T HE B RIGHT AND DARK S IDE OF C OOPERATION FOR
R EGIONAL I NNOVATION P ERFORMANCE ∗
Tom Broekel $ , §
$
Andreas Meder #
Department of Economic Geography,
Faculty of Geosciences, Utrecht University, The Netherlands
#
Friedrich Schiller University, Jena, Germany
29th February 2008
Abstract
In the literature on ‘innovative mileus’ and ‘regional innovation systems’ it is frequently argued that intra-regional cooperativeness plays a crucial role for firms’ innovation performances.
While there exists substantial qualitative evidence for this, the quantitative literature is rather
thin. The paper provides a quantitative-empirical analysis on this issue for the Electrics & Electronics industry and 270 German labor market regions. Using robust nonparametric frontier
techniques, intra- and inter-regional cooperativeness measures are tested for their influence on
regional innovation performance. It is found that below a certain level both types of cooperativeness have positive effects, while exceeding this point, they yield negative effects.
JEL codes: R12, O18, O31
Keywords: regional innovation performance, cooperation, nonparametric frontier analysis, German electrics and electronics industry
§
Corresponding author: Tom Broekel, Department of Economic Geography, Urban & Regional Research Centre
Utrecht (URU), Faculty of Geosciences, Utrecht University, Heidelberglaan 2, 3584 CS Utrecht, The Netherlands.
Phone: +31 30 253 3206. Fax: +31 30 253 2037. E-mail: [email protected]
∗
The authors would like to thank Thomas Brenner and Ron Boschma for helpful comments and suggestions.
1
1
Introduction
Concepts like the ‘innovative milieu’ (Aydalot and Keeble, 1985) and ‘regional innovation system’
(Cooke, 1992) emphasize that cooperativeness can foster innovation performance by triggering collective learning processes. These concepts particularly highlight a positive side of intra-regional
cooperativeness. It is however also well-known that intensive intra-regional cooperativeness which
is not accompanied by sufficient inter-regional linkages can result in lock-in situations that yield
negative effects on regional innovation performance (Grabher, 1993).
In light of this we argue that cooperativeness can have a ‘bright’ side in fostering actors’ innovation
performances. At the same time it can be characterized by a ‘dark’ side causing comparatively low
innovation performance.
Despite the numerous studies investigating the role of cooperativeness for regional innovation performance (see, e.g., Asheim and Isaksen, 2002; Boschma and ter Wal, 2007), the empirical evidence
is mainly of qualitative nature, i.e. it is based primarily on case study research. The few existing
quantitative approaches are often restricted in covering only a very limited number of regions (see,
e.g., Frenkel and Schefer, 1998; Fritsch, 2004).
This paper contributes to the literature by providing a quantitative empirical analysis of the effects
of intra-regional and inter-regional cooperativeness on regional innovation performance. Moreover,
it is discussed and investigated how a region’s non-technological factor endowment can influence
these effects. While the analysis concentrates on a single industry (Electric & Electronics) we make
use of data for 270 German labor market regions and the years 1999-2002.
Following recent critiques on the use of production function approaches by Bonaccorsi and Daraio
(2006) and Broekel (2008), a robust nonparametric production frontier technique is employed. In
addition, to its methodological advantages, its nonparametric nature allows for an unrestricted exploration of the relationship between cooperativeness and innovation performance.
In general, we find a ‘bright’ and ‘dark’ side of intra- and inter-regional cooperativeness to be existing for regional innovation performance. The influence shows to be curvilinear in that below a
certain level, increasing cooperativeness, of either kind, yields positive effects. If the level of cooperativeness increases beyond the turning point regional innovation performance decreases. The
results also suggest intra-regional and inter-regional cooperativeness to be complements. Furthermore, evidence is found that the negative effects of lock-in situations can partially be compensated
by a supportive regional factor endowment, e.g. the presence of universities.
The paper is organized as follows. Section 2 gives an overview of the literature on cooperativeness and regional innovation performance. In Section 3, the empirical methodology is extensively
described. The employed data on intra- and inter-regional cooperativeness, regional factor endowment, and regional innovation performance are presented in Section 4. The results of the empirical
analyses are described and discussed in Section 5. Section 6 concludes.
2
2
2.1
Cooperativeness and regional innovation performance
The influence of cooperation on innovation activities
In the field of economic geography, cooperativeness has been recognized as being a crucial element for determining regional innovation performances. In particular, two phenomena have drawn
researchers’ attention on the level of intra-regional cooperativeness and its impact on regional innovation performances. On the one side, it has been shown that some regions’ outstanding innovativeness can be attributed to intensive inter-firm communication and the existence of a location specific
cooperation culture that stimulate collective intra-regional learning processes (Asheim and Isaksen,
2002). This observation stimulated the vast literature on ‘innovative milieus’ (Aydalot and Keeble,
1985; Camagni, 1991) and ‘regional innovation systems’ Cooke (1992, 2001).
On the other side, while these concepts highlight mainly the potential positive influence of strong
intra-regional cooperativeness on innovation performance, there is also a substantial literature showing that a too strong focus of actors’ cooperation activities on intra-regional partners can have negative effects (see, e.g., Camagni, 1991; Grabher, 1993). Taken to an extreme this can result in regiona
lock-in situations as described for the case of the Ruhr-Area in Grabher (1993). Here, regional innovation performance is reduced unless access to non-regional knowledge networks provides fresh
ideas helping to avoid and overcome these situations (Camagni, 1991; Asheim and Isaksen, 2002).
In the light of this, we argue that there seem to exist ‘bright’ and ‘dark’ side effects of the level of
intra-regional cooperativeness for firms’ innovation performances.
However, the described observations are mainly supported by qualitative case-study research that
moreover concentrates strongly on ‘extreme’ positive or negative cases. There are few studies that
explicitly analyze in a quantitative-empirical manner, the relationship between the level of intraregional cooperativeness and regional innovation performance. For example, Brenner (2005) finds
cooperation with suppliers and universities to be important for the emergence of local industrial
clusters in Germany. However, in his study no explicit innovation performance measure is included.
Fritsch (2004) uses firm-level data and aggregates it for eleven European regions. He finds no support for a conducive role of cooperation for innovation activities, though.
Thus, it is still largely unclear if and how on a general basis the level of intra-regional cooperativeness relate to regional innovation performance.
2.2
A challenging empirical endeavor
One reason for the missing quantitative empirical studies might be the complexity of the relationship between intra-regional cooperativeness and regional innovation performance. This shows in that
e.g., it is of little value to investigate intra-regioanl cooperativeness separately from inter-regional
and inter-national cooperativeness. This is nicely illustrated by the lock-in scenario in which it is
not so much the strong intra-regional orientation that reduces the innovation performance, but rather
3
the missing non-regional linkages. Hence, when analyzing the impact of intra-regional cooperativeness on innovation performance, inter-regional and inter-national cooperativeness have to be taken
into consideration as well (Breschi and Lissoni, 2001; Bathelt et al., 2002). In empirical approaches
this requires a very detailed database. In this paper we were not able to construct a meaningful
international-cooperativeness measure due to lacking data, see Section 4. Therefore, we have to
restrict the analysis and the discussion to intra- an inter-regional cooperativeness, i.e. only those
cooperation between actors located within the same nation (Germany).
