260C. Antonelli et al.Table 1.Summaryof themajorcontributionstothefield of innovationpersistenceDataResultsAuthorsMethodologyPatent data analysesMalerba, Orsenigo, andPatent data fromDynamic panelThe econometricPetretto (1997)OTAF-SPRUdata modelevidence shows thatdata base forinnovative activityfive EU countriesis persistent(19691986)Gerosky, Van Reenen,Patent records andProportionalOnly a minorityand Walters (1997)"major"innovationshazardof firms (majorofa sampleof UKfunctioninnovators)firms (19691988)are found tobe persistentlyinnovativee[eeeTPMCefis and OrsenigoPatent data on aEvidence of weaksampleof 1400(2001)persistence; bothmanufacturing firmslowinnovatorsand(19781993) ingreat innovatorsGermany, Italy,generally remain inJapan, US andtheir classesFranceData on 577 UKTPMCefis (2003)Evidence of littlepatenting firmspersistence(19781991)characterised bya strong thresholdeffect. Only greatinnovators haveastronger probabilitytokeep innovatingCefis and CiccarelliData on 267 UKBayesianThe study shows that(2005)patenting firmseconometriccurrent innovative(19881992)modelsactivity can bepositively influencedby past innovationvia the greateravailability offinancial resourcesInformation on 16,698Time seriesAifranca,Rama,andvonTheevidenceconfirmsTunzelmann(2002)patents granted inanalysisthat global firms inthe USA from 1977this industry exhibitto 1994 to 103 globala stable patternfirms in the food andof technologicalaccumulation inbeverage industrywhich'successbreeds success'.Latham and Le BasPatent data for 3347DurationThe persistence(2006)French firmseconometricof innovation is(19691985)modelstronger amongindividuals thanamong firmsHuang (2008)Patent and R&DDynamicEvidence supportingdata on 246random effectthe existence ofelectronics firmsprobit modelpersistent innovationafter controlling forlisted on theTaiwanStock Exchangefirm heterogeneity(19982003)(continued)
260 C. Antonelli et al. Table 1. Summary of the major contributions to the field of innovation persistence. Authors Data Methodology Results Patent data analyses Malerba, Orsenigo, and Petretto (1997) Patent data from OTAF-SPRU data base for five EU countries (1969–1986) Dynamic panel data model The econometric evidence shows that innovative activity is persistent Gerosky, Van Reenen, and Walters (1997) Patent records and ‘major’ innovations of a sample of UK firms (1969–1988) Proportional hazard function Only a minority of firms (major innovators) are found to be persistently innovative Cefis and Orsenigo (2001) Patent data on a sample of 1400 manufacturing firms (1978–1993) in Germany, Italy, Japan, US and France TPM Evidence of weak persistence; both low innovators and great innovators generally remain in their classes Cefis (2003) Data on 577 UK patenting firms (1978–1991) TPM Evidence of little persistence characterised by a strong threshold effect. Only great innovators have a stronger probability to keep innovating Cefis and Ciccarelli (2005) Data on 267 UK patenting firms (1988–1992) Bayesian econometric models The study shows that current innovative activity can be positively influenced by past innovation via the greater availability of financial resources Alfranca, Rama, and von Tunzelmann (2002) Information on 16,698 patents granted in the USA from 1977 to 1994 to 103 global firms in the food and beverage industry Time series analysis The evidence confirms that global firms in this industry exhibit a stable pattern of technological accumulation in which ‘success breeds success’. Latham and Le Bas (2006) Patent data for 3347 French firms (1969–1985) Duration econometric model The persistence of innovation is stronger among individuals than among firms Huang (2008) Patent and R&D data on 246 electronics firms listed on the Taiwan Stock Exchange (1998–2003) Dynamic random effect probit model Evidence supporting the existence of persistent innovation after controlling for firm heterogeneity (continued) Downloaded by [Wilfrid Laurier University] at 00:11 02 August 2016
261Economicsof lnnovationandNewTechnologyTable 1.ContinuedDataResultsAuthorsMethodologyJang and ChenSurvivalPatent data on 125 publiclyEvidence of the state(2011)listed IT firms in Taiwananalysisdependent but transient(1990-2001)nature of the competitiveadvantage attributable toinnovative persistenceSurvey data analysesDuguet andPropensityStrong evidence ofInnovation and censusMonjondata on621Frenchscoreinnovation persistence(2004)firms operating inmatchingassociated with size andmanufacturing sectorsmodelsformal R&Dactivities(1986-1996)e[eoTPMRoper andData on 3604 plantsBoth product and processHewitt-covered by the Irishinnovations arefoundtoDundasInnovative Panelbe strongly persistent(2008)(19912002)Community InnovationTPM andPeters (2009)High levels of persistence inSurvey (CIS) data ondynamicundertaking innovationGerman manufacturingprobitactivitiesand service firmsmodels(19942002)Martinez-RosESEE survey on SpanishRandomEvidence of persistence withand Labeagamanufacturing firmseffect