Modeling the Innovative Behavior of Firms within the Manufacturing
Sector in a Border Regional Perspective
Nils Karl Sørensen∗ and Andreas Cornett∗∗
University of Southern Denmark
E-mail: [email protected] and [email protected]
Abstract
This paper examines innovative behavior of firms by use of probit models. Based on a
questionnaire undertaken in Western Denmark (Fuen and Jutland) and Northern Germany
(Schleswig-Holstein and Mecklenburg-Vorpommern) a model is set up to identify the
factors influencing the probability of a successful innovative action.
After a presentation of the design of the questionnaire, and a discussion of the quality of the
sample several probit models are estimated. In order to examine the influence of the bias
imposed by firms not undertaking any innovative behavior at all the sample is reestimated
by use of the Heckman procedure set up in a bivariate set up.
It is found that factors such as efficient use of technology and business culture contribute
positively to a successful innovation, whereas supply chains contribute negatively. Among
the background variables joint ventures contribute positively, whereas public support not
has any significant influence. Many of these results are confirmed in a border regional
perspective. Finally, taking into account the bias strengthens the results.
Key-words: Innovation, regional development, clusters, probit models, sample selection
bias.
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The present paper is a revised version of a paper entitled “Systems of Innovation and Linkages in an Interregional
Perspective: A Comparative Analysis of Northern Germany and Western Denmark” published as Chapter 17 (page 475498) in: Karlsson C., Johansson B, and Stough R.R. (editors) (2005) “Industrial Clusters and Inter-Firm Networks”.
Edgar Elgar Publishers ISBN 1-88542-010-1.
The paper was originally prepared for ‘The Uddevalla Symposium 2004’ “Regions in Competition and Co-operation” in
Fredriksstad, Norway, June 17–19, 2004. The authors like to thank for valuable comments from the discussants at the
seminar especially Pillip McCann, University of Reading, UK.
∗
Department of Environmental and Business Studies, Niels Bohrs vej 9, DK-6700 Esbjerg, Denmark. Phone +45 6550 4183.
Grundtvigs Allé 150, DK-6400 Sønderborg, Denmark. Phone +45 6550 1211.
∗∗
1. Introduction
Innovation and innovation policy has become a central element in regional development
policy in most industrialized countries. It is widely accepted that companies and firms
conduct their innovative activities within a framework of intra-firm relations and external
linkages to other companies, the advisory system and R & D institutions.
The findings presented in the present paper are based on a survey of small and medium
sized firms within the manufacturing sector undertaken in the winter 2001/2002 in Western
Denmark and Northern Germany conducted in co-operation between the University of
Flensburg and the University of Southern Denmark and supported by the EU Interreg IIIA.
This paper aims to identify determinants of innovative processes in a regional perspective in
two different countries as well as for the border region between them. This is undertaken by
use of a probit model set up determine the probability for the success or failure of an
innovative action. The primary research theme is to identify differences, and to examine
whether they are caused by differences in the national systems of innovation, sectoral
affiliation of the firms, or size.
The paper is organized as follows. Section 1 provides a brief presentation of the theoretical
concept of regional and national systems of innovation and the methodology, Section 3 is
the empirical set up with results from estimation of probit models. Finally, some
conclusions from the study are drawn in Section 4.
2. Analytical Framework
Innovation policy is an important aspect of industrial policy and regional development
policy nowadays, and comparative studies have focused on the differences of the national
and regional systems of innovation and their impact on regional development and
disparities, see Asheim et.al. (2003) as well as Acs and Varga (2002a,b). For a
comprehensive discussion of the theoretical aspects of the project see Cornett and Sørensen
2003 as well as Cornett and Freytag (2002). In the analysis of innovative behavior and the
2
targets of innovation policy, the distinction between product and process innovation is
useful, not only in the investigation of innovation patterns of firms or industries, but also
with regard to an evaluation of innovation as an instrument in regional development policy.
