OCU-E Discussion Paper Series

What Drives People to Return to Fukushima?:
An Assessment using Decision tree Approach

Mohammad Sanaei , Shinya Horie,
Keyvan Kashkulinejad and Shunsuke Managi

February 3, 2023

Discussion Paper No.009

Graduate School of Economics, Management, and Information Science,
Onomichi City University

What Drives Evacuees to Return toFukushima?: A Decision Tree Approach∗
Mohammad Sanaei
Petropack Co.

Shinya Horie †Onomichi City University

Keyvan Kashkulinejad
GoogleShunsuke Managi
Kyushu University

February 3, 2023

Abstract
Machine Learning’s Decision Tree approach is used to gain new
insights by assessment of eﬀects of various factors which impact the
habitants of the areas eﬀected by 2011Tohoku Earthquake to returnto their original residences. The learning model has 70% prediction
accuracy (based on out of sample prediction). The eﬀect of various
variables aﬀecting the return decision, categorized into hazard, ﬁnanceand time related variables as well as interactions, have been assessed;using marginal eﬀect diagrams. The learning model predicts that radiation decontamination of the aﬀected areas will not have signiﬁcant
impact on changing of the opinion of individuals to return, and 43%
return rate of the people will not increase in case of decontamination.

Key words: 2011 Tohoku Earthquake, migration, return decision, machine learning, decision tree analysis.
JEL classiﬁcations: H84, Q54, O53, Q01.
∗

This work was supported by JSPS KAKENHI Grant Number 19K01629.
Corresponding author: Department of Economics and Information Science, Onomichi
City University, 1600-2 Hisayamada-cho, Onomichi, Hiroshima,722-8506, Japan. e-mail:s-horie@onomichi-u.ac.jp
†

1

1

Introduction

Disasters impact various aspects of the community, such as economy, health(psychological and physical) and environment. Post-disaster recovery requires understanding existing risks, rethinking land use, ﬁnding ways to correct deﬁciencies in public infrastructure, and providing incentives for economic recovery that will give ﬁrms and households an opportunity to survive
and thrive (Landry et al., 2007). All of which rest on having profound understanding of the behavior of the population eﬀected by the disaster. For
example, the economic, social, and cultural feature of the region would besigniﬁcantly inﬂuenced by who will return (Landry et al., 2007). Moreover,migration could have other eﬀects on the economy. For instance, net migration is one of the forces, which causes removal of earning diﬀerentials. Somedevelopments have followed natural disasters (Strobl and Walsh, 2009). They
reported that destruction caused by hurricanes in US has caused 25 percent
increase in country level employment in construction industry 2011 Tohoku
Earthquake has made this disaster a unique case. The disaster caused byGreat East Japan Earthquake and Tsunami has been described as “low probability, high impact” in the report prepared by world bank (Bank, 2012; Rose,2011). They have investigated major types of post disaster resiliencies, andconcluded that only those disasters that are related to nuclear contamination(Chernobyl and Fukushima) seem to have threatened the survival of the hostregion.
The economic, social, and cultural features of the region aﬀected by thedisaster depends on who decides to return. Moreover, the recovery would notseem possible without return of the local population, as human capital is oneof the major (if not the main) drivers of economic growth. Therefore, thispaper focuses on evaluating the eﬀects of various factors, which have impact
on the decision of the local population to return. Moreover, having better
understanding about the decision criteria of the residents of these regions isessential for making eﬀective policies for recovery and improvement of theconditions of the region.
The analysis of the return decision to the original residence after disasteris categorized under the broader subject of return migration analysis (Landry
et al., 2007). This is because all factors that eﬀect the normal return migration are found to be eﬀective in the case of migration due to disaster.
Sjaastad (1962) has deﬁned migration as a decision, which involves the problem of a household’s attempt to maximize the economic return on human2capital. In same way, Landry et al. (2007) have shown that post-disaster
return decision is inﬂuenced by economic factors, such as real wage diﬀerence between home and host region and ownership. The potential eﬀects ofvarious factors have been examined on the return decision.
Elliott and Pais (2006) have considered the eﬀect of social diﬀerences,such as race and class on the return decision after occurrence of hurricane
Katarina, and showed that these factors have relatively smaller eﬀects comparing to other factors such as ﬁnance related factors. Myers et al.(2008) havefound correlation between the independent variables such as percent of disadvantaged population, density of built environment and percent of housingwith damage. Roﬁ et al.(2006) have considered the mortality eﬀect of 2004
Asian tsunami on displacement of the local population in Aceh province ofIndonesia. In a more broader approach, Fussell et al.(2014) have approachedthe human migration inﬂuenced by disaster more systematically, basing theirstudy on demographic data and methods such as fertility, mortality, and migration. Groen and Polivka (2010) have shown that evacuee’s age and theseverity of damage in an evacuee’s county of origin are important determinants of whether an evacuee returned during the ﬁrst year after the storm ornot. The experience of disaster has psychological eﬀect on disaster victims,
as it was shown in studies such as Neuner et al. (2006) investigated about
the post-traumatic stress disorder caused by 2004 tsunami on children due
to trauma exposure and loss of family members. The psychological eﬀects
caused by experiencing disasters could eﬀect the return decision. Looking at
various methodological approaches for empirical analysis of social behaviors
related to disasters in mentioned papers, it could be seen that regression
methods have been widely used for analysis of eﬀect of various parameters.
Although regression methods have been widely accepted and practiced dueto their eﬀectiveness in identifying the correlations between various control
variables and the predicted variable, use of other methodologies for assessment of data such as machine learning techniques have been shown to beeﬀective in gaining new insights and understandings. It could be recognizedfrom previous researches that the trend of the individual’s response towards
diﬀerent variables could change in diﬀerent circumstances. For example variation in the trend of eﬀect of diﬀerent variables’ was observed as the data
was categorized and analyzed according to the age and region (Sanaei et. al.,2018). This observation implies nonlinear the existence of nonlinear correlations between some of the decision variables and the dependent variable. Useof a modeling tool, which could best reﬂect such variation, could be advan3

