of the firms in the sample. Inthis final sample, 300 firms received a public R&D subsidy fromthe Flemish government.
12 According to the 3rd edition of the Oslo Manual – which is the definition fol-lowed by the CIS – an innovative firm is one that has implemented an innovationduring the period under review. An innovation is defined as the implementation ofa new or significantly improved product (good or service) or process or service (seeOECD/Eurostat, 2005).
In the first part of our analysis, we consider R&D investment,.e. the ratio of internal R&D expenditures13 to sales (multiplied by00) as the outcome variable (RDINT). In the second part, estimat-
ng firms’ innovation performance, the outcome variable is defineds sales generated from market novelties as percent of total salesNOVEL).14
.2. Explanatory variables
The receipt of a subsidy form the IWT is denoted by a dummyariable that is equal to one for firms that received public R&Dunding and zero otherwise (SUBS). Moreover, we employ severalontrol variables in our analysis that are likely to influence theelection into a public funding scheme or the firms’ innovationerformance. The number of employees (EMPL) takes into accountossible size effects. We also allow for a potential non-linear rela-ionship by including (ln EMPL2). As the firm size distribution iskewed, these variables enter in logarithms. We further include aummy variable that is equal to one if a firm qualifies as an SMESME).15
In addition, we include a dummy variable capturing whether orot a firm is part of an enterprise group (GP). Firms that belong to aroup may have a lower incentive to apply for subsidies since firmshat have a large majority shareholder do not qualify for the SMErogram in which higher subsidy rates are granted, even if they aremall. On the other hand, firms belonging to a group may benefitrom better communication structures and thus are better informedbout possible funding sources including public technology pol-cy programs. Furthermore, firms belonging to a larger network
ay be preferred by the funding agency as the group member-hip possibly promises knowledge spillovers and thus economiesf scope from the R&D process to a larger extent than for stand-lone companies. This might be even more pronounced for firmshat have an (international) network. For this reason, we accountn addition for the collaboration patterns at the sector level, cap-uring the collaboration propensity in the different industries andub-regions (COOP industry). In other words, this variable takes intoccount that firms active in certain industries might be more proneo engage into collaboration agreements, susceptible to influenceoth the likelihood of applying as well as of receiving a subsidy.ubsidiaries with a foreign parent (FOREIGN) may be less likely toeceive subsidies as the parent may prefer to apply in its homeountry or because the funding agency gives preference to localrms. Furthermore, foreign parents with Flemish subsidiaries areypically large multinational companies and thus the local sub-idiary does not qualify for special SME-support which reduces itsikelihood to apply. As a consequence, it is a priori unclear whetherhe effect of these variables is positive or negative because of thepposing arguments outlined above.
The log of the firm’s age (ln AGE) is included in the analysis aslder firms may be more reluctant to pursue innovation, and hencere less likely to apply for R&D funding, all else constant. Further-
Please cite this article in press as: Hottenrott, H., Lopes-Bento, C., (Itargeted public R&D support schemes. Res. Policy (2014), http://dx.do
ore, younger firms may be more likely to apply given that theyre more likely to be financially constrained.
R&D experience, especially if successful, may be a crucial deter-inant of applying for public subsidy schemes for future projects.
13 The CIS definition of R&D expenditure follows the Frascati Manual (OECD, 1993).14 Note that due to missing values in the dependent variable (NOVEL), the numberf observations drops from 1973 to 1533 observations in this part of the analysis.15 According to the EU’s definition, an SME should have less than 250 employeesnd has either sales less than 50 million euros (or a balance sheet total of less than3 million euros).
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Moreover, it may increase chances of a proposal being approved ifgovernments adopt a picking-the-winner strategy and favor firmswith previously successful R&D. Patents may thus signal R&D qual-ity and increase chances for future project proposals to be granted.To capture these dynamics, we include the firms’ past patent stock(PS) in our regression. The patent information stems from thedatabase of the European Patent Office (EPO). Patent stocks arecomputed as a time series of patent applications with a 15% rate ofobsolescence of knowledge capital, as is common in the literature(see e.g. Griliches and Mairesse, 1984; Jaffe, 1986):
PSi,t = (1 − ı)PSi,t−1 + PATAPPLi,t (8)
where PATAPPL is the number of patent applications in eachyear. The patent stock enters into the regression as patent stockper employee to avoid potential multicollinearity with firm size(PS/EMP).
