European Economic Review 161 (2024) 104632

Contents lists available at ScienceDirect

European Economic Review
journal homepage: www.elsevier.com/locate/eer

On the stability of preferences: Experimental evidence from twodisasters✩
Yusuke Kuroishi a , Yasuyuki Sawada b ,∗a Graduate School of Economics, Hitotsubashi University, 2-1 Naka, Kunitachi, Tokyo, 186-8601, Japanb Faculty of Economics, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan

ARTICLEINFO

JEL classification:
C93
D81
O12
Keywords:
Convex time budget experimentMultiple price list experiment
Disasters
Risk and time preference
Hyperbolic discounting

ABSTRACT
We investigate the impacts of two disasters in Japan and the Philippines on preferences using
the convex time budget experiments and multiple price list experiments with monetary rewards.
By exploiting natural experiments which are combined with lab-in-the-field experiments, we
aim to investigate whether and how long preferences are affected by extreme events. We findevidence supporting preference instability caused by exposure to natural hazards: in both ourstudy sites, disaster exposure seems to make individuals more present-biased even though theydiffer in socioeconomic conditions and disaster types. The estimated impacts are persistent overthe short and long time intervals in both disaster-affected areas and are robust to the methodof measuring preferences.

1. Introduction
The literature on the endogenous formation of individual preferences reveals that these preferences are not constant acrosstime, and they change under some circumstances (Fehr and Hoff, 2011). Since natural hazards are traumatic events, exposure
to them are likely to affect an individual’s preference and behavior. However, the empirical findings are mixed and inconclusive(Chuang and Schechter, 2015; Schildberg-Hörisch, 2018). For example, Bchir et al. (2013), Bourdeau-Brien and Kryzanowski (2020),
Cameron and Shah (2015) and Cassar et al. (2017) consider the cases of Peru, the U.S., Indonesia and Thailand, respectively, findingthat exposure to natural hazards make people more risk averse. In contrast, results of Eckel et al. (2009) on a hurricane in the
U.S., Hanaoka et al. (2018) on an earthquake in Japan, and Page et al. (2014) on floods in Australia show that victims become lessrisk averse systematically. Moreover, Becchetti et al. (2017) finds no change in risk aversion in those exposed to a tsunami in Sri✩ We would like to thank our project members from the Philippines, Nobu Fuwa, Kei Kajisa, Takahiro Sato, Fe Gascon, and Ed Lucio. We are also gratefulto the Iwanuma project members, Toyo Ashida, Hikichi Hiroyuki, Ichiro Kawachi, Katsunori Kondo, and Ken Osaka for their cooperation in the surveys andexperiments. We would also like to thank the editor, associate editor, and two anonymous referees of this journal for very constructive comments whichstrengthened our paper substantially. We appreciate useful comments on the earlier versions of our paper at various seminars, workshops and conferences from
Jim Andreoni, Nick Bloom, Jiahua Che, Syngjoo Choi, Federico Echenique, Lorenz Goette, Chie Hanaoka, Keigo Inukai, Hidehiko Ichimura, Taisuke Imai, Gaurav
Khanna, Dean Karlan, Bryant Kim, Fukunari Kimura, Michael Kuhn, Jumgmin Lee, Jaimie Lien, Mushfiq Mobarak, Takeshi Murooka, Ilan Noy, Masao Ogaki,
Fumio Ohtake, Kota Saito, Hitoshi Shigeoka, Yoshito Takasaki, Alessandra Voena, Yasutora Watanabe, Leeat Yariv, Joanne Yoong, Jie Zheng, and participantsof JAGES study meetings, FY2013–2014 ERIA project meetings, 2014 Hayami Conference, 2015 World Congress of Econometric Society in Montreal, Canada,
2019 Yale-Nankai International Conference on Development Economics and 2nd Asian Workshop on Econometrics and Health Economics. The funding for thisresearch has been provided by the Economic Research Institute for ASEAN and East Asia (ERIA), Indonesia as well as JSPS KAKENHI Grants-in-Aid for Scientific
Research, Japan (Numbers 26220502 & 22K21341). The survey and experimental procedures were approved by the human subjects committees of the Universityof Tokyo (No. 13-165 for the East Laguna Village in FY2013; and No. 14-26 for Iwanuma city in FY2014). The authors are responsible for any remaining errors.
∗ Corresponding author.
E-mail addresses: y.kuroishi@r.hit-u.ac.jp (Y. Kuroishi), sawada@e.u-tokyo.ac.jp (Y. Sawada).

https://doi.org/10.1016/j.euroecorev.2023.104632
Received 11 February 2022; Received in revised form 30 October 2023; Accepted 3 November 2023
Available online 4 November 2023
0014-2921/© 2023 The Author(s).
Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

European Economic Review 161 (2024) 104632

Y. Kuroishi and Y. Sawada

Fig. 1. Comparison of age, income and education levels.

Lanka. Concerning time discounting, although Callen (2015) finds that the Asian tsunami disaster in 2004 decreased impatience in
Sri Lanka, it reinforced impatience in Thailand (Cassar et al., 2017) and present biasedness in the Philippines (Sawada and Kuroishi,
2015a) and Japan (Sawada and Kuroishi, 2015b; Akesaka, 2019).
The mixed empirical results have been attributed to the following three possible factors in the existing literature. First, subject’s
socioeconomic conditions, disaster types, and methods of eliciting preference parameters may generate seemingly inconclusive
results (Schildberg-Hörisch, 2018). In other words, different disasters might have different effects on the preferences of people
from different socioeconomic backgrounds. Hence, when studies on the spectrum of different disasters from diverse parts of theworld show heterogeneous effects on the samples, it may be natural to observe differentiated effects.
Second, specification errors may exist in estimation (Vieider, 2018). While the underlying economic model requires thesimultaneous incorporation of risk and time preferences (Andreoni and Sprenger, 2012; Andersen et al., 2008; Cheung, 2016),
to the best of our knowledge, studies on the nexus between disasters and preferences have not considered the joint inference of all
the parameters that collectively guide an individual’s choices.1 Additionally, failure to consider possible market frictions, such as
binding liquidity constraints, can bias the estimated parameters (Carvalho et al., 2016; Dean and Sautmann, 2021).
Third, inaccurate data on disaster exposure and experimental results can generate systematic biases in estimating the impact
of disasters on preferences, making it difficult to precisely identify causal relationships (Vieider, 2018; Schildberg-Hörisch, 2018).
For example, disaster exposures and damage can be captured imprecisely due to subjective reporting bias (Higuchi et al., 2019)or the unavailability of data on individual-level disaster exposure (Hanaoka et al., 2018). Additionally, noisy experimental results
can distort the estimation results if data quality is systematically correlated with the literacy, education, and cognitive capacityof the subjects (Andersson et al., 2016; Chakraborty et al., 2017; Schildberg-Hörisch, 2018). In this respect, a carefully designed
lab experiment by Imas (2016) shows that a discrepancy between realized and non-realized losses makes parameter estimation
systematically biased if these two types of losses are not properly distinguished.
While these points have been studied in the literature, there is little consensus on whether disaster exposure affects preferences
persistently in the short-run and long-run periods. Since the lag between disaster exposure and preference measurement varies
substantially, different studies could possibly yield different estimates of the effect of a disaster on preferences if such an effectexists only temporarily or changes over time.
Focusing on this preference instability in the time domain, our study aims to rigorously examine the short-term and long-term
impacts of disaster exposure on the present bias, exponential time discounting, and curvature parameters (i.e., the intertemporalelasticity of substitution and the degree of risk aversion) of a utility function. For this purpose, we conduct lab-in-the-field
experiments in Japan’s Iwanuma city, which was hit by a strong earthquake and tsunami in 2011, and in the East Laguna Village
in the Philippines,2 which was affected by serious floods triggered by the monsoon (‘‘habagat’’ in 2012). Preferences are elicited in
an integrated manner with joint determination by two incentivized experiments with monetary rewards: the convex time budget(CTB) experiments developed by Andreoni and Sprenger (2012), Andreoni et al. (2015) and the multiple price list (MPL) experimentdeveloped by Andersen et al. (2008). These experiments were conducted twice in both areas, approximately 2-3 and 6 years after
each disaster. Accurate information on the damage, collected from government metrical surveys in Iwanuma city and from satellite
images in East Laguna Village, as well as our sui generis survey data sets, which cover different disaster events in distinct settingsin terms of the age, income, and educational profiles of subjects (Fig. 1), allows us to investigate whether and how the effect of adisaster on preferences tends to be long-lasting.
We believe that eliciting preference measurements over the same short and long time intervals from two independent sites
affected by separate natural disasters is unique. Combined with the consistent and rigorous simultaneous estimation method, our

1 For example, it is well known that elicitation of the time-discounting parameter using only a discounting multiple price list will seriously bias the estimate(Andersen et al., 2008; Cheung, 2016).
2 For anonymity, we refer to this village as the East Laguna Village throughout the paper.

2European Economic Review 161 (2024) 104632

Y. Kuroishi and Y. Sawada

study would make a significant contribution to the literature because the existing studies predominantly report the short-time or
long-time effect depending on their research time horizons.3
Our analysis leads to the following four findings. First, the application of the two experimental methods to the two distinctive
disasters suggests that disaster exposure commonly makes individuals more present-biased in the two locations, despite their very
different socioeconomic characteristics. Second, we find evidence supporting that this impact persisted at least for six years in bothcountries. Third, our main results may not necessarily be driven by variations in the educational level of subjects or by potential
market frictions in the form of binding liquidity constraints. Rather, our results seem consistent with the psychological frameworks
emphasized by Callen et al. (2014), Callen (2015). Finally, our findings are robust to the method of measuring preferences.
The rest of this paper is organized in the following way. Section 2 briefly overviews two disasters in Japan and the Philippines.
In Section 3, we explain the parameter estimation strategies used in this study. Sections 4 and 5 present our findings from Japanand the Philippines, respectively. Section 6 presents results related to potential selection bias. Section 7 discusses the implicationsof our findings which is followed by the concluding remarks in the final section.

2. Two disasters and experiments

Our data are collected from Iwanuma city in Japan, which was hit by a strong earthquake and tsunami in 2011, and from theEast Laguna Village, which was affected seriously by strong floods in 2012. Socioeconomic characteristics of subjects from thesetwo communities are distributed very differently in terms of age, income, and educational level (Fig. 1).

2.1. The great East Japan earthquake
Concerning the data from Japan,4 we employ three waves of panel survey in 2010, 2013, and 2016 in Iwanuma city of Miyagi
Prefecture collected as part of the Japan Gerontological Evaluation Study (JAGES),5 together with additional data we gathered fromtwo rounds of the lab-in-the-field experiment implemented in Iwanuma city in 2014 and 2017. This is part of large-scale JAGES
surveys which were conducted with 169,215 community-dwelling individuals aged 65 years or above residing in 31 municipalitiesof 12 Japanese prefectures from July 2010 to January 2012. Of these individuals, we selected JAGES respondents living in one of
the 31 municipalities, Iwanuma city, which suffered considerable damages caused by the Great East Japan Earthquake on March
11, 2011 and the subsequent tsunami. In the city, 180 lives were lost, and 2766 homes were either destroyed or seriously damagedby the disasters. The proportion of the area submerged by the tsunami wave was the largest in Iwanuma city, among all the areas
affected by the earthquake and tsunami. Thus, we considered JAGES survey’s respondents in Iwanuma city as the best candidates
to investigate the impact of disasters on individual preferences.
Unlike typhoons, epidemics, and man-made disasters, tsunamis and floods present a clear ‘‘discontinuous’’ variation of local
damages in a homogeneous area with an unknown disaster border before the event, providing random assignments of ‘‘treatment’’and ‘‘control’’ groups of disaster damages. We exploit this natural experimental situation to identify causal impacts of disasters on
individual preferences. Of all the 2013 JAGES census respondents in Iwanuma city, 1023 residents agreed to participate in theexperiments. Out of these people, we sent invitations to 346 respondents who lived along the tsunami border areas, which were
unknown beforehand but became clear afterward. Eventually, a total of 186 individuals participated in our laboratory experiments
in 2014.6 We asked participants of our 2014 experiments to join again in the experiments in 2017. We also sent additional invitations
to those who lived in the tsunami affected areas but had not participated in our 2014 experiments. Out of the 225 invitees, a totalof 179 individuals participated in our field experiments in 2017.7 , 8

3 While Eckel et al. (2009) finds the diminishing effect of disaster exposure on risk preferences within one year, Hanaoka et al. (2018) demonstrates increasedrisk-taking even five years after the disaster.
4 Japan is vulnerable to a variety of natural disasters, such as earthquakes, tsunamis generated by earthquakes, volcanic eruptions, typhoons, floods, landslides,
and avalanches. Of these natural disasters, earthquakes are the most serious and frequently occurring disasters. The continuous seismic activity is attributed tothe country’s location on a subduction zone, where four of the more than ten tectonic plates covering the globe are crushed against each other.
5 JAGES aims to empirically survey risk factors for and social determinants of health and frailty (i.e., need for long-term care) among elderly people. It
focused on those who did not already have a physical or cognitive disability, defined by not receiving public long-term care insurance benefits, as its baseline
survey sample.
6 We conducted a series of experiments on the following dates in 2014: May 15 (38 participants), May 16 (47 participants), May 19 (29 participants), May
20 (47 participants), and May 21 (25 participants).
7 Among 186 individuals in our first laboratory experiment, 117 individuals also participated in our second laboratory experiment.
8 In 2017, we conducted experiments on the following dates: February 8 (26 participants), February 9 (11 participants), February 10 (21 participants),
February 11 (16 participants), February 14 (15 participants), February 21 (26 participants), February 27 (29 participants), February 28 (24 participants), March
28 (6 participants), and March 29 (5 participants).

