Tsukuba Economics Working Papers
No. 2011-004

How is disaster aid allocated within poor villages?
by
Yoshito TakasakiMay 2011

UNIVERSITY OF TSUKUBA
Department of Economics
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPANHow is disaster aid allocated within poor villages?
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
May 11, 2011

Acknowledgements
I wish to thank my field team – Jonati Torocake, Viliame Manavure, Viliame Lomaloma,
and 19 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. This paper has benefited significantly from thecomments and suggestions of Takashi Kurosaki, Towa Tachibana, and seminar
participants on an earlier version of this work presented at Hitotsubashi University. This
research has been made possible through support provided by the Sumitomo Foundation,
the Japan Society for the Promotion of Science, and the Ministry of Education, Culture,
Sports, Science and Technology in Japan. Any errors of interpretation are solely theauthor’s responsibility.

How is disaster aid allocated within poor villages?
Abstract
How disaster aid is allocated within poor villages is little understood. This paper
examines risk-sharing institutions and social hierarchies as village self-allocation
mechanisms. Original survey data from Fiji contain rich information about cyclone
damage, traditional kin status, and aid allocations over post-disaster phases, at bothhousehold and kin-group levels. The paper shows under what conditions the performanceof targeting aid to victims can significantly differ from overall risk-sharing outcomes
determined by private transfers and aid (i.e., targeting gap). Elite domination can occur
not only in aid allocation independent of damage, but also in targeting on damage (i.e.,targeting bias).
Keywords: disaster aid; risk sharing; hierarchy; targeting; Pacific; Fiji

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1. INTRODUCTION
Vulnerability to natural disasters is a major barrier to development and poverty
alleviation (Skoufias, 2003), and effectively allocating disaster aid is critically important.
In rural areas, aid agents distribute private goods (e.g., food, water) and public goods(e.g., shelter, infrastructure) across villages. How aid in the form of private goods
distributed to a village is allocated within the village is a critical question that has notbeen sufficiently answered because of a paucity of data. Morris and Wodon (2003), forexample, examine across-household allocation of disaster aid, but their data, whichcontain only five households per village, cannot address the issue of within-village
allocation. As aid agents’ capacity and resources are limited in developing areas, village
mechanisms play major roles in aid allocation. Using original, post-cyclone data in ruralFiji, this paper examines risk-sharing institutions and social hierarchies as self-allocation
mechanisms. 1
Informal risk-sharing institutions are critically important in poor populations (see,for example, Dercon, 2002; Morduch, 1999 for reviews). Although a natural disaster is aregion-wide covariate shock, it may contain significant idiosyncratic components that canbe locally shared, as shown by recent empirical studies (e.g., Mozumder et al., 2009;
Sawada and Shimizutani, 2008; Takasaki, 2011a). I assume that although disaster aid is
distributed through public transfers to the village, its allocation within the village follows
private risk-sharing arrangements (Dercon and Krishnan, 2005, for example, show thatfood aid is allocated as part of informal risk sharing in Ethiopian villages). How well
local ex post risk sharing can work depends on how much pooled resources that can be2shared among people are reduced by the covariate disaster shock and then augmented bydisaster aid.
Targeting disaster aid toward victims – the greater the damage, the higher the
probability of receipt or the greater the amount received – is a common goal. Frequent
reports point to an inefficient distribution of disaster relief by uncoordinated agents wholack pertinent information about the damage (Amin and Goldstein, 2008). Importantly,
when aid is allocated through risk sharing in a village, targeting performance no longer
measures the effectiveness of disaster management. This is because what determines
victims’ welfare is their overall risk-sharing outcome, i.e., how much net aggregate
private transfer, including aid, they receive. Researchers have not yet explored a potential
difference between targeting performance and overall risk sharing, which I call atargeting gap. Distinct from targeting errors (e.g., inclusion/exclusion errors) determining
targeting performance, the targeting gap determines the usefulness of the targeting itself.
The paper shows under what conditions a targeting gap can be significant.
Independent of risk sharing, a social hierarchy can strongly shape the village
governance that determines aid allocation. The paper shows how bargaining between
elites and non-elites can lead to elite domination: Elites are more likely to be recipients or
to receive a greater amount than non-elites. In kin-based Fijian society, hereditary elite
status is of central importance (Turner, 1992); such hierarchical lineage-based societies
are also common in Sub-Saharan Africa (Platteau and Abraham, 2002). Since my survey
stratified households in each village by their kin group and elite status, direct measures of
elite status at the household and kin-group levels are available; in standard household
surveys, in contrast, elite status is often unobservable to researchers, and even if it is3
observable, there are too few elites/elite groups to make a statistical analysis possible. As
such, I can directly capture elite dominance.
Building on kinship, risk-sharing institutions and social hierarchies are notindependent of each other. In particular, kin groups are a major village subgroup in bothrisk sharing and bargaining. The paper shows that the interaction of these two
mechanisms can lead to elite domination in targeting on damage, which I call targetingbias. Targeting bias is a potential source of systematic targeting errors.
The analysis compares emergency food aid in the relief and early recovery phases(6 months after the cyclone) and the provision of housing construction materials mostly
in the recovery/reconstruction phase (a few years after) (see de Ville de Goyet, 2008 for a
description of these three phases). It also compares their allocations within the kin group
and across kin groups, as well as the allocations of food aid on housing and crop damage.
The paper demonstrates how the targeting gap, elite domination, and targeting bias varyover post-disaster phases, at different levels of allocation, and across different shocks.
The rest of the paper is organized as follows. Section 2 describes the Fijian data
and kin-based hierarchy. Section 3 provides descriptive statistics of cyclone damage,relief, and reconstruction. Section 4 develops a conceptual framework of aid allocation
through risk sharing, social hierarchy, and their interaction, deriving testable hypotheseson the targeting gap, elite domination, and targeting bias. Section 5 develops empiricalmodels to test the hypotheses, followed by the results in Section 6. The last section
summarizes major findings and discusses implications for disaster management.
2. DATA, KINSHIP, AND HIERARCHY(a) Data

