THE EFFECT OF THE INCREMENTAL R&D TAX CREDIT ON THE PRIVATE FUNDING OF R&D: AN ECONOMETRIC EVALUATION ON FRENCH FIRM LEVEL DATA

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1 THE EFFECT OF THE INCREMENTAL R&D TAX CREDIT ON THE PRIVATE FUNDING OF R&D: AN ECONOMETRIC EVALUATION ON FRENCH FIRM LEVEL DATA Emmanuel Duguet To cte ths verson: Emmanuel Duguet. THE EFFECT OF THE INCREMENTAL R&D TAX CREDIT ON THE PRIVATE FUNDING OF R&D: AN ECONOMETRIC EVALUATION ON FRENCH FIRM LEVEL DATA <halshs > HAL Id: halshs Submtted on 27 Feb 202 HAL s a mult-dscplnary open access archve for the depost and dssemnaton of scentfc research documents, whether they are publshed or not. The documents may come from teachng and research nsttutons n France or abroad, or from publc or prvate research centers. L archve ouverte plurdscplnare HAL, est destnée au dépôt et à la dffuson de documents scentfques de nveau recherche, publés ou non, émanant des établssements d ensegnement et de recherche franças ou étrangers, des laboratores publcs ou prvés.

2 DOCUMENT DE TRAVAIL THE EFFECT OF THE INCREMENTAL R&D TAX CREDIT ON THE PRIVATE FUNDING OF R&D: AN ECONOMETRIC EVALUATION ON FRENCH FIRM LEVEL DATA Emmanuel DUGUET Unversté Pars Est, ERUDITE (EA 437) and TEPP (FR CNRS 326) Address: Unversté Pars-Est, ERUDITE, Mal des Mèches, 6 avenue du Général de Gaulle, 9400 Crétel cedex. Emal: emmanuel.duguet@u-pec.fr. The author thanks B. Guédou for hs suggestons and comments on a prevous verson of ths paper, as well as the partcpants to the EPEE Semnar (Unversty of Evry), to the «Economcs and Econometrcs of Innovaton» Semnar (Unverstes of Pars I-CES, Pars II-ERMES and ENST-SES) and to the OECD «TIP Workshop» (December 2007, Pars).

3 Résumé Nous étudons, sur données d entreprses, s le crédt d mpôt recherche (CIR) en accrossement a augmenté le fnancement prvé de la recherche et développement sur la décenne Afn de répondre à cette queston, nous utlsons les enquêtes annuelles du Mnstère de la Recherche, ans que les données fscales ndvduelles relatves au CIR. La queston prncpale est de détermner s les entreprses auraent quand même augmenté leur fnancement prvé en l absence d nctaton fscale. Nous utlsons une méthodologe à la Rubn. Dans un premer temps, nous étudons les détermnants de la probablté d obtenton du crédt d mpôt recherche, c'est-à-dre le processus de sélecton des partcpants à cette mesure. Nous trouvons que la probablté d obtenton du crédt d mpôt recherche en accrossement augmente avec le rato R&D/Ventes et décroît avec l obtenton de subventons à la R&D. Après avor évalué la probablté d obtenton du CIR de chaque entreprse nous corrgeons les bas de sélecton. Dans un second temps, nous évaluons l effet du CIR en accrossement sur le fnancement prvé de la recherche (net de subventons attrbuées par d autres sources). Nous trouvons que, globalement, le crédt d mpôt s ajoute au fnancement prvé de la recherche, de sorte qu un euro de CIR mène à un accrossement d un peu plus d un Euro de recherche totale. Nous trouvons également que le CIR en accrossement augmente le taux de crossance du nombre de chercheurs employés dans les entreprses. Mots-Clés : crédt d mpôt, évaluaton, recherche et développement Classement JEL : H25, O32, O38 Abstract We study, at the frm level, whether the ncremental R&D tax credt ncreases the prvate fundng of R&D. In order to answer ths queston, we use the yearly surveys of the Mnstry of Research over the perod , as well as the correspondng frm-level tax fles. The man ssue s whether the frms would have ncreased ther R&D expendtures wthout ths tax ncentve. We make use of the Rubn methodology. In a frst step, we study the determnants of the probablty to beneft from the R&D tax credt, that s the selecton process at work n the recpents sample. We fnd that the probablty to obtan a R&D tax credt ncreases wth the R&D/Sales rato and decreases wth the drect R&D subsdes. Once we have evaluated the probablty to get the R&D tax credt, we are able to correct for the selecton bas. In a second step, we evaluate the effect of the ncremental R&D tax credt on the prvate fundng of research (once subtracted the drect subsdes from all the mnstres). We fnd that, overall, the tax credt adds to the prvate fundng of R&D: Euro of tax credt would gve slghtly more than one Euro of total R&D. We also fnd that the ncremental R&D tax credt ncreases the growth of the number of researchers. Keywords: tax credt, evaluaton, research and development JEL Classfcaton: H25, O32, O38 2

