the nature of market competition and innovation: does competition improve innovation output?

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The nature of market competition andinnovation: does competition improveinnovation output?Syoum Negassia & Tsu-Yi Hungb

a UFR 06 – Gestion et Economie d'Entreprise, Université de Paris 1 –Panthéon Sorbonne, 17 rue de la Sorbonne, 75231 Paris Cedex 05,Franceb Institute of Services and Technology Management, National TaipeiUniversity of Technology, 1, Chung-Hsiao E. Rd. Sec. 3, Taipei10608, TaiwanPublished online: 25 Aug 2013.

To cite this article: Syoum Negassi & Tsu-Yi Hung (2014) The nature of market competition andinnovation: does competition improve innovation output?, Economics of Innovation and NewTechnology, 23:1, 63-91, DOI: 10.1080/10438599.2013.811936

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Economics of Innovation and New Technology, 2014Vol. 23, No. 1, 63–91, http://dx.doi.org/10.1080/10438599.2013.811936

The nature of market competition and innovation: does competitionimprove innovation output?

Syoum Negassia* and Tsu-Yi Hungb

aUFR 06 – Gestion et Economie d’Entreprise, Université de Paris 1 – Panthéon Sorbonne,17 rue de la Sorbonne, 75231 Paris Cedex 05, France; bInstitute of Services and Technology

Management, National Taipei University of Technology, 1, Chung-Hsiao E. Rd. Sec. 3,Taipei 10608, Taiwan

(Received 27 November 2012; final version received 26 April 2013 )

The modelling of the relationship between innovation and competition through the theoryof auction is simplistic. Researchers are re-evaluating previously tenuous assumptions inorder to validate these thoughts for empirical use. However, the empirical verificationsof this modelling remain fragmented and often unsatisfactory. Our study proposes differ-ent empirical approaches by testing the type of competition (Bertrand versus Cournot)between industries and by doing a more rigorous (by incorporating all specificationsof auction models including uncertainty, extension of the property rights and capitalconstraints) and general (by constructing multi-sector data) verification. This paperexplores also the rich information of the Community Innovation Survey. It groupstogether detailed innovation data sets and the patent data from the European Patent Officefor France. The constructed data come from 612 firms financed by public Research andDevelopment (R&D) investment (hereafter called public sector) and 3240 firms financedby private R&D investment (hereafter called civil sector). Our results, based mainly ona random coefficient model, illustrate that at the public sector, competition index is notcorrelated with innovation output. This is consistent with the belief that product marketcompetition does not stimulate product innovation in this sector. At the civil sector,the competition index is positively and strongly correlated with innovation output. Thisresult is expected since innovation for conquering new markets seems to be importantfor the civil sector. The market drives innovation output.

Keywords: competition; innovation; Cournot competition; Bertrand competition

1. IntroductionOne of the most discussed relationships regarding technological change is that between thelevel of innovation by firms and competition intensity. Innovation is an important driver ofindustries’ productivity growth. Many policy-makers and researchers believe that competi-tion promotes innovation. Initially, arguments focused on the impact of market concentrationlevel on the type of innovation. In the recent years, discussion has extended to include theeffects of different types of competitions on innovation (Bertrand versus Cournot).

*Corresponding author. Email: [email protected]

© 2013 Taylor & Francis

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64 S. Negassi and T.-Y. Hung

The best known debate on the issue is between Schumpeter and Arrow. Schumpeter(1950) predicted that weak competition would be accompanied by a decline in innovation,as less competition for firms which innovate translates into larger potential future profit.This is often called the ‘Schumpeterian’ effect.

Other models have opposing predictions. For the authors of technological race theory,the dominant firm tends to innovate in order to preserve its future profits in the patent race. Ifthis firm did not continue to innovate, then a competitor would. This is the efficiency effect(Gilbert and Newbury 1982; Reinganum 1982). The replacement effect, accredited to Arrow(1962), suggests that more intense competing market structures favour the emergence of alarge number of innovations.

Still other models depict that increased competition produces a positive effect on inno-vation by creating incentive with the aim of convincing managers and employees to abandonlow-profit sectors and focus instead on those areas stimulating growth. This idea relates to‘agency models’, the best known of which being Hart’s (1983).

Tang (2006) developed new measures of competition by arguing that the firm’s per-ceptions about their competitive environment are important for innovation and are bettermeasures of firm-specific competition, while Lee (2003) indicates that one of the ways toreflect market structure is to include simultaneously in a model both the number of firmsand the size of market in which they operate.

However, the theoretical argument of a direct effect of market power on innovation isoften challenged.

On the empirical front, the evidence on the relationship between innovation andcompetition is also ambiguous.

After surveying the literature on testing the Shumpeterian hypothesis on the relationshipbetween monopoly power and Research and Development (R&D) spending, Baldwin andScott (1987) conclude: ‘There is no unambiguous evidence of important, generally valid,relationship between competition and innovation activity’ (145).

Scherer (1965) is known as the first to indicate that the relationship between competitionand innovation may be nonlinear. Aghion et al. (2005) examined the relationship betweeninnovation and product market competition (PMC). By using panel data, their study indi-cated that the relationship between competition and innovation is an inverted-U shape inthe UK. They had two opposite effects in market: one is the Schumpeterian effect statingthat competition reduced the innovative incentive of laggard firms; the other is the escape-competition effect depicting that competition urges neck-and-neck firms to innovate. Whencompetition is low, a larger equilibrium fraction of sectors involves neck-and-neck compet-ing incumbents, so that, overall, the escape-competition effect is more likely to dominatethe Schumpeterian effect. On the other hand, when competition is high, the Schumpete-rian effect is more likely to dominate, because a larger fraction of sectors in equilibriumhas innovation being performed by laggard firms with low initial profits. Correa (2010)analysed the relationship by using the same data set adopted by Aghion et al. (2005). Heindicated that the relationship has a positive influence in 1973–1982. However, he furtherinvestigated and found that the inverted-U is unstable, and revealed that the governmentchanging its policy influences the relationship (Correa 2012).

Symeonidis (2003) analysed the relative efficiency of quantity and price competitionwith the product of R&D (i.e. a specific innovation) and demonstrated that R&D expenditure,product price and net profits of firms were always higher in Cournot competition than inBertrand. Conversely, the output, the consumer surplus and the total welfare were all higherin Bertrand than in Cournot when R&D spillovers were weak or when the products weresufficiently differentiated. Qiu (1997) demonstrated that Cournot was more efficient than

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Economics of Innovation and New Technology 65

Bertrand, which implies that (1) firms operating in Cournot invest more in R&D than thosein Bertrand when R&D is not costly and (2) social well-being increases significantly frommore R&D investment when spillovers are sufficiently large. However, the product price islower and the output is larger in Bertrand than in Cournot. Lin and Saggi (2002) revealed thatfirms invest more in product R&D when in Bertrand, while they invest more in process R&Dwhen in Cournot. Vickers (1986) analysed the evolution of the market structure and R&Dcompetition when there are a series of innovative opportunities over time. The conclusionhe found was that if the market is very competitive (i.e. Bertrand competition) in the staticsense, then there is increasing dominance over time. If it is not competitive (i.e. Cournotcompetition), then there may be an action–reaction mechanism.

Whereas the modelling of a specific innovation by an auction game is certainly simplis-tic, several models have tried to re-evaluate tenuous assumptions that reduced the empiricalsignificance of this modelling. Reinganum (1983, 1985) demonstrated the uncertainty andthe temporal aspect of the innovation. Katz and Shapiro (1987) highlighted the imperfectionof the patent against competitors. Leininger (1991) introduced capital constraints.

The empirical verifications of this modelling remain fragmented and are oftenunsatisfactory.

Unlike previous studies, our paper examines the determinants of innovation output inmanufacturing industries, looking particularly at the impact of PMC on innovation output byincluding two types of competition: Cournot competition and Bertrand competition. Thiswork presents analysis by using separate samples of public R&D performers (hereaftercalled public sector) and private R&D performers (hereafter called civil sector).1 PublicR&D performers are defined as firms reporting public investment in R&D expenditures andprivate R&D performers reporting private R&D expenditures within observation periods.Moreover, we would like to show that market power, as measured by either individualfirm’s price–cost margin or the Lerner Index, has no impact on innovation output of publicR&D performing firms. Thus, it proposes different empirical approaches by testing the typeof competition (Bertrand versus Cournot) between industries and doing a more rigorous(by including all specifications of auction models including uncertainty, extension of theproperty rights and capital constraints) and general (by constructing multi-sector data)verification on the Cournot industries and Bertrand industries. Furthermore, this paperexplores the rich information of many data sets2 by using a special pooling method. Ourapproach and the results we obtained are new in certain ways. In particular, the poolingmethod we propose is based on the segmentation of the firms by groups or industries ratherthan by individual firms, which prompts using a greater number of general theories thanthose normally considered in the usual panel models.3

1.1. Principal hypotheses testedThe output of innovation is explained by competition type as well as other variables. Therelationship between innovation and competition is interpreted as a long-term relationship.