4
On January 13, 2003, Cyclone Ami swept over the northern and eastern regions of
the Fiji Islands. I conducted two rounds of household surveys in intentionally chosen
native Fijian villages with distinct environmental and economic conditions in thenorthern region (Ami was the only cyclone from 1991 through 2005).2 In each village,
households were stratified by the smallest kin-group unit (defined shortly) and a
combination of individual leadership (also defined shortly) and major asset holdings (e.g.,
shops) (all kin groups are sampled); in each stratum, households were randomly sampled.
The first-round interviews conducted between late August and early November
2003 among 374 households in 9 villages (including 43 clans, defined shortly) cover therelief and early recovery phases (henceforth called relief sample; the analysis is based on
340 households with complete data). The second-round interviews conducted between
July and September 2005 among 906 households in 43 villages (including 7 villages
covered in the first round, and 146 clans) cover the recovery/reconstruction phase(henceforth called reconstruction sample). Both surveys collected information about
demographics, assets, production, income, shocks, disaster aid, and private transfers (butnot consumption).3 Labor-transfer data were not collected in the first round, and labortransfers only in the past one year were collected in the second round. The data lackinformation about specific process of aid allocation.
(b) Kinship and hierarchy
Each native Fijian belongs to a lineage of the vanua-yavusa-mataqali-tokatoka
hierarchy: Vanua consists of several yavusa; yavusa consists of several mataqali; andmataqali consists of several tokatoka (Ravuvu, 1983). Although vanua ranges over
several villages, a village consists of one or few yavusa; mataqali and tokatoka are village

5subgroups. Fijians’ social status is clearly defined as follows. First, among mataqali(henceforth called clan), one to which a yavusa or mataqali chief (clan chief) belongs
holds a higher status than others (where there is no takatoka chief). Second, amonghouseholds, one with a clan leader (either a clan chief or non-chief leader, whose status
is lower than the chief) who plays a major role in the clan’s decision-making andnegotiations among clans holds a higher status than other households. Village chiefs areeither a clan chief or non-chief leader. These kin groups and patrimonial status are ofcentral importance not only for village governance and ritual, but also for livelihoods(Turner, 1992); in particular, land is communally owned by mataqali (about 83% of thecountry’s total land is communal and cannot be sold by law), and customary rights forcoastal fishing are held by vanua or several yavusa.
In the relief sample, about 9% of households have a clan leader and about 22%
belong to clan-chief’s clans (clan chiefs themselves are very uncommon, see Table 1).
About 14% of households have a clan leader or a leader of tokatoka (henceforth called
sub-clan) (sub-clan leaders’ status is lower than clan leaders’), and about 42% belong toclan- or village-chief’s clans. The distributions of these elites and elite groups in thereconstruction sample are similar.
3. CYCLONE DAMAGE, RELIEF, AND RECONSTRUCTION(a) Cyclone damage
The total damage across the country caused by Cyclone Ami is estimated at
F$104 million (F$1 = US$.60), of which dwelling damage is F$22 million and crop
damage is F$40 million (National Disaster Management Office, 2003). In the sample
villages, public-health problems were not a major issue: Respondents reported no6casualties and very limited injuries and illnesses caused directly by the cyclone.
Household migration after the cyclone was almost nonexistent.
According to respondents’ subjective assessments, in the relief sample, 8% and
45% of their main houses were completely destroyed and partially damaged, respectively,
and the comparable figures in the reconstruction sample are 19% and 34% (Table 1)4(although many, but not all, households also had other free-standing units, such as
kitchens, showers, and toilets, the paper focuses on damage to the main houses). Almost
40% of households with housing damaged in the relief sample became refugees whostayed in others’ residences in the same village. About two thirds of the refugees lived
with households in the same clan; hence, the clan served as a risk-sharing group.
Almost all households engaged in cropping and fishing. In the relief sample,
cropping accounted for over one half of the total income before the cyclone, followed byfishing, with a 30% income share. About 87% of households experienced crop damage,and the mean value of damaged crops was F$35 per adult equivalent, which was about
60% of the mean monthly pre-cyclone crop income.5(b) Relief
The Red Cross, other nongovernmental organizations, and governments
provisioned relief, and food aid was the largest form of relief in the region. In the relief
sample, respondents were asked the quantity of food aid received in each monthmeasured in the number of days it would have taken to consume the food in normal
periods, not the actual duration (foodstuffs in relief were mostly uniform across villages).
The main delivery started in March; by the end of March, over three fourths ofhouseholds received food aid; and by April almost all got some (Table 1). In the first7
three months, recipient households received about 13 days worth of food per month, on
average, and the mean amount of food aid received in the six-month period among all
households was about 10 days per month. The value of 60 days ration for six months isequivalent to about F$100 per capita, almost three times the average crop damage per
adult equivalent. As households also collected harvestable damaged crops, food shortage
was not a major issue. Households individually rehabilitated cropping; they planted fastgrowing crops (e.g., sweet potato) after seeds were provisioned as part of the relief.
Although the harvest had already started at the time of interviews in 2003, the mean crop
income was still over 40% lower than the pre-cyclone level; cropping recovered after thattime.
(c) Reconstruction
In the second-round survey, each household was asked whether it had received
construction materials, and the recipient was asked about the year and month of receipt
and its monetary value. Provisions in the first year (2003) were very limited; only 5% ofhouseholds were recipients (Table 1).6 In addition to the co-residence for refugees, people
helped others’ housing rehabilitation; at the time of interviews in 2003, 38% of houses
completely destroyed had been rebuilt, 62% of dwellings (including free-standing units)
had completed repairs, and refugees were uncommon.
Even after the provision of construction materials augmented in the second year,
mutual help was crucial; although carpenters were sometimes sent to villages to help
build new houses, villagers did most of the work. At the time of interviews in 2005,
recipients reached 25% of households; although almost no households without dwelling
damage were recipients (leakage), the limited supply resulted in significant under-

