GraSPP-DP-E-09-001 and RMPP-DP-E-09-001

Household Responses to Extreme Shocks:
Evidence from the Kobe Earthquake

Yasuyuki Sawada (corresponding author)
and Satoshi Shimizutani
January 2009

RMPP
GraSPP Discussion Paper E-09-001

RMPP Discussion Paper E-09-001

GraSPP-DP-E-09-001 and RMPP-DP-E-09-001

Household Responses to Extreme Shocks:
Evidence from the Kobe Earthquake
Yasuyuki Sawada(corresponding author)
Faculty of Economics,University of Tokyo
7-3-1, Hongo ,Bunkyo-ku, Tokyo, 113-0033, Japan
Email:sawada@e.u-tokyo.ac.jp
andSatoshi Shimizutani
Institute for International Policy Studies (IIPS)
Toranomon 30 Mori Bldg., 6F, Toranomon 3-2-2
Minato-ku, Tokyo 105-0001, Japan
Email: sshimizutani@hotmail.com
Running title: Kobe earthquake
This version: January 2009

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Discussion Papers are a series of manuscripts in their draft form. They are not intended
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Household Responses to Extreme Shocks:
Evidence from the Kobe Earthquake*byYasuyuki Sawada (corresponding author)
Faculty of Economics, University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
Email: sawada@e.u-tokyo.ac.jp

andSatoshi Shimizutani
Institute for International Policy Studies (IIPS)
Toranomon 30 Mori Bldg., 6F, Toranomon 3-2-2
Minato-ku, Tokyo 105-0001, Japan
Email: sshimizutani@hotmail.com

Running title: Kobe earthquake
This version: January 2009*

We would like to thank the Quality of Life Policy Bureau, Cabinet Office, and Hyogo Prefecture for
permitting us to use their valuable micro dataset. We are also grateful to Hidehiko Ichimura for theirconstructive comments and Sompo Japan for their financial support. The views expressed in this study
are our own and do not represent those of any of the organizations we are affiliated with.

1

Abstract

Using a unique household-level dataset pertaining to the situation of the 1995 Kobe earthquake,we address how households deal with extreme shocks. Our results show that changes in the
expenditure of different consumption items before and after the disaster depend on the type ofdamage caused by the earthquake to each household in terms of income, housing, assets, andhealth. Moreover, we find that households’ utility across different expenditure items is notseparable. These results suggest the difficulties in carrying out in-kind transfers to theearthquake victims and the superiority of cash transfers when damages are easily verifiable.
Moreover, risk-coping strategies depend on the damage: it was found that households borrowed
extensively against housing damage but relied on dissaving to cope with smaller asset damage,
implying that the risk-coping strategies are specific to the nature of the loss caused by theearthquake.

JEL codes: D12; D52; E21
Keywords: Natural disasters; Risk-coping strategies; Earthquake21. Introduction

In a review article on the Kobe earthquake published in this journal, Horwich (2000)
pointed out that events preceding and following an earthquake are seldom analyzed in terms ofbasic economic concepts.1

This seems to be a serious omission in the relevant literature,

because a number of natural disasters have taken place with increasing frequency in bothdeveloped and developing countries, and with climate change, this number is expected toincrease (Khan 2005; EM-DAT 2007).2

The purpose of this study is to fill this gap in theliterature by addressing how households deal with extreme risks posed by natural disasters. For
this purpose, in this study, we analyze households’ responses to the Kobe earthquake by utilizing
a unique household-level dataset collected from the Kobe area in Japan shortly after thedevastating earthquake of 1995.
Along with other natural disasters, earthquakes surely result in extreme shocks that areexogenous, unexpected, and which often transmit a variety of risks to households. Moreover, inthe case of the Kobe earthquake, there were no effective public compensations for thereconstruction of houses in spite of severe damages (Sawada and Shimizutani 2008, 2005); hence,
this particular situation is ideal to examine households’ pure responses to extreme events.
Utilizing this natural experimental situation, we conduct two analyses: first, we explore whether
the change brought about in different household expenditure items depends on the loss to income
and damage to housing, assets, and health caused by the Kobe earthquake; second, using theframework developed by Fafchamps and Lund (2003), we investigate how the earthquake
victims utilized risk-coping strategies to mitigate changes in consumption. While most of theprevious studies have examined these issues separately, we adopt an integrated approach,3

examining both the consumption response to a variety of unexpected shocks and the factors
determining what risk-coping strategies are adopted.
By conducting these analyses, we aim to quantitatively corroborate what Horwich(2000) pointed out: in spite of the tremendous damage, economic recovery in the Kobe area wasmuch faster than initially expected. Lessons from the “successful recovery” in the wake of theKobe earthquake may be crucial for designing appropriate policies to deal with future natural
disasters in both developed and developing countries.
To preview our empirical results, our analyses show that the expenditures of mosthousehold consumption items are affected by the damage caused by the earthquake and thathouseholds’ utility across different expenditure items is not separable. Our results indicate thatwhen examining consumption response to a variety of shocks, it is inappropriate to impose theseparability assumption that has commonly been imposed in most of the previous studies on thebasis of the Panel Study of Income Dynamics (PSID) data (Blundell, Pistaferri, and Preston2006). While we acknowledge the limitations of our dataset, we provide new evidence
invalidating the separability assumption. Our result is consistent with that of Attanasio andWeber (1995), who show that the consideration of food consumption in isolation can yield
misleading results.
Second, on conducting a comparative analysis on three different risk-coping strategies,
namely, dissaving, borrowing, and transfers from private and public sources, we found that thesestrategies are specific to the nature of the loss caused by the earthquake. Households borrowextensively to compensate for housing damage but rely on dissaving to compensate for damage
to other assets.

