ECONOMIC RESEARCH CENTERDISCUSSION PAPER
E-Series

No.E23-1

From Hospitality to Hostility:
Impact of the Rohingya Refugee Influx on the
Sentiments of Host Communities
byYuki Higuchi
Keisaku Higashida
Mosharraf M. Hossain
Mohammad Sujauddin
Ryo Takahashi
Kenta Tanaka

January 2023

ECONOMIC RESEARCH CENTERGRADUATE SCHOOL OF ECONOMICSNAGOYA UNIVERSITY

From hospitality to hostility:
Impact of the Rohingya refugee influx on the sentiments of hostcommunities

Yuki Higuchi*

Keisaku Higashida

Mosharraf M. Hossain

Mohammad Sujauddin

Ryo Takahashi

Kenta Tanaka(This version: 22 December 2022)
Abstract
In 2017, Bangladesh received a sudden influx of Muslim Rohingya refugees from neighboring
Myanmar. While the predominantly Muslim host communities initially expressed sympathy
for the refugees, the prolonged stay substantially affected their livelihoods, and public
sentiment gradually deteriorated. Using an incentivized joy-of-destruction game, we found thatas many as 58% of the hosts paid to reduce support for the refugees. Our regression analysis
revealed that the more exposed hosts paid significantly more but were not more likely to
express negative opinions toward the refugees. This contrast indicates that the incentivized
experiment elicited real (not cosmetic) hostility toward the outgroup.
Keywords: Forcible displacement, refugee, intergroup contact, joy-of-destruction game,
Bangladesh
JEL codes: F2, J1, O1, Z1

Higuchi (corresponding author): Faculty of Economics and Sophia Institute for Human Security, Sophia
University (email: higuchi@sophia.ac.jp); Higashida: School of Economics, Kwansei Gakuin University(email: keisaku@kwansei.ac.jp); Hossain: Institute of Forestry and Environmental Sciences, University of
Chittagong (email: mosharraf@ifescu.ac.bd); Sujauddin: Department of Environmental Science andManagement, North South University (email: mohammad.sujauddin@northsouth.edu); Takahashi: Facultyof Political Science and Economics, Waseda University (email: ryo@waseda.jp); Tanaka: Faculty of
Economics, Musashi University (email: k-tanaka@cc.musashi.ac.jp). We thank Saiful Momen for sharing
valuable data and Daiki Nakamoto for excellent research assistance. We also thank Jun Goto, Yasuyuki
Sawada, and Junichi Yamasaki, as well as the seminar and conference participants at young JADE and
VEAM for their helpful comments and suggestions. We appreciate the survey respondents for providing theirvaluable information. This research was ﬁnancially supported by MEXT/JSPS KAKENHI (Grant Numbers:
20H04394 and 20KK0035), Grant-in-Aid for Research at Sophia Institute for Human Security, and Special
Research Fund of Kwansei Gakuin University. All survey respondents provided informed consent, and thestudy design was approved by the institutional review board of Waseda University (Application Number:2020-314). We have no conflicts of interest to declare, and all errors are our own.
1*

I. Introduction
The United Nations High Commissioner for Refugees (UNHCR) reported that in 2022,
the count of forcibly displaced people exceeded 100 million for the first time in history. Given
the significance of the issue, the forthcoming volume of the World Development Report (WDR),a World Bank flagship report, will be titled “Migrants, Refugees, and Societies.” Refugees
predominantly populate in the developing world, where international aid agencies and hostcountries strive to meet their basic humanitarian needs (Devictor et al., 2021; Hatton, 2020).
The influx of refugees substantially affects the livelihoods of hosts. For example,
increased demand raises commodity prices and exacerbates natural resource extraction, and anaugmented supply of unskilled labor lowers wages. When refugees are unlikely to be
repatriated to their homeland or resettled in a third country, they are left with the sole option of
staying in the host country. However, a prolonged stay may trigger social unrest and violence.
To facilitate social cohesion, the emotional aspects of the hosts should be carefully considered.
In reality, aid agencies and host governments are busy providing support to refugees; theirsupport to hosts, especially with regard to their emotional wellbeing, is limited. Furthermore,
scholarly investigations into such emotional wellbeing are lacking.
To empirically analyze the emotional effects of refugee inflows on host communities,
this study focused on Rohingya refugees, one of the most populous and concentrated refugees
in global history. The genocidal campaign in Myanmar in 2017 triggered a massive influx of
the Rohingya, a Muslim minority group, into neighboring Bangladesh. The predominantly
Muslim host communities initially sympathized with the refugees because of their shared
religion and language. However, the prolonged stay and high fertility among refugees
substantially affected their livelihood, and public sentiment gradually deteriorated (Ansar andKhaled, 2021). Ullah et al. (2021) surveyed over 2,000 host households and found that 72% of
the respondents retrospectively indicated a positive view of refugees at the time of influx.
2However, this percentage decreased to 18% by the time of their survey in 2020.
We conducted an incentivized lab-in-the-field experiment on 1,754 host households torigorously measure the state of public sentiment. We adopted the joy-of-destruction (JOD)
game to elicit their willingness to pay to reduce their support for refugees. To identify the causal
impacts of refugee influx, we used the distance to the refugee camp as a proxy for refugee
exposure. More than 700,000 refugees unexpectedly and suddenly flooded Bangladesh, andthe existing camps were substantially expanded to accommodate them (Hussam et al., 2022).
Camp expansion can be considered an exogenous event, and we used the distance to the camp
as an identifying variation.
We found that 58% of the sample paid a non-zero amount in the incentivized JOD gameto reduce (destroy) support for refugees. Furthermore, as many as 15% of the respondents paid
the highest possible amount, which was close to their average daily per capita income. These
results reveal the deteriorated hosts’ sentiments and call for serious attention by policymakers
to provide decent compensation for the hosts and facilitate social cohesion.
In our ordinary least squares (OLS) regressions, where we used distance to the refugee
camp as an explanatory variable, households living closer to the camp proved significantly
more likely to pay and to pay a larger amount. For every kilometer closer to the camp, thepayment increased by 1.3%. In addition, our instrumental variable (IV) regression, where weused distance as an instrument for the damage caused to the hosts, indicated that households
closer to the camp were more adversely affected, and the more affected households paid more.
These findings suggest that increased exposure to refugees causes damage and worsens public
sentiments. In contrast, we found that households closer to the camp or those that were more
affected were no more likely to express negative opinions toward the refugees. This contrast
indicates that the incentivized experiment revealed real (not cosmetic) hostility toward refugees.
This study contributes to two strands of the literature. First, we add to the emerging
3

literature on the refugee impact on host communities. Verme and Schuettler (2021) reported
that the Syrian refugee crisis in 2011 and the European Union (EU) migration crisis in 2014–2015 evoked scholarly interest in the refugee impact on host communities. Existing studies
mostly focus on the impact on commodity and labor markets (e.g., Akgündüz and Torun, 2020;
Alix-Garcia et al., 2018; Taylor et al., 2016). According to Maystadt et al.’s (2019) survey,
while investigations have also been conducted on health, security, and the environment, more
research is warranted on social interactions and perceptions between refugees and hosts. We
share this view and believe that a good understanding of hosts’ emotional wellbeing will help
policymakers mitigate intergroup tension and facilitate social cohesion.
To our knowledge, Hangartner et al. (2019) is the only study to investigate the causal
impact of refugee influx on hosts’ sentiments. They examined the case of Muslim refugees in
Greek islands and demonstrated that exposure induced sizable and sustained hostility toward
refugees and Muslim minorities. Our study differs by three important dimensions. First, weinvestigated the host community in a developing country in which the hosts were as equallypoor as the refugees. Second, while they investigated the impact of transitory exposure, as mostrefugees left the islands within a short period (typically within 24 h), we explored the impactof the longer-term presence of refugees. In the case of the Rohingya, other solutions, including
repatriation, resettlement, and third-country migration, have failed, and the only possible option
is to keep the refugees in camps. Third, while the former study held interviews via telephone,
we conducted face-to-face interviews to collect information and for an incentivized experiment.
Second, this study adds to the large and expanding literature on the impact of intergroupcontact on attitudes and behaviors toward an outgroup: Bazzi et al. (2019) and Finseraas et al.
(2019) on ethnicity; Boisjoly et al. (2006), Corno et al. (2022), and Enos (2014) on race;
Bursztyn et al. (2022), Mousa (2020), and Scacco and Warren (2018) on religion; Lowe (2021)
and Rao (2019) on caste and social class; and Broockman and Kalla (2016) and Dahl et al.
4(2021) on gender. Our study is unique in that it focuses on the relationship between refugees
and their hosts. While a few recent studies have focused on refugee-host contact byexperimentally creating short-term interactions (Bezabih et al., 2021; Loiacono and SilvaVargas, 2022), we analyzed real-world longer-term interactions using natural experimental
variation. We established that proximity to the camp led to hostile behavior among the hosts,
but did not translate into negative perceptions. Such contrasting results support the effective
use of the JOD game to elicit real hostility toward an outgroup. In this regard, our work relates
to Blouin and Mukand (2019), which conducted public and private variants of public goods
games and considered the sending amount in public as a cosmetic attitude and that in private
as a real attitude.

