Network Structure and Organizational
Cooperation: What Estuaries Tell Us
Wendy Xinfang Gao and Frances Stokes Berry
Askew School of Public Administration & Policy
Florida State University
Tallahassee, FL 32306-2250
xxg6851@fsu.edu/ fberry@garnet.acns.fsu.edu
Network Structure and Organizational Cooperation: What Estuaries Tell Us
This research studies how different network structures affect the cooperation
between public organizations within the network. Building upon previous research
(Agranoff & McGuire, 1991, 1998, 1999, 2001; Mandel, 1988, 1990, 1994; O’Toole,
1986, 1990; Provan & Milward, 1991, 1995, 1998), this paper uses the survey data
gained from twenty-two estuaries nationwide to examine this relationship. Twelve
estuaries in the National Estuary Program (NEP) and ten Non-NEP estuaries were
surveyed and interviewed; two different network properties, namely, centrality and
density were studied to explore how they affect organizational cooperation within the
estuaries. Organizations surveyed include different levels of government agencies,
business organizations, non-profit organizations, and research organizations including
universities and research institutes.
The paper proposes two hypotheses based on previous research. The network
density and centrality are generated by UCINET (Borgatti, Everett and Freeman, 2002).
Measurements of centrality include degree centrality, betweenness centrality and
closeness centrality. Measurements of density are ego networks density and the average
distance of nodes. Two dummy variables, the NEP status of individual organizations and
the status of government agencies are introduced as control variables. The measurement
of organizational cooperation is Yes/No answers to seven questions on collaboration and
cooperation of the organization with other stakeholders. It is treated as count data,
ranging from zero (when all seven questions were answered “No”) to seven (when all
seven questions were answered “Yes”). The paper uses Poisson/ Negative binomial
regression to test proposed hypotheses. Preliminary empirical results agree with previous
research, and also present some new findings that need further explorations. Supporting
previous research, betweenness centrality stands out as the significant factor to positively
affect perceived organizational cooperation.
1
Network Structure and Organizational Cooperation: What Estuaries Tell Us
Background
An estuary is a place where freshwater from rivers and streams flows into the sea.
Estuaries are important to the health of coastal environments and survival of many
species including human beings. Policies on estuaries, and proper management of
estuaries, thus, become very important issues.
In 1987 Congress created the National Estuary Program (NEP), which was
administered by the U. S. Environmental Protection Agency through the Water Quality
Act of 1987. As a voluntary program, NEP began in 1987 with six local NEPs of
national importance scattered around the country. It has grown to 28 estuaries in eighteen
states and Puerto Rico by the year 2003. Each NEP is administered under the Clean
Water Act Section 320, and must establish a Comprehensive Conservation and
Management Plan for attaining or maintaining water quality in an estuary
(http://www.epa.gov, 2003).
The requirement to establish the Comprehensive Conservation and Management
Plan makes it possible for stakeholders to interact and network. In addition, wide
geographical coverage sometimes makes policy network a more effective way to manage
estuaries. Due to this fact, from 1998 to 2001, a research group led by Professor John
Scholz in the Florida State University conducted two rounds of surveys to identify and
examine the policy network in twenty-two estuaries nationwide. Among them, twelve of
them were in the National Estuary Program, and ten non-NEP estuaries were selected to
match the regional distribution and physical characteristics of the NEP estuaries (Scholz,
2
Kile, & Berardo 2003). The first round were mailed questionnaires mainly to identify
networks within estuaries, the second round included telephone interviews and mailed
questionnaires, collecting information on network structure, level of cooperation, and
effectiveness.