Moreover, the importance of intra- and inter-regional cooperativeness can be conditional on certain
regional characteristics. Trivially, it needs at least actors in a region that can serve as potential cooperation partners: “[i]n many areas a regional innovation system does not exist due to lack of relevant
actors (i.e. organization ‘thinness’)” (Isaksen, 2001, p. 109). In this case, lacking the opportunities to cooperate intra-regionally, firms are forced to expand their search for cooperation partners to
more geographically distant locations. This relates to the recent debate on the importance of places
or networks (see Castells, 1996; Boschma and ter Wal, 2007). The importance of places refers to
the idea that mere location, or co-location with other relevant actors, matters for firms’ innovation
performance. Hence, it counts for firms to be located in the right place. This literature shows that
regions with more favorable conditions in terms of the presence of organizations offering consulting
and financial support, the existence of a well-developed science & technological infrastructure, and
the availability of human capital, etc., achieve higher levels of innovation performances (see, e.g.,
Feldman and Florida, 1994; Audretsch, 1998). Parts of the benefits of being in the ‘right’ place are
argued to stem from that geographic proximity fosters knowledge sharing, collective learning processes, and innovation (Feldman and Florida, 1994).
It has been put forward though that “geographical proximity only creates a potential for interaction,
without necessarily leading to dense local relations” (Isaksen, 2001, p. 110). Hence, ‘mere location’
is not a sufficient condition. Instead, firms’ embeddedness into knowledge networks promotes their
innovative activities as well. This corresponds to the network approach, “meaning that being in the
right network is of utmost importance” (Boschma and ter Wal, 2007, p. 179).
In the following we condense the ‘network’ argument to the influence of cooperation as we only
have data on this issue. However, it is acknowledged that the embeddedness into networks influences firms’ R&D activities in more ways than what is represented by cooperation activities.
In the light of the above, we identify three interrelated aspects that play a role for regional innovation
performance. Firstly, certain characteristics of firms and regions can influence regional innovation
performance without that cooperation activities come into play. The firm internal R&D efforts are
most important in determining the resulting innovation output. In addition, certain firm external but
regional factors (regional factors in the following), e.g. the presence of a universities, can contribute
to firms’ innovation performances. For example, a local university can offer possibilities to share
laboratories, or promotes the access to human capital. In this case, firms’ R&D activities benefit
from the mere co-location to a university, without needing to establish cooperation. This denoted as
4
endowment effect.
Secondly, some regional factors can take effect on the level of intra- and inter-regional cooperativeness which in turn influence regional innovation performance. This effect has two parts. On the
one hand, these factors can become relevant in that e.g., actors located in regions with a sufficent
number of potential cooperation partners are likely to be less interested in engaging in inter-regional
cooperativeness. This refers mainly to the technological potential to cooperate that exists in a region
(see on this issue Cantner and Meder, 2007). This is defined as the technological effect. On the other
hand, some regional factors can influence the likelihood that this potential is actually exploited. For
example, a university may function as intermediary in cooperation projects and increase the likelihood that regional firms cooperate. This is considered as the non-technological effect.
Thirdly, intra- and inter-regional cooperation can shape regional innovation performance independently of the regions’ factor endowments. This takes up the idea that the culture of cooperation is
not only learned and motivated by economic, but also by cultural, political, or ideological reason
(Cooke et al., 1997). This is called pure cooperativeness effect.
This study aims mainly at analyzing the pure cooperativeness effect and hence the other two effects
need to be controlled for. In addition, the mentioned possibility of a ‘bright’ and ‘dark’ side of
cooperativeness have to be taken into consideration in the empirical assessment. In the following
it is shown how this can be accomplished by employing a non-parametric production frontier approach. Moreover, using cooperativeness measures that are free of the technological effect allows to
shed light on the relevance of the non-technological effect. This paper does not however provide an
analysis on the regional factors’ influence on the levels of intra- and inter-regional cooperativeness.
Also, the influence of the regional factor on innovation performance is not in the focus (endowment
effect).1 Instead, the relevance of these are assumed to be given and taken into consideration in the
investigation.
3
Method
3.1
Nonparametric frontier analysis
Recently, it has been argued that in contrast to traditional production function approaches (e.g.,
regression models), applying production frontier techniques in general, and non-convex, nonparametric production frontier techniques (NNPF) in particular, is more appropriate for analyzing (regional) technology and innovation systems (Bonaccorsi and Daraio, 2006; Broekel, 2008). This is
motivated by the fact that the NNPF relaxes a number of critical assumptions that are inherent to
parametric production function approaches. For example, its nonparametric nature reduces the risk
of mis-specification. Moreover, in this approach, the performance is not estimated on the basis an
universal pre-defined functional relationship between regional characteristics and innovation out1
See the previously cited literature on these issues.
5
come. Instead, the relationships are allowed to differ between regions. Regions are evaluated with
respect to best practice instead of being compared to average practice which makes the approach
more interesting for policy implications (see for an extensive treatment Broekel, 2007).
Following Broekel (2008) we employ the non-convex order-m frontier approach introduced by
Cazals et al. (2002) in order to calculate regions’ innovation performances. In addition, the conditional order-m analysis developed by Daraio and Simar (2005a,b) and extended to a multivariate
scenario by Daraio and Simar (2007), is used for estimating the influence of cooperativeness on innovation performance.2
Similar to the traditional production function approaches (e.g. the knowledge production function),
the starting point is that innovations do ‘not fall from heaven’ but are results of economic efforts.
For analyzing innovation performance this implies that the innovation outcome has to be seen in
relation to the efforts into innovation activities. With respect to regional innovation performance, we
differentiate firm internal efforts, regional factors that are relevant for firms’ R&D activities, as well
as the aggregated innovations of regional firms. While the first two can be conceptualized as input
factors is the latter are seen as innovative output (Broekel and Brenner, 2007).
The idea behind the NNPF approach is that a region’s innovation performance is evaluated with respect to the performance of other regions. In a first step, the production frontier is estimated. This
frontier consist of the best-practice, or most innovative, regions given a certain level of input factors.
In the NNPF these most-innovative regions are identified using the principle of weak dominance. On
the basis of this most-innovative regions are characterized by that no other region with an equal or
lower level of input factors, shows a higher level of innovative output.3 All regions that fulfill this
criteria make a regions’ individual reference group.
If in comparison to this reference group a region’s innovation output is higher the region becomes
part of the frontier and it is declared well performing. If its level of innovation output is below that
of its reference group, i.e. it is located below the frontier, this region is declared less performing.
The distance to the frontier indicates its degree of (less) performance. Here, this distance (the performance measure) is represented by the vertical (euclidean) distance between the observations and the
frontier.4 The larger the distance the less performing is a region.5 Hence, by using this simple idea
of comparing regions that are similar with respect to certain input factors, there is no need to specify
ex-ante a functional relationship between the input factors and the innovation output measures.