3

European Economic Review 161 (2024) 104632

Y. Kuroishi and Y. Sawada

2.2. Habagat in the Philippines
We also study residents in the East Laguna village which is located in approximately 80 kilometers towards south of Metro
Manila, facing the east coast of the lake Laguna de Bay. Its proximity to the International Rice Research Institute (IRRI) has enabledresearchers to conduct long-term panel surveys since 1966.9
In August 2012, the village was unexpectedly hit by serious out-of-season floods due to the southwest monsoon winds called‘‘Habagat’’ and the resulting overflow of lake water. The floods negatively impacted rice production because the timing of the floods
was detrimental to the crop’s growth.10 In the village data sets covering the last five decades, there has been no record of otherfloods at this scale (Estudillo, 2013).
In the East Laguna village’s study, we employ the same type of surveys and experiments as the ones adopted for Iwanuma
city. The subjects comprised farmers in the East Laguna village and the two surrounding villages. In 2014, a total of 158 farmers
participated in our field experiments.11 In 2018, a total of 141 farmers participated in our field experiments.12
3. Parameter estimation strategies
We employed Andreoni et al. (2015)’s CTB experiment twice, both in Japan (2014 and 2017) and the Philippines (2014 and2018), and Andersen et al. (2008)’s MPL experiment once in Japan (2017) and twice in the Philippines (2014 and 2018).
3.1. The Convex Time Budget (CTB) experiment
The CTB experiment of Andreoni et al. (2015) allows us to separately identify the three key parameters of the time-separable
CRRA utility function with the 𝛽-𝛿 discounting (Strotz, 1955; Phelps and Pollak, 1968; Laibson, 1997)—the inverse of intertemporalelasticity of substitution, 𝛼; the exponential time discounting factor, 𝛿; and the quasi-hyperbolic discounting factor showing present
bias, 𝛽. This function is expressed as 𝑈 (𝑥𝑡 , 𝑥𝑡+𝑘 ) = 𝑢(𝑥𝑡 ) + 𝛽𝛿 𝑘 𝑢(𝑥𝑡+𝑘 ) if 𝑡 = 0; and 𝑈 (𝑥𝑡 , 𝑥𝑡+𝑘 ) = 𝛽𝑢(𝑥𝑡 ) + 𝛽𝛿 𝑘 𝑢(𝑥𝑡+𝑘 ) if 𝑡 ≠ 0, where𝑥1−𝛼𝑡, and the values 𝑥𝑡 and 𝑥𝑡+𝑘 denote experimental earnings. While present bias is associated with 𝛽 < 1, 𝛽 = 1 and 𝛽 > 1𝑢(𝑥𝑡 ) = 1−𝛼correspond to the cases of standard exponential discounting and future bias, respectively.
In our experiments, each subject faces 24 convex budget decisions involving combinations of starting times, 𝑡; delay lengths, 𝑘;
and annual interest rates, 𝑃 . We combine three early payments’ date, 𝑡 = (0, 35, 63) days from the experiment date (i.e., 𝑡 = 0) andtwo time intervals, 𝑘 = (35, 63) days from the early payments’ date, 𝑡. Specifically, we construct four (𝑡, 𝑘) cells—(𝑡, 𝑘)=(0, 35), (0,63), (35, 70), and (63, 126); each cell contains six CTB questions with different payoffs and interest rates 𝑃 , generating 24 choices
for each subject.
Delayed payments were scheduled to arrive on the specified day exactly by mail in Iwanuma and in person in East Laguna
Village for which we provided signed payment certificates and detailed contact numbers of the principal investigator in the case of
Iwanuma and the local head investigator in the case of East Laguna Village. In Iwanuma, the experiments were organized with fullendorsement by its city government which was stated in the invitation letters. To secure confidence of the subjects further, meetingrooms in the local government office were used for our experiments. The later payments were placed in the sealed envelope withthe address of a subject on the day of each experiment. In East Laguna Village, the experiment team members were recruited froma pool of IRRI retirees who used to work for the village studies in the last few decades. In the signed certificates of future payments,
we included detailed contact information of the head enumerator who has been known very well among the villagers. Before we
let subjects make decisions, we explained these procedures repeatedly. Hence, we believe that uncertainties associated with future
repayments are unlikely to affect decisions.
In each CTB question, subjects are given the choices between two corner points—(𝑋, 0) and (0, 𝑌 )—in which 𝑋 denotes theearliest and smallest payment, and 𝑌 denotes the latest and largest payment. Other three interior choices are located along the𝑌represents the gross interest
intertemporal budget constraint connecting these points such that 𝑃 𝑥𝑡 + 𝑥𝑡+𝑘 = 𝑌 , where 𝑃 = 𝑋

9 The earliest documented survey of the village dates back to 1966, when a Japanese geographer, Hiromitsu Umehara, carried out a census survey of thevillage (Umehara, 1967). Since then, 18 rounds of household surveys were conducted from 1974 to 2007, in collaboration with IRRI (Estudillo et al., 2010).
Surveys in the 1970s, 1980s, and 1990s were organized mainly by Yujiro Hayami and Masao Kikuchi (Hayami and Kikuchi, 1999). Surveys in the 2000s wereorganized by other researchers (Fuwa, 2011; Kajisa, 2007). The village has been repeatedly surveyed, which has led to the collection, compilation, and analyses
of useful benchmark information.
10 Over an eight-day period, from August 1 to August 8, 2012, torrential rains and thunderstorms hit the Philippines. The effects centered on Metro Manila,
the surrounding provinces of the Calabarzon region (Quezon, Cavite, Laguna, and Rizal provinces), and the provinces of Region III (Bulacan, Pampanga, andBataan provinces). While the storm cannot be categorized as a typhoon, it was a strong movement of the southwest monsoon wind ‘‘Habagat’’ caused by thepull of the Typhoon Saola (Gener) from August 1 to August 3, 2012, and strengthened by the Typhoon Haikui. It caused typhoon-like damage, such as river
overflow and landslides, to the entire region. In the Laguna province, where the East Laguna village is located, ‘‘Habagat’’ spawned flooding that submerged
low-lying villages in 19 towns and cities, including the village, destroying P410.3 million worth of agricultural products. Comprising rice and corn, the damaged
crops were planted in about 11,000 hectares of inundated farmlands; additionally, ‘‘Habagat’’ affected some 6000 farmers in the whole area. More than a halfof the village area was submerged in flooded water, destroying paddy seriously.
11 We conducted a series of experiments on the following dates in 2014: March 20 (33 participants), March 21 (33 participants), March 22 (36 participants),
March 23 (39 participants), and March 24 (17 participants).
12 In 2018, we conducted several experiments on the following dates: March 3 (31 participants), March 4 (36 participants), March 5 (25 participants), March
6 (29 participants), and March 7 (20 participants).

4

European Economic Review 161 (2024) 104632

Y. Kuroishi and Y. Sawada

rate.13 Given the intertemporal budget constraint, each subject maximizes own utility function. As Andreoni et al. (2015) shows,the first-order necessary condition of this maximization problem is a standard consumption Euler equation, which is log-linear in
the experimental variations of 𝑡, 𝑘, and 𝑃 :
𝑙𝑛(

𝑥𝑡
𝑙𝑛(𝛽)
𝑙𝑛(𝛿)
1
)=−
1[𝑡 = 0] −𝑘 − 𝑙𝑛(𝑃 ),
𝑥𝑡+𝑘
𝛼
𝛼
𝛼

(1)

where 1[𝑡 = 0] is an indicator function which takes 1 if the argument is true and 0 otherwise. Assuming a well-behaved additive
error term, this equation can be estimated at a group level or an individual level by the ordinary least squares (OLS) method bywhich we can exactly identify the three parameters, 𝛼, 𝛿, and 𝛽.14 However, when subjects choose a corner point, (𝑋, 0) or (0, 𝑌 ),𝑥
the allocation ratio 𝑙𝑛( 𝑥 𝑡 ) is not well-defined. To address this problem, we follow Andreoni et al. (2015) to estimate the parameters𝑡+𝑘using an alternative representation of the first-order condition by the non-linear least squares (NLS) method:15
1𝑥𝑡 =𝑌 (𝛽 1[𝑡=0] 𝛿 𝑘 𝑃 )− 𝛼

1

(2)

.

1 + 𝑃 (𝛽 1[𝑡=0] 𝛿 𝑘 𝑃 )− 𝛼3.2. The multiple price list (MPL) experiment
The second experiment is the MPL experiment of Andersen et al. (2008), which consists of two related experiments. The firststage is designed to provide information on the utility function curvature through a lottery choice MPL experiment of Holt andLaury (2002). The second stage is designed to identify time discounting parameters by a time preference MPL experiment.16 As fordelayed payments, we arranged in the same way as the CTB experiments explained above.
1−𝛼̃
𝛼̃
∑
𝑥1−
𝑡
𝑘 𝑥𝑡+𝑘+𝛽 ∞̃In this experiment, we also assume a time separable CRRA utility function: 𝑈 (𝑥𝑡 , 𝑥𝑡+1 𝑥𝑡+2 , …) = 1−
𝑘=1 𝛿 1−𝛼̃ where 𝛼𝛼̃represents the coefficient of the relative risk aversion.17 As in the CTB experiment, the parameter 𝛿 captures the standard exponential
discounting factor, and the parameter 𝛽 denotes the quasi-hyperbolic discounting factor.
Specifically, in the first-stage Holt and Laury (2002)’s risk MPL experiment, subjects face a series of lottery choices, denoted by𝑗, between a safe lottery, A, and a risky lottery, B. For each outcome of each lottery A and B, the probability 𝑝(𝑀𝑗𝑖 ), 𝑖 = A or B, isassigned to the two payoffs 𝑀𝑗𝑖 by the experimenter. Thus, the expected utility for each lottery 𝐸𝑈𝑖 , 𝑖 = A or B; the ratio of expectedutilities, ∇𝐸𝑈 ; and the conditional log-likelihood function can be defined as follows (Andersen et al., 2008):∑
𝐸𝑈𝑖 =(𝑝(𝑀𝑗𝑖 ) × 𝑢(𝑀𝑗𝑖 )),
(3)
𝑗=1,2
1

∇𝐸𝑈 =

𝐸𝑈𝐵 𝜇

,
1
1
𝐸𝑈𝐴 𝜇 + 𝐸𝑈𝐵 𝜇∑𝑙𝑛𝐿𝑅𝐴 (𝛼,̃ 𝜇; 𝐴, 𝐵) =(𝑙𝑛(∇𝐸𝑈 |𝐶𝑅𝑖 = 1) + 𝑙𝑛(1 − ∇𝐸𝑈 |𝐶𝑅𝑖 = 0)),

(4)
(5)

𝑖

where 𝜇 is a structural noise parameter, and 𝐶𝑅𝑖 takes 1 when Lottery B is chosen and takes 0 when Lottery A is chosen.
In the second-stage time preference MPL experiment, individuals make a series of binary choices between smaller early payments
X and larger delayed payments Y. Then, the present value of choosing the smaller early payments X, denoted by 𝑃 𝑉𝑋 , in the multiple
price list can be formalized as:⎧ 𝑋 1−𝛼̃⎪𝑃 𝑉𝑋 = ⎨ 1−𝛼̃ 1−𝛼̃⎪𝛽𝛿 𝑡 𝑋1−𝛼̃⎩

𝑖𝑓 𝑡 = 0(6)
𝑖𝑓 𝑡 ≠ 0

The present value of choosing the larger delayed payment with delay lengths 𝑘, 𝑃 𝑉𝑌 , is
⎧ 𝑘 𝑌 1−𝛼̃⎪𝛽𝛿 1−𝛼̃𝑃 𝑉𝑌 = ⎨1−𝛼̃⎪𝛽𝛿 𝑡+𝑘 𝑌1−𝛼̃⎩

𝑖𝑓 𝑡 = 0(7)
𝑖𝑓 𝑡 ≠ 0

Then, an index of difference between these discounted values can be formed as follows.
1∇𝑃 𝑉 =𝑃 𝑉𝑌 𝜈
1

1

(8)