8
coverage. Provisions of full construction materials for new house building were delayed
until the fourth quarter of the second year. The mean amount received among allrecipients was F$2,680. Among households with housing completely destroyed, 40% had
rebuilt their houses; 51% of recipients and 20% of non-recipients had done so(information about repairing is lacking in the reconstruction sample). Hence, provisionsof construction materials greatly helped housing rehabilitation, but the supply was far
short of demand, and the amount received by recipients was insufficient, especially for
new house building. As a result, self-reconstruction (with mutual help) was relatively
common.
4. RISK SHARING AND HIERARCHY(a) Risk sharing
I assume that households seek to smooth utility determined by consumption,
leisure, and housing quality against crop damage (income shock) and housing damage(preference shock). There is no housing market, and health shock, savings, credits,migration, and across-village transfers are ignored. Ex post risk sharing consists of nonlabor sharing – cash and inkind (e.g., food) – to smooth consumption and housing quality
and labor sharing for housing rehabilitation within the village; crop rehabilitation does
not involve labor sharing.7 How well risk sharing can work depends on pooled resourcesthat can be shared. On one hand, non-labor resources are greatly reduced by crop damage
and for smoothing consumption and rehabilitating housing; on the other hand, given thatlabor endowment is largely intact, labor resources do not decrease much, except for labor
supply for own crop and housing rehabilitation and earning extra income.8 Thus, thepotential for labor sharing is higher than that for non-labor sharing. How well labor9sharing can work depends on the distribution of potential recipients (those with housing
damaged) and donors (those without); the smaller the ratio of the former to the latter, i.e.,the smaller the degree of covariate shocks, the more effective is the labor sharing (thisratio was about one half in Fiji). Overall risk-sharing outcome is measured by net
aggregate private transfers (labor and non-labor) received by households against theirshocks.
I assume that disaster aid, consisting of non-labor resources distributed to thevillage by aid agents, is allocated in the village as part of risk sharing. Disaster aid
augments non-labor resources, thereby substituting for non-labor transfers (for simplicity,
perfect substitutability is assumed). Then, overall risk-sharing outcome is determined byaggregate private transfers, including aid; in contrast, targeting performance is measured
by aid responses to household shocks. The greater the gap between these two, i.e., thetargeting gap, the less useful is targeting performance. If risk-sharing groups are clusteredin the village so that risk sharing consists of one among households within the group andanother across groups, the targeting gap across groups is defined on group-level covariate
shocks.
What determines the magnitude of the targeting gap? First, on one hand, in therelief and early recovery phases, when emergency food aid is the dominant form of aid,
housing rehabilitation relies on risk sharing; as labor/non-labor transfers for housing
rehabilitation and non-labor transfers, including food aid for consumption smoothing,coexist, the targeting gap can be large. On the other hand, in the recovery/reconstruction
phase, when consumption smoothing is not a major concern (after crop rehabilitation)and construction materials are the only aid, labor/non-labor transfers for housing

10
rehabilitation are major risk-sharing arrangements. In contrast to food aid, which
substitutes for non-labor transfers, construction materials complement labor transfers,
because they facilitate rebuilding/repairing; thus, the targeting gap should be small.
Hypothesis 1-1: The earlier the post-disaster phase, the greater is the targeting gap.
Next, the stronger the risk sharing, the greater is the potential targeting gap.
Suppose that risk sharing is stronger within the group than across groups, because of thecloser connection among group members and a smaller degree of covariate shocks within
the group than across groups;9 then,Hypothesis 1-2: The targeting gap in the within-group allocation is greater thanthat in the across-group allocation.
The targeting gap of food aid also depends on whether or not shocks involve labor
sharing.
Hypothesis 1-3: The targeting gap on housing damage is greater than that on crop
damage.
(b) Hierarchy
I assume that the allocation of disaster aid in the village is determined bybargaining between elites and non-elites, independent of risk sharing. Elite domination
can take a form of either elite capture or norm-based prioritization without involving
capturing (henceforth called elite norms). Elite capture has received much attention incommunity-based development (e.g., Bardhan, 2002; Conning and Kevane, 2002;
Platteau and Abraham, 2002); in kin-based societies with hereditary elite status like Fiji,strong elite norms underlie social equilibrium. What distinguishes between elite capture
and norms is the status quo. On one hand, in elite capture, with elite neutrality as the11status quo, elites compare benefits and costs of capturing disaster aid, where social costs
can include reputation damage and non-elites’ antipathy. It is possible that elites will
allocate aid to non-elites (i.e., elite inferiority) if the benefits of doing so (social benefits
and prioritization in private transfers as a counterpart) outweigh the costs (giving up theaid). Some studies show that elites’ capturing program benefits is not necessarily
pernicious to community development, because elites might take actions that benefit nonelites (i.e., ‘benevolent capture’, Mansuri and Rao, 2004). On the other hand, according
to elite norms with elite dominance as the status quo, non-elites compare benefits andcosts of not conforming to such norms (getting the aid vs. social sanction).
Differentiating between elite capture and norms requires detailed data on the allocation
process (which the Fijian data lack).10 Bargaining between elite and non-elite groups isanalogous.
The magnitude of elite dominance in aid allocation is determined by thedifference in social ranks between elites and non-elites. In both elite capture and norms,
the larger the rank difference, the smaller is the elites’ costs of capturing and the larger is
non-elites’ costs of non-conformity to the norms;11 i.e.,
Hypothesis 2: The stronger the social hierarchy, the greater is elite dominance.
In other words, as the rank difference gets smaller, elite dominance becomes weaker;
with no rank difference, elite dominance vanishes.
(c) Interaction of risk sharing and hierarchy
Risk sharing and social hierarchy can interact with each other in the allocation ofdisaster aid in two ways. First, independent of risk sharing, the greater elites’ damage
relative to non-elites’, the smaller are elites’ costs of capturing and the greater are non-

12elites’ costs of non-conformity to norms (i.e., elites’ relative damage serves like theirstatus).
Hypothesis 3-1: The greater elites’ damage relative to non-elites’, the greater iselite dominance.
A difference in damage by rank can augment or reduce elite domination; it is possiblethat elites of the same status exhibit inferiority and domination in the allocation
depending on their damage.
Second, I conjecture that elites are prioritized in overall risk-sharing arrangements,
i.e., aggregate private transfers, including disaster aid, more strongly respond to elites’
damage than non-elites’. Whether this pattern is observed in aid allocation depends on themagnitude of the targeting gap.
Hypothesis 3-2: When the targeting gap is small, aid allocation more strongly
responds to elites’ damage than non-elites’.
Then, targeting performance is stronger for elites than non-elites; that is, elite domination
also exists in targeting on damage, i.e., targeting bias. The pattern in hypothesis 3-1 also
effectively gives rise to targeting bias. As the targeting gap makes targeting performanceless useful, targeting bias loses its usefulness as a potential source of systematic targeting
errors when the targeting gap is large; for example, elite inferiority in aid allocation may
reflect strong elite domination in other private transfers. Analogous to hypothesis 2, therank difference between elites and non-elites determines the magnitude of targeting bias:
i.e.,Hypothesis 3-3: The stronger the social hierarchy, the greater is the targeting bias.
5. ECONOMETRIC SPECIFICATION

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I employ three empirical models for food aid (relief model) and housing
construction materials (reconstruction model). The first model focuses on the allocationof disaster aid within clans. I conjecture that aid allocation to household i, yi, isdetermined by within-clan risk sharing against household-level shocks (housing and crop
damage), Xi, and its social status, Zi. A reduced-form model is:yi

