OSIPP Discussion Paper: DP-2012-E-008

Earthquake Risk Information and Risk Aversive Behavior:
Evidence from a Survey of Residents in Tokyo Metropolitan Area

October 16, 2012
Yasuo Kawawaki
Visiting Professor, Osaka School of International Public Policy (OSIPP)【Key Words】CVM, WTP, Earthquake Insurance, Risk Aversion, Subjective Probability of Loss
【JEL Classification】D81, D83, R28【Abstract】This paper analyzes the relationship between provision of earthquake risk information
and residents’ willingness to pay (WTP) for disaster risk reduction by the Contingent Valuation
Method (CVM), using questionnaire survey data on the purchase of earthquake insurance in theTokyo Metropolitan Area, Japan. Degree of disaster risk aversion and subjective probability of loss
are estimated as parameters of expected utility function in a discrete choice model. The results
suggest that when more precise and specific earthquake risk information is provided, residents ofvulnerable houses are willing to pay more for disaster risk reduction, with larger subjective
probability of loss, while those in safe houses are willing to pay slightly less, with a larger degree ofrisk aversion.

Address: International Recovery Platform Secretariat, 1-5-2 Wakinohama Kaigan-dori, Chuo-ku, Kobe, 651-0073,
Japan, Tel.: +81 78 262 6041; Fax: +81 78 262 6046, Email address: kawawaki@recoveryplatform.org The author
thanks Naoto Yamauchi, Ryuji Kutsuzawa and Nobuo Akai for research guidance, and appreciates helpful andencouraging comments from Masatsugu Tuji, Yoshimasa Kato and Masahiro Arima. The author is also grateful toparticipants at the Third Conference of the International Society for Integrated Disaster Risk Management for theirconstructive comments.

11. Introduction

The Great East Japan Earthquake, which hit the northeast region of Japan on March 11, 2011,claimed about 20 thousand lives, caused the asset loss of 16.9 trillion yen (about 200 billion US
dollars, largest ever in the world), and also had a great impact on disaster management policy inJapan. However, Japan is still facing the great danger of other earthquakes. For example, anearthquake expected in the Tokyo Metropolitan Area, which has a 70% probability of occurrence
within 30 years, may cause the asset loss of 112 trillion yen (about 1.4 trillion US dollars). The large
amount of non-earthquake-resistant buildings and densely populated districts of wooden buildings,
widespread in the central part of cities, are major issues for disaster reduction policy. The low ratioof earthquake insurance policyholders to households (21.5% as of the end of March 2008, Non-Life
Insurance Rating Organization of Japan) is another significant issue.
On the other hand, project-based disaster reduction policies such as urban redevelopment
projects usually require long-term processes of consensus building, and moreover, the government
faces financial difficulties to support them, causing the delay of effective disaster reduction measuresin the cities.
Under these circumstances, the development of hazard maps and disclosure of earthquake risk
information have recently become popular policies adopted by the government1. These policies aim
to induce residents to prepare for disasters by providing them with objective risk information.
However, it seems unlikely that residents can make rational decisions which reflect objective risk2.

1 Since March 2005 the Headquarters for Earthquake Research Promotion of the central government has released‘Probabilistic Seismic Hazard Maps,’ which show the occurrence probability of earthquakes with a given seismic
intensity at a fairly disaggregated geographical level (1km x 1km grid cells) throughout Japan, on the website(http://www.j-shis.bosai.go.jp/). Recently local governments have also been proactive in providing their residents
with such risk information as hazard maps and estimations of damage by natural disasters. (see Nakagawa(2003))
2

For example the ratio of earthquake insurance policyholders to households usually increases when the earthquake

causes huge damages somewhere in Japan. The rate of increase was 28.6% in FY 1994, 28.9% in FY 1995 (The great
Hanshin-Awaji Earthquake occurred in January 1995; Japan’s FY turns over in April of each year), and 8.6% in FY

2

In order to make these policies work effectively, it is important to understand residents’ risk
perception mechanisms, especially the relationship between available risk information and residents’risk aversive behavior.
Residents are generally considered to have difficulty to prepare themselves in an economicallyrational manner because they don’t have direct experiences of, or enough information about,low-frequency, large-scale disasters such as major earthquakes. Residents’ risk perception towarddisasters often amounts to a vague feeling of uneasiness. So the preparedness level for thosedisasters (risk aversive behavior) is considered to be greatly influenced by (1) individual attitudes
toward avoiding uncertainty (degree of risk aversion) and (2) individual subjective prediction of loss
(subjective probability of loss), which are affected by personal preferences as well as external
information sources such as sensational news in the media.
Based on the above recognition, this paper empirically examines residents’ disaster risk aversive
behavior. More specifically this paper estimates the degree of risk aversion and the subjective
probability of loss as parameters of expected utility function in a discrete choice model and analyzes
the relationship between provision of earthquake risk information and willingness to pay (WTP) for
disaster risk reduction using the Contingent Valuation Method (CVM).
The unique methodological contribution of this study is the construction of individual data which
includes the correspondence relationship between the subjective probability of loss and the objective
probability of loss. An internet survey on the purchase of virtual earthquake insurance wasconducted to construct this dataset.
The results of analysis suggest that when earthquake risk information, such as hazard maps andprobability of asset loss, is provided and made more specific to each resident, those in vulnerable
houses are willing to pay more for disaster risk reduction while those in safe houses are willing to2005 (Niigata Prefecture Chuetsu Earthquake occurred in October 2004), while the average rate of increase from FY
1991 to FY 2008 was 7.5%. These behaviors were far from rational decisions which reflected their own objective
earthquake risks.

3

pay slightly less than before. As an explanation for these changes in risk aversive behaviors, it issuggested that residents of vulnerable houses have larger subjective probability of loss while those insafe houses have a larger degree of risk aversion as well as slightly smaller subjective probability ofloss.
This implies that the disclosure of earthquake risk information, especially the provision of
specific risk information such as the probability of asset loss for each resident, can effectively induce
the residents living in non-earthquake-resistant houses or other high-risk houses located in densely
populated districts of wooden buildings to make more investments in disaster risk reduction.
This paper is organized as follows. Section 2 reviews the previous studies of disaster risk
information and people’s risk aversive behaviors. Section 3 presents the empirical specifications of
this study. Section 4 describes the questionnaire survey using CVM. Section 5 reports the estimatedresults and interpretations. Section 6 concludes by discussing the policy implications for disaster risk
reduction.

