Journal of Natural Disaster Science, Volume 36, Number 1, 2015, pp13-24

Where do Individuals Seek Opinions for Evacuation?
A Case Study from Landslide-prone Slum Communities in Mumbai
Subhajyoti SAMADDAR*
Hirokazu TATANO**Disaster Prevention Research Institute, Kyoto University, Japan.
(Received January 28, 2015 Accepted May 13, 2015)
ABSTRACT
Thousands of residents living in slums on small hillocks in Mumbai are at risk due
to recurring landslides. The evacuation of these dwellers from the hills before the rainyseason is considered essential to avoid the landslide risks. Although the city government
has made several efforts to encourage residents to evacuate, few are willing to do so. The
city government is, therefore, seeking effective risk communication strategies to encourage
more slum dwellers to evacuate. Given this challenge, the present study examines thefactors related to residents’ evacuation behavior including their risk perception andevacuation intention. Unlike previous studies on household disaster preparedness behavior,
which are predominantly cognitive modeling based and fail to address collective and social
accounts of human behavior, the present study examines how various societal factors,
such as social networks and group norms govern household evacuation decisions. Ourfindings corroborate the hypothesis that social networks do play an important role in theevacuation decision. The study found that residents’ risk perceptions are shaped to a great
extent by their neighbors and cohesive group partners with whom they have direct andstrong interconnections. However, residents’ intentions to evacuate are largely influenced
by the people who are indirectly and weakly connected with them, such as the members ofsocial groups based on religion, caste and language. Some policy implications related to thefindings are discussed.
Keyword: Evacuation, Social Networks, Landslide, Mumbai

1. Introduction

evacuation of those dwellers before the rainy season isconsidered to be instrumental to reducing the life risksof thousands of people. The local government hasexerted enormous efforts to evacuate people fromhillocks before the rainy season, but in reality, few haveagreed to follow the recommendation. The city
government is struggling to produce effective risk
communication strategies to address this challenge.
Toward this end, a comprehensive understanding on
the evacuation behavior of households is an urgent

In Mumbai, thousands of people living on small
hillocks are at risk due to landslides. These hillocks are
the residences of thousands of poor and sociallymarginalized populations who cannot afford formal
housing in the financial capital of India. Dense,haphazard and unplanned construction as well as poor
infrastructure cause soil erosion and hence enhance therisks of landslides, particularly in the rainy season. The
13

S. SAMADDAR, H. TATANO

requirement.
Most studies on evacuation and other disaster
preparedness behavior follow a cognitive or heuristic
approach. Some of these prominent heuristic models
are the Protection Motivation Theory (Rogers, 1983),
Planned Behavior (TPB) (Ajzen, 1991), and Person
Relative to Event Theory (PRE) (Duval and Mulilis,
1999) etc. These models consider the risk preventive
behavior of individuals as merely a reflection of the
dynamic interactions of various intellectual variables,such as risk perception, self-efficacy, outcome
expectancy and many others. It is assumed that anindividual makes a decision as an atomized unit, and,
therefore, his/her information collection andprocessing activities take place in social isolation.
These models emphasize only the internal factors andthe role individuals play in their own behavior. The
influence of social groups and organizations in an
individual’s decision-making process remains
untouched in cognitive-based models. The serious
intellectual lacuna in the heuristic-based approach isthat it ignores the critical role of various social andcollective factors including group affiliation, cultural
practices, socialization processes, social change andso forth in the individual adoption of the decisionmaking process (Scherer and Cho, 2003; Bhandari etal., 2011). It is argued that cognitive-based models
fail to provide adequate accounts for preparedness
behavior as the world continues to see examples oflow preparedness that cause the loss of life anddamage to property. Field studies across the world
report that evacuation readiness remains low among
disaster-prone communities (Sagala et al., 2009;Samaddar et al., 2014). Planners and practitioners
who are at the sites are struggling to produce effective
strategies to encourage households to adopt preventive
measures (Sagala et al., 2009). Therefore, there is anurgent need to look beyond the interpretations ofevacuation behavior provided by the cognitive-basedmodels.
An alternative and supplementary approach could besocial network analysis, which postulates that
individuals make an adoption decision based on theirrelation with others in society, their social position insocial network(s) and observing the behavior of peers,
neighbors and relatives with whom they are sociallyconnected. Frequent interaction or social roles provide
shared contexts for interpreting prior behavior andattitudes that influence subsequent attitudes (Deanand Brass, 1985). Empirical observation suggests that
individuals make decisions not in social isolation, but
in interaction with others (Townshend et al., 2014).
Social intimacy, frequent interaction, and social
norms all create shared contexts for information
sharing and can be used to interpret prior behavior
and attitudes that influence subsequent attitudes andbehavior (Sagala et al., 2009; Bhandari et al., 2011;
Townshend et al., 2014). Studies using this line ofpursuit in disaster and other forms of risk management
are rare (Scherer and Cho, 2003; Muter et al., 2013).
Therefore, this study examines the evacuation
behavior of Mumbai slum dwellers under landslide
risks in light of the diffusion of innovation and social
network approaches to provide a comprehensivesocial account of the evacuation behavior.
1.1 Roles of Social Networks on Households’
Evacuation Decisions
In the present study, we consider evacuation as aninnovative idea that members of the community haveto adopt in order to reduce their disaster risks. The
diffusion of innovation studies considers that the
adoption of an innovation is not an instantaneous
process, but there are several phases before an
individual reaches a decision (Rogers, 1983b).
Diffusion of innovation studies (Rogers, 1983b)
postulate that the innovation adoption process is acombination of three critical junctures or phases –individuals learn about the innovation; then theydecide that innovation would be desirable or useful;and finally they adopt the innovation. In the disasterrisk context, previous research has shown that there isanother critical stage called risk appraisal in the
adoption process of disaster preventive innovation(Paton, 2003). Before considering adopting anevacuation plan, an individual must first conclude thatthe hazard specifically affects him or her. If an
individual believes the hazard can adversely affecthim or her, the individual will look forward to
discovering possible actions and measures (Rogers,
1983; Paton, 2003). Therefore, to adopt an evacuation
plan, an individual may go through the following
stages – whether or not a disaster risk exists, whether
or not evacuation is effective and then develop an
intention to evacuate and finally actual evacuation.
Social network theory postulates that in the process of14