probitrelevant complementari-(2009)(1990-1999)modelsties between product andprocess innovationUnbalanced panel of 2764MaximumRaymond et al.The study finds true(2010)likelihoodenterprises from thepersistence in theDutch Communitydynamicprobability of innovatingInnovation Surveystobit modelsin high-tech industries(1994-2000)and spurious persistenceinthe low-tech categoryClausen et al.Panel database constructedDynamicR&D-intensiveand science-(2012)fromR&Dandrandombased companies areCommunity Innovationeffectsfound tobemore likelytoSurveys in Norwayprobitbe persistent innovatorsmodelsLe Bas, Mothe,MultinomialPanel data on287firmsOrganisational innovation isand Nguyenfrom Luxembourg (CISprobitshown to be a determinant(2011)2006,2008)modelsfactor for innovationpersistenceAntonelli,TPM andData on 451 ItalianClearer evidence ofCrespi, andmanufacturingdynamicpersistence in the caseScellatocompanies observedprobitof product innovation(in press)during the yearsmodelwithrespecttoprocess1998-2006innovation whencomplementarity effectsaretaken intoaccount(Swann, Prevezer, and Stout 1998; Baptista and Swann 1999), we claim that the degree ofaccesstothestockofknowledgeof otheragentsinthesystemislikelytoplayamajorrole inassessing innovationpersistence.Thepersistenceofinnovationisthen determinedbythetwineffectsofknowledgecumulabilityinternaltofirmsandexternal tofirmsbut
Economics of Innovation and New Technology 261 Table 1. Continued. Authors Data Methodology Results Jang and Chen (2011) Patent data on 125 publicly listed IT firms in Taiwan (1990–2001) Survival analysis Evidence of the state dependent but transient nature of the competitive advantage attributable to innovative persistence Survey data analyses Duguet and Monjon (2004) Innovation and census data on 621 French firms operating in manufacturing sectors (1986–1996) Propensity score matching models Strong evidence of innovation persistence associated with size and formal R&D activities Roper and HewittDundas (2008) Data on 3604 plants covered by the Irish Innovative Panel (1991–2002) TPM Both product and process innovations are found to be strongly persistent Peters (2009) Community Innovation Survey (CIS) data on German manufacturing and service firms (1994–2002) TPM and dynamic probit models High levels of persistence in undertaking innovation activities Martínez-Ros and Labeaga (2009) ESEE survey on Spanish manufacturing firms (1990–1999) Random effect probit models Evidence of persistence with relevant complementarities between product and process innovation Raymond et al. (2010) Unbalanced panel of 2764 enterprises from the Dutch Community Innovation Surveys (1994–2000) Maximum likelihood dynamic tobit models The study finds true persistence in the probability of innovating in high-tech industries and spurious persistence in the low-tech category Clausen et al. (2012) Panel database constructed from R&D and Community Innovation Surveys in Norway Dynamic random effects probit models R&D-intensive and sciencebased companies are found to be more likely to be persistent innovators Le Bas, Mothe, and Nguyen (2011) Panel data on 287 firms from Luxembourg (CIS 2006, 2008) Multinomial probit models Organisational innovation is shown to be a determinant factor for innovation persistence Antonelli, Crespi, and Scellato (in press) Data on 451 Italian manufacturing companies observed during the years 1998–2006 TPM and dynamic probit model Clearer evidence of persistence in the case of product innovation with respect to process innovation when complementarity effects are taken into account (Swann, Prevezer, and Stout 1998; Baptista and Swann 1999), we claim that the degree of access to the stock of knowledge of other agents in the system is likely to play a major role in assessing innovation persistence. The persistence of innovation is then determined by the twin effects of knowledge cumulability internal to firms and external to firms but Downloaded by [Wilfrid Laurier University] at 00:11 02 August 2016
262C.Antonelli etal.internal totheir localised context ofaction.Accesstotheknowledgebase outsideofeachfirmisnecessaryfortheintroduction oftechnological innovations.Atthesametime,how-ever, external knowledge provided by the location continues to change over time,albeitslowly.The architecture of interactions and transactions that are the carriers ofknowledgeexternalities change gradually over time as a result of thegrowth performances of firms,their entry,decline and exit and ultimately the introduction of innovations (Antonelli andScellato, forthcoming).Furthermore, because evidence of persistence has been shown to be dependent in parton the specific innovation activity scrutinised, we will use TFP growth to obtain a generalmeasure of the extentto which innovation is persistent at thefirmlevel.Theempirical testswill develop the transition probability matrix (TPM)methodology implemented by manyauthors, such as Cefis andOrsenigo (2001),Cefis(2003),Peters(2009),David and