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Uddannelses- og Forskningsudvalget 2015-16
UFU Alm.del
Offentligt

The impacts of cluster policy in Denmark

- An impact study on behaviour and economical effects of Innovation Network Denmark

Publisher:

Danish Agency for Science, Technology and Innovation

This publication is available to download from the website of the

Danish Agency for Science, Technology and Innovation at

http://en.fi.dk/publications

ISBN (web): 978-87-92776-10-5

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18/2011 in the series of Innovation: Analysis and Evaluation

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Content

Summary & conclusions.............................................................................. 4

Introduction ....................................................................................... 6

Innovation Networks.......................................................................... 7

Descriptive statistics..........................................................................13

Results ..............................................................................................18

Behavioral effects..............................................................................21

Possible economic impacts ................................................................27

References ................................................................................................29

Appendix 1: Impact study methodology.....................................................30

Appendix 2: Data.......................................................................................33

Appendix 3: The Danish Innovation Network .............................................35

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Summary & conclusions

This is the results of the first quantitative impact assessment of

innovation networks. The analysis has been conducted by DAMVAD for

The Danish Agency for Science, Technology and Innovation.

The impact assessment is based on longitudinal data from 2002 to

2008 and is based the newest econometric methodologies in terms of

setting up controls groups and calculating effects. Hence the control

groups are established using econometric methodologies, where each

of the participating companies are matched with 10 twinning

companies. A twin company is a company with similar characteristics

to participants, but with the difference, that it did not participate in

the innovation networks.

By doing so we use the same approach that is well known from

medical research, in which a group of test people are treated with

some medication and another group is treated with placebo. Here the

treats are the companies who participate in the innovation networks.

Establishing a treatment and control group allows for testing the

effects of given one of the groups the medicine, i.e. participating in the

innovation network, and compare them to the control group, that

does not receive the medicine. This creates a counterfactual situation

allowing analysis of what would have been the situation without the

innovation networks.

Participation increases the probability to innovate by more that 4,5

times year 1 after participation.

Companies participating in different

innovation networks have an increased probability of being innovative

with the effects on innovation showing from the first year of

participation. The probability of being innovative is 4.5 times higher for

innovation network participating companies in innovation networks

compared to a control group composite of other similar companies not

participating in networks. This means that for every time 10 companies

in the control group turns innovative 45 participating companies in

innovation network will turn innovative. Among the participating

companies in innovation networks we can identify 102 companies that

become innovative of a treated population of 1,225 participating

companies in innovation networks an increase of 8.3 percent. On the

contrary for every 1,225 participating company in the control group 22

companies turn innovative an increase of 1.7 percent. It is a

difference of 6.5 percentage points. It is worth mentioning that a lot

of these companies in both groups already are innovative. The figures

above thus relates to numbers of new innovative companies adding to

the number of companies already innovative.

Increasing the number of innovative companies potentially has an

economical impact. The most comprehensive Danish study of private

return on investment in R&D and innovation has proven a significant

return on investments in innovation of 30 percent

. Thus an increased

ability to be innovative and increased probability to be innovative is

expected to have a significant economical impact on the participating

companies in innovation networks.

Participation increases the probability of R&D collaboration by 4

times year 1 after participation.

Innovation networks assists

companies in entering joint R&D and innovation projects by providing

Innovation networks increases the companies ability to

innovate and collaborate on R&D

The impact assessment covers 1,225 participating companies in

innovation networks. The results show that:

The Danish Agency for Science, Technology and Innovation, 2010

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the companies with the competencies required for this complex task

(and which SMEs, in particular, do not possess prior to participating).

Additionally, innovation networks provide a platform within which

participating companies in innovation networks identify potential

collaboration partners. Already within the first year of participation,

the probability of entering R&D collaboration increases by 95 percent,

and thus almost doubles their probability of entering R&D

collaboration. Thus every time a company in the control group

composited by other similar companies not participating in innovation

networks enters R&D collaboration, two new participating companies

in innovation networks enters R&D collaboration.

The year after participating in an innovation network, the probability

of entering R&D collaboration is almost 300 percent higher than other

similar companies not participating in networks. In other words

Participating companies in innovation networks increase their

probability of entering R&D collaboration by four times.

Another study has analyzed the impact of entering R&D collaboration

The study shows that companies, who are entering R&D collaboration,

have significant higher growth rates in productivity compared to other

similar and high productive companies. Entering R&D collaboration

increases productivity with an average of 9 percent a year over a 9

years time period. Thus, increased R&D collaboration is expected to

have a significant economical impact on the participating companies in

innovation networks.

The results indicate a possible economical impact of company

participation in innovation networks. One cannot expect the

economical impact to show within the first few years after

participation. The economical impact will not show before the

increased ability to innovate and the effects from R&D collaboration

materialise in terms of new products or efficiency in the production

process. This will in turn either increase revenue or reduce production

cost, which in turn both will result in improved bottom line.

The data available does not have the longitude and time span that will

enable us to carry out extensive and comprehensive studies of the

economical impact. This will be possible within the following years as

the time span will increase and thus enable more in dept studies of the

economical impacts of company participation in innovation networks.

Setting innovation networks apart

Innovation networks are a result of a wish to boost the innovation

efforts of Danish companies. Innovation networks have a particular

role to play when it comes to helping small and medium-sized

enterprises (SMEs) access the innovation system. They do that by

providing a platform within a specific technical or professional area

where companies, universities, research institutions and other

relevant players can meet to exchange ideas, knowledge and launching

new projects.

Thus, contrary to other R&D and innovation programmes, the

innovation networks specifically focus on inexperienced users of the

knowledge system. As an innovation program, the innovation

networks are based upon an informal approach. Its main function is to

prepare participating companies in innovation networks to take part in

and benefit from the knowledge system by increasing the innovation

level, enable R&D collaboration and participation in other R&D and

innovation programmes.

In order to meet these ambitions, the scope of activities in the

innovation networks is very broad and includes activities ranging from

informal meetings to collaborative innovation projects. The set of

activities that comprise the innovation networks also have a broader

range compared to that of other innovation programs with a more

narrow and focused angle, i.e. innovation consortia and others.

The Danish Agency for Science, Technology and Innovation, 2011

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increased innovation and R&D, which later will lead to an economical

impact.

1 Introduction

This report has been prepared by DAMVAD. It presents an analysis of

the behavioural and economical impact of the Danish Innovation policy

programme of Innovation Network ( Innovationsnetværk ). The

behavioural impact focuses on increased ability to innovate, increase

in R&D collaboration and a better use of the innovation system in

Denmark. A change in the behavior is expected to lead to economical

impact for the participating companies in innovation networks.

Innovation networks aims at Danish companies and knowledge

institutions that are active in specific defined areas or clusters. These

clusters are assembled in individual networks. Thus, firms in each

network have in common that they operate with the same discipline.

Although companies have the same technical focal point their

motivation to engage in innovation networks have a divergent nature.

Among other things motivation depend on the company s knowledge

level prior to participating in the networks. This impact assessment

tries to incorporate the diversity in the way controls groups are

defined.

The purpose of this study is to show company effects of participation

in innovation network. The participating companies in innovation

networks have very diverse prerequisite related to their experience

with the knowledge system. Some are novices and lack experience in

innovation, R&D collaboration and participation in the Danish system

for public research and innovation promotion system, while others are

experts in the knowledge system.

This also set up demands on how to design the impact assessment.

Most of the participating companies in innovation networks are

novices in the knowledge system. This in turn means that the main

focus will be on behavior impacts as these are a prerequisite for

In order to conduct a solid impact assessment the analysis identifies a

control group of companies that does not participate in innovation

networks. The identification builds upon propensity score matching

procedures in order to identify control companies that are as identical

as possible to the participating companies in innovation networks. The

control group is identified based upon firm specific information

regarding sector, firm size, and educational level among employees,

previous performance and R&D activities.

This report is divided into 5 central chapters:

Chapter 2

describes the goals of the innovation networks, as a policy

instrument and the expected effects that companies will experience

from participating in the innovation networks. This is done with the

purpose to set the stage for the impact assessment that follows later

on in the report.

Chapter 3

gives a brief view of the participating companies in the

Innovation Network program.

Chapter 4

describes how the different impacts are measured.

Chapter 5

analyses the impact of innovation network on participating

companies in innovation networks in terms of behavioural impact in

terms of increased ability to innovate, increased probability to enter

R&D collaboration and change in use of other R&D and innovation

programmes, and

Finally

chapter 6

put the results of the impact assessment into

perspective.

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Figure 2.1: Expected relation between R&D intensity and productivity

2 Innovation Networks

This chapter describes the goals of the innovation networks as a policy

instrument and the expected effects that companies will experience

from participating in the innovation networks. This is done in order to

set the stage for the impact assessment later in the report.

