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RESEARCH NOTE
A Network Analysis of Global Trust Across 11
Democratic Countries
Robert Jiqi Zhang
1
, James H. Liu
1
, Gary
Brown
2
, and Homero Gil de Zu´~niga
3,4
1
School of Psychology, Massey University, Auckland, New Zealand;
2
Department of Psychology,
Royal Holloway, University of London, Egham, UK;
3
Democracy Research Unit, Political
Science, University of Salamanca, Spain;
4
Media Effects Research Lab, Donald P. Bellisario
College of Communications, Pennsylvania State University, PA, USA
Trust is seen as a glue in modern societies that facilitates economic prosperity and polit-
ical functioning (e.g., Fukuyama, 1995;Newton, 2001;Putnam, 2000; see a more critical
view of trust in Warren, 1999). In this literature, there exists a conventional division
between social and political trust, as they are believed to have different foundations
(e.g., Uslaner, 2002,2018) and therefore should be treated as distinct concepts.
However, more recently, the interconnected nature of trust has been increasingly
acknowledged: Newton, Stolle, and Zmerli (2018)argue that social and political trust
are “closely tied together in a mutually reinforcing manner that underpins social har-
mony, economic efficiency, and democratic government” (Newton et al., 2018,p.2; see
also Newton & Zmerli, 2011). Consistent with this idea, Liu, Milojev, Gil de Zu´~niga,
and Zhang (2018)propose a Global Trust Inventory (GTI), which captures “a system
of meaning that encompasses both the subcomponents of and an overall grasp of the
risks of opening oneself up to a range of dependencies on others” (p. 790). Here we
introduce network analysis as a relatively novel and useful tool to empirically examine
the interconnectedness of multiple types of trust derived from the GTI.
The debates around the interconnectedness of trust classically have centered on a
binary distinction between social and political trust. Social capital theorists propose a
bottom-up model, where social trust and the civic associations formed among citizens is
the key to improved functioning for a democratic society (e.g., Putnam, 1993,2000).
Political theorists, in contrast, propose a top-down model, where trust in formal political
institutions premises a high level of social trust (e.g., Levi & Stoker, 2000;Seifert, 2018;
Sønderskov & Dinesen, 2016). Despite contradicting assumptions on causal direction,
All correspondence concerning this article should be addressed to Robert Jiqi Zhang, School of Psychology,
Massey University, Private Bag 102904, Auckland 0745, New Zealand. E-mail: jiqi.zhang@foxmail.com
International Journal of Public Opinion Research
V
CThe Author(s) 2020. Published by Oxford University Press on behalf of The World
Association for Public Opinion Research. All rights reserved.
doi:10.1093/ijpor/edaa002
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these views have in common the assumption that trust is interconnected. In other
words, they accept the possibility that social and political trust can influence each other.
In contrast, Uslaner (2002,2018) argues that social and political trust have fundamental
differences, where the former has a moralistic nature based on individual optimism and
a sense of control, but the latter has a rationalistic (or instrumental) nature rooted in the
performance of governmental institutions or officials.
This debate becomes more complicated when the social–political binary distinction
is enlarged into a complex system, premised on more highly specified classifications of
trust. Rothstein and Stolle (2008)distinguish trust in the representative government
(i.e., elected officials) from trust in politically neutral “order institutions” that imple-
ment the law and deliver policies. Newton and Zmerli (2011)emphasize the importance
of integrating particularized trust, which refers to trust within the small personally
known circle of families and friends, into the trust discussion in addition to generalized
trust, which refers to trust toward nonspecific others. Drawing on this literature, Liu
et al. (2018)integrate a broad range of trust types into the GTI and use factor analysis
to group them into seven subcomponents/factors. These include three trust factors that
are aligned with political trust but with more nuanced distinctions in terms of their
functions (i.e., trust in representative government, trust in governing bodies, and trust
in security institutions); two factors that correspond to social trust but distinguish
people within the intimate circle (i.e., trust in close relations) from people with shared
identity but not necessarily personally known (i.e., trust in community). In addition,
two factors that capture trust in nongovernmental institutions (i.e., trust in financial
institutions and trust in knowledge producers) are also included. It is believed these
types of trust mirror important aspects of life in a democratic society. Given that meas-
urement structure and invariance across cultures have been established in their original
study, it is the logical next step to examine how these types of trust are interconnected
with one another as a complex system.