H0: The coefficient of the competition variable is not statistically significant, thisimplies weak competition. There is then a problem of mark-up, i.e. the price ishigher than the marginal cost, which can be characterized as Cournot.H1: The coefficient of competition variable is statistically significant, and thereforethe competition is fierce. This can be characterized as Bertrand.

Section 2 presents the theoretical and background of innovation and types of compe-tition (Bertrand versus Cournot). Section 3 illustrates the estimated model and explains

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how certain variables are constructed. Section 4 outlines the description of data. Section5 demonstrates the econometric methodology. Section 6 contains the results and finally,Section 7 presents our observations, analysis of results and concluding comments.

2. Theoretical background of innovation and type of competition4

Let us imagine two firms i and j. The competition between these two firms is either Cournotand/or Bertrand. Firm i has a production cost c, and firm j has a production cost c + β.

The two firms are also competing with each other on the development of a specificinnovation with a product reduction cost of α.

If we suppose a deterministic auction model (i.e. the firm with the most R&D expenditureis the one which innovates) then the solution of the model is as follows:

In Cournot competition, a linear demand with the following form:

p = d − q,

where p is the price of the goods and q the total quantity produced by the two firms.If now c (respectively c + β) is the production cost of firm i (respectively j), the Cournot

profit for firm i (i or j) is

�i = (d + cj − (1 + σ)ci)2

(2 + σ)2 ∀i �= j, when d + cj − (1 + σ)ci〉0,

where σ is the coefficient of substitution between the two products (produced by i and j):there is very low substitutability between these products when σ is weak (when σ = 0, eachfirm is in a monopoly situation and meets a demand q = D(p) = dp). Conversely, there isa great degree of substitutability when σ tends towards 1.

Now suppose an innovation permits firm i to reach the cost level ci − α. In other wordsN = d − c, an index of the market size.

Three cases must be explored:Case 1: N ≤ α: In this case, the innovation is drastic and the two firms have the same

incentives.Case 2: N − 2β ≤ α < N : In this case, i innovates because the innovation will enable

it to become a monopoly. A firm in a monopolistic situation has incentives to innovatebecause the profits from its innovation are monopolistic, while its adversary, firm j, canonly expect duopoly profits.

Case 3: N − 2β > α: This is the most complicated case. In fact, the two firms stay in thesame sector no matter which firm innovates. Empirically, this is probably the most frequentcase. We determine the profits of i when j comes up with the innovation and the profits ofj when i comes up with the innovation.

If j innovates first, the profits of i are

�i = (d + c − β − (1 + σ)c)2

(1 + σ)2 = (N − β)2

(1 + σ)2 ; N = d − c;

and in this case, the profits of j are

�j = (d + c − (1 + σ)c + (1 + σ)β)2

(2 + σ)2 = (N + (1 + σ)β)2

(2 + σ)2 .

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If i innovates first, the profits of j are

�j = (d + c − α − (1 + σ)C − (1 + σ)β)2

(2 + σ)2 = (d − c − α − (1 + σ)β)2

(2 + σ)2

= (N − α − (1 + σ)β)2

(2 + σ)2 ;

and the profits of i are

�i = (d − c + β − (1 + σ)c + (1 + σ)α)2

(2 + σ)2 = (N + β + (1 + σ)α)2

(2 + σ)2 .

It is the comparison of the total profits in each case (whether i or j innovates) that determinesthe winner of the innovation race.

The total profits of the sector when i wins the innovation race are equal to

�(c − α; c + β) = [(N + β + (1 + σ)α)2 + (N − α − (1 + σ)β)2](2 + σ)2 .

The total profits of the sector when j wins are

�(c; c + β − α) = [(N + (1 + σ)α)2 + (N − α)2](2 + σ)2 .

The comparison of �(c − α; c + β) and �(c; c + β − α) shows that j innovates when

N >(5β + 8α)

2and σ = 1.

In other words, the chances that j innovates increase with the market size (N ) and diminishwith the initial disparity between the two firms (β) and with the efficiency of the innovation(α).

Therefore, three cases can be distinguished:

(1) If the innovation is important enough to take the firm into a monopoly situation(i.e. no substitute product can be found on the market), i and j then have identicalincentives to innovate.

(2) If the realization of the innovation by i induces the exit of j, the innovation iscarried out by i. This implies that α is sufficiently high compared with β, or that β

is sufficiently low compared with α.(3) In other cases, the more important the innovation, the more inclined firm i is to

carry it out. We also observe that the more β increases, the more the innovation iscarried out by i, and that the more the market size increases, the more innovationis carried out by i.

This demonstrates that Bertrand competition leads to the realization of all of the innova-tions by i. Our study concentrates on the empirical verification of one of the two competitions(Cournot or Bertrand).

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Nevertheless, the test requires answers for several measurement problems:

• How to distinguish the leaders from the followers?

To deal with this issue we have the data for firms’ market shares and the profits. Anotherproblem is the oligopoly structure of the sectors (and not the duopoly structure): so whenis a firm a leader? When is it a follower?

• How to distinguish major innovations from minor ones?

For this, we have the sales turnover data specifically allocated to the realization of theinnovation.

Finally, our test can be summarized with a very simple idea: the more the innovationproduces profits, the more a leading firm spends a great deal on R&D expenditure, andtherefore the greater the probability of success with the innovation. This model is basedon several very restrictive assumptions as follows: the degree of competition, the lack ofinnovation ownership, research uncertainty and financial constraints.

3. Model and variables of Cournot–Bertrand competition3.1. Modelling competition and innovation activitiesWe propose mid/long-term models of competition, which are based on the productioncapacities, the choice of product lines, the R&D and the innovation of the firms. Themodelling of the firm’s strategy is carried out using the industrial organization theory, whichuses microeconomics and game theory. Two oligopoly concepts are mainly utilized here.The first is the Cournot concept, which models competition on quantity over the mid/longterm. The second is the Bertrand concept, which focuses on competition on price over theshort term.

The innovation function is

Profits related to the innovation = f (degree of competition other variables).

The principal variable is the degree of competition, the other variables being market share(this variable represents both leadership and financial constraints), profits, capacity toself-finance, advertising expenditure, index of appropriability of innovation, index of thecorrelation between R&D and the innovation, the number of granted patents, etc.

In order to test the robustness of our basic model, we propose two logarithm models basedon two different measures of innovation. We successively take the turnover in innovativeproducts (logarithm of turnover in innovative products: lsip and the number of patents: bvtas a measure of innovation). The first one is

Lsipist = β0 + β1Lbvtist + β2Capist + β3Ladvist + β4Lrdist + β5Mksist + β6Idcorist

+ β7lnrist + εist, (1)

and the second is

Bvtist = γ0 + γ1Capist + γ2Ladvist + γ3Lrdist + γ4Mksist + γ5Idcorist + γ6lnrist + υist.(2)

The choice of these two variables forces us to use three econometric methods: randomcoefficient model, Tobit model (our sample includes innovating and non-innovating firms)and econometric analysis based on Count Data.

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3.2. Variables constructionWe have calculated some of our variables as follows:

3.2.1. Competition indicatorPrice–cost margin or the Lerner Index would be, arguably, a more desirable measure togauge the extent of competition, especially in certain manufacturing industries which areconfronted with intense international competition.

The endogenous variable – the sales turnover from innovative products (or the numberof patents) – is explained by several exogenous variables, one of which being the LernerIndex which is an indicator of type of competition. The relationship between these twovariables (turnover and type of competition) is considered a long-term one. If the impactof the competition coefficient (β7 in Equation (1) and γ6 in Equation (2)) on an innovationis not statistically significant, then we can consider the competition as weak. Thus, thereis a problem with mark-up, i.e. the price is higher than the marginal cost. In this case, themarket competition can be described as Cournot. In the opposite case (coefficient statisticallysignificant), the impact is significant and the competition is fierce, that is to say that theprice tends towards the marginal cost. This market competition is described as Bertrand.

