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The Role of Collective Efficacy in Climate Change Adaptation in India
JAGADISH THAKER
School of Communication, Journalism and Marketing, Massey University, Manawatu Campus,
Palmerston North, New Zealand
EDWARD MAIBACH
Department of Communication, George Mason University, Fairfax, Virginia
ANTHONY LEISEROWITZ
School of Forestry and Environmental Studies, Yale University, New Haven, Connecticut
XIAOQUAN ZHAO
Department of Communication, George Mason University, Fairfax, Virginia
PETER HOWE
Department of Environment and Society, Utah State University, Logan, Utah
(Manuscript received 3 September 2014, in final form 7 September 2015)
ABSTRACT
Research on adaptive capacity often focuses on economics and technology, despite evidence from the social
sciences finding that socially shared beliefs, norms, and networks are critical in increasing individuals’ and
communities’ adaptive capacity. Drawing upon social cognitive theory, this paper builds on the first author’s
Ph.D. dissertation and examines the role of collective efficacy—people’s shared beliefs about their group’s
capabilities to accomplish collective tasks—in influencing Indians’ capacity to adapt to drinking water
scarcity, a condition likely to be exacerbated by future climate change. Using data from a national survey
(N54031), individuals with robust collective efficacy beliefs were found to be more likely to participate in
community activities intended to ensure the adequacy of water supplies, and this relationship was found to
be stronger in communities with high levels of community collective efficacy compared to communities with
low levels of community collective efficacy. In addition, community collective efficacy was positively associated
with self-reported community adaptation responses. Public education campaigns aimed at increasing collective
efficacy beliefs are likely to increase adaptive capacity.
1. Introduction
Several countries are already experiencing negative
impacts because of climate change (IPCC 2014). De-
veloping countries such as India are considered to be
particularly vulnerable to climate change impacts because
of other stressors such as high incidence of poverty, il-
literacy, and lack of resources (IPCC 2014;INCCA
2010). Scientists and policymakers increasingly stress
the need to urgently take measures to prepare and adapt
for climate change impacts, especially in developing and
underdeveloped countries facing disproportional im-
pacts (IPCC 2014).
Adaptation to climate change refers to anticipatory or
reactive actions to reduce harm and benefit from op-
portunities, if any, from climate change impacts (Adger
et al. 2007). Adaptation to climate change depends on
the social system’s adaptive capacity—defined as a sys-
tem’s access to resources and its capacity to effectively
Corresponding author address: Jagadish Thaker, School of
Communication, Journalism and Marketing, Massey University,
Room SST 2.01, Social Science Tower, University Avenue, Palmerston
North 4442, New Zealand.
E-mail: j.thaker@massey.ac.nz; emaibach@gmu.edu; anthony.
leiserowitz@yale.edu; xzhao3@gmu.edu; peter.howe@usu.edu
JANUARY 2016 T H A K E R E T A L . 21
DOI: 10.1175/WCAS-D-14-00037.1
!2016 American Meteorological Society
use such resources (Adger et al. 2007). Adaptive ca-
pacity assessments frequently feature biophysical, eco-
nomic, and technological variables (e.g., O’Brien et al.
2004), yet they often ignore the important human attri-
butes necessary for adaptation planning and implementa-
tion (e.g., Adger et al. 2007;Grothmann and Patt 2005;
Thaker 2012).
While economic resources are important in adapta-
tion planning and implementation, they are not suffi-
cient. For example, Aldrich (2010, p. 3) found that after
the 2004 tsunami in the Indian Ocean, the state of Tamil
Nadu in India, where 8000 people died and 310 000 were
left homeless, recovered relatively quickly as the state
‘‘rebuilt almost all of its schools, fixed 75% of the
damaged housing stock, and put most of its fishermen
back to work’’ within a year of the disaster. Hurricane
Katrina on the U.S. Gulf Coast provides a coun-
terexample. Although fewer people were harmed by
Katrina—1600 killed and 250 000 left homeless—many
communities in coastal Louisiana and Mississippi were
far from recovery even one year after the crisis, despite
having much higher per capita incomes than communi-
ties in Tamil Nadu. By contrast, the low-income Viet-
namese community in New Orleans was more resilient
to Katrina because of its strong community organization
and social capital (Airriess et al. 2008). Several studies
show that people process risk information and respond
in complex ways, and social and cultural factors play an
important role in how individuals and communities react
to risks and crises (e.g., Adger et al. 2007;Bord et al.
2000;Kahneman et al. 1982;Lorenzoni et al. 2007;
Weber 2006).
Moreover, many studies that explore adaptive ca-
pacity do so at the individual level (e.g., Grothmann and
Patt 2005), even though many of the most important
adaptation measures require collective action and mul-
tiple levels of governance (Adger et al. 2007). For ex-
ample, adaptation to drinking water scarcity not only
requires individual households to use water more effi-
ciently, but also requires communities to build water-
harvesting and storage systems and local and national
governments to incentivize efficiency and implement
policies to increase water supply because of increasing
demands from industry, agriculture, and households.
This paper builds on a Ph.D. dissertation by the first
author to introduce the concept of collective efficacy—
people’s perception about their collective abilities to
overcome challenges facing their group or community—
to the climate change adaptation literature and to test if
individuals with stronger perceptions of collective effi-
cacy are more involved in community adaptation and
if communities with higher levels of community col-
lective efficacy are more likely to undertake proactive
adaptation actions. This paper is based on Thaker
(2012), with additional analyses and discussion included
and one additional investigator added to the team.
Specifically, this study tests hypotheses using multilevel
models to account for nonindependence of an in-
dividual’s collectivity efficacy perceptions within a geo-
graphic locale, ignored in a previous analysis, and with
additional demographic control variables.