D1  E1 X i  J 1 Z i  G 1Wi  G  ei ,(1)where Wi is other household characteristics that determine the allocation (defined below);
G is clan dummies, which control for clan-level covariate shocks, total aid allocated tothe clan, and clan’s social status; and ei is an error term.12 Targeting performance ismeasured by positive ȕ1, and elite domination/inferiority is measured by positive/negative
Ȗ1.
Equation (1) can be extended to net aggregate private transfers received(including disaster aid and co-residence); let ȕ*1 denote the corresponding coefficient for
Xi. The targeting gap is ȕ*1 - ȕ1. For a given level of overall risk sharing ȕ*1, the smallerthe ȕ1, the greater is the targeting gap; ȕ1 can even be negative. The lack of complete
information about labor transfers in the current data precludes me from estimating ȕ*1 andthus the targeting gap. My empirical strategy to capture the targeting gap relies on aid
allocation negatively responding to shocks; negative ȕ1 indicates compensated privatetransfers in other forms, because targeting errors per se do not make ȕ1 negative. If
estimated ȕ1 is negative for food aid and positive for construction materials, thenhypothesis 1-1 holds.13
The second model captures within- and across-clan allocations in the village:yi

D  E1 X i  J 1Z i  G1Wi  E 2 X g  J 2 Z g  G 2W g  V  ei ,

(2)

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where Xg ,Zg, and Wg, respectively, are clan g’s cyclone damage, social status, and othercharacteristics that affect the allocation; and V is village dummies, which control forvillage-level covariate shocks and total aid allocated to the village (as well as village’s
social status, Takasaki, forthcoming-a). If aid is allocated across households at only thevillage level, clan-level factors are redundant; that is, clans do not serve as a risk-sharing
group, or aid allocation is part of risk sharing only at the village level. In contrast, thesignificant impacts of both clan- and household-level shocks suggest that not only thevillage but also clans serve as a risk-sharing group, as assumed in equation (1) (Morduch,
2005; Munshi and Rosenzweig, 2009), and aid allocation is part of risk sharing at boththe village and clan levels. In the relief model, negative ȕ1/ȕ2 for housing damage andnegative ȕ1/positive ȕ2 for crop damage support hypotheses 1-2 and 1-3 (that ȕ1 is greaterfor housing damage than crop damage in magnitude cannot be tested, as shown below).
Positive/negative Ȗ2 captures elite clans’ domination/inferiority. If the estimation resultsof household variables in equation (2) are similar to those of (1) with clan factors fully
controlled for, then unobserved clan factors in (2) are unlikely to cause significant bias.
The third model captures the potential interaction of risk sharing and hierarchy byadding interaction terms of cyclone damage and social status to equation (2):yi

D  E1 X i  J 1 Z i  K1 X i Z i  G 1Wi  E 2 X g  J 2 Z g  K 2 X g Z g  G 2Wg  V  ei 㸬(3)Targeting bias is captured by positive Ș1/Ș2: Hypotheses 3-1 and 3-2 can be tested byexamining the marginal effects of social status and cyclone damage, respectively.
All equations are estimated by OLS. Allocation rules may be distinct between the
allocations of recipients and amount received among recipients and may change as aid
supplies augment over time. In the relief model, I first analyze receipt (linear probability

15
model) and the amount received per month (log), conditional on receipt, in the first threemonths (relief phase), and then the amount received per month (log) in six months(including the early recovery phase). In the reconstruction model, receipt and the amountreceived, conditional on receipt, are estimated in the first year (early recovery phase), in
two years (including the recovery/reconstruction phase), and in two years and 9 months(up to the interviews in 2005) separately. This hurdle model is commonly used inprevious works (e.g., Dercon and Krishnan, 2005; Jayne et al., 2002).14 If negative ȕ1 isfound only in the first three months, then hypothesis 1-1 is further supported.
Household damage is captured by a dummy for damaged housing and the value ofcrop damage per adult equivalent (log) in the relief model,15 and two dummies forhousing completely destroyed and partially damaged in the reconstruction model. Clandamage is measured by the proportion of households with damaged housing in the clan
and the clan-mean of crop damage per adult equivalent in the relief model, and twovariables for the proportions of households with complete and partial housing damage inthe clan in the reconstruction model. Two sets of household and clan status are
considered: clan leader and clan-chief’s clan vs. clan/sub-clan leader and clan-/villagechief’s clan. Recall that the former (the latter’s subset) captures higher ranks than thelatter. If the estimated positive Ȗ1/Ȗ2 and Ș1/Ș2 of the former are greater than those of thelatter, then hypotheses 2 and 3-3, respectively, are supported. Other household and clan
factors are standard ones, such as demographic factors and assets.16
6. ESTIMATION RESULTS(a) No interaction of risk sharing and hierarchy

16
Estimation results of cyclone damage and social status in the relief andreconstruction models are reported in Tables 2 and 3. In each table, panels A and B,
respectively, show results for models with clan leader and clan-chief’s clan and models
with clan/sub-clan leader and clan-/village-chief’s clan (results of cyclone damage notshown in panel B are very similar to those in panel A); in each panel, results of equations(1) and (2) are organized by period of interest and then receipt/amount (robust standard
errors are reported and clustered by clan in models with village dummies).17
Estimation results of household variables in equation (2) are almost the same as
those in (1). In the first three months, food-aid recipients are more common amonghouseholds without damaged housing that offered help for refugees and housing
rehabilitation than among others that received such help (qualitatively the same
comparison holds in the descriptive statistics, Takasaki, 2011d). Among recipients, agreater amount is allocated to clans with larger crop damage and then households withsmaller crop damage, which could contribute more to labor sharing because of theirsmaller crop rehabilitation. These findings are consistent with hypothesis 1-2; as private
risk sharing, especially labor sharing within the clan, against housing damage wasprioritized, a large targeting gap emerged. Consistent with hypothesis 1-3, the allocationin six months is negatively associated with clan-level housing damage only. It is neutral
to all other shocks; thus, the allocation rule in the relief phase was reversed later,
supporting hypothesis 1-1.
In contrast, the allocation of construction materials – both receipt and amountreceived – strongly responded to household damage over time. Thus, combined with therelief results, hypothesis 1-1 strongly holds. As the supplies of construction materials

17(especially large ones) augment, targeting performance improves: The probability ofreceipt by households with housing completely destroyed increased from .20 in the firstyear to .41 in two years and then .63; the probability of receipt by those with partial
damage increased from .17 to .31 in two years and then was stable. The allocation isneutral to clan-level shocks; the only exception is that clans with greater damage – bothcomplete and partial damage – receive larger amount in two years. It thus appears thatclans play a limited role in the late post-disaster phase (I return to this below).
Social status does not strongly alter the allocation of food aid; although clan
leaders are less likely to be recipients in the first three months, this pattern loses
statistically significance in equation (3) (according to the joint significant test, as shown
below). In contrast, the social status of households, but not clans, positively affects the
allocation of construction materials: Clan leaders dominate receipt in two years and in
two years and nine months (20% and 14% marginal effects, respectively, in the models
with clan dummies) and amount received in two years (54% marginal effect); clan/subclan leaders do not significantly affect receipt in two years and nine months. That clan
leaders’ domination persists longer than sub-clan leaders’ supports hypothesis 2. As such,it appears that elite domination at the clan level is nonexistent over the post-disaster
phases (I return to this shortly).
(b) Interaction of risk sharing and hierarchy
Estimation results of equation (3) are reported in Tables 4 and 5. Althoughconsidering four interaction terms of cyclone damage and social status – two at thehousehold level and another two at the clan level – is possible, I can include only those ofcrop damage with elite status (household and clan levels) in the relief model and those of