3.2.1.1 How do we calculate the degree of competition or type of competition? This ismeasured by the ratio li = (p − c′)/p, where p is the price and c′ the marginal cost. Thisformula is known as the Lerner Index. However, as mentioned by Motta (2004), the notionof the marginal cost is very theoretical and unobserved. In fact, the Lerner Index is theratio between the gross operating profit (GOP) and sales. We have the data for these twovariables (GOP and sales) and can therefore calculate the type of competition indicator (theLerner Index) as follows: li = GOP/sales.

3.2.2. Measuring mechanisms of appropriation (measuring the capacity of the firm toappropriate the return from industrial R&D)Katz and Shapiro (1987) depicted that if innovations can be easily imitated, the proportionof innovations carried out by the followers will be greater. In other words, the less effectivethe patents, the more ‘follower’ companies will innovate.

3.2.2.1 How do we calculate the capacity of the firm to appropriate the return from indus-trial R&D? The capacity of the firm to appropriate the return from industrial R&D iscalculated as follows:

CAP = the capacity of self-financetotal expenditure for R&D by the enterprise

.

3.2.3. Market shares (measuring leadership or financial constraints)There is a long tradition in industrial economics of linking innovation to market share(Scherer 1983; Lunn 1986, 1989). Certainly, firms with a large market share may be capableof avoiding disclosure, either of the fact that they innovate or the details of their innovationand capable of splitting the fixed costs of their development between a larger number ofcustomers. Literature on game theory has also stressed that firms with a large market powermay seek to pre-emptively innovate in order to deter innovation strategies by competitors.

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Blundell, Griffith, and Van Reenen (1999) have found that firms with a large market shareinnovate more than others.

However, several papers (Ghemawat 1991; Lerner 1997) have shown numerous excep-tions to this rule. It is relatively easy to build a theoretical framework where edge competitorshave the greatest incentives to innovate (Reinganum 1983), and organization theory alsostresses the various inefficiencies associated with a large market share (Henderson 1993).

Although market share is conventionally used as an index of market power, there area lot of reservations to use. Nickell (1996, 733) enumerates the caveats of market share asfollows: (i) collusion depends not only on the size of the various firms involved relativeto the market, but also on other factors that are hard to control; (ii) potential as well asactual competition influences market power; (iii) market share does not fully reflect foreigncompetitors and (iv) market share uses industrial sales as the denominator, but it is notcertain that this represents the actual market.

Therefore, price–cost margin or the Lerner Index would be, arguably, a more desirablemeasure to gauge the extent of competition, especially in certain manufacturing industrieswhich are confronted with intense international competition than mark share.

One can first consult the Community Innovation Survey (CIS) where a great deal ofdata in this regard is included. This survey also shows that financially powerful firms areless susceptible to imitation than their competitors who have financial constraints. Suchconstraints can be measured by market share or realized profits.

A problem then arises when we have an identical variable for two different phenomena:the profit (or the market share) represents both leadership and the financial constraints.

In this work, we use market share as a leadership indicator than a financial constraintvariable.

Logically, in industries where ownership is judged to be lower, the market share andthe profits will have a positive impact on the innovation if ownership actually depends onfinancial capacities. However, if ownership is independent of financial capacities, marketshare and profits will have a negative impact.

It is widely recognized that small and medium enterprises (SMEs) have some uniquecharacteristics to carry out innovations but at the same time have resource constraints.

The market share of firm i is defined as its sales divided by the value of the totalproduction in firm i’s primary industry (hereafter S). The indicators of market share and themeasure of the Herfindahl index introduced are weighted and calculated for the differentmarkets (hereafter k) of the concerned firm. The market share of a firm can therefore becalculated as follows:

MSi =∑

k

Sik

Si· Sik

Sk.

The Herfindahl index of the markets where firm i is active (index of concentration H )

Hi =∑ Sik

Si· Hk , Hk =

∑j

(Sjk∑h Shk

)2

,

or simply

Hi =⎧⎨⎩

N∑j=1

S2it

⎫⎬⎭ , where sit = salesit∑N

i=1 salesit.

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3.2.4. Research uncertaintyAs shown by Reinganum (1983) and Reinganum and Wilde (1986), the models of Vickers(1986) and Katz and Shapiro (1987) are based on a context that an incontestable innovationexists. However, the introduction of uncertainty substantially modifies the results obtainedby the traditional models. We especially notice that the larger the research uncertainty, themore followers will tend to come up with important innovations.

3.2.4.1 How do we measure research uncertainty? This uncertainty, conceived by Rein-ganum, is demonstrated as follows: there is a probability that the firm that has spent most onR&D expenditure does not come up with an innovation. Since, we have detailed informationon the R&D expenditure of firms and their granted patents (in our data set), we can calculatean index of correlation between the R&D expenditure and patents for each industry sector.However, it may be necessary to correct these calculations when we account for secrecy orfor cooperation agreements (the latter also enables a firm that does not spend a great dealin innovative R&D).

Icor = Cov(R& D, Bvt).

Justifications of endogenous variables and all exogenous variables in our models are inTable 1. The Lerner Index and Market shares as measure of leadership and financial con-straints have different focus, which to some extent is supported by the small correlationcoefficient among the indicator (Table 2).

4. Description of data4.1. Datasets to test our hypothesisUnlike many previous studies that use seller concentration or other measures based onindustrial statistics to measure competition, this paper uses data on firm’s perceptions abouttheir competition environment.

Linked to four datasets (the annual company surveys Enquête Annuelle d’Entreprises,i.e. Annual Survey of Enterprises (EAE), R&D database, Statistiques Annuelles sur laDéfense, son Industrie et sesEntreprises, i.e. Annual Statistics on Defence, its Industryand Enterprises (SANDIE) database and European Patent Office (EPO) database), the CISincorporates additional information on firm’s activities, such as value-added, number ofresearchers, uncertainty, extension of the property rights, capital constraints, etc. Thus, thefinal sample for the analysis in this paper contains data on 3800 firms. These firms span 14industries at three-digit North American Industry Classification System (NAICS) level.

To test our hypothesis, the innovation activities include two innovation inputs (R&Dlinked to new or significantly improved products or processes and acquisition of technologythrough machinery, equipment or other technology) and two innovation outputs (productinnovation and process innovation).

Tables 3–9 present the descriptive statistics of our surveyed industries. They areused to examine several technological innovation activities, notably innovation input andinnovation output.

We provide a short overview of the public surveys (our sources of information) whichwe use to study the dynamics of innovation and markets linked of the civil and the publicsectors.

Our working database consists of all the firms listed in the annual company surveysEAE of the ‘Service des Statistiques Indsutrielles-Ministère de l’Economie, des Finances etde l’Industrie’, i.e. Industrial Statistics Service-French Ministry of Economy, Finance and

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72S.N

egassiandT.-Y.H

ung

Table 1. Justification of variables.

Sources Justification of variables

Endogenous variablesLsip: Turnovers of innovative

productsCIS and EAE of SESSI-MEFIa This endogenous variable is a measure of innovation. The innovation

survey gives the sales turnover of enterprises in innovative products andprocesses. In this study, the turnover is the sum of turnovers of productsnew to the market and turnovers of products new to the enterprise

Bvt: Number of granted patentsof enterprise

EPO An important contribution is from Gilbert and Newbery (1982), whoindicated that leadership was more difficult to retain when there were moretechnological opportunities. We can especially measure the technologicalopportunities by the total number of granted patents in the sector. Thisvariable is a means to measure juridical protection. The EPO offersthe number of patents of the enterprises. The appropriation mechanismis an important factor affecting the innovation activities of a firm, asidentified in the theoretical literature but ignored in most empiricalstudies. Different appropriability mechanisms can be recognized. In thiswork, an industry-level variable of legal protection is constructed. Itencourages each firm to capture the exogenous nature of the appropriationmechanisms. Some industries may naturally be more innovative thanothers. One can hypothesize that firms can obtain technology throughlicensing, R&D contracting and consulting agenciesif legal protection iseffective. In this case, they are unlikely to acquire technology externally.There is a significant negative effect on innovation output. If protection istight, firms tend to realize more benefits from external sourcing; this wouldexplain the positive coefficient of protection. The greater the number ofdeposited patents, the more diverse the technological trajectories and inturn the more difficult it becomes for the leader to preserve a technologicaledge over its competitors