2. Previous studies

There are not so many studies which address the relationship between the provision of disasterrisk information and residents’ disaster risk investments. One of the difficulties of this kind of study
is that residents’ risk aversive behavior (or WTP for risk reduction) is not directly affected by theobjective risk but is affected by the degree of disaster risk aversion and the subjective probability ofloss involving the residents’ risk perception biases (see Figure 1). Some related studies have been
conducted in the fields of civil engineering, economics, and psychology.

<Figure 1>

4

Among the studies in the fields of economics and civil engineering, Matsuda et al. (2005), Kaoru(1998), Willis and Asgary (1997), and others have tried to estimate risk premium and WTP using
CVM questionnaire surveys. However, since these studies are based on data without thecorrespondence information of each resident’s subjective and objective probability of loss (or someof them are based on data of the virtual objective probability of loss), it is impossible for them toanalyze "risk perception bias" in detail.
Moreover, in order to induce residents to take proper action for disaster risk reduction, it isthought that the provision of “general risk information for an entire city,” such as a hazard map, is
not sufficient, but rather the provision of easily understandable "specific risk information on
individual house" is needed (Nakagawa (2003)). However, the correspondence between provided
information level and WTP for disaster risk reduction is also not yet empirically clarified in theprevious studies.
There are also literatures such as Brookshire et al.(1985) and Nakagawa et al.(2009) which
estimated the depreciation ratio of the hedonic housing price (or land price) caused by theearthquake risk and calculated the WTP for earthquake risk reduction (or risk premium) based on
this ratio. However, since the housing price (or land price) reflects people’s comprehensive judgmenton the multiple disaster risks as well as various neighborhood environment factors, those studies
have difficulties to analyze the impact of specific disaster risk such as earthquake risk on the housing
price (or land price). Moreover, they had another difficulty to examine the magnitude of people’s"risk perception bias" since the analysis is only based on the objective risk data.
On the other hand, in the field of psychology, there are studies such as Slovic (1997),
Lichtenstein et al. (1978), Kunreuther et al. (1978), Ganderton et al. (2000) which tested whether the
expected utility theory in the field of economics is adequate to explain people’s risk aversive
behavior. These studies indicated the existence of "risk perception bias" which usually makes the
subjective probability of loss larger than the objective probability of loss in the low frequency events.
5

In this case low frequency natural disasters such as earthquakes tend to be considered as higher
frequency events than they are in reality. As for this risk perception bias, it is indicated by Viscusi(1998) and other related studies that the subjective probability of loss is recognized based on the
person’s "experience" and cannot be easily changed. To adjust people’s subjective probability of loss
closer to the objective probability of loss, the accuracy of information and the mutual trust between
provider and recipient of the information are considered to be the key elements, according to thesestudies.
But since most of these studies were conducted in the laboratory without considering people’ssocial conditions and based mainly on qualitative analysis, they have not succeeded in quantifying
the degree of disaster risk aversion in real social situations.
This study examines residents’ WTP for disaster risk reduction when "the specific risk
information on actually existing houses" is provided, and tries to quantify the changes in degree ofrisk aversion and subjective probability of loss (or risk perception bias) according to the level of
information quantity and specificity.

3. ModelThe analytical model for this study follows Matsuda et al.(2005). We use the discrete choicemodel of Hanemann et al. (1984, 1991) to explain purchase or non-purchase of the disaster risk
avoidance option (e.g. earthquake insurance), and estimate the degree of relative risk aversion γ
and the subjective probability of loss p as parameters of the indirect utility function. Then we
calculate the WTP for disaster risk reduction and the risk premium ρ based on the estimation
results of γ and p. We analyze the impact of provision of information on resident’s disaster risk
aversive behavior by comparing the magnitude of these estimates among the subsamples with thedifferent information levels.
6

First, we assume the resident behaves in accordance with the expected utility maximization
hypothesis and we use the measurable von Neumann-Morgenstern (VNM) utility function. If theresident’s expected utility is EV1 in the case of purchase of risk avoidance option and EV0 in the caseof non-purchase, each expected utility can be written as follows.

EV1 = p V11 + (1 – p) V01(1)

EV0 = p V10 + (1 – p) V00(2)

Here for the sake of simplicity we assume 4 cases: s = 1 when the resident suffers loss due todisasters and s = 0 when no loss, as well as a = 1 when the resident purchases the risk avoidance
option and a = 0 when non-purchase. The indirect utility Vsa is described according to the above
mentioned 4 cases.
Next, we consider the expected utility difference (EUD) between the purchase of risk avoidance
options and non-purchase of them, and introduce the following random utility model which consistsof the fixed term ΔEV and the stochastic error term ε which is supposed to be normally distributedwith variance σ.

(Fixed term)

ΔEV = EV1 – EV0(Stochastic error term)= p (V11 – V10) + (1 – p) (V01 – V00)

(3)

ε～N (0, σ2)(4)Then, we suppose that the utility function exhibits Constant Relative Risk Aversion (CRRA).
CRRA function can be written as follows.

V(y;γ) = y1-γ/ (1 –γ)

for γ ≠ 1

7

(5)

for γ = 1

V(y) = ln yV(y) : indirect utility(6)y : household wealth

We also suppose that the cost of purchasing risk avoidance option is c, the resident’s wealth at thenormal situation is y0, the loss of resident’s wealth due to disasters in the case of purchase of risk
avoidance option is l1, and the loss of resident’s wealth due to disasters in the case of non-purchaseof risk avoidance is l0. Then the equation (3) can be rewritten as follows.

ΔEV = [ p {(y0 – c – l1)1-γ– (y0 – l0)1-γ}+ (1 – p) {(y0 – c)1-γ– y01-γ}] / (1 – γ)

(7)

In addition, in order to examine the detailed structure of the degree of relative risk aversion γ, we
also conduct the empirical analysis on the assumption that γ is the function of resident’s attribute
vector X.
γ(X) = X’α

α: coefficient vector(8)

On the other hand, the probability of purchase of risk avoidance option and that of non-purchase isrespectively given as follows based on the normal distribution of error term ε.

