Evaluating the Impacts of Innovation Policy: Evidences from Brazil

Transcription

1 Evaluatng the Impacts of Innovaton Polcy: Evdences from Brazl Ana Paula Avellar Professora do Insttuto de Economa da Unversdade Federal de Uberlânda Pesqusadora FAPEMIG Emal: Abstract The objectve of ths paper s to evaluate the mpact of publc polces of fscal ncentves and fnancal support n the nnovatve effort of Brazlan frms. The methodology appled to evaluate the effects of dfferent nstruments n prvate expendtures on R&D and nnovaton actvtes s propensty score matchng, usng data from PINTEC (2010). Ths research contrbutes to the emprcal lterature by provdng frm-level evdence from Brazl, and dscussng the effectveness of the dfferent nstruments to support nnovaton. The conclusons suggest that the two types of nstruments evaluated generate postve effects con nnovatve effort of frms. However, the fscal ncentve mpact s hgher than the fnancal support. Keywords: Innovaton Polcy, Evaluaton, Matchng, Brazl. Resumo O objetvo desse artgo é avalar o mpacto das polítcas públcas de ncentvos fscas e fnanceros sobre o esforço novatvo das empresas brasleras. A metodologa utlzada para avalar os efetos desses dferentes nstrumentos nos gastos prvados em P&D e em atvdades novatvas é propensty score matchng, usando dados da PINTEC (2010). Essa pesqusa contrbu para a lteratura empírca apresentando evdêncas no nível da frma para o Brasl e dscutndo a efetvdade desses dferentes tpos de nstrumentos de apoo à novação. As conclusões sugerem que os dos tpos de nstrumentos avalados geram efetos postvos sobre o esforço novatvo das empresas. Todava, as polítcas de ncentvos fscas são mas efetvas em relação às polítcas de ncentvos fnanceros. Palavras-Chave: Polítca de Inovação, Avalação, Matchng, Brasl. JEL classfcaton: 032, 038, C12. Área ANPEC 8 Economa Industral e da Tecnologa 1

2 1 Introducton The nnovaton polcy has been actvely practce among several countres amng at both strengthenng ther own technologcal capacty, by stmulatng prvate nvestments and, smultaneously, creatng an nsttutonal envronment wth adequate nfrastructure to encourage nteracton among enterprses, unverstes and research nsttutons. Therefore, the governments apply varous polcy nstruments to promote R&D and nnovaton n the busness sector. In ths context, the man concern of governments and polcy-makers n many countres would be to dentfy the best polcy to stmulate the nnovaton actvtes of the country, dsregardng the publc polcy as a replacement for a market wth statc mperfectons, but as a supportng agent n resource allocaton, whle assurng the compettveness, n termsof dynamcs. Consequently, the dscusson about the publc nterventon n the economy s losng mportance to the dscusson abouthow the publc polcy should ntervene n the promoton of technologcal development. To accomplsh ths, a number of polcy nstruments could be appled by the polcy-makers to stmulate the nnovatve behavor of the frms: tax credt, publc subsdes, target credt and ntellectual property system, venture captal, and every one can generate dfferent mpact on the prvate nnovatve expendture. For example, tax credt to R&D s consder an ex-post nstrument, because ts do not antcpate fnancal support to technologcal actvtes but only refund expenses already done, whch exclude many frms that cannot afford ths nvestment. Ths mechansm s dfferent of the publc subsdes because do not cause any effect on rsk percepton ofthe frm, affectng ts expenses structure only. Based on ths debate, ths artcle ams to evaluate the mpact of the two nnovaton programs appled n Brazl: fscal ncentve (Law n. 8661, Law n.11196, Law n and Law n ) and fnancal support (provde fundng for expendtures on R&D, new machnes and equpment, tranng, software), and to dscuss about the effectveness of each nstrument, takng nto account ther characterstcs and specfctes of the benefcares frms. The data come from the Brazlan Innovaton Survey (2010) that provdes nformaton on government support programs and also some ndcators of nnovaton. By means of a comparatve evaluaton of these cases, for the perod , t wll be possble to dentfy f the knd of nstrument explans ts mpact on the expendture behavor of frms engaged n nnovatng actvtes. Ths study wll estmate the mpact of these programs usng quas-expermental technque, n order to control for a possble selecton bas, to compare benefcares and non-benefcares frms to reduce any selecton bas. The mpact wll be evaluated on the nnovaton varables: expendture on R&D (ER&D), expendture on nnovatve actvtes (EIA), expendture on R&D n relaton to the net revenue of sales (ER&D/NRS) and expendture on nnovatve actvtes n relaton to the net revenue of sales (EIA/NRS). The results show that these two nstruments fscal ncentves and fnancal support - n fact have a postve mpact on nnovaton performance of the frms. On the benefcares frms the behavor of these nnovaton varables s hgher than twn non-benefcares frms. Ths paper s organzed n fve sectons. After ths ntroducton, secton 2 presents the lterature revew about evaluaton of nnovaton program presentng some cases of the nternatonal lterature. Secton 3 summarzes some characterstcs of the benefcares frms of the two Brazlan nnovaton programs: fscal ncentves and fnancal support. Secton 4 shows the database that was used and the methodologcal notes wth the descrpton of the 2