WHERE DO INDIVIDUALS SEEK OPINIONS FOR EVACUATION? A CASE STUDY FROM LANDSLIDE-PRONE SLUM COMMUNITIES IN MUMBAI

adopting a new technology or knowledge, anindividual observes the behavior of other members ofa community (Valente, 1995). Learning from otherexperiences through social interactions helps
individuals to make appropriate decisions based on
their own subjective reality. Becker (1970) mentionedthat to make or develop a decision, particularly tomake an adoption decision of new technology, social
networks help an individual in three ways – first, toprovide information about an innovation that
otherwise an individual might have missed; second, toprovide social support for an individual’s adoption
decision and thus to legitimize the innovation; third,
to create social influence on an individual to accept or
reject the innovation. Studies on attitude change
conclude that group norms have a strong influence on
information, values, perceptions, norms and behavior,such as adopting an innovation (Zaltman and Duncan,1977). Therefore, the role of social networks in the
diffusion of the innovation process is important,partly because networks are considered to provide
opportunities to exchange information andknowledge; and also because social networks impose
constraints on behavior. The Social Network
Threshold Model of Collective Behavior (Granovetter,1978) postulates that in order to reduce theuncertainties of adopting an innovation, individuals
observe the behavior of other members of thecommunity and for that reason an individual’s
adoption behavior is a function of the behavior ofothers in a group or community (Granovetter, 1978).
Therefore, it is logical to argue that an individual’srisk perception, evacuation outcome expectancy andfinally evacuation intention are constructed andshaped by his/her social network types and nature.

network (Valente, 1995). For example, individuals
who belong to the same group and interact frequently
with each other may have similar risk perception andadoption opinions (Townshend et al., 2014; Scherer
and Cho, 2003). Burt (1987) found that an individual’s
decision-making process is influenced by thosehaving structurally similar positions. Conversely,
Granovetter (1973) showed that weaker social ties are
more important in obtaining information on
innovations. Therefore, exploring who influencesindividuals in making decisions on various aspects ofevacuation would further expand our understanding
on individuals’ evacuation behavior. In this study, thefollowing social networks and groups are consideredcentral to shaping individuals’ risk perception andformulating evacuation intention.
Cohesive Group: Several scholars have argued thatpeople are influenced most by the cohesive groupwith whom they have very frequent, intensive anddirect social relations (Coleman et al., 1975). A
cohesive group is marked by the degree ofinterpersonal contacts or ties of its members.
Individuals have various direct and indirect social ties
with others, but their cohesive group consists of thosewith whom they have the highest social interaction.
Social network studies have reported that cohesive
group members foster more intense information
sharing than amongst non-cohesive members(Wasserman and Faust, 2006). The direct relation
between cohesive group members not only provides
channels for quick information flow, but often
influences individual perception, such as thatexemplified in the risk perception case by Scherer andCho (2003). Cohesive group members share anddiscuss an issue together and help each other to reachdecisions (Coleman et al., 1957; Becker, 1970). It isreported that cohesive group network partners play asignificant role in obtaining new ideas and puttingsocial pressure (both negative and positive) on theinnovation adoption decision (Gayen and Raeside,2010). Conversely, some researchers argue thatcohesive groups inhibit the infiltration of new ideas
and only provide redundant information (Granovetter,1973).
Neighborhood and Spatial Group Networks: Thebehavioral similarities of individuals are common
when they live in geographical proximity (Rogers,
1983b). Physical proximity allows individuals to

