Productivity Growth, R&D and the role of international collaborative agreements: Some evidence for Belgian manufacturing companies

Transcription

1 Productivity Growth, R&D and the role of international collaborative agreements: Some evidence for Belgian manufacturing companies Work in progress (September 2003) Please do not quote without permission Paper to be presented at the 4 th INIR workshop "The Economics of Knowledge Spillovers" Université Libre de Bruxelles September 13th, 2003 Cincera Michele (ULB and CEPR), Kempen Lieselot (KUL), van Pottelsberghe Bruno (ULB and Solvay Business School), Veugelers Reinhilde (KUL and CEPR) and Carolina Villegas Sanchez (ULB) 1

2 1. Introduction The role of technology, or more generally knowledge, as one of the most important determinants of economic growth has already been examined extensively in the literature (a.o. Romer 1986, 1990, Lucas 1988, Grossman and Helpman, 1991). An economy s ability to understand, exploit and adapt to a rapidly changing technological environment is seen to be central to its prospects for improving standards of living and prosperity. A wide range of empirical work at the firm, sectoral and aggregate level supports the effects of increased R&D activity on productivity growth (a.o. Mohnen 2001, OECD, 2000). Beyond the private returns from R&D, the literature also points to significant positive spillover effects from private R&D activity, resulting from the partial non-excludability feature of the knowledge good (a.o. Jaffe, 1986; Bernstein and Nadiri, 1988, 1989). Innovation and technological development depend increasingly not only on own R&D activities but also on the ability to utilise new knowledge produced elsewhere and to combine it with the available stock of knowledge. Empirical studies show that spillovers do exist and are likely to be substantial (Griliches (1992)). On average the social rate of return on R&D (i.e. private return plus all the indirect effects) exceeds the private rate of return by 50% to 100% (Mohnen (1996), Nadiri (1995)). The first empirical studies on technological spillovers have been confined to spillovers within the borders of a country, analysing either inter-or intra-industry spillovers within a specific economy (Griliches 1979, 1995; Scherer, 1984). However, because of globalisation forces and international economic integration, R&D spillovers are not confined to national borders. Ample evidence exists in the literature on the importance of international R&D spillovers (Mohnen, 1996, Braconier and Sjöholm, 1997, Cincera and Van Pottelsberghe de la Potterie, 2001). A major issue in the literature on international technology diffusion is the channel through which know-how flows internationally. The most important ones that have already been extensively studied are trade and FDI. (see a.o. Coe and Helpman, 1995; Eaton and Kortum, 1996; Keller 1998; Lichtenberg and Van Pottelsberghe de la Potterie, 1998). Our study, using Belgian firm level data on R&D and productivity, provides further evidence on the important role of knowledge in explaining performance at the firm level, by augmenting the classical productivity growth approach with own R&D expenditures. In addition, we do not restrict our attention to internal R&D activities only, as part of this knowledge reaches the firm from external sources. Taking the perspective that co-operation in R&D gives access to external know-how, the paper provides a new perspective on the mechanisms through which R&D spillovers may impact firm s output growth. Results 2

3 indicate that R&D co-operation, and more in particular international R&D co-operation indeed has a significant positive effect on the firm s output growth. The remainder of the paper is organised as follows. Section 2 briefly summarises the existing theoretical and empirical evidence on the different topics discussed in this paper. Section 3 specifies our empirical framework, describes the data and presents some descriptive statistics. Section 4 examines the regression results and finally Section 6 contains our concluding remarks. 2. Literature Review 2.1. Private and social returns from R&D Since the classic Solow residual paper (Solow, 1957), it has been recognised that rates of factor accumulation do not account for the major part of economic growth. Instead, there is little theoretical controversy nowadays over the importance of technological change as a major driving force of economic growth. The endogenous growth literature (see Romer, 1990, 1994; Grossman and Helpman, 1991, 1994) considers commercially oriented innovation efforts as a major engine of this technological progress and productivity growth. A vast literature has developed which relates own R&D expenditures to total factor productivity growth (see a.o. Griliches, 1988 and Nadiri, 1993). Studies have found high rates of return on R&D investment (Griliches, 1979). In some countries, the average rate of return on R&D investment is more than twice the rate of return on investment in capital equipment (Mohnen, 1992). All this evidence suggests that inventive activities can be considered as being extremely important. Beyond the private returns from R&D, the literature also points to significant positive spillover effects from private R&D activity. The partial non-excludability of knowledge suggests that R&D may indeed generate technological spillovers. The difficulties in appropriating know-how allow for knowledge to diffuse and external know-how to be accessed without necessarily any explicit involvement from the sending party and even despite attempts from firms generating know-how to keep this proprietary. These spillovers create external benefits from the creation of technological knowledge that accrue to parties other than the original inventor. Two main sources of positive externalities can be distinguished (Griliches, 1979): rent spillovers on the one hand and knowledge spillovers on the other hand. Rent spillovers arise because prices are not completely adjusted to incorporate technological improvements resulting from R&D activities. Knowledge spillovers result from the difficulties in appropriating know-how. Contrary to rent spillovers, they are not necessarily synonymous to economic transactions or measurement errors. However, as knowledge spillovers channels 3