This type of frontier which is used in the traditional Free Disposal Hull (FDH) approach developed
by Deprins et al. (1984), is determined by the extreme positive (most innovative) observations. This
makes the performance analysis very sensitive with respect to the existence of outliers and noise in
2
3
4
5
See on this issue Bonaccorsi and Daraio (2006) and Broekel (2008). For an extensive treatment of the methodology
see also Daraio and Simar (2007).
Following (Bonaccorsi and Daraio, 2007) we add 0.001 to the input factors as well as to the innovation outputs. This
avoids distortion in the estimation but does not influence the results.
This view corresponds to the output-oriented type of analysis which has been argued to be most appropriate in this
context by Broekel and Brenner (2007).
There are alternative methods available for estimating the frontier function and different ways how to estimate the
distances to the frontier (see, e.g., Scheel, 2000; Daraio and Simar, 2007).
6
the data (see, e.g., Wilson, 1993). This drawback has been overcome by the introduction of robust
nonparametric frontier techniques (see for an introduction Daraio and Simar, 2007). One of the
robust versions of the FDH approach is the order-m frontier approach developed by Cazals et al.
(2002).
In contrast to the FDH approach, the idea behind the order-m approach is that instead of evaluating a
region’s innovation performance with respect to the performance of all other regions, it is compared
a to a randomly drawn (sub-) sample of regions showing equal or lower levels in the input factors.6
The nonparametric frontier function becomes a partial frontier because not all observations are enveloped but only a sub-sample. Based on the partial frontier the evaluation of regions’ innovation
performance as well as the estimation of the performance scores are as presented above.
3.2
Analyzing the effect of cooperativeness on innovation performance
In order to empirically estimate the impact of certain variables, (denoted as ‘external factors’) on
this kind of performance measure, Daraio and Simar (2005a) suggest the estimation of two different measures.7 The first, the unconditional performance measure, is calculated as described above:
regions are evaluated with respect to a randomly drawn sub-sample of other regions which are characterized by equal or lower levels of input factor endowment.
The second measure, the conditional performance measure, is estimated similarly to the unconditional one. In this case the sub-sample of regions, with equal or lower levels of input factors, used
for the comparison is not drawn randomly. Instead, it is drawn conditional on the values (density)
of the external factors.8 The conditional drawing is done in a way that the sample of regions by
which a region’s performance is evaluated is positively biased towards those regions with similar
values in the external factors. In other words, the likelihood that a region is part of another region’s
comparison group, depends negatively on the difference between the values of the regions’ external
factors.
Further, the ratios between the conditional and unconditional performance measures Qz are set into
relation with the regions’ values in the external factors. From this relation inference can be made
on the effects of the external factors on the regional innovation performance. In a setting with two
external factors are analyzed at the same time, Daraio and Simar (2007) suggest to estimate threedimensional regression plots showing the non-parametrically estimated surfaces of Qz in dependence
of the two external factors. In addition, non-parametric regressions are conducted for different subsamples. This allows a more detailed analysis the relationship between the external factors and Qz .
From the shape of the surfaces and curves the following inference can be made. An increasing re6
7
8
The sub-sample’s size has to be specified by the researcher and is denoted by m, giving the name to the procedure.
We follow Bonaccorsi et al. (2005) in setting the level of robustness to below ten percent, i.e. ten percent of the units
are outside the frontier. Given 258 valid observations this is true for m = 70.
See for an extensive presentation of this method Daraio and Simar (2007).
For the estimation of the probability we use the truncated gaussian kernel as well as the bandwidth selection method
for multivariate cases given in Daraio and Simar (2007).
7
gression surface (curve) indicates a positive influence, while a decreasing curve (surface) hints at a
negative impact. In this paper, intra-regional cooperativeness and inter-regional cooperativeness are
defined as two external factors that take effect on the regions’ innovation performances.
The performance analyses are conducted separately for each year. However, the subsequent analyses of the influence of the two cooperativeness types are conducted on the basis of the pooled yearly
performance measures. Hence, in the plots, and the estimated regressions, each region is represented
as many times as years are considered.9 The motivation for this is that by using the pooled ratios the
impact of short term change (statistical noise) is reduced. Moreover, the robustness of the nonparametric regressions used to illustrate potential trends in the data is increased.
From a methodological point of view such an endeavor is appropriate if the underlying mechanisms
determining a region’s innovation performance do not change significantly in the considered time
period. This implies that the relationships between the two types of cooperativeness and regional
innovation activities do not change at large within a couple of years. The theories on cooperativeness
give little reason for why this would be the case, see Section 2.
This is however not to say that the levels of cooperativeness and the regional innovation performances cannot change on a short term basis. Indeed, the two later introduced cooperativeness measures show considerable variance between the years: from year to year they are weakly correlated,
the correlation coefficients are in all cases smaller than 0.25.10 This indicates the presence of short
term noise in their empirical estimation which is likely to be caused by variance in the employed
patent data.
3.3
The definition of the regional innovation performance
While this approach seems to be very conclusive an open issue is the definition of the input factors.
The problem is that the very nature of R&D processes makes the exact identification of the set of
relevant input factors impossible (Broekel and Brenner, 2007). For example, there may be regions
in which the local university is an active intermediate in R&D processes. In other regions it can play
a rather passive role. Should the university then be considered as a general input factor and be taken
into account when evaluating regions’ innovation performance? At the same time does the outcome
of the performance analyses depends strongly on the choice of the input factors (see for an extensive
discussion Broekel and Brenner, 2007).
In this paper we make a ‘virtue out of a necessity’ by following Broekel and Brenner (2007) emphasizing that “different approaches measure different things, and measuring innovation performance
in different ways provides us with additional information about the causes of the different performances” (p. 12). More precise we estimate the impact of intra- and inter-regional cooperativeness as
previously described. This is however done using two different definitions of the set of input factors
9
In this paper this corresponds to four times because the performances are estimated for the years 1999-2002, see
Section 4.
10
Pearson’s rank correlation coefficient is used.
8
in the performance analysis. In the first case, the firm-oriented analysis, only firm internal R&D
efforts are defined as input factors. In the second case, the region-oriented analysis, in addition to
the firm internal R&D efforts a number of regional factors are considered.
This setting serves two goals. First, it functions as robustness check whether the obtained results are
driven by varying definitions of the considered input factor set.
Second, and this is more important, by comparing the results of the two analyses we gain insights on
the impact of the regional factors on the importance of intra- and inter-regional cooperativeness for
regional innovation performance. The idea is as follows. By considering firm internal R&D efforts
as well as regional factors as input factors, the performance approach allows to control for the endowment effect when estimating the regional innovation performance. Further, it will be shown that
by using specific cooperativeness measures the technological effect can be eliminated.
Lets suppose that we find the importance of intra-cooperativeness to be higher in the firm-oriented
analysis than in the region-oriented analysis. This suggests that the regional factors account for some
of cooperativeness’ effect. In light of Section 2.2 this reflects the case in which the importance of
intra-regional cooperativeness depends on regions’ endowment with the considered factors. In other
words, the impact of intra- and inter-regional cooperativeness is shaped by the non-technological
effect. This is not the case when in both analyses a similar influence of intra- and inter-regional cooperativeness is detected. From this it can be concluded that the importance of intra- and inter-regional
cooperativeness is independent of the regions’ factor endowment, i.e. the pure cooperativeness effect
is of relevance.
