Tsukuba Economics Working Papers
No. 2013-002	
 Do natural disasters beget fraud victimization? :Unrealized coping through labor migration among the poorby
Yoshito Takasaki	 April 2013

UNIVERSITY OF TSUKUBA
Department of Economics
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPAN

Do natural disasters beget fraud victimization?:
Unrealized coping through labor migration among the poor
Yoshito Takasaki*
University of Tsukuba
April 28, 2013

Acknowledgements
I wish to thank my field team – Jonati Torocake, Viliame Manavure, Viliame Lomaloma, and 16
enumerators – for their advice, enthusiasm, and exceptional efforts on behalf of this project.
Special thanks are owed to the Fijians of the region who so willingly participated in the survey.
The Cakaudrove Provincial Office in Fiji offered valuable institutional support. This research has
been made possible through support provided by the Sumitomo Foundation, the Japan Society
for the Promotion of Science, and the Ministry of Education, Culture, Sports, Science andTechnology in Japan. Any errors of interpretation are solely the author’s responsibility.
*Corresponding author. Faculty of Humanities and Social Sciences, University of Tsukuba, 1-11 Tennodai, Tsukuba, Ibaraki 305-8571 Japan, Tel./fax: +81 29 853 6280. E-mail address:
takasaki@sk.tsukuba.ac.jp.

1

Do natural disasters beget fraud victimization?:
Unrealized coping through labor migration among the poor
Abstract
Although international remittances are important insurance against natural disasters indeveloping countries, fraud is a pitfall of international labor migration. This paper addresses an
unexplored question about the disaster-fraud nexus: Do natural disasters beget fraud
victimization among the poor as they seek labor migration for coping? I exploit a naturalexperiment: Two years after a cyclone, a huge number of Fijian males were defrauded ofapplication fees for labor migration to the Middle East in 2005. My household survey data,
which by chance I collected before the fraudulence was noticed, are free fromunderreporting/misreporting out of embarrassment. Controlling for the endogeneity of household
housing damage reveals that housing damage strongly increases individual member’s job
application that later turned out to be fraud victimization. Households resort to high-risk, highreturn labor migration because their domestic coping options are constrained by their laborendowment.
Keywords: labor migration; fraud victimization; natural disaster; risk coping; poverty; Fiji
JEL classification: K42; O15; Q54

1. Introduction
International labor migration and remittances play important roles in many developing
countries (e.g., Lucas and Stark 1985; Taylor 1999; Adams and John 2003; Hanson 2010). In
particular, international remittances can play a significant insurance role as both an ex ante
strategy (e.g., Rosenzweig and Stark 1989; Paulson 1994; Amuedo-Dorantes and Pozo 2006) andan ex post strategy (e.g., de la Briere et al. 2002; Gubert 2002; Halliday 2006; Yang and Choi2007). This is especially so for major covariate shocks, such as natural disasters, because local
risk sharing is weak or even nonexistent, because disaster shocks greatly reduce pooled resourcesthat can be shared. Using country panel data, Yang (2008) shows that migrants’ remittances
strongly respond to hurricane damages in developing countries. Poor populations in ruraldeveloping areas are especially vulnerable to increasingly frequent and extreme weather events,
as they rely heavily on weather-dependent livelihood activities, such as agriculture, livestock,and fishery (e.g., Adger et al. 2003; Pelling 2003; Eriksen and O'Brien 2007; Sawada 2007;

2

Strömberg 2007), and international remittances are expected to play an increasing role asinsurance among them (Marchiori and Schumacher 2011 theoretically show that climate change
increases international migration).
A pitfall in international labor migration, especially from developing countries, is fraud:
Many labor migrants rely on recruitment agencies having information about and market power inforeign job markets, and their potential for fraud and abuse has been noted (World Bank 2006b).
All that is known about recruitment agencies’ fraud, however, is based on anecdotal evidence
and limited case studies. 1 As far as I know, no survey on victimization of recruitment fraud – thescamming of people seeking, not performing, employment – exists. 2 More broadly, most extant
crime and victim studies in developing areas (including those discussed below) focus on violent
and property crimes, and the literature virtually ignores fraud and fraud victimization amongpoor populations (some Living Standard Measurement Surveys of the World Bank, for example,
cover crime victimization, but not fraud victimization). This paper addresses an importantquestion about the disaster-fraud nexus that has not yet explored in the literature: Do natural
disasters beget fraud victimization among the poor as they seek labor migration to cope withdisaster shocks? If so, private risk coping is a channel for fraud victimization among the poor,and through this channel, natural disasters exacerbate the pitfalls of international labor migration.
A major challenge in fraud studies is that victim data are potentially underreported and/or
misreported out of embarrassment. Victims of white-collar crimes, as well as some other crimes
such as sexual assault, are reluctant to report the incidents (Ennis 1967): Many victims “often are
viewed with a mixture of skepticism, suspicion, and disbelief, and viewed as unworthy of1

In their study of Sri Lankan labor migrants to the Middle East in the mid-1980s, Eelens andSpeckmann (1990, p319) mention the prevalence of fraudulent recruitment agencies: “Almost
weekly, serious cases of fraud by bogus agents are reported in the local newspapers. Frequently,
these types of agents promise the prospective migrants foreign employment, collect the fees andthey disappear. Cases are known in which a single fraudulent agent cheated several hundreds
would-be migrants. Although the Sri Lanka Fraud Bureau has taken action against such
malpractices, many poor people are victims of these unscrupulous individuals.” Spaan (1994)
studies the role of recruitment agencies in Indonesian international migration.
2
The International Crime Victim Survey of the United Nations Center for International Crime
Prevention, for example, covers only consumer fraud. In developed countries, recruitment fraudin out-migration has received little attention; for example, recruitment fraud is not listed as acommon type of fraud in Levi and Burrows’s (2008) extensive study of available fraud data in
the United Kingdom.