18clan status with housing damage (complete and partial damage) in the reconstruction
model; variations in those of housing damage and household status, respectively, are too
limited. In the reconstruction model, no amount equation with interaction terms is
estimated, because of limited variations among recipients.
I first focus on models with clan leader and clan-chief’s clan (panel A). In the
allocation of the amount of food aid received, chief’s clans with small and large crop
damage show elite inferiority and domination, respectively, in both three months and in
six months (positive Ș2) (the joint significance test for clan status is statisticallysignificant in three months and the result in six months is statistically weaker); clan
leaders are nonsignificant (this is also true when equation 1 is extended by adding thehousehold-level interaction term). In the reconstruction model, receipt in the first year isdominated by clan-chief’s clans with more housing damage (positive Ș2 for complete
destruction) (the joint significance test for clan status is statistically significant). These
results support hypothesis 3-1.
In the allocation of the amount of food aid received in response to clan-level crop
damage in three months, the response of clan-chief’s clans is over four times more thanthat of other clans (the joint significance test for clan-level damage is statisticallysignificant at a .1% significance level). The allocation of receipt of construction materials
in the first year responds to completely destroyed houses in clan-chief’s clans only (thejoint significance test for clans’ housing complete damage is statistically significant).
These results are consistent with hypothesis 3-2 (recall that the targeting gap is small inthese allocations). Clan-chief’s clans are more likely to receive construction materials in
two years and in two years and nine months, unless partial housing damage is very

19
common (I return to this shortly). Note that although clan status with no interaction andclan status interacted with complete housing damage are nonsignificant according to the ttest, their estimated coefficients are large and jointly significant at least at a 1%
significance level. Hence, distinct from the earlier findings on equation (2), in both relief
and reconstruction models, clan-chief’s clans with large damage are always prioritized;that is, a strong targeting bias exists in the across-clan allocation.
Estimation results on clan-level partial housing damage in the reconstruction
model (negative Ș2 for partial damage) are opposite to hypotheses 3-1 and 3-2. First, inthe allocation of receipt in two years and in two years and nine months, clan-chief’s clans
with more partial damage exhibit elite inferiority. Second, the allocation of receipt in two
years and nine months negatively and positively responds to clan-chief’s clans’ and otherclans’ partial damage, respectively. These patterns coincide with clan leaders’ dominancefrom the second year, when the provision of construction materials augmented. It seems
that although households with partial damage were relatively prioritized in the clan in thefirst year, from the second year, those with complete damage became the dominantpriority at the cost of those with partial damage, while maintaining clan leaders’ priority.
Estimation results for models with clan/sub-clan leader and clan-/village-chief’sclan (panel B) are much weaker, and almost all joint significance tests (corresponding tothose shown in panel A) are nonsignificant (results not shown; as the only exception,clan-level crop damage on the amount of food aid received in three months is statisticallysignificant at a 10% significance level). Hence, hypotheses 3-1 and 3-2 more strongly
hold for households and/or clans with higher elite status in both relief and reconstruction
models; that is, hypothesis 3-3 holds over post-disaster phases. Nonsignificant results on

20
partial housing damage in panel B buttress the importance of clans’ high rank underlying
the potential interaction effects discussed above.
7. CONCLUSION
This paper examined risk-sharing institutions and social hierarchies as selfallocation mechanisms of disaster aid within poor villages. The paper highlighted atargeting gap, a difference between targeting performance and overall risk sharing, whichmakes standard targeting less useful. First, the earlier the post-disaster phase, the greater
is the targeting gap, because private risk sharing significantly makes up limited aid.
Second, the targeting gap within kin groups, a major risk-sharing group, is greater than
the targeting gap in the across-group allocation. Third, the targeting gap of food aid on
housing damage, against which risk-sharing’s making-up plays a role, is greater than thaton crop damage. Bargaining on aid allocation between elites and non-elites can lead toelite domination, through elite capture or norms, which are difficult to distinguish. Risk
sharing and social hierarchy can interact with each other, leading to elite domination intargeting, i.e., targeting bias. The stronger the social hierarchy, the greater is the elite
dominance and the targeting bias.
Using original post-cyclone survey data in rural Fiji, the paper showed supporting
evidence for these hypothesized relationships. First, households with damaged housing
and greater crop damage are allocated less food aid in the early phase, because theyreceive greater net private transfers in other forms, especially in labor sharing for housing
rehabilitation; this form of targeting gap is especially strong within kin groups and on
housing damage. In contrast, the allocation of housing construction materials in the latephase is strongly targeted on housing damage. Second, elites, especially highly ranked

21ones, dominate the allocation of construction materials. Third, there exists a targetingbias toward highly ranked kin groups in both food aid and construction materials.
These results lead to the following implications for disaster management:
1) Risk-sharing institutions can serve as village self-allocation mechanisms ofdisaster aid. Maintaining and strengthening local safety nets is an effective policy,
which is consistent and can be linked with broad community-based development(Mansuri and Rao, 2004; World Bank, 2002).
2) Targeting errors are only a partial problem. In the relief phase with a large
targeting gap, overall risk sharing needs to receive direct attention; however,information about it is lacking the most at that time. Thus, strengthening existing
local institutions ex ante is critically important.
3) In hierarchical societies, elite domination in aid allocation and targeting can bestrong in any post-disaster phase. As risk-sharing institutions can be strongly built
on traditional local hierarchies, policies neutralizing elite capture may weaken
local safety nets; such intervention is questionable for norm-based domination.
Policymakers need to tackle a challenging tradeoff between the efficiency ofoverall risk sharing and the equity of aid allocation.
Overall, for better allocation of disaster aid within villages, policymakers and researchers
need to pay attention to targeting gaps (risk-sharing link), elite domination (hierarchy),and targeting bias (hierarchical risk sharing).
Notes
1

Obtaining a better understanding of the allocation of disaster aid is critically importantin small island states (Bertram, 1986); some researchers criticize the deterioration of

