Socioeconomic Resilience of Local Communities in the Face of Climate Change‐Induced Hazards: The Role of Social Capital

Abstract

This study analyses the socioeconomic resilience of local communities in Fiji in the face of climate change‐induced hazards. Drawing upon two comprehensive datasets, we computed the households’ socioeconomic resilience capacity index (SERCI), following the FAO's Resilience Index Measurement and Analysis II (RIMA‐II) methodology. Our findings revealed that the socioeconomic resilience of Fijian households exhibited an improvement from 1996 to 2007, followed by a stagnation period between 2007 and 2014. iTaukei (indigenous Fijian) households demonstrated lower asset‐based socioeconomic resilience compared to other ethnic groups across the two decades we analysed. Nonetheless, accounting for the role of social capital in the socioeconomic resilience capacity of households substantially reduced the gap between the resilience capacity of the different ethnic groups, as iTaukei households demonstrated higher level of social capital than other ethnic groups. Our results underline that in societies such as those in Fiji where social networks play an important role in times of emergencies and disaster, omitting social capital from the analysis of socioeconomic resilience capacity could lead to flawed policies. Our findings call for holistic approaches that account for social as well as economic aspects of resilience to gain a clearer understanding of the socioeconomic resilience capacity of communities prone to the impacts of climate change.

Full text

Climate Resilience and Sustainability
ORIGINAL ARTICLE
Socioeconomic Resilience of Local Communities in the Face
of Climate Change-Induced Hazards: The Role of Social
Capital
Tsegaye T. Gatiso Suzie Greenhalgh
Manaaki Whenua—Landcare Research, Landscape Policy and Governance, Auckland, New Zealand
Correspondence: TsegayeT.Gatiso(gatisot@landcareresearch.co.nz)
Received: 4July2024 Revised: 24 February 2025 Accepted: 23 March 2025
Keywords: climate change | Fiji | Pacific region | social capital | socioeconomic resilience
ABSTRACT
This study analyses the socioeconomic resilience of local communities in Fiji in the face of climate change-induced hazards.
Drawing upon two comprehensive datasets, we computed the households’ socioeconomic resilience capacity index (SERCI),
following the FAO’s Resilience Index Measurement and Analysis II (RIMA-II) methodology. Our findings revealed that the
socioeconomic resilience of Fijian households exhibited an improvement from 1996 to 2007, followed by a stagnation period
between 2007 and 2014. iTaukei (indigenous Fijian) households demonstrated lower asset-based socioeconomic resilience
compared to other ethnic groups across the two decades we analysed. Nonetheless, accounting for the role of social capital in
the socioeconomic resilience capacity of households substantially reduced the gap between the resilience capacity of the different
ethnic groups, as iTaukei households demonstrated higher level of social capital than other ethnic groups. Our results underline
that in societies such as those in Fiji where social networks play an important role in times of emergencies and disaster, omitting
social capital from the analysis of socioeconomic resilience capacity could lead to flawed policies. Our findings call for holistic
approaches that account for social as well as economic aspects of resilience to gain a clearer understanding of the socioeconomic
resilience capacity of communities prone to the impacts of climate change.
1 Introduction
The climate has been changing dramatically, and our planet has
been continuously warming. Since 1880 the Earth has warmed by
1.1 degrees Celsius (Hansen et al. 2020). During the same period
global sea level has, on average, risen by 210–240 millimetre
(WMO 2023; World Bank 2023).1In the Pacific region the sea-level
rise has been higher than the global average of 3.4 mm per year
(WMO 2023). In some islands of the Pacific region, the sea-level
rise has been four times higher than the average global sea-level
rise (IPCC 2013a; Climate Data Information 2023).
In the past few decades, extreme weather events have become
more frequent and intense in the Pacific region (IPCC 2023a).
These extreme weather events increase stress on communities
and erode their socioeconomic resilience, making them more
vulnerable to future shocks from extreme weather events (Adom
2024;IPCC2023b).
Among the nations highly vulnerable to the effects of climate
change, Fiji (an archipelago in the South Pacific Ocean) stands
out due to its geographical location and socioeconomic context
(e.g., heavy dependence on climate-sensitive sectors such as
agriculture, fisheries, and tourism) (Shiiba et al. 2023). The
increasing frequency and intensity of climate change-induced
shocks, such as cyclones, sea-level rise, and coastal erosion, have
been posing a significant challenge to the well-being of Fijian
communities (Kumar et al. 2020;WHO2023). In Fiji, where
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly
cited.
© 2025 The Author(s). Climate Resilience and Sustainability published by Royal Meteorological Society and John Wiley & Sons Ltd.
Climate Resilience and Sustainability, 2025; 4:e70012
https://doi.org/10.1002/cli2.70012
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around 76% of the population lives within 5 km and 91% lives
within 10 km of the shoreline (Andrew et al. 2019), climate change
and sea-level rise have significant implications for the well-being
of the population. This study aims to assess the socioeconomic
resilience capacity of Fijian communities to effectively cope with
and recover from shocks (e.g., natural hazards).
Broadly, resilience is defined as the ability of communities to
withstand the impacts of shocks (e.g., extreme weather events)
and be able to bounce back from the impacts of adversities and
maintain their pre-disaster living standard as fast as possible
(Alinovi et al. 2010; Ansah et al. 2019; d’Errico et al. 2018;
Lascano Galarza 2020). Thus, the ability to maintain a relatively
higher level of socioeconomic well-being in the face of shocks
and stressors is a strong manifestation of households’ resilience
capacity (Barrett and Constas 2014; Egamberdiev et al. 2023).
Shocks would have relatively less long-lasting adverse conse-
quences on the well-being of resilient households or communities
(d’Errico et al. 2018). Conversely, even small adversities would
have disastrous consequences on the well-being of less resilient
households or communities (Adger 2006).
While the occurrence of extreme weather events could be random
for any individual or community, the long-term impacts of those
events may vary across individuals or communities depending
on their vulnerability and resilience capacity.2Thus, optimal
policy interventions to enhance the socioeconomic resilience
of communities require a clear understanding of the existing
capacities of the local communities to absorb, adapt and bounce
back from the impacts of climate-related shocks. As Fiji is a
country prone to the effects of climate change (Shiiba et al.
2023) and as climate change-induced hazards are becoming more
frequent and intense over time (IPCC 2023a), the assessment of
the socioeconomic resilience of Fijian communities is important
for helping chart a pathway to greater resilience and enhance
their adaptation to climate change.
Socioeconomic resilience is a complex and multifaceted concept,
and various indicators need to be assessed collectively to gain
a comprehensive understanding of the households’ capacity to
withstand and recover from the effects of shocks (Alinovi et al.
2010; Ansah et al. 2019). By understanding the vulnerabilities,
strengths, and opportunities of different segments of communi-
ties, policymakers can design effective strategies to enhance the
socioeconomic resilience of communities (Walker et al. 2004).
