Marke Concenraion and Innovaion: New Empirical Evidence on he Schumpeerian Hypohesis Philip G. Gayle Deparmen of Economics Kansas Sae Universy 302D Waers Hall Manhaan, KS 66506-4001 e-mail: gaylep@ksu.edu Ocober 2001 (Revised April 2003) Absrac This paper conducs a new empirical examinaion of he Schumpeerian hypohesis ha more concenraed indusries simulae innovaion. I is found ha he lack of evidence for he hypohesized relaionship in recen empirical work is largely due o he use of simple paen couns as he measure of innovaive oupu. When caion-weighed paen coun is used o measure innovaive oupu, his paper finds empirical evidence in suppor of he Schumpeerian hypohesis. Because caion-weighed paen coun accouns for he well-known heerogeney in echnologies covered by paens, is an improved measure compared o simple paen coun. Furher, he empirical model considers he naure of exernalies in he R&D process; and compares he relaive imporance of firm level adverising and successful innovaion in affecing a firm s marke share. JEL classificaion: L1; O31; C3 *This is a revised version of Chaper 3 of my Ph.D. disseraion compleed a he Economics Deparmen of Universy of Colorado a Boulder, in May 2002. I would especially like o hank Prof. Bronwyn H. Hall a UC Berkeley for her expedious provision of he daa used in his research and for helpful discussions. In preparing his paper I benefed from invaluable commens from Professors Rober Mcnown, Yongmin Chen, Jose Canals-Cerda, Randy Walsh, Mura Iyigun and Nicholas Flores. I am also indebed o Thima Puanun for aking he ime o proof read and offer valuable commens. I am responsible for any remaining errors.
1 1. Inroducion An imporan issue in economics is how marke srucure affecs innovaion. In his seminal conribuion, Schumpeer (1942) claimed ha sociey mus be willing o pu up wh imperfecly compeive markes in order o achieve rapid echnical progress. He argued ha large firms in imperfecly compeive markes are he mos conducive condions for echnical progress. To he exen ha firms in more concenraed indusries operae in a way ha more closely approximaes imperfecly compeive markes in which firms possess marke power, his led o he long-sanding and much debaed hypohesis ha more concenraed indusries 1 are more conducive for innovaion. The Schumpeerian hypohesis challenged convenional economic hinking on he ideal marke srucure for opimal resource allocaion and sparked a preponderance of boh heoreical and empirical papers on he opic. Based on Shumpeer s argumen, policies ha seek o lim or eliminae imperfec compeion could simulaneously reduce he amoun of innovaion ha a sociey enjoys. A review of he empirical leraure up o he lae 70 s by Kamien and Schwarz (1982) revealed an inconclusiveness of he relaionship beween marke srucure and innovaive acivy 2. Resuls ranged from finding ha imperfecly compeive markes are beer a simulaing innovaive acivy (suppor for Schumpeerian hypohesis), o finding he complee oppose. Subsequenly, researchers such as Geroski (1990), Blundell, Griffh and Van Reenen (1995), Levin, Cohen and Mowrey (1985), and Cohen, Levin and Mowery (1987), among ohers, have found disproporionae evidence agains he Schumpearian hypohesis. These newer sudies argued ha echnological opporuny, which varies across indusry, is an 1 The larger he percenage of indusry oupu conrolled by leading firms, he larger is indusry concenraion [see Tirole (1988), pp. 221, for measures of concenraion].
2 imporan deerminan of innovaive acivy and mus be conrolled for when invesigaing he relaionship beween marke srucure and innovaion. They used various mehods o conrol for hese echnological opporunies and poin o his as he main reason ha swung he evidence agains he Schumpearian hypohesis. In his paper, I shall argue ha he measure of innovaive oupu plays a key role in esing he Schumpeerian hypohesis. The exising sudies have relied heavily on he number of paens awarded (simple paen coun) as a measure of innovaive oupu 3. One problem wh simple paen coun as a measure of innovaive oupu is ha assumes ha all echnologies covered by paens are equal in heir economic and social value. Major innovaions usually require significan amoun of resources ha only large firms end o have. These large firms are mos likely o be locaed in concenraed indusries, while less concenraed indusries end o have many small firms, who lack he resources o produce major innovaions bu can sill produce minor innovaions. In highly compeive indusries where concenraion is usually low, we ofen see a significan amoun of produc differeniaion. Since produc differeniaion ofen leads o paening of minor changes o exising echnologies, we may observe exensive paening in compeive indusries ha resuls from produc differeniaion. Given ha simple paen coun reas echnologies covered by paens as equal in heir economic and social value, his measure is likely o lead o empirical resuls suggesing ha less concenraed indusries produce more paens and hus more innovaion (rejecion of Schumpeerian hypohesis). This would no be an accurae conclusion however, because mos of he 2 Cohen and Levin (1989) provide anoher excellen review of he leraure. 3 Oher measures used include number of imporan innovaions and sales of new producs.
