Tsukuba Economics Working Papers
No. 2011-002

Fraud and Poverty: Exploring Ex Ante Victim Databy
Yoshito TakasakiFebruary 2011

UNIVERSITY OF TSUKUBA
Department of Economics
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPANFraud and Poverty:
Exploring Ex Ante Victim Data
Yoshito Takasaki
University of Tsukuba

Contact Information:
Yoshito Takasaki
Graduate School of Humanities and Social Sciences
University of Tsukuba
1-1-1 Tennodai, Tsukuba
Ibaraki 305-8571 Japan
Tel./fax: +81 29 853 6280
E-mail address: takasaki@sk.tsukuba.ac.jp

February 1, 2011

Acknowledgments
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 for this project. Ryoko Sato served as an excellent research assistant.
This paper has benefited significantly from the comments and suggestions of Oliver
Coomes, and conference and seminar participants at the Far East and South Asia Meetingof the Econometric Society and the University of Kobe. This research has been madepossible 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.

1

Fraud and Poverty:
Exploring Ex Ante Victim Data
Abstract
Fraud studies rely on potentially underreported/misreported victim data in developed
countries, virtually ignoring developing countries. This paper proposes using ex ante
victim data, to be collected before attempted victims become aware of the fraudulence,and examines recruitment fraud, which is tightly linked with poverty. In rural Fiji, almost
one quarter of households were defrauded of application fees for labor migration. The
bigger problem is indirect costs: Controlling for victim endogeneity reveals thathouseholds’ false expectations about international remittances led to a significant
reduction in the domestic private transfers victims received. The analysis sharply
identifies who was victimized and why/why not.
Key Words: Recruitment fraud; Poverty; Ex ante victim data; Labor migration; Privatetransfers; FijiJEL Codes: K42, O12.
I. Introduction
Fraud – “deceit or intentional misrepresentation of fact with the intent of
unlawfully depriving a person of his or her property rights” (Rush 1986, p. 103) – is amajor crime in human society. This paper addresses two fundamental lacunae in fraudstudies. The first lacuna, which is empirical, is that the literature virtually ignoresdeveloping countries, especially poor populations. Fraud in developed countries is anenormous problem. Anderson (1999) estimates the total annual fraud losses (excluding
costs for preventing and responding to fraud) in the United States to be about $440 billion.
In a U.S. national survey in 1991, Titus, Heinzelmann, and Boyle (1995) find that 31% of
respondents had experienced victimization or attempted victimization of consumer fraudin the preceding year, and 15% of the total sample suffered successful victimizations,with an average loss of $216, suggesting over $40 billion annual losses among Americans(cf. the total cost of street crime in 1992 is estimated at about $17.6 billion, Klaus 1994).

2
In contrast, fraud in developing countries has received very limited attention and most
extant crime and victim studies focus on violent and property crimes. 1 Some Living
Standard Measurement Surveys (LSMS) of the World Bank, for example, cover crime
victimization, but not fraud victimization.2
What underlies the virtual ignorance of fraud among the poor is a common
perception that fraud is a problem of the wealthy: Those with limited incomes and assets
are unattractive to fraudsters and even if they are defrauded, their loss cannot besignificant. 3 I argue that crime and development studies miss a potentially huge problem
of fraud, which is directly linked with poverty. First, the poor in developing countries are
more vulnerable to fraud in productive activities than consumer fraud, because they arewilling to pay for better earning opportunities. Second, victims can suffer from much
more than defrauded money (direct costs), because with false expectations about earnings
induced by the fraud, people alter their behaviors in adverse ways (indirect costs, orexternalities). Fraud in productive activities may significantly undermine peoples’ risk
attitudes, investments, trust, and informal institutions; fraud may be a hidden cause ofpoverty traps, as theorized by Mehlum, Moene, and Torvik (2005) for crime in general(see also Lloyd-Ellis and Marceau 2003).
This paper examines recruitment fraud in developing countries. 4 Althoughinternational labor migration and remittances play important roles in many developing
1

Since Becker’s (1968) pioneering work, there has been a surge of economic studies oncrime, victimization, and punishment. Although most extant studies focus on urban crimein both developed and developing countries (e.g., Glaeser and Sacerdote 1999), some
empirical works highlight crime in rural developing areas (e.g., Fafchamps and Minten
2006; Miguel 2005). This paper adds to the growing literature on poverty and crime inrural developing areas.
2
In contrast, significant attention has been given to corruption – “acts in which the powerof public office is used for personal gain in a manner that contravenes the rules of thegame” (Jain 2001, p.73). Seligson (2006) examines household-level corruption
victimization.
3
In developed countries, policing agencies, news media, and survey firms also give muchless attention to frauds against poor consumers than those against businesses and publicbodies (e.g., Levi and Burrows 2008).
4
Recruitment fraud is part of employment fraud, involving the scamming of people
seeking, not performing, employment. Although in-house recruitment can be a fraud,
recruitment fraud targeting the poor is mostly recruitment agencies’ fraud. In this paper, I
use recruitment fraud and recruitment agencies’ fraud interchangeably.

3
areas, labor migrants often rely on recruitment agencies with information about andmarket power in foreign job markets and their potential fraud and abuse have beenfrequently noted (World Bank 2006b). All that is known about recruitment agencies’
fraud, however, is based on anecdotal evidence and limited case studies. 5 As far as I
know, there exists no victim survey on recruitment fraud. The International Crime Victim
Survey (ICVS) of the United Nations Center for International Crime Prevention covers
only consumer fraud. In developed countries, recruitment fraud in out-migration hasreceived little attention. 6
The second lacuna in the literature, which is methodological, is that it relies on
incomplete victim data. A fundamental difference exists between fraud and other crimes:
When responding to fraud attempts, victims are unaware of the offense. The decisions
that attempted victims had thought were good later turned out to be bad. This gap
between expectation and reality not only brings fraud into existence, but also strongly
shapes attempted victims’ responses to survey questions. It is well known that victims ofwhite-collar crimes are reluctant to report the incidents because they feel embarrassed(Ennis 1967): Many victims “often are viewed with a mixture of skepticism, suspicion,and disbelief, and viewed as unworthy of society’s protection” (Walsh and Schram 1980,
pp.46-47). Titus, Heinzelmann, and Boyle (1995) find that only one third of self-reported
consumer-fraud victims report their experiences to the authorities; Mason and Benson(1996) show an even smaller rate of reporting (23%). Underreporting of victimization per
se is also very likely in fraud victim surveys, though this critical problem has not yet beensystematically addressed. Other information related to fraud victimization also can bemisreported by respondents who hide the incidents, for coherence. Such
5

In their study of Sri Lankan labor migrants to the Middle East in the mid-1980s, Eelens
and Speckmann (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 and they 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 victimsof these unscrupulous individuals.” Spaan (1994) studies the role of recruitment agencies
in Indonesian international migration.
6
For example, recruitment fraud is not listed as a common type of fraud in Levi and
Burrows’s (2008) extensive study of available fraud data in the United Kingdom.

4
underreporting/misreporting problems can be significant in any fraud victim surveys thatcollect information about the victimization (reality) after victims and/or the public
become aware of the fraudulence. Now, imagine a survey for attempted victims
conducted before they become aware of the fraudulence. As respondents nevermanipulate records to cover up (or exaggerate) the victimization experience before theyknow about it, the survey is free from fraud-induced measurement errors. The distinctionof ex ante victim data on expectation from ex post victim data on reality is critical for
fraud (but not for other crimes). This distinction has been ignored in the literature,
however; 7 all available fraud victim data are ex post victim data.
The gap between expectation and reality in fraud victimization also strongly
differentiates its consequences before the fraudulence is noticed (ex ante impacts) fromthose afterward (ex post impacts). Researchers have not yet explored ex ante impacts,
although they are indispensable to measure indirect costs of fraud victimization. As fraud
victimization is not an exogenous shock, but a bad decision made by victims, controllingfor its endogeneity is crucial to identify its true impacts – either ex ante or ex post; victim
endogeneity, however, has not yet been addressed in the fraud literature. 8 Ex ante victim
surveys can collect not only reliable information about outcomes, but also instrumental
variables (IV) needed for identification.
This paper conducts a first ex ante victim data analysis. Section II offers aconceptual framework of fraud victimization, victim data, and victim endogeneity. At
first glance, collecting ex ante victim data seems infeasible, because researchers andprofessionals cannot tell the fraudulence until victims or the public notice it; moreover,such data collection can be morally wrong, because even if such early detection of fraud
is made, it should be used to prevent the fraud per se. I propose feasible data-collection
designs. Section III describes my own ex ante victim data on an organized recruitment
fraud in Fiji. In 2005, a recruitment agency defrauded more than 20,000 individuals of7

Criminologists consider the time gap between the commission of the offence andawareness, reporting, and recording as a data problem in fraud statistics (e.g., Levi and
Burrows 2008).
8
Some economic works examine broad consequences of crime – other than fraud – andvictimization by addressing their endogeneity. Crime can have a long-run effect, such as
child abuse as a source of future crime (Currie and Tekin 2006), and can involve negative
externalities, such as the effect on property value (Linden and Rockoff 2008).