Figure 1: Aspects of the concept of Innovation
Old product
New product
Old process
No innovation
Product innovation
New process
Process innovation
Product and Process
innovation
Source: Cornett and Freytag (2002) p. 205.
Figure 1 attempt to set up the various types of innovative behavior according to Cornett and
Freytag (2002). Product innovation deals with the process of launching a new product, both
technically and in a marketing perspective, whereas process innovation focuses on the
process of production and has usually a broader impact than product innovation on the
competencies of a firm. The figure below summarizes the two concepts briefly.
Regional Development and Innovation Policy
From a regional economic point of view, the extra-firm spin off of the innovative activity
are the single most beneficiary aspects. In this perspective, process innovation is probably
most important due to the broader potential regional impact on the regions level of
competence and knowledge base.
The creation of clusters and the nature of region or national systems of innovation have
become a central building block in regional and industrial policy. Within the last decade,
more formal systems of (regional) knowledge management have been added. During the
eighties and nineties, there was a tied competition for funding between industrial and
regional targets in business development policy in many countries often reinforced by the
3
participation in EU research and development programs (R&D). Particularly in North
Western Europe, the regional oriented R&D policy was the main beneficiary.
Political signals in Denmark, but also in Germany, have shifted to some extent since the late
1990s. Increasing attention has been on the reorganisation of regional development and
innovation policy on both sides of the Danish German border with the aim to intensify and
improve regional development initiatives. Growing attention has been given to the
importance of supporting the creation of clusters and the regional aspects of innovation
supporting measures. In this perspective, innovation policy became a tool in regional policy
- or to put it in another way, regional policy adapted innovation policy as an integrated part
of business development policy. The mushrooming of business incubators, Science Parks
and Technology Centres is the best proof of this tendency; see Ministry of Economics and
Business (2003).
Figure 2: Innovative Space: Actors and Relations
Innovative space
Border
Regional
Firm
National system
of innovation
Relation included in analysis
Knowledge Institution
Source: Cornett et. al. (2002).
4
To what extent this can contribute significant to the main objective of regional policy, to
improve living conditions and equalize disparities within a given area, is of course another
question. The strategies used in regional business development policy have changed over
time, mostly reflecting the general approach to economic policy. Figure 2 above sketches
the principal components and linkages.
3. Empirical Set Up
Description of Sample
As stressed the aim of the study has been to analyze the conditions for undertaking
innovations by firms located in three different environments, namely in Northern Germany,
Western Denmark and the Danish-German border region. Geographically, our analysis is
characterized by the fact that the area is located outside the metropolitan regions of the two
countries, but contains economically strong as well as week regions. In particular the
German part of the border region falls into the last group. The county of Sønderjylland in
Denmark represents very much the Danish average from an overall economic point of view.
The Hamburg region in Germany is among the wealthiest regions in Europe, and also parts
of western Denmark are performing above the Danish national average, i.e. the Aarhus area
and the so-called Triangle Region in Jutland (Kolding-Fredericia-Vejle).
Geographically, the German parts of our survey are the three northern states (Bundesländer)
Mecklenburg-Vorpommern, Hamburg, and Schleswig-Holstein. Totally, in the area there
were 221,500 registered firms within the sectors considered. The Western Denmark
constitutes of all Danish countries of Fyn and Jylland. The motivation of the choice of the
Danish area is that the Danish Ministry of Economics and Business has undertaken a special
attempt of regional economic policy. In total, the number of firms in this area within the
sectors in consideration accounts to about 8,900. Finally, the Danish-German border region
constitutes on the German side “Planraum V”. This is equal to the counties Nordfriesland,
Schleswig-Flensburg and the city of Flensborg. On the Danish side the border region
5
constitutes the County of Sønderjylland. Together the parts forming the Danish-German
border region equal the historical three Duchies’. The division of the area into a German and
a Danish part was undertaken as a part of the Versailles treaty, and constituted by a vote
followed by a separation into a Danish and a German part in 1920.
The study focuses on seven sectors or branches within the manufacturing sector all given
priority as a part of the Danish regional policy attempt mentioned above. The targets for the
two surveys are small and medium-sized firms.
