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TECHNOLOGICAL COMPETITION, ECONOMIC PERFORMANCE AND STRATEGIC BEHAVIOUR OF INTERNATIONAL FIRMS Henri Capron Michele Cincera DULBEA-CERT Résumé: Cet article analyse la relation entre le niveau de concurrence en R&D, les externalités technologiques et la productivité au niveau de la firme. Une attention particulière est apportée à la formalisation des externalités. L’analyse se fonde sur un échantillon composé de 625 entreprises mondiales intensives en R&D dans le secteur manufacturier. L’information collectée couvre la période 1987 à 1994. Etant donné la structure de données de panel caractérisant l’échantillon, des méthodes économétriques prenant en compte l’hétérogénéité des entreprises et l’exogénéité faible des régresseurs sont mises en oeuvre. Les fonctions de réaction R&D estimées pour les principales industries intensives en R&D indiquent que si les entreprises sont à des degrés divers sensibles aux investissements R&D de leurs concurrents, ces comportements ne sont pas homogènes entre secteurs et entre pays. Il résulte des estimations que les externalités influencent de manière significative la productivité des entreprises. Néanmoins ces effets apparaissent substantiellement différents au sein des piliers de la Triade. Les Etats-Unis sont principalement sensibles à leur propre stock national d’externalités tandis que le Japon apparaît puiser dans le stock international. De son coté, l’Europe montre des faiblesses à internaliser les externalités.
Abstract: This paper analyses the relationship between R&D rivalry, spillovers and productivity at the firm level. A particular attention is put on the way technological spillovers are formalised. The analysis is based upon a data set composed of 625 world-wide R&D-intensive manufacturing firms whose information has been collected for the period 1987-1994. Given the panel data structure of the sample, econometric techniques which deal with both firm’s unobserved heterogeneity and weak exogeneity of the right hand-side variables are implemented. The R&D reaction patterns for the main R&D-intensive industries show that if firms are to different degrees sensitive to what competitors allocate to their activities, the behaviours are not homogeneous across industries and among countries. The empirical results give clue that spillover effects influence significantly firm’s productivity. Nevertheless the effects differ substantially among the pillars of the Triad. The United States are mainly sensitive to their national stock of spillovers while Japan appears to draw from the international stock. On its side, Europe shows some pains to internalise spillovers.
I. Introduction This paper aims at analysing R&D strategies developed by large multinational companies firms of the Triad. The focus is put upon the impact of technological spillovers and strategic interactions. Indeed, besides the effect of technological spillovers on productivity and R&D intensity, we appreciate to what extent strategic technological choices carried out by firms to cope with the technological race matter. The study concerns the appreciation of the reaction patterns to the present technological challenge shown by firms in Europe, the United-States and Japan as well as its economic impact. The stress is put on the output modelling and the identification of strategic behaviours of firms belonging to each economic area in order to improve the knowledge of interdependencies between rival firms and of their influence on economic performance. The analysis is based upon a sample of main international R&D intensive firms and covers the period 1987-1994. First, output models
are estimated by including, beside traditional economic variables, a measure of technological spillovers. This measure is based on the positioning of firms inside the technological space. Second, as firms are presently engaged in a technological race, we investigate the direction of R&D strategic adjustments inside the Triad. So, the estimation of R&D reaction functions is expected to help us to evaluate the differentiated behaviours of the three geographical areas as regards technological competition. In the second section, we focus the attention on the alternative ways to appreciate the impact of technological spillovers on firms’ technological activity with reference to the main approaches proposed in the literature. In the following section, we discuss the methodological framework necessary to characterise and to differentiate the technological determinants. A particular attention is paid to the way in which firms are classified into technological clusters. In the fourth section, we describe the international R&D database. Then, the econometric models to be estimated by panel data methods are presented and the empirical results obtained from the sample discussed. We conclude by underlining the main observations resulting from the econometric analysis as well as some points deserving further research. II. The sources of technological interdependencies: spillovers and rivalry The productivity performance of firms depends on their own R&D efforts as well as the pool of accessible knowledge developed by other firms. The pool of available knowledge however, does not only influence the productivity but can also give an impulse to own R&D outlays of firms. It is largely accepted that the pool of knowledge at the source of spillover effects results mainly from R&D investments. Consequently, R&D expenditures from other firms as a source of technological spillovers can be thought to affect jointly productivity as well as own R&D expenditures of a firm. Besides this conception of R&D efforts as a source of externalities, we cannot ignore that firms evolve in a competitive environment and that their R&D decisions are not taken independently from R&D choices of competitors. So, R&D efforts are also a source of competitive interactions. As stressed by Griliches (1992) in his review of studies dealing with the measure of spillovers, there is often a confusion about the real definition of technological spillovers. A first kind of spillovers – rent spillovers - is related to new products and processes which embody technological change and are bought by other firms at less than their “ full quality adjusted ” prices. The second kind of spillovers – pure knowledge spillovers - can be defined as the potential benefits of the research activity of other firms for a given firm. These technological externalities exist because of the non-rival and partially excludable property of technology. Non rivalry means that the costs required to reproduce an innovation once it is made are negligible with respect to the original investments involved to discover it so that technology can be seen as a public good. Partial excludability means that the owner of an innovation cannot exclude others from obtaining a part of the benefits free in charge. A distinction can be made between the spillovers generated by the firms 2
of a same industry from those that emanate from firms located in other industry sectors. According to the firm’s country, a similar distinction can be made between the domestic and the foreign or international nature of knowledge spillovers1. The technological opportunity, as well as the technological spillovers, affects the costs of innovation. If the appropriability of knowledge is imperfect and if many firms are involved in similar technological activities, then the costs of innovation for a given firm are likely to be affected by these activities. For instance, if the technological spillovers and the firm’s own R&D are complementary, then an increase of these spillovers should lead the firm to intensify its R&D effort. Referring to the concept of technological space, Jaffe (1986, 1988) has proposed an original approach to formalise spillovers. The firm’s position in the technological space is characterised by the distribution of its patents over technological classes. Jaffe observed significant positive effects of technological spillovers on the firm R&D intensity and on its productivity growth. He also took other variables into account such as the technological opportunity and the market forces in order to avoid biases in the measure of spillovers. The main drawback of this way of formalising technological spillovers is certainly the use of patents as an indicator of the output of the innovation activity2. Nevertheless, comparatively to alternative weighting functions, Jaffe’s one appears very attractive despite its weaknesses3. Behavioural models under oligopolistic market environments have been developed in economic theory. Rather than competing by price changes, oligopolistic firms prefer to turn to product differentiation and quality improvements in order to preserve their market share. In industries characterised by a high R&D intensity, technology is a main component of the non-price competition. As pointed out by Cohen (1995), the empirical literature on technological strategic interactions remains a largely neglected issue. Indeed, there is an astonishing gap between the abundance of theoretical models of R&D rivalry and the miss of real empirical examination of the extent of R&D competition. Yet, the first theoretical arguments developed by Scherer (1967) in the sixties showed that the increase of R&D efforts of a firm will generally invigorate R&D expenditures of competitors. In the eighties, game-theoretic models of R&D rivalry rejuvenated the question of the role of strategic interactions. As shown by these models, the competitive threat resulting from higher engagements of rivals in R&D is a key determinant to explain the amount of resources allocated to R&D by a firm4. The limited empirical evidence on technological strategic interactions does not allow one to conclude if this point really matters. From a study at the firm level, Grabowski and Baxter (1973) conclude to a very scattered but significant competitive interaction pattern in the U.S. chemical industry regarding R&D behaviour. More recently, Scherer (1992) and Scherer and 1 2