tageous in comparison to the conventional regression methodologies –such as
logit method.
This paper aims to use one of the machine learning methods known as
decision tree method to assess the eﬀects of various parameters on the decision to return to the original residences in the area’s eﬀect by 2011 Tohoku
Earthquake. Machine Learning techniques have been eﬀectively used in various ﬁelds including social sciences. Oh and Kim (2010) used decision tree
method for making a model to predict the demand for health metrologicalinformation. In the mentioned paper, the individual’s decision was modeled
as a binary variable. Individual customer’s credit risk has been evaluated using various machine learning techniques, such as Random Forests, k-Nearest
Neighbor methods (Kruppa et al., 2013). The machine learning techniques
have proven to be eﬀective for prediction, for example forecasting the next
day electricity prices in European energy market using Support Vector Machine technique (Papadimitriou et al., 2014). In cases where understanding
each parameter’s eﬀect and it’s impact are important, methodologies suchas decision tree analysis are much of use. Kim et al.(2005) have used thedecision tree analysis for investigating the eﬀect of various parameters on
the customer behavior change.
The remainder of this article is organized as follows: The description of
the machine learning methodology used and the independent variables used
for empirical analysis in the Methodology section. The numerical results arepresented in the Results and Analysis section. Summery of ﬁnding and main
conclusions will be discussed in the Conclusion section.