Often governments do not only look at previous experiencewith conducting R&D projects when attributing a subsidy to a firm,but also at previous experience with a specific funding scheme.Hence, we also control for publicly supported R&D projects in thepast. We include a variable equal to the number of IWT co-fundedprojects a firm has completed within the three preceding years(SUBS PAST3YRS).
We also control for the firms’ activities in foreign marketsreflecting the importance of international competition by includ-ing a dummy equal to one if a firm is export active (EXPORT). Firmsthat engage more heavily in foreign markets may be more innova-tive than others (Bernard and Jensen, 1999, 2004) and, hence, morelikely to apply for subsidies. We further include the labor produc-tivity as a covariate, measured as sales per employee, LABPRO, sincehigh labor productivity may be a relevant determinant for receiv-ing public funds if the government follows a picking-the-winnerstrategy rigorously.
We further control for the firms’ collaboration activity. We canderive directly from the survey whether a firm collaborated forits R&D activities (CO). In addition, firms are asked to indicatethe partner’s location. Thus, we identify international collabora-tors as firms that have at least one partner outside of Belgium(CO INTERNATIONAL) and national collaborators as firms that haveexclusively Belgian collaborating partners (CO NATIONAL).
Finally, ten industry dummies control for unobserved hetero-geneity and technological opportunity across sectors and threetime dummies, one for each wave of the survey, are included tocapture macroeconomic shocks.
5.3. Timing of variables
Given that each wave of the survey covers a three-year period,we employ lagged values wherever possible in order to avoid directsimultaneity between the dependent variables and the covariatesto the largest possible extent. For instance, if the dependent vari-ables are measured in period t, then EMP, PS/EMP, LABPRO andEXPORT are measured at the beginning of the survey period, i.e.in t − 2.
Attributes that are usually highly persistent over time like theinformation on group membership and foreign ownership (GP andFOREIGN), are available such that they refer to the whole 3-yearperiod, i.e. t − 2 to t. For instance, “Did your firm belong to a groupduring the period 2004–2006?”. Likewise, we consider AGE as trulyexogenous and hence it is measured in period t.
nternational) R&D collaboration and SMEs: The effectiveness ofi.org/10.1016/j.respol.2014.01.004
5.4. Descriptive statistics
Table 1 shows the descriptive statistics for the variablesemployed at the first stage of our analysis. As shown by the t-tests,
Log-likelihood −599.207Joint sig. of time dummies �2(2) = 16.10***Joint sign. of industry dummies �2(9) = 56.74***
RDINT Ratio 7.932
otes: *** (**, *) indicate a significance level of 1% (5%, 10%).
lmost all variable means are significantly different between thereated and the non-treated firms.
For instance, on average, treated firms are larger than non-reated firms. While, on average, a treated firm has some 100mployees, an untreated firm employs about 45. Treated firmslso belong more often to a group and are more export orientedhan non-treated firms. Furthermore, we can see that while a non-reated firm has 3 patents per 1000 employees, a treated group hasn average 6 times more patents per 1000 employee. In addition,ubsidized firms belong more often to an industry prone to collab-rate and engage significantly more in collaboration agreements,oth nationally and internationally. For instance, while 66% ofreated firms engage in national collaboration agreements (18% innternational ones), less than half as many untreated firms engagento national collaborations, with a mere 30% (with 11% engagingnto cross-border collaboration). Further, treated firms have had
ore previously government co-funded projects. Interestingly, weo not see a difference between the shares of firms with a foreigneadquarter in the subsidized and un-subsidized sub-samples ando difference in terms of average firm age and labor productivity.ith respect to the outcome variable (RDINT), we find – as expected
that subsidized firms are more R&D-intensive. At this point, how-ver, it is not clear how much of this difference can be attributed tohe financial support provided by the subsidy and how much to theact that R&D-active companies are more likely to apply for R&Dubsidies.
. Empirical findings
.1. The average treatment effect on the treated
As previously explained, in order to apply the matching esti-ator, we first estimate a probit model to obtain the predicted
robability of receiving a grant from the Flemish funding agency.s we can see in Table 2, with the exception of labor productiv-
ty and belonging to a group, all of our covariates are statisticallyignificant and hence important characteristics in driving the selec-ion into the public funding scheme. Even though the share ofollaborators by industry is not individually significant, a test onoint significance on the share of collaborators, national collabo-ators and international collaborators displays highly significantesults (�2(3) = 85.61***). As a consequence, we let all three con-
Please cite this article in press as: Hottenrott, H., Lopes-Bento, C., (Itargeted public R&D support schemes. Res. Policy (2014), http://dx.do
rols enter the model. The same is true for the size variables.ven though they are not individually significant, jointly theest displays that these characteristics should be controlled for�2(3) = 20.80***).