22islanders’ indigenous mechanisms in coping with cyclones because of their increasing
dependency on emergency aid (e.g., Campbell, 1984).
2

Almost all villages are located on Vanua Levu and Taveuni Islands, the second- andthird-largest islands in the country, which significantly lag behind the largest island, Viti
Levu, where the state capital, two international airports, and most tourism businesses are
situated. The study does not cover Indo-Fijians.
3

Like other post-disaster surveys, information about cyclone damage and aid allocation

was collected retrospectively. Takasaki (2011d, forthcoming-a) demonstrates thatsystematic measurement errors are unlikely to be a major concern.
4

Relief officers used the same damage categories for their assessments, and thus thedamage status of each house was common knowledge among villagers.
5

Crop damage was calculated based on the quantity damaged for each major crop, asreported by respondents. Households employ traditional cropping practices (using no
mechanized equipment or animal traction and limited purchased inputs) to produce
mainly taro, cassava, coconut, and kava.
6

In the relief sample, 16% of households received tarpaulins that could be used as

emergency shelters and for temporary repair (Takasaki, 2011d analyses their allocation).
7

Takasaki (2011d) offers the following evidence for labor sharing against housing

damage. First, households with damaged housing and larger crop damage contribute lesslabor for village rehabilitation. Second, crop income (at the time of interviews in 2003) isneutral to crop damage as a result of own crop rehabilitation being intensified inproportion to crop damage. Accordingly, households with greater crop damage contribute23

less to labor sharing and receive smaller net labor/non-labor transfers, being less likely tocomplete housing rehabilitation. Takasaki (forthcoming-b) addresses the disaster-gendernexus, showing that female-headed households are disadvantaged because of a newly
emerging gendered division of labor for housing rehabilitation.
8

Takasaki (2011a) finds that households augmented fishing and handicraft selling in

response to crop damage.
9

The decomposition of the variance of housing and crop damage indicates that mostvariance exists at the household level, followed by clan-level variance (Takasaki, 2011a).
In addition to co-residence for refugees, the importance of clans as a risk-sharing group isevident from the following: 1) Households without housing damage helped with the
housing rehabilitation of other clan members by intensifying fishing (Takasaki, 2011a);2) Clan members formed major household transfer networks (Takasaki, 2011b); 3) Risk
sharing against illness (through non-labor transfers) was arranged mainly among clan
members in 2005 (Takasaki, 2011c).
10

Noncompliance to elite norms is unlikely in the study area (there is no anecdotal

evidence for that); then, elite inferiority, if any, is based on elites’ decisions.
11

It is possible that the higher elites’ status relative to non-elites’, the smaller are elites’

benefits of capturing and the greater are non-elites’ benefits of non-conforming, because
social status can be positively correlated with household asset holdings, which determine
the household’s self-coping capacity. If the elasticity to the rank difference of elites’ costsof capturing and non-elites’ costs of non-conforming is greater than that of theircorresponding benefits, then hypothesis 2 still holds.

24

12

Whether households rebuild/repair their housing without receiving construction

materials certainly affects the allocation, but this endogenous decision does not appear as
an explanatory variable in the reduced-form equation (1).
13

Although crop damage is less observable than housing damage, the observability

problem may not be so significant among clan members who own land communally.
14

An alternative sample-selection model is infeasible with the current data, which lackthe identifying instruments required to credibly estimate the selection equation.
Estimating the relief model in the second three months and the reconstruction model inthe second and third year could be considered, but this would require using the receipt or
the amount received in previous period(s) as a lagged dependent variable, theendogeneity of which cannot be controlled for with these data.
15

Differentiating between complete and partial damage is infeasible, because the former

is relatively uncommon (cf. reconstruction sample). Adding an interaction term between
housing and crop damage does not alter the results reported below (Takasaki, 2011d).
16

In the relief model, other household factors include: income per adult equivalent permonth (log), land holdings (log), fishing capital (log), a dummy for secondary education
among any adults, household adult equivalent size (log), proportions of children andelderly, age of household head (log), and a dummy for female head. All are measured
before the cyclone. Although post-disaster income is affected by disaster aid and privatetransfers, as well as crop damage and rehabilitation, pre-disaster income is not. It is still
possible that unobserved factors determining income, such as ability, affect aid allocationas part of risk sharing; the same concern also applies to productive assets. I estimated25

models excluding income, land, and fishing capital, finding almost the same results forthe remaining variables. Other clan factors consist of the share of households belonging
to the same clan in the village (in the population), the clan-mean of income, land, andcapital. Clan size can affect bargaining power; as elite clans tend to be large, controllingfor clan size is crucial to identify the effects of clan status. In the reconstruction model,
all household factors in the relief model except for income, land, and capital, measured at
the time of interviews, are used as controls. Clan factors are clan size and clan-mean land.
As almost no households newly emerged or vanished after the cyclone, these measureslargely capture pre-cyclone characteristics. The appendix reports the descriptive statisticsof these controls; their estimation results are mostly the same as those in Takasaki (2011d,
forthcoming-a).
17

The relief equation (1) focuses on clans including both recipients and non-recipients;

otherwise, clan dummies perfectly predict the allocation of receipt. Similarly, equations(2) and (3) can be applied to villages with such variations (villages with only one clan are
excluded). The numbers of observations for the amount equation conditional on receipt
further decline. In the reconstruction sample for receipt in the first year, 16% ofhouseholds are recipients (cf. 5% in the whole sample); the corresponding amountequation is not estimated because of the small number of recipients. Probit estimates forreceipt in both relief and reconstruction models are very similar to the OLS results.
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29
Table 1. Means of social status, cyclone damage, food aid, housing
construction materials, and rehabilitations per household.

Social status:
Clan chief (0/1)
Clan leader (0/1)
Clan/sub-clan leader (0/1)
Clan chiefs' clan (0/1)
Clan-/village-chiefs' clan (0/1)
Cyclone damage:
Housing damaged (0/1)
Housing completely damaged (0/1)
Housing partially damaged (0/1)
Crop damaged (0/1)
Crop damage per adult equivalent (F$)
Food aid:
Receipt (0/1)
In 3 months
In 6 months
Amount per month (days)
For 3 months (recipients only)
For 6 months (whole sample)
Construction materials:
Receipt (0/1)
In 1 year
In 2 years
In 2 years and 9 months
Amount received (recipients only) (F$)
In 1 year
In 2 years
In 2 years and 9 months
Rehabilitations:
Crop income per adult equivalent per month (F$)
Before the cyclone
At the time of interviews
New house building at the time of interviews(households with housing completely damaged
only) (0/1)
Recipients of construction materials
Non-recipients of construction materials
Complete dwelling repair (households withhousing damaged only) (0/1)
No. observations
Note - Standard deviations are shown in parentheses.