To this end, in this study we assess the socioeconomic resilience
capacity of Fijian communities across 14 provinces over two
decades, examining the key drivers and the role of social capital
in supporting local communities’ socioeconomic resilience.
To measure the socioeconomic resilience of local communities,
we used the FAO’s Resilience Index Measurement and Analysis II
(RIMA-II) methodology (FAO 2016), which is based on measures
of access to basic needs, access to basic services, access to safety
net programmes, ownership of household assets, and adaptive
capacity of the households. Based on these pillars, we develop a
Socioeconomic Resilience Capacity Index (SERCI) for Fijian com-
munities from 1996 to 2014. The RIMA-II methodology doesn’t
account for social capital, which is an important contributor to
socioeconomic resilience (Aldrich and Meyer 2015;Carmenetal.
2022; Pacoma and Delda 2019; Panday et al. 2021). Therefore, we
extend the FAO RIMA-II methodology to include social capital as
one of the components of the SERCI. This is to emphasise the role
of social capital in enhancing the resilience of local communities
in the face of climate change-induced hazards. In this study we
show how the omission of social capital from resilience analysis
may lead to flawed conclusions and policy directions. Moreover,
we assess how gender and ethnicity are correlated with the
socioeconomic resilience of communities in Fiji.
While numerous studies have explored resilience, gender, social
networks, and ethnicity, our study stands out as novel in two
ways. First, most existing research examines these concepts in
isolation, whereas our study adopts a holistic approach. This inte-
grative perspective is crucial, as we demonstrate that significant
differences exist across various groups in terms of their achieve-
ments in these interconnected dimensions. By examining these
factors together, we provide a more comprehensive understand-
ing of their interplay. Second, much of the existing research in the
Pacific region is anecdotal, qualitative, or based on small sample
size. In contrast, our study employs a dynamic, large-scale quan-
titative analysis, enabling us to systematically compare socioeco-
nomic resilience over time and across ethnic groups and genders.
2Review of Related Literature
2.1 Resilience and Vulnerability to Climate
Change
Resilience and vulnerability are foundational concepts in climate
change research. Vulnerability in the context of climate change
is defined as ‘the degree to which a system (e.g., ecosystem,
community) is susceptible to, and unable to cope with adverse
effects of climate change’ (IPCC 2007,p.783).Itencompasses
three key dimensions: exposure (how much a system is exposed
to a hazard), sensitivity (how sensitive and susceptible it is to
that hazard), and adaptive capacity (ability to adapt and cope
with potential impacts of the hazard) (Jamshidi et al. 2019;
Thomas et al. 2019). These three dimensions determine the level
of vulnerability of a system. Resilience to climate change, on the
other hand, refers to the ability of a system to anticipate, absorb,
recover, and adapt to climate-related hazards (Folke 2006; Koliou
et al. 2018).
While both frameworks aim to understand the capacity of
systems to deal with climate shocks, vulnerability often highlights
risks and weaknesses, whereas resilience focuses on strengths,
capabilities and opportunities for adaptation (Lv et al. 2024; Miller
et al. 2010). Accurate and timely assessments of resilience and
vulnerability are critical for effective climate adaptation planning.
These assessments enable stakeholders to identify at-risk com-
munities, guide resource allocation, and support evidence-based
policymaking. Our study assesses the socioeconomic resilience of
Fijian communities towards climate change-induced hazards.
2.2 Social Capital and Communities’ Resilience
Towards Climate Change
The concept of social capital has emerged as a critical determinant
of a community’s resilience to climate change (Aldrich and
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Meyer 2015;Carmenetal.2022;Liuetal.2022; Masud-All-
Kamal and Monirul Hassan 2018;Pandayetal.2021; Zhao et al.
2024). Social capital is defined as the networks, norms, and trust
that enhances collective action among community members to
achieve a common goal (Putnam 2000) or to access resources
(financial, emotional) to survive or recover from disasters (Pan-
day et al. 2021). It can be categorised into three forms: bonding,
bridging, and linking (Azad and Pritchard 2023;Carmenetal.
2022; Pacoma and Delda 2019). These dimensions represent
relationships within and between groups and across hierarchi-
cal structures, playing distinct roles in enhancing community
resilience (Azad and Pritchard 2023).
Bonding social capital focuses on relationships and trust within
a given group or community. Strong bonds among community
members foster internal cohesion and cooperation, which are
crucial during times of crisis (Nguyen-Trung et al. 2020). This
form of social capital is essential for providing immediate support
during crises, such as collective action to prepare for and recover
from climate-related hazards (Aldrich and Meyer 2015;Carmen
et al. 2022;Liuetal.2022; Zhao et al. 2024). In the face of climate
change impacts, such as extreme weather events, strong bonding
social capital enables community members to support each
other emotionally, physically, and economically. This mutual
support is vital for coping with trauma and rebuilding. Studies
demonstrate that tightly knit communities exhibit higher levels
of trust and reciprocity, enabling rapid mobilisation of resources
in emergencies (Islam and Walkerden 2014).
Bridging social capital refers to connections and relationships
between individuals and groups across different social net-
works (e.g., religion, race, community organisations) (Azad and
Pritchard 2023). In the context of climate change, these connec-
tions create a web of support and information-sharing within and
beyond the local community in case of emergencies (Masud-All-
Kamal and Monirul Hassan 2018). It enhances adaptive capacity
by fostering access to diverse perspectives, broader networks and
additional resources (Dressel et al. 2020).
Linking social capital involves relationships between local com-
munities and external institutions, such as governmental bodies,
NGOs, or aid agencies (Azad and Pritchard 2023a; Zhao et al.
2024). These connections are vital for accessing resources, exper-
tise, and support during and/or in the aftermath of disasters
(Aldrich 2011; Behera 2021; Khalil et al. 2021), though sometimes
their benefits may be limited to only a few individuals or groups
of individuals (Masud-All-Kamal and Monirul Hassan 2018).
2.3 The Role of Gender in Enhancing
Community Resilience to Climate Change
Gender dynamics play a significant role in shaping the resilience
of local communities to climate change. Women, particularly in
developing countries, often face systemic inequalities (Panday
et al. 2021) that hinder their capacity to adapt to the impacts of
climate change. Households headed by women are disproportion-
ately affected by the impacts of climate change due to limited
access to resources, education, and decision-making processes
(Acheampong et al. 2024). Cultural norms and gendered labour
divisions further exacerbate these challenges, leaving women
with fewer opportunities to build adaptive capacities (Khalafzai
and Nirupama 2011; Jesús Carrasco-Santos et al. 2024;Lwamba
et al. 2022; Staab et al. 2024; Tschakert and Machado 2017)
In the Pacific region, women are traditionally assigned to
domestic and mostly unpaid labour (ESCAP 2019). While this
positions women as critical agents of socioeconomic resilience,
their contributions are frequently undervalued and their views
are ignored in formal decision-making (Singh et al. 2022).