Rullani(2008)andAntonelli,Crespi,and Scellato(2012).In particular,weproposean approachthateeconsiders observing different TPMs for specific sub-periods within a longer time interval.This type of analysis enables the identification ofchanges in the transition probabilities andtheinterpretationofthemascluestotheeffectsoftheexternaleventsonpersistence3.HypothesesandresearchdesignThe generation of technological knowledge is an activity characterised by significantindivisibility and learning.Knowledge indivisibility and learning exert strong cumulativeeffects.Withincorporations,thegenerationofnewknowledgeandtheintroductionofinnovations are theresultofthecreation ofnewfunctional routines,ofresearch and developmentlaboratories and of the communication networks that allow access to externalknowledge.Thegeneration of newknowledge and therelated introduction of innovation are shapedbythejointeffectof internal cumulativeforcesand external positivefeedbackexertedbythesystem in which firms are embedded.Therefore, we retain the hypothesis that innovation is a path-dependent, rather than apast-dependent,processdetermined byseveral internal andexternalfactors.External factorsarecharacterised byhighlevels ofcontingency,as such,theirchanges affectthedynamics ofpersistence.Following the resource-based theory ofthefirm, we suppose that thefollowingfactors are important.(A)The size of firms.The generation of technological knowledge is characterised bysubstantial sunk costs. Corporations that have innovated once are more likely to continueinnovating simplybecausethe incremental costs ofthe internal facilitiesdesignedtogener-ate new technologicalknowledge and introduce innovations are low (Penrose 1959;Arrow1974;ConnerandPrahalad1996).(B)The wage level.The well-knowndynamics oftheMatthew effect are likely to applynotonlyto scientists,butalsotofirmsforatleasttworeasons.First,itseemsplausiblethat innovatingfirms are abletopayhigherwagesand,therefore,attractmore creativeandtalentedemployees.Second, innovatingfirms arelikelyto interactwithinnovative suppliersand innovative customers and, therefore, participate in more fertile and productive user-producer interactions.The repeated interaction between the accumulation of knowledgeand the creation of routines to valorise and exploit it eventually leads to the creation ofdynamic capabilities thatfavour the systematic reliance on innovation as a competitivetool(Stiglitz1987;TeeceandPisano1994;LangloisandFoss1999)costmargins onthepersistenceof inno-(C)Price-costmargins.Theeffectsofpricvation are twofold. On the one hand, large price-cost margins should provide access to
262 C. Antonelli et al. internal to their localised context of action. Access to the knowledge base outside of each firm is necessary for the introduction of technological innovations. At the same time, however, external knowledge provided by the location continues to change over time, albeit slowly. The architecture of interactions and transactions that are the carriers of knowledge externalities change gradually over time as a result of the growth performances of firms, their entry, decline and exit and ultimately the introduction of innovations (Antonelli and Scellato, forthcoming). Furthermore, because evidence of persistence has been shown to be dependent in part on the specific innovation activity scrutinised, we will use TFP growth to obtain a general measure of the extent to which innovation is persistent at the firm level. The empirical tests will develop the transition probability matrix (TPM) methodology implemented by many authors, such as Cefis and Orsenigo (2001), Cefis (2003), Peters (2009), David and Rullani (2008) and Antonelli, Crespi, and Scellato (2012). In particular, we propose an approach that considers observing different TPMs for specific sub-periods within a longer time interval. This type of analysis enables the identification of changes in the transition probabilities and the interpretation of them as clues to the effects of the external events on persistence. 3. Hypotheses and research design The generation of technological knowledge is an activity characterised by significant indivisibility and learning. Knowledge indivisibility and learning exert strong cumulative effects. Within corporations, the generation of new knowledge and the introduction of innovations are the result of the creation of new functional routines, of research and development laboratories and of the communication networks that allow access to external knowledge. The generation of new knowledge and the related introduction of innovation are shaped by the joint effect of internal cumulative forces and external positive feedback exerted by the system in which firms are embedded. Therefore, we retain the hypothesis that innovation is a path-dependent, rather than a past-dependent, process determined by several internal and external factors. External factors are characterised by high levels of contingency; as such, their changes affect the dynamics