2

Methodology

This paper aims to take advantage of the machine learning tool and its potential abilities to gain additional meaningful insight into how each parametereﬀects individual’s decision to return. In its very core, machine learning isbased on improving the performance of a program (a model) by learning fromprevious experiences (Mitchell, 1997). The trained model is able to obtaina generalized relationship between the independent variables and dependent
variable with certain degree of precision. Using this generalized rule, themodel is able to predict the outcome of other cases not experienced. The
consistency of the logic of the chosen learning model with the problem understudy is one of the deciding factors, which impact how well a trained model4

could be generalized. For instance the Neural Networks uses concepts borrowed from an understanding of human brain in order to model the arbitrary
functions (Lantz and Brett, 2013). In terms of ability to interpret the results and complexity of the mathematical system used, the machine learning
methods are in general either transparent or black box. The methods such as
Neural Networks or Support Vector Machine are known as black box methods (Lantz and Brett, 2013). These methods have displayed good predictive
abilities. However, there are cases where model’s transparency is important;such as cases when the criteria should be transparent for legal purposes. For
example, credit scoring, which is a criterion, which the applicant is acceptedor rejected, should be well speciﬁed. There are many cases such as present
work, which there is need to interpret the eﬀect of various variables andprediction is not the sole purpose. In these cases the black box methods cannot be normally used and more transparent methods such as decision tree
method is useful. The decision tree algorithm makes a model in the form of atree structure. Each branching point resembles a logical decision, which eachof its branches (decisions) lead to other branching points (decisions) or leafs
nodes where the branching stops. The leaf nodes are the result or outcomeof making a series of choices. Since the decision tree is a ﬂow chart, it could
clearly display the relation of each variable on the dependent variable.
Decision tree method is based on a heuristic called recursive partitioning,which could be classiﬁed under divide and conquer algorithm. Beginning at
the root node, the algorithm chooses the most predictive feature and divides
the values according to it. It continues this process of divide and conquer tillit reaches a stopping criteria (Lantz and Brett, 2013). Looking at the process of formation of the tree, in which the process of decision-making is broken down into smaller manageable decisions and formation of ﬂow diagram,its similarity to human’s decision-making process can be realized. Understanding this, its not surprising that processes, such as medical diagnostic,equipment malfunction and their cause, and evaluation of loan applicants(which people have been involved in), are also cases which use of Decision
Tree methods are known to have success in(Mitchell, 1997).
The general mathematical formulation of the Decision Tree method is asfollows (Hastie, et. al, 2009):
fˆ(x) =

n∑cm I{X ∈ Rm }

m=1

5

(1)

In the above equation, fˆ(X) is the prediction of the dependent variable.
The estimated value assigned to each partition is cm ; the overall predictedvalue is the summation of the eﬀect of points within each partition, is determined by the points within each partition I{X ∈ Rm }. As it was mentioned
before, an approach based on greedy algorithm is used for building the decision tree. That is in every step of building the decision tree, the best splitting
variable and splitting point is found. This is shown in the equation below:min min
j,s

c1

∑

(yi − c1 ) + minc2

xi ∈R1 (j,s)∑

(yi − c2 )2 

(2)xi ∈R2 (j,s)

As it could be seen in the above equation, the choice of the splitting
variable j and splitting point s, which determine the structure of the decision
tree, depends on inner minimization from which the constant value for eachregion is found. In a similar way, this process is repeated for each of thetwo decided regions. The tree grows as this process is repeated, and later
the size of the tree is chosen in a way that over ﬁtting or loss of important
information is avoided. More detail could be found in (Hastie et al., 2009;
Lantz and Brett, 2013).
There are wide variety of methods for increasing the accuracy of themachine learning models. One of the methodologies that have been provento be eﬀective is called Boosting. The basic idea of boosting method is
to combine the output of many weak classiﬁers (models with slightly better
performance than random model) to produce a powerful committee. The gbm
package is used in this paper for implementing the Boosting Tree method in
R programming environment. The prediction error is calculated using thefollowing formula:1 ∑I (y1 ̸= G(xi ))
N i=1
N

err
¯ =

(3)

where the mean error is equal to the ratio of the points wrongly classiﬁedby the boosting model G(·). The general concept in the boosting model’stree is to start by assigning equal weights to each of the points and afterﬁnding the classiﬁcation tree in the ﬁrst step, higher weights are assigned
to the point that have been misclassiﬁed, and the weight of the points that6

have been classiﬁed correctly are reduced. This process is repeated for M
successive iterations. This way the observations that are diﬃcult to classify
receive more attention. Separate classiﬁers are built, every time the weightsof the observations are modiﬁed. As described before the overall outcome
depends on the decision of the committee of the classiﬁers. However the ﬁnal
classiﬁer is determined by weighted sum of the M classiﬁers, as shown inEquation 2.
[M]∑
G(x) = sign
αm Gm (x)(4)
m=1

