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CHAPTER 3
DISASTER RISK PERCEPTION
3. INTRODUCTION
Risk perception among the communities is an important determinant of the behavior
towards disaster risk reduction. There is an underlying belief that perceptions steer
decisions about the acceptability of risks and influence the behavior during and after the
disaster. Impact of natural disasters on local communities varies with their understanding
and appraisal of risk exposure and its subsequent management (Prater and Lindell, 2006).
If risk perception of people living in high risk prone areas is known, effective disaster
management strategies for mitigation measures can be designed more effectively.
The main objective of this study was to assess how people perceive natural hazards, if
they sense natural hazards to be the major risk, and whether these perceptions and beliefs
make a difference in adopting mitigation. In addition to demographic variables, this study
focuses on psychological variables such as perceived vulnerability, risk perception and
social trust. These psychological variables are not limited to a theoretical decision
framework, which typically includes perceived likelihood of the hazards and severity of
the impacts. Research within these paradigms attempts to answer following research
questions: What does the community perceive as risk? What makes people stay in high-
risk areas? Does risk perception vary with age, sex, education, experience, income, and
landholding? Does risk perception affect the disaster mitigation process?
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3.1. REVIEW OF LITERATURE
The management of risks and threats is a fundamental dimension in a modern society. It
encompasses a wide range of activities which have developed to tackle the emerging risks
and societal changes that followed the transition from an industrialized to a modern
society (Krimsky and Golding, 1992; Lupton, 1999; Kemshall, 2002; Hovden, 2004;
Johansson et al., 2006; Olsen et al., 2007). A plethora of studies are available on the
question of assessing how people perceive the risk across different disciplines (Renn,
2008). Areas in which risks are being addressed range from natural hazards, technological
threats, working conditions, ambient health impacts, crime, terrorism, and pollution to
leisure activities (Renn, 2008).
Assessing risk perception in natural disaster management has gained immense momentum
in last decades (Fischhoff et al., 1978; Slovic et al., 1980; Slovic, 1987; O'Connor,1999;
McKenna ,1993; Lindell and Perry, 2000; Lindell and Prater, 2002; Kasperson, 2005
;Paton 2005; Lin et al., 2008; Lindell and Hwang, 2008; Mishra et al., 2009 and Mishra et
al., 2010). There is an underlying belief that perceptions steer decisions about the
acceptability of risks and influence the behavior during and after the disaster. Risk
perception research in the domain of disaster risks has shown that affected peoples’
perception of risk is subject to many factors influencing cognitive, personal, situational
and contextual dimensions (Sjöberg, 2000; Lin et al., 2008; Renn, 2008 and Mishra et al.,
2010). Knowledge about the level of risk perception of community members living in
risk-prone areas is relevant whenever risk management strategies are to be developed or
applied.
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Though there are a number of publications on disaster risks and each day the application
of the risk concept in various contexts increases, the definition of the term ‘risk’ is highly
contested. At any time, an individual, an organization or a society as a whole faces
several options for taking action (including doing nothing) against a disaster, each of
which is associated with potential positive or negative consequences. Thinking about
risks helps people to select the option that promises more benefit than harm. If this
argument holds true, the term “risk” denotes the likelihood that an undesirable state of
reality (adverse effects) may occur as a result of natural events or human activities (Kates
et al., 1985). This definition implies that humans will make causal connections between
actions. Consequences can be altered either by modifying the initial activity or event, or
by mitigating the impacts. The definition of risk, therefore, contains three elements:
events that have an impact upon what human’s value; the likelihood of occurrence
(uncertainty); and a specific context in which the risk may materialize. (Renn, 2008).
In recent years, risk has come to prominence with a stronger “dread” element to the term
than was previously the case. When Beck (1992) coined the phrase, “risk society”, he was
identifying a form of disaster risk associated with industrialisation and extreme, although
in probabilistic terms often highly unlikely, catastrophic events. Ballard (1992) suggests
that in industry, “Risk = Frequency x Consequences”. This definition suggests an
expectation of system failure and risk management is about ensuring that “events which
happen often must have a low consequence, or events involving serious consequences
must be rare” (Ballard, 1992).
There are six social science based theoretical approaches to risk: the rational choice
approach (Jaeger et al., 2001); the reflexive modernization approach by Beck (1992) and
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Giddens (2000); the systems theory approach of Luhmann (1993); the critical theory
approach based on the seminal work of Habermas (1984, 1987); the post-modern
perspective introduced by Foucault (1982) and further developed by Dean (1999) and
others; and a cultural theory approach, originally introduced by Douglas (1966) and
Douglas and Wildavsky (1982), recently represented by Adams (1995) and Lupton and
Tulloch (2002).