Research shows that empowering women—through education,
financial inclusion, land ownership rights, and participation
in governance—significantly enhances communities’ socioeco-
nomic resilience (Azad and Pritchard 2023b; Adera and Abdisa
2023; Hendriks 2019; Khalafzai and Nirupama 2011; Khadka and
Schuett 2024; Meinzen-Dick et al. 2019; Tschakert and Machado
2017; Singh et al. 2022; Staab et al. 2024). Additionally, involving
women in climate-related planning and governance fosters more
inclusive and effective decision-making processes (Deininger
et al. 2023).
While the existing literature underscores the importance of
gender in building resilience, there is a lack of empirical research
quantifying these impacts in the Pacific region (Singh et al. 2022).
2.4 Ethnicity and Climate Resilience
Ethnicity is a social construct encompassing shared cultural
practices, values, and norms (Suyemoto et al. 2020). Studies
have shown that ethnic identity shapes access to resources,
social capital, and decision-making processes (Anshuka et al.
2021; Gawith et al. 2016; Nakamura and Kanemasu 2020), all of
which are critical components of resilience. For example, ethnic
minorities in various regions of the world often face systemic
barriers, including limited access to education, healthcare, and
economic opportunities (The World Bank Group 2019).
In the Pacific region, particularly in Fiji, ethnicity has played a
complex role in shaping climate resilience. Indigenous Fijians
(iTaukei) and Indo-Fijians, the two major ethnic groups, expe-
rience differing levels of vulnerability and resilience to climate
change (Gawith et al. 2016; Nakamura and Kanemasu 2020;Yila
et al. 2013). Research indicates that iTaukei communities tend to
have stronger social networks and cultural practices that promote
collective action (Tuimavana 2020; Yee et al. 2022), which can
enhance resilience. However, they may also face challenges
due to traditional land tenure systems that limit flexibility in
land use. In contrast, Indo-Fijians, who often lack access to
land, face different vulnerabilities, such as economic dependency
on leasehold agricultural systems, making them particularly
susceptible to climate-induced disruptions (Anshuka et al. 2021;
Gawith et al. 2016).
Although there is growing recognition of the role of ethnicity
in shaping climate resilience, empirical studies in the Pacific
region remain sparse. Existing research is often qualitative,
anecdotal or focuses on case studies with small sample size
or only one aspect of resilience, with limited large-scale and
longitudinal quantitative analyses that systematically compare
resilience across ethnic groups.
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3 Methods
3.1 Study Sites
The study uses data collected from households across 14 provinces
of Fiji. Fiji is one of the Small Island developing states (SIDS)
in the Pacific region, with a total population of 935,974 as of
2023 (Country Reports 2023). In terms of human development
index, Fiji ranked 99th out of 191 countries and territories in
2021 (UNDP 2023). The adult literacy rate in the country is
high (99.08%) compared to many nations around the world. The
major ethnic groups of the nation are iTaukei (51%), Indo-Fijians
(44%) and other ethnic groups such as Europeans, Chinese and
others constituting 5% of the total population (Country Reports
2023). Livelihoods in Fiji mainly depend on agriculture, fisheries,
tourism, and handicrafts (Mikhailovich et al. 2023). As of 2020 the
service sector (dominated by tourism) contributed around 53.4%
of Fiji’s GDP and employed around 68.3% of country’s labour
force (World Bank 2023). The second most important sector in
terms of its contribution to employment is the agricultural sector,
employing 17.6% of the population and accounting for 14.5% of the
GDP. The industrial sector contributes 16.9% of GDP and employs
14.1% of the country’s labour force (World Bank 2023).
Fiji has a tropical climate (Climates to Travel 2024)withanaver-
age temperature of 23◦C–25◦Cinthedryseasonand26
◦C–27◦Cin
the wet season (World Bank Group Climate Change Knowledge
Portal 2025). Annual rainfall varies from 1500–2000 mm (low
altitude areas) to 3000–6000 mm (high altitude areas) (FIJI
Shores and Marinas 2024).
The data3on the frequency of cyclones in Fiji show that the most
affected provinces during our study periods were the Ba and Ra
provinces of the Western Division (see Figure S15). They faced
4 tropical cyclones within 5 years leading up to both the 2007
population and housing census and the 2014 census. In most
of the provinces, the number of tropical cyclones has increased
between 2007 and 2014 (see Figure S15).
The social vulnerability of Fijian communities to climate change
is shaped by a combination of geographic exposure, economic
dependence on climate-sensitive sectors, and socio-cultural
dynamics. Geographically, Fiji, an archipelago in the south Pacific
Ocean, is highly susceptible to cyclones, sea-level rise, and coastal
erosion. Rural and coastal communities, where subsistence
farming and fishing dominate (Mikhailovich et al. 2023), are
particularly vulnerable due to limited access to alternative income
sources, financial resources, and adaptive infrastructure.
The ethnic background of communities also plays a crucial role
in shaping their vulnerability and resilience to climate change.
Indigenous Fijians (iTaukei) generally have stronger social net-
works, which enhance collective action and resource-sharing
during crises (Gawith et al. 2016; Nakamura and Kanemasu 2020;
Yila et al. 2013). In contrast, Indo-Fijians, many of whom rely
on leasehold agricultural land or urban employment, may face
weaker communal support systems (Anshuka et al. 2021;Gawith
et al. 2016), affecting their socioeconomic resilience.
Fiji has four administrative divisions, namely northern, central,
western, and eastern divisions, which are further subdivided
into 14 provinces, and 195 districts and 1193 villages (Fiji Budget
Vacation 2023). In this study we present data grouped into 10
provinces. The IPUMS data is organised in such a way that the
data from provinces with population size less than 20,000 as of
2007 are combined with bigger close by provinces (see Figure 1).
3.2 Sources of Data
In this study, we utilised two datasets: the Fijian Population
and Housing Census data for 1996, 2007, and 2014 from IPUMS
(Integrated Public Use Microdata Series) (IPUMS 2023), and a
household survey conducted by Manaaki Whenua—Landcare
Research, New Zealand (MWLR) in 2013–2014.
The IPUMS dataset is the world’s largest collection of pub-
licly available census data, compiled from national population
censuses (IPUMS 2023). The IPUMS project is a collaboration
of the University of Minnesota, National Statistical Offices,
international data archives, and other international organisations
(IPUMS 2023). In our study, the IPUMS dataset provided com-
prehensive information for 19,320 households across 14 provinces
of Fiji. However, this dataset lacked variables related to social
capital. Hence, we obtained social capital data from the MWLR
household survey conducted for a disaster risk assessment fol-
lowing multiple severe natural disasters (e.g., floods, tropical
cyclones such as Evan) in 2012 in the Ra and Ba provinces.
For this survey, respondents were selected from 36 villages and
settlements using the probability sampling method (Gawith et al.
2016). The survey was conducted face-to-face using a structured
questionnaire (Gawith et al. 2016).The survey was approved by
the Social Ethics Committee of MWLR, with participation being
voluntary and informed consent obtained from each participant.