Network analysis has unique advantages in modeling the interconnectedness of
trust. A network is an abstract model consisting of the entities (called nodes) and
connections between entities (called edges, see Schmittmann et al., 2013). Networks
have long been used both metaphorically and empirically to study social structures (see
Scott, 1988). Recently, network analysis has been increasingly applied to draw insight
into the interconnectedness of different psycho-social phenomena, including psycho-
pathology (e.g., Borsboom & Cramer, 2013), personality (e.g., Costantini et al., 2015),
and political attitudes (e.g., Boutyline & Vaisey, 2017;Brandt, Sibley, & Osborne,
2019). We believe a network analysis of global trust could add unique insights in follow-
ing ways: (a) network analysis can operationalize the idea of trust as a system of meaning
with subcomponents/factors interconnecting with one another; (b) network analysis is
able to identify the factor(s) that have potentially the greatest overall influence through
the provision of centrality measures (e.g., Epskamp, Borsboom, & Fried, 2018); (c) net-
work analysis allows empirical examination on the generalizability of network structure
and centrality measures across countries (Danaher, Wang, & Witten, 2014).
We expect trust in representative government, trust in government bodies, and
trust in security institutions to form a closely interconnected cluster (Hypothesis 1), as
these political institutions are functionally distinct but closely collaborate in performing
democratic duties (Rothstein & Stolle, 2008). We also expect trust in close relations
and trust in community to form another cluster (Hypothesis 2), as these two both
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correspond to social trust, and trust within the intimate circle has often been theorized
to facilitate trust in a broader circle of people (Newton & Zmerli, 2011; see a different
view in Fukuyama, 1995;Yamagishi & Yamagishi, 1994). We leave it as a research ques-
tion whether and how these two clusters of social and political trust interconnect.
Method
Participants
This study analyzed data from the Digital Influence survey. A representative sample
from 11 countries was used (N¼11,917,53% female, M
age
¼43.76,SD
age
¼15.711).
Specifically, respondents were from Argentina, Brazil, Chile, Estonia, Italy, Poland,
Spain, Germany, New Zealand, the United Kingdom, and the United States. The aver-
age sample size per country was 1,083.3, ranging from 964 in Chile to 1,168 in Estonia.
See the detailed sampling information in Liu et al. (2018), where the same data were
used to establish measurement invariance across cultures.
Measures
The 21-item GTI (Liu et al., 2018) was used to assess trust in different sectors of soci-
ety. Participants were asked to rate their feelings of trust toward different people and
organizations on a 7-point scale (1¼do not trust at all,7¼trust completely). As a degree
of measurement invariance was established for the 7-factor structure using the same
data (see Liu et al., 2018), factor scores were used in this study, including scores for rep-
resentative government, governing bodies, security institutions, financial institutions,
knowledge producers, community, and close relations.
1
There were very few missing
values across all 21 trust items (ranging from 0.6% for trust in friends to 2.5% for trust
in oil companies).
Analysis
Gaussian graphical models (GGMs) were used to represent the network of trust
based on the pooled data. Trust types were treated as nodes and regularized partial
correlations were used as estimates of edge weights. Conventionally, regularization is
used to estimate a more parsimonious and interpretable network (Epskamp & Fried,
2018). In this study, the graphical LASSO (least absolute shrinkage and selection
operator) in combination with the extended Bayesian information criterion was used
to determine the optimal tuning parameter for the regularized network (see Epskamp
et al., 2018).