4
The employed data
4.1
Data on patent applications and R&D
The 270 German labor market regions are chosen as units of analysis, because they seem to fit
best to the theoretical arguments of a regional dimension of innovation processes (see, e.g., Broekel
and Binder, 2007).11 As it is common in innovation research, the output of innovation activities is
approximated by patent applications. The data on patent applications for the years 1999-2003 are
published by the Deutsches Patent- und Markenamt (German Patent Office) in Greif and Schmiedl
(2002) and Greif et al. (2006) (called Patentatlas in the following). The applications by public research institutes, e.g., universities and research societies (e.g. Max Planck Society) as well as the
patent applications by private inventors are not included because the corresponding R&D employment data covers only industrial R&D.
Data on R&D employees is obtained from the German labor market statistic. Following Bade (1987)
the R&D personnel is defined as the sum of the occupational groups: agrarian engineers (032), engineers (60), physicists, chemists, mathematicians (61) and other natural scientists (883). The R&D
11
We use the up-to-date definition of labor market regions in contrast to the older definition used in Greif and Schmiedl
(2002) and Greif et al. (2006).
9
personnel is organized according to the NACE classification. In order to match them with the patent
application that are organized in 31 technological fields we rely upon the concordance developed by
Broekel (2007).
In this paper we concentrate on one industry, the Electrics & Electronics (ELEC). Its definition with
respect to the considered technological fields and NACE industries is presented in Table 5. For
this industry patenting represents an important property rights protection mechanism (Arundel and
Kabla, 1998). This ensures that the innovation output measure captures most, or at least a significant
share of, innovations in this industry.
In this paper the patent applications of the five technological fields assigned to ELEC by Broekel
(2007) are summed resulting in a single innovation output measure. This is motivated by the existence of a great number of zeros in most of these fields which can bias the results (see for a discussion
Broekel and Brenner, 2007). Furthermore, we consider a time-lag of one year to the R&D employment data.
A similar problem occurs on the side of the R&D employees in which one of the three relevant
two-digit NACE industries (DL30, DL31, DL32) is characterized by a large number of zero values.
Out of 270 labor market regions DL30 shows 147 zero values in 1999. We therefore add this industry’s R&D employees to that industry with which it is correlated the highest. In this case this
is DL32 (r = 0.72∗∗∗ ).12 After excluding those regions which still show no R&D employment in
DL32 DL30 (sum of DL32 and DL30) and DL31, this leaves 258 valid observations. The exclusion
is motivated by that a performance analyses with zero values in all input factors are improper.13 .
As has already been mentioned above, in the firm-oriented analysis apart from firms’ R&D employees no further regional input factors are considered. Therefore, the first analysis is conducted with a
single innovation output measure, the sum of patent applications of ELEC, and the two input factors
DL31 and DL32 DL30.
4.2
Regional factor endowment
For the region-oriented analysis in addition to ELEC’s R&D employment, we consider regional factors that have purely intra-regional effects and factors with inter-regional effects. These variables
have been chosen because they are commonly argued to influence the regional innovation performance directly (endowment effect). At the same time some of them may however also contribute to
the non-technological effect in that they influence the level of intra- and inter-regional cooperativeness. All variables are only briefly presented due to space limitations. A more detailed description
and literature references of most of the employed variables can be found in Broekel and Brenner
(2005) and Broekel and Brenner (2007).
The first set of factors influencing firms’ innovation activities need to be located in the same region
in order to become effective. In contrast, the effects of inter-regional factors are to a lesser extent
12
13
Pearson’s correlation coefficient is used.
Remember that in the firm-oriented analysis only the R&D employees are considered as input factors
10
regionally bounded. In their case, firms might benefit from the presence of these factors in neighboring region. In addition to firms’ R&D employees that are already considered as input factors, nine
firm-external input factors are considered in the region-oriented analysis.
In order to account for urban agglomeration advantages on the innovation performance, which in
addition may also effect the cooperation behavior, the population density is employed (POP DEN).
The local demand, public financial situation, and economic activity in the region is approximated by
the gross domestic product (GDP) per capita (see, e.g., Broekel and Brenner, 2005).
Furthermore, the literature highlights the importance of business services (see, e.g., Feldman, 1994)
so that the variable SERVICE has been added. It represents employees in industry KA74 (according
to WZ03) in a region. In a common fashion we compute the influence of SERVICE by using the
‘production structure specialization index’ (PS) (see Feldman and Audretsch, 1999). The index (PS)
+ 1.
is made symmetric as proposed by Laursen (1998) in a different context by calculating PP S−1
S+1
This index ranges from 0 to +2 with one indicating average specialization.
The potential impact of the share of employees with high qualifications (EMP HIGH) is also considered because it is an often used measure for the quality of local human capital (Weibert, 1999).
An industry specific variable (ELEC PS) accounts for the specialization of a region with respect
to ELEC. In a common fashion it is approximated by the PS of the employees of ELEC which is
also made symmetric as described above. It enters the analysis as variable ELEC PS. In order to
account for effects stemming from the absolute regional employment of ELEC (see Brenner, 2004),
we include it as EMPL ELEC. Accounting for the effects of the presence of large firms in ELEC,
the average firm size (SIZE) is considered as well.
These seven input factors are argued to affect only firms located in the same region. In contrast, the
input factors presented below, are argued to be regionally less bounded. For example, this applies
to university graduates that do not stay in the region where they obtained their degree (Mohr, 2002)
and hence other regions benefit from the educational efforts conducted in universities’ host regions.
However, in the statistics they are assigned only to these host regions. Following Broekel and Brenner (2007) we use a distribution procedure to assign adequate shares of such inter-regionally ‘mobile’
resources to the regions in which they are potentially effective. The parameters of the hyperbolic
function that ‘distributes’ the quantities across the regions are fitted by a maximum likelihood calculation, using geographic coordinates and population counts for the German five digit postal code
areas as well as empirical findings from the literature on the mobility of graduates (see Legler et al.,
2001; Mohr, 2002).14 . Table 4 summarizes the distribution parameters.
To control for size effects regarding a region’s industry endowment, the distributed graduate counts
enter the analysis as ratios of the region’s total employment. For technological innovations in particular, graduates of engineering and natural science faculties of technical colleges and universities are
of special interest. They enter the analysis in form of the variables GRAD ENG and GRAD NAT.15
14
15
See Broekel and Brenner (2007) for a more detailed description of this procedure.
It is shown in Broekel (2008) that these variables are strongly correlated to similarly distributed employment data of
public research institutes and hence it accounts for their effect as well.