Problem Statement
Implementation and administration of environmental policies usually demand
different approaches from other policy domains. As Schneider, Scholz, Lubell, Mindruta
and Edwardsen (2003) argued, in administrating environmental “commons”, “approaches
that rely on hierarchical command-and-control are being replaced by policies that seek to
create more community-based and less coercive solutions to policy problems,” and
cooperation and “networks form the core of these new governing structures” that
“determine the authoritative allocation of resources” (p. 142). As a way to promote
regional integration and cooperation within the federal system, networks are developed
and used widely in implementing environmental policies. Furthermore, not only popular
in environmental policy domain, networks have become an increasingly used
organizational form, “the network is emerging as the signature form of organization in
the information age, just as bureaucracy stamped the industrial age, hierarchy controlled
in the agricultural era and the small group roamed in the nomadic era” (Lipnack and
Stamps, 1994, p. 3). Networks are becoming important elements of public management
and intergovernmental cooperation.
However, as Schneider et al (2003) noted, the understanding of these networks
still lacked “the systematic exploration of the impacts of networks on the policy outputs
and outcomes of agencies involved in local network” (p. 154).
3
This paper, in an effort to do such an exploration, using data gained from the
surveys on twenty-two estuaries, studies how different network structures affected the
trust and outcomes of networks in estuaries. The paper analyzes characteristics of policy
networks in each estuary through Ucinet (Borgatti, Everett, M.G. and Freeman, 2002),
and examines how these different network properties, namely, centrality and density,
affect social capital and cooperation levels of different estuaries.
Theoretical Framework
While studies of networks have been undertaken for decades, sociologists,
political scientists and public management researchers often have approached networks
from very different perspectives. Though recently the gap between them has been
bridged, comparatively speaking, network research is still conducted using different
approaches.
Sociologists use cognitive approaches to conduct social network analysis, discuss
concepts such as cohesion, centrality, stability, and density of networks, and study
structure, power and influence of organizations (Freeman, 1979; Burt, 1976, 1980, 1992;
Krackhardt, 1990; Granovetter, 1973, 1983; Knoke, 1990; Wellman, 1983, Topper &
Carley, 1999, Scott, 1991). Political science network researchers focus on policy changes
including adoptions and policy diffusion represented by Mintrom, Sabatier, and JenkinsSmith. They have studied power, coalition and politics of agenda setting and policy
adoptions within the federal system. In political science networks are often interest
groups and people who act as a subsystem for policy management, or as structures for
overcoming collective action problems. Public management network researchers have
attempted to develop a network management paradigm comparable to the hierarchical
4
organizational authority paradigm of bureaucratic management (Agranoff, 1991;
Agranoff & McGuire1998, 1999 2001; Mandell, 1984, 1994; Mandell, O'Toole, 1986,
1990; Provan &Milward, 1991, 1995, 1998).
The view of networks in these studies has varied widely. Provan & Milward
(1991) regarded networks as linkages among separate organizations. Some other
researchers defined networks as relations among separate organizations, “relations within
a system of actors” (Burt, 1980, p. 81). Other researchers viewed networks as more
formal types of organized efforts (Agranoff, 1991; Agranoff & McGuire, 1998; Mandell,
1988). Schneider et al. (2003) agreed with Heclo “it is through networks of people who
regard each other as knowledgeable, or at least needing to be answered that public policy
issues tend to be refined, evidence debated, and alternative options worked out-though
rarely in any controlled, well-organized way” (2003, p. 142).
Networks provide platforms where highly interdependent policy actors are
contacted and interact frequently to deal with common problems. “The resulting formal
and informal interactions have the potential to increase policy effectiveness at less cost
than authority-based structural changes arrived at through formal reorganization”
(Schneider et al., 2003, p. 143). This paper adopts the network concept used by Schneider
et al (2003) since both papers discuss natural resources management in big regional
districts across local jurisdiction and administration.
Network management’s “potential to increase policy effectiveness at less cost”
(Schneider et al., 2003, p. 143) and the fact that so many programs are now implemented
through network structures has focused attention from researchers. Public management
network research, led by Agranoff, McGuire, O’Toole, Meier, Provan and Milward has
5
been studying which types of network structures are best able to improve efficiency and
accountability of public agencies and public programs. Agranoff and McGuire (2001)
regarded “decisions made in networks may simply be better decisions”, “more effective”
(p. 24) because networks involved stakeholder and many other interest groups.