3 methodology adopted n ths research. Fnally, secton 5 gves the emprcal results and secton 6 presents some concludng remarks. 2 Theoretcal and emprcal lterature The quanttatve evaluaton of mpact of nnovatng polcy usng econometrc models started wth Mansfeld and Swtzer (1985), who carred out a research wth R&D busness to understand the percepton of the group n relaton to tax ncentve. The results show that the 1% R&D cost reducton culmnates n a 0.3% ncrease n R&D expendture, of the frms. Hall (1992) comes to an even more expressve result when he observes, based on data of the Unted States, that a 1% R&D cost reducton leads to a short term ncrease of 0.84% n R&D spendng of the frms and to a long term ncrease of 1.5%. However, ths matter s not restrcted to the analyses only of tax ncentve effects, but also nvolves the analyses of other ncentve polces of nnovaton lke the fnancal support. The studes presented on table 1 show a predomnance, n the nternatonal lterature, of researches whch report the exstence of the postve effect of the publc polces n the analyzed cases of dfferent countres where government ncentve polces amed at ncreasng prvate expendture n nnovatng actvtes. These results suggest that the partcpaton n nnovaton programs stmulate the R&D expendtures on benefcary frms. Busom (1999) evaluate the mpact of the fnancal program to R&D expendtures n Spansh frms appled n The author estmate a probt model to analyses the effects n 154 frms. The result shows that the fnancal publc support s capable to ncrease the prvate R&D nvestment, and do no occur crowd out effect. However, the author emphaszes the heterogenety of the effects, consderng the characterstcs of the benefcares frms. Davd et al. (2000) developed ths dscusson and presented a summarze of the lterature revew. The results show that n the major of the studes analyzed the fnancal publc support promotes the addtonalty effect on prvate expendtures. Wallsten (2000) evaluated the mpact of the Amercan fnancng program Small Busness Innovaton Research Program (SBIR) between 1990 and In 1998, for example, approxmately one bllon US dollars had been allocated to the fnancng of projects. Ths study deserves promnence for showng the presence of the crowdng out effectbetween publc and prvate expendture n R&D. In other words, the study ndcates that prvate nvestment would occur regardless of government fnancng. The author justfes ths result by the selecton tself of the programs by the executng agency, as prorty s gven to projects that a pror show hgh probablty of market success, thus allocatng a role of lttle mportance to the polcy. Hall and Van Reenen (2000) contrbuted to ths dscusson through a comparatve study on the effects caused dfferent ncentve polces of nnovaton carred out by countres of the OECD such as Canada, Germany, France, Italy, The Unted Kngdom and Belgum, among others. They compare the effects of fnancng programs to tax ncentve and conclude that the latter s more effcent when t comes to stmulatng prvate expendture n R&D. Lach (2002) evaluates the effects of the R&D fnancng projects n Israel and suggests the absence of the crowdng out effect of publc expendture by prvate expendture n R&D. Panel data analyss s used for frms from 1990 to 1995 and dentfes an ncrease n R&D spendng n frms that receve federal R&D support, especally smaller frms. The evaluated fnancng program had a 310 mllon dollar budget n

4 Czarntzk and Fer (2002) nvestgate the effects of government fnancng on R&D actvtes n 210 frms n Germany between 1994 and The matchng method s used and they conclude that a 1,0 fnancng generates an ncrease of 1,3 to 1,4 n prvate expendture. In other words, they reject the hypothess of a crowdng out effect between publc and prvate expendture n the case that was nvestgated. Bloom et al (2002) evaluate some experences of the fscal ncentves to R&D usng panel data for analyses dfferent countres. It s observed the presence of a relatve nternatonal consensus upon the effects of ths nstrument: t s expected that a 10% reducton n the cost of actvtes of R&D provdes a 1% nr&d expenses n the short term (2 or 3 years) and a 10% rase n the long term. Almus and Czarntzk (2003) use the matchng method to fnd out f the partcpaton of the frms n federal supported nnovaton programs contrbuted to an ncrease n R&D expendture of frms n Germany. The study here shown, ndcates that the effect of the program s sgnfcant, postve and dfferent from zero, ndcatng more ntense R&D actvtes n benefcary frms n relaton to the control group consstng of smlar frms that were non-benefcary. In monetary terms, fnancngs of 100,000 monetary unts can generate prvate R&D nvestments of 4,000 monetary unts (4%). Duguet (2004) carres out an analyss of the effects of federal fnancng on prvate expendture on R&D n frms n France. After the nvestgaton of more than 1,600 frms n France from 1985 to 1997, the author concludes to absence of the crowdng out effect, but confrms the presence of the addtonalty effect as complementary prvate expendture occurs n relaton to publc expendture, except for the year Aerts and Czarntzk (2004) present an analyss smlar to the one carred out by Bloom et al. (2002) for the specfc case of Belgum usng the database of the Communty Innovaton Survey (CIS) of After several econometrc studes, they conclude that the hypothess of crowdng out effect must be rejected when t comes to nnovatng frms. Kaser (2006) developed an evaluaton by means of the matchng method and the model of Heckman for the Dansh R&D fnancng program, consstng of data from 1974 to He reached no conclusve results as regards thecrowdng out effects between publc, and prvate expendture. Lööf and Hesmat (2005), smlar to Kaser (2006), make an analyss for the Swedsh R&D fnancng program from 1998 to They come to the concluson that the addtonalty effect occurs, or n other words, there s complementary expendture, but only n small benefcary frms. The table 1 shows that the nternatonal studes of mcroeconometrc evaluaton are concentrated n cases of the developed countres polces. Hall and Maffol (2008) organzed the frst research to evaluate the effects of nnovaton programs n emergng countres. They evaluate the Technology Development Funds (TDF) n Argentna, Brazl, Chle and Panama, and the emprcal results shows that the effectveness of the nnovaton program n found to depend on the fnancng mechansm used and on the characterstcs of the target benefcares. Usng dfferent methods the paper shows that the mpacts of the programs are dfferent and the publc subsdes were more effectve than grants. Especally about the Brazl s case, some recent emprcal studes of the evaluaton of nnovatng polces show the effcency of these polces to ntensfy the nnovatng efforts of the benefted frms n relaton to those whch were not benefted. Avellar (2008) evaluates the Program of Technologcal and Industral Development (PDTI) and concludes that the partcpaton of a company n the program culmnated n a 190% ncrease n spendng on technologcal actvtes, ncludng R&D expendtures, new machnes and tools expendtures 4

