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Tsukuba Economics Working Papers
No. 2009-004

Targeting of Cyclone Relief within the Village:
Kinship, Sharing, and Captureby
Yoshito Takasaki
March 2009

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

1

Targeting of Cyclone Relief within the Village:
Kinship, Sharing, and Capture*
Yoshito Takasaki
University of Tsukuba

Running Title:
Targeting of Cyclone Relief

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

March 17, 2009*

I wish to thank my field team – Jonati Torocake, Viliame Manavure, Viliame Lomaloma, Maria Torocake,and Anaseini Savuiwasa – for their advice, enthusiasm, and exceptional efforts on behalf of this project.
Special thanks are owed also to the Fijians of the region who so willingly participated in the survey.
Xiaowen Mo undertook the laborious task of data entry and cleaning. I wish to thank Takashi Yamano who
encouraged me to study the topic of this paper. Comments provided by two anonymous referees were very
useful to improve the paper. This research has been made possible through support provided by Sumitomo
Foundation, Japan Society for the Promotion of Science, and Ministry of Education, Culture, Sports,
Science and Technology in Japan. Any errors of interpretation are solely the responsibility of the author.

2

Targeting of Cyclone Relief within the Village:
Kinship, Sharing, and Capture
Abstract
This paper investigates targeting of cyclone relief within the village in Fiji. We
focus on two issues, the link of relief allocation with informal risk sharing and elite
capture, both of which are directly related to kinship. We find the following. First, foodaid is first targeted toward kin groups according to their aggregate shocks and then shared
among group members. Right after the cyclone when aid supply is scarce, householdswith housing damaged and greater crop damage are allocated !"## aid within the group.
Our conjecture is that they receive greater net private transfers in other forms especiallyin labor sharing. Consistent patterns are found in village, cropping, and housing
rehabilitations. Second, there is no elite capture of food aid in the kin group andtraditional kin leaders rather share it for others; contrarily, non-kin-based community
leaders capture aid when it is allocated across kin groups. Third, distinct from food aid
demanded by all, tarpaulins demanded only by the needed are strongly targeted on
individual housing damage at the village level – not kin group – independent of social
status. Like food aid, victims with greater crop damage are !"## prioritized. Implications
for relief policies are discussed.

3
1. Introduction
Although timely delivery of relief to victims of natural disaster is critical(Skoufias 2003), inefficient distribution of disaster relief with lack of information ofdamages and coordination among relief agents is frequently reported (e.g., Banerjee2007). However, empirical research on targeting of disaster relief is very scant due topaucity of data. As an exceptional study among Honduran victims of Hurricane Mitch,
Morris and Wodon (2003) find that the receipt of relief is targeted on pre-shock asset,
asset loss, and incidence of housing damage among households but the amount received
is not, concluding that “because the bulk of emergency aid consists of food, clothing, andmedicine, and because the needs for these goods are relatively #$%$!&' between
households, there is only limited scope for providing more relief to those who suffered
great.” (p1280, italics added).
Recent empirical works on targeting of food aid in Africa reveal similar poor
targeting performance. Jayne et al. (2002) find evidence of targeting of aid receipt on
income and rainfall shock in Ethiopia – both at the regional level and at the household
level within the region – and of lack of targeting of the amount received; “(w)ithin local
areas it does not appear that field-level rainfall shocks affect local allocations tohouseholds, so that a potential insurance role of food aid is not being used.” (p286)
Contrarily, using other Ethiopian data, Dercon and Krishnan (2005) show that food aid isshared within the village and this sharing complements weak targeting. Using original
household survey data gathered in Fiji, this paper examines targeting of cyclone relief
within the village, an issue Morris and Wodon (2003) do not address with their IFPRI
data which contain only five households per village.

4
The paper focuses on two important issues of disaster relief which have been
unexplored in the literature. The first is its connection with informal risk sharing. Whilemost empirical works on risk sharing focus on consumption smoothing using the full-risk
sharing model (Cochrane 1991; Mace 1991; Townsend 1994), it is not the only concern
among victims of natural disasters. In our sample, in addition to crop damage
experienced by most households, about half suffered from damages on their houses andcommon village facilities were also damaged. Theoretically, under imperfect labor andhousing markets, people seek to smooth utility determined by consumption, leisure, andhousing quality (a decrease in housing quality due to the cyclone is a preference shock).
($#)*#+&'$,- consists of !&./'*#+&'$,- and ,/,0!&./'*#+&'$,- (we focus on ex post risk
sharing as a joint coping mechanism not ex ante risk management). Labor sharing takes
two forms: 1/%%2,&!*!&./' for 3$!!&-"*'"+&.$!$4&4$/, – repair and reconstruction ofvillage structures and facilities – and !&./'*4'&,#5"' across households for +/2#$,-*'"+&.$!$4&4$/, – repair and rebuilding of household dwellings. Contrarily, 1'/6*'"+&.$!$4&4$/, – collecting harvestable damaged crops, cleaning fields, and planting – isindividually done without using shared labor. In all cases, hired labor is very rarely used.
Private non-labor transfers (cash and inkind) are made to help households copewith crop damage, help victims purchase construction materials, and contribute to village
rehabilitation. Following Dercon and Krishnan (2005), we assume that cyclone relief isshared within the village as part of informal risk sharing.1 In our study area, while almost

1

While to develop a theory connecting public transfer (disaster relief) with informal risk sharing is beyond
the scope of this paper, the former has two counteracting effects on the latter, a positive effect augmenting
resources to be shared and a negative crowding-out effect lowering people’s incentive for sharing.
Crowding-out occurs because in the risk-sharing arrangement with limited enforceability public transfer
that increases the value of autarky relative to the value of staying in the contract will reduce the degree ofrisk sharing (Attanasio and Rios-Rull 2000; Ligon, Thomas, and Worrall 2002).

5
all households received generous emergency food aid, only primitive tarpaulins – to be
used as emergency shelter and for temporary housing repair – were provided for a small
proportion of victims and no support for major repairs or reconstruction was available
during our survey periods. We reveal that households who suffered more actually receive!"## food aid. Our conjecture is that they receive greater net private transfers in otherforms: in particular, overall risk sharing is mainly driven by labor sharing because
whether households experienced housing damage sharply distinguishes their position as a
net donor or recipient of labor transfer. To prove this conjecture requires us to compare
all major forms of transfers, but we lack key data on labor transfer. Alternatively, weshow that communal labor contribution and the outcomes of cropping and housing
rehabilitations are consistent with relief allocation patterns.
The second issue we highlight is aid allocation rules and their implementation at
the village level about which we know so little (Jayne et al. 2002). As the kin-basedhierarchy of the Fijian society is well known among anthropologists (Turner 1992), we
shed light on this black box by examining the role of kinship in relief allocation. Our
inquiry is directly related to recent studies on the structure of risk-sharing network (e.g.,
Fafchamps and Lund 2003; De Weerdt and Dercon 2006). Fafchamps and Gubert (2007),for example, attribute their finding of geographical proximity as a major determinant of
network formation in Philippines to kith and kin relationships. We show that food aid isallocated to kin groups in the village and then shared within the group.
The significant role played by kin groups as a risk-sharing group in relief
allocation leads to a concern about elite capture. Distinct from standard household
surveys, our data stratified by kin group allow us to examine how relief allocation is6
influenced by individual social status (e.g., kin leaders) and group status (e.g., chiefs’ kingroups). Theoretical predictions are ambiguous. On one hand, the political economy
literature address elite capture as a drawback of decentralization (Grossman and Helpman
1996; Bardhan and Mookherjee 2000; Platteau and Abraham 2002; Bardhan andMookherjee 2006). On the other hand, local elites may rather take less relief to maintaintheir reputation. We also consider other groups than kin groups like women’s groups.
Development agencies increasingly consider village organizations as essential local
partners to implement their community-based development projects (World Bank 2002;
Mansuri and Rao 2004). We find that traditional kin leaders share food aid for others andnon-traditional community leaders capture it.
To deepen our inquiry about risk sharing and elite capture in relief allocation, we
consider different shocks, different relief items, and changes over time. We distinguishcrop and housing damages which affect various forms of risk sharing in distinct manners.
We compare food aid, a focus of previous studies on targeting, with tarpaulins. Whilefood aid as a positive income shock is demanded by all households, tarpaulins are needed
only by those with housing damaged. In developing countries their reliance on
emergency aid from outside donors and their limited capability to handle distribution
often lead to delayed delivery. Our data of relief provisioned over time within a year
allow us to see the evolution of allocation rules.
In general, contrary to extensive anthropological studies, economic studies basedon household survey data are almost nonexistent in the Pacific region. To better
understand the nature and performance of targeting of disaster relief is of considerable
importance in small island states (Bertram 1986) as researchers criticize their increasing