,

𝑃 𝑉𝑋 𝜈 + 𝑃 𝑉𝑌 𝜈

13

The exact interest rates, experimental budgets, and delay lengths in the experiment in Japan and the Philippines are shown in Online Appendix.
This equation clarifies the mapping from the variation of experimental parameters to structural parameter estimates. Variation in the gross interest rate, 𝑃 ,delivers the utility function curvature, 𝛼. For a fixed interest rate, variation in delay length, 𝑘, delivers, 𝛿, and variation in whether the present, 𝑡 = 0, delivers
𝛽. Thus, these experimental variations allow us to estimate these three parameters via the delta method.
15 This strategy allows us to estimate three parameters when 𝛼 ∈ (0, 1).
16 The details about the Holt and Laury (2002) and time discount MPL experiments are described in the online appendix.
17 With this functional form, 𝛼̃ = 0 denotes the risk-neutral behavior, 𝛼̃ > 0 denotes risk aversion, and 𝛼̃ < 0 denotes risk tolerance.
14

5

European Economic Review 161 (2024) 104632

Y. Kuroishi and Y. Sawada

where 𝜈 is a structural noise parameter. The conditional log-likelihood function is as follows.
∑(𝑙𝑛(∇𝑃 𝑉 |𝐶𝐷𝑖 = 1) + 𝑙𝑛(1 − ∇𝑃 𝑉 |𝐶𝐷𝑖 = 0)).
𝑙𝑛𝐿𝐷𝑅 (𝛽, 𝛿, 𝛼;̃ 𝑋, 𝑌 ) =(9)𝑖where 𝐶𝐷𝑖 takes 1 when the delayed payment is chosen and takes 0 when an early payment is chosen. Following Andersen et al.
(2008), we combine the two conditional log-likelihood functions (5) and (9) in the two stages to obtain the joint likelihood, which
is a function of the coefficient of relative risk aversion (𝛼),̃ hyperbolic and exponential discount rates (𝛽 and 𝛿, respectively), andtwo noise parameters (𝜈 and 𝜇) as follows:𝑙𝑛𝐿(𝛽, 𝛿, 𝛼,̃ 𝜈, 𝜇; 𝑋, 𝑌 , 𝐴, 𝐵) = 𝑙𝑛𝐿𝐷𝑅 (𝛽, 𝛿, 𝛼;̃ 𝑋, 𝑌 ) + 𝑙𝑛𝐿𝑅𝐴 (𝛼,̃ 𝜇; 𝐴, 𝐵).

(10)

4. Data and results I: The great East Japan earthquake
First, we check whether damages caused by the Great East Japan Earthquake were unpredictable and thus exogenous tohouseholds. Second, we use CTB data in 2014 and 2017 to examine whether the earthquake caused short-term- and long-term
impacts on preferences. Finally, we employ the MPL data collected in 2017 to crosscheck the disaster impact on preference
parameters.
4.1. Data and baseline covariate balance test
In Iwanuma city, local government conducted detailed metrical surveys of home damages and issued official damage certificatesfor each house, with which households could obtain government compensation and reallocation of donations. We collect officiallycertified damage information from each of the respondents of the JAGES survey conducted in November 2013. House damages are
divided into five categories depending on the extent of the damage: totally collapsed (𝑍𝑒𝑛𝑘𝑎𝑖), almost collapsed (𝐷𝑎𝑖𝑘𝑖𝑏𝑜 𝐻𝑎𝑛𝑘𝑎𝑖),half collapsed (𝐻𝑎𝑛𝑘𝑎𝑖), minor damage (𝐼𝑐ℎ𝑖𝑏𝑢 𝑆𝑜𝑛𝑘𝑎𝑖), or no damage (𝑆𝑜𝑛𝑔𝑎𝑖 𝑁𝑎𝑠ℎ𝑖). Based on these categories, we divide thesampled subjects with house damages into three groups. The first group includes subjects whose houses were not damaged. The
second group includes subjects whose houses suffered minor or half damage. The third group includes subjects whose houses had
almost or totally collapsed.
To check the exogeneity of damages, we regress each of the pre-disaster characteristics of the subjects on these three variables,
depicting different damage levels with the no damage group as a reference category.18 Based on the two methods of multiple
hypothesis testing with multiple treatments, i.e., Romano-Wold and Westfall-Young tests, which are suitable for our data with threedamage levels, we do not find systematic correlation between the damage level and each of the observed pre-disaster characteristics
for subjects in either the 2014 or 2017 experiments (Table A.1).
In particular, we include a pre-disaster hyperbolic discounting measure collected from a retrospective survey in 2017: We
follow Ikeda et al. (2010), Kang and Ikeda (2014, 2016) to capture each individual’s timing of completing homework assignments
during elementary school summer vacations and to construct a proxy variable of hyperbolic discounting, 𝐻𝑜𝑚𝑒𝑤𝑜𝑟𝑘, to capture eachindividual’s timing for completing homework assignments during elementary and junior high school summer vacations. Elementaryand junior high schools are compulsory in Japan. Since summer vacation is the longest holiday for students at that level, lastingaround 40 days, most schools provide a substantial amount of homework for students during the long vacation. When to complete
the homework depends on each student’s self-control, and since it is not a pleasant task in most cases, we believe that it is the best
measure to capture present bias or hyperbolic discounting during the respondents’ adolescence. This variable, 𝐻𝑜𝑚𝑒𝑤𝑜𝑟𝑘, takes thevalue one if a subject completed homework assignments before the end of the summer vacation; and zero otherwise.
As shown in the last column of Table A.1 for the 2017 subjects, the pre-disaster hyperbolic discounting measure, 𝐻𝑜𝑚𝑒𝑤𝑜𝑟𝑘, anddisaster exposure are uncorrelated. In addition, considering the causal impacts of hyperbolic discounting on harmful health behaviors
such as over-eating, drinking, and smoking (Dupas, 2011; Story et al., 2014), we also use pre-disaster health behavior variables inbaseline-balancing tests. The results are shown in columns (10), (11), and (12) of Table A.1 for 2014 and 2017 separately. We canverify that these health variables are not correlated with disaster exposure.
However, selection would threaten our causal identification of the impact of a disaster on preference parameters. According
to Callen (2015), there are two problems in identifying the causal impacts of disasters on preference parameters with post-disasterdata. The first is a ‘‘selective exposure’’ problem where individuals may be located according to their preferences before the disaster.
Second, there may be a ‘‘selective migration’’ problem because individuals might selectively migrate out of affected zones based on
their preferences in the wake of the disaster.
In Iwanuma data, dropouts mainly occur when a respondent passes away, enters a care facility, moves out of the city, or refuses
to respond. Our data show that the migration rate is only 2.0–2.3%.19 The lack of extensive migration may justify our empiricalstrategy that the earthquake provides a natural experimental situation to identify the causal effect of the disaster on households’
preferences. However, the possibility remains that selection through exposure and migration would threaten our causal identificationof the disaster impact on preference parameters. We carefully revisit this potential problem in Section 6.

18

The data presents age and educational levels as of each interview date in November 2013.
Out of 4568 valid respondents of the pre-disaster Iwanuma JAGES panel dataset in 2010, 577 participants had to be dropped out during the follow-up
survey in 2013 because of death (434), moving out (92), loss of tracking information (17), and inability to participate owning to sickness (34).
196European Economic Review 161 (2024) 104632

Y. Kuroishi and Y. Sawada

Table 1Aggregate and individual utility parameters estimated by the Convex Time Budget (CTB) experiments allowing for heterogeneity by damages in Japan (2014and 2017).
Panel A: Aggregate analysis
Estimated parameters

(1)

(2)
(3)
Experiments in 2014

(4)

(5)
(6)
Experiments in 2017

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

1.057
(0.0366)
1.022(0.0226)0.937(0.0432)1.001(0.00121)
1.002(0.000766)
1.001(0.00126)

0.262(0.0289)
0.247(0.0178)
0.234(0.0248)1.045(0.0219)0.980(0.0304)
0.893(0.0402)

1.001(0.000738)
1.001(0.000773)
1.001(0.000871)

0.177(0.0126)
0.202(0.0137)0.181(0.0145)

P-value of the null hypothesis for homogeneous parameters0.0958

0.993

0.772

0.002900.721

Panel B: Individual analysis(1)

No house damage
Minor or half-damaged house
Almost or totally-collapsed house
Observations

4464

Dependent variables
Minor or half-damaged house dummy
Almost or totally-collapsed house dummy

4296

(2)
(3)
Experiments in 2014

(4)

0.381

(5)
(6)
Experiments in 2017

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

−0.0394(0.0637)−0.167(0.0803)0.0307(0.0974)
0.0290(0.109)−0.00249(0.00469)−0.00431(0.00509)−0.0162(0.0554)−0.146(0.0654)0.0545(0.0462)−0.0211(0.0471)

0.00626(0.00433)−0.00153(0.00406)

Observations

186

186

186

179

179

179

P-value of the null hypothesis for homogeneous parameters0.0997

0.933

0.698

0.0752

0.322

0.187

Notes: Individually clustered standard errors are reported in parentheses in panel A and robust standard errors are reported in parentheses in panel B. No House
Damage is the group indicating subjects whose houses were not damaged. Minor or Half-damaged House is the group indicating that subjects whose houses
suffered minor or half damage. Almost or Totally-collapsed House is the group indicating subjects whose houses almost or totally collapsed. The CTB experimentof Andreoni et al. (2015) allows us to separately identify the three key parameters of the time-separable CRRA utility function with the 𝛽-𝛿 discounting (Strotz,
1955; Phelps and Pollak, 1968; Laibson, 1997)—the quasi-hyperbolic discounting factor showing present bias, 𝛽; the exponential time discounting factor, 𝛿; andthe degree of intertemporal elasticity of substitution, 𝛼.

4.2. The CTB experiment results
We estimate the model of Eq. (1) by allowing a heterogeneous degree of intertemporal rate of substitution, 𝛼; exponential time
discounting factor, 𝛿; and quasi hyperbolic discounting factor, 𝛽, depending on each of the three house-damage categories.20 We
also estimate 𝛽, 𝛿 and 𝛼 at the individual level and examine the disaster impact on each of the estimated parameters. The results areshown in Table 1. While the estimated 𝛽 exceeds one, suggesting the existence of overall future bias, it is not necessarily inconsistent
with other findings in the existing literature. According to Andreoni and Sprenger (2012), Andreoni et al. (2015), the present bias
parameters, 𝛽, estimated by the CTB experiments with monetary rewards are 1.004 and 1.010, respectively. Abdellaoui et al. (2010)
proposes another experimental framework with monetary incentives and shows that the median of 𝛽 is 0.99. The estimated 𝛽 reported
by Augenblick et al. (2015) exploiting real-effort choices is 0.89. DellaVigna and Pope (2018) combines monetary and non-monetary
incentives and finds that the estimated 𝛽 is 1.17.
According to Table 1, as house damages become more severe, the estimated 𝛽 becomes systematically lower. In fact, when wecompare the status ‘‘No House Damage’’ with ‘‘Almost or Totally-collapsed House’’ caused by the disaster, 𝛽 decreases by 0.120 from1.057 to 0.937 in 2014 (column (1)) and 0.152 from 1.045 to 0.893 in 2017 (column (4)) at the aggregate level, and by 0.167 in2014 (column (1)) and by 0.146 in 2017 (column (4)) at the individual level. We reject the null hypothesis that the parameters, 𝛽, arethe same, irrespective of house damages in both years. In contrast, we do not observe the earthquake’s impacts on other parameters,that is, 𝛼 and 𝛿 at aggregate and individual levels. These findings indicate that while moderate exposure to house damages causedby the Great East Japan Earthquake in 2011 seems to fix future bias towards exponential discounting, extreme exposure seems to
make people present-biased consistently in both 2014 and 2017. The pooled and balanced panel data confirm the finding that thedisaster’s effect persisted for at least 6 years.21

20 In Appendix Table A.2 shows the aggregate estimation results of homogeneous parameters by data from subjects who participated in 2014 and 2017,
respectively. In these two tables, the first two columns report the estimated parameter based on Eq. (3) using OLS and the last column shows results based on
Eq. (4) using NLS. There are several differences between the results in 2014 and those in 2017. First, in all specifications of Appendix Table A.2, we cannot reject
the null hypothesis in which the present bias parameter, 𝛽, equals to one in the 2014 data. Second, both results in 2014 and 2017 show that the exponential
time discounting parameter, 𝛿, is within a reasonable range. Finally, the degree of intertemporal substitution parameter, 𝛼, in 2014 and 2017, ranges from 0.08
and 0.25.
21 These results are shown in Online Appendix (Table A.3).