2. Literature review
a. Collective efficacy: A conceptual analysis
According to social cognitive theory (Bandura 1997),
human behavior is regulated by personal and social
factors and is primarily driven by an individual’s self-
efficacy, that is, the ‘‘beliefs in one’s capacity to organize
and execute the courses of action required to produce
given attainments’’ (Bandura 1997, p. 3). Self-efficacy
beliefs are primary drivers of behaviors aimed to
achieve individuals’ goals (Bandura 1997). Individuals,
however, often face collective tasks, such as community
adaptation to climate change, and may benefit from
acting in coordination with others by pooling their re-
sources for common goals (Thaker 2012). Self-efficacy
may play an important role in collective tasks and is
often associated with collective efficacy; however, a group
of highly self-efficacious individuals may perform poorly
in tasks that require group members to coordinate.
A substantial body of evidence suggests that groups
with high collective efficacy are more likely to set higher
goals, mobilize better resources, coordinate and per-
form behaviors that increase their group’s chances to
succeed, and persevere in spite of initial setbacks or
growing opposition (Bandura 2000;Goddard et al. 2004;
also see Thaker 2012). The importance of the collective
efficacy construct has been demonstrated experimen-
tally (e.g., Durham et al. 1997;Earley 1994) and through
survey research in diverse domains, including educa-
tional systems (Bandura 1997;Goddard et al. 2004),
athletic teams (e.g., Feltz and Lirgg 1998), combat teams
(Jex and Bliese 1999), business organizations (Zellars
et al. 2001;Little and Madigan 1997), and political sys-
tems (e.g., Pollock 1983;Lee 2006,2010).
Efficacy perceptions are behavior- or behavioral-
domain-specific beliefs; behavior and domain-linked
indices of perceived efficacy have greater explanatory
and predictive value than do generalized efficacy beliefs
(Bandura 1997). A given person’s efficacy beliefs may
vary considerably between behaviors (e.g., reducing
residential water use versus reducing residential energy
use) and between behavioral domains (e.g., plumbing
versus carpentry), and the difference in efficacy
22 WEATHER, CLIMATE, AND SOCIETY VOLUME 8
perceptions across domains holds true even among
groups, with individuals being more efficacious about
their collective abilities in reducing crime than altering
economic crises or dealing with terrorism (Fernández-
Ballesteros et al. 2002). In addition, the degree of
interdependence between members of a group for a par-
ticular group goal also affects collective efficacy per-
ceptions (e.g., Gully et al. 2002). Efficacy perceptions
are only moderately related to people’s and groups’
actual abilities; indeed, it is helpful if people and groups
slightly overestimate their capabilities, as it can increase
their motivation to set and achieve higher targets
(Bandura 1997).
b. Measuring collective efficacy
Three different approaches exist to measure collective
efficacy (Bandura 1997; also see Thaker 2012). One
approach is to aggregate the self-efficacy assessments of
all members of the group (e.g., ‘‘How confident are you
that you can do [X]?’’). Such measures, however, ignore
the ‘‘coordinative and interactive aspects operating
within groups’’ (Bandura 2000, p. 76). A better, and
most often used, method to measure collective efficacy is
to aggregate measures to responses to collective referent
statements (e.g., ‘‘How confident are you that you and
your neighbors can work together to do [X]?’’). A third
approach to measuring collective efficacy is to ask group
members to discuss group capabilities and reach a con-
sensus about the group’s collective efficacy; however,
Bandura (1997) argues this method is susceptible to
social desirability bias, as well as ignoring within-group
differences of collective efficacy beliefs.
c. Antecedents and consequences of collective efficacy
Efficacy assessments are influenced by mastery ex-
periences, vicarious experiences, social persuasion, and
affective states. Prior experience of success is one of the
most important determinants of efficacy beliefs, as per-
sonal experiences provide the most credible evidence
for individuals and groups to assess their abilities. Vi-
carious learning—observing other people or groups
successfully perform the behavior of interest—is a sec-
ond powerful source of efficacy beliefs, especially when
people or groups deem themselves to be as capable as
the behavioral model. Verbal persuasion by trusted,
insightful others—such as teachers, coaches, opinion
leaders, and accomplished peers—can also have a strong
influence on efficacy beliefs. Finally, at least with regard
to self-efficacy (although it is less clear how, if at all, it
pertains to collective efficacy) affective states can affect
the judgment of competence; for example, positive
moods increase perceived efficacy, whereas sad moods
diminish it (Bandura 1997;Goddard 2002).
Collective efficacy beliefs regulate human behavior
through four major processes: cognitive, motivational,
emotional, and decisional. Perceived collective efficacy
beliefs affect how people or groups assimilate and pro-
cess information, what goals they set for themselves, and
how they anticipate and prepare for barriers, thereby
increasing their odds of group goal attainment. The
stronger the perceived collective efficacy is, the higher
the motivational investment of group members to mo-
bilize resources at their command and to persist despite
setbacks. Perceived collective efficacy also regulates how
people or groups respond emotionally to challenging
situations. Finally, collective efficacy also influences the
decisions people or groups make in order to control their
future (Bandura 1997,2000;Goddard 2002).