The model contains a constant, industry and year dummies (not presented).*** (**, *) indicate a significance level of 1% (5%, 10%).
We also included interaction terms between the policy featurecharacteristics, i.e. between size and collaboration status. How-ever, the latter were neither individually nor jointly significant.As a consequence, we dropped them from the probit estimation(joint significance of SME*NATIONAL and SME*CO INTERNATIONALis rejected with �2(2) = 4.00).
A precondition for the matching to be valid is to have commonsupport. We reinforced this condition by imposing a caliper. In total,we lose 17 observations because of the common support conditionand 11 because of the caliper. Our final sample hence consists of272 subsidized firms.
As displayed in Table 3, all our covariates are well balanced afterthe matching as we no longer find significant differences in thevariable means. We can thus conclude that our matching was suc-cessful. The only difference that remains is in our outcome variable.Hence, we can conclude that this difference can be attributed tothe treatment, and that we can reject the null hypothesis of totalcrowding-out. The estimated treatment effect on R&D intensity isabout 3.033 percentage points which is very similar to previouslyfound treatment effects for Flemish firms.
6.2. The impact of specific policy features on the estimated
nternational) R&D collaboration and SMEs: The effectiveness ofi.org/10.1016/j.respol.2014.01.004
treatment effects
A central question that arises from the design of the Flem-ish innovation policy is whether the specific features do indeed
** (**, *) indicate a significance level of 1% (5%, 10%).
ave the desired positive impact on the estimated treatment effect.sing the obtained treatment effect from the matching estimations our new dependent variable, we run several OLS regressions inrder to analyze the impact of certain specific policy features on thereatment effect. In order to do so, we regress the individual treat-
ent effect ˛TTi
on firm size and collaboration dummies. Besideshe policy design dummies, we further control for the number ofubsidized project a single firm has at the same time. Indeed, it isossible for a same firm to submit several projects and hence to getubsidies for more than one project at the same time. Based on thendings of Czarnitzki and Lopes-Bento (2013), concluding that thereatment effect increases with the number of subsidized projects arm has at the same time, we control for this possibility by includ-
ng a variable taking into account the number of simultaneouslynanced projects one firm has (SUB PROJECTS).16 The equation toe estimated can be expressed as:
TTi = ˇ0 +
∑m
1ˇ(policy design dummies)i
+ ˇn(SUB PROJECTS)i + ε, (9)
here the m policy design dummies comprise: (i) an SME dummy,ii) two dummies equal to one if a firm qualifies as a smallespectively a medium-sized firm, (iii) two dummies for national,espectively international collaboration as well as (iv) dummies forpecific collaboration partner location. 48% of the firms in our sam-le do engage in some form of collaborative R&D. 12% collaborateith other firms in Belgium, but not with firms abroad. 36% have at
east one international partner. These partners are located in withinhe European Union in most cases (for 87% of the firms). 34% have
partner in the US and 20% somewhere in the rest of the world.f course firms can have multiple partners in several locations.escriptive statistics of these variables are presented in Table A.3
n Appendix A.The results of the impact of collaboration status and firm size are
isplayed in Table 4. As we can see in Model 1, SMEs have on aver-
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ge a higher treatment effect compared to larger firms. In Model the effect of collaborating (differentiating between national and
nternational collaboration) is included. While qualifying as an SME
16 The number of simultaneously financed projects enters the equation as a slopeoefficient, having the same slope for all the firms in the sample, independent ofrm size or collaboration status. When interacting the number of financed projectsith firm size, for instance, we did not find evidence that the slope would be signif-
cantly different for large rather than medium or small sized firms. We thus leavehis variable in without interacting it with other firm characteristics.