Furthermore, network decision-making could be viewed “as being more rational than
individual decision-making” (p. 24) and “may also occur as a result of a synergy that can
develop when multiple players pursue a common solution” (p. 24), which implied a better
rationality, more acceptability and more effective implementation.
Provan and Milward (1995) argued that studies of networks had been “guided
primarily by two theoretical perspectives: resource dependence and related exchange
perspectives, and transaction cost economics, with most recent work focusing on the
latter approach.” (p. 1). The transaction cost literature argues that the motivation and
rationale for networking activities is more efficiency related to reducing transaction costs
(Williamson, 1985). Individual organizations made strategic choices to form or become
part of a cooperative network of other organizations when it appeared that the advantages
to such an arrangement outweighed the costs of maintaining the relationship (Provan and
Milward, 1995).
The transaction cost approach was used widely by sociologists in the organization
theory literature. Using it in the policy arena is still quite new, though some pioneers
include Feoick, Carr, Lubell, Schneider and Scholz. Feiock and Carr (2001) observed
partnership in the creation of a municipalities and special districts when potential benefits
of cooperation outweighed the transaction costs of forming new institutions. Schneider et
al. (2003)’s study in the National Estuary Program argued that networks increased the
6
available “information” about myriad details of organizational decision-making as well as
potential implementation problems in each organization. Based on the political
contracting theories of collective action, these authors thought networks increased the
credibility of commitments by transforming short-term interactions into repeated games
in which a reputation for reciprocity and trustworthiness could potentially mitigate the
problem of opportunism involved in single exchanges.
Do networks produce results or discover processes that would not have emerged
from work through a single organization? Obviously, as we already note, many
researchers believe so, though some researchers offer a different view. Sabatier and
Jenkins-Smith (1993) argued that tight-knit networks and shared belief-systems within
each coalition might actually inhibit cooperation between them.
Empirical research trying to answer these questions, however, still remains in a
preliminary stage. Milward and Provan’s (1995) study on four community mental health
systems provided some clues. One of their findings was network effectiveness could be
explained by network structure and context, namely, centralized integration, external
control, stability and resource munificence. Networks integrated and coordinated
centrally through a single core agency, were more likely to be more effective for the
outcomes they examined. In their findings, monopoly was the most effective network,
and network effectiveness would be highest when the network was integrated with a
centralized core agency. Milward and Provan’s findings have significant policy
implications, and test their propositions in more networks and more settings may expand
the generalizability of their findings.
This paper studies networks in twenty-two estuaries, hoping to solve the
7
limitation in Milward and Provan’s research. The network effectiveness examined in this
paper is collaboration and cooperation between organizations within the network.
To further test Milward and Provan’s (1998) arguments on network centralization
and network effectiveness, this paper, thus, develops its first hypothesis:
1) Estuary networks centralized through core agencies have higher levels of
organizational cooperation
How to describe network centralized through core agencies? Previous research
provided some examples. According to Wasserman and Faust (1994), “both centrality
and prestige indices are examples of measures of the prominence or importance of the
actors in a social network” (p. 170). They presented four centrality measures, namely
degree centrality, closeness centrality, betweenness centrality, and information centrality
and three measures of prestige, based on degree, proximity, and status or rank. These
indices were discussed according to actor and group-level to capture structural and
locational properties of networks.
Previous research on centrality and prestige indices showed different results.
Comparing the actor and group-level degree, closeness and betweeness centrality
measures, Freeman (1979) demonstrated that the betweenness indices best captured the
essence of the important actors. Later, Freeman, Roder, and Mulholland (1980) found
that betweenness indices best measured which actors in the set of actors was viewed most
frequently as a leader, and both the degree and betweenness indices were important
indictors of group performance. These centrality measures and prestige indices, thus, are
adopted to capture integration through core agencies within networks.