of persistence. Following the resource-based theory of the firm, we suppose that the following factors are important. (A) The size of firms. The generation of technological knowledge is characterised by substantial sunk costs. Corporations that have innovated once are more likely to continue innovating simply because the incremental costs of the internal facilities designed to generate new technological knowledge and introduce innovations are low (Penrose 1959; Arrow 1974; Conner and Prahalad 1996). (B) The wage level. The well-known dynamics of the Matthew effect are likely to apply not only to scientists, but also to firms for at least two reasons. First, it seems plausible that innovating firms are able to pay higher wages and, therefore, attract more creative and talented employees. Second, innovating firms are likely to interact with innovative suppliers and innovative customers and, therefore, participate in more fertile and productive user– producer interactions. The repeated interaction between the accumulation of knowledge and the creation of routines to valorise and exploit it eventually leads to the creation of dynamic capabilities that favour the systematic reliance on innovation as a competitive tool (Stiglitz 1987; Teece and Pisano 1994; Langlois and Foss 1999). (C) Price–cost margins. The effects of price–cost margins on the persistence of innovation are twofold. On the one hand, large price–cost margins should provide access to Downloaded by [Wilfrid Laurier University] at 00:11 02 August 2016
263Economics of Innovation and NewTechnologyinternal funds and favour the innovative efforts of firms;therefore,the effect should be positive.Onthe other hand, however, largeprice-costmargins are clear indicators of barrierstoentryandmarketpower.Firms thatenjoymarketpower have less incentiveto continuefundinginnovationactivities.Therefore,theeffectsshouldbenegative,especiallywhentheprice-cost margin levels are very high (Aghion et al.2005; Antonelli and Scellato 2011).(D)The investment in intangible capital.The intangible assets intensity captures firmsefforts to build innovative competencies.R&D expenditures are the traditional indicatorused to measuretheinternal efforts togeneratenewtechnological knowledge.However.R&D statistics measure onlypart of the overall effort that firms make to introduce newtechnologies.Accountancy rules provide suitable evidence of stocks of intangible capitalthat include capitalised research expenditures,purchasing costs for patents and licencesand the costs incurred to build and implement the brand and know-how (Teece, Pisano, andShuen 1997).In addition to the internal factors that the literature on innovation persistence hasaddressed, we argue that extermal factors play a crucial role.External factors are also con-tingent because the structure of the system in which external knowledge and rivalry occurchange as a result of the introduction of innovations.Ateach point intime,thenetworksof interactionsandthetypesoftransactionsonfactorandproductmarketschange.Yet,ateachpointintime,thearchitectureofthesystemandthemarketexerta strongeffectontheabilityoffirmstoaccessanduseexternalknowledgeandtorelyonitfortheintroductionoffurther innovations as a competitive tool.Because we expect that innovation is a persistentprocess that occurs when external knowledge and external, local feedback play a positiverole,we introduce,in additionto the internalfactors considered sofar,twoexternalfactors:(E)Theaccesstolocalknowledge stockgeneratedbythespilloverofotherfirmsinnovative activity provides a key contribution to the persistence of innovative activitiesSuch effects are typically inter-industrial:knowledge generated in an industry maybe usefulinother activities(Jacobs1969).Hence,weexpectthatthelevelsofTFPoffirmslocated inthe sameregion,irrespective of the industrial sector,favour the persistence of innovationThe higher the levels of TFP of all the firms that are co-localised, the higher we expect theinnovationpersistencetobe.(F)Thelevels ofinnovative activity offirms within the same industrymeasure the extentto which thetypical Schumpeterian rivalry,based upon the introduction ofinnovation, is atwork.The higher the levels of TFP of rival firms are, the stronger the competitive pressureis.The Schumpeterian rivalry pushes firms to innovate to survive.Therefore,we expectthat thehigher the efficiency ofthe rivals within the same industry,thehigher the likelihoodthateach firm relies on the introduction ofinnovation as a competitive tool and the strongerthe persistence of innovation will be (Aghion et al. 2005).These hypotheses are consistentwith the model described byGruber (1992)about the role of sequential product innovationsin maintaining leadership in markets characterised by vertical differentiation.Externalfactorsaddtointernalfactorsandshapethecontextinwhichthepersistenceofinnovation occurs.Theexternal conditions,namely thequality oflocal pools ofknowledgeand the strength ofthe Schumpeterian rivalry,together with the internal conditions(thatis,thelevel ofdynamic ability,asproxied bywagelevelsandfirm size),exerta specificand localised effect onthepersistentintroduction of innovations.Becauseexternalities areinternal to the local system in which firms are embedded, the changing conditions exert apath-dependent effect on the sequence of innovations.To study the persistence of innovation,we rely on a classic indicator such asTFP.Weassumethatinnovationhasamuchbroaderscopethanindicatorsfocusedonthegenerationand introduction of new,science-based technologies suchaspatentstatistics oraimed at