The study has been undertaken by a questionnaire consisting of similar questions in the two
countries. However, the conduct of study in the two countries has been different. In
Denmark, the questionnaire was sent to the firms by mail, whereas in Germany the firms
were reached by telephone interviews. The sample of firms was drawn by random selection
of telephone numbers. In the German survey, the response ratio accounted to 30.7 per cent
or 380 firms out of a total of 1,237 firms that were contacted. In Denmark, the response
ratio accounted to only 12.1 per cent or 340 firms. In Denmark, the sample of the
questionnaire was drawn from a private company (Købmandsstandens Oplysningsbureau)
that collects and organizes firm-specific information from the authorities to who all
companies in Denmark have a legal obligation to report their economic performance. From
this source, a list of firms was obtained by use of the “COM-Direct” data base. A simple
uniform stratification by sector has been undertaken, and a total of 2,800 firms all located in
the Fyn and Jylland area (Western Denmark) was selected for the questionnaire, i.e. 400
firms per sector.
Out of a total of the 720 firms in the total study, 63 could be identified as located in the
Danish-German border region. Among these, 42 firms were located on the Danish side of
the border (the county of Sønderjylland), whereas 21 firms were located on the German side
of the border (Plannungsraum V or Schleswig). The identification of the firms relative to the
border region was undertaken by an investigation of a list of the relevant postal zip-codes.
For this study, the Danish-German border region is considered as a sub group. This means
that these statistics also is included as a part of the two national groups. Further, although
6
the advisory systems, etc. may be different across borders, we consider the Danish as well
as the German border statistics as one single group. The motivation for this is twofold. First,
we are interested in identifying a special “border-spirit” or attitude. Second, as stressed
above the number of observations is quite limited. With only 63 observations available for
estimation purposes the results for the border region should be interpreted with care.
Table 1: Industries and Presentation of Survey Statistics
Sector:
Denmark
Reply
Sample
Number
Food
Textiles
Wood and furniture
Rubber and plastic
Iron and metals
Electronics
Transport equipment
Not to locate
Total
Note:
Source:
Per cent
Per cent
Border region
Sample
Total
Number
Per cent
52
38
49
57
86
46
12
15.3
11.2
14.4
16.8
25.3
13.5
3.5
13.0
9.5
12.3
14.3
21.5
11.5
3.0
1,418
1,013
2,037
410
2,562
1,004
412
16.0
11.4
23.0
4.6
28.9
11.3
4.7
340
100.0
12.1
8,856
100.0
Number
10
4
10
5
16
9
3
6
63
Per cent
17.5
7.0
17.5
8.9
28.1
15.8
5.2
100.0
Germany
Sample
Number
Per cent
38
4
32
22
57
72
11
144
380
16.1
1.7
13.6
9.3
24.2
30.6
4.5
100.0
“Food” covers NACE group15-15.999, “Textiles” NACE group 17-17.999, “Wood and furniture” NACE
group 20-20.999 and 36-36.999, “Rubber and plastic” NACE group 25-25.999, “Iron and metals” NACE
group 27-28.999, “Electronics” NACE group 31-32.999, and “Transport equipment NACE group 3535.999. Statistics on the total number of firms has been drawn from Statistics Denmark.
For comparison purposes the group “not to locate” is excluded when calculating the per cent distribution.
Own survey and Statistics Denmark.
Table 1 brings some survey statistics. For the Danish material it is possible to obtain
information on the distribution of the total volume of firms by use of material from
Statistics Denmark (The column “total”). This enables us to examine the representative of
the Danish survey. Especially for “textiles” and “transport equipment”, the response rate has
been very low. This is far below the response rate in e.g. the official statistics. The
efficiency of the distribution of firms by sector in the sample can be examined by
performing a setting up a chi-square test for independence using the total as the expected
reference. A chi-squared value of 36.8 with 6 degrees of freedom is obtained. With a critical
value equal to 5.35 we accept that the two distributions not are equal. This result is solely
due to the strong under-representation of the sector “Wood and furniture” in the sample.