Pr(a = 1 | y) = Π1 = 1 – Φ(–ΔEV /σ)(9)Pr(a = 0 | y) = Π0 = Φ(–ΔEV /σ)(10)

Πa: probability in the case of a,

Φ: probability distribution function of standard normal distribution

Based on this, we introduce the two-stage binomial discrete choice model. The questions with 2
alternatives (purchase or non-purchase of the risk avoidance option) are asked in 2 stages. Let 1
represent purchase and 0 non-purchase in the first stage and the second stage; then the set of8alternatives consists of 4 elements {a = 11, a = 10, a = 01, a = 00}. In this case, the log-likelihood
function can be formulated as follows. (see Hanemann et al. (1991)).

ln L = ΣiΣa d ia lnΠia= Σi {d i11 ln(1 – Φ(–ΔEVU /σ))+ d i10 ln(Φ(–ΔEVU /σ) – Φ(–ΔEV1 /σ))+ d i01 ln(Φ(–ΔEV1 /σ) – Φ(–ΔEVL /σ))+ d i00 ln(Φ(–ΔEVL /σ))}(11)

da: 1 in the case of a, 0 otherwise, ΔEV1: EUD in the first stage, ΔEVU: EUD in the second stage after purchase in
the first stage, ΔEVL: EUD in the second stage after non-purchase in the first stage, i: household number

Based on equations (7) and (11), we estimate the parameters (γ, p, σ) by the maximum
likelihood method, using the data from the questions with 2 alternatives in 2 stages about purchaseor non-purchase of disaster risk avoidance options.
∧∧Finally, using the estimated parameters γ, p, we calculate the WTP for disaster risk reduction
and the risk premium ρ of a representative resident (see Figure 2). The calculation is conducted by
the following equation, where yE is the expected resident’s wealth including subjective loss by thedisaster, and y* is the certainty equivalent.

WTP = y0 – y*(12)∧

ρ= yE – y* = (y0 – p l0) – y*
∧

∧

(13)
∧

∧

∧

y*= V -1(p V(y0 – l0;γ) + (1 – p) V(y0;γ) ;γ)<Figure 2>

9

(14)

4. Questionnaire survey on purchase of earthquake insurance

4.1 Methodology of survey

In this study we carried out the following CVM questionnaire survey via Internet (see Table 1).
We selected the Tokyo Metropolitan area as a target area since detailed earthquake risk indicators foreach district have been developed by the Tokyo Metropolitan Government. We used earthquake
insurance as a resident’s earthquake risk avoidance option and asked the question whether topurchase the hypothetical earthquake insurance under the condition that a certain level of earthquake
risk information was given.

<Table 1>

In this study, the “earthquake risk” a resident faces is defined as the “objective probability ofloss”, which is obtained as a product of “probability of earthquake occurrence” and the “ratio ofasset loss to resident’s risk asset in case the earthquake occurs”. “Risk asset” is defined as theamount of a resident’s housing assets exposed to earthquake risk, and it consists of the building asset
value (excluding the land value) and the value of home contents. Risk asset also means the amountof asset the earthquake insurance can cover.
“Expected loss of asset by earthquake” is regarded as the “objective loss” which is obtained as a
product of “risk asset” and “earthquake risk”. (See Figure 3)<Figure 3>

We assume that the “earthquake insurance payment” covers up to 50% of the risk asset and is10

decided according to the damage level3. We asked the residents whether they would like to purchasethe earthquake insurance for the hypothetical “annual insurance cost”4. Then we asked those who
answered “yes” the second question, whether they would like to purchase the same insurance fortwice as much as the cost of the first question, and asked those who answered “no” the alternative
second question, whether they would like to purchase the same insurance for half as much as the costof the first question (the questions with 2 alternatives in 2 stages).
In this survey we provided each resident with 2 kinds of information just before the questions
about the purchase of earthquake insurance: the “earthquake risk information” and the information
on “amount of risk assets”
As for the “earthquake risk information”, we divided the total sample of 1,000 into three equal
subsamples, and provided one of the following three levels of earthquake risk information to theresidents in the respective subsample (see Table 2).

Level 1: No earthquake risk information availableLevel 2: Earthquake risk information easily obtained in daily life such as hazard maps ((1) and(2) were provided.)

Level 3: Earthquake risk information which requires some expertise or cost to obtain, but which
can clearly identify the objective risk of individual housing ((1) - (6) were all provided.)

As for the information on “amount of risk assets”, we provided all residents with the precise
calculation result of each resident’s amount of risk assets.

3

This is the same as the payment rule of the current Japanese earthquake insurance.

4

The hypothetical annual insurance cost was determined by multiplying the earthquake insurance payment by any

one of the five levels of insurance rates. We set the insurance rate of 0.06%, 0.1%, 0.2%, 0.4%, and 0.6% based on
the result of preliminary questionnaire survey in a small group, and randomly used one of them. Meanwhile, theactual earthquake insurance rates in the Tokyo Metropolitan Area are 0.169% for non-wooden houses and 0.313% forwooden houses.

11

<Table 2>

In this survey, by taking advantage of the internet-based questionnaire, we automaticallycalculated each resident’s “earthquake risk (objective probability of loss)” and “amount of risk assets”
based on the resident’s housing attributes (address (district or street), building structure, year oforiginal construction, gross floor area of house, purchase price of house, family structure) input by
each respondent5, and displayed them on the survey sheets (or controlled the amount of information
displayed).
Therefore, it became possible to determine the WTP by asking the resident the questions in therealistic situation where the actual house may be affected by an earthquake. In addition, since the“amount of risk assets” was standardized and presented on the survey sheets (the resident usuallydoesn’t know this precisely, resulting in considerable variation of resident’s prediction and unreliable
WTP estimation), it became possible to develop accurate individual data of the correspondence
relationship between each resident’s risk perception and WTP for disaster risk reduction, and also thecorrespondence relationship between the objective probability of loss and the subjective probabilityof loss. (See Figure 4, 5, 6 for the survey sheets)