4.065 11.249 ***
remains highly significant, being engaged in (international) collab-oration does not display any significant impact on the magnitude ofthe treatment effect. These conclusions hold when differentiatingbetween small and medium sized firms in Model 3. However, wedo not find a significant difference between the coefficients of smalland medium firms (see test at the bottom of Table 4), reaffirmingthe effectiveness of an overall SME policy.17 In light of these find-ings and given the important number of SMEs in the Flemish region,one interesting question would be to assess whether internation-ally collaborating SMEs differ in their treatment effect from otherfirms. For this purpose, we introduce interaction terms betweensize and international collaboration status. While the current policyoffers a higher subsidy rate to collaborators provided that at leastone qualifies as an SME or one is an international partner, we areinterested in knowing whether further incentivizing internationalcollaborating SMEs would display significant impacts. Especially forSMEs, cross-border collaboration may be an appealing strategy tointernationalize their R&D activities. When introducing an interac-tion term between being an SME and an international collaboratorin Model 4, we indeed find that the coefficient is positive, albeitonly at a 10% level. When separating between small and medium-sized firms in Model 5, we find a positive interaction term for both,small as well as medium-sized firms. These findings suggest thatspecial features for SMEs that engage into cross-border collabo-ration might be effective. In other words, the current R&D policymay be more effective if it targeted international R&D collabora-tion in SMEs more directly or intensively. Put differently, instead ofincentivizing partnerships with either an SME or an internationalpartner, the policy could pay closer attention to partnerships withininternational SMEs.
6.3. The impact on innovation performance
We turn next to our assessment of innovation performance,measured as sales generated from market novelties as percentof total sales. Specifically, we report in Table 6 the results of the
nternational) R&D collaboration and SMEs: The effectiveness ofi.org/10.1016/j.respol.2014.01.004
heteroscedasticity-robust Tobit model on NOVEL. The averagesales share from NOVEL in our sample is 9.77 (percent of turnover).Table 5 presents the average value for NOVEL for different
17 According to the EU’s definition, a firm qualifies as small-sized firm if it has fewerthan 50 employees and a turnover of less than 10 million euros or a balance sheettotal of less than 10 million euros. A firm is considered medium-sized if it employsbetween 50 and 250 employees and has a turnover of more than 10 but less than 50million euros.
Overall model significance 6.72** 3.80** 3.21** 3.18*** 2.59**Test SMALL = MEDIUM (§interactions) 1.22 §0.47
*
sgf8ninm5
tsecRis1siIiotRs
TM
*
** (**, *) indicate a significance level of 1% (5%, 10%).
ub-samples and reveals interesting differences between theseroups. SMEs achieve a significantly higher share of their turnoverrom market novelties compared to larger firms (10.27 versus.13). Likewise, collaborating firms show higher values thanon-collaborators (11.11 versus 8.30). Interestingly, the difference
s only significant for international collaborators, not for firms withational partners only. Finally, we see that the sales share fromarket novelties within the group of subsidized firms is on average
percentage points higher than within non-subsidized firms.We can see that in all the Models presented in Tables 6a and 6b,
he R&D spending in the counterfactual situation (R&DC ) – i.e. R&Dpending in absence of the subsidy – exhibits a significant positiveffect on the share of sales from market novelties. For instance, wean see that in Model 1, an increase of 10% in the counterfactual&D intensity would lead to an increase of 5 percentage points
n the estimated latent dependent variable, i.e. the estimatedales share in market novelties, on average. Furthermore, Model
shows that the subsidy-triggered R&D has a positive impact onales market novelties. With respect to the coefficient, we find thatt is similar in size to the coefficient of the privately induced R&D.n other words, the impact inflicted by private and public R&Dnvestment is of similar magnitude. On top of estimating the effectsf privately financed and publicly induced R&D, Model 1 shows
Please cite this article in press as: Hottenrott, H., Lopes-Bento, C., (Itargeted public R&D support schemes. Res. Policy (2014), http://dx.do
hat collaborating has a positive effect on NOVEL. Collaborating in&D activities induces, on average, an increase in the estimatedales share from market novelties by 6 percentage points. When
able 5arket novelties in targeted groups.
Variable NOVEL*
# obs. Mea
SME = 1 1175 10.2SME = 0 358 8.1CO = 1 801 11.1CO = 0 732 8.3CO INTERNATIONAL = 1 610 11.6CO INTERNATIONAL = 0 923 8.5CO NATIONAL = 1 191 9.4CO NATIONAL = 0 1342 9.8SUBS = 1 270 13.6SUBS = 0 1263 8.9
** (**, *) indicate a significance level of 1% (5%, 10%).* Note that the total sample size is reduced to 1533 for NOVEL due to missing values.
interacting collaboration with the privately (R&DC ∗ CO) as wellas the publicly induced part of R&D (CO*˛TT), we see that theprivately financed R&D is significant for both collaborating aswell as non-collaborating firms. The policy-induced investment,however, is only significant for collaborators (Model 2).