7
With regard to the German survey it has not been possible to classify 144 firms by sector.
This equals nearly 38 per cent of the sample! Compared to the Danish sample it seems as if
“textiles” and “wood and furniture” is strongly underrepresented, whereas the reverse is true
for “electronics”. However, the information on this sample is extremely poor.
Description of Methodology
As stressed, each firm was asked to identify the most successful and the most unsuccessful
product development ever undertaken. For each innovation, an evaluation was undertaken
and ranked with regard to the use of seven items, namely technology, distribution chains,
input suppliers, production units, divisions and department corporations, the influence of
firm culture, and marketing costs. For each item a ranking into four categories was
undertaken, i.e. 0= no knowledge, 1= not true, 2= partly true, and 3= precisely true, all in
perspective to the success or failure of an innovative action.
As all firms were asked to identify the best as well as the worst product development case
there is a total data set of 1,440 observations, i.e. associated with the 720 firms listed in
Table 1. Therefore, for each firm it is possible to identify the characteristics of a successful
innovation as well as a failed innovation. We can access these events with the probability
one if the event (innovation) is successful, and zero if it is a failure. Using standard notation,
see for example Gujarati (2003) or Green (2000), such a model can be written as:
P( IBW ) = X ′β + ε
where X’ is a column vector of explanatory variables, β is a column vector of explanatory
variables’ parameters, and ε is the error term. IBW is the probability variable. If IBW=1 the
innovation is a success, whereas if IBW=0 the innovation is a failure. Summing up we
estimate P (success of a specific innovation project) given the included variables.
Estimation of several probit models by use of maximum likelihood method can be found in
Table 2. All estimations has been undertaken in RATS. The table presents two sets of
8
estimates for three areas, i.e. Western Denmark, the Danish-German border region, and the
Northern Germany as defined earlier. The left side of the table brings the estimates for the
“full data set”. From the Danish material, 464 out of 680 observations had a complete set of
answers with 267 successful innovations (denoted in the table by “cases correct). For the
German material, 401 out of 760 observations had a complete set of answers with 269
successful innovations. In the German set, especially many failed to provide information
with regard to the unsuccessful innovations. Finally, for the border regional innovations 60
out of 126 observations had a complete set of answers with 45 successful innovations.
Table 2: Results from Probit Estimations of Firm Behaviour on Innovation
Full data set
Denmark
coef
sd
Constant
Technology
Distribution
Supplier
Production unit
Department
Culture
Marketing cost
Joint venture
Public support
Age of firm
Employment
Ownership
Tec. complex
Patent
Turnover
Export share
Export age
Cases correct
Log L
Observations
Pseudo R2
−.21
.14
.03
−.20
.04
.09
.17
−.05
(.16)
(.09)*
(.09)
(.09)**
(.09)
(.10)
(.09)*
(.09)
267
−310.29
464
0.12
Border region
coef
sd
−.31
.29
−.71
.12
.67
.76
.77
−.06
(.62)
(.27)
(.30)**
(.27)
(.28)
(.31)**
(.39)**
(.33)
45
−27.76
60
0.35
Reduced data set
Germany
coef
sd
Denmark
coef
sd
−.08
.30
−.37
−.02
.01
.11
.32
−.09
.44
.12
−.15
−.13
−.11
.02
.20
−.10
.18
.03
.07
−.07
−.06
−.08
.28
−.04
−.01
−.02
(.24)
(.08)***
(.08)***
(.09)
(.08)
(.10)
(.10)***
(.10)
269
−236.70
401
0.21
Border region
Coef
Sd
(.66)
−1.08 (.55)**
**
(.06)
−.48 (.63)
(.08)*
−1.46 (.81)*
(.07)*
−1.98 (.97)**
(.16)
−.39 (.57)
(.13)
2.10 (.95)***
*
(.11)
2.22 (.98)***
(.20)
2.40 (1.63)
(.09)**
2.24 (.64)***
(.22)
2.75 (1.44)*
*
(.04)
−.50 (.46)
(.13)
.58 (.43)
(.12)
−.01 (.39)
(.16)
2.10 (1.14)*
*
(.15)
1.57 (.81)*
***
(.01)
.89 (.61)
(.04)
.01 (.02)
(.06)
.54 (.92)
112
36
−89.38
−14.88
160
41
0.16
0.54
Germany
coef
Sd
.72 (.47)
.38 (.15)**
−.54 (.14)***
−.28 (.14)***
−.03 (.13)
.04 (.17)
.33 (.16)**
−.36 (.15)**
.23 (.13)**
−.07 (.28)
−.04 (.11)
.08 (.13)
.06 (.12)
−.02 (.17)
.25 (.14)*
.09 (.12)
.03 (.01)***
−.48 (.26)*
148
−88.44
189
0.24
Note: sd is the standard deviation. A * indicates significance at the 10 percent level, a ** indicate significance at the 5
percent level, whereas a *** indicates significance at the 1 percent level by use of the t-test of coefficient significance.