Cap: Capacity of the firm toappropriate the return fromindustrial R&D

Ministère de l’Education Nationale, de laRecherche et de la Technologie – Ministryof National Education, Research andTechnology (MENRT)

This variable is a measure of capacity of the firm to appropriate the returnfrom industrial R&D. We calculated the percentage of self-finance of theenterprise in relation to its R&D expenditure (self-finance/total R&D).The greater the capacity of self-finance, the more the firm can defend itspatent (i.e. its innovation)

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Econom

icsofInnovation

andN

ewTechnology

73

Exogenous variablesLadv: Advertising expenditure EAE of SESSI-MEFIa This variable is the total of the advertising expenditure. Advertisement is

a major intangible investment. In France, the advertising investments ofenterprises stimulate consumption, innovation and competition betweenindustries. In fact, advertisement can balance R&D expenditure. It plays anessential role in rapidly transforming a technological advance, somethingthat is always considered but not always useful, in the innovation ofprofitable products, because it can satisfy demand more rapidly. Thegreater the advertising expenditure, the more the innovating firm will beable to exclusively benefit from its innovation

Lrd: R&D expenditure MENRT The design and development of new products come from a specific activityof a firm: R&D. R&D is the source of technological progress. It playsan essential role in innovation and also because the only resource whichprovides us with abundant and reliable statistics (at least in France). R&Dexpenditure is an investment that depends on the increase in capital stock.The product of research, i.e. increased knowledge, is something which isboth perishable and very cumulative. In our case, this variable is the total ofthe internal R&D expenditure DIRD (Dépenses Intérieures en Rechercheet Développement) and the external R&D expenditure of sub-contractedpartners DERD (Dépenses Extérieures en Recherche et Développement).It does not include public subsidies of R&D in the civil sector. The greaterthe R&D/innovation correlation, the more the technological context iscertain (i.e. expenditures are not a waste as the firm may spend and inventnothing). In this case, a leading firm will tend to carry out the most radicalinnovations

Mks: Leadership indicator EAE of SESSI-MEFIa In this work, we use market share as leadership indicator variable. Wedefine the market share of the enterprise without considering its size. Thisvariable is the ratio of the sales amount of the enterprise and that of itssector. The bigger the market share (or the profits) by a leading firm, lessradical innovations will be carried out

(continued)

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74S.N

egassiandT.-Y.H

ung

Table 1. (Continued)

Sources Justification of variables

Idcor: Uncertainty of R&D(correlation between R&D andinnovation)

EPO and MENRT The uncertainty conceived by Reinganum (1983) is the probability that thefirm has spent the R&D expenditure which does not carry out innovation.As we have the detailed information on the R&D expenditure of firmsand their patent numbers, we can, in each sector, calculate the index ofcorrelation between the R&D expenditure and the patents. This calculatedvariable measures the ‘productiveness’ of technology of the enterprise.In fact, the production of new goods comes from the R&D carried outby an enterprise. However, a research project may completely fail, or thediscovery may be surpassed by a competitor which patented the inventionthereby shattering the firm’s innovation efforts. By integrating this variable,we try to measure the output from R&D investment. Moreover, if thetechnological context is assured, the leading firm tends to carry out themost the best performing innovations

Lnr: Lerner Index (degree ofcompetition)

EAE of SESSI-MEFIa The level of competition that exists in a product market is measured by theLerner Index. The Lerner Index is the relationship between the differenceof price and marginal cost and (divided by) the price. Motta (2004) showedthat the marginal cost is very theoretical and cannot be observed. We thuspropose to measure the Lerner Index by the EBE ratio (Excédent Brutd’Exploitation, i.e. Net Profit) and the sales amount. We have the data onthese two variables (EBE and the sales amount). This variable is a measureof the degree of competition: EBE/sales amount

aSESSI-MEFI: Statistics and Industrial Studies (SESSI)-Ministry of Economy, Finance and Industry (MEFI).

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Table 2. Correlation coefficients among exogenous variables.

Lrd Idcor Cap Mks Ladv Lbvt Lnr

Lrd: R&D expenditure 1Idcor: Research uncertainty 0.062 1Cap: Capacity of the firm to

appropriate the return fromindustrial R&D

−0.089 −0.050 1

Mks: Leadership indicator 0.040 −0.011 0.029 1Ladv: Advertising

expenditure0.070 −0.028 −0.046 0.005 1

Lbvt: Patents taken out by thefirm

0.076 −0.003 −0.087 0.102 −0.074 1

Lnr: Lerner Index (degree ofcompetition)

0.109 0.036 −0.114 0.132 0.09 0.072 1

Table 3. Research data sets.

Database Variables Period Number of firms Sources

EAE (EnquêteAnnuelled’Entreprises-Annual Surveyof Enterprises)

Enterprise data 1990–2004 22,000 SESSI-MEFI

CIS Data on innovationand competition

1990–2004 4800 SESSI-MEFI (France).Four innovationinquiries enable usto study the sourcesof technologicalknowledge utilized bythe firms in order to beinnovative: CIS 1 forthe period 1990–1992,CIS 2 for the period1994–1996, CIS 3 forthe period 1998–2000and CIS 4 for the period2002–2004. Theseenterprises may beinnovative or not

R&D Inquiry‘Researchers’

Information onthe researchers(number ofresearchers,diploma,mobility, etc.)

1990–2004 3600 MENRT

R&D Inquiry‘Expenditure’

Expendituredevoted to R&D

1990–2004 3600 MENRT (France)

SANDIE List of the publicfirms

1990–2004 12,000 SANDIE of ObservatoireEconomique de laDéfense– EconomicObservatory of Defence,which distinguishes thefirms performing in themilitary sector

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Table 4. Surveyed public sectors.

NAF 36 NES 114 NAF700 Number ofName of industry Contenta enterprises

E1 E11 Shipbuilding 50351A: Construction and repair of battleships M 8351B: Construction of civil ships C 23351C: Naval repair C 19

E13 Aeronautical and space engineering 116353A: Construction of engines C + M 22353B: Construction of airframes C + M 88353C: Construction of launchers and

spacecraftC + M 6

E2 E28 Manufacture of weapons and ammunition 19296A: Manufacture of armament M 14296B: Manufacture of hunting, shooting and

defence weaponsC 5

E33 E33 Manufacture of emission and transmissionapparatuses

144

322A: Manufacture of emission andelectromagnetic transmission

M + C 72

322B: Manufacture of telephonic apparatuses M + C 72E35 Manufacture of measurement and control

materials278

332A: Manufacture of assistance equipmentfor navigation

M + C 81

332B: Manufacture of scientific and technicalinstrumentation

M + C 197

F4 F43 Para-chemistry 10246A: Chemistry and manufacture of

explosive productsM + C 10

Total 617

Notes: aM, 100% of military enterprises working in the military sector; C, 100% of civil enterprises workingpartly or totally in the military sector; M + C, mainly military enterprises and partly civil enterprises in themilitary sector; C + M, mainly civil enterprises and partly military enterprises in the military sector.

Table 5. Surveyed civil sectors.

NAF 36 Name of industry Contenta Number of enterprises

C1 Clothing and leather C 334C2 Publishing, printing and reproduction C 269C3 Pharmacy, perfumery and health care C 162C4 House equipment C 266D0 Automobiles C 126E1 Shipbuilding, aeronautical and railway construction C 40E2 Mechanical equipment C 424E3 Electrical and electronic equipment C 147F1 Material products C 227F2 Textiles C 247F3 Wood and paper C 251F4 Chemistry, rubber and plastic C 335F5 Metallurgy and transformation of metals C 307F6 Electrical and electronic components C 105Total 3240

Note: aC, 100% civil enterprises working in the civil sector.

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Table 6. Number of innovative enterprises in the public sector.