Another explanation of network dynamics is concerned with social capital,
8
network density and strength of network ties. Coleman (1988) argued that social capital
was a facilitation to networking actions. Putnam (2000) developed Coleman’s idea,
saying “social capital here refers to features of social organization, such as trust, norms,
and networks, that can improve the efficiency of society by facilitating coordinated
action” (1993, p. 167).
Defined as the average strength of connection between contacts, network density
was one form of network closure (Burt, 2000). As a source of social capital (Burt),
network closures were usually positively related to coordination and communication.
Individuals in networks adopted networking strategies around similar or like-minded
individuals. Contacts in a dense network tended to keep close communication through
numerous communication channels, and could readily punish those individuals who
violated shared beliefs or norms of behavior. Density of networks, therefore, was closely
related to trust. In Burt’s words, “Dense networks of positive relations increase the
probability of trust” (2000, p. 7). Coleman (1998) further argued social capital, such as
trust, norms and networks, could improve the efficiency of society by facilitating
coordinated action. Putnam agreed and said “networks facilitate coordination and
communication … and thus allow dilemmas of collective choice to be resolved” (Putnam,
1995, p. 67).
Research done by Schneider, Scholz, Lubell, and Edwardsen (2003) agreed with
these arguments. They found through informal and formal interactions, networks
produced social capital through closure, and thus, had the potential to increase policy
effectiveness. Their research concluded that networks in NEP areas nurtured stronger
interpersonal ties between stakeholders.
9
The estuary networks studied by this paper had multiple levels of governmental
organizations, namely, federal, regional and local. They also involved different types of
organizations including government organizations, non-profit agencies including
environmental organizations, research institutes, universities, etc., and business
organizations. Thus, similar to networks explored by Provan and Milward (1995) and
Schneider, et. al (2003), the estuary networks were more or less weak-tie networks.
Therefore, based on the above theoretical arguments between network density and
cooperation within weak-tie networks, the paper develops another hypothesis to test:
2) More densely connected estuaries enjoy higher levels of organizational
cooperation
Data and Measurements
The data of this research were collected through two rounds of mailed
questionnaire and interviews to stakeholders in twenty-two estuaries. These surveys were
done from 1999 to 2001. They were to identify and examine the policy network in
twenty-two estuaries nationwide. Among them twelve were selected from the National
Estuary Program. These included Albemarle-Pamlico Sounds, Barnegat Bay, Casco Bay,
Charlotte Harbor, Corpus Christi Bay Estuary, Lower Columbia River Estuary, Delaware
Inland Bays, Long Island Sound, Maryland Coastal Bays, Mobile Bay, New Hampshire
Estuaries, and Tampa Bay. The other ten non-NEP estuaries were selected from the
approximately 100 major estuaries in the United States to match the regional distribution
and physical characteristics of the NEP estuaries (Scholz et al 2003). They were
Apalachicola Bay Estuary, Atchafalaya Bay Estuary, Cape Fear River, Gray's Harbor
10
Estuary, Lower Saint John's River Estuary, Martha's Vineyard, Penobscot Bay Estuary,
Saco Bay Estuary, Pensacola Bay Estuary, and St Andrew's Bay Estuary.
The first round surveys were conducted in spring 1999, mainly to identify
different level of organizations and networks within estuaries. The response rates for this
round were 64 percent for NEP estuaries and 65 percent for non-NEP estuaries. Based on
the results of the first round survey, the second round found out the properties and levels
of cooperation in the networks identified in the first round. The second round of survey
tried to contact all respondents from the first round. Researchers conducted telephone
interviews to these respondents and mailed follow up questionnaires. The response rate
were 77 percent for NEP estuaries and 81 percent for non-NEP estuaries. Both surveys
covered roughly 840 respondents (Lubell 2002). As a result, they generated rich data on
properties and effectiveness of network structures of twenty-two estuaries.
