154
VI. Exploring the Spillover Impact on Productivity of World-wide
Manufacturing Firms
SUMMARY
This chapter analyzes the relationship between R&D activity, spillovers and
productivity at the firm level. A particular attention is paid on the formalization of
technological spillovers. The analysis is based on the dataset S625 composed of 625
worldwide R&D-intensive manufacturing firms over the period 1987-1994. Given
the panel data structure of the sample, ad hoc econometric techniques which deal
with both firm’s unobserved heterogeneity and weak exogeneity of the right hand-
side variables are implemented. The empirical results indicate that spillover effects
significantly influence 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 difficulties in internalizing spillovers.
Ch. VI: Impact of technological spillovers on productivity growth
155
“In pharmaceuticals, there have been remarkable advances drawing upon findings in such fields as biochemistry, molecular and cell biology, immunology, neurobiology, and scientific instrumentation.”
Nathan Rosenberg (1994: p.148)
6.1. INTRODUCTION
118One of the central tenets of the ‘new growth economics’
119is that R&D activities not only affect the productivity of firms that undertake them but also have some repercussions on the economic performances of other firms. These R&D spillovers or technological externalities arise because of the partially public good nature of knowledge. Though the contribution of R&D spillovers to productivity growth has been acknowledged a long time ago, it is only recently that the empirical measure of the magnitude and the direction of such effects has become a major point in the research agenda on the economics of innovation. Indeed, the measurement and assessment of the impact of R&D spillovers within and between industry sectors, not to mention among different countries, should help governments to better identify the Science and Technology policies to be implemented for enhancing firms innovative activities and the overall competitiveness. Indeed, if technological spillovers are more important in some sectors, then the R&D effort devoted by firms from which these effects emanate may be socially sub-optimal. Hence, identifying these sectors would allow policy makers to better target the measures for encouraging firms to engage in innovative activities
120. On the other hand, in an era of increasing ‘techno-globalization’ of our industrialized countries, the question of how and to what extent R&D spillovers generated in one country affect the economic performances of other countries is a highly important one. Here also very little work has been done along this line and further investigation of the impact of international and intra- national R&D spillovers within and across fields of technological specialization can be expected to lead to useful findings for policy intervention.
The purpose of this chapter is to measure the impact of the main determinants of firms’
technological activity on their economic performance as measured by productivity growth.
118
Some sections of the chapter borrow heavily from Capr on and Cincera (1998a). Also, this chapter substantially updates prior work related to the measurement of spillover impact on firms’ productivity (Cincera and Capron, 1995a and Cincera, 1995). In particular, the dataset is ‘enriched’. The treatment of firms’
technological positions is further investigated and the estimators implemented are consistent in the presence of
‘not-quite fixed effects’.
119
For a review of this literature, see Grossman and Helpman (1991) or Amable and Guellec (1992).
120
See Section 2.4.4 of Chapter 2 for a review of the main S&T policies.
Ch. VI: Impact of technological spillovers on productivity growth
156
More specifically, the stress is put on R&D spillovers which are often described as a main source of technology-push
121. Besides R&D spillovers, firms’ own R&D capital, technological opportunity and market factors are considered as well.
These determinants are quantified from the main dataset S625 constructed for the purposes of the thesis, i.e. the international dataset of 625 worldwide R&D-intensive firms over the period 1987-1994. We will deal with the measurement of technological spillovers at the micro level on the basis of a new sample, gathering information from large companies representative of industrialized countries. To this end, Jaffe’s methodology (1986, 1988), which associates econometrics and data analysis, has been adopted. In this chapter, the stress is put on an enlargement of the analysis to the international dimension as well as on an extension of the appropriability hypothesis in the construction of spillovers to take into account the geographical origin of firms in addition to their technological proximity. The method at the basis of the classification of firms into technological clusters as well as the metric considered to appreciate firms’ technological proximities which enter the construction of spillovers have been further investigated. Finally, given the panel data structure of the sample, more sophisticated econometric techniques which deal with both firm’s unobserved heterogeneity and weak exogeneity of the right hand-side variables are implemented.
Section 6.2 reviews the alternative ways to appreciate the impact of R&D spillovers on firms’ economic performances with reference to the main approaches proposed in the literature to formalize these spillovers. The results of some selected empirical studies at the micro level in this area are then presented. The following section discusses the methodological framework necessary to characterize and to differentiate the technological determinants. A particular attention is paid to the way firms are classified into technological clusters. The question of the sensitivity of results towards alternative ‘metrics’ positioning firms into the technological space, is also investigated. Then, the dataset, the specification relating productivity to the spillover variable and the implemented econometric framework are presented in Section 6.4.
Section 6.5. exhibits the main empirical findings. First, basic results are provided on the relationship between firms’ output and the total pool of spillovers and its different components, i.e. local, external, national and international. Second, additional estimates that control for opportunity as well as industry and geographic effects are discussed. Alternative results obtained from different ‘cuts’ of the sample are also shown. In particular, the differentiated importance of spillover effects across firms of different geographic areas is explored. Section 6.5.3 compares our findings with those reported in related empirical studies at the micro level.
Finally, further results are given in order to assess the ‘robustness’ of the spillover variable.
121
Schmookler (1966) underscored the demand pull factors, while Rosenberg (1974, 1983) did the same for the
technological opportunity.
Ch. VI: Impact of technological spillovers on productivity growth
157
These estimates hinge on different constructions of the spillover variable and its components.
The main observations resulting from the econometric analysis as well as some points deserving further research are underlined in the conclusion.
6.2. R&D SPILLOVERS: REVIEW OF THE LITERATURE
The purpose of this section is twofold. First, the main approaches adopted by economists to formalize and measure technological spillovers are reviewed. Second, the main findings of some selected empirical studies at the firm level that have estimated their impact on firms’ economic performances are summarized. Before discussing the alternative methods for formalizing and measuring the concept of technological spillovers, it is useful to briefly recall the concept of technological spillovers. As it has been discussed in Chapter 2, it is common to operate a distinction between rent spillovers and pure knowledge spillovers (Griliches, 1979).
