University versus Firm Knowledge Spillovers - a ...

University versus Firm Knowledge Spillovers - a Comparison of Productivity E¤ects in Swedish Regions¤ Olof Ejermo and Martin Anderssony [email protected], [email protected] January 3, 2002

Abstract This paper uses a new Swedish dataset of 299 …rms, aimed at analysing within-regional spillover e¤ects of …rms’ and univeristies’ research on the productivity of …rms for 1997. The regional unit of analysis is the labourmarket region, a functional region de…ned based on commuting patterns. We believe this is a more appropriate region for analysis than those previously used. Due to statistical problems, which may be the result of spatial autocorrelation, we …nd no evidence whatsoever of spillover e¤ects of knowledge, but the results must be considered as preliminary.

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Introduction

This paper studies empirically the relative importance of di¤erent kinds of localized knowledge spillovers. The purpose is to …nd out to what extent geographical proximity to university research and other …rms’ R&D activities contribute to the productivity of a …rm. The main idea is that proximity stimulates knowledge spillovers, which in turn have a positive impact on a …rm’s productivity. However, we also account for technological closeness. By technological closeness (or distance) we refer to distance between technologies through which actors can communciate e¤ectively. For example, knowledge generated by university research can more easily be adopted by a …rm whose research activity and/or production occurs within a similar scienti…c …eld. Similarly, …rms can more e¤ectively learn from each other if they work on similar products. Knowledge spreads in many forms though, which makes it inherently di¢cult to quantify. ¤ Preliminary and incomplete paper, please don’t quote without the authors’ consent. Methods used will be developed, see the section extensions. y PhD candidates, Jönköping International Business School, P.O. Box 1026, SE-551 11 Jönköping, Sweden

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While knowledge certainly has public features, its spread is by no means automatic. It is hindered by both geographic and technological distance. The role of proximity for knowledge spillovers stems mainly from the conception that knowledge is most e¤ectively transmitted through face-to-face (FTF) contacts. The importance of FTF-contacts is often explained by stressing the role of tacit knowledge. In fact, in the context of spillovers it seems to be a consensus among researchers that much relevant knowledge is tacit in nature (see e.g. Maillat and Kebir 2001; Lorenzen 1996). In contrast to knowledge that easily can be transformed to information and therefore can be transmitted via telecommunications (such as the internet), tacit knowledge is semi- and unconscious knowledge and does not exist in printed explicit forms (Leonard and Sensiper 1998). Hence, exchange of tacit knowledge requires FTF-contacts. Since the mobility of engineers and other important knowledge handlers decreases with distance, making the likelihood of FTF-contacts lower, proximity is indeed important. Although it is hard to imagine a kind of knowledge that cannot be transformed into information, di¤erent forms of knowledge certainly requires di¤erent amounts of e¤orts to be transformed into information, making the distinction between information and knowledge a useful concept for analysis as maintained by Karlsson and Manducchi (2001). The localized nature of knowledge and the importance of technological closeness have traditionally been covered separately in the literature. Technological distance measures have been best developed in studies on the importance of interindustry (or more seldom between …rms) technology spillovers.1 Often due to datalimitations, studies on the geographical importance of spillovers have been con…ned to study spillover within narrowly de…ned technology classes. Another limitation has been that administrative units such as federal states have been used. See the next section for a review on the literature on geography spillovers. A merger of these two strands of the literature is according to our view partially missing, which motivates this study. Several reasons why …rms co-locate have been advanced in the literature2 : Marshall (1920) emphasized three forces driving external economies of scale. First, suppliers become specialized and are able to tailor products to the speci…c needs of …rms. Second, labour market pooling is important because search costs for labour are reduced and/or quali…cations of workers may be more suited to the needs of the company. Third, there are knowledge spillovers. Ohlin (1933) in his characterization of agglomeration forces in turn divided them into 1) within-…rm economies of scale, 2) localization economies of scale, external to the …rm but dependent on the size of the local industry, 3) urbanization economies of scale, external to the local industry but arising from the size of the local economy and 4) inter-industry linkages. This suggests that the forming of clusters could be a function of the relative importance of some of these forces. 1 See for example Terleckyj (1974, 1980), Scherer (1982), Griliches and Mairesse (1984) Englander et. al. (1988), Verspagen (1997a, b), Verspagen and Loo (1999), Los and Verspagen (2000) and Ejermo (2001). 2 Pettersson (2001) and Krugman and Obstfeld (2000) were helpful here.