3 paening in less concenraed indusries would be for minor echnologies possibly resuling from produc differeniaion driven by siff compeion. In esing he Schumpeerian hypohesis, he conribuion of his paper o he leraure is o use a more accurae measure of innovaion, caion-weighed paen coun, ha accouns for he heerogeney of echnologies covered by paens. A measure of innovaive oupu ha accouns for he heerogeney of echnologies covered by paens is no disored by minor paening and hus more reflecive of rue innovaion. Using caion-weighed paen coun, I show ha he empirical evidence suppors he Schumpeerian hypohesis, even afer conrolling for boh observable and unobservable indusry and firm specific characerisics which includes echnological opporuny, normally ced as crical in esing Schumpeer s hypoheses. I is suggesed ha marke power ineracs wh a firm s decision o innovae via anicipaed and curren possession of marke power [Kamien and Schwarz (1982)]. Innovaors will have more incenive o innovae he greaer he anicipaed marke power associaed wh he pos-innovaion indusry. The promised exraordinary profs in he fuure will more han compensae for he curren R&D invesmen. Thus is no conroversial in he leraure ha greaer anicipaed marke power simulaes greaer innovaive acivy. Where conroversy creeps in is wheher curren possession of marke power simulaes greaer innovaion. There are heoreical argumens ha pos boh posive and negaive relaions beween curren marke power and innovaive acivy. There are several argumens why he curren possession of marke power should resul in greaer innovaive acivies. Firs, marke power wh respec o curren producs may be exendable o new producs, for example, hrough a dominan firm s
4 command over channels of disribuion ec. Wh he abily o exend marke power o new producs, a curren monopolis should find innovaion more aracive. Second, as suggesed by Arrow (1962), due o moral hazard problems, here may be a need o finance innovaion inernally, which pus firms wh marke power a an advanage since hese firms may have supernormal profs. Third, firms wh curren marke power usually have more resources and hus more likely o hire he mos innovaive people. Of course he hird reason is relaed o he imperfec capal marke argumen underlying he second reason. There are also disadvanages o curren marke power in performing innovaion. Firs, monopoly may regard addional leisure as superior o addional profs. This may be due o he lack of acive compeive forces and hus generaes an x-inefficiency effec. Second, a firm realizing monopoly profs on s curren produc or process may be slower in replacing wh a superior produc or process han a newcomer. This is because he firm realizing monopoly profs on s curren produc calculaes he prof from innovaion as he difference beween s curren profs and he profs could realize from he new produc, whereas he newcomer regards he profs from he new produc as he gain (see Kamien and Schwarz (1982)). As such, he larger curren monopoly profs are, he less incenive he monopolis has o replace his own produc or process. Theoreical models comparing an incumben s and an enran s incenives o innovae also give mixed predicions abou he impac of monopoly power on innovaive effor. Gilber and Newbery (1982) sugges ha a monopolis has more incenive o win a paen race because s win avoids dissipaion of rens ha would occur if an enran wins he
5 paen race. Oher heoreical models, including Reinganum(1983), Chen (2000), and Gayle (2002), sugges ha facors such as uncerainy in he innovaion process and he sraegic relaion beween new and exising producs may moivae enrans o spend more on R&D relaive o incumbens. Since here are forces boh in favor of and agains a posive relaion beween marke power and innovaive acivy, he ne resul is an empirical maer. To he exen ha pure monopoly is rare in he real world, exising empirical sudies have focused on he relaion beween marke concenraion and innovaion, wh he underlying assumpion ha firms in more concenraed markes end o have more marke power. The presen paper will ake he same approach o revis he empirical evidence on he Shumpeerian hypohesis. The res of he paper is organized as follows. Secion 2 discusses he measuremen of innovaive oupu. I sugges ha a more precise measure of innovaive oupu, caionweighed paen coun, can be used o es he empirical relaion beween marke concenraion and innovaion. Secion 3 discusses he daa, secion 4 presens he empirical model, secion 5 discusses esimaion and resuls, and secion 6 concludes. 2. The Measure of Innovaive Oupu For a long ime now, researchers have recognized ha simple paen coun is no a very accurae measure of innovaive oupu 4. Simple paen coun as a measure of innovaive oupu has been used exensively in he empirical leraure, (for review see Griliches, 1990). As suggesed before, one reason why simple paen coun is no an 4 See Lanjouw, Pakes and Punam (1998).
6 accurae measure of innovaive oupu is ha he echnologies covered by paens are very heerogeneous in heir economic and social value while, simple paen coun values all paened innovaions equally. Recognizing his problem, Pakes (1986), Pakes and Schankerman (1984), and Schankerman (1991), among ohers, aemped o measure he qualy raher han quany of innovaive oupu using paen renewal daa. In many paening regimes, paen holders mus pay an annual renewal fee in order no o forfe he paen before s sauory lim of proecion (approximaely 20 years). The paen renewal leraure poss ha informaion on he value of paens can be exraced from paen renewal paerns since raional agens will only renew paens if he benef of renewal is greaer han he cos. This leraure finds ha a subsanial number of paens were no renewed o he full sauory lim. Esimaion of hese models required fairly lenghy ime series o observe each cohor of paens and heir respecive dropou daes. The majory of hese models were esimaed on European daa raher han U.S. daa, probably because U.S. only sared requiring paenees o pay a renewal fee in 1982. In oher words, many of he paen cohors in U.S. daa were no observable for he full sauory lim. Recenly, using U.S. daa, Jaffe and Trajenberg (1996), Hall, Jaffe and Trajenberg (2000), and Lanjouw and Schankerman (1999) have found more creaive ways o measure he value of paens by using he number of caions received by a paen. An invenor mus ce all relaed prior U.S. paens in he applicaion process, much like how auhors of journal aricles mus ce relaed previous research. A paen examiner is responsible for insuring ha all appropriae paens have been ced. Again, his is analogous o he academic world where referees of journals are responsible for
7 ensuring ha appropriae research has been ced. These caions help o define he righs of he paenee. Researchers have posed ha he number of caions ha a paen receives can be used o measure he relaive value/imporance of he echnology proeced by he paen. As such, researchers have developed new and more precise measures of innovaive oupu using paen caions. Once more, his idea is analogous o how we measure he relaive imporance of published research aricles. The more caions ha a research aricle receives he more likely is ha he ced aricle has made an imporan conribuion o he leraure. The measure of innovaive oupu used in his paper is caion-weighed paen couns, ha is, each paen coun is weighed by he number of caions received. A brief descripion of he consrucion of he caion-weighed paen coun variable is as follows. Le ( s) n, be number of ces received a ime s o paens applied for a ime. Therefore, () = n( s) T n, is he oal number of ces whin ime inerval T-, o s = paens applied for a ime. The same lengh ime inerval is used o coun caion informaion for each paen, irrespecive of applicaion dae, in order o allow for comparable measures. For example, if an inerval of en years is used, hen he caion measure is number of ces received by a paen whin en years afer applicaion dae. The variable n () is caion-weighed paen coun. This measure of innovaive oupu reas each paen as if is worh he number of caions received. Thus a measure of oal innovaive oupu in a given year is he sum of caions over all he paens applied for in ha year. n () is calculaed for each firm for each year in he daa se. 5 5 For a more deailed derivaion of he caion-weighed paen sock measure used in his paper see Hall, Jaffe and Trajenberg (2000).