Rent spillovers exist because capital goods are purchased by firms at less then their full quality adjusted price. On the one hand, output or capital prices indices are generally not corrected for quality improvements and on the other hand, competitive pressures on the final good market ensure that innovations are rarely sold at prices that entirely reflect quality improvements. The second type of spillovers refers to as the results (or part of it) of innovative activities generated by firms that can be obtained free of charge by other firms. These knowledge spillovers arise because the fruits of innovative activities are not fully appropriable.
6.2.1. Formalizing and measuring R&D spillovers
In what follows, we are mainly concerned with knowledge spillovers. In order to formalize this second type of spillovers, several approaches have been proposed and developed in the literature
122. All these approaches are based on the construction of a variable representing an outside pool of R&D knowledge. This pool is built as the sum of the R&D capitals or stocks of all other firms in the economy. Mohnen (1991, 1996) distinguishes several approaches that may be listed into two categories according to whether the sum of all R&D stocks are weighted or not. The first category formalizing externalities is the simplest to compute and is also the most used
123. The main drawback of this approach is that it attaches the same weights to all R&D stocks. In the second category of formalization, the R&D stocks are weighted according to the technological linkage or proximity between the sender and the
122
See Griliches (1979, 1992), Mohnen (1991) or Capron (1993) for reviews on this topic.
123
This kind of formalization has been implemented in Chapter 4.
Ch. VI: Impact of technological spillovers on productivity growth
158
recipient of spillovers. Hence a matrix W of dimension n x n is constructed, where n denotes the number of firms and wij represents the technological proximity between i and j. In order to measure the share of j’s research that goes to i, this weight, wij is multiplied by the amount of R&D activities undertaken by j. Hence we have:
S i w R ij j
j
= ∑ n
=1
(6.1)
where S i is the pool of spillovers available to i and R j represents the R&D activities performed by j.
Various weights or technological proximities can be used. Mohnen (1996), in his review, distinguishes between two kinds of technological proximities. In the first set of proximities, the weights are measured on the basis of inter-firm or inter-industry flows of good and services, capital goods, R&D personnel, patents, innovations, citations or R&D cooperation agreements. As underlined by the author, these proximities follow the argument that the more i purchases intermediate inputs or capital goods from j, hires scientists from j, manufactures goods patented by j, uses innovations discovered by j, cites j’s patents in his own patent applications, or cooperates with j on R&D, the more it is technologically close to j.
The second set of technological proximities departs from the construction of vectors characterizing the firms’ or industries’ technological positions into different spaces. The argument relies on the closer firms are in such spaces, the more they can benefit from each other research activities. Various vectors or spaces can be used. Having the number of patent applications classified across technological classes, lines of business, categories of R&D activities or qualifications of R&D personnel allows one to construct firm’ vectors and position firms into technological spaces. For instance in the approach developed by Jaffe (1986, 1988, 1989), the firm’s position in the technological space is characterized by the distribution of its patents over patent classes. Jaffe observed significant positive effects of the technological externalities on the firm R&D intensity and on its productivity. He also took other variables into account such as the technological opportunity and the market forces in order to avoid measure biases of the externalities. Goto and Suzuki (1989) consider industry sectors to be technologically close to each other if they perform the same kind of R&D. In Adams (1990), technological proximities are constructed on the basis of the number of scientists hired with the same type of qualification.
It should be noted that each of these technological proximities has its own drawbacks
and it is not obvious how to make the relevant choice among these proximity measures. As
Ch. VI: Impact of technological spillovers on productivity growth
159
pointed out by Verspagen (1997), the first kind of technological proximities is based on a user- producer principle while the second set of proximities adopt more explicitly a technology perspective for measuring spillovers. In the author’s opinion, both perspectives are aimed at measuring knowledge spillovers but they place different emphasis on different aspects of the complicated process of knowledge spillovers. The user-producer method tends to stress transaction based linkages while the second approach tends to stress technology-based linkages between firms and sectors. Hence, the two categories of proximities should be viewed more as complementary rather than as substitutes.
According to van Meijl (1995), the user-producer approach to modelling knowledge spillovers tends to under estimate the real magnitude of pure knowledge spillovers. One reason put forward by the author is that the user-producer approach is related to economic transactions rather than pure technological links between the two actors. Indeed, to the extent that a knowledge producer can appropriate the returns of his innovative activities, the price at which new products or processes are sold will be higher than the marginal costs involved to produce them. Hence, the potential technological spillovers existing between the producer and the user of this innovation are likely to embody important rent spillovers besides pure knowledge spillovers. On the other hand, while technological innovations may be useful to other R&D performers, they are not always subjected to user-producer relationships. Taking the example of Verspagen (1997: p.49), “ one may think of sectors such as rubber and plastic products which, by the chemical nature of their technology base, may benefit from technical knowledge on fertilizers, although their relationship in terms of user-producer interactions with the fertilizer industry will be marginal ”.
Van Pottelsberghe (1997) analyzes various types of weighting matrix in order to measure interindustry R&D spillovers measures. The author concludes that it is hard to empirically make a distinction between rent spillovers and knowledge spillovers. Mohnen (1996) also finds it difficult, both theoretically and empirically, to distinguish between rent and pure knowledge spillovers. As pointed out by the author, proximity measures based on intermediate input flows are more likely to reflect rent spillovers while the weighting matrices based on patent and innovation flows should be viewed more as indicators of knowledge transmission. Finally, in the author’s opinion, the proximities based on distances in technological spaces are the most likely to represent knowledge spillovers of a tacit kind.