Relief sample

Reconsturction
sample0.01 (0.11)
0.09 (0.29)
0.14 (0.34)
0.22 (0.42)
0.42 (0.49)

0.03 (0.16)
0.11 (0.31)
0.19 (0.40)
0.27 (0.44)
0.47 (0.50)

0.53 (0.50)
0.08 (0.27)
0.45 (0.50)
0.87 (0.33)
35.1 (46.1)

0.53 (0.50)
0.19 (0.39)
0.34 (0.47)

0.77 (0.42)
0.95 (0.21)
12.9 (7.9)
9.8 (6.5)

0.05 (0.21)
0.19 (0.39)
0.25 (0.43)
2159 (2888)
2137 (2868)
2680 (3032)

60.9 (90.0)
34.7 (59.0)

0.38 (0.50)

152.2 (325.9)

0.40 (0.49)
0.51 (0.50)
0.20 (0.41)

0.62 (0.49)
340

906

30
Table 2. Allocation of emergency food aid - OLS with no interaction term.
Period after the cyclone

3 months
Receipt(1)(2)
A. Models with clan leader and clan-chief's clan.
Cyclone damage:
Housing damaged (0/1)-0.127 ** -0.122 **(0.053)(0.057)Log of crop damage per adult-0.023-0.023equivalent (F$)(0.016)(0.015)
Proportion of housing damaged-0.096in the clan
(0.112)Clan-mean of log of crop
0.027
damage per adult equivalent (F$)(0.053)Social status:
Clan leader (0/1)-0.185 *-0.213 *(0.111)(0.117)Clan-chief's clan (0/1)
0.010(0.068)6 months

Log amount permonth amongrecipients (days)(3)(4)

Log amount permonth (days)
(5)

(6)

0.055
0.055
(0.068)(0.081)-0.085 *** -0.084 ***(0.023)(0.030)-0.219(0.143)0.254 ***
(0.085)-0.128(0.087)-0.019(0.027)-0.133(0.083)-0.023(0.031)-0.459 **(0.222)0.039(0.141)

0.138(0.176)-0.180(0.150)-0.170(0.176)0.083(0.125)

Yes
No
0.237
325

No
Yes
0.158
327

-0.091(0.115)-0.140(0.152)-0.089(0.113)0.100(0.188)-0.155(0.116)

Clan dummies
Yes
No
Yes
No
Village dummies
No
Yes
No
Yes
R-squared
0.163
0.118
0.480
0.448
No. observations
325
327
249
252
B. Models with clan/sub-clan leader and clan-/village-chief's clan.
Social status:
Clan/sub-clan leader (0/1)-0.103-0.1280.060
0.067(0.082)(0.085)(0.109)(0.124)Clan-/village-chief's clan (0/1)-0.003-0.138 *(0.059)(0.071)*10% significance, **5% significance, ***1% significance. Robust standard errors are shown in parentheses;
those in columns (2), (4), and (6) are clustered by clan. Other controls not shown here are pre-cyclone
income per adult equivalent per month (log), land holdings (log), fishing capital (log), a dummy for secondary
education among any adults, household adult equivalent size (log), proportions of children and elderly, age ofhousehold head (log), a dummy for female head , and constant. Proportion of households belonging to thesame clan in the village, and clan-means of pre-cyclone income per adult equivalent per month, landholdings, and fishing capital are also included in columns (2), (4), and (6). Cyclone damage variables are also
included in panel B.

31

Table 3. Allocation of housing construction materials - OLS with no interaction term.
Period after the cyclone

2 years

1 yearReceipt(1)(2)
A. Models with clan leader and clan-chief's clan.
Cyclone damage:
Housing completely destroyed
0.202 *** 0.166 **(0/1)(0.066)(0.066)
Housing partially damaged (0/1)
0.166 *** 0.081 **(0.056)(0.032)
Proportion of housing completely-0.035destroyed in the clan
(0.111)
Proportion of housing partially
0.062
damaged in the clan
(0.070)Social status:
Clan leader (0/1)
0.028
0.037
(0.093)(0.049)Clan-chief's clan (0/1)
0.040(0.035)

Receipt(3)(4)

2 years and 9 months

Log amount amongrecipients (F$)
(5)

(6)

Receipt(7)(8)

Log amount (F$)
(9)

(10)

0.411 *** 0.360 ***(0.053)(0.066)0.314 *** 0.228 ***(0.046)(0.046)0.079(0.137)-0.024(0.096)2.655 *** 2.150 ***(0.297)(0.511)0.611 **
0.529
(0.295)(0.393)2.070 **
(0.963)2.321 **
(0.911)0.628 *** 0.577 ***(0.048)(0.053)0.312 *** 0.227 ***(0.041)(0.048)0.034(0.113)0.094(0.082)2.734 *** 2.416 ***(0.233)(0.356)0.758 *** 0.757 **(0.236)(0.351)-0.133(0.643)-0.283(0.693)0.203 *** 0.131 **(0.073)(0.052)0.022(0.046)0.540 *
(0.278)0.977 **
(0.394)0.164(0.664)0.137 **
0.109 **
(0.063)(0.051)
0.045(0.033)0.375(0.301)0.512(0.418)0.371(0.514)

Yes
No
0.685
129

No
Yes
0.661
95

Yes
No
0.387
615

No
Yes
0.376
652

Yes
No
0.671
173

No
Yes
0.617
137

0.054
(0.051)0.070(0.044)-0.013(0.033)0.410(0.308)0.290(0.364)0.214(0.352)Clan dummies
Yes
No
Yes
No
Village dummies
No
Yes
No
Yes
R squared
0.127
0.093
0.277
0.255
No. observations
262
404
546
568
B. Models with clan/sub-clan leader and clan-/village-chief's clan.
Social status:
Clan/sub-clan leader (0/1)-0.0070.013
0.143 **
0.128 ***
(0.078)(0.041)(0.059)(0.041)Clan-/village-chief's clan (0/1)-0.022-0.035(0.029)(0.046)

0.615 **
0.890 **
(0.294)(0.379)-0.398(0.395)*10% significance, **5% significance, ***1% significance. Robust standard errors are shown in parentheses; those in columns (2), (4), (5), (6), (8), (9), and (10)are clustered by clan. Other controls not shown here are a dummy for secondary education among any adults, household adult equivalent size (log), proportionsof children and elderly, age of household head (log), a dummy for female head, and constant. Proportion of households belonging to the same clan in the village
and clan-mean of land holdings are also inlcuded in columns (2), (4), (6), (8), and (10). Cyclone damage variables are also included in panel B.