5 The "probability of earthquake occurrence" was read from the 1 km mesh data of PSHM using the typical latitude

and longitude of the district (chō-me) where the resident is living, and the “probability of total collapse and halfcollapse” was calculated using the damage function based on the building damage data from the 1995 Great
Hanshin-Awaji Earthquake in Kobe (Murao and Yamazaki(2000)) and the data on each resident’s housing attributes
such as building structure and year of original construction. The "Potential earthquake damage level for the district"
was derived from the Comprehensive Hazard Map corresponding to the district where the resident was living.
The "amount of risk assets" was calculated by adding the value of home contents to the value of building asset.
The value of home contents was determined from the data on standard value of home contents corresponding to thefamily structure (age, number of family members, etc.) developed by Nissay Dowa General Insurance Co., Ltd.. The
value of building asset was calculated using the average construction unit cost of wooden or non-wooden structures in
the Tokyo Metropolitan Area based on the Annual Report on Building Statistics, the gross floor area of the house,
and also the age of the house in order to take into account age degradation. (See Kawawaki (2009) for a more detailed
numerical calculation.)12

<Figure 4><Figure 5><Figure 6>

In the meantime however, it is pointed out that CVM involves various biases associated withstated preference data and its estimation. Moreover, in the Internet survey, there is a question as towhether the monitors in the research company adequately represent general consumers.
In this survey the demographic composition of respective three subsamples such as age andgender is adjusted as close as possible to that of the census result of the Tokyo metropolitan area, andthe sample size ratio among municipalities is also confirmed to be not much different from the real
population distribution among municipalities. However, meticulous attention to bias is required forthe proper interpretation of this survey result.

4.2 Results of survey

The descriptive statistics of the survey result are shown in Table 3. As for the ratio of building
types, wooden detached housing was 40.0%, non-wooden detached housing was 9.9%, non-wooden
multifamily housing such as condominiums was 49.1%, and wooden apartment housing was 1.0%.
The average purchase price of a house was 46,860 thousand yen, the average construction year was
1990, the average number of family members was 2.9, and the average age of head of household was
48.5 years old. The average amount of gross asset (including liabilities) was 56,460 thousand yen(the median was 40,000 thousand yen) since all of the respondents were homeowners.
13

In addition, the average objective “probability of earthquake occurrence” of the districts where
the residents were living was 15.5% (the earthquake with ground motions equal to or larger than
JMA seismic intensity 6- within 30 years was assumed), and the average "ratio of asset loss toresident's risk asset" in case of earthquake was 16.5% (the earthquake with ground motions equal to
JMA seismic intensity 6+ was assumed)6.

<Table 3>

The average earthquake insurance cost presented in the first stage was 34.2 thousand yen (themedian was 23.4 thousand yen), and based on this, 53.7% of the residents answered “yes” topurchase the insurance.
In the second stage 31.3% of the residents who answered “yes” in the first stage answered “yes”
again to purchase the same insurance for the twice as much as the previous cost. On the other hand
46.2% of the residents who answered “no” in the first stage answered “no” again to purchase thesame insurance for the half as much as the previous cost (see Figure 7).

<Figure 7>

5. Estimation results

We estimated the two-stage binomial discrete choice model by the maximum likelihood method.
The descriptive statistics of the variables used for the estimation 7 are shown in Table 4, the6

Since the damage level was assessed as ‘total collapse’ when the asset loss was more than 50% and ‘half collapse’

when the asset loss was from 20% to 50%, we used the intermediate value of asset loss of 75% for ‘total collapse’and 35% for ‘half collapse’ to calculate the “ratio of asset loss to resident’s risk asset”.
7

Since we have built the model with the stock concept such that the resident’s utility level is a function of the14

estimation results of whole sample models are shown in Table 5, 6, and the estimation result ofsegmentation models is shown in Table 7.

<Table 4>

5.1 Estimation results of whole sample model

In the estimation result of the whole sample model, the degree of relative risk aversion was
estimated to 0.399 and the subjective probability of loss was estimated to 0.00184. Both of themwere significant at 1% level. (see Table 5)
The positive degree of relative risk aversion means resident’s risk-averse preference, which isconsistent with the theory8. The estimated subjective probability of loss was larger than the averageof the objective probability of loss among residents which was 0.00082.9 This is likely to be due tothe bias in risk perception of low frequency events, which is also consistent with the risk perception
theory. This indicates that it is not the residents’ underestimation of earthquake risk which causes the
low ratio of earthquake insurance policyholders to households.10
amount of gross asset (see Section 3), the income which comes from the flow concept is not included in the
explanatory variables. So we tested the effect of not including the income variable in the model by comparing theestimation results with and without the income variable using an expenditure function model. We found that theeffect of not including the income variable was primarily reflected in a little larger coefficient of “wealth” variable
and not so much reflected in the coefficients of “resident attributes” variables.
8Since most of the degree of relative risk aversion estimated by previous studies were 1.0 or larger (concerning thedemand for risky assets such as shares, Friend and Blume (1975) etc), the estimation result of this study seems to be alittle smaller. But when we used the objective probability of loss as a value of subjective probability of loss instead,the estimation result of degree of relative risk aversion became 1.647. It is considered that compared with the model
assuming people’s precise risk perception the estimation result of this study was smaller due to the effect of risk
perception bias.
9

The PSHM showed the probability of earthquake occurrence within 30 years. We converted the probability within

30 years (p(30)) into the probability within one year (p(1)) using the equation ( p (1) = 1 - exp (1/30 ln(1 - p(30))) )assuming that the probability of earthquake occurrence follows a Poisson distribution.
10

Fujimi and Tatano (2006) has demonstrated that the "ambiguity" inherent in the earthquake insurance contract

made it difficult for residents to purchase the earthquake insurance even if they had earthquake risk information and15

Another whole sample model including resident’s attributes which affect relative risk aversion isshown in Table 6.
The residents who were carrying the earthquake insurance had a larger degree of risk aversion,and the residents who had greatly raised awareness of disaster prevention after the Hanshin-Awaji
Earthquake also had a larger degree of risk aversion.