Weber (2001) reviews three approaches by which risk perception has been studied: the
axiomatic measurement paradigm, the socio-cultural paradigm, and the psychometric
paradigm. Studies within the axiomatic measurement paradigm have focused on the way
in which people subjectively transform objective risk information, i.e., possible
consequences of risky choice options such as mortality rates or financial losses and their
likelihood of occurrence, in ways that reflect the impact that these events have on their
lives. Studies within the socio-cultural paradigm have examined the effect of group- and
culture-level variables on risk perception. Research within the psychometric paradigm has
identified people’s emotional reactions to risky situations that affect judgments of the
riskiness of physical, environmental, and material risks in ways that go beyond their
objective consequences.
Psychological risk perception focuses on personal preferences, and attempts to explain
why individuals do not base their risk judgements on expected values (Lopes, 1983;Luce
and Weber, 1986). Second, more specific studies on the perception of probabilities in
decision-making identified strong biases in people’s drawing inferences from
probabilistic information (Festinger, 1957; Tversky and Kahneman, 1974 and Renn,
2008).Risk perceptions differ considerably among social and cultural groups. However, it
37
appears to be a common characteristic in almost all countries in which perception studies
have been performed, that most people from their beliefs by referring to the nature of the
risk, the cause of the risk, the associated benefits, and the circumstances of risk taking
(Renn and Rohrmann, 2000).
In this study, we define risk perception as an everyday subjective assessment process that
is based on experience and on available information without referring to reliable data,
series and complex models. Individual’s subjective risk judgments are often assumed to
be intuitive of which major parts of the underlying processes pass unconsciously. In more
sociological terms, risk perception is a construction process embedded into and
determined by society and culture. Risk judgements therefore imply value judgements.
“Risk perception is all about thoughts, beliefs and constructs.” (Sjöberg, 2006) In this
construction process, possible consequences or outcomes (negative and positive), possible
cause-effect relationships, and situations experienced are attributed to hazardous events,
situations or activities. Risk here, consequently is defined not in mathematical or
technical terms, but as a multidimensional concept that comprises subjective
“quantitative” assessments based on experience and information as well as perceived or
attributed “qualitative” risk characteristics within a certain social, cultural and historical
contexts (Renn, 1995).
Risk perceptions vary among individuals and groups Whose perceptions should be taken
into consideration to make decisions on risk? At the same time, however, these
perceptions reflect the real concerns of people, and include those undesirable effects that
the technical analyses of risk often miss. Because of its complexity, it is very difficult to
deduce general statements or a general theory of risk perception(Renn, 1995).
38
Nevertheless, knowledge about the risk perception of persons living in risk-prone areas is
relevant whenever risk management strategies are to be developed or applied. Risk
perception of the community influences the mitigation and adaptation strategies
undertaken. If risk perception of people living in high risk prone areas is known, effective
disaster management strategies for mitigation measures can be more effectively designed
(Sjöberg, 2000; Lin et al., 2008; Renn, 2008 and Mishra et al., 2010).
3.2. METHODOLOGY
Participatory methods as well as questionnaire surveys were used to collect data on the
major risks as perceived by the community (namely focus group discussions and
participatory ranking exercise)( Paul and Routray, 2010).
A focus group discussion (FGD) is a good way to gather together people from similar
backgrounds or experiences to discuss a specific topic of interest. The group of
participants is guided by a moderator (or group facilitator) who introduces topics for
discussion and helps the group to participate in a lively and natural discussion amongst
them. The strength of FGD relies on allowing the participants to agree or disagree with
each other so that it provides an insight into how a group thinks about an issue, about the
range of opinion and ideas, and the inconsistencies and variation that exists in a particular
community in terms of beliefs and their experiences and practices (Overseas
Development Institute,2009).
FGDs can be used to explore the meanings of survey findings that cannot be explained
statistically, the range of opinions/views on a topic of interest and to collect a wide
variety of local terms. In disaster research too FGD is useful in providing an insight intot
39
risk perception among different stakeholders exposed to disaster, enabling the designing
of effective disaster management policy. It is also a good method to employ prior to
designing questionnaires.Many of the disaster risk assessment studies have used FGD’s as
data collection tools(Legesse and Drake, 2005and Terpstra et. al, 2009).