However, various studies based on this way of modelling spillovers have reported some
conflicting conclusions to the extent that on the basis of a common datasample different results
have been obtained according to the weighting matrix considered.
Ch. VI: Impact of technological spillovers on productivity growth
160
Another approach to formalize the impact of technological spillovers on firms economic performances consists in adding industry dummies in the set of explanatory variables entering the production function (in the case of the primal approach). Since these dummy variables take, in essence, the same value for all firms in a given industry and to the extent that R&D performed outside an industry has a similar effect on all firms of that industry, these dummy variables can be interpreted in terms of inter-industry spillovers. However, as pointed out by Mohnen (1996: p.50), these variables are also likely to pick up opportunity effects such as “ the closeness to science, technical knowledge from upstream materials and equipments suppliers, downstream users, governments agencies and research labs ”.
An alternative method for assessing the effect of R&D spillovers is based on the comparison between its estimated rate of returns at the industry level and the estimates at the firm level. Indeed, if significant spillovers exist within an industry, then the computed rate of returns should be higher at the meso- than at the micro level. Mairesse and Mohnen (1990) in their study of the impact of spillovers do not however report higher estimates at the industry level than at the firm level. The argument invoked by the authors for explaining this result rests in the fact that the assumed rate of obsolescence for computing the spillover stock should actually be much less than the corresponding one used for constructing the firm’s own R&D capital. To the extent that the pool of R&D spillovers embody a large component of social returns, the rate at which this pool depreciates over time can be expected to be lesser than private R&D stocks. Hence the assumption of a same rate of obsolescence for both spillover and own R&D stocks may explain why the authors’ estimates do not differ significantly at the aggregated and firm levels respectively.
The last approach estimating the impact of R&D spillovers considers the R&D capitals of different industry sectors as separate regressors. But as emphasized by Griliches (1992), this approach is not really feasible because of multicollinearity issues among the regressors and because of lack of degree of freedom, e.g. there are in general more industry sectors or firms than available time periods for each sector or firm.
Turning now to the measuring issue, there are two ways of estimating the magnitude and the direction of R&D spillovers reported in the literature. The first approach is based on case studies which “ examine in detail all the costs and benefits, direct and indirect, present and future, related to a particular R&D project in a particular sector ”
124. Another approach is to estimate the impacts of R&D spillovers using econometric methods. This approach consists of relating an innovative or economic performance measure such as counts of patent applications
124
Mohnen (1996: p.41). The author cites four case studies in four different sectors: hybrid corn (Griliches,
1958), computers (Bresnahan, 1986), forest products (Seldon, 1987) and space programmes (Bach et al., 1992).
Ch. VI: Impact of technological spillovers on productivity growth
161
or innovations, R&D intensity, (total factor) productivity growth, profitability, exports, production costs, of one firm or in one sector to the knowledge capital accumulated by other firms or in other sectors. In order to construct this knowledge capital, different technological proximities are used to aggregate the R&D capital of other firms or sectors in the economy.
The knowledge capital or spillover stock is entered as a separate regressor in addition to conventional inputs and own R&D stock into an extended Cobb-Douglas production function
125.
6.2.2. Review of selected econometric studies at the micro level
Table 6.1 summarizes the findings reported in some selected studies that have focused on the measurement of technological spillovers on firms’ economic performances. Like it was the case for the studies discussed in the previous chapter, it is a difficult task to try to compare the findings reported in these studies to the extent that substantial differences characterize them. Moreover, only a few number of studies in this literature have estimated the impact of spillovers at the firm level
126. Some of these studies have based their investigation by considering the dual approach which relates the impact of spillovers on production costs incurred by firms rather than on productivity gains (total factor productivity growth) as it is the case in the primal approach. Another important point that differentiates these studies is the proximity measure considered to build the spillover pool. Among the studies reported in Table 6.1, two main approaches for modelling these proximities have to be distinguished. The first one attaches the same weight to the R&D of all other firms and the second one locates firms into a patent space.
All studies reported in Table 6.1, except the last two, examine the impact of technological spillovers on firms’ economic performances as measured by productivity growth.
This exercise is carried out by using the standard residual methodology discussed in Chapter 5.
Almost all studies are based on samples of firms operating in a single country. The only exception is Brandstetter (1996) who considers Japanese firms in addition to US firms.
Regarding the weighting matrix used to construct the spillover variables, the first four and the last two studies consider an unweighted sum of all other firms’ R&D. Studies 7 to 9 implement Jaffe’s framework (1986, 1988, 1989) in which the technological proximity between firms is characterized by their relative position into a patent space. Los and Verspagen (1996) also consider this kind of proximities in addition to the one which attaches no weights. However,
125
This framework has already been discussed in the previous chapter.
126
Indeed, a majority of studies have used data at the industry level (see Mohnen 1996 for a review). The main
reason is that most studies make use of inter-industry flows matrices (mainly input/output matrices) to construct
their proximity measures. At the firm level, such information is not available.
Ch. VI: Impact of technological spillovers on productivity growth
162
the authors do not make use of technological distances between each pair of firms but instead between each pair of industries
127. Finally, in Fecher (1990), the firm’s spillover stock is defined as a weighted sum of R&D performed by all industry sectors in the economy less the firm’s own R&D. The weights are proportional to the input-output flows across the industry sectors.
Besides these differences, some studies distinguish between local and external or domestic and foreign components of the spillover pool. For instance Jaffe (1986) explores the effect of spillovers generated by firms which are engaged in R&D activities within the same field of technological specialization than the recipient firm (technological local component).