d. Individual-level collective efficacy and behavioral
involvement in climate change–relevant adaptation
activities
Evidence across several domains of group activity
show that people’s beliefs about their group’s collective
abilities positively affect their own degree of in-
volvement in collective tasks (Jex and Bliese 1999;
Walumbwa et al. 2004;Zellars et al. 2001). For example,
Goddard and Salloum (2011, p. 11) argued that, ‘‘col-
lective efficacy beliefs may thus foster decisions to
gather health-related resources, eliminate environmen-
tal hazards to health, and promote communication
among neighbors, each of which in turn could facilitate
dissemination of health information, prevent disease,
and increase the likelihood of treatment.’’ Individuals
with high levels of collective efficacy are found to persist
longer in group goals and tasks than individuals with
lower levels, even under difficult circumstances; they
also display more job satisfaction and express less in-
tention to quit the team even when experiencing high
degrees of stress and strain (Jex and Bliese 1999;Zellars
et al. 2001). Further, Lee (2006,2010) found a positive
association between collective efficacy and intentions to
participate in political protests in support of more
democratic reforms in Hong Kong. Benight (2004; also
see Benight and Bandura 2004) found that when re-
source loss was high, individuals with low perceived
collective efficacy experienced higher distress than in-
dividuals with high collective efficacy.
Recent studies indicate efficacy beliefs may play an
important role in public engagement with climate
change adaptation relevant attitudes and actions (e.g.,
Lorenzoni Nicholson-Cole and Whitmarsh 2007;
Maibach et al. 2008;Roser-Renouf and Nisbet 2008).
For example, evidence from the field of development
communication indicates that a media can play an en-
abling role to increase community members’ collective
JANUARY 2016 T H A K E R E T A L . 23
efficacy perceptions, which in turn influences individual
participation in community activities. In public health
and community development research, for example,
communities that are more efficacious have been found
to experience better outcomes over time (e.g., Papa
et al. 2000;Singhal and Rogers 1999).
Based on these previous studies, this paper tests the
following hypothesis (hypothesis 1): individuals’ collec-
tive efficacy regarding their community’s capacity to
ensure the adequacy of its drinking water supply will be
positively associated with their participation in com-
munity activities to address drinking water scarcity.
e. Community-level collective efficacy and
community drinking water adaptation
High group collective efficacy establishes a strong
normative influence of the group that affects ‘‘the dili-
gence and resolve with which groups choose to pursue
their goals’’ (Goddard et al. 2004, p. 8; Thaker 2012).
Further, collective efficacy establishes a social norm
where ‘‘collective efficacy beliefs serve to encourage
certain actions and constrain others’’ (Goddard et al.
2004, p. 8). For example, evidence from the field of
community water management projects suggests that
communities with prior experience of successful inter-
ventions are more likely to seek and find opportunities
to help their community members adapt to drinking
water scarcity (e.g., Cohen and Uphoff 1980;Manikutty
1998;Murtinho 2010;Narayan 2005). Experience of
successful management of resources in the past, or such
mastery of experience in coordinating with internal
stakeholders and external agencies, is an important
source of efficacy for community members. By cultivating
a sense of collective achievement, a community is more
likely to enhance the ability of its members to pool their
resources together and work toward group goals. In ad-
dition, communities with high levels of collective efficacy
are more likely to form powerful collectives and put more
pressure on external agencies to provide necessary re-
sources to help their community members adapt to local
vulnerabilities. For example, Murtinho (2010), in a study
of water user associations (community-based organiza-
tions to manage water resources), found that community
members’ perceptions about water scarcity as well as the
community’s prior success in securing external funding
was associated with implementing adaptation strategies
to cope with water source degradation. Based on the
above findings, the following hypothesis is proposed
(hypothesis 2; Thaker 2012): aggregate community-level
perceptions of collective efficacy regarding the com-
munity’s ability to ensure the adequacy of its drinking
water supply will be positively associated with commu-
nity adaptation responses.
3. Methods
a. Data collection
The data for this study are from a national sample sur-
vey conducted in India to understand Indian citizens’
perceptions about climate change (see Thaker 2012). The
target population for this survey was all adults in India (18
years of age and above), drawn from urban, semiurban,
and rural communities. The stratified random sampling
plan was as follows: parliamentary constituencies that re-
fer to the federal-government-level electoral units served
as primary sampling units. From each randomly sampled
parliamentary constituency unit, an assembly constituency
was randomly selected. Then polling locations (or polling
stations) within an assemblyconstituencywererandomly
selected. From each of the randomly selected polling sta-
tions, using the electoral rolls provided by the Election
Commission of India, the first respondent was randomly
selected, after which every tenth subsequent respondent
on the list was selected. From each polling station, the
target was to achieve at least 10 completed surveys.
Using the above sampling plan, 10 153 respondents
were contacted, out of which 4031 completed the survey,
resulting in a response rate of 39.7%, with a 1.5% margin
of error. The survey was administered face to face at the
home of the selected respondents and took approxi-
mately 45 min to complete. The interviews were con-
ducted in November and December 2011 by employees
of two survey companies (C-Voter and Markelytics).
Interviews were conducted in Hindi, Marathi, Punjabi,
Bengali, Tamil, Telugu, Urdu, Kannada, English, Ma-
layalam, Oriya, Assamese, and Gujarati. The final data
were weighted to match the age, gender, religious, and
regional distribution of the target population—adults 18
years and above, using parameters from the 2001 Census
of India. The demographic characteristics of the sample
are listed in Table 1.
b. Measures
1) INDIVIDUAL COLLECTIVE EFFICACY
Two items were used to assess individuals’ percep-
tions about their community’s abilities in the domain of
drinking water adaptation: ‘‘How confident are you that
your community can work together to increase access to
safe drinking water?’’ and ‘‘How confident are you that
your community can work together to make sure that
everyone has enough safe drinking water even during
difficult times like floods or droughts?’’ Both items were
assessed with a four-point scale, ‘‘not at all confident’’ (1)
to ‘‘very confident’’ (4); ‘‘do not know’’ was also given
as a response option (which was treated as missing data).