Innovation networks are the result of a wish to boost the innovation

effort of Danish companies. The main focus of the innovation networks

is to help small and medium-sized enterprises (SMEs) gain access to

the innovation system. The innovation network provides a platform

within a specific technical or professional area where companies,

universities, research institutions and other relevant players can meet

and exchange ideas and knowledge and launch shared projects. The

innovation networks thus build a bridge between Danish companies

and universities and other research institutions and the large

accumulation of knowledge that happens there.

As the innovation networks dissemination their knowledge to

participating companies in the network, the companies gradually

experience a stepping upwards on the knowledge ladder

. The

companies knowledge progress is presented in Figure 2.1 which

illustrates the relation between intensity of research and development

activities and economic growth.

Productivity per

employee

R&D active and

R&D collaboration

R&D active

Innovative

Not R&D active

Time from

participation

Source: DAMVAD 2011

The figure thus demonstrates how companies that were at the bottom

of the ladder prior to joining the innovation network may climb up the

ladder once they absorb knowledge from the network and increase

their innovation and R&D competencies. Hence, the figure is dynamic

in the sense that companies at various stages may progress even

further following their participation in the network activities.

Accordingly, the innovation networks will include different sets of

activities that contribute to companies progress on the knowledge

ladder which are also adjusted to the companies existing level of R&D

intensity. The advanced players will be drawn to particular activities,

such as how to establish R&D collaboration, whereas the more

inexperienced players will be attracted to other activities that may

help them advance their innovation competencies.

Hence, the aim of the innovation network is to provide a platform that

will add value to the participating companies in innovation networks.

The emphasis is on:

The concept of the knowledge ladder is developed based on two reports from the

Danish Agency for Science and Innovation. The reports document empirical effects

that companies experience from investing in R&D and innovation, and from

participating in R&D collaborations with public knowledge institutions.

Providing access to professional competencies from scientists,

users, and specialised companies

Providing room for a common generation of ideas and

knowledge-sharing

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Providing the opportunity to identify collaboration partners

and to launch projects

Providing the opportunity to strengthen and develop relations

with research environments, companies, lead users, GTS etc.

Being a stepping stone for internationalisation of companies

professional area can meet. Thus the very nature of the network gives

it a more informal character.

The overall difference between the innovation networks and other

innovation programs such as the Strategic Research or Innovation

Consortiums is highlighted in the figure below. The figure categorises

the programs as to whether they are informal or formal, and whether

they focus on execution of research or preparation of research.

Figure 2.2 Innovation networks placement as a policy tool

Formal

Informal

As the above list indicates, the scope of the innovation networks is

very broad. The set of activities that make up the innovation networks

also have a broader range compared to that of other innovation

programs that have a more narrow and focused angle.

The scope of activities and the variety of the participating companies

innovative competencies prior to participation present specific

premises for measuring the effects of participating in the innovation

networks. The diversity of activities as well as company characteristics

necessitates the establishing of parameters that will assist the impact

assessment with large degrees of variation and with the hoped-for

results taken into consideration. This will be dealt with in more depth

in section 2.3.

In the following, the innovation networks as part of the knowledge

system will be introduced with emphasis on what distinguishes the

innovation networks from other innovation instruments. Next, the

kinds of companies that participate in the innovation networks will be

presented. These two dimensions will be central to the impact

measurement and the expected results of participating.

Preparational for

research

Innovation networks

Platform

Prioritising

Relevance

Strategicresearch

Innovation Consortiums

EU s framework programmes

Execution of

research

Soruce: DAMVAD 2011

2.1 A policy instrument

The aim of the innovation networks sets the network apart as a policy

instrument compared to other innovation programs. Unlike other

programs that focus on funding projects, the innovation networks

provide a platform where players within a specific technical or

As the figure indicates, the innovation networks as an innovation

program are characterised as an informal policy instrument, its main

function being to prepare network participating companies to

participate in and benefit from the knowledge system and thus

potentially participate in more formal policy instruments later. This

type of program is contrasted with programs that provide specific

project support and thus focus on the execution of research, giving the

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activities a more formal character, e.g. Innovation Consortiums or EU s

Framework Programs.

The figure also illustrates how the innovation network supply a

platform that should focus on assisting companies in prioritising

research through shared development of strategies and execution of

plans and by bringing participating companies in innovation networks

together in order to ensure a critical mass in relation to projects.

Accordingly, research relevance is a focal point in order to make the

needs of companies a more direct part of the research conducted at

universities. Secondly it is also a point that a broader scopes how the

needs of companies become a part of research and innovation

programs. As knowledge-sharing and the dissemination of knowledge

are central activities in the innovation network, the participating

companies in innovation networks will contribute to the prioritization

of both public and private research.

As the innovation network is to work as a bridging element for

companies on the verge of unfolding their innovation potential by

providing connections to other companies, universities, and research

institutions in the networks, the innovation network thus potentially

fills a gap in the innovation system. An additional feature of this

characteristic is its ability to reach a broad range of companies that

covers both very experienced players in the knowledge system as well

as more inexperienced companies.

The innovation networks attract both experienced as well as

inexperienced players in the knowledge system. This provides

opportunities for the inexperienced players who are not familiar with

the system and as such will gain practical knowledge about the

knowledge system from the experienced players. Contrary it also set

requirements for the activities in the networks that need to be

targeted at different levels of innovation capacity and overall

experience. As the preparation part of the innovation networks is

distinct, the focus of the majority of activities in the network supports

this dimension. However, this part is also strengthened by the

existence of the more advanced companies.

The different types of companies will be elaborated below.

2.2 Company participation on different levels

The innovation networks reach out to different kinds of companies

because of the diversity of activities in the networks. The various

activities in the innovation networks also illustrate that companies

participate with differing intensity and differing purposes.

It is therefore useful to provide a generic characterization of the

different types of companies participating in the networks. In

particular, the categorisation of companies depends on the existing

level of innovation capacity, i.e. their existing R&D expertise and their

experience with the innovation system prior to participating in the

network.

Inexperienced players in the knowledge system:

Companies

belonging to this category demonstrate a low degree of innovation

capacity prior to joining the network. Their motivation for participating

is focused on getting to know the central players and building relations

with these players in order to get access to knowledge and

competences that the company itself does not possess. The main

activities that these companies participate in when they initially join

the network are thus informational activities such as meetings and

conferences.

New players in the knowledge system:

Companies belonging to this

group have some experience with the knowledge system. Their main

motivation is to use the network to make more progress with their

innovation attempts. A need of these companies is to get access to

knowledge resources that the company does not already possess and

thus the targeted activities are match-making, idea generation,

counseling, and knowledge exchanges.

Advanced players in the knowledge system:

This group of companies

have a significant experience in operating in the knowledge system.

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Their main motivation for participating in the innovation networks is

thus to use their existing knowledge combined with new knowledge

for targeting new projects. Their main activities are the pre-projects

that are offered as a testing platform for potentially larger projects.

Experts in the knowledge system:

These companies are the

experienced players in the knowledge system. They participate in

innovation networks as part of their overall palette of innovation

programs. These companies are also motivated by an interest in

gaining access to knowledge and competencies. Their main activity is

participation in innovation and R&D projects with other key

stakeholders from the network including international R&D players.

Figure 2.3: Change in level within the knowledge system from participation

in innovation network

Level in the

knowledge system

Experts

Advanced

player

New player

Inexperienced

Initial

participation

Time from initial

participation

2.3 Effects of participation

The innovation networks contribute to companies development and

innovation efforts in different ways. Consequently, there will be

different effects of the companies participation in the innovation

networks. The different effects are related to the companies existing

competencies, previous experiences of the knowledge system and

involvement in innovation and research programs as well as formal

joint knowledge collaboration attempts.

The different effects are presented in Figure 2.4. This figure illustrates

how the effects of the innovation network depend on the type of

network participant using it. The primary effect for inexperienced

participating companies in innovation networks is different from the

experienced network participant. Companies that participate in the

network over a long period will gradually experience the strengthening

of their competencies and benefit accordingly from the network and

learning externalities provided through the network participation. The

figure below shows how companies in theory move upwards in the

knowledge system after participating in innovation networks.

Source: DAMVAD 2011

In the short run, participation in the innovation networks will be

related to behavioral effects such as changes in the characteristics of

the company s R&D activities. An example could be a company that

due to participation in the innovation network increases their

investments in R&D, or pursues further innovation activities.

In the long run, the innovation networks will enable the companies to

participate in knowledge collaboration and interaction. Thus, it is in

the long term perspective that economic effects associated with

participating in the innovation networks will be visible. An example of

this kind of economic effect are increases in the company s

productivity or increase in exports.

Companies that participate in innovation projects through

collaboration with other companies and knowledge institutions

facilitated by their participation in the innovation network are

expected to experience economic effects sooner than companies that

have only participated in meetings and workshops, regardless of the

duration of their network membership.

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Figure 2.4: Effects of participation in innovation network

Box 2.1 What are the objectives of the innovation networks

Access

to know-

ledge

Colla-

boration

Participation

in programs

Invest-

ments

in know-

ledge

R&D

investments

innovation

Econo-

mic

effects

Value

added

growth

Export

The main objectives of the innovation networks are:

To strengthen public-private collaboration and

knowledge transfer between public universities and

private companies on research and innovation.