Adams and Jaffe (1996) examine the issue of geographic localization. They decompose the available pool of spillovers into a share that is close to the recipient firm (geographic local component) and a share that is far (geographic external component). Then, they estimate the relative contribution of each share on productivity growth. Brandstetter (1996) operates a similar distinction by decomposing the spillover pool into a national component and an international one. The international spillover stock is constructed as before but the weighted sum of R&D stocks is computed on the basis of all firms located outside the country of the firm which receives the spillover. Klette (1994) and Adams and Jaffe (1996) investigate these effects at a sub-level. They examine the impact of parent firm R&D on plant-level productivity.
On the whole, the estimated elasticities and/or rates of returns of R&D spillovers are significant, positive and exceed the ones obtained for the own R&D stock. Quoting Mohnen (1996: p.51), “In a world of certainty and free disposal, R&D spillovers are expected to have beneficial effects, since it is reasonable to assume that firms do not adopt new ideas which reduce their profits. However, one can raise a number of arguments claiming that R&D can have detrimental effects on profit, productivity growth or welfare. For strategic reasons, firms may feel obliged to enter an R&D race without necessarily benefiting from it. R&D spillovers can increase or decrease the price that a producer can charge for his product, depending on whether the new product from outside R&D is substituable or complementary to the firm’s own product. New products can displace old ones. This process of creative destruction can be harmful if innovators do not have time to recover their R&D investments. Firms may have to incur heavy adjustment costs to learn the new technologies. Finally, R&D can reduce welfare when firms use R&D as a strategic tool to raise entry barriers, or when firms are obliged to duplicate R&D to stay in the race.”
In Jaffe’s opinion (1986: p. 984) too, “from a purely technological point of view, R&D spillovers constitute an unambiguous positive externality. Unfortunately, we can only observe various
127
The authors use a concordance table (Verspagen et al., 1994) which maps the 4 patent digit IPC codes into
one or more of the 22 ISIC manufacturing industries into which they classified the firms in their sample. It
should be noted that this way of formalizing spillover variables only consider intra-industry spillovers.
Ch. VI: Impact of technological spillovers on productivity growth
163 economic manifestations of the firm’s R&D success. For this reason, the positive technologically externality is potentially confounded with a negative effect of other’s research due to competition”.
The author continues by stating that it is not possible with available data, to distinguish between these two effects but in his study, he finds evidence that both are present.
Jaffe (1988) finds a positive effect of spillovers generated by firms which are technologically close. Moreover, in his 1989’s study, high-tech firms appear to further benefit from these effects. Brandstetter’s results (1996) lead one to conclude that spillover effects are more national than international in scope. Indeed, for both Japan and the U.S., the international spillover stock is not significant. Comparing his results for US firms with those of Jaffe (1988, 1989), leads us to conclude that the spillover impact is higher in the mid 80’s. However this higher estimated elasticity may be a consequence of the smaller number of firms included in the later study and as a consequence of the higher average R&D intensity characterizing these firms. Los and Verspagen (1996) also find positive and significant impact of R&D spillovers.
In addition, their results do not significantly differ according to whether the total stock of spillover is weighted or not. Finally Adams and Jaffe (1996) find that the effects of parent firm R&D on plant-level productivity are diminished by both the geographic and technological distance between the research lab and the plants and that spillovers transmitted by technologically similar firms are significant but depend on R&D intensity more than total industry R&D.
The last two studies in Table 6.1 measure the spillover effects on the basis of the dual
approach and by considering an unweighted outside pool of R&D stock. Bernstein (1988)
analyzes the impact of both intra- and inter-industry spillovers in seven Canadian industries,
while Berstein and Nadiri (1989) investigate this impact in four US industries. Both studies are
an application of the theory of dynamic duality in which physical and R&D capitals face
internal adjustment costs and the operating costs are the variable factors, i.e. labor and
materials. The main results are that the social rates of return to R&D are higher than the
privates ones in R&D intensive industries. In Bernstein (1988), the intra industry spillovers are
more important than the inter industry which are similar across industries (rate of return about
.02).
Table 6.1
Summary of econometric studies assessing the impact of spillovers on firms’ economic performances
# study data sample specification weighting matrix spillover
variable
elasticity or rate of return (%) PRIMAL APPROACH
1 Raut (1995) 75-86, 192 Indian firms Cobb-Douglas Unweighted sum ∆NS .06%* to .36%*
2 Antonelli (1994) 84-85, 92 Italian firms Cobb-Douglas, F.D. Unweighted sum ∆ NS insignificant
3 Adams and Jaffe (1996) 74-88, 19561 US plants,Chemicals Cobb-Douglas, level Unweighted sum of firms’ plant R&D
firm K .08*
fractional effect of far away K 10%*-30%*
differential effect of firm K in other product fields 2%-6%
Position (of firm within industry) in patent space
.24*
Unweighted sum of R&D in plants:
4 Klette (1994) 89-90, 804 Norvegian plants, 3 industries
non-parametric productivity analysis
within same business line within same group
D significant%
across lines of business within same firm
D insignificant%
across lines of business within same group
D significant%
5 Fecher (1990) 81-83, 292 Belgian firms Cobb-Douglas L.D. I/O flows K/S 2%*
NS/S .5%*
IS/S -1%
6 Harhoff (1994) 443 German firms Position (of firm) in R&D space .03*
source: adapted and extended from Mohnen (1996)
Table 6.1. (con’t)
Summary of econometric studies assessing the impact of spillovers on firms’ economic performances
study data sample specification weighting matrix spillover
variable
elasticity or rate of return (%) PRIMAL APPROACH
7 Jaffe (1988) 72-77, 434 US firms Cobb-Douglas L.D. Position (of firm) in patent space K/S 1.3*-15%*
" ∆ln(LS) .10*-.25%*
" ∆(ES/LS) .00035
8 Jaffe (1989) 72-77, 434 US firms, Cobb-Douglas L.D. Position (of firm) in patent space ∆K .03*