4 (FDI, R&D co-operation, technology payments, ) are often associated with an economic transaction, it is not always clear to which extent they also reflect some rent spillovers. The distinction between the two type of spillovers is ambiguous, which is further discussed by Cincera and Van Pottelberghe de la Potterie (2001). As a result, most studies focus on the broader concept of R&D spillovers instead of distinguishing between rent and knowledge spillovers. Because knowledge transfers are not perfect, a measure of distance is typically included. The closer generator and receiver are, the higher the level of spillovers is. Several weighting schemes for composing the external knowledge of the receiving agent are used, based on economic, technological or trade relations. A first methodology assumes that spillovers follow the pattern of economic transactions (i.e. supplier customer relations). This approach based on input-output tables measures merely the so-called rent-spillovers and not necessarily knowledge spillovers 1 (Griliches (1979)). A second more frequently used methodology is targeted to measure the pure knowledge spillovers and is principally based on patent information. 2 There is little controversy about the existence of spillovers, however the magnitude varies according to the level of aggregation of the study. Griliches (1991) points out that comparisons should not be done unless a differentiation between private and social rates of return is possible. The rate of return of R&D intensity has two components that depreciate at different rates: the private return obsolescence is greater than the social one 3. Industry level studies contain a broader component of social returns and therefore tend to overestimate the rate of return of R&D intensity compared to firm level studies. 1 Rent spillovers arise because prices of intermediate inputs are not fully adjusted for quality improvements resulting from R&D-investments in upstream industries. Knowledge spillovers arise when the knowledge embodied in an industry s innovation contributes to innovations in another industry. 2 Patent information is used to identify producers and users of knowledge (Scherer, 1982)); The socalled Yale studies use information on producing sectors and principal uses, information which is directly available in some patent administrations (e.g. Canada). Another approach developed by Verspagen (1997a) uses the distinction between main versus supplementary sectors available in the EPO-office, in order to identify users and producers of knowledge. Patent-citations are another source of information to trace spillovers, used e.g. by Jaffe, Henderson & Traijtenberg (1993) to look for geographical clusters. A somewhat different approach is used by Jaffe (1986), who constructs a technological distance between agents on the basis of the technological overlap between patents of different firms. A few papers use innovations as information source (Sterlachini, 1989; Acs et al. 1992). A last approach developed by Bernstein & Nadiri (1988), who instead of measuring spillovers, directly estimate spillover effects based on an adjustment-cost model of investment and factor demand. 3 On average the social rate of return on R&D (i.e. private return plus all the indirect effects) exceeds the private rate of return by 50% to 100% (Mohnen, 1996; Nadiri 1995). 4

5 2.2. International R&D spillovers The first empirical studies on R&D spillovers have been restricted to spillovers within the borders of a country, hereby analysing either inter-or intra-industry spillovers within a specific economy (Griliches 1979, 1995; Scherer 1984). However, R&D spillovers are not necessarily contained within national boundaries. The nations of the world economy are becoming increasingly open and interdependent. As openness fosters new ideas and their diffusion, external knowledge is increasingly likely to originate from outside the national borders (Eaton and Kortum, 1999; Keller, 2001). Once we are in the context of an open economy where international trade and FDI prevail, the possibilities multiply for the country in terms of technology availability. The country is not restricted to its own R&D efforts but can potentially benefit from the international pool of R&D. This is the main reason why, in recent years, the international diffusion of technology has received particular attention. The empirical literature was pioneered by Caves (1974) and Globerman (1979). Since then, an extensive array of empirical studies have emerged that have been searching for international spillovers of various forms, on the macro, meso and micro level. (Blomstrom & Kokko, 1998; Nadiri and Kim, 1996; Bernstein and Mohnen, 1998; Capron and Cincera, 1998, 2001; Branstetter, 2001). A major issue in the literature on international technology diffusion is the channel through which know-how flows. Various channels of transmission are considered in the literature (for an overview, see Cincera and Van Pottelsberghe de la Potterie, 2001; Keller, 2002). The most important ones that have already been extensively studied are trade and FDI. Trade was one of the first channels studied in the literature on international transmission of technology. Based on the non-excludability feature of knowledge, the idea is that countries importing both intermediate and final goods from more technologically advanced partners, will be able to enjoy the improvements embodied in those goods, without necessarily undertaking further investment (see Coe and Helpman, 1995; Eaton and Kortum, 1996). However, later empirical research a.o. by Keller (1998) raised doubts about the importance of trade as a mean of technology transfer. There are other means through which technological knowledge can flow across national boundaries. An obvious alternative is foreign direct investment (FDI). Although the entry of foreign affiliates increases the competition for local producers, the production and/or research activities undertaken by multinational affiliates can confer spillover benefits to the local economy. Knowledge may flow from the affiliate to local producers through formal and informal contacts, or trained affiliate personnel switching jobs to the local economy. Despite all these potential benefits, the available empirical evidence is mixed: regarding the effect from FDI on productivity, usually studies on plantlevel data find positive spillover effects while studies using firm-level data find either negative or positive results conditioned to firm specific characteristics. Analyses have been 5