The items were highly correlated (r50.63, p,0.001) and
24 WEATHER, CLIMATE, AND SOCIETY VOLUME 8
were summed to create a seven-point collective efficacy
scale [mean (M)55.22, standard deviation (SD) 51.76].
2) BEHAVIORAL INVOLVEMENT
Four yes/no items (‘‘no’’ coded as 0 and ‘‘yes’’ coded
as 1) were used to assess behavioral involvement in com-
munity activities related to drinking water adaptation:
1) Have you encouraged other members of your com-
munity to waste less water?
2) Have you participated in community activities to
increase the amount of safe drinking water?
3) Have you demanded that your community leaders or
government officials improve the amount of safe
drinking water for your community?
4) Have you participated in social demonstrations—
such as gheroas (sit-ins), rasta rokos (blocking roads),
or bands (blockades)—to demand more safe drink-
ing water for your community?
Responses were summed to create an index of behav-
ioral involvement (M51.65, SD 51.46).
3) PERCEIVED RISK
Two items were used to measure perceptions of
drinking water scarcity:
1) If a one-year-long severe drought happened in your
local area, how big of an impact would it have on
your household’s drinking water supply?
2) Would you say a one-year-long severe flood would
have a large impact, a medium impact, a small impact,
or no impact atall on your household’s drinking water
supply?
Both items were assessed with a four-point scale: no
impact at all (1), a small impact (2), a medium impact
(3), and a large impact (4). The items were highly cor-
related (r50.60, p,0.01) and were summed to create a
risk perception scale (M56.07, SD 51.93).
4) CONTROL VARIABLES
Demographic variables were used as control variables to
examine the unique variance in the outcome variable that
can be attributed to the independent variable(s) of in-
terest. Twelve variables were used as control variables in
this study: respondent’s sex, age, income levels, educa-
tional attainment, caste groups (as identified by the Gov-
ernment of India), source of drinking water, payment for
water, time to collect water, access to sanitation, agricul-
tural land ownership, house type, and location of the re-
spondent’s household.
For sex, dummy codes were used such that female
(48%) was the reference category, coded as 0, compared to
male (52%), coded as 1. The caste variable was dummy-
coded into three categories, comparing upper castes with
other lower castes. Income was measured using eight cat-
egories (‘‘up to 1000 rupees a month’’ to ‘‘more than 20 000
rupees a month’’) and education was measured using 10
categories and recoded into four primary categories (‘‘il-
literate’’ to ‘‘postgraduate and above’’). The source of
drinking water variable was dummy-coded such that re-
spondents with a tapped or piped water connection within
the household premises were coded as 1, and the rest were
coded as 0. Similarly, respondents who pay for drinking
water access were coded as 0, and those who do not pay
any monthly fee at all were coded as 1. Respondents who
spent some time to collect drinking water were the refer-
ence category, coded as 0, while those who do not spend
any time to collect water were coded as 1. Access to san-
itation was coded such that respondents who said their
household has access to public sewer system were coded as
1andothersas0.Respondentswhosehouseholdshave
agricultural land were the coded as 1, and those without
any agricultural land ownership were coded as 0. Re-
spondents living in independent house or living in flats
were coded as 1 and others as 0. Respondents in urban
areas were the reference category compared to re-
spondents living in rural areas. The descriptive analysis of
the variables is presented in Table 1.
5) COMMUNITY-LEVEL COLLECTIVE EFFICACY
To compute community-level constructs, respondents’
assembly constituency was used as the unit of aggrega-
tion for individual scores. An assembly constituency is a
basic political unit at the state level, with one member
representing a constituency at the state legislative as-
sembly. For example, the community-level collective
efficacy was computed as the aggregate mean of in-
dividuals’ perceptions within an assembly constituency.
Community adaptation responses. At the individual
level, two items were used to measure self-reported
community adaptation responses using a dichotomous
scale (‘‘no’’ coded as 0 and ‘‘yes’’ coded as 1): ‘‘Over the
past one year, has your community 1) taken steps to help
people waste less water at home or 2) taken steps to in-
crease the amount of safe drinking water for the com-
munity?’’ The two items were moderately correlated (r5
0.57, p,0.01) and were summed to create a response
variable indicating self-reported community adaptation
responses (M50.97, SD 50.88).
To build community-level sociodemographic pro-
files, median age, median education, and median
household were used, in addition to four district-level
variables adopted from the Census 2011 figures (Census
of India 2011): sex ratio (number of females per 1000
JANUARY 2016 T H A K E R E T A L . 25
TABLE 1. Descriptive statistics (data weighted to match target sample characteristics for age, gender, religion, and region). Note that
percentages do not always add up to 100% because of missing values. Asterisks indicate where data were not available.