3society’s protection” (Walsh and Schram 1980, pp. 46-47). 3 Distinct from conventional fraudvictim data gathered after the fraudulence is noticed by victims or the public, by chance I
conducted a household survey before fraud victims and non-victims became aware of thefraudulence of a recruitment agency (discussed shortly), but after they decided whether or notthey would apply for labor migration. In my survey, respondents never manipulate records tocover up (or exaggerate) victimization experiences before they know about them. Thus, I candirectly examine people’s job applications without relying on information about their
victimization experiences, which their decisions later turned out to be.
Recruitment fraud is especially relevant in Pacific island states with small economies,
which are often dependent on international labor migrations (Bertram 1986; World Bank
2006a). 4 In 2005, a private recruitment agency defrauded more than 20,000 Fijian males ofapplication fees for labor migration to the Middle East. This large-scale recruitment fraud
occurred two years after a tropical cyclone hit part of the country in January 2003, when theregion was still in the reconstruction phase. The fraud per se was not necessarily related to thecyclone, because the fraud was prevalent across most of the country, including regions not hit by
the cyclone. Exploiting this unique sequence as a natural experiment, I show that household
disaster victimization increases individual members’ job applications, i.e., ex post fraud
victimization. Even though village-level covariate disaster shocks are exogenous to individual
households, household-level disaster damage can be correlated with unobserved
individual/household heterogeneity affecting job applications (as discussed below). Controlling
for the potential endogeneity of household housing damage by using exogenous flood shock as
an instrument reveals even stronger coping responses than those otherwise. I then explore whyhouseholds seek coping through high-risk, high-return labor migration by examining how theircoping responses are differentiated by their coping capacity. Only when damage endogeneity iscontrolled for, results indicate that households resort to labor migration because their domestic
coping options are constrained by their labor endowment. Thus, the poor’s limited coping
capacity underlies the risk-coping channel for their post-disaster fraud victimization.

3

In a U.S. national survey conducted in 1991, Titus et al. (1995) find that only one third of selfreported consumer-fraud victims report their experiences to the authorities.
4
In contrast with anthropologists' in-depth ethnographic studies, systematic economic works
using micro-level survey data are scant in the Pacific region.

4

The disaster-fraud nexus is related to two lines of literature other than that addressing
migration and remittances, as discussed above. First, economists extensively study how poverty
and inequality can potentially cause crime, and a growing number of related works have been
recently conducted in developing countries (e.g., Fajnzylber et al. 1998; Bourguignon 2000;
Gaviria and Pages 2002). Identifying causality is a central challenge – poverty and crime are
jointly determined (Ehrlich 1973) and related with unobserved factors. A possible identification
strategy is to use shocks as exogenous factors. Miguel (2005) examines how the number ofmurders (of old women) in Tanzanian villages corresponds to rainfall shocks (which determine
income) in his reduced-form analysis; Fafchamps and Minten (2006) examine how the numberof crimes in communes in Madagascar responds to transitory poverty (head count ratio), using
the fuel crisis as a natural experiment. Distinct from these studies based on village-/communelevel data, my analysis focuses on individual-/household-level factors and thus can capture
heterogeneity according to micro-level coping capacity. 5
Second, although criminologists have paid increasing attention to abuses and offences in
the aftermath of natural disasters (e.g., Davila et al. 2005; Zahran et al. 2009; Harper and Frailing2010; Brown 2012; White 2012), especially in the chaotic relief phase (the worst behaviors ofhumankind occur during the crisis, as discussed, for example, by Teh 2008 about the Indian
Ocean tsunami), economic studies on post-disaster crime are very scarce. A growing number of
economists examine various impacts of natural disasters, such as education (e.g., Yamauchi et al.
2009; Sacerdote 2012; Deuchert and Felfe 2013), labor (e.g., Sarmiento 2007; Belasen andPolachek 2009), and migration (e.g., Boustan et al. 2012), but not crime; in a related study,
Leeson and Sobel (2008) show that disaster aid increases public corruption across U.S. states(Cavallo and Noy 2009; Kellenberg and Mobarak 2011 review macro impacts of natural
disasters). As far as I know, this paper is the first economic study on post-disaster fraud. Fijians’
fraud victimization through their coping responses in the reconstruction phase intimates thatnatural disasters can adversely affect crime and crime victimization in a broader and more
persistent way than current criminology research suggests.

5

Some economic works examine crime and conflicts as determinants of migration; using
household-level data in Colombia, Rodrigues and Villa (2012), for example, show that exposure
to kidnap risk increases international labor migrations (see also Engel and Ibáñez 2007).

