Tsukuba Economics Working Papers
No. 2009-012	
 Do Local Elites Capture Natural Disaster Reconstruction Funds? 	
 by
Yoshito Takasaki
October 2009

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

Do Local Elites Capture Natural Disaster Reconstruction Funds?
Yoshito Takasaki
University of Tsukuba

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

October 1, 2009

Acknowledgments
I wish to thank my field team – Jonati Torocake, Viliame Manavure, Viliame Lomaloma,
and 16 enumerators – for their advice, enthusiasm, and exceptional efforts on behalf of
this project. Special thanks are owed to the Fijians of the region who so willingly
participated in the survey. 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. Anyerrors of interpretation are solely the author’s responsibility.

1

Do Local Elites Capture Natural Disaster Reconstruction Funds?
Abstract
This paper examines the allocation of natural disaster reconstruction funds among
cyclone victims in rural Fiji. During post-emergency periods, when good information
about cyclone damage is available, do local elites, a powerful minority, capture housing
construction materials? With effective targeting in both receipt and the amount received,
local elites do not capture larger benefits. More severely affected victims are not earlyrecipients, though, because the supply of reconstruction funds is limited during early
periods. This invites early capture: Traditional kin elites receive benefits earlier thanothers in recipient villages.

I. Introduction
Vulnerability to natural disaster is a major barrier to development, andaugmenting the capacity for effective disaster management is critically important.
Frequent reports point to an inefficient distribution of disaster relief by uncoordinatedrelief agents who lack pertinent information about the damage. This is not surprising,
because relief agents give a higher priority to the speed of response than to evidencebased decision making (de Ville de Goyet, 2008). Only recently has empirical researchstarted to shed light on the performance of relief targeting. Morris and Wodon (2003)
find that among Honduran victims of Hurricane Mitch, the receipt of emergency aid istargeted on pre-shock assets, asset loss, and incidence of housing damage, but the amountreceived is not. In Fiji, Takasaki (forthcoming) demonstrates that the allocation of

2
cyclone relief within villages is linked with informal risk sharing (in Ethiopia, Dercon
and Krishnan, 2005 also find evidence that food aid is shared within the village).
As time passes, the main actions of disaster management shift from relief torecovery and reconstruction, and the allocation of relief, recovery, and reconstruction
funds becomes more efficient as damage information accumulates and agents’coordination becomes more effective. 1 Probably because of this perception, as well as alack of data, empirical research on targeting reconstruction funds during post-emergency
periods is lacking. This paper examines the allocation of housing reconstruction funds
among cyclone victims in rural Fiji. Understanding the performance of targetingreconstruction funds is important, but is not a main focus of the paper. My main goal is toaddress a question that has not yet been explored by researchers, but has potential to be
critically important: Do local elites capture reconstruction funds?
Elite capture of a particular program occurs when a powerful minority alters thenature of the program in their favor. As the participatory or decentralized approach todevelopment has become mainstream (World Bank, 2002; Mansuri and Rao, 2004), elite
capture as its potential drawback has been receiving considerable attention fromresearchers. Bardhan and Mookherjee (2000) theoretically examine the factors that might
affect whether elite capture is more likely to occur at the local or national levels, showingthat higher income inequality results in more local capture. Consistent empirical findings
have been obtained in community-based programs in Asia and Latin America, such as
India’s employment generation program (Bardhan and Mookherjee, 2006), Bangladesh’s
Food-for-Education Program (Galasso and Ravallion, 2005), and Ecuador’s Social Fundinvestment projects (Araujo et al., 2008). 2

3
Platteau and Abraham (2002) argue that capture problems are also significant inmore egalitarian countries in Sub-Saharan Africa because of community imperfections
entailed in the personalized character of human interactions in small groups:
In lineage-based societies, local chiefs and elders from dominant lineages are
ideally positioned to … “capture” the benefits of decentralized development
programmes or projects. In fact, they may easily respond to new prospects ofwindfall gains by transforming themselves into all-powerful patrons. Instead of“father figures” clinging to their traditional duties of redistributing wealth andsettling conflicts in such a way as to maintain the existing social order, theerstwhile elite become greedy individuals who show all the less restraint inenriching themselves at the expense of their community as they are actuallylegitimated by outside actors. By virtue of their dominant position, they can thus
manipulate participatory methods by subtly representing their own interests as
community concerns expressed in the light of project deliverables. (p. 122)In Fiji and many other Pacific Island states, kin-based hierarchies play a central role inlocal governance:
Hierarchy is defined here as the ranking of the elements of a whole (society) in
relation to the whole. In this sense, the elements that are ranked are social
categories or positions defined in terms of age, seniority of descent, and gender,
and the whole in relation to which they are ranked is a social system grounded inritual. Elder is superior to junior, chief to commoner, and male to female. But
while age, rank, and gender differences entail relations of superiority/inferiority
among persons, they also create interdependence. . . . These relations ofinequality and interdependence (which do not preclude conflict) are expressed andreproduced in the practice of everyday life. (Turner, 1992, p.291)
Kin elites may become capturers of reconstruction funds even when such funds arestrongly targeted toward victims. If elite capture deteriorates the equitability of disastermanagement, policymakers need to pay serious attention to recipient communities. As arelated study in a different context, Leeson and Sobel (2008) find that disaster relief isassociated with an increase in public corruption across the United States.
To empirically examine elite capture, most extant studies on community-based
development programs rely on measures of consumption, income, or asset inequality with4
the assumption that power is correlated with wealth. A straightforward alternative is touse direct measures of elite status as determinants of benefit allocation. In standard
household surveys, however, elite status is often unobservable to researchers, and even if
it is observable, there are too few elites to make a statistical analysis possible. A uniquefeature of the Fijian data is that in each village, households are stratified by their kingroup (clan) and elite status, and thus rich, direct measures of local elites are available.
The paper is based on original survey data gathered in 2005 in the same area as
Takasaki’s (forthcoming) study, but from many more villages and households than his
original sample collected in 2003. Distinct from this earlier work on the relief allocation
within the village in the six months after the cyclone, this paper investigates the
allocation of housing reconstruction funds at three different levels – village, clan, andhousehold – over three years. Comparing reconstruction with relief in response to thesame cyclone allows me to better understand capture problems in disaster management. 3
The paper examines in which form – receipt, the amount received, or the timingof these two – local elites capture reconstruction funds. Examining receipt and theamount received separately is important because their determinants can be distinct, asshown by Morris and Wodon (2003) for emergency relief (see also Jayne et al., 2002;
Dercon and Krishnan, 2005 for similar empirical findings on targeting food aid in Africa).
Strong targeting in receipt and the amount received does not necessarily mean targetingin timing: The larger the damage, the earlier the receipt or the greater the amount received
in early periods. Targeting in timing can be weak or even nonexistent, because
reconstruction usually takes time and the distribution of scarce funds is often delayed.
Thus the limited supply of funds during early periods may lead to early capture of receipt

