does disaster experience undermine risk perception and ... apts ppt final/sessi… · does disaster...

Does Disaster Experience Undermine Risk Perception and Response: An Empirical Analysis of

Typhoon-Morakot

Ching-Cheng CHANGInstitute of Economics, Academia Sinica,

Dept. of Agricultural Economics, National Taiwan University

Wan-Jung ChouAECOM Capital

Yu-Chia HuangDept. of Agricultural Economics, National Taiwan University

APEC Typhoon Symposium–Lessons Learned from Disastrous TyphoonsInstitute of Manila, the Philippines, Nov 24-25, 2015

Introduction

• Typhoon Morakot

August 7 2009, Taiwan

AfterMorakot

Warning end

Weather Watching

in typhoon Season

Focus on weather systems in South China and Taiwan region and typhoon info in North west Pacific and South China Sea

Sea warning

Land warning

Emergency response

centerTyphoon forms

12th 02:30 12th 08:30

14th 14:30 17th 09:13

14th 15:16

10th 20:00 20 times in 8 days

Provide typhoon news

in 09:00

Provideinformation twice a day

Provideinformation 4 times a day

Analysis and discussion

Schematic diagram of stages of guidance and information provided during

typhoon season in Taiwan

• Did people take precautionary actions before the

event?

• Did this event have any influence on their

perception of risk exposure?

• How would they accordingly adjust the ways of

coping with typhoon risks in the future?

Research Questions:

Purpose

• Investigate the causal relationship between risk perceptions and precautionary actions The influence of previously adopted protection

actions on risk perceptions observed at the time of observation and

in turn the effects of risk perceptions on individuals’ intention to take precautionary actions in the future.

HypothesisPrevious

experience, trust, socio-econ factors

Risk perception

Precautionary behavior

Mediating Effect

two-stage approach

Statistical Analysis

1 • Predict perceived risk levels using ordered probit model

2 • Examine its influence on precautionary intentions using probit model

Two-Stage approach

Data1. Survey Data• Where: Household Survey of Post-Morakot Social Impact and

Recovery-Wave 1.• When: June, 2010• How: face to face interview

2. Interviewees• Age: over 20• Status: household heads, primary financial supporters or the ones

most capable of answering questions in an interview

3. Observations• Raw data contains 1658 observations• representing 1658 households

QuestionsPlease indicate which of the following measures you took (1) before the typhoon Morakot. In addition, please indicate which of the following measures (2) you intend to take in the future (before next typhoon event).

• Obtain the information about this typhoon from the TV, radio and other sources (INFORMATION)

• Take part in local disaster drills(DRILLS)

• Strengthen your house’s resistance to typhoons, e.g. sandbags, water pumping machines (HOUSE)

• Prepare food, clothes and other necessities (FOOD)

• Understand and make plans of evacuation routes and temporary shelters (EVACUATION)

• Purchase or renew personal accident insurance (INSURE_ACCIDENT)

• Purchase or renew typhoon and flood insurance for your property (INSURE_PROPERTY)

• The information revealed is twofold: 1. First question (1) exhibits whether or not households took

precautionary measures before the typhoon Morakot

2. Second question (2) shows whether or not households, at the time of interview, had the intention to take the same precautionary measures in the future

• According to the phases of the hazard life cycle:

Source: Lindell and Perry (2004)

PrecautionaryMeasures Mitigation measures

Preparedness measures

Recovery measures

Precautionary measure Description Before Morakot

% of the sampleAfter Morakot

% of the sample

Preparedness

INFORMATIONObtain the information about this typhoon from the TV, radio and other sources

91% 76%

FOOD Prepare food, clothes and other necessities 85% 76%

HOUSEStrengthen your house’s resistance to typhoons, e.g. sandbags, water pumping machines

62% 66%

Mitigation

EVACUATIONUnderstand and make plans of evacuation routes and temporary shelters

44% 69%

DRILLS Take part in local disaster drills 39% 61%

RecoveryINSURE_ACCIDENT Purchase or renew personal

accident insurance 24% 48%

INSURE_PROPERTY 11 6% 35%

Table 1: Precautionary measures

Risk Perception Description Mean Std Devi.