See Cincera and van Pottelsberghe (2001) for a review of the empirical literature on this subject. For the relevance of patent statistics as an indicator of the technological output, see Griliches (1990) for instance. 3 For a discussion of alternative weighting function, see Mohnen (1996). 4 See Reinganum (1989) and Beath, Katsoulakos and Ulph (1995) for a review of models. 3
Huh (1992) reported results of submissive R&D reactions from U.S. firms to competition of foreign firms. Given that a proxy for the R&D of rivals is not available, he used a measure of import penetration. His results give clue that multinational corporations reacted more aggressively than firms performing R&D only in the United States. In a study of technological competitive interdependencies at the firm level for ten high- and medium- intensive industries, Capron (1994) obtained results indicating that the United States had a leader behaviour in these industries over the period 1982-1991. In the sectors of chemicals, motor vehicles, drugs and aerospace, the results gave evidence of aggressive reactions from European firms while Japanese drew a similar behaviour in chemicals, electronics, electrical machinery and photographic instruments. In electronics, European firms appeared to proceed according to a submissive reaction pattern. Regarding US firms, they did not seem to take the decisions of their rivals into account when they determine their R&D budget. This behaviour is typical of a leadership situation in which rivals strategies do not matter since they are not in a position to challenge the leader. At the macro level, Levy and Terleckyj (1985) tested the changes in the R&D reaction pattern between the United States and Japan. They found a decrease in U.S. R&D outlays opposing to the increase of Japanese R&D expenditures in the sixties but with increasingly positive change in the eighties. Scherer (1991) reached a similar conclusion in a study of R&D competition between the United States and Japan. While studies converge to estimate that the rate of return to R&D spillovers is significantly large, the observations remain very scattered and face with lots of criticisms (Griliches, 1992). If firms can benefit from R&D carried out outside through spillover effects which can concretise directly through improvements of productivity or indirectly through a stimulation of their R&D efforts, their own R&D investment policy can also be sensitive to what competitors allocate to R&D. Disentangling between both categories of outside R&D effects is certainly not an easy task as the basic variables are intrinsically the same. III. Data sample and formalisation of technological spillovers and R&D rivalry effects 3.1. Description of data The R&D database has been constructed with the view of setting up a representative sample of the largest firms at the international level that reported R&D expenditure. The initial data set consists of an unbalanced panel of 2676 firms from 1984 to 1995. For each firm, information is available for net sales, number of employees, net property, net plant, property & equipment, annual R&D expenditure and major industry group according to the Standard Industrial Classification (SIC-2 digits). The information on company profiles and financial statements comes from the Worldscope/Disclosure database5. All variables have been converted into constant 1990 dollars. Because of the non-availability of output deflators at the industry level for each country, net sales 5
See Cincera (1998) for more details as regards this information. 4
and R&D expenditures have been deflated using the GDP deflators of respective countries. For the net property, plant & equipment variable, the deflator of physical capital has been used. The stock of R&D capital has been built on the basis of the permanent inventory method with a depreciation rate equal to 15 percent and an initial stock of R&D capital calculated by assuming a growth rate of R&D expenditure equal to 5 percent. The second source of information is the firm’s patent applications across technological classes according to the International Patent Classification (IPC) for the whole period from 1978 to 1994 as published by the European Patent Office (EPO). Although all firms reported R&D expenditures, not less than 1058 firms (mainly non-European firms) did not apply for any patents to the EPO over the period 1978-1994. Because of this “ zero ” issue, it was not possible to build the index of technological closeness of these firms, which represent 30.6% of the whole sample. The two-digit IPC classification allows one to identify the technological classes of patent applications. In order to ease calculations, the 118 classes were grouped into 50 broader classes. On this basis, a table of contingency, i.e. a table reporting the distribution of the firms’ patents across the 50 IPC classes, has been constructed in order to compute the index of technological closeness and consequently the stocks of spillovers. In order to build a consistent sample, a cleaning procedure6 has been applied to this unbalanced sample of firms in order to reject firms whose variables displayed very high and often irrelevant variations. In a second step, firms for which data were not available for the whole period 19871994 have been excluded. Our final data set is composed with a balanced inter-industry panel of 625 manufacturing firms over the period 1987 to 1994. Finally, additional industry specific samples have also been constructed for the estimation of the intra-industry R&D reaction functions. Here, the objective has been to jointly optimise the number of firms as well as the number of time periods for each industry analysed. The features of these ‘intra-industry’ samples in terms of the number of firms and time periods are presented later in the empirical section.
With sixty per cent of firms, the United-States are largely over-represented in the sample7. The weight of American firms is particularly important in some sectors: computer & office equipment, professional goods and software. European firms account for only sixteen percent while Japanese firms cover twenty one per cent of the sample. So, a main drawback of the sample is the underrepresentativeness of European firms. This is mainly a consequence of the miss of data availability for these firms for the first years covered by the sample.
6
This procedure is similar to the one applied by Hall and Mairesse (1995). For descriptive statistics about the geographical and sectoral composition of the sample, see Capron and Cincera (1998) and Cincera (1998).
7
5
The representativeness of the ‘inter-industry’ sample comparatively to total business enterprise R&D expenditures8 in the different geographical areas is respectively 48 percent for Europe, 38 percent for Japan, 53 percent the United-States and 18 percent for the Rest of the world for the first sample. 3.2. Measuring technological spillovers Locating firms into the technological space allows one to formalise the technological spillovers. Indeed, this way of formalising spillovers is closely related to the notion of technological proximity: the closer two firms are in the technological space, the more the research activity of one firm is supposed to be affected by the technological spillovers generated by the research activities of the second firm. Hence, it is assumed that each firm faces a potential ‘stock’ of spillovers, which is a weighted sum of the technological activities undertaken by all other firms. In order to measure the technological closeness between two firms, Jaffe used the ‘angular separation’ between them. This measure of closeness takes values between one and zero according to the common degree of research interest of both firms. It is purely directional, i.e. it does not depend on the technological vector’s length. In order to measure the distribution of the firm’s research interests across the different technological areas, we use the patent distribution over 50 technological sectors according to the International Patent Classification (IPC). The patent distribution relies on the whole number of patent applications9 filed to the European Patent Office during the period 1978-1994. Once the measure of closeness between firms i and j is calculated, the potential stock of technological spillovers of the ith firm can be evaluated as follows: Si = ∑ Pij K j
(1)
j j ≠i
where:
Si = stock of spillovers of firm i, Pij = technological closeness between firms i and j, Kj = R&D capital stock of firm j.