The process of including social capital into the socioeconomic
resilience assessment comprised two stages. In stage 1, we
estimated the SERCI without the social capital component from
the MWLR dataset and compared the results with the SERCI from
the census (IPUMS) dataset, which did not include social capital
data. For this comparison, we limited our analysis of the IPUMS
data to rural households, as the MWLR data were collected only
from rural villages in the Ra and Ba provinces. The results were
very similar between the two datasets (see Figure S9a and b).
This analysis demonstrated that the IPUMS and MWLR data were
compatible and comparable.
In stage 2, since the results from stage 1 (i.e., results without social
capital) showed comparability between the two datasets, we
incorporated a social capital component into the SERCI obtained
from the MWLR dataset. We then compared the results from the
MWLR dataset with and without social capital (see Figure 4). This
approach allowed us to assess the role of social capital on the
socioeconomic resilience capacity of households in Fiji.
Here we would like to emphasise that while the most recent
census data we had was from 2014, the findings from this study
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FIGURE 1 Map of the study provinces.
remain highly relevant even today. One of the key justifications
for the continued relevance of our study’s insights is the relative
stability of socioeconomic conditions in Fiji over the past decade.
For instance, the poverty rate, a critical indicator of economic
well-being, was 28.1% in 2013/14 and slightly increased to 29.9%
in 2019/20. In addition, there was relatively small change in
human development index (HDI) since 2014, which was 0.719 in
2014, 0.721 in 2016, and 0.715 in 2021 (UNDP 2022). Further, the
contribution of the service sector to the country’s GDP remained
stable from 2015 (55.4%) to 2023 (55.1%) (World Bank 2023). These
marginal changes suggest that while there have been fluctuations,
the overall socioeconomic landscape has not undergone drastic
transformations in Fiji. Therefore, the foundational patterns and
dynamics observed in our study likely persist.
Moreover, our study assesses the crucial role of social capital
in enhancing the socioeconomic resilience of households, par-
ticularly in the face of climate change-related hazards. Social
networks, community trust, and institutional support are endur-
ing aspects of Fijian society. Despite potential changes in specific
economic conditions, the importance of social capital remains
stable. The bonds within communities, the bridging connections
between different groups, and the linking networks with insti-
tutions continue to play a pivotal role in how households cope
with and recover from shocks. This aspect of resilience is likely as
relevant today as it was a decade ago.
In the IPUMS data, around 49% of the households live in rural
areas, while the remaining 51% live in urban areas (i.e., towns or
cities) (Table S1). A slight majority of the households had iTaukei
heads (50.2%), while 44% of the households had Indo-Fijian
heads and around 5.8% households were headed by other ethnic
groups such as European, Chinese and others. The overwhelming
majority of the households were headed by men (85%) and the
remaining 15% of the households were headed by women. On
average, the household heads were 47 years old and had 9.17 years
of education. The average household size across the three years
we analysed (1996, 2007, and 2014) was 5, with a declining trend
between 1996 and 2014 (see Table S1). The descriptive statistics of
these variables are consistent with other reports such as Narsey
(2011) and Fiji Bureau of Statistics (2020).
3.3 Measuring Resilience
In this study, we used the FAO’s RIMA-II methodology to mea-
sure the socioeconomic resilience of Fijian communities towards
shocks and stresses in general (FAO 2016). The FAO’s RIMA-II
methodology uses five pillars of resilience: access to basic needs
(ABN), access to basic services (ABS), assets ownership (produc-
tive or durable) (HHA), access to social safety net programs (ASN)
and the adaptive capacity of the households (HAC) (Alinovi et al.
2010; Ansah et al. 2019;FAO2016). In this study, we extended
the FAO’s RIMA-II methodology by adding social capital to the
analysis of households’ socioeconomic resilience capacity. One
of the major advantages of using FAO’s RIMA-II methodology is
that it provides a comprehensive and context-specific assessment
of resilience to identify key factors contributing to vulnerability
(FAO 2016). It also enables us to analyse how households cope
with shocks across different contexts and what is the likelihood
of them to bounce back to their pre-disaster status after the
shocks (FAO 2016). A description of the different measures and
indicators used in our study to estimate SERCI are outlined in
Table 1.
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TABLE 1 Components of SERCI and their indicator variables.
Components of
resilience Definition Indicator variables
Source(s) of
data used
Ability to meet basic
needs (ABN)
This pillar represents the ability of the households
to meet their basic needs.
Employment, Income,
housing condition
IPUMS
Ownership of
household assets (HHA)
This pillar stands for the ownership of both
productive and non-productive (durable) household
assets.
The ownership of land,
livestock, car, furniture, TV,
radio etc.
IPMS/ MWLR
Access to basic services
(ABS)
This pillar represents the ability of households to
access basic services to support their well-being.
Access to electricity,
clean/improved water
facility, improved toilet
facility.
IPUMS
Access to safety net
programs (ASN)
This pillar is about the availability and access to
assistance from relatives, neighbours, friends,
government, NGO, international community, etc.
Remittances, gifts, aid IPUMS/ MWLR
Adaptive capacity of
households (HAC)
This pillar is about the capacity of households to
quickly adapt to a new situation after they have
encountered a shock.
Education level, diversity of
income sources
IPUMS/MWLR
Social capital (SC) This pillar measures the connectedness of
households to their communities, and how much
they trust the government and NGOs that would
support them during shocks. These could be in
terms of bonding, bridging, and linking.
Trust in one’s community,
trust towards government,
NGOs, aid agencies.
MWLR
3.4 Description of the Variables, Proxies and
Data Used to Assess Resilience
3.4.1 Ability to Meet Basic Needs (ABN)
Access to basic needs is a critical determinant of households’
ability to meet their basic needs before, during, and after a
disaster. It can serve as an indicator of households’ capacity to
recover from the effects of shocks. The underlying hypothesis
is that households capable of meeting their basic needs in the
face of shocks are more likely to recover successfully after facing
disasters. Conversely, households already grappling with unmet
basic needs even in ’normal’ times could be less likely to recover
soon after a disaster. For the proxy variables we used in this study
to measure ABN, see Figure S1.
3.4.2 Ownership of Household Assets (HHA)
This component of SERCI involves two types of household assets:
productive assets and non-productive (durable) assets (see Figure
S2). Productive assets form the resource base of the households,
and they are used by households to produce goods and services
for their own consumption or for sale. They are usually gauged
by the ownership of livestock and land resources. The ownership
of livestock is proxied by Tropical Livestock Unit (TLU), which
is a composite measure that stands for the number of livestock
owned by households. TLU is a weighted sum of different types of
livestock owned by households. We utilised the commonly used
weights of horses =0.8, cattle =0.7, pigs =0.2, goats =0.1 in
our TLU calculation (Jahnke 1982; Rothman-Ostrow et al. 2020).
In addition to livestock, we used land ownership to develop a
composite index for this component.