We intend to analyze individuals’ decision to return. A dummy variable
is used for accounting for the return decision as shown below:{
1 returning,yi =(5)0 otherwise.
The review of previous literature brings to light various parameters, which
impact the individuals’ decision to return to their original residence. These
parameters are classiﬁed into four major groups taken from Sanaei et.al.,(2016), which are: time related connections, ﬁnance related connections, hazard related variables, and interaction variables. Age, duration of residence,ﬁrst generation, express the time related connections. These variables intend
to reﬂect individuals’ root attachment to the region, which is formed by time.
Variables such as individuals’ income, ownership of residence, and job related
to region, could be categorized under the ﬁnance related variables. The ownership of residence is costly and it requires long-term investment by mostindividuals. Having job related to the local region is another ﬁnance related
attachment to the local region. In addition the ﬁnance related factors areimportant since they play a decisive role in migrations in general. In generalmigration is not expected if it’s not ﬁnancially feasible for the individuals.
Sanaei, et. al, (2016) have found that having related job to the region andalso ownership status have the largest impact on the return decision. Factors
reﬂecting feeling of safety and security are also referred to as hazard related
variables and are: experience of disaster, the number of death and lost in
the region, and radiation level. Having in mind that feeling of safety in individual’s living environment is a basic necessity, some previous researchers
have located their main focus on the eﬀect of the hazard related variables
7

on individual’s decision to return. For example (Alistar and Managi, 2014)
have considered the eﬀect of radiation level on decision of people eﬀected by2011 Tohoku Earthquake to return to their original residences.
For considering the interaction between variables belonging to each of thementioned groups, interaction variables have been deﬁned. The interaction
between the age and having related jobs to the region is considered in order toinvestigate the degree of which having job related to the region eﬀects peopleof various ages. Moreover, the interaction between the number of dependents
and number of death losses are considered for enabling us to have a judgment
on how the number of death losses in same region would impact individuals
who have dependents. Due to the importance of understanding the eﬀect ofamount of region’s degree of radiation on peoples’ opinion and also havingin mind that decontamination requires large amount of investment by government.,the eﬀect of potential radiation exposure with some key variablesfrom various groups is considered. By doing this additional insight could
be gained on the eﬀect of radiation level on various individuals’ opinions.
The radiation contamination is a source of concern and various previous researches have shown its eﬀect on migration decision. A practical applicationof the present research is to approximate the eﬀect of reduction of amountof radiation in the eﬀected areas, on decision to return, if decontamination
is carried out.
The data used for the imperial analysis in this paper is gathered by interviewing 1510 individuals in various areas aﬀected by 2011 Tohoku Earthquake. The summary of various described variables is shown in Table 1. As
it could be seen, 884 (about 58%) of the individuals provided an answer tothe question to whether they going back to their original residence or not.
As it could be seen there are some variables with even smaller number than884. The reason for this is because the decision tree method is able to handle
incomplete data for independent variables.

3

Results and Analysis

The described methodology for building decision trees are used for construction of decision trees for modeling the decision to return using variables
described in Table 1. To maximize the accuracy of the learning model, manydecision trees are constructed, which based on each trees performance (error
in prediction) its contribution to the learning model is deﬁned (as described8