7
dependency on emergency aid and associated deterioration of indigenous mechanisms(e.g., Campbell 1984). Our findings in Fiji suggest a considerable scope for bettertargeting of cyclone relief through existing informal institutions in the village.
The rest of the paper is organized as follows. Section 2 describes the study areaand data; cyclone damage, relief, and rehabilitation; and kinship and village governance.
Section 3 discusses the econometric specification followed by the estimation results in
Section 4. Section 5 summarizes major findings and offers implications for relief policies.
2. Study area, cyclone, and village governance7898 :42;<*&'"&*&,;*;&4&*
On January 14, 2003, Cyclone Ami swept over the northern and eastern parts of
the Fiji Islands. Differing from other parts of the country, the area is less prone to be hit
by cyclones and Ami was the only cyclone in the year. The Red Cross, other NGOs, andgovernments delivered relief.2 After visiting many native Fijian villages for initial field
assessment early March, we intentionally chose nine costal villages to cover distinct
environmental and economic conditions.3 In each village households were stratified by
the smallest kin group unit (defined below) and the combination of leadership status (alsodefined below) and major asset holdings like shops, and in each stratum households wererandomly sampled (n = 374).4 Interviews were conducted between late August and early2

The total cyclone damage across the country is estimated at F$104 million, of which dwelling damage is
F$22 million and crop damage is F$40 million (National Disaster Management Office 2003). Fourteen
people were killed. In our sample villages no casualties and very limited injuries and illnesses caused by
the cyclone were reported.
3
Six villages are on Vanua Levu Island and three are on Taveuni Island. These second and third largest
islands in the country significantly lag behind the first largest island Viti Levu where the state capital, twointernational airports, and most tourism businesses are situated. Our study does not cover Indo-Fijians.
4Households with leadership status and/or major assets were oversampled to guarantee their enoughobservations. All analyses presented in the paper were conducted with and without considering thestratification and weighting design. All results are very similar to each other while significance levels
improve in the former. This paper presents the latter conservative results. As the paper examines aid

8
November. Pre-disaster information and damage measures were thus retrospectively
collected (the next section discusses potential measurement errors in our data).5
Among our sample households, cropping counted for over half total income
before the cyclone, followed by fishing with a 30% income share (Table 1).6 About 10%households earned significant income from permanent wage labor – especially in the
public and tourism sectors – in a stable manner independent of the cyclone. Contrarily,
casual wage labor was very uncommon with negligible contribution to total income.
7878 =/2#$,-*;&%&-">*4&'6&2!$,#>*&,;*'"+&.$!$4&4$/,*
According to respondents’ subjective assessment, the cyclone damaged more thanhalf of residents’ main houses (26 houses or 15% damaged houses were completely
destroyed and the rest were partially damaged). Figure 1 depicts the proportion ofhouseholds with main housing damaged who were refugees in each month. In January
almost 40% victims were refugees. Households intensively shared male labor for
emergency repair (hired labor was very rare). As more and more houses were repaired,refugees diminished (less than 5% at the time of interviews). Almost all refugees stayed
temporarily in others’ residence in the same village and about two thirds lived with thosein the same clan (defined below). Permanent migration was nonexistent.

allocation within the clan (defined below), several clans consisting of only one household in our sample are
dropped. Our analysis is based on 333 households with complete data.
5
In any post-disaster survey conducted several months after the disaster, to gather comprehensive data onlabor-time transfer, time allocation, and consumption '$-+4*&54"' the disaster is a daunting task.
6
Almost all households employ traditional farming practices using no mechanized equipment or animal
traction and limited purchased inputs. Main food crops are taro and cassava. Main cash crops are coconut(sold to government-run mills for oil production in addition to own consumption) and kava plant locallyknown as <&?/,& (a pepper plant used to make a local beverage kava which is a dominant symbol in Fijian
culture, Turner 1986). Most households conduct subsistence fishing using lines and hooks, simple spear
guns, or rudimentary nets, and more commercially-oriented fishermen use boats and engine along withmore valuable nets. Questions about cropping and fishing in the past one month were asked at the interview,and then monthly production a year before was asked in comparison with the latest figures.

9
About half households in our sample also experienced damages on otherindependent dwelling units than the main house like kitchen, shower, and toilet (15%households own none of them as they are inside the main house). Housing rehabilitation
involved repairing or rebuilding a main house and/or other unit(s), and our data containthe status of rehabilitation only at the time of interviews. About two thirds households
completed rehabilitation (16 victims built a new house).7 In the remaining analyses, we
focus on the damage on main housing not other units as the former shock was muchseverer than the latter relevant only to owners. We call households with main housing
damaged and undamaged 3$14$%# and ,/,03$14$%#, respectively.
Public supports for housing rehabilitation were limited to the provision of
tarpaulins. Figure 1 shows the cumulative proportion of households who received
tarpaulins in each month in seven villages excluding the other two which received almost
none. Delivery started in February and peaked in March – as we witnessed in the field –&54"' most refugees got back to their own residence, followed by minor deliveries until
June. Only 16% households got tarpaulins and victims were more likely to receive it thannon-victims. All recipients but few received one uniform tarpaulin. Our analysis thus
focuses on tarpaulin receipt not amount by June. The government provisioned
construction materials more than one year after the cyclone. Thus, housing rehabilitation
was determined by labor support and construction materials people could secure.
78@8 A$!!&-"*;&%&-">*1/%%2,&!*!&./'>*&,;*'"+&.$!$4&4$/,*
7

Almost all main houses built before the cyclone have metal roof and about two thirds wood wall and floor
as primary construction materials. While houses with thatched wall or ground floor covered by mat (nofloor construction) were more likely to be damaged by the cyclone than others, no other significant
difference exists between damaged and undamaged houses in other construction materials. Among newly
built houses after the cyclone only about half and one third have wood wall and floor, respectively, and lesspreferred materials, metal and thatched walls and ground floor covered by mat, are commonly used. Thus,
the quality of rehabilitated housing is likely to be lower than the pre-cyclone level.

10
All nine villages experienced damages on their structures and facilities but welack complete information of such village-level damages and corresponding relief.
Communal labor – mostly male – played a main role in village rehabilitation. We asked
communal labor each household contributed 5/'*&,<*'"&#/,#*$,*4+"*6&#4*/,"*<"&' (itincludes one prior to the cyclone and for other purposes than rehabilitation). An average
household offered 19 days communal labor per adult equivalent.8 At the time ofinterviews most facilities were not fully recovered due to lack of construction materials.
78B8 C'/6*;&%&-">*5//;*&$;>*&,;*'"+&.$!$4&4$/,*87% households experienced crop damage. The mean value of damaged crops
was 35 Fiji dollar per adult equivalent (F$1 = US$.60), or equivalently 57% mean
monthly pre-cyclone crop income (crop damage was calculated based on the quantity
damaged for each major crop reported by respondents). Crop rehabilitation was
individually done without using shared or hired labor. People planted ‘catch’ crops(which grow fast) after their seeds were provisioned as part of relief and their harvest
already started before our interviews. Recovery was still incomplete: the mean postcyclone crop income was lower than the pre-cyclone level by more than 40%.
Emergency food aid was the largest relief in the region (the total cost of foodration was 20 times that of tarpaulins in the country, National Disaster Management
Office 2003). Other relief items like water, clothing, and medicine were almost
nonexistent in our sample. The proportion of households who received food aid in eachmonth is depicted in Figure 1. Like tarpaulins the delivery was delayed: by March overthree fourths households received aid and by April almost all got some. Accordingly, our
8

They are almost equally divided into village and community groups (defined below) and only 3% was forkin groups (also defined below). As most facilities are owned and/or used by village or community groups,
a significant part of communal labor was probably used for village rehabilitation.