5or the amount received: Local elites receive benefits earlier or receive greater benefits
during early periods. Lanjouw and Ravallion (1999) find early capture of benefits fromschooling and antipoverty programs by the nonpoor in India.
Main findings of the paper are summarized as follows. Allocations of housing
reconstruction funds – both receipt and the amount received – across villages, clans, andhouseholds are strongly targeted on damage, and accordingly, local elites do not capture
larger benefits at any level (in any period). A limited supply of funds during early periods,
however, precludes targeting in timing, resulting in early capture of receipt by local elites
within the recipient village.
The rest of the paper is organized as follows. Section II describes elite status in
Fijian society, the sampling design, and the data. Section III provides a description of
cyclone damage and reconstruction. Descriptive evidence of targeting and elite capture of
housing reconstruction funds is offered in Sections IV and V, respectively. Section VI
discusses the econometric specification, which is followed by estimation results in
Section VII and discussions in Section VIII. The last section concludes.
II. Elite status and data
The hierarchical Fijian kin structure is well known among anthropologists: The
bottom is tokatoka, followed by mataqali, yavusa, and vanua, and all native Fijians
belong to one tokatoka, which belongs to one mataqali, and so forth (Ravuvu, 1983).
Vanua ranges over several villages, roughly matching districts, and there is one or a few
yavusa in a village; mataqali and tokatoka are village sub-groups.
I define kin-based elite status at the household, mataqali (henceforth called clan),and village levels as follows. First, in the village, individuals who hold a traditionally6
assigned, permanent leadership position in their kin group – yavusa, mataqali, or tokatoka– play a major role in the group’s decision making and negotiations among groups (clanleaders). Second, a small number of clan leaders are yavusa or mataqali chiefs withspecial social status who assume various traditional duties in the village (no tokatoka
chiefs exist; village chiefs are shared by some clan leaders and are not necessarily yavusa
or mataqali chiefs). These chiefs come from only specific mataqali (chief’s clans). Third,
vanua chiefs who are highly ranked and assume traditional duties across villages areavailable in or originated from selected villages (chief’s villages). Since many vanua
chiefs live in a city, the data contain a small number of vanua chiefs who share yavusa
chiefs (i.e., clan leaders). Local elite status is also held by leaders of groups other than kingroups in the village, such as church, women’s, and school groups (non-clan leaders).
Non-clan leadership is neither permanent nor directly related to kinship. 4
In July-September 2005, I conducted a survey among native Fijian households in
Cakaudrove Province in the northern region of the country. 5 The province has 134
villages in 16 districts. Households were sampled as follows. First, in each district,villages were stratified by chief’s village status; all 15 chief’s villages were sampled, andother villages with distinct environmental and economic conditions were intentionallychosen. Next, in each village, all tokatoka were sampled. Lastly, in each tokatoka,
households were stratified by a combination of clan and non-clan leadership status andmajor asset holdings (like shops), and households were randomly sampled in eachstratum. Overall, the survey covered 906 households, 234 tokatoka, and 146 mataqali in
43 villages (with no overlap of tokatoka or mataqali across villages). While the data

7
represent neither the province nor the nation, the sample villages well capture various
types of villages in Fiji’s underdeveloped islands.
In the sample, 19% of households, 17% of clans, and 35% of villages are clan
leaders, chief’s clans, and chief’s villages, respectively; 10% of households are non-clan
leaders, and there are non-clan leaders in 55% of clans and in all villages (see Table 1).
Hence, the present data contain sufficient frequencies of local elites for statistical analysis.
At the time of interviews, households in the sample earned F$10,972 annual
income, or F$2,515 per adult equivalent (F$1 = US$.60), on average; cropping, fishing,and permanent wage labor accounted for 65%, 11%, and 10% of income, respectively. 6
III. Cyclone damage and reconstruction
On 13 January 2003, Cyclone Ami swept over the northern and eastern regions of
the Fiji Islands. 7 According to respondents’ subjective assessments, Ami damaged 62%
of residents’ dwellings in the sample (panel A of Table 1): 19% and 34% of main houses
were completely destroyed and partially damaged, respectively; and 54% of households
experienced damage of independent dwelling units other than the main house, such as the
kitchen, shower, and toilet (not all households have such units, as such facilities are often
located inside the main house). The mean value of total dwelling damage, including
independent units, in the whole sample was F$1,074. 8
The Red Cross, other nongovernmental organizations (NGOs), and governments
provisioned emergency relief. While almost all households received generous food aid(30% of their food consumption over six months, on average), as well as seeds for crop
rehabilitation, primitive tarpaulins – to be used as emergency shelters and for temporary
dwelling repair – were given to a small proportion of victims (Takasaki, forthcoming).

8
Housing reconstruction programs followed. Construction materials were
provisioned, and if needed, carpenters were sent to villages to help build new houses(villagers helped each other with rebuilding and repairing). In the survey, each household
was asked whether it received construction materials, and the recipient was asked about
the year and month of receipt and the monetary value of construction materials received(measurement errors in these retrospective data are discussed in Section VI). A quarter ofhouseholds were recipients, and the mean amount in the whole sample was F$685. The
mean amount among recipients was F$2,821, which is almost the same as the mean valueof total dwelling damage in the same sub-sample: That is, housing reconstruction funds
and dwelling damages were balanced among recipients, on average. 9
IV. Targeting
Household-level targeting
Housing reconstruction funds were strongly targeted toward victims. Panel A of
Table 2 compares the receipt of construction materials (of any amount) with theincidence of dwelling damage (of any magnitude) among households. While 2% ofrecipients were households that experienced no dwelling damage, 60% of victims with adamaged dwelling were not recipients. That is, while the error of inclusion (leakage) isvery small, the error of exclusion (under-coverage) is very large. While households witha completely destroyed main house were targeted (48% of recipients), under-coverage
was still common among them (35% of such victims were non-recipients) (panel B).
Thus, the supply of full construction materials for new house building was limited, andsmall provisions were targeted toward victims with a partially damaged main house anddamaged independent units (42% and 8% of recipients, respectively).

