Tsukuba Economics Working Papers
No. 2010-006

Natural disasters and informal risk sharing against illness:
networks vs. groupsbyYoshito Takasaki	 July 2010

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

1

Natural disasters and informal risk sharing against illness:
networks vs. groups *Yoshito Takasaki
University of Tsukuba

Running Title:
Natural disasters and risk sharing

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

July 13, 2010*

I wish to thank my field team – Jonati Torocake, Viliame Manavure, Viliame
Lomaloma, and 19 enumerators – for their advice, enthusiasm, and exceptional efforts onbehalf of this project. Special thanks are owed to the Fijians of the region who sowillingly participated in the survey. This research has been made possible throughsupport provided by the Sumitomo Foundation, the Japan Society for the Promotion of
Science, and the Ministry of Education, Culture, Sports, Science and Technology inJapan. Any errors of interpretation are solely the author’s responsibility.

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Natural disasters and informal risk sharing against illness:
networks vs. groups
Abstract
Using original household panel survey data collected in rural Fiji, this paper demonstrates
how informal risk-sharing institutions upon which poor people heavily rely in times of
illness are vulnerable to natural disasters. First, household private cash-inkind transfers
do not serve as insurance against illness in the relief phase (several months after thedisaster); they do so only after pooled resources are recovered in the reconstruction phase(a few years later) (i.e., the resource effect). Second, risk-sharing arrangements are
dependent on the history of labor-time transfers corresponding to housing damage: Only
disaster non-victims are insured against illness, because victims have already received
labor help for their rehabilitation from non-victims (i.e., the reciprocity effect). The paper
also reveals that resource/reciprocity effects exist in endogenously formed networks andpre-formed groups, as risk-sharing pools to a similar degree. Not only do private transfers
exchanged among households serve as insurance, but also, household contributions
directly made to groups – such as ritual gifts and religious donations – contain risksharing components against illness among group members. Although the former finding
is commonly evident in the literature, the latter is new. Network formation is directly
related to pre-formed groups, especially kin and religious ones.

3
I. Introduction
Informal risk-sharing institutions are critically important in poor populations (see,e.g., Alderman and Paxon 1994; Morduch 1999; and Dercon 2002 for reviews of
informal insurance). They are particularly important for health shock, because neither
health insurance nor public safety nets are available among the poor (see Strauss andThomas 1998 for their extensive review of the health-development nexus). Numerous
studies have shown that informal risk sharing against idiosyncratic shocks, such as illness,is available in developing areas, although it is far from complete (e.g., Townsend 1994;
Kochar 1995; Dercon and Krishnan 2000; Gertler and Gruber 2002; Asfaw and vonBraun 2004). In contrast, such informal institutions are considered to be ineffective
against covariate shocks, such as natural disasters, because shocks are highly correlated
over space. Although extant works on risk sharing against natural disasters are scarce,
recent studies provide evidence for such arrangements against household-level disastershocks (e.g., Sawada and Shimizutani 2008; Mozumder et al. 2009). Of course, disaster
relief plays a central role as a safety net. Post-disaster management is a time-consuming
process, consisting of relief, recovery, and reconstruction phases (de Ville de Goyet
2008). This paper addresses a question that researchers have not yet explored but iscritically important for post-disaster development: How does a natural disaster affectinformal risk sharing against illness over time? Although adverse effects of natural
disasters on various dimensions of well-being, such as consumption, child nutrition, andpublic health, have received much attention from researchers (e.g., Noji 1997; Skoufias
2003), no previous works explicitly address the link between natural disasters andinformal risk sharing against health shocks.

4
I hypothesize two links. First, the degree of sharing cash and inkind (e.g., food) tosmooth consumption against illness (non-labor sharing) depends on the amount ofpooled resources that can be shared among people, i.e., the resource effect. In the relief
phase right after the disaster, risk sharing against illness is weak or even nonexistent,
simply because the covariate disaster shock greatly reduces pooled non-labor resources.
As rehabilitation progresses, pooled resources and thus risk sharing make a recovery.
Second, in the risk-sharing arrangement with limited enforceability, current
transfers are dependent on the past history of transfers, i.e., the reciprocity effect (Ligon,
Thomas, and Worrall 2002). The simulation analysis of Foster and Rosenzweig (2001,p390) demonstrates that “the existence of binding imperfect commitment constraints
implies that households that have made net transfers in previous periods are less likely toprovide subsequent transfers, given the current state of the world, than are households
that have been net recipients of transfers” (they call this the transfer asset effect). La
Ferrara (2003) theoretically and empirical examines the reciprocity effect in credit
transactions among kin members in Ghana.
Natural disasters can elicit the reciprocity effect as follows. Although a natural
disaster is a region-wide covariate shock, it may contain significant idiosyncraticcomponents at a local level; for example, a tropical cyclone may damage some, but not
all dwellings within villages. Imagine a situation where there are disaster victims andnon-victims within villages and in the relief phase non-victims help victims’
rehabilitation by providing labor time (labor sharing). Note that even if the resource
effect precludes non-labor sharing against the disaster damage, labor sharing can stillwork unless the disaster significantly lowers labor endowment among villagers (e.g.,5casualties, disease outbreak, out-migration). Thus, the reciprocity effect suggests thatvictims are less insured against illness than non-victims are.
As such, natural disasters may adversely affect informal risk sharing not only for
all contemporaneously, but also for some in a persistent manner; in particular, disastervictims may suffer from a lack of private safety nets against illness over time. Then, even
if disaster-induced public-health problems are not a major issue, “hidden” healthproblems exacerbated by the disaster – through endogenous adjustments in informal risksharing arrangements – can be considerable. Using original household panel survey datacollected in rural Fiji, the paper shows that a tropical cyclone has strong resource andreciprocity effects: Sick persons are insured in the reconstruction phase, but not in therelief phase; sick non-victims are insured, but sick victims are not.
To test the reciprocity effect, the paper directly analyzes household privatetransfers; distinct from many extant studies of risk sharing that focus on consumption
smoothing, it thus explores how people share risk, in the same spirit as Udry (1994) andFafchamps and Lund (2003). Although economists have extensively studied privatetransfers exchanged among households (across-household transfers) (see, e.g., Cox and
Fafchamps 2008 for a review), transfers exchanged directly with groups to which thehousehold belongs – such as ritual gifts for kin groups, village communal work, andchurch donations (household-group transfers) – have received very limited attention. 1
This is a significant lacuna in the risk-sharing literature, because household-grouptransfers may contain a significant risk-sharing component, such that group members