To strengthen innovation and research in Danish

Companies and thus promote knowledge based

growth in business and industry.

Below is a brief overview of the 22 existing innovation networks and

the identified participating companies in innovation networks. A more

comprehensive, but still brief description of each innovation network

can be found in appendix 3.

Short term: Network- and learning externalities

Long term: Economic value

Source: DAMVAD 2011

2.4 The Danish Innovation Network

This gives a brief presentation of the Danish innovation networks. In

appendix 3 there is a more thorough presentation of each network.

The text box below presents the more formal objectives of the

innovation networks:

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Table 2.1. Overview of innovation network

Network

AluCluster - Knowledge and technology centre for aluminium

Animation HUB

Biopeople Innovation Network for Biotech

Innovation Network for Biomass

Danish Sound Technology Network

FoodNetwork - Fødevaresektorens Innovationsnetværk

Infinit The Danish ICT Innovation network

InnoBYG - Innovation Network for Energy efficient and Sustainable construction

Innonet Lifestyle Interior & Clothing

The Innovation network for Market, Communication and Consumption

The Innovation network for Environmental Technology

Offshore Center Denmark

Innovation Network for Danish Lighting

Plastic and Polymer Network

InViO - Innovation network for knowledge-based experience economy

No Age innovative solutions for elderly people

RoboCluster

Service Platform Service Cluster Denmark

The Transport Innovation Network - TINV

UNIC Use of New technologies in Innovative solutions for Chronic patients

Water in Urban Areas

VE-NET

Source: DAMVAD 2011 based on participation list for innovation networks.

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3 Descriptive statistics

The following is aiming at giving a descriptive status on the

participating companies of the Innovation Network program. Two of

the key performance indicators for this program are the focus on

regional distribution and inclusion of small companies.

In order to provide further information on the participating companies

in innovation networks, data is merged with other statistics. Merging

Statistic on Research, Development and Innovation

and

General

enterprise statistics

leads to a further reduction of companies. The

final number of participating companies in innovation networks is

3,031.

The total number of companies participating in current innovation

networks will not correspond with the numbers of participating

companies in innovation networks in the impact analysis. The impact

analysis cover participating companies in innovation networks in the

time span 2003 to 2008. The innovation networks are rather dynamic

and their scope changes from year to year, some stays the same, some

are closed down, new ones appear and some are merged.

The study covers 3,031 participating companies in the Danish

Innovation policy program of Innovation Networks. The majority of

participating companies in innovation networks come from the

manufacturing, business, commerce and transportation sectors. These

industries account for 67 percent of the participating companies in

innovation networks. Table 3.1 shows the distribution of participating

companies in innovation networks by industry.

Table 3.1. Participating companies in innovation networks by industry

Industry

Agriculture, forestry &

Manufacturing

Construction

Trade and transport ect.

Information and

Financial and insurance

Real estate & renting

Other business services

Knowledge-based services

Travel agent, cleaning, ect.

Arts, entertainment & other

Public administration,

Undisclosed activity

All

Number of

participating

companies

844

558

361

569

182

174

3.031

Fraction of

participating

companies

2,0%

27,8%

1,6%

18,4%

11,9%

2,6%

0,9%

9,5%

4,9%

6,0%

5,7%

2,3%

Fraction of

total

companies

12,3%

6,7%

11,6%

24,0%

3,9%

3,5%

8,6%

18,8%

1,9%

6,7%

8,0%

0,3%

Source: DAMVAD 2011 based on General enterprise statistics (2008) and participation

list for innovation networks.

The regional distribution of participating companies in innovation

networks matches the general distribution of companies. The

Innovation Networks thus lives up to the aim of having a wide regional

distribution of activities

. Geographically, most participating

companies in innovation networks are based in the capital and central

Jutland. This is most likely because the concentration of companies in

general in these areas is higher than in other parts of the country. It is

noteworthy that there are relatively few participating companies in

innovation networks from Region Zealand. The results of table 3.2

The aim is part of the key performance indicators as is presented in the action plan

for The Danish Council for Technology and Innovation 2010 2013.

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show that besides Region Zealand the different Danish regions are

represented on a level that matches the relative distribution of

companies in general.

Table 3.3. Participating companies in innovation networks by size

Company size in

full-time

equivalent

0 to 19

20 to 49

50 to 99

100 +

All

Amount of

participating

companies

1,730

441

270

590

3,031

Fraction of

participating

companies

57,1%

14,5%

8,9%

19,5%

Fraction of

total

companies

96,0%

2,5%

0,8%

0,7%

Table 3.2. Participating companies in innovation networks by region

Name of Region

Capital Region

Central Denmark Region

North Denmark Region

Region Zealand

Region of south Denmark

All

Number of

participating

companies

908

797

498

175

653

3,031

Fraction of

participating

companies

30,0%

26,3%

16,4%

5,8%

21,5%

Fractions of

total

companies

30,7%

22,9%

10,7%

14,6%

21,0%

Source: DAMVAD 2011 based on General Enterprise Statistics (2008) and participation

list for innovation networks.

Note: 346 companies have more than 250 employees. This mean that 11,3 percent of

the innovation network participant have more than 250 employees.

Source: DAMVAD 2011 based on General enterprise statistics (2008) and participation

list for innovation networks.

3.1 Innovation profile

The following presents the level of innovative participating companies

in innovation networks. Tabel 3.4 shows that 51,2 percent of the

participating companies in innovation networks in 2004 are innovative.

This is compared to that 42 percent of the Danish companies were

innovative in 2004. 73.1 percent of the companies participating in

2007 are innovative. This is compared to that 42.8 percent of the

Danish companies were innovative in 2007.

Another focus in the key performance indicators for the Innovation

Networks program is a focus of including small companies. In numbers,

most companies participating in innovation networks are small. But

compared with the general distribution by size, large companies are

over-represented among the participating companies in innovation

networks. In Denmark in general, 1.5 percent of businesses have more

than 50 employees. By comparison 28.4 percent of firms in the

innovations programs have more than 50 employees.

Compared to other Danish programs for R&D and innovation, the

share of small companies is rather high. E.g. in the program for

Strategic Research 40 percent of the participating companies have

more than 250 full time equivalent employees and in Innovation

Consortia the figure is 27 percent. The Innovation Network programme

11.3 percent of the participating companies in innovation networks

have more than 250 full time equivalent employees.

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Table 3.4. Participating companies in innovation networks Innovative

Innovative in year

of participation

2004

2007

2008

Fraction of

participating

companies

51.2 %

73.1 %

73.6 %

Fraction of total

companies

42.0 %

42.8 %

41.0 %

Source: DAMVAD 2011 based on innovation statistics (2004, 2007 and 2008) and

participation list for innovation networks.

Another interesting point in the analysis will be the focus on R&D

collaboration. The participating companies of Innovation Networks are

more likely to cooperate on R&D than other companies. Around 30 per

cent of participating companies in innovation networks cooperate,

where only 5.4 percent of all Danish companies cooperate on R&D. It

will thus be interesting to see whether the higher share of R&D

collaboration is due to participation in the Innovation Network

program.

Table 3.6. R&D-cooperation among companies in innovation networks

3.2 R&D-profile

68.3 per cent of the participating companies in Innovation Networks

are engaged in R&D activities. Compared with the total amount of

companies in Denmark, participating companies in innovation

networks are more likely to be involved in R&D activities as only 35.6

percent of all Danish companies are engaged in R&D. This is of cause

due to self-selection, as the companies analysed here by definition are

engaged in innovation. The question is whether the companies are

active in R&D because they participate in the Innovation Networks

program, or if they were R&D active before entering the program.

Table 3.5. Participating companies in innovation networks R&D activity

R&D

activity

Yes

All

Amount of

participating

companies

203

438

641

Fraction of

participating

companies

31.7%

68.3%

Fraction of total

companies

64.2%

35.8%

R&D-

cooperation

Yes

All

Amount of

participating

companies

427

189

616

Fraction of

participating

companies

69.3 %

30.7 %

Fraction of total

companies

94.6 %

5.4 %

Source: DAMVAD 2011 based on R&D-statistics (2008) and participation list for

innovation networks.

3.3 Network participation

As mentioned above small or middle sized are most likely to

participate in Innovation Networks. The table below shows that 58.1

percent of the participating companies in innovation networks have

less than 20 employees whereas 11.3 percent have more than 250

employees.

Source: DAMVAD 2011 based on R&D-statistics (2008) and participation list for

innovation networks.

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Table 3.7. Network participation by firm size

Network

Company size in full-time

equivalent

0 to 19

20 to 49 50 to 99

1,730

58.1%

438

14.7%

252

8.5%

Table 3.8. Network participation by activity

Conf. or

Seminars

2,965

98%

Thematic

matching

1,537

51%

Con-

sulting

592

20%

Preli-

minary or

small

projects

240

R&D or

inno.

projects

419

14%

All

100 to

249

221

7.4%

250 +

337

11.3%

Network

All

Percentage

Source: DAMVAD 2011 based on General Enterprise Statistics (2008) and participation

list for innovation networks.