Participatory ranking and scoring of participants’ perceived risks was done using the
methods suggested by Lopez et al. (2009). Participatory ranking and scoring was used to
elicit the risks that people perceive. For this study, “risk” is conceptualized in terms of the
things that constantly occupy people’s thoughts, the immediacy of an event a person
believes he or she might experience, and something that is severe and that can result in
harmful impacts or can create unfavorable conditions for people or things they value
(Smith et al., 2001; Lindell and Perry, 2004; Armaş, 2006). In this study, the words
“risk,” “concern,” “worry,” “problem,” or “stressor” were used interchangeably; all fit
well with the colloquial terms used by people in the study. The Oriya word chintawas
used by the investigator while conducting the field activity; the term “concern” and
various forms of that word will be used here. Participatory visualization (Chambers,
1997; Kumar, 2002) was used to guide a total of 50 participants through the steps of the
ranking and scoring exercise. The participants were chosen from age group of 18- 60 who
actively volunteered for the participatory exercise. First, participants were asked to list
issues concerning them. Participants were not limited in the type or the numbers of
concerns they listed; on the contrary, they were encouraged to list as many concerns as
they wanted. They were asked to write down each concern on a separate card. When
participants did not know how to write or read, they drew or made symbols to represent
their concerns.
40
Second, participants were asked to rank their concerns by order of importance on a big
sheet of paper. Third, they were asked to assign a severity value to each concern, with
severity meaning how much of a threat that concern constituted for them or things they
valued. The severity score ranged from one (least severe) to five (most severe). Finally,
participants had an opportunity to explain why they listed specific concerns, possible
solutions to the concerns, and their ability or inability to solve them. If they did not
mention floods as a concern, the researcher asked them why they had not done so at the
very end of the activity.
Later a questionnaire was developed based on detailed PRA (details given in Table
10) pretested on a smaller representative population of 50. The questionnaire was
also reviewed by experts in the field for face validity. The variables chosen for the
questionnaire are given in Table 11. The questionnaire was focused on the
subjects’ responses to the items in the following four categories (see Table 11):
Risk Perception (seven items, revised from Fischhoff et al., 1978; Slovic, 1987);
Trust (three items); Vulnerability (five items); and Risk Mitigation Intentions
(seven items) (adapted from Lin et al., 2008). All items are measured on a 4-point
bipolar scale (it's called bipolar because there is a neutral point and the two ends of
the scale are at opposite positions of the opinion) (Lin et al., 2008).
Adult members who were 18 years of age or above were included in the sample. A
maximum of two questionnaires were collected from each household. The
questionnaires were collected personally. The purpose of the study was briefed to
each respondent. Respondents were assured confidentiality of their answers and
41
were told that their responses would be used for research purpose only. Head of
the household were chosen first and then with their consent one of the women was
interviewed with the questionnaire. Following this method 541 questionnaires
(24% sampling) were collected.
42
TABLE 10: Methodology Table
Methodology Methods of data collection Variables studied Type of data
Source of data Data analysis
Participatory ranking, Focus Group Discussion Open- ended questions
Largest considered danger Reaction to a disaster
Measures to protect material goods
Primary Field work Descriptive analysis
Social/questionnaire survey
A household census using systematic stratified random sampling Every Nth house was sampled, after a randomly chosen starting point N was calculated by dividing the total number of households in the sampling frame (usually in all the villages) by the sample size required.
Risk perception, Trust, Vulnerability,
Mitigation intentions
Primary Field Work
Factorial analysis, Correlation
43
TABLE 11: Variables of the Questionnaire
Item type Item Key term and scale 1 2 3 4
Risk perception
Have you experienced any natural disaster earlier in life?
Experience
Never 1-2 times 2-4 times >4
In the community in which you live, how likely is it that a flood/cyclone will occur?
Likelihood of cyclone
Very small Small Large Very large
How clearly do you know what mitigation actions you can adopt?
Knowledge about mitigation
Not clear at all Not clear Clear Very clear
Do you think you can control a loss due to a flood/cyclone event?
Manageability Cannot control
at all Cannot control Can control Can totally control
To what extent would a flood/ cyclone threaten your life?
Fatality
Not severe at all Not severe Severe Very severe
To what extent would a flood/ cyclone affect the quality of your life?
Quality of life
Not severe at all Not severe Severe Very severe
To what extent would a flood/ cyclone cause financial loss to you?
Financial loss
Not severe at all Not severe Severe Very severe
In general how afraid are you of a Dread
44
flood/cyclone? Not afraid at all Not afraid Afraid Very afraid
Trust
In general, do you trust the government’s capability with regard to crisis management?
Trust on government
Do not trust at all Do not trust Trust Trust a lot
In general do you trust the capability of experts to give flood/ cyclone warnings?
Trust on experts
Do not trust at all Do not trust Trust Trust a lot
In general, do you trust the mass media’s capability to report flood/cyclone warnings?
Trust on media
Do not trust at all Do not trust Trust Trust a lot
Vulnerability
Encountering a major flood/cyclone disaster would be just due to fate of which I have little control over?
Fatalism
Strongly disagree Disagree Agree Strongly agree
Do you often worry about the threat of flood/ cyclone in your daily life?
