measuring the impact of cluster programmes presentation at the ngp cluster excellence conference...

Measuring the impact of cluster programmes

Presentation at the NGP Cluster Excellence Conference 2011May 26th 2011

By Michael Mark

DAMVAD, Economics

10 persons•Ministries•Academic & research•Internationally

•R&D and innovation•Public innovation•Labor market•Business development•Impact assessments

•Knowledge, analysis and strategy

•Governments, regions og municipalities in DK og internationally •Private organizations •Int. organizations

Baseline of this presentation

• A national and international wish to conduct solid and quantitative impact assessments and evaluations.

• Return on investment from public funding. • Fact based knowledge about what works and for whom. • Quantifying and explaining effects are at the core of evaluating

socio-economical programmes.

Objectives of the innovation networks

• To strengthen public-private collaboration and knowledge transfer between public universities and private companies on innovation and R&D.

• To strengthen innovation and R&D in Danish companies.

Not R&D active

R&D active

R&D active and R&D collaboration

Productivity per employee

Time from participation

Innovative

R&D collaboration increase productivity

0

2

4

6

8

10

12

14

16

18

20

1 2 3 4 5 6 7 8 9

Perc

ent

Years after initial collaboration

Collaboration with universities and research institutions

Sign. 10%

Sign. 5%

Treatment

Control

Source: DASTI(2011) (in Danish): Økonomiske effekter af erhvervssamarbejde om forskning, udvikling og innovation

Return on investment from innovation and R&D

Source: DASTI (2010) (in Danish): Produktivitetseffekter af erhvervslivets forskning, udvikling og innovation.

Return on investment

An additional euro invested in company innovation 30 %

An additional euro invested in company R&D 66 %

Impacts of participation in innovation networks

• Colla-boration

• Participation in programs

Access to know-

ledge

• R&D investments

• More innovation

Invest-ments

in know-ledge

• Value added growth

• Export

Econo-mic

effects

Short term: Network- and learning externalities

Long term: Economic value

More than 4 times as many new innovators• Participation implies learning externalities • Participants gain new knowledge• Participation in common idea generation

102

22

0

20

40

60

80

100

120

Participants Control group

Number of new innovators

year 1 after participation

Actual growth as a consequence of participationNumber of innovative companies

560

580

600

620

640

660

680

700

720

740

Year of participation Year 1 after participation

Projected number of new innovators WITHOUT the cluster programme

Projected number of new innovators WITH the cluster programme

More than 4 times as many new collaborators

• Participation implies increased networks externalities • Provide opportunities to identify collaboration partners

58

13

0

10

20

30

40

50

60

70

Participants Control group

Number of new R&D collaborators

After participation

Increased participation in other programmes

• Providing the participating companies with the overview of other programmes and contacts

• Turning inexperienced companies into experts

Time from initial participation

Level in the knowledge system

Initial participation

Inexperienced

New player

Advancedplayer

Experts

How to measuring impacts

• Isolating the impact of participation in an innovation network• Create a statistically counterfactual situation• Concept is well known from medical science – where a

medical intervention is simulated • Transferred to socio-economic evaluations and impact

assessments

Comparing ”alike” with ”alike”

Estimating a counterfactual situation

• Important to compare situations that are statistically alike

Creating one or several control groups

• Extremely important in order to create a counterfactual situation

• Identify a statistically identical twin

Steps in creating a counterfactual situation

Identify participants and the control group

Pairing the twins:

Estimating the probability of participating in an innovation network

Comparison of the two groups

The trick: matching control and treatment groups

”Treatment group”

”Control group”

PropensityScore

Number of observations

0 1

Contakt

Michael Markmma@damvad.dk

P: + 45 2993 1312

Possibilities in knowledge

DAMVAD Badstuestræde 201209 København KDenmark