<Table 5><Table 6>

However, the residents who had experienced natural disasters didn’t always have a larger degreeof risk aversion. This suggests that earthquake risk and other natural disaster risks are considered tobe different types of risks in terms of residents’ risk perception.
The residents who thought that earthquake countermeasures taken by the government were more
important than those taken by residents themselves had a higher degree of risk aversion than theresidents who thought the reverse. This is the opposite result to our initial expectation that thoseresidents who think that earthquake countermeasures taken by the government are more important
would not be risk averse and would not invest in their own disaster risk reduction, as they would
expect a government bail-out after a disaster (the moral hazard). In reality, those residents have ahigher priority for safety and behave more risk aversively by themselves.
In addition, those who were 60 years old or over had a lower degree of risk aversion than others,
and men had a higher degree of risk aversion than women. The reason for these differences may bethat the elderly are not so much interested in the risk avoidance option for long-term housing asset
holdings, and that men are more deeply involved in the management of housing asset than women.
found the purchase of the insurance is advantageous to them on an actuarial basis.

16

5.2 Estimation results of Segmentation Model

This time, to analyze the effect of provision of risk information on the vulnerable houses andsafe houses respectively, we divided the whole sample into two subsamples of different
vulnerabilities (larger or smaller than the average of objective probability of loss), and also divided
each of them into three subsamples of different information levels. Then we estimated theparameters using those divided subsamples (see Table 7).
The estimation result shows that the degree of relative risk aversion was not significant in thevulnerable houses. The residents in the vulnerable houses had risk-neutral preference and did notchange their preferences according to the risk information level. On the other hand, the degree ofrelative risk aversion was larger and significant at 1% level in the safe houses. The residents in safe
houses had risk-averse preferences. Moreover, the degree of risk aversion became larger at
information levels 2 and 3. The provision of risk information might increase the degree of risk
aversion of the residents in safe houses11.
However, when we look at the estimate value closely, we can find it became largest at
information level 2.12 It is assumed that when only the hazard maps were presented, the mere
existence of danger was communicated to the residents, resulting in the increase of risk aversion of
the residents in safe houses. But when the specific probabilities of loss to each resident were
presented, the low earthquake risk of the safe houses became explicit and the uncertainty was
reduced, resulting in the decrease of risk aversion of the residents in safe houses.13

11

In a similar finding, Viscusi et al. (1999) reported that non-smokers tended to take information about risk forsmoking more seriously than smokers did.
12

To confirm whether there are statistically significant differences between the estimate values of relative risk

aversion, we tested it based on the variance of estimator. We found significant differences at 5% level only between
the information level 1 and 2 (the effect of provision of hazard map information) in the safe houses.
13

From this, the information level and the degree of risk aversion were not in a simple proportional relationship.

17

<Table 7>

Next, regarding the subjective probability of loss, it increased as the information level increased,
and became largest at information level 3 in the vulnerable houses 14 . The provision of risk
information increased the subjective probability of loss of the residents in the vulnerable houses.
However, the gap between subjective probability of loss and objective probability of loss (bias inrisk perception) became also larger as the information level became higher, and this contradicts thetheory. One of the possible explanations for this is that the information on large earthquake risk hada larger impact on the residents’ risk perception than the real impact on asset loss, since it is difficult
for them to understand the hazard map and the probability of loss precisely and respond correctly
according to the real risk level. The other possible explanation is that in this survey the informationof each district’s earthquake risk was also provided in addition to each house’s objective probabilityof loss, and it became clearer that a lot of vulnerable houses were located in the dangerous districts15
as the information level became higher.
On the other hand, there was no major change in the subjective probability of loss in the safe
houses even though it became slightly smaller when some information was provided. However, theThere are various types of risk information such as hazard maps, probability of loss, video image of damages, and soon. The degree of risk aversion is considered to be affected by the types of information or the way of providing
information. Hershey et al. (1982) demonstrated that, based on the data in the laboratory and the review of previous
studies, the way information is presented will make people focus attention on different aspects of things and change
their preferences. (This is called “context effect”.) It seems that in this study the presentation of the hazard map
shifted the residents’ interest on the aspect of existence of the risk while the presentation of the specific information
on probability of loss shifted the residents’ interest on the aspect of level of the risk. However, there are not sufficient
previous studies on this matter. Future research is necessary to understand this phenomenon in detail.
14

To confirm whether there are statistically significant differences between the estimate values of subjective

probability of loss, we tested it based on the variance of estimator. We found significant differences at 5% level only
between the information levels 1 and 3 (the effect of provision of specific probability information) in the vulnerable
houses.
15

35.1% of vulnerable houses were located in the district of potential damage level 3-5 of the Comprehensive

Hazard Map, while 23.8% of safe houses were located in the district of potential damage level 3-5.

18

risk perception bias in the safe houses was much larger than that in the vulnerable houses. Thissuggests that the residents in safe houses had not only risk-averse preference but also a tendency toestimate their probability of loss excessively. The subjective probability of loss was more than threetimes as much as the objective probability of loss, even though the specific information on theobjective probability of loss was provided for the residents in safe houses. With these facts it is notedthat changing residents risk perception is not an easy thing. 16

5.3 Willingness to pay and risk premium

Using the estimation results of the degree of relative risk aversion and the subjective probabilityof loss obtained by the above model, we calculated the WTP and the risk premium of arepresentative resident (see Table 8). We used the gross asset of 40 million yen (median of thesample) and the risk asset of 23.64 million yen (median of the sample) for a representative resident.
According to the calculation result, the WTP for earthquake risk reduction was 50,900 yen peryear when whole sample was used, and as for the breakdown of WTP, 43,500 yen per year was for
subjective loss and 7,400 yen per year was for risk premium. (The WTP for earthquake insurance
was 23,200 yen per year since we assumed the earthquake insurance which would cover up to 50%
of residents’ risk assets in this study.)
On the other hand, the objective loss was 19,300 yen per year. (We calculated this using theobjective probability of loss and risk asset of a representative resident. See Fig. 3.) The gap between
the WTP and the objective loss amounted to 31,600 yen. This gap includes the overestimation of16

One possible explanation for this is that it was difficult for residents to understand the earthquake risk presented in

the long-term probability and to reflect it on risk avoidance behavior such as purchase of earthquake insurance. The
other possibility is that there still existed uncertain events which might cause the unexpected loss such that theconsequent fire might break out after the earthquake or some unknown active faults might move. For example, theGreat East Japan Earthquake caused unexpected large scale of tsunami wave and also caused unexpected Fukushima
Daiichi Nuclear Power Plant accident.

19earthquake risk associated with the risk perception bias. If the model does not take into account therisk perception bias, the calculation result of risk premium may capture this "superficial risk
premium" resulting in the overestimation of risk premium (see Figure 8).