This research uses data from both surveys. The paper analyzes network
characteristics of undirected respondent matrixes of twenty-two estuaries generated by
the Ucinet (Borgatti, Everett, and Freeman, 2002).
In order to measure the network integration through the core agencies, adopting
arguments by Wasserman & Faust (1994), actor level of centrality and prestige indices
are used in this paper. According to Wasserman & Faust (1994), a prestigious actor is the
one who is the object of extensive ties. The prestige of an actor increases as the actor
becomes the object of more ties. Actors who are prestigious tend to receive many
nominations or choices. Since core agencies usually are more prestigious, this paper
adopts an index of degree prestige (prestige) to identify core agencies. Prestige indices
are in degrees of each actor divided by the group size, give us the proportion of actors
11
who choose a particular actor.
The three independent variables of centrality introduced are the degree centrality,
closeness centrality and betweenness centrality. According to Faust and Wasserman
(1994), degree centrality is the degree of the nodes; closeness centrality measures how
close an actor is to all the other actors in the network. An actor is central if he can quickly
interact with all others. Betweenness centrality measures how two nonadjacent actors
might depend on the other actors in the network, especially the actors who lay on the
paths between the two. The actor betweenness index for ni is simply the sum of the
estimated probabilities over all pairs of actors not including the ith actor. Based on
previous research, the authors believe these variables captured the structural and
locational properties of each actor, thus, can identify the prominent and important actors
in the estuary networks.
The paper’s measurements of centrality of networks are also consistent with what
Ucinet (Borgatti, Everett, and Freeman, 2002) defines as centrality. Variables on
centrality are node measurements generated by Ucinet (Borgatti, Everett, and Freeman,
2002), and are normalized centrality measures. According to Ucinet (Borgatti, Everett,
and Freeman, 2002), the number of vertices adjacent to a given vertex in a symmetric
graph is the degree of that vertex. The normalized degree centrality is the degree divided
by the maximum possible degree expressed as a percentage. The farness of a vertex is the
sum of the lengths of the geodesics to every other vertex. The reciprocal of farness is the
closeness centrality. The normalized closeness centrality of a vertex is the reciprocal of
farness divided by the minimum possible farness expressed as a percentage. The
betweenness of vertex i is the sum of all bjk where i, j and k are distinct. Betweenness is
12
therefore a measure of the number of times a vertex occurred on a geodesic. The
normalized betweenness centrality is the betweenness divided by the maximum possible
betweenness expressed as a percentage.
In addition, two independent variables are chosen as measurements of density.
Among them one is the ego network density (Density), defined as the number of
undirected ties divided by number of ordered pairs. The other measurement of density is
the average distance (avgdist), defined as the average geodesic distance. This variable is
introduced to supplement information not provided by ego network density. Ucinet
(Borgatti, Everett, and Freeman, 2002) calculates density as the average value within
clusters for similarity and the sum for distance data.
The dependent variable, the network’s level of cooperation, was obtained from
perceived data of respondents obtained from the second round of survey. This paper
treats Yes/No answers to seven questions on collaboration and interaction with other
stakeholders as the dependent variable. These seven questions asked the respondent
organizations whether or not they had engaged seven types of collaborations. Questions
includes whether or not the organization provided information to other organizations,
whether or not they shared personnel, whether or not they collaborated on joint research
projects, whether or not they had collaboration on joint grant/funding proposal, whether
or not they created an interagency taskforce, whether or not they signed a memorandum
of understanding/agreement and whether or not they shared permitting or regulatory
activities. The variable is treated as a count data, ranging from zero {when all seven
questions were answered “No”}) to seven {when all seven questions were answered
13
“Yes”}). These data nicely measured how networks affected the interagency cooperation
and collaboration within estuaries.