5
application fees for labor migration to the Middle East. 9 I conducted a survey about
people’s job applications, before they had become aware of the offense. Sections IV andV, respectively, investigate who applied for the jobs and why/why not. In Section VI, as
an example of ex ante impacts of fraud victimization, I examine household domestic
private transfers, which play a central role in kin-based Fijian society (Hann 2006). Withstrong expectations and beliefs about labor migration among potential donors, thesubstitution effect – newly available international remittances reduce domestic privatetransfers – may already be at work. 10 Because the expected substitution is not realized, all
reduced private transfers, if any, are indirect costs borne by victims.
The major findings can be summarized as follows. First, recruitment fraud
victimization is highly prevalent in rural Fiji. Almost one quarter of households in theregion were defrauded, with direct costs in a range of monthly per-capita earned income.
Second, victims suffer much more. The prospective labor migration greatly lowered thedomestic private transfers victims received. Third, preventing fraud victimization is very
difficult. Only better domestic employment is shown to be a solution. The last section
concludes by synthesizing the major findings and discussing promising data collection
and questions for new fraud study.
II. Conceptual framework and fraud-victim data
A. Fraud victimization
Consider one fraudster and one individual. Fraud victimization consists of fourstages, as depicted in Figure 1 (recruitment fraud is used as an example here, but theframework applies to other types of fraud, such as consumer fraud). Stage 1 occurs prior
to the fraudster’s fraud attempt. Stage 2 begins once the fraudster targets the individual;
he/she becomes an attempted victim before he/she knows. The individual’s decision

9

Recruitment fraud as a pitfall in international labor migrations is particularly relevant in
Pacific island states, which are often dependent on them (Bertram 1986; World Bank
2006a). In contrast with extensive ethnographic studies by anthropologists, systematic
economic works using micro survey data are very scant in the region.
10
Following seminal works by Becker (1974), Barro (1974), and Cox (1987), economists
have extensively studied household private transfers. Numerous studies examine motives
for migrants’ remittances and their consequences (e.g., Lucas and Stark 1985) (seeRapoport and Docquier 2006 for a review). My inquiry is closely related to the crowdingout of private transfers (see Cox and Fafchamps 2008 for a review).

6
involves a tradeoff – taking an attractive job offer requires an advance payment. Stage 2,
during which the attempted victim can make a decision, is usually short, because thelonger the wait, the higher the risk of the fraudulence being detected. Stage 3 begins once
the individual’s decision determines his/her expected status; only if he/she takes thefraudster’s offer, the status changes to an attractive one (such as employment) and thefraudster’s status changes correspondingly – successful or unsuccessful attempt. Stage 4
begins once the fraudulence is detected; 11 the individual becomes aware of his/her
realized status – victim or non-victim. Fraud is sharply distinguished from other crimes
by this gap between expected and realized status; in other crimes, stages 2 and 3 arenonexistent and ex ante impacts/victim data are irrelevant. This gap is also irrelevant for
the fraudster (the shift to stage 4 alters risk of being captured though).
Ex ante and ex post impacts of fraud victimization are realized in stages 3 and 4,
respectively. Whether ex ante impacts matter and ex ante victim data at least theoretically
can be collected strongly depend on stage 3’s duration, which can significantly vary. On
the one hand, it can be virtually instant (e.g., online consumer fraud); then, ex ante
impacts are nonexistent and there is virtually no chance to collect ex ante victim data. On
the other hand, if the fraudulence remains unknown, stage 3’s duration is infinite and thus
only ex ante impacts matter; however, victimization per se is never noticed (stage 4 does
not emerge). In practice many frauds are in between these two extremes. 12
B. Ex post vs. ex ante victim data
Surveys that collect crime-victim data are either victim surveys or household
surveys with a victimization module. They have distinct potential advantages incollecting ex post fraud-victim data in stage 4; although a victim survey on one specificfraud (i.e., no fraud heterogeneity) enables a sharp identification of determinants of fraud
victimization,13 a household survey can capture its broad impacts.
11

In practice, the transition from stage 3 to stage 4 may be gradual as the attempted
victim’s information and/or suspicion grows. The timing of transition also can vary
among attempted victims and between them and others (e.g., news media).
12
Although ex ante impacts may be persistent in stage 4, it is very difficult to distinguishthem from ex post impacts, because both are caused by the same decision.
13
Although a small number of victims of a specific crime often make statistical analysis
infeasible, a particular fraud may involve a large number of victims; however,criminologists’ studies often cover only victims, lacking a non-victim sample (e.g.,

7
The ex ante victim survey to be conducted in stage 3 is totally different from theex post survey; standard questions about victimization do not work, simply because
respondents have not yet become aware of their realized status. The key is whether any of
the household survey’s modules other than victimization capture respondents’ expected
status. The following three designs are possible:
1) Passive design: A household survey is accidentally conducted in stage 3, and this isnoticed only after the survey was completed.
2) Proactive design: Researchers or professionals conduct a household survey on
potential fraud, hoping that it covers stage 3.
3) Reactive design: Researchers or professionals who conduct a household survey get
suspicious about fraudulence by chance, before attempted victims do so (stage 3), andthey add questions about the potential but concrete fraud.
Although all these three designs rely heavily on chance (as any natural experiment does),they have distinct strengths and weaknesses as follows.
The passive design in principle can be applied to any standard household survey.
It depends on pure luck not only to encounter the fraud at the “right” time, but also toaccidentally capture fraud-related data, along with fraud information that can be matchedwith the survey data (e.g., identification of victims). Although the odds that a particularsurvey satisfies these conditions are negligible, there may exist such surveys somewherein the world. All that is needed is to search accidental data collection among available
household surveys by using specific widespread frauds as a marker and to pay attention
to such a possibility in future surveys.
The proactive survey captures household behaviors corresponding to potential
frauds, about which only incomplete information is available (e.g., the authorities’). For
example, in the case where recruitment agencies’ fraud is known to be prevalent, thesurvey can ask questions not only about realized migration (e.g., migrant’s attributes,
remittances), but also about household efforts to send a migrant laborer, including nonTrahan, Marquart, and Mullings 2005). Large-scale general victim surveys (e.g., ICVS)
potentially capture not only fraud victims and non-victims, but also individuals on whom
fraud attempts were not made (not shown in Figure 1) (e.g., Titus, Heinzelmann, andBoyle 1995); this is also the case for large-scale household surveys (e.g., LSMS) if a
fraud-victimization module is added.

8materialized ones. 14 The proactive design is effective only in fraud-prone locales. With arisk of missing stage 3 and fraud-related data, a cross-cutting design for other purposes(e.g., realized migration) is recommended.
Compared to the proactive design, the reactive survey using richer priorinformation about potential fraud has significant advantages: The probability of covering
stage 3 is much higher, survey questions can be more concrete and focused, and it can beapplied in any locale (it is a cross-cutting design by definition). The possibility of using areactive design is more restrictive, however, for the following reasons. First, it strongly
relies on researchers’ and practitioners’ flexibility. It probably fits best in researchers’
small-scale surveys with limited institutional constraints. Second, it depends on
researchers’ strong sensitivity and luck to notice a weak sign of the potential fraud earlier
than attempted victims do. Third, researchers must be truly unsure of their suspicion;
otherwise, they instead should report it to the authorities.
C. Victim endogeneity
To identify ex ante and ex post impacts of endogenous fraud victimization,
researchers can use IVs that determine the attempted victim’s decision in stage 2, but do
not directly influence the outcome of interest in stages 3 and 4, respectively. 15 Good
candidates for IVs include factors that alter the attempted victim’s perception about the
fraud attempt and/or the benefit-cost calculation in his/her decision making. This isespecially so for an ex ante impact analysis, because the attempted victim has not yet
considered such factors as those determining his/her realized status.
In practice, some information about frauds and/or fraudsters needs to be collected,
and tradeoffs exist between ex post and ex ante surveys. On the one hand, in the ex post
survey, researchers who can identify realized frauds (and maybe also fraudsters) cancollect detailed, specific data; they need to rely on respondents’ retrospection about stage
2, however, which might involve considerable misreporting, as discussed above. On the14

It is even possible to design a panel survey with many waves with short intervals – tobe set depending on stage 3’s duration – hopefully covering different victimization stagesof the same household. Such detailed data are extremely useful to dissect the mechanismof fraud victimization.
15
Alternatively, a natural experiment such that attempted victims’ decisions are
considered to be almost random with respect to the outcome of interest can be looked for,especially in the passive design.