This index of technological distance relies on the strong assumption that the appropriability conditions of knowledge are the same for all firms (Jaffe, 1988)10. Another drawback of this method is that firms, which encounter a rather diversified technological activity, will benefit to a lesser extent from the stock of spillovers. Indeed, the more the firm’s R&D activities are diversified, the more its patent distribution over technological classes is uniform and the more the 8 9
As reported in the OECD’s BSTI report (1997). Patents are classified by date of application rather than by date of issue. Patents classified by date of application are preferable because they reflect the moment when a firm makes out itself to have generated an innovation and because of the long time lags in the patent’s application process. 10 According to Spence (1984), an imperfect appropriation can be defined as the proportion Φ of the output of each firm’s technological activity that is disclosed. If Φ = 0, then appropriability is perfect, if Φ = 1, then R&D is a pure public good. 6
index of technological closeness is likely to be close to zero. A firm, which is technologically diversified, will be located in the central region of the technological space so that it will not be close to any firm. An alternative standpoint is to say that firms are aware of the research activities undertaken by only a few technologically similar firms. In that sense, even if all stocks of technological spillovers are relevant, they will probably not be taken into account completely due to imperfect information about the content of R&D realised by rivals. In order to examine this possibility, Jaffe divided the potential stock of spillovers into two distinct components obtained by applying a clustering method: a local stock which corresponds to the sum of R&D stocks of firms belonging to a same cluster of technological activities and an external stock which is computed from the other firms. Thanks to the international dimension of our sample, Jaffe’s methodology has been extended by distinguishing, besides the local and external stocks, national stocks11 from international ones. In this way, we have been able to appreciate to what extent geographical and cultural contiguity matters. Furthermore, in order to consider the technological as well as the geographical closeness, the potential stock of spillovers was dissociated into four components: the local national stock, the local international stock, the external, i.e. non local, national and international stocks. So, in a second step, firms have been grouped into homogeneous categories or clusters on the basis of their technological ‘proximity’. Because of this closeness, firms belonging to a same cluster are assumed to face the same state of technological opportunity. Among the several techniques available to combine firms into clusters, the K-means clustering method is one of the most commonly used12. In the present paper, we experimented this technique as well as two others: the K-Means clustering with ‘strong centres’ and the agglomerative hierarchical clustering methods13. The algorithm, which has been used to combine firms into clusters, works on the factorial coordinates of a preliminary principal component analysis. The advantage of this method is the use of an Euclidean distance between firms, allowing to considere an objective criterion in order to evaluate the quality of the firms’ partition. This distance is used for measuring how far apart two firms are in the factorial space. Besides the benefit of the orthogonality of factorial axes, another advantage of this method is that it does not take into account the last factorial axes which often carry random components. Given the nature of our data, the analysis of binary correspondences of the contingency table, i.e. the table of the firm’s patent distribution across 50 IPC classes, has been performed to compute the factorial axes. A common difficulty to all clustering techniques is to fix the number of clusters present in the data. Different procedures for determining the ‘optimal’ number of clusters have been proposed in the literature14. In this study, the three experimented clustering techniques are based on Ward’s 11
In this paper, we consider Europe as a whole. Jaffe (1986) derived a modified version of this method, which allows him to take the multinomial structure of the firm’s patent distribution into account. 13 See Lebart, Morineau and Fenélon (1979) for a description of these methods. 14 For an examination of some of these procedures, see Milligan and Cooper (1985). 12
7
aggregation criterion. This criterion allows one to measure the quality of the firm’s partition into technological clusters by considering the within and the without cluster inertia. The within cluster inertia represents the mean of the squared distances between the firms’ cluster and its centre of gravity, while the without cluster inertia consists in the mean of the squared distances between all cluster centres of gravity and that of the whole data sample. Ward’s criterion for forming the clusters consists of maximising the ratio between the two inertia in order to get the most homogenous and the most distant possible clusters. It should be noticed that such a ratio does not allow one to compare two partitions with a different number of clusters. Actually, the partition into k+1 clusters will always have a higher ratio of inertia than a partition into k clusters. Ultimately, the best possible partition would be the one, which has as many clusters as the number of firms. In this case the ratio of inertia is equal to zero given that each firm is blended with its cluster centre. To choose the number of clusters, a two step procedure has been applied. In a first step, the agglomerative hierarchical and the K-Mean with strong centres clustering methods have been identified as the best candidates in terms of the inertia criterion. In a second step, different stocks of local spillovers constructed from different partitions into k clusters according to the agglomerative hierarchical method for values of k ranging from 15 to 29 have been experimented. These stocks have been systematically tested in the productivity regression model. The local stock based on k equal to 18 has appeared the most satisfactory both in terms of the regression’s overall fit and the estimated standard error associated with the local stock coefficient. For these reasons, we decided to retain the partition with 18 clusters, against 21 clusters in Jaffe’s analysis. Once the technological proximities of each pair of firms have been calculated15 it is worth having a look at average proximities within and across industry sectors given that there is no ‘natural’ order of technological closeness among industries (e.g. is textile closer to software than to instruments?), it may be interesting to look at such proximities from a technological perspective. Table 1 exhibits the Herfindhal index (H) computed for each industry as well as technological proximities within and across industries. These indexes and proximities represent industry averages of corresponding measures for each firm. These measures have been performed on the basis of the 625 firms’ patent distribution16 across 50 IPC classes and over the entire period 1978-1994. The last row in Table 1 indicates that technological activities are more concentrated in the software industry (Herfindhal index, H = .79), computers (H = .52), paper (H = .50), drugs (H = .49), electrical (H = .48) and instruments (H = .48). Conversely, firms in aircraft, petroleum industries and stone appear to have far most diversified technological activities (Herfindhal indexes of .22, .24 and .26 respectively). The main diagonal of Table 1 shows the technological proximities within industries. It can be observed that drug, food and textile are the industries that display the highest technological proximities. At the other end, fabricated metal products, machinery, ‘other’ and paper are the industries for which firms have the lowest technological proximities on average. Looking at the 15 16
Since there are 625 firms, this makes 195625 proximity measures. The total number of patents applied by these firms is 169820 over the whole period 1978-1994. 8
off-diagonal cells of Table 1 gives an idea of how technologically distant the industries are. On the whole, the technological distances reported in Table 1 seem to be consistent with reality. Moreover, except for a few industries, technological proximities are always higher for firms within an industry than for firms in different industries. This is quite normal since firms classified in a same industry are likely to benefit more from each other’s research activities. However, large firms in our data set generally have several establishments in several industries, this may explain why, in some cases, firms of different industries are on average closer to each other than to themselves. Interestingly, the closest industries in terms of technological proximity are aircraft, instruments and motor vehicles; chemicals, drugs, petroleum industries and textile and computer, electronics and software. Table 1. Technological proximities within and across industries (firms' averages) INDUSTRY