19 industries, 21 technology classes " ∆NS .13*
" ∆K x ∆NS .01
72-77, low tech firms " ∆NS .13*
72-77, medium tech firms " ∆NS .15*
72-77, hi-tech firms " ∆NS .17*
9 Branstetter (1996) 83-89, 209 US firms Cobb-Douglas L.D. Position (of firm) in patent space ∆K .36*
" ∆NS .83**
" ∆IS -.48
83-89, 205 Japanese firms " ∆K .01
" ∆ NS .70*
" ∆IS .38
10 Los & Verspagen (1996) 74-93, 485 US firms Cobb-Douglas Within Position (of industry) in patent space K .02*
" Position (of industry) in patent space TS .51*
" Unweighted sum TS .53*
DUAL APPROACH
11 Bernstein (1988) 78-88, 680 Canadian Translog Pool Unweighted sum IntraS 17%* to 24%*
firms, 7 industries InterS 19%* to 26%*
12 Bernstein & Nadiri (1989) 65-78, 48 US firms, 4 industries Translog Pool Unweighted sum IntraS 9* to 16*
source: adapted and extended from Mohnen (1996)
Ch. VI: Impact of technological spillovers on productivity growth
166
6.3. LOCATING FIRMS INTO THE TECHNOLOGICAL SPACE
In this chapter, we have adopted a technology perspective for formalizing R&D spillovers. When locating firms into a technological space, we are able to measure their R&D efforts across various technological fields of specialization. In such a framework, the stress is put on technology-based linkages between firms rather than transaction-based linkages. Two reasons can be put forward for explaining such a choice. On the one hand, assuming that R&D spillovers between firms are proportional to the similarity and intensity of their research activities rather than to the amount of their economic transactions allows one to mitigate the main criticism addressed by van Meijl (1996). That is, since not all technological innovations that are relevant and useful to other R&D performers lead to user-producer relationships, the assessment of R&D spillovers on the basis of an user-producer approach may in fact underestimate the impacts of such effects. On the other hand, the former approach is more likely to measure a ‘potential’ pool of spillovers that includes knowledge transmission of a tacit nature between firms. According to Griliches (1995), the first method comes closest in measuring pure knowledge spillovers to the extent that the proximity measure between firms in a technological research space does not imply flows of any kinds and in any particular direction. On the other hand, the lack of information at the micro level with regard to the industrial destination of a firm’s R&D efforts as well as the absence of detailed data reflecting the inter firm flows of goods and services, e.g. input/output matrices, are two major impediments for applying the so-called user-producer approach to construct technological proximities.
The approach for modelling R&D spillovers considered in this chapter builds on the methodology suggested by Griliches (1979) and first empirically implemented by Jaffe (1986).
Among the many approaches which weigh the R&D stocks of other firms, Jaffe’s one appears as the most attractive as well as the least open to criticism. The firm’s position in the technological space, as well as the distinction between global and local stocks of technological spillovers (the local stock refers to externalities that arise from firms operating in narrowly defined technological fields of specialization) are the key points of this approach. In what follows, we shall first present Jaffe’s methodology to construct the R&D spillover variable.
This methodology also allows one to formalize the other technological determinants, i.e.
technological opportunity, markets and geographic effect that influence firms’ economic
performances. After having discussed the main issues and weaknesses characterizing this
methodology as well as their possible remedies, some illustrating examples are given to show
what these technological proximities look like. These examples are performed on the basis of
the sample of 625 large R&D intensive firms (dataset S625). Then, three alternative methods
Ch. VI: Impact of technological spillovers on productivity growth
167
for splitting the total pool of R&D spillovers into several components, in particular local an external ones, are exposed. Thanks to the international dimension of the dataset, it is interesting to separate the national stock of spillovers from its international counterpart. On the one hand, it can be imagined that the technological externalities not only depend on the technological proximity among firms but also on the geographic one. Such an exercise should improve our understanding of the importance of domestic and foreign sources of R&D spillovers on firms’ economic performances. On the other hand, as it is the case for technological opportunity, a test for a significant geographic opportunity effect can also be performed. Finally, in order to assess the relevance of the concept of technological proximity, alternative proximity measures are proposed in order to experiment with their effects on the sensitivity of estimates.
6.3.1 Technological and geographical opportunities and market factors
As Jaffe (1988) pointed out, the typical manufacturing firm is diversified at the level of the goods it produces, as well as at the level of its research activities. Even if both diversification structures are not independent from each other, they are nevertheless distinct.
Thus, a firm which sells an unique product will undertake R&D in several related technological fields. The purpose here is not to understand the relationship between these structures of diversification, but to characterize the firm’s research interests, as well as the distribution of its industrial activities. Taking the international dimension into account, a similar question can be asked about the geographic distribution of the firm’s productive activities. To this end, three vectors of distribution are computed for each firm in an analogous way. These vectors allow one to locate the firms into three distinct spaces: a technological space, a market space and a geographic one. For instance, the vector of technological position of firm i, T
i= ( t , . . . , t
i1 iK) , indicates the share of its R&D effort carried out in the K different technological areas. Its vector of market position, M
i= ( m , . . . , m
i1 iL) , denotes the distribution of its sales among the L definite industrial sectors. Finally, its vector of geographic position, G
i= ( g , . . . , g
i1 iH) , reveals its sales’ distribution among the H individual domestic markets. The position into the technological space represents the technological opportunity which renders technological activity more prolific in some technological areas
128.
128
Other methods have been used for empirically proxying opportunity effects. See Levin et al. (1985), Levin
and Reiss (1988) or Cohen and Levinthal (1989) for more details about these methods.