9other hand, although such misreporting is not a major concern in the ex ante survey,
researchers employing the proactive design, with limited information about specificfrauds, necessarily rely on less focused questions; this problem is smaller in the reactive
design using richer prior information.
III. Fijian recruitment fraud and ex ante victim data
Since the invasion of Iraq in 2003, U.K.- and U.S.-based private security
companies have been 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 thejobs are dangerous. With a large pool of former army personnel, Fiji has been a major
labor supplier. Even though some casualties had been reported, the Fijian government
welcomed this movement as a solution for its unemployment. In late 2004 and early 2005,
a Fijian recruitment agency conducted the largest recruitment drive in the country. All
healthy males between the ages of 18 and 60 were eligible to apply. According to news
media, the agency collected US$2 million fees from at least 20,000 applicants.
In June-September 2005, I conducted a survey among 906 randomly selectedhouseholds in 43 native Fijian villages in Cakaudrove Province, which was extensively
covered by the agency’s recruitment drive. 16 The sampling design was not affected by job
applications at all. 17 The survey was designed to collect information about demographics,
assets, production, income, and transfers (but not consumption). While I was pretesting
the questionnaires in May 2005, I became aware of the unusual recruitment drive andquickly added questions about job applications. I was not very suspicious about potential
16

Fiji is divided almost evenly between native Fijians and Indo-Fijians. My study focuseson native Fijians. The fraudulent recruitment agency is staffed by only native Fijians andits recruitment drive in rural areas covered native Fijian villages only on the three largest
islands in the country – Viti Levu, Vanua Levu, and Taveuni. Cakaudrove 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.
17
There are 5,117 households in 134 native Fijian villages in the province (in the
population). In each of 16 districts, villages were intentionally chosen to cover distinct
environmental and economic conditions, before I became aware of the recruitment drive.
In each village, households were stratified by a smallest kin-group unit (sub-linage or
extended family, locally known as tokatoka, Ravuvu 1983) and the combination ofleadership status (e.g., kin leaders) and major asset holdings (e.g., shops); in each stratum,
households were randomly sampled.

10
fraudulence. During interviews several months after people’s application decisions,
respondents were still unaware of the fraudulence (stage 3). In this way, I accidentally
implemented the reactive design. This household survey with a module on one specificpotential fraud combines advantages of the different types of surveys discussed above. In
particular, as victims were defrauded of similar amounts of money, heterogeneity in theseverity of victimization is very small, further sharpening the identification ofdeterminants and impacts of victimization.
As I became more aware of this recruitment agency (especially through aninterview with its employees), my suspicion about potential fraudulence grew during thesurvey. Right after the survey was completed, news media started to report its
fraudulence, as most applicants got neither a job nor a refund (stage 4 began). At thattime, the agency’s director, who originated from the study region, had already left thecountry. 18 Unfortunately, the Fijian coup in December 2006 precluded me fromconducting the ex post victim survey I had planned. As such, quite distinct from extant
fraud studies on ex post victim data, I collected only ex ante victim data.
IV. Determinants of fraud victimization
A. Descriptive statistics
The empirical analysis covers 1,247 males aged between 18 and 60, who were
eligible for the jobs offered by the recruitment agency; this sample includes 787
households (87% of the whole sample) in all 43 sample villages (see panel A of Table1). 19 All these eligible individuals/households are attempted victims; the fraud attempt
was at least indirectly made on all individuals in the region, because individuals invillages uncovered by the recruitment drive were privately informed of the job
opportunity (which villages in the sample were directly covered is unknown). No
respondents had difficulty answering questions about their job applications; 238
individuals (19% of the eligible) of 212 households (27% of households with eligible

18

According to news media, the director returned to Fiji in late 2009, and a police
investigation is still in process at the time of this writing. Thus, fraud victims examined inthis paper are not legally “fraud victims.”
19
The mean monthly household pre-transfer income – including a very small amount ofpublic transfers (mostly pension) – is F$1,703, or F$290 per capita. Farming accounts for
two thirds of total income, followed by fishing, handicrafts, and permanent wage labor.

11
members, or 23% of the whole sample) applied (panel B). Although 36% of households
have more than one eligible individual, only one application was made by over 90% ofhouseholds with an applicant(s); that is, in most cases each household selected one
migrant laborer, indicating that migration is a collective family decision.
In order to apply, an average applicant spent F$230 (F$1 = US$.60), and an
average victimized household spent F$242 (close to the mean monthly per-capita
income). 20 This amount includes application fees and all other related expenses,
especially for transportation; to file an application, applicants travelled to the recruitment
agency’s office in the biggest town (with a population of about 3,000) in the province,and many revisited to check their status (most sample villages lack phone access). 21
B. Econometric specification
Who was vulnerable to this recruitment fraud? I estimate determinants of job
application among eligible individuals and households, using the following models:

d ihv

1
1
1
2
1
1
x ihv
x ihv
x v1  Į 32
x v2  Iv  eihv , Į 12 Į 21 x hv  Į 31
Į01  Į 11

(1.1)

d hv

1
2
2
2
2
x hv
x v1  Į 32
x v2  Iv  ehv ,D 02  Į 112 x hv
 Į 12 Į 22 x hv  Į 31

(1.2)

where i, h, and v stand for individual, household, and village, respectively; dihv and dhv,
respectively, are a dummy variable for job application for eligible individual i and any
eligible member of household h; ࢞૚࢏ࢎ࢜ and ࢞૛࢏ࢎ࢜ , respectively, are a vector of individual
factors for individual i and household members other than individual i; ࢞૚ࢎ࢜ and ࢞૛ࢎ࢜ ,respectively, are a vector of eligible and non-eligible members of household h; xhv is avector of household factors; ࢞૚࢜ , and ࢞૛࢜ , respectively, are a vector of village factors
unrelated and related to the fraudster; ‫׋‬v is unobservable village factors that determine job
application; and eihv and ehv are residuals. Following the migration literature, potential
migrants and other members staying home are differentiated. Equations (1.1) and (1.2)
are estimated by a linear probability model (LPM) and probit.
20

Simple extrapolation suggests that approximately 1,540 individuals and 1,300
households were victimized across the whole province, and the total loss reached over
F$350,000, which is comparable to media reports.
21
At the time of interviews, 80% of applicants were still waiting for the result, 10% were
accepted, 8% were rejected, and 2% withdrew their application. None of the accepted
applicants got the job. I repeated all regression analyses for applicants whose status was
still unknown, finding results very similar to those presented below.

12
The selection of potential determinants follows the literature on both migrationand crime victimization. 22 All explanatory variables are listed in Table 2, along with theirdescriptive statistics. First, individual factors capture demographics (headship and age), 23
health (disability), education (adults’ secondary education), and employment (permanent
wage labor). 24 Good health is an eligibility criterion for the job other than sex-age.
Permanent employment, the opportunities for which are very limited and mostly in the
public and tourism sectors, is considered exogenous, because individuals cannot flexibly
adjust it (its status did not change at all after application decisions were made); dropping
the employment variables significantly alters none of the remaining results (this is also
true for the models discussed in Sections V and VI).
Second, household factors capture demographics (female headship, age ofhousehold head, and the size of four cohorts) and assets (agricultural land), but not
22

Sociologists categorize determinants of victimization into exposure, guardianship,
attractiveness of potential targets, and proximity to potential offenders (Cohen, Kluegel,and Land 1981). The first three are often captured by demographic factors, police access,and asset/income, respectively; clear categorization is not always possible – for example,
some measures, such as age, capture both exposure and attractiveness (e.g., Barslund et al.
2007). Guardianship matters relatively little for fraud victimization; as police stations inthe study area are located with close proximity to local markets, police access is captured
by market access anyway. Proximity to potential offenders is directly captured by theproximity measures defined below. Extant studies of consumer fraud victimization also
highlight the role of cognitive deficiency and social interaction/status as determinants ofvictimization (Lee and Soberon-Ferrer 1997). The former is captured by education, age,and health. As kin-based hierarchy plays a central role in villagers’ lives in Fiji (Turner
1992), I added kin-status variables as covariates, finding no significant results (household
private transfers discussed later also are neutral to kin status, Takasaki forthcoming).
Determinants of migration include demographic factors, assets, location, and migrant
network (e.g., Munshi 2003). Although the Fijian data lack information about a migrant
network in the Middle East, the next section gives evidence that it plays a limited role.
23
Some criminologists find that younger people are more likely than the elderly to be
victimized by consumer frauds, challenging the common belief among practitioners andprofessionals that the elderly are the main victims (Titus, Heinzelmann, and Boyle 1995).
Van Wyk and Mason (2001) attribute this to distinct socialization and risk-taking among
different cohorts. The analysis below shows that job application is neutral to age.
24
Some variables defined for eligible individuals (eligible household members) are notused for other household members (non-eligible household members), because they are
redundant with similar household factors defined next. A potentially important individual
factor that the present data cannot capture is military experience; however, anecdotal
evidence suggests that such individuals are not so common in the sample. Indeed,households with a member currently working in the military are rare.

13
income. Though income is often considered as an important determinant of crime
victimization, it is endogenous as a determinant of job application for the following
reasons: 1) in anticipation of labor migration and remittances, the household may adjust
its earning efforts, and 2) any unobservable factors that determine income, such as skills,
may also influence migration decisions (even income measured before the fraud attempt,
which the present data lack, would be endogenous). Still, household permanent income iscontrolled for by permanent wage labor (of any household members) and land holdings,
as well as household demographic factors. 25 Third, village factors unrelated to thefraudster capture market access (travel time to the closest local market), village size (total
number of households in the village, in the population), welfare, and inequality (villagemean and standard deviation of agricultural land in the whole sample). 26
I conjecture that two eligibility factors – sex-age and health – are significant
determinants. Specifically, households with more eligible male adults are more likely toapply (endowment effect); individuals in households with more eligible members are lesslikely to apply, because others can apply (multiple applications from the same household
are rare, as discussed above). Disabled individuals are less likely to apply. I also
conjecture that own and other family members’ permanent employment discourages job
application, because of the high opportunity cost of migration and its relatively low
benefit with secure employment in the family, respectively. If victimization is associatedwith low welfare, not only permanent employment – both own and others’ – but also landholdings should discourage job applications. Nonsignificant results suggest that welfare
difference within the sample does not differentiate victimization; that is, most households
are so poor that they were potentially attracted by this job opportunity.