Since previous research has found big differences between networks in NEP and
Non-NEP estuaries, a dummy variable is introduced with NEP as 1, Non NEP as 0. In
addition, since government agencies tend to have institutional and financial support to
form and maintain networks, a dummy variable with government agencies as 1 and nongovernment agencies as 0 is introduced to the regression equation.
Findings and Analyses
Using UCINET (Borgatti, Everett and Freeman, 2002) generated data on
centrality and density, a diagnostic test for multicollinearity is done at first. The
following are the results.
Figure 1 Multicollinearity Test of Independent Variables
Prestige
Density
Average distance
Closeness
Betweenness
Degree
Prestige
Average
Density
distance Closeness Betweenness Degree
1.0000
-0.9916
1.0000
-0.3109 0.3117
1.0000
-0.7392
0.7656
0.4146
1.0000
-0.6656
0.6509
0.7254
0.7432
1.0000
-0.4945
0.4914
0.4175
0.4726
0.6048
1.0000
Since there is serious collinearity between Density and Average Distance, and
density and average distance are two variables to describe node density, the variable of
Average distance is dropped. We decided the less serious collinearity between Density
and Betweenness, and closeness and degree is not large enough to require eliminating a
variable chosen on theoretical grounds.
In order to run possion regression on count data, the dependent variable needs to
meet a strict possion assumption. The dependent variable of this research is the answers
14
to seven questions, treated as count data. Its distribution is showed as the following
histogram.
.4
0
.2
Density
.6
.8
Figure 1. Histogram of Distribution of Dependent Variable 1
0
2
4
depvar
6
8
Unfortunately, it does not follow a poisson distribution. To convert it to follow a
poisson distribution, we create a new dependent variable, dependent variable 2, which
equals 7 minus the original dependent variable. Since seven is the highest possible
cooperation score, this new dependent variable simply is the negative cooperation. This
variable is created only for the convenience of statistical analysis. Its distribution
histogram follows:
Figure 2. Histogram of Distribution of Dependent Variable 2
15
.8
.6
.4
0
.2
Density
0
2
4
depvar2
6
8
Furthermore, an overdispersion test is run. The result further proves there is no
overdispersion, and poisson regression can be done using the new dependent variable.
Table 1: Overdispersion Test Results
Source |
SS
df
MS
-------------+-----------------------------Model |
0
0
.
Residual | 315.638498
368
.85771331
-------------+-----------------------------Total | 315.638498
368
.85771331
Number of obs
F( 0,
368)
Prob > F
R-squared
Adj R-squared
Root MSE
=
=
=
=
=
=
369
0.00
.
0.0000
0.0000
.92613
-----------------------------------------------------------------------------z |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------_cons |
.0519745
.0482123
1.08
0.282
-.0428317
.1467807
Thus, we run the poisson regression, and obtain the following results.
16
Table 2: Poisson Regression Results
Iteration 0:
Iteration 1:
Iteration 2:
log likelihood = -663.25475
log likelihood = -663.25002
log likelihood = -663.25002
Poisson regression
Number of obs
=
369
LR chi2(7)
=
30.68
Prob > chi2
=
0.0001
Log likelihood = -663.25002
Pseudo R2
=
0.0226
-----------------------------------------------------------------------------depvar2 |
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------gov | -.1369286
.0712988
-1.92
0.055
-.2766717
.0028145
nep | -.0083963
.0737181
-0.11
0.909
-.152881
.1360885
prestige |
.1406773
.1115847
1.26
0.207
-.0780248
.3593794
density |
-.000468
.0015846
-0.30
0.768
-.0035738
.0026377
betweeness |
-.024248
.0076361
-3.18
0.001
-.0392145
-.0092816
closeness |
.0011681
.0024395
0.48
0.632
-.0036132
.0059493
degree | -.2292946
.2444457
-0.94
0.348
-.7083993
.2498101
_cons |
1.024475
.150263
6.82
0.000
.7299646
1.318985
------------------------------------------------------------------------------
The dummy variable that whether the organization is a government agency or not
and the variable of betweenness have significant effects on the created dependent variable.