Source: DAMVAD 2011 based on General Enterprise Statistics (2008) and participation

list for innovation networks.

From the data on participating companies in innovation networks it is

possible to see the different kinds of activities that each company has

participated in. The different activities increase in strength and

formality so that conferences and seminars are the most casual and

informal activity whereas R&D and innovation project are the most

formal.

Overall, most companies participate in the informal conferences or

seminars. Almost 3,000 companies have been participating in these

activities, while only 419 companies have been participating in formal

R&D and innovation projects. It is possible for each company to

participate in five activities altogether. Table 3.7 presents the results.

Most companies participate in conferences or seminars. This is 98

percent of the participating companies in innovation networks. 14

percent are involved in innovation and R&D projects.

3.4 Participation in other programs

One of the main aims of the Innovation Networks program is to

increase participation in other R&D and Innovation programs, inviting

novices to enter into the innovation system. By merging the

participation list with the DAMVAD Database on Knowledge

Collaboration, it is possible to see how many of the innovation

network participating companies that are also participating in other

programs. It is an aim of the Innovation Networks program to improve

each company s ability to innovate, increase the overall investments in

R&D and innovation and increase the number of joint R&D and

innovation projects between companies and knowledge institutions.

Around 25 per cent of the participating companies in innovation

networks have participated in a project recorded in the DAMVAD

Database on Knowledge. The participation in other programmes shows

that the participating companies in innovation networks also

participate in other parts of the knowledge system. It is part of this

analysis to see whether participation in innovation network will

enhance company participation in other programmes.

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Table 3.9. Network participation by activity

Network

Participating

companies in

Innovation

Network

4,021

Participating in

other

programmes

1,022

Program

Share in per cent

ABT fonden

25,4

Table 3.10. Network participation by activity

0 to 49 full

time

employees

...

378

50 to 249 full

time

employees

...

243

+ 250 full time

employees

...

109

543

All

User Driven innovation

ELFORSK

EU's 4. Framework

programme

EU's 5. Framework

programme

EU's 6. Framework

programme

EU's 7. Framework

programme

EUDP

Food Innovation 2007

High Technology

Foundation

Innovation Consortia

Regional Programmes

Council for Strategic

Research

Knowledge Coupon

All

Source: DAMVAD 2011 based on DAMVAD co and participation list for innovation

networks.

Table 3.10 show companies that participate in innovation network and

how they participate in other R&D and innovation programmes.

Further the figures is divided into firm size. By doing so we see how

many small, medium sized and large companies participating in

innovation networks also participates in other programmes. It is an

aim for the innovation network to help and enhance the use of other

programmes, especially for small and medium sized companies. And it

is in particular programmes focusing on innovation, which attracts

participating companies from the innovation networks. Table 3.10

shows that the programmes User Driven Innovaiton , Innovation

Consortia and Knowledge Coupons all attract a great share of small

or medium sized companies.

Source: DAMVAD 2011 based on General Enterprise Statistics (2008) and participation

list for innovation networks.

Note: mean that the figure left out due to discretion matters.

Note: The figure of larger companies exceed figure in table 3.6. This is due to the fact

that one company can participate in several programmes.

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Figure 4.1: The effect of participating in the Innovation Network program

4 Results

It is expected that companies will experience different effects from

participating in Innovation Network. As described in Chapter 2 there

are different patterns of participation. These different patterns have

different aims and thus their impact should be viewed separately. It is

crucial to focus on different impact measurements that focus

exclusively on economic impacts, such as productivity and exports, but

also changes in behaviour that in the end will imply economic growth.

This analysis focuses on two themes of the overall impact of

participating in Innovation Networks:

Behavioural effects

Economic impacts

The time span of the individual company s participation in an

Innovation Network will be of great importance as to which kinds of

effects can be expected. The figure below illustrates the expected

coherence between effects and the time span of participation. The

figure shows how the initial phase of participation leads to potential

behavioural effects, e.g. increased R&D spending or establishment of

own R&D department.

The ever growing knowledge base gained by participation, combined

with behavioural changes such as increased investments in R&D, will

affect the economic impact of the individual company, e.g. by

increased productivity, revenue or employment.

Effect of participating in

an Innovation Network

Firm-specific

economic effect

Behavioral

effect

Time

Source: DAMVAD, 2011.

4.1 What is meant by impact

Behavioral effects

The behavioural impact of participating in an Innovation Network is

measured by an increased probability of being innovative, increased

probability of entering into a R&D collaboration and by a better use of

existing national and international R&D and innovation programs,.

One behavioural effect is measured by increased ability to be

innovative. This is direct key performances aim with the Innovation

Network program. Increased innovation is a possible driver of

improved economic performance. An analysis on the return of

increased investments in innovation conducted by DAMVAD and the

Danish Centre for Studies in Research and Research Policy proved that

investing one additional Euro in innovation yields a return of 30 cents.

More innovation will thus increase the company growth.

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A second behavioural effect studied here is the level of R&D

collaboration. A recent study conducted by DAMVAD proved that a

business that enters into an R&D collaboration with universities and

other knowledge institutions on average experiences a 9 percent

increase in productivity a year in a 9 year period. Therefore it is

important to increase R&D collaboration, and it is also a key

performance indicator for the Innovation Network program in the

action plan Innovation Denmark 2010-2013.

A better use of existing national and international R&D and innovation

programs covers several of key performance indicators for the

Innovation Network program. Participation in other programs will

most likely increase the individual company s ability to innovate,

increase investments in R&D and innovation and increase the general

level of collaboration between companies, universities and other

knowledge institutions. Two Danish studies have proven a relationship

between, respectively, increased innovation, increased R&D and R&D

collaboration on the one hand, and business growth in terms of

increased productivity on the other. This was shown in Figure 2.1 and

is known as the knowledge ladder. It is therefore highly relevant to aim

for a better use of existing national and international R&D and

innovation programs.

Economical effects

The economic impact of participating in Innovation Networks is

measured in terms of productivity, exports, turnover and employment.

The measuring of the economic impact has been carried out from two

different perspectives. First, the general effect is measured regardless

of the character of participation. Secondly, the impact is estimated

while the character of participation is taken into account.

For each of the two perspectives we use a matching approach to

estimate the causal effect of participation in Innovation Networks. This

approach matches each participant with a similar non-participant,

hereby simulating a counterfactual situation.

When estimating the impact of participation, causality is a very

important issue. The question is whether companies perform better as

a result of participating in Innovation Networks, or whether high

performance companies are more likely to join Innovation Networks.

This issue is addressed first by matching the participating companies in

innovation networks with non-participating but similar companies (the

control group).

Secondly, the performance of the participating companies in

innovation networks is compared to the performance of the control

group over time. The performance of the control group is assumed to

illustrate the performance paths that the participating companies in

innovation networks would have followed had they not participated.

For each of the indicators our methodology focuses on causality. We

match control companies so that they are similar to Innovation

Network participating companies in innovation networks in the year

that the latter participate in a network. The similarity is based on a

range of company specific indicators and the economical performance

of each company, in order to ensure the similarity between the

participating companies in innovation networks and the control group.

4.1.1 Establishing control groups

In this impact study we establish one solid control group and use it to

compare the before and after behavioural activities between

participating companies in innovation networks and similar non-

participating companies.

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Companies are selected for a control group according to the matching

method called propensity score matching . This method estimates for

each company the probability of participating in innovation network

conditional on company specific characteristics such as industry,

company size, exports, educational background of employees,

previous performance, R&D activities and research collaborations.

According to the estimated probability, participants are matched to

similar non-participants according to

nearest neighbour

matching

method. This is further elaborated in appendix 1.

As a result, the control groups consist of companies that are similar to

participating companies in innovation networks, where the only

observed difference is the fact that these companies did not

participate in an innovation network.

It is important to consider the quality of the control group. This is done

by testing if observations with the same propensity score have the

same distribution of observable covariates independent of treatment

status. In other words, it has been tested whether there are any

observed systematic differences between participating companies in

innovation networks and the control group consisting of the matched

non-participating

companies.

The economic methodology is further elaborated in appendix 1.

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5 Behavioral effects

The behavioral effect is not immediately transferrable to economic

measures and as such economic impacts. But they can be a

prerequisite to later economical impacts, e.g. increased probability to

innovate or increased probability of engaging in R&D collaboration

with universities and other knowledge institutions.

The analysis of behavioral effects focus on three different types of

effects from participation:

Higher probability of being innovative

Higher probability of R&D collaboration.

A better use of existing national and international R&D and

innovation programs.

In order to analyze whether participation in innovation networks

increases the probability after we utilize a probability model that is

modeling the probability of being innovative for each company. The

model is in principle equal to the one used to investigate a better use

of other R&D and innovation programmes.

