1. Complex climate change adaptation network in Bhutan;
how actor type impacts actor inclusion and clustering
Master’s Thesis
Institute of Political Science
University of Bern, Switzerland
Presented by:
Jessica Russell
17 September 2015
Thesis supervisor:
Prof. Dr. Karin Ingold
Institute of Political Science
University of Bern, Switzerland

2. Background
> Policy processes and governance as networks:
 Actors participate in collective decision-making process’ called an “institution”
 Repeated transactions between actors become institutionalized in a “network”
 Actors collaborate within the network on a defined project, creating a “tie”
Stakeholder inclusion, scales and multi-level approaches:
 Climate change adaptation is local in scope, with the structure of institutional
arrangements being critical, local actor inclusion is important
 The multilevel governance framework involves two levels of action:
1) Vertical = crossing multiple government scales - local to national
2) Horizontal = across government departments, line ministries
 Vertical/horizontal integration through top-down/bottom-up institutional designs
 A “polycentric approach” to governance
Case study: Bhutan

3. Research Question 1
> Aim: Examine how actor inclusion in climate change adaptation institutions is
shaped in a complex network in Bhutan, by applying a multilevel governance
framework to explore links between actors and institutions within networks
> Research Question: Which type of actors are most included within complex
networks and projects in climate change adaptation policy in Bhutan via the
embededness of national and local actors in top-down and bottom-up institutional
designs
> Hypothesis: National actors are well embedded in top-down institutional designs,
and local actors are well embedded in bottom-up institutional designs
 Independent Variable: Actor type
 Dependent Variable: Actors’ inclusion

4. Data and Methodology – Research
Questions 1, 2, 3
> 130 key actors were identified from a literature review and defined as nodes,
categorised into 7 actor types: international organisation, national government,
foreign government, local government, NGO, corporation or community
> 73 climate change adaptation-related policies/projects defined and categorised
into:
 Institutional types: climate change adaptation, sustainable forestry, disaster
management, communication (14)
 Institutional designs: top-down (69) or bottom-up (4)
 Social Network Analysis (SNA). Software- UCINET:
 Ties between actors and institutions coded as a dummy variable, with 1: tie
being present, and 0: no tie present
 Binary ties make up the 2 mode (actor x institution) asymmetric network matrix
 2 mode data transformed into a 1 mode (actor x actor) symmetric network
matrix to run analysis
 Ties are undirected

5. Research Question 1: Methodology
Networks Analysed:
 Total network: All actors as nodes, with ties (both top-down and bottom-up),
in a transformed 2 mode to 1 mode network
 Sub-networks: Either top-down or bottom-up institutional designs, are also
made up of actors as nodes, with ties, in a transformed 2 mode to 1 mode
network
> Method- Research Question 1
Social Network Analysis (SNA): three measures of centrality to assess
embededness:
 Degree centrality: the number of ties an actor directly shares with others in
the network
 Betweeness centrality: the number of times an actor connects two
disconnected actors
 Eigenvector centrality: higher when an actor is connected to institutions that
are also well connected

6. Research Question 1: Methodology
> Analysis:
 T-test: compare the mean centrality: degree, betweeness and eigenvector of
national government actors and all other groups (total network and top-down
designs), and local government actors and all other groups (bottom-up
designs)
 Two-way ANOVA: tests the level of significance of differences in normed
centrality: degree, betweeness, and eigenvector means between the 7 actor
types, with the p-value
 Regression model: to fit the data and estimate the significance and strength
of the relationship between independent variable actor type and dependent
variable actors’ inclusion (dependent variable: centrality)
 If an actor has a high degree centrality, betweeness centrality and eigenvector
centrality, relative to others = actor is well embedded in the network

7. Table 3. Normed betweeness centrality means for the total network and sub-networks

8. Research Question 1: Results
> Hypothesis: National actors are well embedded in top-down institutional designs,
and local actors are well embedded in bottom-up institutional designs
 National governments do not have a high degree centrality (Table 2- Annex),
but high betweeness centrality (Table 3) and eigenvector centrality (Table 4-
Annex) in top-down designs
 Local actors have a slightly higher degree centrality, no betweeness centrality,
but a high eigenvector centrality, in bottom-up institutional designs
 No significant effect of actor type on degree, betweeness, eigenvector
centrality
 Conclude: National actors are relatively better embedded in top-down
institutional designs, and local actors are relatively better embedded in
bottom-up institutional designs

9. The Relevance of Clustering
Research Question 2: Part A and B
> Clusters of actors collaborate inside closed groups, having repeated transactions
leading to trust, thus supporting cooperation
> Core/Periphery- Actors in the core are more able to coordinate their activities
> Aim: Examine whether clustering occurs according to actor type in climate change
adaptation institutions in a complex network in Bhutan, by applying a multilevel
governance framework to explore links between actors and institutions
> Research Question 2. Part A: Do actors of the same type cluster together within
networks?
 Hypothesis: Actors of the same type collaborate by clustering; the density
between within-group ties and outside-group ties are significantly different
 Independent Variable: Actor type
 Dependent Variable: Clustering
> Research Question 2. Part B: Which actors make up the core and periphery of
the network?