6 conducted for both developed and developing countries, as host nations for FDI 4. Most of the empirical studies on developing countries have failed to find robust evidence of positive knowledge spillovers from multinational investment, accounted for by the lack of absorptive capacity in these host countries (e.g. Aitken and Harrison, 1999; Blömström and Sjöholm, 1999; Blömström and Kokko, 1998, for a review). Empirical studies on developed countries find mixed results, although most indicate some evidence for FDI as a transfer channel. In the case of the UK for instance, a wide range of analyses has been carried out Spillovers and Collaboration in R&D The existing empirical studies on (international) spillovers have focused mostly on the impact of involuntarily flows of knowledge on performance. However there remains a large area of voluntary spillovers, present in several forms of cooperation and joint ventures. Geroski (1996) concludes that: The rich information flows which connect innovation producers and users seems to me to be much more important than other involuntary flows between more widely dispersed agents. Whether upstream/downstream flows are truly spillovers is not clear, but there is a lot of evidence to suggest that many firms try to nurture them. Case studies often suggest that cooperative relations between innovation users and producers are a prime determinant of the success of innovative activities. Also Griliches (1998) points to the importance of interaction between producers and users of knowledge instead of the rather freewheeling involuntary character of spillovers. Alliances can be considered as a spillover channel to access externally available know-how, as alternative to the traditional spillover measures. The pervasiveness of networking has become a significant feature in current innovation practices (Mowery, 1992; Teece, 1997; Hagedoorn et al. 2000). The growing role of R&D collaboration in firms innovative activities has spurned research into the determinants of such R&D cooperation and the performance of cooperative R&D. Theoretical contributions in the management literature have stressed that R&D collaboration stems from the search for complementary know-how between partner firms and the sharing of costs and risks (e.g. Kogut, 1988; Das and Teng 2000). The Industrial Organization (IO) literature has extensively examined the incentives and performance effects of R&D cooperation among competing firms focusing on the role of R&D spillovers and appropriability (see De Bondt, 1996, for an overview). This literature suggests a positive association between spillovers and 4 Xu (2000) states that strong evidence exists of diffusion to developed countries while there is only very weak evidence of diffusion to low developed countries. 5 Girma, Greenaway and Wakelin (2002) find a priori no evidence of productivity spillovers on average. However, positive spillovers are to be found in those sectors characterised by high import competition. Haskel et al. (2001) conclude that technological proximity matters for FDI spillovers, while regional proximity does not. Moreover, whether positive spillovers occur, depends on the origin of FDI. According to Liu et al (2000), spillovers through FDI are negatively correlated to the technology gap between foreign and locally owned firms. 6

7 R&D cooperation. In a world of imperfect appropriability of know-how, cooperation can be a vehicle to internalize the effect of involuntary transfers that occur. Spillovers increase the profitability of R&D cooperation and once spillovers are sufficiently high, further increases in spillovers make R&D cooperation more attractive as compared to non-cooperative R&D (De Bondt & Veugelers, 1991). Moreover firm can increase the effectiveness of incoming spillovers by investing in absorptive capacity (Kamien et al. 2000; Cohen and Levinthal, 1989). Overall, these models thus predict that involuntary spillovers are an important positive driver of R&D cooperation, especially for firms with an own R&D base. In addition, cooperation can be seen as an instrument to more efficiently manage transfers of know-how among partners. When spillovers are considered to be at least partly voluntary, firms that are partners in R&D co-operation can improve on the knowledge transfer among themselves, (e.g. Kamien et al., 1992). 6 Larger incoming spillovers for partners through managing and sharing information make R&D cooperation even more profitable. Hence, firms will typically have an incentive to maximally share information among partners. At the same time, information sharing stifles the incentives to cheat and hence makes cooperation more stable (Kesteloot and Veugelers, 1994; Eaton & Eswaran, 1997). Empirical evidence confirms that R&D co-operation has a positive impact on research productivity, which is attributed to increased spillovers (see a.o. Henderson and Cockburn, 1996; Branstetter and Sakakibara, 1998). Closely related to this literature, Cassiman and Veugelers (2003) find that firms which rate incoming spillovers as more important, are more likely to co-operate. Following the perspective that cooperation can be seen as a vehicle for voluntary know-how transfers and an instrument to respond to involuntary spillovers, in our search for characteristics which explain productivity performance differences among innovative firms, we will consider R&D co-operation as an innovation activity that provides access to external know-how. In this respect, a firm can get voluntarily or unvoluntarily access to knowledge by co-operating with partners and hence internalise R&D spillovers. Given the small open character of the Belgian economy, we will focus especially on international technology diffusion and the role of cooperation with partners abroad. In this respect, a firm gets voluntarily access to foreign knowledge by co-operating with foreign partners and by doing this, the firm benefits from the internalisation of international knowledge flows. 6 Endogenous spillover model are increasingly being used, see a.o. Bhattacharya, Glaser & Sappington (1987). 7