Transfers are likely to be effective against mild shocks and are adopted

particularly by multigeneration households.

4

By performing these analyses, we also aim to contribute to the debates on effective risk
management policies against natural disasters. When the different types of damages are evident,
it facilitates “targeted” in-kind transfers such as the provision of physical assets and houses.
Our results, however, indicate that income, asset, housing, and health shocks affect different
expenditure items simultaneously and that households’ utility across different expenditure items
is not separable.

These results suggest difficulties in carrying out targeted in-kind transfers to

the earthquake victims, because providing in-kind transfers to a targeted expenditure item will
also change the marginal utility from other expenditure items.

This interrelationship among

different expenditure items complicates the procedure of fine-tuning the optimal mix of in-kindtransfers. This is consistent with the emerging consensus among development policymakers on
the suitability of cash and voucher transfers, rather than in-kind transfers, in complex
emergencies arising from natural disasters (Harvey 2007). Alternatively, as discussed later, thegovernment may organize transfers in more liquid forms or subsidized loan programs.
Although this study is not the first to examine the effects of the Kobe earthquake, wewould like to emphasize that this study differs from the previous ones in two important respects.
First, there has been no research examining the consumption responses to a variety of shocks thatconsiders the nonseparability of goods in the context of natural disasters.3

Second, we

explicitly examine the relative effectiveness of various risk-coping strategies against unexpectednatural disasters. As per our knowledge, only a few studies have employed household-level
data to jointly and quantitatively investigate the role of savings, borrowing, and other risk-coping
strategies (Rosenzweig 2001). However, an exception is a study by Fafchamps and Lund
(2003), which investigated the joint determination of risk-coping strategies in the rural
Philippines; we take a similar approach to examine households’ choice of risk-coping strategies

5against natural disasters.
In another paper using the same dataset (Sawada and Shimizutani 2008), we separately
examined the determinants of mutual insurance mechanisms through borrowing and privatetransfers, mainly focusing on the difference between borrowing-constrained and nonconstrainedhouseholds. However, the paper does not address the joint determination of risk-coping
strategies, nor does it consider self-insurance (dissaving), and thus, it did not allow us to examine
the choice of risk-coping strategies under nonseparability in response to extreme shocks.

Thus,

we bridge this gap in the previous studies by employing a general model that takes into
consideration both separable and nonseparable cases.
The remainder of this paper is organized as follows: Section 2 provides a brief overview
of the dataset employed in this study; Section 3 examines whether consumption response
depends on the damage incurred by each household; Section 4 presents the estimation results ofthe determinants of risk-coping strategies; and finally, Section 5 presents the concluding remarks.

2. Dataset and Summary Statistics

We take advantage of a rich household-level dataset from the Shinsai-go no Kurashi noHenka kara Mita Shohi Kozo ni Tsuite no Chosa Hokokusho (Research Report on Post-Disaster
Changes in Lifestyles and Consumption Behavior). The survey was conducted by the Hyogo
Prefectural government in October 1996—twenty months after the disaster—in five areas thatwere seriously damaged by the earthquake: Kobe’s Higashinada, Kita, and Suma wards; Akashi
City; and Nishinomiya City (Hyogo Prefecture 1997).4

The survey was completed by 1,589

women aged over 30, who were selected on the basis of a stratified random sampling method.

6

The definitions and summary statistics of the variables employed in this study areprovided in Table 1. First, we observe a variety of shocks caused by the earthquake.5
Approximately 70% of the sampled households suffered damage to their housing: approximately
30% suffered major or moderate damage, while approximately 40% suffered minor damage.
Moreover, over 90% of the respondents experienced some sort of damage to household assets:
approximately 70% of the sampled households suffered major damage, while approximately
20% suffered minor damage. In addition, approximately 20% of the respondents observed
adverse effects on the health of a family member. With regard to the income changes before
and after the earthquake, approximately 6% and 34% of all the respondents faced positive andnegative income shocks, respectively, while approximately 60% of the respondents reported no
income shock. These figures demonstrate the gravity of the economic loss caused by theearthquake.
Second, we summarize the household responses against these damages. We find thatover 60% of the respondents answered that their household’s overall consumption changed after
the earthquake. Although our data do not allow us to identify whether consumption increased
or decreased, it is clear that a large proportion of victims altered their consumption behavior after
the event.6 The survey also presented ten different expenditure items and asked the respondents
with regard to which item their consumption changed. We observe a large variation across theexpenditure items.

Approximately 30% of the households brought about a change in theirexpenditure on furniture, reflecting the large proportion of residents whose household assets
suffered damage.

Moreover, approximately 25% of the households altered their expenditure on

clothing and over 20% altered their expenditure on daily necessities.

In contrast, less than 10%of the households altered their expenditure on luxuries, leisure, or gifts. We will later examine

7

the types of shocks that brought about changes in the consumption behavior in each item.
Third, the survey asked respondents if they had experienced an unexpected, forced
increase in expenditure due to the earthquake, and if so, how they had coped with such anincrease. Over 80% of the respondents replied that they had experienced an increase in theirexpenditure.7 Among the households that increased their expenditure because of the earthquake,approximately 25% managed to cope by altering the composition of their consumption and over
50% relied on their savings. Borrowing and receiving transfers constituted importantrisk-coping strategies for approximately 10% and 12% of valid responses, respectively.
Finally, with regard to household characteristics, the rate of house ownership was
approximately 70% prior to the earthquake and approximately 30% of all households hadoutstanding housing loans.

The average age of the respondents was 51 years, and the educationlevel of a majority of the respondents was high school graduate or lower. A majority of the
respondents lived with their children, while approximately 20% lived with their parents or
grandchildren.