25

Land is communally owned by a within-village clan (locally known as mataqali, which
consists of several tokatoka), is privately used, and by law cannot be sold (about 83% of
the country’s total land is communal). Land holdings at the time of the fraud attemptshould be almost the same as those at the time of interviews used here. Information about
non-land asset holdings and shocks such as sickness (other than chronic illness captured
by disability status) prior to the fraud attempt is lacking, however (only information about
sickness in the past one year is available). Thus, the model cannot tell whether job
applications responded to transitory shocks.
26
Many extant studies highlight inequality as a cause of crime (e.g., Bourguignon 2000).

14
Last, village factors related to the fraudster capture the physical and social
distance between the fraudster and attempted victims. The physical distance – measured
by villagers’ travel time to the recruitment agency’s office – determines the travel cost of
lodging an application, as well as the agency’s recruitment drive (indeed, there were no
applicants in six remote villages in the sample). The social distance is captured by thedummy for two villages in the home district of the agency’s director. 27 The director had
built a good reputation there by offering casual labor employment and making school
donations (he was viewed as a hero). 28 I conjecture that as attempted victims become
physically and socially closer to the fraudster, they are more likely to apply, because
applying is less costly and they are more positive about the job (as confirmed in Section
V). These proximity measures serve as excluded IVs in Section VI.
In an alternative specification, all village factors, including ‫׋‬v, are fully controlled
for by village dummies. Similar results of the individual/household factors with village
dummies and observable village factors, as reported next, give evidence thatunobservable village factors are unlikely to cause bias in the latter specification.
C. Estimation results
LPM results are reported in Table 3, where robust standard errors are shown inthe models with village dummies and standard errors are clustered by village (43 clusters)in the models with observable village factors (probit estimates are very similar). First,
eligibility – sex-age and health – and proximity to the fraudster are strong determinants.

27

It is assumed that these two proximity measures, as well as other covariates, areuncorrelated with unobservable village factors ‫׋‬v. Although the recruitment drive can be
related with such factors (the fraudster may have prior information about good targets),the physical distance and the director’s birthplace are not. An exception for the latter may
be found in large cities with strong, crime-driven segregation, where future offenders
commonly originate from certain areas. Rural Fiji is far from such a case.
28
Two pieces of evidence strongly indicate that discounted fees were charged in the
director’s home district. First, the mean cost for application per victimized individual –including transportation – in the director’s home-district villages was about half of thatelsewhere (F$125 vs. F$263). Second, regressing the application cost on the two
proximity variables among victimized individuals shows that it is lower in the fraudster’shome district by F$125 and becomes higher with greater distance (F$31 per 100%
increase); both results are significant at least at a 1% significance level and the R squaredis .24. The next section gives evidence that the home-district dummy captures the social
distance more strongly than discounted fees.

15
In particular, the probability of applying for a job in the fraudster’s home district is higher
by about 34% and a 100% increase in the travel time to the fraudster’s office lowers theprobability by .08-.10 (no other village factors have significant effects). Second, although
individuals in the household with a permanent wage laborer are less likely to apply (theresults in the household-level analysis are statistically weak), neither eligible individual’s
own employment status nor household land holdings are significant (all other factors arealso nonsignificant). Thus, what matters is not one’s own opportunity cost of migration,
but secure employment in the family, and households at any welfare level are vulnerable.
V. Reasons for fraud victimization
A. Descriptive statistics
The survey asked each applicant why he applied (up to three reasons) and eachhousehold with no applicants why none of its eligible members applied (up to threereasons) (panels B and C, respectively, of Table 1). 29,30 While almost 80% of applicants
were attracted by high salary, many households did not apply because of a concern about
qualifications and high fees. While security mattered for 14% of non-applicant
households, almost 20% of them questioned the contract’s authenticity; that is, about
12% of households in the whole eligible sample took precautions to avoid victimization.
Job type and location (abroad) were also relatively common reasons for both applicationand non-application; that is, individual job preference mattered in the opposite way. In
contrast, alternative activities and acquaintance with someone who already had gotten thesame job were uncommon reasons for both application and non-application, reflecting thesmall opportunity cost of migration, as found above, and a limited migrant network in the
Middle East (in another words, the recruitment agency was the only network available).
B. Econometric specification
To examine which reasons mattered for whom, I estimate determinants of reasonsfor application among applicant individuals and reasons for non-application among nonapplicant households, using the following models:
29

Directly asking reasons in this manner is effective only in the ex ante victim survey;
misreporting is most likely in the ex post victim survey. It also much better works for a
victim survey on a specific fraud than one covering various frauds.
30
Panel B also shows reasons for applying at the household level. Not surprisingly, theresults are almost the same as the individual-level results.

16
j
pihv

j
1
1j 2
1j 1
1j 2
D 01 j  Į 111j x ihv
 Į 12x ihv  Į 21j x hv  Į 31
x v  Į32xv  Ivj  eihv
if dihv = 1, (2.1)

phvj

1
2j 2
2j 1
D 02 j  Į 112j x hv
 Į 12x hv  Į 22j x hv  Į 31
x v  Į322 j xv2  Ivj  ehvj if dhv = 0,

௝

(2.2)

௝

where ‫݌‬௜௛௩ and ‫݌‬௛௩ , respectively, are a dummy for the reason j for individual i's
application and household h’s non-application (three and six most common reasons
discussed above are considered). 31 Equations (2.1) and (2.2) are estimated by LPM andprobit. All explanatory variables in equations (2.1) and (2.2), respectively, are the sameas those in (1.1) and (1.2), with an exception that the fraudster’s physical distance is notincluded in ‫ݔ‬௩ଶ (which is a scalar). The analyses including the fraudster’s physicaldistance as a control show that it has no significant effects; that is, although it strongly
determines individuals’/households’ application decisions, as found above, it does notaffect the reasons for their decisions. 32 Excluding this variable does not significantly alter
results of the remaining variables, either. These subsample analyses involve potential
selection bias, because application decisions are endogenous. I estimate a selection probit
model (Greene 2000, p.857), where the sample-selection equations for equations (2.1)
and (2.2), respectively, are (1.1) and (1.2) with the fraudster’s physical distance as an
excluded variable for identification. Results are very similar to the probit estimates. 33
C. Estimation results
LPM estimates with observed village factors are reported in Table 4, where
standard errors are clustered by village (probit estimates are very similar). 34 The
following results buttress earlier findings on determinants of job application. First, asmaking a job application is neutral to welfare level, high salary is attractive at every
31

The regression analysis of reasons for applying at the household level – equation (2.2)
for dhv = 1 – yields very similar results to the individual-level results.
32
The nonsignificant result for high fees as a reason for non-application may seemcounterintuitive. However, what the survey asked about is application fees unrelated tothe physical distance, not application costs including transportation (application fees are
separately controlled for by the fraudster’s home district dummy, as discussed above).
33
Estimated covariance of error terms in the selection probit is not significantly different
from zero in most cases; in the few cases where it is significant, the estimated coefficientsare very similar to the probit results. Thus, selection bias is unlikely to be a major
concern.
34
In equation (2.1), the dummy for eligible individual’s disability and the dummy forfemale head are dropped, because their variations in the subsample are limited and they
perfectly predict some reasons.

17
welfare level (measured by permanent wage labor and land holdings); at the same time,
the poor do not apply because of high fees. Second, applicants in the fraudster’s home
district are more likely to have been attracted by the job type and location than others
were; non-applicants there are less likely to have been concerned about qualification, job
type, and security. Thus, the fraudster’s reputation altered peoples’ perceptions about theprospective job, as hypothesized above. 35 Third, households with more eligible males areless likely to be cautious (precaution is neutral to other factors). That is, the endowment
effect shapes victimization, partly because it alters people’s taking precautions. 36
VI. Ex ante impacts of fraud victimization on household private transfers
A. Descriptive statistics
Most households in the eligible sample had participated in private transfers in thepast one month (i.e., several months after their job application decision) (see Table 5). 37
Household private transfers consist of cash, inkind, and labor time; in the calculation of
total transfers, labor time is monetized using the mean daily wage of casual labor in eachvillage (it ranges from F$10 to F$20, with a median of F$15). The sample means of
35

The dummy for the fraudster’s home district is significant for neither high salary norhigh fees, suggesting that it is likely to capture the social distance more strongly than
discounted fees.
36
The following findings about people’s preferences are obtained. First, security
concerns are stronger in a village with smaller land endowment and larger inequality.
This indicates that land distribution shapes risk preference (when village dummies areused as controls, disability status, permanent employment, and the number of eligible
males, which show significant results in Table 4, lose their statistical significance).
Second, the job type is attractive to applicants (with small land holdings) in a small
village, it is not attractive to non-applicants in a large village with large land holdings,
and the job location is attractive for applicants in a small village with small land holdings.
This suggests that village economic opportunities shape preferences for job type andlocation. Other significant results are as follows: non-applicant households with an oldhead are more likely to be concerned about qualifications; households with a disabled
non-eligible member are more concerned about security; and high salary strongly attracts
households with a disabled member.
37
Respondents were asked about each major transfer received from and given to otherhouseholds in the past month and then in the past year. Although the latter annual transfer
data contain transfers made in part of stage 1, all of stage 2, and part of stage 3, thesethree cannot be distinguished from each other (the month when transfers were made is
unknown). The ex ante impact analysis in this section focuses on transfers in the pastmonth (stage 3). Informal loans were much less common and much smaller than gifttransfers examined here.