5

The rest of the paper is organized as follows. Section 2 describes the data, the recruitment
fraud, and the disaster. Section 3 presents the econometric specification to identify the effects ofdisaster damage on job applications that later turned out to be fraud victimization, which isfollowed by the estimation results in Section 4. Section 5 examines the heterogeneous damage
effects by individual/household coping capacity. The last section concludes.
2. Data, fraud, and disaster2.1. Fijian kinship and data
I start with a description of Fijian kinship, which shapes my survey’s sampling design
and underlies my empirical strategy. Each native Fijian belongs to a lineage of the vanuayavusa-mataqali-tokatoka hierarchy: Vanua consists of several yavusa; yavusa consists ofseveral mataqali; and mataqali consists of several tokatoka (Ravuvu 1983). Roughly matching an
old district (tikina) in the administrative unit, vanua ranges over several villages (koro); a village
consists of one or a few yavusa, which includes several lower-order units, mataqali, and thentokatoka. These kin groups are of central importance for village governance, ritual, andlivelihoods (Turner 1992); in particular, rural land is communally owned by mataqali and isprivately used; by law it cannot be sold (communal land consists of about 83% of the country’s
total land).
In June-September 2005, I conducted a household survey among 906 households in 43
native Fijian villages in Cakaudrove Province in the northern region of the country. 6 In each of
16 districts in the province, villages were intentionally chosen to cover distinct environmental
and economic conditions. In each village, households were stratified by tokatoka and a
combination of individual leadership status (e.g., kin leader) and major asset holdings (e.g.,
shops) (all tokatoka were sampled); in each stratum, households were randomly sampled (50% of
the population in each stratum, on average). The 43 villages in the sample cover 20 vanua, 53
yavusa, 146 mataqali, and 234 tokatoka in total (an average village consists of 3.4 mataqali and

6

The province, consisting of part of Vanua Levu, all of Taveuni, and other small islands,
significantly lags behind the main island, Viti Levu, where the state capital, two international
airports, and most tourism businesses are situated. Fiji is divided almost evenly between native
Fijians and Indo-Fijians. My study focuses on native Fijians. The fraudulent recruitment agency
discussed below is staffed by only native Fijians, and its recruitment drive in rural areas covered
only native Fijian villages.

6

5.6 tokatoka, and on average, each mataqali and tokatoka consists of 14 and 8 households,
respectively, in the population). 7
At the time of interviews, the mean monthly total income earned by sample households is
F$1,582, or F$288 per capita (F$1 = US$.60). Virtually all households employ traditionalfarming practices, using no mechanized equipment or animal traction to produce taro, cassava,
coconut, and kava plants. Many households also engage in artisanal fishing and handicraft
making. Farming, fishing, and handicraft making, respectively, account for 66%, 11%, and 10%of income earned.
2.2. Recruitment fraud
Since the invasion of Iraq in 2003, U.K.- and U.S.-based private security companies havebeen seeking personnel for their operations in the Middle East (e.g., delivering supplies to U.S.
armed forces in Iraq) (MacLellan 2006). Pay is good, but the jobs are dangerous (some casualties
have been reported). With a large pool of former army personnel, Fiji has been a major labor
supplier. The Fijian government welcomed this movement as a solution for its unemployment(the government was not directly involved). In late 2004 and early 2005, a private Fijian
recruitment agency conducted the largest recruitment drive in the country. All males between theages of 18 and 60 were eligible to apply by paying a fee in advance (applicants were notnecessarily promised a job). According to news media, the agency collected US$2 million feesfrom at least 20,000 applicants.
The agency’s recruitment drive extensively covered Cakaudrove Province. In my survey,
village selection was made before I became aware of the recruitment drive, and the recruitment
drive did not affect the household sampling design, as discussed above. While I was pretesting
the questionnaires in May 2005, I first noticed the unusual recruitment drive and quickly added
questions about job applications. I was not very suspicious about potential fraudulence, andduring interviews several months after people’s application decisions, respondents were still
unaware of the fraudulence. Right after the survey was completed, news media started to reportits fraudulence, as a huge number of applicants got neither a job nor a refund. At that time, theagency’s director had already left the country.
2.3. Fraud victimization
7

Marriage across different kin groups in the village or different villages is common, and thispaper focuses on the kin groups to which households currently belong.

7

The empirical analysis covers 1,239 males aged between 18 and 60, who were eligible forthe jobs offered by the recruitment agency; this sample includes 783 households (87% of thewhole sample) – with complete data on the damage caused by the cyclone in January 2003,
discussed shortly – in all 43 sample villages. Although the agency’s recruitment drive did notcover all villages in the region, all eligible individuals are effectively attempted victims, because
people in villages not covered by the recruitment drive were privately informed of the job
opportunity. 8 No respondents had difficulty in answering questions about their job applications;
19% of eligible individuals (27% of households with eligible members, or 23% of the wholesample) applied. Most application decisions were made in early 2005, i.e., at least two years after
the cyclone. Although 36% of households with eligible members have more than one eligible
individual, only one application was made by over 90% of households with an applicant(s); thatis, in most cases, each household selected one migrant laborer, indicating that migration is acollective family decision.
To apply, an average applicant spent F$230, and an average victimized household spent
F$242 (close to the mean monthly per-capita income). This amount includes application fees andall other related expenses, especially for transportation; to file an application, applicants travelled
to the recruitment agency’s office in the biggest town in the province, and many revisited tocheck their status.
2.4. Disaster and aid
On 13 January 2003, Cyclone Ami swept over the northern and eastern regions of thecountry; Ami was the only cyclone in the northern region from 1991 through 2005 (McKenzie etal. 2005). According to respondents’ subjective assessments, about 64% of residents’ dwellings –a main house and/or free-standing units, such as the kitchen, shower, and toilet (not allhouseholds have such units, as these facilities are often located inside the main house) – weredamaged (henceforth disaster victims), and the mean value of total housing damage in the wholesample was about F$1,100 (the descriptive statistics of housing damage and aid among all
households in the sample, including those with no members eligible for the job, are very similar

8

Since which villages in the sample were directly covered is unknown, I cannot examine thetargeting of fraud; the month when fraud attempt was made – directly or indirectly – is notknown, either.