7

European Economic Review 161 (2024) 104632

Y. Kuroishi and Y. Sawada

Table 2
Utility parameters estimated by the Multiple Price List (MPL) experiments allowing for heterogeneity by damages in Japan (2017).
Estimated parameters

𝛿

𝜈

𝛽

𝛼̃

𝜇

No house damage

0.999(0.000276)
0.999(0.000291)
0.999(0.000563)

0.0585(0.0195)
0.128(0.0251)
0.131(0.0298)

1.015(0.0111)0.999(0.0156)0.930(0.0352)0.634(0.0773)
0.422(0.0617)0.292(0.0897)

0.349(0.0778)
0.327(0.0410)0.395(0.0751)

0.459

0.03490.01130.727

Minor or half-damaged house
Almost or totally-collapsed house
Observations
P-value of the null hypothesis for homogeneous parameters

25776
0.0632

Notes: Individually clustered standard errors are reported in parentheses. No House Damage is the group indicating subjects whose houses were not damaged.
Minor or Half-damaged House is the group indicating that subjects whose houses suffered minor or half damage. Almost or Totally-collapsed House is the groupindicating subjects whose houses almost or totally collapsed. The MPL experiment of Andersen et al. (2008) consists of two MPL experiments. The first stage
is designed to provide information on the utility function curvature through a lottery choice MPL experiment of Holt and Laury (2002). The second stage isdesigned to identify time discounting parameters by a time preference MPL experiment. This experiment allows us to separately identify the three key parametersof the time-separable CRRA utility function with the 𝛽-𝛿 discounting (Strotz, 1955; Phelps and Pollak, 1968; Laibson, 1997)—the quasi-hyperbolic discounting
factor showing present bias, 𝛽; the exponential time discounting factor, 𝛿; and 𝛼̃ the coefficient of the relative risk aversion with two noise parameters, 𝜈 and 𝜇.

4.3. The MPL experiment results
With experimental data in 2017, we also estimate the MPL model by allowing heterogeneous parameters, depending on the
degree of the house damage (Table 2).22 We find that house damages make people fix future bias towards exponential discounting
or present bias. With undamaged houses, the present bias parameter, 𝛽, is 1.015; with minor or half damages, the present bias
parameter is 0.999; and, with almost totally or totally collapsed house, the estimated 𝛽 decreases further to 0.930. A joint test also
confirms this pattern. However, the disaster impact on 𝛽 is smaller with the MPL method than that observed with the CTB method.
Part of the decline in 𝛽 may be absorbed by a change in the curvature, 𝛼,̃ in the case of MPL—the risk aversion parameter, 𝛼,̃ dropsfrom 0.634 to 0.292 with an increase in the damage level, indicating that disaster damages make people less risk averse.
We can graphically show these results. In Figs. 2 and 3 which show the relationship between gross interest rates and proportionof subjects choosing sooner payments, we compare the case {𝑡 = 0, 𝑘 = 35} or {𝑡 = 0, 𝑘 = 63} with the one where {𝑡 = 35, 𝑘 = 35}
or {𝑡 = 63, 𝑘 = 63}. While the choice between today and a future date is systematically related to damages, the choice between two
points in the future are not necessarily sensitive to damages. We clearly observe that the extreme disaster damage makes people
present-biased.
As for the disaster impact on risk attitudes, we present (Holt and Laury, 2002) experiment results allowing heterogeneity acrossdegree of damages. According to Fig. 4, those who suffered house damage are less likely to choose the safer lottery even when theprobability of winning the larger prize is close to zero. This figure indicates that individuals with larger house damages show lessrisk aversion.
5. Data and results II: The Habagat Spawned floods in the Philippines
We also undertake the following three procedures using the Philippines data. First, we check the exogeneity of the damage causedby the floods induced by ‘‘Habagat’’. Subsequently, we employ the CTB data to estimate present bias, exponential discounting, andthe intertemporal elasticity of substitution. Finally, we also use MPL data to obtain estimates of present bias, exponential discounting,and the relative risk-aversion parameters.
5.1. Data and baseline covariate balance test
The flood’s damage levels are captured objectively and subjectively. Concerning the objective damage information, we employ
the satellite imageries, which identify whether households suffered farm-level damages due to the submergence of paddy fields by
the flood (i.e., the overflow of the lake water). By comparing two 5 km by 5 km images taken by IKONOS before (May 23, 2012)
and after (August 11, 2012) the floods, we can identify the flood border shown by the red line of Fig. 5 .23 Since the ground level
geographic information systems (GIS) data on landownership data can be matched with the satellite image only in the East Laguna
Village, but not the two surrounding villages, we could obtain 99 and 91 observations, respectively, in 2014 and 2017.

22 The estimation results using the MPL and Holt and Laury (2002) are shown in the Online Appendix. Table A.4 shows estimation results of the threeparameters together with the error parameters using the MPL experiment conducted only in 2017. The estimated present bias parameter, 𝛽, is 0.994, and hence
we cannot statistically reject the null hypothesis that 𝛽 equals to one. The estimated risk aversion parameter, 𝛼,̃ is 0.443. Since the estimated intertemporalelasticity of substitution, 𝛼, by CTB, ranges from 0.144 to 0.247 in 2014 and from 0.082 to 0.189 in 2017 (Table A.2), the estimated intertemporal elasticity,𝛼,̃ by MPL, and the relative risk-aversion coefficient, 𝛼,̃ by MPL, are quite different, rejecting the expected utility framework. We also find that the two noise
parameters are not substantial, suggesting that subjects made theoretically consistent decisions—noise parameter in the MPL experiment, 𝜈, is 0.109, and noise
parameter in the (Holt and Laury, 2002) experiment, 𝜇, is 0.359.
23 In Fig. 5, we overlay the post-flood satellite image with the flood border in red on self-reported flood water depth with five categories in blue: lightest
blue (below ankle depth < 10 cm), light blue (below knee depth < 40 cm), blue (below hip depth < 80 cm), dark blue (below chest depth < 120 cm), anddarkest blue (above chest depth > 120 cm). These two pieces of information seem to be consistent.

8European Economic Review 161 (2024) 104632

Y. Kuroishi and Y. Sawada

Fig. 2. Summary of MPL data by house damage status in Japan (2017).
Notes: Fig. 2 shows the relationship between gross interest rates and proportion of subjects choosing sooner payments in the cases {𝑡 = 0, 𝑘 = 35} and {𝑡 = 0, 𝑘 = 63}.
In the Multiple Price List (MPL) experiments, individuals make a series of binary choices between smaller early payments and larger delayed payments involving
combinations of starting times, 𝑡; delay lengths, 𝑘; and annual interest rates.

Fig. 3. Summary of MPL data by house damage status in Japan (2017).
Notes: Fig. 3 shows the relationship between gross interest rates and proportion of subjects choosing sooner payments in the cases {𝑡 = 35, 𝑘 = 35} and{𝑡 = 63, 𝑘 = 63}. In the Multiple Price List (MPL) experiments, individuals make a series of binary choices between smaller early payments and larger delayed
payments involving combinations of starting times, 𝑡; delay lengths, 𝑘; and annual interest rates.

Concerning the subjective damage levels, we construct a disaster damage variable from self-reported damage information,
including home damage, farm damage, asset damage, income decline, debt increase, and injury and/or sickness, available for all
three villages.
9European Economic Review 161 (2024) 104632

Y. Kuroishi and Y. Sawada

Fig. 4. Summary of holt and laury data by house damage status in Japan (2017).
Notes: Fig. 4 shows the relationship between the probability of winning a larger prize and the probability of choosing a safer lottery. In the first-stage Holt andLaury (2002)’s risk MPL experiment, subjects face a series of lottery choices between a safe lottery and a risky lottery.

Fig. 5. The satellite image of farms.
Notes: The data on self-reported water depth is overlaid on the satellite image with five categories: lightest blue (below ankle depth <10 cm), light blue (below
knee depth <40 cm), blue (below hip depth <80 cm), dark blue (below chest depth <120 cm), and darkest blue (above chest depth >120 cm). (For interpretationof the references to color in this figure legend, the reader is referred to the web version of this article.)

Accordingly, we define a damage variable combining objective and subjective information, ‘‘Flood Damage’’, which is an indicatorvariable based on the subjective and objective reports, which takes one when households suffered from at least three kinds ofdamages, out of the satellite-based objective farm damage and the five self-reported subjective damages except for the subjective
farm damage.
Based on this damage variable, we perform a covariate balance test by regressing each of the following observed pre-disastercharacteristics of subjects before the floods (i.e., household size, indicator variable of head’s sex, head’s age, head’s years of
10European Economic Review 161 (2024) 104632

Y. Kuroishi and Y. Sawada

Table 3Aggregate and individual utility parameters estimated by the Convex Time Budget (CTB) experiments allowing for heterogeneity by damages in the Philippines(2014 and 2018).
Panel A: Aggregate analysis
Estimated parameters

(1)

(2)
Experiments in 2014

(3)

(4)

(5)
(6)
Experiments in 2018

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

0.864
(0.0247)0.703(0.0628)

0.993(0.000811)
0.995(0.00159)

0.130(0.0138)
0.164(0.0284)

0.903(0.0359)0.746(0.0423)

0.995(0.000836)
0.994(0.00114)

0.120(0.0123)
0.123(0.0166)

P-value of the null hypothesis for homogeneous parameters0.0165

0.348

0.282

0.00470.806

Panel B: Individual analysis

(1)

(3)

(4)

No flood damage
Flood damage
Observations

2352

Dependent variables

2184

(2)
Experiments in 2014

0.897

(5)
(6)
Experiments in 2018

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

Flood damage dummy−0.210(0.0915)0.00455(0.0144)−0.164(0.299)−0.328(0.104)−0.0164(0.0204)−0.441(0.444)

Observations

98

98

98

91

91

91

Notes: Individually clustered standard errors are reported in parentheses in panel A and robust standard errors are reported in parentheses in panel B. No Flood
Damage is the group indicating subjects whose households experienced less than three kinds of damages, including farm damage, as identified by the satellite
image. Flood Damage is the group indicating subjects whose households experienced at least three kinds of damages, including farm damage, as identified by
the satellite image. The CTB experiment of Andreoni et al. (2015) allows us to separately identify the three key parameters of the time-separable CRRA utility
function with the 𝛽-𝛿 discounting (Strotz, 1955; Phelps and Pollak, 1968; Laibson, 1997)—the quasi-hyperbolic discounting factor showing present bias, 𝛽; theexponential time discounting factor, 𝛿; and the degree of intertemporal elasticity of substitution, 𝛼.

Table 4
Utility parameters estimated by the Multiple Price List (MPL) experiments allowing for heterogeneity by damages in the Philippines (2014 and 2018).
Estimated parameters

No flood damage
Flood damage

Experiments in 2014
𝛿

𝜈

𝛽

𝛼̃

𝜇

0.996
(0.00133)
0.993(0.00231)

0.150(0.0475)0.303(0.103)0.960(0.0245)0.762(0.0823)

0.554(0.130)0.199(0.214)0.208(0.0641)0.302(0.0856)

0.177

0.0209

0.156

0.382

Observations

14112

P-value of the null hypothesis for homogeneous parameters

0.250

Estimated parameters

No flood damage
Flood damage

Experiments in 2018
𝛿

𝜈

𝛽

𝛼̃

𝜇

0.998
(0.000818)
0.998(0.000857)

0.0920(0.0355)
0.0995(0.0421)0.945(0.0226)
0.823(0.0701)

0.632(0.131)
0.552(0.174)0.162(0.0576)
0.196(0.0769)

0.892

0.0978

0.714

0.729

Observations

13104

P-value of the null hypothesis for homogeneous parameters

0.742

Notes: Individually clustered standard errors are reported in parentheses. No Flood Damage is the group indicating subjects whose households experienced lessthan three kinds of damages, including farm damage, as identified by the satellite image. Flood Damage is the group indicating subjects whose households
experienced at least three kinds of damages, including farm damage, as identified by the satellite image. The MPL experiment of Andersen et al. (2008) consistsof two MPL experiments. The first stage is designed to provide information on the utility function curvature through a lottery choice MPL experiment of Holtand Laury (2002). The second stage is designed to identify time discounting parameters by a time preference MPL experiment. This experiment allows us
to separately identify the three key parameters of the time-separable CRRA utility function with the 𝛽-𝛿 discounting (Strotz, 1955; Phelps and Pollak, 1968;
Laibson, 1997)—the quasi-hyperbolic discounting factor showing present bias, 𝛽; the exponential time discounting factor, 𝛿; and 𝛼̃ the coefficient of the relativerisk aversion with two noise parameters, 𝜈 and 𝜇.