“Cases correct” is the number of success, i.e. that the value of the dependent variable equals one (IBW=1 meaning that
the project has been a success).
9
Discussion of Results of the Estimations from the “Full Data Set”
Turning first to the results for the “full data set” both differences and similarities are
observed. For all of the estimations three coefficients are significant. For the German as
well as the Danish sample, knowledge of the technology and an efficient business culture
contribute positively to innovative processes. The last finding is also true for the borderregional firms. For the Danish firms it is found that the presence of suppliers of intermediate
components influences the process negatively. For the border-regional firms as well as the
German sample the use of new distributional canals contributes negatively to the innovative
process. For export orientated firms introduction of new products on foreign markets could
give rise to a meeting with barriers to entry, different legislation, language problems etc.
Discussion of Results of the Estimations from the “Reduced Data Set”
The results shown on the right side of the table refer to the “reduced data set”. In this data
set a number of firm characteristics are related to the innovative process. In this case a
process of reduction of the information available is necessary in order not to over identify
the model. This is so because the full data set features positive as well as negative
innovations for each firm.
For each of the 699 firms in the two surveys the innovation data has been examined, and the
successful and the failure innovations have been ranked according to performance of the
answers of the seven items with regard to the four categories of answers. For each firm the
most “true” answers have been selected, and this has classified the firms as “innovative” or
“not innovative”. In total, 160 Danish and 189 German cases turned out to be useful. One
reason for this quite large reduction in the number of cases origins from the fact that only
firms that have reported external orientation (export performance) are included in the
analysis. Luckily, most of the border-regional firms are engaged in trade (across the border),
so happily the number of cases here is not reduced substantially.
The number of “cases correct” in the table reflects the number of positive innovations
undertaken (IBW=1). In general, we observe a bias towards positive innovations in the
10
surveys. For the Danish survey, 70 per cent of the cases considered are positive, whereas
this number for the German sample equals 78 per cent. Finally, for the border region the
percentage equals 88 per cent. Please note that this only relates to the innovations in
consideration, at another point in time this behaviour may be different.
On comparing the seven parameters included in the “full data set” as well as the “reduced
data set”, it is observed that the size of the estimated coefficients in general increases when
estimating the “reduced data set”. This is especially true for the border-regional sample.
This is due to the smaller number of observations used in the “reduced data set”.
Compared to the results for the “full data set” for the seven parameters directly related to the
innovative process it is evident that the coefficients are quite consistent with regard to sign.
However, the inclusion of the background variables increases the number of significant
variables. For all the samples the coefficient related to distribution and the influence of
supply chains is negatively significant.
Discussion of Results of the Estimates of Background Parameters from the “full data set”
The first two variables considered is the significance of joint corporations, and the
influences of public support, e.g. the use of knowledge institutions like universities, service
environments etc. According to Figure 2, these variables seek to determine the nature of the
innovative space relative to the process of development. Although both variables contribute
positively to the innovative process besides from the German case on public support, only
the joint corporations seem to be of significance. Establishment of clusters outside the
public innovative environments is a result of importance.