Ch. VI: Impact of technological spillovers on productivity growth
168 Figure 6.1
Technological position vector of firms
1
3
K 2
k i
j
For instance, in Figure 6.1, firms i and j are closer to each other in the technological space than they are to firm k. This means that the ‘portfolio’ of i and j’s research activities is similar. Because of this similarity, i and j are likely to encounter the same difficulties or costs in performing R&D. They also will have access to the same stock of scientific or technical knowledge, downstream users etc. In short, i and j can be assumed to face the same state of technological opportunity. Hence, the technological location may also be interpreted as reflecting the extent to which firms devote their resources to R&D. Considering the location on the market and geographical spaces respectively, similar questions about the effects of market factors and of the geographical opportunity on productivity performance can be asked.
In order to measure the distribution of the firm’s research interests in the various technological areas, we make use of their patents distributed over 50 technological sectors on the basis of the International Patent Classification (IPC). The patent distribution relies on the whole number of patent applications
129filed by these firms to the European Patent Office during the period 1978-1994. Ideally, we should have the firm’s sales distribution over industrial sectors in order to represent its market position’s vector. Given the international dimension of the database, it has not been possible to construct such a sales’ distribution.
Therefore, we restrict ourselves to qualitative dummies which indicate the firm’s main industrial sector of activity. A similar problem arises for the geographical distribution of the sales. Here again, geographical dummies for the country of the firm’s registered office are computed. It should be noted that these industrial and geographical dummies only add qualitative information.
129
The patents considered here are those classified by date of application rather then by date of issue as is it the
case in Jaffe’s study. As discussed in Chapter 3, patents classified by date of application are preferable because
they reflect the moment when a firm realizes having generated an innovation and because of the existence of
long time lags in a patent application process.
Ch. VI: Impact of technological spillovers on productivity growth
169
6.3.2. Technological proximities and R&D spillovers
Locating firms into the technological space allows one to formalize the technological spillovers as well. Indeed, this way of formalizing 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 other 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 firm i and j, Jaffe used the ‘angular separation’ between them, i.e. he computed the uncentered correlation
130between their respective vectors of technological position, T
i= ( t
i 1, . . . , t
i K) and
( )
T
j= t , . . . , t
j 1 j K:
P
T T
T T
ij
ik jk K
ik 2 K
jk 2
=
= K= =
∑
∑ ∑
k
k k
1
1 1
(6.2)
This measure of closeness takes values between one and zero according to the common degree of research interest of both firms. Once the measure of closeness between firms i and j is computed, the potential stock of technological spillovers of the i th firm can be evaluated as follows:
S i P K ij j
j i
= ∑
≠(6.3)
where: Si = stock of spillovers of firm i, and Kj = R&D capital stock of firm j.
It should be noted that this method of formalizing R&D spillovers is not free of criticism. In what follows, we address the main limits of such a formulation, as well as, some suggestions on how to deal with them. First of all, the index of technological distance relies on the strong assumption that the appropriability conditions of knowledge are the same for all firms (Jaffe, 1988). 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. Hence, the more the outcomes of R&D activities are appropriable, the less there will be flows of knowledge
130
Alternative distance measures may be considered. See section 6.3.4 for a discussion.
Ch. VI: Impact of technological spillovers on productivity growth
170
between the R&D performers and the potential users of this knowledge. In chapter 2, we have seen that among the main factors that affect the appropriability conditions, one has to distinguish between firm and industry specific characteristics. For instance, the nature of technology, i.e. its complexity, its rate of change, the legal and institutional characteristics of markets, can be viewed as conditions that are more likely to differ across industries or fields of research activities and be more or less constant within firms operating in a given industry. The usual way to take these industry specific effects into account, when estimating the impact of spillovers, consists in adding industry or technological narrowly defined sectors dummies.
Otherwise, factors such as the internal capabilities of the innovator are more firm specific.
Since these variables are generally not observable at the firm level, their direct assessment is less obvious. However, in the context of panel data, it is possible to circumvent this issue by assuming that these firms specific unobserved effects are constant over the period under investigation
131.
A related issue arises when using patent statistics by patent classes to characterize the firms’ technological vectors. Here also, the propensity to patent will generally differ across industry sectors. This problem can be tempered by including dummies in the set of explanatory variables. Yet, the number of patents applications by firms constitutes an issue per se. Indeed, firms might not apply for any patents or only for a small number of them due to different reasons, e.g. the failure to succeed in R&D, a strategy of secrecy to avoid disclosure of new knowledge, or simply the size of the firm’s R&D activities. These issues have been encountered in the empirical analysis. Indeed, although all firms in the data sample reported R&D expenditures, some of them did not apply for any patents. Because of this ‘zero’ issue, these firms had to be removed from the analysis given that it was not possible to build their index of technological closeness. This issue is a major drawback of patent statistics used as a measure of technological output. In addition, firms that applied for a small number of patents during the period under investigation are likely to be wrongly located in the technological space because their patent distribution may not reflect the true one. In order to reduce this risk, all firms which applied for less then 5 patents were systematically checked by comparing the technological classes in which their patents have been assigned (according to the IPC) to their main industry sector (SIC). On this basis, firms for which an erroneous location was suspected were excluded.
Besides the issue of erroneous position, the question of whether firm’s position into the technological space is fixed or not is another point which empirically is difficult to tackle. As a matter of fact, firms’ R&D activities evolve over time and, as a result, so does their technological position. However, there are good reasons to think that over a short time period
131
For more details, see Section 5.2 of the previous chapter.
Ch. VI: Impact of technological spillovers on productivity growth
171
the firms’ position in the technological space is fixed
132. In the present analysis, the firms’
technological position vector is assumed to be fixed over the investigated time period. This vector has been calculated for each firm in the dataset S625 on the basis of the firms’ patent applications over the whole period 1978 to 1994. Of course, it would have been interesting to test whether this hypothesis of fixed technological position over time holds or not. However, this would have required to remove a substantial number of firms from the analysis because of the ‘zero issue’ discussed before. Indeed, many firms only apply for patents in certain years or time sub-periods and not over the entire period.