5 and tranng expendtures. Ths proves that the PDTI managed to reach ts objectve of ncreasng the spendng of benefcary frms on nnovaton actvtes. Table 1. Internatonal Studes of Mcroeconometrc Evaluaton of the effects of the publc polces to prvate R&D expendture Year Author Country Perod Incentve Method 1993 Leyden and Lnk US, Canada, Sweden 1997 Lattmore Australa Japan, 1987 Fscal OLS Fscal Cost-Beneft OLS 2000 Busom Span 1998 Fnancal OLS 2000 Wallsten USA Fnancal IV 2002 Lach Israel Fnancal Panel 2002 Czarntzk and Fer Germany Fnancal OLS 2003 Almus and Czarntzk Germany 1995, 97 e 99 Fnancal Matchng 2002 Bloom et al 9 OECD countres 2003 Hussnger Germany Fscal Panel data Fnancal Matchng and Heckman 2004 Duguet France Fnancal Matchng 2004 Czarntzk et al Canada Fscal Matchng 2004 Aerts and Czarntzk Belgum 2001 Fnancal Matchng 2005 Lööf and Hesmat Sweden Fnancal Matchng 2006 Kaser Denmark Fnancal Matchng 2008 González and Pazó Span Fnancal Matchng 2008 Hall and Maffol Emergng countres Source: Personal Elaboraton. Dfferent perods Fnancal Matchng, Panel Data Based on ths debate, the present artcle wll present the results of the evaluaton of two nnovaton publc programs mplemented n Brazl. Intally n the next secton, there wll be present the characterstcs of the benefcares frms on these programs of ncentve to nnovaton. 3 Data and descrptve analyses The am of ths secton s to present the characterstcs of the benefcares frms and to show some detals about the Brazlan programs evaluated. Ths study s based on database Technologcal Innovaton Research (PINTEC), from the Brazlan Insttute of Geography and Statstcs (IBGE) publshed n 2010, ncludng questons about the perod The varables used are descrbed n table 2. Table 2. Varable defntons Varables Sze Net Sales Revenue (NRS) Expendture on R&D (ER&D) Expendture on Innovatve Actvtes (EIA) Labor Productvty Defnton Number of Employees Net Sales Revenue n 2008 (R$ 1000) Expendture on R&D n 2008 (R$ 1000) Expendture on Innovatve Actvtes, ncludng R&D, Machne and Equpment, Tranng, Software (R$ 1000) (Net Sales Revenue Producton Cost) / Number of Employees n