This result is quite interesting. Based on our samples it seems that the role of for example
public innovation programs is of limited influence at least for firms within the sectors in
consideration. In order to investigate this issue further, see Table 3 below. For each survey
the table brings information on the relation between the success of the innovative process
11
and joint ventures and public support respectively for the cases considered in the “reduced
data set”.
Table 3: The Influence of Joint Ventures and Public Programs on the Innovative Behaviour
Joint
venture
Public
support
Innovation:
Used
Not used
Total
Used
Not used
Total
Success
68
47
115
17
72
89
Denmark
Failure
28
17
45
37
34
71
Total
96
64
160
54
106
160
Border region
Success Failure
Total
13
10
25
11
5
16
24
15
41
8
4
12
18
11
29
26
15
41
Success
102
34
136
28
119
147
Germany
Failure
35
18
53
10
32
42
Total
137
52
189
38
151
189
Analyzing first the case of joint venture the rate of success amounted to 42.5 per cent for the
Danish survey, 31 per cent for the border-regional firms, and finally 54 per cent for the
German survey. With regard to the success of the public supported innovations, the rate of
success is much lower and amounts to 10.6, 19.5, and 14.8 per cent in the three cases,
respectively. However, the use of public support seems rather limited. In the Danish case
about 33.8 per cent of the cases took advantage of public support or advisory systems,
whereas the figures for the two other samples are 29.3 and 20.1 per cent respectively.
Especially the figure for the German survey seems to be significant lower.
Age of the firm seems only to be of positive significance for the Danish firms. Of the firms
before 1950 a total of 64 per cent had success with innovations whereas the same number in
the German sample was 10 per cent point lower. The reason for a positive contribute from
the age variable is doubtful The reason for age to be significant could be that established
firms have more experience from former innovative efforts, and this could serve as help for
the firm in a new innovative endeavor. One could argue that the German firms may be
newer, and primarily establish in the post war period. However, this is not confirmed by
data. About 26 per cent of the Danish firms were established before 1950, whereas the same
number for the German sample equals nearly 32 per cent.
12
Ownership and employment are not found to have any significant influence on innovative
processes in any of the surveys. This may be surprising. For instance, one could expect that
large firms had more potential to innovate, or reverse that small firms could generate
positive synergy effects (like e.g. the “Intel”-effect) that could serve as basis for an
innovative environment. The parameter technological complexity relates to the complexity
of the production process relative to the innovation. This parameter is found only to be weak
positively significant for the border-regional survey. Complex innovative firms are found in
the Danish-German border region, and these firms could make the positive contribution.
In general, the presence of patents is found to have a weak positive significance on the
innovative behavior. This issue is further investigated in Table 4. Generally, as the number
of patents increases the relative success of an innovation increases. However, in the
majority of cases patent rights were not present, and this did not prevent an innovation from
being a success. In the Danish case patents were present in 30 per cent of the successful
innovations. The similar figures for the border region and Germany amounted to 31 and 57
per cent respectively.
Table 4: The Influence of Patent Ownerships on the Innovative Behaviour
Innovation:
Patents:
None
1 to 3
4 or more
Total
Total, pct.
Success
Denmark
Failure
76
21
12
109
68
41
7
3
51
32
Total
117
28
15
160
100
Success
Border region
Failure
Total
18
2
6
26
63
10
3
2
15
37
28
5
8
41
100
Success
58
30
47
135
71
Germany
Failure
21
15
18
54
29
Total
79
45
65
189
100
If the turnover is high, this revenue could be spent on the development of new products.
Such a hypothesis is not confirmed by the data set. For the Danish survey, a reverse pattern
is found, and for the border region as well as for Germany, the coefficient is not significant.