11European Economic Review 161 (2024) 104632

Y. Kuroishi and Y. Sawada

education, married dummy, widow dummy, the proportion of food consumption out of the total monthly consumption before thefloods, and the amount of cash loans taken before the floods) on the damage variable.24 With either 2014 or 2018 data, the RomanoWold and Westfall-Young tests of multiple hypothesis show that each of the pre-disaster household characteristics is uncorrelatedwith the damage level (Tables A.5 and A.6).25
As for the selective migration issue, according to the complete lists of farmers in 2007 and 2013 which are available from theprevious surveys conducted by IRRI, we verify that no one migrated out between the timing of Habagat in 2012 and the IRRI survey
in 2013. While we believe that the issue of selective migration is not a major concern in the Philippines, we will revisit the potential
problem carefully in Section 6.
5.2. The CTB experiment results
In order to examine the impact of the floods, we estimate the model by allowing the heterogeneous curvature parameter, 𝛼;
exponential time discounting parameter, 𝛿; and the present bias parameter, 𝛽, depending on the damage category we defined,‘‘Flood Damage’’.26 We also estimate 𝛽, 𝛿 and 𝛼 at the individual level and examine the damage impact on each of the estimatedparameters. The estimation results in Table 3 show that when we compare the status ‘‘No Flood Damage’’ with ‘‘Flood Damage’’caused by the flood, 𝛽 decreases substantially, suggesting that exposure to the disaster seems to reinforce people’s present bias. In
contrast, other parameters, 𝛼 and 𝛿, are largely unaffected by the disaster.
5.3. The MPL experiment results
We also employ MPL experiment data from the Philippines in 2014 and 2018 to estimate the impact of the floods on
preference parameters. Tables 4 for 2014 and 2018, respectively, show the estimation results, wherein the estimated quasi-hyperbolic
discounting factor, 𝛽, is consistently smaller for those who were exposed to the floods than those who were unaffected withreasonable level of statistical significance. Qualitatively, these results may be seen as being comparable to the ones we obtainfrom the Japanese data.27
6. Possibility of selection
As we discussed, selective exposure and migration would threaten our causal identification of the disaster impact on preference
parameters (Callen, 2015). Our baseline balancing test results, both in Iwanuma and the Philippines, show the non-existence ofsystematic correlation between the damage level and each of the observed pre-disaster characteristics. Yet, there still is a concern
for selective exposure and selective migration.28 To handle this concern, we follow Oster (2019) and quantify omitted variable bias
arising from endogenous migration. According to Oster (2019), the inclusion of observed controls allows us to infer the bias fromthe unobservables and to calculate bounds on the disaster effect. In our specification, we control for sex, age and smoking dummy
variables. Among these variables, the smoking dummy variable is a proxy for time and risk preferences (Kan, 2007; Anderson and
Mellor, 2008; Dupas, 2011; Story et al., 2014). Thus, we believe that the inclusion of the smoking dummy variable captures omitted
variable bias at least partially and derives bounds for the causal impact of the disaster. The estimation results are reported in Table 5.
Our bound estimates are similar to the ones reported in Table 1 and we only observe the statistically significant bound corresponding
to the effect of the disaster on the present bias. Hence, we believe our results are unlikely to be driven by selective exposure andmigration bias.
Yet, there is a possibility that our estimate might be different from the effect of a disaster on a sample of the (pre-disaster) exposed
population. To strengthen our argument, we use the key observables as weights and run the weighted regression, where we employ
the following weights interchangeably: age, sex, education and smoking dummy for Japan; and household size, head’s sex, head’s
age and head’s years of schooling for the Philippines. In Japan, we make the sample representative of the non-migrants in 2010,
i.e. ‘‘stayers’’ of the population, as well as the entire population from 2010. In the Philippines, we make the sample representativeof the exposed population in the East Laguna village as of 2012. The estimation results are reported in Tables 6 and 7 for Japan and

24

These data were collected before the floods in August 2012, except for the age information, which is as of each experiment date in March 2014.
The covariate balance test may seem underpowered with less than 100 observations. To check the power empirically, we regress the individually estimatedpost-disaster present-bias 𝛽 on the damage status. The result shows overall statistically significance which supports that the sample size is enough to performthe covariate balance test.
26 Table A.7 shows the estimation results of three parameters using the CTB experiment data from the Philippines. In all specifications, the estimated present
bias parameter, 𝛽, falls below one and is smaller than the estimated 𝛽 in Japan. This result suggests that subjects in developing countries show substantial
quasi-hyperbolic discounting. The estimated exponential time discounting and intertemporal elasticity of substitution parameters are within reasonable ranges.
27 Table A.8 shows estimation results of the three parameters, together with error parameters, derived using the MPL experiment data. Subjects also show
their present biasedness in MPL—the present bias parameters, 𝛽, are 0.880 in 2014 and 0.913 in 2018, and we statistically reject the null hypothesis that 𝛽equals to one. The estimated risk aversion parameters, 𝛼,̃ are 0.32 in 2014 and 0.533 in 2018, which are plausibly consistent with the existing studies. However,these risk aversion coefficients deviate from the estimated intertemporal elasticity of substitution, 𝛼, particularly in 2014, using the CTB experiment, that is,
0.147–0.282 in 2014 and 0.124–0.241 in 2018.
28 Mironova et al. (2019) examines risk tolerance among rebel combatants and civilians in Syria and identifies a sorting mechanism during conflict whererisk averse individuals select out of conflict.
25

12

European Economic Review 161 (2024) 104632

Y. Kuroishi and Y. Sawada

Table 5
Individual utility parameters estimated by the Convex Time Budget (CTB) experiments allowing for heterogeneity by damages in Japan (2014 and 2017): Boundapproach.
Dependent variables

(1)

(2)

𝛽

𝛽

Minor or half-damaged house dummy−0.0405(0.0957)

Almost or totally-collapsed house dummy
Observations(3)(4)
Experiments in 2014
𝛿

186

(6)

(7)

(8)

𝛼

𝛼

𝛽

𝛽

0.0605(0.124)−0.00235(0.00671)−0.197(0.0712)

186

𝛿

(5)

186

186

186

186

𝛿

−0.158(0.0718)179

𝛿

0.00722(0.00813)−0.0332(0.0758)
0.0825(0.143)−0.00498(0.00751)(9)(10)
Experiments in 2017

179

(11)

(12)

𝛼

𝛼

0.0705(0.0749)−0.000788(0.00737)

179

179

−0.0124(0.0732)
179

179

Notes: We follow Oster (2019) to calculate bounding values for unbiased coefficients. Bootstrap standard errors are reported in parentheses. We control for sex, age and smoking
dummy and set the maximal 𝑅2 to 1.3 times the 𝑅2 of each regression. Minor or Half-damaged House is the group indicating that subjects whose houses suffered minor or half
damage. Almost or Totally-collapsed House is the group indicating subjects whose houses almost or totally collapsed.

the Philippines, respectively. The results show that exposure to the disaster reinforces only present bias of the exposed population
both in Japan and the Philippines.
Moreover, individuals who agreed to participate in our experiments may be systematically different from the rest of the
population. When the probability of an individual’s participation in experiments is correlated with the outcomes of the experiments,
there will be site selection bias in estimating the impacts of disasters (Allcott, 2015; Banerjee et al., 2017). To check and handle
the existence of such a bias, we employ the following control function approach to the JAGES census data from Iwanuma city. In
this approach, we estimate the probability of participation in the experiments jointly with the main Euler equation for the CTB
experiments, that is, Eq. (1), using a two-step estimation method under the joint normality assumption with bootstrapped standard
errors. To estimate the probability of controlling for sample selection bias, we include the smoking dummy variable, a proxy measureof pre-disaster time and risk preferences, as an independent variable. Then, we regress each of the estimated individual preference
parameters on the damage variables, covariates, and selection correction term derived from the first-stage equation.
Table 8 presents the estimation results for the second-stage model. The coefficients of the selection correction term derived
from the first-stage equation are mostly statistically significant, indicating nonrandom selection. Importantly, the qualitative results
for both the 2014 and 2017 experimental data are maintained, even after correcting for potential site-selection bias. These results
suggest that such bias is not necessarily serious in our setting.
7. Discussion
7.1. Possible time trajectory
One might think our results on time discounting in Japan and the Philippines differ from those of Callen (2015) which examines
the impact of the Indian Ocean Earthquake tsunami in Sri Lanka: While Callen (2015) finds that exposure to the disaster has apositive effect on the time discount factor after two and a half years, our study shows no effect of the tsunami and floods on the
exponential discount factor, 𝛿. However, our individual analyses for Japan (Table 1, Panel B) and the Philippines (Table 3, Panel B)
show that the effect on the discount factor, 𝛿, is positive in the first wave of experiments after three and two years, respectively, butmainly negative in the second wave of experiments after six years, although neither is statistically significant. These findings, alongwith those of Callen (2015), suggest that the effect is positive in the short-term and diminishes over time. Indeed, the following two
existing studies seem to be consistent with this conjecture: While Chantarat et al. (2019) finds that victims of the 2011 mega floodin Cambodia show higher patience after three years, Cassar et al. (2017) reports that Thai people who experienced the December
2004 Indian Ocean tsunami appear to discount the future more four and a half years after the disaster. However, it should be notedthat this explanation is speculative, and further careful investigations are needed to address this issue in the future.
7.2. Potential frameworks
Why do disasters make people ‘‘seemingly’’ more present-biased in the short and long-term periods? To address this question,
we follow existing studies to consider two possible frameworks for the nexus between disaster exposure and present bias: Callen
et al. (2014), Callen (2015).
First, Callen et al. (2014) finds a relationship between trauma and a preference for certainty. Callen et al. (2014) demonstrates
that fearful recollections trigger an increased preference for certainty among individuals exposed to violence.29 We predict that theexacerbated certainty effect caused by traumatic exposure would make individuals more present-biased. To investigate this potential
channel empirically, we analyze whether the exposed individuals in Japan show psychological responses.
In the JAGES data from Japan, we employ questions on three binary variables taken from the clinically-validated K6 depression
measure of Kessler et al. (2002): First, ‘‘Desperate Feeling’’ takes 1 when the respondents feel that their lives are desperate and 0

29 An intuition behind this finding is provided by Lerner and Keltner (2001), who explains that the sense of uncertainty and lack of control associated withfear should lead fearful individuals to make certainly enhancing choices.

13

(A) Weight variable: Stayer
Experiments in 2014

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

Dependent variables

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

Minor or half-damaged house dummy−0.0139(0.0733)−0.147(0.0825)−0.00229(0.00481)−0.00628(0.00473)

0.0398(0.104)0.00212(0.102)−0.0423(0.0640)−0.169(0.0803)−0.00263(0.00471)−0.00446(0.00506)

0.0312(0.0988)
0.0261(0.107)−0.0415(0.0677)−0.182(0.0826)−0.00260(0.00505)−0.00532(0.00500)

0.0200(0.0939)−0.0234(0.0970)−0.0405(0.0639)−0.171(0.0806)−0.00248(0.00471)−0.00435(0.00511)

0.0298(0.0973)
0.0308(0.111)186
0.0989
Age

186
0.373
Age

186
0.924
Age

186
0.0956
Sex

186
0.678
Sex

186
0.937
Sex

186
0.0696
Education

186
0.547
Education

186
0.927
Education

186
0.0922
Smoking186
0.696
Smoking186
0.932
Smoking

Experiments in 2017

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Dependent variables

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

Minor or half-damaged house dummy−0.0269(0.0541)−0.137(0.0627)0.00457(0.00397)−0.00173(0.00390)

0.0438(0.0426)−0.0205(0.0447)−0.00835(0.0572)−0.149(0.0653)

0.00668(0.00445)−0.00216(0.00395)

0.0643(0.0468)−0.0302(0.0474)−0.00783(0.0639)−0.136(0.0750)

0.00653(0.00494)−0.00133(0.00539)

0.0621(0.0513)−0.0100(0.0561)−0.00642(0.0559)−0.150(0.0660)

0.00700(0.00441)−0.00147(0.00404)

0.0619(0.0467)−0.0200(0.0467)

Observations
P-value of the null hypothesis for homogeneous parameters
Weight variable

178
0.0915
Age

178
0.326
Age

178
0.406
Age

179
0.0615
Sex

179
0.118
Sex

179
0.183
Sex

179
0.152
Education

179
0.277
Education

179
0.375
Education

179
0.0620
Smoking179
0.147
Smoking179
0.264
Smoking

Experiments in 2014

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Dependent variables

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

Minor or half-damaged house dummy−0.00796(0.0834)−0.118(0.0914)−0.00356(0.00478)−0.00612(0.00465)

0.0539(0.133)0.0268(0.109)−0.0419(0.0639)−0.169(0.0803)−0.00261(0.00470)−0.00444(0.00506)

0.0311(0.0986)
0.0265(0.107)−0.0385(0.0638)−0.161(0.0820)−0.00244(0.00468)−0.00388(0.00527)

0.0351(0.1000)
0.0534(0.116)−0.0395(0.0637)−0.168(0.0803)−0.00249(0.00470)−0.00432(0.00509)

0.0306(0.0974)
0.0292(0.109)

186
0.251
Age

186
0.422
Age

186
0.913
Age

186
0.0961
Sex

186
0.681
Sex

186
0.936
Sex

186
0.133
Education

186
0.758
Education

186
0.869
Education

186
0.0988
Smoking186
0.698
Smoking186
0.933
Smoking

Experiments in 2017

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Dependent variables

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

Minor or half-damaged house dummy−0.0269(0.0541)−0.137(0.0627)0.00457(0.00397)−0.00173(0.00390)

0.0438(0.0426)−0.0205(0.0447)−0.00859(0.0571)−0.149(0.0653)

0.00667(0.00445)−0.00214(0.00396)

0.0640(0.0467)−0.0299(0.0474)−0.0179(0.0548)−0.148(0.0649)

0.00621(0.00430)−0.00158(0.00389)

0.0529(0.0460)−0.0239(0.0464)−0.00917(0.0558)−0.148(0.0658)

0.00679(0.00438)−0.00149(0.00404)

0.0598(0.0466)−0.0203(0.0468)178
0.0915
Age

178
0.326
Age

178
0.406
Age

179
0.0619
Sex

179
0.120
Sex

179
0.186
Sex

179
0.0683
Education

179
0.177
Education

179
0.316
Education

179
0.0655
Smoking179
0.157
Smoking179
0.280
Smoking

Almost or totally-collapsed house dummy
Observations
P-value of the null hypothesis for homogeneous parameters
Weight Variable

Almost or totally-collapsed house dummy(12)

Y. Kuroishi and Y. Sawada

Table 6
Individual utility parameters estimated by the Convex Time Budget (CTB) experiments allowing for heterogeneity by damages in Japan (2014 and 2017): Weighted approach.