A I R C
C H E M
C O M P
D R U G
E L E C
E T R O
F A M P
F O O D
I N S T
M A C H
M E T A
O T H E
P A P E
P E T R
R U B B
S O F T
S T O N
T E X T
V E H I
Aircraft .27 Chemicals .14
.29
Computer .10
.03
.32
Drugs .11
.31
.02
.62
Electrical .14
.05
.09
.02
.20
Electronics .14
.03
.20
.02
.21
.34
Fabbr. metal prod. .14
.07
.04
.05
.09
.09
.09
Food .05
.12
.02
.17
.02
.01
.03
.44
Instruments .20
.13
.11
.18
.12
.15
.08
.02
.28
Machinery .17
.08
.04
.04
.09
.06
.10
.04
.09
.16
Metals .17
.10
.05
.07
.11
.11
.12
.02
.11
.12
.31
Other .11
.10
.08
.07
.10
.12
.09
.04
.10
.13
.11
.17
Paper .09
.12
.06
.08
.06
.04
.06
.03
.08
.07
.07
.06
.18
Petroleum ind. .16
.27
.04
.21
.08
.07
.08
.06
.11
.10
.14
.14
.09
.39
Rubber .16
.15
.04
.10
.06
.04
.08
.04
.09
.10
.09
.07
.12
.14
.21
Software .10
.01
.34
.01
.04
.17
.02
.00
.10
.02
.03
.06
.02
.03
.02
.33
Stone .15
.14
.05
.05
.13
.11
.11
.06
.09
.12
.12
.13
.11
.12
.11
.02
.33
Textile .11
.22
.02
.19
.06
.03
.06
.12
.08
.05
.09
.05
.11
.18
.09
.01
.14
.41
Vehicules .22
.06
.06
.03
.13
.12
.13
.01
.11
.18
.14
.11
.06
.07
.14
.04
.12
.03
.33
Herfindhal index .22
.32
.52
.49
.48
.44
.45
.37
.48
.36
.32
.43
.50
.24
.30
.79
.26
.45
.37
IV. R&D & productivity equations 4.1. R&D Equation In order to assess the impact of technological spillovers as well as of R&D competitive interactions, the following equation has been selected:
9
∆ln Rit = αi + β1∆ln Rit-1 + β2∆ln Sit + β3∆ln Cit + δ∆ln Xit +γ∆ln Zit + ΣφMmTm + ΣφLlTl + ΣφGgTg + εit (2) where
ln = the natural logarithm, ∆ = the first-difference operator, Rit = the annual R&D expenditures of firm i at time t, Sit = the net sales, Cit = the stock of physical capital, Xit = a vector of spillover components, Zit = a vector of R&D of competitors, Tm, Tl, Tg = respectively vectors of dummies for technological, sectorial and geographical opportunities17, αi = the firm fixed effect, εit = the disturbance term.
Three alternative specifications of Xit have been estimated for testing the impact of the stock of total spillovers (specification I) and the impacts of local spillovers (specification II) and national spillovers (specification III) in addition of total spillovers: specification I: δ∆ln Xit = δT∆ln TSit,
where TS is the stock of total spillovers
specification II: δ∆ln Xit = δT∆ln TSit +δL ∆[LSit / TSit]
where LS is the stock of local spillovers
specification III: δ∆ln Xit = δT∆ln TSit +δN ∆[NSit / TSit]
where NS is the stock of local spillovers
Specifications II and III results from the following approximations: TSit+ = TSit + δL+ LSit= TSit [1 + δL+ LSit/TSit] so that provided that δL+ LSit/TSit is small: δ+lnTSit+ ≅ δTlnTSit +δLLSit/TSit Taking this last equation in first differences leads to the stock specification II. Given that, the impact of the R&D realised by rival firms is examined on the basis on two complementary samples, i.e. intra-industry and inter-industry samples, two different specifications
17
Industry dummies were also introduced. As stressed by Griliches and Lichtenberg (1984) and Jaffe (1988), it can be expected that in a perfect world total factor productivity is not explained by factors specific to industries. However, there are at least three good reasons to take the market factors into account. First, in order to get a measure of the real output, we need price deflators. This variable is not perfect and the errors related to it have a structure, which differs from an industry to another. Second, as long as the inputs are not corrected by the utilisation rate of the maximal production capacity, variations of these inputs affect the productivity measurement. Finally, including the market position should attenuate the simultaneity biases, which are generated by the expected growth rate of the R&D demand. A firm that expects an increase of its sales in the future will probably intensify its R&D activity today. Given that the expected rate of growth of R&D differs among industrial sectors, for instance computers versus textiles, including the market position will reduce the simultaneity biases between sales and R&D demand. 10
of the R&D stocks of competitors have been retained. For the inter-industry R&D equation (estimated on the basis of the balanced sample of 625 firms), the specification is as follows: γ∆ln Zit = γ∆ln SRit = γK∆lnΣi and j ∈ K, i≠j Rjt
(3)
where K represents the industrial sectors, i.e. K ∈ {aircraft, ..., wood}. For the intra-industry R&D equation (estimated on the basis of the set of industry specific samples of firms), the specification of competitors’ R&D is as follows: γ∆ln Zit = γ∆ln RG = Σ G ∉ L [γG∆lnΣj ∈ G Rjt]
(4)
where G and L represent the three geographic areas, i.e. G ∈ {EU, JP, US} and L the geographic region for which the equation is estimated. The imperfect technological appropriability reduces the incentive for R&D intensive firms to invest in innovative activities. Nevertheless, firms facing an oligopolistic situation should be less worried by such spillovers and hence might be more R&D intensive. The firm’s R&D stocks are affected by technological spillovers, which increase the firm productivity. If there is a complementary relationship between own R&D and technological spillovers, then any firm maximising its profits would intensify its R&D activity and this would be done proportionally to the gained stock of spillovers. Moreover, if the technological opportunity differs among different technological areas and if these differences are relevant in explaining the costs related to the R&D activity, then we expect technological dummies to be significant, i.e. the null hypothesis of equality among the technological dummies should be rejected. Likewise, if the geographic location is important for the R&D activity, then the geographical dummies should be significant too. 4.2. The productivity equation The R&D activity implemented by firms is expected to stimulate their productivity. Besides the impact of the firm’s own R&D capital as well as the influence of labour and of physical capital stock on productivity, it is worth examining to what extent the spillover stocks improve firm’s productivity. In order to answer this question, the Cobb-Douglas production function framework is used. Formally, we have: ln Sit = αi + λt + β1ln Lit + β2ln Cit + β3ln Kit + γln Xit +εit where
(5)
ln is the natural logarithm, Lit is the employment of firm i at time t (i = 1 to 625, t = 1 to 8), 11
Kit is the stock of R&D capital, Sit is the net sales, Cit is the stock of physical capital, αi is the firm’s specific effect, λt is a set of time dummies, Xit is a vector of spillover components, γ is its associated vector of parameters and εit is the disturbance term. Differentiated impact of the national and international spillover stocks have been estimated: γln Xit = γNln NSit + γIln ISit,
(6)
where NS, IS are the national and international spillover stocks respectively. The R&D stock represents the firm’s research activity. As Griliches and Mairesse (1984) pointed out, the omission of materials as a production factor in the equation above can lead to misspecifications and hence biases in the estimated coefficients. In order to avoid this issue, it is possible to use added values instead of sales. Some authors tested whether the use of sales versus added values give different results. The conclusion of Griliches and Mairesse (1984), Cunéo and Mairesse (1984) and Mairesse and Hall (1996) is that the use of sales or added value as a dependent variable leads to similar results. On the other hand, Schankermann (1981) and Hall and Mairesse (1995) indicate that the estimated R&D elasticities are sensitive to the double-counting between R&D and capital expenditures as well as between total employees and the workers allocated to R&D activities. According to these authors, correcting for double-counting tends to slightly increase the R&D elasticity. Consequently, our results should be interpreted cautiously as materials are not included in the model and the R&D estimated coefficients are a measure of the ‘excess’ R&D elasticity of output. 4.3. Econometric panel data models Standard panel data estimation procedures that address the issue of firm’s unobserved over time fixed effects have been implemented to estimate the equations specified above. These effects take into account permanent differences among firms such as for instance the ability of engineers to discover new inventions. Actually, these unobservables are likely to affect R&D decisions since firms facing higher abilities will generally invest more in research activities. Hence neglecting these effects as it is the case in cross-section estimates may lead to some omitted variable biases. In the context of panel data, it is possible to get around this issue by appropriate transformations of data in order to ‘eliminate’ unobserved fixed effects.
