What policies to support

regional networking capabilities?

Evidence from a regional innovation policy framework

2002-2008

Annalisa Caloffi, Federica Rossi, Margherita RussoUniversità di Padova, Birkbeck College, Università di Modena e Reggio Emilia

Complexity in the Real World: From policy intelligence to intelligent policyECCS’12, Bruxelles, 5-6 September 2012

Introduction

General features of a regional policy_SPD 2000-2006 (2002-2008)

Heterogeneity of project networks

Stability of participants and relationships over time

Effects of participation to policies on organizations’ ability to:

– form heterogeneous networks

– engage in stable relationships

– activate relationships in general

Some preliminary conclusions & further developments

• Public policies in support of innovation networks:

– increasingly popular

– usually aimed at promotion of R&D collaborations, technology

transfer, innovation diffusion

– do they help organizations improve their networking

capabilities?

• being able to access external knowledge through networking is

increasingly important to innovate and compete successfully

• not all organizations are equally able to engage in effective

networking

Introduction 1/3

• Objective of analysis:

– exploit a very original and comprehensive dataset of innovation

networks policy interventions funded by Tuscany’s regional

government in 2002-2008 (SPD 2000-2006)

– assess whether these policies have contributed to improving the

participants’ ability to form “successful” innovation networks

with external partners

– “learning” / “behavioral additionality” effects

Introduction 2/3

• Key elements of network structure associated with

success in collaborative innovation and knowledge transmission

– heterogeneity in participants’ competences and outlook: balance

between cognitive distance and proximity

– novelty vs. stability: balance between new and stable relationships

– involvement of “intermediaries” to facilitate contact and

communication among organizations with different competences,

languages, cognitive frameworks

• The policy programmes we analyze included some requirements

(“constraints”) aimed at promoting heterogeneity, repeated

collaborations and the involvement of intermediaries

Introduction 3/3

Policy Programmes

RPIA_ITT 2002 16SPD 171 2002 18SPD172 2002 18SPD 171 2004 4SPD 171 E 2004 4SPD 171 2005 10RPIA_VINCI 2006 12SPD 171 2007 10SPD 171 2008 10

2002 2003 2004 2005 2006 2007 2008Avg.

Lengthmonth

General features of policy programmes_2002-2008 1/5timeline

• Constraints in networks’ basic composition imposed only in the 200-2005 programmes

• Two periods: network formation (2002-2005) vs. network consolidation (206-2008)

• Did participation in the first period influence the organizations’ ability to network “successfully” in the second period?

programmes 9

Submitted projects 309

Funded projects 168

Cost of funded projects 44,004,349 €

Funds 36,790,543 €

Public co-funding 75-100 % (avg. 83,4%)

Avg. length of funded projects 12.8 months

Min – max number of projects funded by programme 1 – 41 projects

Min – max funds per project 20 thousand € – 3 million €

Smallest / largest size of the project network 3 – 35 organizations

Programmes’ funding as a share of total innovation policy funding

40% (remaining 60%: incentives to individual firms)

Share of regional firms involved in these programmes 1%

General features of policy programmes_2002-2008 2/5

RPIA_ITT 2002

SPD 171 2002

SPD172 2002

SPD 171 2004

SPD 171 E 2004

SPD 171 2005

RPIA_VINCI 2006

SPD 171 2007

SPD 171 2008

4,617,490.000

1,465,820.000

1,550,479.000

751,500.000

377,458.820

4,047,785.120

2,859,639.800

10,010,692.040

11,111,677.800

Total funding

12.6 4.04.22.01.011.0

7.827.3

30.3

% funding by programme_ITT 2002171 2002172 2002171 2004171A 2004171 2005_VIN 2006171 2007171 2008

• Funding by programme

General features of policy programmes_2002-2008 3/5

• Funding by technological field

Ent

Opubl.

Uni

PR

SC

SP

LG

LA

CC

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.045.6

3.1

12.9

1.6

7.5

7.6

8.8

10.4

2.5

36.3

2.2

20.0

1.5

16.9

10.9

1.9

8.2

2.2

% funds % participants

• Participants by number and funding

General features of policy programmes_2002-2008 4/5

bubbles proportional to project funding

2002_ITT 2002_171 2002_172 2004_171 2004_171E 2005_171 2006_VIN 2007_171 2008_171

hete

roge

neity

inde

x

Heterogeneity of networks and programmes 1/3• Heterogeneity of project networks

within each programme

programme without minimum heterogeneity constraint

0

1

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7

2002_ITT

Serie1 Serie2 Serie3 Serie4 Serie5

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2002_171

Serie1 Serie2 Serie3 Serie4 Serie5

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2002_172

Serie1 Serie2 Serie3 Serie4 Serie5

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2004_171

Serie1 Serie2 Serie3 Serie4 Serie5

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2004_171E

Serie1 Serie2 Serie3 Serie4 Serie5

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2005_171

Serie1 Serie2 Serie3 Serie4 Serie5

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2006_VIN

Serie1 Serie2 Serie3 Serie4

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2007_171

Serie1 Serie2

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2008_171

programme without minimum heterogeneity constraint

hete

roge

neity

inde

xHeterogeneity of networks and programmes 2/3• Heterogeneity of project networks

within each programme

box plot

Heterogeneity of networks and programmes 3/3• Heterogeneity of project networks

and number of participants

2002

2002

2002

2004

2004

2005

2006

2007

2008

_IT

T1

71

17

21

71

17

1A

17

1_

VIN

17

11

71

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

223

27

65

25

47

467

50

138

83

8

11

11

19

12

2

2

2

25

2

2

2

61

18

130

157

8

3

3

Since 2002 Since 2004

Since 2005 Since 2006

Since 2007 One (or more) of the previous year(s)