1. 2 Types of Social Networks and Behavioral
Patterns
Individuals are connected to different types of social
networks and all such networks play a critical role inthe decision-making processes of individuals (Valente,1995). Sometimes, the extent of an individual’s social
network is confined within primary social relations,such as extended family and kinship structure, friends
and co-workers. But one’s social network may extend
at a greater social system level, such as city, state etc.
Social network studies have found that individuals are
influenced by many actors in the social system or
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S. SAMADDAR, H. TATANO

population of the city (Samaddar et al., 2015). A large
number of such urban poor have illegally constructedhouses on the slopes of abandoned and vulnerable
hills in sub-urban areas including Ghatkopar,
Sakinaka, Vikhroli, Bhandup, Chembur, which are
hilly terrains, i.e. mostly small hillocks. These houses
do not have proper foundations and are constructedwithout consideration of any engineering parameters;
in fact, most of such houses are made from temporary
materials (called semi-pucca or kuchcha houses)(Emmel and Soussan, 2001; Samaddar et al., 2015).
Kale (2009) posits that most of the risk-prone areas
are at the foot and edge of hills. Haphazard andunplanned building construction and poor
infrastructure cause soil erosion and hence increase
the risks of landslides. The absence of trees in thearea also has an adverse impact on the binding
capacity of the soil and thus poses further risks. This
leads to a huge life threat to the people living in theselandslide-prone areas, especially during themonsoons. About 9.5% (almost 1 million people) of
the population of Mumbai _ lives in a landslide-prone
area (41 sq. km) (School of Planning and Architecture,2010). Almost every year, landslides take place in
Mumbai. Major landslides occurred in Mumbai in
2000, 2005, 2009 and 2010 killing many anddestroying many shanties (Kale, 2009; School ofPlanning and Architecture, 2010). The landslides
cause small chunks of land from the hillocks to fallfrom above, but as the areas are very densely
populated, this results in the collapse of shanties andthe deaths of local people.
Recently, the local government, the Municipal
Corporation of Greater Mumbai (MCGM), which issolely responsible for the city’s disaster risks
management, has identified landslide-prone areas andset up guard walls around hillocks (Kale, 2009).
However, all the hillocks could not be guarded due tothe densely populated nature of the areas and funding
shortages. Alternatively, evacuation of the citizens
from landslide-prone areas is considered the mostviable solution to reduce risks. Since 2006, every
year, just before the monsoon, MCGM has issued anevacuation order in landslide areas, particularly tohouseholds within the landslide risk zone, that is, thefoothills and edges of the hills (Kale, 2009). MCGM
has regularly organized door-to-door campaigns,
putting up posters and notices in public places, andobserve, learn about other experiences and share ideas
and values with each other. These interactions help anindividual not just to become aware of an innovation,
but also to minimize risks, such as the disadvantages
associated with the innovation adoption decision(Samaddar et al., 2014b). Individuals who live in thesame neighborhood generally have greater social
interactions and their adoption behavior is channeledthrough social learning and social influence generated
through neighborhood membership (Rogers, 1983b).
Based on the above discussion, this study investigateshow various social network groups influence thehousehold evacuation decision-making processincluding risk perception, evacuation outcome
expectancy, and evacuation intention in landslideprone slums in Mumbai, India.
2. Background: Landslide Risks in Slums on
Hillocks, Mumbai
The island city of Mumbai is located on the western
coast of India and is considered the commercial
capital and economic growth center of the country.
Originally composed of seven small islands, land
reclamation and infill conducted during the 18th and19th century integrated these islands into a continuous
peninsula (Someshwar et al., 2009). This city
originally developed as a seaport on the west coast of
the Indian peninsula; then in the 19th century diverse
economic activity and growth occurred, particularlyin the manufacturing and financial sectors (Deshpandeand Arunachalam, 1981). Today, Mumbai isconsidered as not only the financial capital of India,
but also an important business hub of south Asia.
Mumbai city contributes over 25% to the country’s
tax revenues and generates about 5% of India’s Gross
Domestic Product (GDP) (Gupta, 2007). With therapid industrialization and economic boost, people
from all over India come to the city in search of jobs.
Hence, intra and interstate migration increased thecity’s population from 7.7 million in 1971 to 18.3
million in 2001 (Census of India, 2001) and this is
projected to increase to 22.4 million by 2011
(MMRDA, 1999). A great number of those migrants
are poor farmers or landless laborers from rural areas,
who cannot afford formal housing in Mumbai. Highrents force them to choose abandoned, fragile, andhazardous housing, which constitutes 65% of the16WHERE DO INDIVIDUALS SEEK OPINIONS FOR EVACUATION? A CASE STUDY FROM LANDSLIDE-PRONE SLUM COMMUNITIES IN MUMBAI