12
The fixed effects can be removed through the so-called within transformation, which can be estimated consistently by OLS provided that the αi are fixed over time and the regressors are strictly exogenous18. Unfortunately, the strict exogeneity condition is a hypothesis, which is hard to maintain in the productivity framework. According to Griliches (1995) for instance, when one measures the elasticities of the right hand side variables in such a framework, it is not clear to what extent the explanatory variables depend on past, current or future values of the dependent variables or inversely. In other words, does R&D for instance causes output or is it output which causes R&D? A solution to this simultaneity problem is to use an instrumental variable approach, but quoting Hall and Mairesse (1996), applying this approach to the within transformation often invalidate the estimates because the only available instruments are generally lagged values of explanatory variables and in short panels, these variables are likely to be correlated with the disturbances, once the firms means have been removed. Another way to eliminate the unobserved fixed effects consists in taking the productivity equation in first-differences. An advantage of this transformation is that it does no longer require the strict exogeneity of regressors. However due to possible measurement errors in all the variables, this approach leads generally to estimates which are more biased towards zero than does the within correction (Griliches and Hausman 1986). Following Hall and Mairesse (1996), a General Method of Moment (GMM) estimators have been performed in order to allow for all effects to be present, i.e. correlated fixed effects and simultaneity19. It is worth noticing that although this GMM framework appears quite attractive in terms of the modelling possibilities it contains and the weak distributional assumptions it relies on, it nevertheless rests on an instrumental variable approach, and as pointed by Griliches and Mairesse (1995), the past levels as instruments for current growth rates of regressors such as R&D capital are likely to be quite poor and their resolving power is quite low. As far as the intra-industry R&D estimates (based on the industry sub-samples of firms) are concerned, GMM estimates have also been implemented. However, it turned out that this framework did not work because of the much weaker number of observations available for each industry sub-sample. First-differences Instrumental variables (IV) estimation techniques for panel data have been implemented instead. The results obtained regarding the variable instrumented (the one year lagged R&D) should be interpreted cautiously, given the measurement error issue discussed before.
18
Removing individual means from equation above eliminates the fixed effects (provided that they are constant over time) but contaminates the εit’s with the disturbances from the other years, εi1,...,εiT. Hence strict exogeneity of the regressors is required, i.e. E[xisεit]=0, ∀s=1,...,T and ∀t=1,...,T. 19 This methodology is based on that of Arellano and Bond (1991) and Keane and Runkle (1992). 13
V. R&D Rivalry 5.1. Inter-industry sample The estimates obtained for the alternative specifications of the R&D intensity equations are given in Table 2. We begin by interpreting the statistical results regarding the simultaneity and the overidentification tests. The simultaneity statistical test shows that regressors are weakly exogeneous. The results of the overidentification lead one to conclude that the alternative specifications of the R&D intensity equation are acceptable for the most relevant results. The second column of Table 2 exhibits the estimates obtained by considering the total pool of spillovers. The estimated elasticities for lagged R&D, sales and physical capital appear to be significant at the 5% level. The significant positive impact for the latter means that there is a complementarity between this variable and the firm’s own R&D expenditures. If we turn to the coefficient obtained for the total pool of spillovers, it is non-significant. The test performed with the lagged spillover stock has not improved the result. When the current intra-industry R&D flow is introduced, as shown in the third column, the coefficient associated with the lagged R&D variable double and becomes highly significant. Yet, the spillover variable remains nonsignificant. The coefficient for the current intra-industry R&D flow is significant and gives a short-term own R&D elasticity with respect to intra-industry R&D equal to .20. An important question concerns the real interpretation of this coefficient: is it a measure of the short-term effect of current intra-industry spillovers or a measure of the reaction pattern? In our view, it appears very doubtful that the content of current intra-industry R&D could spill out without gestation lag. If one agrees with this argument, the coefficient of current intra-industry R&D can be expected to give an assessment of the extent of the technological rivalry. Globally, the firms of the sample react aggressively to an increase of R&D outlays of competitors. Given the low value associated with the lagged own R&D, the adjustment process would be very short, the long-term reaction elasticity varying from .23 to .31. The other results of the table confirm that a significant effect is obtained for spillovers only when the current intra-industry R&D is included in the specification. The coefficients measuring the local and national premium effects are non-significant. Consequently, firms seem to dip into the stock of knowledge without marking the difference between its local and external sources as well as its national or international origins. It is worth underlining that very similar results have been obtained when the lagged spillover variables are introduced instead of current ones. While it is understandable that firms take advantage of any profitable idea from the stock of knowledge wherever the geographic origin of ideas, it is more astonishing that no premium is detected for the local stock of knowledge given it could be thought to be the prime source of spillovers for any industry.
14
Table 2. R&D investment growth: GMM estimates. dependent Variable: ∆ln R instruments: lag 2 and lower values of regressors specification model ∆ln R(t-1)
I .067
∆ln S ∆ln C ∆ln TS
sample: 625 firms x 8 years IIA III IIIA .132 .312 .147
(.037)a
IA .123
II .101
(.029)
(.027)
(.024)
(.061)
.400
.356
.460
.387
.439
.340
(.101)
(.072)
(.056)
(.051)
(.071)
(.045)
.443
.426
.341
.353
.287
.290
(.096)
(.073)
(.058)
(.050)
(.091)
(.045)
.549
.223
.509
.623
-.118
.931
(1.01)
(.499)
(.380)
(.354)
(.575)
(.343)
∆ln SR
.197
.229
.262
(.084)
(.063)
(.065)
∆LS/TS
-.063
.022
(.086)
(.079)
∆NS/TS overidentificationb simultaneityc
χ 2 (D.F.) [prob.]d
χ 2 (D.F.) [prob.]