Non-productive assets also play a critical role in sustaining the
economic resilience of households. They serve as financial cush-
ions during emergencies. Households could use durable assets
as a coping strategy by selling them and obtaining income to
support their family. The ability to afford and maintain household
assets often reflects some level of economic stability. Resilient
households are better equipped to invest in non-essential items
like furniture, suggesting financial security and the capacity to
withstand shocks (see Figure S2).
3.4.3 Access to Basic Services (ABS)
Access to essential services, including amenities like toilets,
electricity, and clean drinking water, is a strong indicator of
household living standards (see Figure S3). The better the living
standard of a household, the higher the probability of having
access to these essential services, and subsequently, the capacity
to withstand the impacts of disasters would be higher.
3.4.4 Access to Safety Net Programs (ASN)
Access to safety net programs, such as gifts, remittances, and
aid from friends, NGOs, the international community, plays
a crucial role in strengthening the resilience of households
and communities (see Figure S4). Safety nets act as financial
shields during times of crisis by diversifying the resource pool,
reducing dependence on a single income source, and insulating
households from the full brunt of shocks. In the absence of
safety nets, households often resort to ‘negative coping strategies’,
such as taking on high-interest debt, selling productive assets,
or cutting essential expenditures. Safety nets, particularly remit-
6of16 Climate Resilience and Sustainability,2025

tances, often support investments in education and healthcare.
These investments enhance the skills and health of household
members, enhancing the adaptive capacity of the households and
ultimately bolstering their resilience.
3.4.5 Adaptive Capacity of Households (HAC)
The adaptive capacity of households is about adapting to the
impacts of shocks, which could be enhanced by the level of
education attained by household members and the diversity of
household income sources (see Figure S5). Education empowers
households with knowledge and skills, enabling them to access
information and make informed decisions. Furthermore, edu-
cation increases the employability of the household members
by diversifying skill sets within the household. In the event
of shocks, individuals with diverse skills are more likely to
secure alternative employment or even create income-generating
opportunities. Income diversity serves as a safeguard against the
impacts of adversities, reducing vulnerability, mitigating risk, and
providing financial flexibility. If one source falters, other sources
can compensate, reducing the economic impact of shocks.
3.4.6 Social Capital (SC)
Social capital (in the form of linking, bonding and bridging)
plays a key role in fostering the resilience of local communities
(Pacoma and Delda 2019) (see Figure S6). It facilitates the flow of
information within and outside the community. This is essential
for early warning systems, preparedness, and timely response to
climate-related threats. The strength of social capital also enables
collective action (Auer et al. 2020). Communities with robust
social ties are better positioned to collaborate in implementing
adaptive strategies, sharing resources, and collectively addressing
challenges posed by climate change. Moreover, social capital
supports adaptive learning (Slijper et al. 2022). Through shared
experiences and knowledge exchange, communities can learn
from each other’s successful strategies and adopt successful
practices to become more resilient over time. In addition, the
emotional and psychological support provided by social capital
helps individuals and communities cope with the stress and
trauma associated with climate change impacts.
3.5 Statistical Models
To analyse the data, we used Structural Equation Models (SEM)
(Harlow 2023;Oberski2014) and mixed effect linear regression
models (Bates et al. 2015). SEM models combine the measure-
ment (factor analysis) and causal inference assessment (e.g.,
regression) (Harlow 2023). In the SEM models, we estimated the
latent variable (i.e., SERCI) based on different factors associated
with socioeconomic resilience outlined in Table 1, and using the
observed variables and proxies outlined in Figures S1–S6.We
estimated two SERCIs based on the two datasets: IPUMS and
MWLR datasets (see Equations 1and 2).4
𝐼𝑃𝑈𝑀𝑆 ∶ 𝑆𝐸𝑅𝐶𝐼 =𝑓(𝐴𝐵𝑁, 𝐴𝐵𝑆, 𝐻 𝐻𝐴, 𝐴𝑆𝑁, 𝐻𝐴𝐶 ),(1)
𝑀𝑊𝐿𝑅 ∶ 𝑆𝐸𝑅𝐶𝐼 =𝑓(𝐻𝐻𝐴,𝐴𝑆𝑁,𝐻𝐴𝐶),(2a)
𝑀𝑊𝐿𝑅 ∶ 𝑆𝐸𝑅𝐶𝐼 =𝑓(𝐻𝐻𝐴,𝐴𝑆𝑁,𝐻𝐴𝐶,𝑆𝐶),(2b)
where Equation (2a) is for SERCI without social capital (SC) and
Equation (2b) is the one with SC.
To estimate the SEM models, we used lavaan R-package (Rosseel
2012). We transformed all observed variables into the same scale
of 0 to 1 before we included them in our models to estimate
the latent variable SERCI. The tests of the goodness of the fit of
our SEM model (using chi-square =3122, df =40, p=0.000),
Comparative Fit Index (CFI) =0.955, Tucker-Lewis Index (TLI)
=0.913 and Root Mean Square Error of Approximation (RMSEA)
(0.046, p(RMSEA ≤0.05) =0.969) show that our model had a
reasonable fitness to our data (see Figure S7).5
To check the robustness of our results from the SEM models,
we developed a separate composite SERCI by aggregating the
different components of SERCI using Equations (3)and(4).
𝐼𝑃𝑈𝑀𝑆 ∶ 𝑆𝐸𝑅𝐶𝐼 =𝐴𝐵𝑁 +𝐴𝐵𝑆 +𝐻𝐻𝐴 +𝐴𝑆𝑁 +𝐻𝐴𝐶
5,
(3)
𝑀𝑊𝐿𝑅 ∶ 𝑆𝐸𝑅𝐶𝐼 =𝐻𝐻𝐴 +𝐴𝑆𝑁 +𝐻𝐴𝐶 +𝑆𝐶
4.(4)
After obtaining the SERCI from Equations (3)and(4), we
fitted linear mixed effects regression models to examine the
factors associated with the socioeconomic resilience capacity
of households. We included residence (Urban or Rural), the
ethnicity of household heads (iTaukei, Indo-Fijian or Others), the
intensity6of cyclones reported within 5 years leading up to the
survey year, gender, age and the education level of the household
heads, household size and a dummy variable for the year in
which the survey was conducted as explanatory variables (see
Table S1 for the descriptive statistics of the variables). We checked
the robustness of our results using the frequency of cyclones as
an explanatory variable instead of the intensity of cyclones, but
the results were similar (see Figure S14). In our mixed effect
models, we included provinces as random effects. All the mixed
effect models were fitted in R 4.2.1 (R Core Team 2021) using the
function ‘lmer’ from the ‘lmer4’ R-package (Bürkner 2017).
To compare the SERCI from the aggregation method (Equations 3
and 4) with the SERCI obtained from SEM, we computed the
predicted values of SERCI from the SEM. For easy comparison
of the values of SERCI from the two approaches and to facilitate
the interpretation of the results, we rescaled the predicted values
of SERCI obtained from SEM models to range from 0 to 1 (i.e.,
to the scale in which SERCI values from the aggregation method
already are).