distributed handbills to ensure the evacuation ofhouseholds living in those landslide-prone slums.
However, on September, 2009, after the city
Government’s initiation of landslide risk reduction
initiatives, 12 people were killed and 13 others injured
in a landslide in a Mumbai slum. Many more smallscale landslides and injuries are reported. According
to the MCGM field engineers and local newspapers,
the evacuation rate is still very low, and the loss ofhuman life and damage to assets is increasing every
year. The low evacuation rate raised major criticalquestions, such as - Are the local communities
worried about landslide risks when, as slum dwellers,
they face so many other risks daily, such as job risk,
shelter risk, security risk? Do they believe evacuation
is a useful and meaningful countermeasure when they
have few socio-economic resources to execute any
such disaster preparedness plan? We urgently need to
expand our understanding of these questions andexamine the underlying factors influencing thecommunity’s evacuation behavior.

the patchworks of communities already established on
the hillside. The infrastructure of the settlement is invery poor condition. There are no roads or drainage
systems. The lack of proper drainage systems,
sanitation facilities and water supply is accelerating
the landslide likelihood and susceptibility of the area.
Landslides are common phenomena in this area;major landslides observed in the area occurred in theyears of 2000, 2001, 2005, 2009, and 2010. In the2000 landslide, 67 people died. Before the monsoon,
the local Ward office sent out a notice to every
household imploring them to evacuate their houses. In
some part of the slums, the Mumbai Metropolitan
Regional Development Authority (MMRDA) had set
up guard walls. However, the guard walls could not
be set up for all the hillocks due to the densely
populated condition of the areas and funding
shortages. Alternatively, evacuation of the citizens
from landslide-prone areas is considered the mostviable solution to reduce risks. Since 2006, every
year, just before the monsoon, MCGM has issued
evacuation orders in the landslide areas, particularly
to households in the landslide zone, that is, thefoothills and edge of the hills (Kale, 2009). MCGM
has regularly organized campaigns through door-todoor visits, posters in public places, and the
distribution of handbills to ensure that slum dwellers
threatened by landslides evacuate in time.

2.1 Case Study Area: Landslide-prone slums in
Ghatkopar (West)
The data for this study were collected through fieldsurveys in a landslide-prone slum community in
Ghatkopar (West) in L-Ward, Municipal Corporationof Greater Mumbai (MCGM). This landslide-prone
slum in Ghatkopar (West) consists of several small
clusters of information settlements or slums on
hillocks, namely Himalaya Society, Milind Nagar,
Netaji Nagar, Adarsha Society, Jai Santoshi Mata
Nagar, Sidhart Nagar, and Rajiv Nagar. The total
population of the entire area is approximately 25,000,
living in 4000 huts. Oral history has revealed that in
the 1970s the present settlement was uninhabited
scrubland on which a number of communities
gradually built a village-like settlement. People fromdifferent parts of India have come and built theirhouses in this slum. Several of the communities
moved from squatter sites and pavement dwellings in
the city of Mumbai. Other groups were new migrants
to the city from different states. Communities
involved in the initial establishment of the slum
consolidated their position within it and increased insize by drawing in relatives and caste groups fromtheir rural locality. At the same time, more groups ofmigrants moved in to take over the spaces between

3. Method
3.1 Field Survey
This study employed the snowball sampling method
to obtain social network data and information. A
snowball sample is a non-probability samplingtechnique that is appropriate to use in research whenthe members of a population are difficult to locate(Wassermann and Faust, 2006). A snowball sample is
one in which the researcher collects data on a few
members of the target population he or she can locate,then asks those individuals to provide information
required to locate other members of that population
whom they know. Those participants then suggest
additional participants, and so forth, thus gathering
volume like a snowball rolling down a hill. In thisstudy, each respondent was asked to name their social
network partners (including friends, relatives, withwhom they share an intimate relationship) who live
17