4 Introducton The many emprcal studes that have been conducted n OECD countres over the past ffteen years show that research and development sgnfcantly mproves the frms performances, however measured (Klenknecht ed, 996; Klenknecht and Mohnen eds, 2002). The economc performance of a country therefore depends on ts capacty to provde the rght ncentves to research and development (R&D) n the prvate sector. Unfortunately, R&D rases specfc nvestment ssues that do not always occur for physcal nvestment. Frst, R&D creates nformaton, and t cannot always be protected effcently from compettors. Through mtaton, some frms can get a part of the benefts of ther compettors nvestments. Second, n some specfc markets, the competton focuses on nnovaton tself more than on the organzaton of producton. A frm that launches a new product gets a strategc advantage. Thrd, the output of research s naturally random. These three elements reduce the prvate ncentves to perform research nvestments compared to physcal nvestments. If no polcy measure was to be taken, frms would show a tendency to under nvest n R&D (D Aspremont and Jacquemn, 988; Kamen, Muller and Zang, 992). In order to acheve hgher ncentves for R&D, one needs to make ths actvty more proftable from the frms vewpont. Two types of measures have been mplemented: On the one hand, enforcng stronger property rghts and, on the other hand, promotng fnancal support to R&D. The frst type of measures reles on patents and brands. By forbddng mtators to get, freely, the benefts from the nvestments of nnovators, the property rghts am to ncrease the gross return on R&D. Ths frst array of polces s often not suffcent, because t s hard to apply property rghts n some lnes of busness. Moreover, n order to protect themselves from mtaton, the frms have to ncur addtonal secrecy costs that reduce the return on R&D (Levn et al., 987, Duguet and Kabla, 998). The second type of measure s based on R&D subsdes and R&D tax credt. Ther goal s to reduce the sunk cost of R&D. Compared to subsdes, the fscal regulatons present some advantages and one drawback. Among the advantages, fscal ncentves apply equally to all the frms whatever ther lne of busness or ther sze are; they also let the ntatve of the technologcal choces to the market, that s to the consumers of the new products; moreover, they reduce the admnstratve burden to fulfllng a smple form. The man drawback, compared to subsdes, s that there s no selecton of the R&D projects by an ndependent nsttuton. Therefore, there s a hgher probablty that frms that dd not need any publc support for ther R&D projects apply for the R&D tax credt. In ths last case, t s dffcult to predct the effect of the tax credt on the prvate fundng of R&D. 3

5 Accordng to a recent study (Warda, 2006), the practce of R&D tax credt s expandng among the OECD countres, from 2 countres n 996 to 9 countres n Ths expanson of fscal deductons rases the addtonal ssue of fscal competton among countres, whch could nfluence the locaton of the prvate R&D laboratores. Therefore, n addton to the classc ssue of the prvate ncentves for R&D, the expanson of R&D tax credt could also exert an nfluence over the locaton of the future knowledge producton. In France, the R&D tax credt («Crédt d Impôt Recherche», CIR) was ntroduced n 983. Untl 2003, t was computed on the growth of R&D nvestments so that only frms wth a steadly ncreasng R&D budget could beneft fully from t. Ths tax credt was equal to the half of R&D growth, and was refundable when the frms pad no taxes. Snce 2004, ths frst tax credt has been completed by a volume tax credt, whch s a tax credt computed on the amount of R&D regardless of the R&D growth. Up to 2005, the frms located n France have a rght to a refundable R&D tax credt equal to the sum of 5% of ther R&D total amount and to 45% of ther R&D growth. Ths polcy drecton has been extended n 2006 wth 0% for the amount and 40% for the growth of R&D expendtures. It s notceable that ths doublededucton system now exsts n several countres, even though the elgble expendtures dffer from one country to another, that the tax credt s not always refundable, and sometmes even taxable (n 9 countres out of 9). Out of 9 OECD countres wth a R&D tax credt, 8 countres apply a volume tax credt (the excepton beng the USA) and 8 countres apply a growth tax credt. Seven countres apply a double-deducton system but wth dfferent elgblty rules (Australa, Austra, France, Ireland, Korea, Portugal and Span). Ths tax measures also nvolve a revenue loss for the State. It s qute dffcult to make an nternatonal comparson on ths varable. For France, averaged over the decade , the R&D tax credt represents 465 mllons Euros a year. The extenson to a volume tax credt n 2004 almost doubled ths fgure, up to 890 mllons Euros, and has strongly ncreased afterwards (.5 bllons Euros n 2006). For 2008, the ncremental part of the tax credt has been suppressed and the frms can get 30% of ther total expendtures, wth an estmated cost of 4 bllons Euros. From the vewpont of the socety, these fscal deductons are nterestng only as far as they really ncrease the prvate R&D nvestments. The fact that they are taken on publc funds (exactly lke subsdes, from a publc fnance vewpont) calls for the evaluaton of ths fscal measures. Ths frst motvaton can be renforced by notcng that, for France, ts budget cost has consderably ncreased and, more generally, that these measures have been extended to larger number of nnovatng countres. 4

6 The publc support to R&D faces the followng evaluaton problem. Two stuatons can occur when a tax credt s granted. On the one hand, a frm can ntegrate the tax credt nto ts nvestment decson process and decde to nvest more because the deducton exsts (.e. because the prce of R&D s lower wth the tax deducton). But, on the other hand, t s possble that a frm does not account for ths measure at the nvestment decson stage, realze afterwards that t has the rght to a tax deducton, and asks for t n order to ncrease ts proft. In the former case, the tax measure has the expected effect, whle n the latter case t has no effect at all on prvate R&D. At the frm level, t s lkely that both stuatons occur, therefore we wsh to evaluate the average effect of the measure, n order to know whether the postve effects domnate or not. An mportant pont when performng the evaluaton s to consder a perod wth no polcy change on the R&D tax credt at all. For ths reason, we have restrcted our data to , where the R&D tax credt s computed on the same elgble expendtures, on the growth of R&D, wth a flat rate of 50%, refundable and not taxable. 2 Data sources and sample statstcs An nterestng qualty of our data set s that t ncludes true fscal data at the frm level and not a theoretcal computaton of frms rghts. It s mportant snce all frms that could apply to the R&D tax credt do not apply for t n practce. 2 Ths feature of the data allows to defne a control group and to apply the Rubn methods explaned below. Ths would not be possble wthout such data snce a theoretcal computaton would allocate a tax credt to all frms wth a R&D growth. Here, among frms wth a postve R&D growth, we can separate frms that get a tax credt from the other frms. 2. Data Sources The data comes from three dfferent sources: - the «enquêtes annuelles d entreprses» (yearly frm census) provde nformaton on the accountng data (sales, lne of busness) ; 2 Ths fact s well known at the Mnstry of Research and seems do derve from ether a bad knowledge of tax deductons or from the erratc nature evoluton of growth tax credt. The latter pont s consstent wth the fact that when the system was extended to the level of R&D n 2004, some frms entered the system for the frst tme, whle they were performng R&D for a long tme. 5