The R-package qgraph (Epskamp, Cramer, Waldorp, Schmittmann, & Borsboom,
2012) was used to visualize the networks. The Fruchterman–Reingold algorithm
(Fruchterman & Reingold, 1991) was used to compute the layout, where the length of
edges is the absolute edge weight. A thicker edge indicates a stronger relationship
between two nodes. A solid edge indicates a positive relationship, whereas a dashed one
indicates a negative relationship (see Figure 1).
Strength centrality was used as the main indicator to identify the central types(s)
of trust, as previous research has shown strength centrality is more reliable than
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betweenness centrality and closeness centrality (Epskamp et al., 2018). Strength central-
ity is the sum of the weights of the significant connections (in absolute value) to the
focal node. A higher strength centrality indicates that the focal node may directly influ-
ence or be influenced by other nodes to a larger extent. Betweenness centrality, close-
ness centrality, and network stability test were estimated and reported in
Supplementary Material.
A fused graphical lasso (FGL; Danaher et al., 2014) was used to estimate the network
of trust across 11 countries by using R-package EstimateGroupNetwork (Costantini &
Epskamp, 2017). When compared with the conventional GGM, FGL improves network
estimation by including an extra tuning parameter, which is determined by the k-fold
cross-validation in our study, to regularize network similarities and differences in the joint
estimation involving multiple groups (see the tutorial paper in Costantini et al., 2019).
Figure 1.
Overall network of global trust. RepGov ¼trust in representative government; GovBody ¼
trust in governing bodies; Security ¼trust in security institutions; Financial ¼trust in finan-
cial institutions; Knowledge ¼trust in knowledge producers; Community ¼trust in commu-
nity; Close ¼trust in close relations.
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A cross-sample variability network was estimated, which estimates the standard de-
viation of each edge across countries (see Rhemtulla et al., 2016). A thicker edge indi-
cates a higher cross-country variation of the focal edge (see Figure 3).
Results
Overall network based on the pooled data
Mean scores and the correlational matrix of seven trust factors of the overall pooled
sample are presented in Table 1. The overall network of global trust based on the pooled
data is presented in Figure 1. There were strong links between trust in governing bodies
and representative government, as well as between trust in governing bodies and secur-
ity institutions. However, trust in representative government was negatively associated
with trust in security institutions. Except for this unexpected negative link, this pattern
is largely consistent with Hypothesis 1, where trust in neutral (nonpartisan) institutions
are distinct from but still closely associated with trust in representative (partisan) gov-
ernment (Rothstein & Stolle, 2008). The negative link may suggest a tension between
security institutions and representative government. It is worth noticing that trust in fi-
nancial institutions was also associated with governing bodies and security institutions,
which suggests that confidence in corporate power intertwines with political evaluation
in these democratic societies.
There was a strong and positive association between close relations and community,
which is consistent with Hypothesis 2and confirms that trust in intimate circles facili-
tates trust in broader circles. Trust in knowledge producers is also associated with trust
in community but not political institutions, which may suggest that confidence in know-
ledge production is rooted in trust in the civic society rather than institutionalized
power.
Perhaps most importantly, there were positive associations between trust in govern-
ing bodies and security institutions on the one hand, and trust in community on the
other hand. This is in line with both social capital theorists’ and political theorists’
claims about the interconnectedness of social and political trust. In contrast, other types
Table 1.
Mean scores and correlational matrix of different types of trust.
1234567
1. Representative
government
2. Governing bodies 0.740*
3. Security institutions 0.517*0.681*
4. Financial institutions 0.614*0.677*0.572*
5. Knowledge producers 0.356*0.468*0.437*0.411*
6. Community 0.494*0.621*0.593*0.524*0.573*
7. Close relations 0.213*0.274*0.322*0.209*0.370*0.502*
M2.75 3.26 3.97 2.85 4.57 4.05 5.48
SD 1.40 1.41 1.56 1.20 1.42 1.19 1.10
*p<.01.
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of trust had weak cross-cluster associations. Particularly, trust in close relations had lit-
tle direct associations with any type of political trust, and trust in representative govern-
ment also had little association with any type of trust except governing bodies and
security institutions. This suggests that these two are relatively independent from other
types of trust.