6 (R$ 1000) Skll Full Tme Workers Completed Hgh School (%) Market share Sales each Frm / Total Sales Sector Foregn Captal Frms wth more than 10% of foregn captal (%) Group Frms that partcpate n busness group (%) R&D Contnuous Frms wth R&D contnuous (%) Innovaton Product Frms that nnovate n product (%) Innovaton Process Frms that nnovate n process (%) Patent Frms that regster patent (%) Source: PINTEC (2010). The table 3 shows the partcpaton of benefcares frms on nnovaton programs by sze. The small frms (10-29 employees) have a bg representaton on the benefcares frms. In the case of fscal ncentves, the second group of benefted frms s wth more than 500 employees. However, n fnancal support program, the second group more benefted s the frms wth employees. Table 3.Number of Benefcares Frms on Innovaton Programs by Sze - PINTEC 2008 Sze (number of employees) Fscal Incentves Fnancal Support , , More than Number of Frms 1,176 6,388 Source: PINTEC (2010). Table 4 presents the characterstcs of the benefcares frms, on average, by the programs. It s possble to observe the heterogenety of the characterstcs of the benefcares frms by program, and consequently s possble suppose that effects of these programs on prvate R&D expendture could be dfferent n each one. The fnancal support covers more frms than the fscal ncentves (1,176 frms and 6,388 frms, respectvely) durng the perod In relaton to the profle of the frms partcpatng n the programs, t can be verfed, as regards the average sze of the frms, that the partcpants of the fnancal support present profles smaller (135 workers) than the frms of the fscal ncentves (737 workers). However, ths fact s not exclusve of ths study as the majorty of the frms that have already beneftted from fscal ncentves, for nnovatng actvtes, are mostly large frms. Data of PINTEC 2003 show that over 70% of R&D resources are allocated to frms wth more than 500 workers. Consderng the net revenue of sales (NRS), the frms of the fnancal support have smaller revenues, an average, n relaton to the frms of other programs, wth R$ 64.8 mllon and R$ 533 mllon, respectvely. The partcpaton of multnatonal frms s hgh n fscal ncentves. In relaton to the nnovatng profle of partcpatng frms n the both programs, t can be observed that the nnovatve efforts - expendture on R&D (ER&D) and expendture on nnovatve actvtes (EIA) - of benefcares frms on fscal ncentves are hgher than frms 6

7 on the fnancal support. However, 64% of the frms on the fnancal support possessed, n 2008, some patent. The frms of the fscal ncentves, n turn, only 28.8% possessed some patent n Regardng labor, the proporton of workers wth 3rd degree educaton s of about 5.8% n frms of the fscal ncentves, 1.7% n frms partcpatng of the fnancal support. In summary, s possble to fnd dfferences between the benefcares frms by program. The fnancal support s a program that target a hgh number of frms (6,388 frms), wth a small number of employees (135 employees), wth small effort to nnovaton (expendtures on R&D), but that fnd mportant nnovatve results (new products, process and patents). By other sde, the frms benefted by fscal ncentves are bgger, wth hgher net revenue of sales (R$ 533 mllon) and labor productvty, more ntensve n qualfed employees, wth more foregn captal and hgher nnovatve effort (expendtures on R&D and on nnovatve actvtes). Table 4. Characterstcs of Benefcares Frms by Program - PINTEC 2008 Varables Fscal Incentves Fnancal Support Sze (number of employees) Net Revenue of Sales (R$ 1000) Expendtures on R&D (R$ 1000) Expendtures on Innovatve Actvtes (R$ 1000) (121.06) 533, (227,966.56) 9, (3,473.45) 16, (4,533.35) (28.99) 64, (58,675.66) 1, (921.44) 3, (1,207.86) Productvty (R$ 1000) (16.06) (4.35) Skll (%) 5.82% 1.74% Market share (%) 1.9% 2.9% Foregn Captal (%) 49.50% 29.96% Group (%) 23.89% 42.42% P&D Contnuous (%) 32.24% 62.22% Innovaton Product (%) 17.33% 58.52% Innovaton Process (%) 12.16% 70.59% Patent (%) 28.80% 63.97% Number of Frms 1,176 6,388 Source:PINTEC (2010). In ths way, based on the presentaton and a bref comparson of the two programs of ncentve to nnovaton one cannot dsregard the exstence of an effort made by Brazlan publc polces to stmulate the accomplshment of nnovatng actvtes. It s, however, fundamental to dentfy the effects of these programs on the decson of expendture on R&D actvtes of the frms and of expendture n nnovatve actvtes, so as to measure ther nnovatng efforts. It s mportant to menton the unfamlarty wth systematc and more elaborate evaluatons of the effects of these programs on the behavor of partcpatng frms as regards expendture on nnovatng actvtes, and specfcally on R&D actvtes. To brdge ths gap, ths paper presents, as follows, an applcaton of the evaluaton methods for the Brazlan cases. 7