7 - The R&D survey s collected by the Mnstry of Research and provdes nformaton at the frm level on R&D, and especally the amount of subsdes from all the mnstres. Matchng wth ths data set s mportant because t allows to correct the R&D amounts from subsdes and to determne the part of R&D expendtures that s prvately funded; - The tax credt (CIR) fle s collected by the fscal admnstraton (Drecton Générale des Impôts). It s not a survey; t s exhaustve and ncludes the amount of tax credts granted at the frm level. 3 It allows to perform a study on the real recpents of the tax credt and to remove the bas related to self-selecton. We use the Frascat (OECD, 2002) defnton of R&D provded by the R&D survey. Our man performance varable s computed from the prvate fundng of R&D that we defne as the dfference between the total R&D expendtures on the one hand and the sum of all subsdes and tax credt on the other hand. It represents the amount of R&D actually pad by the frm. We also use data on R&D personnel n order to examne the robustness of our results. In order to evaluate the effect of the R&D tax credt on the growth rates of the prvate fundng of R&D, we have matched our samples by couple of years over the perod Ths conventon nsures that there reman a large number of frms n the sample. 5 After these operatons, we have 0 samples coverng the two-year perods to A descrpton of the samples s provded n Table. The samples nclude between 33 and 645 frms each year, n all lnes of busness (ncludng servces). Approxmately 20% of the frms beneft from the tax credt alone or from a subsdy only and 7% beneft from both a subsdy and the tax credt. Over the decade, between 50% and 60% of the frms have benefted nether from a subsdy nor from a tax credt. 6 Ths pont s mportant because the evaluaton reles on a comparson between the recpents and the other frms. In the evaluaton lterature, the nonrecpents represent the reservor from whch we extract the counterfactuals (Rubn, 2006). The sze of ths reservor seems reasonable. The amounts of publc supports to prvate R&D are reported n Table 2. As expected, the R&D subsdes represent the most mportant amounts, whle the R&D tax credt reach 0% of the support gven to frms. Ths s related to ts ncremental nature. However, t s mportant to 3 Another property of ths data set s that t cannot be accessed by the standard procedure n France. The data were made avalable because there s a law that compels to provde an evaluaton of the R&D tax credt to the Parlament. Access to the data set was decded after a call for offer on the tax credt evaluaton among economc research laboratores. 4 The French frms have a compulsory dentfcaton number (the SIREN number) that s avalable n all our fles. Ths precludes losses due to matchng. 5 A balanced sample over the perod would reduce to about 200 frms only, nstead of The effect of subsdes has been studed separately n Duguet (2004) wth comparable evaluaton methods. 6

8 keep n mnd that these are not the same frms that get these two knds of publc support. The R&D tax credt, by ts fscal nature, s open to all the frms. It s often argued that the frms do not have the same sze dependng on they beneft from subsdes or from the tax credt. Fgure shows the medan sze of the recpents dependng on the R&D support that has been granted. Fgure 2 present the correspondng average sze. We fnd that whle the medan sze does not dffer between the two R&D mechansms (a medan of about 300 employees), there s a strong dfference n the average sze. The dfference between these two measures comes from the bggest frms that are more present among the subsdy recpents. The average sze s around 500 employees for the tax credt recpent and around 2500 employees for the subsdy recpent. A part of ths dfference can be attrbuted to the fact that, over ths perod, the tax credt s computed on the growth of R&D, and that the hgher the level of R&D the harder t s to make t grow. The comparson of these two fgures also confrms that the tax credt s more favorable to the small and medum szed frms. Fgure Medan number of workers dependng on the R&D support mechansm Subsdy CIR Both None 7

9 Fgure 2 Average number of workers dependng on the R&D support mechansm Subsdy CIR Both None 2.2 Sample Statstcs In a frst step, we wll smply perform a comparson of the average growth rate of prvate R&D, n order to assess the descrptve effect of the tax credt. Two types of comparsons can be made. A frst comparson, statc, compares the growth rates of the prvate fundng of R&D dependng on the mechansm of the recpents beneft from. A second comparson, dynamc, examnes the same growth rates, dependng on the poston of the frm nsde the tax credt mechansm. Four cases are possble: no tax credt for two years, tax credt durng two years, entry nto the tax credt and ext from the tax credt mechansm. The statc comparson s presented n Table 3. We fnd, as expected, that the frms that beneft from the tax credt have the strongest growth rate, partly because t s attrbuted on that bass (and not fully, because subsdes are deducted from the elgble R&D). Then come the frms that got both the tax credt and the subsdes. The lowest growth rates have been acheved by the frms that receved subsdes alone and the frms that benefted no support. Our defnton of prvate R&D excludes both subsdes and the tax credt; therefore a zero growth rate for the prvate R&D varable means that the R&D tax credt would be effcent snce 8

10 t nvolves that the frms have not reduced ther prvate R&D when they have benefted from the tax credt. In ths case, the total R&D expendtures ncreases by the amount of tax credt granted. When the effect s postve, there s a multpler effect, the total R&D ncreases by more than the amount of tax credt granted. However, ths frst comparson suffers from a serous lmtaton. It does not allow seeng the effect of the tax deducton snce what really matters s how the frms change ther behavor when they get the publc support. It s why we nsst on the dynamc comparson. The dynamc comparson s performed n Table 4. Here we examne how the growth rate of prvate R&D s affected when the frms enter or ext the tax credt mechansm. The varaton of the growth rates should be a better proxy for the R&D tax credt than ther levels, even though we are conscous that there remans a selecton bas ssue. An ncentve effect of the measure would nvolve that the prvate R&D does not decrease when a frms enters the mechansm. The dynamc comparson shows that the frms that enter the tax credt mechansm mantan a sgnfcant growth rate of ther prvate R&D expendtures, whch s often above 0%. The frms that enter n or ext from the mechansm do not behave sgnfcantly dfferently from the non recpents. Overall, we fnd that the frms add exactly the tax credt to ther prvate fundng when they enter the mechansm, and add more than the tax credt to ther prvate fundng when they stay nsde the mechansm. 2.3 The naïve estmator and a crtc of sample statstcs The naïve estmator s defned as the dfference of means. Its defnton comes from the fact that ths estmator s the one we should use on expermental data. The result s clear-cut: over all the decade the frms that have benefted from the R&D tax credt had a stronger growth rate of ther R&D tax credt, except n whch corresponds to the burst of the dot-com bubble. In practce, R&D data are not expermental and are affected by selecton bases. The frms that get the R&D tax credt do not have the same characterstcs that the frms that get t, and f these unbalanced characterstcs are correlated to our performance varable, we should expect a bas when comparng the mean performances of the recpents to the mean performance of the non-recpents. Ths dfference s gven n Table 5. Another crtc s related to the type of tax credt that s gven: when the tax credt s computed on the growth of R&D, the frms that do not experence a growth of ther R&D are 9