Finally, two variables measuring the international orientation of the firm are included,
namely the export share and export age. Both variables are significant for the German
13
survey. Export age comes out with a negative significant coefficient. Perhaps the reverse
should be expected. Firms that have been established on the export markets for a long time
should have more knowledge with regard to the market structure, market characteristics etc.
The export share of the firms is considered more in detail in Table 5. Here, the average
export share is shown along with the standard deviation. In general, it is observed that the
firms that undertake successful innovations are more open, and consequently more
competitive than the non successful innovators. Although the German firms are the most
closed of the firms, the difference in export share between the successful and unsuccessful
firms is so large that the variable turns out to be significant.
This Table also provides a fine statement of the “Linder”-hypothesis of economics
openness. The Danish economy is smaller and more open than the German. This is reflected
in the higher export share. Interestingly, the difference between the two export shares for
Denmark is quite low. For the border region we should expect the highest export share, and
this pattern is also confirmed by the survey.
Table 5: Descriptive Statistics on Export Share and Innovative Behaviour
Innovation:
Mean, pct
Std.
Observations
Success
Denmark
Failure
46.2
32.4
109
42.6
34.8
51
Total
44.3
34.2
160
Success
Border region
Failure
Total
50.1
31.0
26
44.3
32.2
15
46.7
31.5
41
Success
Germany
Failure
38.7
27.1
135
30.9
27.5
55
Total
31.8
27.8
189
Common for both sets of estimations in Table 2 is that the Danish results seem to be poorly
measured by the pseudo R2, and as indicated by the log likelihood. For some reason there
seems to be more “noise” in the Danish questionnaire. Besides a real statistical inference
this could be a result of the survey method, i.e. that the telephone interview method
undertaken in the German survey produced more reliable results.
14
Examination of Sample Selection Bias
To this point we have considered two outcomes relative to innovative behaviour namely
successful and non-successful actions. However, a third outcome is also possible according
to Figure 1. Consider a firm with “business as usual” behaviour. This firm is just supplying
its products to a market. During the survey period such a firm has not undertaken any
innovative actions. As a result such a firm may have responded to the questionnaire, but
without answering the questions related to the innovative behaviour.
Actually this has been true for quite a large number of cases. Consider for example the 340
cases for the Danish survey reported in Table 1 giving a total of 640 positive or negative
innovative cases to be used for estimation purposes in the first column in Table 2.
Inspection of this table reveals that only 464 cases involved answers related to innovative
behavior whereas the remaining 176 cases could not be used, because they did not relate to
innovative behavior. These “business as usual cases” consisted of “blank” responses, and
were excluded from the estimation procedure. The 176 cases equal 27.5 percent of the
material. For the Border region and the German survey the similar shares equal 52.4 and 51
percent respectively. Consequently, the bias involved by the magnitude of the shares must
be examined.
Potential problems with sample selection bias are handled in the econometric model
outlined above by employing an extension of the standard Heckman (1976, 1979) procedure
in a probit set up. This model was introduced by Van De Ven and Van Praag (1981).
Results from re-estimation of the probit model taking into account the bias from noninnovating firm are found in Table 6.
There are several complications accompanied with this estimation procedure. In probit
models the likelihood function may be more complicated. In addition, the truncation of the
data set has to be identified. We here assume that “business as usual” has no influence at all
on innovative behaviour. Consequently, we use a model with bottom truncation. Within a
loop with 100 interactions of the Heckman transformation based on the initial ML-estimates
15
from Table 2 we obtain the results in Table 6. Indirectly, we apply a two-step procedure, see
also Green (2000).