The results again represent a logistic model that allows for including

other predictor variables that will affect the probability of being

innovative. The model is build upon merging data from the

participation list, the General Enterprise Statistics and Innovation

Statistics in 2002, 2004, 2007 and 2008. Innovation is defined based on

the Innovation Statistics and for 2002 and 2004 it includes product and

process innovation whereas 2007 and 2008 also includes

organizational and marketing innovation. Further the results are

conditioned that companies not previously have been innovative.

Increasing the level of innovation among participating

companies in innovation networks

Tabel 5.1 shows that compared to similar companies participating in

Innovation Networks increases the probability of being innovative.

Already the same year as participating in an innovation network

companies tend to be more innovative. Year 1 after participation the

probability of being innovative is 366 percent higher for innovation

network participating companies in innovation networks than other

similar companies. The not available in the tabel indicates that there

weren t enough observation on innovative companies. This will be due

to the lack of innovation statistics in the years of 2005 and 2006.

5.1 Increased ability to innovate

Another interesting area to explore is whether participation increases

the innovation ability among companies. The return on investment in

innovation is 30 percent and innovative companies in general are 66

percent more productive than non-innovating companies

. Further it is

one of the key performance indicators to increase innovation as a

mean of the Innovation Networks.

Cf. The Danish Agency for Science, Technology and Innovation, 2010, 2011

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Table 5.1: Level of innovation after participating in Innovation Networks

Year after participating in

Innovation Networks

The same year

Year 1 after participation

Year 2 after participation

Year 3 after participation

Year 4 after participation

Year 5 after participation

Increased probability of being

innovative

0.4786**

3.6678***

N/A

4.6386***

3.2029***

Interpreting the results

The results of the impact assessment show that participation in an

innovation network significantly increases the probability of being

innovative. There are several possible explanations to why this is the

case:

Participation implies learning externalities

as we shaw in chapter 2 in

figure 2.4 it is expected that participating companies in innovation

networks strengthening of their competences and accordingly benefit

from the learning externalities provided through the network

participation. This will potentially lead to innovation.

Participant gains access to new knowledge

one of the key points in

the activities in the networks is bridge builder function where

participating companies in innovation networks gain access to

professional capabilities from researchers, users or other companies.

This knowledge help develops ideas into new products or services and

provides inputs and advice for solving problems with product

development and innovation.

Participation in common idea generation

as part of the knowledge

transfer participating companies in innovation networks can achieve

counseling and individual feedback from experts. The counseling and

feedback is based on the latest knowledge within certain areas. The

network can act like an expert itself or putting up the frame for a

meeting between participating companies in innovation networks and

other experts.

Source: DAMVAD, 2011.

Note: The significance is marked with asterisk : *** = 1 pct.-level, ** = 5 pct.-level and * = 10 pct.-

level.

N: 5,201

The results imply that every time two company in the control group

turn innovative 9 companies will turn innovative in the treatment

group. In the time span we can locate 102 participating companies in

innovation networks turning innovative out of a population of 1,225.

Contrary 22 companies out of every 1,225 companies in the control

group turns innovative. This means that 8.3 percent of the

participating companies in innovation networks turn innovative. In the

counterfactual situation without participation in innovation networks

only 1.7 percent of the companies will turn innovative. That is a

significant difference of 6.5 percentage points.

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5.2

Increase the probability of R&D collaboration

almost 300 percent higher than other similar companies not

partipating in innovation networks .

It not was possible to obtain a satisfactory number of observations in

the third, fourth and fifth year after participation. Therefore there are

no results of these models.

Table 5.2: Level of R&D collaboration after participating in an innovation

network

Year after participating in

Innovation Networks

The same year

Year 1 after participation

Year 2 after participation

Year 3 after participation

Year 4 after participation

Year 5 after participation

Increased probability of being

innovative

0.9459***

2.9978***

3.8494***

N/A

One of the objectives of the innovation networks is to strengthen

public-private collaboration and knowledge transfer between public

universities and private companies regarding research and innovation.

And as such it is a key performance indicator. A recent study has

proven the significant effects for companies of entering public-private

collaboration

In order to investigate whether an innovation network is an effective

instrument to stimulate knowledge sharing and interaction between

companies, universities and other public knowledge institutions

through increased R&D collaboration.

R&D collaboration is defined as in the official R&D statistics and covers

active participation in common projects regarding R&D with other

companies or institutions. With the different R&D statistics it is

possible to see whether companies have R&D collaboration in the

years of 1999, 2001, 2003 2004, 2005, 2007 and 2008. The years 1999

and 2001 will solely be used to check that companies not previously

have had R&D collaboration. The following years will be used to

analyze whether the companies are entering R&D collaboration.

Source: DAMVAD, 2011.

Note: The significance is marked with asterisk : *** = 1 pct.-level, ** = 5 pct.-level and * = 10 pct.-

level.

N: 2,350

Increasing R&D collaboration

Interpreting the results

Table 5.2 shows that compared to other similar companies

participating in an innovation network have a higher probability of

entering R&D collaboration after their participation. Already within the

same year as participation the probability of entering R&D

collaboration increases by 95 percent. The year after participating in

an innovation network the probability of entering R&D collaboration is

The results of the impact assessment show that participation in an

innovation network significantly increases the probability of having

R&D collaboration. There can be several explanations to the results:

Increased network externalities

as explained under the analysis of

better use of other R&D and innovation programmes the ability to

enter joined R&D and innovation projects is not straight forward. It

requires absorption capacity and a certain level of intern knowledge

within the company. A lot of companies, particularly smaller

Cf. The Danish Agency for Science, Technology and Innovation, 2011

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companies, do not possess these capacities and as such then

innovation network assist the companies in overcoming this drawback.

The same explanation can be implied to why innovation network

should increase the probability of entering and having R&D

collaboration.

Providing the opportunity to identify collaboration partners and to

launch projects

is one of the main objectives for the innovation

networks. One of the activities in the networks is bridge builder

function where participating companies in innovation networks

receive assistance in order to identify suitable partner to a R&D or

innovation project. Further the access to a comprehensive network of

researchers, technological service providers and private companies

implies an increase in R&D collaboration. Finally the fact that there is

R&D collaboration projects within the different innovation network

yields itself a higher level of R&D collaboration.

role in catalyzing participation in other more formal R&D and

innovation programs both nationally and internationally.

Tabel 5.3 presents the results of the analysis on whether participation

in Innovation Networks increases the use of other R&D and innovation

programs compared to similar companies that have not participated in

Innovation Networks. The results represent the outcome of a logistic

regression modelling the probability of entering other programs when

participating in Innovation Network. The logistic model also allows for

including other predictor variables that will affect the probability of

entering R&D or innovation programs

. In all 6 models are estimated

ranging from effect the same year to participation 5 years after

entering Innovation Networks.

There is a better use of other programmes

Tabel 5.3 shows that compared to other similar companies Innovation

Networks participating companies in innovation networks experience a

significantly higher probability of participating in other programmes

two and five years after participation in an innovation network.

Two years after participating in an innovation network participant

have an increased probability by 141 percent of participating in other

programmes compared to other similar companies. Five years after

participating in an innovation network this have increased to 215

percent. Eventhough the results are general positive and shows an

increased probability of participating in other programmes the

significance differs throughout the period.

5.3 Better use of other R&D and innovation

programmes

It is possible to analyze whether participation in innovation networks

leads to an increased the use of national and international programs.

By establishing a control group of companies that have not yet

participated in innovation networks and over time investigate their use

of other national and international programs.

Participation in other programs is identified through the DAMVAD

Database on R&D and innovation programs. The database is build on

information regarding company participation in national programs,

e.g. Innovation Consortia or the program for strategic research and

international programs such as the EU framework programs.

The use of the database provides us with the possibility to answer

whether participation in Innovation Networks leads to increased use of

the innovation system. If so Innovation Networks plays a significant

In the report The Impact on Company Growth of Collaboration with Research

Institutions , The Danish Agency for S

cience, Technology and Innovation, 2011 it is

shown that factors such as sector, firm size and employees with tertiary education

have a high impact on the probability of R&D collaboration.

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Table 5.3: Better use of other programmes

Year after participating in

Innovation Networks

The same year

Year 1 after participation

Year 2 after participation

Year 3 after participation

Year 4 after participation

Year 5 after participation

Increased probability of

participation in other programmes

0.1704

0.5237

1.4087**

0.8043

0.5121

2.1499***

Turning inexperienced users into experts

While other R&D and

innovation programmes often attracts the elite of research and

knowledge intensive companies, the innovation networks also is

relevant for more inexperienced companies. The Innovation Network

helps inexperienced users of the research and innovation promotion

system to become more familiar with the system.

It is argued that participation in knowledge intensive and advanced

R&D and innovation projects requires an initial knowledge base among

participating companies in innovation networks. Companies

participating in joined R&D collaboration have to provide knowledge

and other inputs to the project. Otherwise they will be excluded from

the project and receive a bad reputation and seen as free-riders, cf.

Robertson and Gatingnon (1998). Innovation networks assist and help

companies to establish the right initial knowledge base that will help

them be successful in other programmes.