11
In order to avoid including variables which are statistically redundant, i.e. highly correlated, we
check the input factors with respect to their correlation structure. In case that two or more variables
are correlated with r = 0.8 (Spearman rank correlation) or above, the variables with less theoretical
relevance are excluded.16
Applying this rule, seven variables enter the analysis as input factors: the two R&D employee variables (DL32 DL30 and DL31); average firm size (SIZE), gross domestic product (GDP), the share
of highly qualified employees (EMP HIGH), spatially distributed graduates of engineering faculties (GRAD ENG), and the regions’ specialization in business services (SERVICE). The variables
GDP is kept instead of POP DEN; DL31 for EMPL ELEC; GRAD ENG for GRAD NAT; SIZE for
ELEC PS. Tables 8 and 9 in the Appendix show the corresponding correlation coefficients. Table 6
summarize the variables and Table 7 illustrates the selection.
In the nonparametric performance analysis the selected variables enter simply as additional input
factors.
4.3
Two cooperativeness measures
As pointed out in Section 2.2 our main concern in this study is on the pure cooperativeness effect and
its impact on regional innovation performance. This requires that we control for the technological
effect meaning that regions in which a multitude of actors are located that can potentially serve as
cooperation partners, are likely to show higher numbers of realized cooperation. However, this does
not say that the actors are more intra-regionally cooperative than actors located in regions that are
characterized by less potential cooperation partners. This is to some extent a ‘size’ effect regarding
the cooperation opportunities that exist for actors to cooperate intra- and inter-regionally.
In order to avoid this bias we make use of the cooperativeness measure that has been developed
recently by Cantner and Meder (2007). This measure is characterized by the absence of this ‘technological effect’. Thereby, it comes closer to the representation of actors’ cooperative attitude as we
account for the existing technological opportunities to cooperate.
Data on cooperation are obtained from German patent data published in the German “Patentblatt”.
More precisely, a cooperation is defined as a co-application of a patent, i.e. two organizations that
jointly apply for a patent within the IPCs classes assigned to ELEC.17
The methodology includes three steps. First, the general cooperation propensity of ELEC is calculated by the dividing the total number of collaborations by the total number of innovations within
this technology. In a second step, the patents are assigned to the labor market regions according
to the inventor principle. This reflects the regions’ technological endowment. Next, the number of
16
The threshold of r = 0.8 seems to be well fitting in this context because the number of variables is moderately but
sufficiently reduced. In addition, more than eighty percent of an excluded variable’s variance is still ‘explained’ by one
variable included in the analysis. Robustness checks have been conducted showing that the results are not sensitive
with respect to the inclusion of variables that are correlated with 0.8 or above with already considered variables.
17
Lacking data on the quality, i.e. intensity, of the cooperation we observe only cooperation counts. However, it is
acknowledged that the quality of the cooperation is a crucial aspect as well. Note furthermore that the cooperativeness
measures are constructed from patent data while the innovation output measures are obtained from patent applications.
12
cooperation is calculated that can be expected within a region according to this technological endowment. This expectation is estimated by the multiplication of the number of innovations in ELEC
(technological endowment) and the cooperation propensity of ELEC which has been calculated in
step one. In the final step, for each region the number of expected cooperation is divided by the number of observed cooperation. The idea is that in the case of both, expected and observed, cooperation
numbers being equal, this indicates that the regional actors’ cooperation behavior is influenced by
technological patterns alone (see for an extensive description Cantner and Meder, 2007).
Regions in which no cooperation is observed, and given their technological profile, no cooperation
can be expected, are treated the same as regions with no observed cooperation but for which positive
levels of cooperativeness are expected. Empirically, both regions are indicated by zero values. We
are expecting an influence of cooperativeness on regional innovation performance and, thus, regions
lacking cooperativeness are expected to show lower performance levels independently of the reason
for this lack.18
+ 1. This allows an easier interpretation as well as more
The index is made symmetric by I−1
I+1
meaningful graphical representation. Hence, a value of one implies a level of cooperativeness that
corresponds to what can be expected given the technological structure of the region. Lower values
indicated cooperativeness below the expected level and values above one the opposite.
The intra-regional cooperativeness measure (CoopIntra) is constructed on the base of cooperation of
the inventors being located within the same region but are part of different organizations. In case of
inter-regional cooperativeness (CoopInter), cooperation are indicated by the inventors being located
in different German regions. The latter implies that in this paper inter-regional cooperativeness refers
to national cooperation. International cooperation are not considered because of too many zero values.19
Because the cooperativeness measures are based on patent data, we consider a time lag of one year
with the input factors, i.e. the cooperative measures for the year 1999 are based on the patent data of
2000. Surprisingly, intra- and inter-regional cooperativeness measures are only weakly correlated.
Hence, there seems to be a difference in actors’ cooperative attitude with respect to their cooperation
partners’ geographic location.
Moreover, they also show weak correlations with the innovation output measures. For the pooled
data of 1999-2002, the correlation between the two cooperativeness measures and the numbers of
patent applications in ELEC (PAT) is just r = 0.29∗∗∗ for CoopIntra and r = 0.08∗∗∗ in the case of
CoopInter. The correlation between the two measures is only r = 0.01∗∗∗ . This is illustrated also in
Fig. 1 and 2 showing the density of the two cooperativeness measures as well as the corresponding
contour plot.20 In addition, both measures’ histograms are included in the Appendix, see top left
graphs in Fig. 9 and 10.
18
We also estimated the results when assigning a value of one to those regions that are not expected to show cooperations.
The results proofed to be not sensible to this, though.
19
This is certainly an interesting aspect for future research.
20
For the estimating the density we used the truncated gaussian kernel proposed by Daraio and Simar (2007).
13
Contour plot of coop. measures, year 2000
40
of coop.
30
20
10
1.5 p.
o
al
ion
eg
1.0
co 0.5
op
.
co
r
0.5 ra−
Int
0.0
Int 1.5
er
−r
eg 1.0
ion
al
1.0
Inter−regional coop.
50
0.5
Density
1.5
Density of coop. measures, year 2000
0.0
0.5
1.0
1.5
Intra−regional coop.
Figure 1: Density of coop. measures
Figure 2: Contour of coop. measures
The plots illustrate that the masses of observations are characterized by values around one in both cooperativeness measures. However, there is also a significant mass with zero values in both measures.
In addition, a considerable number of observations shows close to zero or zero values in CoopIntra
and values of one or close to one, in CoopInter. Not surprisingly the latter ones are regions with
comparatively low R&D employment in ELEC. Lacking regional alternatives, firms located in such
regions need to cross regional boundaries in order to find cooperation partners.
It strikes furthermore that both measures show almost no correlation to the variables approximating the regions’ input factor endowment, see Table 9 in the Appendix. This may hint at that the
non-technological effect is likely to be of little relevance.
5
Results
5.1
General results
At this point, the analysis does not allow to estimate the significance of the estimated relationship
between cooperativeness and regional innovation performance, i.e. confidence intervals can not be
calculated for Qz .21 Hence, it is necessary to take a look at the numbers of observations backing
the estimated relationships. The histograms for the two cooperativeness measures can be found in
the Appendix in the top left corner of Fig. 9 and 10. They show that for both cooperativeness variables only few observations show values between 0 and 0.5 as well as above 1.5. We refrain from
interpreting these areas. However, while we cannot discuss the interior of the interval ]0,0.5] due to
few observations, there are many observations on both sides of this interval’s borders. Though, the
relationship can be analyzed with respect to differences between regions with zero values and those
with values larger than 0.5, i.e., whether a positive or negative influence on innovation performance
exists when the values in the cooperativeness variables ‘jump’ from 0 to above 0.5.