(.024)
98.9 (88) [.201] lags+0 178.8 (39) [.000]
126.1 (112) [.171] lags+0 108.7 (50) [.000]
138.1 (112) [.047] lags+0 171.1 (72) [.000]
156.1 (136) [.115] lags+0 177.1 (83) [.000]
.301
.131
(.220) 69.6 (62) [.236] lags+2 53.6 (25) [.001]
(.118) 161.1 (136) [.068] lags+0 155.9 (83) [.000]
notes: a: heteroskedastic-consistent standard errors in brackets b: test of the validity of moment restrictions c: test of the most recent lag admitted as instruments d: upper tail area
5.2. Intra-industry samples In order to deepen the analysis of R&D competitive reactions, firms belonging to the more R&Dintensive industries have been selected and R&D reaction functions estimated for each industry as well as for each geographic area. The search for representative industry sub-samples in each geographic area has led us to construct different samples according to the time periods available. So, the reduced time periods available have to be kept in mind along with the interpretation of results. Yet, we think that the estimates may give some information of interest about the average direction of R&D reactions in each geographic area. Table 3 summarises the estimates obtained for the seven selected industries: aircraft, chemicals, computer, drugs, electronics, instruments and motor vehicles. Unfortunately, it has not been possible to include the software industry in our sample. Firms included in the samples vary from 4 for the European aircraft industry to 97 for the US instruments industry. The period covered by samples is 4 to 6 years and mainly related to the beginning of the 90’s. The time length as well as the number of firms are reported in Table 3 for each estimation. In a majority of equations the lagged R&D variable is not significant. To some extent, the coefficients obtained for both sales and physical capital variables are significant. Regarding the reaction patterns, the main observations that emerge from the estimates can be summarised as follows: 15
• •
•
•
In the aircraft industry, only US firms appear to react to European firms. This R&D reaction could reflect the response of US firms to the European competitive pressure from Airbus. European firms are characterised by strong reactions to modifications in R&D investments of Japanese and US competitors in chemicals. R&D reactions of Japanese firms are not influenced by foreign competition. Astonishingly, US firms adopt submissive reactions to Japanese competition in this industry. No reaction pattern is detected in the computer industry except for Japanese firms with respect to US ones. The absence of effects for Europe reflects the lack of competitiveness of European firms in this industry. The similar effect observed for US firms is certainly not due to a similar reason. Despite the high competitiveness characterising these firms, they could continue to adopt a leader behaviour. A reaction pattern from US firms with respect to European competitors is observed in the drugs industry. At the opposite, submissive reaction of European firms with respect to Japanese ones is detected. Table 3. R&D reaction functions: First Differences IV estimates dependent Variable: ∆ln R Industry Geographic area Number of firms Number of time periods Laga ∆ln R(t-1)b ∆ln S ∆ln C ∆ln REU ∆ln RJP ∆ln RUS R² ad. Industry Geographic area Number of firms Number of time periods Lag ∆ln R(t-1) ∆ln S ∆ln C ∆ln REU ∆ln RJP ∆ln RUS R² ad. Industry Geographic area Number of firms Number of time periods Lag ∆ln R(t-1) ∆ln S ∆ln C ∆ln REU ∆ln RJP
EU 4 6 1
AIRCRAFT JP 1 6
-.030 (.035)c .98 (.401) -.447 (.399)
-.015 (.023) .420 (.436) .293 (.277) 1.18 (.660)
-.799 (.913) .88 EU 34 4 1 -.016 (.006) .442 (.216) .012 (.078) .444 (.216) .659 (.338) .73 EU 13 4 0 -.005 (.012) .351 (.245) .283 (.130) -.086 (1.09)
US 17 6 1
.03 CHEMICALS JP 40 4 1 -.015 (.009) .602 (.236) -.027 (.168) -1.03 (.873) 1.94 (2.32) .65 COMPUTER JP 9 4 0 .016 (.006) .274 (.161) .111 (.132) -.138 (.558)
US 53 4 1 .002 (.005) .853 (.252) .346 (.118) -.063 (.113) -.447 (.245) .77 US 76 4 0 -.005 (.006) .301 (.077) .103 (.065) .159 (.537) .151 (.232)
16
∆ln RUS R² ad.
-.983 (2.69) .41
.944 (.418) .39
.47
Table 3. R&D reaction functions: First Differences IV estimates (con’t) Industry Geographic area Number of firms Number of time periods Laga ∆ln R(t-1)b ∆ln S ∆ln C ∆ln REU ∆ln RJP ∆ln RUS R² ad. dependent Variable: ∆ln R Industry Geographic area Number of firms Number of time periods Lag ∆ln R(t-1) ∆ln S ∆ln C ∆ln REU ∆ln RJP ∆ln RUS R²ad. Industry Geographic area Number of firms Number of time periods Lag ∆ln R(t-1) ∆ln S ∆ln C ∆ln REU ∆ln RJP ∆ln RUS R² ad. Industry Geographic area Number of firms Number of time periods Laga ∆ln R(t-1)b ∆ln S ∆ln C ∆ln REU ∆ln RJP ∆ln RUS R² ad.
EU 22 5 0 -.004 (.007)c .766 (.120) .144 (.067) -1.09 (.645) .391 (.322) .80
EU 13 5 0 -.002 (.008) .679 (.168) .173 (.090) .115 (.349) -.812 (.768) .34 EU 16 5 0 -.010 (.010) .388 (.165) .172 (.124) 1.21 (.722) -.819 (.714) .80 EU 25 4 0 .011 (.010)c .230 (.176) -.194 (.144) -.173 (.377) .727 (.448) .67
DRUGS JP 22 5 0 .016 (.006) .312 (.114) .107 (.050) .164 (.516) .301 (.306) .82 ELECTRONICS JP 16 5 0 -.009 (.011) .097 (.229) .572 (.203) .324 (.672) .921 (1.20) .76 INSTRUMENTS JP 9 5 0 .003 (.015) .106 (.139) .333 (.201) 1.12 (.661) -.218 (.466) .78 MOTOR VEHICLES JP 8 4 0 -.012 (.016) .216 (.158) .032 (.145) .402 (.865) 1.01 (.667) .83
US 90 5 0 -.014 (.005) .183 (.132) -.042 (.074) 1.28 (.610) -.478 (.722) .76
US 90 5 0 -.007 (.006) .354 (.080) .276 (.062) -.176 (.207) .408 (.224) .38 US 97 5 0 -.009 (.005) .473 (.115) .238 (.076) -.139 (.254) -.512 (.240) .73 US 21 4 0 -.001 (.011) 1.02 (.242) .108 (.137) .415 (.530) -.740 (.441) .87
notes: a: lag for outside R&D effect (∆ln REU, RJP, RUS): 0=t and 1=t-1, b: instrument: R(t-2), c: heteroskedastic-consistent standard errors in brackets.
•
In electronics, it appears that European do not react to R&D of Japanese and US competitors. An explanation of this lack of reaction, again can be found in the technological weakness of 17
• •
Europe in this industry. More surprisingly, Japanese firms do seem to not react as well. The only significant estimated reaction concerns US firms with respect to Japanese ones. This submissive reaction can be due to the larger number of firms composing the US sample. These firms are smaller in size and hence are more likely to behave as followers in this industry. The industry of instruments is characterised by strong mutual aggressive reactions between both European and Japanese firms and a negative response from US firms to Japanese ones. The last industry investigated is motor vehicles. We can observe that in this sector European and to a larger extent Japanese firms adopt aggressive reactions against US R&D, while the latter react submissively to Japanese R&D.