II. Setting
The Rohingya is an ethnic minority group in Myanmar, a predominantly Buddhist
country. The Burmese government has had a history of persecuting the Rohingya since the late
1940s, and their citizenship was stripped in 1982 (Islam et al., 2022; Mahmood et al., 2017).
In response to occasional fights between Rohingya militants and the Burmese government,
which began in 2016, the government launched a massive Rohingya clearance operation in
August 2017. The operation killed at least 6,700 Rohingya, and within a very short period,
more than 700,000 Rohingya fled to neighboring Bangladesh as refugees (Albert and Maizland,2020; Hussam et al., 2022). Although the Rohingya had intermittently become refugees overthe past several decades, the 2017 refugee influx was unprecedented in terms of number andmomentum.
Owing to the intermittent flow of Rohingya refugees, two registered refugee camps,
Kutupalong and Nayapara, were established in the 1970s. In 2015, before the recent influx,
31,000 registered refugees lived in these camps, and an additional 230,000 Rohingya were5

scattered throughout Bangladesh (Mahmood et al., 2017). They lived harmoniously with thehost communities, even in cases of intermarriage. After the unexpected and sudden influx in2017, the existing camps, particularly Kutupalong, were substantially expanded within weeks,primarily by clearing forest areas (Hussam et al., 2022). The expanded Kutupalong camp andnewly established neighboring camps are commonly called the “mega camp.” The mega camp
was 5.5 kms north–south and 4.5 kms east–west, and accommodated most of the 2017 refugees.
Panels A and B in Figure 1 present land cover classification maps based on the random
forest algorithm of Landsat 8 satellite imagery, thus illustrating the sudden and large expansionof the camp. Red indicates build-ups, and the largest red area in the upper center of the 2016
map (Panel A) denotes the Kutupalong refugee camp formed in the 1970s. The red dots
appearing sporadically to the south of the Kutupalong camp indicate the residence of the hosthouseholds and the Nayapara camp, which is smaller and not as concentrated as the Kutupalongcamp. After the 2017 influx, the surrounding forest, which is shown in green, was erased, andthe camp area (red) expanded substantially by 2018 (Panel B). Minor camp expansions havebeen ongoing since then; however, the red zone in the 2018 map suggests the boundary of thecamp that continues to the present day.
In the past, while the Rohingya occasionally entered Bangladesh as refugees, mostreturned to Myanmar. However, the repatriation of recent refugees has been severely limited;even five years after the influx, only 1% of refugees have been resettled or moved to a third
country (Ullah et al., 2021). A reason for the failure of the repatriation process is that a large
number of refugees made diplomatic negotiations difficult. Further difficulties were also addedby the political instability in Myanmar since 2021. Moreover, the refugees had a higher fertility
rate, and over 30,000 infants were born every year in the camp (Tayeb, 2021).
Initially, the host Muslim Bangladeshi were sympathetic to the Rohingya refugees.
However, as their stay was prolonged, the socio-economic and environmental impacts thereof
6became apparent. In addition, the population explosion in the camp triggered social anxiety,and the sentiment toward the Rohingya deteriorated. Ansar and Khaled (2021) conducted indepth interviews with hosts and found that economic instability, political uncertainty, andunequal access to humanitarian aid shifted the host’s sentiment from solidarity to resistance.
Massive humanitarian aid has been provided to Bangladesh by the international
communities. The refugee crisis evoked wide international attention during its outbreak, and in
the six months since August 2017, 344 million USD had been provided for their aid. The
amount increased to 656 million USD in 2018, but aid started to decrease as international
attention waned (Tayeb, 2021). While most aid was used for refugees at the beginning of thecrisis, a part of the aid was spent for the host communities as the emergency phase ended.
However, the support given was limited, and most host communities expressed dissatisfaction
with it, and thus, compensation for the host population and reconciliation between hosts andrefugees have become emerging issues for the Bangladeshi government and international aid
agencies.

III. Data
We mainly used data collected from our original survey, which included an incentivized
lab-in-the-field experiment to measure hostility toward Rohingya refugees. We supplemented
such data using the Household Income and Expenditure Survey (HIES) 2016 data collected by
the Bangladesh Bureau of Statistics to check for preexisting differences before the refugee
influx.

SurveyOf the 64 districts in Bangladesh, we selected Cox’s Bazar, where the camp is located,
and Bandarban, which is next to Cox’s Bazar, as our study area. The population of Cox’s Bazar
7

district was 2.3 million before the refugee influx, after which 700,000 refugees arrived; thenumbers illustrate the scale of refugee influx. We performed stratified random sampling inthese two districts to choose 120 villages. These included 64 villages in the 4 most exposed
sub-districts in Cox’s Bazar, 32 villages in the remaining 4 sub-districts in Cox’s Bazar, and 24
villages in 3 sub-districts in Bandarban. In each village, we randomly selected 15 households,
and the expected sample size was 1,800 households. Appendix A provides more details on thesampling design and survey implementation.
Trained enumerators, mostly graduate students, visited each household to conduct aninterview survey from January 2021 to January 2022. Each interview took 45 minutes at themedian to complete the structured questionnaire and joy-of-destruction game, which were preprogrammed in a tablet computer-assisted personal interview (CAPI) application. We
interviewed the household heads. When a head was unavailable, the enumerator made an
appointment to revisit the household. When a head temporarily lived outside the village andwas unavailable for interview, we interviewed a spouse or any household member withdecision-making power when the head was absent. The response rate was high, and we obtained
reliable answers from 1,754 households; in 95.8% of cases, a household head responded to oursurvey. Panel C in Figure 1 shows the locations of our sample households based on Global
Positioning System (GPS) coordinates.

Joy-of-destruction (JOD) gameAs an honorarium for participating in our survey, we offered 400 Bangladeshi Taka
(BDT), which is equivalent to 4.7 USD, to the respondents. 1 Using this honorarium, we
conducted an incentivized JOD game to measure respondents’ willingness to harm others at apersonal cost (Abbink and Herrmann, 2011; Abbink and Sadrieh, 2009). Prediger et al. (2014)
This conversion uses the exchange rate in January 2021. The rate had been stable until September 2021,
and it increased by about 3% by January 2022.
8
1

is the first study, according to our knowledge, to adopt this game in the field by targeting a nonstudent sample, and Bauer et al. (2018) is a novel study that used this game to measure hostility
toward an ethnic minority.
We gave the respondents the option to pay to reduce the donation we were making to aninternational non-governmental organization (NGO) working in the refugee camp. We
anonymized the names of the NGO so that a particular name would not influence their decisions.
The donation amount was 400 BDT and five times the amount paid was deducted from the
donation. Each respondent had the option to pay 0, 20, 40, 60, or 80 BDT. We prepared discrete
choice sets instead of continuous values to simplify the decision-making process. The
economically rational strategy is to pay 0 BDT as the amount is deducted from earnings. In thiscase, both the respondent and the NGO received 400 BDT. If the respondent chose to pay 80
BDT, their remaining honorarium was 320 BDT, and the donation amount was 0 BDT. The
maximum payable amount of 80 BDT is close to the daily per capita income. Since our sample
households were poorly educated, and we carefully explained the amount of their earnings anddonations in each scenario.
We believe that the JOD game is the most suitable for measuring hosts’ hostility in an
incentivized manner. No material gain was achieved by making a payment and no wrongdoing
was punished (as in the punishment against free riders in the public goods game). Furthermore,as the initial endowment for the respondent and the refugee (i.e., through an NGO) was anequal amount of 400 BDT, the payment would reduce no inequality. However, the hosts may
feel that they were deprived of their real lives because of the massive support received byrefugees from the international community. They may have burned their money to damage suchgains among refugees and reduce relative inequality. Hence, we collected information on ahypothetical inequality aversion measure and explicitly controlled for it in our regression

9analysis.2 In addition, the payment may be motivated by general antipathy to an outsider, andthus, we controlled self-reported distrust toward a foreigner in the regression analysis.
Therefore, the payment in our JOD game, conditional on the inequality aversion and foreigner
distrust measures, indicates the host’s hostility toward refugees.