Table 6: Estimates of the Probit Model with Heckman Correction on Innovation Behaviour
Full data set
Denmark
coef
sd
Constant
Technology
Distribution
Supplier
Production unit
Department
Culture
Marketing cost
Joint venture
Public support
Age of firm
Employment
Ownership
Tec. complex
Patent
Turnover
Export share
Export age
Log L
Pseudo R2
−1.56
.11
−.01
.07
.05
.02
.11
−.09
(.99)
(.42)
(.38)
(.34)
(.43)
(.42)
(.39)
(.46)
− 318.51
.07
Reduced data set
Border region
coef
sd
Germany
coef
sd
Denmark
coef
sd
−.03
.16
−.34
.15
−.37
.37
.29
−.01
−.05
.21
−.27
.01
.01
.05
.20
−.05
.49 (.91)
.16 (.08)*
−.17 (.04)***
−.07 (.02)***
−.20 (.37)
.03 (.09)
.22 (.09)***
−.21 (.22)
.09 (.05)*
−.01 (.41)
−.04 (.17)
−.02 (.13)
−.01 (.24)
−.11 (.45)
.17 (.10)
−.00 (.02)
−.00 (.01)
−.01 (.01)
− 90.17
.28
(.37)
(.17)
(.20)*
(.20)
(.28)
(.19)**
(.13)***
(.13)
− 29.18
.55
(.25)
(.09)**
(.10)***
(0.9)
(.08)
(.09)
(.09)**
(.09)
− 241.70
.42
Border region
coef
sd
−.40 (.81)
−.20 (.50)
−.28 (.16)*
−.25 (.14)*
−.12 (.46)
.67 (.38)*
.69 (.68)*
.09 (.52)
.63 (.36)*
−.97 (.94)
.07 (.31)
−.02 (.36)
−.00 (.37)
−.45 (.55)
−.13 (.41)
.07 (.33)
.00 (.01)
.74 (.88)
− 15.32
.61
Germany
coef
sd
.33 (.10)***
.14 (.07)**
−.23 (.07)***
−.09 (.05)**
.01 (.05)
−.02 (.07)
.12 (.07)*
−.13 (.07)*
−.10 (.11)
−.01 (.10)
−.02 (.04)
.01 (.05)
−.03 (.05)
.01 (.06)
−.02 (.07)
.03 (.06)
.01 (.02)
−.14 (.07)*
− 89.11
.45
Note: See notes to Table 2. Number of observations and “Cases Correct” are as reported in Table 2.
By comparison of the results in Table 2 and 6 an interpretation of the bias can be obtained.
Moving first to the “full data set” many of the conclusions observed from Table 2 are still
valid. However, the model for Denmark is especially poor. In general, the size of the
coefficients approaches more closely around zero in the bias corrected estimates stressing
the impact of the truncation. Especially the model for the German survey is very consistent
with the results reported earlier.
4. Conclusion
Innovation is one of the central elements of the business development policy in Germany as
well as in Denmark, and measures of restructuring have taken place in both countries on the
16
national as well as on the regional level. Structural changes in the regional economies are
needed due to changes in the international production system as well as a consequence of
changes in the internal economic landscape.
Based on a probit estimation, several factors are found to influence on the success on an
innovative action. These findings are for the full data set also valid when estimations are
provided by sectors.
Developed technology, and efficient business culture influence innovation positively,
whereas supply chains influence negatively. This may be due to the time horizon involved
in changing a supply chain. This result is true both for an over identified data set as well as
for a reduced data set. Based on the latter, the existence of joint ventures among firms
significantly contributes to the success of an innovation whereas public support is not found
to be significant. Further factors like patents, high profit rate, and firm experience contribute
significantly to the success of an innovative action. Finally, international orientation of the
production with a high content of trade is found not to influence innovation.
Regarding the cross border co-operation we only found minor difference, but institutional
differences still seem to be of some importance. A closer investigation of the role of the
advisory system on both sides of the border will probably provide some further information
on these aspects. For this reason we only were able to give some tentative answers to the
research problems raised in the introduction with regard to the importance of institutional
frameworks and peripheral location of parts of the area analysed.
In quite a large share of the considered cases innovation actions have not taken place. In
order to account for the bias imposed by this “business as usual” behaviour the models were
estimated in a bivariate set up by use of the Heckman correction procedure. The results
indicate that the conclusions reached by the probit models are even stronger than suggested
without taking the bias into consideration. For the “reduced data set” the influence of
several of the background variables like patents and turnover disappears.
17
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