8 There exis empirical evidence ha suppors he hypohesized idea ha paens ha are frequenly ced have made larger impac on echnological improvemens and hus more valuable. A review of he empirical leraure ha suppors his idea can be found in Hall, Jaffe, and Trajenberg (2000), however, I will briefly ce some specific examples discussed by hem. Trajenberg (1990) used paens relaed o a class of medical insrumens, and relaed he flow of paens over ime o he esimaed social surplus aribued o scanner invenions. Trajenberg found ha simple paen couns have no correlaions wh esimaed social surplus, bu caion-weighed paen couns have exremely high posive correlaions wh esimaed social surplus (0.5 and above). We can only conclude from his ha caions are a measure of paen qualy as indicaed by he generaion of social welfare. Using survey daa, Harhoff e al (1999) find ha more valuable paens are more likely o be renewed o full erm and ha he esimaed value is posively correlaed wh subsequen caions o ha paen. In oher words, he paens ha had he highes economic value based on survey daa were also more highly ced. They esimaed ha a single U.S. caion imply on average more han $1 million of economic value. Lanjouw and Shankerman (1999) also uses caions, ogeher wh number of claims and number of differen counries in which an invenion is paened o consruc a measure of paen qualy. They find ha heir measure of paen qualy has significan power in predicing which paens will be renewed o full erm, and which ligaed.
9 3. Daa The daa se used in his paper is he NBER-Case-Wesern Universy R&D paens daa se [see in references, Trajenberg, Manuel, Adam Jaffe, and Bronwyn Hall (2000)]. This is a new and comprehensive daase conaining over 4800 U.S. Manufacuring firms over he period 1965 o 1995. The daa se conains usual firmspecific daa (2-dig indusry code, sales, R&D expendure, adverising expendure, capal sock, asses, Tobin s q ec.) along wh firms paening acivies. Firm-specific paening informaion includes number of paens applied for in a given year ha were evenually graned and he oal number of ces received by hose paens. The daa se conains caion informaion saring only from 1976. As such, he sample used for analysis in his paper sars from 1976. Paening informaion comes from he Uned Saes Paen Office while oher firm-specific daa are drawn from he Compusa file, which comprises all firms raded on he U.S. sock marke [see Hall, Jaffe, and Trajenberg (2000) for deailed descripion of daa se]. Summary saisics and simple correlaions of he variables used in his sudy are shown in ables 1 and 2. A lis of broad indusry caegories covered by he daa se is presened in able A.1 in he appendix. Several poins are worh menioning abou he correlaions shown in able 2. Firs, a firm s R&D spending is posively correlaed wh boh s simple paen coun and caion-weighed paen coun. In fac, hese correlaions are among he larges displayed in he marix. Second, more concenraed indusries, as measured by he
10 Herfindahl index 6, are also more R&D inensive as exemplified wh a correlaion coefficien of 0.50. Third, indusry concenraion is slighly more highly correlaed wh firm level R&D expendure han wh innovaive oupus (simple paen coun and caion-weighed paen coun). This suggess ha indusry concenraion migh influence innovaion indirecly hrough R&D expendure. Many empirical papers have posed a direc raher han indirec relaionship beween indusry concenraion and innovaion. The heoreical srucure of he model in his paper poss an indirec Table 1 Descripive Saisics of he NBER-Case-Wesern Universy R&D Paens Daase Variable Mean Sd. Dev. Min Max Year - - 1976 1992 Firm R&D expendure (M$) 15.92 81.58 0 2224.498 Firm adverising expendure (M$) 14.28 76.94 0 2693 Indusry level R&D expendure (M$) 2439.68 2831.11 2.077 13187.46 Firm sales (M$) 723.15 2741.19 0.001 59946 Capal sock (M$) 550.34 2310.25 0.045 47911.38 Number of Paen applicaion 6.83 31.58 0 775 Number of Ces o paens 32.37 163.5 0 3713 Marke share 0.011 0.032 2.36e-08 0.638 Indusry Concenraion (Herfindahl index) 0.122 0.098 0.024 0.478 N=33121 n 6 The Herfindahl index is calculaed by C = s 2 where s is firm i marke share a ime, and n i = 1 is he number of firms in he paricular indusry [see Tirole (1988), pp. 221 for more on concenraion indices]. Indusry subscrips are suppressed for noaional convenience bu noe ha he index is calculaed for each indusry a each ime period.