S. SAMADDAR, H. TATANO

within the landslide-prone area. The individuals (headsof the households) named in the first round ofinterviews then became the sample of the second
round. This referral process continued until no newindividuals were named.
In order to obtain social network data and conduct thesnowball survey method, it was necessary to fix thegeographical boundary of the survey area and makeprospective respondents aware of it. Therefore, before
conducting the field survey, we attempted to acquire
the landslide zoning map prepared by the local ward
office (L-Ward, Municipal Corporation of Greater
Mumbai). Simultaneously, we attempted to locate thelandslide zoning boundary on the actual site
demarcated by the Ward office. However, the zoning
map was not available and the onsite boundary
markings were not clearly visible at the time of thesurvey. Therefore, we prepared our own landslide
zoning map with the help of field engineers andofficials of the local municipal ward office. For this,
we first gathered a basic map available from the ward
office and asked the local ward office (L-Ward) tomark the landslide-prone areas as officially declared.
Once the map was prepared, our research team,including the first author of the paper, walked along
the boundary of the landside zone, took photographsof local landmarks, such as large trees, religious
buildings, tall public buildings etc. and simultaneously
marked them on the map. Thus, the landslide map andlocal landslide markings were ready before the fieldsurvey was conducted.
Before conducting interviews, surveyors first
introduced the goals and significance of the survey toprospective respondents. If a prospective respondent
agreed to answer the questions, the surveyor thenintroduced the landslide zoning map along withphotographs of local landmarks. Afterwards, thesurveyor first confirmed from each respondent that bylooking at the photographs and maps they could
recognize the total area coming under the landslidezone. In instances where respondents could notunderstand the map (12 such cases occurred), thesurveyor reintroduced the map and photographs.
Heads of households were the target respondents andif the head of the household was not at home during
the first visit, information about his/her availability
was collected and noted; the visit was repeated up to
two times. If the selected household’s head could not
be reached after these repeated visits, another elder
and important decision-maker of the household, forexample the wife, was selected for the interview. The
language used during the interview was Hindi, which
was considered to be the most appropriate in alinguistically heterogeneous and cosmopolitan
Mumbai urban community. Research assistants withdata collection experience were recruited and trainedfor 3 days involving lectures, mock interviews, andpretests to ensure the quality of data. The fieldprocedures were also closely monitored by theprincipal investigators, including the first author of
the paper, to ensure that the field assistants adhered tothe procedures laid down. A total of 86 individuals
were interviewed in the survey between May and Junein 2012.
3.2 Data and Methods of Analysis
Dependent Variables
Based on the study objectives, we collected threedependent variables – landslide risk perception,
evacuation outcome expectancy and evacuation
intention. Table 1 shows the questionnaires orquestions used for data collection of dependent

Table 1: Questionnaires Used for Dependent Variables a
Variable

N

Mean

SD

Landslide Risk Perception Are you worried that a landslide will take place again
and cause loss and damage to your life and property?

86

3.46

1.18

Outcome Expectancy of Do you think evacuation is an important and effective
Evacuation
countermeasure to reduce risks of landslide?

86

3.74

1.19

86

2.19

1.15

Evacuation Intention

Questionnaire

Do you want to evacuate before the next rainy season
if an order is issued by the BMC (Bombay Municipal
Corporation)?

a

All items were measured on a 5-point Likert-type scale; 5 = Absolutely/Obviously, 1 = Not at all.
18

WHERE DO INDIVIDUALS SEEK OPINIONS FOR EVACUATION? A CASE STUDY FROM LANDSLIDE-PRONE SLUM COMMUNITIES IN MUMBAI

variables. The matrix for each dependent variable wasconstructed using a “Euclidean Distance” method,which is discussed in detail later in this section.
Independent Variables
Cohesive Group: The composition of the cohesive
group is determined by the degree of interpersonal
contacts or ties of its members. For data on generalinterpersonal ties, we collected sociometric data on
households’ personal interaction in day-to-day life.
Respondents were asked: “Please name three
individuals in the area within this landslide zone in
Ghatkopar area with whom you most often interact andshare spare time in your daily life.” These sociometricdata (See Fig. 1) were used to group the individuals
into cohesive groups. The social network matrix wasformed in such a way that cell entry Xij equaled one if
actor i selected actor j for the social interaction. For
example, if actor i mentioned his or her daily social
network partner was actor j, the cell entry Xij equaled
one, and all other entries equaled zero.
These sociometric data were used to group theindividuals into cohesive groups, as shown in Fig. 1,
using the FACTIONS method technique by running
UCINET Social Network Software 6.0 version 1.00