9Panel A of Table 3 shows the correlations of construction materials – receipt andamount received – with cyclone damage in the whole sample. The allocation is strongly
positively correlated with all damage measures, except for a negative correlation between
the amount of construction materials received and partial main house damage, the reason
for which is given below. Panel A of Table 1 shows comparable patterns of recipients andnon-recipients.
Targeting in timing
Provisions of construction materials took time. Figure 1 depicts the numbers ofhouseholds that received construction materials and the mean amounts among recipientsby quarter (the record is incomplete in the last quarter, 2005-3, when interviews wereconducted). The numbers of recipients and the amounts received were small during earlyquarters and increased later, since 2004-1 and 2004-4, respectively. That is, housing
reconstruction programs augmented in scale one year after the disaster, and provisions offull construction materials for new house building were further delayed (in 2003-3, only
one recipient received them). A comparison across years reveals that 58% of recipients
received construction materials in 2004, and the mean amount received among recipientsin 2005 is more than two times that in 2003 and 2004.
Panel B of Table 3 shows correlations of cyclone damage with the receipt of
construction materials in each year and the amount received in three years and in eachyear, among recipients in the corresponding year(s). Receipt is negatively correlated withdwelling damage value and complete main house destruction and positively correlatedwith partial main house damage in 2004; in 2005, opposite correlation patterns hold in astatistically significant manner (the results in 2003 are statistically nonsignificant). At the10same time, the amounts received are always positively correlated with dwelling damage
value and complete main house destruction and negatively correlated with partial mainhouse damage (the last relationship significantly appeared also in the whole sample
discussed above). These patterns indicate that households with severer damage received
construction materials later, not earlier, simply because their supply was limited during
early periods, while benefit amounts were always strongly targeted on damage. 10
Clan- and village-level targeting
Cyclone damage and construction materials at the clan (mataqali) level arereported in panel B of Table 1 (all mataqali-level analyses in the paper were repeated at
the level of tokatoka, a sub-group of mataqali, yielding very similar results). Comparable
village-level figures appear in panel C. While 88% of clans and all villages experienced
dwelling damage (i.e., at least one victimized household was located there), 54% of clans
and 88% of villages, respectively, were recipients (i.e., at least one recipient household
was located there). Not surprisingly, clan and village means of all damage andconstruction material measures are comparable to the original household-level figures
reported in panel A.
Using these clan- and village-level measures, I examine targeting performance at
the clan and village levels in the same manner as the household-level analysis.
Corresponding to panel B of Table 3, I consider the years of the earliest receipt – thisdummy for each year takes one if a first-recipient household in the clan/village appears inthat year – and the amount received among recipient clans/villages – with at least one
recipient household – in each year. While by definition the former dummies for theearliest receipt are mutually exclusive across years, dummies for receipt corresponding to11
the latter recipient clans/villages are not, because different households in the sameclan/village can be recipients in different years. Qualitatively the same results as thosereported in Tables 2 and 3 are obtained (results not shown); as an exception, clans andvillages with severer damage did not necessarily receive construction materials later (theresults are statistically nonsignificant).
Synthesis
To sum up, while targeting in receipt and the amount received was strong at thevillage, clan, and household levels, limited supply of reconstruction funds resulted in
nonsignificant under-coverage and precluded targeting in timing. This indicatesconsiderable room for early capture by local elites.
V. Elite capture
Descriptive evidence of elite capture is limited to the following. First, as shown inpanel B of Table 1, chief’s clans and clans with non-clan leaders are more likely to berecipients (comparable results are obtained from correlation analyses at the clan level andby comparing the proportion of recipients in these leaders’ clans with that of other clans).
Second, as suggested from panel B of Table 3, the amounts received in 2003 and 2004
(and in 2003-2005) among recipients are significantly larger for clan leaders than nonleaders (comparable results are obtained by comparing the means of the amounts
conditional on recipient between these two). There is no statistically significant
difference in receipt, however, between clan leaders and others in any year, and neither
receipt nor the amount received significantly differs between non-clan leaders and others.
Clans’ elite status does not significantly differentiate the years of the earliest receipt andthe amount received among recipient clans in each year, and qualitatively the same12
results hold at the village level (results not shown). To sum up, it appears that chief’sclans and non-clan leaders’ clans are capturers, but not early ones, in the allocation ofrecipients, and clan leaders are early capturers of larger benefits, but not receipt ofbenefits.
These patterns could be spurious, however. Let me consider the clan-level
capturing to illustrate why. First, chiefs’ clans and non-clan leaders’ clans are positively
correlated with each other (the correlation is .27 with a 1% statistical significance). Only
one of them may be a real capturer. Second, these two leaders’ clans can be correlatedwith other factors that determine receipt of benefits. An obvious example is clan size:
Larger clans with more victims are more likely to be recipient clans, and these leaders’clans tend to be larger than other clans. Systematically controlling for other determinants,
which is a task in the remaining sections, is thus crucial to identify elite capture.
VI. Econometric specification
Empirical models
I conjecture that allocation of construction materials y is determined not only bycyclone damage X (targeting) but also by social status Z (elite capture). I employ thefollowing reduced-form models at the village (v), clan (g), and household (i) levels:
yv = α1 + β1 X v + γ 1Z v + δ1Wv + ev ,(1)

y g = α 2 + β 2 X g + γ 2 Z g + δ 2Wg + V + e g ,(2)

yi = α 3 + β 3 X i + γ 3 Z i + δ 3Wi + V + ei ,(3)where Wv, Wg, and Wi, respectively, are other village, clan, and household characteristics
that affect the allocation; V is village dummies; and ev, eg, and ei are error terms. Whether
victims reconstruct or repair their dwellings without receiving construction materials