1This is especially so in developing countries; in developed countries, in contrast,
transfers to community institutions in general (e.g., charitable giving) have been wellstudied (see, e.g., Schokkaert 2006 for review).

6with adverse shock contribute less than others do. As a unique feature, the Fijian data
include comparable household information about these two forms of transfers, enabling
their direct comparison. Osili, Deb, and Okten (forthcoming) conduct a similar
comparison using Indonesian Family Life Surveys, though risk sharing is not their focus.
The paper finds resource and reciprocity effects of the cyclone in both transfers.
Although economists often highlight the village as a risk-sharing pool because of
its information and enforcement advantages (e.g., Townsend 1994; Ligon, Thomas, andWorrall 2002), recent works directly address the question of among whom people sharerisk. Some researchers focus on pre-formed groups other than village, such as kin, caste,
and ethnic groups (e.g., Grimard 1997; Morduch 2005; Munshi and Rosenzweig 2009),
while others study the formation of risk-sharing groups and networks (e.g., Murgai et al.
2002; Fafchamps and Lund 2003; De Weerdt and Dercon 2006). The paper examines not
only which pre-formed groups serve as risk-sharing groups in household-group transfers,
but also how those groups form household transfer networks and what networks serve as
risk-sharing networks in across-household transfers. The findings reveal that kin andreligious networks and groups are important.
The rest of the paper is organized as follows. Section II describes the study area,
the cyclone, and health shock. Section III explains household private transfers. Section
IV develops empirical strategies to test the resource and reciprocity effects, which isfollowed by the results in Section V. The last section concludes.
II. Data, cyclone, and health
A. Study area and data

7
On January 13, 2003, Cyclone Ami swept over the northern and eastern regions of
the Fiji Islands. 2 Seven native Fijian villages on the coast in the northern region, with
distinct environmental and economic conditions, were intentionally chosen for thesurvey. 3 After being stratified for each of the selected villages by the smallest kin group
unit (defined shortly), as well as by a combination of leadership status (e.g., kin leader)and major asset holdings (e.g., shops), households were randomly sampled in eachstratum. Household interviews were conducted between late August and early November
2003, collecting information about demographics, assets, production, income, shocks,
relief, and transfers (but not consumption). As such, like other post-disaster surveys (e.g.,Morris et al. 2002), the survey collected disaster information retrospectively (I will
discuss retrospective errors in Section IV). In July-September 2005, the second wave of
the survey was implemented. Analyses in this paper are conducted for 226 householdswith complete panel data. All monetized values presented in the paper are real values,
with 2003 as the base year.
B. Cyclone shock
All seven sample villages experienced damage to their structures and facilities,
and housing damage and crop damage are the two major damages that individual
households experienced. According to respondents’ subjective assessments, the cyclone2

Ami was the only cyclone in the northern region from 1991 through 2005 (McKenzie,
Prasad, and Kaloumaira 2005). The total damage caused by Cyclone Ami across thecountry is estimated at F$104 million, of which housing damage is F$22 million and crop
damage is F$40 million (National Disaster Management Office 2003).
3
Two other villages were also surveyed in 2003, but not in 2005. Four and three villages,
respectively, are located on Vanua Levu and Taveuni Islands, the second- and thirdlargest 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. Fiji is divided almost evenly between native Fijians and Indo-Fijians. The study
focuses on native Fijians.

8damaged 58% of residents’ houses: 9% were completely destroyed and 49% werepartially damaged (see Table 1). Households with and without damaged housing did not
significantly differ from each other in their crop damage (discussed next), earned
incomes, asset holdings, and other household characteristics at the time of interviews in2003 (nor were they different before the cyclone, Takasaki forthcoming-b). Amonghouseholds that experienced housing damage, 36% became refugees who stayed inothers’ residences in the same village (permanent migration was nonexistent). About two
thirds of those refugees lived with households in the same kin group; that is, kin networks
served as a major risk-sharing pool. Households without damaged housing also helped
others’ rehabilitation (I return to this below).
Almost all households engaged in cropping (and fishing), 4 and 82% experienced
crop damage. The mean value of damaged crops was F$44 per capita (1 Fiji dollar =US$.60), which was 11% of the mean annual crop income at the time of interviews in2003 (crop damage was calculated based on the quantity damaged for each major crop, asreported by respondents). 5 Distinct from housing rehabilitation, households individually
rehabilitated cropping by collecting harvestable damaged crops, cleaning fields, andplanting seeds with no labor sharing involved. Annual total earned income in 2003 wasabout half of that in 2005; thus, aggregated resources that could be shared amonghouseholds were limited after the cyclone.

4

Farming and fishing, respectively, accounted for 50% and 27% of total earned income
in 2003 and 55% and 14% of total earned income in 2005. Households employ traditionalfarming practices, using no mechanized equipment or animal traction to produce taro,cassava, coconut, and kava plants, and engage in artisanal fishing, using lines and hooks,
simple spear guns, or rudimentary nets.
5
Correlations of housing damage with crop damage and crop-damage value are .041
and .079, respectively, with no statistical significance.