<Table 8><Figure 8>

The provision of risk information resulted in a great increase of WTP in vulnerable houses(54.6→88.2 thousand yen) and a slight decrease of WTP in safe houses (50.4→45.4 thousand yen).
The decrease of WTP in safe houses was not as large as the increase of WTP in vulnerable houses.
This suggests that the provision of risk information made residents in safe houses more risk averse.

6. Conclusion

This paper analyzed the relationship between provision of earthquake risk information andresidents’ WTP for disaster risk reduction by the Contingent Valuation Method, using questionnaire
survey data on the purchase earthquake insurance in the Tokyo Metropolitan Area, Japan.
The results suggested that when more precise and specific earthquake risk information isprovided, residents of vulnerable houses are willing to pay more for disaster risk reduction, while
those in safe houses are willing to pay slightly less than before.
As an explanation for these changes in risk aversive behaviors, it was suggested that residents ofvulnerable houses had larger subjective probability of loss while those in safe houses had a larger
degree of risk aversion as well as slightly smaller subjective probability of loss. (See Table 9)20

<Table 9>

In addition, there was a bias in residents’ risk perception. The subjective probability of loss wasmore than twice as much as the objective probability of loss. These biases were quite large in both
vulnerable houses and safe houses at all information levels. It didn’t seem to be easy to change
residents’ risk perception.
Figure 9 shows the changes of WTP of a representative resident for earthquake insurance
according to the risk information level. In the case of no information (information level 1) there was
not a large gap between WTP of vulnerable houses and that of safe houses; however, when more
information was provided (information levels 2 and 3), the gap between them became larger.
At information level 3, the WTP of vulnerable houses went up to a level which was higher than
the real earthquake insurance cost of a representative wooden house in Tokyo. This implies that thedisclosure of earthquake risk information can induce residents’ purchase of earthquake insurance
who are living in the high-risk houses located in densely populated districts of wooden buildings.
However, it should be noted that the “specific risk information” for each residence is needed toinduce this behavior and that what kind of risk information is provided makes a difference in the
degree of risk aversion. Especially the provision of “general risk information,” such as a hazard map
for the entire Tokyo metropolitan area, just increases the degree of risk aversion of the residents insafe houses who are originally behaving risk aversively, and does not increase WTP of the residents
in vulnerable houses sufficiently.

<Figure 9>

On the other hand, at information levels 2 and 3, the WTP of safe houses went slightly down to21

the level which was close to the real earthquake insurance cost of a representative non-wooden
house in Tokyo. This means some residents in safe houses such as newly built condominiums may
stop purchasing insurance once they come to know their houses are safe enough.
The increase of earthquake insurance holders in vulnerable houses and the decrease of those insafe houses will cause an imbalance between revenue and expenditures in the earthquake insurance
business. The current earthquake insurance rate is the same in each prefecture in Japan. This rateneeds to be more segmented reflecting earthquake risk of each building and district. The equalizationof earthquake insurance cost per risk is necessary.
Kunreuther (2008) explained that insurance premiums should be based on risk to provide signals
to individuals as to the hazards they face and to encourage them to engage in cost-effective
mitigation measures to reduce their vulnerability to catastrophes 17 . Integrated disaster risk
management, such as the combination of disclosure of risk information and the improvement of theearthquake insurance system, will be necessary.

References
Brookshire, D. S., Thayer, M. A., Tschirhart, J. and Schulze, W. D. (1985) “A Test of the Expected
Utility Model: Evidence from Earthquake Risks”, Journal of Political Economy, 93, 2,
pp.369-389.
Bureau of Urban Development, Tokyo Metropolitan Government (2008) The Sixth Survey ofDistrict-based Vulnerability to Earthquake Disaster .(in Japanese).
Friend, I. and Blume, M. E. (1975) “The Demand for Risky Asset”, American Economic

Review, 63, 5, pp. 900-922.
Fujimi, T. and Tatano, H. (2006) “Ambiguity, Risk and Earthquake Insurance Premiums: An
Empirical Analysis”, Annuals of Disaster Prevention Research Institute Kyoto University, No.

17

There is a concern for some residents in vulnerable houses who will be faced with large premium increases if

insurers adhere to the risk-based premiums policy. In this case Kunreuther (2008) insists that any special treatment
given to residents in vulnerable houses (for example, low income homeowners) should come from general publicfunding and not through insurance premium subsidies.

22

49C.
Ganderton, P. T., Brookshire, D. S., Mckee, M., Stewart, S. and Thurston, H. (2000) “Buying
Insurance for Disaster-Type Risks: Experimental Evidence”, Journal of Risk and Uncertainty,
20, 3, pp.271-289.
Hanemann, M. (1984) “Welfare Evaluations in Contingent Valuation Experiments with Discrete
Responses”, American Journal of Agricultural Economics, 66, pp.332-341.
Hanemann, M., Loomis, J. and Kanninen, B. (1991) “Statistical Efficiency of Double - Bounded
Dichotomous Choice Contingent Valuation”, American Journal of Agricultural Economics, 73,
pp.1255-1263.
Headquarters for Earthquake Research Promotion, Government of Japan (2008) “The Probabilistic
Seismic Hazard Map”.
Hershey, C. J., Kunreuther, C. H. and Schoemaker, J. H. P.(1982) “Sources of Bias in Assessment
Procedures for Utility Functions”, Management Science, 28,8, pp.936-954.
Kaoru,Y. (1998) “Abating Coastal Flood Risk through Insurance: Evaluation of the Risk Perception
and Willingness to Pay”, Nanzan Management Review, 13, 2, pp.285-307.
Kawawaki, Y. (2009) Economic Analysis of Asset and Risk, Doctoral Thesis of Osaka School ofInternational Public Policy, Osaka University. (in Japanese).
Kunreuther, H. (2008) “Reducing Losses from Catastrophic Risks through Long-Term Insurance and
Mitigation”, Social Research, 75, 3, pp.905-930.
Kunreuther, H., Ginsberg, R., Miller, L., Sagi, P., Slovic, P., Borkan, B. and Katz, N. (1978) Disaster
Insurance Protection: Public Policy Lessons. New York: John Wiley and Sons.
Lichtenstein, S., Slovic, P., Fichhoff, B., Layman, M. and Combs, B.(1978) “Judged Frequency ofLethal Events”, Journal of Experimental Psychology: Human Learning and Memory, 4, 6,
pp.551-578.
Matsuda, Y., Tatano, H. and Okada, N. (2005) “Measuring Risk Premium and Disaster Risk
Preference of Households in Use of CVM”, Infrastructure Planning Review, 22, 2, pp.325-334.
(in Japanese).
Murao, O. and Yamazaki, F. (2000) “Development of Fragility Curves for Buildings Based on
Damage Survey Data of a Local Government after the 1995 Hyogoken-Nanbu Earthquake”,
Transactions of AIJ. Journal of Structural and Construction Engineering, 527, pp.189-196. (inJapanese).
Nakagawa, M. (2003) Economic Analysis of Urban and Housing Policy: Experimental andEmpirical Approach on Discrimination and Risk in Cities, Nihon Hyoron Sha. (in Japanese).
Nakagawa, M., Saito, M. and Yamaga, H. (2009) “Earthquake Risks and Land Prices: Evidence fromthe Tokyo Metropolitan Area” The Japanese Economic Review, 60, 2, pp.208-222.
Slovic, P. (1997) The Perception of Risk, Earthscan.
23