Further the absorption capacity of a company has a huge impact on

their ability to gain from the knowledge provided by others in R&D and

innovation projects. Cohen and Levinthal (1990) argues that the

absorption capacity is of greatest importance to discover, understand

and embed new knowledge. Further the absorption capacity will be of

great importance to forward exploit the knowledge gained from joined

R&D and innovation projects, cf. Kastelli (2004) and Vinding (2002).

Other studies have shown that it largely is larger companies that

possess the basis capabilities and absorption capacity that enables

them to gain from joined R&D and innovation. That is often referred to

as one of the main reasons to why larger companies tend to be over-

represented in joined R&D and innovation programmes as well as

when focusing on R&D collaboration, e.g. 40 percent of the

participating companies in innovation networks in the Programme for

Strategic Research have more than 250 employees and 27 pct. in

Innovation Consortia. While in the Innovation Networks it is just 11

pct. that have more than 250 employees.

Source: DAMVAD, 2011.

Note: The significance is marked with asterisk : *** = 1 pct.-level, ** = 5 pct.-level and * = 10 pct.-

level.

N: 6,066

Interpreting results

The results of the impact assessment show that participation in the

Innovation Network programme increase the probability of

participating in other R&D and innovation programmes. There can

several explanations to this result:

Providing participating companies in innovation networks with

overview and contacts

One of the cornerstones of the Innovation

Networks is their ability to provide participating companies in

innovation networks with an overview of and contact to other R&D

and innovation programmes. Further they provide the participating

companies in innovation networks with access to a comprehensive

network of researchers from universities, technological service

providers as well as other private companies, which all potentially

have experience with other parts of the public research and innovation

promotion system. As such they will be able to direct the participant to

other relevant programmes.

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As such the innovation network programme is playing a central role in

enabling inexperienced and smaller companies without the basic

knowledge skills into the public research and innovation promotion

system and thus is assisting companies in moving up the knowledge

ladder .

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6 Possible economic impacts

In general, the analyses of the behavioural changes of the companies

indicate that they change their behaviour as a result of their

participation in the innovation networks. On the other hand, it takes a

number of years before these behavioural changes affect their

economic performance. For instance, Rouvinen (2002) points out that

investments in R&D do not improve the general performance until 3 to

5 years after the initial investment. When the investment is eventually

developed into new products, more efficient processes or new services

that increase earnings, this will, however, affect the general

performance. The available data does not include time series extensive

enough for calculating the exact economic effects at this time. In a few

years, we expect to able to do just that.

This chapter looks at the possible economic effects of participating in

innovation networks. The previous chapter focussed on the change in

behaviour of the participating companies in innovation networks. The

results showed that,

the public intervention that boost knowledge creation and relations

among agents in the innovation system.

6.1 Possible impact of increased innovation

The results from the previous chapter show that participating

companies in the innovation networks increase their probability of

being innovative by a factor of 4.5 compared to a control group of

companies with similar characteristics. This means that for each 10

companies in the control group that turn innovative, 45 companies in

the innovation networks will turn innovative.

Innovation leads to the creation of new products, processes and

services in businesses, increasing earnings and at the same time raising

the level of knowledge. They will thus make the innovative companies

more competitive in the long run, benefiting productivity and growth.

Innovation networks are a result of a wish to give the innovation effort

of Danish companies a boost. The result of the analysis of the changed

behavior of the participating companies in innovation networks

indicated a far greater likelihood of becoming innovative after having

participated in an innovation network.

A recent Danish study

shows substantial effects of investment in

innovation. Additional investments in innovation activities yield a

return on investment of an average of 30 percent based on calculation

of the impact that innovation has on labour productivity. Labour

productivity is a measure of the average value created by a business

per labour year performed. Growing labour productivity means that

businesses are improving the size of their income relative to their

expenses and thus becoming more competitive.

Participating companies are far more likely to become

innovative after having participated in an innovation network

than other, similar companies are.

Participating companies are far more likely to enter into R&D

collaborations after having participated in an innovation

network than other, similar companies are.

These behavioural changes indicate that the companies will experience

learning externalities. Autio m.fl. (2008) point to this as a so-called

second order effects of participating in public innovation programs.

These effects primarily allude to the learning capabilities enhanced by

The Impact of Business R&D and Innovation on Productivity in Denmark,

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The study is the most comprehensive conducted in Denmark covering

2,694 companies in the years from 1997 to 2005. Furthermore, the

analysis focuses on the solidity of the results and shows that the effect

does not differ over time. The result is therefore considered solid.

Furthermore, the econometric model used controls for intermediate

variables that otherwise could explain growth in labour productivity.

This means that an increasing the number of innovative companies is

expected to have a significant economical impact.

Strengthening the skills and knowledge through collaboration

with researchers

Strategic outsourcing of research to public research

institutions to focus more on its core research activities

6.2 Possible impact of increased R&D collaboration

The results from the previous chapter showed that participating

companies in the innovation networks increase their probability of

entering R&D collaborations by a factor of 4 compared to a control

group of companies with similar characteristics. This means that for

each 10 companies in the control group that enter R&D collaborations,

40 companies in the innovation networks will enter R&D collaboration.

R&D collaboration has many potential advantages. On a theoretical

level, companies can gain from R&D collaborations in many different

ways:

An overall better ability to absorb and translate new

knowledge and technology.

Faster and easier access to knowledge and technology.

Economies of scale, which is especially likely to be the case

among research-intensive companies.

Cost minimization in research and innovation projects.

Reducing the financial risks associated with long-term research

investments.

Access to a wider knowledge and understanding of the latest

international research trends and research results

A recent Danish study has tested if R&D collaborations with research

institutions result in subsequently higher labour productivity growth.

The analysis shows that the companies with R&D collaborations have a

significantly higher growth in labour productivity in each of the years

after the collaboration compared to other, very similar companies. The

analysis finds an increased annual value added per employee of 9

percent.

The effect is biggest in the first two years after collaboration, as

indicated by a significantly higher value added growth per employee

compared to companies who have not engaged in collaboration but

have the same probability of engaging in this kind of collaboration.

This means that an increasing the number of companies with R&D

collaboration is expected to have a significant economic impact.

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References

Autio m.fl. (2008),

First and Second-order Additionality and Learning

Outcomes in Collaborative R&D Programmes,

Research Policy, Vol 37,

Issue 1, pages 59-76.

DAMVAD (2011),

Økonomiske effekter af erhvervssamarbejde om

forskning, udvikling og innovation

for the Danish Agency For Science,

Technology and Innovation (DASTI), to be published 2011.

DAMVAD (2010),

Produktivitetseffekter af erhvervslivets forskning,

udvikling og innovation

for the Danish Agency For Science, Technology

and Innovation (DASTI), ISBN: 978 87 923 7249 9, 2010,

Kastelli m.fl. (2004)

, Cooperative R&D as a means for knowledge

creation , International Journal of Technology Management, Vol. 27,

Number 8, pages 712-30.

Robertson and Gatingnon (1998),

Technology development mode: a

transaction cost conceptualization , S

trategic Management Journal,

Vol. 19, pages 515-31.

Rouvinen (2002),

The Existence of R&D Spillovers: A Cost Function

Estimation With Random Coefficients,

Economics of Innovation and

New Technology, Taylor and Francis Journals, Vol. 11(6), pages 525-

541.

Vinding

(2002), Absorptive capacity and innovative performance: A

human capital approach. Kapitel 7 i Interorganisational Diffusion and

Transformation of Knowledge in the Process of Product Innovation,

Institut for Erhvervsstudier, Ålborg Universitet

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Appendix 1: Impact study methodology

This analysis focuses on two different types of impacts of participating

in innovation networks:

Behavioural effects

Economic impacts

Firstly, this section will present the methodology behind the

behavioural impact study. Secondly, it will describe the method used

when conducting the economic impacts study.

participated in innovation networks. Clearly it is not possible to

observe the participating companies outcome with and without

treatment. We must therefore find a proper substitute for the

outcome of participating companies had they not participated.

Assuming that the average outcome of the population of

non-

participating companies is a valid approximation for the counterfactual

situation is, however, not a viable solution as participating companies

in innovation networks and

non-participating

companies may differ in

the absence of treatment. This selection problem arises because

participating companies in innovation networks may be more likely to

participate and possibly more likely to benefit from participation.

To circumvent the challenges from selection bias we employ a certain

matching technique which identifies

non-participating

companies that

are similar to participating companies in innovation networks given

several company specific characteristics.

Estimating the behavioural impact

The economic impact of participation in innovation networks has been

carried out from two different perspectives,

1. A general perspective that attempts to identify the overall

participation effect by using the full sample of participating

companies in innovation networks disregarding the variation in

participation type.

2. A perspective that subdivides participation according to

participation type and conducts the impact analysis for each

type separately.

For each of the two perspectives, we use a matching approach to

estimate the causal effect of participation in innovation networks. This

approach matches each participant with a similar

non-participant,

hereby simulating the counterfactual situation.