In Model 3, we go a step further and distinguish betweennational and international collaboration. We can see that thesignificant result of collaboration was driven by international col-laboration as it captured the full effect from collaboration in generaland the coefficient of CO NATIONAL is insignificant. In Model 4we distinguish between partner locations and find that having apartner within the EU has a significant impact on sales in marketnovelties.
When interacting both types of R&D investment with interna-tional collaboration (Model 5), we find that the private part of theR&D investment is significant for both, international collaboratorsas well as for the other firms, whereas the policy-induced part onlydisplays a significant result when received by international collabo-rators. In other words, while the private part of invested R&D alwayshas a positive impact on marketable products, the governmentalsupport only displays an effect when the recipient firm collabo-rates with one or more partners. This finding may suggest thatknowledge spillovers from partner firms contribute substantially
nternational) R&D collaboration and SMEs: The effectiveness ofi.org/10.1016/j.respol.2014.01.004
to the firms’ success when introducing radical innovations. Thismay be attributed to the fact that, in line with the policy’s objec-tive, firms’ may be incentivized to undertake riskier, more basic and
tandard deviations in parentheses are bootstrapped (200 replications). Time dummf the Tobit models. All models contain a constant, industry and year dummies (not** (**, *) indicate a significance level of 1% (5%, 10%).
ore radical R&D projects, which are also more resource intensivend therefore might only become feasible when undertaken by con-ortia. Being engaged in collaboration contributes to both increasedncentives to invest in R&D as free-riding is reduced and higher R&Droductivity as a result of pooled of knowledge and exploitation ofomplementary assets.
In order to be able to assess whether international collabora-ion has an added value compared to national collaboration only,e reduce the sample to collaborating firms only in Model 6.hile in Model 5 the term 1-CO INTERNATIONAL included also non-
ollaborating firms, in Model 6, it will capture exclusively nationalollaborators. The results show that for both types of R&D spending,
Please cite this article in press as: Hottenrott, H., Lopes-Bento, C., (Itargeted public R&D support schemes. Res. Policy (2014), http://dx.do
he firms’ investment is more productive if the firms are engaged innternational collaboration as compared to national collaborationnly, reaffirming our previous findings.18
18 We also tested the effect of national collaboration versus no collaboration inhe sub sample of firms that excluded international collaboration. The interactionlope coefficient of CO NATIONAL and the policy induced investment is statisticallyot significant, neither for national nor for non-collaborators. These results confirm
nsights from Model 6 that the added-value stems for international collaboration.herefore, the results are not reported in detail.
dustry dummies) are jointly significant in the individual models in each replicationnted).
In Model 7 we interact R&DC and the treatment effect with theSME dummy. We see that both types of R&D investment have asignificantly positive effect on NOVEL for SMEs when compared tolarge firms.19 As could already be gathered by the descriptive statis-tics, this was to be expected. Indeed, it is often smaller and youngerfirms that undertake more basic and more radical research, able totranslate into market novelties.
Finally, we find for all models that age and size have non-lineareffects, with a significant negative impact on market novelties salesfor firms larger than about 115 employees and for firms older thanabout 17 years of age. This finding is in line with our expectations,given that often younger and smaller firms pursue more radicalinnovation that make up for a larger share of market novelty sales.
nternational) R&D collaboration and SMEs: The effectiveness ofi.org/10.1016/j.respol.2014.01.004
We also controlled for other characteristics likely to influence mar-ket novelty sales like for instance the patent stock per employeeand the number of competitors, as well as for headquarter location.
19 We also tested whether there was an effect if one differentiates between smalland medium sized firms individually given the large number of SMEs in our sample.However, there is no significant difference between small and medium firms interms of the productivity of the policy-induced R&D. Therefore, we do not reportthe results in details.
Table 6bHeteroscedasticity-robust Tobit results on innovation success (NOVEL).