8 4 Estmaton method To evaluate the mpact of these Brazlan nnovaton programs on the nnovatve effort of benefcares frms, wll be appled a counterfactual method to reduce potental selecton bas (HALL and MAFFIOLI, 2008). Imbens and Wooldrdge (2009) present a recent survey about econometrcs methodology to evaluaton program. Propensty score matchng 1 s a technque to determne quas experments, based on balancng algorthms for pertanng ndvduals of dstnct groups wth the purpose of evaluatng the effects of a determned treatment. The technque conssts n a probt model on vary of classfcaton of observatons and later use of estmated probabltes ( X ) to form a controllng group. In 1983, Rosenbaum and Rubn publshed ther semnal paper that frst proposed ths approach. Accordng to Deheja and Wahba (1998), deals wth the nference n samples wth selecton bas n non-expermental unts n whch some unts of the non-expermental group are compared to the ones that receved the treatment. Dependent varable s defned as beng the partcpaton of frms n the fscal ncentves and fnancal support to nnovaton. In present artcle, the use of matchng technque wll result n 4 groups: (1) Benefcary frms of the program whch do not have smlar characterstcs wth any other frms (Sngular Benefcares) (2) Benefcary frms of the program whch have smlar characterstcs wth other non-benefcary frms (Non-Sngular Benefcares) (3) Non-benefcary frms of the program whch have smlar characterstcs wth other non-benefcary frms (Non-Sngular Non-Benefcares) (4) Non-benefcary frms of the program whch do not have any smlar characterstcs wth other benefcary frms (Sngular Non-Benefcares) A late analyss of matchng wll be done through a comparng test of averages and regresson models and wll focus on group (2) and (3) frms, comparng comparable frms. Formally, consderng the practce of an experment, ndexng the nterested populaton as. If Y s the varable value of nterest when the same unt subject to the treatment applcaton 1 and Y 0 the varable value of nterest when the unt s subject to the use of controllng or absence of treatment. The effect of treatment ( ) for a unt s defned through Y1 Y 0 and the expected effect of treatment n all populaton wll be: Y T E Y T 1 E Y T 1 (1) E Y T 0 Where T 0. 1, means that the same expermental unt has been undergone to controllng treatment. 1 We used the psmatch2 program developed by Leuven and Sanes (2003), avalable n software Stata 11. 8

9 As hghlghted by Deheja and Wahba (1998), the man problem n non-expermental stuatons s that t can be estmated E Y 1 T 1, although t s not possble to obtan b E and the dfference E Y T E Y T 0 Y 0 T 1 s a based estmator for Once the unts of treatment and controllng systematcally dffer n ther characterstcs, observng Y 0 T 0 do not correctly estmate Y 0 for control group. The purpose of an experment s to prevent ths bas. However, as t s shown through the use of a co-varable group n whch Y Y T X, Y T Y T Y and the ndependence among groups symbolzed by. 1, 0, beng Gvng the probablty of a unt been submtted to treatment defned as p X Pr T 1 X E T X, demonstrated n Deheja and Wahba (1998), the value of the varable of nterest Y 0 e Y 1, wll be ndependent of the use of treatment or, n other terms, b Y Y T p X, E Y T E Y T 0, become unbased for 1, 0. The estmator 1 1 0, dependng on the probablty of ncluson n the treatment. p X Pr T 1 X. The matchng estmator appled s the nearest neghbor (NN), consdered n the lterature the most straghtforward matchng estmator (IMBENS and WOOLDRIDGE, 2009). Consderng T the set of receved treatment unts and Cthe set of control unts.bengy e Y the observed answer of the treated unts and the control unts, respectvely. C s the set of unts of control group matched wth the unts of treatment group, wth a score of p. The matchng of the nearest neghbour chooses: C p mn p p (2) k Ths means an exclusve matchng for each unt of treatment (BECKER and ICHINO, 2004). In addton of propensty score matchng method, the lterature shows others methods that can be appled to evaluate the nnovaton programs, for example, dfference n dfference estmaton, nstrumental varable estmaton and panel estmaton (HECKMAN et al., 2000; BLUNDELL and COSTA-DIAS, 2008; CALIENDO and KOPEINIG, 2005; IMBENS and WOOLDRIDGE, 2009). T C 5 Emprcal Results The am of ths secton s reports the emprcal results of the nnovaton programs effects on R&D nvestment of benefcares frms.based on the analyss procedure called propensty score matchng, a t test was run for the comparson of the means of benefcares frms of the complete sample (all frms benefcares of the three programs) of the fscal ncentves and of the fnancal support, and frms that were non-benefcares, before data matchng. It must be consdered that the group of frms that were not benefted present n ths analyss s centered n the group of nnovatng frms accordng to PINTEC concepts, n other words, they are the ones who declared beng nvolved n some knd of nnovatng actvty, between 2006 and 2008, or who n ths perod had ncomplete nnovatng projects. If the group of nonbenefcal frms conssted of the total number of frms n ths research (nnovatng and nonnnovatng), the dfference between the two groups would be even bgger. 9