PROB_DISASTER

In a scale from ‘1’ (very unlikely) to ‘4’ (very likely), how likely do you think that a typhoon disaster would occur in the region of your residency?

3.29 0.87

IMPACT_SAFETY

In a scale from ‘1’ (very mildly) to ‘4’ (very seriously), to what extent do you think a typhoon disaster would threaten the safety of your life?

3.58 0.60

IMPACT_PROPERTY

In a scale from ‘1’ (very mildly) to ‘4’ (very seriously), to what extent do you think a typhoon disaster would result in the loss of your personal property?

3.50 0.67

Table 2: Risk perceptions

• 1st question measures one’s subjective judgment of the chances of a typhoon-induced disaster in the future.

• 2nd and 3rd question stated one’s perceivedlevels of potential impact on his/her personal safety and of the loss of his/her personal property, as a result of a typhoon disaster.

Table 3: Summary Statistics of the Sample

Variable Mean Variable MeanAlready adopted precautionary measures Geographic factors

The number of preparedness measures taken 2.38 Nantou 3%The number of mitigation measures taken 0.83 Chiayi 14%The number of recovery measures taken 0.30 Tainan 5%

Experience_disaster 0.63 Kaohsiung 48%Level of trust Pintong 17%

In central government 3.10 Taitong 11%In community 3.66 Tainan City 1%

Socio-demographic factorsHealth impact in Morakot

Female 40% The number of death 0.15Age 52.37 The number of injury 0.11Income

Below 12k 21%12k and above, below 36k 38%36k and above, below 60k 27%60k and above, below 108k 11%108k and above 3%

Education (illiterate) 11%Aboriginal 39%

Results1.Risk perceptions

• Confirm the association between precautionary behaviour taken before Morakot and households risk perceptions after typhoon Morakot.

• Whether or not former actions could reduce risk perceptions in a later stage is inconclusive.

• A negative correlation between trust in the central government and perceived impact of property loss is observed

Table 4 : The estimation results for three different types of risk perception

Ordered probit PROB_DISASTER IMPACT_SAFETY IMPACT_PROPERTYCoef. Std. Err. Coef. Std. Err. Coef. Std. Err.

The number of preparedness measures taken

-0.166*** 0.046 -0.022 0.048 0.078 0.046

The number of mitigation measures taken 0.175*** 0.049 0.238*** 0.056 0.194*** 0.053

The number of recovery measures taken -0.140** 0.065 -0.113 0.072 -0.028 0.070

Experience_disasterbefore Morakot -0.037 0.075 0.043 0.082 0.125 0.079

‘***’ denotes at 99% confidence level; ‘**’ for at 95% confidence level

Level of Trust PROB_DISASTER IMPACT_SAFETY IMPACT_PROPERTY

Coef. Std. Err. Coef. Std. Err. Coef. Std. Err.

In central government 0.209*** 0.041 -0.010 0.046 -0.168*** 0.046

In community -0.154*** 0.044 -0.053 0.049 -0.039 0.048

Socio-demographic PROB_DISASTER IMPACT_SAFETY IMPACT_PROPERTY


Female 0.256*** 0.074 0.146 0.079 0.071 0.077

Age 0.005 0.003 -0.009*** 0.003 -0.004 0.003

Income 0.098*** 0.028 0.051 0.030 0.140*** 0.031

Education (illiterate) -0.341*** 0.124 0.049 0.134 0.032 0.130

Aboriginal 0.140 0.084 -0.183 0.095 -0.329*** 0.090


Geographic factors(base = Tainan City) PROB_DISASTER IMPACT_SAFETY IMPACT_PROPERTY