With regard to marital status, 5% of the respondents were single.8

Finally, inorder to control for average consumption changes and unobserved heterogeneity, which may
result from differences in the impact of the earthquake, we include district-specific dummy
variables. Since, the average effects of the earthquake are determined by, among other things,
the proximity to its hypocenter, we believe that the inclusion of the district dummies isreasonable.

3. Consumption Response to an Extreme ShockFirst, we examine whether change in different expenditure items before and after the8earthquake depends on the damage incurred by each household. We employ the following
specification to address this issue:

K

Δcim = ∑ δ k Rka + Siγ + X i β + uim ,

(1)

k =1

where m refers to each item of goods (m = 1, 2,…, 10); k is an identifier of regional insurance
networks; and Ra is a dummy variable that is equal to 1 if the ith household is located in theregion k. We use the area dummies for the variable Ra to control for the consumption averages.
The matrix S comprises the indicators of the loss to income and damage to housing, household
assets, and health, while the matrix X consists of household characteristics to control for theobserved household taste shocks.

The final term on the right-hand side of the equation is awell-behaved error term.
Unfortunately, we do not have data on the expenditure amounts. However, qualitative
information on changes in the expenditure on the following ten consumption items is available:
food, daily goods, clothing, luxuries, leisure, gifts, furniture, electronic products, housing, andemergency supplies. We construct an indicator variable, Ic, which takes the value of 1 if achange in a household’s expenditure on item m is observed and the value of 0 if no change in
consumption is observed.

K ~~I im = 1⎡ ∑ δk Rka + Si ~γ + X i β + u~im ≠ 0⎤⎢⎣ k =1⎥⎦(2)

As discussed in Section 2, it is natural to assume that the households’ preferences with regard to9multiple goods are not additively separable in the face of an unexpected disaster shock; hence,
we consider the joint determination of expenditures on each item. Under the assumption of thejoint normality of the error terms, our model is a ten-equation multivariate probit model thattakes into account the fact that household expenditure is determined by a simultaneousdecision-making process.

As Sawada and Shimizutani (2007b) have shown by using theframework developed by Mace (1991), if the utility function is separable, error terms should be
uncorrelated across consumption items, but if it is not separable across goods, the error term ineach equation is correlated with those in the other equations. For identification, we need to
impose the conditions that var(ũim) = 1 for all m and that the variance-covariance matrix, ũim, issymmetric. In order to estimate the parameters under this setting, we employ a log-likelihood
function, which depends on the joint standard normal distribution function. We utilize thealgorithm given in Cappellari and Jenkins (2003) in order to estimate the multivariate probit
model using the method of simulated maximum likelihood, which is also known as theGeweke-Hajivassiliou-Keane (GHK) estimator.
Table 2 reports the estimation results. First, we observe that the negative income shock
significantly affects the consumption behavior for all the items, except gift and emergency
supplies; however, this is not the case for positive income shock. This implies that the income
shock effect on consumption by items is asymmetric and also different from the pattern observedin the effect of damages to housing and household assets. Second, we find that all the
expenditure items, except luxuries and gift, depend on the damage caused to housing and/orhousehold assets of each household. On closely examining each item, we find that theexpenditure on furniture and housing is proportional to the housing damage; however, in the caseof other items, most of the coefficients on housing damage are not statistically significant.

The

10

households that suffered damage to their household assets were more likely to alter theirexpenditure on daily goods, clothing, furniture, electronic products, and emergency supplies.
Third, it is interesting to note that changes in the expenditure on five out of ten items arecorrelated with the variable indicating that the earthquake adversely affected the health of afamily member, thus implying that health shocks substantially alter consumption behavior.
Finally, although we do not report the covariances for each pair of error terms, all thecovariances are positive and statistically significant at the 1% level, indicating that ourassumption of nonseparability across goods is supported empirically.
In summary, our analyses produce two main findings. First, we conclude that change
in most of the expenditure items before and after the disaster depends on the damage incurred byeach household. Specifically, we find that income declines affect most of the expenditure items,
except gifts and emergency supplies; housing damages affect not only housing expenditure but
also nonhousing expenditures such as leisure and furniture, and asset damages affect nonasset
expenditures such as daily goods, clothing, and emergency supplies. These results suggest thedifficulty in carrying out targeted in-kind transfers to the earthquake victims, because providing
housing support to housing damages, for example, may also alter the expenditure of other items
such as electronic products.

Presumably, fine-tuning transfer amounts to achieve the optimal

expenditure allocation pattern will not be easy. Second, we find corroborative evidence thathouseholds’ preferences were not separable across multiple goods and that the various types ofdamage sustained caused a substantial variation in expenditure on each item. When theassumption of separability does not hold, the optimal consumption condition for the individualcommodity depends on the consumption of other commodities as well; this also indicates thedifficulty of using targeted in-kind transfers to compensate for specific losses suffered by

11expenditure items.

In other words, these results suggest the superiority of transfers in more

liquid forms, such as cash transfers. This may be considered as counterintuitive, because
theoretical works such as Besley and Coate (1991) concluded that in-kind transfers can lead to aself-targeting mechanism that induce only the intended recipients to participate with the others
opting out. In contrast, our results imply that cash transfers allow recipients to optimize what
they should spend the money on under a situation of imperfect observability of household
responses. We should also note that an earthquake creates observable and verifiable damages,
and thus, screening problems analyzed by Besley and Coate (1991) seems to be less problematic.
In practice, targeting cash support does not seem to be more problematic than targeting in-kindassistance (Harvey 2007).