9
Variance of the household-level cyclone-damage measures is decomposed into
year, village, and household levels by allowing for year-level or time-varying villagelevel means. 6 Apart from the major contribution of the year-level variance to the total
variance for the cyclone-damage dummies, most variance exists at the household level(57-90%). In contrast, the contribution of village-level variance to the total variance isnegligible (less than 3%). This is because as the survey covers only the northern region in
the small island state, variations in village-level shocks in the study area are limited. As
such, although the cyclone is a region-wide covariate shock, household-level cyclone
damages in 2003 are largely idiosyncratic within villages.
C. Relief and reconstruction
The Red Cross, other nongovernmental organizations, and governments
provisioned relief, and interviews in 2003 were conducted at the end of the relief phase.
Almost all households received emergency food aid, and the mean amount per capita was
F$95, which was more than twice the mean crop damage; in contrast, only a small
proportion of victims received tarpaulins that could be used as emergency shelters and for
temporary dwelling repair. At the time of interviews in 2003, refugees were almost
nonexistent and about two thirds of households with damaged housing had completed
rehabilitation: 12% had built a new house and 52% had completed repairs. As thegovernment provisioned most construction materials from 2004, these housing
rehabilitations were accomplished through people’s mutual help. By the time ofinterviews in 2005 in the late reconstruction phase, construction materials had been

6

In practice, the year-level variance (percent of total variance) is the R-squared of aregression on a year dummy; the village-level variance is the R-squared of a regression
on a full set of village-time dummies, minus the year-level variance.

10
provisioned to one quarter of households in the sample. Takasaki (forthcoming-a;forthcoming-b) details and analyzes allocations of reconstruction and relief funds,
respectively.
D. Health shock
Respondents were asked each household member’s health conditions over the past
year. In both 2003 and 2005, about one third of households had one or more sickmembers – 72% and 84% of those had one or more sick adults, respectively – and 12% ofhouseholds experienced illness in both years (Table 1). According to the variancedecomposition, household-level illnesses are mostly idiosyncratic shocks within villages.
Public-health problems were not a major issue after the cyclone in the sample villages –respondents reported no casualties and very limited injuries and illnesses directly causedby the cyclone. Illness, however, was more common among households with damaged
housing than others in 2003 (with a .13 correlation); 7 the prevalence of illness was almost
the same for refugees and non-refugees. Although illness was not more pervasive in 2003
than in 2005, housing damage, not refugee status, may have caused some healthproblems; housing damage did not cause chronic illnesses though, because these twovariables were uncorrelated for 2005.
III. Household private transfers
A. Groups
Apart from the village, kin, religious, and social groups play major roles in
Fijians’ life. First, each native Fijian belongs to a lineage of the vanua-yavusa-mataqali-

7

Illness was less common among households with damaged crops than others (with a .14 correlation); there was no significant correlation between illness and crop damage
value.

11
tokatoka hierarchy: Vanua consists of several yavusa; yavusa consists of several
mataqali; and mataqali consists of several tokatoka (Ravuvu 1983). Although vanua
ranges over several villages, there is just one yavusa in each of the sample villages (i.e.,
village formation is based on yavusa); mataqali and tokatoka are village subgroups (thesample covers 22 mataqali and 35 tokatoka). Many ritual activities, such as funerals andweddings, are organized by mataqali and yavusa. 8 Second, Christianity underlies Fijiansociety, and church donations are quite significant, as shown below. A religious groupformed for each church, which often covers more than one nearby village, is available inall villages in the sample – 3.9 church groups per village on average – and almost allhouseholds are members. Third, social groups consist of women’s, school, and youth
groups in all villages (market-oriented groups such as cooperatives are almost
nonexistent). Although membership is fixed for kin and religious groups (without
conversion to another religion), 9 participation in social groups is based on individual
decisions among the eligible – determined by gender, child schooling, and age – and 86%
of households belong to at least one social group. When these pre-formed groups are
considered as potential risk-sharing groups, group formation is irrelevant.
B. Transfer data

8

The dominant symbol of Fijian culture is kava (a beverage infused from the root of apepper plant, Piper methysticum), and kava rituals frequently involve exchanges of
ceremonial goods, such as food, mats, and bark cloth (Turner 1987). Land is communallyowned by mataqali (about 83% of the country’s total land is communal), and customary
rights for coastal fishing are held by vanua or several yavusa.
9
Marriage across different kin groups is common. This paper focuses on the kin groups
to which households currently belong; if the kin groups to which individuals used tobelong prior to marriage are considered, transfer networks concentrate more on own kingroups (especially tokatoka) than what is shown below.

12In both 2003 and 2005 surveys, respondents were asked not only about eachmajor transfer received from and given to other households, but also the transfers they
contributed to and received directly from each kin, religious, and social group to which
they belonged, as well as the village, in the past year. Three caveats are noted. First,
distinct from extant studies in the Pacific region (Bertram 1986), overseas remittances arealmost nonexistent. 10 Second, although transfer measures capture not only cash andinkind, but also labor time in 2005, labor-time transfer data in 2003 are limited tocommunal labor contributed to groups. Third, although the transfers that the household
offers to groups include all the resources it contributes, those it receives from the group
capture only partial benefits, excluding those of local public goods that the groupprovides, such as social activities and village upkeep. Measuring such benefits is very
difficult, because they often include unobservable, non-economic benefits and can berealized over a long time horizon (Clotfelter 1992). Reflecting this imbalance, transfers
given to groups are much more common and greater than those received from groups. In
contrast, the across-household transfer data are balanced in coverage between receipt andgiving.
Proportion of participation in and mean amounts of annual transfers received andgiven per capita in each year are reported in Table 2 – cash-inkind in panel A and labortime in panel B (labor time is monetized based on men’s daily wage in each village, the10

In contrast, according to the household survey conducted in five major towns and ninevillages in Viti Levu in 2005 by the World Bank (2006), 26% of 211 native Fijian
households have overseas migrants and 34% received overseas remittances. This
indicates a potentially significant difference in Fijians’ transfer patterns between the mainisland and other islands and between urban and rural areas (cf. note 3). This issue
deserves more research.