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The forming of high technology clusters in Silicon Valley and Boston among others suggest that knowledge has the potential be an important agglomeration factor.3 In Arrow (1962) knowledge-production was a side-e¤ect of goods production, as a function of capital accumulation, being purely external to the …rm, an e¤ect referred to as learning-by-doing. Romer (1986) develops a theoretical model where specialization fosters growth. Marshall (1920), Arrow (1962) and Romer (1986) are together often referred to as MAR-e¤ects, thus not specifying the relative importance of agglomeration e¤ects. A partially opposite idea was proposed by Jacobs (1969), that …rms can actually bene…t from having technologically di¤erent …rms at close range. Evidence for Jacobs hypothesis was for instance found in Feldman and Audretsch (1999). However, for our purposes this distinction is not important. Indeed, our objective is more modest; we do not intend to model which kind of agglomeration force is the more important, only to empirically test which e¤ect is the strongest: that of having other …rms’ or university research nearby. We continue the paper by reviewing some of the studies on knowledge spillovers in regions and e¤ects of academic research. Then we go on to describe the dataset and give an overview of the geographical distribution of research in Swedish …rms and universities for 1997. Thereafter, the model used for empirical estimation is presented, followed by empirical results and comments. The …nal section concludes and lays out our plan for further improvement.

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Studies of Geography Spillovers and University E¤ects

Ja¤e (1989) examined the geographical coincidence of university research with that of research labs within 29 US states for 1972-77, 1979 and 1984 respectively. He found a strong relationship between corporate lab patenting and university research in the areas drugs, chemicals and electronics. Furthermore, it seemed that industrial R&D was induced by presence of university research. Ja¤e et al (1993) examined localization of citation patterns. It was found that US citations to domestic patents were more likely to be domestic. Further, citations were more likely to come from the same state or Standard Metropolitan Statistical Area. Some evidence was also found that geographic localization fades over time. Also, spillovers in speci…c technology areas were found to be less geographically localized. In a study across European regions, Maurseth and Verspagen (1999) also found compelling evidence of a localization pattern of patent citations. However, national barriers were important so that patent citations occured more often between regions belonging to the same country. 3 See

Saxenian (1994).

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Fischer and Varga (2001) examined spillovers of knowledge from universities on patent application activity in 1993. Their sample consisted of …rms belonging to one of six technology classes in 99 political units in Austria. Employing a spatial econometric approach, the authors …nd evidence of spillovers across regions, but that this is linked to a spatial decay e¤ect. Wallsten (2001) examines …rms that received US federal SBIR (Small Business Innovation Research) grants in 1993-94 and 1995-96 respectively. Using modern GIS (Geographic informations systems) analysis he …nds, by the position of …rms in space, that …rms that apply for SBIR grants are much more likely to receive such if they are co-located with …rms that have received grant(s) earlier. This result also holds when the …rms have received grants earlier. The results are stronger the closer …rms are to each other. Also, results are stronger if they belong to the same out of seven technology classes.

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Database of …rms

The original dataset has been constructed by a matching procedure between two sources of data: ”Financial accounts for enterprises 1997” (referred to as FA) and ”Research and Development in the Business Enterprise Sector 1997” (called BERD). In this manner, 299 …rms could be matched, which constituted 74 per cent of the number of man-years in research from BERD. An original 299 …rms have been possible to match. The information that has been possible to collect in this manner is unusually rich, encompassing nearly 200 hundred variables per …rm. BERD also gives information on in which county research takes place on a …rm-by-…rm basis. By checking the locations of …rms in the dataset through the corresponding yellow pages of the phonebook from 1997 on the …rms, research e¤orts could be matched by functional regions, namely local labour market regions. These regions have been constructed on the basis of commuting patterns (see NUTEK 1998). This concordes with our belief that they are a more appropriate economic unit of analysis than administrative units commonly used in the literature. The geographical landscape of R&D man-years in …rms in the dataset is given in Figure 1. As can be seen, company research is largely con…ned to three or four main areas: Stockholm, Gothenburg, MalmöLund, possibly Helsingborg (north of Malmö-Lund) and Linköping (south-west of Stockholm), in decreasing order of importance. This pattern coincide with the three largest cities (Stockholm, Gothenburg and Malmö) and to a large extent, also to (nearby) presence of universities, see Figure 2. The BERD data set was used for several purposes. Knowledge input from the own …rm was proxied by number of research personnel of the …rms. It would probably have been more appropriate to measure this input by expenditure or investment in R&D, but the regional distribution of this data was not available, forcing us to use amounts of research personnel. However, the latter has the advantage of being of the same unit as labour. 4

R&D man-years, all branches Stockholm

Gothenburg

Lafou.shp 0 - 202.47 202.47 - 677.94 677.94 - 1970.28 1970.28 - 3496.96 3496.96 - 13951.8

Malmö-Lund

Figure 1: Personnel, engaged in Research and Development activities in Swedish …rms, 1997. Classi…cation based on natural breaks by Jenk’s optimization method. Data organized by local labour market regions, according to NUTEK (1998).