Number of Number of Number ofinnovative innovative innovative

NAF Number of enterprises enterprises enterprises Number ofNES 700 enterprises in product in product in process innovative114 Name of industry Content by sector and process only only enterprises

(1) (2) (3) (4) (5)

E11 Shipbuilding 50 6 4 5 15351A: Construction and

repair of battleshipsM 8 0 2 2 4

351B: Construction ofcivil ships

C 23 6 2 3 11

351C: Naval repair C 19 0 0 0 0E13 Aeronautical and space

engineering116 39 11 11 61

353A: Construction ofengines

C + M 22 13 0 7 20

353B: Construction ofairframes

C + M 88 23 10 4 37

353C: Constructionof launchers andspacecraft

C + M 6 3 1 0 4

E28 Manufacture of weaponsand ammunition

19 9 4 1 14

296A: Manufacture ofarmament

M 14 7 3 0 10

296B: Manufacture ofhunting, shooting anddefence weapons

C 5 2 1 1 4

E33 Manufacture of emissionand transmissionapparatuses

144 37 22 4 63

322A: Manufactureof emission andelectromagnetictransmission

M + C 72 15 9 2 26

322B: Manufactureof telephonicapparatuses

M + C 72 22 13 2 37

E35 Manufacture ofmeasurement andcontrol materials

278 74 50 7 122

332A: Manufacture ofassistance equipmentfor navigation

M + C 81 25 14 3 31

332B: Manufac-ture of scientificand technicalinstrumentation

M + C 197 49 36 4 91

F4 Para-chemistry 10 5 3 0 8246A: Chemistry and

manufacture ofexplosive products

10 5 3 0 8

Total 617 170 94 28 292

Notes: Column (1) is the content of sector: M, military; C, civil.Column (2) is the number of enterprises by sector simultaneously listed in the four following databases: SANDIE,CIS, MENRT R&D and EAE between 1992 and 2004.Columns (3), (4) and (5) are the number of innovative enterprises in the three types of innovation: innovation inproduct and process; product innovation only; and process innovation only?

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Table 7. Percentage of public innovative enterprises in different types of innovation.

% of % of % ofinnovative innovative innovative

Number of enterprises in enterprises in enterprises in % ofNES enterprises product and product process innovative114 Name of industry by sector process only only enterprises

E11 Shipbuilding 50 12 8 10 30E13 Aeronautical and space

engineering116 34 9 9 52

E28 Manufacture of weaponsand ammunition

19 47 21 5 73

E33 Manufacture of emissionand transmissionapparatuses

144 26 15 3 44

E35 Manufacture ofmeasurement andcontrol materials

278 27 18 3 48

F4 Para-chemistry 10 50 30 0 80

Table 8. Number of innovative enterprises in the public sector.

Number of Number of Number ofinnovative innovative innovative

Number of enterprises enterprises enterprises Number ofNAF enterprises by in product and in product in process innovative36 Name of industry sector process only only enterprises

(1) (2) (3) (4)

C1 Clothing and leather 334 27 29 23 79C2 Publishing, printing

and reproduction269 28 17 36 81

C3 Pharmacy, perfumeryand health care

162 58 38 10 106

C4 House equipment 266 74 42 22 138D0 Automobile 126 38 16 12 66E1 Shipbuilding,

aeronautical andrailway construction

40 10 6 2 18

E2 Mechanical equipment 424 93 77 24 194E3 Electrical and

electronic equipment147 48 37 10 95

F1 Material products 227 47 40 26 113F2 Textiles 247 56 29 19 104F3 Wood and paper 251 64 31 26 121F4 Chemistry, rubber and

plastic335 116 59 28 203

F5 Metallurgy andtransformation ofmetals

307 60 37 36 133

F6 Electrical andelectroniccomponents

105 35 21 9 65

Total 3240 754 479 283 1516

Notes: Column (1) is the number of enterprises by sector simultaneously listed in the four databases: SANDIE,CIS, MENRT R&D and EAE between 1992 and 2004.Columns (2), (3) and (4) are the number of innovative enterprises in the three types of innovation: innovation inproduct and process; product innovation only; and process innovation only?

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Table 9. Percentage of civil innovative enterprises in different types of innovation.

% of % of % ofinnovative innovative innovative

Number of enterprises in enterprises in enterprises in % ofNES enterprises product and product process innovative36 Name of industry by sector process only only enterprises

C1 Clothing and leather 334 8 9 7 24C2 Publishing, printing and

reproduction269 10 6 13 30

C3 Pharmacy, perfumery andhealth care

162 36 23 6 65

C4 House equipment 266 28 16 8 52D0 Automobile 126 30 13 10 52E1 Shipbuilding, aeronautical

and railway construction40 25 15 5 45

E2 Mechanical equipment 424 22 18 6 46E3 Electrical and electronic

equipment147 33 25 7 65

F1 Material products 227 21 18 11 50F2 Textiles 247 23 12 8 42F3 Wood and paper 251 25 12 10 48F4 Chemistry, rubber and

plastic335 35 18 8 61

F5 Metallurgy andtransformation ofmetals

307 20 12 12 43

F6 Electrical and electroniccomponents

105 33 20 9 62

Industry (SESSI-MEFI), R&D innovation databases from 1990 to 2004 and the SANDIE(database from the Financial Affairs Department of the Ministry of Defense).

Four novel surveys enable us to study the sources of technological knowledge used byfirms in order to ensure high-performance (innovation): CIS 1 for the period 1990–1992;CIS 2 for the period 1994–1996; CIS 3 for the period 1998–2000; and CIS 4 for the period2002–2004.

This study includes 3857 manufacturing firms, where:

• 617 firms are in the public sector (Table 4) belonging to either 14 economic sec-tors defined in the NAF36 classification (‘Nomenclature d’Activités Française’,i.e. French Industry Classification (NAF)) sectors or six NES114 (‘NomenclatureEconomique de Synthèse’, i.e. Summary Economic Classification (NES)) sectors(cf. Lhuillery et al. 2002; Negassi 2004).

• 3240 firms in the civil sector (Table 5) belonging to 14 economic sectors of the NAF36classification or NES114 classification.

4.2. Innovating activities of French civil and public firms: descriptive statisticsNearly 38% of the civil firms and 42.8% of the public firms innovate primarily in products.The electrical and electronic equipment industry is the most innovative industry (48% inthe public sector, Table 7; and 65% in the civil sector, Table 9). Forty-five percent of thepublic firms and 58% of the civil firms innovate in products, especially in informationtechnology. This industry supplies the whole economy with new products incorporatingadvanced technologies, which are in turn the basis for new innovations.

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The ‘chemistry, rubber and explosive products’ industry (Tables 7 and 9, line F4 with61% for civil and 80% for public) is the most innovative, both in terms of product andprocess. This is a very strong French industry (fourth largest in the world) even thoughFrench industrial groups are not the largest in the world. To innovate, these firms utilizepublic research sources, patent publications, other firms in the industry and, to a lesserdegree, conferences, meetings, reviews and databases. Client and supplier involvement ininnovation is weak within this industry. The source, which is used the most, continues to bethe hard core of external scientific knowledge. Surprisingly, the internal resources of thisindustry’s firms do not seem to be greatly utilized (i.e. the importance of transfering R&Dactivities to subsidiaries). This industry develops from scientific advances and updating ofproducts in order that they will remain a long time in the market (product life cycle is notshort probably because of the lesser importance of innovative products). On the other hand,the penetration by foreign groups into this market is very strong (60% of the production in1995 came from foreign firms).

For the public sector, Table 6 demonstrates that near a half of the firms belong to theindustry of manufacture of measurement and control materials (E35). The ‘manufacturingof weapons and ammunition’ industry is very dynamic, with 73% of its firms engaged ininnovation.

In the ‘mechanical equipment’ (E2) industry there are firms working in the civil sectorand others in the ‘emission and transmission apparatuses’ industry in the defence sector,the latter representing 20% of our sample around 638 firms (424 firms in civil and 114firms in public sector). Twenty-six percent of them innovate in product and process, 18% inproduct only and 6% in process only. The renewal of products in this professional marketis very fast and is regulated by legislation. We find large operators of complex systems thatintegrate software and services. These industries (E2 and E33) have been technologicallyrevolutionalized by digitalization. It is not greatly penetrated by foreign capital (Alcatel,Thomson, Matra, SAT, etc.). At the beginning, the French groups were not positioned in themost promising niche markets. The firms in these industries usually utilize several sourcesto innovate: internal group sources, their competitors, the main material and componentsuppliers, universities and public research organizations, meetings and conferences, reviewsand databases. On the contrary, equipment and software suppliers are cited less often in termsof being sources for innovation. The firms in this industry must have their own competence ifthey want to innovate. The client has a moderate impact in a field where large industrializedcountries constitute an important outlet for innovation.