8

to those discussed here). The cyclone caused no casualties, and permanent migration was
virtually nonexistent in its aftermath.
The provision of emergency relief (see Takasaki 2011b for details) was followed byhousing reconstruction programs. About one quarter of households received construction
materials (henceforth aid recipients), and the mean amount received in the whole sample wasalmost F$700. Although almost all aid recipients were disaster victims (i.e., virtually no leakage),
about 40% of disaster victims were aid recipients (i.e., large under-coverage), and aid wasstrongly targeted on the value of housing damage (Takasaki 2011a). Although aid was mostcommonly received in 2004, i.e., before job application decisions were made, a significant
proportion of full construction materials was provisioned in 2005, i.e., during or after job
application decisions were made; in particular, although about 23% of recipients received aid in
2005, 42% of the amount was provisioned then. Although there was at least one disaster victim
in each village, a few villages had no aid recipients. Post-disaster antisocial or conflict behavior
was not reported in the region.
2.5. Disaster vs. fraud victimization
This sub-section provides descriptive evidence for the damage effects on job applications,or equivalently ex post fraud victimization, and the potential role of disaster aid in mitigatingsuch damage effects. First, if housing damage increases fraud victimization, we would expectthat job applications are more common among disaster victims than non-victims and housing
damage is more common and larger among job applicants than non-applicants. Supporting
results are found for job application (9% difference) and the incidence of housing damage (14%difference), though there is no significant difference in the value of housing damage by job
application (see panels A and C of Table 1). Next, if housing aid decreases fraud victimization,
we would expect that job applications are less common among aid recipients than non-recipientsamong disaster victims, and housing aid is less common and smaller among job applicants than
non-applicants. No supportive results are found regardless of the timing of aid: None of thesedifferences are significantly different from 0 at conventional levels (see panels B and C, theformer of which reports results for aid in 2003-04; those for aid in 2003-05 are very similar).
3. Econometric specification
I start with the following reduced-form specification:

y1 = α + βy2 + γx + V + u .

(1)

9

where y1 is a dummy variable for individual job application (i.e., ex post fraud victimization); y2
is a dummy variable for housing damage; x is a vector of exogenous individual and household
controls (defined below); V is a vector of village dummies; and u is an error term. I consider
disaster aid as an additional determinant in the next section. Village dummies fully control forvillage-level factors affecting job applications, such as distance to the recruitment agency’s
office and local labor-market conditions, as well as village-level covariate disaster shocks andaid supply. Equation (1) is estimated by Ordinary Least Squares (OLS) (linear probability model).
As disaster damage is measured at the household level, standard errors are clustered byhousehold. If disaster victimization increases job applications, β > 0. The assumption of aconstant damage effect in equation (1) is relaxed in Section 5.
The identification assumption in equation (1) that household-level housing damage isuncorrelated with unobserved individual/household heterogeneity determining job application
decisions, such as ability and preference, may not hold, as follows. With village-level covariate
shocks controlled for, whether or not housing is damaged in the village depends on its
unobserved quality prior to the disaster, in particular, its durability against cyclone shocks (e.g.,heavy wind, rain, flood), which is a function of cumulative investments in housing that thehousehold had made, as well as its location in the village. If housing quality is positively(negatively) correlated with say, preference for labor migration, i.e., households with housing
less (more) vulnerable to cyclones tend to apply for the job, the estimated damage effect isbiased downward (upward).
My empirical strategy is two-fold. First, since housing location is fixed with mataqali (avillage subgroup owing land), it is effectively captured by mataqali fixed effects. Second, I
endogenize household housing damage by using flood shock as an excluded instrumental
variable (IV). The household survey asked about the magnitude of flood, not damage caused by
the flood, that the household had experienced on its land, based on a five-point scale (0: none, 1:
small, 2: some, 3: large, 4: very large); 45% of households had experienced a flood. The
identification assumption is that household flood shock affects its housing damage within
mataqali and is uncorrelated with unobserved individual/household heterogeneity in job
applications. In particular, household housing investments correlated with housing vulnerability
to flood on its micro location, if any, are assumed to be uncorrelated with household unobserved
heterogeneity. If flood shock augments housing damage and the damage effect on job

10applications is positive, we would expect that flooding is more common and larger not only
among disaster victims than non-victims, but also among job applicants than non-applicants.
These expectations are strongly supported by the results reported in panels A and C, respectively,of Table 1, which indicate a 30% and 16% difference in flood incidence and almost a 1 and .5
difference in flood magnitude.
The two-equation system is

y1 = α1 + β1 y2 + δ1 x + G + u1 ,(2)

y2 = α 2 + γ 2 w + δ2 x + G + u 2 ,(3)where w is flood shock; G is a vector of mataqali dummies; and u1 and u2 are error terms. The
endogeneity of housing damage y2 is controlled for by estimating equation (2) using a two-stage
least squares (2SLS) estimator with the first-stage equation (3), where w is an excluded IV.
The selection of individual and household controls follows the literature on both crime
victimization and migration.9 Individual factors capture demographics (household headship andage [minus 18]; recall that age 18 is the youngest in the sample), education (primary-school
completion, see below), and employment (permanent wage labor). Permanent employment, theopportunities for which are limited and exist mostly in the public sector, is considered exogenous,
because individuals cannot flexibly adjust it (its status did not change after application decisions
were made); dropping the employment variable significantly alters none of the remaining results.
Household factors capture demographics (the size of male/female working adults [ages 18-60],
children, and elderly) and assets (agricultural land), 10 but not income. Though income is often
considered as an important determinant of crime victimization, it is endogenous as a determinant9

Sociologists categorize determinants of crime victimization into exposure, attractiveness ofpotential targets, guardianship, and proximity to potential offenders (Cohen et al. 1981). The first
three are often captured by demographic factors, asset/income, and police access, respectively(e.g., Barslund et al. 2007). Determinants of migration often include demographic factors,
education, asset/income, home location, and migrant network, as well as shocks (e.g., Borjas
1989; Stark 1991). Police access, proximity to potential offenders (e.g., direct coverage by therecruitment drive), and home location are fully controlled for by village (mataqali) dummies inour models. Migrant networks in the Middle East are very limited – acquaintance with someone
who already had gotten the same job is uncommon in the sample. In another words, therecruitment agency is the only network available.
10
Household land holdings (under the customary tenure) at the time of the fraud attempt shouldbe almost the same as those at the time of interviews used here. The present data lackinformation about non-land asset holdings prior to the fraud attempt.