Actor

(Borgatti et al., 2002). The FACTIONS routine in
UCINET takes the bipartite graph as input and uses a
combinatorial optimization algorithm called Tabu
Search (Glover, 1989) to assign nodes to as manyclusters as hypothesized by the user (researcher) so as
to maximize a fit criterion. The fit criterion is acorrelation between the observed data and idealized
pattern in which the density of ties between groups is
0% (Borgatti and Everett, 1997).
Neighborhood Networks: To construct neighborhood
networks, we used the records of individuals’
neighborhood affiliation, that is locally known as a
Chawl. Thus to formulate the neighborhoodsociomatrix, cell entry Xij equaled one if two actors
belong to the same neighborhood, and zero if they havedifferent neighborhood affiliations. The number ofneighborhood groups is 6; sizes of groups are 16, 20, 7,
8, 13, and 12.
Cultural Affiliation: To recognize the cultural
affiliations of individuals, three types of culturalgroup data were collected including religion,
linguistic group and native district place (fromwhence the individuals originally came). These data
were first used to formulate an affiliation matrix. The

Tie

Figure 1: Interpersonal contacts and cohesive (FACTIONS) group patterns in a landslide-prone slum,
Ghatkoper, Mumbai. (The number of cohesive groups is 17. Sizes of groups are – 2, 3, 4, 4, 4, 4, 4, 5,
5, 5, 5, 6, 6, 6, 6, 6, 8. The location of nodes (actors) is random. The color indicates the groupaffiliation of actors).
19

S. SAMADDAR, H. TATANO

affiliation matrix records the affiliation of each actor
with each event or group affiliation. The affiliation
matrix, A = {aij } , is a two-mode sociomatrix inwhich rows represent actors and columns represent
events or group affiliations (in the context of thepresent study, this is cultural background, such as thename of a particular religion, or the name of the placefrom whence the individuals migrated). There is anentry of 1 in the (I,j)th cell if row actor i is affiliatedwith column event j, and an entry of 0 if row actor i is
not affiliated with column event j. Each row of thematrix indicates an actor’s affiliation with a particulargroup. This two-mode affiliation matrix data was thenconverted to one-mode sociomatrix data by using
UCINET Social Network Software 6.0 version 1.00
(Borgatti et al., 2002). In the affiliation sociomatrix, if
actor i and j are both affiliated with event, k, then aij= ajk = 1. The number of co-memberships for actors
i and j is equal to the number of times that ajk = ajk =1 for k = 1, 2, …, h.
In addition, four control variables were included ineach regression test; these are education level, periodof stay in the area, household size, and housing
typology. Education level, age and period of stay were
measured as continuous variables. The study foundthat 53% of respondents had primary education, 19%
had attended class 8 or junior high school, 13% had asecondary degree (up to class 10) and only 6% had ahigher secondary degree (class 12). The average
period of stay in this area was established as 10 years(minimum 4 and maximum 30 years). The average
household size was 5 (Minimum 2 and Maximum 11).
The sociomatrices for these three variables wereconstructed using the “Euclidean Distance” method
through UCINET Social Network Software 6.0

version 1.00 (Borgatti et al., 2002). For example,
number “5” was entered in cell xij if member i in a
dyad has lived in the place (period of stay) for the last
15 years (coded as 15) and the other dyad member, j,
has lived in the place for the last 10 years (coded as10). The higher the number in cell Xij, the greater thedifference in period of stay between members of the
dyad. The same rule is followed for two other variables,
education and household size. Housing typology wasmeasured through observation. We found three
predominant housing typologies - Kachha Ghar
(Thrash Housing ) = 41%; Semi Pacca Ghar (Semiconcrete Housing) = 36%; and Pacca Ghar (Brick andCement Housing) = 23%. For the sociomatrix ofhousing typology, cell entry Xij equaled one if two
actors had the same housing type and zero if they were
not the same.
We used the Quadratic Assignment Procedure (QAP),a multi-regression technique, provided by UCINET
Social Network Software 6.0 Version 1.00 (Borgatti etal., 2002) to determine how various social networks
influence individuals’ landslide risk perception,
outcome expectancy of evacuation and evacuation
intention. This approach is similar to ordinary
multiple regression. However, it enables analysis of
matrix data. The equation used was Y = B0 + B1
(cohesion) + B2 (cultural affiliations) + B3
(neighborhood networks) + B4 (period of stay) + B5
(household size) + B6 (housing type) + B7
(education).
4. Results
The QAP multiple regression analysis result displayedin Table 2 shows that individuals’ landslide risk