13
mean of which is about F$14). 11 Household-group transfers are quite significant,especially in 2005: Gross cash-inkind and labor-time transfers contributed to groups are
2.8 and 7.4 times, respectively, those given to other households.
C. Relief vs. reconstruction phases
A comparison of the relief period 2003 with the reconstruction period 2005
reveals a sharp contrast between cash-inkind and labor-time transfers. On the one hand,
non-labor resources that could be shared among households were limited: Cash-inkind
transfers received from other households and given to groups in 2003 were much lesscommon and smaller than those in 2005 (cash-inkind transfers given to other households
were similar over time, mainly because of large transfers made for funerals in 2003). On
the other hand, group members contributed significant labor time to rehabilitate groupfacilities, such as village facilities (e.g., community halls), churches, and schools (i.e.,labor sharing against group-level covariate shock): Labor-time transfers given to thevillage and religious and social groups in 2003 were more common and much greaterthan those in 2005; the converse holds true for kin groups, as no kin groups owned or
managed group facilities, and ritual transfers to them increased in 2005. 12 Although thecyclone significantly reduced pooled, non-labor resources, labor-time endowment waslargely intact, because of no cyclone-induced casualties and permanent migration andlimited cyclone-induced diseases. Along with the patterns of housing rehabilitations

11

Informal loans were much smaller than gifts, and when informal loans are added to
private transfers, results are almost the same as what are presented here.
12
Kin-group transfers include those made with yavusa, mataqali, and tokatoka, because
comparable data for vanua are lacking for 2003 (transfers with vanua were minor in2005).

14
discussed above, this suggests that across-household, labor-time transfers in 2003 werealso more common and greater than those in 2005.
D. Transfer networks
Respondents were also asked about the characteristics of each household withwhich transfers were made. Major transfer networks are in-village, kin, and religious
ones (Fafchamps and Gubert 2007 obtain similar findings in the Philippines): Cashinkind transfers received from other households in the village, in the same tokatoka, andin the same religious group are more common and much greater than those out of thevillage, in other tokatoka, and in other church groups, respectively, in both 2003 and2005 (disaggregated data by religion are lacking in 2003); this is also mostly true forlabor-time transfers in 2005. Although transfer networks are endogenously formed byindividual households, their network formation is directly related to kin and religious
groups they cannot choose.
IV. Econometric specification
A. Base model
I start by estimating the across-household transfer equation using the following
standard, fixed-effects specification:
yit = αhit + ∑η k X itk + Vt + ui + eit ,(1)kwhere yit is household i’s net transfer received from other households in time t; hit is adummy for illness among any household members; Xitk is a series of household-levelfactors that affect transfer decisions; Vt is time-varying village dummies, which capture
village-level covariate shock; ui is household heterogeneity; and eit is a time-variant error
term that is individually and independently distributed. This base specification is the

15same as equation (1) in Gertler and Gruber (2002), although their focus is not on privatetransfers. Fafchamps and Lund (2003) derive equation (1) from a full risk-sharing model(Cochrane 1991; Mace 1991; Townsend 1994). The fixed-effects estimator controls for
all household and village fixed effects. If private transfers are ex-post, risk-sharing
arrangements among villagers with given pooled resources in the village, householdswith illness receive more transfers, i.e., α > 0.
B. Resource and reciprocity effects
Theoretically, under imperfect labor and housing-market conditions, people seek
to smooth utility determined by consumption, leisure, and housing quality (a decrease in
housing quality because of the disaster is a preference shock), and risk sharing consists of
non-labor sharing to smooth consumption against illness (income shock) and labor
sharing for housing rehabilitation. I extend equation (1) in the following four steps.
First, I add household-level disaster damage, in particular, housing damage(dummy), dit, as a control. 13 The panel data consist of relief period 1 and reconstruction
period 2, and di2 = 0 for all i (no disaster in period 2). In period 1, households are either
disaster victims or non-victims, and in the labor sharing against housing damage, victims
receive labor-time transfers from non-victims, i.e., β1 > 0, where β1 is a coefficient of di1.
Second, I allow heterogeneous responses of private transfers to illness over time, by
replacing α with αt. The resource effect suggests that non-labor sharing against illness
better works in period 2 than in period 1; it is ineffective in period 1 if pooled resources
are sufficiently low, i.e., 0 ≤ α1 < α2. Third, I make transfer responses to shocks in period
1 – cash-inkind transfers to illness and labor-time transfers to disaster damage –13

If illness is correlated with housing damage (as found above), omitting the latter causes
bias in the estimated α.

16heterogeneous by adding an interaction term, hi1di1. This captures the contemporaneous
link of household-level disaster damage with risk sharing against illness.
Last, to capture the reciprocity effect, I allow transfer responses to illness inperiod 2 to vary, depending on the disaster damage experienced in period 1, by adding aninteraction term, hi2di1. The final model is
yit = α t hit + β1d i1 + γ 1hi1d i1 + γ 2 hi 2 d i1 + ∑η k X itk + Vt + ui + eit .