New participants

Stability vs. novelty 1/3• of network participants

2002

2002

2002

2004

2004

2005

2006

2007

2008

_IT

T1

71

17

21

71

17

1A

17

1_

VIN

17

11

71

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Since 2002 Since 2004

Since 2005 Since 2006

Since 2007 One (or more) of the previous year(s)

New relations

Stability vs. novelty 2/3• of relationships between participants

Note to table: The index is calculated as the ratio between the share of stable/continuous relationships of a certain type and the share of stable/continuous relationships overall. The index is zero when no stable/continuous relationships of that type were present in the programme. The earliest programme, RPIA ITT_2002, is not displayed since, by definition, it includes only relationships that are new to the policy.

Stability vs. novelty 3/3• of relationships between participants by participants’ types

By participating in policy-supported innovation networks,

do organizations improve their networking abilities?

• Effects of participation to policies on organizations’ ability to

– form innovative heterogeneous networks

– engage in stable relationships:

two periods analysis: formation vs. consolidation stage

The “learning effects” of policies 1/8

Variable Obs Mean Std. Dev. Min Max

Dependent variable 1 avgdiversity20068 197   2.88 1.14 1.00 6.76

Dependent variable 2 Pctrepeated20068 197 0.37 0.32 0.00 1.00

Policy constraints

p2002ITT 205 0.32 0.72 0.00 5.00

p2002171 205 0.09 0.28 0.00 1.00

p2002172 205 0.16 0.37 0.00 1.00

p2004171 205 0.09 0.29 0.00 1.00

p2004171E 205 0.11 0.37 0.00 2.00

p2005171 205 1.74 1.73 0.00 14.00

Behaviour of agent in previous period

avgdiversity20025 205 3.68 0.95 1.28 5.95

avgfunding20025 205 12482.96 16518.83 0.00 87908.33

avgpctSC20025 205 0.07 0.05 0.00 0.33

avgp_20025 205 20.89 6.44 3.00 36.00

Behaviour of agent in current period

avgfunding20068 197 32664.61 38336.69 0.00 207925.00

avgpctSC20068 197 0.28 0.87 0.00 5.43

avgp_20068 197 9.64 3.32 2.00 18.00

Controls: type of organization, % projects in each technology field 2/8

Average heterogeneity of organization’s networks in 2006-8   Coefficient

Robust standard error

Sign. 

p2002ITT -0.007 0.089  

p2002171 -0.391 0.230 *

p2002172 0.080 0.221  

p2004171 0.318 0.222  

p2004171E 0.448 0.225 **

p2005171 -0.088 0.039 **

avgdiversity_20025 0.122 0.103  

avgfunding_20025 0.000 0.000  

avgpctSC_20025 -3.289 1.773 *

avgp_20025 0.004 0.018  

avgfunding_20068 0.000 0.000 *

avgpctSC_20068 0.110 0.072  

avgp_20068 0.094 0.024 ***

Ent -***, CC -***, %Opto-**, %OrgChem-**, %Biotech-**,%Multi-*Number of obs: 197; Prob > F: 0.0000; R-squared: 0.4229; sign. * 0.1, ** 0.01 *** 0.001 3/8

Share of organization’s relationships in 2006-8 that were already active in 2002-5

  CoefficientRobust standard

errorSign. 

p2002ITT 0.025 0.032  

p2002171 0.220 0.099 **

p2002172 -0.004 0.058  

p2004171 0.100 0.072  

p2004171E 0.035 0.055  

p2005171 0.069 0.017 ***

avgdiversity_20025 0.034 0.028  

avgfunding_20025 0.000 0.000  

avgpctSC_20025 0.103 0.466  

avgp_20025 0.001 0.004  

avgfunding_20068 0.000 0.000  

avgpctSC_20068 0.022 0.025  

avgp_20068 0.015 0.007 **

LG +***, %OrgChem-*, %Biotech-**,%Multi-**Number of obs: 197; Prob > F: 0.0000; R-squared: 0.4853; sign. * 0.1, ** 0.01 *** 0.001 4/8

Effects of participation to policies on organizations’ ability to activate relationships: SNA approach

• Indicators of network structure

IndexNetwork formation:

2002-2005Network consolidation:

2006-2008

1. N vertices 856 476

2. N edges 12606 2681

3. Density 0.037 0.0236

4. Average degree 29.444 11.264

5. N. of components 3 5

6. Connectedness (% of vertices in largest component) 97.5% 96.4%

7. Clustering coefficient (actual network) 0.990 0.8765

8. Clustering coefficient (random network) 0.036 0.0236

9. Average path length (actual network) 2.576 3.6706

10. Average path length (random network) 2.261 2.798

11. Small world index 24.13 28.31

5/8

Likelihood to activate relationships in 2006-8 Variables Description Obs Mean Std.