7of16

TABLE 2 SERCI among ethnic groups over time.
1996 2007 2014
SERCI t*SERCI t*SERCI t*
Ethnicity
iTaukei 0.43 −25.48, p=0.00 0.6 −27.09, p=0.00 0.64 −26.27, p=0.00
Indo-Fijian 0.57 16.84, p=0.00 0.74 29.77, p=0.00 0.76 38.22, p=0.00
Others 0.69 118.81, p=0.00 0.75 13.34, p=0.00 0.76 12.44, p=0.00
National average 0.51 0.67 0.69
*tvalues are obtained by comparing the average SERCI of households from different ethnic groups with the national average resilience capacity index.
Source: IPUMS (2023).
4Results
4.1 Components of the Socioeconomic Resilience
Capacity of Fijian Households
Based on the analysis conducted using the IPUMS dataset, all
the fundamental components constituting the SERCI exhibited
statistically significant correlations with the overall SERCI score
(Figure S7). Among these components, the predominant dimen-
sions correlated with households’ socioeconomic resilience were
access to basic services (corr =0.90, p=0.00),7ownership of
household assets (corr =0.84, p=0.00) and access to basic needs
(corr =0.62, p=0.00) (see Figure S7). Specifically, households
having higher access to essential services (such as electricity
and improved toilet), along with a greater ability to meet their
basic needs, have demonstrated higher SERCI. Moreover, house-
holds possessing a larger array of household assets exhibited
an enhanced resilience capacity to withstand adverse shocks
and challenges. These findings align with the existing literature,
particularly in the context of developing countries, where similar
dimensions have been identified as pivotal factors shaping the
resilience capacity of households (FAO 2017). Notably, studies in
countries like Senegal have underscored the significance of access
to basic services, ability to meet basic needs, and ownership of
assets in determining household resilience (FAO 2017).
Our analysis also revealed a positive trend in Fijian households’
resilience capacity over time, particularly between the years 1996
and 2007. The improvement in SERCI between 2007 and 2014 was
more modest (see Table S2).
4.2 Households’ SERCI by Ethnic Groups
Our data (based on IPUMS dataset) show that households
headed by iTaukei have lower material possession-based SERCI
(without accounting for social capital) than all other ethnic
groups throughout the time period we analysed, which was also
significantly lower than the national average (see Table 2). This
discrepancy was mainly driven by the household assets, basic
services, and basic needs components of SERCI (see Figure S8).
There was no significant difference between iTaukei households
and Indo-Fijian households in terms of adaptive capacity index
and access to safety net programs (see Figure S8). Similar patterns
were observed in the sample of only rural households, even
though the gaps between the ethnic groups were lower in
rural areas (see Figure S9). Over time, however, all households
(regardless of ethnic background) haveexhibited an improvement
in their socioeconomic resilience between 1996 and 2014 (Table 2).
4.3 SERCI by the Gender of Household Heads
Our results based on IPUMs show that women-headed house-
holds had relatively lower socioeconomic resilience capacity
index (0.55) than households headed by men (0.59), which was
statistically significant (t=18.312; p=0.00). This difference
was mainly driven by significantly lower adaptive capacity (i.e.,
lower educational achievement) of women-headed households
(0.65) compared to men-headed households (0.72) (see Figure
S10) and this difference was statistically significant (t=20.1; p
=0.00). There was no statistically significant difference between
households with men and women heads in terms of other
dimensions of resilience capacity.
4.4 The Role of Social Capital
To demonstrate the role of social capital, we used the MWLR
dataset, as the IPUMS dataset did not have relevant variables for
the social capital component of SERCI. Our analysis shows that
social capital is the most important component of the resilience
capacity of the local communities in Fiji (see Figure 2), mainly for
iTaukei communities (see Figure 3). The second most important
component is the adaptive capacity of the households based on
the education level of the household heads and the diversity of
household income as a proxy for adaptive capacity. According to
MWLR dataset, access to safety nets and household assets carry
less weight in their contribution to the overall socioeconomic
resilience capacity of the households.
Figure 3shows that iTaukei households reported significantly
higher levels of social capital (mean =0.748) than Indo-Fijian
households (mean =0.636; t=6.934, p=0.00), as well as
higher adaptive capacity (which includes education and diversity
of sources of income) (t=2.41, p=0.02). In contrast, iTaukei
households reported a significantly lower amount of household
assets than Indo-Fijian households (t=–11.633, p=0.00). We
found no statistically significant difference between iTaukei and
Indo-Fijian households in terms of their access to safety nets (–
0.612, p=0.541) (Figure 4). Our results showed no statistically
8of16 Climate Resilience and Sustainability,2025

0.132
0.243
0.634
0.701
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
ASN
HH
A
AC
SC
FIGURE 2 Key components of the SERCI. Source: MWLR
(2013/2014).
significant difference in the social capital between households
headed by men and women (t=–0.053, p=0.96) (see Figure S11).
The regression results using the mixed effects model (based on
MWLR dataset) show that the inclusion of the social capital
in the calculation of SERCI has substantially reduced the gap
between the socioeconomic resilience capacity of iTaukei and
Indo-Fijian households (Figure 4). Without social capital, the
resilience capacity index of Indo-Fijian households was 0.066
points higher than that of iTaukei households, which was sta-
tistically significant (confidence interval falling above the zero
line (red line) in Figure 4(upper panel) and t=7.245). When
social capital is included in the SERCI, the difference between
the iTaukei and Indo-Fijian households fell by almost half to
0.03 and the difference became statistically weaker (i.e., the value
approached the zero line in Figure 4(lower panel) and t=3.73).
This suggests that failing to account for social capital in the
analysis of socioeconomic resilience capacity of household could
lead to flawed conclusions and policies.8
4.5 Predictors of Households’ SERCI
Based on the IPUMS dataset, to analyse the factors that predict
the SERCI of households (along with gender and ethnicity), we
used three different models: SEM, and two mixed effect models
depending on how the SERCI is calculated (see Table 3and Figure
S12).
Our results (based on IPUMS dataset) across all models show
that, keeping other things constant, households from urban areas
(i.e., cities or towns) are statistically significantly more resilient
than households from rural areas (Table 3and Figure S6). In
addition, Indio-Fijian households, and households headed by
other ethnic groups (e.g., European, Chinese) were found to
be more resilient compared to iTaukei headed households. This
gap was mainly driven by the big difference in the household
assets ownership (Figure S8). The intensity of cyclones was not
statistically significantly correlated with SERCI, which might be
due to low variability of the frequency and intensity of cyclones
across the provinces, as the cyclone data was at province level. We
also found the socioeconomic resilience of households increases
with the age of household heads, but the positive impact of
age doesn’t continue indefinitely. Our results show that initially
the socioeconomic resilience of households increases as the age
of the household head increases, but after 50 years of age the
socioeconomic resilience of households start to decline (i.e., an
inverted U-shape relationship between the age of the household
head and the socioeconomic resilience the household) (see Figure
S13).