The public and civil firms in the ‘aeronautic space and railway engineering’ industry(E13 in Table 7, and E1 in Table 9) innovate mainly in both product and process. Franceis a world class leader in this industry with an open rate (export over local sales turnover)higher than 200%. It is an industry where international – especially European – cooperationis strong and where the government plays an important role in some firms. These firmsusually utilize their internal sources, patent publications, conferences and meetings, reviewsand databases to innovate. To a smaller extent, they also seek relatively more assistancefrom universities and public research organizations. On the contrary, competitors are citedless often in questionnaires dealing with innovation. Two sources are more often cited:internal sources and public research organizations. The primary material and componentsuppliers are cited less often.

Beyond the decision to innovate, one can evaluate the engagement and success of thetechnological innovation strategy of firms (public and civil sectors) by the place technolog-ically innovative or improved products hold in the firms’ markets. The military firms in theindustry sub-sectors E13, E33 and E35 obtain a little more than 30% of their sales turnover

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from the sale of innovative products. In the civil sector, this figure is 10% and 13.1% fromtechnologically improved products. Moreover, in France, firms that innovate consider that8.3% of the industry’s sales turnover is from products which are not only innovative forthem but also innovative for the market.

For all French firms, the innovation process is constantly changing, as almost 35% offirms are conducting projects on innovation which have not yet met with market approval.For the most part, these projects are the result of firms having introduced innovations duringthe surveyed period (92% of respondents). It is the firms that constitute the extension of thematrix of innovation to the whole French industry (cf. questionnaire CIS 3).

5. Econometric methodology: random coefficient model in the case of a singleequation5.1. JustificationIn France, panel information, i.e. quantified data pertaining to a set of firms observed overseveral periods, are accessible thanks to developments in statistical processing. Furthermore,the diversity and dynamics of individual behaviour of firms can be more closely examined.In our case, the hypothesis of perfect homogeneity among the different industries and firmsis rejected (different tests have already shown this: tests of heteroscedasticity). Our datasets have several indexes (industrial or sectoral, individual and time dimensions). Thereis a need to explore these three effects contained within our panel data. However, a morepractical justification of our method concerning the use of random coefficient model isthat the traditional methods of estimation developed for single equations can no longer bedirectly applied. We aimed to extend the econometric research initiated by Hildreth andHouck (1968) and Swamy (1970). In fact, our approach and the results we obtained are newin certain ways. In particular, the pooling method we propose is based on the segmentationof the firms by groups or industries rather than by individual firms, which prompts usinga greater number of general theories than those normally considered in the usual panelmodels. Such group modelling presents the following advantages: (1) there remains thepossibility of having a different number of observations for firms belonging to the samegroup (or industries). As an example, with an empirical study of firms, we can integraterecently merged firms that have created a new firm; (2) the possibility of pooling severalfirms with similar behaviour in the same group or industry and (3) the possibility for eachgroup to have its own coefficient of reaction.

5.2. Random coefficient modelWe have adapted the well-known double index Swamy method to a situation where thestatistical data are distinguished by a triple index: an industry index g (an industry containsa set of individuals firms pooled according to well-defined criteria), an individual index I ,and a time index t. When assuming homogeneity in individual behaviour within the sameindustry, we can obtain the Swamy findings.

The general specification of the model is as follows:

ygit =K∑

k=1

βkgitXkgit + εgit , g = 1 . . . G, i = 1 . . . N , t = 1 . . . T , (3)

where ygit is the endogenous variable of the individual i belonging to industry g to timet; Xkgit the kth exogenous variables of the individual i belonging to industry g to time t;

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βkgit the reaction coefficient of the variable Xkgit , considered random and εgit a residual termexpressing, inter alia, the effects of omitted variables.

The statistical properties of the model and the estimation methods depend on hypothe-ses that one wishes to make on the variability of the reaction coefficients and on thecharacteristics of the residual terms. We adopt the main contents of the following hypotheses:

The following equation stacks the observations for all groups:

⎡⎢⎢⎢⎣

y1y2...

yg

⎤⎥⎥⎥⎦ =

⎡⎢⎢⎢⎣

X1X2...

Xg

⎤⎥⎥⎥⎦ β̄ +

⎡⎢⎢⎢⎣

ν1ν2...νg

⎤⎥⎥⎥⎦ ,

or in a more compact form

y = X β̄ + ν. (4)

With y a (�Tg) × 1 vector, X a (�Tg) × K vector and ν a vector of the same size as y.The estimation of Equation (4) requires the introduction of an additional assumption

relating to the random terms between different groups.

5.2.1. Pure generalized least-squaresThe Aitken formula instantly provides the following estimator (which is BLUE when A isgiven):

ˆ̄β = (X ′A−1X )−1X ′A−1y =[∑

g

X ′gA−1

g Xg

]−1 ∑g

X ′gA−1

g yg , (5)

the variance–covariance matrix of this is

ν( ˆ̄β) =[∑

g

X ′gA−1

g Xg

]−1

. (6)

With the help of a little algebra, expression (5) provides an enlightening interpretation. Letus first note that

XgAg = X ′gXg�X ′

g + σ 2g X ′

g

= X ′gXg[� + σ 2

g (X ′gXg)

−1]X ′g

= X ′gXgRgXg . (7)

We can hypothetically recognize the absence of collinearity, and this means that X ′gXg

is consistent and that as a consequence the matrix Rg = � + σ 2g (X ′

gXg)−1 is positive and

definite. From Equation (7) we obtain

R−1g (X ′

gXg)−1X −1

g = X −1g A−1

g

and

X ′gA−1

g Xg = R−1g ,

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and the introduction of both these expressions in Equation (5) gives

ˆ̄β =[∑

R−1g

]−1 ∑R−1

gˆ̄βg , (8)

with ˆ̄βg = (XgX ′g)

−1Xgyg . ˆ̄βg is obtained by applying the ordinary least-squares (OLS)(equal to generalized least-squares (GLS)) to each group. Thus, the Aitken estimator appearsto be a matrix weighted mean of the individual estimators (by group), as the loads areinversely proportional to their variance–covariance matrix.

5.2.2. The feasible GLSFormula (8) does not provide an estimator, strictly speaking, because it depends on unknownelements appearing in the matrix. There are two element orders: the parameter and thematrix. To achieve an efficient (feasible) estimator, it is normal to use a two-step procedure:one step for the convergent estimation of the unknown parameters of R and the other toapply formula (8) with an estimated R to replace the real R.

First step. For σ 2g , a convergent estimation is provided by the individual OLS (by

group). Given that the matrix � represents the variance–covariance structure of the coef-ficient βg , it is possible to propose the usual estimator of the second-order moment of adistribution, i.e.

�̂ = 1G − 1

∑g

( ˆ̄β − ˆ̄β)( ˆ̄β − ˆ̄β0)′, (9)

where ˆ̄β0 = (1/G)∑

gˆ̄βg .

If the vector β0 is directly observed, its use in Equation (9) can provide an adjusted esti-mator of �. Unfortunately, this is not the case here and its replacement by ˆ̄β0 in Equation (9)results in a biased but convergent estimator. In accordance with the Swamy article, it is easyto demonstrate that the expected value is given by

E(�̂) = � + 1G

∑g

σ 2g (X ′

gXg)−1,

and that consequently, the bias is equal to the last term of the above expression.The bias can be eliminated by defining the following estimator:

�∗ = � − 1G

∑g

σ̂ 2g (X ′

gXg)−1,

where σ̂ 2g is the adjusted estimator of σ 2

g . Nevertheless, the disadvantage of this estimatoris that it does not guarantee that � is a ‘definite non-negative’ within the sample. For thisreason �̂ will be kept as an estimator of � despite its bias. Furthermore, the only prerequisitein the first step is the convergence (for T to infinite) which is assured for both �̂ and �∗.

Second step. By using σ̂ 2g and �̂, an estimation of Rg can be made

R̂g = �̂ + σ 2g (X ′

gXg)−1,

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which is used in formula (8) and instead of the real unknown R. Swamy demonstrates thatthe two-step estimator thus obtained is asymptotically normal (under fairly general andsteady conditions). The asymptotic variance–covariance matrix will be estimated by

[∑g

R̂−1

]−1

.

6. ResultsSee Tables 10 and 11.

7. Comments7.1. Effect of competition index on innovation outputThe results are given in Tables 10 and 11. They show the results of three econometricmethodologies: the random coefficient method (RCM), the Tobit method and the counterdata model (our sample includes innovating and non-innovating enterprises). Tobit andRCM provide similar results. We will discuss only the RCM results in this section.