How much are the effect of these variables? The paper explains them through the
standard deviation change in Xk. For a standard deviation change in Xk, the expected
count changed by a factor of exp(βk * Sk), holding all other variables constant.
Specifically, they display following changes. Using betweenness as one example, one
standard deviation change in Xk, the expected cooperation count changes by 0.8261.
Table 3: The Expected Cooperation Changes
Independent
Variables
gov
nep
prestige
density
betweeness
closeness
degree
17
Coefficient
-0.13693
-0.0084
0.140677
-0.00047
-0.02425
0.001168
-0.22929
Standard
Deviation
0.494951
0.496995
0.402707
31.92891
7.880667
29.09011
0.317218
Changes
0.934473
0.995836
1.058287
0.985168
0.826058
1.034564
0.929846
Based on the poisson regression results on the new dependent variable,
government agency status is positively related to organizational cooperation in networks,
significant at 90% level. NEP status of respondents, though still positive to the network
cooperation, has no statistical significance.
The relationship between centrality and organizational cooperation, however,
presents a perplexing result. On the one hand, betweenness stands out as a positive
important factor to affect cooperation, significant at 99% level. In addition, degree
centrality shows a positive effect, though not statistically significant. On the other hand,
closeness centrality and prestige indices pose negative effects on cooperation, though
neither is statistically significant.
Similarly, results show a positive relationship between density and network
cooperation, though the relationship is not statistically significant.
These results are based on a new dependent variable created just for the
convenience of statistical analysis. The paper further run negative nominal regression to
test these results because the original dependent variable does not follow a strict poisson
distribution, yet negative nominal regression can be run since their assumptions are much
loose.
Table 4 is the results obtained.
Table 4: Negative Binomial Regression Results
Fitting comparison Poisson model:
Iteration 0:
Iteration 1:
log likelihood = -729.10297
log likelihood = -729.10295
Fitting constant-only model:
Iteration 0:
Iteration 1:
Iteration 2:
log likelihood = -978.84315
log likelihood = -735.44668
log likelihood = -735.44668
Fitting full model:
18
(not concave)
Iteration 0:
Iteration 1:
Iteration 2:
log likelihood = -729.11374
log likelihood = -729.10295
log likelihood = -729.10295
Negative binomial regression
Log likelihood = -729.10295
(not concave)
Number of obs
LR chi2(7)
Prob > chi2
Pseudo R2
=
=
=
=
369
12.69
0.0801
0.0086
-----------------------------------------------------------------------------depvar |
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------gov |
.0699433
.0508776
1.37
0.169
-.029775
.1696616
nep | -.0032455
.0503746
-0.06
0.949
-.1019778
.0954868
prestige |
-.066817
.0798091
-0.84
0.402
-.2232399
.0896059
density | -.0001986
.001134
-0.18
0.861
-.0024211
.0020239
betweeness |
.0066428
.0034278
1.94
0.053
-.0000756
.0133612
closeness | -.0005924
.0015863
-0.37
0.709
-.0037014
.0025167
degree |
.1590162
.1558501
1.02
0.308
-.1464443
.4644767
_cons |
1.474927
.1066967
13.82
0.000
1.265806
1.684049
-------------+---------------------------------------------------------------/lnalpha | -21.86837
.
.
.
-------------+---------------------------------------------------------------alpha |
3.18e-10
.
.
.
-----------------------------------------------------------------------------Likelihood-ratio test of alpha=0: chibar2(01) =
0.00 Prob>=chibar2 = 1.000
The results largely agree with the previous poison regression on the created
dependent variable, betweenness centrality has positive effect on perceived cooperation,
significant at 95% level. The status of a government agency still has positive effect,
though lost statistical significance. Prestige indices and closeness centrality, agreeing
with previous results, have negative relations on perceived network cooperation. The
status of NEP and density variables, though, show negative signs, which are different
from previous poisson regression results. However, because of their tiny coefficients and
P values, the paper treats them as variance that could be ignored.