11
analysis examines aid receipt by March. This first wave of delivery continued until May,and after two months interval the second wave arrived in August and September.9
Because the monetary value of food aid received was difficult for respondents toanswer, we asked the quantity measured in days it would have taken to consume innormal periods (not actual duration). As foodstuffs in relief were mostly uniform acrossvillages, quality difference is not an issue. Mean amounts of food aid &%/,-*'"1$6$",4#*$,*"&1+*%/,4+, especially in the months when receipt was common, are mostly in the rangeof 30-40 days (Figure 1). Our monthly data generate two quarterly data in January-Marchand April-June and one half year data in January-June. ("1$6$",4 households received
about 12 days food per month on average $,*"&1+*?2&'4"'*and the mean amount of foodaid received in the six months period among &!!*households (including non-recipients ineither quarter) was 9.5 days per month (these two measures are used as a dependent
variable in our regression analysis). Our sample households could thus rely on aid tocover about 30% food consumption. This is a huge injection: based on the government
estimate of the cost of food ration (F$1.73 per person per day, National Disaster
Management Office 2003), the value of 57 days ration for six months is equivalent toF$99 per capita, almost three times average crop damage per adult equivalent.10
78D8 C/''"!&4$/,#*/5*1<1!/,"*'"!$"5*E$4+*1<1!/,"*;&%&-"#*
The correlations of relief with cyclone damages give us initial evidence ofallocation rules (Table 2). Food aid receipt is negatively and positively associated with
9

Four Vanua Levu villages interviewed from late August through mid-September, earlier than the other
five, received almost no food aid in July or later, but they might receive some during the second wave ofdelivery after we conducted interviews. With the incomplete data in these four, our analysis focuses on thefirst wave and all descriptive data about food aid after June presented in the paper, including ones in Figure
1, are based on the remaining five villages with complete data of the second wave.
10
In the five villages with the complete data of the second wave of food aid delivery (note 9), households
received about 11 days of food per month over the nine months period from January to September: that is,adding the second wave, total ration becomes almost 100 days food on average.

12
housing damage in the first and second quarters, respectively (the same pattern also
appears in Table 1). Our interpretation is that households who received labor support andcontributed less to communal work were less likely to receive aid when its supply was
scarce and after the supply augmented this allocation rule was reversed. Food aid receipt
is always negatively associated with the incidence not magnitude of crop damage. Ourinterpretation is that households with crop damage who contributed less to labor sharing
for the sake of own crop rehabilitation were less likely to be a recipient. On the other
hand, tarpaulin receipt is positively correlated with house damage (and crop damage
value), indicating strong targeting. The amounts of food aid received among recipients
exhibit no significant correlations. Whether these results and interpretations hold in
systematic regression analyses is a question to be examined in the next section.
78F8 G$,#+$6*&,;*3$!!&-"*-/3"',&,1"*
Three leaders play an important role in the village governance in Fiji. The first is
1!&,*!"&;"'#. Every native Fijian belongs to a clan (%&4&?&!$).11 There are 36 clans in ournine sample villages (2-8 clans per village with no overlap across villages). Land iscommunally owned by clan, is privately used, and is not allowed to be sold by law(communal land consists of about 83% total land in the country). Some individuals
holding a traditionally-assigned permanent leadership position in their clan play a majorrole in decisions made by the clan and negotiations among clans. Some clan leaders arechiefs with special social status who assume various traditional duties.

11

The hierarchical Fijian kin structure is well known among anthropologists: the bottom is 4/)&4/)&,
followed by %&4&?&!$, <&32#&, and 3&,2&, and all native Fijians belong to one tokatoka which belongs to
one mataqali, and so forth (Ravuvu 1983). Vanua ranges over several villages, there is only one yavusa inseven out of the nine sample villages, and mataqali and tokatoka are sub-groups within the village. This
paper focuses on mataqali which we call 1!&,. We repeated our regression analyses using tokatoka as analternative definition of clan, finding similar results to what are presented here.

13
The second is 3$!!&-"*!"&;"'#. In our sample, village chiefs are shared by clan
leaders (village chiefs are not necessarily clan chiefs). The second important village
leader next to village chiefs is -&4"*)""6"'# (42'&-&*,$*)/'/) who handle most matters inconnection with the local government. Gate keepers receive information and materials
from the government and NGOs, distribute them to villagers, and coordinate village
meetings. Nine gate keepers in our sample received relief items delivered to the village
and distributed them to villagers (their comprehensive records are unavailable). Whilegate keepers are a deliverer of relief, how its allocation decision is made is unknown.
The third is 1/%%2,$4<0-'/26*!"&;"'# (community groups are other groups than
kin groups in the village). Some groups like church, women’s, and school groups
received relief through their own network (which cannot be distinguished from relief
provisioned to the village in our data). Community leadership is neither permanent nor
directly related to kinship. With a limited number of observations of gate keepers andcommunity-group leaders, we combine these two as ,/,01!&,*!"&;"'#. Clan and non-clan
leaders, respectively, belong to 8% and 5% of our sample households (Table 1).
We also consider potential capture by three elite groups: clans with a clan orvillage chief (henceforth, 1+$"5#H*1!&,#), clans with a gate keeper, and clans with
community-group leaders. Clan and village chiefs come only from specific clans (weexclude other clan leaders than chiefs because they exist in most clans) and gate keepers
are also frequently appointed from certain clans; on the other hand, community-groupleaderships are unrelated to clans. In this way, we differentiate kin-based traditional elites
and other non-traditional elites at both the individual and clan levels. In our sample, 4250% households belong to leaders’ clans, that is, those leaders come from large clans.

14
3. Econometric specification@898 I%6$'$1&!*%/;"!#*/5*'"!$"5*&!!/1&4$/,*Extant empirical analyses on targeting focus on cross-section analysis using““reduced form” specifications in which as little structure is put on the decision rules as
possible, because so little is known about these decision rules and their implementation at
the village level.” (Jayne et al. 2002, p248) To examine relief allocation at the village
level, we employ a reduced-form model,<$ " & ! %1 L $ ! $ 1K $ ! #1J$ ! A ! "$ ,(1)where <$ is the relief allocated to household $, L$, K$ , and J$, respectively, are vectors ofhousehold-level 1<1!/,"*#+/1)#, #/1$&!*#4&42#, /4+"'*6'"01<1!/,"*5&14/'#*which candetermine relief allocation, A is village dummies which fully control for village-levelfactors including village facility damages and total relief delivered to the village, and "$ isan error term. If relief allocation is independent of informal risk sharing, targeting
performance is measured by a positive element of !9. If relief allocation is part of
informal risk sharing among villagers, however, the element of !9 is unsigned because theopposite allocation rule is possible when households who suffered more receive greaternet private transfers in other forms. The capturing and sharing by leaders, respectively,
are measured by positive and negative elements of "9.
To examine roles of clans in relief allocation, we extend Model 1 to<$ " & ! %1 L $ ! $ 1K $ ! #1J$ ! % 2 L - ! $ 2 K - ! # 2J - ! A ! "$ ,(2)where L-, K-, and J-, respectively, are vectors of clan-level cyclone shocks, social status,and other pre-cyclone factors for clan -. If relief is allocated across households only at
the village level, the addition of clan-level factors is redundant. In the case where relief

15
allocation is part of risk sharing, this means that clans do not serve as a risk-sharing
group or relief allocation is part of risk sharing only at the village level. Contrarily, thesignificant impacts of both clan- and household-level shocks suggest that not only thevillage but also clans serve as a risk-sharing group and relief allocation is part of risk
sharing at both the village and clan levels. The element of !7 is unsigned because clans
which experienced greater damage may receive larger net private transfers in other forms.
Across-clans allocation rules can be distinct from within-clan allocation.12
We conjecture that overall risk sharing is mainly driven by labor sharing because
housing damage sharply distinguishes households’ position as a net donor or recipient oflabor transfer for housing rehabilitation. In particular, we hypothesize that: (1)households with housing damaged (victims) are a net recipient of labor transfer andcontribute less to communal work (+<6/4+"#$#*9) and (2) households with greater crop
damage intensify own crop rehabilitation and thus contribute less to labor sharing(+<6/4+"#$#*7). Then, households who suffered great can be allocated less relief in
exchange for favorable labor-sharing arrangements and relief allocation rules can be
distinct between victims and non-victims. We run separate regressions for these two subsamples. Pre-cyclone income, asset holdings, and other household characteristics are notsignificantly different between victims and non-victims (Table 1), suggesting that
housing damage is considered exogenous and selection bias is not a major concern.13

12

We also estimated models with clan dummies which fully control for clan-level factors (village dummies
are excluded). This alternative specification focuses on within-clan allocation. The estimation results ofhousehold-level factors did not significantly change. Thus, unobserved clan-level factors in Model 2 areunlikely to cause significant bias in our estimates.
13
Households with an older head and belonging to leaders’ clans are more likely to experience housing
damage. Our data show that these households rely more on inferior construction materials discussed in note
7 (because leaders’ clans are large clans, this relationship also holds regarding clan size). That is, inequality
in construction materials is higher in leaders’ clans than the others. Inequality in general in the kin-based
traditional society deserves further research.