13certainly affects the allocations of reconstruction funds (see note 9), but this endogenous
decision does not appear as an explanatory variable in the reduced-form equations (1)-(3)(examining self-reconstruction is not a focus of the paper). As village dummies fully
control for village-level factors, including total construction materials allocated to thevillage, equations (2) and (3), respectively, focus on the allocations across clans andhouseholds within the village. I also estimate equation (3), replacing village dummies V
with clan dummies G, which fully control for clan-level factors. If household-levelfactors are a driving force, then significant findings in the original equation (3), which
does not control for clan-level factors – both observable and unobservable ones – must be
robust to this alternative specification focusing on allocations within the clan.
I conduct two analyses, one ignoring the timing of receipt and the otherhighlighting it. The first analysis estimates the determinants of receipt in the three-year
period, 2003-2005, using probit for the whole sample and those of log of the amountreceived among recipients using Ordinary Least-Squares (OLS) (the village and clanmeans of the amount received is used in equations 1 and 2, respectively). This hurdle
model is commonly used in previous studies (Jayne et al., 2002; Dercon and Krishnan,2005). 11 Targeting performance and elite capture, respectively, are measured by positive
βj and γj (or their positive elements if they are a vector); 12 if the allocation corresponds toonly damage, then γj and δj should be zero.
The second analysis employs the same hurdle model in the first year, 2003, and in
the first two years, 2003-2004. At the village and clan levels, the year of the earliest
receipt and the recipients in the corresponding year(s) defined above are considered. For
example, the dummy dependent variable for 2003-2004 takes one if at least one recipient14household in a clan appears in the first two years, and the corresponding amount equation
is estimated for clans with at least one recipient household in the same period. Targetingin timing and early capture, respectively, are measured by positive βj and γj with greater
magnitudes than those for the three-year period.
I also estimate the determinants of the year of receipt – 2003, 2004, or 2005 –among recipients using ordered probit (the year of the earliest receipt in the village- andclan-level analyses). As the greater the dependent variable, the later the receipt, negative
βj and γj, respectively, indicate targeting in timing and early capture of receipt. While theprobit model shows whether elites are more likely to be recipients during early periods,
the ordered-probit model examines the order of receipts among recipients. 13
While retrospective errors in the receipt of construction materials should be
minimal and those in the year of receipt should be also small (those in the month ofreceipt could be significant), those in the amount received could be considerable. A key
question is whether errors are correlated with covariates. In particular, households withmore damage may have felt that the amount received was too small, causing a downward
bias in estimated impacts of the damage. This means that estimated positive βj in theamount equation (suggesting good targeting) should be qualitatively robust.
Covariates
Three groups of covariates – cyclone damage X, social status Z, and othercharacteristics W – are measured as follows. At the household level, Xi is captured by thelog value of total dwelling damage or two dummies for a completely destroyed andpartially damaged main house. The former damage measure is comprehensive, as itcovers total damage of all dwelling units, but its measurement errors could be15considerable and systematic. Although the latter damage measure does not capture
independent units owned by some households, retrospective errors in the damage statusof each main house reported by individual households are minimal, because relief
officers used the same three categories for their damage assessments, and thus thedamage status of each house was common knowledge among villagers. As such,qualitatively similar results of these two damage measures give me confidence about thefindings’ robustness. Xv is measured by the village mean of total damage value or two
variables for the proportions of households with completely destroyed and partiallydamaged main houses in the village; Xg is measured analogously. When main housedamage measures are used, βj (which is a vector) should indicate a greater marginal effectfor complete damage than partial damage.
Social status Zv, Zg, and Zi, respectively, are measured by a dummy for chief’svillages, two dummies for chief’s clans and non-clan leaders’ clans, and two dummies forclan leaders and non-clan leaders defined above.
Village characteristics Wv include village size and access, measured by the total
number of households in the village (in population) and travel time to a market (log),
respectively; only clan size, measured by the total number of households in the clan (inpopulation), is considered for clan characteristics Wg. While clan size should positively
affect the allocation in the village, as discussed above, how village size matters is
ambiguous: Larger villages may be prioritized or they instead may be given a lower
priority with a limited supply of funds. While geographical location certainly determines
the delivery of emergency relief, whether this is also the case for reconstruction funds
during post-emergency periods is an empirical question. In equations (2) and (3), all16geographical factors are captured by village dummies. Household characteristics Wi arecaptured by asset holdings and demographic factors in a standard manner. 14 If theallocation is determined by targeting on housing damage and social status potentiallycausing elite capture, as conjectured here, household characteristics should not matter.
VII.

Estimation results
Estimation results of the models with dwelling damage value and main housedamage are reported in Tables 4 and 5, respectively. In each table, results of theallocations across villages, clans, and households are shown in panels A, B, and C,
respectively. In each table, columns (1)-(3), respectively, show probit results for receipts
in 2003, 2003-2004, and 2003-2005 (marginal effects at means are reported); columns (4)and (5), respectively, show OLS results for the amounts received among recipients in
2003-2004 and 2003-2005 (the analysis in 2003 is infeasible because of the small numberof observations); and column (6) gives ordered probit results for the year of receipt
among recipients. 15 I first discuss targeting and then elite capture and other factors.
TargetingTargeting performance is very consistent with earlier descriptive findings. Whenthe timing of receipt is ignored (columns 3 and 5), the receipt and the amount received
are strongly targeted toward more severely affected victims, according to both dwelling
damage value and main house damage, at the village, clan, and household levels. Almost
all results are statistically significant at least at a 5% significance level. 16 Allocations
strongly respond to complete main house destruction. At the household level, for example,
the probability of being a recipient is higher by .72 and the amount received (conditional

17
on receipt) is 261% larger among households with a completely destroyed main housethan others. 17
There is no strong evidence of targeting in timing (columns 1, 2, 4, and 6).
Overall fitness of the models of the receipt in 2003 and of the year of receipt is weak atthe village and clan levels. Only in receipt at the village level do the marginal effects ofdamage decrease over time, and correspondingly, the estimated coefficients of thedamage in the year-of-receipt equation are negative, but not statistically significant.
According to both the receipt and year-of-receipt equations, clans with more severely
affected victims are rather late recipients. 18
Elite capture
Consistent with the earlier descriptive finding at the clan level, when clan size is
not controlled for, the probability of receipt is significantly higher among the chief’sclans than other clans (results not shown); once clan size is controlled for, however, thisresult loses statistical significance and the estimated marginal effect decreases (column 3
in panel B of Table 4). The former result is thus biased upward for the reason discussed
above. A new finding is that the chief’s clans strongly affect the receipt in 2003-2004 –about .40 marginal effects with a near 10% statistical significance (column 2). At thesame time, chief’s villages do not significantly influence across-villages allocations.
Two new findings at the household level are obtained: (1) the probability ofreceipt in 2003-2004 is higher by .10-.12 among clan leaders than others, and this pattern
is not statistically significant in 2003 or 2003-2005 (panel C of Table 4); and (2)according to the year-of-receipt equation among recipients, the probability of receipt in2003 and 2005 is higher and lower by about .14-.15 and .12-.13, respectively, among clan

18
leaders than non-leaders (column 6 in panel C of Table 6). There are no other significant
results indicating capturing in receipt or the timing of receipt, and when I estimate
equation (3) with clan dummies, these two significant results hold, confirming thathousehold-level elite status is a driving force. Hence, individual clan leadership mattersin across-households allocations within villages and clans.
Consistent with the earlier descriptive finding, the clan leader dummy exhibits apositive impact on the amount received in 2003-2004; however, this is statisticallysignificant only in models with dwelling damage value and the result loses statisticalsignificance in equation (3) with clan dummies, indicating that household-level elite
status is not a strong determinant. According to the regression results, traditional local
elites do not capture larger amounts at any level of allocation in any period. Nor is there
evidence of capturing by non-traditional elites in any form of allocation – at the clan orhousehold level – over time.
To sum up, elite capture exists in two forms: (1) chief’s clans, but not non-clan
leaders’ clans, are early capturers of receipt in the village; and (2) clan leaders are early
capturers of receipt of benefits, but not larger benefits, in the village and the clan. Notethat as there are clan leaders in most clans, the latter household-level capturing is notnecessarily a primary cause of the former clan-level capturing. These patterns differ fromearlier descriptive findings: The descriptive evidence of capturing by non-clan leaders’clans is an artifact caused by their positive correlation with chief’s clans; chief’s clans are
early capturers, as clan leaders are; and clan leaders are capturers of receipt of benefits, as
chief’s clans are.
Other factors