11of job application, because in anticipation of labor migration and remittances, the household may
adjust its earning efforts, and any unobserved factors that determine income, such as skills, mayalso influence migration decisions (even income measured before the fraud attempt, which thepresent data lack, would be endogenous). Still, household permanent income is controlled for by
employment and assets, as well as demographic factors. The descriptive statistics of all controls
reported in panel C of Table 1 show that compared to non-applicants, job applicants are younger,
more educated, and in households with fewer male working adults. 11
4. Estimation results
OLS and 2SLS estimation results of equation (2) are reported in panel A of Table 2 (in all
regression analyses, mataqali with only one household in the sample are dropped). The OLS
estimate of the damage effect without individual/household controls is about .08 and statisticallysignificant at a 1% significance level (column 1). The comparable 2SLS estimate is about .25,
i.e., about three times the OLS estimate, and also significant at a 1% significance level (column
4). Flood shock – on a normalized five scale (with 1 = very large flood) – strongly determines
housing damage in the first-stage equation (3) (F value for this excluded IV is 69), and the
reduced-form result – equation (2) with housing damage replaced with flood shock – is also
statistically significant (the table reports estimated coefficients only for flood shock); in
particular, experiencing a very large flood increases the probability that housing is damagedby .44 and that the individual applies for the job by .11. When individual and household controls
are added, the estimated damage effects do not significantly change in either OLS or 2SLS
(columns 2 and 4). 12
Thus, housing damage is endogenous in equation (2); that assuming its exogeneity leads
to strong downward bias in its estimated effects suggests a significant positive correlation ofunobserved housing quality and individual/household heterogeneity. Disaster victimization
increases the probability that the individual is victimized by fraud by .25; then, if there had been
no cyclone, the fraud-victimization rate among disaster victims would have been 17.9%(=.224/1.25) and that among disaster victims and non-victims combined would have been 16.2%
11

Although the present data cannot capture military experience, anecdotal evidence suggests that
individuals with such experience are not very common in the sample; indeed, households with amember currently working in the military are rare.
12
The OLS estimates of equation (1) with village dummies – either without or withindividual/household controls – are very similar to those of equation (2).

12(= (787×.179 + 452×.133)/1239) (Table 1), and thus, out of the actual overall fraud victimization(19%), about 3% was brought by disaster victimization.
Estimation results for controls are consistent with the descriptive findings above (theresults are similar when disaster damage is excluded in equation 2). On one hand, young andeducated household heads of households with fewer male working adults are more likely toapply. 13 I interpret these results when I consider the potentially heterogeneous impacts of thesecontrols by disaster victimization below. On the other hand, neither employment nor assets
significantly alter application. These results suggest that the opportunity cost of migration –losing the current employment – is low, because migrant jobs are better than domestic ones, andapplication is largely neutral to household welfare; that is, most households are so poor that theywould potentially be attracted by this job opportunity.
For a robustness check, I estimate the damage effect at the margin by redefining y2 inequations (2) and (3) as housing damage value (log). Distinct from damage incidence, in which
recall errors are minor, measurement errors in damage value can be significant, causing
attenuation bias that also can be controlled for in the 2SLS estimation. Estimation results
reported in panel B of Table 2 (controls are not shown for brevity) are qualitatively the same as
those for the damage dummy. In particular, the 2SLS estimates of the damage effect are about
three times the OLS estimates; according to the former, a 10% increase in damage value
augments the probability of job application by about .003. Flood shock is a strong instrument (avery large flood increases damage value by over 300%) and the corresponding reduced-form
results are consistent with the 2SLS results.
So far, I have not considered disaster aid as a potential determinant of job applications.
As housing aid is positively correlated with housing damage (targeting), whether this omitted
variable causes bias in the estimated damage effects depends on whether it influences individual
job applications. With a lack of valid instruments for housing aid, I estimate models withhousing aid as an additional exogenous control to see whether the remaining estimation resultschange significantly. Adding the dummy for aid receipt in 2003-04 to the models with thedamage dummy does not significantly alter the remaining results, nor does adding the amount of
13

When three dummies for secondary-school completion or above (18% of eligible individuals),secondary-school incompletion (37%), and primary-school completion (28%) are used (withprimary-school incompletion or below as a base), none of the three estimated coefficients are
significantly different from each other (results not shown).