11 Firm Firm Table 2 Correlaion Marix Paens Ces Indusry Marke Indusry Adverising R&D Sales R&D Share concenraion Expendure Firm R&D 1 Firm 0.71 1 Sales Paens 0.68 0.58 1 Ces 0.56 0.49 0.90 1 Indusry R&D Marke Share Indusry Concenraion Adverising Expendure 0.13-0.02 0.06 0.03 1 0.62 0.73 0.51 0.48-0.12 1 0.07-0.01 0.02 0.05 0.50-0.01 1 0.40 0.52 0.31 0.26-0.02 0.47-0.025 1 relaionship beween indusry concenraion and innovaion as suggesed by he daa. The las poin I wan o menion before moving on o he nex secion is ha adverising expendure is posively correlaed wh firm s marke share as expeced, bu he correlaion beween firms marke share and innovaion is even higher. This seems o sugges ha successful innovaion could be a sronger deerminan of marke share compare o adverising expendure. 4. The Empirical Model The economeric model consiss of hree equaions, one for research, one for innovaion and one ha akes accoun of he endogenous effec of innovaion on marke
12 share. Each equaion uses a differen economeric reamen much like in Crepon, Dugue and Mairesse (1998). The firs equaion models he magnude or inensy of research acivies and is given by: = (1) * r γ s + β1x1 + µ 1i + ε1 where r * is he rue research inensy of firm i a ime, s is firm i' s marke share a ime wh γ being he corresponding coefficien, x 1 is a vecor of explanaory variables wh β 1 being he corresponding coefficien vecor, µ 1i conrols for firm specific effec, and ε 1 is a random error erm. In his equaion he righ hand side variables are firm and indusry characerisics such as firm s marke share, firm size, and indusry concenraion/compeiveness. Having conrolled for indusry compeiveness and firm s marke share, we would expec larger firms o be more R&D inensive as is well documened in work by Cohen and Klepper (1996) and Scherer (1965a, 1965b). As such, he sign of he coefficien on firm size is expeced o be posive. As saed previously, more recen empirical leraure swung he balance of evidence agains he Schumpeerian hypohesis. Tha is, recen evidence suggess ha indusry concenraion eher have no effec or have a negaive impac on innovaion [Geroski (1990), Blundell, Griffh and Van Reem (1995), Levin, Cohen and Mowrey (1985)]. In he srucural model of his paper I have posed ha indusry concenraion direcly influences firms R&D inensy, which in urn affecs firms level of innovaion (his will be more apparen when I specify equaion 2). As such, he effec of indusry concenraion on innovaion is indirec.
13 Following Crepon, Dugue and Mairesse (1998), and Blundell, Griffh and Van Reem (1995), I also include firm s marke share since hese previous sudies found ha marke share is a significan deerminan of innovaive effor. A firm s marke share can also be viewed as a measure of dominance and hus heoreically should affec a firm s R&D inensy. Crepon, Dugue and Mairesse (1998) found a posive and saisically significan coefficien for he effec of marke share on R&D inensy. Blundell, Griffh and Van Reem (1995) also found a posive and saisically significan coefficien bu his is for he effec of marke share on innovaive oupu. As will be explained laer, he srucural parameers of equaion 1 will no be esimaed because we are more ineresed in he resuling parameers when equaion 1 is combined wh equaion 2. Equaions 2 is he innovaion equaion and is modeled as a random/fixed-effecs negaive binomial regression given by: * * ( n r, x, µ, ε ; α, β ) exp( αr + β x + µ + ε ) E 2 2i 2 2 2 2 2i 2 = (2) where n is caion-weighed paen coun of firm i in year. Since he dependen variable falls in he caegory of coun daa (only ineger values), we specify he equaion as a heerogeneous coun daa process condional on research inensy and oher variables. Recall ha r * is our R&D inensy variable from equaion 1. x 2 is a vecor of explanaory variables, µ 2i conrols for firm specific effec (heerogeneous abily o innovae), and ε 2 is a random error erm. Since x 2 only conains one variable which is indusry level R&D, he righ hand side variables in equaion 2 are firm level R&D spending and indusry level R&D spending. Based on previous sudies such as Crepon,
14 Dugue and Mairesse (1998), Pakes and Greliches (1984), Lanjouw and Schankerman (1999), we expec firm s R&D spending o be posively correlaed wh innovaion. As such, he sign of he coefficien on firm level R&D inensy should be posive. We wan o emphasize he imporance of µ 2i in equaion 2. Empirical sudies have found ha indusries vary wh respec o heir echnological opporunies and appropriabily condions. Technological opporunies include facors such as he echnological base of an indusry, ha is, wha is he body of scienific knowledge relevan o research in an indusry and how easily can his knowledge be accessed. Geroski (1990), Levin, Cohen and Mowrey (1985), Cohen, Levin and Mowery (1987), Blundell, Griffh and Van Reenen (1995), sress he imporance of conrolling for echnological opporunies and appropriabily condions when esing he Schumpeerian hypohesis. In fac, hese papers show ha wheher you conrol for hese facors makes he difference beween acceping and rejecing he Schumpeerian hypohesis. The problem is ha hese facors are generally no observable. Levin, Cohen and Mowrey (1985), and Cohen, Levin and Mowery (1987) made an aemp o measure hese facors via survey daa. Geroski (1990) conrolled for hese effecs via he usual fixed effec procedure applicable o panel daa. Whou good measures for hese facors, he argued ha he usual fixed or random effec procedures done wh panel daa are appropriae. Therefore, his explains he imporance of µ 2i in equaion 2, which is also a feaure of he empirical model found in Geroski (1990) and many oher papers. In fac, is a general heme in all of our equaions o conrol for unobservable specific effecs. Following Crepon and Dugue (1997), indusry level R&D is used o measure R&D exernalies among firms in he same indusry. According o Kaz and Ordover