Variable Unweighted (%) Weighted (%) Census 2001
Sample size 4031 4000 1 028 737 436
Gender
Male 2397 (59.5) 2090 (52) 52
Female 1634 (40.5) 1910 (48) 48
Age groups (years)
18–24 378 (9.4) 791 (20)
25–34 1074 (26.6) 1015 (25)
35–44 962 (23.9) 880 (22) *
45–54 780 (19.4) 569 (14)
55–64 489 (12.1) 410 (10)
651344 (8.5) 331 (8)
Caste groups
Scheduled tribe 293 (7.3) 301 (8) 8
Scheduled caste 728 (18.1) 729 (19) 16
Other backward classes 1153 (28.6) 1204 (32) *
Upper caste 1535 (38.1) 1515 (40) *
Education levels
Primary education 1060 (26.3) 987 (24.7) *
Secondary education 1141 (28.3) 1042 (26)
Higher secondary 908 (22.5) 952 (23.8)
Graduate and above 922 (22.9) 1020 (25.5)
Monthly household income (rupees)
Up to 1000 158 (3.9) 146 (3.6) *
1001 to 2000 241 (6) 265 (6.6)
2001 to 3000 236 (5.9) 223 (5.6)
3001 to 4000 269 (6.7) 300 (7.5)
4001 to 5000 482 (12) 479 (12)
5001 to 10 000 1093 (27.1) 1049 (26.2)
10 001 to 20 000 845 (21) 872 (21.8)
Above 20 000 707 (17.5) 667 (16.7)
Source of drinking water
Tap/piped into house 2330 (57.8) 2415 (60.4)
Tap/piped into yard/plot 654 (16.2) 700 (17.5)
Public/community tap 468 (11.6) 449 (11.2)
Open well in dwelling 84 (2.1) 76 (1.9)
Open well in yard/plot/homestead 72 (1.8) 59 (1.5)
Open public/community well 40 (1) 32 (0.8)
Protected well in dwelling 22 (0.5) 14 (0.4)
Protected well in yard/plot 34 (0.8) 24 (0.6)
Protected public/community well 17 (0.4) 7 (0.2)
Spring 2 (0) 1 (0)
River/stream 7 (0.2) 4 (0.1)
Pond/lake 10 (0.2) 6 (0.1)
Dam 14 (0.3) 13 (0.3)
Rainwater 7 (0.2) 5 (0.1)
Tanker truck 69 (1.7) 65 (1.6)
Bottled water/water bag/sachet 67 (1.7) 43 (1.1)
Others 134 (3.3) 88 (2.2)
Payment for drinking water
Do not pay any money 942 (26.8) 908 (26.7)
Less than 50 rupees 396 (11.3) 489 (14.4)
50–100 rupees 535 (15.2) 522 (15.3)
100–200 rupees 642 (18.2) 623 (18.3)
200–300 rupees 471 (13.4) 433 (12.7)
300–400 rupees 305 (8.7) 242 (7.1)
More than 400 rupees 229 (6.5) 187 (5.5)
Time to collect drinking water
No time 190 (5.1) 173 (4.6)
26 WEATHER, CLIMATE, AND SOCIETY VOLUME 8
males), literacy rate, population density, and per-
centage of households whose drinking water source
is outside household premises. The Census of India
maintains exhaustive administrative-level data, with
the most recent census estimates at the district level
released in early 2011. Although a district is a higher-
level administrative unit, whereas an assembly segment—
the unit of aggregation for community adaptation re-
sponses and community collective efficacy in the
dataset as mentioned above—is a state-level electoral
unit, for the purposes of this study, an assembly con-
stituency is assumed to be more or less representative
of the district characteristics. While matching assembly
constituencies in the dataset to their respective
districts, eight pairs of assembly constituencies were
located in eight districts, indicating a minor non-
independence of observations.
c. Analysis
A variety of statistical tests were used to test the
construct validity of collective efficacy measure and
examine the hypothesis. Correlational analysis, ttests,
and analysis of variance (ANOVA) were used to test
the construct validity of collective efficacy measure
used in the survey. Specifically we expected collective
efficacy to be higher for males, older respondents, up-
per castes, higher income, and more educated in-
dividuals. Moreover, we expected that people who own
TABLE 1. (Continued)
Variable Unweighted (%) Weighted (%) Census 2001
Less than 30 min 1107 (29.4) 1103 (29.5)
30–60 min 1081 (28.7) 1124 (30.1)
1–2 h 707 (18.8) 684 (18.3)
2–3 h 333 (8.9) 328 (8.8)
More than 3 h 343 (9.1) 321 (8.6)
Access to sanitation
Connection to a public sewer
Connection to a septic system 1542 (38.3) 1560 (39)
Pour flush latrine 879 (21.8) 771 (19.3)
Simple pit latrine 585 (14.5) 746 (18.7)
Ventilated improved pit latrine 269 (6.7) 246 (6.2)
Public or shared latrine 75 (1.9) 62 (1.6)
Open pit latrine 94 (2.3) 101 (2.5)
Bucket latrine 105 (2.6) 95 (2.4)
Other 390 (9.7) 336 (8.5)
Agriculture land ownership
Yes 681 (16.9) 649 (16.2)
No 2850 (70.7) 2821 (70.5)
Refused/do not know 500 (12.4) 529 (13.2)
House type
Hut 224 (5.6) 241 (6)
Kutcha house (if wall materials include
wood/bamboo/mud and roof is
thatched/wooden/tin/asbestos
sheets, etc.)
322 (8) 328 (8.2)
Kutcha-pucca (if walls are made up of
pucca materials such as burnt brick
but roof is not concrete/cemented)
438 (10.9) 420 (10.5)
Mixed houses (if some rooms are
pucca and other rooms are
kutcha-pucca or kutcha)
382 (9.5) 344 (8.6)
Pucca independent house (both walls
and roofs are made up of pucca
materials and built on separate plot)
2006 (49.8) 2022 (50.6)
Flats 618 (15.3) 589 (14.7)
Other 41 (1) 54 (1.4)
Geographic location
Urban Tier 1 2094 (51.9) 1810 (45) 28
Tier 2 459 (11.4) 1076 (27)
Tier 3 517 (12.8) 338 (8)
Rural 961 (23.8) 776 (18) 72
JANUARY 2016 T H A K E R E T A L . 27
the houses they live in and who feel they live in co-
hesive communities are more likely to have high de-
gree of collective efficacy.