Other key variables
To measure self-reported perceptions of refugees, we collected four sets of information.
The first was self-reported trust, which was based on the Social Value Orientation module. We
asked how much the respondents trusted the Rohingya refugees, with the answer options
ranging on a 4-point Likert scale. Second, we asked how much they thought Rohingya refugees
would pay if they played a hypothetical JOD game to reduce support for the host communities.
The third question was, “Do you think the Rohingya integrate well into the local Bangladeshi
community?” The fourth was, “How well do you think you get on (communicate in general)with the Rohingya?” Both questions were borrowed from a household survey conducted by aninternational NGO called Xchange in 2018, and the answer options ranged on a 5-point Likert
scale. Xchange surveyed 1,697 households in two sub-districts closest to the camp.
The influx of refugees has substantially affected the host communities’ livelihoods.
Although the movement of refugees is restricted outside the camp and they are not allowed towork in the host community, the restriction is not strictly imposed. Refugees visit the local
market to purchase food and non-food items not included in the food aid package, which
increases prices. They also engage in unskilled labor activities, mainly agricultural work or
fishing, which puts downward pressure on wages. In addition, Rohingya refugees went outside
the camp to collect non-timber forest products, particularly in the immediate aftermath of the

To measure inequality aversion, we followed Engelmann and Strobel (2004) and asked respondents tochoose from the three allocations they would prefer. This choice enables us to disentangle inequality aversion
from selfishness, efficiency concern, and maximin preferences.
102

influx. Resource extraction caused massive deforestation, and the refugees traveled tens ofkilometers to collect forest resources once the surrounding forest was cleared. Following severe
deforestation even in the Bandarban district, aid organizations started to distribute cooking gas
cylinders about a year after the influx, which substantially reduced refugees’ collection of forest
resources. Several environmental assessments have been conducted using satellite imagery, andenvironmental damage has been reported (e.g., Ahmed et al., 2019; Hassan et al., 2018;
Sakamoto et al., 2021). The influx also deteriorated hosts’ sense of public security. Some
refugees engage in criminal activities related to drugs and human trafficking, but these facts
are exaggerated in the media and trigger anxiety among hosts.
To quantify the damage caused by the refugee influx, we collected information on thefollowing aspects: commodity price, income, perceived level of crime, and forest degradation.
For the first three, we asked the respondents to report changes therein in the past five years on
a 5-point Likert scale, and we coded them as 1 if they responded “very much” to each question.
For the environment, we asked if the respondents were aware of the forest degradation causedby the Rohingya.

IV. Results
This section describes the collected data, explains our empirical model, then presents theestimated results.

Descriptive results
Table 1 presents the characteristics of our sample households according to the three
categories of their residential sub-districts. In the Muslim-majority district, Bengali Muslims
were the majority. In the tribal-majority district, half were Bengali, and half were Muslim. The
average household head was male, in their mid-40s, and had completed only three years of11

education. According to the 2015 Barro-Lee data, the average completed years of education forcohorts 35–55 was approximately four years. This comparison indicates that our study area,
particularly the tribal majority district, was underdeveloped. The average monthly householdincome was about 16,000 BDT in the Muslim-majority district, which was close to the national
average. The average distance to the camp was 22 km in the exposed areas, whereas it wasmore than double in the other areas.
Panel A in Table 2 presents the descriptive results for the JOD game. In total, 58% of thesample paid part of their earned compensation to reduce support for refugees. Although weneed to be cautious with comparisons across studies because of the differences in context,
framing, or stake size, the proportion was higher than that observed in the existing studies. In
the survey of the JOD game by Sanjaya (2021), 10 studies conducted the game with a costly
destruction option and targeting a non-student sample; the average proportion was 36%.
Furthermore, 15% of our respondents paid the maximum payable amount of 80 BDT (0.9 USD)
to reduce support altogether. According to Table 1, the average monthly per capita income wasapproximately 3,000 BDT (dividing household income by household size), and the average
daily per capita income was 100 BDT. Therefore, 80 BDT was close to our sample’s average
daily per capita income. Our finding that the majority of our sample reduced support for
refugees in the game with a relatively high stake indicates an alarming tension between thehosts and the Rohingya refugees. The breakdown reveals that the respondents in the exposed
areas paid more. Approximately two-thirds paid a non-zero amount, and 17% paid the
maximum. Panel A in Appendix Figure B1 visualizes the results.
Panel B in Table 2 presents the descriptive statistics for the four perception variables.
For the self-reported distrust, most respondents did not trust the Rohingya at all. However,unlike in the JOD game, there seems to be no systematic pattern between location and selfreported opinion measures. In the original survey conducted by Xchange in 2018, hosts’
12opinions were much better. When asked the integration question, 81% answered yes. In ourdata, only 19% answered “very well” or “somewhat well” (and 26% responded neutrally). This
comparison indicates the deterioration of sentiments over time.
As the scale of each question was different, we standardized each question (with a meanof 0 and a standard deviation of 1). We created an aggregated index by adding standardized
scores. Aggregation was used to mitigate the problem of multiple hypothesis testing. The
aggregated index reported toward the bottom shows that households living in the remote partof Cox’s Bazar expressed more negative opinions than those living closer to the camp or
Bandarban. Appendix Table B1 presents the correlation matrix of the JOD game, theaggregated index, and its components. It shows a positive correlation between the JOD
payment and opinion index, but the correlation is weak, with a correlation coefficient of 0.213.
Finally, Panel C presents the descriptive statistics of the damage caused by the refugee
influx. In all four categories, more households living closer to refugee camps experienced
damage. A smaller share of households in other areas also reported damage, particularly withrespect to environmental degradation. Others reported damage not necessarily caused by the
refugee influx, as we asked about the changes in the past five years. We created a damage index
by summing these four dummies, with the index ranging from 0 to 4. The index reported toward
the bottom shows that households closer to the camp experienced more damage.

Regression specification
We performed regression analyses to examine the relationship between distance to thecamp, damage caused by the refugee influx, and sentiment toward the refugees. First, weformulated our OLS model as follows:𝑦𝑖 = β0 + β1 × 𝐷𝑖𝑠𝑡𝑖 + 𝑿𝒊 𝛄 + θj + θk + θt + ϵi
13(1)

yi indicates the outcome variable of household i, which can be the payment in the JOD gameand the reported opinion of the refugees. Disti is the distance from the location of eachhousehold to the nearest border of the camp. The rationale behind this specification is that thecamp expansion was sudden and unexpected; thus, the distance captured exogenously induced
exposure to refugees. Xi is a vector of household characteristics that may affect the outcome
variables. It has all the variables presented in Table 1 except for household income. As
household income at the time of the survey may have been affected by the refugee influx, we
controlled for household income from five years ago, which was collected retrospectively. We
also controlled for the dummy variable taking the value of 1 if a non-household head responded
to the survey.
We likewise controlled for various fixed effects. θj indicates the sub-district fixed effects.
As households in the same village share similar values of Disti, and our primary interest was
not the within-village difference in exposures but the between-village difference, we did notcontrol for village fixed effects. Panel C in Figure 1 illustrates that the sample villages arescattered around the sample districts. To control for the unobserved autocorrelation ofoutcomes among villagers, we clustered the standard error, εi, at the village level. We also
controlled for the enumerator fixed effect, θk. Since our outcome includes sensitive information,
how the enumerator asked the question or how they behaved may have influenced the response.
We controlled for the survey phase fixed effects, θt, to control for overall changes in thepolitical or other environmental aspects surrounding the Rohingya issue.
β1 in our OLS model indicates the effect of living close to the camp. To examine theimpact of damage on sentiment, we conducted an analysis using the distance to the camp as an
instrument for the damage index. Our two-stage IV model is postulated as follows:14