8 3. Data, estimation equation and econometric issues 3.1. Data used in the empirical analysis We apply the empirical analysis on productivity growth to firm-level data for Belgian manufacturing. The data we use are drawn from the R&D Survey 1996 conducted on a biannual basis by the Belgian Federal Office for Scientific, Technical and Cultural Affairs (OSTC) and the Belgian regional authorities in charge of Science and Technology policies, who survey on a regular basis a permanent inventory of firms that are registered as being active in R&D. The survey provides broad information on firms own R&D expenditures as well as on other innovation activities, like co-operative agreements in R&D. Our dataset allows to distinguish between a wide range of co-operations: with universities or research centres vs. with customers, suppliers or other companies 7 ; with foreign vs. Belgian partners. Additionally, these data are matched 8 at the firm level with financial data on sales, physical capital and employment. In order to compute the (foreign) sectoral R&D intensity, we made use of the OECD ANBERD (data on foreign industry-level R&D) and STAN (data on sectoral output by country of interest) databases. This combination of data sets allows us to assess the efficiency of technology flows and their impact on growth and performance. In particular, the dataset includes all manufacturing and service firms operating in Belgium that are registered in the Belgian permanent inventory of national scientific potential 9. The available version of the dataset consists of an unbalanced panel of 1426 firms distributed in 19 broad sectors (2 digit NACE codes) over the period After a cleaning procedure (see Appendix 1) by which 531 firms were deleted, our final sample consists in a balanced panel of 895 firms. Since the firms in the sample are restricted to firms that are R&D active, our data might suffer from a possible sample selection bias, which we need to properly take into account. Appendix 1 gives an overview of the distribution of firms across the different manufacturing industries and the representativeness of the data; while Appendix 2 gives details about the construction of the variables for estimation purposes. 7 Note that we cannot distinguish horizontal co-operation, as the other company might as well be a competitor as another company (eg. member of the same group). 8 In the matching process we used variables such as VAT and name and address of the firm. 9 Data is collected for institutions and companies conducting R&D in Belgium. Including data on financial resources and staff devoted to R&D activities as well as ongoing research projects with Universities and research centers. 8

9 3.2 Basic Regression Following other previous analyses of R&D contributions to productivity growth, we estimate as our baseline regression, a simple extended Cobb-Douglas production function, (see a.o. Kinoshita, 2000) 10. The production function of firm i is defined as: Y = A L C (1) i i β 1 β 2 i i where Y represents output (proxied by firm s sales), L and C the input factors labour and physical capital and finally, β 1 and β 2 represent output elasticities with respect to labour and capital respectively. Taking natural logarithms and first differentiating equation (1), we can estimate the following linear relationship: y i = β 0 + β 1 l i + β 2 c i + ε i (2) where indicates growth rate and lower cases represent logarithms. We estimate the production function in its growth rate form ( ) 11, and augment it to include R&D capital. The R&D capital variable is meant to account for changes in productivity derived from technological change. Following most previous studies in this research field, we include firm R&D intensity (1995). R&D intensity RDI is defined here as the ratio of firm s i own intramural R&D expenditures to sales (R&D/Y) i. Using R&D intensity rather than the stock of R&D capital, makes the rate of return to R&D investments our parameter of interest (as opposed to the direct output elasticity). Our final basic equation to be estimated adopts the form: y i = β 0 + β 1 l i + β 2 c i + β 3 RDI i + ε i (3) with Yi β 3 = K i being the rate of return to R&D investment (with K i firm i s R&D capital stock). 10 Despite the popularity of this functional form given its desired properties of linear homogeneity and constant elasticity of substitution, some authors question its suitability when modelling the production process (see Duffy and Papageorgiou, 2000). One could of course consider more complicated functional forms, such as the CES or translog functions. However, Griliches (1979) stresses the insensitivity of the results to the functional form chosen. 11 An important issue when accounting for the effect of R&D and spillovers on productivity is the time lag inherent in the production process. As Capron and Cincera (1998) point out, the real impact of R&D and spillovers on productivity will depend upon the time necessary for the R&D to materialise in an increase in productivity. We tried different lag structures, giving 1995/1999 the best results in terms of significance and size of the coefficients. 9

10 Our basic equation is complemented with industry dummies, to capture sector-specific effects (like e.g. technological opportunity), as well as with region dummies, to control for different productivity effects for the three Belgian regions (Brussels, Flanders, Wallonia). This baseline regression, which serves as our empirical framework, will be augmented further, as will be shown in the next two subsections. 3.3 R&D co-operation as an innovation activity Next to own Research and Development, we extend the analysis of productivity growth differences by also incorporating R&D co-operation as another possible innovation activity of the firm 12. Following the Industrial Organisation literature, co-operation in R&D gives access to external know-how, allowing to internalize flows of knowledge, both voluntarily and involuntarily. Augmenting our productivity base line equation (3) with R&D co-operation as another explanatory variable in the analysis, leads to the following equation to be estimated: yi = β 0 + β1 li + β 2 ci + β 3RDI i + β 4COOPi + ε i (4) where COOP i reflects the cooperative R&D strategy of firm i. More particularly, COOP i takes the value of 1 if firm i has at least one co-operation partner in R&D, and = 0 else. By decomposing the COOP variable further according to the type of partner (geographic origin and nature), we examine the types of R&D co-operation that are of particular interest for enhancing firm s performance. Also the number of R&D alliances has been included as an explanatory variable. To test for the importance of absorptive capacity to be able to benefit from external sourcing through cooperation, we include an interaction term between the co-operation dummy on the hand and the firm s research intensity on the other hand. We expect that own R&D resources can be used as absorptive capacity, most notable the more basic, generic research activities The existence of (inter)national R&D spillovers To further zero in on the contribution of R&D cooperation in firm s innovative strategies and ultimate productivity performance, we further examine the role of COOP as a spillover mechanism, providing access to external know-how. In line with the existing empirical 12 The importance of other innovation activities, like for instance the acquisition of external knowledge through buying technology (patents, trademarks, software, ), although recently studied in literature (see e.g. Cassiman and Veugelers, 2002), will not be analysed in this paper. 10