Viscusi, K. W. (1998) Rational Risk Policy, Oxford University Press.
Viscusi, K. W., Magat, A. W. and Huber, J. (1999) “Smoking Status and Public Responses toAmbiguous Scientific Risk Evidence”, Southern Economic Journal, 66, 2, pp.250-270.
Willis, K. G. and Asgary, A. (1997) “The Impact of Earthquake Risk on Housing Markets: Evidence
from Tehran Real Estate Agents”, Journal of Housing Research, 8, 1, pp.125-136.
Yamaguchi, K., Tatano, H., Okada, N. (2000) “Information Provision and Risk Perception on Fatal
Disaster Risk in a Monocentric City”, Infrastructure Planning Review, 17, pp.327-336. (inJapanese).

24

Tables and Figures
Table 1. Outline of the survey
Date

20 August 2008～1 September 2008

Respondents

Male and female, aged 20 years or over, whohave their own houses

Number ofsample1,000 (Component ratio of sex and age
followed the census result in Tokyo)

MethodInternet survey(“Goo Research”, a research company, wasentrusted to conduct the survey)

Table 2. Provided earthquake risk information

(1)

(2)

(3)

(4)

Earthquake risk information

Note

Distribution map of earthquake

Earthquakes of Japan Meteorologicaloccurrence probability for the entire

Agency (JMA) seismic intensity scale > 6-

Tokyo metropolitan area[1]in the district within the next 30 years

Distribution map of potential earthquake

5-level index of potential building damage

damage level for the entire Tokyo

in the district due to earthquake shocks andmetropolitan area[2]consequent fires

Probability of earthquake occurrence for

Earthquakes of JMA seismic intensity scale

the district where the resident is living[1]> 6- in the district within the next 30 years

Probability of total collapse and half

The earthquake with the ground motion

collapse of the house where the resident

level of JMA seismic intensity scale 6+

is living in the case of earthquake

(5)

[3]

Potential earthquake damage level for the
district where the resident is living[2]Ranking of potential earthquake damage(6)

level for the district where the resident isliving among all of the districts in Tokyo

Level 2
information

Level 3
information

assumed5-level index of potential building damage
in the district due to earthquake shocks andconsequent fires
Ranking of the district from 1 to 5099
among all of the 5099 districts in Tokyo

metropolitan area
metropolitan area[2]Source: [1]The Probabilistic Seismic Hazard Map (PSHM), [2]The Survey on Regional Earthquake Risk: The
Comprehensive Hazard Map, [3]Murao and Yamazaki (2000)

25

Table 3. Descriptive statistics of resident’s attributes
Variables

Mean

S.D.

MedianPurchase price of the house (in 10 thousand yen)

4686

4670

4000

Year of home purchase

1993

13.94

1998

Year of home building

1990

15.03

1995

Gross floor area of the house (in square meter)

91.03

52.04

80

Number of family members

2.94

1.15

3

Age of head of household

48.46

11.54

50

Amount of gross asset(including liabilities) (in 10
thousand yen)

5646

5452

4000

Objective probability of earthquake occurrence (%)

15.53

8.42

11.9

16.52

13.63

12.6

2.06

1

2

2180

1348

2041

Objective ratio of asset loss to resident's risk
asset (%)
Potential damage level by earthquake for the
district (1:lowest～5:highest)
Ranking of potential damage level by earthquake
for the district (1～5099)

Table 4. Descriptive statistics of variables
Variables

Purchase of
insurance

Description

Mean

S.D.

MedianPurchase of insurance (first stage) (=1 if purchase)

0.537

0.499

1

0.313

0.464

0

0.538

0.499

1

3.422

3.362

2.34

6.843

6.724

4.68

1.711

1.681

1.17

7046

6104

4999

2603

1427

2364

Carrying earthquake insurance (=1 if yes)

0.417

0.493

0

Awareness of disaster prevention raised after Hanshin-Awaji earthquake(=1 if raised very much)

0.271

0.445

0

Having experience of natural disasters (=1 if yes)

0.067

0.25

0

The thought whether responsible actors for earthquake disaster prevention aregovernments or residents including a respondent himself (=1 if governments)

0.472

0.499

0

Age (=1 if 60 years old or over)

0.251

0.434

0

Sex (=1 if male)

0.494

0.500

0

Purchase of insurance(second stage; twice as much as insurance cost provided)(=1 if purchase) Number of observations: 537
Purchase of insurance(second stage; half as much as insuranace cost provided)(=1 if purchase) Number of observations: 463

Insurance cost provided (first stage) (in 10 thousand yen)
Insurance cost
Insurance cost provided (second stage; twice as much as insurance cost provided)
provided(in 10 thousand yen)（c）
Insurance cost provided (second stage; half as much as insurance cost provided)(in 10 thousand yen)
WealthAmount of gross asset (in 10 thousand yen)（y0）※Largest value of the band is employed
Risk asset
Amount of asset covered by earthquake insurance (in 10 thousand yen)（l0）(Present value of building assets and home contents)

Resident
attributes（X）※To avoid the case the amount of asset becomes negative when the resident suffered loss, the largest value of each band(～499,
500～999, 1000～1999, 2000～2999, 3000～4999, 5000～9999, 10000～19999, 20000～; in 10 thousand yen) was employed.