𝐷𝑎𝑚𝑎𝑔𝑒𝑖 = 𝛽0 + 𝛽1 × 𝐷𝑖𝑠𝑡𝑖 + 𝑿𝒊 𝜸 + 𝜃𝑗 + 𝜃𝑘 + 𝜃𝑡 + 𝜖𝑖(2)̂ i + 𝑿𝒊 𝜸 + 𝜃𝑗 + 𝜃𝑘 + 𝜃𝑡 + 𝜖𝑖𝑦𝑖 = 𝛽0 + 𝛽1 × Damage(3)

In the first stage, the damage index was regressed on Disti, controlling for the same variables
as in Equation (1). The two-stage model examined how exposure to refugees, measured bydistance, caused damage to host households. The outcome variable was then regressed on thepredicted damage index in the second stage. β1 in the second stage indicates how the damage
caused by the refugee influx was translated into hostility.

Validity of empirical strategy
Before presenting the estimated results, we discuss the exogeneity of our distancevariable by examining the relationship between distance and pre-existing socioeconomic
conditions. To do so, we employed the HIES data, which are based on a nationally
representative survey. We used the data from the 2016 round, which collected data from 46,076
households.3 The HIES adopted stratified sampling, and the 2016 data covered 57 villages
located in our study sub-district. Appendix Figure C1 presents the locations of the sample
villages. Twenty households were randomly sampled from each sample village, and we had
1,140 observations in our study area. We regressed various characteristics of these 1,140
households on the distance to the camp so that we could examine whether the pre-existing
differences were correlated with distance.
Appendix Figure C2 illustrates the regression coefficients and confidence intervals forthe distance variables. The outcome variables were standardized, sub-district fixed effects were
controlled for, and standard errors were clustered at the village level. The coefficient of distance

We initially planned to also use the 2010 round to construct repeated cross-section data to check pre-trends,
but the 2010 survey was much smaller in sample size and contained only 10 villages (200 households) in
our study area. Hence, we decided to use only the 2016 data.
153was not significant for all individual characteristics. It is significant for the log of non-food
expenditure, but the point estimate is not large. Furthermore, the coefficient is insignificant forfood and total expenditure. These results indicate that the distance to the camp, conditional on
the sub-district fixed effect, was not correlated with various pre-existing measures, including
human capital stock, health status, living standards, the experience of shocks, and migration.
Therefore, we can interpret the distance to the camp as a measure of exposure to Rohingya
refugees.

Regression results
Panel A in Table 3 presents our main results, in which the outcome is the amount paid in
the BDT. The odd-numbered columns show a parsimonious model controlling only the subdistrict, enumerator, and survey phase-fixed effects. In the even-numbered columns, we
included the full set of household characteristics. While the distance to the camp was notcorrelated with pre-existing observable characteristics, there might be some differences
between households closer to and farther away from the camp. The inclusion of householdlevel control variables may have improved the precision of our regression analysis.
Columns (1) and (2) present the OLS results. The distance to the camp is negative andsignificant, and the coefficient does not differ significantly with the inclusion of control
variables. These results indicate that households exposed to refugees paid significantly more
to reduce their support. According to Table 1, the median distance to the camp was 23.2 km in
the adjunct areas and 68.2 km in the remote areas. The point estimate of -0.33 in column (1)means that the paid amount differed by 14.9 BDT between a household with median remotenessto the camp in the adjunct areas and that with median remoteness to the camp in the remoteareas. Panel B in Appendix Figure B1 graphically illustrates the non-parametric relationshipbetween distance and paid amount (without controlling for any fixed effect). It demonstrates
16

that the negative relationship consistently holds for all the distance ranges.
There are three points to note regarding the control variables, whose point estimates arepresented in Appendix Table B2. First, the coefficient of completed years of schooling is
negative and significant, thus indicating that educated households pay less. It may be because
they understood that payment would not increase their payoff. This result may be consistent
with the general pattern obtained from the cross-country analysis by Mayda (2006) that theeducated population has a positive view of immigrants. Second, the Muslim dummy variable
was negative and significant. As the vast majority of Rohingyas believe in Muslim, Muslim
hosts were more sympathetic toward them and did not dare reduce the support for them. Thisfinding reiterates the fact that the JOD game successfully measured the real sentiment. Third,
the distrust in a foreigner is negative and significant. This suggests that, even after controllingfor general antipathy to an outsider, the exposed households paid more, which indicates their
hostility directed toward the refugees.
Columns (3) and (4) present the first-stage IV results. The distance was negative andsignificant, thus suggesting that households living closer to the camp were adversely affected.
The F-statistics reported toward the bottom are greater than the conventional level of 20, thus
validating our IV strategy. Columns (5) and (6) present the second-stage results. The coefficientis positive and significant, thereby indicating that households that experienced damage by
refugees paid more. The point estimate reported in column (1) was 19.9, and the damage index
ranges from 0 to 4. Hence, the households that experienced all the damages paid almost 80
BDT more than households that did not experience any damage. For the control variables, theeducation and Muslim variables are negative and significant while the distruct variable ispositive and significant, as in Columns (1) and (2). Overall, Panel A shows that the households
more exposed to the refugees, particularly those adversely affected by them, exhibited hostile
attitudes toward the refugees.
17

Table 2 shows that the results of the JOD game had twin peaks at 0 BDT and 80 BDT,
and thus, there may be a distinction between the households that did not pay at all and those
who paid at least the smallest possible amount of 20 BDT. Hence, we estimated the Linear
Probability Model, where the outcome is a dummy variable taking the value of 1 if a household
paid at least 20 BDT, and 0 otherwise. This model examines if exposure to the refugees
influences the extensive margin of the decision to pay in the JOD game. Appendix Table B3
presents the results. The first-stage regression is exactly the same as that reported in Table 3,
and its results are not presented. The results present qualitatively the same pattern. The distanceis negatively correlated with the payment in the OLS model, and the damage index is positively
correlated with the payment in the IV model.
Subsequently, we analyzed how opinions were related to exposure. We used theaggregated standardized score as an outcome variable and estimated the same OLS and IV
models. Panel B of Table 3 presents the results. In the OLS model, the coefficient of distanceis small and insignificant (see Columns 1 and 2). Similarly, the coefficient of distance isinsignificant in the IV model (see Columns 3 and 4). The results indicate that households more
exposed to refugees or those more affected by their inflow were no more likely to express
negative opinions toward refugees. In other words, the households less exposed and lessaffected were equally likely to express negative opinions. Although the estimated coefficientsof control variables are only reported in Appendix Table B4, the Muslim dummy was negativeand significant, thereby indicating that Muslims expressed more favorable opinions toward
refugees of the same religion. Upon investigating each of the four components of the opinion
measures, there was no relationship. The results are reported in Appendix Table B5, and noneof the distances or damage coefficients is significant.
Panels A and B in Table 3 present a clear contrast. The exposed and affected households
paid significantly more, but were no more likely to express negative opinions toward the18refugees. This result indicates that the expression of negative opinion was cheap talk, and the
majority of Bangladeshi hosts expressed negative opinions toward the refugees. However, only
those exposed and directly affected were willing to pay real money. This contrasting patternsuggests that the incentivized game revealed real (and not cosmetic) hostility toward refugees.