First, the competition index (Lnr) has a positive and not statistically significant coeffi-cient at public sector (with an elasticity of 0.09 and the student t statistic is 0.48). On thecontrary, the competition index is statistically significant at civil sector (with an elasticityof 0.16 and the student t statistic is 3.74). Second, the magnitude of the coefficient of thecompetition index at civil sector is greater (and significant) than at the public sector. Thisseems to imply that the degree of rivalry perceived by individual firms is more intenseamong firms competing at the civil sector. The competition is fierce, that is to say that the

Table 10. Estimation of the Cournot–Bertrand model in the public sector (1992–2004).

Estimation method RCMa Tobit Poisson

Endogenous variable Lsip: Turn overof innovativeproducts

Lsip: Turn overof innovativeproducts

Bvt: Patents takenout by theenterprise

Constant −0.57 (2.61)∗∗ −22.14 (18.34)∗∗ –Lbvt: Patents taken out by the

enterprise0.55 (5.70)∗∗ 0.67 (1.96)∗∗ –

Cap: Capacity of the firm toappropriate the return fromindustrial R&D

−1.39(5.14)∗∗ −1.00 (1.00) −0.26 (0.79)

Ladv: Advertising expenditure 0.31 (10.71)∗∗ 1.30 (9.73)∗∗ 0.19 (2.38)∗∗Lrd: R&D expenditure 0.37 (14.20)∗∗ 0.98 (10.12)∗∗ 0.22 (6.86)∗∗Mks: Leadership indicator 3.00 (4.86)∗∗ 7.84 (3.39)∗∗ −2.79 (2.69)∗∗Idcor: Research uncertainty 0.48 (1.63) 1.70 (1.25) −3.03 (3.87)∗∗Lnr: Lerner Index (degree of

competition)0.09 (0.48) 0.77 (0.59) −0.25 (4.13)∗∗

R̄2 0.23 – 0.08Number of enterprises 617 617 617

Note: (.), Student t test.All variables with capital ‘L’ are logged (except Lnr).aRandom coefficient model.∗Significant at 10%.∗∗Significant at 5%.

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Table 11. Estimation of the Cournot–Bertrand model in the civil sector (1992–2004).

Estimation method RCMa Tobit Poisson

Endogenous variable Lsip: Turnoverof innovativeproducts

Lsip: Turnoverof innovativeproducts

Bvt: Patents takenout by the firm

Constant 0.70 (13.80)∗∗ −10.79 (60.42)∗∗ –Lbvt: Patents taken out by the firm 1.02 (16.80)∗∗ 1.65 (11.14)∗∗ –Cap: Capacity of the firm to

appropriate the return fromindustrial R&D

0.00004 (14.30)∗∗ 0.00002 (8.45)∗∗ 0.000005 (9.83)∗∗

Ladv: Advertising expenditure 0.43 (49.36)∗∗ 1.09 (44.07)∗∗ −0.02 (0.75)Lrd: R&D expenditure 0.06 (4.85)∗∗ 0.16 (4.94)∗∗ 0.33 (25.00)∗∗Mks: Leadership indicator 2.95 (10.19)∗∗ 4.81 (6.52)∗∗ −2.13 (1.62)Idcor: Research uncertainty 2.01 (25.36)∗∗ 5.47 (24.79)∗∗ −0.43 (1.16)Lnr: Lerner Index (degree of

competition)0.16 (3.74)∗∗ 5.11 (8.33)∗∗ 0.007 (0.72)

R̄2 0.12 – 0.03Number of enterprises 3240 3240 3240

Note: (.), Student t test.All variables with capital ‘L’ are logged (except Lnr).aRandom coefficient model.∗Significant at 10%.∗∗Significant at 5%.

price tends towards the marginal cost. On the contrary, if the coefficient at the public sectoris very small (and not significant), then we can consider the competition as weak and thusthere is a problem with mark-up, i.e. the price is higher than the marginal cost.

7.1.1. What do we learn about competition and innovation?At the public sector, competition index is not correlated with innovation output. This isconsistent with the belief that PMC does not stimulate product innovation in this sector.This sector is large firms’ sector. Large firms are more likely to introduce an innovation,mainly process innovation only or the combination (process and product innovation) thansmall firms. These results confirm the findings of Scherer (1991) and Cohen and Klepper(1996), who show that as firm size increases, R&D for process innovation increases rel-ative to R&D for product innovation. R&D is an important determinant of innovation inboth sectors. The innovation rate seems higher in the public sector than in its civil coun-terpart. Public enterprises carry out a greater percentage of R&D than do civil enterprises.The main objectives of innovation of public sector firms are to improve product qualityand extend product lines. These objectives become important if a firm carries out productand process innovation, which strongly indicate that these objectives can carry out productinnovation based on a new method of production (process). Unlike their civil counter-parts whose innovations are focused on reducing production costs and prices, public sectorfirms seem to face competition pressure through the innovation of products whose purposeis to directly capture the market share from competing products by targeting customerswho are sensitive to the characteristics and novel performance capabilities of new prod-ucts. This strategic choice, recently reaffirmed for product and process innovation, wasmore tangibly noticed by the firms in the 1994–1996 period than firms’ future intentionsin 1993 led us to expect. Indeed, at that moment (1993 and recently), with France (likeothers European countries) in the throes of an economic crisis, firms were more interested

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in process innovation, which they saw as the principal means to meeting the requirementof competitiveness. The recovery in 1994 seems to have reduced this pressure on pricesand thus reoriented firms’ choices towards product innovation. In short, product innova-tion (based on process innovation) seems more than ever the principal means to ensurecompetitiveness.

At the civil sector, the competition index is positively and strongly correlated withinnovation output. This result is expected since innovation for conquering new marketsseems to be important for the civil sector. The market drives innovation output. In fact, themotivation to increase market share is recognized as the first priority by nearly 60% of theinnovating firms, followed closely by the extension of the product line and the improvementof quality (50% for these two objectives) in all CIS surveys.

Innovation is first driven by competition: The principal problem facing civil firms is tobe able to sell their product. To do so, they must have competitive products not only in termsof price but also in terms of quality (for the public industries the price is not an importantdeterminant). The reduction of wage costs and increased production flexibility are two ofthe objectives of more than 70% of innovating civil firms (cf. CIS).

7.1.2. Why Bertrand competition explains more innovation output than Cournotcompetition?These results suggest that the public sector seems to represent Cournot oligopoly betterwhile the civil sector represents Bertrand. When R&D investment is controlled througheither subsidization or taxation, the welfare superiority of Bertrand competition is rein-stated. We can show that for Cournot competition, the R&D subsidy is optimal, i.e. asubsidy is preferred to ‘laissez faire’ and taxation. Since under-investment is more seriousin Bertrand competition than in Cournot competition, a larger subsidy could be requiredin Bertrand competition. If that is the case, the R&D differential between Cournot andBertrand competition would be reduced. Recall that a larger R&D investment in Cournotcompetition is the reason that the welfare of Cournot competition is greater than that ofBertrand competition.

We previously demonstrated that firms produce more when in Bertrand competitionthan in Cournot competition. Indeed, in the duopoly of Cournot, each firm supposes thatits competitor keeps its quantity constant. It also clearly predicts that any increase in offerwill reduce the market price. This prevents an increase in production. On the other hand,in the duopoly of Bertrand, each firm considers that the price of its competitor is constantand that there is no limit to the expansion of output, something which provokes overpro-duction and fierce competition. When the differentiation is very strong, we almost havetwo independent monopolies. Equilibrium prices become identical in both cases becausethe equilibrium of the monopoly does not depend on the strategy of the firm (price orquantity).

7.2. The role of leadership and the index of correlation between the R&D andinnovation: the most powerful enterprises modify their intellectual property policiesThe results (for civil and public sectors) demonstrate that firms with a large position ofleadership (represented by the variable Mks) and strong innovation capacity (representedby the variable Idcor) utilize the patents to protect their intellectual property rights less (Bvtin Tables 10 and 11). They also commercialize their innovative products more efficiently(Lsip in Tables 10 and 11). In fact, firms face future expanding market demands by increasing

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their investment in high-tech (the elasticities are positive and significant in model Lsip).This entrepreneurial approach is novel. However, because of various advances in scientificprogress within the field of new materials, R&D expenditure can accelerate innovation (theelasticities of the R&D variable, Lrd, are positive and significant in all models). Firms thusrecruit well-educated researchers to carry out R&D.