Worry
Strongly disagree Disagree Agree Strongly agree
When flood/cyclone occurs, you likely feel helpless because of lack of assistance from the friends and neighbor
Selfish neighbors
Strongly disagree Disagree Agree Strongly agree
When flood/cyclone occurs, you likely feel helpless because of lack of assistance from the government
Government Apathy
Strongly disagree Disagree Agree Strongly agree
You often feel helpless because of the Threats to livelihoods
45
lack of capability to better the livelihood of your family Strongly disagree Disagree Agree Strongly agree
Mitigations intentions
Do you agree on the government’s plan to alert the public about a flood/cyclone hazard in your area?
Prior warning by Govt agencies
Strongly disagree Disagree Agree Strongly agree
If you can afford it, would you be willing to relocate?
Willingness for relocation
Very unwilling Unwilling Willing Very willing
If it is necessary would, you be willing to take mitigation measures at your own expense?
Willingness for self mitigation
Very unwilling Unwilling Willing Very willing
Would you be willing to purchase a governments flood/cyclone insurance plan to protect against potential loss?
Willingness to insure
Very unwilling Unwilling Willing Very willing
If it were necessary would you be willing to accept inconvenience in your life due to government’s mitigation plans?
Acceptability of inconvenience
Very unwilling Unwilling Willing Very willing
If it were necessary would you be willing to accept finanancial loss due to government’s mitigation plan
Acceptability of financial loss
Very unwilling Unwilling Willing Very willing
How much attention did you pay to flood / cyclone information?
Information seeking
Not at all attentive Not attentive Attentive Very attentive
46
3.3. RESULTS AND DISCUSSION
3.3.1. Participatory Risk Perception
It is important to know what risk really occupies the mind of the villagers and among the
risks as perceived which risk are most important to in their daily lives. The participatory
exercise was conducted in all the study villages and the average size of group varied from
8-10. Total number of risk/ concern that were listed by the participants as part of life was
reported in Table 12 with the respective ranking. The respondents found natural disasters
as a major risk followed by the resultant environmental changes happening around the
villages. It was followed by concern regarding livelihoods and managing overall family
budget.
3.3.2. Questionnaire
A total of 541 questionnaires were collected from all the selected villages for individual
risk perception. The sample profile of the respondents containing age, gender, caste,
education level, occupation and income level details are given in the Table 13. The
Kruskal-Wallis test was applied to emphasize whether or not there is a significant
difference between the samples collected from the five villages on disaster risk perception
(Armas, 2009). At the p-value = 0.0935 ≤ 0.05 level of significance, there exists enough
evidence to conclude that there is no significant difference between the five samples.
Hence the data was pooled and analyzed.
47
TABLE 12: Participatory Ranking of Risks
Type of concern Concern Description of concern Total score Ranking
Human
Health Poor health conditions, illness 130 6
Family Desire for family’s well being (shelter, food, values) 112 7
School (In)ability of children for attending higher school 70 12
Social
Solidarity
Lack of solidarity and sociability among people 65 13
Displacement Fear of being displaced from the community 84 10
Inequality Government
Not being treated fairly and equally by the government 78 11
Economic
Economy Being in debt, bankrupt, lack of money, inability to sustain Household
152 5
Livelihood Lack of work, lack of alternative livelihood generating activities
160 4
Move
Inability to move out of the community 60 14
Physical/Material
House Poor housing conditions 92 9
Infrastructure
Poor infrastructure conditions within the community (water, port, lack of drainage, bad road conditions)
45 15
Environmental
Floods
Floods occurrence and negative effects 68 3
Cyclones Cyclone occurrence and negative effects 200 1
Coastline change Increasing tide line 180 2
Wildlife Crocodiles 100 8
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The Planning Commission of India, 2011 described people who are below poverty line
are those spending less than Rs 965 per month in urban India and Rs 781 in rural India.
Updating the poverty line cut-off figures, the commission said those spending in excess of
Rs 32 a day in urban areas or Rs 26 a day in villages will no longer be eligible to draw
benefits of central and state government welfare schemes meant for those living below
the poverty line.
TABLE 13: Details of Sample Profile
Variables Groups Percentage of respondents
Age 18-35 54 36-55 36 56+ 10
Sex Male 67.1 Female 32.9
Caste
General 30.9 Other backward class 34.8 Scheduled caste 30.5 Scheduled tribe 3.9
Education
Uneducated 7.9 Primary 76.2 Secondary 12.4 College 3.5
Annual income Below poverty line 76.0 Above poverty line 23.3 Higher class 0.7
Occupation
Primary 60.4 Secondary 6.3 Tertiary 25.1 Unemployed 5.2
Land holding size
Big farmers 1.7 Marginal farmers 5.9 Small farmers 5.0 Share croppers 8.7 Landless 68.8
Years of stay In community 45years In house 8.6years
49
3.3.3. Factor Analysis
Factor analysis of the 16 items (out of the categories of trust, risk perception and
vulnerability) was done by pooling the questionnaire collected from all the five villages
as the resulting factor structures are the same for all the villages. The Kruskal-Wallis test
was applied to emphasize whether or not there is a significant difference between the
samples collected from the five villages on disaster risk perception (Armas, 2007) The
data show common factors of perceiving the risk of flood, the value of the Kaiser- Meyer-
Olkin index (0.680) being statistically acceptable (Morrison, 1990). Moreover, the level
of the Bartlett sphericity test (120) = 659.913, sig. = 0.001) justifies the application of a
factorial reduction procedure.