11 literature on spillovers, we distinguish national and international intra- and intersectoral spillovers. In all cases the spillovers are the result of formal or informal contacts. Intra-industry spillovers take place within the same sector while inter-industry spillovers arise across different sectors. For intra-industry spillovers the stock of accessible know-how can be constructed from considering the R&D expenditures by all firms in the sector. This stock does not vary for firms within the same sector. The stock of knowledge that firms can access through inter-industry spillovers, requires a specification of the direction of these inter-industry linkages. Since we lack the information to include the precise destination of the relationships between sectors, we can only proxy for the total stock of know-how available across all sectors. This stock does not vary across firms and sectors and is hence a constant in our sample. National spillovers occur within the boundaries of a country and are usually referred to as domestic intra- and inter-industry spillovers. Open economies such as Belgium benefit not only from domestic R&D (both intra- and inter-industry spillovers) but also and perhaps even more importantly, from international R&D spillovers. 13 In our study, for the construction of the national and foreign intra-industry R&D intensity, we follow previous work by Coe and Helpman (1995), Keller (1998) and Lichtenberg and van Pottelsberghe (1998). Our proxy for foreign and national sectoral R&D intensity will adopt the form: N R & D j= 1 j k j k F j= 1 indrdi k = N (5) Y D k N k indrdi = (6) i= 1 N R & D k i= 1 Y ik ik where R&D j k are the private R&D expenditures in sector k of country j, R&D ik are the R&D expenditures of firm i in sector k in Belgium, this sector comprising N k firms. Y j k is the output in sector k of country j and Y ik is firm s i (in Belgian sector k) output. i = 1,,N k (firms in sector k in Belgium), k=15,, 74 (sectors) and j=1,,8 (countries 14 ). 13 However, Fecher (1990) found no significant relationship between international R&D expenditure and productivity growth for Belgium. 14 The foreign countries included in the computation of the index are: Czech Republic, Germany, Korea, Netherlands, Norway, Sweden, United Kingdom and United States. Despite the fact that the criterion for selection was mainly the availability of data, imports from the selected countries under analysis into Belgium represent around 87% of total Belgian imports. 11

12 To test for the existence of intra-industry and foreign spillovers, we include the foreign and national sectoral knowledge stock as variable influencing output growth of firm I belonging to sector k. As already mentioned, the main contribution of this paper lies in the consideration of R&D co-operation as a mechanism to access external knowledge. In this way, R&D co-operation can be seen as one channel of transmission through which the benefits of external R&D spill over to the own firm. To bring some evidence to bear on this issue, we interact the coop dummy CP with the sectoral R&D intensity indrdi: In doing so, we can distinguish between national and international intra-industry spillovers: CP F * indrdi F k and CP D * indrdi D k where CP F/D = 1 if the firm has at least one foreign/domestic partner in R&D co-operation. By estimating this model, we test more in particular whether international R&D cooperation can be considered as a channel of transmission for knowledge that is situated abroad. If the interaction term turns out to be positive this suggests that the effect of cooperative agreements on firm performance are higher when the external know-how base that can be assessed through cooperation is larger. Despite the richness of information on cooperative partners, the data nevertheless are restricting the analysis. The data we have available on R&D cooperation and stocks of knowledge only allow one to make the distinction between domestic and foreign spillovers. But we cannot identify the foreign country of the cooperating partners, which implies that we have to aggregate the foreign stock of knowledge. The distinction between inter- and intrasectoral linkages is even more difficult. We can only single out as cooperating partners clients/customers and other firms, while we do not know the precise sectoral origin of these partners. Furthermore, for inter-industry spillovers, since we lack the information to include the precise destination of the relationships between sectors, we can only include the total stock of know-how available across all sectors. Since this stock does not vary across firms in industries, the interaction term would be fully encompassed by the cooperation variable. Furthermore, given the highly aggregated nature of our sector-classification (typically at the two-digit level), most clients and customers would be situated in the same sector. This implies that our measure for intra-industry linkages would already pick up a vast majority of vertical cooperative linkages. The information on R&D cooperative partners not only includes information on linkages between firms, but also when the firm would cooperate with research institutes. This type of R&D cooperation can be considered to provide access to scientific knowledge. However, in the absence of information on the relationship between firms or sectors and which parts of the scientific knowledge base, we can only interact the scientific cooperation variable with the total national or international stock of scientific knowledge. 12