13

aid received (log) to the models with the damage value significantly change the remaining results(columns 3 and 6); using housing aid in 2003-05 yields very similar results (results not shown).
Hence, the potential bias caused by omitted disaster aid is unlikely to be significant. 14
5. Coping capacity
How distinctly do disaster victims employ high-risk, high-return labor migration as a
coping strategy, depending on their coping capacity? I focus on individual- and household-level
labor capacities, which are shown to directly affect job applications above: individual laborproductivity (quality) captured by headship, age, and education (primary-school completion) andhousehold labor endowment (quantity) captured by the number of male working adults. 15
Specifically, I augment equation (2) by adding an interaction term of y2 and one of these four
factors separately. In the 2SLS, housing damage and its interaction term are two endogenous
variables to be controlled for by using flood shock and its interaction with the corresponding
control as two excluded IVs.
The OLS and 2SLS estimation results for the dummy for housing damage without aid
receipt as a control are reported in Table 3, where columns (1) and (6), respectively, replicate
those with no interaction term reported in columns (2) and (5) of panel A of Table 2. For clarity,each of the remaining columns reports only a selected control interacted (the results of controls
not reported are very similar to those reported in Table 2). The results with aid receipt – in 200304 or 2003-05 – as an additional control are very similar. Across specifications, flood shock andits interaction term are strong instruments (almost all the F-values for the excluded IVs in thefirst stage are greater than 30), and the corresponding reduced-form results are consistent withthe 2SLS results discussed next and statistically significant (results not shown).
The estimated potentially heterogeneous damage effects by coping capacity are quitedistinct between OLS and 2SLS.

14

Although consistent with the descriptive findings above, the estimated aid effects in OLS – foreither aid dummy or amount – are negative and very small in magnitude with no statisticalsignificance, those in the 2SLS are considerable in magnitude with statistical significance andstill much smaller in magnitude than the corresponding damage effects; interpreting these resultswith potential bias requires strong caution, however.
15
Analyzing assets (land) in the same way shows that assets do not significantly differentiate thedamage effects; doing so for employment is infeasible because it is relatively uncommon (only
9% of male working adults are employed).

141) Headship: Although the estimated damage effect is statistically significant only for maleadults other than heads and headship augments job applications only among disaster nonvictims in the OLS (column 2), the converse holds true in the 2SLS – the damage effect isstatistically significant only for heads and headship augments job applications only among
disaster victims (the joint significance tests are significant at a 1% significance level)(column 7).
2) Age: Although the estimated damage effects are smaller among older adults than younger
ones in the OLS (column 3), in the 2SLS the estimates are not differentiated by age and thenegative impact of age becomes statistically weak (column 8).
3) Education: Although the OLS estimates of the damage effects are not differentiated byeducation (column 4), in the 2SLS the damage effect is statistically significant only for theeducated, and education significantly augments job applications only among disaster victims(the joint significance tests are significant at a 1% significance level) (column 9).
4) Male labor endowment: Although the OLS estimates of the damage effects are notdifferentiated by labor endowment (column 5), in the 2SLS it significantly decreases thedamage effects – they are statistically significant for households only with one or two eligible
members (mean size is 2.1). It also decreases and increases job applications among disastervictims and non-victims, respectively, with almost the same magnitude at the margin(about .09) (these results are significant at least at a 5% significance level) (column 10).
These 2SLS results suggest that household/individual labor capacities strongly
differentiate coping responses among disaster victims. The household with small laborendowments employs labor migration for coping by selecting a member with high laborproductivity – an educated head, i.e., a main bread earner, regardless of his age – as a migrantlaborer; in contrast, the household with relatively large labor endowments can better cope withdisaster damage through domestic labor supply without relying on risky migration. Thus,
households resort to coping through labor migration when their domestic coping options areconstrained by their labor endowment. Among disaster non-victims, the household with large
labor endowments can better seek labor migration for risky investment (not coping), and aselected migrant laborer is not necessarily a member with high labor productivity. This positive
labor-endowment effect on migration does not appear among disaster victims, because it first
determines their domestic labor supply for coping; disaster victims with limited domestic options

15

rely more heavily on risky migration by utilizing a productive migrant laborer than disaster nonvictims. The biased OLS estimations fail to capture the differentiating role of coping capacity;
according to the OLS results, regardless of labor endowments, a young male adult but a head(regardless of education) would tend to be a migrant laborer among disaster victims.
For a robustness check, I repeat all analyses for housing damage value (log) and its
interaction terms; in the 2SLS estimation, the same set of excluded IVs as above are used. The
OLS and 2SLS results without the amount of aid received as a control are reported in Table 4,
the format of which is the same as that of Table 3 (the results with the log amount of aid received– in 2003-04 or 2003-05 – are very similar). Almost all estimation results are qualitatively thesame as those for the damage dummy; as the only exception, the estimated coefficient for
housing damage value interacted with age is statistically nonsignificant also in the OLS (column
3). 16
6. Conclusion
Although international remittances are important insurance against natural disasters indeveloping countries, fraud is a pitfall of international labor migration. This paper addressed an
unexplored question about the disaster-fraud nexus: Do natural disasters beget fraud
victimization among the poor as they seek labor migration for coping? I exploited a naturalexperiment: Two years after a cyclone, a huge number of Fijian males were defrauded ofapplication fees for labor migration to the Middle East in 2005. My household survey data,
which by chance I collected before the fraudulence is noticed, are free fromunderreporting/misreporting out of embarrassment. Controlling for the endogeneity of household
housing damage by using exogenous flood shock as an instrument reveals that housing damagestrongly increases individual member’s job application that later turned out to be fraud
victimization. Households resort to high-risk-high-return labor migration because their domestic
coping options are constrained by their labor endowment.
Hence, the significant potential of international labor migration as insurance underlies
recruitment fraud as its pitfall, and this pitfall is exacerbated by natural disasters because the16

In the 2SLS, the estimated coefficient for housing damage value interacted with household
head is statistically significant at almost a 10% significance level and the estimated damage
effect for household head (.050) is significant at a 1% significance level; the estimated effects ofmale labor endowment among disaster non-victims and at the mean housing damage value (.059
and -.024, respectively) are significant at a 15% and 10% significance level, respectively.