Heterogeneity
Lastly, we turn to heterogeneous effects of exposure to the refugees. For readability,
Table 4 only presents results on the OLS estimation that included the control variable (i.e., theresults comparable to those reported in column 2 in Table 3). There are three points to note.
First, the coefficient of the distance variable is stable both for the JOD payment and the opinion
index, even when the interaction term with each control variable was added. This indicates thatno particular sub-group drives our results and reinforces our conclusion. Second, the interaction
with the religious identity variable is positive and significant. We interpret that the hosthouseholds who strongly believe in Muslim innately felt sympathy for Rohingya, and their
sentiment was not much affected by negative exposure to them. Third, the interaction with theinequality aversion is negative and significant. Together with the coefficient of inequality
aversion itself, the inequality-averse households paid more than non-averse households only ina remote area. This may be because households in remote mountainous areas are poor, and they
particularly envied the massive humanitarian support enjoyed by the refugees.

V. Conclusions
While it is important to consider the sentiments of the host population to implement
policies for their compensation and reconciliation with the refugees, the literature has notanalyzed the impact of refugee influx on the emotional aspects of hosts. To fill this importantresearch gap, this study collected unique data on hosts’ sentiments in an incentivized manner.
19

The deterioration of the hosts’ feelings became evident, and policymakers and aid workers need
to be aware of these results when formulating policies for refugees and host communities.
An important next step is to find an effective program to reduce the tension between
hosts and refugees. A joint sports event is a typical attempt by aid organizations for such
purposes. Lowe (2021) and Mousa (2020) conducted a field experiment and found theeffectiveness as well as the limitations of such interventions. Despite its effectiveness,organizing a sports event or other social events involving different groups is usually costly andnot scalable. An alternative option is the provision of information. Blouin and Mukand (2019)
found that a radio campaign significantly improved attitudes and behavior toward a previously
fighting ethnic group in Rwanda. There is room for investigating what types of informationand media (including traditional media, online media, or social networking services) are mosteffective in facilitating social cohesion.
A broader implication of our study is the efficacy of an incentivized experiment to elicit
real sentiments. The usual opinion survey is hypothetical and the responses may be cheap talk.
We found that less affected households also expressed negative opinions while paying
significantly less in the JOD game. Although the full-scale implementation of an incentivized
game is costly, it can be implemented among a subset of survey respondents to better
understand their real preferences toward an outgroup such as immigrants, political parties, or
religious or ethnic groups.

20References
Abbink, K. and Herrmann, B. 2011. The Moral Costs of Nastiness, Economic Inquiry, vol. 49, no. 2, 631–
33
Abbink, K. and Sadrieh, A. 2009. The Pleasure of Being Nasty, Economics Letters, vol. 105, no. 3, 306–8
Ahmed, N., Islam, M. N., Hasan, M. F., Motahar, T., and Sujauddin, M. 2019. Understanding the Political
Ecology of Forced Migration and Deforestation through a Multi-algorithm Classification Approach:
The Case of Rohingya Displacement in the Southeastern Border Region of Bangladesh, Geology,
Ecology, and Landscapes, vol. 3, no. 4, 282–94
Akgündüz, Y. E. and Torun, H. 2020. Two and a Half Million Syrian Refugees, Tasks and Capital Intensity,
Journal of Development Economics, vol. 145, 102470
Albert, E. and Maizland, L. 2020. What Forces Are Fueling Myanmar’s Rohingya Crisis?, Council on
Foreign Relations, https://www.cfr.org/backgrounder/rohingya-crisis
Alix-Garcia, J., Walker, S., Bartlett, A., Onder, H., and Sanghi, A. 2018. Do Refugee Camps Help or Hurt
Hosts? The Case of Kakuma, Kenya, Journal of Development Economics, vol. 130, 66–83
Ansar, A. and Khaled, A. F. Md. 2021. From Solidarity to Resistance: Host Communities’ Evolving Responseto the Rohingya Refugees in Bangladesh, Journal of International Humanitarian Action, vol. 6, no.
1, 16
Bauer, M., Cahlíková, J., Chytilová, J., and Želinský, T. 2018. Social Contagion of Ethnic Hostility,
Proceedings of the National Academy of Sciences, vol. 115, no. 19, 4881–86
Bazzi, S., Gaduh, A., Rothenberg, A. D., and Wong, M. 2019. Unity in Diversity? How Intergroup Contact
Can Foster Nation Building, American Economic Review, vol. 109, no. 11, 3978–4025
Bezabih, M., Bezu, S., Getahun, T., Kolstad, I., Lujala, P., and Wiig, A. 2021. Inter-group Interaction and
Attitudes to Migrants, CMI Working Paper, vol. WP 2021:02
Blouin, A. and Mukand, S. W. 2019. Erasing Ethnicity? Propaganda, Nation Building, and Identity in
Rwanda, Journal of Political Economy, vol. 127, no. 3, 1008–62
Boisjoly, J., Duncan, G. J., Kremer, M., Levy, D. M., and Eccles, J. 2006. Empathy or Antipathy? The Impact
of Diversity, American Economic Review, vol. 96, no. 5, 1890–1905
Broockman, D. and Kalla, J. 2016. Durably Reducing Transphobia: A Field Experiment on Door-to-door
Canvassing, Science, vol. 352, no. 6282, 220–24
Bursztyn, L., Chaney, T., Hassan, T. A., and Rao, A. 2022. The Immigrant Next Door: Long-Term Contact,
Generosity, and Prejudice, Mimeo
21

Corno, L., La Ferrara, E., and Burns, J. 2022. Interaction, Stereotypes, and Performance: Evidence fromSouth Africa, American Economic Review, vol. 112, no. 12, 3848–75
Dahl, G. B., Kotsadam, A., and Rooth, D.-O. 2021. Does Integration Change Gender Attitudes? The Effectof Randomly Assigning Women to Traditionally Male Teams, Quarterly Journal of Economics, vol.
136, no. 2, 987–1030
Devictor, X., Do, Q.-T., and Levchenko, A. A. 2021. The Globalization of Refugee Flows, Journal of
Development Economics, vol. 150, 102605
Engelmann, D. and Strobel, M. 2004. Inequality Aversion, Efficiency, and Maximin Preferences in Simple
Distribution Experiments, American Economic Review, vol. 94, no. 4, 857–69
Enos, R. D. 2014. Causal Effect of Intergroup Contact on Exclusionary Attitudes, Proceedings of the
National Academy of Sciences, vol. 111, no. 10, 3699–3704
Finseraas, H., Hanson, T., Johnsen, Å. A., Kotsadam, A., and Torsvik, G. 2019. Trust, Ethnic Diversity, andPersonal Contact: A Field Experiment, Journal of Public Economics, vol. 173, 72–84
Hangartner, D., Dinas, E., Marbach, M., Matakos, K., and Xefteris, D. 2019. Does Exposure to the Refugee
Crisis Make Natives More Hostile?, American Political Science Review, vol. 113, no. 2, 442–55
Hassan, M., Smith, A., Walker, K., Rahman, M., and Southworth, J. 2018. Rohingya Refugee Crisis andForest Cover Change in Teknaf, Bangladesh, Remote Sensing, vol. 10, no. 5, 689
Hatton, T. J. 2020. Asylum Migration to the Developed World: Persecution, Incentives, and Policy, Journalof Economic Perspectives, vol. 34, no. 1, 75–93
Hussam, R., Kelley, E. M., Lane, G., and Zahra, F. 2022. The Psychosocial Value of Employment: Evidence
from a Refugee Camp, American Economic Review, Advance Access published 2022:
doi:10.1257/aer.20211616
Islam, A., Mozumder, T. A., Rahman, T., Shatil, T., and Siddique, A. 2022. Forced Displacement, Mental
Health, and Child Development: Evidence from the Rohingya Refugees, Mimeo
Loiacono, F. and Silva-Vargas, M. 2022. Matching with the Right Attitude: The Effect of Matching Firms
with Refugee Workers, Mimeo
Lowe, M. 2021. Types of Contact: A Field Experiment on Collaborative and Adversarial Caste Integration,
American Economic Review, vol. 111, no. 6, 1807–44
Mahmood, S. S., Wroe, E., Fuller, A., and Leaning, J. 2017. The Rohingya people of Myanmar: health,human rights, and identity, The Lancet, vol. 389, no. 10081, 1841–50
Mayda, A. M. 2006. Who Is against Immigration? A Cross-Country Investigation of Individual Attitudes
toward Immigrants, Review of Economics and Statistics, vol. 88, no. 3, 510–30
22