in previous section). Two of the decision trees made by the learning model
are shown in Figures 2 and 3. The numbers next to the nodes are, node
numbers and the numbers within the parentheses indicate the variable used
for splitting. For example in node number 0 in Figure 2, number 8 is theeighth independent variable in Table 1. The splitting value for branching is
written on the right side branches; as shown in the Figure 2. The predicted
decision at each node is written using a plus or minus sign with in each node.
The plus sign is for returning and minus for not returning. The nodes, which
are the root for other nodes in the lower levels, are parent nodes. The nodes
with out branching are referred to as leaves, and they indicate the ﬁnal decision. Based on the values of each variable, the decision is directed to one of
the leaves. As mentioned before, the decision tree method manages missing
data. This could be seen in Figure 2, the nodes connected to the parent
nodes using hatched lines, represent missing values. The boosting model inthis case uses 531 trees (in general similar to the trees shown). This numberof trees is the number of trees at which the accuracy of the boosting model
improves to.
Table 2 shows the signiﬁcance and relative inﬂuence of each variable. Similar to regression methods, the low values under the signiﬁcance column (lowp-value) reﬂect the likelihood that the variable is meaningful in explaining
the dependent variable. Moreover, the relative inﬂuence of each variable isalso shown. The variables with larger relative inﬂuence values are found tohave more impact on individual’s decision to return.
It is intended to have an assessment of the eﬀect of the signiﬁcant independent variables on the return decision. All the variables have been centeredand scaled. The average value of the variables has been used for entering andstandard deviation of each data has been used for scaling. The eﬀect of age
has been shown in Figure 3. As it could be seen the model indicates thatpeople of diﬀerent age groups behave diﬀerently. A negative trend could
be seen in the eﬀect of age from -2 to -1; which suggests that people fromage 20 to about 33 are not likely to return. This is the age range where
normally individuals choose their career directions. This result may suggest
that: given the choice, people of this age range prefer to choose their carrier
in other places. In the age range from about 33 years to about 38, there isa positive tendency to return; establishment of roots in the region such as
having jobs related to the region may be a causal eﬀect. However there isanother decrease in the tendency to return for the people of age ranges fromapproximately 38 to 50. Normally the people of this age range are specialized9