(2)kThis reduced-form specification does not identify how the history of transfers affects thecurrent transfer; a lack of labor-time transfer information in period 1 in the Fijian dataprecludes a structural-form specification – using yi1 as a determinant of yi2 with cyclone
damage di1 as an excluded instrument. Equation (2) assumes that risk sharing against
illness in period 2 depends on the outcome of risk sharing against disaster damage, notillness, in period 1; that is, it captures the potential reciprocity effect of disaster damage
only. Since researchers cannot observe the complete history of transfers, this empiricalstrategy is practically attractive if they know what particular shocks can cause thereciprocity effect. An advantage of this reduced-form specification is that the history ofrisk sharing is inclusive, capturing all forms of mutual help among households, including
those that are not measured by standard transfer data, such as co-residence for refugees.
The reciprocity effect suggests that risk sharing against illness in period 2 works
better among non-victims than victims, i.e., γ2 < 0; the marginal effects of illness inperiod t are αt for non-victims and αt + γt for victims, and in an extreme case, risk sharing
against illness is still ineffective among victims in period 2, i.e., γ2 = -α2. I also estimate
equation (2) for the non-victim (N) sample and the victim (V) sample separately (di1,hi1di1, and hi2di1 vanish); the reciprocity effect suggests that 0 ≤ α2V < α2N.

17
C. Cash-inkind vs. labor-time transfers
Ideally, I would conduct complete tests of my conjectures by estimating equation(2) for cash-kind and labor-time transfers separately, but this is infeasible with the lack of
a complete panel of labor-time transfers in Fiji. All I can do is test the resource andreciprocity effects on non-labor sharing against illness; I cannot test whether labor time isshared against disaster damage in period 1. I compare housing damage and crop damage(another income shock) as a source of the potential reciprocity effect (β1 and γt arevectors). I conjecture that the reciprocity effect of housing damage is stronger than that ofcrop damage. This is because labor-sharing against housing damage is stronger than thatagainst crop damage (the latter was actually nonexistent). Put differently, if the strong
reciprocity effect of housing damage is found despite the absence of non-labor sharing
against housing damage, this gives indirect evidence for strong labor sharing against
housing damage in period 1, as I conjecture.
D. Household-group transfers
I analyze household-group transfers in a way comparable to the analysis ofacross-household transfers. If household-group transfers in period 2 – mainly for local
public-goods provisions – are risk-sharing arrangements against illness among groupmembers, those with illness contribute less to groups than others do. If groups are thesame as villages, group-level covariate shocks, as well as all time-variant, group-levelfactors, are captured by village-time dummies, and equation (2) can be directly used totest the resource/reciprocity effects on household-group transfers. In addition to net
transfers received from groups, I also estimate gross transfers contributed to groups

18
separately, because decisions about transfers received from and given to groups are madeby different agents and what these two cover is unbalanced in the transfer data.
The reciprocity effect on household-group cash-inkind transfers can occur in two
ways. First, household-group labor-time transfers in period 1 – mainly for group-level
rehabilitation – may also serve as risk-sharing arrangements against household-leveldisaster damage. This reciprocity within groups can be tested by estimating equation (2)for gross labor-time transfers given to groups. Second, across-household labor-timetransfers in period 1 – for housing rehabilitation – may affect non-labor sharing amonggroup members in period 2. This type of reciprocity is likely if household risk-sharing
networks significantly overlap pre-formed groups. In Fiji, because major transfer
networks consist of kin and religious affiliations, that the reciprocity effect exists mainly
in kin and religious networks and groups provides evidence for this reciprocity between
networks and groups, which suggests that the formation of not only transfer networks ingeneral, but also risk-sharing networks are directly related to pre-formed groups.
E. Covariates
Household-level disaster damage dit is captured by a dummy for housing damage,the value of crop damage per capita, and their interaction. Household crop damage isendogenous, because unobservable household and village characteristics, such as land
quality, farming skills, and market and environmental conditions, which affect household
pre-cyclone cropping decisions and thus crop damage, can be correlated with its transfer
decisions. Most of these unobservable factors are fixed effects, which can be controlled
for by the fixed-effects estimator. Housing quality, such as construction materials andmicro location within villages, which might influence housing damage, may be correlated19with household transfers; such pre-cyclone housing quality is also a fixed effect. Timevariant household characteristics Xitk are captured by household size. 14 Village-time
dummies capture all village-level factors: time-variant market and environmental
conditions, village-level shocks to housing and crops (which are shown above to besmall), damage to village structures and facilities, and relief and construction materials
received by the village. 15 For disaggregated transfers, alternative specifications are
employed to better capture network- and group-level covariate shocks, as detailed in thenext section. A time dummy controls for region-level covariate shocks, seasonality, andother common events or trends.
F. Measurement errors
Although errors in the measurement of housing damage are minimal, because
relief officers used the same categories for their damage assessments (the damage statusof each house was common knowledge among villagers), errors in the value of crop
damage could be considerable and systematic. I repeated the analyses using the cropdamage dummy, the errors of which should be minimal, finding qualitatively the same
results; dropping the crop-damage variable (and its interaction terms) does not
significantly alter the remaining results, either. Although the subjective health measure,which is commonly used in household surveys, can contain significant measurement
errors because of heterogeneous definitions of illness among respondents and their14

As an alternative specification, I use land and fishing capital holdings as additional
controls, finding results very similar to those presented below. Though these productive
assets could be endogenous if they are adjusted to shocks (Rosenzweig and Wolpin 1993),
the results suggest that this is unlikely to be the case.
15
Relief and construction materials received by individual households are not included as
explanatory variables, because they are endogenously determined as part of private risk
sharing within villages (Dercon and Krishnan 2005; Takasaki forthcoming-a, b).