Conclusion and Discussions
Centrality and density, as two common and important network characteristics, are
examined and discussed to test their relationship with cooperation within estuary
networks. The empirical analyses in this paper present some very interesting results:
some confirm with previous researches, and some are puzzling.
As found in previous research, actor betweeness centrality in the estuary appear
19
to be a significant factor to affect perceived cooperation in the estuary. Freeman(1979)’s
research showed that betweenness indices were the best to identify the important actors.
In Freeman, Roder, and Mulholland (1980) research, betweenness indices, along with
degree indices, were found to be important indicators of group performance. Our analyses
find that betweenness centrality has significant positive relations with cooperation within
estuary networks. On the other hand, the degree centrality, though having a positive sign,
does not have statistical significance, as showed by Freeman, Roder, and Mulholland
(1980)’s research. In addition, our results show that closeness indices can be virtually
ignored, as they have little impact on cooperation.
The fact that prestige indices are negatively related to cooperation is a little
perplexing at first, though. The result indicts that when organizations receive more
referrals, showing by the in-degree of each actor, they will have less cooperation with
other organizations. Why will a more prestigious organization be less cooperative? One
explanation may lie in the transaction cost theory. Network formation and maintenance is
a costly behavior. Generally speaking, an actor who has a higher prestige index is already
important and influential in the network. His cooperation with other organizations in the
network involves cost including time and other resources, yet he will probably not reap
the same level of benefits as a peripheral organization will from similar investment. If the
benefits to cooperate cannot match the cost, it is more likely that the organization will
withhold its cooperation with others.
Furthermore, networks may incur another kind of problem, which Ostrom (1990)
defined as the second-level collective action problem of institutional undersupply.
Prestigious actors, even play major roles in resolving the transaction costs arising from
20
the second-level collective action problem, networks, however, are public good. It’s
impossible for them to gain the full benefits from their efforts. Contacts cultivated mostly
through their investment and contribution will aid the estuary as a whole, benefiting free
riders as well. Prestigious agencies, thus, lack the incentive for cooperation.
The positive sign between network density and cooperation shows that estuaries
with more dense networks lead to higher cooperation between organizations, which
supports arguments by Coleman (1988), Putnam (2000), and Burt (2000). Though there
are suspicions that in highly over-connected networks, density actually inhibits
cooperation, networks within estuaries are usually “weak-tie” networks. The social
capital in these weak-tie networks will increase as the interactions between organizations
increase. Contacts in these networks expand the channels of effective communication
within the network, reduce uncertainty for cooperation and other transaction costs, and
therefore, improve the possibility for cooperation.
The status of being a government agency has a positive impact on cooperation. As
shown in previous research, government agencies tend to have both financial and
personnel resources to encourage network formation and maintenance, and to facilitate
cooperation.
Knowing how network structures affect network effectiveness will provide us
some guidance on how to manage policy networks. Nurturing a dense network with high
levels of betweeness centrality will be a good way to improve the effectiveness of the
network policy community. Being aware of the high transaction costs of creating and
maintaining networks and the possible discouragement of these costs on prestigious
organizations within the network, we may need to establish some mechanisms to share
21
the costs, and to minimize the externality problems.
This research is based on the dataset that was from one span of two years,
especially a cross-sectional design. As researchers, unfortunately, we cannot identify the
changes that happened before or after the networks were formed. This makes it harder to
be certain of the direction of causality between network operations and level of network
cooperation. There is the possibility that a great deal cooperation have actually happened
before the network is formed and these cooperation lead to the formation of network, not
vice versa. However, theoretical arguments lead us to conclude even if this were the case,
the network will still facilitate the communication and cooperation within the network,
which makes our research meaningful.
22
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