15 (1990), wo main ypes of exernalies have been repored in he heoreical leraure: a compeive exernaly and a diffusion one. Theoreical models by Loury (1979), Lee and Wilde (1980), Delbono and Denicolo (1991), and Gayle (2002), all incorporaed compeive exernalies via a paen race, where firms inves in R&D aiming o be he firs o discover an innovaion. In hese models, winning depends on boh individual and compeors R&D invesmen: a rise in a firm s R&D spending, ceeris paribus, increases s probabily of winning and lowers ha of s compeors. This would sugges a negaive sign for he coefficien on indusry level R&D in equaion 2. Oher heoreical models such as Kaz (1986) examine diffusion exernalies. In hese models a firm benefs from oher firms R&D hrough a spillover effec. As such, a firm s probabily of success in innovaion is enhanced by more R&D of oher firms in he indusry. This suggess a posive sign of he coefficien on indusry level R&D in equaion 2. Therefore, in general heory is inconclusive as o wha sign we should expec for he coefficien on indusry level R&D in equaion 2. Equaion 3 models he effec of innovaion on marke share and is given by: s ϕ + β3n + φa + µ 3i + ε3 = (3) where s is firm i' s marke share a ime, ϕ is an inercep coefficien, n is caionweighed innovaion coun from equaion 3, a is he log of firm i' s adverising expendure a ime, µ 3i conrols for firm specific effec, and 3 ε is a random error erm. Equaion 3 is esimaed by he usual fixed/random effecs procedure when he dependen variable is coninuous and normally disribued. Specificaion of equaion 3 is
16 a direc aemp o model he endogeney of he relaion beween innovaion and marke srucure. From equaion 1 we see ha a firm s marke share affecs s R&D inensy which in urn influences he firm s probabily of successful innovaion as seen in equaion 2. However, equaion 3 recognizes ha successful innovaion in urn affecs a firm s marke share. We would expec ha successful innovaion increases a firm s marke share. Also is expeced ha a firm s marke share should increase wh s adverising expendure since ha is usually he goal of adverising. Wha is ineresing is ha we can use equaion 3 o compare he relaive imporance of successful innovaion o adverising in affecing marke share. Having specified each equaion, I close his secion by collecing all he equaions ha summarizes he full srucural model as follows: = (1) * r γ s + β1x1 + µ 1i + ε1 * * ( n r, x, µ, ε ; α, β ) exp( αr + β x + µ + ε ) E 2 2i 2 2 2 2 2i 2 = (2) s ϕ + β3n + φa + µ 3i + ε3 = (3) 5. Esimaion and Resuls Recall ha he main ineres of his paper is o explore how firm and indusry characerisics, especially indusry concenraion, affecs firms innovaion, where innovaion can eher be measured by simple paen coun (sandard in he leraure) or caion-weighed paen coun. To conduc his analysis we plug equaion 1 ino equaion
17 2. This allows us o obain an equaion ha expresses innovaive oupu as a funcion of indusry concenraion among oher variables. Having plugged equaion 1 ino equaion 2, he main equaion of ineres is: ( n C, s, x,, ε ; ϖ, λ, β ) = ( ϖc + λs + β x + µ ε ) E 2 2i 2 2 exp 2 2 2 i + 2 µ (2 / ) where C measures indusry concenraion a ime, x 2 is a vecor of explanaory variables which includes firm size and indusry level R&D. In equaion 2 /, he sign of ϖ is our main ineres 7. If ϖ > 0, hen here is suppor for he Schumpeerian hypohesis bu ϖ 0 is a rejecion of he hypohesis. n is measured eher by simple paen coun or by caion-weighed paen coun. The full model o be esimaed consiss of equaions 2 / and 3. Thus here are now only wo endogenous variables, s and n. In any simulaneous equaion sysem, wo major concerns are he problem of simulaney bias and he issue of idenificaion. Firs, I discuss he problem of simulaney bias and hen move on o he issue of idenificaion. Broadly speaking, here are wo approaches o esimaing he model ha solves he problem of simulaney bias. One approach involves esimaing each equaion separaely, using a limed informaion esimaor. Anoher approach is o use a full informaion sysem esimaor. In boh approaches we can find esimaors ha are 7 We could have gone he roue of specifying boh a direc and an indirec effec of marke concenraion on innovaive oupu by inially including he marke concenraion variable in boh equaions 1 and 2. Afer plugging equaion 1 ino equaion 2, ϖ would hen be inerpreed as he oal effec comprising boh a direc and indirec effec. Noe ha he naure of he analysis would no change if his roue had been chosen.