26

Table 5. Estimation result of whole sample model
Relative risk aversion（γ）

0.399(16.16)***

Subjective probability of loss（p）

0.00184(23.12)***

Subjective probability of loss /Objective probability of loss
Standard deviation（σ）

Log likelihood（L）
Number of observations（N）

2.25
0.143
(4.55)***-1780.63
1000

Table 6. Estimation result of whole sample model with resident’s attributes
Relative risk aversion (γ(X))
Constant
Carrying earthquake insurance （1,0）
Awareness of disaster prevention raised verymuch after Hanshin-Awaji earthquake （1,0）
Having experience of natural disasters （1,0）
Thought that responsible actors for earthquake
disaster prevention are governments （1,0）
Age(60 years old or over) （1,0）
Sex(male) （1,0）
Subjective probability of loss(within a year)（p）Standard deviation（σ）
Log likelihood （L）
Number of observations （N）

0.341(11.84)0.0387(6.98)
0.0315(5.87)
0.0102(1.37)
0.0205(3.71)-0.0262(-4.03)0.0310(5.41)***

0.00167(23.66)***0.152(4.02)***-1747.86
1000

Notes: Numbers in parentheses are t-values. *** indicates the 1% levelof significance.

27

***
***

***
***
***

Table 7. Estimation results of segmentation model
Sample of vulnerable houses[Sample whose objective probability of loss is larger than average]

Relative risk aversion（γ）

Subjective probability of loss（p）

Informationlevel 1

Informationlevel 2

Informationlevel 30.163
(1.64)-0.127(-1.15)0.00863(0.06)

0.00217(5.35)***

0.00282(7.18)***

0.00372(6.30)

Subjective probability of loss /Objective probability of loss

1.34

1.58

2.01

Standard deviation（σ）

0.76(1.17)

9.13(1.07)

3.66(0.82)

Log likelihood（L）-156.02-174.33-180.08

93

100

98

Number of observations（N）***

Sample of safe houses[Sample whose objective probability of loss is smaller than average]

Relative risk aversion（γ）

Subjective probability of loss（p）

Informationlevel 1

Informationlevel 2

Informationlevel 30.336
(4.51)***

0.583(10.39)***0.463(6.12)***

0.00187(8.83)***

0.00148(8.58)***

0.00160(11.82)***

Subjective probability of loss /Objective probability of loss

4.16

3.39

Standard deviation（σ）

0.231(1.56)

0.0308(1.99)

Log likelihood（L）-409.73-407.08-426.96

234

234

241

Number of observations（N）

3.70

**

0.0864(1.51)

Notes: Numbers in parentheses are t-values. ***, ** and * indicates the significance level of
1%, 5% and 10% respectively. Subjective probability/Objective probability is calculated by
dividing estimate of subjective probability of loss by average of objective probability ofconcerning samples.

28

Table 8. WTP of a representative resident for earthquake risk reduction(Thousand yen per year)
Informationlevel 1

Informationlevel 2

Informationlevel 3Whole
sample

50.5

51.5

50.7

50.9

・Subjective loss

44.4

43.3

43.7

43.5

・Risk premium

6.0

8.3

6.9

7.4

WTP of vulnerable houses

54.6

63.6

88.2

69.1

・Subjective loss

51.3

66.7

87.9

69.5

・Risk premium

3.3

-3.1

0.3

-0.4

50.4

44.2

45.4

46.2

・Subjective loss

44.2

35.0

37.8

38.1

・Risk premium

6.2

9.2

7.6

8.1

WTP of whole sample

WTP of safe houses

Table 9. The effect of provision of earthquake risk information
Risk preference

Subjective
probability of loss

WTP for disaster risk
reduction

Vulnerable
housesStay risk neutral

Become much larger

Increase greatly

Safe houses

Become more risk
averseNo big change(Slightly smaller)

Decrease slightly

29

Figure 1. Provision of risk information and change in WTP

Figure 2. VNM utility function and risk premium
V（Level of Indirect utility）
V（Normal situation）

O

E[V]（Expected utility）

V（In case of loss）

V(y;γ )B'
B

A

Risk premiumρ

Subjective loss
yE

y0-l 0y*y（Level of Wealth）

WTP

y0

Figure 3. Evaluation of earthquake risk

30

Figure 4. A part of the survey sheet (Questionnaire on purchase of earthquake insurance)

31

Figure 5. A part of the survey sheet (The earthquake disaster risk information (1))

32

Figure 6. A part of the survey sheet (The earthquake disaster risk information (2))

33

Figure 7. Flow chart of the purchase of earthquake insurance(First stage)(Second stage)purchase
53.7%

Information on
double insurance
cost provided

purchase
31.3%

not purchase
68.7%

46.8 thousand yen/year(Median)Information on
Insurance costprovided
23.4 thousand yen/year(Median)not purchase
46.3%

Information on
half insurance
cost provided

purchase
53.8%

not purchase11.7 thousand yen/year(Median)Figure 8. Relation between WTP and risk premium

WTP for earthquake disaster risk reduction
50.9 thousand yen

Subjective loss
43.5 thousand yen

Risk premium
7.4 thousand yen

Bias in risk perception

Objective loss
19.3 thousand yen

Superficial risk premium
31.6 thousand yen

34

46.2%

Figure 9. Changes of WTP for earthquake insurance according to the risk information level
WTP f or ea rthqua k e i ns ura nce(thousand yen)
③ 44.0 Vulnerable houses【Infromatnion level 3】40● 37.0 Ea rthqua k e i ns ura nce cos t i n Tok y o( Wooden hous es )②

32.6 Vulnerable houses【Information level 2】①

26.4 Vulnerable houses【Information level 1】30① 23.4◎ 20.7③ 20.420● 20.0② 19.3

Safe houses【Information level 1】
Objective loss of vulnerable houses × 1/2
Safe houses【Information level 3】
Ea rthqua k e i ns ura nce cos t i n Tok y o( N on-w ooden hous es )Safe houses【Information level 2】10◎ 5.2 Objective loss of safe houses × 1/2

35