Five factors were identified, based on the Eigen [1] criterion and the resulting factor
structure is presented in Table 14. As shown in Table 14 six out of eight risk perception
items are grouped into two factors perceived IMPACT and perceived CONTROL of the
consequences. Perceived ‘‘likelihood of cyclone’’ of the flood/landslide, however, is
grouped with worry and fatalism (vulnerability items). A person who rated high in this
factor indicated if he/she perceived the chances of a hazard to be high, often worrying
about it, but believed that little can be done about the risk. Thus, this factor was labelled
as POWERLESS. Factor HELPLESS contains three remaining vulnerability items, while
three trust items constitute the TRUST factor. All together, five factors account for 46%
of the variance (Table 15). Because factor analysis here merely serves as a data reduction
tool, all five factors were utilized for further analysis.
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TABLE 14: Factor Loadings across Sixteen Predictors
N=541 Impact Powerless Trust Helpless Control Fatality .424 - - - - Quality of life .545 - - - - Financial loss .607 - - - - Dread .730 - - - - Knowledge about mitigation - - - - .446 Manageability - - - - .754 Experience - - - - .509 Likelihood of cyclone - .492 - - - Worry - .785 - - - Fate - - - .508 - Selfish Neighbor - - - .705 - Govt apathy - - - .476 - Helpless livelihood - - - .489 - Trust on Govt - - .544 - - Trust on expert - - .415 - - Trust on media - - .702 - - Variance explained 15.520 8.573 8.300 7.555 6.572 Cumulative variance 15.520 24.093 32.393 39.948 46.519 Eigen values 2.483 1.372 1.328 1.209 1.051
Extraction Method: Principal Component Analysis. a 5 components extracted
TABLE 15: Total Variance Explained
Com
pon
ent Initial Eigen values
Extraction Sums of Squared Loadings
Rotation Sums of Squared Loadings
Total % of Variance
Cumulative % Total % of
Variance Cumulative
% Total % of Variance
Cumulative %
1 2.483 15.520 15.520 2.483 15.520 15.520 1.904 11.899 11.899 2 1.372 8.573 24.093 1.372 8.573 24.093 1.738 10.862 22.761 3 1.328 8.300 32.393 1.328 8.300 32.393 1.339 8.371 31.132 4 1.209 7.555 39.948 1.209 7.555 39.948 1.290 8.063 39.195 5 1.051 6.572 46.519 1.051 6.572 46.519 1.172 7.325 46.519 6 .990 6.185 52.704 - - - - - - 7 .983 6.143 58.847 - - - - - - 8 .930 5.812 64.659 - - - - - - 9 .878 5.490 70.149 - - - - - -
10 .838 5.239 75.388 - - - - - - 11 .763 4.767 80.156 - - - - - - 12 .722 4.515 84.671 - - - - - - 13 .698 4.360 89.031 - - - - - - 14 .655 4.095 93.125 - - - - - - 15 .594 3.710 96.836 - - - - - - 16 .506 3.164 100.00 - - - - - -
51
3.3.4. Correlation Between Variables
Spearman rank correlations (Table 16) were done first among the first sixteen variables with socio-demographic variables to understand the
inter-correlation between the variables. Later the process was repeated between mitigation intention variables and socio-demographic variables.