10 It s worth mentonng that the results that are reported n Tables 4 and 6, as follows, show the behavor (before and after matchng) of the four nnovaton varables: expendture on R&D (ER&D), expendture on nnovatve actvtes (EIA), expendture on R&D n relaton to the net revenue of sales (ER&D/NRS) and expendture on nnovatve actvtes n relaton to the net revenue of sales (EIA/NRS). The results presented n table 5 show that all dfferences are sgnfcant before the matchng, for the two programs evaluated. Wthn group of frms, those who partcpated n the fscal ncentves, on average, present hgher of three nnovaton varables (ER&D, EIA, ER&D/NRS) n relaton to the benefted frms of the other program. Ths result was foreseen, once partcpatng of a tax ncentves program s only possble, f the partcpant company has carred out expendture on R&D, beng therefore a company that s already predsposed to nnovate. Dfferent behavor s expected of the frms who receve resources through the fnancal ncentves and who a pror had carred out none, or low actvty of R&D. Table 5. Test t for Comparson Averages of the R&D Expendtures and Innovatve Actvtes Expendtures (R$ a thousand) of the Benefcary Frms by Program PINTEC 2008 Programs Fscal Incentves Average Dfference Sgnf. No Benefc. Benefc. Expendture on R&D (ER&D) 20,888 *** ,112 Expendture on Innovatve Actvtes (EIA) 36,191 *** ,793 Expendture on R&D/ NRS (ER&D/NRS) ** Expendture on Innovatve Actvtes /NRS (EIA/NRS) * Number of Frms 13, Fnancal Expendture on R&D (ER&D) 5,067 *** 441 5,508 Support Expendture on Innovatve Actvtes (EIA) 12,466 *** 1,673 14,139 Expendture on R&D / NRS (ER&D/NRS) *** Expendture on Innovatve Actvtes /NRS (EIA/NRS) *** Number of Frms 12,791 1,157 Source: PINTEC (2010). *** Sgnfcant at 1%; **Sgnfcant at 5%; * Sgnfcant at 10%; ns. Non-sgnfcant. The second stage of ths frst procedure resdes n the estmaton of the probablstc model whch s dentcal to the one used n the prevous chapter. A lkelhood rato test was carred out for the verfcaton of the null hypothess, H0 : 1 k 0. The statstcs of lkelhood account for the comparson between the complete model and a model wth only the ntercept, where 1.The result of the lkelhood tests shows rejecton of the probt 0 model contanng only the ntercept and the valdty of at least one of the varables used n the model. More specfcally the lkelhood statstcs compares the complete model and a model only wth the ntercept. Sze Export ForegnCaptal MarketShare HghTech (3) versus 0 (4) j The selected explanatory varables for the probablstc model were: 10

11 Sze: Logarthm of the number of persons employed by the frm,to evaluate the sze frm effect on the probablty of partcpatng n the publc program. Export: Dummy for the frm as an ndcator for nternatonal compettveness. Foregn Captal: Dummy to ndcate the foregn captal on the frm. Market Share: Frm revenue partcpaton n the sector of economc actvty as an ndcator of natonal compettveness. Hgh Tech: Dummy for the frm that s classfed as a hgh technology sector, accordng to the technologcal ntensty classfcaton of the OECD. j : Geographcal localzaton regon of the company, consderng the fve largest regons of the country. Among the varables used n the set of explanatory varables the market share of the frms was constructed through the net frms revenue partcpaton on the net total revenue of the economc actvty sector, defned accordng to the three dgts classfcaton from the CNAE (economc actvty natonal classfcaton). The model has also ncluded the geographcal regon localzaton (α j ) consderng the largest fve regons n Brazl and the economc actvty sector (γ k ) accordng to the technologcal ntensty classfcaton from the OECD. Table 6.Probt Models on Fscal Incentves and Fnancal Support Varables Fscal Incentves Fnancal Support Sze ( )*** ( )*** Foregn Captal ( )n.s. ( )*** Export ( )*** ( )n.s. Market Share (4.3089)*** (2.4888)*** Hgh Technology ( ) n.s. ( )*** Geographc Regon (0.3669) n.s. ( )n.s. Geographc Regon (0.4449) n.s. ( ) n.s. Geographc Regon ( ) n.s. ( ) n.s. Geographc Regon ( ) n.s. ( ) n.s. Constante ( )*** ( )*** N. Observatons 13,948 13,948 Pseudo R Log pseudo-lkelhood The pseudo R 2, obtaned by comparng the maxmum lkelhood functon of the complete model wth the maxmum lkelhood of the model wth only the ntercept, was 22.97% n the 11