11 not elgble to the tax credt. Therefore comparng the recpents wth all the non-recpents s less relevant. Indeed what we would lke to measure s the dfference the R&D nvestment the frms have made and the R&D nvestment they would have made wthout the polcy measure. For ths, we need a counterfactual computed on the frms that dd not beneft from the polcy measure but the rght counterfactual set seems to be the set of frms that have experenced a growth of ther R&D expendtures and dd not ask for the tax credt. 7 In ths paper, we wll account for that crtc by reportng the results restrcted to the frms wth a postve growth of R&D. 8 3 Methodology In order to measure the effect of CIR, one should evaluate the dfference between the performance that a frm acheved wth the CIR and the performance the same frms would have acheved wthout ths mechansm. The latter quantty s called the counterfactual. There are many ways to estmate a counterfactual. In ths paper, we consder two famles of methods: standard regresson analyss and matchng methods. Among the latter we dstngush kernel matchng and the weghted estmator. Let y, the performance (.e., the growth rate of prvate R&D) of frm when t benefts from CIR and y 0, ts performance when t does not. The evaluaton problem comes from the fact that we cannot observe both quanttes at the same tme. Ether we observe frm benefts from CIR or we observe y when the y 0 when t does not. The observable data s therefore: ( T ) y0 T y y = + wth T = 0 f frm beneftsfromcir otherwse 3. Regresson methods The smplest method s the naïve estmator that takes the dfference of the average performance of recpents and non-recpents. Techncally ths reduces to perform an OLS regresson of the performance on the ntercept and a CIR dummy varable (equal to for 7 The lack of nformaton and the fear of a fscal control are often used to explan such behavor. 8 Ths crtc also rases an ssue when the data comes from the nnovaton surveys. The fscal deducton dummy varable should be merged wth R&D growth data n order to get the rght counterfactual when the tax credt s defned on the growth of R&D. 0

12 recpents, 0 for the other frms). The OLS coeffcent of the CIR dummy varables gves the dfference of the mean performances n both groups: ĉ = N I y N 0 I 0 y where I s the ndex set of recpents (number: N ), and I0 the ndex set of non-recpents (number N 0 ).Ths s the «nave» estmator. A second method extends the prevous model by addng explanatve varables prevous regresson. The model becomes: y = a + X b + ct + u. where u s the usual dsturbance. From ths model, we get the two cases: T = 0, wth an expected average performance E( y T = 0) = a X b, and + T =, wth an expected average performance E( y T = ) = a + X b c + Ths mples that the effect of CIR for the ndvdual s equal to: E ( y T ) E( y T = 0) c =. = X nto the Compared wth the naïve estmator, ths regresson allows droppng from the evaluaton the part that s attrbutable to the explanatve varables But, strctly speakng, the prevous estmators are not fully consstent wth the evaluaton problematc. A thrd regresson method s more rgorous. We assume that there are two X. equatons correspondng to each of the potental outcome, so that: y 0 = a 0 + Xb0 + u 0 and y = a + Xb + u, And the observable performance s: ( T ) y + T y = ( T )( a + X b + u ) + T ( a + X b u ) y = +, 0 After some smplfcaton, we get: y = β + X β + T β + T X β + u, whch mples that one should estmate a model wth the cross product of the explanatve varables and the CIR dummy. Moreover, f the varables X are centered, we can show that the

13 coeffcent of the CIR dummy, β 2, measures the average effect of the tax credt on the performance. 9 The results produced wth the standard regresson methods are presented n Table 6. The smple OLS regresson method, dependng on the year, gve results that vary between no effect of the CIR on prvate R&D (n ) and an mportant effect of +,4%. The effects are globally lower than the ones obtaned wth the naïve estmator (up to +2.2%), whch shows that the explanatve varables explan a part of the growth dfference between the recpents and the non-recpents. When we nclude the cross products of the explanatve varables wth the CIR dummy we get an effect that vares, dependng on the year, between 0 and +0.4%, whch shows that the cross product themselves explan a part of the performance dfferences between the recpents and the other frms. Overall, the growth rate gap of prvate R&D remans n favor of the CIR recpents. The standard OLS regresson fals to control for all the dfferences between these two groups of frms. Qualtatvely, these frst results ndcate that the effect of CIR would vary between a smple addton effect of CIR and prvate R&D (so that the measure s just effcent), and a multpler effect of CIR on prvate R&D (so that frms would ncrease ther R&D expendtures by more than the amount of the CIR). But t s well known the regresson estmators can suffer from selecton bases, therefore we turn to the matchng methods. 3.2 Methods that account for the selecton bas The evaluaton methods are the most mportant n ths paper snce the nave regresson methods do not account for the fact that the ndvduals are not comparable n the recpent and the non recpent groups. We follow the propensty score approach ntated by Rosenbaum and Rubn (983, 985) and surveyed n Lee (2005) and Rubn (2006). We am to estmate the three followng parameters: c = E ( y - ) y 0, the average effect of the treatment, 9 The structure of ths model also mples that the dsturbance of the model s heteroskedastc snce the dsturbance s dfferent dependng on T = 0 or T =. We account for ths property n our estmatons. 2