16
To analyze aid receipt and amount received separately is important because theirdeterminants are shown to be distinct in extant studies. To examine how allocation ruleschange over time, we first analyze food aid receipt by March and amount received in
January-March among recipients using Probit and OLS, respectively, and then amountreceived in January-June using OLS. The former hurdle model is commonly used inprevious studies (Jayne et al. 2002; Dercon and Krishnan 2005).14 For comparison, we
analyze tarpaulin receipt by June using Probit.
@878 C/3&'$&4"#*
In Model 1, household-level cyclone shocks L$ are captured by a dummy for main
housing damaged, the value of crop damage per adult equivalent (log), and theirinteraction term (which captures their interactions in risk sharing).15 In the sub-sample
analysis for victims and non-victims, only the value of crop damage is used (!9 is a scalar
not a vector). Social status K$ is captured by two dummies for clan and non-clan leaders.
Other pre-cyclone factors J$*include standard variables used in extant works such as
income, asset holdings, and demographic factors as detailed in Appendix (descriptive
statistics of all covariates are shown in Table 1). Pre-cyclone factors are measured a year
before our interviews. Our income measure thus rules out the possibility that income isaffected by transfers due to disincentive effects (Clay and Stokke 2000; Barrett 2002).
Contrary to housing damage, crop damage is not random but related with landholdings and pre-cyclone cropping practices. Even though covariates are added to control
14

An alternative sample selection model is infeasible with our data which lack identifying instruments
required to credibly estimate the selection equation.
15
We do not distinguish houses partially damaged and completely destroyed because the latter are muchless common than the former. We repeated our analyses excluding households with housing completely
destroyed. The results are almost the same as those presented here. To control for potentially distinct
patterns among refugees, we also repeated our analyses excluding them. This mostly further reduced thesize of the victim sample. We found no significantly different results.

17
for these factors and ex ante risk management is unlikely to play a major role in our lesscyclone-prone area, crop damage may be still correlated with unobservable factors
affecting relief allocation. Similarly, pre-cyclone income may be also endogenous. To
control for the potential endogeneity of crop damage and income requires valid
instruments for them which our data lack. We ran regressions excluding them to checkthe robustness of estimates of other variables, finding no significantly different results.
In Model 2, clan-level cyclone shocks L- are captured by the proportion of
housing damaged in the clan and the clan mean of crop damage per adult equivalent (log).
In the sub-sample analysis, two clan means of crop damage*$,*4+"*E+/!"*#&%6!", one
among victims and another among non-victims, are used to allow their potentially distinct
effects. In the victim sample, for example, crop damages experienced by other victims
and non-victims in the same clan may affect relief allocation in different manners as theyplay distinct roles in labor sharing. Social status K- includes three dummies for chiefs’clan, gate keepers’ clan, and community-group leaders’ clan. Other pre-cyclone factors
J- are detailed in Appendix. Our clan-mean estimates should be reasonably accurate
because on average we sampled about 70% households in each stratum.
@8@8 I%6$'$1&!*%/;"!#*/5*'"+&.$!$4&4$/,*We test hypotheses 1 and 2 by examining how cyclone shocks affect village, crop,and housing rehabilitations. Since our goal is not to identify impacts of relief allocated tohouseholds (which is endogenously determined), we employ the same reduced-form
specifications as relief allocation.16 First, we directly test both hypotheses regarding
communal labor using OLS. Next, we test the first half of hypothesis 2 regarding the16

If allocation rules were exogenously set by relief agents and were perfectly implemented in the village,
household-level relief would appear as an explanatory variable as a determinant of rehabilitation.

18
intensification of crop rehabilitation. With lack of labor input data, we examine theoutcome of crop rehabilitation by employing the first-difference estimator using pre- andpost-cyclone crop incomes as a dependent variable. Only time-variant shock variables(plus village-time dummies) appear as covariates (this specification is a direction
extension of the full-risk sharing model, Fafchamps and Lund 2003). The effect ofhousehold-level crop damage on post-cyclone crop income is the combination of thenegative and positive effects of the direct damage and own labor input for rehabilitation
corresponding to the damage, respectively. Our null hypothesis is that post-cyclone crop
income is insensitive to own crop damage, that is, households intensify rehabilitation $,*6'/6/'4$/,*4/ the magnitude of crop damage (this does not necessarily mean thatcropping was fully recovered).17 Then, households with greater crop damage do notnecessarily earn less cash at the time of interviews but contribute less to labor sharing(the second half of hypothesis 2). While we directly test the latter regarding communal
labor, lack of data precludes us from doing so for labor transfer. Alternatively, we
examine the outcome of housing rehabilitation – completed or not – among victims using
Probit. Households with greater crop damage should be less likely to complete housing
rehabilitation as they receive smaller labor and non-labor transfers including relief.
@8B8 M"&#2'"%",4*"''/'#*
Special attention needs to be given to measurement errors in our retrospective
data. First of all, errors in our measure of housing damage reported by individual17

We think that households can give a high priority to crop rehabilitation because of the following reasons.
First, distinct from housing and village rehabilitations relying heavily on labor sharing, crop rehabilitation
is individually done and its benefits are fully captured by cultivators. Second, distinct from housing andvillage rehabilitations involving costly construction materials, crop rehabilitation can be done only byadjusting own labor supply. Third, with very limited public support for housing and village rehabilitations,
extra cash was greatly needed for purchasing construction materials. Fourth, before the supply of food aid
augmented, food shortage was a major concern.

19households are minimal because relief officers also used three categories – no damage,
partial damage, and complete destruction – for their damage assessment and thus thedamage status of each house was common knowledge among villagers. Errors in crop
damage value may cause bias. We repeated our analyses using a dummy for crop damage
the measurement errors in which should be minimal, finding qualitatively similar results.
Hence, systematic errors in crop damage value are unlikely to be a major concern.
Even if retrospective errors in aid receipt are small, errors in the timing of receipt
and amount received can be considerable. A key question is whether these errors arecorrelated with the covariates. In particular, households with more damage might tend tofeel that the delivery was too late and the amount was too small, causing ;/E,E&'; bias
in our estimates of shock variables. Measurement errors in the timing of tarpaulin andfood aid in the six months period are not an issue because it covers all tarpaulins
delivered and errors across two waves of food aid delivery are unlikely. Our mainconcern is errors in the timing between March and April which are mainly caused byhouseholds who received aid in either March or April not those who received in both orneither of them. With systematic recall bias, the correlation between aid receipt $, Marchand shocks in this sub-sample should be smaller than that of receipt .< March in thewhole sample. This pattern holds for housing damage but not for crop damage dummy orvalue (Table 2). Thus, recall bias in timing can be a concern for housing damage in thewhole sample analysis (it is irrelevant in the sub-sample analysis for victims and nonvictims). We cannot tell how serious other recall bias than that caused by the error intiming is in the amount of food aid received. It is important that whenever we interpret
negative impacts of adverse shocks on the amount we consider the possibility of bias.