12 fscal ncentves model and 3.16% n the fnancal support model, ndcatng a moderate degree of explanaton of the model and compatble wth the goals of the model adjustment. In both cases, the probt models presented n table 6 shows a sgnfcant and postve mpact for the varables sze and market share. The varable export frm has a postve mpact n the probablty to frm partcpate of fscal ncentves program, and the varable foregn captal has a postve mpact n the probablty to frm partcpate of fnancal support. The nonsgnfcance of some classes for regon of localzaton shows that they do not dffer among them wth respect to the probablty of partcpatng n the fnancng program. The post-matchng test presented n table 7 shows that the R&D expendture varable has a relevant contrbuton to the two programs for remanng sgnfcant n relaton to the t test before the matchng. The expendtures on R&D (ER&D) and nnovatve actvtes (EIA) of benefcary frms reman superor to the expendtures on R&D of the non-benefcary through the programs after the matchng. The results of the two models as regards the behavor of the ER&D, EIA, ER&D/NRS and EIA/NRS varables are presented n table 7. The results show that all the dfferences of these four nnovaton varables reman sgnfcant after the matchng. The benefcary frms of the fscal ncentves and fnancal support present the bggest medan dfference n relaton to the group of twn frms, but whch had not partcpated of the program. Table 7. Post-Matchng: Test t for Comparson Averages of the R&D Expendtures and Innovatve Actvtes Expendtures(R$ a thousand) of the Benefcary Frms by programs PINTEC 2008 Programs Fscal Incentves Average Dfference Sgnf. No Benefc. Benefc. Expendture on R&D (ER&D) 14,994 ** 6,118 21,112 Expendture on Innovatve Actvtes (EIA) 20,867 ** 16,926 37,793 Expendture on R&D / NRS (ER&D/NRS) ** Expendture on Innovatve Actvtes /NRS (EIA/NRS) * Number of Frms Fnancal Expendture on R&D (ER&D) 4,716 ** 792 5,508 Support Expendture on Innovatve Actvtes (EIA) 11,110 *** 3,029 14,139 Expendture on R&D / NRS (ER&D/NRS) *** Expendture on Innovatve Actvtes /NRS (EIA/NRS) *** Number of Frms 1,157 1,157 Source: PINTEC (2010). *** Sgnfcant at 1%; **Sgnfcant at 5%; * Sgnfcant at 10%; ns. Non-sgnfcant. In concluson, the publc programs evaluated ncrease the nnovatve effort of Brazlan frms. 2 For expendture on R&D (ER&D), expendture on nnovatve actvtes (EIA) and ER&D/NRS, the fscal ncentves ncrease more the nnovatve efforts of the frm than the fnancal support program. However, the fnancal support program generates a hgher EIA/NRS compared wth the fscal ncentve mpact. In general, the mpact of the fscal ncentves shows superorty to the mpact generated by the fnancal support. A possble explanaton for ths result could be that the frst one s a tax ncentve program whose profle of partcpant frms s concentrated n frms that are already nnovatve, and for beng an ex-post beneft, whch s well argued n nternatonal lterature. 2 In ths emprcal study we also appled others matchng estmators, such as: Kernel, Mahalanobs and Calper estmators. The results obtaned wth theses estmators are consstent wth the Nearest Neghbor estmator. 12

13 Dfferently, the fnancal support programscan fnance new nnovatve frms who are stll ntatng ther R&D actvtes, whch could subestmate the measurement of the mmedate results of ths program as regards the technologcal behavor of these benefcary frms. 6 Concludng remarks Many studes concentrated to measure the effects of publc support on busness R&D have been realzed and the emprcal lterature contnues to ncrease. Some of these studes usually apply quas-expermental (propensty score matchng) to comparng the behavor of benefcary frms n relaton to non-benefcary frms.the practce of evaluaton the mpact of publc support to prvate R&D expendtures n Brazl s stll an ntal stage, but the nsttutons responsble for R&D plannng and management were establshed ths practce. Accordng to what has been exposed, t can be concluded that the government polces of promoton to nnovaton n Brazl are stmulatng changes n the behavor of the frms as regards expendture on R&D and nnovatve actvtes, because n the cases evaluated, the effect of the publc support s postve, ncreasng the nnovatve effort. However, the fscal ncentve mpact s hgher than the fnancal support. A possble explanaton for ths result s that the frst s a tax ncentve program, an ex-post beneft, and the target s nnovatve frms. It can also be concluded that the type of nstrument s mportant, manly for dealng wth heterogeneous target publc and temporalty. Therefore, the effectveness of the nnovaton program depends on the fnancng mechansm used and on the characterstcs of the target benefcares. In Brazl case the emprcal evdences show that f the country wants to deep the ntensty of the nnovatve effort of the prevously nnovatve frms, t s approprate appled fscal ncentves. And f the publc polces want to enlarge the base of nnovatve frms, ncreasng the number of nnovatve frms, the fnancal support to R&D, to buy machnes and equpment, software and tranng, could be more approprate. The next step of ths research s to evaluate other fnancng programs of nnovatve actvtes to Brazlan frms (scholarshp for researchers n frms, venture captal, for example) usng new methodologes, to brng new contrbutons regardng the effects of nnovatve publc polces n Brazl. References ABADIE, A.; IMBENS, G., Matchng on the Estmated Propensty Score. NBER Workng Paper Seres, 38 p., 2011 AERTS, K.; CZARNITZKI, D Usng Innovaton Survey Data to Evaluate R&D Polcy: the case of Belgum. ZEW Dscusson Paper ALMUS, M.; CZARNITZKI, D The effects of publc R&D subsdes on frms nnovaton actvtes: the case of Eastern Germany. Journal of Busness and Economc Statstcs 21 (2), AVELLAR, A. P Impact of Fscal Incentves to R&D: evaluatng the Effects of the PDTI n Brazl. LACEA Conference. Ro de Janero. Brazl. AVELLAR, A.P., Impacto das Polítcas de Fomento à Inovação no Brasl sobre o Gasto em Atvdades Inovatvas e em Atvdades de P&D das Empresas. Estudos Econômcos, São Paulo, v. 39, n