4. Determinants of Risk-Coping Strategies

Next, we apply the empirical model of Fafchamps and Lund (2003) and investigate
households’ risk-coping strategies against damages caused by the Kobe earthquake. Since theadoption of a particular risk-coping strategy is observed as a discrete variable in our data, we
jointly estimate three binary-dependent variable models on the basis of different risk-coping
strategies. On the basis of the correlations of error terms, we assume that the three different
risk-coping strategies are interdependent.

Δbi = Siθ1S + Xiβ1 + ε1i,

(3)ΔyTi = Siθ2S + Xiβ2 + ε2i,(4)Δdi = Siθ3S + Xiβ3 + ε3i,

(5)

12

p1i = 1[Δbi > 0],(6)p2i = 1[ΔyETi > 0],(7)p4i = 1[Δdi > 0].

(8)

We do not directly observe the intensities of the risk-coping strategies, that is, Δb, ΔyT, and Δd;rather, whether a particular risk-coping strategy is adopted is observed as a discrete variable.
Hence, our dependent variables indicate whether a household adopted a particular risk-coping
strategy, which can be represented by three indicator variables: pj, j = 1, 2, and 3. We assume
that the variance-covariance matrix, εji, is symmetric and the covariances are not necessarily 0.
For identification, we need to impose the condition var(ε1i) = var(ε2i) = var(ε3i) = 1. Under theassumption of the joint normality of the error terms, our model is a three-equation multivariate(trivariate) probit model. Similar to the estimation carried out in the previous section, we
employ the algorithm given in Cappellari and Jenkins (2003) to obtain the GHK estimator.
Table 3 reports the results, which can be summarized as follows. First, the negative
income shock variable has a positive coefficient for the borrowing equation, indicating thatborrowing is effective against negative income shock. The positive income change variable
positively affects transfers, possibly suggesting that more transfers are made if incomes increase;this may be considered as an example of the self-interested exchanges in Cox (1987). However,these income change coefficients are largely insignificant.
Second, the column for borrowing reveals that people primarily coped with major orminor housing damages by borrowing. In addition, we observe that borrowing was possible
particularly for those who owned houses prior to the earthquake; this highlights the importanceof land collaterals in obtaining a loan after the earthquake (Sawada and Shimizutani 2008).

13

Alternatively, credit-constrained households might have been unable to utilize borrowing as arisk-coping strategy against the negative shocks caused by the earthquake. The marginallysignificant and positive coefficient on the dummy indicating whether a respondent lived withparents or grandchildren, is consistent with anecdotal evidence suggesting that using housing
loans for the construction of nisetai jutaku (two-generation houses) gained immense popularity
among the households that lost their houses to the earthquake, since households with multiple
generations found it easier to borrow and fund the construction of new houses.
The column for transfers in Table 3 shows the results for the determinants of aggregatetransfers from private and public sources. While the results are less evident than those on
borrowing, we find that the coefficient on the moderate housing damage is statisticallysignificant, suggesting that households weathered such damage with the help of transfers.

The

correlation between transfers and the extent of the damage is partly explained by the fact that the
public committee in charge of disbursing funds expended larger amounts to households that
suffered greater housing damage.9 In contrast to borrowing, the coefficient on the dummy
variable indicating whether a respondent lived with parents or grandchildren is negative andsignificant, implying that those households are less likely to depend on transfers; this is natural
since the family members of such households are more likely to suffer from the same disasterand such households are less affordable than those where family members live separately and are
more likely to be insured against damages caused by the earthquake. Moreover, mostcoefficients on the area dummies are significantly negative, implying that the transfers areaffected by each region’s indigenous character.
Third, the last column reports the results on the effectiveness of self-insurance. Since
the coefficient on the dummy variable for minor household asset damage is positive and14marginally significant, we may conclude that dissaving was employed as a risk-coping strategy
only in the case of minor damage to households’ assets. In addition to the finding thathouseholds relied on dissaving to compensate for smaller losses but coped with larger shocks byborrowing, our empirical findings suggest the existence of a hierarchy of risk-coping measures,from dissaving to borrowing. Moreover, those living with children were less likely to use
dissaving as a risk-coping strategy, as they probably had not accumulated sufficient
precautionary savings. These overall results on risk coping present a different picture from thesubjective responses in Table 1, where more than half of the respondents reported that dissaving
was the most important risk-coping strategy.

This may suggest a potential problem in relying

on subjective damage and risk-coping assessments of disaster victims.
Finally, the estimated correlations between the error terms in the trivariate probit models
are shown in Table 4. The correlations overwhelmingly reject the null hypothesis ofindependent error terms, a finding that supports the adoption of the trivariate probit model.
More importantly, the covariances for the error terms of both the borrowing and dissaving
equations, and the transfer and dissaving equations, are negative. These findings imply that
there exists an unobservable factor accounting for the negative correlation between dissaving andother risk-coping strategies, and they suggest that self-insurance acts as a compensation for thelack of mutual insurance. On the other hand, the covariance of the error terms of borrowing
and transfers is positive, suggesting a complementary relation between borrowing and transfers,
though the coefficient is not significant.10

5. Concluding Remarks

15

In this study, we examined how people in the areas damaged by the 1995 Kobe
earthquake altered their expenditure and compensated for the losses sustained. We utilized aunique household-level dataset collected shortly after the earthquake. First, according to ourestimation results, the changes in the expenditure before and after the earthquake depend on thedamage incurred by each household; moreover, our results support a model that shows thathouseholds’ preferences are not separable across consumption items. Second, we investigatedthe effectiveness of households’ strategies to cope with losses caused by the earthquake, because
we observed that households were able to adopt a wide variety of risk-coping strategies against
the negative shocks caused by the earthquake. We found that the coping strategies were
specific to the nature of the shock sustained: borrowing was extensively used to cope withhousing damage, while dissaving was used for compensating smaller asset damages. Thesefindings suggest the existence of a hierarchy of risk-coping measures, from dissaving toborrowing.
Two policy implications can be drawn from these findings. First, our empirical results
show that the shock effects of the Kobe earthquake were diverse across households: while
negative income shock altered consumption behavior in most of the items, asset shock affectedcertain items.