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Standard production variables have been derived from FA. Production value is measured by value added of …rms. Gross capital stocks have been constructed from the summation of the posts buildings, land improvements and plant machinery tools. Corresponding posts used for research were available from BERD, thus these were deducted from the gross capital variable. Ignoring such double-counting e¤ects bias results and were already investigated by Schankerman (1981). Labour is simply the number of employees from FA, with the deduction of research personnel in line with the previous discussion. University knowledge spillover e¤ects were tested for in two distinct ways. First, from BERD is given amount of research, as measured by number of manyears, in di¤erent scienti…c disciplines. This was taken as an indicator of the potential bene…t of university spillovers, either coming from labour market pooling or knowledge spillovers. Thus, the relative shares of research undertaken in di¤erent scienti…c disciplines by the …rm were used to weigh the relative importance of the number of people employed in university research by this discipline. However, only in case the …rm had research acitivity in the region of the university were such e¤ects counted.4 Number of research employees by scienti…c discipline in universities were taken from Statistics Sweden (1999). The total number of research sta¤ among universities in Swedish labour market regions is shown in Figure 2. University research is apparently even more highly concentrated than that of …rms. The most research-intensive regions in 1997/98 were Stockholm, Uppsala, Malmö-Lund and Gothenburg in decreasing order, Note that the order of importance compared to …rm research-intensity and population density has changed. This is to a high extent re‡ected by the high amounts of research at the old universities in Uppsala and Lund. Uppsala, is however the fourth largest city in the country. Our second measure, from the BERD, instead uses the amount of external funding from the …rm towards universities. These funds are in turn separated into two components: a) where the …rm has an exclusive right for use of the research results and b) where there is no exclusive right. This type of university variable is aimed at capturing the degree of interaction between the …rm and university.

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Model of …rms

This section describes how we intend to model the production function and its relation to locally orginated knowledge. We model the setup such that …rms’ production functions are considered separately. However, we believe that the appropriate unit of analysis is the concern and not the …rm. Clearly, cooperative behaviour of R&D between …rms in a concern must be much larger than if they 4 This is clearly a limitation of our approach, because …rms may of course bene…t from cross-regional spillovers. See the section on extensions for our planned improvements of the paper. For now, we believe that our second measure is more appropriate.

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Luleå Umeå

University research, man-years 1997/98 Uppsala Stockholm Linköping Gothenburg

Lafou.shp 0 - 104 105 - 308 309 - 1326 1327 - 3407 3408 - 4738

Malmö-Lund

Figure 2: Personnel, engaged in Research and Development activities in Swedish universities, 1997/98. Classi…cation based on natural breaks. Source: Statistics Sweden (1999).

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are not part of a larger organization. Still we believe that the correct unit for analyzing spillovers is the …rm. Data on concern structure for 1997 was not available to us in the time of writing. Therefore only the …rm approach was pursued here.5 The knowledge production function is modelled to be dependent on locally produced knowledge, where knowledge is proxied by number of researchers in relevant technology …elds. To take into account relevant sources, we model the local knowledge stock to be dependent on three sources: 1. investments in knowledge arising from amount of own R&D personnel in the region, 2. knowledge coming from spillover from other …rms/concerns, whose personnel are working on projects technologally close to the own …rm/concern and 3. university research in areas relevant to the …rm, measured by the number of locally employed researchers. For spillover e¤ects of …rms, a …rms technological vector is de…ned according to Ja¤e’s (1986) formulation. He used shares of patents in technology classes to proxy technological closeness.6 This was later extended by Goto and Suzuki (1989) who used R&D expenditures by product group and was also used by Vuori (1997) and Ejermo (2001). Here, following the latter authors, a …rm i’s technology row vector, Fi , is de…ned by the shares of R&D expenditure in di¤erent product groups. Thus the technological closeness between two …rms can be written

wij =

Fi Fj0 ; 0 [Fi Fi ] [Fj Fj0 ]1=2

i 6= j

(1)