Economics of Innovation and New Technology 263 internal funds and favour the innovative efforts of firms; therefore, the effect should be positive. On the other hand, however, large price–cost margins are clear indicators of barriers to entry and market power. Firms that enjoy market power have less incentive to continue funding innovation activities. Therefore, the effects should be negative, especially when the price–cost margin levels are very high (Aghion et al. 2005; Antonelli and Scellato 2011). (D) The investment in intangible capital. The intangible assets intensity captures firms’ efforts to build innovative competencies. R&D expenditures are the traditional indicator used to measure the internal efforts to generate new technological knowledge. However, R&D statistics measure only part of the overall effort that firms make to introduce new technologies. Accountancy rules provide suitable evidence of stocks of intangible capital that include capitalised research expenditures, purchasing costs for patents and licences and the costs incurred to build and implement the brand and know-how (Teece, Pisano, and Shuen 1997). In addition to the internal factors that the literature on innovation persistence has addressed, we argue that external factors play a crucial role. External factors are also contingent because the structure of the system in which external knowledge and rivalry occur change as a result of the introduction of innovations. At each point in time, the networks of interactions and the types of transactions on factor and product markets change. Yet, at each point in time, the architecture of the system and the market exert a strong effect on the ability of firms to access and use external knowledge and to rely on it for the introduction of further innovations as a competitive tool. Because we expect that innovation is a persistent process that occurs when external knowledge and external, local feedback play a positive role, we introduce, in addition to the internal factors considered so far, two external factors: (E) The access to local knowledge stock generated by the spillover of other firms’ innovative activity provides a key contribution to the persistence of innovative activities. Such effects are typically inter-industrial: knowledge generated in an industry may be useful in other activities (Jacobs 1969). Hence, we expect that the levels of TFP of firms located in the same region, irrespective of the industrial sector, favour the persistence of innovation. The higher the levels of TFP of all the firms that are co-localised, the higher we expect the innovation persistence to be. (F) The levels of innovative activity of firms within the same industry measure the extent to which the typical Schumpeterian rivalry, based upon the introduction of innovation, is at work. The higher the levels of TFP of rival firms are, the stronger the competitive pressure is. The Schumpeterian rivalry pushes firms to innovate to survive. Therefore, we expect that the higher the efficiency of the rivals within the same industry, the higher the likelihood that each firm relies on the introduction of innovation as a competitive tool and the stronger the persistence of innovation will be (Aghion et al. 2005). These hypotheses are consistent with the model described by Gruber (1992) about the role of sequential product innovations in maintaining leadership in markets characterised by vertical differentiation. External factors add to internal factors and shape the context in which the persistence of innovation occurs. The external conditions, namely the quality of local pools of knowledge and the strength of the Schumpeterian rivalry, together with the internal conditions (that is, the level of dynamic ability, as proxied by wage levels and firm size), exert a specific and localised effect on the persistent introduction of innovations. Because externalities are internal to the local system in which firms are embedded, the changing conditions exert a path-dependent effect on the sequence of innovations. To study the persistence of innovation, we rely on a classic indicator such as TFP. We assume that innovation has a much broader scope than indicators focused on the generation and introduction of new, science-based technologies such as patent statistics or aimed at Downloaded by [Wilfrid Laurier University] at 00:11 02 August 2016