In the case of the Kobe disaster, effective compensations for housing were notavailable in spite of huge damages incurred by households, but the recovery was relatively quick.
This finding poses an important issue on the modality of disaster relief programs, that is, cash or
in-kind transfers.
In the literature focusing on poverty, the exclusion and inclusion errors of means-tested
targeting have been a serious concern under asymmetric information between government andtransfer recipients (Besley and Coate 1992).

An earthquake, however, generates tangible and16verifiable damages, and thus, inclusion and exclusion errors in targeting policies seem to be lessproblematic. Moreover, our results indicate that income, asset, housing, and health shocks
affect different expenditure items simultaneously and that households’ utility across different
expenditure items is not separable. These results suggest difficulties in carrying out targetedin-kind transfers to the earthquake victims, because providing in-kind transfers to a targeted
expenditure will change the marginal utility from other expenditure items as well.

Thisinterrelationship among different expenditure items complicates the procedure of fine-tuning the
optimal mix of in-kind transfers. Alternatively, the government may organize transfers in more
liquid forms. Hence, our finding implies that cash transfers may be preferable when damages
are easily identifiable and utility function is not separable across goods and services. This isconsistent with the emerging consensus among development policymakers on the suitability of
cash and voucher transfers, rather than in-kind transfers, in complex emergencies arising fromnatural disasters (Harvey 2007).
Second, the quick recovery process in Kobe can be attributed to self-insurance andmutual insurance through borrowing. After the earthquake, the central and local governments
provided financial support—the largest in the history of Japan—for the reconstruction of theaffected areas and the victims’ welfare.

However, because of the large number of victims, theaverage direct transfers to them were small.

The earthquake victims apparently combined

self-insurance and mutual insurance schemes effectively and did not depend heavily on
government support, which explains the expeditious recovery. Our empirical results suggestthat providing subsidized loans to victims may be effective in facilitating risk-coping behavior in
the form of self-insurance. More importantly, such interventions are less likely to create serious
moral hazard problems which are pointed by Horwich (2000). Although we do not deny that17

the government has to play a role in the face of natural disasters, policies to ensure that people
take necessary precautions independently and self-insurance are necessary for preparing
well-designed social safety nets against future natural disasters.

18

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Blundell, Richard, Luigi Pistaferri, and Ian Preston. 2006. “Imputing Consumption in the PSID
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Cappellari, Lorenzo, and Stephan P. Jenkins. 2003. “Multivariate Probit Regression Using
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Chosa Hokokusho (Research Report on Changes in Lifestyles and Consumption
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20

Table 1: Descriptive Statistics of the Variables
Description of Variables

Mean(Standard
Deviation)

Shock Variables

Dummy = 1 if the earthquake caused major housing damage
Dummy = 1 if the earthquake caused moderate housing damage
Dummy = 1 if the earthquake caused minor housing damage
Dummy = 1 if the earthquake caused major household asset damage
Dummy = 1 if the earthquake caused minor household asset damage
Dummy = 1 if the earthquake adversely affected the health of a family
memberDummy = 1 if income did not change (default category)
Dummy = 1 if income increased
Dummy = 1 if income decreased
Dummy = 1 if information on income change is missing

0.129
0.175
0.409
0.079
0.707
0.177
0.593
0.062
0.336
0.009

Expenditure shock

Dummy = 1 if household consumption behavior changed after the earthquake
Dummy = 1 if expenditure on food changed
Dummy = 1 if expenditure on daily goods changed
Dummy = 1 if expenditure on clothing changed
Dummy = 1 if expenditure on luxury goods changed
Dummy = 1 if expenditure on leisure goods and services changed
Dummy = 1 if expenditure on gifts changed
Dummy = 1 if expenditure on furniture changed
Dummy = 1 if expenditure on electronic products changed
Dummy = 1 if expenditure on housing changed
Dummy = 1 if expenditure on emergency supplies changed

0.627
0.188
0.215
0.249
0.056
0.081
0.073
0.291
0.152
0.120
0.164

Coping Variables

Dummy = 1 if the household experienced an increase in expenditure becauseof the earthquake
Dummy = 1 if adjustment between expenditure items was the most important
coping strategy (default category)
Dummy = 1 if dissaving was the most important coping strategy
Dummy = 1 if borrowing was the most important coping strategy
Dummy = 1 if receiving transfers was the most important coping strategy

0.803
0.250
0.537
0.096
0.117

21

Table 1: Descriptive Statistics of the Variables (continued)
Description of Variables

Household Characteristics
Dummy = 1 if the household owned a house prior to the earthquake
Dummy = 1 if the household had outstanding housing loans prior to
the earthquake
Age of the respondentAge squared
Dummy = 1 if the respondent was a high school graduate
Dummy = 1 if the respondent was a junior college graduate orequivalent
Dummy = 1 if the respondent was a university graduate
Dummy = 1 if the respondent was single
Dummy = 1 if the respondent lived with children
Dummy = 1 if the respondent lived with parents or grandchildren
Regional Dummy Variables
Dummy = 1 if the respondent lived in Higashinada Ward (default
category)
Dummy = 1 if the respondent lived in Kita Ward
Dummy = 1 if the respondent lived in Suma Ward
Dummy = 1 if the respondent lived in Akashi City
Dummy = 1 if the respondent lived in Nishinomiya City
Dummy = 1 if the respondent lived in any other area

Mean
(Standard
Deviation)

0.670
0.316
51.168(11.479)2749.872(1202.06)
0.508
0.221
0.135
0.049
0.614
0.184
0.125
0.170
0.145
0.334
0.210
0.016

Note: Numbers in parentheses represent standard deviations.