19
Findings about village, clan, and household characteristics are as follows (results
not shown). While village access does not influence the allocation, smaller villages are
more likely to be recipients in 2003-2004, but not in 2003-2005 (with .07-.13 marginal
effects in magnitude). As expected, larger clans are more likely to be recipients in 20032004 and 2003-2005 (with .28-.40 marginal effects); the results are statisticallysignificant, however, only in the models with main house damage. Lastly, almost no
household characteristics are strong determinants in any form of allocations. 19
VIII. Discussions
To better interpret the findings on elite capture in the last section, this section
compares the findings with those regarding emergency relief. Recall that with a large
supply of food aid, almost all households are recipients. Takasaki (forthcoming) showsthat neither clan leaders nor chief’s clans capture food aid, even though crop damage is
not very observable to other households and everyone demands food aid, and thus there
exist significant room and demand for capture. This is because in a closely knit kinsociety, capturing relief allocated as part of risk sharing can greatly deteriorate thecapturer’s reputation.
In contrast to emergency food aid, the following relationships hold forconstruction materials. First, as only households with a damaged dwelling demand
construction materials, capturing by non-victims is limited. Indeed, leakage was minimal(Takasaki, forthcoming shows a similar pattern in the receipt of tarpaulins). Second,
information problems in housing damage among villagers and relief officers are verysmall. This precludes local elites from capturing larger benefits in any period, because theamount provisioned can be well determined based on the damage. Third, a limited supply

20
of construction materials gives rise to significant under-coverage, especially during early
periods, making considerable room for the early capture of the receipt of benefits.
Whether local elites actually capture benefits earlier depends on their benefit-cost
calculation of doing so. Benefits are much higher for receiving construction materials
than for receiving food aid (the mean value of construction materials received per adultequivalent among recipients is 6.5 times the mean value of food aid received per capita,
as reported by Takasaki, forthcoming). Benefits peak in 2004 when the amountprovisioned – especially provisions for new house building – started to augment. The
social costs of capturing can be also high, but those of manipulating the timing of receipt
by one year or so are much smaller than manipulating the recipients themselves.
Traditional local elites can exercise their power most within the village, while nontraditional elites have no such power. 20 As a result, early capture of the receipt of benefits
by traditional elites emerges in the recipient village.
IX. Conclusion
This paper examined the allocation of natural disaster reconstruction funds among
cyclone victims in rural Fiji. Using original survey data with rich, direct measures of
traditional, kin-based elite status, the paper investigated whether and how local elites
capture housing construction materials during post-emergency periods when good
information about the damage is available. With effective targeting in both receipt andthe amount received at the village, clan, and household levels, local elites do not capture
larger benefits at any level. More severely affected victims are not early recipients,
however, because the supply of reconstruction funds is limited during early periods. This
invites early capture within the recipient village: Clan leaders and elite clans receive

21
benefits earlier. At the same time, there is no evidence of capturing by non-traditional
elites of village organizations other than kin groups. Descriptive results of targeting wellmatch regression results, except in the instance of elite capture.
Policy implications for disaster management are the following. First, good dataand information management for effective targeting (Amin and Goldstein, 2008) greatly
help preclude elite capture. Second, timely provision of sufficient reconstruction funds is
crucial not only to better support disaster victims, but also to reduce early capture by
local elites. Third, in kin-based societies like Fiji, traditional elite status is likely to be asource of power fuelling elite capture, and not only individual elites but also elite kingroups can be capturers in the recipient community. Fourth, identifying elite capture ismore difficult than evaluating targeting performance, and thus it is likely to require
careful econometric analysis of direct measures of elite status. Capture problems in
disaster management may be also prevalent in other kin-based societies, such as those in
Sub-Saharan Africa, and need to receive more attention from researchers andpractitioners.
Notes
1

Post-disaster management consists of three phases – relief, early recovery, and recovery

and reconstruction (de Ville de Goyet, 2008). Relief emphasizes the urgent but temporary
nature of the assistance, such as search and rescue, evacuation, food and water
distribution, temporary sanitation and health care, temporary shelter, and restoration of
the access to transport. Relief is mostly a humanitarian response by nongovernmentalorganizations and the United Nations (UN) Office for the Coordination of Humanitarian
Affairs. Development agents, such as bilateral donors, the UN Development Programme,

22

and the World Bank, are primarily concerned about recovery and reconstruction, aiming
at restoring heavy infrastructure and the normal life of business with a long-term vision.
Early recovery consists of continuing basic needs support, providing mental health care,
restoring education, and restoring livelihoods:
It is often assumed that early recovery implies emergency or temporary measures.
However, early recovery is gradually coming to include permanent solutions suchas the construction of housing or water systems and the establishment of primary
health care centers or schools staffed by local people, thereby blurring thedistinction between delayed relief and reconstruction. Emergency activities
undertaken by relief agencies following hurricanes or earthquakes, which used to
be run for only a few weeks or months, are now spanning years. (de Ville deGoyet, 2008, pp.31-32, emphasis added)
2

To the contrary, Yamauchi (forthcoming) finds no evidence of elite capture inIndonesia’s anti-poverty program, nor does elite capture appear in India’s credit andagricultural minikit programs (Bardhan and Mookherjee, 2006).
3

Better understanding the effectiveness of disaster management is of great importance insmall island states (Bertram, 1986), as researchers criticize their increasing dependency
on emergency aid from donors and the associated deterioration of indigenous
mechanisms (e.g., Campbell, 1984). In contrast to extensive anthropological studies,
economic studies of the Pacific region based on household survey data are greatly scarce.
4

Non-clan leaders also include gatekeepers (turaga ni koro), who handle most matters inconnection with the local government. Gatekeepers receive information and materials
from the government and NGOs, distribute them to villagers, and coordinate village
meetings. While gatekeepers are deliverers of disaster relief, whether and how they affectallocation decisions are unknown.

23

5

The province is mainly located on Vanua Levu Island and Taveuni Island, the second-

and third-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. While Fiji is divided almost evenly between native Fijians and Indo-Fijians,
this study focuses on the former.
6

Almost all households employ traditional farming practices, using no mechanizedequipment or animal traction and limited purchased inputs to produce taro, cassava,
coconut, and kava plant (locally known as yaqona, a pepper plant used to make a local
beverage kava which is a dominant symbol in Fijian culture, Turner, 1986). Land iscommunally owned by mataqali, is privately used, and by law cannot be sold (about 83%
of the country’s total land is communal). Most households engage in subsistence fishingusing lines and hooks, simple spear guns, or rudimentary nets, and more commerciallyoriented fishermen use boats with engines, along with more valuable nets. Somehouseholds engage in permanent wage labor, especially in the public and tourism sectors.
7

The northern region is much less prone to being hit by cyclones than other regions of

the country, and Ami was the only cyclone in the northern region from 1991 through2005 (McKenzie et al., 2005).
8

The total cyclone damage across the country is estimated at F$104 million, of whichdwelling damage is F$22 million and crop damage is F$40 million (National Disaster
Management Office, 2003). Fourteen people were killed. In the sample villages, nocasualties and very limited injuries and illnesses caused by the cyclone were reported.