TABLE 16: Inter-Correlation between Risk Perception Variables and Socio Demographic Variables
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
AGE 1 -.180 (**)
-.139 (**)
-.115 (**) 0.011 0.04 -.152
(**) .576 (**) -0.06 0.001 0.032 0.07 -.092
(*) 0.027 0.082 -0.03 -0.07 0.025 0.027 -0.06 -.085 (*) -0.03 0.009
SEX 1 0.056 -0.072 0.022 -.263
(**) -
0.023 -.147 (**) -0.05 0.076 -0.01 0.065 0.006 0.06 -0.06 0.005 0.022 .089
(*) -0.06 0.013 0.003 0.009 0.063
CASTE 1 -.093 (*)
-.087 (*)
-.110 (*)
.353 (**)
-.162 (**) -0.00 0.058 -.142
(**) 0.014 .193 (**)
-0.049
-0.032 0.035 0.08 0.02 -.150
(**) .138 (**) 0.052 0.001 0.03
EDUCATION 1 0.012 -.125 (**)
-.138 (**) 0.032 .199
(**) -.104 (*) 0 .095
(*) 0.011 0.059 0.07 .117 (**)
-.112 (**) -0.06 .156
(**) 0.053 .179 (**)
.140 (**) 0.065
ANNINC 1 0.024 -.255 (**) 0.082 -0.00 .101
(*) 0.02 -0.07 -.147 (**)
-.156 (**) 0.03 .113
(**) 0.035 .087 (*) 0.008 -.159
(**) -.236 (**) 0.017 -.105
(*)
OCCUPATION 1 0.072 0.057 -0.07 0.073 0.051 -0.04 -.166 (**)
-0.061
-.100 (*)
-0.011
-0.041 0.061 -
0.011 -
0.015 -.132 (**) -0.05 -.118
(**)
LANDHOLDING 1 -.177 (**)
-.103 (*) 0.081 -.143
(**) -0.05 .304 (**) 0.013 -
0.077 -
0.047 .099 (*) 0.034 -.181
(**) .157 (**) 0.065 0.023 .087
(*)
EXPERIENCE 1 0.004 -0.021
.104 (*) -0.02 -.086
(*) -0.00 .086 (*) -0.02 -
0.078 0.054 0.017 -.102 (*)
-.160 (**) -0.03 -0.02
LIKELIHOOD OF CYCLONE 1 -.112
(**) -
0.071 .089 (*)
-0.019
.088 (*)
-.116 (**) 0.001 -.147
(**) -.140 (**)
.204 (**) 0.062 .271
(**) .132 (**) 0.022
KNOWLEDGE ABOUT MITIGATION
1 -0.02 -0.06 -0.00 -.128 (**) 0.021 .119
(**) .204 (**)
.176 (**) -0.08 -.113
(**) -.255 (**)
-.254 (**)
-.209 (**)
CONTROL 1 -.119 (**)
-.100 (*) -0.06 -0.03 -0.01 0.061 0.007 0.019 -.129
(**) -.109 (*)
-0.024
-0.007
FATAL 1 0.051 .102 (*)
-.115 (**) -0.08 -.169
(**) 0.001 0.072 .157 (**)
.267 (**)
.089 (*)
.182 (**)
AFFECT QUALITY OF LIFE
1 0.045 -0.06 -0.00 .117 (**)
-.104 (*) -0.06 .128
(**)
.127 (**)
.119 (**) 0.066
FINANCIAL LOSS 1 -.089
(*) -0.07 -.142 (**)
-0.081 -0.02 .147
(**) .173 (**)
.118 (**) 0.071
52
DREAD 1 -0.03 0.016 0.007 0.033 -.094 (*) -0.03 0.02 -0.00
TRUST ON GOVEERMENT 1 .188
(**) 0.035 0.019 -.101 (*)
-.116 (**) -0.03 -.185
(**)
TRUST ON MEDIA 1 -0.01 0.069 -.206
(**) -.27
8(**) -.089 (*)
-.259 (**)
TRUST ON EXPERT 1 -0.00 0.04 -.155
(**) -.125 (**) -0.08
FATALISTIC 1 -0.03 0.064 .113 (**)
-.094 (*)
WORRY 1 .289 (**)
.187 (**)
.201 (**)
SELFISH NEIBOUR 1 .255
(**) .213 (**)
GOVERNMENT APATHY 1 .176
(**)
THREATS TO LIVELIHOOD 1
53
People’s attitude to risk is affected by several factors such as age (Greening et al., 1996;
Millstein and Halpern-Felsher, 2002), gender (Rogers, 1985; Flynn et al., 1994 and
Slovic, 1997), social structure (Rogers, 1985 and Heimer, 1988), disaster experience
(Weinstein et al., 2000), trust (Slovic, 1990, 1993), the possibility of a large-scale
disaster (von Winterfeldt et al., 1981), personal belief (Fishbein and Stasson, 1990 and
Dake, 1991). Similarly disaster preparedness is positively affected by age, marital status,
children living at home, home ownership and length of residence in the same location,
and previous disaster experience,among others (Dooley et al.,1992; Mishra &Suar, 2005;
Miceli et.al., 2008 and Mishra et al., 2009). The general inference is that there is a
difference in the level of household preparedness which is based on risk perception of the
household (e.g. Drabek,1986; Mileti& Darlington,1995; Lindell& Perry, 2000 and Miceli
et al., 2008). Jackson (1981) claims that people living in earthquake zones with
structurally inadequately-resistant housing perceive more risk and show readiness for
disaster preparedness.In the study area, age was positively correlated with experience but
negatively correlated with variables like quality of life, selfish neighbor and government
apathy. People who are older feel more afraid of disaster and feel they have less control
over the disasters they encounter. It is possible that older people have more resources than
younger people, and therefore people who are older perceive a higher threat of possible
resource loss due to floods. It can be interpreted that experience of disasters increases the
understanding of the phenomenon and adds to the disaster preparedness (Mishra &Suar,
2005 and Mishra et al., 2009). They become more self reliant in adapting to changes
brought about by the disaster.