To test the two hypotheses, multilevel models were
tested using the lmer function in R, available as part of
lme4 package (Bates et al. 2013;Gelman and Hill 2006).
A previous analysis to test the hypothesis ignored mul-
tilevel theoretical framework of collective efficacy and
missing values (Thaker 2012).
4. Results
a. Psychometric analysis
To verify the construct validity of collective efficacy
used in this study, the following psychometric analyses
were performed. The ttest between sex and collective
efficacy scale indicated a significant difference in col-
lective efficacy perceptions, with women (M55.28,
SD 50.04) being more efficacious compared to men
(M55.16, SD 50.04; t52.20, p50.03, Cohen’s
d(d)50.07). Further, a one-way ANOVA was con-
ducted to compare the differences in collective efficacy
perceptions among the four caste groups. As expected,
there was a statistically significant difference between
caste groups on collective efficacy [F(3, 3748) 53.27,
p,0.05, h
2
(h2
p)50.004]. Post hoc comparisons using
Gabriel’s procedure test for different group sizes in-
dicated that the mean collective efficacy for the upper
castes (M55.34, SD 51.68) was significantly higher
than that of other backward castes (M55.16, SD 5
1.78), scheduled castes (M55.15, SD 51.72), and
scheduled tribes (M55.19, SD 51.55). However,
collective efficacy levels of other backward castes,
scheduled castes, and scheduled tribes were largely
similar, suggesting that compared to upper castes,
other caste groups may face similar experiences in
dealing with water scarcity. The results partially sug-
gest that collective efficacy perceptions differ between
caste groups, as expected. As also expected, individuals
who own their houses (M55.27, SE 50.03) have
significantly stronger perceived collective efficacy be-
liefs than people who live in rented houses [M55.02,
SE 50.07; t(3934) 523.24, p,0.01, h2
p50.002]. As
anticipated, there is a positive association between
perceived community cohesion and collective efficacy
(r50.13, p,0.01) as well as education and collective
efficacy (r50.06, p,0.001). Contrary to what was
expected, collective efficacy was not significantly as-
sociated with age.
In addition, the two collective items were moder-
ately correlated (r50.63, p,0.01), indicating internal
consistency. Overall, partial support was found for
construct validity of collective efficacy used in
this study.
b. Hypothesis 1: Collective efficacy and behavioral
involvement
Three multilevel models with increasing complexity
were tested: null model, random intercepts, and the ran-
dom intercepts and slopes. The models were fit by re-
stricted maximum likelihood (REML), and model fit was
compared using ANOVA and was based on values of
Akaike information criterion (AIC), Bayesian informa-
tion criterion (BIC), and the log likelihood (logLik). Prior
to analysis, the hot-deck imputation method (Myers
2011) was used to impute missing data on all the variables
considered in the study using gender and age as matching
variables to impute missing values (see Table 1).
The null model was specified to test the proportion of
variance in behavioral involvement that can be attrib-
uted to differences at the community level and as a
baseline to examine if more complex models fit the data
better. Results from the null model, with no predictors
except specifying random effects for each community,
indicated that 46% of the variance in behavioral in-
volvement could be attributed to differences at the
community level.
The random intercept model was specified using
individual-level sociodemographic (gender, age, in-
come, education, and dummy variables for caste), risk
perception, and collective efficacy predictors. Results
indicated that individual perception of collective effi-
cacy is a significant and positive predictor of behavioral
involvement across communities and after holding
sociodemographic and risk perception variables con-
stant. On average, a one-point increase in perceived
collective efficacy was found to increase behavioral in-
volvement by 0.07 points. Moreover, education and in-
come were also significantly associated with behavioral
involvement. Perceived risk was negatively associated
with behavioral involvement.
Next, as part of post hoc analysis, a random intercept
and random slopes model was tested. The model
allowed the slope of individual perceived collective ef-
ficacy to vary between two categories of communities:
those with high aggregate collective efficacy and those
with low aggregate collective efficacy. The two cate-
gories were assigned using a mean split. We predicted
that the slope of individual collective efficacy–
behavioral involvement would be steeper in communi-
ties with high levels of collective efficacy as compared to
communities with low levels of collective efficacy be-
cause of a social norm effect. In other words, individuals
in communities with high levels of community collective
efficacy would be expected to be more motivated to be
28 WEATHER, CLIMATE, AND SOCIETY VOLUME 8
involved in their communities, for example, by watching
others similar to them succeed (Table 2).
Results indicated that the community-level random
effects were significantly different at the 95% confidence
level. The slope of collectiveefficacy–behavioralin-
volvement in communities with high levels of community
collective efficacy was slightly steeper compared to com-
munities with low levels of community collective efficacy.
On average, a one-point increase in individual perceived
collective efficacy was associated with a change in be-
havioral involvement of 0.11 points in communities with
high levels of community collective efficacy, versus a
change in behavioral involvement of 20.03 points in
communities with low levels of community collective ef-
ficacy (Fig. 1).
c. Hypothesis 2: Collective efficacy and community
adaptation responses
Similarly, a set of multilevel models was used to test if
self-reported community adaptation responses differ
across communities. The null model—without any pre-
dictors apart from specifying community name as a
random effect—indicated that 47% of the variance in
community adaptation responses can be attributed to
differences at the community level.
Results suggested that community collective efficacy
is a positive and significant predictor of difference between
communities in self-reported community adaptation
responses. On average, a one-point increase in commu-
nity collective efficacy was associated with an increase of
an individual’s self-reported community adaptation re-
sponses by 0.20 points (Table 3).