14 BECKER, S.; ICHINO, A., Estmaton of Average Treatment Effects based on Propensty Scores. The Stata Journal, 2, 4, BLOOM, N.; GRIFFITH, R.; VAN REENEN, J., Do R&D Tax Credt work? Evdence form a panel of countres Journal of Publc Economcs, n.85, BLUNDELL, R.; COSTA DIAS, M., Alternatve approaches to evaluaton n emprcal mcroeconomcs. CEMMAP Workng Paper CVP 26/08. BUSOM,I., An emprcal evaluaton of the effects of R&D subsdes. Economcs of Innovaton and New Technology 9, CALIENDO, M.; KOPEINIG,S., Some Practcal Gudance for the Implementaton of Propensty Score Matchng. IZA Dscusson Paper No CAMERON, A.C.; TRIVEDI, P.K., Mcroeconomcs Usng Stata. Stata Press Publcaton. CZARNITZKI, D.; FIER, A., Do nnovaton subsdes crowd out prvate nvestment: evdence from the German servce sector. Appled Economcs Quarterly 48 (1), CZARNITZKI, D.; HANEL, P.; ROSA, J. M Evaluatons the Impact of R&D Tax Credts on Innovaton: a Mcroeconometrc Study on Canadan Frms. ZEW Dscusson PaperNo DAVID, P. A.; HALL, B. H.; TOOLE, A.A., Is publc R&D a complement or substtute for prvate R&D? A revew of the econometrc evdence. Research Polcy29, DEHEJA, R. H; WAHBA, S.,1998. Propensty Score Matchng Methods for Non- Expermental Causal Studes. Natonal Bureau of Economc Research. Workng Paper Seres DUGUET, E.,2004. Are R&D subsdes a substtute or a complement to prvately funded R&D? Evdence from France usng propensty score methods for non-expermental data. Revue d EconomePoltque 114 (2), GONZÁLEZ, X.; PAZÓ, C., Do publc subsdes stmulate prvate R&D spendng? Research Polcy 37, GRIFFITH, R.; REDDING, S.; VAN REENEN,J., Measurng the Cost Effectveness of an R&D Tax Credt for the UK. Center for Economc Performance. London School of Economcs and Poltcal Scence. HALL, B.H.,1992. R&D tax polcy durng the eghtes: success or falure? NBER Workng Paper Seres n , Cambrdge. HALL, B.H.,2002. The Fnancng of Research and Development.Oxford Revew of Economc Polcy, Vol.18, No.1, pp HALL, B.H.; MAFFIOLI, A., Evaluatng the Impact of Technology Development Funds n Emergng Economes: evdences from Latn Amerca. NBER Workng Paper Seres n , Cambrdge. HALL, B.H.; VAN REENEN, J., How effectve are fscal ncentves for R&D? A revew of the evdence. Research Polcy 29, pp HALL, B.H.; WOSINSKA, M., Effectveness of the Calforna R&D Tax Credt. Report Prepared for The Calforna Councl on Scence and Technology. June. 14

15 HECKMAN, J. J.; LALONDE, R.; SMITH, J The economcs and econometrcs of actve labor market programs. In: ASHENFELTER, O. C. D. (eds.). The Handbook of Labor Economcs, North-Holland. HUSSINGER, K R&D and subsdes at the frm level: an applcaton of parametrc and sem-parametrc two-step selecton models. ZEW Dscusson Paper No IMBENS, G.; WOOLDRIDGE, J., 2009.Recent developments n the econometrcs of program evaluaton. Journal of Economc Lterature. KAISER, U., Prvate R&D and publc subsdes: mcroeconomc evdence from Denmark. Dansh Journal of Economcs 144 (1) LACH,S., Do R&D subsdes stmulate or dsplace prvate R&D? Evdence from Israel. Journal of Industral Economcs LATTIMORE, R.,1997. Research and Development Fscal Incentves n Australa: Impacts and Polcy Lessons. Conference Polcy Evaluaton n Innovaton and Technology. Scence and Technology Polcy Dvson. OECD, Pars. LEUVEN, E.; SIANESI, B., PSMATCH2: Stata module toperform full Mahalanobs and propensty score matchng. common support graphng. and covarate mbalance testng. Verson LEYDEN, D. P.; LINK, A. N., Tax polces affectng R&D: an nternatonal comparson. Technovaton13, LÖÖF, H.; HESMATI,A., The mpact of publc fundng on prvate R&D nvestment. New evdence from a frm level nnovaton study. CESIS Workng Papers No. 06. MACDONALD, C.; R&D Tax Incentves: a Comparson of the Incentve Effects of Refundable and Non-refundable Tax Credts. Summer Research Paper. Unversty of Waterloo. MANSFIELD, E.; SWITZER, L.,1985. The effects of R&D tax credts and allowances n Canada. Research Polcy OZCELIK, E.; TAYMAZ, E., R&D support programs n developng countres: The Turksh experence. Research Polcy 37, p PINTEC Pesqusa de Inovação Tecnológca. IBGE, Ro de Janero. ROSENBAUM, P.; RUBIN, D., The central role of the propensty score n observatonal studes for casual effects. Bometrca70, WALLSTEN,S., The effect of government-ndustry R&D programs on prvate R&D: the case of the small busness nnovaton research program. Rand Journal of Economcs, WOOLDRIDGE, J.M., Econometrc Analyss of Cross Secton and Panel Data. The MIT Press. Cambrdge. Massachusetts. 15