20systematic misreporting (Strauss and Thomas 1998), the fixed-effects estimator helps
reduce these problems.
The correlation of recall errors in private transfers with household-level shocks
can cause bias. Specifically, a positive (negative) correlation – households with larger
shocks tend to report higher (lower) net private transfers received than actual transfers –causes upward (downward) bias. If such a potential correlation with illness does notchange significantly over time, it is controlled for by the fixed-effects estimator, and theestimated positive α2 (resource effect) should be robust. This correlation still matters fordisaster damage that occurred only in period 1. Unless the correlation is negative andlarge in magnitude, estimated negative γ2 (reciprocity effect) should be qualitatively
robust; its robustness is further buttressed by consistent results of the subsample analysis.
V. Estimation results
A. Aggregated transfers
The fixed-effects estimates of determinants of annual net cash-inkind transfers
received per capita from other households and groups are shown in Table 3 (village-time
dummies are used as controls and robust standard errors are reported). When cyclone
damage is ignored (equation 1), transfers positively respond to illness, but the result is notstatistically significant (column 1). When cyclone-damage variables are added, theestimated coefficient for illness does not change and no cyclone-damage variables yieldsignificant results (column 2); that is, non-labor sharing was ineffective against housing
and crop damage. The resource effect strongly holds: Although transfers were insensitive
to illness in 2003, they significantly responded to illness in 2005 (the estimated marginal
effect is F$100, or equivalently about two thirds of the mean gross transfers received)

21(column 3). None of the interaction terms of illness in 2003 with cyclone damage yieldedsignificant impacts (column 4); that is, household-level cyclone damage does notcontemporaneously affect risk sharing against illness. The reciprocity effect of housing
damage is strong: In 2005, transfers responded to illness among households without
damaged housing (about F$250 marginal effect), but not among others (the joint
significance test for α2 + γ2 = 0 is insignificant); other interaction terms with crop damage
are insignificant (column 5). This finding is consistent with my working hypothesis that
housing damage distinguished between recipients and donors in labor sharing in period 1.
In the remaining disaggregated analyses, I use the interaction term of illness in2005 with the housing-damage dummy only (γ2 is a scalar) to increase the degrees of
freedom. Results of the estimated coefficients for illness in 2005 (α2) and its interaction
with housing damage (γ2) in the whole sample and for illness in 2005 among households
without damaged housing (α2N) and among those with damaged housing (α2V) are
reported in Table 4. Potential selection bias in this subsample analysis is unlikely to be amajor concern, because early descriptive findings suggest that housing damage isconsidered largely exogenous. Results for the aggregated transfers are almost the same as
those in column (5) of Table 3, and the subsample analysis confirms that disaster nonvictims are insured against illness, but victims are not (panel A1 of Table 4).
B. Disaggregated transfers
The resource and reciprocity effects hold not only in across-household transfers,
but also in household-group transfers: When these two are estimated separately, 16 results

16

In almost all disaggregated analyses discussed here, most households participate intransfers received or given in either 2003 or 2005 (Table 2); the only exception istransfers with social groups simply because 14% of households are not their members.

22
for the two effects are qualitatively the same, and marginal effects of illness in 2005 fornon-victims (α2 or α2N) are similar to each other (panel A1).
It appears that the reciprocity effect on household-group transfers exists between
networks and groups, and not within groups, for the following reasons. First, labor
sharing within groups does not serve as risk sharing against housing damage: Gross(monetized) labor-time transfers given to groups are neutral to all household-level shocks(column 6 of Table 3); this is also true for disaggregated groups.
Second, risk-sharing against illness through across-household transfers is mainly
arranged in in-village and kin networks: Disaggregated results for in-village and tokatoka
networks are similar to those for the aggregated networks, and the estimated marginal
effects of illness in 2005 for non-victims are 70-83% of those for the aggregatednetworks (in proportion to the shares of in-village/in-tokatoka transfers in 2005) (panel
A2 of Table 4). Although a similar analysis is infeasible for religious networks (with alack of disaggregated panel data), the share of in-church transfers in 2005 is at acomparable level (Table 2). Replacing village-time dummies with tokatoka-time
dummies for tokatoka networks does not significantly alter the results. Note thattokatoka-time dummies fully capture covariate shocks in the tokatoka networks,
including out-of-village ones; on the other hand, village-time dummies fully capture
covariate shocks in the in-village networks, but not in the aggregated networks, including
out-of-village ones.
Third, among pre-formed groups, kin and religious groups are major risk-sharing
ones: Although results for the village and kin, religious, and social groups are
When transfers with social groups only among members are considered, the results arevery similar to those for the whole sample presented here.

23
qualitatively the same as those for the aggregated groups (with the exception that theestimated γ2 for the village is non-negative), the estimated marginal effects of illness in2005 for non-victims are considerable only for kin and religious groups (34-50% of those
for all groups combined, which is greater than their shares in the aggregated grouptransfers given in 2005, 25-29%), and only those for religious groups are statisticallysignificant (panel A3). Village-time dummies fully capture covariate shocks in the village,
but not other groups. Using tokatoka-time dummies, which fully capture covariate shocks
in kin groups (tokatoka is the smallest unit in the hierarchical kin structure), does not
significantly alter the results. Similar results for religious groups are obtained under twoalternative specifications: one using church-time dummies (three church dummies aredefined for Methodist, Catholic, and other small sects combined) and another using
village-church-time dummies (they are coarser and finer, respectively, than local churchgroups, which are formed for each church across nearby villages). It is notstraightforward to construct group dummies for social groups that consist of women’s,
school, and youth groups with overlapping memberships.
All results for gross cash-inkind transfers given to groups are similar to those fornet transfers received, with opposite signs (panel B), though the marginal effect for
aggregated transfers in the subsample analysis is smaller in magnitude, with weakerstatistical significance. Hence, risk-sharing arrangements against illness take place mostly
in household contributions to groups.
VI. Conclusion
Using original household panel survey data collected in rural Fiji, this paper
demonstrated how informal risk-sharing institutions upon which poor people heavily rely