18
monthly gross private transfers received and given are F$141 and F$104, respectively,
the difference of which mainly comes from across-village cash and inkind transfers.
Specifically, although most households participated in within-village transfers and theamounts received and given were almost balanced, across-village transfers received weremore common and larger than those given; most across-village transfers were cash andinkind (exchanged through kin network Takasaki forthcoming), while labor-timetransfers consisted of one quarter of within-village transfers. Almost all across-villagetransfers were domestic ones; distinct from smaller island states, such as Tonga, where
overseas migrations and remittances are common (Bertram 1986), international transfers
were negligible among Fijians in the sample. 38
B. Econometric specification
To identify how job application dhv affects household private transfers (ex ante
impacts), I employ the following endogenous dummy variable model:

y hv

12
E 0  E1d hv  ȕ1 x hv
 ȕ 2 x hv  ȕ 31 x v1  U v  H hv ,

(3)

d hv

12
D 0  Į1 x hv
 Į 2 x hv  Į 31 x v1  Į 32 x v2  V v  Z hv ,(4)

where yhv is the amount of net transfers received (gross transfers received minus grosstransfers given) by household h in the past month; ࢞૚૛ࢎ࢜ is a vector of individual factors forany household members summarized in Table 2; ȡv and ıv are unobservable village
factors that determine private transfers and job application, respectively; and İhv and Ȧhv
are residuals. 39 For the same reasons given above, observed earned income is not

38

In contrast, according to the household survey conducted in five major towns and ninevillages in the main island Viti Levu in 2005 by the World Bank (2006a), 26% of 211
native Fijian households had overseas migrants and 34% received overseas remittances.
This indicates a potentially significant difference in Fijians’ transfer patterns between themain island and other islands and between urban and rural areas. The World Bank
(2006a) also attributes the survey result to growing international labor migration,
especially to the Middle East, emphasizing its importance. This paper’s finding on
recruitment fraud highlights a significant pitfall in this development path andcorresponding policy making.
39
This model can also estimate ex post impacts of endogenous fraud victimization. Witha lack of ex post victim data, I cannot examine how households – both victims and nonvictims – made readjustments in their private transfers after becoming aware of thefraudulence (in stage 4).

19
included as a household factor. 40 Note that ࢞૚૛ࢎ࢜ is the only difference in equation (4) from
(1.2).
The substitution effect indicates that domestic private transfers are reduced byprospective remittances, i.e., negative ȕ1 (constant effect is assumed). The endogeneity ofhousehold application decision dhv is apparent: It is influenced by unobservable factors
such as preference and skills in İhv that determine private transfers. Since households that
tend to send a migrant laborer are likely to be those that rely more on private transfers,
the OLS estimates for ȕ1 are expected to be biased upward. The identifying assumption
for the IV estimation is that proximities to the fraudster ࢞૛࢜ affect private transfers only
through job application; that is, it is uncorrelated with any unobservable village factors ȡv,as well as unobservable household factors İhv. Moffitt (1996) clarifies the assumptions
necessary for the two-stage least squares (2SLS) estimator to represent a causal effect ofan endogenous dummy variable at the individual level with an aggregate-level IV (thesame is illustrated by Wooldridge 2002, pp. 133-134).
First, preferential treatment in the fraudster’s home district was made, not becauseof village characteristics that could be related with unobservable village factors affecting
private transfers, but because of the place of his birth. Still, if ȡv is different between the
fraudster’s home district and other districts in the absence of the fraud attempt, theexclusion restriction does not hold. With a lack of transfer data prior to the fraud attempt(stage 1), showing direct counterevidence against this possibility is infeasible. A piece of
indirect counterevidence is that self-reported measures of village social problems, which
serve as proxies for unobservable social ties determining private transfers, are notsignificantly different across the districts. 41

40

With a lack of valid IVs, controlling for the income endogeneity is infeasible (cf.
Jensen 2004; Juarez 2009; Kazianga 2006). In equation (3), permanent income iscaptured by a set of controls, but transitory income is not, as discussed above.
41
Villagers’ social ties not only directly determine within-village transfers, but also
indirectly influence across-village transfers; for example, weak social ties in the village
may be associated with weak or strong household transfer networks out of the village.
The social-problem measures are according to village leaders’ subjective assessments
about low attendance at communal activities/ceremonies, decreasing sharing practices,and conflicts among households/villages (incidence was reported by 53%, 37%, and 26%,
respectively, of the sample villages).

20
Next, the identifying assumption for the fraudster’s physical distance does nothold if the agency’s location coincides with other facilities that influence private transfers
or other households with whom distinct across-village transfers are made. The former
include postal offices where villagers can receive/send cash and inkind from/to cities.
Most postal offices are located closely to local markets, the access to which is separately
controlled for. The latter transfers exchanged with households in the town are notsignificantly different from those in other locations. The social-problem measures
discussed above are also uncorrelated with the fraudster’s physical distance. An
assumption that ‫ݔ‬௩ଶ is uncorrelated with İhv is supported analogously.
I employ three specifications of 2SLS: an overidentified model with thefraudster’s social and physical distance as excluded IVs and just-identified models 1 and
2 with the former and the latter, respectively, as a unique excluded IV. Although theover-identified model is not unbiased in small samples, the just-identified models areapproximately median-unbiased. I also estimate the limited information maximum
likelihood (LIML) estimator for the over-identified model, which is less precise than
2SLS, but less biased (under constant effects) (e.g., Davidson and MacKinnon 1993;
Mariano 2001). Standard errors are clustered by village (Shore-Sheppard 1996 highlights
the importance of this cluster adjustment with an aggregate-level IV).
Two extensions are made to further test the substitution effect. First, I compare
gross transfers received and given. If potential donors respond to prospective remittances,
households with an applicant should receive less gross transfers than others do. 42 Second,
I compare within- and across-village transfers received. Since international remittances
are the more “direct” substitute for the latter cash and inkind transfers, job application
should reduce them more than the former. 43

42

If households received more transfers to finance their applications several months ago,
they are likely to have received smaller transfers in the past month, even in the absence ofthe substitution effect, and may offer larger transfers for reciprocation. Almost no
respondents, however, reported job application as a reason for the transfers they received
in the past year, which include those received in stage 2. Consumption and ritual (funeral,
wedding, and other ritual events) were the most common reasons for transfers made inboth the past month and the past year (Takasaki forthcoming).
43
While an average household in the sample is a net recipient of across-village transfers,
within-village transfers are balanced between receiving and giving. Then, reducing

21
Distinct from gross within-village transfers received, many households are notrecipients of across-village transfers (Table 5). I first employ 2SLS for amounts withmany zeros. This model captures the combination of impacts on receipt and amountreceived conditional on receipt, without making any distributional assumptions (Angrist
2001). 44 Next, to identify the impact on receipt, I employ 2SLS with a dummy for receiptof transfers as a dependent variable in equation (3).
C. Estimation results
The estimated coefficients (marginal effects) of job application (ȕ1) are reported
in Table 6. Column (1) shows results for net transfers received, with no exogenous૚
covariates – ࢞૚૛ࢎ࢜ , xhv, and ࢞࢜ . In all cases, the excluded IVs strongly determine job

application in the first-stage equation (4) (as in equation 1.2 above). The fraudster’ssocial distance is a much stronger determinant than the physical distance (53.1 vs. 13.9 of
F value); when these two are jointly used, the F value is 26.6 and the overidentification
test (Hansen J statistic) does not reject a null hypothesis that they are uncorrelated withthe error term in equation (3). Although the OLS estimates of ȕ1 are negative and small inmagnitude with no statistical significance, the 2SLS estimates are much larger inmagnitude with strong statistical significance in all specifications, and their point
estimates across specifications are in a similar range; in particular, the LIML estimates
are almost the same as the 2SLS estimates. 45 When the exogenous covariates are added(column 2), the estimation results for ȕ1 do not significantly change (those of theexogenous covariates – for the OLS and the over-identified 2SLS – are reported in thetransfers to households in the village is probably less costly for potential donors outside
the village, who rely less on transfers received from them, than those in the same village.
44
Although a two-part model is commonly used in empirical works on private transfers,
the identifying assumption does not hold in 2SLS conditional on positive (receipt),because excluded IVs affect participation (Angrist 2001). A tobit model relies on theassumption of the normally distributed error term in equation (3), against whichcounterevidence is found below. For the robustness check, I analyze not only the level ofgross transfers received, the results of which are shown below, but also their natural log,
finding qualitatively the same results.
45
Table 6 also reports results of a reduced-form analysis of equation (3), where theendogenous variable dhv is replaced with the excluded IVs ࢞૛࢜ . Their estimatedcoefficients take the expected signs and are statistically significant (the fraudster’s
physical distance in the overidentified model is significant at almost a 10% significancelevel).