Table 2: Matrix Regression Analysis (QAP) Results Showing Standardized Beta
Risk Perception

Evacuation Outcome Expectancy

Evacuation Intention

Cohesive Networks- .042**- .034**- .010Cultural/ Social Affiliations.011- .078**- .194****Neighborhood Networks- .132****

.020

.021

Period of Stay in the area

.013

.251****.163***

Household Size

- .013

.060

.057*

Education- .040- .002.022

Housing Typology

.025

.020

- .031R- Square

.026

.074

.068

*P> .05; **P> 0.01; *** P> 0.005; ****P> 0.001
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WHERE DO INDIVIDUALS SEEK OPINIONS FOR EVACUATION? A CASE STUDY FROM LANDSLIDE-PRONE SLUM COMMUNITIES IN MUMBAI

perception is significantly associated with two factors,
neighborhood networks and cohesive group networks;
no other factors are significant. People, who live inthe same place or neighborhood, perceive risk in asimilar fashion, whereas outcome expectancy, that is,perceived effectiveness of evacuation in reducing
landslide risk is most strongly associated withindividuals’ cultural and social affiliation. Two otherfactors significantly associated with outcome
expectancy are period of stay and cohesive networks.
In the case of evacuation intention, cultural affiliations
and period of stay are both strongly significant
denominators. Household size is also significantly
associated with evacuation intention. Individuals
living for a long time in the area and having large
household size do not intend to evacuate.

However, the connection between cohesive networks
and risk perception could be observed from theperspective that neighborhood members are often
cohesive group partners. Living in geographicalproximity may foster social association and bonding
among individuals. One’s perception about thepossibility of landslide or possible losses is influenced
by friends, relatives and co-workers living in the sameneighborhood. The association between risk
perception and interpersonal networks has also beenreported by Scherer and Cho (2003) and very recently
by Muter et al. (2013).
Evacuation Decision and Social Networks:
Though landslide risk perception is very high among
the slum dwellers, only a few like to evacuate andbelieve evacuation would be an effective mechanism
to reduce the risk to their life and property. Like risk
perception, residents’ beliefs about the effectivenessof evacuation (outcome expectancy) are greatly
influenced by their cohesive group partners, but whenit comes to intention to evacuate, the influences ofcohesive groups or direct networks are insignificant.
There are other factors that influence the residents’evacuation intention – cultural affiliation, period ofstay and household size. Why it this so? Possible
explanations could be that those living in the area fora long time are well settled in the slum in terms of
having permanent housing, a steady source of income,and greater household assets. Moreover, a great senseof belonging exists among the residents that have
lived in those hillocks for a long period of time.
Therefore, evacuation is not a better choice because
evacuation during the rainy season every year may
disrupt these establishments and bring more intense
livelihood challenges to those families. Some slum
dwellers reported that because of long absence ofresidence due to evacuation, gangs and new settlers
will come and occupy their houses and land and cantake over their property. Legitimacy of tenure isalways an issue for the slum dwellers in Mumbai(Parthasarathy, 2009), and consequently, relocation,even a temporary one, aggravates the insecurity oflosing property rights. Therefore, the relatively
permanent residents are unwilling to evacuate,
whereas the recently-migrated households have amore favorable attitude towards evacuation.
The significant association between household sizeand perceived effectiveness of evacuation is obvious

5. Discussion:
The results show that the evacuation decision-making
processes of slum dwellers are greatly influenced bytheir social group and social network partners
including cohesive groups, socio-cultural groups andneighbors. Dwellers in those landslide-prone hillocks
very rarely make decisions to evacuate just from theirown individual perspectives in social isolation, butthey mutually influence each other, discuss withothers and pass opinions to fellow residents. The
present study reveals the following important
findings.
Risk Perception –The landslide risk perceptions of slum dwellers arestrongly associated with two factors – theirneighborhood affiliations and cohesive group
networks. Neighborhood association was the mostinfluential factor for formulating risk perception.
Landslides in these hillocks are locally confined andless catastrophic; as a result, the entire area is notequally affected. The frequency and magnitude oflandslides vary from one neighborhood to another,even though the slums are not very large. The
settlements or neighborhoods located at the foot andedge of the hill experience more frequent and severe
landslides. Higher frequency and greater severity oflandslides, therefore, induced higher risk perception
among the inhabitants of those neighborhoods than
the neighborhoods experiencing less frequent andminor landslides.
21