24
when experiencing illness are vulnerable to natural disaster. First, household private,
cash-inkind transfers do not serve as insurance against illness in the relief phase (several
months after the cyclone); they do so only after pooled resources recover in thereconstruction phase (a few years later) (i.e., the resource effect). Second, risk-sharing
arrangements depend on the history of labor-time transfers corresponding to housing
damage: Only disaster non-victims are insured against illness, because victims alreadyreceived labor help for their rehabilitation from non-victims (i.e., the reciprocity effect).
The paper also revealed that the resource/reciprocity effects exist in endogenously
formed networks and pre-formed groups, which serve as risk-sharing pools, to a similar
degree. Not only do private transfers exchanged among households serve as insurance,
but also, household contributions made directly to groups – such as ritual gifts andreligious donations – contain risk-sharing components against illness among groupmembers. Although the former finding is common in the literature, the latter is new.
Network formation is directly related to pre-formed groups, especially kin and religious
ones.
These findings lead to the following policy and research implications. First,
although it is crucial to better design and implement disaster relief/reconstruction (Aminand Goldstein 2008) and public-health programs to combat disaster-induced diseases(Noji 1997), these are not enough to prevent chronic health poverty. Policymakers needto strengthen broad public safety nets as a substitute for weakened private safety nets
over extended post-disaster periods. Such efforts are necessary even if public health does
not appear to be a major problem after the disaster; in fact, they may be even morenecessary then, because available public-health programs are limited in such cases.

25
Second, it is necessary to augment local safety nets ex ante to combat less visible,
post-disaster health problems. To this end, a better understanding of informal risk-sharing
mechanisms among the poor is crucial. In Fiji, though the scope of informal risk sharing
is greater than normally thought, fixed social relations – not only via kinship but also
through religious affiliation – underlying local institutions need to receive explicit
attention.
Third, although economists have not paid much attention to informal risk sharing
against natural disasters, labor sharing against their idiosyncratic components can besignificant, determining subsequent risk-sharing arrangements against non-disastershocks. More research on the link between natural disasters and informal risk sharing isneeded. How much does labor sharing help rehabilitation from a disaster? How persistentis the reciprocity effect? How do people react to the reciprocity effect? These areimportant questions that this paper did not explore.
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31

Table 1. Shocks and household characteristics by housing damage.
Varianceb
decomposition
2003&2005aHousehold means
2003

c

2005

2003

Housing
damaged

Mean/
prop.
test
(p-value)Year

VillageHousehold

All

All

Housingundamaged

Housing damaged dummy
Crop damaged dummy

0.58 (0.49)
0.82 (0.39)

0.00 (0.00)
0.00 (0.00)

0.00 (0.00)
0.80 (0.40)

1.00 (0.00)
0.83 (0.38)

0.537

40.8
69.3

2.2
0.8

57.0
29.9

Crop damage value per capita (F$)
Illness dummycIllness in 2005 dummy

44 (107)0.35 (0.48)-

0 (0)0.32 (0.47)
0.32 (0.47)

34 (57)0.27 (0.45)
0.32 (0.47)

52 (132)
0.40 (0.49)
0.33 (0.47)

0.235
0.054
0.843

7.9
0.1
-

2.6
11.1
-

89.5
88.9
-

813 (1045) 1674 (1695)

823 (818)805 (1186)

0.900

Annual public transfer received per capita (F$)
Household size

18 (81)
6.5 (2.9)

11 (48)5.8 (2.7)

20 (87)
6.3 (2.7)

17 (77)
6.6 (3.0)

0.784
0.353

Age of household head
Female head dummy

50 (14)0.13 (0.34)

51 (14)0.14 (0.34)

51 (14)0.13 (0.33)

49 (13)0.13 (0.34)

0.228
0.939

Adult secondary education dummyLand per capita (acre)
Fishing capital per capita (F$)

0.87 (0.34)
0.92 (1.19)
50 (150)

0.81 (0.39)
0.69 (1.55)
46 (228)0.88 (0.32)
0.95 (1.13)
43 (154)0.85 (0.35)
0.89 (1.23)
56 (148)

0.522
0.720
0.530

Shocks:Household characteristics:
Annual earned income per capita (F$)

No. observations
226
226
Household means are shown along with standard deviations in parentheses.
b
T hese are percents of total variance.
c2005 for illness in 2005 dummy.
a

95

131

32
Table 2. Household annual private transfers.
2003(n=226)
A. Cash-inkind transfers
A1. Aggregated transfers

Received
Mean
Partici- amounts
pation (F$ per
capita)Both45%
43 (246)
Across-household
42%
37 (240)
Group-household
4%
6.0 (58)
A2. Disaggregated across-household transfers

2005

Given
Mean
Partici- amounts
pation (F$ per
capita)Received
Mean
Partici- amounts
pation (F$ per
capita)Given
Mean
Partici- amounts
pation (F$ per
capita)

87%
63%
68%

110 (172)67 (169)
44 (61)

95%
94%
33%

145 (215)
106 (170)
39 (85)

100% 232 (262)87%
61 (87)
99% 171 (235)

Location:
Same village
Other village or city
Kinship:35%
12%

26 (220)
11 (59)54%23%

46 (146)
21 (77)88%
35%

59 (80)44 (128)86%15%

52 (74)8.5 (37)