13 Again, since this variable would not vary across firms and industries, we cannot use an interaction term Descriptive statistics We restrict our sample to Belgian firms in the manufacturing sector 15, and the data have been aggregated in such a way that we are left with 12 industries to analyse. We exclude the regulated sector of public utilities (NACE code 40) but include construction (NACE code 45). Some simple statistics for the regression variables 16 are presented in Table 1. It can be seen from this table that sales and capital have on average grown within the period under consideration. Labour growth however has been smaller and negative on average. The correlation between sales on the one hand and labour, capital, R&D intensity and R&D cooperation on the other hand is high and significant. Table 1. Descriptive Statistics (manufacturing) y l c RDI COOP CP F Mean S.D Min Max Correlation matrix y l c RDI COOP CP F y 1 l 0.429*** 1 c 0.395*** 0.233*** 1 RDI 0.194*** ** 1 COOP 0.12*** ** 1 CP F 0.111* Notes: ** significant at the 5% level; *** significant at the 1% level. Table 2 provides us with some simple statistics on R&D co-operation within our manufacturing sample. Table 2. Statistics for the R&D and Cooperation variable Number of firms Percentage R&D> % R&D variable R&D= % Missing % Total % Coop= % Coop variable Coop= % Missing % Total % 15 We tried several estimations including the different service sectors (See Appendix4 Table12). However, because of the specific character of R&D in these sectors, we decided to focus our attention on manufacturing. When considerably different results appear for services, we report them. 16 See Appendix 2 for a definition of the variables. 13

14 Table 3. Number of Firms that invest in R&D and Cooperate Cooperation Coop 0 Coop 1 Missing Total R&D= R&D Missing R&D> Total From table 2 it is possible to observe that there are 599 firms in our sample of manufacturing firms, of which 222 report positive R&D expenses (37 % of the sample), 275 report zero investment on R&D and for 102 firms data is not available. In addition, the examination of the Cooperation variable reveals that 148 firms (25% of the sample) cooperate in R&D. Turning to the analysis of the distribution of firms that cooperate and invest in R&D, we have a number of 121 firms (20% of the sample) who both cooperate and invest in R&D. Table4. Cooperation profile of R&D active manufacturing firms No cooperation only universities/ research centres only customers/ suppliers/ other companies Universities/ research centres + customers/ suppliers/ other companies TOTAL No cooperation % 0 0% 0 0% 0 0% % 45.5% only Belgian partners 0 0% % % % % 16.7% 62.1% 20.6% 16.2% only foreign partners 0 0% % % 0 0% 9 100% 33.3% 3.4% 0% 4.1% Belgian + foreign partners 0 0% % % 54 72% % 50% 34.5% 79.4% 34.2% TOTAL % % % % % 100% 100% 100% 100% 100% Pearson s chi²(9) = Pr=0.000 How does the cooperation profile of R&D active manufacturing firms look like? Table 4 reveals that a majority of R&D active firms has a wide portfolio of partners, combining cooperation with other companies with universities, both nationally as internationally. 4. Empirical findings Throughout the empirical analysis, the dependent variable is sales growth during the period We employ a simple OLS framework to estimate the importance of various determinants of the firm s sales growth. In section 5.1., we start with the basic model that we will augment with additional variables. 14

15 However, before turning to the estimation of our model, we first discuss the issue of selectivity bias. In what follows, the empirical analysis is performed using the sample of 599 manufacturing firms, of which 222 have reported R&D budgets and 148 cooperate in R&D Our dataset might suffer under two possible selection biases. First, the analysis of only those firms that are registered in the Permanent Inventory and secondly, the inclusion of those firms for which data on R&D is missing. In order to test for sample selection bias in our model, a generalised Heckman two-step procedure was implemented, where we corrected for two decisions: first, the decision to invest in R&D and secondly, the decision to report R&D expenses (see Appendix 3). In both cases the insignificant coefficient of lambda (mills ratio) do not indicate the existence of a selection bias. Moreover, the size and significance of the coefficients on capital, labour, RDI and cooperation remain unaffected. Therefore we continue our analysis without correcting for selectivity bias. The sample of analysis will include those firms that reported positive R&D expenditures The Basic Regression: Internal R&D and output growth Table 5 reports the results from the estimation of the baseline equation (1). R&D intensity RDI has a positive and significant influence on sales growth ( y), with a rate of return to R&D investment approaching 13%. This implies that one more unit of R&D will lead to an increase in output of 13%. This rate of return is comparable with results of Kinoshita (2001), who finds a rate of return of about 15%. However it is considerably lower than the average estimated rate, which is around 20% to 30% (Nadiri, 1993). Our rate of return is very robust across the different model specifications, which is also true for the estimates for labour and capital, which point to output elasticities of 46.2% and 29.5%, respectively. Table 5.- Impact of R&D on output growth c *** (0.095) l *** (0.141) RDI *** (0.012) Industry Dummies Yes N 222 R-squared F(14,207) prob>f 0.00 Notes: Dependent variable: ln sales growth ( y); * significant at the 10% level of significance; ** significant at the 5% level of significance; *** significant at the 1% level of significance. 15