24

9

The provisions of construction materials greatly helped reconstruction as follows (asystematic analysis of impacts of housing reconstruction programs is beyond the scope of
the paper). I focus on new main house building, for which information is available(information of repairing is lacking). In the whole sample, 9% of households built a newhouse and almost all of them had experienced dwelling damage, especially complete
destruction of their main house; among households with a completely destroyed mainhouse, more than half of recipients built a new house and 20% of non-recipients did so(results not shown). Thus, the provisions of construction materials for new house building
were insufficient and constructing a new house without receiving them (i.e., selfreconstruction) was relatively common.
10

Correspondingly, among recipients, new main house building is positively correlatedwith the amount of construction materials received, but not receipt itself, in each year; inthe whole sample, new main house building is positively correlated with cyclone damage
and both receipt and the amount received (Table 3).
11

An alternative sample selection model is infeasible with these data, which lack theidentifying instruments required to credibly estimate the selection equation.
12

If the allocation of construction materials is part of informal risk sharing among

villagers and clan members, β3 is generally unsigned, because the opposite allocation rule
is possible when households that suffered more receive greater net private transfers, as
Takasaki (forthcoming) finds for emergency food aid. It is most unlikely, however, thatprivate transfers outweigh provisions of construction materials.

25

13

At the household and clan levels, respectively, I repeated all these analyses forhouseholds with a damaged dwelling and clans containing at least one such victimized
household. In this way, I can examine capture problems as well as targeting performance
among eligible potential recipients. All results of this sub-sample analysis are almost thesame as what are presented below based on the whole sample. Of course, because of theminimal error of inclusion found above, recipients in the victim sub-sample are almost
the same as those in the whole sample analysis.
14

Household characteristics include land holdings (log), fishing capital (log), a dummy

for secondary education among adults (capturing human capital), household adultequivalent size (log), proportions of children and elderly (capturing labor resources), theage of household head (log), and a dummy for female head. All of these are measured at
the time of interviews. It is better to use measures before the cyclone or right after the
cyclone, but such data are lacking. In particular, land holding and fishing capital can beendogenous, because the receipt of construction materials can alter household investment
decisions. To address this problem, I estimate models excluding these two assets, finding
very similar results on all remaining variables. For the same reason, income is notcontrolled for, though household characteristics still control for permanent income. Notealso that village dummies control for income inequality (also in equation 2).
15

Equation (2) can be applied only to villages in which there exist across-clans variationsin the receipt in the period of interest in the probit and the years of receipt amongrecipient clans in the ordered probit; without such variations, village dummies perfectlypredict them. Analogously, equation (3) can be applied only to villages with sufficient26

across-households variations. Accordingly, the numbers of observations for theseanalyses greatly decrease. The numbers of observations for the analyses conditional onreceipt – OLS and ordered probit – further decline, and this is especially so in 2003.
16

The result of receipt at the village level is weak, because non-recipient villages areuncommon (only 5 out of 43 villages). Indeed, the result is stronger in 2003-2004, when
non-recipient villages are more common.
17

At the clan level, a .1 increase in the proportion of households with completely

destroyed main houses in the clan augments the probability of receipt by .31 and theamount received by 27%; and the comparable marginal effect on the amount received atthe village level is 71%. In both the receipt and the amount received at any level ofallocation, partial main house damage exhibits much smaller impacts than complete
destruction. While marginal effects of dwelling damage value on receipt are small, thoseon the amount received are large – its 10% increase augments the amount received by
18%, 7.8%, and 4.8% at the village, clan, and household levels, respectively.
18

This result is not strong. A 10% increase in dwelling damage value reduces theprobability of receipt in 2003 by .015 and increases that in 2004 and 2005 by .010
and .004, respectively, and the result is not statistically significant in the model with mainhouse damage. Consistent with the earlier descriptive finding, a similar pattern is found at
the household level, but in equation (3) with clan dummies, the estimated coefficient fordwelling damage value in the year-of-receipt equation loses statistical significance,
indicating that household-level damage is not a driving force.

27

19

The only exception is that households with more children (in proportion) tend toreceive larger amounts of construction materials in 2003-2004 and 2003-2005. Becausehousehold size, as well as other demographic factors, is separately controlled for, thisresult suggests that the allocations favor children.
20

Non-clan leaders’ clans, not non-clan leaders themselves, capture emergency food aid

provisioned to their groups (Takasaki, forthcoming). Because construction materials were
not provisioned to village organizations, non-clan leaders could not use the traditional kin
structure for capturing, as they did for food aid.
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28
Campbell, J. R. (1984) Dealing With Disaster: Hurricane Response in Fiji. (Honolulu:
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Lanjouw, P. and Ravallion, M. (1999) Benefit Incidence and the Timing of Program
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Leeson, P. T. and Sobel, R. S. (2008) Weathering Corruption. Journal of Law andEconomics, 51(4), pp.667-81.
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University of the South Pacific.

29
Morris, S. S. and Wodon, Q. (2003) The Allocation of Natural Disaster Relief Funds:
Hurricane Mitch in Honduras. World Development, 31(7), pp.1279-89.
National Disaster Management Office (2003) Report on Tropical Cyclone Ami. Ministryof Regional Development, Fiji.
Platteau, J.-P. and Abraham, A. (2002) Participatory Development in the Presence of
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Ravuvu, A. (1983) Vaka I Taukei: The Fijian Way of Life. (Suva: Institute of PacificStudies).
Takasaki, Y. (forthcoming) Targeting cyclone relief within the village: kinship, sharing,and capture. Economic Development and Cultural Change.
Turner, J. W. (1986) "The water of life": kava ritual and the logic of sacrifice. Ethnology,
25(3), pp.203-214.
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(Washington, D.C.: World Bank).
Yamauchi, C. (forthcoming) Community-based Targeting and Initial Local Conditions:
Evidence from Indonesia's IDT Program. Economic Development and Cultural
Change.

30
Table 1. Means of elite status, housing damage, construction materials, and reconstruction by receipt.

All

Non-recipients

Recipients

Mean.
test
(p-value)

Clan leader dummy

0.19 (0.40)

0.20 (0.40)

0.18 (0.38)

0.412

Non-clan leader dummy

0.10 (0.30)

0.09 (0.29)

0.14 (0.34)

0.055

A. Household

Damaged dwelling dummy

0.62 (0.49)

0.50 (0.50)

0.98 (0.15)

0.000Completely destroyed main house dummy

0.19 (0.39)

0.09 (0.28)

0.48 (0.50)

0.000Partially damaged main house dummy

0.34 (0.47)

0.32 (0.46)

0.42 (0.50)

0.003

Damaged independent units dummy

0.53 (0.50)

0.41 (0.49)

0.86 (0.35)

0.000

Total dwelling damage (F$)

1074 (2138)

466 (1160)

2881 (3134)

0.000

Construction materials receipt dummy

0.25 (0.43)

0.00 (0.00)

1.00 (0.00)

n.a.