By de…nition, wij has to take a value betwen 0 and 1. In the paper, product groups are given by the Industry Classi…cation codes from BERD. Data is given on the 5-digit level, meaning that it would close to impossible to get …rms to be technologically close and therefore an aggregation procedure was done based on the scheme in Table 1. BERD gives information on the relative importance of di¤erent scienti…c disciplines in the research of the …rm. In the …rst variant for measuring university knowledge spillovers, information on the number of researchers within the …rm and scienti…c disciplines, is used to weigh the number of research employees in university research in corresponding disciplines, thus giving a measure of the potential importance of university spillovers. The research potential of discipline k in region r is equal to $ik RUkr , where $ik are the shares in discipline k, RUkr are the number of university researchers in the discipline in the region. 5 Negative value added for nine observations, which were deleted from the regressions, further strengthen our belief that …rms should be aggregated into concerns. 6 As will be later explained in the endnotes, we do not consider this formulation to be satisfactory, and in a later version of this paper we plan to rely on patent citations instead.

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Group

SNI92

1 2 3 4 5 6 7 8 9

01-05 10-14 15-16 17-19 20 21 22 23 24

10 11 12 13 14 15 16 17

25 26 27 28 29 30 31 32

18 19 20 21 22 23 24 25

33 34 35 36-37 40-41 45 50-52 63

26 27 28 29

64 72 73-75 80-95

Description Agriculture, Forestry and Fishing Mining and quarrying Food product, beverage and tobacco industry Textiles, clothing and leather Wood and woodproducts Pulp, paper and paper products Publishers and printers Coke and petroleum products Chemicals and chemical products (incl. pharmaceutical preparations) Rubber and plastic products Other non-metallic mineral products Basic metalls Fabricated metal products Machinery and equipment. O¢ce machinery and computers Electrical machinery and apparatus Radio, television and communication equipment and apparatus Precision, medical and optical instruments Motor vehicles, trailers and semi-trailers Other transport equipment Manufacturing industry n.e.c. Electricity, gas and water Construction Wholesale and retail trade Supporting and auxiliary transport activities; activities of travel agencies Post and telecommunications Data consultancy and data service companies Research Community, social and personal service

Table 1: Product groups classi…cation scheme for R&D expenditure based on the Swedish Industry Classi…cation, SNI92.

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In the second version of university knowledge spillover is simply used external funding of university research activities with a) exclusive right to the scienti…c result for the …rm and b) non-exclusive rights. P The …rm’s knowledge production is a sum of Pall locally produced knowledge A , where i is the …rm, r the region. r ir r Air is in turn a function of knowledge coming from the own …rm, that spilling over from other …rms and that spilling over from university research. Weighting schemes are based on the formulations above.

Ai = C

X r

Air =

à X r

RDir

!°

¢

0 1± Ã !³ XX XX ¢@ Dir wij RDjr A Dir $ik RUkr ; i 6= j r

r

j

(2)

k

C is a constant and can be regarded as a measure of ”basic” or ”common” knowledge of the …rm’s employees, RD denotes number of people devoted to R&D-activities, j is used for other …rms. Dir is a dummy, taking the value 1 if …rm i has research activities in region r and 0 otherwise, thus guaranteeing that only in case the …rm has research activities in the region does knowledge spill over. The formulation does not make a di¤erence for the location in which knowledge is produced. Logarithms of (2) gives e i + ³f ai = °rdi + ± rd rui

(3)

Yi = Ai Ki® L¯i ei

(4)

³ ´ P e i ´ ln P P Dir wij RDjr small letters denote logs and rdi ´ ln ( r RDir ), rd r j P P and rf ui ´ ln ( r k Dir $ik RUkr ). The production function of the …rm is written as:

ei is an error term. Taking logarithms of this equation and insertion of (3) leaves us with e i + ³f yi = c + °rdi + ± rd rui + ®ki + ¯li + "i

(5)

where "i ´ ln ei . We follow Los and Verspagen (2000) in writing (4) in labour-intensive form: e i ¡ li ) + ³(f (yi ¡ li ) = c + °(rdi ¡ li ) + ±(rd rui ¡ li ) + ®(ki ¡ li ) + ¹li + "i (6) 10

where ¹ ´ (¯ + ® + ° 1 + ° 2 ¡ 1). Thus constant returns can easily be checked by seeing if ¹ is signi…cantly di¤erent from zero. The transformation has been done to reduce problems of heteroskedasticity and multicollinearity and constitutes the …rst of our estimated equations.7 This formulation implicitly assumes that knowledge freely ‡ows within the …rm, so that geographical distance is not important. This may or not be a questionnable assumption. It can for instance be argued that knowledge has to be close to production, while it could also be implicitly assumed that …rms optimize their behaviour so that knowledge spreads very easily through the organization. In any case, it has not been possible from the data to derive the relative amounts of production in the location over space.