Mousa, S. 2020. Building Social Cohesion between Christians and Muslims through Soccer in Post-ISIS
Iraq, Science, vol. 369, no. 6505, 866–70
Prediger, S., Vollan, B., and Herrmann, B. 2014. Resource Scarcity and Antisocial Behavior, Journal of
Public Economics, vol. 119, 1–9
Rao, G. 2019. Familiarity Does Not Breed Contempt: Generosity, Discrimination, and Diversity in Delhi
Schools, American Economic Review, vol. 109, no. 3, 774–809
Sakamoto, M., Ullah, S. M. A., and Tani, M. 2021. Land Cover Changes after the Massive Rohingya Refugee
Influx in Bangladesh: Neo-Classic Unsupervised Approach, Remote Sensing, vol. 13, no. 24, 5056
Sanjaya, M. R. 2021. Antisocial Behaviour in Experiments: What Have We Learned from the Past Two
Decades?, Mimeo
Scacco, A. and Warren, S. S. 2018. Can Social Contact Reduce Prejudice and Discrimination? Evidence
from a Field Experiment in Nigeria, American Political Science Review, vol. 112, no. 3, 654–77
Tayeb, T. 2021. Four Years on, No End in Sight to the Plight of the Rohingya, The Daily Star,
https://www.thedailystar.net/opinion/closer-look/news/four-years-no-end-sight-the-plight-therohingya-2159946 (date last accessed 14 December 2022)
Taylor, J. E., Filipski, M. J., Alloush, M., Gupta, A., Rojas Valdes, R. I., and Gonzalez-Estrada, E. 2016.
Economic Impact of Refugees, Proceedings of the National Academy of Sciences, vol. 113, no. 27,
7449–53
Ullah, S. M. A., Asahiro, K., Moriyama, M., and Tani, M. 2021. Socioeconomic Status Changes of the Host
Communities after the Rohingya Refugee Influx in the Southern Coastal Area of Bangladesh,
Sustainability, vol. 13, no. 8, 4240
Verme, P. and Schuettler, K. 2021. The Impact of Forced Displacement on Host Communities: A Review of
the Empirical Literature in Economics, Journal of Development Economics, vol. 150, 102606

23

Table 1: Characteristics of sample household

Household Survey

Muslim majority
sub-district(near the camp)

Muslim majority
sub-district(far from the camp)

Tribal majority
sub-district

Mean

Mean

Mean

SD

N = 955

SD

N = 471

SD

N = 328

Characteristics of HH head
=1 if male

0.79

0.40

0.84

0.36

0.84

0.37

=1 if Bengali

0.97
3.4
47.3

0.17
4.3
14.8

1.00
3.4
47.7

0.05
4.2
14.0

0.50
2.1
45.6

0.50
3.3
13.7

0.52

0.50

0.65

0.48

0.39

0.49

0.64

0.48

0.69

0.46

0.78

0.41

3.0
0.33

1.02
0.47

3.3
0.04

0.93
0.20

3.3
0.08

0.91
0.28

=1 if migrated in the past 5 years

0.04

0.20

0.07

0.26

0.03

0.16

Number of HH members

5.7
16,66767.6

2.2
18,90088.7

5.6
16,04893.5

2.2
17,497116.0

5.4
10,090
133.1

2.1
9,981
130.5

Completed years of schoolingAge (at the time of the survey)= 1 if feel belonged more to areligious group=1 if averse to inequality
Distrust in a foreigner (4 scale)=1 if interacting with Rohingya
Characteristics of HH

Monthly HH income (BDT)Distance to the city (minutes)
Remote Sensing Data a
Distance to the camp (km)Min
MedianMax

N = 934
21.8
12.6
0
23.2
62.9

N = 464
63.6
12.3
38.3
68.2
82.1

N = 289
48.5
15.2
15.3
51.6
68.0

Note: (a) GPS location of some sample households was not recorded in CAPI either because the interview
was conducted outside the household (0.9%) or because the GPS signal was not captured by the tablet (2.9%).

24

Table 2: Descriptive statistics of main variables
Muslim majority Muslim majority(near the camp) (far from camp)
N = 955
N = 471
Panel A: JOD game

Tribal majority

Total

N = 328

N=17540 BDT
20 BDT

26.3(29.0)[38.9][28.6]

22.5(27.4)[44.4][29.1]

19.2(25.2)[49.1][28.9]

24.0(28.0)[42.2][28.8]

40 BDT
60 BDT
80 BDT[11.7][3.6][17.2][9.3][4.0][13.2]

[7.2]
[6.3]
[8.5]

[10.3][4.2][14.5]

Panel B: Opinion
Self-reported distrust

3.7

3.8

3.7

3.7

(4 Likert scale)
JOD by Rohingya(0, 20, 40, 60, 80 BDT)
Opinion integration (reverse)(0.66)
26.1(33.6)
3.6(0.63)
27.6(34.3)
3.8(0.65)
16.5(26.2)
3.4(0.65)
24.8(32.8)
3.6(5 Likert scale)Opinion getting on (reverse)(5 Likert scale)Standardized Index(Aggregated)(1.23)
3.2(1.23)-0.07(2.51)(1.06)
3.5(1.10)
0.53(2.46)(1.08)
3.0(0.81)-0.64(1.97)(1.17)
3.3(1.14)
0.00(2.44)

Panel C: Damage
HH income declined(=1 if yes)
Commodity price increased(=1 if yes)

0.37
(0.48)
0.19(0.39)

0.30(0.46)
0.17(0.37)

0.30(0.46)
0.10(0.30)

0.34(0.47)
0.17(0.37)

Forest degraded(=1 if yes)
Level of crime increased(=1 if yes)
Damage index(0 – 4)0.59(0.49)
0.20(0.40)
1.34(1.07)

0.35(0.48)
0.04(0.20)
0.86(0.93)

0.30(0.46)
0.04(0.21)
0.75(0.85)

0.47(0.50)
0.13(0.33)
1.10(1.03)

Note: Numbers are the means, numbers in parentheses are standard deviations, and numbers in brackets areproportions.

25

Table 3: Main estimation results
Panel A: JOD
(1)

(2)

(3)

JOD payment (BDT)

(5)

IV

Damage index

JOD payment (BDT)-0.33***-0.33***-0.017***-0.013***(0.13)(0.12)(0.0041)(0.0041)

Damage index
Control variables
N
First-stage F-statistics(6)

First stage

OLS

Dependent variable
Distance to the camp (km)(4)

19.9***

25.5***(6.35)(8.74)

N

Y

N

Y

N

Y

1687

1687

1687

1687

1687

1687

32.9

29.3

Panel B: Opinion index
(1)

(2)

(3)

OLS

Dependent variable
Distance to the camp (km)IV

Negative opinion index
0.0011-0.0013(0.0078)(0.0076)

Damage index
Control variables
N(4)-0.0670.10(0.47)(0.56)

N

Y

N

Y

1687

1687

1687

1687

Note: Estimated coefficients are reported. ***, **, and * indicate the 1%, 5%, and 10% levels of statisticalsignificance, respectively. Numbers in parentheses are standard errors clustered at the village level. Subdistrict fixed effects, enumerator fixed effects, and survey phase fixed effects were controlled.