14 ( y - y T ) c = E 0 =, the average effect of the treatment on the treated; ( y - y T 0) c0 = E 0 =, the average effect of the treatment on the not treated. These three quanttes are related by the followng relatonshp: c 0 = = c Pr[T = ] + c Pr[T 0]. Followng Rubn (974) and Rosenbaum and Rubn (983, 985), we make the condtonal ndependence assumpton: (, y ) T X ( y, y ) T P( T X) y0 0 = The ntuton of ths result s the followng: f two frms have the same probablty to get the CIR, and the frst frm does have t whle the other has not, then the allocaton of CIR can be consdered as random between these two frms, and we can use the second frm as a counterfactual for the frst frm. Estmaton of the average effect on the treated c. We consder the CIR recpents and, for each of them, we estmate the performance they would have had wthout the CIR. In practce, we compare the performance of the CIR recpent wth the average performance of the non recpents that have the same probablty to get the CIR. In ths paper, we use the method of Heckman, Ichmura and Todd (998). The average performance that the CIR recpents ( I ) would have had wthout the CIR s estmated by: ŷ K [(pˆ j pˆ ) / h] y j =, j I K [(pˆ j pˆ ) / h] 0 0, I j I 0 Where K(.) s a Gaussan kernel, h the rule-of-thumb wndow and pˆ the estmated probablty to get the CIR for the frm. Symmetrcally, the performance that the non recpents ( I0 ) would have had f they had benefted from CIR s estmated by: ŷ K [(pˆ j pˆ ) / h] y j =, 0 j I K [(pˆ j pˆ ) / h], I j I, Therefore, the average effect of CIR on the treated s defned as: 3

15 ĉ, { y, 0,} = N ŷ I the average effect on the not treated by: ĉ, { ŷ, 0,} 0 = N y 0 I 0 and the average effect on the whole populaton by: ĉ = { } + { } + ŷ, y0, y, ŷ0,. N 0 N I 0 I The standard errors of these statstcs are complcated to wrte, so that we use the bootstrap method wth 500 repettons (ncludng the Probt step for pˆ ). The weghtng approach, surveyed n Lee (2005), uses the same assumptons than kernel matchng, but merely expresses the non observable sample moments by ther observable counterparts, and replaces them by the correspondng emprcal moments. We get the followng results: Effect of the treatment on the not treated: ĉ N N0 T 0 = y N = N pˆ Effect of the treatment on the treated: c = N c N = N N T pˆ y pˆ Effect of the treatment on the whole populaton: ĉ = N N = T pˆ y pˆ ( pˆ ) Where pˆ s the estmated value of the propensty score for the ndvdual, N the number of frms n the common support, N 0 the number of not treated frms n the common support and 4

16 N the correspondng number of treated frms. The varances of theses estmators must be corrected to account for the estmaton of the Probt model n the frst step, but t can be determned by the delta method. The detals are gven n appendx. It s possble that the regresson methods gve nconsstent estmators because the CIR s not dstrbuted at random to the frms. As for all polcy measures, there are attrbuton procedures that determne who s elgble. Ths creates dfferences between the sample of recpents and the other frms and, provded that these dfferences are correlated wth the performance measure, the OLS regresson estmates can be nconsstent. It s therefore desrable to balance the covarates so that the frms are comparable. In practce, ths creates an ncentve to match each recpent frm to the non-recpent frms that share the same value of the observable covarates. There are many matchng methods n the lterature. In ths paper, we follow the approach by Rosenbaum and Rubn (983, 985), and match our frms on the propensty score, whch s smply the probablty to get the CIR. The ntuton of ths theoretcal result s the followng: f two frms have the same probablty to get the CIR, that the frst one got t and the second one dd not, everythng happens as f the CIR was allocated at random among these two frms. Therefore, for each recpent, we look for all the non-recpents that have the same probablty to get the CIR and then we compute the dfference between the performance of the recpent and the average performance of the other frms (the counterfactual). These operatons are performed for all the recpents, and the effect of CIR s gven by the average of all these dfferences (.e., the effect on the treated ). The same method can be used to determne the potental effect of CIR on the frms that dd not get t. One just needs to match each non-recpent frm wth the recpents that have the same probablty to get the CIR. The only problem that remans s therefore to estmate the probablty to get the CIR. We estmate a Probt model and use the predcted probabltes. 0 Notce that the replacement of the true probabltes by the estmated probabltes mples to modfy the way to compute the Student statstcs. We have used the bootstrapped Student statstcs to account for t. The last pont s to make sure that the probabltes between the recpents and the non-recpents have a 0 We have performed Vuong test of the Probt model aganst the Logt model. We fnd that the models are equvalent for all the couples of years, except for were the Probt model outperforms the Logt model. Therefore, we kept the Probt model for all our regressons. 5

17 suffcent overlap so that we compare frms wth smlar probabltes. We have used the common support of the predcted probabltes to fx ths problem. Last, notce that ths method s related to the nstrumental varable method n the followng way. The estmaton reles on the estmated probabltes to get the CIR, and these estmated probabltes depend on nstrumental varables only (mostly lagged varables n our applcaton). In our applcaton, we have appled three dfferent estmaton methods on the common support of the CIR probabltes: - the OLS regresson methods, so as to see what s the nfluence of changng the data set by droppng the extreme probabltes; - The kernel matchng method (Rubn approach). It allows for dstngushng the effect on the treated from the effect of the non-treated; - The weghted estmator (or moment estmator ), whch s equvalent to kernel matchng but much faster to compute snce t replaces the bootstrap method by the delta method (see the appendx). The Rubn methodology mples to drop some observatons from the sample when the probabltes take extreme values (close to 0 or to ). The estmatons are performed on the common support of the estmated CIR probabltes. Therefore, f we wsh to compare ths method wth the OLS regresson methods, we should do t on the same sample, on the common support, so as to make sure that the dfferences comes from the method tself and not from a sample dfference only. Table 7 presents the OLS regresson methods on the common support. We do not fnd mportant dfferences wth the regressons over the whole sample, so that the dfference of samples cannot be advocated to explan dfference of results among the standard and Rubn methods. 3.3 Emprcal strategy A problem specfc to the tax credt systems computed on the growth of R&D comes from the fact that our performance varable s correlated to the attrbuton condtons. Therefore we cannot use the standard method wthout cauton. There are two ways to tackle that problem: In practce, we defne the probablty support by the frst and 99th percentles n order to suppress the nfluence of outlers. 6