The counterfactual situation describes the performance paths

participating companies would have followed had they not

Matching approach

This paper uses a particular matching approach called propensity score

matching which for all companies involves the estimation of the

probability of participating in innovation networks based on observed

relevant company specific characteristics. For the specific perspective

we model the probability of participation in each of the specific

participation types based on a set of observed characteristics.

The probability of participation is estimated using a logit model, which

relates the probability of being treated with several company specific

characteristics. Thus, the logit model estimates the following

conditional probability:

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where

treatment

is a variable that takes the value one in the event of

participation and zero otherwise and where

covariates consisting of:

1. Industry

2. Company size

3. Exports

4. Educational background of employees

5. Previous performance

6. R&D activities

7. Research collaboration

represents a vector of

Skilled

Short education

Further education

Higher education and research education

The predicted probability from the logit model is interpreted as the

propensity score and therefore constitutes the specified probability of

participating based on company specific characteristics.

Participating companies in innovation networks are matched with

non-

participating companies according to a matching algorithm which for

each treated unit identifies companies in the population of

non-

participating companies with identical or similar propensity score. This

matching algorithm is called Nearest Neighbour matching

In order to increase the estimation precision, the Nearest Neighbour

matching algorithm is augmented so that ten control units are

identified and selected for each participant according to the

propensity scores. In other words, this matching algorithm picks for

each participant those ten non-participating companies that come

closest in terms of propensity score.

It is important to consider the quality of the matching, which can be

done by testing whether there is additional explanatory power stored

in the covariates considering businesses treatment status after the

matching procedure has been carried out. In other words, it has been

tested whether there are any observed systematic differences

between participating companies in innovation networks and the

control group consisting of the matched

non-participating

companies.

The probabilities are estimated based on five different industries,

three different size levels, and five different educational levels.

Industries are subdivided as:

Low tech manufacturing

High tech manufacturing

Wholesale and retail trade

Knowledge-intensive business services

Other

Company size is subdivided as:

1 to 50 full time equivalent

50 to 250 full time equivalent

Larger than 250 full time equivalent

Educational background of employees is subdivided as:

Unskilled

Other matching algorithmS such as the calliper algorithm has been implemented

although not resulting in any significant differences regarding the outcome of the

matching model compared to the implementation of the nearest neighbour

algorithm.

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For each matching procedure performed in this analysis we have

carried out a test of the existence of systematic differences between

the treatment group and the control group. We are able to reject the

presence of such differences, which indicates that the matching

procedure throughout the analysis results in a proper balance between

participating companies in innovation networks and the selected non-

participating companies.

The methodology encounters the possibility of selection bias. By

establishing the control group from a certain number of explainable

factors the control group will have the same probability to participate

in the innovation network programme. As such the control group are

not randomized but defined especially to match the participating

companies. As such they should have the same requisite to enter the

programme, to become innovative and to engage in R&D

collaboration.

With this result in hand we are able to interpret any differences in

outcomes between the well selected and adequate control group and

of participating companies in innovation networks as the causal effect

of participating in innovation network.

The table below presents the number of participant by participation

type.

Table A.1: Amount of observations according to participation type

Number of participating companies in

innovation networks

1,225

199

148

Source: DAMVAD 2011

Performance criteria

The presence of extreme observations may distort the participation

effect and reduce the estimation precession. Data can contain extreme

values due to the occurrence of measurement errors or due to

mergers and split offs of businesses. Such extreme observations can

have a disproportionately large impact on the analysis. Another

example is young companies that carry out grand investment projects

that affect the company s productivity per full time equivalent for a

while. Such companies can experience extreme fluctuations in

performance.

To avoid the distorting impact of outliers this paper implements

certain performance criteria, which serve as thresholds for which

outliers are corrected. In the research literature it is common to

remove companies that experience a tripling or a halving in

performance between two successive years

. This methodology has

been implemented throughout the analysis

In order to minimise the impact of outliers and to reduce the variance

of the estimator, the 5 percent best and worst performances for each

year for participating companies in innovation networks and

non-

participating companies are removed from the sample. This correction

is implemented to secure a high degree of solidity and reliability of the

estimated participation effect.

General model

Participation type

Seminar

Matchmaking

Counselling

Projects

Only participations before 2008 are included in the analysis since Statistics Denmark

holds no information on companies performance hereafter.

See e.g. Mairesse, Jacques and Hall, Bronwyn Hughes, 1995,

Exploring the Relationship Between R&D and Productivity in French

Manufacturing Firms .

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Appendix 2: Data

The report is based on microeconomic data, primarily from corporate

financial accounts and information on research and development

activities at corporate level. This section contains a detailed

description of the data used in the analysis. The period covered is

2003-2008 unless otherwise stated.

The data was obtained from the following sources:

Information on the participating companies in the Danish

Innovation policy program of Innovation Network (In Danish:

Innovationsnetværk ).

Statistics on Research, Development and Innovation in the

Danish Business Sector.

General enterprise statistics.

DAMVAD Database on Knowledge Collaboration a database

covering participating companies in major R&D and innovation

programs both nationally and internationally.

Information on the participating companies

in the Danish Innovation

policy program of Innovation Network was provided by The Danish

Agency of Science, Technology and Innovation. These data enable us to

identify and characterize the companies participating in Innovation

Network. Furthermore, they contain information concerning the time

and character of participation.

The

Statistics on Research, Development and Innovation (RDI)

in the

Danish Business Sector contains detailed information on the expenses

and resources allocated to research, development and innovation in

Danish businesses. Furthermore, it describes the framework for

innovation in Denmark. Data are survey-based, which facilitates

comparisons over time. The survey is conducted by Statistics Denmark

in accordance with OECD s guidelines for R&D and innovation

statistics

The purpose of

General enterprise statistics

is to give a coherent and

consistent description of Danish business conditions through

economic, employment, and accounting figures at enterprise level. All

companies subject to registration according to Danish law are

included. The information on economic, employment, and accounting

figures is collected every year, which makes it possible to present a

detailed set of panel data where key economic variables and company

characteristics are monitored over time. This analysis uses data from

the General enterprise statistics covering the period from 2001 to

2008.

Finally, DAMVAD Database on Knowledge Collaboration covers

projects in all major research and innovation programs, including

Innovation Consortia, The Program for Strategic Research, 4

-7

Framework Program. Overall, the database contains more than 3,800

projects and includes almost 7,800 Danish companies (approx. 2,200

unique companies). The database constitutes an unparalleled source

for analysing the extent and nature of the interactions between

companies and knowledge institutions in Denmark and their

In the years 2004 and 2006 the data were collected as part of the Community

Innovation Survey.

For R&D statistics the data collection follows the Frascati Manual while the

innovation statistics were collected in accordance with the Oslo Manual.

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participation in the national as well as the international innovation

system.

The information on participation in innovation networks contains

6,006 observations. After initial quality assurance and data cleaning,

the number of observations falls to 4,664. This is primarily due to

foreign and public companies not being included in the analysis and

therefore deleted.

A number of companies appear several times in the same network.

This is dealt with by keeping the observation with the highest ranked

activity and deleting the others. The final list of participating

companies in innovation networks thus ends with 4,021 observations.

It is noted that the same company may participate in more than one

network. There are 3,388 unique companies in the data set. The

distribution of participating companies in innovation networks by

network is shown in the table below.

Table A.2. Participating companies in innovation networks by network

Network

A-netværk

AluCluster

Animation HUB

Apex

Bio-netværk

Biomasse

CISS

CSDR

CSI

Center for Sundhedsteknologi

Danvifo

InfinIT

Knowledgelab

Livsstil B&B

NFBi

NIK-VE

OC DK

Participating companies in innovation networks

383

279

206

108

279

354

265

345

252

358

PlastNet

SUPPLYNET

Seedland

VE-Net

VIFU/Regionalt Teknologicenter

SundhedsITnet

All

113

123

184

311

105

4,021

Source: DAMVAD 2011 based on participation list for innovation networks.

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Appendix 3: The Danish Innovation

Network

Danish Innovation Networks and clusters

AluCluster - Knowledge and technology centre for aluminium

Contact: Michael Nedergaard tel:+ 45 7473 3040, e-mail:

[email protected]

AluCluster, a centre within practical utilisation of aluminium, offers a

wide range of competences within the aluminium area. AluCluster's

objective is to create value for the customer through overall solutions.

AluCluster's mission is to meet the need for highly qualified

consultancy now and in the future within practical utilisation of

aluminium and thereby become the preferred partner in the field of

consultancy and development of aluminium solutions.

www.alucluster.com

involved in innovation processes, developing new concepts, marketing

as well as learning, guiding and interaction design.

www.animationhub.dk

Danish Sound Technology Network

Contact: Jan Larsen tel: +: 45 2243 0025, e-mail: [email protected]

Danish Sound Technology Network embraces individuals, organizations

and businesses involved with sound technology. They will create a new

space for innovation, collaboration and dissemination of knowledge.