These results differ from the ones obtained by Capron (1994), what could mean that competitive behaviours are changing over time. A main observation is that American companies are now reacting to competitive moves from rivals. The results give clue that these companies now see their technological leadership position as increasingly challenged. VI. Spillover Impact on Productivity Table 4 reports the results for each geographical area taken individually. In summarising the results obtained by Capron and Cincera (1998) in this section, we borrow liberally from that paper. A first observation is that the coefficients obtained for the explanatory variables are significantly different among the three areas. The elasticities for the labour and the physical capital are similar in Europe and the United-States and significantly different in Japan. Yet, their values are inferior to those obtained in some studies, what can be explained by the fact that we use sales instead of added values. While materials were not introduced in the equation due to data unavailability, the coefficients are comparable to those obtained by studies using sales. If we suppose constant return to scale for traditional production factors, the elasticity for materials should be expected to be about .3 to .4 for the United States and Europe. Regarding the own R&D stocks, the results are more controversial. The first-difference estimates are similar while the within estimates are significantly different and the GMM estimates are in an intermediary position. The estimates give globally coefficients whose values are higher than the measures reported in the literature, what could be explained by the high proportion of R&Dintensive companies in our sample. Indeed, a split of data into two sub-samples has given R&D elasticities which are respectively .43 and .44 for high R&D-intensive firms against .12 and .22 respectively for other firms. Turning to the effects of spillovers, it appears that their influences are drastically different for each geographical area. In the United States, the national stock affects significantly the output what is clearly not the case for the international stock. An opposite observation emerges for Japan, which appears to benefit from the international stock. So, Japan seems to depend, to a large extent, on technologies developed outside while American firms are mainly turned to their domestic 18
technologies. Interestingly, Bernstein and Mohnen (1998) in their study of the effects of international R&D spillovers on productivity growth of R&D intensive sectors, find that international spillovers exist from the U.S. to Japan, but not in the converse direction. Branstetter (1996) too, reports some evidence that Japanese firms benefit positively from R&D undertaken by U.S. firms while no effect of Japanese R&D on U.S. firms’ output growth is found. As far as Europe is concerned, no consistent effect is obtained for this continent. Consequently, the receptivity and absorptive capacity of European firms to new technologies can be questioned. These empirical observations are, to a large extent, in accordance with the positioning often emphasised for the three geographical areas. As a technological leader, the United-States is principally concerned by its own technological development. On its side, Japan has demonstrated that it was highly successful in implementing technologies developed outside, particularly in the United-States. The weakness of European countries in fast growing technological fields, their higher specialisation in slow growing or declining activities and a more defensive and/or passive behaviour regarding R&D intensive activities induce a lesser sensitiveness to spillovers. Consequently, the lesser R&D intensity of European countries combined to a weaker propensity to internalise technological spillovers might jeopardise its long-term competitiveness. Table 4. Productivity estimates by geographic area dependent Variable: ln S WITHIN Level est. s.e.a
OLS F.D. GMM-IV F.D. est. s.e. est. s.e. US sample 3024 (2646) obs. LnL .66 (.030)* ∆lnL .47 (.031)* ∆lnL .51 (.012)* LnC .11 (.027)* ∆lnC .13 (.025)* ∆lnC .10 (.001)* lnK .18 (.024)* ∆lnK .28 (.039)* ∆lnK .25 (.013)* lnNS .69 (.179)* ∆lnNS .59 (.202)* ∆lnNS .56 (.075)* lnIS -.02 (.155) -.43 (.273) ∆lnIS -.35 (.122)* ∆lnIS Ra² .995 Ra² .468 X² [d.f.]b 239.8 [195] c Sim. : S JP sample 1064 (931) obs. lnL .23 (.053)* ∆lnL .11 (.040)* ∆lnL .09 (.001)* lnC .28 (.033)* ∆lnC .18 (.035)* ∆lnC .12 (.001)* lnK .07 (.040)** ∆lnK .28 (.114)* ∆lnK .10 (.001)* lnNS -.17 (.149) -.23 (.403) ∆lnNS .28 (.028)* ∆lnNS lnIS .91 (.307)* ∆lnIS 1.46 (.621)* ∆lnIS .97 (.065)* Ra² .992 Ra² .221 X² [d.f.] 122.9 [120] Sim.: W EU sample 808 (707) obs. lnL .63 (.052)* ∆lnL .53 (.066)* ∆lnL .56 (.001)* lnC .18 (.035)* ∆lnC .09 (.040)* ∆lnC .11 (.001)* lnK .04 (.053) .22 (.105)* ∆lnK .15 (.001)* ∆lnK lnNS .13 (.140) .13 (.281) ∆lnNS .12 (.032)* ∆lnNS lnIS .32 (.269) .06 (.565) ∆lnIS -.12 (.030)* ∆lnIS Ra² .996 Ra² .417 X² [d.f] 97.4 [95] Sim.: +L1 a: heteroskedastic-consistent standard errors in brackets (except for JP and EU GMM estimates), * (**) =statistically significant at the 5 (10)% level b: overidentification test
19
c: predeterminancy of Xit: W(S)= weak (strong) exogeneity, +L3 =lag 3 and lower values of Xit as instruments
VII. Conclusion The purpose of the paper has been to assess the importance of the main determinants of technological activity of international firms on R&D and productivity performance. Among the main determinants, the firms’ own R&D capitals as well as the technological spillovers were considered. Technological spillovers have been formalised by weighting the firms’ R&D stocks according to their proximity into the technological space on the basis of the patent distribution of firms across technological classes. The new constructed data set which enlarges Jaffe’s study to the international scope is representative of a main part of R&D expenditures in industrialised countries. In order to provide a distinction between local and external components of the total spillover pool, three clustering procedures have been investigated. National and international spillover stocks have also been constructed on the basis of the geographic location of firms. Despite these improvements, such an approach has some limitations, which are difficult to overcome. The main drawbacks of this methodology are the difficulty to take into account firms, which do not apply for any patents, as well as the risk of erroneous technological location for firms, which applied for a small number of patents. These problems have really been encountered in the empirical analysis. The estimates obtained in this study have been performed by using ad-hoc panel data estimation methods which allow one to test specific hypotheses typically associated with this kind of data, namely, correlated firms’ unobserved fixed effects with the regressors and exogeneous explanatory variables. The main results show that the introduction of technological and geographic dummies are important not only to pick up the effects of technological and geographic opportunities, but also to obtain better measures of the impacts of technological activities on productivity and R&D investment. Some evidence about the effects of technological spillovers has also been found. When we consider the impacts of spillovers on R&D investment, the results give evidence that an increase of one percent of spillovers could stimulate an increase from .6 to 1 percent of firm’s own R&D. The estimations dealing with R&D reaction patterns for the main R&D-intensive industries have shown that if firms are to different degrees sensitive to what competitors allocate to their activities, the behaviours are not homogeneous across industries and among countries. In some case, countries do not react to competitors and, in other cases, they adopt aggressive or submissive reactions. In industries such as Chemicals, Instruments and Motor vehicles, U.S. firms appear to adopt submissive reactions with respect to Japanese competitors while the latter react aggressively in Electronics. Otherwise, their reaction with respect to European firms seems to be limited to Aircraft and Drugs. An aggressive reaction pattern has been observed for Japanese firms in 20
Computer and Motor vehicles with respect to U.S. firms and in Instruments with respect to European firms. Except for Drugs with respect to Japanese firms, European firms react aggressively to R&D competition in Chemicals with respect to Japanese and U.S. firms, in Instruments with respect to Japanese and in Motor vehicles with respect to U.S. firms. The absence of reaction effects from some geographical areas in some industries cannot be systematically assimilated to a leader behaviour but can often be representative of a competitive gap at the international level in many cases such as for example, the absence of European reactions in Computer and Electronics. Given the shortness of the time period studied, these intra-industry results have to be interpreted cautiously. On the whole, results obtained for productivity equations as regards traditional production factors appeared to be consistent with the findings of previous related studies. Some evidence about the effects of technological spillovers on productivity has also been found. The results lead one to conclude that the sensitivity of firms to spillovers differs significantly among the three geographical areas. Indeed, the United States, Japan and Europe seem to adopt very differentiated behaviours. While US firms are mainly concerned with their national spillover stock, Japanese ones are more receptive to the international stock and European ones do not seem to particularly benefit from both sources of spillovers. The search for technological spillover effects and reaction patterns on output and R&D decisions at the firm level is really a challenge due to the miss of retrospective data. In further studies, the stress should be put on new indicators giving a better information of the extent of spillovers as well as of the pattern of responsiveness of firms to the rival R&D outlays. As the extent of spillovers varies among industries, intra-industry and inter-industry measures matter. Regarding R&D reaction patterns, despite a real miss of empirical evidence they also matter. A better understanding of strategic interactions could help to target R&D policies.