22
Appendix). 46 These results buttress the robustness of the estimated marginal effects of the
IV regressions. Households with a job applicant receive smaller net transfers by F$139171 – a range comparable to the mean gross transfers received – than others do; incontrast, the OLS estimates ignoring victim endogeneity are strongly biased upward.
The estimation results of gross transfers received and given strongly confirm myconjecture (columns 3-6). According to the 2SLS and LIML estimates, the estimatedmarginal effects of job application on gross transfers received are very close to those on
net transfers received, and those on gross transfers given are very small and statisticallynonsignificant; 47 in contrast, only the latter results are significant in the OLS estimates.
The estimated marginal effects of job application on gross within- and acrossvillage transfers received are reported in columns (2)-(4) of Table 7. 48 As I conjectured,prospective remittances more strongly reduce across-village transfers than within-villagetransfers. In particular, while results of within-village transfers lose their statisticalsignificance with the exogenous covariates added (panel B), those of across-villagetransfers do not significantly change; the latter results are very similar to each otheracross specifications. The probability of receiving across-village transfers for householdswith a job applicant is almost .5 lower than others’, 49 and they receive smaller amounts
by about F$100. These results suggest that the significant reduction in total grosstransfers received (column 1, which replicates the results in columns 3 and 4 of Table 6)

46

The estimated coefficients of the exogenous covariates are very similar to each otherbetween the OLS and 2SLS estimates, buttressing their exogeneity (Appendix). Although
households with a disabled member are big recipients, those with permanent employment
and large land holdings (i.e., high permanent income) are big donors.
47
The corresponding reduced-form results are statistically significant only for grosstransfers received. Results of the just-identified model 2 are weak: The estimated ȕ1
becomes much smaller with the exogenous covariates added, losing its statisticalsignificance at a conventional level, and the corresponding reduced-form results are also
statistically nonsignificant; in contrast, results of the other IV regressions are very similar
to each other. Since these results suggest that the fraudster’s physical distance is arelatively weak IV, the remaining analyses exclude the just-identified model 2.
48
In all specifications, the (joint) significance tests for excluded IV(s) show strong results
and overidentification tests are satisfactory.
49
The bivariate probit estimate is considerably smaller (about .3 marginal effect),questioning the assumption of the bivariate normal distribution of the error terms.

23
comes mainly from the termination of receipt of across-village transfers. 50 Overall, theseresults suggest that a reduction in the private transfers that victims received during all ofstage 3 (several months) was greater than the money they lost to apply for the job.
VII. Conclusion
Fraud studies rely on potentially underreported/misreported victim data indeveloped countries, virtually ignoring developing countries. This paper proposedcollecting victim data before attempted victims become aware of the fraudulence (ex ante
victim data), and examined recruitment fraud in rural Fiji. Almost one quarter ofhouseholds were defrauded of application fees for labor migration. Although all
households at any welfare level are vulnerable to victimization (all are poor), households
without secure domestic employment, with more eligible members (male healthy adults),
and with “close” proximity to the fraudster (which shapes their perceptions and benefitcost calculation regarding the job opportunity) are more vulnerable.
Although defrauded money among the poor is not a large amount, the muchbigger problem is that people’s behaviors are adversely altered by their false expectations
induced by the fraud (ex ante impacts). In particular, recruitment-fraud victims lost
domestic private transfers they could have received from other households outside thevillage, as a result of the substitution for prospective international remittances. This is
one example of the potentially broad adverse effects of fraud victimization. Fraud in
productive activities may further persistent poverty and underdevelopment (i.e., fraudinduced poverty traps). Then, poverty alleviation as a long-run solution for victimization– especially through better domestic employment – is stuck. Controlling for victim
endogeneity by using instruments – such as proximity to potential fraudsters – is crucial
to identify fraud impacts; failing to do so gives rise to strong bias.
Is fraud prevalent in other developing areas? As a very basic understanding islacking, conventional fraud-victim data first need to be collected in developing countries.

50

Consider a counterfactual with no job application (victimization). The mean amount ofgross transfers received in the whole sample would be F$48 (actual means) + 27%(proportion of victimized households) * F$100 = F$75. The mean amount in the casewhere only the proportion of recipients changes, with no change in the mean amountreceived among recipients, would be (F$48 / 30% (actual proportion of recipients)) *(30% + 27% * 50%) = F$70.

24
The recent expansion of data collection about consumer fraud (e.g., ICVS) is important,but misses bigger problems. Fortunately, it is straightforward to expand available victim
surveys and household surveys with a victimization module (e.g., LSMS) to cover abroader range of frauds, especially in productive activities. Such ex post victim data,
however, are most likely to incompletely or even mistakenly capture the prevalence andseriousness of frauds and cannot be used to identify determinants and ex ante impacts offraud victimization. Ex ante victim data can be a breakthrough in fraud study and policy.
Data collection requires a totally different approach; the passive, proactive, and reactive
designs are all demanding and rely heavily on chance, but none of them are infeasible.
The paper’s ex ante victim data analysis opens up new, promising avenues forresearch. The following three sets of questions about methodology, the victimization
mechanism, and its impacts are of great importance:
1) What difference can ex ante victim data really make? How different would thepaper’s empirical findings have been if ex post victim data had been analyzed?
2) How can the mechanism of fraud victimization, in particular, attempted victims’decisions, be dissected? What policies can effectively prevent victimization?
Structural modeling combined with ex ante victim data seems very promising.
3) How broadly, seriously, and persistently do fraud and fraud victimization affectpeoples’ behaviors and welfare, before and after they become aware of thefraudulence? For example, does informal risk sharing work against fraud
victimization (victims’ bad decision)? How does victimization influence subsequent
production and investments? Does fraud cause poverty traps?
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29
Figure 1. Conceptual framework of fraud victimization.
Successful attempt
FraudsterFraud attempt
Unsuccessful attempt

Stage 1

Fraud attempt ismadeFraudulence is
detectedStage 2

Stage 3

Stage 4

Individual makes
decision

Ex ante victim
data collected

Ex post victim
data collected

Ex ante impacts

Ex post impacts

Prospective
employment

Fraud victim<Take an offer>

Individual

Attempted victim(ex post)<Not take an offer>

Prospective nonemployment

Fraud non-victim

30
Table 1. Recruitment fraud victimization in rural Fiji.
Individual
A. Whole sample
No. eligible male adults in the household:
One
Two
Three
Four
Five
Six
Victimization (job application)
Mean monthly household pre-transfer
income (F$)
No. observations
B. Victim sample
No. victims
One
Two
Three
Four
Mean application costs (F$)

19%

Household

64%
22%
10%
3%
1.0%
0.6%
27%
1703
(2092)

1247

787

230
(127)

91%
7%
2%
0.5%
242
(125)

79%
24%
16%
6%
9%

79%
24%
16%
7%
8%

No. observations
C. Non-victim sample
Reasons for non-application:
Unqualified
Fees
Job type
AbroadSecurity
Precaution
Alternative
Do not know anyone who already works

238

212

No. observations

1009

Reasons for application:
SalaryJob type
AbroadNo alternative
Know someone who already works

38%
26%
31%
33%
14%
19%
10%
9%
575

Notes : Individual and household sample means are shown; allpercentages are sample proportions. Standard deviations are inparentheses.

31
Table 2. Means of explanatory variables.
IndividualHousehold

Eligible individual(x 1 ihv )Eligible household
members (x 1 hv )Individual factors:

Household head dummy
Age
Mean age
Disability dummy
Adults' secondary education dummy
Permanent wage labor dummy

0.50 (0.50)
36.0 (12.1)
0.07 (0.26)
0.57 (0.50)
0.09 (0.29)
Other household
members (x 2 ihv )Disability dummy
Adults' secondary education dummy
Permanent wage labor dummy

0.30 (0.46)
0.78 (0.42)
0.10 (0.31)

37.6 (8.8)
0.11 (0.31)
0.65 (0.48)
0.14 (0.35)
Non-eligible
householdmembers (x 2 hv )0.22 (0.42)
0.65 (0.48)
0.04 (0.20)
Any household
members (x 12 hv )Disability dummy
Adults' secondary education dummy
Permanent wage labor dummyHousehold factors (x hv ):Female head dummy
Age of household heada
No. <18 years old
No. males 18-60 years old
No. females 18-60 years old
No. >60 years old
Agricultural land (acres)
Village factors unrelated to the fraudster (x 1 v ):Market access (min)No. households in village
Village mean agricultural land (acres)b
Village standard deviation ofagricultural land (acres)b
Village factors related to the fraudster (x 2 v ):Fraudster's home district dummy
Access to fraudster (min)No. observations

0.29 (0.45)
0.83 (0.38)
0.17 (0.38)

0.04 (0.21)
50.8 (12.9)
2.47 (2.07)
2.14 (1.25)
1.53 (0.97)
0.42 (0.68)
3.21 (4.95)

0.05 (0.21)
49.2 (13.6)
2.50 (1.97)
1.58 (0.94)
1.35 (0.85)
0.40 (0.68)
2.95 (4.85)

79.5 (66.2)
53.3 (21.6)

81.0 (67.3)
53.5 (21.5)

3.09 (1.81)

3.06 (1.80)

3.57 (3.98)

3.51 (3.94)

0.08 (0.28)
194.9 (125.8)

0.09 (0.28)
195.9 (127.3)

1247

787

Notes : Standard deviations are in parentheses.
aNo. of observations are 1244 individuals and 785 households.
bMeans/standard deviations including households with no eligible members.