16 To check the robustness of our results, we tried several other specifications for this basic regression 17. First of all, we estimated a standard Cobb-Douglas equation, with only labour l and capital c included as input factors, in a level specification. The results were consistent with our expectations, as the estimated factor shares for labour and capital were close to 0.7 and 0.3, respectively. As we were concerned with the fact that R&D intensity RDI does not typically have an immediate effect on productivity, we tried several lag structures for the effects of R&D intensity of which a four-year time period ( ) turned out to be the most appropriate one. Controlling for industry differences proved to be meaningful: some industry dummies are highly significantly positive (at the 0.1% level of significance), like it is the case for Electronics (NACEcode 30), Chemicals and pharmaceuticals (NACEcode 24), Metals (NACEcode 27) and Textiles (NACEcode 17). Firms belonging to these sectors have a significant higher potential for creating sales growth, beyond their use of labour, physical capital and R&D, compared to the reference sector, which is the food sector (NACEcode 15). We checked for regional differences, but found no evidence for the existence of significantly different effects in the three Belgian regions. We tested the hypothesis of nonlinearity in the effect of R&D intensity on sales growth, by including the square of R&D intensity RDI². Although some evidence exists that the level of R&D intensity positively influences sales growth but at a decreasing rate ( inverted U-curve effect), this effect seems to be picked up by the industry dummies. Finally, to test for the effect of being part of a foreign group, we include a dummy FORGROUP which measures whether or not the firm belongs to a group which has its headquarters abroad. Also these estimated coefficients turned out not to be significantly different from zero. The estimated coefficients for labour, capital and R&D intensity proved to be very robust across the different specifications. Taking into consideration these baseline regression results and more in particular the insignificant influence of region, non-linearity of R&D and foreign subsidiaries, we only included the industry dummies as basic control variables in the remainder of the empirical analysis. Consequently, Table 5 can be considered to represent our basic regression framework. 4.2 R&D co-operation and output growth In order to gauge the effect of R&D co-operation on output growth, we use various variables that contain information about the type and the origin of the co-operation partners. In Table 6 we report the results from some of these regressions examining whether there is any effect of R&D co-operation on performance. 17 See Appendix 4. 16

17 Table6.- R&D co-operation vs. output growth (6.1) (6.2) (6.3) (6.4) c *** (0.094) *** (0.096) *** (0.096) *** (0.095) l *** (0.143) *** (0.138) *** (0.139) *** (0.141) RDI *** (0.013) *** (0.012) *** (0.012) *** (0.014) Coop (0.069) CP N ** (0.077) CP F ** (0.076) CP N A (0.106) * (0.086) ** (0.090) CP F A 0.153* (0.087) CP N B (0.106) CP F B (0.108) CP B (0.071) Industry Dummies Yes Yes Yes Yes N R-squared F(n 1,n 2 ) Prob>F Notes: Dependent variable: ln sales growth ( y); * significant at the 10% level of significance; ** significant at the 5% level of significance; *** significant at the 1% level of significance. First of all, regression 6.1 includes COOP, which is a general dummy variable stating whether or not the firm co-operated in Research and Development in This variable was constructed as to take a value of 1 if the firm had at least one partner in R&D co-operation and 0 otherwise. As can be seen from Table 6, the coefficient for COOP is found to be positive but not statistically significant. Given the poor results of the simple COOP dummy we tried other specifications. In particular, we tried to proxy the intensity of cooperation by the number of partners. The number of partners in R&D collaboration, however, did not seem to matter significantly. We thus can conclude that neither the fact of participating in collaborative R&D agreements nor the intensity of co-operating, measured by the number of partners, turn out to be significant. Results show that cooperation in general does not seem to have a significant positive effect on sales growth, however, it can be the case that firms benefit differently from different types of cooperation. In order to find out which kind of co-operation exactly is beneficial to the firm, different specifications of the R&D co-operation variable have been examined, 17

18 hereby making use of the data on co-operation partners the R&D survey consists of. Regression 6.2 presents the results concerning the importance of the geographic origin of the co-operation partner. Both national and international cooperation appear to be significant however of opposite sign. These results indicate that international cooperation CP F positively influences sales growth. In other words, firms that have at least one foreign cooperation partner, irrespective of their number of Belgian alliance partners, seem to be a higher sales growth, which could be accounted for by the granted potential access to a much larger knowledge base through international cooperation. Specifications 6.3 and 6.4. report the results when also taking the type of co-operation partner into account. Here, we distinguish not only between different geographical origin of the co-operation partners, but also the sectoral nature of the partner. We identify co-operation with customers/suppliers/other companies, both nationally and internationally. This type of cooperation is typically more of an applied nature, (CP N A and CP F A ), while co-operation with universities/research centres -(inter)nationally- is more basic research oriented (CP N B and CP B F ). As can be seen from regression 6.3. it is mainly co-operation with foreign customers/suppliers/companies that seems to be interesting for the firms in terms of increasing sales growth. The insignificant results of basic research activities might be due to the fact that basic R&D, resulting from basic R&D co-operation, has a less immediate impact on economic performance to fall within the time period of 4 years that we are considering. The results of regression 6.3. suggest that only for applied research a distinction between national vs. international cooperation is meaningful 18. Hence, specification 6.4. fits the data better and again points to the importance of having at least one foreign cooperation partner in R&D. On the contrary, R&D co-operation with Belgian partners is less desirable since it shows a tendency to negatively impact output growth. These results might indicate that while international co-operation is often part of a market access strategy, Belgian cooperation occurs in the case of market reorganisation in the domestic market International R&D spillovers, Output growth and the role of international collaborative R&D agreements Section 5.2 proved strong support for international R&D cooperation to significantly improve firm productivity growth especially, international co-operation with customers, suppliers or other companies (applied international R&D co-operation). In this section, we will explore the role of R&D cooperation as a channel for spillovers. According to theory, cooperation (both national and international) would give 18 On the one hand, the effect of international applied cooperation is significant while national applied cooperation barely fails to be significant at the 10% level. On the other hand, basic research, no matter the origin of the partner, is insignificant at any reasonable level of significance. 18