21
Several robustness checks have been conducted showing the stability of the results. For example, it has been checked
that regions with identical unconditional and conditional performance scores do not cause some of the observed
patterns.
14
The first impression of the results is that they confirm that cooperativeness plays a role for R&D
activities in the German Electrics & Electronics industry. This is shown by that the surfaces, representing the influence of the two types of cooperativeness on regions’ innovation performances depart
from a horizontal plane, see in Fig. 3 (firm-oriented analysis) and 4 (region-oriented analysis).
Confirming the ‘bright’ and ‘dark’ side argumentation, in both firm-oriented and region-oriented
analyses the surfaces take the form of a bump, i.e. with increasing levels of cooperativeness the
surfaces turn upwards indicating positive effects. When exceeding a certain turning point their slope
becomes negative which indicates the existence of negative effects.
Min.
Max.
Mean
Median
TP
TP
firm* regional*
CoopIntrat−1 0.001 1.81 0.78
0.90
1.33 1.25
CoopIntert−1 0.001 1.82 0.87
0.96
1.18 1.03
* According to the maximum value of surfaces of Qz in Fig. 3 and 4.
Table 1: Descriptives of cooperativeness’ impact
The turning point of cooperativeness’ impact is interesting in itself. In case of CoopIntra it is considerably larger than one, while in case of CoopInter it is just somewhat larger than one, see Table
1. As has been argued in Section 4.3 the cooperativeness measures signal the degree to which actors
exploit their intra-regional and inter-regional technological cooperation potential. With respect to
the difference in the levels of intra- and inter-regional cooperativeness at the turning point it can
be inferred that within a certain range the intra-regional cooperation potential can be exploited to a
higher degree than the inter-regional cooperation potential without that firms suffer from negative
effects. A reason for this can be certain advances of geographic proximity compensating some of
the negative effects resulting from too intense intra-regional cooperativeness.
Firm−orientation
Region−orientation
1.0
0.8
Impact on RegIP (Qz)
Impact on RegIP (Qz)
0.8
0.6
0.6
0.4
0.4
0.2
0.2
Int 1.5
ra−
reg 1.0
ion
al
co 0.5
op
.
Int 1.5
ra−
reg 1.0
ion
al
1.5
.
1.0 l coop
na
0.5 −regio
r
te
In
Figure 3: Surface of Qz, Analysis 1
co 0.5
op
.
1.5
.
1.0 l coop
na
0.5 −regio
r
te
In
Figure 4: Surface of Qz, Analysis 2
A more detailed view at the results can be obtained by slicing the surfaces into six parts. This is to
say that the results are divided the into six sub-samples on the basis of their CoopIntra and CoopInter
15
values. The first sub-sample consisting of the 33 percent of observations with the smallest values in
CoopIntra (low intra-regional cooperativeness). The second covers the 33 percent with the largest
values in CoopIntra (high intra-regional cooperativeness). The third represents the 33 percent in
between (medium intra-regional cooperativeness). In a similar manner the observations are divided
according to their values of CoopInter, given us three additional samples: low, medium, and high
inter-regional cooperativeness.
Table 2 summarizes the results in form of an overview-table that rows (CoopInter) and columns
(CoopIntra) correspond to the mentioned slices. The nine resulting cells represent different situations that can exist in regions with respect to the levels of the two cooperativeness types. For each
situation the corresponding impact on the regional innovation performance is given in parenthesis.
In the table, the extreme situations correspond to a lock-in (high CoopIntra and low CoopInter),
and to a lock-out (low CoopIntra and high CoopInter). The middle position accounts for average
cooperativeness as in this case both cooperativeness measures are close to the national average, see
Section 2. Low intra- and inter-regional cooperativeness describes a low general cooperativeness
behavior. The four mix situation are represented by A, B, C, and D. In addition to the table, for the
Effect of intra−regional coop. (region−orientation)
1.0
0.8
0.6
0.2
0.4
Impact on RegIP (Qz)
1.0
0.8
0.6
0.4
0.2
Impact on RegIP (Qz)
1.2
1.2
Effect of intra−regional coop. (firm−orientation)
Level of inter−coop.
0.0
0.5
1.0
medium
0.0
0.0
Level of inter−coop.
medium
low
high
1.5
0.0
Intra−regional coop.
0.5
low
high
1.0
1.5
Intra−regional coop.
Figure 5: CoopIntra, firm-orient.
Figure 6: CoopIntra, region-orient.
three sub-samples that are defined on the basis of the CoopIntra values, the CoopIntra values are
nonparametrically regressed on the obtained Qz values. In case of the sub-samples defined by the
values of CoopInter, the regressions show the relationship between CoopInter and Qz . The regression plots are shown in two dimensional plots (firm-oriented analysis: Fig. 5, 7 and region-oriented
analysis: Fig. 6, 8).22 The idea behind it is to reduce the three-variate relationship between the two
cooperativeness measures and Qz ratios to a bivariate one (see for further details Daraio and Simar,
2007). The dashed regression curve indicates the relationship of the considered cooperativeness
measure for low levels, the solid line for medium levels and the dotted line for high levels of the
cooperativeness type that does not define the sub-sample.
The histograms for each of the six sub-samples are shown in Fig. 9 and 10 allowing for basic robust22
Note that in the two-dimensional plots the turning points may depart from the one in the three-dimensional surfaces.
However, the two-dimensional plots represent only supportive but not definitive illustrations.
16
ness checks. In Fig. 5 and 6, the curves for medium & high inter-regional cooperativeness and in Fig.
7 and 8 the curves for medium & high intra-regional cooperativeness show the behavior described
for the surface view: a curvilinear relationship between the levels of both types of cooperativeness
and regional innovation performance.
1.0
0.8
0.6
0.2
0.4
Impact on RegIP (Qz)
0.8
0.6
0.4
0.2
Impact on RegIP (Qz)
1.0
1.2
Effect of inter−regional coop. (region−orientation)
1.2
Effect of inter−regional coop. (firm−orientation)
0.0
0.5
1.0
Level of intra−regional coop.:
medium
low
high
0.0
0.0
Level of intra−regional coop.:
medium
low
high
1.5
0.0
Inter−regional coop.
0.5
1.0
1.5
Inter−regional coop.
Figure 7: CoopInter, firm-orient.
Figure 8: CoopInter, region-orient.
In general does the curves for medium cooperativeness show as the strongest ‘bumpy’ development
indicating that the effect of increasing intra- or inter-regional cooperativeness seems to be most positive at medium levels of the other type of cooperativeness.
Intra-regional cooperativeness
low
medium
high
Inter-regional
poor coop.
A
lock-in
low
(-)
(-)
(-)
B
average coop.