Our results also show that households headed by women were
socioeconomically less resilient compared to households headed
by men. The other interesting result was that the relationship
between household size and household socioeconomic resilience
capacity has an inverted U shape (Figure S13). Like the age
of the household head, the resilience capacity of households
initially increases with the household size, but the positive impact
of household size on the resilience capacity has a limit. The
socioeconomic resilience capacity reaches its maximum when a
household has around 10 members, beyond which the increase in
household size leads to a decrease in the resilience capacity of the
households (Figure S13).
5 Discussion
5.1 Social Capital, Ethnicity and Socioeconomic
Resilience to Climate Change
Social capital emerged as a pivotal factor in our analysis to
bolster the resilience of households, particularly that of the
iTaukei communities. These findings are in line with the existing
studies in the literature (Yila et al. 2013; Aldrich and Meyer
2015;Carmenetal.2022;Liuetal.2022; Masud-All-Kamal
and Monirul Hassan 2018; Nakamura and Kanemasu 2020;
Panday et al. 2021;Zhaoetal.2024). In our study, there was a
substantial reduction in the socioeconomic resilience capacity
gap between ethnic groups when social capital is incorporated
into the resilience capacity analysis. When social capital is not
included, the disparity in the socioeconomic resilience capacity
of households headed by iTaukei and Indo-Fijians was high,
which was mainly driven by the household assets component
of SERCI. However, social capital plays a moderation role by
reducing the gap between the socioeconomic resilience of iTaukei
and Indo-Fijians communities.
We found that iTaukei households possess significantly higher
social capital than other ethnic groups in Fiji. Social capital
encompasses the networks, relationships, and shared norms
within Fijian communities that facilitate cooperation and mutual
support. Among iTaukei communities, social capital often thrives
within extended family systems, tight-knit village structures, and
communal rituals (Tuimavana 2020; Yee et al. 2022). Indigenous
Fijian cultures traditionally place a strong emphasis on commu-
nal activities, ceremonies, and shared responsibilities (Tuimavan
2020). These cultural practices reinforce a sense of belonging and
9of16

FIGURE 3 Key components of the SERCI by ethnicity. Source: MWLR (2013/2014).
interconnectedness among iTaukei communities. Many iTaukei
communities reside in close-knit villages where everyone knows
each other. This connectedness fosters strong social ties, enabling
swift and coordinated responses to challenges, including those
posed by climate change. iTaukei communities often operate
within extended family structures, promoting a support system
that extends beyond the nuclear family (Sevudredre 2023). This
extended network in terms of linking social capital is crucial
during times of crisis, enhancing the community’s ability to cope
with and recover from the impacts of adversities.
The higher social capital within iTaukei communities provides
a solid foundation for collective action in the face of climate
change-induced hazards (Auer et al. 2020), which is known as
solesolevaki in Fijian (Movono and Becken 2018;Ratuva2014).
Shared resources, knowledge, and mutual assistance strengthen
their ability to withstand shocks and recover from the effects of
shocks. Social capital also enables communities to adapt well to
changing conditions (Jewett et al. 2021). The collective knowledge
and shared experiences facilitate the development and implemen-
tation of adaptive strategies, ensuring a more resilient response
to climate related challenges. In times of disaster, the social
networks within iTaukei communities become instrumental in
coordinating emergency responses, as it was the case during
the 2009 and 2012 floods of the Ba River (Yila et al. 2013)and
tropical cyclone Winston (Nakamura and Kanemasu 2020). The
robust and strong social capital within iTaukei communities plays
a pivotal role in enhancing communities’ resilience, and crisis
response in the face of climate change-induced hazards and
shocks.
Indo-Fijian communities, on the other hand, are generally
independent of each other, and have lower level of communal
cohesion and weaker networks (Anshuka et al. 2021; Gawith et al.
2016). They tend to respond individually to disasters and hence
face greater challenges in mobilising resources and accessing
support during disasters (Anshuka et al. 2021).
These findings are in line with the findings of Gawith et al. (2016),
who also reported that iTaukei communities demonstrated higher
level of social capital-based community resilience than the Indo-
Fijian households. In their study, they mainly focused on the
social and human capital aspect of resilience. Our study expands
their findings by adding the other components of socioeconomic
resilience based on FAO’s RIMA II methodology (e.g., adaptive
capacity, household assets, access to basic needs). Our results
suggest that there could be some substitutability between social
capital and other components of socioeconomic resilience.
Our findings highlight the imperative of considering all pertinent
components of socioeconomic resilience capacity including social
capital. Careful examination of each component of resilience
10 of 16 Climate Resilience and Sustainability,2025

FIGURE 4 Factors correlated with household resilience in Fiji: with and without social capital. The results in the upper panel are obtained
without accounting for social capital and those in the lower panel are obtained by incorporating the social capital component of SERCI. Source: MWLR
(2013/2014).
capacity is crucial. In the Fijian context, our results suggest that
social capital holds a greater significance to some communities
in facilitating their ability to bounce back from the impacts
of shocks than others. We recommend that analysts (including
those in research and government) should make concerted
efforts to account for all relevant components of socioeconomic
resilience capacity (including social capital) in their analysis of
the resilience capacity of communities, particularly in countries
like Fiji.
5.2 Gender and Socioeconomic Resilience of
Households
In Fijian communities, as in many other parts of the developing
world(FullerandLain2019), households headed by women often
exhibit significantly lower levels of socioeconomic resilience.
This disparity stems from a multifaceted interplay of factors,
encompassing economic, historical, cultural, institutional, and
political dimensions.
In general, unequal access to education for women significantly
constrains the economic prospects available to women in Fiji
(Narsey 2023). Our results (based on both IPUMS and MWLR
datasets) show that the educational achievement of female house-
hold heads was lower than male household heads, which resulted
in women having lower adaptive capacity compared to men (see
Figures S10 and S11). Limited educational attainment restricts the
employability and earning potential of women, impeding their
capacity to establish socioeconomic resilience. Furthermore, a
prevalent gender pay gap, a challenge confronted by women in
Fiji, results in women earning less than their male counterparts
for equivalent work (Asian Development Bank 2022). This leads
to income inequality (COVID-19 Response Gender Working
group 2020), and curtails their financial resources, rendering it
more challenging to save and invest in economic stability.
Cultural norms and expectations in Fijian communities (partic-
ularly in rural areas) have traditionally assigned domestic and
caregiving roles to women (Prasad 2017).9While these roles are
vital, they can reduce the educational achievement of women
and limit their participation in the formal labour market and
their access to economic opportunities, which reduces their
adaptive capacity, and ultimately their socioeconomic resilience
capacity. A report by Fiji Women’s Rights Movement, found
that women participation in paid work is only 34% (Narsey
2023), despite women representing around 49.3% of the Fijian
population (COVID-19 Response Gender Working group 2020).