10. Research Question 2: Part A and B
Methodology
> PART A & B: An MDS plot is used to initially locate clusters:
 Actors located in the centre of the cluster = core actors
 Actors located further away from the centre = peripheral actors
 Analysis: Total network (both top-down and bottom-up institutional designs)
> Part A: Clustering analysed via tie density:
 Structural Block model option of an ANOVA Density model: test whether within
and between group ties are significantly different across actor types
> Part B: Location via a simple Core/Periphery model:
 Core actors = highest density of ties amongst themselves, collaborating in
common institutions
 Peripheral actors = lower density of ties amongst themselves, fewer
institutions in common

11. Figure 5. An MDS plot shows the total network.
Red circles: international organisations, orange: foreign government, yellow: national
government, green: local government, blue: NGO, purple: corporation, pink: community

12. Table 5. Structural Block model option of an ANOVA Density model (a) with model fit (b), and a
Simple Core/Periphery model with density matrix table (c) for the total network

13. Research Question 2: Results
> Part A: Do actors of the same type cluster together within networks?
Hypothesis: Actors of the same type collaborate by clustering; the density
between within-group ties and outside-group ties are significantly different
 Results: There is a statistically significant effect of variable “actor type” on
variable “clustering” in the network; accept that the density between within-
group ties and outside-group ties are significantly different
> Part B: Which actors make up the core and periphery of the network?
 Results: Many national level actors in the core
> Conclude:
 Actors of the same type collaborate by clustering
 National actors cluster and occupy the core of the network, sharing many ties
and cooperating with other actor types

14. The Relevance of Fragmentation
Research Question 3
> We define fragmentation where there is a high proportion of actors unable to reach
each-other in the network:
 Vertical fragmentation: Between institutions at different scales such as from
national to local
 Horizontal fragmentation: Between institutions in different sectors such as
forestry or water
> Fragmentation in institutions:
 Could affect actors’ ability to implement projects effectively, through inhibiting
joint decision-making
 Creates barriers to cooperation
> Aim: Examine fragmentation in climate change adaptation institutions in a complex
network in Bhutan, by applying a multilevel governance framework to explore links
between actors and institutions within networks
> Research Question: Does the network display fragmentation?

15. Research Question 3 - Hypothesis and
Methodology
> Hypothesis: Top-down institutional designs are less fragmented than bottom-up
institutional designs
> Fragmentation analysis:
 Networks: the total network (both top-down and bottom-up institutional
designs), and sub-networks; top-down and bottom-up
 Fragmentation score: the proportion of actors unable to reach each-other
 Compare the fragmentation score of institutional design relative to each-other
> Density score:
 Equal to 1 = all actors within the network are tied directly to each other
 Equal to 0 = network is fully disconnected and therefore fragmented

16. Research Question 3: Results
Table 6. Fragmentation score for the total network, and sub-networks; top-down or bottom-up
institutional designs
> Results:
 Low amount of fragmentation in top-down designs
 High amount of fragmentation in bottom-up designs
 Overall density scores (Table 1- Annex) are low for each institutional design
(top-down = 0.035, bottom-up = 0.052)
 Conclude: Few ties through which information can flow, both networks are
disconnected and therefore fragmented

17. Conclusion
 Some difference between top-down and bottom-up institutional designs:
National actors relatively better embedded in top-down, local actors relatively
better embedded in bottom-up (Table 2- Annex, Table 3, Table 4- Annex)
 Lack of inclusion of local actors (Table 2, 3, 4)
 Some actors may work on climate change projects in isolation- UNFAO (Fig.
5)
 Actors of the same type collaborate by clustering (Figure 5) (Table 5)
 National actors cluster/core of the network, sharing many ties/cooperate with
different actor types (Table 5)
 Top-down institutional designs less fragmented than bottom-up, but with
overall low density scores for each (Table 1-Annex). Conclude that both
demonstrate fragmentation (Table 1- Annex) (Table 6)
 The total network in Bhutan is fragmented (Table 1- Annex) (Table 6), but
displays clustering (Table 5); conclude that information sharing between actors
to plan, monitor and enforce climate change adaptation projects may be low

18. Recommendations
> General recommendations:
 Increased involvement from national government actors: more influence, ability
to collaborate and capacity to coordinate in the network
> Recommendations for a “polycentric” approach to governance:
 A hybrid approach between top-down, and bottom-up designs, with national
government providing a guiding framework, allowing local communities to
make implementation decisions based on community knowledge
 Increase both horizontal and especially vertical collaboration, to increase local
actor inclusion
 Support vertical and horizontal integration, to decrease fragmentation