26

Table 4: Heterogeneity (OLS)

Dependent variableX=1 if maleN
=1 if BengaliN=1 if Muslim
N
Competed years of schooling(=1 if above median)N
Age(=1 if above median)N= 1 if feel belonged to a religion
N=1 if averse to inequality
N
Distrust in a foreigner(=1 if above median)=1 if interacting with RohingyaN=1 if migrated
N
Number of HH members(=1 if above median)N
Log (past per capita income)(=1 if above median)N
Log (distance to the city)(=1 if above median)N=1 if non-HH head responded(1)(2)
JOD payment (BDT)Distance
Distance
interactedwith X-0.31**-0.029(0.13)(0.078)
313
1374
-0.35
0.019
(0.22)(0.19)
179
1508-0.40**
0.072(0.17)(0.12)
280
1407-0.33***-0.0084(0.12)(0.036)856831-0.36***
0.053(0.12)(0.041)876811-0.41***
0.13*(0.14)(0.064)772
915-0.43***
0.16**(0.12)(0.066)554
1133-0.30**-0.055(0.12)(0.051)784
903-0.35***
0.064(0.13)(0.11)
1348
339-0.34***
0.077(0.11)(0.27)
71
1616-0.32**-0.033(0.13)(0.037)921
766-0.36***
0.049(0.12)(0.031)841
846-0.40***
0.14**(0.13)(0.061)1107
580-0.33***
0.095(0.12)(0.14)
1620
67

(3)
(4)
Negative opinion index
Distance
Distance
interactedwith X-0.00170.00042(0.0093)(0.0055)313
1374-0.0067
0.0056(0.013)(0.010)179
1508-0.00440.0032(0.011)(0.0073)280
1407-0.000044-0.0025(0.0082)(0.0034)856
831-0.0047
0.0062*(0.0078)(0.0032)876
811-0.00810.011**(0.0086)(0.0048)772
915
0.00430.064(0.0086)(0.11)
554
1133-0.0035
0.0037(0.0084)(0.0043)784
903
0.0060-0.024**(0.0080)(0.011)1348
339-0.00190.0076(0.0074)(0.015)711616
0.00064-0.0052(0.0077)(0.0032)921
766-0.0034
0.0034(0.0078)(0.0022)841
846-0.0055
0.0088**(0.0074)(0.0040)1107
580-0.0018
0.018(0.0075)(0.013)
1620
67

N
Note: N in columns 1 and 3 presents the sample size of those whose control variable takes the value of 0
while N in columns 2 and 4 presets that takes 1. Estimated coefficients are reported. ***, **, and * indicate
the 1%, 5%, and 10% levels of statistical significance, respectively. Numbers in parentheses are standard
errors clustered at the village level. The control variables, sub-district fixed effects, enumerator fixed effects,
and survey phase fixed effects were controlled.
27

Figure 1: Expansion of the refugee camp and location of sample household
Panel A: 2016

Panel B: 2018

Panel C: Sample household

28Note: Panels A and B show land cover maps in 2016 and 2018, respectively. The maps encompass an area of 39 km from east to west and 65 km from north to south.
The maps were created based on Landsat 8 images. Data on Landsat 8, vegetation index, water index, and built-up index were extracted from Google Colab. The pixel
resolution was about 120 meters. The random forest algorithm was used to classify each pixel into five types; permanent water, temporal water, vegetation, barrens,
and built-up. In the classification process, supervised data were manually obtained from Landsat images to train the algorithm. We adjusted the consistency of the timeseries for pixels classified as built-up using data from 2014 to 2021 by assuming that the built-up will not change to vegetation or barrens in a relatively short periodof time. Panel C shows the location of Cox’s Bazar district is shown in dark gray on the lower left map, which depicts entire Bangladesh. The areas in red on the main
map are refugee camps, while the shaded areas are Myanmar. The black dots show the locations of surveyed households.

29

Appendix A: Sampling Design and Survey Implementation
Bangladesh has 64 districts, and we performed stratified random sampling for two ofthem in this study: Cox’s Bazar and Bandarban. The refugee camp is located in Cox’s Bazar
District. The population of Cox’s Bazar district was 2.3 million before the refugee influx, after
which 700,000 refugees arrived; the numbers illustrate the scale of refugee influx. Most
residents in this district are Bengali Muslim. The Bengali dialect in the district is also similar
to the Rohingya language and can be used to communicate with each other.
Bandarban district is next to Cox’s Bazar and is one of the three provinces of theChittagong Hill Tract, where the minority non-Muslim group is exceptionally dominant in
Bangladesh. While there are restrictions on the movement of refugees outside the camp,
Bandarban District is accessible by land, and environmental damage was reportedly caused byrefugees’ collection of non-timber forest products. Furthermore, as houses in the camp aremainly constructed of bamboo, the surge in demand has resulted in the illegal cutting down ofbamboo groves. In our survey, we included Bandarban to analyze how the deforestation
affected people in this district, and how the sentiment of the non-Muslim population differedfrom that of the Muslim majority camp-adjoining community.
We targeted all eight sub-districts (called Upazilas in Bangladesh) in Cox’s Bazar andthree (out of seven) sub-districts located in Southern Bandarban, which were closer to the camp.
Refugee-induced damage was mostly concentrated in these sub-districts. The 11 sub-districts
consisted of (i) the 4 most exposed sub-districts in Cox’s Bazar, (ii) the remaining 4 subdistricts in Cox’s Bazar, and (iii) 3 sub-districts in Bandarban. Given budgetary and humanresource constraints, we randomly sampled 16 villages from each of the four exposed subdistricts and eight villages each from the other two categories. Based on the list of all villages
in each sub-district, we randomly sampled a fixed number of villages from each sub-district.
The total sample villages are 16 villages × 4 sub-districts + 8 villages × (4+3) sub-districts =30

120 villages. Appendix Table A1 presents the sampling design.
We randomly selected 15 households from each of the 120 sampled villages. In somevillages without an updated list of households, we created a list before sampling. The expected
sample size was 15 households × 120 villages = 1,800 households. Some sampled villages in
Bandarban, which were located in a remote area, had fewer than 15 households; we interviewed
all households in these villages. Furthermore, some data collection interviews were notconducted when the respondent was unfound or unavailable. We ultimately collected data from1,754 households.
Trained enumerators, mostly graduate students, visited each household to conduct aninterview survey. Each interview took 45 minutes at the median to complete the structured
questionnaire and the joy-of-destruction game. The questionnaire and game were preprogrammed in a tablet computer-assisted personal interview (CAPI) application. We
interviewed the household heads. When a head was unavailable, the enumerator made an
appointment to revisit the household. When a head temporarily lived outside the village andwas unavailable for interview, we interviewed a spouse or any household member withdecision-making power when the head was absent. The response rate was high, and we obtained
reliable answers from 1,754 households; in 95.8% of cases, a household head responded to oursurvey.
The survey was conducted in three phases from January 2021 to January 2022. The firstphase was a pilot survey conducted in January 2021 in 5 villages randomly selected from 120
villages. We did not find any major problems. We then added minor edits to the questionnaireand started a full-scale survey soon after the pilot. The majority of sampled households wereinterviewed between January and March 2021. Although we planned to finish all of theinterviews in early 2021, we took a pause in March 2021—at which point we had completedinterviews with 1,701 households—because of coronavirus disease (COVID-19)-related31

restrictions and problems with the CAPI server. We were able to resume the survey inDecember 2021, interview the remaining households, and collect data from 53 households by
January 2022.

32

Appendix Table A1: Sampling scheme
Bangladesh
64 Districts↓↓

Cox’s Bazar District(Muslim majority)
8 Sub-districts

Bandarban District(Tribal majority)
7 Sub-districts

↓

↓

↓

4 Sub-districts
closer to the camp

4 Sub-districts
farther from the camp

3 Sub-districts
closer to the camp

↓

↓

↓

16 villages
in each Sub-district

8 villages
in each Sub-district

8 villages
in each Sub-district

↓

↓

↓

15 HH
in each village

15 HH
in each village

15 HH
in each village

33

Appendix B: Additional Tables and Figures
Appendix Table B1: Correlation matrix of the outcome variables
JOD
Paid amount in the JOD gameSelf-reported trust (A)JOD by Rohingya (B)Opinion on integration (C)Opinion on getting on (D)Standardized index (E)

(A)

(B)

(C)

(D)

0.123
0.197

0.062

0.109

0.212

0.078

0.092

0.215

0.022

0.391

0.213

0.610

0.476

0.689

34

0.667

Appendix Table B2: Estimation results on JOD (full model)
OLS(1)

Dependent variable
Distance to the camp (km)JOD payment (BDT)

(3)

(4)

Damage index-0.33***-0.33***-0.017***-0.013***(0.13)(0.12)(0.0041)(0.0041)