The importance of research uncertainty (Idcor) in the civil sector can be attributedprimarily to the behaviour of enterprises in regard to patents. In fact, first of all, the propertyrights may have short-term anti-competitive effects in that they constitute monopoly rights,of varying durability, which derogate from the principle of freedom of trade. Enterprises donot have any predefined procedure for managing intellectual property, which in itself canbe regarded as a kind of intellectual property strategy.

Nor do they have particular contracts (e.g. non-disclosure contracts) with their personneland only require special laboratory protocols to be implemented when monitoring productupdates?

Furthermore, the persons in charge utilize neither a decision tool to evaluate the strategicor economic value of patents nor other means of market protection during the developmentof new products. Like most SMEs, enterprises recognize the huge difficulties in commer-cializing their products and it is very hard to perform a market study before launching anew product due to the budgetary constraints.

Next, the French market is distinguished by varying sizes of enterprises operating indifferent sectors, something which causes a very competitive environment. Imitating theproduct of a competitor is common in these sectors. The managers of these enterprises arepersuaded that only patents can effectively protect their technological property. Nonetheless,sometimes they must utilize more financial resources and apply an integrated businessstrategy to protect their products even though they obtain patents. One of the most difficulttasks in managing the intellectual property for a company is to monitor all the associateddeadlines. Requesting patents and satisfying annual tax payments by the required date forrenewal is imperative. Enterprises entrust this task to the patent offices in Europe, somethingwhich has also increased their annual R&D expenditure.

Finally, it seems that until now, French enterprises have not utilized traditional meth-ods of financial profits and intellectual properties in order to protect property rights butinstead have relied on patents and non-disclosure contracts. Clearly, they lack the neces-sary integration of company intellectual property policies and their innovation strategies. Ingeneral, French enterprises do not have the possibility to carry out market studies, evaluatetheir product potentials before applying a patent, or even create an action plan for a newproduct. In order to rapidly launch the products to the market, managers of enterpriseshave modified their intellectual property policy; they have decided to keep the activi-ties of their high-tech new products a secret and do not patent a product until its marketlaunch.

The principal conclusion is that a highly competitive environment characterized bymany different firm sizes among competitors is a very difficult domain to operate in. Thisis difficult even for an SME whose products are protected by intellectual property rights.SMEs that operate in such conditions can improve their performance if they integrate theirintellectual property policies with their innovation strategies and if they judiciously chooseand utilize the available tools and financial resources.

The enterprise with leadership position innovate more and more: Competitiveness is aconcept first utilized at the enterprise level. An enterprise is competitive when it has betterperformance than the average enterprise. In this case, competitiveness is usually expressedby a price gap for the same product. The enterprise is competitive when it sets a price

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less than others for the same product. This is called ‘price competitiveness’. However, itis obvious that competitiveness is not just a question of reducing the price gap. Non-pricecompetitiveness or structural competitiveness also exists and is based on product quality.

Price competitiveness Non-price competitiveness

Related level of production costs QualityProducers’ margin driven behaviours InnovationLevel of exchange rate Reputation

Redefining competitiveness by using enterprise performance (profitability, growth and mar-ket share) does not resolve the problem of measuring competitiveness because performanceis decided on by numerous determinants. To face this difficulty, we utilize a conceptual shiftand consider that the notion of market share applies to the analysis of competitiveness. Mar-ket share is an indicator of the enterprise’s performance. Our results demonstrate that thelarger the market share the firm possesses, the more likely the firm will become a leader,and will carry out only efficient innovations (Mks has a positive and significant elasticityin the equation Lsip in Tables 10 and 11).

Patents as strategic tools to increase sales turnover in innovative products: Patents fallunder the category of intellectual property rights. They are granted for new products orprocesses, when the process improves the method of manufacturing of existing products.Their owner has the exclusive right to exploit new products or processes for a duration of atleast 20 years. Patents are a powerful commercial tool and an important link between R&Dand the market.

But patents provide much more than this and can be utilized to identify and develop newbrands as well as to maintain their existing market or create a new one. They can mobilizecapital funding and create new sources of income by granting licenses or by entering certaincountries without incurring large equipment costs. If patents are used carefully, they can bethe deciding factor between the success or failure of the firm.

As an asset, intellectual property rights play an important role in the commercial per-spective of a firm and can have an impact on practically all of its activities – from sales,creation and commercialization of its products to its overall financial position. Each firmmust therefore manage its intellectual property rights and carefully protect these intangibleassets. That said, what is the value of these assets and how can they best be used? Oneof the characteristics shown by all flourishing firms is their attention to innovation andtheir understanding of the value of intangible assets. Firms comprehend the influence thatthese assets have on their financial situation. In particular, they understand the impact thatpatents and brands have on maintaining their competitive advantage and reinforcing theirmarket position. Prosperous firms invariably have a very well-managed intellectual prop-erty portfolio, created to control access to their inventions and to maximize income fromthese inventions through licenses, exploitation fees and solid commercial alliances.

8. ConclusionThe relationship between innovation by firms and competition is discussed here in thefield of technological change. Arguments on this topic initially focused on the impact ofthe concentration on innovation (the Schumpeter/Arrow debate for example). The latterexpanded the concept to encompass the effects of different types of competition (Bertrandand Cournot) and also of international competition.

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Past studies attempted to model innovation through auction game theory but thesemodels were simplistic. Some researchers tried to validate the assumptions that limitedthe empirical range of the modelling; however, the empirical verifications still remain frag-mented and often unsatisfactory. The empirical study outlined here proposes a more rigorous(with all the specifications of the various auction game models in terms of uncertainty, theextension of the property rights, and capital constraints) and general (with multi-sector)verification.

This study rearranges main and detailed innovation data sets in France and also thepatent data from the EPO in order to retain as much of the data as possible since veryfew studies could obtain such complete French data sources. The constructed data contain3857 enterprises with 612 enterprises in the public sector and 3240 in the civil sector. Theresults demonstrate that the competition in the public sector represents Cournot competition(quantity), whereas the civil sector represents Bertrand competition (price).

Notes1. The available data set consists of an unbalanced panel with a large number of cross-section units

of manufacturing more than 3800 firms which are classified by industry codes (‘Siren in France’)in manufacturing. Each firm is observed for the years 1990–2004.

2. Specially, the CIS examines several technological innovation activities, notably innovation inputand innovation output. Linked to the annual survey of manufactures and to patent data from EPO,the CIS permits us to realize high-level econometric analysis.

3. We estimate the equation using a pooled random coefficient effects procedure. That is, we pre-sume that the coefficients have a cross-section component, time-series component, and pooledcomponent and weight observations according to sample estimates of their corresponding condi-tional variances (Balestra and Negassi 1992; Negassi 2009).This method has many advantages.It controls for differences in firm size and industries effects that may be important for innovationactivity. We do not need to introduce dummy variables. The coefficients are estimated at eachindustry level and the final coefficients are a weighted average of all industry-level coefficients.This is also another way to compute the average of each variable across firms within an industryto get for example competition index, etc.

4. For more details regarding Section 2, see Hung (2008).5. The annual company surveys EAE (“‘Enquête Annuelle d’Entreprises”’ i.e. Annual Survey of

Enterprises) of the SESSI-MEFI, R&D innovation databases from 1990 to 2004 and the SANDIE(database from the Financial Affairs Department of the Ministry of Defense).

6. Intellectual property rights are often considered intangible assets. They include the firm’s, itspatents, the R&D strategies and licensing agreements. Other intangible assets are invested inhuman capital and know-how, in particular databases, manuals, specifications of new productsand instructions for their manufacture. All these intangible assets have a value which must beaccurately assessed to find a fair fixed price. This is especially important for technology transfer,for the procurement or sale of the firm and in other situations such as mergers. Indeed, mostSMEs underestimate their intangible assets. The question of the value of a patent often arisesfor different reasons. One example would be an inventor looking to generate capital to launchhis/her invention on the market. For another it may be to try to set a price for the invention. Forothers still it may involve the cost of acquiring a license to use an invention, or conversely, forinventors who cannot penetrate the market and need to remain competitive, the best solution maybe to create license agreements for the utilization of their new or improved technology. Whateverthe case, each invention has a price which generally depends on the following four factors: theimportance of the patent, the market, the duration of validity of the patent and the number ofexisting inventions of the same type.

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the nature of market competition and innovation: does competition improve innovation output?

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