20
4. Estimation results
We first discuss food aid and rehabilitations regarding cyclone shocks and social
status and then compare food aid with tarpaulins. Unless otherwise noted, estimation
results for Model 1 in the whole sample and Model 2 in the whole, non-victim, andvictim samples are shown in Tables 3 and 4 which report cyclone shocks and social status
only;18 other pre-cyclone factors are reported and discussed in Appendix.
B898 C<1!/,"*#+/1)#*
Results for food aid receipt by March, log amount received per month in JanuaryMarch among recipients, and log amount received per month in January-June arereported in Table 3. In the first quarter, households with housing damaged are less likely
to be a recipient (Columns 1-4) and those with greater crop damage receive a smaller
amount (Columns 5-8). The housing damage dummy is jointly significant at least at a
10% significance level, the latter result on amount holds in both sub-samples, and crop
damage also negatively affects receipt among victims. These findings are consistent withour conjecture that relief allocation is driven by labor sharing. In the first quarter withscarce aid supply, labor transfer for emergency repair was of critical importance. Oncethe supply augmented in the second quarter, the allocation rules were reversed so that theallocation for the six months period became neutral to shocks (Columns 9-12).19
Results for log communal labor per adult equivalent, log crop income per adultequivalent per month, and housing rehabilitation among victims are reported in Table 4.

18

Robust standard errors are reported for OLS in Model 1. Standard errors are clustered by clan in Model 2.
Two exceptions are tarpaulin receipt in seven recipient villages and food aid received in January-Marchamong recipients in the victim-sample, both of which contain a small number of clans.
19This is confirmed by estimating the determinants of aid receipt in April-June using the same reduced
form specifications either with or without the amount of aid received in January-March as an additional
covariate treated exogenous (we cannot control for the endogeneity of this lagged dependent variable).

21
Consistent with hypotheses 1 and 2, households with housing damaged and greater crop
damage contribute less communal labor (except for crop damage among victims probably
because victims receiving labor transfer from non-victims cannot lower contributionmuch) (Columns 1-4). The combined results of crop and housing rehabilitations support
hypothesis 2: household post-cyclone crop income is insensitive to own crop damage(Columns 5-8),20 and victims with greater crop damage are less likely to complete
housing rehabilitation (Columns 9 and 10).
Table 3 shows that clan-level cyclone shocks affect food aid allocation as follows.
In the whole sample, clans of which members experienced greater crop damage receive alarger amount in the first quarter (Column 6). In the victim sample, clans in which
victims experienced greater crop damage are more likely to be a recipient in the firstquarter (Column 4); contrarily, in the non-victim sample, such clans receive a smaller
amount in the six months period (Column 11). Results for crop damage experienced by
non-victims in the clan and housing damage among clan members are weak. Thesefindings indicate that food aid is first targeted toward clans according to their crop
damage especially among victims – as victims are prioritized, non-victims are allocatedless – and then it is shared within the clan in connection with labor sharing. Labor sharingis thus important among clan members as implied from the co-residence for refugees.21
Supportive evidence is found in rehabilitations as follows (Table 4). First,
communal labor for village rehabilitation is shared both within the clan and across clans:

20

Predictions regarding own housing damage are ambiguous: households may intensify cropping to earn
extra cash for housing rehabilitation or may downsize cropping to shift labor to housing rehabilitation. Our
results show that the former outweighs the latter (the results are statistically insignificant).
21
This is also supported by our finding that victims in a richer clan are !"## likely to complete housing
rehabilitation (see Appendix). If non-labor sharing were relatively important compared to labor sharing in
the clan, the opposite relationship would be the case.

22
non-victims in a clan in which victims experienced greater crop damage contribute more
to help those victims; contrarily, in the non-victim sample, clans in which non-victims
experienced more crop damage are allowed to contribute less (Column 3). Second,
victims in a clan in which housing damage was more common less rehabilitate croppingas they are busy in helping each other (Column 8).22 Third, households in a clan in which
victims – not non-victims – experienced greater crop damage are more likely to complete
housing rehabilitation (Column 10). This is possible if each victim receives more support
from non-victims, i.e., within-clan labor sharing augments.
B878 :/1$&!*#4&42#*
Social status strongly influences food aid allocation (Table 3). First, clan leaders
are less likely to receive aid in the first quarter (the result among victims is insignificant)(Columns 1-4); all other estimated coefficients for household-level social status are
insignificant (weak results of non-clan leaders may be because they combine gate keepers
and community-group leaders). Second, among victims, chiefs’ and gate keepers’ clans
are less likely to receive aid in the first quarter and receive a smaller amount in the six
months period; the converse holds true for community-group leaders’ clans (Columns 4
and 12). In the whole sample, qualitatively the same results hold for gate keepers’ andcommunity-group leaders’ clans in receipt in the first quarter (Column 2).
These findings suggest the following. First, while food aid received by a village isshared by traditional kin leaders, aid received by a community group is captured by its
leader’s clan not the leader him/herself. In the closely-knit kin society, capturing relief
allocated as part of risk sharing within the clan can greatly deteriorate capturer’s
22

Predictions regarding clan-level shocks are ambiguous: households may intensify cropping against
damages among clan members to augment non-labor sharing or reduce cropping to shift labor to labor
sharing. Regarding housing damage the latter outweighs the former among victims.

23
reputation among clan members. As this social cost is too high for all elites, there is no
capture in the clan. Traditional leaders rather share aid for others to maintain or evenaugment their reputation in the clan and village. Contrarily, since the status of nontraditional leaders is not directly related to their clan, their social cost of capturing aid
allocated among clans is not so high. Non-traditional leaders make use of traditional kin
structure for capturing.23 This finding supports the argument of Platteau and Abraham(2002) that the highly ranked can exercise their authority in other sectors of the village’s
life. Second, clan status affects aid allocation particularly in the first quarter and among
victims, that is, when aid was scarce and greatly demanded (recall that less aid wasallocated to victims then). Third, social status largely alters aid receipt not amountreceived. Put together, the scarcity of food aid augments distinct motives for sharing andcapturing among different elites in their decisions about who are recipients.
B8@8 C/%6&'$#/,*/5*4&'6&2!$,#*&,;*5//;*&$;*
Results for tarpaulin receipt by June are reported in Columns (13) and (14) of
Table 3 (only the results in the whole sample in seven tarpaulin recipient villages areshown). Households with main housing damaged are more likely to be a recipient. Noneof clan-level shocks are significant (the statistical significance of own housing damage
and the overall fitness of the model in Model 2 become weaker) and neither social status
nor other pre-cyclone factors matter (see Appendix for the latter). These sharp results
indicate strong targeting on individual housing damage in the allocation at the village
level not clan. This targeting is weakened by individual crop damage: the marginal effect23

Victims belonging to community-group leaders’ clans are more likely to complete housing rehabilitation(Columns 10 of Table 4). This may be partly because of their capture of food aid. The role of social status
in communal labor is straightforward (Columns 1-4): households in chiefs’ clans which assume greater
traditional roles contribute more (our communal labor data is inclusive) and clan leaders contribute less
especially in the victim sample, i.e., communal labor is more common among commoners.

24of housing damage diminishes as crop damage augments (the highest marginal effectwith no crop damage is about 30% when all other variables take mean values).
We think that the sharp contrast in allocation rules according to cyclone shocks
and social status between food aid and tarpaulins is caused by their distinct characteristics
and the difference of crop and dwelling damages. On one hand, crop damage is not so
observable to other households and food aid is demanded by all. There are thus
significant room and demand for capture. Crop damage is relatively observable amongclan members who jointly own communal land and labor sharing for housing
rehabilitation is probably mainly done among them. As such, food aid allocation amongclan members can be tightly linked with their labor sharing.
On the other hand, dwelling damage is well observable to all villagers and only
those whose main house and/or independent units were damaged demand tarpaulins.
There are thus limited room and demand for capture, resulting in the strong targeting on
victims without involving across-clans allocation. As tarpaulin supply is insufficient(much scarcer than food aid), an additional allocation rule is employed according to crop
damage: victims who contribute less to labor sharing among villagers – not clan members– are less prioritized (this is consistent with our earlier finding that they are less likely tocomplete housing rehabilitation). As such, how relief allocation is linked with otherforms of risk sharing varies across relief items.
5. Conclusion
Using original post-disaster survey data gathered in Fiji, this paper investigatedtargeting of cyclone relief within the village. Our focus was on the link of relief
allocation with informal risk sharing and elite capture, both of which are directly related