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Empirical investigation

Our estimations are very preliminary. Diagnostic tests revealed problems of normality and/or heteroskedasticity.8 This suggests some form of speci…cation problem. We also experimented with the use of the translog production function, which o¤ers more ‡exibility than the standard Cobb-Douglas. However, this was not found to remedy our speci…cation problems. We think that our problems may arise due to problems of spatial autocorrelation, because cross-regional e¤ects are not taken into consideration. Again, see extensions for our suggested plan of improvement of the paper. The results below nonetheless report our estimations using White’s (1980) heteroskedasticity consistent covariance matrix. We report the results of no spillover e¤ects (WITHOUT ), with only …rm spillover e¤ects (FIRM ), only university e¤ects (UNIV )9 and …rm and university e¤ects (FIRM&UNIV ). We …nd that own R&D and capital are highly signi…cant explanatory variables, while spillover e¤ects seem to be non-existent; they have both low values and are insigni…cant. At this stage, we prefer to be cautios in our conclusions, due to our econometric problems.

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Extensions, conclusions

As should by now have been clear from the paper, we put little faith in the results we have received. The reasons are the following. First of all, …rms should be aggregated into concerns, because knowledge can be argued to ‡ow 7 Indeed, White’s (1980) product and crossproduct test reveals problems of heteroskedasticity. 8 Through the Bera-Jarque (1980) normality and White’s (1980) product and crossproduct test respectively. 9 In the table, only the university e¤ect using external funding of university research with an exclusive right for the …rm is reported on. The other university e¤ects were almost the same, i.e. small coe¢cients and non-signi…cant t-values.

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Method WITHOUT FIRM UNIV FIRM&UNIV

c 11.45 11.45 11.52 11.52

WITHOUT FIRM UNIV FIRM&UNIV

¤¤¤

(31.81) (31.84)¤¤¤ (32.18)¤¤¤ (32.23)¤¤¤

-0.03 -0.03 -0.03 -0.03

(rdi ¡ li )

0.22 0.23 0.22 0.23

¤¤¤

(8.35) (8.34)¤¤¤ (8.26)¤¤¤ (8.25)¤¤¤

e i ¡ li ) (rd

(f rui ¡ li )

-0.00 (-0.79) -0.00 (-0.76)

0.00 (0.93) 0.00 (0.90)

(ki ¡ li )

0.21 0.21 0.21 0.21

(6.95)¤¤¤ (6.94)¤¤¤ (6.93)¤¤¤ (6.92)¤¤¤

li

R2

R 2 adj

SEE

df

(-1.02) (-0.98) (-1.11) (-1.06)

0.45 0.46 0.46 0.46

0.45 0.45 0.45 0.45

0.45 0.45 0.45 0.45

286 285 285 284

Table 2: Estimation results. Reported t-values based on White (1980). Stars indicate: * signi…cant at the 10 per cent level, ** signi…cant at the 5 per cent level, *** signi…cant at the 1 per cent level. more freely through the own organization. Indeed, many concerns put their R&D e¤orts in research units. From the data, we also found some examples of negative value added which may be a consequence of this. Therefore, we believe that the ’…rm approach’ is likely to be biased. Secondly, knowledge spreads through space, a fact which is not taken into account by the formulation of the paper, where only within-regional spillovers are considered. This presence of spatial auto-correlation may result in the speci…cation problems we observe when we test for normality or heteroskedasticity. We plan to introduce accessibility across regions into the picture, as a measure of how far regions are between each other. This will be done by using a timedistance measure, based on the shortest time it takes to travel between two regions. The logic behind using this measure is the interaction cost. A third reason why we think the paper can be improved is by measuring technological closeness di¤erently. The Ja¤e (1986) method was tried out in Ejermo (2001) with the BERD data, only on the industrial level. Also here little signs of spillover e¤ects were found. It was concluded that the measures used in that paper may su¤er from problems of capturing timelag e¤ects of spillovers. Patents on the …rm level can probably be linked to space and geography can provide a much clearer picture of the technological networks in a region. We conclude that our work has promising venues to follow, that it is a dataintensive kind of research, but with a lot of potential.

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Wallsten, S. J. (2001): “An Empirical Test of Geographic Knowledge Spillovers Using Geographic Information Systems and Firm-Level Data,” Regional Science And Urban Economics, 31(5), 571–599. White, H. (1980): “A Heteroskedasticity Consistent Covariance Matrix Estimator and a Direct Test of Heteroskedasticity,” Econometrica, 48, 817–838.

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