18 consisen bu, in general, full informaion esimaion is more efficien. A full informaion sysem esimaion of he model requires wring down a likelihood funcion for he sysem. As noed in Lee L.-F (1981), full maximum likelihood esimaion of a simulaneous model wh laen dependen variables are oo complicaed o be useful. To confound a full maximum likelihood esimaion procedure of he model above, each equaion has unobservable specific effec parameers and one of he endogenous variables is a coun daa variable. Thus for pracical purposes I am forced o consider a single-equaion limed informaion approach ha yields consisen esimaes. The procedure used, suggesed by Lee L.-F (1981), is analogous o wo-sage leas squares. Firs, reduced-form equaions (equaions ha only have exogenous variables on he righ-hand side) are esimaed and prediced values of he dependen variables recovered. For example, n is expressed as a funcion of all he exogenous variables in he model hen reduced-form parameers are esimaed using a negaive binomial model. The reduced-form parameers are used o ge prediced values of n. Prediced n is hen used in he esimaion of equaion 3 insead of using n. Likewise, before equaion 2 / is esimaed we ge prediced values of s from he reduced-form esimaion of he s equaion. Since s is a coninuous variable, a normally disribued error erm is assumed for he reduced-form esimaion. Prediced s is hen used in he esimaion of 2 /. Equaion 2 / is esimaed as a fixed effecs negaive binomial model. Having oulined he esimaion sraegy, le me briefly discuss idenificaion issues. Each of he wo equaions in he sysem includes boh endogenous variables. s
19 is on he righ hand side of equaion 2 / while n is on he righ hand side of equaion 3. Equaion 2 / is idenified if equaion 3 has a leas one exogenous variable ha is no in equaion 2 /. The exogenous variable ha idenifies equaion 2 / is adverising expendure found in equaion 3. Equaion 3 is also idenified because here are several exogenous variables in equaion 2 / ha is excluded from equaion 3. Following sandard esimaion procedures ha are usually used o rejec he Schumpeerian hypohesis, his paper shows ha using a more precise measure of innovaive oupu (caion-weighed paen coun) can overurn previous resuls (i.e. find suppor for he Schumpeerian hypohesis). The resuls when innovaive oupu is measured by simple paen coun are presened in able 3 while he resuls when he measure is caion-weighed paen coun are presened in able 4. I repor resuls for boh random and fixed effecs esimaion. Columns 1 and 2 in boh ables 3 and 4 display resuls based on random effecs esimaion while columns 3 and 4 show fixed effecs esimaion. I urns ou ha he Hausman es, repored in each able, always rejec he random effecs model as mos appropriae and hus resuls from he fixed effecs models are used o make conclusions. In boh ables 3 and 4, he firs and hird columns display he negaive binomial equaion for innovaion resuls, and he second and fourh columns display he effecs of successful innovaion and adverising on marke share. However, from his poin forward I will focus he discussion on he fixed effecs models (columns 3 and 4) as suggesed by he Hausman es. Column 3 of ables 3 and 4 display he main resul of his paper. In column 3 of able 3 we see ha he coefficien on concenraion is negaive. This is consisen wh he newer empirical findings when innovaive oupu is measured by simple paen coun.
20 This is evidence agains he Schumpeerian hypohesis. Tha is, as indusries become more concenraed innovaion is reduced. If we urn o column 3 of able 4 where innovaive oupu is measured by caion-weighed paen coun, hen we can see ha he sign of he coefficien on indusry concenraion swches o posive. The resuls in able 4 are hus consisen wh he Schumpeerian hypohesis ha more concenraed indusry encourage innovaion. I is worh emphasizing ha esimaion procedure and all he variables are he same in column 3 of ables 3 and 4 wh he excepion of he measure of innovaive oupu. The swch in sign on he concenraion variable begs a plausible explanaion. Since I argue ha simple paen coun is no an accurae measure of innovaive oupu, why do we observe a saisically significan negaive coefficien on concenraion in able 3? In oher words, simple paen coun could be a fairly accurae measure of some process ha is negaively relaed o indusry concenraion. While here migh be several processes a work ha drive he resul, I will offer an argumen ha is boh consisen wh he daa and races back o he core of Schumpeer s argumen as o why large firms in imperfecly compeive markes have an advanage in he innovaive process. One cricism of simple paen coun as a measure of innovaive oupu is ha he measure capures paening of minor echnologies ha can hardly be considered innovaive and could have resuled from simple produc differeniaion. Significan innovaions (innovaions ha have bigger impac), of which he caion-weighed paen coun is a good measure, end o require subsanial resources ha only large firms are likely o have. More concenraed indusries end o be characerized by large firms who
21 C Table 3 Model Esimaes Using Simple Paen Coun Model Random Effecs Fixed Effecs Simple Marke Share Simple Marke paen couns s paen Share n couns s n (1) (2) (3) (4) Indusry Concenraion, -0.93** - -1.11** - (0.12) (0.13) Marke share, s 17.96** (4.37) Firm size (log of Sales) 0.22** (0.03) - 21.02** (4.76) - 0.15** (0.03) - - Indusry level R&D expendure (in logs) 0.13** (0.02) - 0.07** (0.02) - Simple paen couns, n - 0.00043** (7.09e-06) - 0.0004** (7.12e-06) Firm adverising expendure (in logs) - 0.0019** (0.0001) - 0.001** (0.0001) R-squared - 0.41-0.41 Hausman: H o : E( u X ) = 0 χ 2 (4) = 392. 13 2 probχ (4) = 0.0000 Sandard errors are in parenheses. **indicaes saisical significance a he 5% level. All regressions are fed wh a consan 2 χ (2) = 12868.63 2 probχ (2) = 0.0000