32
Table 3. Determinants of recruitment fraud victimization - linear probability model.
Individual(1)
Individual factors:
Household head dummy
Log of age(2)
Eligible individual
0.051
0.053
(0.039)(0.033)-0.084-0.084(0.047)(0.057)Log of mean age
Disability dummy
Adults' secondary education
dummy
Permanent wage labor dummy

Disability dummy
Adults' secondary education
dummy
Permanent wage labor dummyHousehold factors:
Female head dummy
Log of age of household headNo. <18 years old
No. males 18-60 years old
No. females 18-60 years old
No. >60 years old
Log of agricultural land (acres)-0.076 *-0.083 *(0.035)(0.031)0.030
0.029(0.023)(0.025)-0.023-0.048(0.046)(0.042)Other household members-0.016-0.023(0.026)(0.025)0.032
0.030(0.029)(0.033)-0.071 *-0.104 **(0.035)(0.034)-0.009(0.054)-0.057(0.057)0.001(0.006)-0.020(0.011)
0.003(0.014)0.023(0.019)-0.005(0.017)

Village factors unrelated to the fraudster:Log of market access (min)Log of no. households in thevillageLog of village mean agriculturalland (acres)
Log of village standard deviationof agricultural land (acres)
Village factors related to the fraudster:Fraudster's home districtdummy
Log of access to fraudster (min)0.003(0.055)-0.024(0.051)
0.002(0.006)-0.023(0.012)0.004(0.017)0.026(0.022)-0.004(0.018)Household(3)(4)
Eligible household members-0.025-0.025(0.089)(0.077)-0.134 **-0.135 **(0.049)(0.047)0.054
0.054(0.033)(0.027)-0.068-0.100(0.053)(0.054)Non-eligible household members
0.005-0.004(0.042)(0.041)0.028
0.019(0.036)(0.044)-0.060-0.146(0.074)(0.083)-0.035(0.066)-0.110(0.077)-0.003(0.013)0.057 **
(0.020)0.020(0.023)0.030(0.028)-0.015(0.025)-0.022(0.082)-0.066(0.079)-0.001(0.012)
0.056 *
(0.024)0.018(0.027)0.030(0.030)-0.023(0.023)-0.007(0.012)0.028(0.034)-0.011(0.123)0.015(0.060)-0.009(0.017)0.035(0.048)-0.029(0.167)0.015(0.087)0.340 ***
(0.054)-0.081 **(0.029)0.348 ***
(0.081)-0.102 *(0.039)Village dummies
F
R squaredYes
6.4 ***
0.207

No
12.8 ***
0.131

No. observations

1244

1244

Yes
7.1 ***
0.252

No
8.1 ***
0.141

785

785

Notes : Robust standard errors are shown in columns (1) and (3); standard errors are clustered by village incolumns (2) and (4). Other control not shown here is constant.
*** Significant at 0.1 percent level.
** Significant at 1 percent level.
* Significant at 5 percent level.

33

Table 4. Reasons for recruitment fraud victimization and non-victimization - linear probability model.
Reasons for application among
victimized individuals
Salary(1)
Individual factors:
Household head dummy
Log of age

Job type

Abroad(2)(3)
Eligible individual

0.037(0.105)-0.129(0.103)-0.193(0.127)0.046(0.139)Security

(6)
(7)
(8)
Eligible household members

Precaution
(9)

0.170
(0.089)0.027(0.038)-0.020(0.091)-0.070(0.054)-0.062(0.045)-0.017(0.072)0.000(0.053)0.015(0.048)-0.014(0.073)-0.064(0.051)
0.029(0.035)0.052(0.091)-0.018(0.056)0.073(0.048)0.054(0.104)

0.109 *
(0.047)-0.028(0.034)0.005(0.055)0.015(0.042)-0.008(0.036)-0.058(0.068)-0.076(0.182)0.037-0.067(0.116)(0.079)0.287 ***
0.082
(0.080)(0.108)0.034(0.108)-0.169(0.109)-0.029(0.086)0.079(0.075)-0.019(0.056)-0.034(0.082)-0.071(0.047)-0.098(0.076)(continued)

Disability dummy

0.250 **
(0.080)-0.006(0.049)0.053(0.097)

Household factors:
Female head dummy-0.029(0.227)

Abroad0.123(0.090)-0.058(0.041)0.001(0.037)0.052(0.048)-0.047-0.020-0.008(0.056)(0.044)(0.088)0.014-0.019-0.101(0.098)(0.083)(0.063)
Other household members-0.113(0.149)(5)

Job type-0.1490.173
0.012-0.146(0.105)(0.106)(0.117)(0.096)0.035-0.041-0.0310.149 *
(0.078)(0.049)(0.052)(0.059)-0.039-0.022-0.0420.046(0.034)(0.035)(0.043)(0.026)-0.123 **
0.020
0.120
-0.078 *(0.044)(0.056)(0.072)(0.034)Non-eligible household members

Adults' secondary education
dummy
Permanent wage labor dummy

Log of age of household head

Fees

0.046
(0.103)0.077(0.068)-0.084(0.048)-0.047(0.064)

Disability dummy

Adults' secondary education
dummy
Permanent wage labor dummy

Unqualified(4)-0.029(0.112)0.107(0.124)Log of mean age

0.006(0.102)0.005(0.080)0.032(0.194)

Reasons for non-application
among non-victimized households

34

No. <18 years old

0.001(0.011)-0.015(0.024)0.019(0.017)0.010(0.063)-0.023(0.049)-0.007(0.014)0.011(0.035)0.014(0.035)-0.048(0.065)-0.111 *(0.054)-0.018(0.013)-0.015(0.031)
0.002(0.024)-0.045(0.052)0.018(0.033)0.004(0.016)-0.027(0.024)-0.031(0.029)0.043(0.034)-0.049(0.030)-0.014(0.016)-0.020(0.021)
0.026(0.025)0.028(0.034)-0.099 **(0.031)0.026 *
(0.011)
0.002(0.021)0.014(0.024)-0.004(0.040)0.007(0.027)0.006(0.016)-0.005(0.026)-0.023(0.030)-0.008(0.034)0.028(0.041)-0.017(0.012)-0.026 *(0.011)
0.012(0.025)-0.008(0.030)-0.019(0.024)0.007(0.012)-0.051 *(0.019)0.019(0.030)-0.011(0.028)0.033(0.033)Village factors unrelated to the fraudster:Log of market access (min)-0.031(0.030)Log of no. households in the
0.014
village(0.089)Log of village mean agricultural
0.011
land (acres)(0.415)Log of village standard
0.191
deviation of agricultural land
(0.278)-0.040(0.032)-0.191 **(0.069)-0.223(0.282)0.072(0.188)0.014(0.010)-0.133 *(0.061)-0.519 ***(0.139)0.193 *
(0.091)-0.004(0.014)0.006(0.048)0.167(0.150)-0.079(0.093)-0.019(0.023)-0.018(0.057)0.168(0.147)-0.073(0.100)-0.001(0.014)0.140 *
(0.065)0.472 *
(0.214)-0.247(0.143)0.002(0.021)-0.017(0.047)0.022(0.128)0.000(0.079)0.012(0.013)-0.056(0.047)-0.288(0.158)0.221 *
(0.101)0.000(0.013)-0.025(0.040)-0.140(0.119)-0.009(0.072)Village factors related to the fraudster:Fraudster's home district-0.181dummy(0.137)0.168 **
(0.055)

0.093 **
(0.026)-0.200 **(0.057)-0.056(0.049)-0.122 *(0.057)-0.050(0.035)-0.120 *(0.045)-0.023(0.069)F
R squared

7.9 ***
0.187

6.6 ***
0.117

10.1 ***
0.121

6.9 ***
0.084

2.4 **
0.045

2.7 **
0.059

1.3
0.018

4.6 ***
0.087

2.3 *
0.051

238

238

238

621

621

621

621

621

621

No. males 18-60 years old
No. females 18-60 years old
No. >60 years old
Log of agricultural land (acres)

No. observations

Notes : Standard errors are clustered by village. Other control not shown here is constant.
*** Significant at 0.1 percent level.
** Significant at 1 percent level.
* Significant at 5 percent level.

35
Table 5. Household private transfers.
Gross transfers received
Gross transfers given
ParticiMean amounts
ParticiMean amounts
pation(F$)
pation(F$)
All (F$)92%
141 (183)88%
104 (141)Same village89%
93 (126)87%
89 (117)
Other village or city30%
48 (121)15%
15 (58)Cash and inkind (F$)
92%
114 (160)
88%
78 (118)
Same village
89%
70 (105)87%
66 (94)
Other village or city30%
44 (112)15%
12 (52)
Labor (man-day)25%
1.9 (4.8)26%
1.7 (4.3)
Same village24%
1.6 (4.2)25%
1.5 (4.0)
Other village or city4%0.3 (2.1)
4%
0.2 (1.2)
Notes : These are transfers in the past one month. Standard deviations are in parentheses.
No. of observations vary across variables (771-785).