Strength centrality (see Figure 2) suggested that governing bodies had the highest
centrality, followed by community, and security institutions. Knowledge producers had
the lowest. These results further confirm the central roles of trust in order institutions
(but not representative government) and trust in community (but not close relations) in
the overall flow of global trust.
Jointly estimated networks of global trust
The jointly estimated networks of global trust across 11 countries are presented in the
Supplementary Material. Strength centrality is presented in Figure 2. Trust in govern-
ing bodies had the highest strength centrality in all countries except the United States,
where trust in security had a slightly higher strength centrality. Trust in community
and trust in security institutions also had relatively high strength centrality in most
countries. In contrast, trust in knowledge producers, close relations, representative gov-
ernment, and financial institutions had relatively low strength centrality in most
countries.
The variability network is presented in Figure 3. This shows that the edges between
political trust were in general highly variable across countries, with the edge between
Figure 2.
Standardised strength centrality of different types of trust in the overall network and individ-
ual-country networks.
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security institutions and financial institutions being the most variable. The edges be-
tween social trust were moderately variable across countries. The edges between social
and political trust, in general, had low to moderate variability, except the edge between
security institutions and community was highly variable.
Discussion
We conducted a network analysis of global trust based on representative samples from
11 democratic countries. The overall network and jointly estimated networks allowed an
empirical examination of the interconnectedness of multiple types of trust in a complex
system across countries. Results showed that seven types of trust were grouped into two
clusters that centered around social and political trust, respectively. Largely in accord
with Hypothesis 1, there was a strong and positive association between trust in repre-
sentative government and trust in governing bodies, as well as trust in governing bodies
Figure 3.
Network of variability.
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and trust in security institutions. There was an unanticipated negative link between
trust in representative government and trust in security institutions. In accord with
Hypothesis 2, there was a moderate and positive link between trust in community and
trust in close relations. Perhaps most importantly, we found positives links between
trust in governing bodies and security institutions on the one hand, and trust in commu-
nity on the other hand.
This study may contribute to the trust literature by providing a more nuanced an-
swer to the debate on the interconnectedness of social and political trust. A network
analysis based on the multidimensional measure of trust resulted in the realization that
social and political trust might be neither entirely interconnected nor completely dis-
connected. Instead, there is conditional interconnectedness between them, which was
manifested in particular associations between certain types of trust. Given its cross-
sectional nature, this study cannot speak to directionality in and of itself. But in the con-
text of previous research, where a uni-dimensional political trust was longitudinally
associated with a uni-dimensional social trust (Sønderskov & Dinesen, 2016), it is
speculated that the associations might be predominantly flowing from order institutions
(i.e., governing bodies and security institutions instead of representative government) to
trust in community (instead of close relations). It makes sense that less politicized
branches of government, charged with implementing the law or enacting social policy,
might function as avenues for the extension of trust between bottom-up and top-down
structures of society. Specifically, if these state institutions are conceived as effectively
and impartially carrying out their duties, they encourage the norm of reciprocity either
by reducing the risk of being cheated, or by acting as important moral heuristics that
could “spill over” to the trust in everyday society, transcending partisan political divi-
sions (see Freitag & Bu¨ hlmann, 2009;Rothstein & Stolle, 2008).
In contrast, trust in representative government had little association with social
trust. How much one trusts the representative government is believed to have a strong
partisan component (e.g., Citrin, 1974). For example, people might have very different
levels of trust toward the same president depending on whether the president represents
one’s political view or not. Trusting a president as well as partisan institutions probably
cannot provide necessary incentives equally for all citizens, as it has a partisan compo-
nent. The provision of strength centrality also confirms that trust in order institutions
was more central than trust in representative government. This might have a naviga-
tional function for future research, in that trust in order institutions, particularly trust
in governing bodies, should be considered as the focal point when the research goal is to
determine how to increase the overall level of trust in society.