25
to kinship. We found the following. First, food aid is first targeted toward kin groups
according to their aggregate crop damage especially among those who experienced
housing damage and then shared among group members. Right after the cyclone whenaid supply is scarce and labor help for housing repair is critical, households who suffered
more are allocated !"## aid within the group. Our conjecture was that they receive greaternet private transfers in other forms: in particular, households with housing damaged andgreater crop damage, respectively, are less likely to receive aid and receive a smaller
amount in exchange for favorable labor-sharing arrangements among group members.
After aid supply augments, this allocation rule is reversed. Although lack of dataprecluded us from proving our conjecture, we found consistent patterns in communal
labor for village rehabilitation and cropping and housing rehabilitation outcomes.
Second, there is no elite capture of food aid in the kin group and traditional kin
leaders rather share it for others probably to maintain their reputation in the group andvillage. Contrarily, non-traditional community leaders capture aid when it is allocatedacross kin groups, i.e., they make use of kin structure for capturing. Both sharing andcapturing mainly occur when recipients of scarce aid are determined. Third, distinct fromfood aid demanded by all, tarpaulins demanded only by the needed are strongly targetedon individual housing damage at the village level – not kin group – independent of social
status. Victims with greater crop damage are !"## prioritized presumably because
tarpaulin allocation is linked with the labor sharing among villagers.
These findings lead to the following policy implications. First, while targeting ofdisaster relief on shocks is important, the performance of broad risk sharing needs to begiven greater attention. Contrary to what Morris and Wodon (2003) conclude, the needs

26
for relief are heterogeneous across households even in the same village and there exists a
scope for targeting of both aid receipt and amount received on idiosyncratic shocks
through private risk sharing. This self-targeting can complement weak targeting ofdisaster relief, but existing private mechanisms needs to be better understood first. In
particular, the self-targeting may be attained not only by the village itself but also by subgroups in the village like kin groups. Second, in the kin-based society where social
reputation in the kin group and village is a central concern, the risk of elite capture can berather higher in relief provisioned to non-kin-based community groups than that to thevillage because the former’s reputation-based sharing mechanism is weaker than thelatter. Contrary to community development, village organizations may not be goodtargets or partners in disaster relief management in certain locales. Third, timely deliveryof sufficient relief is important not only to better satisfy the demand of the needed but
also to reduce elite capture and thus improve self-targeting. Lastly, effective relief
policies can be distinct across different shocks and across relief items as how relief andinformal risk sharing are related with each other depends on each shock and each item.
Policy makers need to design a good combination of shock-and-item-specific policies.
Appendix
This appendix discusses other pre-cyclone factors used as covariates and reports
their estimation results (except for the first-difference estimates of crop income). Relief
allocation and rehabilitation results, respectively, are shown in Tables A1 and A2 ofwhich the column numbers match with those in Tables 3 and 4.
Household-level other pre-cyclone factors include income per adult equivalent permonth (log), land holdings (log), fishing capital (log), a dummy for secondary education

27
among adults (capturing human capital), household adult equivalent size (log),
proportions of children and elderly (capturing labor resources), the age of household head(log), and a dummy for female head. Clan-level other pre-cyclone factors consist of theclan mean of pre-cyclone income per adult equivalent per month (log) and clan
population share in the village (based on population not sample) (the mean and standard
deviation of the latter are .39 and .21, respectively).
While none of other pre-cyclone factors affect tarpaulin allocation, theysignificantly alter food aid. In the six months period, households (except for non-victims)
with lower pre-cyclone income and more children receive more aid, indicating targeting
toward the poor and children. Bigger households (except for victims) receive less aid per
capita, indicating the allocation rule not fully reflecting household size (the same finding
is obtained by Jayne et al. 2002). Bigger clans receive more, suggesting an important role
played by across-clans bargaining in the allocation. In the first quarter, household size
and the number of children (only in the whole sample) have qualitatively the same
impacts on amount received as those found in the six months period; pre-cyclone income
shows no significant effects and clan size only affects receipt in the whole sample.
Households holding larger fishing capital receive a smaller amount (except for nonvictims), indicating targeting toward the asset poor. The male headed (except for victims)
and households with secondary education are more likely to receive aid despite that someeducated adults earned secure income from their permanent jobs after the cyclone. Thissuggests that scarce aid was captured by the educated and males.
Larger households, the female headed (except for victims), households with morechildren, and households in a larger clan (except for victims) contribute less communal

28
labor per adult equivalent. Households with educated adults (only in the whole sample)
contribute less probably because of their high opportunity cost of labor time andpermanent jobs held by some of them. Households in a richer clan (except for victims)
also contribute less. This may be because richer clans contribute larger non-labor transfer(for any purposes). Older households, households with educated adults, smaller
households, and households with more children are more likely to complete housing
rehabilitation probably because seniority is an advantage in securing support, someeducated adults hold permanent jobs, household size is positively related with house size,and labor sharing is preferably altered by children as found in communal labor.
Communal labor and housing rehabilitation are neutral to pre-cyclone income and assets.
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31
Table 1. Means of income, cyclone shocks, relief, and rehabiliations, social status, and other pre-cyclone
factors per household.
All(n=333)Pre-cyclone income per adult equivalent per month (F$):
Total
113 (116)
Cropping
60.4 (90.5)
Fishing33.7 (40.8)
Permanent wage labor
10.6 (41.8)
Casual wage labor
0.4 (3.0)
Other
7.0 (44.5)
Cyclone shocks:
Housing damaged dummy
0.53 (0.50)
Independent units damaged dummy
0.52 (0.50)
Crop damaged dummy
0.87 (0.34)
Crop damage per adult equivalent (F$)
34.5 (44.6)
Cyclone relief:
Tarpaulin receipt by June dummy (inseven receipient villages)
0.16 (0.37)
Food aid receipt dummy
By March
0.77 (0.42)
In April-June
0.79 (0.41)
By June0.95 (0.21)
Food aid per month (days)in January-March (recipients only)
12.6 (7.7)
In April-June (recipients only)
11.9 (7.2)
By June (whole sample)
9.5 (6.3)
Rehabilitations:
Communal labor per adult equivalent in
the past one year (days):
18.8 (16.0)
Post-cyclone cropping income per adultequivalent per month (F$)
Housing rehabiliation completion dummy(among households with main house or
independent units damaged)
Social status:
Clan leader dummy
Non-clan leader dummy
Chiefs' clan dummy
Gate keepers' clan dummyCommunity group leaders' clan dummy
Other pre-cyclone factors:
Land holdings (acre)
Fishing capital (F$)
Adults' secondary education dummyHousehold size (adult equivalent)
Proportion of children (<15)
Proportion of elderly (>65)
Age of household head
Female head dummy

Housingundamaged(n=157)

Housing
damaged(n=176)Mean/
prop. test
(p-value)

117 (112)64.8 (91.0)
32.1 (37.4)
12.1 (50.8)
0.4 (2.6)
6.6 (21.3)

110 (119)56.5 (90.1)
35.2 (43.7)
9.3 (31.8)
0.5 (3.4)
7.3 (57.8)

0.57
0.40
0.50
0.54
0.64
0.88

0.00 (0.00)
0.31 (0.46)
0.87 (0.33)
35.1 (49.2)

1.00 (0.00)
0.71 (0.45)
0.87 (0.34)
34.0 (40.2)

0.00
0.00
0.93
0.82

0.09 (0.28)

0.22 (0.42)

0.01

0.85 (0.36)
0.73 (0.44)
0.97 (0.16)

0.70 (0.46)
0.84 (0.37)
0.93 (0.25)

0.00
0.02
0.07

13.2 (8.2)
12.0 (7.9)
10.0 (6.2)

12.0 (7.2)
11.9 (6.7)
9.1 (6.4)

0.22
0.85
0.22

19.9 (17.3)

17.9 (14.7)

0.24

35.0 (59.4)

38.8 (54.7)

31.5 (63.3)

0.26

0.68 (0.47)

0.78 (0.42)

0.66 (0.48)

0.15

0.08 (0.27)
0.05 (0.23)
0.43 (0.50)
0.42 (0.49)
0.50 (0.50)

0.10 (0.29)
0.04 (0.21)
0.36 (0.48)
0.30 (0.46)
0.42 (0.50)

0.06 (0.24)
0.06 (0.24)
0.49 (0.50)
0.53 (0.50)
0.57 (0.50)

0.26
0.47
0.02
0.00
0.01

4.78 (5.65)
473 (1497)
0.84 (0.36)
4.93 (2.23)
0.32 (0.2)
0.06 (0.14)
48.3 (13.7)
0.11 (0.31)

4.60 (5.39)
587 (1935)
0.83 (0.38)
4.79 (2.09)
0.31 (0.2)
0.07 (0.16)
50.1 (13.9)
0.11 (0.31)

4.93 (5.89)
371 (942)
0.86 (0.35)
5.06 (2.35)
0.33 (0.2)
0.04 (0.13)
46.7 (13.4)
0.10 (0.30)

0.60
0.19
0.45
0.26
0.43
0.14
0.03
0.86

Note - Standard deviations are shown in parentheses. t-test and chi-squared tests compare the means andproportions for continuous and dummy variables, respectively. Test results with a 5% significance level are bolded.