14(B) Weight variable: Total

Almost or totally-collapsed house dummy
Observations
P-value of the null hypothesis for homogeneous parameters
Weight variable

Observations
P-value of the null hypothesis for homogeneous parameters
Weight variable

Notes: Robust standard errors in parentheses. Minor or Half-damaged House is the group indicating that subjects whose houses suffered minor or half damage. Almost or Totally-collapsed House is the group indicating
subjects whose houses had almost or totally collapsed. Age refers to the age of the participants. Sex is equal to 1 for male participants. Education takes 1 for participants with complete compulsory education and 0
otherwise. Smoking" takes 1 when they smoke sometimes or almost every day and 0 otherwise.

European Economic Review 161 (2024) 104632

Almost or totally-collapsed house dummyEuropean Economic Review 161 (2024) 104632

Y. Kuroishi and Y. Sawada

Table 7
Individual utility parameters estimated by the Convex Time Budget (CTB) experiments allowing for heterogeneity by damages in the Philippines (2014 and2018): Weighted approach.
Weight variable: Total
Dependent variables

(1)

(2)

(3)

(4)

(5)

(6)

(7)
(8)
Experiments in 2014

(9)

(10)

(11)

(12)

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

Flood damage dummy−0.218(0.0920)0.00221(0.0152)−0.190(0.318)−0.205(0.0921)0.00461(0.0148)−0.171(0.306)−0.255(0.121)−0.00771(0.0245)−0.279(0.490)−0.216(0.0904)

0.00692(0.0134)−0.117(0.277)

Observations
Weight variable

96
Household
Number

96
Household
Number

96
Household
Number

98
Head
Sex

98
Head
Sex

98
Head
Sex

98
Head
Age

98
Head
Age

98
Head
Age

98
Head years of
Schooling98
Head Years of
Schooling98
Head Years of
Schooling

(1)

(2)

(3)

(4)

(5)

(6)

(9)

(10)

(11)

(12)

Dependent variables

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

Flood damage dummy−0.327(0.107)−0.0123(0.0195)−0.310(0.445)−0.334(0.107)−0.0176(0.0213)−0.430(0.455)−0.248(0.140)−0.00273(0.0229)−0.167(0.486)−0.332(0.105)−0.0198(0.0231)−0.339(0.512)

Observations
Weight variable

89
Household
Number

89
Household
Number

89
Household
Number

91
Head
Sex

91
Head
Sex

91
Head
Sex

91
Head
Age

91
Head
Age

91
Head
Age

91
Head years of
Schooling91
Head years of
Schooling91
Head years of
Schooling(7)(8)
Experiments in 2018

Notes: Robust standard errors are reported in parentheses. Flood Damage is the group indicating subjects whose households experienced at least three kinds of damages, including
farm damage, as identified by the satellite image. Household Number is the number of household members. Head Age is age of the head. Head Sex is equal to 1 when their head ismale. Head Years of Schooling is years of schooling of the head.

Table 8
Individual utility parameters estimated by the Convex Time Budget (CTB) experiments allowing for heterogeneity by damages and sample selection in Japan(2014 and 2017).
Individual Analysis(1)

Dependent variables

𝛽

Minor or half-damaged house−0.0523(0.0671)−0.184(0.0791)−0.0971(0.226)

Observations

6422

P-value of the null hypothesis:
All of the three are the same

0.0467

Almost or totally-collapsed house
Sample selection(2)
Experiments in 2014

(3)

(4)

(5)
Experiments in 2017

(6)

𝛿

𝛼

𝛽

𝛿

𝛼

0.00524(0.00823)−0.00664(0.00566)
0.0323(0.0182)−0.227(0.173)−0.0465(0.148)−1.287(1.118)−0.0202(0.0495)−0.149(0.0677)
0.395(0.219)0.00542(0.00451)−0.00159(0.00513)
0.0393(0.0195)

0.0118(0.0695)
0.0321(0.0615)−0.516(0.193)

6422

6422

6423

6423

6423

0.386

0.418

0.0779

0.276

0.872

Notes: Bootstrap standard errors in parentheses. No House Damage is the group indicating subjects whose houses were not damaged. Minor or Half-damaged
House is the group indicating that subjects whose houses suffered minor or half damage. Almost or Totally-collapsed House is the group indicating subjects
whose houses had almost or totally collapsed. The CTB experiment of Andreoni et al. (2015) allows us to separately identify the three key parameters of thetime-separable CRRA utility function with the 𝛽-𝛿 discounting (Strotz, 1955; Phelps and Pollak, 1968; Laibson, 1997)—the quasi-hyperbolic discounting factor
showing present bias, 𝛽; the exponential time discounting factor, 𝛿; and the degree of intertemporal elasticity of substitution, 𝛼. We employ age, age-squared,
job dummy, earthquake insurance subscription dummy and smoking dummy as covariates in the first-stage sample selection equation and age, age-squared, job
dummy, and earthquake insurance subscription dummy for covariates in the second-stage equation. ‘‘Sample Selection’’ reports the coefficient on the sample
selection correction term derived from the first-stage selection equation to correct for the sample selection.

otherwise; second, ‘‘Hopeless Life’’ takes 1 when they feel that their lives are hopeless and 0 otherwise; and third, ‘‘Something Bad’’takes 1 when they feel something bad will happen in the future and 0 otherwise. We regress each of these three binary variables on
the house damage variables. Table 9 presents the estimated results for the years 2014 and 2017. These results suggest that disaster
exposure seems to make subjects feel more ‘‘Desperate’’ and ‘‘Hopeless’’ persistently even after six years, arguably supporting the
proposed psychological framework.
There are potential concerns about selection examined in Section 6 because we exploit the differences in disaster exposure that
may trigger migration. To validate our analysis, we follow the bound and weighted regression approaches as before. The results
are presented in Tables 10 and 11. Since our estimates are comparable to those reported in Table 9, we believe that our results arerobust and unlikely to be driven by selection bias.
Second, Callen (2015) proposes another framework to characterize the three potential channels through which disaster exposure
could affect individuals’ patience: subjective expectations about the future; changes in consumption levels due to economics losses;
and changes in taste. We empirically test the first channel through which a disaster decreases subjective expectations about the
future in the Philippines, leading to less patience and increased consumption from the future to the present.
During the 2018 experiments in the Philippines, we asked whether the subjects perceived a decline in their life expectancy after
the 2012 flood. According to the result reported in Table 12, individuals exposed to the floods systematically tended to perceive a15

European Economic Review 161 (2024) 104632

Y. Kuroishi and Y. Sawada
Table 9Emotional change in Japan (2014 and 2017).
Participants in 2014
Desperate feeling

Hopeless life

Something bad

0.0634(0.0813)0.288(0.104)

0.0974(0.0459)
0.194(0.0756)

0.0138(0.0605)
0.0615(0.0814)

Observations

186

185

183

Dependent variables

Participants in 2017
Desperate feeling

Hopeless life

Something bad

0.0881(0.0784)
0.319(0.0998)

0.100(0.0457)
0.142(0.0674)−0.00417(0.0593)0.0301(0.0770)

179

179

176

Dependent variables
Minor or half-damaged house dummy
Almost or Totally Collapsed House Dummy

Minor or half damaged house dummy
Almost or Totally Collapsed House Dummy
Observations

Notes: Robust standard errors are reported in parentheses. Desperate Feeling takes 1 if individuals feel that their lives are desperate. HopelessLife takes 1 if they feel that their lives are hopeless. Something Bad takes 1 if they feel that something bad will happen in the future. Minor
or Half-damaged House is the group indicating that subjects whose houses suffered minor or half damage. Almost or Totally-collapsed House is
the group indicating subjects whose houses had almost or totally collapsed.

Table 10
Emotional change in Japan (2014 and 2017): Bound approach.
Dependent variables

Minor or half-damaged house dummy

(1)

(3)
(4)
Experiments in 2014

(5)

(6)

(7)

(8)

(9)
(10)
Experiments in 2017

(11)

(12)

Desperate feeling

Hopeless life

Something bad

Desperate feeling

Hopeless life

Something bad

0.0864(0.135)0.135(0.0624)0.0323(0.0665)0.179(0.0997)0.110(0.0524)−0.0115(0.0511)

Almost or totally-collapsed house dummy
Observations

(2)

0.291
(0.135)
186

186

0.227
(0.0849)
185

185

0.0655(0.0741)
183

183

0.384
(0.0861)
179

179

0.162
(0.0568)
179

179

0.0231(0.0589)
176

176

Notes: We follow Oster (2019) to calculate bounding values for unbiased coefficients. Bootstrap standard errors in parentheses. We control for sex, age and smoking dummy andset the maximal 𝑅2 to 1.3 times the 𝑅2 of each regression. Minor or Half-damaged House is the group indicating that subjects whose houses suffered minor or half damage.
Almost or Totally-collapsed House is the group indicating subjects whose houses had almost or totally collapsed.

decrease in their life expectancy. Overall, our results for both Japan and the Philippines seem to be consistent with the frameworks
proposed by Callen et al. (2014), Callen (2015).30
7.3. Alternative explanation: Liquidity constraints
However, as Callen (2015) points out, a change in consumption levels due to economics losses may also affect individuals’
patience: scarcity of monetary resources or simply binding liquidity constraints may make people ‘‘seemingly’’ present biased.31
Here, we examine this possible intervening channel, liquidity constraints.
In the case of Japan, no respondent in the JAGES survey claimed money shortage for food purchases, indicating that liquidity
constraints were not binding among the subjects. In fact, the general probability of binding liquidity constraint is negligible inJapan even during the financial crisis in 1997–98 (Sawada et al., 2011). However, in the Philippines, our data show that 54% of thesubjects could not borrow money from others during their need after the disaster. Hence, we re-estimate the impact of the disasteron preference parameters only for the Philippines.
A long-standing critique of the CTB is that non-liquidity constrained individuals should not reveal any of their intertemporal
preferences in money allocation tasks (Augenblick et al., 2015; Andreoni et al., 2018). Arbitrage over money rewards may be
stripping present bias from the data and invalidating cash for measuring discounting. In other words, deriving the estimation modelfrom a utility maximization is only theoretically appropriate when a subject is constrained (or bracketing narrowly).

30 There are alternative mental health and emotional channels following the psychology literature such as Loewenstein et al. (2001) and Haushofer and Fehr(2014). It would be possible that disaster-hit subjects perceive that they are in a loss domain, making them more open to taking risks. Alternatively, individuals
under extreme stress are simply less likely to analyze the set of gambling choices carefully and use instead a simple rule such as choosing the gamble that cangive the highest payoff (Janis, 1993), leading to a systematic observation that disaster exposure makes lower risk-aversion.
31 Losing financial resources, physical assets, and family members can consume attentional resources, leading to distinctive biases on cognitive function andparticular decisions. Mani et al. (2013) shows that scarcity can impede one’s cognitive functions, leading to seemingly myopic behaviors. Carvalho et al. (2016)
also argues that scarce resources can affect one’s willingness to delay gratification, and those who suffered from liquidity constraints behaved as if they weremore present-biased.