18 - Frst, one can restrct the analyss to the frms that have a postve growth rate of ther R&D. Ths s because only these frms are elgble and t should mprove on the qualty of the matchng. Ths s only possble however because some frms that have the rght to the tax credt do not ask for t. - Second, one can use another varable than the growth rate of R&D. We need a varable that s both related to the R&D effort and not to a short-run effect caused by the CIR. We use the growth rate of the (full-tme) number of researchers. Indeed, the researchers are the ones that hold the knowledge of the frm, so that the frms should be reluctant to hre or lay-off researchers just to beneft from a short-run tax deducton. The number of researchers s an essental nput of the nnovaton process so that s should be a good ndcator of the real nnovaton polcy of the frm. Keepng the researchers nsde the frm avods the dssemnaton of knowledge to compettors and many studes show that the frms care about ths ssue (Levn et al., 987, Duguet and Kabla, 998). We have also tred nstrumental varables methods (not reported) but ther results were smlar to those of the standard regresson methods, and often gave a stronger effect of CIR on the growth of prvate R&D. In order to measure the effect of CIR on prvate R&D, we use the followng emprcal strategy: - Estmators over the whole sample; - Estmators restrcted to the frms wth a postve growth rate of prvate R&D; - Estmators on the growth rate of the number of researchers. 3.4 Multplers computaton There are two ways to compute the tax credt multplers n the lterature: frst, one dvdes the tax credt amount by the amount of R&D the frms would have made wthout t (mpact of the tax credt on R&D); second, one dvdes the ncremental amount of R&D performed under the tax credt by the amount of tax credt dstrbuted (addtonal number of Euros of R&D for one Euro of tax credt dstrbuted). Ths secton wrtes the explct relatonshps between our estmates and these two knds of multplers. We need the followng notatons: - c denotes the effect on the treated; 7

19 - R 0 s the amount of R&D that would have been nvested wthout the CIR and R s the amount of R&D that would have been nvested under the tax credt. - We have the followng relatonshp between these quanttes: - R denotes the prvate R&D expendtures. R R c = +. ( c ) 0 0 R = R R 0 - The R&D subsdes have been subtracted from all our measures of R&D (prvate and total) so that we really measure the net effect of the tax credt on R&D R&D expendtures multplers All our multplers are computed on the subset of CIR recpent frms, snce there s no drect effect on the others by defnton. Ths s a sgnfcant dfference wth aggregated data where total R&D usually refers to country R&D and therefore ncludes the non recpents. Prvate R&D expendtures multpler Consder frst the prvate R&D multpler. Wth the tax credt, a frm nvests R whle wthout t, the same frm would have nvested R R /( + ) multpler s equal to: R = c, R c + m P = + Total R&D expendtures multpler =, therefore the prvate R&D 0 c Consder now the total R&D multpler. The total R&D of a recpent frm s equal to R + CIR under the tax credt, whle t s R = R /( + ) wthout t. The total R&D multpler s therefore equal to: R + CIR = ( + c )( CIR / R ), R c + m T = + 0 c In ths paper, we present the mean value of ths multpler over the sample, whch gves: 8

20 ( + c )( CIR / R ) m T = Tax credt multplers Prvate R&D tax credt multpler The varaton of prvate R&D nduces by CIR s equal to: R c R R 0 = R = R, + c + c so that the multpler equals: M P R R 0 c R = =. CIR + c CIR We estmate t at the mean pont of the sample by: M c = + c R CIR P, Total R&D tax credt multpler The varaton of total R&D nduced by the CIR s equal to: c R + CIR R 0 = R + c + So that the multpler equals: 0 M T = + CIR CIR + R R = M P. CIR 4 Results In a frst subsecton, we report the determnants of the probablty to get the CIR and, n a second subsecton, we comment on the evaluaton. 9

21 4. The probablty to get CIR The man determnant of CIR s ts lagged value (Table 8). Ths result llustrates the mportance of the dynamc comparson reported n Table 4. But other varables also nfluence the probablty to get the CIR: the R&D/sales rato ncreases the probablty to get the tax credt and the beneft from R&D subsdes reduces t. In the latter case, there could be a mechancal effect comng from the fact that the R&D subsdes are not elgble to the tax credt. Fnally, we also fnd that some lnes of busness use more the tax credt on some years, but we do not fnd tme persstence on ths pont. Ths could come from the fact that the tax credt s more open to frms than R&D subsdes. A second nterestng result s the small number of sgnfcant explanatve varables n the Probt regressons. Ths also suggests that entry nto the tax credt mechansm s more open than for R&D subsdes (Duguet, 2004). In order to test the robustness of ths result, we have also run the Probt regressons wthout the lagged tax credt varable (not reported). We fnd that the most mportant determnant of the probablty to get the tax credt s the R&D/sales rato. Therefore the R&D tax credt s drected toward the frms that allocate the bggest part of ther sales to R&D. Ths selecton of recpents certanly explans for a good part, the results of the evaluatons that follow. 4.2 The effect of CIR The kernel matchng and weghtng methods provde smlar results (Table 9). We comment mostly on the weghtng estmators because they provde smaller standard errors. The estmaton on the recpents represents the evaluaton strctly speakng (the «effect on the treated»): we seek to estmate the effect of the tax credt on the frms that have ndeed benefted from t, and not on the whole sample of R&D performers. The basc results show a multpler effect on 6 years out of 0, and an addton effect for the 4 remanng years. The estmaton on the sample of frms that have not benefted from the tax credt represents the effect the CIR would have had f t has been extended to more R&D frms. We fnd that the potental effect of the tax credt on ths subsample of frms s weaker than on the recpents snce we fnd a multpler effect for year and an addton effect on 9 years. However, we do not fnd a (frm-level) «crowdng-out» effect. 20