11 of 16

TABLE 3 Model results obtained from three different models: SEM and two mixed effect models.
SEM
Mixed effect model 1
(predicted RCI from
SEM)
Mixed effect model 2
(aggregated value of
RCI)
Estimate Estimate Estimate
Frequency of cyclones 0.011 −0.002 0.008
(0.022) (0.008) (0.006)
Residence (Urban) 0.058** 0.149*** 0.09***
(0.024) 0.002 0.002
Ethnicity (iTaukei) −0.04** Reference Reference
(0.013)
Ethnicity (Indo-Fijian) Reference 0.104*** 0.1***
0.002 0.002
Ethnicity (others) Reference 0.092*** 0.08***
0.004 0.004
Age of household head 0.107*** 0.25*** 0.45***
(0.026) 0.022 0.02
Age of household head (square) −0.094*** −0.233*** −0.43***
(0.025) 0.022 0.02
Gender of household head (female) −0.004** −0.012*** −0.05***
0.003 0.002
Gender of household head (male) Reference Reference Reference
Household size 0.225*** 0.585*** 0.61***
(0.054) 0.041 0.03
Household size (square) −0.453*** −1.23*** −1.33***
(0.114) 0.147 0.12
Year (2014) 0.007** 0.027*** 0.004**
(0.002) 0.002 0.002
Random effect (province) — Yes Yes
Cluster (province) Yes — —
Signif. codes: 0.01 ‘***’ 0.05 ‘**’.
Source: IPUMS (2023).
The report also underlines that 73% of the unpaid household
work is done by women compared to only 27% by men (Narsey
2023).
In Fiji, many women do not have equal access to land ownership
or control over land resources (Singh-Peterson and Iranacolaivalu
2018), which limits their ability to engage in agriculture and land-
based income-generating activities. Despite substantial efforts to
promote gender equality, institutional and policy barriers persist
in Fiji (Narsey 2023). These include limited access to credit,
challenges in securing land tenure, and low women participation
in formal employment, and limited representation of women in
decision-making roles (Narsey 2023; Singh-Peterson and Irana-
colaivalu 2018). Our results show that households headed by
women possess a significantly lower amount of household assets
and their ability to meet their basic needs is significantly lower
than households headed by men. The combination of all these
factors makes female-headed households less resilient to the
effects of shocks in general and climate change-induced shocks
in particular.
6 Conclusion
In this study we analysed the multifaceted dimensions of
socioeconomic resilience capacity of Fijian communities drawing
on two comprehensive datasets, namely IPUMS and MWLR
datasets. Within this context, we have shed light on the crit-
ical role of social capital, while also examining the role of
gender and ethnicity in shaping the socioeconomic resilience
landscape.
Our study emphasises the pivotal role of social capital in bolster-
ing households’ socioeconomic resilience, particularly in nations
12 of 16 Climate Resilience and Sustainability,2025

like Fiji, where social networks play a crucial role in coping with
and recovering from the effects of shocks. The omission of critical
dimensions (e.g., social capital) from the household resilience
capacity analyses risks yielding flawed conclusions and policies.
Our findings underscore the importance of a holistic approach to
gain a complete understanding of the socioeconomic resilience
of communities and craft optimal climate change adaptation
policies.
Our findings highlight a noteworthy disparity in the socioeco-
nomic resilience capacity of households headed by women com-
pared to their male counterparts. Households headed by women
had a lower level of achievements across critical dimensions of
resilience capacity, including adaptive capacity, household assets,
and the ability to meet basic needs. This is exacerbated by limited
access women have to resources and economic opportunities,
the cause of which is rooted in cultural, economic, institutional,
and political factors. Addressing these challenges is imperative
for enhancing the economic resilience of households headed by
women in Fijian communities.
Although our study focuses on Fiji, we strongly believe that
the interplay of gender, ethnicity, and social capital in shaping
socioeconomic resilience is a critical issue in many developing
countries, particularly in the Pacific region which faces many
climate-induced shocks and socioeconomic vulnerabilities.
While our findings are relevant to the current situation in Fiji,
we acknowledge that the lack of more recent data presents
a limitation in our analysis. To address this limitation, while
preserving the comprehensive scope of our study, future research
should incorporate the critical dimensions of socioeconomic
resilience (e.g., social capital) into assessments of community
resilience more systematically (starting from the research plan-
ning to data collection and analysis), particularly in countries
like Fiji. In addition, comparative analyses across different
regions or countries may enhance the generalisability of our
results.
Author Contributions
Conceptualisation: T.T.G and S.G. Analysis: T.T.G and S.G. Methodology:
T.T.G and S.G. Writing—original draft: T.T.G. Writing—review and
editing: T.T.G and S.G
Acknowledgements
We would like to thank the Ministry of Business, Innovation and
Employment, New Zealand for the financial support we obtained to
conduct this study.
Open access publishing facilitated by Landcare Research New Zealand,
as part of the Wiley - Landcare Research New Zealand agreement via the
Council of Australian University Librarians.
Conflicts of Interest
There is no conflict of interests.
Data Availability Statement
The data that support the findings of this study are openly available in
IPUMS at https://international.ipums.org/international/.
Endnotes
1According to the IPCC, globally the sea level had risen at about 1.5 mm
per year from 1901 to 1990, and at about 3.2 mm per year between 1993
and 2010 (Nurse et al., 2014) and at about 4.6 mm per year from 2013 to
2022 (WMO, 2023).
2More vulnerable members of communities may live in areas more prone
to floods, landslides, erosion etc which makes the impact on those
households more damaging.
3We obtained the data on the incidence of tropical cyclones in Fiji from
the Emergency Events Database (EM-DAT) from Centre for Research
on the Epidemiology of Disasters (CRED) (https://www.emdat.be/).The
number of tropical cyclones included in our study are of category 1 to 5
according to Australian tropical cyclone intensity scale.
4We did not have variables related to social capital in the IPUMs dataset,
and variables related to ABN and ABS in MWLR dataset (see Figures S1,
S2, and S6).
5Note that in the case of large sample size, the Chi-square tests are less
reliable (Sarmento and Costa, 2019).
6To obtain the cyclone intensity variable, we multiplied the number of
tropical cyclones with weights for the category of the tropical cyclones
ranging from 0.2 to 1 (0.2 to category 1 cyclones, 0.4 to category 2, 0.6 to
category 3, 0.8 to category 4 and 1 for category 5 using the Australian trop-
ical cyclone intensity scale. (http://www.bom.gov.au/cyclone/tropical-
cyclone-knowledge- centre/understanding/categories/)
7In 1996 around 76% of the households had at least one of the two basic
services in our data set (access to toilet and electricity).
8The gap between the two ethnic groups is still statistically significant
because of the big difference in other components of socioeconomic
resilience, mainly the household assets component (see Figure 4).
9https://www.globalpartnership.org/blog/fiji-gpe- education-made-
transformational-journey-possible.
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Supporting Information
Additional supporting information can be found online in the Supporting
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