16

(A) Weight variable: Stayer
Experiments in 2014

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Dependent variables

Desperate
feeling

Hopelesslife

SomethingbadDesperate
feeling

Hopelesslife

SomethingbadDesperate
feeling

Hopelesslife

SomethingbadDesperate
feeling

Hopelesslife

Somethingbad

Minor or half-damaged house dummy

0.0743(0.0921)0.285(0.113)0.0906(0.0454)0.170(0.0727)0.00923(0.0588)
0.0936(0.0901)

0.0604(0.0818)
0.286(0.104)

0.0976(0.0459)
0.192(0.0752)0.0140(0.0604)
0.0616(0.0813)0.0331(0.0893)0.275(0.112)0.106(0.0463)0.178(0.0735)

0.0214(0.0572)
0.0813(0.0841)0.0642(0.0810)0.289(0.103)

0.0989(0.0460)
0.197(0.0760)

0.0154(0.0603)
0.0633(0.0814)

Observations
P-value of the null hypothesis for homogeneous parameters
Weight variable

186
0.0355Age

1850.0245
Age

183
0.556
Age

186
0.0183Sex

185
0.0133
Sex

183
0.742
Sex

186
0.0265
Education

185
0.0112
Education

183
0.627
Education

186
0.0168
Smoking

185
0.0115
Smoking183
0.733
Smoking

Experiments in 2017

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Dependent variables

Desperate
feeling

Hopelesslife

SomethingbadDesperate
feeling

Hopelesslife

SomethingbadDesperate
feeling

Hopelesslife

SomethingbadDesperate
feeling

Hopelesslife

Somethingbad

Minor or half-damaged house dummy

0.0344(0.0856)0.300(0.108)0.0773(0.0424)
0.0989(0.0561)−0.0225(0.0590)0.0361(0.0843)0.0765(0.0802)0.318(0.101)

0.105(0.0439)0.147(0.0665)−0.0102(0.0603)0.0299(0.0791)

0.115(0.0870)0.390(0.102)0.105(0.0451)0.175(0.0788)0.00828(0.0669)
0.0292(0.0841)0.0896(0.0780)
0.322(0.0996)0.104(0.0461)
0.146(0.0683)−0.000864(0.0592)
0.0360(0.0779)

Observations
P-value of the null hypothesis for homogeneous parameters
Weight Variable

178
0.0137Age

178
0.0818
Age

175
0.737
Age

179
0.0070Sex

179
0.0124
Sex

176
0.858
Sex

179
0.0008
Education

179
0.0122
Education

176
0.941
Education

179
0.0058
Smoking179
0.0208
Smoking176
0.869
Smoking

Experiments in 2014

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Dependent variables

Desperate
feeling

Hopelesslife

SomethingbadDesperate
feeling

Hopelesslife

SomethingbadDesperate
feeling

Hopelesslife

SomethingbadDesperate
feeling

Hopelesslife

Somethingbad

Minor or half-damaged house dummy

0.0712(0.103)0.249(0.121)

0.0996(0.0471)0.161(0.0711)0.00477(0.0593)0.0855(0.0893)0.0608(0.0817)0.286(0.104)

0.0976(0.0459)
0.192(0.0752)0.0140(0.0604)
0.0616(0.0813)0.0758(0.0804)
0.291(0.104)

0.0939(0.0466)
0.202(0.0784)0.0106(0.0623)
0.0529(0.0827)

0.0635(0.0813)0.288(0.104)

0.0976(0.0459)
0.195(0.0756)

0.0139(0.0604)
0.0617(0.0814)

Observations
P-value of the null hypothesis for homogeneous parameters
Weight variable

186
0.104
Age

185
0.0199
Age

183
0.590
Age

186
0.0182Sex

185
0.0132
Sex

183
0.742
Sex

186
0.0189
Education

185
0.0152
Education

183
0.803
Education

186
0.0175
Smoking

1850.0127
Smoking183
0.741
Smoking

Experiments in 2017

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Dependent variables

Desperate
feeling

Hopelesslife

SomethingbadDesperate
feeling

Hopelesslife

SomethingbadDesperate
feeling

Hopelesslife

SomethingbadDesperate
feeling

Hopelesslife

Somethingbad

Minor or half-damaged house dummy

0.0294(0.0878)
0.281(0.110)0.0802(0.0462)0.0901(0.0565)−0.0149(0.0613)0.0268(0.0824)

0.0769(0.0801)
0.318(0.101)

0.105(0.0440)0.147(0.0666)−0.0101(0.0603)0.0299(0.0791)

0.0821(0.0782)0.301(0.101)

0.0991(0.0464)0.134(0.0665)−0.00692(0.0591)0.0303(0.0773)

0.0892(0.0781)0.321(0.0996)0.103(0.0460)
0.145(0.0680)−0.00179(0.0592)
0.0343(0.0776)178
0.0231
Age

178
0.123
Age

175
0.857
Age

179
0.0069Sex

179
0.0126
Sex

176
0.858
Sex

179
0.0119
Education

179
0.0334
Education

176
0.871
Education

179
0.0060
Smoking179
0.0219Smoking176
0.874
Smoking

Almost or totally-collapsed house dummy

Almost or totally-collapsed house dummy

Y. Kuroishi and Y. Sawada

Table 11
Emotional change in Japan (2014 and 2017): Weighted approach.

17(B) Weight variable: Total

Almost or totally-collapsed house dummy

Observations
P-value of the null hypothesis for homogeneous parameters
Weight variable

Notes: Robust standard errors in parentheses. Minor or Half-damaged House is the group indicating that subjects whose houses suffered minor or half damage. Almost or Totally-collapsed House is the group indicating subjects whose houses had
almost or totally collapsed. Age refers to the age of the participants. Sex is equal to 1 for male participants. Education takes 1 for participants with complete compulsory education and 0 otherwise. Smoking" takes 1 when they smoke sometimes
or almost every day and 0 otherwise.

European Economic Review 161 (2024) 104632

Almost or totally-collapsed house dummyEuropean Economic Review 161 (2024) 104632

Y. Kuroishi and Y. Sawada
Table 12
Emotional change in the Philippines (2018).
Dependent variable

1[Subjective life expectancy declined]

(1)

(2)

Flood damage dummy

0.103(0.0604)

0.120(0.0622)

Observations
Control variables

87
No

87
Yes

Notes: Robust standard errors are reported in parentheses. 1(Subjective Life Expectancy Declined)
takes 1 if subjects perceive a decline in their longevity after Habagat. Flood Damage is the groupindicating subjects whose households experienced at least three kinds of damages, including farmdamage, as identified by the satellite image. Control variables include sex, age, years of education,and expected life expectancy.

Table 13
Individual utility parameters estimated by the Convex Time Budget (CTB) experiments allowing for heterogeneity by damages and liquidity constraints in thePhilippines (2014 and 2018).
Dependent variables

Flood damage dummy
Liquidity constraint dummyln(Income)Observations

(1)

(2)
Experiments in 2014

(3)

(4)

(5)
Experiments in 2018

(6)

𝛽

𝛿

𝛼

𝛽

𝛿

𝛼

−0.195(0.0917)−0.129(0.0913)0.0177(0.00818)

0.00804(0.0159)
0.0176(0.0113)0.00265(0.00211)−0.0903(0.327)0.291(0.263)0.0585(0.0439)−0.325(0.106)−0.0381(0.107)0.0121(0.0118)−0.0132(0.0199)0.0300(0.0143)0.00251(0.00237)−0.374(0.433)0.937(0.371)0.00784(0.0614)

98

98

98

91

91

91

Notes: Robust standard errors are reported in parentheses. Flood Damage is the group indicating subjects whose households experienced at least three kindsof damages, including farm damage, as identified by the satellite image. Liquidity Constraint dummy takes 1 if the participant could not borrow money fromothers during their need after Habagat. Income is the household income before Habagat.

To assuage these concerns, we employ the following approach. First, we follow Andreoni and Sprenger (2012) and model
consumption external to the experiment study in the proceeding method.
𝑙𝑛(

𝑥𝑡 + 𝜔1
𝑙𝑛(𝛽)
𝑙𝑛(𝛿)
1
)=−
1[𝑡 = 0] −𝑘 − 𝑙𝑛(𝑃 ),𝑥𝑡+𝑘 + 𝜔2
𝛼
𝛼
𝛼

(11)

where the terms 𝜔1 and 𝜔2 are additional utility parameters which could be interpreted as background consumption. In our
specification, these parameters are set to 60PHP, the average value of daily consumption in the East Laguna Village in 2012. With
this model, we estimate 𝛽, 𝛿 and 𝛼 at the individual level.
Second, we estimate the following empirical model of the disaster impact, regressing each of the estimated preference parameterson the damage variable as well as the liquidity constraint and income variables:
𝑦𝑖 = 𝑏0 + 𝑏1 𝐹 𝑙𝑜𝑜𝑑𝐷𝑎𝑚𝑎𝑔𝑒𝑖 + 𝑏2 1[𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦𝐶𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡𝑖 ] + 𝑏3 𝑙𝑛(𝐼𝑛𝑐𝑜𝑚𝑒𝑖 ) + 𝜀𝑖 ,(12)where 𝑦𝑖 is the estimated preference parameter (𝛽, 𝛿 or 𝛼), 1[𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦𝐶𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡𝑖 ] is an indicator function, which takes 1 whena subject cannot borrow money from others during their need after the disaster and 0 otherwise, and 𝑙𝑛(𝐼𝑛𝑐𝑜𝑚𝑒𝑖 ) is the subject’s
income. If the subject cannot borrow money from others during their need after the disaster, liquidity constraint is more likely
to be binding. Moreover, if the subject earns more income, the subject has enough money for consumption smoothing andliquidity constraint is less likely to bind. Thus, these two variables, 1[𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦𝐶𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡𝑖 ] and 𝑙𝑛(𝐼𝑛𝑐𝑜𝑚𝑒𝑖 ), are proxies for liquidity
constraints. If damages may make people ‘‘seemingly’’ present biased by reinforced liquidity constraints due to economic losses, 𝑏1
is statistically insignificant due to these two controls.
The results reported in Table 13 for 2014 and 2018, respectively show that even with liquidity constraint controls the estimated 𝛽
is still systematically lower at the 10% significance level for those exposed to the floods than those unexposed. This finding suggeststhat the inclusion of the liquidity constraint cannot fully explain the reinforced present bias caused by a disaster. Our overall results
seem to favor the psychological channel over the liquidity constraint channel.
Yet, we also find that the effect on 𝛽 becomes smaller uniformly once we incorporate the liquidity constraint variables into theestimation model. This could be seen consistent with Carvalho et al. (2016) which finds seeming present bias among those who faceliquidity constraints if allowing heterogeneous behavioral response to binding liquidity constraints in the model. Hence, we cannot
rule out the possibility that the combination of psychological factors and liquidity constraints could be driving our result.
8. Concluding remarks
To investigate whether and how long preferences are affected by extreme events, we adopt the CTB and MPL experiments andemployed accurate damage information from official metrical surveys and satellite images. We find that disaster exposure seems
18

European Economic Review 161 (2024) 104632

Y. Kuroishi and Y. Sawada

to make individuals more present-biased over the short and long time intervals between two distinct sets of subjects in Japan and
the Philippines with different socioeconomic conditions and disaster types. As for the potential channels, our results are largely
consistent with Callen et al. (2014), Callen (2015), suggesting that the psychological responses and changes in survival expectations
contribute to individuals’ impatience.
The literature on hyperbolic discounting attributes harmful behaviors, such as obesity, overeating, debt overhang, gambling,smoking, drinking, and other procrastination behaviors, to naive hyperbolic discounting (Banerjee and Mullainathan, 2010). Our
companion study reveals that present bias reinforced by disasters seems to induce unhealthy behaviors such as overeating, smoking,and drinking among the same subjects as this study (Sawada et al., 2019), as well as depression among tsunami and nuclear
power plant disaster victims in Japan (Sawada et al., 2023). Hence, we believe that our study also sheds new light on post-disasterrehabilitation policies by emphasizing the importance of providing commitment devices to disaster victims for mitigating negative
behavioral consequences of reinforced present bias, in line with Dupas (2011), Giné et al. (2010), and Bryan et al. (2010).
Yet, we should also note that there are potential caveats of our study. The recent experimental literature on hyperbolic
discounting focuses on the difference between the domains of monetary and non-monetary choice. Considering the empirical result
by Augenblick et al. (2015), which finds that present bias in monetary choices is much more limited than that in the allocation ofwork, our results that are fully based on monetary choices may underestimate the true impacts of disasters on real-world behavior.
Using the timings of mailing New Year’s cards to identify present bias and hyperbolic discounting after the disaster in the nonmonetary domain following Sawada et al. (2023)32 , we find 45% of our subjects in Iwanuma show hyperbolic discounting, while
the CTB experiment shows that 36% of quasi-hyperbolic discount parameter, 𝛽, of Iwanuma’s subjects fall below one. This indicatesthat present bias in non-monetary choices is more salient than that in monetary choices. While a recent article by Balakrishnan et al.
(2020) also shows that present bias even for money exists for immediate payments using the Kenyan mobile money system M-Pesa,
we would need to be aware of potential bias in estimating hyperbolic discounting parameter with different monetary incentives.
Also, there are issues which need further investigations. First, as Frederick et al. (2002) pointed, the variability in theestimates might have been driven by other confounding factors which were not considered in this study. Such factors may include
intertemporal arbitrage in general, uncertainty, expectations of changing utility functions, and habit formation. Second, we findthat disaster exposure in Japan reinforces risk tolerance in the MPL experiment, but induces no change in the intertemporalelasticity of substitution in the CTB experiment. Since under the expected utility hypothesis, the coefficient of relative risk aversion
is the reciprocal of the intertemporal elasticity of substitution, our empirical results may be seen consistent with the frameworkof Kreps and Porteus (1978) and Epstein and Zin (1991), rejecting the expected utility hypothesis. Third, since social preferences
are also susceptible to instability over time, as the pandemic has revealed, pro-sociality needs to be examined. Our companion
papers, Kuroishi and Sawada (2019) and Sawada et al. (2023), analyzed altruism using dictator games and real-world decisions,
respectively. We believe these issues are beyond the scope of the current paper and leave them as future investigations.
Data availability
Data will be made available on request.
Appendix A. Supplementary data
Supplementary material related to this article can be found online at https://doi.org/10.1016/j.euroecorev.2023.104632.
References
Abdellaoui, M., Attema, A.E., Bleichrodt, H., 2010. Intertemporal tradeoffs for gains and losses: An experimental measurement of discounted utility. Econom. J.
120 (545), 845–866.
Akesaka, M., 2019. Change in time preferences: Evidence from the great east Japan earthquake. J. Econ. Behav. Organ. 166, 239–245.
Allcott, H., 2015. Site selection bias in program evaluation. Q. J. Econ. 130 (3), 1117–1165.
Andersen, S., Harrison, G.W., Lau, M.I., Rutström, E.E., 2008. Eliciting risk and time preferences. Econometrica 76 (3), 583–618.
Anderson, L.R., Mellor, J.M., 2008. Predicting health behaviors with an experimental measure of risk preference. J. Health Econ. 27 (5), 1260–1274.
Andersson, O., Holm, H.J., Tyran, J.-R., Wengström, E., 2016. Risk aversion relates to cognitive ability: Preferences or noise? J. Eur. Econom. Assoc. 14 (5),1129–1154.
Andreoni, J., Gravert, C., Kuhn, M.A., Saccardo, S., Yang, Y., 2018. Arbitrage or narrow bracketing? On using money to measure intertemporal preferences.
Technical report, National Bureau of Economic Research.
Andreoni, J., Kuhn, M.A., Sprenger, C., 2015. Measuring time preferences: A comparison of experimental methods. J. Econ. Behav. Organ. 116, 451–464.
Andreoni, J., Sprenger, C., 2012. Estimating time preferences from convex budgets. Amer. Econ. Rev. 102 (7), 3333–3356.
Augenblick, N., Niederle, M., Sprenger, C., 2015. Working over time: Dynamic inconsistency in real effort tasks. Q. J. Econ. 130 (3), 1067–1115.
Balakrishnan, U., Haushofer, J., Jakiela, P., 2020. How soon is now? Evidence of present bias from convex time budget experiments. Exp. Econ. 23 (2), 294–321.
Banerjee, A., Banerji, R., Berry, J., Duflo, E., Kannan, H., Mukerji, S., Shotland, M., Walton, M., 2017. From proof of concept to scalable policies: Challenges
and solutions, with an application. J. Econ. Perspect. 31 (4), 73–102.