22 However, these frst estmates must be taken wth cauton. Snce the tax credt s ncremental, we could thnk that the standard evaluaton procedure overestmates the effect of CIR on the growth rate of prvate R&D. Therefore, we also present the estmaton, wth the same methodology, on the subsample of frms that had a postve growth rate of ther prvate R&D and, despte of ths, dd not ask for CIR. The results, reported n Table 0, should represent a lower bound of the effect of the tax credt. Ths «lower bound» shows an addton effect for all the 0 years. Accordng to t, the frms would add the tax credt to ther prvate fundng so that there would be no «crowdng out» effect. It s possble to convert the results reported n Tables 0 and nto multplers of the total R&D expendtures. Snce the study s performed over ten couple of years, we comment on the average value (Tables and 2). The estmatons based on the total sample (Table 0) ndcate that the CIR would have ncreased prvate R&D by 7.9% (Table, multpler.079) and total R&D (wth the CIR) by.2%. The effect would thus be mportant on the recpents. When we restrct the analyss to the frms that have ncreased ther prvate R&D (Table ), the correspondng effects would be between 0% (unt multpler), a smple addton effect, and the total R&D would have ncreased by 3.2% only. A common practce expresses the multplers as a functon of CIR tself. Over the whole sample (Table 0) we fnd that Euro of CIR would generate 2.33 Euros of prvate R&D that s 3.33 (=+2.33) Euros of total R&D (Table 2). When we restrct the analyss to the postve growth rates (Table 2) we fnd a more pessmstc result that Euro of CIR would not generate any Euro of prvate R&D, so that Euro of CIR would generate Euro of total R&D. However, snce our evaluaton s a short-run one (one year), ths fgure s n fact rather hgh. 4.3 Testng the robustness of our estmators: the number of researchers A basc reason that may reduce the trust that we have n the prevous estmates s the ncremental nature of the R&D tax credt. We reply to ths crtc by proposng the followng robustness test. The R&D performers may have an nterest n askng for tax deductons even when they do not need t, but the same R&D performers have no nterest n lettng ther knowledge leak out for free to ther compettors. Therefore, f they do not use the R&D tax credt for research, we should see no R&D personnel varatons. We use growth rate of the 2

23 number of researchers, n equvalent full tme, snce they are the holders of the frm s knowledge. The results are reported n Table 3. We fnd that, over the whole populaton, the R&D tax credt s always assocated to postve growth of the number of researchers, varyng between 7.5% and 2.6% dependng on the couple of years. If we restrct our analyss to the frms that had a postve growth rate of ther prvate R&D, we fnd a postve effect 5 years out of 0 (from 6.6% to 0%) and no effect on fve years. These results suggest that the R&D performers would use the ncremental R&D tax credt n order to nvest n R&D. 5 Concluson We fnd that the ncremental R&D tax credt benefts to a dfferent populaton of frms than the ones that beneft from subsdes. The tax credt recpents have a smaller sze and a hgh R&D to Sales rato. Ths confrms the vew that the tax measures favor equalty among the nnovatve frms. Applyng methods that control for selecton bases, we fnd that the ncremental R&D tax ncentve s effectve, snce t s assocated both to a growth of the prvate fundng of R&D (or adds to t), and to a growth of the number of researchers on most years. However, the results n ths study apply to the ncremental tax credt only, whch s based on the growth of the prvately funded R&D. It does not seem to be extendable to other form of tax mechansm, snce they apply to a dfferent populaton of frms (larger, especally). New research should be done n order to evaluate the reforms that have been taken place n France snce

24 References Aerts C. and D. Czarntzk (2004). «Usng nnovaton survey data to evaluate R&D polcy : the case of Belgum. Workng paper, ZEW N ftp://ftp.zew.de/pub/zew-docs/dp/dp0455.pdf Berger P. (993). «Explct and mplct effects of the R&D tax credt. Journal of Accountng Research, 3(2), 3-7. Busom I. (999). «An emprcal evaluaton of R&D subsdes». Unversty of Calforna, Burch Workng Paper, N B Crépon B. and N. Iung (999). Innovaton, emplo et performances. INSEE, DESE, Workng paper G9904. Czarntzk D., P. Hanel and J.M. Rosa (2004). Evaluatng the mpact of R&D tax credt on nnovaton: A mcroeconometrc study on Canadan frms. Workng paper, N ftp://ftp.zew.de/pub/zew-docs/dp/dp0477.pdf D'Aspremont, C. and Jacquemn, A. (988). "Cooperatve and non cooperatve R&D n duopoly wth spllovers". Amercan Economc Revew, 78, Duguet E. (2004). Are R&D subsdes a substtute or a complement to prvately funded R&D? An econometrc analyss at the frm level, Revue d Econome Poltque, 4(2), Duguet E. and I. Kabla (998). "Appropraton strategy and the motvatons to use the patent system n France: an econometrc analyss at the frm level". Annales d'econome et de Statstque, n 49-50, Efron B. and J. Tbshran (993). An ntroducton to the bootstrap. Monographs on Statstcs and Appled Probablty n 57. Chapman & Hall. ISBN Gouréroux C. (2000). Econometrcs of Qualtatve Dependent Varables. Cambrdge Unversty Press (ISBN ). Guellec D. and B. Van Pottelsberghe (2000). The mpact of publc R&D expendture on busness R&D. Workng paper, OECD, STI, N Hall B. H. and J. Van Reenen (2000). How effectve are fscal ncentves for R&D? A revew of the evdence. Research Polcy, vol. 29, Heckman J. and J. Hotz (988). Choosng among alternatve nonexpermental methods for estmatng the mpact of socal programs : the case of manpower tranng. Journal of the Amercan Statstcal Socety, vol. 84, n 408, Heckman J., H. Ichmura and P. Todd (997). Matchng as an econometrc evaluaton estmator : evdence from evaluatng a job tranng programme. Revew of Economc Studes, vol. 64, Hujer H. and D. Radc (2005). «Evaluatng the mpact of subsdes on nnovaton actvtes n Germany». Workng paper, ZEW, N ftp://ftp.zew.de/pub/zew-docs/dp/dp0543.pdf Hussnger K. (2003). R&D and subsdes at the frm level: An applcaton of parametrc and semparametrc two-step selecton model. Workng paper, ZEW, N ftp://ftp.zew.de/pub/zewdocs/dp/dp0363.pdf Kaser U. (2004). «Prvate R&D and publc R&D subsdes : Mcroeconometrc evdence from Denmark. Workng paper, Center for Economc and Busness Research, N Kamen M., Muller E. and I. Zang (992). Research jont ventures and R&D cartels. The Amercan Economc Revew, 82(5), Klette T.J. and J. Møen (998). R&D nvestment responses to R&D subsdes : a theoretcal analyss and a mcroeconometrc study. Workng paper, NBER Summer Insttute. Klenknecht A. ed. (996). Determnants of nnovaton: The message from new ndcators. Palgrave. ISBN