As it has already been discussed in Chapter 3, it might also be objected that the measure of technological proximities that we get by using European patent applications by non-European firms may be quite distorted or incomplete. On the one hand, quoting Basberg (1987) ‘foreign’ patents are a better indicator than domestic ones to the extent that ‘it is reasonable to assume that only inventions with significant profit expectations in a larger market will be patented abroad because of time and costs involved in such processes’. On the other hand, it can be expected that the propensity of European firms to patent in Europe differs from that of U.S. firms and Japanese firms and that this propensity varies across fields for firms based in different continents. Yet, in the absence of a ‘global’ patent office, we have no choice but to use national or regional patent data.
Another pitfall concerning the uncentered correlation index for measuring technological proximities is that it is a symmetric index. That is the technological proximity between firm A and firm B is the same than the one between B and A. If now we assume that this proximity is equal to .5 and that A’s R&D expenses represent 100 while B spends 5 on the same activities, then the amount of spillovers available to A from B is 2 while B will benefit from 50 of A’s research activities. However, given the possible existence of asymmetrical information flows, to what extent are these figures representative of the true amount of spillovers that benefits to A and B? How can we take into account that A may be better in capturing the fruits of B’s R&D outcomes than the opposite? Do large or diversified firms have relative advantages in appropriating the results from outside R&D?
Another drawback of this method is that firms that 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 index of technological closeness is likely to
132
Quoting Jaffe (1986: p.986), “Firms’ technological positions are, in the long run, a matter of choice for the firms. If technological opportunity affects profitability, then we would expect firms to move to the more profitable positions. However, changes in the technological position of firms can be brought about only slowly.
Expertise in various areas is not easily acquired, and goodwill and reputation in product markets represent
sunk costs that make jumping costly.”
Ch. VI: Impact of technological spillovers on productivity growth
172
be close to zero. A firm which is technologically diversified will be located in the central region of the technological space implying that it will not be close to any firm. In other words, such a firm will benefit from the externalities generated by all firms, but only in a small proportion.
This situation may not appear to be very realistic. An alternative standpoint is to say that firms are aware of the research activities undertaken by a few technologically similar firms. In that sense, even if all stocks of technological spillovers are relevant, they will probably not be taken completely into account due to imperfect information on the content of R&D realized 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, in addition to the local and external stocks, also national stocks
133from international ones. In this way, we will be able to appreciate to what extent geographical and cultural contiguity matters.
Furthermore, in order to consider the technological as well as geographical closeness, the potential stock of externalities has been dissociated into four components: the local national stock (LNS), the local international stock (LIS), the non-local or external national and international stocks (ENS and EIS). The technological clusters obtained by the firm’s clustering procedure allows one to construct a set of technological dummies that identify the firms whose research interests are sufficiently identical to encounter the same technological opportunity. Thanks to the industrial and geographic location of firms, market and geographical dummies have also been constructed.
Other issues associated with this methodology still need to be mentioned. To begin with, it is not clear how important the depreciation of spillover stocks is. According to Pakes and Schankerman (1984), the private rate at which the firm’s own R&D capital becomes obsolete should be much higher than the social rate of depreciation of spillovers since the latter embodies a large component of social returns whose depreciation should be less important.
The timing of spillover effects should also be considered. How much time does it take the spillovers to concretize in new products and processes and as a result in productivity? Because of lags in the diffusion of knowledge, spillover effects are probably not immediate
134.
Finally, if the concept of technological space is very attractive, its measure is not clear- cut and it is not obvious to what extent alternative distance metrics than the one considered by
133
In this chapter, we consider alternatively Europe and the European countries as specific geographic regions.
134
Quoting Griliches (1995: p.70), “...spillovers take more time than ‘own’ effects, both because of secrecy and
the time it may take for them to be expressed in new products and processes and diffused throughout the
relevant industrial structure.” Indeed, the analysis performed in Chapter 4 revealed that the impacts of
spillovers on patenting are less immediate than the corresponding findings for R&D.
Ch. VI: Impact of technological spillovers on productivity growth
173
Jaffe can affect the nature of results. In other words, is the uncentered correlation of technological vectors the most ‘natural’ measure of technological proximity? This question is addressed in Section 6.3.4 by investigating alternative weighting functions to see the sensitivity of results to the initial distance measure.
The next two tables illustrate the properties of the correlation measure defined in the previous section. From Table 6.2, we can observe that Solvay and Du Pont de Nemours are closer to each other than to Renault or Honda. This is quite normal given the nature characterizing the research activities of these firms.
Table 6.2
Example of technological proximity between firms
Firm NPA
aSOLVAY DU PONT RENAULT HONDA
SOLVAY 284 1 .785 .021 .044
DU PONT DE NEMOURS 4412 .785 1 .058 .063
RENAULT 636 .021 .058 1 .946
HONDA 618 .044 .063 .946 1
note: a) # of patent applications to the European Patent Office during the period 1978-1994;
own calculations based on dataset S625
Once the technological proximities of each pair of firms have been calculated
135it is interesting to compute average proximities within and across industry sectors. Since 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 6.3 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 distribution
136across 50 IPC classes and over the entire period 1978-1994. The last column in Table 6.3 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).
135
Since there are 625 firms, this makes 195625 proximity measures.
136
Patent data come from the 625 firms composing the dataset S625. The total number of patents applied by
these firms is 169820 over the whole period 1978-1994. The distribution of these patents by IPC classes and
industry sectors is shown in Appendix A.3.3.