The vision of the network is that Denmark is a leading country with

regard to sound technology in terms of knowledge, research and

education. Danish Sound Technology will be the epitome of high

quality in products and services, as well as in physical rooms and social

contexts.

www.lydteknologi.dk

FoodNetwork

Contact: Britt Sandvad

tel: + 45 9612 7624, e-mail: [email protected]

Animation Hub

Contact: Viggo Johannes Jensen - tel:+ 45 2850 9864, e-mail:

[email protected]

Animation Hub is a cluster experimenting with animation in the

context of development and communication. Based on the

competences within animation, such as generating new ideas,

dramaturgy, cinematography and simulation, the cluster wishes to

help companies find new ways of understanding, accepting and getting

FoodNetwork is an extensive network which includes a large number

of Danish universities, research institutions, Approved Technological

Service Institutions (GTS), innovation and development parks as well as

technical and vocational schools. The aim of the network is to create

growth within the food industry through networks, projects and

activities. It is also to be the link that ensures visibility of the relevant

partners within the food industry and to support and facilitate existing

and new clusters.

www.foodnetwork.dk

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The Innovation network for Environmental Technology

Biopeople Innovation Network for Biotech

Contact: Per Spindler tel: +45 2875 6572, e-mail: [email protected]

Biopeople embraces universities, research organizations, and

hospitals, the Danish Medicines Agency, industry associations as well

as pharma, medtech, medical device, food and biotech companies.

www.biopeople.dk

Infinit The Danish ICT Innovation network

Manager of network Aalborg: Rikke Uhrenholt tel: + 45 9940 7220, e-

mail: [email protected]

Manager of network Copenhagen: Rikke Koch ph: + 45 2126 8724, e-

mail: [email protected]

Infinit is a Danish network for innovative utilization of IT. Its goal is to

convert the infinite possibilities that technology offers into concrete

collaborations between research and industry.

www.infinit.dk

InnoBYG - Innovation Network for Energy efficient and Sustainable

Construction

Contact: Henriette Hall-Andersen

[email protected]

tel: +45 7220 2241, e-mail:

Contact: Jørn Rasmussen - tel:+ 45 4516 9200, e-mail:

[email protected]

The Innovation Network for Environmental Technology, Inno-mt, was

established in December 2010. The focus is to bring the sectors soil,

water, air and waste together in order to turn them into innovative

new products and services across the four sectors.

www.inno-mt.dk

InViO - Innovation network for knowledge-based experience

economy

Contact: Jens F. Jensen tel:+ 45 9940 9028, e-mail:

[email protected]

The objective of Innovation network for knowledge-based experience

economy is to strengthen knowledge sharing, knowledge

development, and cooperation between businesses and institutions of

knowledge relating to innovation and research within the field of

experience economy. In this way, the innovation capacity of the

businesses is reinforced so that knowledge- and experience-based

growth within the industry will be generated.

www.invio-net.dk

The construction industry's new network InnoBYG will facilitate

sustainable and energy efficient development in the construction

industry from 2010-2014. The focus of the network will be on

development projects, knowledge sharing and dissemination and

matchmaking across the industry and between companies and

knowledge institutions/universities.

www.innobyg.dk

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Innovation Network for Biomass

Contact: Michael Støckler tel: +: 45 8999 2504, [email protected]

The purpose of the Innovation Network for Biomass is to facilitate

development within production, handling, and processing of biomass

with the goal of better utilization for energy purposes. Its members are

individuals and companies involved in the field of biomass, agricultural

waste, and manure. The network has an international scope and

welcomes both Danish and international members from private

companies, research institutions, authorities, etc.

www.inbiom.dk

The Innovation network for Market, Communication and

Consumption

Contact: Per Østergaard ph. + 45 6550 3235, e-mail: [email protected]

The network embraces a broad range of core competences that are

essential to understand future markets and consumers. Researchers

from a classic marketing tradition collaborate with researchers from

the humanities, arts, and design. This combination is not common in a

Danish context, but crucial in a market where symbolic and emotional

dimensions of products are becoming more and more important. The

participating researchers come from The University of Southern

Denmark, Aalborg University, Aarhus School of Business Aarhus

University, Copenhagen Business School, and Kolding School of Design.

www.imkf.dk

Innovation Network for Danish Lighting

Contact: Lene Hartmeyer

[email protected]

ph. +: 45 4717 1800, e-mail:

Service Platform Service Cluster Denmark

Contact: Mette Abrahamsen

tel: + 45 2311 3719, e-mail: [email protected]

The object of the Innovation Network for Danish Lighting is to promote

the use of good and appropriate lighting and to advance knowledge

and information on the improvement of the lighted environment to

the benefit of society.

www.dansklys.dk

The vision for Service Cluster Denmark is to contribute to growth,

innovation and competitiveness among service businesses in Denmark.

Service Cluster Denmark aims to create new possibilities for

cooperation between businesses and knowledge institutions, to

strengthen research and innovation in businesses and to incorporate

international knowledge and ideas by involving businesses, research

institutions and networks outside Denmark.

www.serviceplatform.dk

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Innonet Lifestyle Interior & Clothing

Contact: Betina Simonsen tel: + 45 96 16 62 00, e-mail:

[email protected]

Innonet Lifestyle Interior & Clothing is an innovation Network under

the ministry for science, technology and development (STD). The

Network's purpose is to promote growth and innovation in the

industry of home and fashion by identifying, communicating and

embedding new knowledge. The purpose is also to build bridges

between companies and institutions of research and knowledge.

www.innonetlifestyle.com

Plastic and Polymer Network

Contact: Dorte Bælum tel: + 45 36973600, e-mail:

[email protected]

The cluster consists of a number of companies with an interest in

plastic and polymer materials. The aim of the cluster is to increase the

awareness of the materials, promote and innovate the use of the

materials within and across sectors.

www.plastnet.dk

No Age innovative solutions for elderly people

Contact: Gunhild Garsdal, tel:+ 45 3010 8080, e-mail: [email protected]

No Age aims to make the older people of Denmark more resourceful

by supporting their resources and making them capable of taking care

of themselves. Companies, leading research institutions,

municipalities, hospitals and organizations are part of No Age s work to

develop innovative solutions within health, prevention, nursing,

treatment, etc,

www.lvvl.dk

Offshore Center Denmark

Contact: Peter Blach tel: + 45 3697 3670, e-mail:

[email protected]

Offshore Center Danmark is the official national competence and

innovation center for the Danish offshore industry. On behalf of its

+210 member companies and institutions Offshore Center Danmark

push development with the aim of growth within the Danish offshore

industry.

www.offshorecenter.dk

RoboCluster

Contact: Bjarke Nielsen tel: + 45 2119 4797, e-mail:

[email protected]

RoboCluster is a Danish innovation network for robotics and

automation. The object is to maintain and further expand the robotics

sector in Denmark by generating and ensuring optimal conditions for

innovation in new as well as existing enterprises and set robotics into

action in fields as hospitals, farming, industry, play and education. This

is done by initiating technological projects between suppliers,

producers, users, universities and knowledge institutions in the field of

robotics and automation.

www.robocluster.dk

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VE-Net - Renewable Energy Innovation Network

The Transport Innovation Network - TINV

Contact: Steen Sabinsky tel: + 45 2966 2408, e-mail:

[email protected]

The Transport Innovation Network (TINV) is a national, cross

disciplinary network aimed at the Danish Transport sector. The

primary objectives of TINV are to create synergy, encourage match-

making and generate research and development projects between

stakeholders in the transport sector and research and educational

institutions, as well as related sectors such as energy and

infrastructure.

www.tinv.dk

Contact: Grete Bech Nielsen tel: + 45 7220 1113, e-mail:

[email protected]

VE-Net (Renewable Energy Network) is an innovative network related

to energy and funded by the Danish Ministry of Science, Technology

and Innovation. The aim of the network is to create collaboration

initiatives between companies and research institutes with the

purpose of increasing the application of research-based expertise

in the business community and to solve high technology matters.

www.ve-net.eu

Water in Urban Areas

Contact: Ulrik Hindsberger, tel:+ 45 7220 2285, e-mail:

[email protected]

The partnership is directed towards the challenge of adapting cities to

a changed climate, and thus it operates within the topic of energy,

climate and environmental technologies. The partnership will

contribute to realising the vision of Denmark as a climatically strong

and green winner nation and establish Denmark as the global

demonstratorium for viable water technologies, system solutions and

integrated water resource administration. The goal is to develop,

document and present technologies and planning tools for climatic

adaptation of existing urban areas in Europe, USA and Australia, and

for development of new, climatically strong cities in countries in

financial and institutional transition, such as China.

www.vandibyer.dk

UNIC Use of New technologies in Innovative solutions for Chronic

patients

Contact: Dorthe Kjær Pedersen tel: + 45 2498 4155, e-mail:

[email protected]

The number of chronically sick people is increasing, and consequently

the necessity of treatment and nursing for the chronically sick.

Through development of technological and innovative solutions UNIC

aims at reducing the number of hospitalizations, increasing the

number of chronically sick people in jobs, more people being able to

care for themselves, etc.