21
References Arellano M. and S. Bond, 1991, Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations, Review of Economic Studies 58(2), pp. 277-98. Beath J., Y. Katsoulacos and D. Ulph, 1995, Game-Theoretic Approaches to the Modelling of Technological Change, in Stoneman P. (ed.), Handbook of the Economics of Innovation and Technological Change, Blackwell, Oxford, pp. 132-181. Bernstein J. I. and P. Mohnen, 1998, International R&D spillovers between U.S. and Japanese R&D intensive sectors, Journal of International Economics, 44, pp.315-38. Branstetter L., 2001, Are knowledge spillovers international or intranational in scope? Microeconometric evidence from the U.S. and Japan, Journal of International Economics, 53, pp. 53-79. Capron H. and M. Cincera, 1998, Exploring the spillover impact on productivity of World-wide manufacturing firms, Annales d’économie et de statistiques, 49/50, pp. 566-87. Capron H., 1994, Technological Competition and Strategy of Firms inside the Triad, in Khalil T. and Bayraktar B., (eds.), Management of Technology IV, The Creation Wealth, Industrial Engineering and Management Press, Nordcross, pp.467-77. Cincera M., 1998, Economic and technological performances of international firms, Ph.D. Dissertation, University of Brussels (ULB), 242 p. [http://homepages.ulb.ac.be/~mcincera/research] Cohen W., 1995, Empirical Studies of Innovative Activity, in Stoneman P. (ed.), op cit., Blackwell, Oxford, pp. 182264. Cunéo P. and J. Mairesse, 1984, Productivity and R&D at the Firm Level in French Manufacturing?, in Z. Griliches (ed.), R&D, Patents and Productivity., Chicago: University of Chicago, pp. 375-92. Grabowski H. and N. Baxter, 1973, Rivalry in Industrial Research and Development: an Empirical Study, Journal of Industrial Economics, 21(3), pp. 209-235. Griliches Z., 1990, Patent Statistics as Economic Indicators: A Survey, Journal of Economic Literature, 28(4), pp. 1661-707. Griliches Z., 1992, The Search for R&D Spillovers, Scandinavian Journal of Economics, 94(1), pp. 29-47. Griliches Z., 1995, R&D and Productivity: Econometric Results and Measurement Issues, in P. Stoneman (ed.), Handbook of the Economics of Innovation and Technological Change, Oxford, UK: Blackwell Publishers Ltd, pp. 5289. Griliches Z. and F. Lichtenberg, 1984, R&D and Productivity at the Industry Level: Is There Still a Relationship?, in Z. Griliches (ed.), op. cit., Chicago: University of Chicago, pp. 465-502. Griliches Z. and J. Hausman, 1986, Errors in Variables in Panel Data, Journal of Econometrics, 31(1), pp.93-118. Griliches Z. and J. Mairesse, 1984, Productivity and R&D at the Firm Level, in Z. Griliches (ed.), op. cit., Chicago: University of Chicago, pp. 339-74. Griliches Z. and J. Mairesse, 1995, Production functions: the search for identification, NBER Working Paper #5067, Cambridge, National Bureau of Economic Research. Hall B. H. and J. Mairesse, 1995, Exploring the Relationship between R&D and Productivity in French Manufacturing Firms, Journal of Econometrics, 65(2), pp. 263-93. Jaffe A. B., 1986, Technological Opportunity and Spillovers of R&D, American Economic Review, 76(5), pp. 9841001. Jaffe A. B., 1988, R&D Intensity and Productivity Growth, Review of Economics and Statistics, 70(3), pp. 431-37. Keane M. and D. Runkle, 1992, On the Estimation of Panel Data Models with Serial Correlation When Instruments Are Not Strictly Exogenous, Journal of Business and Economic Statistics, 10(1), pp. 1-6. Lebart L., A. Morineau and J.-P. Fenélon, 1979, Traitement des données statistiques, Dunod. Levy D. and N. Terleckyj, 1985, Trends in Industrial R&D Activities in the United-States, Europe and Japan, 196383, National Planning Association paper presented at a NBER conference. 22
Mairesse J. and B. H. Hall, 1996, Estimating the Productivity of Research and Development: An Exploration of GMM Method Using Data on French and United States Manufacturing Firms, NBER Working Paper #5501, Cambridge, National Bureau of Economic Research. Milligan G. and M. Cooper, 1985, An examination of procedures for determining the number of clusters in a data set, Psychometrika, 50(2), pp. 159-179. Mohnen P., 1996, R&D externalities and productivity growth, STI Review, 18, OECD, Paris, pp. 39-66. OECD, 1997, Basic Science and Technology Indicators, Paris, OECD. Reinganum J., 1989, The Timing of Innovation: Research, Development and Diffusion, in Schmalensee R. and Willig R. (eds), Handbook of Industrial Organization op cit., North Holland, Amsterdam, pp. 849-908. Schankerman M., 1981,The Effects of Double-Counting and Expensing on the Measured Returns to R&D, Review of Economics and Statistics, 63(2), pp. 454-58. Scherer F. M., 1967, Market Structure and the Employment of Scientists and Engineers, American Economic Review, 57(2), pp. 524-31. Scherer F. M., 1991, International R&D Races: Theory and Evidence, in Mattsson, I.-G. and Stymne, B., Corporate and Industry Strategies for Europe, North-Holland, Amsterdam, pp. 117-138. Scherer F. M., 1992, International High-Technology Competition, Harvard University Press, Cambridge. Scherer F. M. and K. Huh, 1992, R&D Reactions to High-Technology Import Competition, Review of Economics and Statistics, 74(2), pp. 202-12. Spence A.M., 1984, Cost Reduction, Competition, and Industry Performance”, Econometrica, 52(1), pp.101-22.
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