in their jobs, making them a valuable human resource asset. It’s needed to bementioned that eﬀect of job, which we have used for explaining the behavior
of diﬀerent age groups, is diﬀers from Local Job, which we have used here
(and has small relative inﬂuence). The tendency for return shows an increasing trend for people older than 50 years of age. This could be explained byroot connections to the region.
The duration of residence of individual in the region is found to havethe highest inﬂuence on return decision (according to Table 2), but small
signiﬁcance. As it could be seen in Figure 4, the average trend of the eﬀectof duration of residence on the decision to return is increasing. This again
explains the decision of the people above 50 years of age, which was previously
explained. The other time related variable: First generation, is found tohave small relative inﬂuence. This suggests that attachments that could be
imagined to exist due to living of previous family generations do not havestrong inﬂuence on the decision to return; in comparison with other factors.
Looking at the eﬀect of death toll (Figure 5), as one of the hazard related
variables, we could see that the model has not been able to capture thedecision response under inﬂuence of this variable, consistently. The sharp
ﬂuctuation up to and little over the average death toll ﬁgure is the reason
for this.
Another important hazard related variable is the amount of radiation levelin the region. As it could be seen in Figure 5, the tendency to return, is likely
to sharply decrease as the radiation exposure level increases. The decreasing
trend is immediately followed by constant trend which reduces stepwise atabout 1.75 (equivalent to 4.5µSv). The sharp decrease observed followed
by a constant trend could suggest that the sole knowledge that the amountof radiation level has increased in a region (even by and small amount) is
suﬃcient for having a negative impact on the decision to return. The increasein the amount of radiation level is likely to not eﬀect the return decision; until
the radiation exposure levels reach high values (such as 4.5µSv and more).
This kind of reaction also suggests that there is not much understanding
about the values of the radiation levels, and the mere knowledge on increaseof radiation is enough for impacting the decisions for return.
The eﬀect of having experienced previous disasters could be seen in Figure 6. It could be seen that people, who have experienced previous disasters,
are less likely to return. Having experienced previous disasters and/or experienced the harm from may have caused these individuals to take the possible
continuing risks from this disaster and other possible disasters more seriously;
10and therefore reducing their incentive to return to their original residence.
Analysis of the eﬀect of the income value on the return decision (Figure7) suggests that in general (with the exception of small income range) there
is more tendency to return, for individuals with higher income. The cause
for this behavior maybe the tendency to return to their previous conditions
that they were relatively successful at. Moreover, the ability to rebuild andrestart life in devastated areas is ﬁnancially demanding and this task becomeseasier for people with higher income values.
Individuals, who own residential units, are more likely to return according
to Figure 8. Ownership of one’s own residence is normally considered a large
ﬁnancial step, which is not possible for everyone. It would be expectedthat the value of residential property would drop following the occurrenceof disaster in the region. Therefore, the owner would be forced to sell theresidential unit under its normal market value. This loss of asset resulting
from undervalue selling of the house, would make it diﬃcult for the owner topurchase a house in other places (Sanaei et al, 2018).
By looking at relative signiﬁcance of each variable in Table 2, it is noteworthy that the learning model has found that the eﬀect of the ﬁnance related
variables: Income, Home Ownership, Local Job, in terms on their estimatedinﬂuence on individuals’ decision to return is relatively small. However, theeﬀect of ﬁnancial factors is shown to enable one to compare the results obtained in this research with other researches, which have considered the eﬀect
of ﬁnance related variables.
As it is shown in Figure 9, having local jobs, such as farming, ﬁshery,teaching, provides positive incentive for people to return to their originalresidences. In addition to social attachments, some jobs such as farming andﬁshery require local investments.
The interaction variables have been considered for gaining a more indepth insight into partial eﬀect of various variables on the return decision.
Looking at the interaction of radiation exposure level and number of dependents in Figure 11, we can see agreement with the overall trend of the eﬀectof the radiation on the decision to return. This similarity suggests that bothpeople with and without dependents respond in the same way to increase of
the level of radiation.
The death toll in the region has been classiﬁed as a hazard related variable. Similar to the eﬀect of the number of death losses (Figure 12) previously
discussed, similar conclusion could be drawn by looking at Figure 12. Theinteraction of the number of dependents and death losses, suggests that peo11

ple who have dependents act diﬀerently towards the number of death losses
in their region; however in the areas where there has been a large number ofdeath losses, the number of death losses does not have impact on individual’s
decision to return.
It could be understood from Figure 13, that the tendency for returning
increases as the amount of income increase -even in presence of radiation.
However, for the people with higher income, the increase of radiation does
not eﬀect their decision.
One important factor that has impact on population’s decision to returnis their feeling of safety from potential eﬀects of radiation exposure in thearea where they lived. Understanding this there has been a considerable
debate on whether reduction of the level of radiation by decontamination
is eﬀective or not. Due to high economic cost of decontamination, it isimportant to have estimation about the extent at which decontamination
would eﬀect the return decision. The developed learning model is used forpredicting (with certain accuracy) the eﬀect of decontamination on increase(or decrease) of percentage of the people whom would decide to return. As it
could be seen in ﬁgure 15, the developed learning model predicts that about
43% would return; and decontamination does not have any signiﬁcant eﬀect
on population’s decision to return.

4

Conclusion

In this paper assessment of the eﬀect of various factors on the decision -of
individuals that have been aﬀected by 2011 Tohoku Earthquake to return totheir original residences is performed.
In contrast to regression methods, taking advantage of the machine learning’s Decision Tree method has enabled us to gain additional insight in to
dynamic eﬀect of each of the variables on the return decision. Marginal eﬀect
diagram for each variable has been used for displaying the changing trend
in the return decision, as each variable changes. By using this tool, various
thresholds for each variable have been recognized. For example, the marginal
eﬀect diagram for age (Figure 3), implicitly categorizes the people aﬀectedby the disaster in to four age groups: 20 to 33, 33 to 38, 38 to 50, and above
50 years old. The classiﬁcation for age and other variables such as income
becomes evident by observing the variations in the trend of each variable s
eﬀect on the return decision. This result is robust in a sense that classiﬁca12