19 access to a greater pool of technology knowledge. In order to test for this hypothesis we constructed two measures of national intra-sectoral R&D intensity and foreign intra-sectoral R&D intensity 19. The impossibility of classifying partners by sector made it unfeasible to include weights across firms and therefore we were left with two proxies for intra-sectoral R&D intensity that vary across sectors but not across firms of the same sector. Hence, our main constraint was that although coefficients for these variables were positive and significant (see Table 7, regressions (7.1) and (7.2)), we could not interpret them as supporting the hypothesis of positive spillovers. Because they could only picking up industry characteristics, reflecting the technological opportunities of the sector. Moreover when both measures are included in the same regression equation (regression (7.3) of Table 7) the national intra-sectoral R&D intensity is negative but no longer significant, which may be due to multicollinearity problems between the two variables. Table 7. Intra-industry R&D spillovers (7.1) (7.2) (7.3) (7.4) (7.5) (7.6) (7.7) (7.8) (7.9) c *** (0.952) *** (0.096) *** (0.096) *** (0.095) *** (0.097) *** (0.097) 0.304*** (0.097) 0.302*** (0.097) 0.304*** (0.097) l *** (0.141) *** (0.014) *** (0.140) *** (0.140) *** (0.139) *** (0.139) 0.446*** (0.141) 0.450*** (0.140) 0.446*** (0.141) RDI *** (0.012) *** (0.012) *** (0.012) *** (0.012) *** (0.012) *** (0.012) 0.129*** (0.013) 0.131*** (0.013) 0.129*** (0.013) indrdi D *** (0.021) (0.028) (0.027) (0.027) (0.030) indrdi F *** (0.013) *** (0.017) ** (0.019) 0.048*** (0.018) 0.045** (0.020) N CP A * (0.085) (0.154) * (0.090) (0.154) F CP A ** (0.084) (0.168) 0.170** (0.086) (0.168) CP N A * ndrdi D * (0.025) * (0.025) (0.042) (0.042) CP F A * indrdi F ** (0.012) ** (0.012) (0.024) (0.024) Industry Yes Yes Yes Yes Yes Yes Yes Yes Yes Dummie s Num observ R squared F(n 1,n 2 ) Prob>F Dependent variable: ln sales growth ( y) * significant at the 10% level of significance ** significant at the 5% level of significance *** significant at the 1% level of significance Nevertheless, our main concern was to test for cooperation as a channel for spillovers. Therefore, we interact the national/foreign cooperation variable with the national/foreign 19 See Section 3.4 in the paper. 19

20 intra-sectoral R&D intensity. Table 7 shows the results of our empirical analysis on the role of cooperation with national/foreign customer, suppliers and other firms (applied cooperation) as a channel for spillovers. Regression (7.4) recovers one of the results of Table 6 (6.4), but in this case concentrating only in the role of national and foreign applied cooperation on sales growth. This result confirms our previous findings, hinting the positive effect of foreign cooperation on sales growth. Regression ( ) are our specifications of interest since it tests the relative importance of national and foreign applied cooperation as a means to access external know-how. The positive and significant coefficient of the interaction term (CP F A *IndRDI F ) in (7.5) is supportive of our hypothesis that the foreign applied cooperation gives access to the foreign technological pool thus generating a positive effect on sales growth. This means internalizing existing spillovers in the foreign sector derive from the know-how of those firms that are collaborating in the same sector abroad. The inclusion of the national and foreign R&D intensity measures in the regression (7.6) also yields a positive and significant interaction term (CP F A *IndRDI F ) beyond the positive effect for IndRDI F suggesting that firms that are engaged in applied cooperation with an international partner have a larger positive effect from the international intra-sectoral R&D intensity, as compared to firms without such cooperation. Including both cooperation and the interaction terms in (7.7) and (7.9) would allow to test whether the positive effect of cooperation would be higher if the pool of accessible knowledge would be higher. Unfortunately, no significant results are obtained here, which is not surprising given that, due to the rough proxy for the pool of accessible know-how, most of the variation in the interaction term is due to the cooperation. Our major constraint is, as noted before, that the variable indrdi F may not be specific enough as proxy for the know how base that the cooperating firm gets access to. Being constructed as reflecting the R&D intensity of all countries in the aggregate sector to which the firm belongs, it is questionable whether this is the true know-how base the firm gets access to as soon as it has a foreign partner in applied R&D cooperation CP F A. And therefore it is not necessarily a proof of the existence of international intrasectoral spillovers. Further research should refine the measures for national and foreign intra-sectoral R&D intensity, making them more firm specific. 6. Conclusion and policy implications Following the perspective that cooperation can be seen as a vehicle for voluntary know-how transfers and an instrument to respond to involuntary spillovers, we include R&D co-operation in our analysis of productivity performance differences among innovative firms. Cooperative R&D agreements can be seen as an innovation activity that provides access to 20