32
Table 2. Correlations of cyclone relief with cyclone damages.

Tarpaulin receipt by June dummy (in sevenrecipient villages)
Food aid receipt by March dummy

Food aid receipt in April-June dummy

Food aid per month in January-March (amongrecipients) (days)
Food aid per month in April-June (amongrecipients) (days)
Food aid per month in January-June (in the wholesample) (days)
Food aid receipt in March among households who
received either in March or April (not both)

Housing
damageddummy

Crop damaged
dummy

Crop damage
per adult
equivalent
(F$)

0.18
(0.01)

0.07(0.31)

0.17(0.01)-0.18(0.00)-0.17(0.00)

0.03(0.60)

0.13(0.02)-0.09(0.10)

0.02(0.78)-0.08(0.22)

0.00(0.95)

0.05(0.41)-0.01(0.85)

0.10(0.12)

0.05(0.39)-0.07(0.22)-0.06(0.28)

0.07(0.22)-0.25(0.00)-0.13(0.06)

0.15(0.02)

Note - p-values are shown in parentheses and those with a 5% significance level are bolded.

-0.090(0.442)-0.011(0.122)-0.131(0.154)-0.147(0.344)0.350(0.214)-0.079(0.319)-0.199(0.160)-0.212(1.048)0.018(0.155)(3)-0.773 ** -0.713 ** -1.126 **(0.312)(0.318)(0.439)
0.161
0.207
0.695
(0.311)(0.324)(0.836)-0.307-0.106(0.235)(0.485)-0.442 ** -0.209(0.200)(0.556)
0.441 ** 0.862(0.219)(0.545)-0.191(0.407)-0.022(0.101)-0.109(0.126)(2)(1)

N onvictims
(5)

All
(6)

All
(7)

Nonvictims(8)

Victims(9)All-0.6210.107(0.534)(0.138)0.068
0.024(0.354)(0.095)-0.623 *(0.332)-1.077 **(0.463)1.303 ***
(0.449)0.092(0.184)0.093(0.082)-0.129 *(0.073)-0.099 *(0.057)-0.010(0.078)0.140(0.299)0.161(0.138)-0.118(0.106)-0.091(0.137)-0.043(0.123)-0.005(0.202)-0.079(0.110)-0.190(0.171)-0.233(0.193)0.101(0.274)-0.242(0.160)0.000(0.113)-0.152(0.177)-0.022(0.095)-0.117(0.119)0.005(0.130)0.123(0.124)0.008(0.162)0.003(0.054)-0.048(0.056)-0.465(0.326)0.080(0.177)

(10)

All

0.321
(0.622)-0.045(0.034)(12)

Victims-0.180(0.224)0.082(0.135)-0.157(0.149)0.158(0.159)0.035(0.183)

Recipientvillages
(14)

0.334
(0.650)-0.092(0.433)-0.401(0.514)0.081(0.564)0.417(0.687)1.890 ** 1.719 *(0.865)(0.917)0.429 ** 0.451 **(0.216)(0.229)-0.396 * -0.383(0.236)(0.248)0.816(0.906)-0.665(0.631)

Recipientvillages(13)

Tarpaulin receipt
by June(Probit)-0.2010.267(0.293)(0.585)-0.1080.026(0.160)(0.412)-0.364 **(0.177)-0.543 *(0.281)0.644 *
(0.353)

0.000
0.086
(0.099)(0.127)-0.147 ** 0.369(0.059)(0.254)-0.602(0.469)-0.021(0.051)(11)

Nonvictims

Food aid received per month in JanuaryJune (days)(OLS)-0.0620.002-0.040(0.104)(0.123)(0.145)-0.228 *** -0.087 *** -0.097 ** -0.091 ** -0.073 ** -0.002(0.071)(0.030)(0.037)(0.040)(0.035)(0.042)0.032
0.019-0.036(0.037)(0.037)(0.051)0.689-0.053-0.4530.465(0.893)(0.144)(0.349)(0.344)0.217 **
(0.091)
0.014
0.118
0.078
(0.163)(0.095)(0.090)1.214 ***
0.014
0.077
(0.276)(0.061)(0.197)(4)

Victims

Food aid received per month in JanuaryMarch among recipients (days)(OLS)

F/Wald tests (p-value)
for housing damaged
0.012
0.068
0.670
0.727
0.242
0.277
0.035
0.125
for crop damage
0.294
0.200
0.006
0.027
0.538
0.285
0.117
0.105
0.016
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.020
0.061
F/Chi-squared (p-value)
0.109
0.121
0.254
0.161
0.425
0.451
0.445
0.567
0.123
0.157
0.208
0.215
0.151
0.178
R-squared/Pseudo-R squared-156.9-154.7
-48.3
-89.1
-83.5
-80.9
Log likelihood
327
327
153
174
252
252
130
122
327
327
153
174
222
222
No. observations*10% significance, **5% significance, ***1% significance. Robust standard errors in (1), (5), (9), (12), cluster-adjusted standard errors in (2)-(4), (6)-(8), (10)-(11), and (14), and standard errors are in
the parentheses in Column (13). Other pre-cyclone factors are reported in Table A1. Other controls which are not shown here or in Table A1 are constant and village dummies.

Community-group leaders' clan
dummy

Gate keepers' clan dummy

Chiefs' clan dummy

Non-clan leader dummySocial status:
Clan leader dummy

Log of crop damage per adultequivalent (F$)
Log of crop damage per adultequivalent*Housing damaged dummy
Proportion of housing damaged in theclan
Clan mean of log of crop damage per
adult equivalent (F$)
Clan mean of log of crop damage per
adult equivalent among non-victims
Clan mean of log of crop damage per
adult equivalent among victims (F$)

Cyclone shocks:
Housing damaged dummy

All

Food aid receipt by March(Probit)All

Table 3. Determinants of relief allocation.

33

0.018
0.007
0.000
0.391
327

327

-0.238(0.177)-0.040(0.168)0.375 ***
(0.101)-0.018(0.085)-0.261(0.167)-0.406 **(0.177)-0.135 ***(0.040)0.112(0.072)0.128(0.227)-0.188(0.114)

0.114
0.003
0.000
0.369

-0.224(0.168)-0.022(0.152)-0.400 **(0.195)-0.145 ***(0.042)0.109 *
(0.064)

(2)

(1)

0.031
(0.057)-0.032(0.155)-0.219 **(0.083)0.106 *
(0.054)

153

0.000
0.492

174

0.000
0.365

-0.357 **(0.150)-0.079(0.282)0.302 **
(0.128)-0.099(0.181)-0.249(0.247)0.395(0.306)-0.496(0.464)-0.141(0.252)-0.036(0.131)0.458 **
(0.187)0.042(0.155)0.070(0.176)-0.047(0.081)(4)

Victims-0.115 **(0.045)

Nonvictims
(3)

324

0.438
0.754
0.000
0.111

0.187
(0.474)0.059(0.104)-0.101(0.135)

324

0.285
0.472
0.000
0.116

0.278
(0.366)0.063(0.078)-0.106(0.086)-0.507(0.300)-0.028(0.180)

154

0.000
0.141

0.063
(0.246)0.008(0.083)0.032(0.360)0.072(0.079)

170

0.000
0.160

0.091
(0.121)-0.246(0.208)-1.287 **(0.613)-0.028(0.068)Crop income per adult equivalent per month(F$)(first-difference)
NonVictims
AllAll
victims
(5)
(6)
(7)
(8)

0.050
0.143
-94.3
174

-0.543(0.516)-0.565(0.398)-0.318 ***(0.098)(9)Victims

0.000
0.194
-88.7
174

-0.557(0.537)-0.660 *(0.357)0.160(0.268)-0.163(0.540)0.905 *
(0.533)0.069(0.178)1.104 ***
(0.361)

0.987(0.959)-0.401 ***(0.086)(10)

Victims

C omplete housing
rehabilitation(Probit)*10% significance, **5% significance, ***1% significance. Robust standard errors are in the parentheses in Column (1) and (5), cluster-adjusted standard errors in (2)-(4),(6)-(8), and (10), and standard errors in (9). For OLS and Probit other pre-cyclone factors are reported in Table A2 and other controls which are not shown here or in
Table A2 are constant and village dummies. For first-difference other controls which are not shown here are constant and village-time dummies.