36
Table 6. Estimated marginal effects of recruitment fraud victimization on household private transfers.
Net transfers received

Gross transfers
received

Gross transfers given

(1)

(2)

(3)

(4)

(5)

(6)

-19.4
(15.9)-14.7(16.4)

14.1(19.6)

24.6(20.8)

33.9 *(14.9)

39.4 **(12.7)2SLS - overidentified model-152.7 **(59.1)-158.1 ***(36.0)-180.1 ***(45.0)-141.4 ***(37.1)-26.4(65.0)

17.6(34.5)2SLS - just-identified model 1-162.8 **(74.5)-171.6 ***(39.9)-145.6 ***(26.1)-144.6 ***(29.1)

17.6(86.1)

27.4(36.3)2SLS - just-identified model 2-136.9 *(64.5)-138.5 *(58.3)-233.9 *(99.2)-136.9(87.6)-95.5(77.9)

3.3(60.3)

LIML - overidentified model-152.9 **(59.2)-158.5 ***(36.1)-183.1 ***(46.3)-141.5 ***(37.1)-29.6(68.2)

17.5(34.6)First stage: Fraudster's home
district dummy a

0.37 ***(0.07)

0.34 ***(0.08)First stage: Log of access tofraudster (min)a-0.10 **
(0.03)-0.10 *
(0.04)

Excluded IVs: Fa
Overidentification test:
Hansen J

26.6 ***

28.6 ***

0.12

0.24

0.94

0.01

1.75

0.15

Reduced-form: Fraudster's
home district dummy-62.0 **
(22.9)-62.3 ***
(13.1)-44.5 *
(19.8)-50.0 **
(18.1)

17.6(39.2)

12.3(17.2)

Reduced-form: Log of accessto fraudster (min)

12.2
(7.1)

12.4
(7.5)

26.3 *(10.8)

13.6(11.9)

13.9
(8.5)

1.0
(9.4)

First stage: Fraudster's home
district dummy a

0.43 ***(0.06)

0.42 ***(0.05)

Excluded IV: Fa
Reduced-form: Fraudster's
home district dummy

53.1 ***

69.1 ***-70.7 **
(24.1)-72.1 ***
(11.7)-63.3 ***
(16.1)-60.8 ***
(11.5)

7.6(36.9)

11.5(14.9)First stage: Log of access tofraudster (min)a-0.13 ***
(0.03)-0.14 ***
(0.04)

Excluded IV: Fa
Reduced-form: Log of accessto fraudster (min)13.9 ***

12.4 ***

17.7 *(8.7)

19.6 *(8.3)

30.3 **(10.8)

19.4(10.9)

12.3
(8.9)

-0.5
(8.8)

Fraud victimization dummy:OLS

2SLS - over-identified model:

2SLS - just-identified model 1:

2SLS - just-identified model 2:

Exogenous covariates

No

Yes

No

Yes

No

Yes

No. observations

771

769

772

770

771

769

Notes : Fraudster’s home district dummy and log of access to fraudster are excluded IVs in over-identified model;
fraudster’s home district dummy is an excluded IV in just-identified model 1; and log of access to fraudster is an
excluded IV in just-identified model 2. Standard errors clustered by village are in parentheses. Exogenous
covariates in columns (2), (4), and (6) are those shown in appendix.
*** Significant at 0.1 percent level.
** Significant at 1 percent level.
* Significant at 5 percent level.
aResults in columns (3) and (5), respectively, are almost and exactly the same as those in (1); results in (4)and (6), respectively, are almost and exactly the same as those in (2).

37
Table 7. Estimated marginal effects of recruitment fraud victimization on household
private gross transfers received by location.

A. Models with no exogenous covariates
OLS
2SLS - over-identified model
2SLS - just-identified model 1
LIML - over-identified modelAll

Withinvillage

Amounts

Amounts

Amounts

Receipt

(1)

(2)

(3)

(4)

14.1
(19.6)-180.1 ***(45.0)-145.6 ***(26.1)-183.1 ***(46.3)

7.7(12.6)-57.3 *
(22.8)-49.7 **
(18.2)-57.4 *
(22.8)

5.8(11.2)-117.3 ***(29.3)-94.0 ***
(13.5)-119.3 ***(30.2)-0.01(0.04)-0.48 ***
(0.10)-0.49 ***
(0.07)-0.48 ***
(0.10)-0.33 ***
(0.05)

772

777

784

784

24.6
(20.8)-141.4 ***(37.1)-144.6 ***(29.1)-141.5 ***(37.1)

13.3(13.2)-31.3(22.7)-41.1 *
(20.5)-31.4(22.8)

10.4(12.0)-103.6 ***(23.7)-97.7 ***
(18.1)-103.8 ***(23.8)

0.00(0.04)-0.45 ***
(0.10)-0.52 ***
(0.09)-0.45 ***
(0.10)-0.31 ***
(0.07)

770

775

782

782

Across-village

Bivariate probit (at means)
No. observations
B. Models with exogenous covariates
OLS
2SLS - over-identified model
2SLS - just-identified model 1
LIML - over-identified model
Bivariate probit (at means)
No. observations

Notes : Fraudster’s home district dummy and log of access to fraudster are excluded IVs in overidentified model and excluded variables in the transfer-receipt equation in the bivariate probit; andfraudster’s home district dummy is an excluded IV in just-identified model 1. Standard errors
clustered by village are in parentheses. Exogenous covariates are those shwon in appendix.
Results in column (1) is the same as those in columns (3) and (4) of Table 6.
*** Significant at 0.1 percent level.
** Significant at 1 percent level.
* Significant at 5 percent level.

38
Appendix. Determinants of household private transfers.

Fraud victimization dummy aNettransfers
received

OLS
Grosstransfers
receivedGrosstransfers
given

(1)

(2)

(3)

-14.7
(16.4)

24.6(20.8)

39.4 **(12.7)

2SLS
Grosstransfers
receivedGrosstransfers
given

(4)

(5)

(6)

-158.1 **(36.0)-141.4 ***(37.1)

17.6(34.5)

Net
transfers
received

Individual factors (any household members):
Disability dummy

48.3 **(14.3)

43.2 *(16.7)-5.1(13.5)

36.2 *(14.1)

29.2(18.2)-6.9(13.5)

Adults' secondary education
dummy

2.7
(11.4)

13.1(15.6)

10.1(11.6)

12.8(11.2)

24.7(17.9)

11.6(12.4)

Permanent wage labor dummy-3.4(14.8)

40.4(22.3)

42.7 **(13.8)-23.0(16.7)

17.5(23.6)

39.7 **(14.5)

Female head dummy

38.2(36.3)

28.9(32.8)-9.5(26.0)

35.2(43.5)

25.3(39.3)-9.9(24.6)

Log of age of household head

12.0
(27.3)

6.7(28.1)-3.6(29.9)

4.3(29.9)-1.9(32.9)-4.8(30.0)

No. <18 years old

-0.3
(2.7)

1.7
(2.7)

2.1
(2.0)

-0.2
(2.7)

1.8
(2.7)

2.1
(1.9)

No. males 18-60 years old

-9.1
(7.7)

-5.2
(7.7)

3.8
(7.1)

-1.5
(8.3)

3.6
(9.2)

5.0
(7.4)

No. females 18-60 years old

-6.0
(7.4)

-7.1
(5.6)

-0.8
(6.3)

-3.3
(8.7)

-4.0
(7.5)

-0.4
(6.0)

No. >60 years old-12.1(13.8)

3.6
(9.7)

15.5
(9.9)

-2.6
(14.6)

14.5(11.1)

16.9(9.3)

Log of agricultural land (acres)-13.1(11.8)

10.0(11.7)

23.3 *(9.2)-15.7(10.9)

7.0(12.4)

22.9 *(9.6)

Log of market access (min)

1.3
(2.9)

1.3
(6.4)

-0.1
(4.5)

1.3
(4.4)

1.2
(8.2)

-0.1
(4.6)

Log of no. households in thevillage

19.8(18.2)

14.6(24.7)-5.7(12.4)

25.2(15.5)

20.8(21.5)-4.8(12.2)

Log of village mean agriculturalland (acres)

63.3(54.2)

182.5 *
(82.2)

118.7 *
(46.7)

18.1(52.3)

130.0(82.0)

111.8 *
(49.7)

Log of village standard deviationof agricultural land (acres)-49.9(32.9)-96.7 *
(47.8)-46.6(27.1)-25.7(30.7)-68.7(46.7)-42.9(28.5)

3.4 ***
0.032

2.1 *
0.044

4.8 ***
0.076

769

770

769

Household factors:

Village factors:F
R squared
No. observations

4.1 ***

3.0 *

6.4 ***

769

770

769

Notes : Standard errors clustered by village are in parentheses. Other control not shown here is constant.
*** Significant at 0.1 percent level.
** Significant at 1 percent level.
* Significant at 5 percent level.
a
The endogenous variable in 2SLS; fraudster's district dummy and log of fraudster's access are excluded IVs.
The results in columns (1) and (4), (2) and (5), and (3) and (6) are the same as those in columns (2), (4),and (5), respectively, of Table 6.