5. Discussion
The results suggest that collective efficacy is a vital
component of Indians’ adaptive capacity to drinking
water scarcity. Based on Bandura’s social cognitive
theory (Bandura 1997) and previous research (Thaker
2012), two hypotheses about the potential influence of
collective efficacy were tested. Results suggest that
individuals’ collective efficacy is significantly and pos-
itively associated with behavioral involvement to en-
sure drinking water adequacy in their communities.
TABLE 2. Multilevel linear regression model predicting behavioral involvement. Standard errors are in parentheses, ‘‘CE’’ stands for
collective efficacy, n54022, and the number of communities is 138.
Null model Model 1 (random intercepts)
Model 2 (random intercepts
and random slopes–split mean
community CE)
Intercept 1.6 (0.07) 1.64
a
(0.18) 1.88
b
(0.17)
Gender (male) 0.06 (0.04) 0.06 (0.04)
Age 0.01 (0.01) 0.02 (0.01)
Education 0.07
a
(0.02) 0.06
a
(0.02)
Monthly income 20.05
a
(0.01) 20.04
a
(0.01)
Caste 20.06 (0.04) 20.06 (0.04)
Source of drinking water 0.09
c
(0.05) 0.08 (0.05)
Payment for drinking water 20.17
b
(0.06) 20.18
b
(0.06)
Time to collect drinking water 20.09 (0.09) 20.08 (0.09)
Access to sanitation 20.09 (0.05) 20.11 (0.05)
Agriculture land ownership 20.03 (0.06) 20.04 (0.06)
House type 0.01
c
(0.05) 0.08 (0.05)
Rural 20.09 (0.16) 20.09 (0.16)
Perceived risk 20.05
a
(0.01) 20.05
a
(0.01)
Individual CE 0.07
a
(0.01)
Individual CE (high-CE communities) 0.11 (0.01)
Individual CE (low-CE communities) 20.03(0.02)
High community CE 0.14 (0.05)
Low community CE 20.42 (0.09)
Community-level variance 0.73 0.65 0.62
Individual-level variance 1.25 1.22 1.21
AIC 12 645 12 531 12 512
BIC 12 664 12 638 12 632
Log likelihood 26319.5 26248.3 26237.2
Deviance 12 639 12 497 12 474
a
p,0.001.
b
p,0.01.
c
p,0.05.
JANUARY 2016 T H A K E R E T A L . 29
Moreover, post hoc analysis shows that the strength of
this relationship varies among communities, such that
individuals with high levels of collective efficacy living in
communities where others also generally have high
levels of collective efficacy are more involved in their
community adaptation activities than similar individuals
who live in communities with others who have low levels
of collective efficacy. The second hypothesis was also
fully supported: community-level collective efficacy is
significantly and positively associated with community
adaptation responses. Previous research (Thaker 2012)
ignored clustering of individuals within communities, as
well as missing values. Multilevel models, as used in this
study, account for interdependence of individuals’ col-
lective efficacy perceptions within a community. More-
over, results show that the strength of the individual’s
collective efficacy behavior is influenced by community
collective efficacy.
To our knowledge, this is the first study (see Thaker
2012) to provide evidence that, at the individual and
collective levels, perceived collective efficacy predicts
the capacity of communities to adapt to drinking water
scarcity in India. Individuals who are most convinced of
their community’s ability are likely to be the most
FIG. 1. Differences in the individual collective efficacy and behavioral involvement relationship betweenhigh and
low levels of community collective efficacy. Shaded areas represent 95% confidence intervals based on 1000
simulations.
TABLE 3. Multilevel linear regression model predicting community adaptation responses. Standard errors are in parentheses, n54031,
and the number of communities is 138.
Null model
Model 2 (random intercepts
without community CE)
Model 3 (random intercepts
with community CE)
Intercept 0.976 (0.05) 1.434 (0.918) 0.432 (0.891)
Median age 0.067 (0.108) 0.075 (0.102)
Median income 20.022 (0.032) 20.012 (0.031)
Median education 0.054 (0.071) 0.017 (0.067)
Sex ratio 20.001 (0.001) 20.001 (0.001)
Literacy rate 0.007 (0.005) 0.067 (0.005)
Population density 20.001 (0.0001) 20.001 (0.014)
Percentage households drinking 20.006 (0.004) 20.005 (0.004)
Water source outside premises
Community collective efficacy 0.206
a
(0.04)
Community-level variance 0.29 0.28 0.24
Individual-level variance 0.49 0.49 0.49
AIC 8914 8919.5 8902.5
BIC 8932.9 8982.5 8971.9
Log likelihood 24454 24449.7 24440.3
Deviance 8908 8899.5 8880.5
a
p,0.001.
30 WEATHER, CLIMATE, AND SOCIETY VOLUME 8
motivated members in a group and are more likely to be
involved in community activities. Moreover, the col-
lective efficacy–behavioral involvement relationship is
stronger in places with higher levels of community col-
lective efficacy, probably because being part of a highly
motivated community serves as an important normative
cue to community members, resulting in increased in-
volvement in community tasks. In addition, communities
that foster stronger perceptions of collective capabil-
ities among its members are more likely to collectively
organize actions and to overcome obstacles and setbacks,
which can increase the odds of group goal attainment
(Bandura 1997;Goddard et al. 2004).
Findings from this study provide important lessons in
the domain of climate change communication. Efficacy
beliefs are domain-specific constructs, and people’s ef-
ficacy beliefs vary in different domains of activity (water
scarcity adaptation versus saving energy) and at differ-
ent levels of activity (individual versus collective). While
most of the research on collective efficacy has often
featured academic, sport, and organizational settings,
scholars have argued for a need to identify the role of
collective efficacy to help explain behaviors and policy
preferences at the individual and community level of
analysis (e.g., Roser-Renouf and Nisbet 2008). This
study provides evidence that high levels of collective
efficacy are associated with greater individual behav-
ioral involvement in community activities.