Variables Model 7
SME*R&DC 0.671***(0.205)
(1-SME)*R&DC 0.206(0.489)
SME*˛TT 0.506*(0.306)
(1-SME)*˛TT 0.503(1.578)
SME 4.648(4.064)
CO INTERNATIONAL 6.729***(2.407)
CO NATIONAL 4.101(3.731)
ln(AGE) −8.440**(3.826)
ln(AGE)2 1.452**(0.652)
ln(EMP) −5.346**(2.450)
ln(EMP)2 0.713*(0.371)
# Observations 1533� 16.556***
N
Gt
7
eoctp
tdssfSftnim
aoa
(4.137)
otes: see Table 6a.
iven that we did not find significant effects for these variables,hey were not included in the final models.20
. Discussion and conclusions
The present paper provides new insights with respect to thevaluation of direct subsidies for R&D and innovation. The aimf the analysis was firstly to evaluate if specific policy featuresurrently in place in Flanders are effective in terms of input addi-ionality, and, secondly, whether the effect triggered by theseolicies also translates into higher output additionality.
With respect to input, we can, in line with the literature, rejecthe null hypothesis of total crowding-out of firms’ own R&D effortsue to public support. We indeed find that subsidies trigger R&Dpending in the private sector. When analysing the impact of thepecific policy features on the treatment effect, we find evidenceor the efficacy of the policy currently in use. The results show thatMEs do have a larger treatment effect than larger-sized firms. Weurther find that internationally collaborating SMEs have a larger
Please cite this article in press as: Hottenrott, H., Lopes-Bento, C., (Itargeted public R&D support schemes. Res. Policy (2014), http://dx.do
reatment effect than internationally collaborating larger firms oron-internationally collaborating SMEs, and that there is no signif-
cant difference between small international collaborators versusedium-sized ones. This finding may provide the grounds on which
20 In order to test whether our collaboration variables are endogenous, we used structural equation model (Smith and Blundell, 1986) to test for the endogeneityf those variables. We find that exogeneity cannot be rejected. Detailed results arevailable upon request.
PRESSrch Policy xxx (2014) xxx–xxx
the existing policy design can be improved so as to target thesegroups in particular, i.e. conditioning the percentage of costs cov-ered not on either having an SME or and international partner,but further favor the firms that fulfill both conditions simultane-ously.
The implementation of the results from the treatment effectsanalysis into a series of innovation output models brought for-ward additional insights. Both, privately financed as well as publiclyinduced R&D have significant positive effects on firms’ innova-tiveness. Leading to higher sales from market novelties, publicco-financing of R&D projects seemed to have encouraged R&D ofmore fundamental nature. Further, we find that the effect of policy-induced R&D on market novelties is highest for internationallycollaborating firms. With respect to firm size, we find that both,privately as well as publicly induced R&D, have a positive impacton sales from market novelties for SMEs. This is not necessarilysurprising as smaller and younger firms often undertake more basicand radical innovation, which would be the kind of research result-ing in product market novelties. Interesting is, however, that R&Dinduced by public subsidies does indeed contribute to innovationperformance of SMEs.
While this paper provides new insides to the effect of R&D poli-cies on firms’ innovative behavior, it has some caveats that oughtto be addressed by future research. First, it would be advanta-geous to have longer time lags between the receipt of a subsidyand market novelty sales. Second, given that governments alsoaim at stimulating employment, evaluating whether and to whichextent the higher innovation performance translates into employ-ment growth could constitute an interesting extension to thisstudy. Third, it would be interesting to see if and how the resultswould be affected if partner type and mode of collaboration wastaken into account. Finally, our results are based on data for theregion of Flanders. It would thus be of particular interest for pol-icy makers to know whether these findings are specific to Flanders,a small open economy, or whether some of these seemingly effi-cient policy features might also be effective in larger regions orcountries.
Acknowledgments
We thank René Belderbos, Dirk Czarnitzki, Bernd Ebersberger,Jeffrey Furman, Alfonso Flores-Lagunes, Katrin Hussinger, PierreMohnen, Otto Toivanen, Philippe Van Kerm, Reinhilde Veugelersand two anonymous referees for very helpful comments. Finan-cial support from the IWT is gratefully acknowledged. Furthermore,Cindy Lopes-Bento is grateful for financial support by the National
nternational) R&D collaboration and SMEs: The effectiveness ofi.org/10.1016/j.respol.2014.01.004
Research Fund, Luxembourg, cofounded under the Marie CurieActions of the European Commission (FP7-COFUND) and HannaHottenrott appreciates research funding from the Research Foun-dation Flanders (FWO).
Supplementary data associated with this article can beound, in the online version, at http://dx.doi.org/10.1016/j.respol.014.01.004.
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