22

Table 2: Consumption Response to the Earthquake Shocks
Explanatory Variables

Food
Daily Goods
Clothing
Luxuries
LeisureCoefficient
Coefficient
Coefficient
Coefficient
Coefficient(Std. Err.)(Std. Err.)(Std. Err.)(Std. Err.)(Std. Err.)
Dummy = 1 if income increased
0.068–0.181–0.119–0.148–0.356(0.177)(0.178)(0.173)(0.278)(0.290)
Dummy = 1 if income decreased
0.349
0.146
0.281
0.237
0.247
(0.088)***(0.084)*(0.082)***(0.121)*(0.109)**Dummy = 1 if information on income change is
0.076
0.240
–0.322–2.749–3.131
missing(0.593)(0.498)(0.562)(150.512)(109.702)
Dummy = 1 if the earthquake caused major–0.090
0.096
0.137
0.053
0.093
housing damage(0.169)(0.158)(0.153)(0.223)(0.220)
Dummy = 1 if the earthquake caused moderate
0.090
0.145
–0.1680.059
0.331
housing damage(0.133)(0.130)(0.128)(0.187)(0.176)*
Dummy = 1 if the earthquake caused minor–0.0630.141–0.117–0.0690.182
housing damage(0.109)(0.105)(0.103)(0.161)(0.149)Dummy = 1 if the earthquake caused major
0.261
0.510
0.396
0.038
0.236
household asset damage(0.202)(0.191)***(0.189)**(0.283)(0.241)
Dummy = 1 if the earthquake caused minor
0.213
0.246
0.315
0.208
–0.124household asset damage(0.120) *(0.117)**(0.114)***(0.182)(0.150)
Dummy = 1 if the earthquake adversely affected
0.214
0.281
0.343
0.356
0.074
the health of a family member(0.107)**(0.101)***(0.099)***(0.137)***(0.134)
Dummy = 1 if the household owned a house prior–0.225–0.077
0.080
0.202
0.111
to the earthquake(0.111)**(0.105)(0.103)(0.152)(0.138)Dummy = 1 if the household had outstanding
0.140
0.045
0.020
–0.178–0.014
housing loans prior to the earthquake(0.109)(0.102)(0.100)(0.153)(0.135)Age of the respondent
0.019
0.075
0.038
–0.0030.051(0.029)(0.028)***(0.027)(0.038)(0.038)Age squared–0.0002–0.0006–0.00020.00004–0.0004(0.0003)(0.0003)**(0.0003)(0.0004)(0.0004)
Dummy = 1 if the respondent was a high school–0.052–0.011
0.124
0.281
0.424
graduate(0.128)(0.122)(0.123)(0.200)(0.181)**
Dummy = 1 if the respondent was a junior college–0.103
0.113
0.296
0.347
0.433
graduate or equivalent(0.155)(0.147)(0.146)**(0.232)(0.211)**
Dummy = 1 if the respondent was a university–0.144
0.168
0.301
0.465
0.415
graduate(0.173)(0.162)(0.160)*(0.254)*(0.235)*
Dummy = 1 if the respondent was single
0.147–0.087
0.277
0.706
0.576
(0.201)(0.197)(0.186)(0.239)***(0.223)***0.088
Dummy = 1 if the respondent lived with children–0.027
0.052
0.042
0.126
(0.125)(0.096)(0.091)(0.089)(0.138)Dummy = 1 if the respondent lived with parents–0.004
0.116
0.204
0.024
0.013
or grandchildren(0.105)(0.098)(0.097)**(0.145)(0.132)
Dummy = 1 if the respondent lived in Kita Ward
0.148
0.062
–0.082–0.138–0.011(0.156)(0.153)(0.150)(0.238)(0.222)
Dummy = 1 if the respondent lived in Suma Ward
–0.126–0.115–0.091–0.0830.096(0.157)(0.153)(0.146)(0.223)(0.205)
Dummy = 1 if the respondent lived in Akashi City
–0.1380.048–0.1700.033
0.091(0.132)(0.126)(0.124)(0.176)(0.177)
Dummy = 1 if the respondent lived in–0.217–0.027–0.1150.164
0.116
Nishinomiya City
(0.152)(0.144)(0.141)(0.214)(0.200)
Dummy = 1 if the respondent lived in any other–0.722–0.008
0.437
0.155
0.532
area
(0.388)*(0.311)(0.291)(0.383)(0.337)
Constant–1.316–3.324–2.595–2.326–3.722(0.764)*(0.750)***(0.730)***(1.067)**(1.059)***Note: The dependent variable is a dummy variable that takes the value of 1 if household expenditure on each itemchanged after the earthquake, and a value of 0 otherwise. The Wald test is performed for the null hypothesis that thecoefficients on the shock variables are jointly 0 (p-value). Coefficients, rather than marginal effects, are reported.
The Huber-White consistent, robust standard errors are shown in parentheses. *, **, and *** indicate statisticalsignificance at the 10%, 5%, and 1% level, respectively.