C
cooperativeness medium
(-)
(+)
(+)
lock-out
D
excessive coop.
high
(-)
(+)
(-)
(+) indicates positive, and (-) negative effects on regional innovation performance
Table 2: Effects of intra- and inter-regional cooperation
Interesting cases are the regression curves for the low intra- and inter-regional cooperativeness. They
represent a relationship that is difficult to be noticed when looking at the surfaces alone. In three out
of the four cases (firm-oriented and region-oriented analyses), they are characterized by a monotonic
decreasing trend, Fig. 5, 7, and 8. Only in Fig. 6 does the curve for low CoopInter and increasing
CoopIntra correspond to an almost horizontal line. This case will be discussed later in the paper.
The monotonic decreasing trend in the other three cases indicates that a sole orientation on intraor inter-regional cooperation lowers the innovation performance. This implies that for cooperativeness values below the turning point, intra- and inter-regional cooperativeness are to a certain extent
17
complements, i.e. they foster innovation performance only in combination.23 The economic rationale behind this finding is that firms need to access specific resources from within as well as from
outside the region. One might think of these as ‘necessary cooperation’ with close customers or
suppliers which cannot be done without. Lacking parts of these necessary cooperation the innovation performance cannot be increased and even decreases when taking additional, but not necessary,
cooperation efforts. Because this is the case for both types of cooperativeness this suggests that the
necessary cooperation partners are located within as well as outside firms’ home regions.
5.2
The impact of the resource endowment
In Section 2.2 we argued that by comparing the results of the firm-oriented and region-oriented analysis insights on the relevance of the non-technological effect can be gained. The effect describes
that certain regional factors not only promote regional innovation performance directly, but in addition can take effect on the level of regional cooperativeness. This in turn influences the innovation
performance. It is however striking that the shape of the surfaces does not change strongly when
considering the regional factors in addition to firm internal R&D efforts as input factors, see Fig. 3
and 4. As discussed in Section 3.3 this can suggests two things. First, the results are fairly robust
with respect to the choice of the considered input factors.
Second, it indicates that the non-technological effect is only weakly relevant for the impact of cooperativeness (network) on regions’ innovation performances. Given the weak correlation of the
cooperativeness measures with the regional factors (see Tab. 8 and 9) this is not too much of a surprise.
While the regional factors seem to be generally of rather small importance for cooperativeness’
effects on innovation performance, in a specific situation however, they are relevant. It has been
pointed out above that the curve for low CoopInter and increasing CoopIntra in the region-oriented
analysis shows almost as a horizontal line (Fig. 6). This behavior is however not observed in
the corresponding curve in the firm-oriented analysis (Fig. 5). The missing decreasing trend in
the region-oriented analysis indicates that the consideration of the regional factors levels out some
(explains parts) of the negative effect of increasing intra-regional but constant low inter-regional cooperativeness.
This is particularly interesting in the extreme situation of a regional lock in as this implies that its
negative effects stem partly from an insufficient endowment with regional factors. Or put differently,
the negative effects of the regional lock-in can be compensated by a supportive factor endowment
in the region. It has to be emphasized though that this observation is supported by a low number of
observations (see the top right histograms in Fig. 9 in the Appendix).
23
Note however that the curves in Fig. 7 and 8 (low intra-regional cooperativeness) are supported by few observations
in the interval ]0,0.7], see top right histogram in Fig. 9 in the Appendix.
18
6
Conclusion
In concepts like the ‘innovative milieu’ and ‘regional innovation system’ it is emphasized that strong
intra-regional cooperativeness particularly enhances regional innovation performance. In contrast,
the lock-in case of the ‘Ruhr-area’ shows that a too strong focus on intra-regional interactions and
a missing willingness or ability to engage in inter-regional cooperation can yield negative effects on
actors innovation activities. In this paper, we conceptualized this as that intra-regional cooperation
can be viewed to have a ‘bright’ side resulting in high innovation performance. At the same time it
can yield a ‘dark’ side causing comparatively low innovation performance. Further, whether a region
profits from the ‘bright’ or suffers from the ‘dark’ side depends not only on the level of intra-regional
cooperativeness but also on the actors’ abilities to cooperate inter-regionally.
While this is commonly accepted and confirmed by qualitative case study research, large-scale quantitative evidence is still missing. Hence, it is still unclear whether these findings can be generalized.
This paper contributes to the literature by providing a quantitative empirical analysis of the effects
of intra-regional and inter-regional cooperativeness on regional innovation performance. More precisely, analyses have been conducted for the case of the Electric & Electronics industry and the
German labor market regions.
By employing cooperativeness measures that are characterized by the absence of ‘technological effects’, introduced by Cantner and Meder (2007), we were able to investigate the role of what can be
considered pure cooperativeness, i.e. the level of cooperativeness that is not affected by the technological potential to cooperate in a region.
We made use of a robust nonparametric production frontier technique developed by Daraio and
Simar (2007). Besides a number of methodological advantages, the nonparametric nature of this
approach allowed to explore the relationship between cooperativeness and innovation performance
in an unrestricted manner.
In general, evidence was found supporting the idea of the existence of a ‘bright’ and a ‘dark’ side
of cooperativeness for regions’ innovation performance. For low as well as for excessive intra- and
inter-regional cooperativeness negative effects have been observed. Below a certain turning point,
increasing cooperativeness of either kind, yields positive effects, though.
In addition, it was confirmed that intra- and interregional cooperativeness are complements. Moreover, the results suggest that the negative effects on innovation performance by lock-in situations,
can partly be compensated by a rich endowment with supportive regional factors, e.g. the presence
of universities.
The present study represents a first attempt to quantitative empirically explore the impact of intraand inter-regional cooperativeness on regions’ innovation performances. Most importantly in future
research the investigations need to be extended to other industries in order to verify the results on a
more general basis. Further, the inclusion of a third cooperativeness measure, namely inter-national
cooperativeness, will certainly be helpful to gain a deeper understanding of how cooperativeness
influences actors’ innovation activities.
19
7
Tables & Figures
Distance
< 50km # 50 < 200km # Share 1999 Share 2000
GRAD ENG (university) 48.8%
29.8%
41.6%
40.7%
GRAD ENG (tc)
42.3%
35.5%
58.4%
59.3%
GRAD NAT (university) 61.2%
14.9.9%
89.7%
89.8%
GRAD NAT* (tc)
45.4%
36.0%
10.3%
10.2%
* No data available, the shares of all technical graduates taken together are used.
# Data based on Legler et al. (2001) but adjusted for inner Germany mobility
Table 3: Graduates Mobility
Empirical values
Estimation
Spill-over source < 50km 200km < < 50km 200km <
GRAD ENG
45,1%
33,1%
44.70% 34.35%
GRAD NAT
60.0%
17.0%
56.34% 29.29%
Estimation based on sum of 1999 and 2000 data.
