Composition of environmental decision-making networks – a case study in Helsinki, Finland

M.Soc.Sc. Arho Toikka

HERC Seminar Series 22.11.2007

Department of Social Policy, Faculty of Social Sciences

Composition of decision-making networks

In networks of organizations in charge of making policy

decisions and draft policies,

some actors are more

popular negotiation

partners than others.

Why?

The City of Helsinki Environmental Governance Data Set

Based on 70 interviews and archival data

Main interest the Ecological Sustainability Programme of

Helsinki The programme is the environmental strategy of Helsinki

The most important determinator of Helsinki environmental

action

Including three types of variables Network variables

Preference variables

Outcome variables

Modelling the choice of negotiation partners

Exponential Random Graph Modeling ERGM

Possible ties in the network treated as random,

dependent variables in the analysis

Network statistics and actor attributes as independent

variables

Dependence assumptions

Helsinki Environmental Governance Analysis

The network had considerable impact on the final policies

and how different subfields of environmental policy were

emphasized

Influence of a single organization over the final policy was

generated in the network, through the various

partnerships

What made an organization an attractive an sought-after

negotiation partner in environmental policy-making?

Helsinki Environmental Governance Analysis

Hypotheses for partner choice

Network characteristics Well-connected actors are more attractive – you can spread

your message

Less popular actors are more attractive – you have more

direct influence

Actors in tight subgroups are more attractive – you can build

a base of influence

Actor attributes Official status of the organization

Issue area expertise

Helsinki Environmental Governance Analysis

The model to test these hypotheses includes estimates

for the parameters for: Generalized popularity

- alternating-k-stars

Generalized subgroups

- alternating-k-triangles

Expertise

Status popularity

Status homophily

Helsinki Environmental Governance Analysis

Parameter estimate for effect on tie generation

Popularity 0,1545

Subgroups 1,3471

Expertise 0,1999Status popularity -0,1207

Status homophily 0,4678

Conclusion

In the drafting process of environmental policy in Helsinki,

the various organizations involved in drafting the policy

document were free to choose the collaborators in the

process

Contrary to some political science theories, the strongest

effects in partner choice were the building of subgroups

and status similarity, with small effects for expertise,

overall status and popularity