32 Sawada et al. (2023) uses information about the timing of mailing New Year’s cards, a unique Japanese custom. According to the Japan Post, the company
sold a total of 3.2 billion cards for 2016, meaning that Japanese people sent around 30 New Year’s cards each. Since New Year’s cards are ideally supposed
to arrive on January 1st, people need to send them at least a week in advance (i.e., on or before December 25th) according to the Japan Post. Yet, it is
not uncommon for people to delay sending cards because cards received by January 7 are still socially acceptable, albeit not necessarily desirable. Hyperbolic
discounters would procrastinate writing and sending cards due to time devoted to the costly activity of writing new year cards.

19

European Economic Review 161 (2024) 104632

Y. Kuroishi and Y. Sawada

Banerjee, A., Mullainathan, S., 2010. The shape of temptation: Implications for the economic lives of the poor. National Bureau of Economic Research Working
Papers 15973, National Bureau of Economic Research.
Bchir, M.A., Willinger, M., et al., 2013. Does the exposure to natural hazards affect risk and time preferences? Some insights from a field experiment in Perú.
Becchetti, L., Castriota, S., Conzo, P., 2017. Disaster, aid, and preferences: The long-run impact of the tsunami on giving in Sri Lanka. World Dev. 94, 157–173.
Bourdeau-Brien, M., Kryzanowski, L., 2020. Natural disasters and risk aversion. J. Econ. Behav. Organ. 177, 818–835.
Bryan, G., Karlan, D., Nelson, S., 2010. Commitment devices. Annu. Rev. Econ. 2 (1), 671–698.
Callen, M., 2015. Catastrophes and time preference: Evidence from the Indian ocean earthquake. J. Econ. Behav. Organ. 118, 199–214.
Callen, M., Isaqzadeh, M., Long, J.D., Sprenger, C., 2014. Violence and risk preference: Experimental evidence from afghanistan. Amer. Econ. Rev. 104 (1),
123–148.
Cameron, L., Shah, M., 2015. Risk-taking behavior in the wake of natural disasters. J. Hum. Resour. 50 (2), 484–515.
Carvalho, L.S., Meier, S., Wang, S.W., 2016. Poverty and economic decision-making: Evidence from changes in financial resources at payday. Amer. Econ. Rev.
106 (2), 260–284.
Cassar, A., Healy, A., Von Kessler, C., 2017. Trust, risk, and time preferences after a natural disaster: Experimental evidence from Thailand. World Dev. 94,
90–105.
Chakraborty, A., Calford, E.M., Fenig, G., Halevy, Y., 2017. External and internal consistency of choices made in convex time budgets. Exp. Econ. 20 (3), 687–706.
Chantarat, S., Oum, S., Samphantharak, K., Sann, V., 2019. Natural disasters, preferences, and behaviors: Evidence from the 2011 mega flood in cambodia. J.
Asian Econ. 63, 44–74.
Cheung, S.L., 2016. Recent developments in the experimental elicitation of time preference. J. Behav. Exp. Finance 11, 1–8.
Chuang, Y., Schechter, L., 2015. Stability of experimental and survey measures of risk, time, and social preferences: A review and some new results. J. Dev.
Econ. 117, 151–170.
Dean, M., Sautmann, A., 2021. Credit constraints and the measurement of time preferences. Rev. Econ. Stat. 103 (1), 119–135.
DellaVigna, S., Pope, D., 2018. What motivates effort? Evidence and expert forecasts. Rev. Econom. Stud. 85 (2), 1029–1069.
Dupas, P., 2011. Health behavior in developing countries. Ann. Rev. Econ. 3 (1), 425–449.
Eckel, C.C., El-Gamal, M.A., Wilson, R.K., 2009. Risk loving after the storm: A Bayesian-network study of hurricane katrina evacuees. J. Econ. Behav. Organ. 69(2), 110–124.
Epstein, L.G., Zin, S.E., 1991. Substitution, risk aversion, and the temporal behavior of consumption and asset returns: An empirical analysis. J. Polit. Econ. 99(2), 263–286.
Estudillo, J.P., 2013. The response of Rural Coastal households to typhoon milenyo in the Philippines. In: The Environments of the Poor in Southeast Asia, East
Asia and the Pacific. Institute of Southeast Asian Studies, p. 243.
Estudillo, J.P., Sawada, Y., Kajisa, K., Fuwa, N., Kikuchi, M., 2010. The transformation of hayami’s village. In: Community, Market and State in Development.
Springer, pp. 224–244.
Fehr, E., Hoff, K., 2011. Introduction: Tastes, castes and culture: the influence of society on preferences. Econom. J. 121 (556), F396–F412.
Frederick, S., Loewenstein, G., O’donoghue, T., 2002. Time discounting and time preference: A critical review. J. Econ. Lit. 40 (2), 351–401.
Fuwa, N., 2011. Should we track migrant households when collecting household panel data? Household relocation, economic mobility, and attrition biases in
the rural Philippines. Am. J. Agric. Econ. 93 (1), 56–82.
Giné, X., Karlan, D., Zinman, J., 2010. Put your money where your butt is: A commitment contract for smoking cessation. Am. Econ. J.: Appl. Econ. 2 (4),
213–235.
Hanaoka, C., Shigeoka, H., Watanabe, Y., 2018. Do risk preferences change? Evidence from the great east Japan earthquake. Am. Econ. J.: Appl. Econ. 10 (2),298–330.
Haushofer, J., Fehr, E., 2014. On the psychology of poverty. Science 344 (6186), 862–867.
Hayami, Y., Kikuchi, M., 1999. A Rice Village Saga: Three Decades of Green Revolution in the Philippines. Rowman & Littlefield Publishers.
Higuchi, Y., Fuwa, N., Kajisa, K., Sato, T., Sawada, Y., 2019. Disaster aid targeting and self-reporting bias: Natural experimental evidence from the Philippines.
Sustainability 11 (3), 771.
Holt, C.A., Laury, S.K., 2002. Risk aversion and incentive effects. Amer. Econ. Rev. 92 (5), 1644–1655.
Ikeda, S., Kang, M.-I., Ohtake, F., 2010. Hyperbolic discounting, the sign effect, and the body mass index. J. Health Econ. 29 (2), 268–284.
Imas, A., 2016. The realization effect: Risk-taking after realized versus paper losses. Amer. Econ. Rev. 106 (8), 2086–2109.
Janis, C.M., 1993. Tertiary mammal evolution in the context of changing climates, vegetation, and tectonic events. Annu. Rev. Ecol. Syst. 467–500.
Kajisa, K., 2007. Personal networks and nonagricultural employment: The case of a farming village in the Philippines. Econom. Dev. Cult. Chang. 55 (4), 669–707.
Kan, K., 2007. Cigarette smoking and self-control. J. Health Econ. 26 (1), 61–81.
Kang, M.-I., Ikeda, S., 2014. Time discounting and smoking behavior: Evidence from a panel survey. Health Econ. 23 (12), 1443–1464.
Kang, M.-I., Ikeda, S., 2016. Time discounting, present biases, and health-related behaviors: Evidence from Japan. Econ. Hum. Biol. 21, 122–136.
Kessler, R.C., Andrews, G., Colpe, L.J., Hiripi, E., Mroczek, D.K., Normand, S.-L., Walters, E.E., Zaslavsky, A.M., 2002. Short screening scales to monitor population
prevalence and trends in non-specific psychological distress. Psychol. Med. 32 (6), 959–976.
Kreps, D.M., Porteus, E.L., 1978. Temporal resolution of uncertainty and dynamic choice theory. Econometrica 185–200.
Kuroishi, Y., Sawada, Y., 2019. Motivations behind prosocial behavior: Evidence from the Philippines. J. Asian Econ. 64, 101127.
Laibson, D., 1997. Golden eggs and hyperbolic discounting. Q. J. Econ. 112 (2), 443–477.
Lerner, J.S., Keltner, D., 2001. Fear, anger, and risk. J. Personal. Soc. Psychol. 81 (1), 146.
Loewenstein, G.F., Weber, E.U., Hsee, C.K., Welch, N., 2001. Risk as feelings. Psychol. Bull. 127 (2), 267.
Mani, A., Mullainathan, S., Shafir, E., Zhao, J., 2013. Poverty impedes cognitive function. Science 341 (6149), 976–980.
Mironova, V., Mrie, L., Whitt, S., 2019. Risk tolerance during conflict: Evidence from Aleppo, Syria. J. Peace Res. 56 (6), 767–782.
Oster, E., 2019. Unobservable selection and coefficient stability: Theory and evidence. J. Bus. Econom. Statist. 37 (2), 187–204.
Page, L., Savage, D.A., Torgler, B., 2014. Variation in risk seeking behaviour following large losses: A natural experiment. Eur. Econ. Rev. 71, 121–131.
Phelps, E.S., Pollak, R.A., 1968. On second-best national saving and game-equilibrium growth. Rev. Econom. Stud. 35 (2), 185–199.
Sawada, Y., Ashida, T., Iwasaki, K., 2023. Heterogenous effects of the great east Japan earthquake on prosociality of people depending on their age. Sci. Rep.
13 (1), 3211.
Sawada, Y., Kuroishi, Y., 2015a. How does a natural disaster affect People’s preference? The case of a large scale flood in the Philippines using the convex timebudget experiments. In: Disaster Risks, Social Preferences, and Policy Effects: Field Experiments in Selected ASEAN and East Asian Countries. pp. 57–84.
Sawada, Y., Kuroishi, Y., 2015b. How to strengthen social capital in disaster affected communities? The case of the great east Japan earthquake. In: Disaster
Risks, Social Preferences, and Policy Effects: Field Experiments in Selected ASEAN and East Asian Countries. pp. 163–199.
Sawada, Y., Kuroishi, Y., Ashida, T., Aida, J., Kajisa, K., Hikichi, H., Sato, T., Lucio, E., Kondo, K., Osaka, K., Kawachi, I., 2019. Disasters, scarcity, and unhealthy
behaviors: Understanding the mechanisms. In: Mimeographed, Faculty of Economics. University of Tokyo.
Sawada, Y., Nawata, K., Ii, M., Lee, M.J., 2011. Did the financial crisis in Japan affect household welfare seriously? J. Money Credit Bank. 43 (2–3), 297–324.
Schildberg-Hörisch, H., 2018. Are risk preferences stable? J. Econ. Perspect. 32 (2), 135–154.
Story, G., Vlaev, I., Seymour, B., Darzi, A., Dolan, R., 2014. Does temporal discounting explain unhealthy behavior? A systematic review and reinforcement
learning perspective. Front. Behav. Neurosci. 8, 76.
Strotz, R.H., 1955. Myopia and inconsistency in dynamic utility maximization. Rev. Econom. Stud. 23 (3), 165–180.
Umehara, H., 1967. Philippine no beisaku noson. In: Takigawa, T., Saito, H. (Eds.), A Rice Village in the Philippines). 262, Ajia no Tochi Seido to Noson, pp.
317–344.
Vieider, F.M., 2018. Violence and risk preference: Experimental evidence from afghanistan: Comment. Amer. Econ. Rev. 108 (8), 2366–2382.

20