22 C Table 4 Model Esimaes Using Caion-Weighed Paen Coun Model Random Effecs Fixed Effecs Caion-Weighed Marke Share Caion- Marke paen couns s Weighed Share n paen s couns n (1) (2) (3) (4) Indusry Concenraion, 1.18** - 1.09** - (0.15) (0.15) Marke share, s 8.80* (4.66) Firm size (log of Sales) 0.30** (0.03) - 9.56** (4.82) - 0.27** (0.031) - - Indusry level R&D expendure (in logs) 0.10** (0.017) - 0.065** (0.018) - Caion-Weighed paen couns, n Firm adverising expendure (in logs) - 0.00008** (1.59e-06) - 0.002** (0.0001) - 0.00006** (1.60e-06) - 0.001** (0.0001) R-squared - 0.41-0.41 Hausman: H : E( u X ) = 0 χ 2 (4) = 360. 28 o 2 probχ (4) = 0.0000 Sandard errors are in parenheses. **indicaes saisical significance a he 5% level. *indicaes saisical significance a he 10% level. All regressions are fed wh a consan. 2 χ (2) = 19660.49 2 probχ (2) = 0.0000
23 are more able o produce hese innovaions. On he oher hand, less concenraed indusries end o have more small firms who end o lack he resources for major innovaion, bu can sill produce minor innovaions. These minor innovaions could be mere produc differeniaion by small firms in highly compeive indusries. This reasoning fs he original idea behind Schumpeer s argumen why more concenraion would facilae innovaion, if wha he hinks is ha imporan innovaions end o require significan resources ha only large firms end o possess. Based on he argumens above, a negaive sign on he concenraion coefficien when simple paen coun is used as he measure of innovaive oupu is no surprising. The simple paen coun measure is picking up a lo of minor paening (possibly driven by he need o produc differeniae) ha is more prevalen in less concenraed indusries. Caion-weighed paen coun is designed o purge simple paen coun of paens ha cover minor echnologies ha can hardly be considered innovaive. As such, caionweighed paen coun should give us a more accurae measure of he relaionship beween indusry concenraion and innovaion. I is possible o furher verify ha he daa is consisen wh hese argumens. Recall ha he caion-weighed paen measure is obained by summing up caions received by a paen. Thus he caion-measure of a paen ha is never ced is zero. A sufficien condion o conclude ha a firm has paens ha are never ced is o check if he caion-weighed paen coun is less han he corresponding simple paen coun for sricly posive simple paen couns. I proceed by selecing wo indusries ha have conrasing levels of concenraion from he daa se. The firs indusry, Moor Vehicle, is consisenly among he five mos concenraed indusries beween 1976 and 1992, and he
24 second indusry, Texile, Apparel and Foowear, has consisenly been among he leas concenraed over he same period. I urns ou ha he rae a which minor paens are applied for is almos hree imes (2.83 imes) higher in he Texile, Apparel and Foowear indusry compared o he Moor Vehicle indusry 8. This is a clear example where less concenraed indusries end o paen more minor innovaions. There are oher ineresing resuls in column 3 of ables 3 and 4. The posive sign of he coefficiens on marke share and firm size sugges ha dominan and large firms end o be more innovaive. This finding is consisen wh Blundell, Griffh and Van Reenen (1995). The sign of he coefficien on indusry level R&D is posive in boh ables 3 and 4. This is evidence in suppor of posive spillover effecs presen in he R&D process: a firm s probabily of success in innovaion is enhanced by more R&D of oher firms in he indusry. As menioned earlier, his is ermed diffusion R&D exernaly in he heoreical leraure [Kaz (1986)]. Column 4 in boh ables 3 and 4 also presens some ineresing resuls. The coefficiens on innovaion and adverising are boh posive across boh ables. No surprisingly, his suggess ha boh successful innovaion and adverising are sraegic ools ha can be used o increase marke share. Since innovaion and adverising expendure are measured in differen uns, boh coefficiens mus be adjused appropriaely o facilae a meaningful comparison of relaive size. The sandard 8 In he Texile, Apparel and Food Indusry, approximaely 17% of he observaions had he caionweighed paen coun measure being less han he simple paen coun measure. On he oher hand, in he Moor Vehicle indusry only a mere 6% of he observaions had caion-weighed paen coun being less han he simple paen coun measure.
25 mehod 9 o adjus hese coefficiens is given by ˆ * i β ˆ βisx = i, where ˆi* β is he adjused s y coefficien, βˆ i is he unadjused coefficien ha appears in he regression, s x i is he sample sandard deviaion of independen variable x i (innovaion and adverising expendure), and s y is he sample sandard deviaion of he dependen variable of he regression (which in his case is marke share). Afer applying hese adjusmens o he coefficiens in column 4 of able 4, he adjused coefficiens on innovaion and adverising expendure are 0.158 and 0.043 respecively. In able 3 he corresponding adjused coefficiens are 0.183 and 0.047 respecively. Thus across boh ables he adjused coefficien on innovaion is larger han he adjused coefficien on adverising. This implies ha, on average, successful innovaion is more powerful in increasing a firm s marke share compared o adverising. This should be useful sraegic informaion for managers. Before concluding, is worhwhile o go hrough some comparaive saic exercises o beer undersand he economics behind some of he coefficien esimaes. The esimaes used for comparaive-saics are aken from columns 3 and 4 of able 4. For a meaningful comparaive-saic exercise of he simulaneous sysem, we mus ake accoun of boh direc and indirec effecs of changes in exogenous variables. As such, I resor o elasicies based on oal derivaives as oppose o parial derivaives. For example, ineresing elasicies o analyze are: ξ nc, ξ nr andξ sa, where ξ nc is he elasicy of innovaive oupu wh respec o indusry concenraion, ξ nr is he elasicy 9 See Ramanahan (1989), Inroducory Economerics wh Applicaions, Harcour Brace Jovanovich, Inc., pp. 160