Nantou 1.126*** 0.385 -0.206 0.402 -0.035 0.386Chiayi 0.562 0.332 0.370 0.363 0.702** 0.347Tainan -0.001 0.346 -0.384 0.375 -0.053 0.359Kaohsiung 0.741** 0.323 0.436 0.352 0.606 0.335Pintong 0.719** 0.335 0.129 0.366 0.116 0.347Taitong 0.615 0.342 0.420 0.376 0.760** 0.360

Health impact in Morakot PROB_DISASTER IMPACT_SAFETY IMPACT_PROPERTY


Death 0.248*** 0.095

Injured 0.414*** 0.126


• Focused on the determinants of households’ intentions to take 7 different types of precautionary measures, respectively.

• each measure functions uniquely towards reducing the impact of typhoon events on households.

• Households may exhibit various degrees of willingness to take each of these measures and the knowledge behind,

• conveys useful implications for hazard risk management strategies at the household level.

2.Precautionary intentions

INFORMATION FOOD HOUSEProbit model estimation Coef.

(Std. Err.)dy/dx

(Std. Err.)Coef.

(Std. Err.)dy/dx

(Std. Err.)Coef.

(Std. Err.)dy/dx

(Std. Err.)

PREVIOUSLY ADOPTED THE SAME MEASURE

-0.177(0.186)

-0.050(0.052)

-0.055(0.145)

-0.015(0.041)

0.633***(0.096)

0.212***(0.030)

RISK PERCEPTIONProb_Disaster (very high) 1.864***

(0.593)0.522***

(0.165) 2.182***

(0.605)0.612***

(0.168) 2.660***

(0.532)0.889***

(0.172) Impact_Safety (very

seriously)2.000***

(0.706)0.560***

(0.196) 1.383**(0.690)

0.388**(0.193)

1.434**(0.624)

0.480**(0.207)

Impact_Property (very seriously)

-3.473***(0.652)

-0.973***(0.177)

-3.427***(0.652)

-0.962***(0.177)

-3.228***(0.585)

-1.079***(0.189)

EXPERIENCE_DISASTER -0.399***(0.095)

-0.112***(0.026)

-0.325***(0.094)

-0.091***(0.026)

-0.296***(0.086)

-0.099***(0.028)

LEVEL OF TRUSTCentral government+ -0.311***

(0.079)-0.087***

(0.022)-0.342***

(0.082)-0.096***

(0.023) -0.401***

(0.073)-0.134***

(0.023) Local community+ 0.169***

(0.060)0.047***

(0.017)0.190***

(0.061)0.053***

(0.017) 0.284***

(0.056)0.095***

(0.018)

Table 6: Predictors of the intention to take preparedness measures

‘***’ denotes at 99% confidence level; ‘**’ for at 95% confidence level ‘+’ For the purpose of simplicity, these variables were treated as quasi continuous variables. However, these variables are categorical in nature. One must note that a general trend of their association with households’ intention to take precautionary actions can be confidently identified in this approach but at the same time, one must interpret the corresponding marginal effects with caution.

θφθ

INFORMATION FOOD HOUSE

Probit model estimation Coef.(Std. Err.)

dy/dx(Std. Err.)

Coef.(Std. Err.)

dy/dx(Std. Err.)

Coef.(Std. Err.)

dy/dx(Std. Err.)

SOCIO-DEMOGRAPHIC FACTORS

Female -0.139(0.102)

-0.039(0.028)

-0.121(0.102)

-0.034(0.029)

-0.124(0.093)

-0.041(0.031)

Age -0.011**(0.004)

-0.003**(0.001)

-0.015***(0.004)

-0.004***(0.001)

-0.012***(0.004)

-0.004***(0.001)

Income+ 0.031(0.043)

0.009(0.012)

0.026(0.042)

0.007(0.012)

-0.005(0.039)

-0.002(0.013)

Education (illiterate) 0.101(0.173)

0.028(0.048)

0.112(0.171)

0.031(0.048)

0.205(0.160)

0.069(0.053)

Aboriginal -0.937***(0.116)