32
Table 4. Allocation of emergency food aid - OLS with interaction terms.
Period after the cyclone

A. Models with clan leader and clan-chief's clan.
Cyclone damage:
Housing damaged (0/1)

3 months
Log amountper month
Receipt
amongrecipients(days)(1)(2)

6 months
Log amountper month(days)(3)-0.121 **(0.056)-0.020(0.014)-0.089(0.111)0.028(0.054)0.055(0.083)-0.086 ***(0.030)-0.100(0.167)0.223 **
(0.091)-0.133(0.084)-0.024(0.031)-0.412 *(0.238)0.033(0.143)-0.052(0.295)-0.394(0.452)-0.060(0.332)-2.213 ***(0.617)-0.246(0.389)-1.539 *(0.896)Clan-mean of log of crop damage per adult equivalent ×
Clan-chief's clan

-0.052(0.083)0.143(0.165)0.055(0.076)0.733 ***
(0.222)0.022(0.090)0.580 *
(0.312)

F tests (p-value)
for log of crop damage per adult equivalent
for clan leader
for clan-mean of log of crop damage per adult equivalent
for clan-chief's clan

0.332
0.149
0.589
0.687

0.018
0.572
0.000
0.002

0.723
0.594
0.183
0.115

R-squared
0.121
No. observations
327
B. Models with clan/sub-clan leader and clan-/village-chief's clan.
Social status:
Clan/sub-clan leader (0/1)
0.014(0.182)Clan-/village-chief's clan (0/1)
0.164(0.251)
Cyclone damage-social status:
Log of crop damage per adult equivalent × Clan/sub-clan-0.046leader
(0.050)Clan-mean of log of crop damage per adult equivalent ×-0.062Clan-/village-chief's clan (0/1)(0.092)

0.455
252

0.161
327

0.030
(0.196)-0.390(0.383)0.040(0.288)-0.490(0.546)0.011(0.043)0.096(0.139)-0.062(0.088)0.156(0.195)Log of crop damage per adult equivalent (F$)
Proportion of housing damaged in the clan
Clan-mean of log of crop damage per adult equivalent (F$)
Social status:
Clan leader (0/1)
Clan-chief's clan (0/1)
Cyclone damage-social status:
Log of crop damage per adult equivalent × Clan leader*10% significance, **5% significance, ***1% significance. Standard errors in parentheses are clustered byclan. Other controls not shown here are pre-cyclone income per adult equivalent per month (log), landholdings (log), fishing capital (log), a dummy for secondary education among any adults, household adultequivalent size (log), proportions of children and elderly, age of household head (log), a dummy for female
head, proportion of households belonging to the same clan in the village, and clan-means of pre-cyclone
income per adult equivalent per month, land holdings, and fishing capital, village dummies, and constant.
Cyclone damage variables are also included in panel B.

33
Table 5. Receipt of housing construction materials - OLS with interaction terms.
Period after the cyclone
A. Models with clan leader and clan-chief's clan.
Cyclone damage:
Housing completely destroyed (0/1)
Housing partially damaged (0/1)
Proportion of housing completely destroyed in the clan
Proportion of housing partially damaged in the clan
Social status:
Clan leader (0/1)
Clan-chief's clan (0/1)
Cyclone damage-social status:
Proportion of housing completely destroyed in the clan ×
Clan-chief's clan (0/1)
Proportion of housing partially damaged in the clan × Clanchief's clan (0/1)

1 year

2 years(1)(2)

2 years and9 months(3)

0.165 **
(0.066)0.081 **
(0.032)-0.061(0.109)0.060(0.071)0.360 ***
(0.066)0.228 ***
(0.046)0.100(0.131)
0.063(0.087)0.577 ***
(0.053)0.227 ***
(0.048)0.039(0.103)0.177 **
(0.077)0.039(0.049)-0.120(0.077)0.139 ***
(0.052)0.159(0.107)0.115 **
(0.051)0.139(0.086)

0.623 ***
(0.218)0.137(0.148)0.341(0.308)-0.539 ***(0.196)0.375(0.258)-0.450 ***(0.170)F tests (p-value)
for proportion of housing completely destroyed in the clan
for proportion of housing partially damaged in the clan
for clan-chief's clan
for clan-chief's clan (interacted with proportion of housing
completely destroyed in the clan only)

0.021
0.458
0.025

0.402
0.026
0.001

0.334
0.010
0.000

0.012

0.003

0.000

for clan-chief's clan (interacted with proportion of housing
partially damaged in the clan only)

0.186

0.004

0.009

0.100
R squared
No. observations
404
B. Models with clan/sub-clan leader and clan-/village-chief's clan.
Social status:
Clan/sub-clan leader (0/1)
0.013(0.041)Clan-/village-chief's clan (0/1)-0.060(0.065)
Cyclone damage-social status:
Proportion of housing completely destroyed in the clan ×
0.185
Clan-/village-chief's clan
(0.189)Proportion of housing partially damaged in the clan × Clan0.006/village-chief's clan
(0.120)

0.263
568

0.383
652

0.130 ***
(0.042)-0.025(0.087)0.070(0.044)0.015(0.080)0.262(0.194)-0.152(0.183)0.169(0.193)-0.161(0.174)*10% significance, **5% significance, ***1% significance. Standard errors in parentheses are clusteredby clan. Other controls not shown here are a dummy for secondary education among any adults,
household adult equivalent size (log), proportions of children and elderly, age of household head (log), a
dummy for female head, proportion of households belonging to the same clan in the village, clan-mean ofland holdings, village dummies, and constant. Cyclone damage variables are also included in panel B.

34
Appendix. Means of household and clan characteristics per household.
Reconstruction
sample114.1 (116.3) 227.3 (316.4)
4.94 (6.04)
2.83 (4.82)
484 (1505)
313 (2139)
0.84 (0.37)
0.81 (0.40)
4.95 (2.25)
4.36 (2.15)
0.32 (0.21)
0.28 (0.22)
0.06 (0.14)
0.09 (0.21)
48.4 (13.7)
51.4 (14.6)
0.11 (0.31)
0.09 (0.29)
0.38 (0.21)
0.42 (0.29)

Relief sample
Earned income per adult equivalent per month (F$)
Land holdings (acre)
Fishing capital (F$)
Adults' secondary education (0/1)
Household size (adult equivalent)
Proportion of children (<15)
Proportion of elderly (>65)
Age of household head
Female head (0/1)
Clan's household share in the village
No. observations

340

906

Note - Standard deviations are shown in parentheses. These characteristics aremeasured before the cyclone in the relief sample and at the time of interviews in thereconstruction sample.