25 Klenknecth A. and P. Mohnen eds. (2002). Innovaton and frm performance: Econometrc exploratons of survey data. Palgrave. ISBN Lach S. (2002). Do R&D subsdes stmulate or dsplace prvate R&D? Evdence from Israël. The Journal of Industral Economcs, vol. L(4), Lee Myoung-Jae (2005). Mcro-Econometrcs for Polcy, Program and Treatment Effects. Oxford Unversty Press. ISBN Levn, R., A. Klevorck, R. Nelson and S. Wnter (987). Appropratng the returns from ndustral R&D. Brookngs Papers on Economc Actvty, Maresse J. and B. Mulkay (2004). «Une évaluaton du crédt d mpôt recherche en France : ». Workng paper, CREST, N Mansfeld E. (986). The R&D tax credt and other polcy ssues. The Amercan Economc Revew, 76(2), OECD (2002). Frascat manual : Proposed standard practce for Surveys on Research and Expermental Development. OCDE (2003). Tax ncentves for research and development : trends and ssues. Workng paper, OECD, STI. Rosenbaum P. and D. Rubn (983). The central role of the propensty score n observatonal studes for causal effects. Bometrka; vol. 70, Rosenbaum P. and D. Rubn (985). Constructng a control group usng multvarate matched samplng methods that ncorporate the propensty score. The Amercan Statstcan; vol. 39, n, Rubn, D. (974). Estmatng causal effects of treatments n randomzed and non-randomzed studes, Journal of Educatonal Psychology, 66, pp Rubn D. (997). Estmatng causal effects from large data sets usng propensty scores. Annals of Internal Medcne, Part 2, n 27, Rubn D. (2006). Matched samplng for causal effects. Cambrdge Unversty Press, ISBN Wallsten S. (2000). The effect of governement-ndustry R&D programs on prvate R&D : the case of Small Busness Innovaton Research program. RAND Journal of Economcs, vol. 3(), Warda J. (2006). Tax treatment of busness nvestments n ntellectual assets: an nternatonal comparson. Workng paper, OECD, STI, N

26 Appendx: Estmaton of the varance of the weghtng estmators Slghtly extendng the analyss n Crépon and Iung (999), we wrte the three estmators under the followng form: θ ˆ = N N = y g ( bˆ ) where bˆ s the estmated parameter from the Probt model. Usng the delta method, we can estmate the varance of our estmate by: Vˆ N ( θˆ ) = ( ϕˆ ϕˆ ) N = 2 wth ϕ ˆ = y g N g ( bˆ ) + y ( bˆ ) J ( bˆ ) N s ( bˆ ) N = b' where J 2 lnf b b' ( b) = E ( T X,b), s ( b) lnf = ( T X,b), b and lnf ( T X,b) = T lnφ( X b) + ( T ) ln Φ( X b) ( ). Notce that these formulas are vald for any bnary model estmated by the maxmum lkelhood method, provded that one replaces Φ( b) Φ by F( X b) X where F(.) s the cdf of the dsturbance of the new model (or by F( X b) f the dstrbuton s symmetrc). 25

27 Year Subsdy Alone Table : Publc Support to Prvate R&D Tax Credt Alone Both Subsdy and Tax Credt No Publc Support Sample sze 994 2,% 9,5% 8,6% 50,9% 553 (00%) 995 9,% 8,4% 7,4% 55,% 645 (00%) 996 9,5% 6,7% 7,0% 56,9% 639 (00%) 997 8,8% 7,3% 6,% 57,9% 570 (00%) ,% 7,2% 6,3% 56,3% 482 (00%) ,% 7,2% 7,% 55,5% 49 (00%) ,0% 7,6% 8,5% 53,9% 23 (00%) 200 6,8% 9,% 7,9% 56,2% 33 (00%) ,0% 8,3% 6,5% 58,% 299 (00%) ,6% 6,7% 5,8% 59,8% 542 (00%) Table 2: Amount of Publc Support to R&D Year Subsdes Tax Credt Total* ,8% 5,2% (00%) ,5% 7,5% (00%) ,0% 8,0% 542 (00%) ,2% 9,8% 253 (00%) ,4%,6% 207 (00%) 999 9,% 8,9% (00%) ,% 6,9% 727 (00%) ,7% 2,3% 573 (00%) ,5% 9,5% 90 (00%) ,3% 9,7% 94 (00%) Notes *In mllons Euros, computed on the sample 26

28 Table 3: Statc comparson of the growth rates of the prvate fundng of R&D * Sgnfcant at the 5% level Subsdy Tax Credt (CIR) Both None Average -,5% -6,3% 0,9% -8,3% Student 4,99* 3,07* 0,29 2,7* Average 3,2%,6% 9,% 2,9% Student,72 6,46* 3,3* 2,9* Average 0,6% 6,8% 3,3%,% Student 0,3 3,54*,5, Average -,6% 9,7% 7,2% 2,3% Student 0,89 5,9* 2,3* 2,34* Average,6% 0,3% 2,4%,0% Student 0,83 5,53* 0,65 0, Average -2,% 0,5% 8,6% 0,2% Student 0,84 4,2*,83 0, Average -0,3% 6,9% 3,% -,% Student 0,2 2,84* 0,68 0, Average 6,5% 6,6% 8,% 3,9% Student 2,5* 2,58* 2,8* 2,* Average 0,0%,5%,0% -,4% Student 0,00 5,24* 0,22 0, Average -2,6% 8,% -,2% -,8% Student 0,88 3,28* 0,32,32 27