Construction materials received (F$)

685 (1983)

0 (0)2821 (3191)

0.000

New main house building dummy

0.09 (0.3)

0.03 (0.2)

0.28 (0.5)

0.000

No. observations

903

676

227

Chief's clan dummy

0.17 (0.38)

0.10 (0.31)

0.23 (0.42)

0.049

Non-clan leaders dummy

0.55 (0.50)

0.40 (0.49)

0.67 (0.47)

0.001

Damaged dwelling dummy

0.88 (0.32)

0.75 (0.44)

1.00 (0.00)

0.000
0.000

B. Clan

Proportion of damaged dwelling

0.59 (0.32)

0.41 (0.34)

0.73 (0.21)

Proportion of completely destroyed main houses

0.18 (0.23)

0.06 (0.12)

0.27 (0.26)

0.000

Proportion of partially damaged main houses

0.32 (0.28)

0.28 (0.30)

0.34 (0.26)

0.189

Proportion of damaged independent units

0.49 (0.31)

0.34 (0.32)

0.62 (0.25)

0.000

Clan mean of total dwelling damage (F$)

994 (1165)

352 (539)

1538 (1274)

0.000

Construction materials receipt dummy

0.54 (0.50)

0.00 (0.00)

1.00 (0.00)

n.a.

Clan mean of construction materials received (F$)

680 (1366)

0 (0)1257 (1654)

0.000

Proportion of new main house building

0.08 (0.2)

0.01 (0.1)

0.15 (0.2)

0.000

No. observations

146

67

79

C. Village
Chief's village dummy

0.35 (0.48)

0.40 (0.55)

0.34 (0.48)

0.804

Non-clan leaders dummy

1.00 (0.00)

1.00 (0.00)

1.00 (0.00)

n.a.

Damaged dwelling dummy

1.00 (0.00)

1.00 (0.00)

1.00 (0.00)

n.a.

Proportion of damaged dwelling

0.64 (0.21)

0.37 (0.24)

0.68 (0.18)

0.001

Proportion of completely destroyed main houses

0.21 (0.16)

0.07 (0.08)

0.23 (0.16)

0.031

Proportion of partially damaged main houses

0.34 (0.15)

0.26 (0.15)

0.35 (0.14)

0.162

Proportion of damaged independent units

0.55 (0.21)

0.33 (0.22)

0.58 (0.19)

0.009

Village mean of total dwelling damage (F$)

1160 (771)287 (203)

1275 (744)0.006Construction materials receipt dummy

0.88 (0.32)

0.00 (0.00)

1.00 (0.00)

n.a.

Village mean of construction materials received (F$)

775 (849)0 (0)877 (852)

0.006

Proportion of new main house building

0.10 (0.1)

0.02 (0.0)

0.12 (0.1)

0.028

No. observations

43

5

38

Note - Standard deviations are shown in parentheses. Mean test results with a 5% significance level are bolded.

31
Table 2. Targeting of construction materials among households.
A. Targeting on dwelling damage.

Undamaged

Damaged

Total

Receipt of construction materials
Non-receipt
Receipt
Total
338
5
343
99%
1%
100%
50%
2%
38%
338
222
560
60%
40%
100%
50%
98%
62%
676
227
903
75%
25%
100%
100%
100%
100%

B. Targeting on main house damage.
Receipt of construction materials
Non-receipt
Receipt
Total
Undamaged
404
22
426
95%
5%
100%
60%
10%
47%
Partially damaged
213
96
309
69%
31%
100%
32%
42%
34%
Completely destroyed
59
109
168
35%
65%
100%
9%
48%
19%
Total
676
227
903
75%
25%
100%
100%
100%
100%
Note - In each cell of panels A and B, the frequency, the proportion ofrecipient status (in rows), and the proportion of damage category (incolumns), respectively, are shown at the top, in the middle, and at thebottom.

32

Table 3. Correlations of housing damage, construction materials, reconstruction, and elite status among households.
Dwelling
damagevalue (F$)
A. Whole sample
Dwelling damage value (F$)
Completely destroyed main house dummy
Partially damaged main house dummy
Construction materials receipt dummy
Amount of construction materials (F$)
New main house building dummyClan leader dummy
Non-clan leader dummy

Construction Amount ofPartially
Completely
materials construction
damageddestroyedmaterials
receipt
main house main house
(F$)
dummy
dummy
dummy

New mainhousebuilding
dummy

Clan leader
dummy

Non-clanleader
dummy

1.000
0.661
(0.000)-0.079(0.018)0.491(0.000)0.616(0.000)0.473(0.000)0.014(0.687)0.048(0.149)1.000-0.345(0.00)
0.438(0.000)0.612(0.000)0.518(0.000)-0.027(0.420)0.035(0.299)

1.000
0.099
(0.003)-0.164(0.000)-0.149(0.000)-0.066(0.047)-0.014(0.674)

B. Recipient sample-0.061-0.102
0.106
2003 construction materials receipt dummy
among recipients(0.361)(0.130)(0.114)
2004 construction materials receipt dummy-0.199-0.1770.140
among recipients(0.003)(0.008)(0.036)
2005 construction materials receipt dummy
0.290
0.302
-0.263among recipients(0.000)(0.000)(0.000)Construction materials received among
0.545
0.690
-0.552
recipients (F$)(0.000)(0.000)(0.000)Construction materials received among 2003
0.677
0.579
-0.426
recipients (F$)(0.000)(0.000)(0.007)
Construction materials received among 2004
0.528
0.693
-0.555
recipients (F$)(0.000)(0.000)(0.000)Construction materials received among 2005
0.390
0.619
-0.455
recipients (F$)(0.006)(0.000)(0.001)
Note - p-values are shown in parentheses. Results with a 5% significance level are bolded.

1.000
0.611
(0.000)0.381(0.000)-0.027(0.412)0.064(0.055)

1.000
0.536
(0.000)0.043(0.198)0.049(0.147)1.000-0.021(0.531)-0.007(0.838)

1.000
0.017
(0.601)1.000-0.025(0.706)-0.029(0.670)0.057(0.394)0.504(0.000)0.501(0.001)
0.561(0.000)0.409(0.004)-0.040(0.556)0.100(0.135)-0.081(0.228)0.142(0.036)
0.345(0.032)0.169(0.059)0.081(0.581)-0.027(0.689)0.023(0.734)-0.002(0.979)0.019(0.778)-0.044(0.790)0.030(0.737)0.025(0.865)

33
Table 4. Allocation of construction materials - dwelling damage value.
W hole sample

2003
(1)

Receipt
20032004(2)

Recipients
Amount received
2003200320032005
2004
2005
(3)
(4)
(5)
A. Village.