α
1.4851
1.6358
Table 4: Range of spill-overs of graduates, hyperbolic distribution
Technological fields *
NACE industries**
TF27, TF28, TF29, TF30, TF31 DL31, DL32, DL30
* As defined in Greif and Schmiedl (2002) ** According to the GIC DESTATIS (2002)
Table 5: Definition of the Electrics & Electronics industry according to Broekel (2007)
20
Variable
PAT
Description
Sum of patent applications of
TF27, TF28, TF29 ,TF30, TF31 in Greif and Schmiedl (2002); Greif et al. (2006)
DL 31
R&D employees of DL31
DL 32 30
Sum of R&D employees of industries DL32, DL30
POP DEN
Inhabitants per kilometer2 land area
GDP
Gross domestic product per inhabitant
SERVICE
Production structure specialization index of
WZ03’ category 74: ’other business activities’ (business services) industry
EMP HIGH
Share of employees with high qualification
ELEC PS
Production structure specialization index (PS) of RD ELEC)
EMPL ELEC Absolute regional employment of ELEC
GRAD ENG Distributed engineering graduates per employee
GRAD NAT Distributed natural and science graduates per employee
All shares refer to total employment
Table 6: Variables, estimation base, and sources
Variable
Firm-level orientation Regional orientation
Innovation output measures
P ATt
×
×
Input factors
DL32 30t−1
×
×
DL31t−1
×
×
SIZEt−1
×
EM P L ELECt−1
−
EM P HIGHt−1
×
GDPt−1
×
ELEC P St−1
−
P OP DENt−1
−
SERV ICEt−1
×
GRAD EN Gt−1
×
GRAD N ATt−1
−
× Indicates inclusion, − exclusion because of correlation.
Table 7: Variables and their employment
21
DL32 DL30
DL31
EMPL ELEC
ELEC PS
POP DEN
DistriNAT
DistriENG
GDP
EMP High
Service
Firm size
CoopIntrat−1
CoopIntert−1
DL32 DL30
1∗∗∗
0.48∗∗∗
0.75∗∗∗
0.62∗∗∗
0.5∗∗∗
−0.05∗
−0.12∗∗∗
0.45∗∗∗
0.38∗∗∗
0.11∗∗∗
0.53∗∗∗
0.22∗∗∗
0.07∗∗∗
DL31
0.48∗∗∗
1∗∗∗
0.83∗∗∗
0.73∗∗∗
0.44∗∗∗
−0.1∗∗∗
−0.16∗∗∗
0.45∗∗∗
0.33∗∗∗
0.02
0.65∗∗∗
0.17∗∗∗
0.06∗∗
EMPL ELEC
0.75∗∗∗
0.83∗∗∗
1∗∗∗
0.84∗∗∗
0.59∗∗∗
−0.12∗∗∗
−0.2∗∗∗
0.58∗∗∗
0.43∗∗∗
0.09∗∗∗
0.78∗∗∗
0.23∗∗∗
0.1∗∗∗
ELEC PS
0.62∗∗∗
0.73∗∗∗
0.84∗∗∗
1∗∗∗
0.23∗∗∗
0.02
0.01
0.25∗∗∗
0.08∗∗∗
0.02
0.85∗∗∗
0.14∗∗∗
0.05∗
POP DEN
0.5∗∗∗
0.44∗∗∗
0.59∗∗∗
0.23∗∗∗
1∗∗∗
−0.21∗∗∗
−0.32∗∗∗
0.81∗∗∗
0.77∗∗∗
0.29∗∗∗
0.26∗∗∗
0.2∗∗∗
0.17∗∗∗
DistriNAT
−0.05∗
−0.1∗∗∗
−0.12∗∗∗
0.02
−0.21∗∗∗
1∗∗∗
0.8∗∗∗
−0.08∗∗∗
−0.21∗∗∗
−0.12∗∗∗
−0.02
0.06∗∗
−0.02
DistriENG
−0.12∗∗∗
−0.16∗∗∗
−0.2∗∗∗
0.01
−0.32∗∗∗
0.8∗∗∗
1∗∗∗
−0.16∗∗∗
−0.31∗∗∗
−0.13∗∗∗
−0.05∗
0.02
−0.06∗∗
Table 8: Pearson’s correlation coefficients I
DL32 DL30
DL31
EMPL ELEC
ELEC PS
POP DEN
DistriNAT
DistriENG
GDP
EMP High
Service
Firm size
CoopIntrat−1
CoopIntert−1
GDP
0.45∗∗∗
0.45∗∗∗
0.58∗∗∗
0.25∗∗∗
0.81∗∗∗
−0.08∗∗∗
−0.16∗∗∗
1∗∗∗
0.68∗∗∗
0.22∗∗∗
0.32∗∗∗
0.2∗∗∗
0.11∗∗∗
EMP High
0.38∗∗∗
0.33∗∗∗
0.43∗∗∗
0.08∗∗∗
0.77∗∗∗
−0.21∗∗∗
−0.31∗∗∗
0.68∗∗∗
1∗∗∗
0.19∗∗∗
0.08∗∗∗
0.11∗∗∗
0.16∗∗∗
Service
0.11∗∗∗
0.02
0.09∗∗∗
0.02
0.29∗∗∗
−0.12∗∗∗
−0.13∗∗∗
0.22∗∗∗
0.19∗∗∗
1∗∗∗
0.05∗
0.06∗∗
0.03
SIZE
0.53∗∗∗
0.65∗∗∗
0.78∗∗∗
0.85∗∗∗
0.26∗∗∗
−0.02
−0.05∗
0.32∗∗∗
0.08∗∗∗
0.05∗
1∗∗∗
0.12∗∗∗
0.02
CoopIntrat−1
0.22∗∗∗
0.17∗∗∗
0.23∗∗∗
0.14∗∗∗
0.2∗∗∗
0.06∗∗
0.02
0.2∗∗∗
0.11∗∗∗
0.06∗∗
0.12∗∗∗
1∗∗∗
0.08∗∗∗
Table 9: Pearson’s correlation coefficients II
22
CoopIntert−1
0.07∗∗∗
0.06∗∗
0.1∗∗∗
0.05∗
0.17∗∗∗
−0.02
−0.06∗∗
0.11∗∗∗
0.16∗∗∗
0.03
0.02
0.08∗∗∗
1∗∗∗
Freq. CoopIntra for low CoopInter
0
0.0
0.5
1.0
1.5
2.0
0.0
0.5
1.0
1.5
CoopIntra
Freq. CoopIntra for medium CoopInter
Freq. CoopIntra for high CoopInter
2.0
80
60
0
20
20
40
40
Frequency
100
60
80
140
CoopIntra
0
Frequency
100
Frequency
50
200
0
100
Frequency
300
150
400
Freq. CoopIntra
0.0
0.5
1.0
1.5
2.0
0.0
CoopIntra
0.5
1.0
CoopIntra
Figure 9: Histograms of CoopIntra
23
1.5
2.0
Freq. CoopInter for low CoopIntra
100
80
0
0
20
50
40
60
Frequency
150
100
Frequency
200
120
250
Freq. CoopInter
0.0
0.5
1.0
1.5
2.0
0.0
1.0
1.5
2.0
CoopInter
Freq. CoopInter for medium CoopIntra
Freq. CoopInter for high CoopIntra
40
0
20
20
40
Frequency
60
60
80
80
CoopInter
0
Frequency
0.5
0.0
0.5
1.0
1.5
0.0
CoopInter
0.5
1.0
CoopInter
Figure 10: Histograms of CoopInter
24
1.5
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