Trust in community also had relatively high centrality and was widely associated
with varying types of trust, which is in line with social capital theorists’ claim that trust
in a wider circle is more pivotal to large-scale modern societies than trust within a close-
knit circle (e.g., Fukuyama, 1995;Putnam, 1993). Trust in close relations, in compari-
son, had relatively low strength centrality and limited connectivity with all types of pol-
itical trust. This may seem to contradict Newton and Zmerli’s (2011)findings that
particularized trust (a concept similar to trust in close relations) was associated with pol-
itical trust. Given that strength centrality is an aggregated measure of direct impacts, we
speculate that trust in close relations might be associated with other types of trust indir-
ectly via trust in community. In other words, trust in community might act as a bridge
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that allows the flow of trust from micro-level close relations to macro-level institutional
powers of society.
Trust researchers used to exclusively focus on comparing levels of trust either
across countries or across time. However, recent advances suggest this may not be the
only thing that matters for the functioning of democracy. For example, Delhey,
Newton, and Welzel (2011)proposed the concept of “radius of trust”, which assesses
how wide is one’s trust circle, as an additional and probably better feature of trust to
predict civic attitudes and behaviors. Moreover, Wu and Wilkes (2018)examined critic-
al trust, which refers to the ability to critically evaluate political trust toward functional-
ly different institutions. Augmenting these efforts, we believe the interconnectedness of
trust might be another important feature to evaluate trust’s impact on democratic
functioning.
This study has limitations. First, this analysis was based on cross-sectional data:
therefore, it cannot be used to infer causality. The relatively consistent findings across
11 countries could give us some confidence in the conditionally interconnected view of
trust and the central role of trust in governing bodies in democratic societies. However,
experiments or longitudinal studies are needed to ascertain causality. Second, a network
of seven types of trust is not an exhaustive representation of all possible important rela-
tionships to a person in modern society. We believe the GTI covers some major social
relations and forces, but we are open to refinement regarding the range of trust types
included. Finally, we focused on the dynamics of trust in countries that share democrat-
ic political arrangements and a cultural and religious heritage of Christianity. These
findings are not necessarily applicable to societies with different cultural and political
characteristics, as macro-level power structures might shape the structure and dynamics
of trust (e.g., Zhang et al., 2019).
Supplementary Data
Supplementary Data are available at IJPOR online.
Funding
This research was supported by Grant FA2386-15-1-0003 from the Asian
Office of Aerospace Research and Development.
Notes
The 7-factor structure: Representative Government (national government, local govern-
ment, and president), Governing Bodies (judiciary, election outcomes, tax system, and
government surveillance agencies), Security Institutions (police and military), Financial
and Corporate Institutions (banks, stock market, multinational corporations, and oil
companies), Knowledge Producers (scientists and universities), Community (neighbors,
ethnic group, and other citizens in one’s country), and Close Relations (friends, imme-
diate family, and extended family).
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Biographical Notes
Robert Jiqi Zhang is a PhD candidate in the School of Psychology, Massey
University, New Zealand. His previous research appears in the Asian Journal of Social
Psychology, Journal of Cross-cultural Psychology, etc.
James H. Liu is a professor in the School of Psychology, Massey University, New
Zealand. His previous research appears in the British Journal of Social Psychology,
Personality and Social Psychology Bulletin, Personality and Social Psychology Bulletin, etc.
Gary Brown is a senior lecturer in the Department of Psychology, Royal Holloway,
University of London, UK. His previous research appears in the Journal of Behavior
Therapy and Experimental Psychiatry, Journal of Obsessive-Compulsive and Related
Disorders, The Cognitive Behaviour Therapist, etc.
Homero Gil de Zu´ ~niga is affiliated with University of Salamanca & Pennsylvania
State University. His previous research appears in the Computers in Human Behavior,
Journal of Computer Mediated Communication, New Media & Society, etc.
12 INTERNATIONAL JOURNAL OF PUBLIC OPINION RESEARCH
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