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