16

poor with limited coping capacity resort to risky migration. The risk-coping channel makes thepoor who are vulnerable to disasters also vulnerable to potential post-disaster fraud. Even if the
post-disaster antisocial or conflict behavior highlighted by criminologists is uncommon or evennonexistent, the risk-coping channel can bring fraud victimization throughout the relief andreconstruction phases, especially among the poor. Researchers and policy makers should pay
greater attention to the risk of post-disaster fraud, which may be greater, be more persistent, andlead to more adverse consequences than normally thought. Methodologically, controlling for theendogeneity of household-level disaster shocks is crucial to identify their impacts. Failing to doso could give rise to strong bias.
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20

Table 1. Job application (ex post fraud victimization), disaster damage/aid, andindividual/household characteristics.
A. Job application by housing damage
Disastervictims

0.19 (0.39)

0.22 (0.42)

0.13 (0.34)

0.09 ***

0.02

0.46 (0.50)

0.57 (0.50)

0.27 (0.44)

0.30 ***

0.04

Flood magnitude (0-4)

1.29 (1.57)

1.65 (1.64)

0.66 (1.22)

0.98 ***

0.12

No. observations

1239

787

452

Job application (0/1)
Flood (0/1)
aDisaster nonDifference
victims

Standard
error ofdifferenceAll

B. Job application by aid receipt among disaster victims
All

Aid
Aid nonreceipients in receipients in Difference
2003-04
2003-04

Job applicantion (0/1)

0.22 (0.42)

0.22 (0.42)

0.22 (0.42)

No. observations

787

250

534

0.00

Standard
error ofdifference
0.03

C. Disaster damage/aid and individual/household characteristics by job application
All

Job
applicants

Job nonapplicants

DifferenceStandard
error ofdifference

Housing damage (0/1)

0.64 (0.48)

0.75 (0.44)

0.61 (0.49)

0.14 ***

0.04

Housing damage value (F$)

1067 (2144)

1197 (2331)

1036 (2098)

161

175

Aid receipt in 2003-04 (0/1)

0.21 (0.41)

0.24 (0.43)

0.20 (0.40)

0.04

0.03

Aid receipt in 2003-05 (0/1)

0.27 (0.44)

0.30 (0.46)

0.26 (0.44)

0.04

0.04

Aid received in 2003-04 (F$)

409 (1503)

276 (1232)

439 (1558)

-163

109

Aid received in 2003-05 (F$)

659 (1910)

571 (1763)

680 (1943)

-110

142

Flood (0/1)

0.46 (0.50)

0.58 (0.49)

0.43 (0.49)

0.16 ***

0.04

Flood magnitude (0-4)

1.29 (1.57)

1.67 (1.62)

1.20 (1.55)

0.47 ***

0.13

Household head (0/1)

0.50 (0.50)

0.52 (0.50)

0.49 (0.50)

0.03

0.04

Age

36.0 (12.1)

34.8 (10.1)

36.3 (12.5)-1.53 *

0.80

Primary-school completion (0/1)

0.83 (0.37)

0.94 (0.25)

0.81 (0.39)

0.13 ***

0.02

Employment (0/1)

0.09 (0.29)

0.08 (0.28)

0.09 (0.29)

-0.01

0.02

Agricultural land (acre)

3.24 (4.96)

3.18 (5.64)

3.26 (4.79)

-0.08

0.54

No. males 18-60 years old

2.14 (1.25)

1.89 (1.13)

2.20 (1.27)-0.30 ***

0.10

No. females 18-60 years old

1.53 (0.98)

1.50 (0.96)

1.53 (0.98)

-0.04

0.08

No. < 18 years old

2.47 (2.07)

2.60 (2.08)

2.44 (2.07)

0.16

0.17

No. > 60 years old

0.42 (0.68)

0.46 (0.69)

0.41 (0.68)

0.05

0.05

No. observations

1239

236

1003

a

Notes: Standard deviations are shown in parentheses. Standard errors are clustered by household. a0= none, 1 = small, 2 = some, 3 = large, 4 = very large. *10% significance, **5% significance, ***1%
significance.

21

Table 2. Damage effects on job application (ex post fraud victimization).

(1)

OLS
(2)

(3)

(4)

2SLS
(5)

(6)

A. Housing damage
Housing damage (0/1)a0.077 *** 0.087 *** 0.091 ***(0.026)(0.025)(0.027)0.244 *** 0.254 *** 0.271 ***(0.090)(0.087)(0.094)

Household head (0/1)

0.092 **
(0.037)0.090 **
(0.036)Age - 18-0.004 *** -0.004 ***(0.001)(0.001)-0.004 *** -0.004 ***(0.001)(0.001)Primary-school completion (0/1)

0.126 *** 0.127 ***(0.022)(0.023)0.141 *** 0.144 ***(0.024)(0.024)

Employment (0/1)

0.027(0.044)0.028(0.044)0.039(0.043)0.043(0.043)Log of agricultural land (m2)-0.002(0.008)-0.002(0.008)-0.004(0.007)-0.004(0.007)No. males 18-60 years old-0.025 **(0.012)-0.025 **(0.012)-0.030 **(0.012)-0.029 **(0.012)

No. females 18-60 years old

0.011(0.015)0.011(0.016)0.016(0.015)0.016(0.015)

No. < 18 years old

0.001(0.007)0.001(0.007)-0.001(0.006)-0.001(0.006)

No. > 60 years old

0.018(0.021)0.017(0.021)0.018(0.021)0.017(0.021)

Aid recipt (0/1)

0.092 **
(0.037)-0.018(0.034)R-squared

0.270

0.301

0.302

No. observations

1201

1189

1189

0.091 **
(0.036)-0.086 *(0.049)
1201

1189

1189

First stage: Flood shock (0-1)