Dev. Min Max

Dependent variables

relation [dependent variable in model 1]

Dummy =1 if relation between the two agents realizes during the consolidation stage 8438 0.052 0.223 0 1

relation_D [dependent variable in model 2]

relation_D=1 if relation develops among two enterprises 8438 0.014 0.116 0 1

relation_D=2 if relation involves a firm and a university 8438 0.013 0.113 0 1relation_D=3 if relation involves a firm and another agent 8438 0.026 0.159 0 1

Heterogeneity

sectoral_het

sectoral_het=0 relation between agents in same 3 digit NACE Rev.2 8438 0.012 0.110 0 1

sectoral_het=1 relation between agents in different 3 digit but same 2 digit sector Nace Rev.2

8438 0.009 0.092 0 1

sectoral_het=2 relation between agents in different 2 digit Nace Rev.2

8438 0.979 0.143 0 1

power Difference between the Bonacich (1987) eigenvector measure of network centrality indices of the two agents in t-1

8438 -24.001 59.450 -321 104

leader Dummy = 1 when at least one of the agents has been leading partner of at least one project at time t-1

8438 0.274 0.446 0 1

funds Difference between the amount of funds that have been collected by the two agents in time t-1

8438 -37484 79612 -391158 81913

Stability previous Dummy = 1 when agents have had at least one relation in t-1 8438 0.069 0.254 0 1

multiple Dummy = 1 when agents have had multiple relations in t-1 8438 0.007 0.083 0 1

Intermediaries

intermediariesDummy = 1 when at t-1 the two agents were indirectly connected through an intermediary (service centres, private services providers, business associations and chamber of commerce)

8438 0.413 0.492 0 1

sc Dummy = 1 when the two agents at t-1 were indirectly connected through an innovation centre.

8438 0.202 0.402 0 1

other_interm Dummy variable = 1 when the two agents at t-1 were indirectly connected through an intermediary which is not an innovation centre.

8438 0.146 0.353 0 1

6/8Controls: programme in which agents may meet (VIN_2006, 2007_171, 2008_171); dummy = 1 if agent type was required by the policy (constrained)

Likelihood to activate relationships in 2006-8 Variables Coef.  Sign.

Dependent variable Relation (yes/no)

sector_het=1 -0.4572 0.512  

sector_het=2 -1.7047 0.299 ***

power -0.0002 0.001  

leader -0.5078 0.170 ***

funds  0.0000 0.000  

previous 2.6274 0.138 ***

multiple 4.4806 0.797 ***

intermediaries 0.5956 0.150 ***

2006_VIN 0.6029 0.269 **

2007_171 -0.0311 0.169  

2008_171 0.6470 0.166 ***

Log pseudolikelihood = -1239.45; Wald chi2(12) = 715.54; pseudo R2 = 0.2851. Sign: *** = 1%; ** = 5%; * = 10%. 7/8

Likelihood to activate relationships in 2006-8

Variables Coef.   Coef.   Coef.  

Dependent variable Firm-firm relation Firm-university relation Firm-other agents relation

power 0.0058 ** -0.0044 ** 0.0006  

leader -1.3078 *** -1.3508 *** -0.0132  

funds  0.0000 *** -0.0000 *** -0.0000  

previous 3.0294 *** 3.5248 *** 3.0731 ***

multiple 5.1451 *** 4.3327 *** 3.8972 ***

sc 0.8260 * 1.2735 ** 0.4870 *

other_interm 0.2259   -1.0703 ** 0.6732 **

2006_VIN 0.8951 *** -0.2802   0.4041  

2007_171 -1.1450 *** 0.2289   0.2161  

2008_171 -0.0484   0.8037 *** 0.8050 ***

Log pseudolikelihood = -1607.29; Wald chi2(33) = 930.07; pseudo R2 = 0.2678. Sign: *** = 1%; ** = 5%; * = 10%.

8/8

Preliminary conclusionsfor more effective policies fostering innovation networks

• What have we learned from policy analysis?

– Policy rules

• and heterogeneity of network composition

• and novelty vs. stability

• and involvement of intermediaries

– Need for instruments to assess the “learning effects”

(or “behavioural additionality”) of policies:

• network construction and analysis

• new indicators

• ABMs?

Further developments

• Has the innovation policy programme produced long lasting results ?

– Innovation and collaborative behaviour of these organizations after the end of

the policy programme (behavioural additionality)

• Role of intermediaries

– Which types of organizations did play the role of brokers and “bridges” in the

programmes?

– To which extent those agents were able to foster the ability of more marginal

agents to become more central?

• Comparative analysis of network policies in other regions in Italy and in

Europe

– Resources, processes and outcomes of regional variety of innovation policies

in SMEs regional systems