Communicating collective efficacy
Increasing public awareness about climate change
risks is an important objective, but without also raising
people’s efficacy beliefs to act on that knowledge—to
act, for example, by performing more climate adaptation
actions—little change is likely to occur. For example,
several public opinion surveys show that although public
awareness of climate change is increasing, such an in-
crease in knowledge levels has not resulted in proactive
public engagement with practices necessary to advance
adaptation and mitigation objectives (e.g., Gifford 2011;
Ockwell et al. 2009;Maibach et al. 2008;Lorenzoni et al.
2007;Whitmarsh 2009;Whitmarsh and Lorenzoni 2010;
also see Witte 1992).
A primary barrier to public engagement of climate
change is the public’s limited understanding of cause
and consequences and, more importantly, the different
ways to mitigate and adapt to climate change. Mass
media, which is the primary source of information on
climate change for most people, often reports the issue
of climate change in the context of natural disasters or
generally emphasizes the catastrophic connotations of
climate change impacts (e.g., Carvalho 2007;Doulton
and Brown 2009;Hulme et al. 2009), with little
information on actions necessary to mitigate the im-
pacts. Mass media in the United States also tends to
focus on skepticism about climate science and un-
certainty about climate change impacts (e.g., Boykoff
2008;Boykoff and Boykoff 2004) and skepticism about
the collective will to address the issue (e.g., Gavin and
Marshall 2011). Such fearful portrayals of climate
change are less likely to motivate positive personal en-
gagement with the issue (e.g., Moser and Dilling 2007;
O’Neill and Nicholson-Cole 2009). A large and sub-
stantial body of literature on fear appeals attests that
‘‘an individual’s perceived sense of action effectiveness
and the individual’s perceived sense of self-efficacy are
imperative for a fear appeal to be successful’’ (O’Neill
and Nicholson-Cole 2009, p. 361; Moser 2010;Moser
and Dilling 2007;Witte 1992).
Communicating the risks of climate change impacts is
important, but without also communicating individual
and collective efficacy to manage those risks, it may be
counterproductive. One of the unanticipated findings of
this study—the negative correlation between perceived
risk and behavioral involvement—is consistent with
protection motivation theory (e.g., Floyd et al. 2000) and
fear appeals literature (e.g., Witte 1992), which suggests
that merely perceiving a high degree of threat alone
will not increase positive behavioral shifts. Increasing
self- and collective efficacy perceptions, for example,
through mass communication campaigns, can poten-
tially strengthen collective efficacy perceptions that may
in turn result in more individual involvement in com-
munity adaptation actions. For example, Morton et al.
(2011) found that efficacy perceptions mediate the effect
of frames (reducing loss versus highlighting loss) on
behavioral intentions. Several mass media interventions
to enhance perceived individual and collective efficacy
levels have resulted in substantial benefits, ranging from
increasing literacy rates, promoting family planning, and
changing social norms about women in traditional so-
cieties (e.g., Bandura 2001;Singhal and Rogers 1999;
Singhal 2004). For example, a postcampaign evalua-
tion of Yeh Kahan Aa Gaya Hum (Where have we
arrived?), a campaign to promote environmental pro-
tection, found that radio listeners self-organized into
groups to promote proenvironmental behaviors such as
improving sanitation by building pit latrines, tree-
planting campaigns, and reducing air pollution from
vehicles waiting at railway crossings (Papa et al. 2000).
Several social scientists have already found that
‘‘barriers to community or individual action do not lie
primarily in a lack of information or understanding
alone, but in social, cultural, and institutional factors’’
(Tompkins and Adger 2004, p. 4). Primary among such
barriers are perceived beliefs about self and collective
JANUARY 2016 T H A K E R E T A L . 31
competencies (e.g., Grothmann and Patt 2005;Lorenzoni
et al. 2007). The findings of this study suggest that in-
creasing collective efficacy beliefs through mass media
channels, and providing communities withmore resources
to manage their local water problems, can have a positive
impact in increasing Indian communities’ adaptive ca-
pacity to drinking water scarcity (see Thaker 2012).
This study built on the first author’s Ph.D. dissertation
and tested, using more appropriate multilevel models, if
collective efficacy perceptions can play a central role in
increasing communities’ adaptive capacity (Thaker
2012). Potential limitations of the study include an in-
ability to establish causality because of the cross-sectional
nature of the data. In addition, the study relied on self-
reported community adaptation responses, which may
not reflect objective assessments of adaptation. Future
research should build on these findings using panel sur-
veys and could use government data such as the number
of water conservation activities undertaken in a com-
munity to decrease self-reporting bias for community
adaptation responses. Moreover, in addition to perceived
risk tested in this paper, future research could also in-
clude other critical variables such as values (e.g., Schultz
and Zelezny 1999;Steg et al. 2012), cultural orientations
(e.g., Markus and Kitayama 1991), and social capital
(Aldrich 2010) to test the relative importance of collec-
tive efficacy and values in enhancing adaptive capacity.
Acknowledgments. Funding for survey data collection
was provided by the Shakti Sustainable Energy Foun-
dation and the Rice Family Foundation. The survey was
conducted by the Yale Project on Climate Change
Communication in collaboration with GlobeScan In-
corporated. Fieldwork inIndia was conducted by C-Voter
and Markelytics. This paper is based on the first author’s
doctorate dissertation, with additional analyses and
discussion included, and one additional investigator
added to the team.
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