23

Table 2: Consumption Response to the Earthquake Shocks (continued)
Explanatory Variables

Electronic
Emergency
Gifts
Furniture
Products
Housing
Supplies
Coefficient
Coefficient
Coefficient
Coefficient
Coefficient(Std. Err.)(Std. Err.)(Std. Err.)(Std. Err.)(Std. Err.)
Dummy = 1 if income increased
0.089
0.040
–0.320–0.270–0.133(0.208)(0.161)(0.189)*(0.218)(0.177)
Dummy = 1 if income decreased
0.137
0.158
0.270
0.185
0.104
(0.111)(0.081)*(0.088)***(0.099)*(0.090)
Dummy = 1 if information on income change is–3.326
0.511
–3.26
0.238
0.086
missing(120.691)(0.485)(85.359)(0.602)(0.608)Dummy = 1 if the earthquake caused major–0.042
0.524
0.057
0.771
–0.179housing damage(0.210)(0.151)***(0.172)(0.180)***(0.180)
Dummy = 1 if the earthquake caused moderate–0.024
0.462
0.199
0.416
0.150
housing damage(0.168)(0.124)***(0.137)(0.159)***(0.137)
Dummy = 1 if the earthquake caused minor–0.073
0.168
0.028
0.213
0.071
housing damage(0.143)(0.102)*(0.113)(0.134)(0.110)
Dummy = 1 if the earthquake caused major
0.259
0.361
0.390
0.193
0.469
household asset damage(0.257)(0.185)*(0.206)*(0.212)(0.212)**
Dummy = 1 if the earthquake caused minor
0.239
0.318
0.259
–0.0003
0.373
household asset damage(0.163)(0.111)***(0.125)**(0.139)(0.124)***
Dummy = 1 if the earthquake adversely affected
0.135
0.103
0.244
0.089
0.109
(0.110)the health of a family member(0.128)(0.099)(0.106)**(0.118)Dummy = 1 if the household owned a house prior
0.112–0.021–0.024–0.050–0.065to the earthquake(0.139)(0.101)(0.114)(0.124)(0.114)
Dummy = 1 if the household outstanding housing
0.048
0.226
0.169
0.030
0.030
loans prior to the earthquake(0.134)(0.097)**(0.109)(0.124)(0.109)Age of the respondent
0.004
0.065
0.007
–0.025–0.027(0.035)(0.026)**(0.029)(0.031)(0.028)Age squared–0.00004–0.0006–0.0001
0.00010.0002(0.0003)(0.0002)**(0.0003)(0.0003)(0.0003)
Dummy = 1 if the respondent was a high school–0.165
0.345
0.209
0.280
–0.079graduate(0.159)(0.121)***(0.137)(0.158)*(0.132)
Dummy = 1 if the respondent was a junior college–0.105
0.313
0.316
0.259
0.099
graduate or equivalent(0.190)(0.145)**(0.161)*(0.185)(0.155)–0.114
0.259
0.235
0.413
0.238
Dummy = 1 if the respondent was a university
graduate(0.211)(0.159)(0.177)(0.201)**(0.168)Dummy = 1 if the respondent was a single
0.271
0.276
0.144
0.285
–0.232(0.235)(0.181)(0.213)(0.223)(0.233)
Dummy = 1 if the respondent lived with children
0.028
0.087
0.093
0.134
0.016
(0.121)(0.088)(0.099)(0.111)(0.097)
Dummy = 1 if the respondent lived with parents or
0.161–0.078–0.067–0.0080.074
grandchildren(0.129)(0.096)(0.107)(0.116)(0.104)
Dummy =1 if the respondent lived in Kita Ward
–0.541–0.168–0.130–0.0620.324(0.205)***(0.147)(0.159)(0.190)(0.161)**
Dummy = 1 if the respondent lived in Suma Ward
–0.245–0.047–0.254–0.1470.020(0.182)(0.142)(0.158)(0.183)(0.163)
Dummy = 1 if the respondent lived in Akashi City
–0.247–0.086–0.2570.089
0.002(0.151)(0.120)(0.131)**(0.148)(0.137)
Dummy=1 if the respondent lived in Nishinomiya–0.569–0.078–0.155
0.027
0.026
City
(0.189)***(0.137)(0.148)(0.171)(0.154)
Dummy=1 if the respondent lived in any other area
–0.2330.294–0.0900.230
0.016(0.366)(0.281)(0.313)(0.312)(0.336)
Constant–1.496–3.088–1.706–1.147–0.733(0.949)(0.700)***(0.768)**(0.831)(0.751)
Note: The dependent variable is a dummy variable that takes the value of 1 if household expenditure on each itemchanged after the earthquake, and a value of 0 otherwise. The Wald test is performed for the null hypothesis that thecoefficients on shock variables are jointly 0 (p-value). Coefficients, rather than marginal effects, are reported. The
Huber-White consistent, robust standard errors are shown in parentheses. *, **, and *** indicate statisticalsignificance at the 10%, 5%, and 1% level, respectively.