F/Wald tests (p-value)
for housing damaged
for crop damage
F/Chi-squared (p-value)
R-squared/Pseudo-R squared
Log likelihood
No. observations

Community-group leaders' clan
dummy

Gate keepers' clan dummy

Chiefs' clan dummy

Non-clan leader dummySocial status:
Clan leader dummy

Log of crop damage per adultequivalent (F$)
Log of crop damage per adultequivalent*Housing damaged dummy
Proportion of housing damaged in theclan
Clan mean of log of crop damage per
adult equivalent (F$)
Clan mean of log of crop damage per
adult equivalent among non-victims
Clan mean of log of crop damage per
adult equivalent among victims (F$)

Cyclone shocks:
Housing damaged dummy

All

All

C ommunal labor per adult equivalent (days)(OLS)

Table 4. Determinants of rehabilitations.

34

-0.027(0.111)0.129(0.156)-0.022(0.028)0.380(0.253)-0.051(0.305)0.198(0.486)-0.157(0.706)0.601(0.419)-0.657 **(0.278)(3)-0.0090.168(0.120)(0.227)0.104
0.459 *
(0.166)(0.278)-0.0270.060(0.025)(0.058)0.438 *
0.877 *
(0.240)(0.511)-0.046-0.007(0.270)(0.638)0.247-0.478(0.449)(1.078)-0.035-0.862(0.765)(1.010)0.570
1.645(0.351)(1.001)-0.619 ** -1.242 **(0.254)(0.613)-0.0940.031(0.310)(0.595)1.543 *** -0.588(0.589)(1.371)(2)(1)

N onvictims-0.082(0.189)-0.020(0.286)-0.057(0.043)0.537 **
(0.255)-0.018(0.386)0.616(0.565)0.619(1.278)0.167(0.380)-0.440(0.409)0.214(0.471)2.257(1.498)(4)

Victims
(6)

All
(7)

Nonvictims(8)

Victims
(9)

All
(11)

Nonvictims-0.120 ** -0.076(0.049)(0.078)0.099
0.232 *
(0.085)(0.120)-0.0150.001(0.014)(0.021)0.090
0.211(0.129)(0.192)-0.253 ** -0.409 **(0.123)(0.198)0.428 ** 0.358 *(0.192)(0.205)0.069-0.175(0.325)(0.469)0.089
0.376(0.190)(0.237)0.069-0.078(0.125)(0.146)0.022
0.237(0.202)(0.231)1.209 *** 1.229 **(0.324)(0.475)(10)

All

Recipientvillages
(13)

0.195
(0.174)-0.365(0.241)0.018(0.043)-0.111(0.362)0.118(0.451)-0.752(0.656)-1.409(1.511)-0.379(0.547)-0.147(0.414)0.614(0.721)0.216(2.008)

Recipientvillages(14)

Tarpaulin receipt
by June(Probit)-0.143 *0.236(0.074)(0.169)0.002-0.386 *(0.173)(0.235)-0.033 *0.025(0.019)(0.040)0.029-0.063(0.235)(0.348)-0.1540.091(0.212)(0.429)0.566 *
-0.708(0.286)(0.628)0.394-1.437(0.628)(1.540)-0.128-0.243(0.288)(0.538)0.211-0.131(0.224)(0.404)-0.088(0.342)2.320 ***
(0.610)(12)

Victims

Food aid received per month in JanuaryJune (days)
(OLS)

0.021
0.033
0.015
0.049
-0.127 **(0.037)(0.042)(0.058)(0.060)(0.056)
0.061
0.037
0.109
-0.0250.104(0.062)(0.060)(0.075)(0.098)(0.094)-0.014-0.017 * -0.003-0.046 *** -0.014(0.011)(0.009)(0.014)(0.015)(0.015)-0.069-0.076-0.030-0.2030.063(0.097)(0.082)(0.133)(0.143)(0.144)-0.415 *** -0.417 *** -0.469 *** -0.274 * -0.246 **(0.108)(0.082)(0.164)(0.155)(0.120)0.430 ** 0.437 *** 0.359
0.378
0.380 *
(0.173)(0.146)(0.321)(0.236)(0.226)
0.208
0.196
0.318
0.335
0.002
(0.263)(0.349)(0.652)(0.365)(0.291)-0.040-0.0230.082-0.2070.127(0.154)(0.158)(0.300)(0.226)(0.211)0.015
0.060-0.0580.062
0.031(0.111)(0.157)(0.189)(0.198)(0.112)-0.013-0.141
0.412(0.091)(0.148)(0.311)
0.368
0.545
0.556
(0.260)(0.426)(0.629)(5)

All

Food aid received per month in JanuaryMarch among recipients (days)(OLS)*10% significance, **5% significance, ***1% significance. Column numbers match with those in Table 3.

Clan mean of log of total income per
adult equivalent per month (F$)
Clan population share in the village

Female head dummy

Log of age of household head

Proportion of elderly

Log of adult equivalent household
size
Proportion of children

Adults' secondary education dummy

Log of fishing capital (F$)

Other pre-cyclone factors:
Log of total income per adultequivalent per month (F$)
Log of land holdings (acre)

All

All

Food aid receipt by March(Probit)

Table A1. Determinants of relief allocation unreported in Table 3.

35

-0.058(0.055)-0.022(0.073)-0.013(0.015)-0.174(0.131)-0.897 ***(0.151)-0.706 ***(0.255)0.116(0.334)-0.282(0.201)-0.219(0.145)-0.052(0.052)-0.015(0.063)-0.007(0.017)-0.173 **(0.085)-0.923 ***(0.109)-0.724 ***(0.231)
0.045(0.302)-0.260(0.251)-0.305 **(0.144)-0.332 ***(0.094)-0.683 **(0.299)(2)(1)-0.070(0.077)-0.074(0.130)0.008(0.030)-0.136(0.172)-0.839 ***(0.201)-0.937 **(0.405)0.217(0.437)-0.363(0.428)-0.497 **(0.225)-0.606 ***(0.185)-0.996 **(0.401)

Nonvictims(3)-0.018(0.078)0.069(0.072)-0.022(0.020)-0.250(0.157)-0.865 ***(0.141)-0.845 ***(0.297)-0.006(0.437)-0.279(0.254)-0.102(0.139)-0.146(0.159)-0.226(0.601)(4)

Victims-0.109(0.159)0.148(0.204)0.012(0.039)0.552(0.361)-0.994 **(0.404)0.785(0.612)-0.267(1.185)0.900 *
(0.509)-0.488(0.390)(9)Victims-0.010(0.174)0.123(0.271)
0.026(0.036)
0.663 **
(0.319)-0.986 ***(0.365)0.836 *
(0.507)0.199(1.062)
1.019 **
(0.499)-0.337(0.433)-1.176 **(0.551)-1.807(1.564)(10)

Victims

Complete housing
rehabilitation(Probit)*10% significance, **5% significance, ***1% significance. Column numbers match with those in Table 4.

Clan mean of log of total income per
adult equivalent per month (F$)
Clan population share in the village

Female head dummy

Log of age of household head

Proportion of elderly

Log of adult equivalent household
size
Proportion of children

Adults' secondary education dummy

Log of fishing capital (F$)

Other pre-cyclone factors:
Log of total income per adultequivalent per month (F$)
Log of land holdings (acre)

All

All

Commu nal labor per adult equivalent (days)(OLS)

Table A2. Determinants of rehabilitations unreported in Table 4.

36

37

80%

80

70%

70

60%

60

50%

50

40%

40

30%

30

20%

20

10%

10

0%

0
Jan

Feb

March

April

May

June

July

Aug

Sept

Oct

Month
Proportion of refugee households among those with housing damaged
Cumulative proportion of households who received tarpaulins
Proportion of households who received emergency food aid
Mean amount of food aid received among households who received (days)

Days

Percentage

Figure 1. Cyclone refugees and relief over time.