0.438 *** 0.448 *** 0.417 ***(0.053)(0.053)(0.051)

Reduced-form: Flood shock (0-1)

0.107 *** 0.114 *** 0.113 ***(0.041)(0.040)(0.041)

B. Housing damage value
Log of housing damage value (F$)a

0.008 **
(0.004)0.009 **
(0.004)Log of aid received (F$)

0.011 ***
(0.004)0.031 **
(0.013)0.032 *** 0.036 ***(0.012)(0.013)-0.008(0.005)-0.020 **(0.008)

Individual and household controls
R-squared

No

Yes

Yes

No

Yes

Yes

0.265

0.294

0.296

0.239

0.268

0.271

No. observations

1185

1173

1173

1185

1173

1173

First stage: Flood shock (0-1)

3.129 *** 3.203 *** 2.952 ***(0.369)(0.371)(0.335)

Reduced-form: Flood shock (0-1)

0.096 **
(0.041)0.103 **
(0.040)0.105 ***
(0.040)Notes: Dependent variables are a dummy variable for job application. Standard errors clustered by household arein parentheses. Other controls not shown here are mataqali dummies and constant; individual and household
controls shown in panel A are not shown in panel B. aEndogenous variable in 2SLS. *10% significance, **5%
significance, ***1% significance.

22

Table 3. Heterogeneous effects of housing damage on job application (ex post fraud victimization).

(n=1189)(1)a

Housing damage (0/1)

(2)

OLS
(3)

(4)

0.087 *** 0.123 *** 0.154 *** 0.079 ** 0.056(0.025)(0.034)(0.041)(0.040)(0.047)

Housing damage × Householdheada

0.254 *** 0.093(0.087)(0.117)-0.004 **(0.002)(9)0.255 *
(0.143)0.110(0.173)0.009(0.045)0.092 ** 0.134 ***(0.037)(0.043)Age - 18-0.004 ***(0.001)Primary-school completion (0/1)

0.126 ***
(0.022)

No. males 18-60 years old-0.025 **(0.012)0.301
F-value for excluded IVs in the first stage-0.185 **(0.078)0.090 ** -0.103(0.036)(0.106)-0.002(0.002)-0.004 ***(0.001)0.120 ***
(0.034)

0.304

0.301

-0.004(0.004)0.141 ***
(0.024)-0.036 **(0.017)0.303

0.614 ***
(0.187)0.169(0.182)0.016(0.019)Household head (0/1)

(10)

0.000
(0.007)

Housing damage × No. males 1860 years olda

Housing damage interacted withselected control(7)0.315 *
(0.171)

Housing damage × Primary-school
completiona

Housing damage(6)-0.069(0.044)

Housing damage × (Age - 18)aR-squared

(5)

2SLS
(8)

0.022
(0.126)-0.030 **(0.012)

0.094 *
(0.054)

0.302
71.3

36.0

35.9

35.7

37.1

33.9

31.9

35.6

19.8

Notes: Dependent variables are a dummy variable for job application. Standard errors clustered by household are in parentheses. Only controls
interacted are shown in columns (2)-(5) and (7)-(10). Other controls not shown here are employment (0/1), log of land (m2), no. females 18-60 years old,no. < 18 years old, no. > 60 years old, mataqali dummies, and constant. a Endogenous variables in 2SLS. Excluded IVs are flood shock (0-1) and its
interaction with the corresponding control. *10% significance, **5% significance, ***1% significance.

23

Table 4. Heterogeneous effects of housing damage value on job application (ex post fraud victimization).

(n=1173)(1)a

Log of housing damage value (F$)

(2)

OLS
(3)

(4)

0.009 ** 0.014 *** 0.016 *** 0.007(0.004)(0.005)(0.006)(0.006)

Log of housing damage value ×Household heada

0.032 *** 0.013(0.012)(0.016)0.000(0.000)(9)0.033 *
(0.019)0.021(0.024)0.002(0.006)

0.093 ** 0.132 ***(0.038)(0.043)Age - 18-0.004 ***(0.001)Primary-school completion (0/1)

0.120 ***
(0.023)No. males 18-60 years old-0.023 *(0.012)

0.294
F-value for excluded IVs in the first stage-0.020 **(0.009)0.089 ** -0.052(0.037)(0.089)-0.002(0.002)-0.004 ***(0.001)0.111 ***
(0.033)

0.296

0.294

-0.004(0.004)0.133 ***
(0.024)-0.033 **(0.017)0.296

0.071 ***
(0.023)0.013(0.025)0.002(0.003)

Household head (0/1)

(10)

0.000
(0.001)

Log of housing damage value ×
No. males 18-60 years olda

Log of Housing damage valueinteracted with selected control

(7)

0.037
(0.023)Log of housing damage value ×Primary-school completiona

Log of Housing damage value

0.004(0.007)(6)-0.010(0.006)

Log of housing damage value ×(Age - 18)aR-squared

(5)

2SLS
(8)

0.076
(0.110)-0.027 **(0.012)0.059(0.040)

0.295
77.9

39.0

38.2

37.4

39.1

35.4

35.8

38.0

19.2

Notes: Dependent variables are a dummy variable for job application. Standard errors clustered by household are in parentheses. Only controls
interacted are shown in columns (2)-(5) and (7)-(10). Other controls not shown here are employment (0/1), log of land (m2), no. females 18-60 years old,no. < 18 years old, no. > 60 years old, mataqali dummies, and constant. a Endogenous variables in 2SLS. Excluded IVs are flood shock (0-1) and its
interaction with the corresponding control. *10% significance, **5% significance, ***1% significance.