Gender was strongly correlated with trust on expert, affect quality of life. Several studies
have shown that men tend to judge risk as being less significant than do women (e.g.,
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Rogers, 1985; Flynn, et al., 1994 and Slovic, 1997). Gender differences with regard to
risk perception may be explained by means of many different approaches. For example,
females generally have lower socio-economic status than males, and therefore females are
more sensitive to the possibility of resource loss (e.g., monetary loss) (Rogers, 1985). In
addition, females are physically more vulnerable than males, and thus females are
sensitive to risks (Baumer, 1978 and OXFAM, 2007).
Education was positively correlated with likelihood of cyclone, fatal, trust on govt and
negatively correlated with trust on media. In the current study, the level of trust in
government is negatively related to consequence and dread,and positively related to
controllability. Many studies support the role of trust in the decrement of perceived risk
(Slovic, 1993 and Viklund, 2003). In modern society, increasingly complex details of
hazard and crisis management are mastered by relatively small numbers of experts and
politicians. Thus, trust in experts and politicians is important for lay people to feel more
secure (Luhmann, 1979, 1988 and Giddens, 1990).
Furthermore, the other possible factor that affects risk perception is resource loss.
Hobfoll, et al. (1995) defined four types of resources: object resources (e.g., housing that
suits needs), condition resources (e.g., status at work), personal resources (e.g., sense of
optimism), and energy resources (e.g., financial resources). Because people are sensitive
to resource loss more than gain (Tversky and Kahneman, 1981), victims tend to feel a
higher likelihood of disaster reoccurrence than the non-victims. This change of attitude
toward the disaster may cause people to improve disaster preparedness among the
households. Hence, occupation was positively correlated with the quality of life and dread
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which can be explained by the fact that they are engaged in primary livelihoods and will
be affected by any natural hazard.
3.3.5. Predicting Mitigation Intentions
Being categorical in nature of the outcome variable, the study uses logit regression model
specification. Logistic regression is a one of the special class of the regression models,
which is used when the dependent (response) variable is a dichotomous variable, (i.e., it
takes only two values, which usually represent the occurrence or non-occurrence of some
outcome event and are usually coded as zero and one. The independent (input) variables
however may be continuous, categorical or both.
A standard regression model has the following general form
Yˆ= b0 + b1x1 + b2x2 + . . . + bpxp
bpxpwhere,is the estimated outcome variable value for the true Y; b0 is the constant of the
equation; b1, . . . , bpare estimated parameters corresponding to predictor values x1, . . . ,
xp; b0 is alternatively called the Y-intercept; and b1, . . . , bpare slopes, regression
coefficients or regression weights. One method used by statisticians to estimate
parameters is the least squares method. The values obtained under the least squares
method are called least squares estimates. In the case of categorical outcome variables the
linear regression model is inadequate for the following reasons. The plot of categorical
data appears to fall on parallel lines, each corresponding to a value of the outcome
variable. Furthermore, the categorical nature of the outcome makes it impossible to
satisfy either the normality assumption for residuals or the continuous, unbounded
assumptions on Y. As a result, significance tests performed on regression coefficients are
not valid, although least squares estimates are unbiased (Menard, 2000). Even if the
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categorical outcomes are reconceptualized as probabilities, the predicted probabilities
derived from the least squares regression models can sometimes exceed the logical range
of zero to one. This results from a lack of provision in the model to restrict the predicted
values, and finally, the R2 index derived from the least squares regression for categorical
outcomes does not render the usual meaning of variance explained, i.e., it does not
correspond to the predictive efficiency and cannot be tested in an inferential framework
(Menard, 2000).
To overcome the limitations of least squares regression in handling categorical variables,
a number of alternative statistical techniques have been proposed. These include: logistic
regression, tobit and probit regressions, discriminant function analysis, log-linear models
and linear probability models. Logistic regression for the dichotomous dependent variable
estimation has been popular and superior because: (a) it can accept both continuous and
discrete predictors; (b) it is not constrained by normality or equal variance/covariance
assumptions for the residuals; and (c) it is related to the discriminant function analysis
through the Bayes theorem (Flury, 1997). Furthermore, in terms of classification and
prediction, logistic regression has been shown to produce fairly accurate results (Fan and
Wang, 1999 and Lei and Koehly, 2000). For these reasons, social researchers have
recognized logistic regression as a viable alternative to linear discriminant function
analysis and other techniques for analyzing categorical outcome variables.