-0.262***(0.030)

-0.998***(0.119)

-0.280***(0.031)

-0.643***(0.113)

-0.215***(0.036)

CONSTANT 2.253***(0.446)

2.568***(0.434)

1.030***(0.397)

Number of obs. 1135 1135 1135 Log likelihood -563.46 -564.75 -666.89Pseudo R2 0.099 0.101 0.082‘***’ denotes at 99% confidence level; ‘**’ for at 95% confidence level ‘+’ For the purpose of simplicity, these variables were treated as quasi continuous variables. However, these variables are categorical in nature. One must note that a general trend of their association with households’ intention to take precautionary actions can be confidently identified in this approach but at the same time, one must interpret the corresponding marginal effects with caution.

Table 7: Predictors of the intention to take mitigation measures

DRILLS EVACUATIONProbit model estimation Coef.

(Std. Err.)dy/dx

(Std. Err.)Coef.

(Std. Err.)dy/dx

(Std. Err.)PREVIOUSLY ADOPTED THE SAME MEASURE

0.278***(0.103)

0.095***(0.035)

-0.335***(0.099)

-0.104***(0.030)

RISK PERCEPTIONProb_Disaster 2.086***

(0.484)0.714***

(0.161) 4.808***

(0.529)1.492***

(0.147) Impact_Safety 1.857***

(0.595)0.636***

(0.201) 1.014

(0.624)0.315

(0.193) Impact_Property -0.825

(0.577)-0.283

(0.197) -0.813

(0.589)-0.252

(0.183) EXPERIENCE_DISASTER -0.078

(0.086)-0.027

(0.030) -0.088

(0.089)-0.027

(0.028) LEVEL OF TRUST

Central government+ -0.136**(0.068)

-0.047**(0.023)

-0.389***(0.072)

-0.121***(0.021)

Local community+ 0.226***(0.056)

0.077***(0.019)

0.340***(0.058)

0.105***(0.017)

Table 7: Predictors of the intention to take mitigation measures


DRILLS EVACUATIONProbit model estimation Coef.

(Std. Err.)dy/dx

(Std. Err.)Coef.

(Std. Err.)dy/dx

(Std. Err.)SOCIO-DEMOGRAPHIC FACTORS

Female -0.028(0.093)

-0.009(0.032)

-0.271***(0.097)

-0.084***(0.030)

Age -0.006(0.004)

-0.002(0.001)

-0.015***(0.004)

-0.005***(0.001)

Income+ -0.085**(0.039)

-0.029**(0.013)

-0.151***(0.040)

-0.047***(0.012)

Education (illiterate) 0.107(0.156)

0.037(0.054)

0.457***(0.162)

0.142***(0.050)

Aboriginal 0.066(0.107)

0.023(0.037)

-0.498***(0.114)

-0.155***(0.034)

CONSTANT -1.338***(0.433)

-0.399(0.432)

Number of obs. 1135 1135Log likelihood -683.38 -623.55Pseudo R2 0.101 0.114


Conclusions1. Certain predictors have both direct and indirect effects on

the intentions to take certain precautionary measures,when indirect effects are mediated by risk perceptions.

2. The direct and indirect effects in some cases cancounteract each other.• For example, Aboriginal households, On one hand, have

lower perceived impact concerning property loss andhence are less likely to buy/renew property insurance.

• On the other hand, they are more likely to buy/renewproperty insurance, when the influence of perceivedimpact remains constant.

households’ attitudes and socio-demographic factorsought to be taken into account in the development ofcommunication strategies..

3. The extent to which one trusts the central government and local communities in their disaster response capacity explains his/her intention to take precautionary actions. • Higher trust in the central government is associated

with weaker intention to take preparedness and mitigation measures.

• Higher trust in local communities’ capacity is correlated to stronger intention to take preparedness, mitigation and recovery measures.

indicates their dependency on the government and hence results in weaker intention to take self-protect actions. recognise the necessity of precautionary actions at the community level.

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