Year ofreceipt
2003/2004
/2005
(6)

Village mean of log dwelling
damage value (F$)

0.230 **
(0.100)0.046 **
(0.081)0.025 **
(0.051)1.279 ***
(0.283)

1.784 ***
(0.181)-0.391(0.278)Chief's village dummy

0.015(0.170)-0.009(0.027)-0.020(0.042)0.025(0.495)0.242(0.312)0.178(0.502)Log likelihood
Chi sq./F (p-value)
Pseudo-R sq./R sq.
No. observations

-26.4
0.176
0.107
43

-9.5
0.000
0.542
43

-7.2
0.002
0.001
0.533
0.358
43
35
B. Clan.

0.000
0.702
38

-32.5
0.573
0.061
38

Clan mean of log dwelling
damage value (F$)

0.071
(0.049)0.168 ***
(0.055)0.380 ***
(0.259)0.908 ***
(0.257)0.781 ***
(0.130)0.394 *
(0.217)

Chief's clan dummy

0.132(0.257)0.406(0.236)0.315(0.274)-0.803(0.679)-0.165(0.536)-1.048(0.737)

Non-clan leaders' clan
dummy

0.100
(0.160)-0.209(0.224)0.016(0.253)0.823(0.485)0.370(0.356)-0.709(0.456)Log likelihood
Chi sq./F (p-value)
Pseudo-R sq./R sq.
No. observations

-32.1
0.799
0.167
58

-27.0
0.001
0.450
71

-22.5
0.000
0.000
0.573
0.762
76
49
C. Household.

0.000
0.813
61

-37.9
0.131
0.255
51

Log dwelling damage value(F$)

0.021 ***
(0.004)0.062 ***
(0.006)0.082 ***
(0.006)0.485 ***
(0.104)0.479 ***
(0.075)0.095 *
(0.057)

Clan leader dummy

0.007(0.031)0.102 **
(0.054)0.030(0.046)0.516(0.320)0.230(0.267)-0.514 *(0.267)

Non-clan leader dummy

0.014(0.035)0.026(0.048)0.051(0.054)0.127(0.345)-0.060(0.290)0.003(0.282)Log likelihood
Chi sq./F (p-value)
Pseudo-R sq./R sq.
No. observations-111.7
0.006
0.187
428

-267.5
0.000
0.311
689

-270.3
0.000
0.421
773

0.000
0.525
153

0.000
0.532
207

-150.8
0.000
0.200
186

*10% significance, **5% significance, ***1% significance. Columns (1)-(3) are marginal effects at means in probit
estimates with standard errors in parentheses. Columns (4)-(5) are OLS estimates conditional on receipt with robust
standard errors in parentheses. Column (6) is ordered probit estimates conditional on receipt with standard errors inparenthesis. Other controls which are not shown here are village characteristics in panel A, clan characteristics inpanel B, household characteristics in panel C, village dummies in panels B and C, and constant in columns (4)-(6).

34Table 5. Allocation of construction materials - main house damage.
W hole sample

2003
(1)

Receipt
20032004(2)

Recipients
Amount received
2003200320032005
2004
2005
(3)
(4)
(5)
A. Village.

Year ofreceipt
2003/2004
/2005
(6)

Proportion of completely
damaged main houses

1.182 **
(0.587)0.498 **
(0.448)0.169(0.302)

6.297 ***
(1.474)7.258 ***
(1.152)-1.731(1.432)

Proportion of partiallydamaged main houses

0.969 *
(0.566)0.248(0.319)0.075(0.161)0.305(2.111)-2.380(1.954)-1.652(1.494)Chief's village dummy-0.002(0.170)-0.036(0.059)-0.027(0.048)0.013(0.474)0.256(0.411)0.283(0.496)Log likelihood
Chi sq./F (p-value)
Pseudo-R sq./R sq.
No. observations

-26.1
0.240
0.114
43

-12.0
0.004
0.420
43

-9.1
0.026
0.000
0.413
0.358
43
35
B. Clan.

0.000
0.564
38

-32.3
0.653
0.067
38

Proportion of completely
damaged main houses

0.082(0.413)2.040 ***
(0.765)3.133 ***
(1.020)2.664 *
(1.453)2.706 ***
(0.980)1.288(1.212)

Proportion of partiallydamaged main houses

0.989 **
(0.429)0.440(0.351)

0.668(0.408)0.434(1.475)0.191(0.967)-1.532(1.056)

Chief's clan dummy

0.414(0.253)0.393(0.218)0.348(0.201)-0.450(0.819)-0.164(0.642)-1.525 *(0.817)

Non-clan leaders' clan
dummy

0.010
(0.173)-0.319(0.211)-0.197(0.216)0.725(0.561)0.365(0.429)-0.779(0.497)Log likelihood
Chi sq./F (p-value)
Pseudo-R sq./R sq.
No. observations

-29.9
0.575
0.224
58

-27.4
0.003
0.441
71

-27.8
0.001
0.000
0.471
0.711
76
49
C. Household.

0.000
0.752
61

-36.5
0.093
0.283
51

Completely damaged mainhouse dummy

0.199 ***
(0.068)0.507 ***
(0.058)0.715 ***
(0.043)2.686 ***
(0.338)2.610 ***
(0.271)0.510(0.350)Partially damaged mainhouse dummy

0.134 ***
(0.042)

0.344 ***
(0.046)0.384 ***
(0.045)0.834 ***
(0.301)0.728 ***
(0.257)-0.139(0.359)Clan leader dummy

0.016(0.039)0.119 **
(0.056)0.056(0.053)0.570 *
(0.309)0.306(0.276)-0.558 **(0.271)

Non-clan leader dummy

0.024(0.044)0.056(0.053)0.081(0.059)0.359(0.322)0.165(0.247)0.050(0.283)Log likelihood
Chi sq./F (p-value)
Pseudo-R sq./R sq.
No. observations-116.4
0.074
0.152
428

-288.2
0.000
0.258
690

-299.2
0.000
0.360
774

0.000
0.634
153

0.000
0.633
207

-147.4
0.000
0.218
186

*10% significance, **5% significance, ***1% significance. Columns (1)-(3) are marginal effects at means in probit
estimates with standard errors in parentheses. Columns (4)-(5) are OLS estimates conditional on receipt with robust
standard errors in parentheses. Column (6) is ordered probit estimates conditional on receipt with standard errors inparenthesis. Other controls which are not shown here are village characteristics in panel A, clan characteristics inpanel B, household characteristics in panel C, village dummies in panels B and C, and constant in columns (4)-(6).

35Figure 1. Evolution of provisions of construction materials
45

10000

40

9000
8000

35

7000
6000
25
5000
20
4000
15
3000
10

2000

5

1000

0

0
2003
1st

2003
2nd

2003
3rd

2003
4th

2004
1st

2004
2nd

2004
3rd

2004
4th

2005
1st

2005
2nd

2005
3rd

Quarters
No. of recipients

Mean amount of construction materials among recipientsF$

Households

30