tion is directly derived from outcome of the model analysis. Moreover, thetransparency of the decision tree method (as shown in Figures 1 and 2) inbeing able to see the role of each variable in the modeling the decision process
makes explanation of the outcome of the model easier and more acceptable.
The ability of the model to be generalized has been tested using 25% of
the original data as test data. The generalization accuracy of the model isfound to be 70%.
In terms of relative inﬂuence of variables on the return decision, the hazard related variables (which reﬂect the feeling of safety), such as number ofdeath and losses in the region and the level of radiation are shown to haverelatively high inﬂuence on the return decision. As for the ﬁnance related
variables, the results show that the individual’s income has higher relative
inﬂuence than having related job to the region. Moreover the inﬂuence ofage on the return decision is notably high.
The predictive ability of the Decision Tree method has been used forpredicting the eﬀect of decontamination on the return decision. The learningmodel s prediction is that decontamination of region will not signiﬁcantly
result in an increase in the percentage of the people returning to their originalresidences, and about 43% of the people are likely to return.

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15

Variable
Returning
Age
Duration of ResidenceFirst Generation
Death Toll
Rad Exposure
Dependents
Disaster Experience
IncomeHome Ownership
Local Job

Observation Mean
884
.33
884
47.84
881
25.47
883
.54
825
879.47
838
1.18
871
1.71
884
.85
859
4.21
884
.60
884
.39

St.Dev.
.47
14.21620.78.501252.11
2.19
1.15
.36
3.17
.49
3.17

Min Max
0
1
20
84
0
81
0
1
0
3961
0
12
0
6
0
1
1
15
0
1
0.49
1

Table 1: Basic statistics of evacuees’ characteristics

16

Variable
Signiﬁcance Relative Inﬂuence
Age
.03
6.54
Duration of Residence.11
16.45 ∗∗First Generation
.01
1.67∗
Death Toll
.07
11.28∗Rad Exposure.03
6.02∗∗
Number of Dependents.04
6.97∗∗
Disaster Experience.00
1.02∗∗
Income.03
7.53∗∗
Home Ownership.02
3.88 ∗∗
Local Job
.00
.04 ∗∗
Age×Local Job
0.02
2.69∗∗Rad Exposure×Number of Dependents.00
8.63∗∗Rad Exposure×Income.036.45∗∗Rad Exposure×Home Ownership.01
3.18∗∗Rad Exposure×Local Job.01
0.87∗∗Rad Exposure×Duration of Residence.06
13.14∗
Note: ∗ and ∗∗ denote that the corresponding variable is statistically signiﬁcantat 10% and 5% level respectively.

Table 2: Relative inﬂuences to the returning decision

17

Figure 1: Sample decision tree 1

Figure 2: Sample decision tree 2

18

Figure 3: Eﬀect of the age on the decision to return19

Figure 4: Eﬀect of the duration of residence on the decision to return.

20

Figure 5: Eﬀect of the death toll in the region due to disaster on the decision
to return.

21

Figure 6: Eﬀect of the radiation exposure level on the decision to return.

22

Figure 7: Eﬀect of the level of income on the decision to return.

23

Figure 8: Eﬀect of the home ownership in the eﬀected area on the decision
to return.

24

Figure 9: Eﬀect of having local job to the region on the decision to return.

25

Figure 10: Eﬀect of interaction of age and having local jobs on the decision
to return.

26

Figure 11: Eﬀect of interaction of the radiation exposure and the number of
dependents on the decision to return.

27

Figure 12: Eﬀect of interaction of the death toll and the number of dependents on the decision to return28

Figure 13: Eﬀect of interaction of the level of radiation exposure and thelevel of income on the decision of to return.

29

Figure 14: Eﬀect of interaction of the level of radiation exposure and theduration of residence on the decision of to return.

30

Figure 15: Prediction of the eﬀect of decontamination on the decision to
return.

31