24

Table 3: Determinants of Different Risk-Coping Strategies
Explanatory Variables

BorrowingTransfers
Dissaving
Coefficient
Coefficient
Coefficient(Std. Err.)(Std. Err.)(Std. Err.)
Dummy = 1 if income increased
0.184
0.692
–0.313(0.365)(0.326)**(0.298)Dummy = 1 if income decreased
0.299
0.205
–0.037(0.174)*(0.191)(0.125)
Dummy = 1 if information on income change is missing
0.929–1.075–1.069(0.788)(59.853)(0.854)
Dummy = 1 if the earthquake caused major housing damage
1.195
0.466
0.213
(0.432)***(0.327)(0.245)
Dummy = 1 if the earthquake caused moderate housing
1.180
0.746
–0.062
damage(0.394)***(0.292)**(0.204)
Dummy = 1 if the earthquake caused minor housing damage
0.803
0.016
0.023
(0.370)**(0.270)(0.180)
Dummy = 1 if the earthquake caused major household asset–0.473–0.3240.362
damage(0.382)(0.405)(0.293)
Dummy = 1 if the earthquake caused minor household asset–0.439–0.3790.334
damage(0.246)*(0.234)(0.187)*
Dummy = 1 if the earthquake adversely affected the health of–0.0240.240
0.045
a family member(0.211)(0.216)(0.152)
Dummy = 1 if the household owned a house prior to the
0.425–0.1310.267
earthquake(0.230)*(0.245)(0.160)
Dummy = 1 if the household had outstanding housing loans–0.0800.185–0.237prior to the earthquake(0.207)(0.216)(0.152)Age of the respondent
0.099
0.074
–0.046(0.069)(0.078)(0.047)Age squared–0.001–0.0010.001(0.001)(0.001)(0.000)
Dummy = 1 if the respondent was a high school graduate–0.3960.092–0.069(0.274)(0.324)(0.214)–0.140
Dummy = 1 if the respondent was a junior college graduate or–0.1510.408(0.353)(0.242)equivalent(0.304)
Dummy = 1 if the respondent was a university graduate–0.293–0.006–0.030(0.354)(0.418)(0.267)
Dummy = 1 if the respondent was single
0.520
0.198
–0.283(0.434)(0.433)(0.312)
Dummy = 1 if the respondent lived with children
0.273–0.013–0.251(0.201)(0.188)(0.140)*
Dummy = 1 if the respondent lived with parents or
0.278–0.469–0.020
grandchildren(0.194)(0.245)*(0.152)
Dummy =1 if the respondent lived in Kita Ward
–0.371–0.838–0.009(0.337)(0.347)**(0.224)
Dummy=1 if the respondent lived in Suma Ward
–0.161–0.4760.256(0.300)(0.297)(0.222)
Dummy=1 if the respondent lived in Akashi City
–0.128–0.311
0.114(0.230)(0.216)(0.176)
Dummy=1 if the respondent lived in Nishinomiya City
–0.029–0.6970.334(0.271)(0.295)**(0.211
Dummy=1 if the respondent lived in any other area
–0.453–0.6050.159(0.735)(1.038)(0.479)Constant–4.347–2.5770.393(1.854)**(2.067)(1.243)Sample size
522
522
522
Note: Coefficients, rather than marginal effects, are reported. The Huber-White consistent, robust standard errors
are shown in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level,
respectively.

25

Table 4
Covariances of Error Terms

Covariance
Covariance between ε1 and ε2
Covariance between ε1 and ε3
Covariance between ε2 and ε3

0.110–0.674–0.736

Std. Err.
(0.132)(0.069)***(0.068)***

Note: *, **, and *** indicate statistical significance at the 10%, 5%, and 1%
level, respectively.

26

1. The Kobe earthquake struck at 5:46 a.m. on January 17, 1995, hitting an area that contains one
of Japan’s main industrial clusters as well as major shipping ports and is home to 4 million
people. The earthquake, which had registered 7.3 on the Richter scale, cost 6,432 lives(excluding 3 missing persons), resulted in 43,792 injured, and damaged 639,686 buildings, ofwhich 104,906 were completely destroyed (Fire and Disaster Management Agency 2006).
Together with Hurricane Katrina, the Kobe earthquake was responsible for the largest economic
damage due to a natural disaster in history (Horwich 2000).
2. In the past few years alone, there have been several major disasters that resulted in tremendous
human and economic losses; these include the Asian tsunami in 2004, Hurricane Katrina in 2005,
and the earthquakes in Pakistan and Indonesia in 2005.

Japan also has suffered severe natural

disasters and probably is more at risk, especially from earthquakes, than many other countries.
3. Kohara, Ohtake, and Saito (2006) rejected the full consumption insurance hypothesis in thecase of the Kobe earthquake, but they did not take into account the direct losses incurred by eachhousehold nor did they examine risk-coping strategies. Sawada and Shimizutani (2007a) used
the same dataset as used in this study to examine the full insurance hypothesis by employing anordered probit model; however, they did not consider nonseparability across multiple goods.
4. The dataset was released on March 25, 1997, by Hyogo-ken Seikatsu Bunka-bu Seikatsu
Sozo-ka Shohi Seikatsu Taisaku-shitsu (Hyogo Prefecture, Department of Livelihood and Culture,
Livelihood Creation Section, Office for Livelihood Policy).
5. First, the survey was carried out in order to record the details of the damage incurred by the
respondents due to the earthquake, such as damage to their housing, household assets, and healthof family members. It should be noted that shortly after the earthquake, the local governments
conducted metrical surveys and issued formal certificates for housing damage, with whichhouseholds could later obtain government compensation. Therefore, we believe that the
information obtained on housing damage is fairly objective and accurate.
27

6. We acknowledge the limitation of the dataset since households were not asked about whether
there was an increase or decrease in the change; however, to our knowledge, this survey is the
only source of information on expenditure on different items with a variety of unexpected shocks,
which is indispensable for the test of nonseparability across goods.
7. Owing to the data availability constraints, the households that did not alter their expenditure or
those that altered their expenditure but did not indicate their coping mechanism are excluded
from our sample for the risk-coping analysis.
8. Moreover, the household income at the time of the survey was recorded by income category.
The median annual household income was ¥6 million–¥8 million (approximately$50,000–$67,000).
9. However, the amount of publicly disbursed funds was negligible—¥100,000 (approximately$1,000) for the owner of each collapsed house.
10. The sign of the coefficient indicates that the rich, with collateralizable assets, can obtain bothloans and transfer incomes, while the poor are excluded from both credit markets and insurance
networks against natural disasters.

28