Binary logistic regression was used to examine the relative importance of the five
psychological factors (derived from factor analysis) and the five social-economic status
variables (age, sex, annual income, education and landholding) in predicting the seven
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mitigation intentions (Refer Table 11). Among other demographic variables these five
variables were selected because they have been found to be associated with risk
mitigation behaviors (Lin et al., 2008). Mitigation intentions were calculated by asking
seven questions and summing up the values assigned for each response. If the summation
scores exceeded more than one then it was coded as one and if zero or less than zero it
was coded as zero. The factor IMPACT was the composite measure of variables Fatality,
Quality of life, financial loss and Dread. TRUST factor was composite measure of
variables Trust on govt, Trust on expert and Trust on media. POWERLESS was
composite measure of the variables Likelihood of cyclone and Worry. HELPLESS was
composite measure of Fate, Selfish Neighbor, Govt apathy and Helpless livelihood.
Similarly factor Control was composite measure of Knowledge about mitigation,
manageability and experience.
The significant standardized regression coefficients are listed in Table 17. Social trust
(TRUST), risk perception (IMPACT, CONTROL) and social economic (EDUCATION,
INCOME) variables are positively associated with mitigation intentions. However,
psychological vulnerability (POWERLESS, HELPLESS) is a negative predictor for all
mitigation intentions. However, as the POWERLESS factor outweighs the impact factor,
it is conceivable that there was less willingness to employ mitigation measures. The
psychological factors are clearly stronger predictors for hazard mitigation than that of
demographic variables (education and income).
A logistic regression analysis was conducted to predict mitigation intention for 541
respondents using Pschychological factors and socio economic factors. Model 1 and
Model 2 are statically significant as null hypothesis can be rejected on HL test (Model1,
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0.121, Model 2, 0.363). In Model 1 age and two psychological variables Powerless and
Helpless predict the mitigations better.In both the models pschychological factors predict
mitigation intensions better than demographic factors of age, sex and education (Lin et.
al, 2008). Nagelkerke’s R square is 0.084 indicated a moderately strong relationship
between prediction and grouping. The Wald criterion for both the models showed that
psychological variables are better predictors for taking mitigation than socio-economic
predictors.
TABLE 17: Mitigation Interest Predictors
Sl. No. Variables in the equation
MODEL 1 B( Sig.)
MODEL 2 B(Sig.)
1 AGE -0.043(0.001)* -0.045(.001)*
2 SEX 0.117(0.622) 0.127(0.935)
3 EDUC_DMY 1.186(0.256) -
4 IMPCT 0.028(0.773) 0.032(0.741)
5 PWRLS 0.240(0.096)* 0.239(0.097)
6 TRST -0.047(0.574) -0.042(0.616)
7 HLPLS 0.175(0.023)* 0.185(0.016) *
8 CNTRL 0.085(0.435) 0.088(0.416)
9 Constant -1.573 (0.428) -1.721(0.387)
-2 log likelihood 526.927(a) 528.724(a)
Cox and Snell R Square 0.054 0.051
Nagelkerke R Square 0.084 0.080
Hosmer and Lemeshow Test 0.121 0.363
** Significant at 0.01 level; *at 0.05 level
This study asserts that any mitigation programme should inculcate disaster risk perception
into account as perceived by the local community. Factors like Social trust, risk
perception (IMPACT, CONTROL) should be included while developing any community
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based disaster management programme. Psychological vulnerability (POWERLESS,
HELPLESS) encourages the people to incorporate mitigation measures. Hence, socio
economic variable Age shows negative relationship with mitigation intentions i.e younger
people are more keen on taking mitigation measures.
3.4. CONCLUSION
The risk behaviour literature (Pratt, 1964; Arrow, 1971; MacCrimmon and Wehrung,
1986; Slovic, 1987 and Pennings and Wansink, 2004) identifies two dimensions that play
an important role in how decision makers respond to risk: the content of the risk and the
likelihood of actual exposure to that content. The present study focuses on the attitudinal
components of people- such as how do they perceive and feel about the natural hazards
and the effectiveness of mitigation measures. People accept risk as a threat and are
occupied by the impact it may cause especially the ones happening in their local
environment. In the study area they believe that cyclones are major threat to their
community and they need participatory disaster reduction programmes. The results
indicate that perception and belief attitude components outweigh the demographic
variables in predicting mitigation intentions. Hence any disaster reduction plan must take
into account community’s perception of risk. Perception to risk is increased when the
changes due natural hazards have a profound impact on the local environment. A natural
disaster over the years can affect the natural capital of the community. The impacts of
cyclones on the natural assets of the community are discussed in the next chapter.
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