26 of innovaive oupu wh respec o indusry level R&D spending, andξ sa is he elasicy of firm s marke share wh respec o s adverising expendure. Formal derivaions of hese elasicies and oal derivaives are shown in he Appendix. Given he non-lineary of he innovaive oupu equaion, derivaives mus be evaluaed holding each variable a some respecive level. For his exercise I use he sample mean of each variable. Since ξ nc equals 0.13, his implies ha for a one percen increase in indusry concenraion, on average, a firm s innovaive oupu will increase by 0.13%. Acivies such as mergers can lead o increases in indusry concenraion. Policy makers mus ake hese poenial benefs ino accoun when deciding wheher or no o preven a merger. As menioned earlier, he posive coefficien on indusry R&D expendure sugges posive spillover effecs o R&D. Furher, he exisence of hese posive spillover effecs provides a basis for governmen subsidizaion of R&D acivies. Since ξ nr equals 0.065, his implies ha a one percen increase in indusry level R&D will increase innovaive oupu of he average firm by 0.065%. As menioned earlier, a posive coefficien on adverising expendure in column 4 of able 4 implies ha increases in adverising expendure will increase a firm's marke share. Since ξ sa equals 0.09, his implies ha a one percen increase in a firm's adverising expendure will increase s marke share by 0.09%. 6. Conclusion This paper has revised he empirical evidence on he relaionship beween marke concenraion and innovaion. I has found ha a more concenraed indusry simulaes innovaion, in suppor of he Schumpeerian hypohesis. I also shows ha he
27 reason ha his resul has eluded recen empirical work is largely due o he use of an inaccurae measure of innovaive oupu (simple paen coun). Once innovaive oupu is measured by caion-weighed paen coun, arguably a more precise measure, a posive empirical relaion beween concenraion and innovaion is esablished. In addion, he empirical resuls suppor diffusion exernalies in R&D; and sugges ha, on average, successful innovaion is more powerful han adverising a increasing a firm s marke share. Even hough his paper found empirical suppor for he Schumpeerian hypohesis, does no advocae ha policies should always seek o embrace imperfec compeion. Based on he relaively large sample of indusries used, he resuls are o be inerpreed as on average relaionships. This implies ha some indusries may operae in a manor inconsisen wh Schumpeerian ideas while oher indusries f he Schumpeerian world more closely. As such, a more accurae characerizaion of he main resul in his paper is as follows: since a sufficienly large number of indusries operae in a manor ha make on average resul consisen wh Schumpeerian ideas, we need o approach anrus policies wh much more cauion han previous resuls end o suppor. Based on he argumens above, a naural direcion for fuure work is o exend he empirical analysis in specific indusries. While analyzing indusries individually for evidence of he Schumpeerian hypohesis is desirable, he daa requiremens for such analysis exceed even he relaively comprehensive daa se used in his paper. Indusry concenraion does no vary much over ime in any paricular indusry and hus, whou very lenghy ime series, precise esimaion of he coefficien capuring he relevan
28 relaionship is difficul. I remain opimisic however, because a decade ago, firm-level daa as comprehensive as he daa se used for his research was only an economerician s dream.
29 Appendix Appendix conains able A.1 and oal derivaives based on economeric model Table A1. 2-Dig Indusry code Indusry 01 Food & Tobacco 02 Texile, apparel & foowear 03 Lumber & wood producs 04 Furnure 05 Paper & paper producs 06 Prining & publishing 07 Chemical producs 08 Peroleum refining & producs 09 Plasics & rubber producs 10 Sone, clay & glass 11 Primary meal producs 12 Fabricaed meal producs 13 Machinery & engines 14 Compuer & com. Equipmen 15 Elecrical machinery 16 Elecronic ins. & comm. Equipmen 17 Transporaion equipmen 18 Moor vehicle 19 Opical & medical insrumens 20 Pharmaceuicals 21 Misc. manufacuring 22 Soap & oileries 23 Auo pars
30 Toal Derivaives Based on Economeric model The wo main equaions in he simulaneous sysem are: ~ ~ n = exp ϖ C + λ s + β 1 Z + θ R (1) ( ) s = ϕ + β + a~ (2) 3 n φ where n is expeced (equilibrium) innovaive oupu of firm i a ime, s is equilibrium marke share of firm i a ime, Z ~ is he size of firm i a ime, R ~ is he log of indusry level R&D a ime, C is indusry concenraion a ime, and a ~ is he log of adverising expendure of firm i a ime. For comparaive-saics I am ineresed in dn dn solving for, dc dr dn dc From equaion (2): ds dc and ds da ds = λ exp( ) + ϖ exp( ) dc dn. From equaion (1): = β 3 (4) dc If we plug equaion (4) ino equaion (3) and rearrange erms, we ge: dn dc ϖ exp( ) = 1 λβ exp( ) 3 Furher, we can use equaion (5) o calculae he elasicy of n wh respec o ξ nc = C n dn dc = ϖc 1 λβ3 exp( ) From equaion (1): dn ds 1 = λ exp( ) + θ dr dr R exp( ) (7) Noe ha he ~ is no longer above R, which implies ha R is expressed in levels no logs. From equaion (2): ds dr dn = β 3 (8) dr (3) (5) (6) C :
31 If we plug equaion (8) ino equaion (7) and rearrange erms, we ge: dn dr = θ exp( ) R 1 λβ exp( ) 3 (9) Equaion (9) can hen be used o calculae he elasicy of n wh respec o R : ξ nr = R n dn dr = θ 1 λβ3 exp( ) (10) Analogous o he process in finding equaions (6) and (10) we can also show ha he elasicy of marke share wh respec o adverising expendure is: φ a ds s ξsa = = (11) s da 1 λβ3 exp( )
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