S. SAMADDAR, H. TATANO

as the heads of the large households need moreresources and alternative livelihood arrangements due
to evacuation, particularly when the evacuation is fora relatively longer period of time.
More interestingly, social networks play strong roles
in shaping individuals’ evacuation decisions also, but
the type of social networks that were critical in
shaping an individual’s risk perception, was notimportant in shaping people’s evacuation intention.
Direct and strong social ties confined within aparticular geographical boundary, such as aneighborhood, are critical in shaping residents’ risk
perception, but not so significant in influencing theirevacuation decisions. To make an evacuation decision,a resident in those hillocks in Mumbai depends onlarge, widely distributed social groups or networks
based on religion, language, caste and ethnic identity.
The possible reasons could be that these cultural orsocial networks, such as religious groups or linguistic
groups, provide opportunities and support duringresettlement and other emergencies. During theevacuation or temporary relocation, religious, culturaland ethnic groups work as lifelines to render support
for obtaining alternative work, finding temporary
shelters, and childcare support, which are necessary,
but difficult to arrange from any other sources forthese marginalized slum communities. The city
authority and non-governmental originations do notprovide such means to these socio-economicallymarginalized communities (Parthasarathy, 2009).
Therefore, cultural and social networks are the only
social capital that replace those needs and play adecisive role in household landslide evacuation
decisions.

investigated the factors accountable for household
evacuation decisions in a landslide-prone slum in
Mumbai. Unlike previous studies, most of which are
cognitive based and consider that individuals makedecisions as an isolated atomized unit, the present
study explored how various social networks andgroups influence an individual’s evacuation decision.
Our findings corroborate the hypothesis that social
networks do play an important role in the evacuation
decision. More specifically, we found that individual
risk perception is shaped to a great extent byneighbors and cohesive group partners with whom theindividual has direct and strong interconnections indaily life. On the other hand, we observed that whenit comes to making decisions for action, such as theevacuation intention, individuals seek informationand follow the opinions of wider social network
partners such as cultural and social groups based onreligion, caste and language.
Policy Implications The most significant implication of this study is that it
is a pioneering attempt to analyze the households’
evacuation decisions beyond the limit of individual or
cognitive perspective. It also investigates how
individuals are influenced by social networks andsocial norms that affect personal beliefs on disaster
preparedness. Therefore, the research on evacuation
behaviors and other disaster preparedness should not
be confined to collecting data on individual
demographics and cognitive characteristics, but also
include information on respondents’ social networks.
The other planning implications that can be drawn
from the present study are that since individuals learn
from and are influenced by their cohesive and sociocultural groups, the city authority may intensify theirdialogues with neighborhood groups and local leaders
in order to locate which areas are more landslide
prone and where these people can evacuate.
Since the city government does not provide any
temporary shelters for evacuation, it would be
effective to provide support to help find alternative
temporary housing and jobs. Providing such support
would be easier if the city government soughtcollaboration with cultural, ethnic and religious groupbased originations.
In this regard, a local participatory platformcomprising local ethnic representatives and leaders
can be formed to intensify the dialogues between the

6. Conclusions and Implications
Evacuation is essential for saving the lives of
thousands of slum dwellers living in landslide-prone
hilly terrain in various parts of Mumbai City, India.
H o w e v e r, t h e c i t y g o v e r n m e n t h a s r e c e n t l yacknowledged that even after investing numerous
efforts and continued intervention, the evacuation ratein those hilly regions is inadequate. The city authority
is, therefore, deliberately seeking plans and strategies
to enhance its risk communication mechanism toensure the planned evacuation of thousands of slum
dwellers. Given this challenge, the present study
22

WHERE DO INDIVIDUALS SEEK OPINIONS FOR EVACUATION? A CASE STUDY FROM LANDSLIDE-PRONE SLUM COMMUNITIES IN MUMBAI

city authority and local residents. The success stories,
such as those evacuated previously, can be madeavailable to others to have them understand how theevacuees were able to manage their lives andlivelihood during the evacuation. This may encourage
the non-evacuees.
Since, there exists a sense of loss of property rights,
the city authority must ensure that the residents canbe resettled in the original place without any politicalor social harassment. It seems that mass-media andleaflet distribution would be an effective mechanism
to encourage residents to evacuate, but the emphasisshould be placed on personal and two-way
communication between residents and the city
government.

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