Table 6.3
Technological proximities within and across industries
a(firms' averages)
INDUSTRY A C C D E E F F I M M O P P R S S T V
I H O R L T A O N A E T A E U O T E E Herfindhal
R E M U E R M O S C T H P T B F O X H index
C M P G C O P D T H A E E R B T N T I
Aircraft .27 .22
Chemicals .14 .29 .32
Computer .10
.03.32 .52
Drugs .11 .31
.02.62 .49
Electrical .14 .05 .09
.02.20 .48
Electronics .14
.03.20
.02.21 .34 .44
Fabbr. metal prod. .14 .07
.04.05 .09 .09 .09 .45
Food .05 .12
.02.17 .02 .01 .03 .44 .37
Instruments .20 .13 .11 .18 .12 .15 .08
.02.28 .48
Machinery .17 .08
.04 .04.09 .06 .10
.04.09 .16 .36
Metals .17 .10 .05 .07 .11 .11 .12
.02.11 .12 .31 .32
Other .11 .10 .08 .07 .10 .12 .09
.04.10 .13 .11 .17 .43
Paper .09 .12 .06 .08 .06 .04 .06
.03.08 .07 .07 .06 .18 .50
Petroleum ind. .16 .27
.04.21 .08 .07 .08 .06 .11 .10 .14 .14 .09 .39 .24
Rubber .16 .15
.04.10 .06 .04 .08
.04.09 .10 .09 .07 .12 .14 .21 .30
Software .10
.01.34
.01 .04.17
.02 .00.10
.02 .03.06
.02 .03 .02.33 .79
Stone .15 .14 .05 .05 .13 .11 .11 .06 .09 .12 .12 .13 .11 .12 .11
.02.33 .26
Textile .11 .22
.02.19 .06
.03.06 .12 .08 .05 .09 .05 .11 .18 .09
.01.14 .41 .45
Vehicules .22 .06 .06
.03.13 .12 .13
.01.11 .18 .14 .11 .06 .07 .14
.04.12
.03.33 .37
note: a) own calculations based on dataset S625Ch. VI: Impact of technological spillovers on productivity growth
175
The main diagonal of Table 6.3 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 off-diagonal cells of Table 6.3 gives an idea of how technologically distant the industries are. On the whole, the technological distances reported in Table 6.3 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 dataset 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.
6.3.3. Firm’s attribution to technological clusters
One contribution of the analysis performed in this chapter is to consider alternative techniques for assigning firms into homogeneous categories or clusters on the basis of their technological ‘nearness’. Because of this closeness, firms belonging to a same cluster are assumed to face the same state of technological opportunity. Hence, once technologically related firms are assigned to technological clusters, it is possible to construct dummy variables associated with these clusters and then analyze the effect of technological opportunity on firms’ economic performances. Furthermore, the assignment to technological clusters provides a framework to split the total stock of spillovers into two components: a local and an external stock. The local stock is constructed as in equation 6.2, but the weighted sum of other firms’
R&D is performed on the basis of the firms belonging to the same cluster. This local stock allows one to examine to what extent the impact of R&D spillovers generated by firms which are technologically similar, differ from the spillovers of firms located far away in the technological space.
In what follows, we present three alternative clustering procedures experimented in this chapter. Among the several techniques available to combine firms into clusters, the K-means clustering method is one of the most commonly used
137. In the present analysis, this technique is investigated, as well as, two others: the K-Means clustering with ‘strong centers’ and the
137
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.
Ch. VI: Impact of technological spillovers on productivity growth
176
‘agglomerative hierarchical’ clustering methods
138. The algorithm, which has been used to combine firms into clusters, works on the factorial coordinates of a preliminary principal components analysis. The advantage of this method is that it uses an Euclidean distance between firms, which permits considering 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, i.e. non systematic elements. Given the nature of our data, the analysis of binary correspondence in 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 literature
139. In this study, the three clustering techniques are based on Ward’s 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 center of gravity, while the without cluster inertia consists in the mean of the squared distances between all cluster centers of gravity and that of the whole data sample. Ward’s criterion for forming the clusters consists of maximizing the ratio between these two inertia in order to get the most homogenous and the most distant possible clusters. It should be noted that such a ratio does not permit 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 confounded with its cluster center.
To perform the clustering analysis, a three step procedure has been applied. In a first step, several parameters have to be fixed before running the clustering algorithms. These parameters which affect the ratio between the within and the without cluster inertia, are the number of factor axes for the analysis of binary correspondence from the contingency table (NFABC), the number of iterations to produce the initial partitions (NIIPA), the number of clusters for the initial partitions
140(NCIPA) and the number of clusters of the final partition (NCFPA). Several values of parameters have been experimented and the results are summarized in Table 6.4. It follows from the results obtained from partitions 6a to 6e, that reducing the number of factor axes retained for the binary correspondence analysis improves
138
See Lebart, Morineau and Fenélon (1979) for a description of these methods.
139
For an examination of some of these procedures, see Milligan and Cooper (1985).
140
The agglomerative hierarchical clustering method is not concerned by these two parameters.
Ch. VI: Impact of technological spillovers on productivity growth
177
the ratio of inertia. However to allow for a ‘reasonable’ representation of the initial 50 dimensions of the technological space, a minimum number of axes have to be retained. In practice, the number of axes to be retained is such that these axes are able to explain 80% of the variance. In our case, the 27 first axes explain 79.3% of the variance. As far as the number of iterations for producing the initial partitions is concerned, increasing this number does not seem to affect the inertia ratio (see partitions 6c and 6d in Table 6.4). Conversely, fixing a higher number of clusters for the initial partition slightly improves the inertia ratio (partitions 21a and 21b).
Table 6.4
Clustering partial results
a: inertia ratio
clustering methods
K-Means ‘strong centers’K-Means
partition # 6a 6b 6c 6d 6e 21a 21b
# factors axes for binary correspondence analysis total 40 27 27 27 27 27
# iterations for producing initial partitions 5 5 5 10 5 5 5
# clusters for initial partitions 20 20 20 20 50 20 50
# clusters for final partitions 20 20 20 20 20 20 20
inertia ratio .442 .481 .546 .546 .641 .660 .663
note: a) complete results are reported in Appendix A.6.1