Damage index=1 if male
=1 if Bengali=1 if Muslim
Competed years of schoolingAge= 1 if feel belonged to a religion=1 if averse to inequality
Distrust in a foreigner=1 if interacting with Rohingya=1 if migrated
Number of HH members
Log (past per capita income)Log (distance to the city)=1 if non-HH head responded
N
First-stage F-statistics

1687

IV

First stage
(2)

(5)

(6)

JOD payment (BDT)

19.9***

25.5***(6.35)(8.74)

1.71

0.064

0.075

(1.87)(0.058)(2.18)

7.32

0.22*

1.67

(5.53)(0.13)(5.76)-9.68**-0.011-9.41**(4.77)(0.10)(4.34)-0.38**

0.013**-0.70***(0.18)(0.0055)(0.24)-0.0470.00041-0.057(0.055)(0.0016)(0.059)-1.34-0.033-0.49(1.61)(0.049)(1.99)-1.57-0.095*0.85(1.61)(0.051)(2.15)

1.65*-0.0312.45**(0.85)(0.026)(1.01)

3.87*

0.26***-2.68(2.13)(0.067)(3.40)

-2.80

0.21

-8.19*(3.89)(0.15)(4.46)-0.26

0.014*-0.61*(0.29)(0.0078)(0.32)

0.36-0.0056

0.51(0.28)(0.0086)(0.33)-0.49-0.0500.79(0.97)(0.034)(1.40)

1.21

0.031

0.43

(3.48)(0.092)(3.87)

1687

1687

1687

32.9

29.3

1687

1687

Note: Estimated coefficients are reported. ***, **, and * indicate the 1%, 5%, and 10% levels of statisticalsignificance, respectively. Numbers in parentheses are standard errors clustered at the village level. Subdistrict fixed effects, enumerator fixed effects, and survey phase fixed effects were controlled.

35

Appendix Table B3: Estimation results on JOD (extensive margin, full model)
OLS(1)

Dependent variable
Distance to the camp (km)

IV
(2)

(4)

=1 if paid a non-zero amount in JOD-0.0051***-0.0050***(0.0018)(0.0017)

Damage index=1 if male
=1 if Bengali=1 if Muslim
Competed years of schoolingAge= 1 if feel belonged to a religion=1 if averse to inequality
Distrust in a foreigner=1 if interacting with Rohingya=1 if migrated
Number of HH members
Log (past per capita income)Log (distance to the city)=1 if non-HH head responded
N

(3)

1687

0.31***

0.39***(0.087)(0.12)

0.028

0.0026(0.035)(0.040)0.019-0.067(0.072)(0.093)-0.14***-0.14***(0.047)(0.052)-0.0039-0.0087**(0.0031)(0.0041)-0.00047-0.00063(0.00090)(0.00092)-0.038-0.025(0.030)(0.035)-0.0170.020(0.026)(0.032)0.035**

0.047***(0.013)(0.015)0.068*-0.032(0.035)(0.054)-0.048-0.13(0.080)(0.090)-0.0031-0.0084(0.0057)(0.0061)0.00130.0035(0.0046)(0.0057)-0.024*-0.0049(0.015)(0.019)-0.012-0.024(0.054)(0.062)

1687

1687

1687

Note: Estimated coefficients are reported. ***, **, and * indicate the 1%, 5%, and 10% levels of statisticalsignificance, respectively. Numbers in parentheses are standard errors clustered at the village level. Subdistrict fixed effects, enumerator fixed effects, and survey phase fixed effects were controlled.

36

Appendix Table B4: Estimation results on opinion (full model)
OLS(1)

Dependent variable
Distance to the camp (km)

IV
(2)

(4)

Negative opinion index
0.0011-0.0013(0.0078)(0.0076)

Damage index=1 if male
=1 if Bengali=1 if Muslim
Competed years of schoolingAge= 1 if feel belonged to a religion=1 if averse to inequality
Distrust in a foreigner=1 if interacting with Rohingya=1 if migrated
Number of HH members
Log (past per capita income)Log (distance to the city)=1 if non-HH head responded
N

(3)

1687

-0.0670.10(0.47)(0.56)

0.15

0.14(0.12)(0.13)

0.62*

0.60*

(0.32)(0.34)-0.57**-0.57**(0.25)(0.24)-0.016-0.017(0.014)(0.015)-0.00036-0.00040(0.0042)(0.0042)

0.099

0.10

(0.11)(0.11)

-0.14

-0.13

(0.12)(0.14)

0.38***

0.38***(0.073)(0.072)-0.0047-0.031(0.22)(0.25)

0.16

0.14(0.39)(0.41)-0.00071-0.0021(0.025)(0.024)

0.014

0.015

(0.019)(0.019)

0.010

0.015

(0.074)(0.085)

-0.14

-0.14

(0.27)(0.27)

1687

1687

1687

Note: Estimated coefficients are reported. ***, **, and * indicate the 1%, 5%, and 10% levels of statisticalsignificance, respectively. Numbers in parentheses are standard errors clustered at the village level. Subdistrict fixed effects, enumerator fixed effects, and survey phase fixed effects were controlled.

37

Appendix Table B5: Estimation results on opinion (OLS, each component, full model)
(1)

(2)

(3)

(4)

(5)

(6)

(7)

Self-reported trust

JOD by Rohingya

Distance to the camp

0.00270.0013-0.11-0.067-0.0038-0.00360.00420.0021(km)(0.0022)(0.0020)(0.14)(0.14)(0.0043)(0.0042)(0.0035)(0.0035)=1 if male
=1 if Bengali=1 if Muslim

Integration(8)

Dependent variable

Getting-on

0.025

0.41

0.050

0.065

(0.040)(1.82)(0.059)(0.061)

0.046

0.58

0.38**

0.24(0.10)(5.66)(0.15)(0.15)-0.0651.38-0.33***-0.26**(0.063)(4.61)(0.12)(0.12)

Competed years of

0.0034-0.35*-0.0053-0.0063

Schooling(0.0036)(0.21)(0.0061)(0.0058)

Age-0.00011-0.022-0.00087

0.0014(0.0011)(0.055)(0.0021)(0.0018)= 1 if feel belonged to a

-0.0075

0.41

0.018

0.095*

religion(0.037)(1.49)(0.051)(0.057)=1 if averse to inequality-0.047-0.43-0.0610.00081(0.037)(1.81)(0.066)(0.052)Distrust in a foreigner

0.19***

0.53

0.025

0.056*(0.025)(0.90)(0.030)(0.030)=1 if interacting with

-0.0446.03**-0.013-0.13*

Rohingya(0.061)(2.34)(0.084)(0.074)=1 if migrated

0.15**-0.074-0.0830.010(0.073)(5.88)(0.20)(0.17)

0.0062-0.0600.0029-0.013

Number of HH members(0.0079)(0.40)(0.011)(0.010)Log (past per capita

0.010*-0.0550.0091-0.0084

income)(0.0057)(0.28)(0.0089)(0.0082)

Log (distance to the city)-0.0042-0.76-0.0100.056(0.019)(1.15)(0.034)(0.035)=1 if non-HH head

0.068

-4.34

-0.12

-0.0089

responded(0.063)(3.45)(0.12)(0.10)

N

1687

1687

1687

1687

1687

1687

1687

1687

Note: Estimated coefficients are reported. ***, **, and * indicate the 1%, 5%, and 10% levels of statisticalsignificance, respectively. Numbers in parentheses are standard errors clustered at the village level. Subdistrict fixed effects, enumerator fixed effects, and survey phase fixed effects were controlled.

38

Appendix Figure B1: Visualization of main results
Panel A: Results of the JOD game

Panel B: Relationship between the JOD game and distance to the camp

39

Appendix C: HIES 2016 data
Appendix Figure C1: Location of sample villages

Note: The blue dots represent sample village and the areas in red are refugee camps.

40Appendix Figure C2: Relationship between the distance and pre-existing conditions

Note: Data source is HIES 2016. The sample size ranges from 2876 to 5360 for the individual characteristics
and 1139 for the household characteristics. Each dot represents the regression coefficient of each variable
outcome on the distance to the camps, and each bar represents a 95% confidence interval. The regressions
controlled sub-district fixed effects, and standard errors were clustered at the village level. All the outcome
variables were standardized with a mean of zero and a standard error of one.

41