Same tokatoka
Other tokatoka
Religion:27%
22%

28 (236)
9.5 (47)37%41%

37 (145)
29 (75)86%
48%

67 (103)38 (125)80%42%

47 (72)15 (44)Same religious group
Other religious group
A3. Disaggregated household-group transfers
Village
Kin groups

-

-

80%
26%

74 (146)
32 (97)74%
21%

49 (84)12 (40)0%2%

0.1 (2.1)
5.5 (58)42%
19%

16 (40)7.1 (22)12%31%

4.0 (18)29 (72)80%
81%

31 (47)42 (69)Religious groups
Social groups
B. Labor-time transfers
B1. Aggregated transfers

1%0%0.3 (4.0)
0.1 (1.1)

32%
36%

11 (24)10 (27)8%5%

4.8 (25)
1.3 (7.7)

96%
79%

49 (84)49 (106)

Both
Across-household

-

-

-

-

46%
28%

33 (96)21 (75)80%28%

104 (129)
12 (33)97%

198 (184)25%

12 (44)80%

92 (112)Group-household
B2. Disaggregated across-household transfers
Location:
Same village
Other village or city

-

-

-

-

26%
6%

14 (41)7.0 (63)28%
4%

12 (32)0.7 (5)

-

-

-

-

26%
13%

11 (37)9.9 (65)26%
13%

8.7 (25)
4.1 (16)

-

-

19%
10%

9.0 (35)
12 (67)20%
9%

6.5 (20)
5.9 (25)

-

87%
19%
73%

99 (92)8.8 (34)
49 (86)8%22%
5%

1.8 (7.8)
6.3 (25)
1.1 (10)

73%
56%
46%

36 (59)23 (41)
12 (21)Social groups
Note - Standard deviations are in parentheses.

67%

42 (63)4%

2.6 (23)43%

21 (46)

Kinship:
Same tokatoka
Other tokatoka

Religion:
Same religious group
Other religious group
B3. Disaggregated household-group transfers
Village
Kin groups
Religious groups-

33
Table 3. Determinants of household private transfers - fixed effects.

(2)
36.2
(49.7)

(3)

(4)

(5)

Gross annuallabor-timetransfers per
capita given togroups(6)-34.9(66.8)-0.34(0.52)-29.9(66.5)-0.38(0.52)-20.5(64.8)-0.60(0.58)-99.1(75.4)-0.60(0.67)

31.3(42.9)
0.22(0.52)

0.55(0.53)

0.54(0.53)

0.61(0.59)

0.68(0.67)-0.27(0.54)

α1-19.7(71.3)-24.2(154.8)-12.2(158.2)-22.0(51.3)

α299.8 *(52.0)

96.5 *
253.6 **
(52.2)(119.2)-87.1(65.7)-30.5(155.1)-47.1(160.0)-18.8(65.0)

Illness in 2003 * Crop damage percapita0.70(1.25)

0.44(1.23)

0.78(0.66)

Illness in 2003 * Housing damaged *Crop damage per capita-0.22(1.31)

0.06(1.28)-0.95(0.72)-285.3 **(133.5)25.7(84.3)-0.50(1.33)
1.40(1.70)

0.95(1.08)
0.25(1.40)

0.156
0.000

0.335
0.000

Net annual cash-inkind transfers
per capita received from otherhouseholds and groups(n=452)Illness dummy
Housing damaged dummy

α

(1)
37.6
(47.9)

β1

Cyclone damage per capita (F$)
Housing damaged dummy * Cyclone
damage per capita
Illness in 2003 dummy
Illness in 2005 dummy
Illness in 2003 * Housing damaged

Illness in 2005 * Housing damaged

γ1

γ2

Illness in 2005 * Crop damage percapitaIllness in 2005 * Housing damaged *Crop damage per capita
R squared
F (p-value)

0.115
0.000

0.119
0.000

0.129
0.000

0.134
0.000

*10% significance, **5% significance. Robust standard errors are in parentheses. Other controls not shown here
are household size, village-time dummies, time-dummy, and constant.

34
Table 4. Effects of illness on annual cash-inkind transfers per capita - fixed effects.
Housingundamaged

All

Housing
damaged

α2

γ2

α2

N

α2

V

(1)

(2)

(3)

(4)

A. Net transfers received
A1. Aggregated transfers
Both237.1 **
(91.8)-247.7 **(102.2)253.4 **
(99.2)-10.5(47.3)

Across-household

129.0 *
(77.7)-133.5(87.9)

157.3 *
(82.0)-4.4(33.9)

Group-household

108.1 *
(55.0)-114.2 *(67.4)

96.0(60.5)-6.2(39.7)

Same village

91.1(62.6)-80.2(76.1)

111.0 *
(64.4)

14.9(26.0)

Same tokatoka

107.7(68.4)-110.5(82.7)

131.1 *
(69.5)

4.7(27.5)

Same tokatoka(tokatoka-time dummies)

127.7 *
(73.2)-115.2(92.7)

132.2(82.6)

25.9(32.5)

Village

12.0(11.9)

1.5(15.3)

8.4(12.4)

14.0(9.5)

Kin groups

36.9(32.9)-32.0(31.7)

45.2(38.2)-4.5(14.6)

Kin groups(tokatoka-time dummies)

28.8(29.0)-28.2(31.0)

41.9(38.3)

0.1(14.2)

Religious groups

39.4 **(17.2)-39.2 *
(22.5)

34.4 *(18.0)

3.3(15.3)

Religious groups(church-time dummies)

38.0 **(17.6)-35.0(24.0)

39.5 **(19.3)

1.7(18.6)

Religious groups(village-church-time dummies)

31.2 *(16.4)-26.6(24.4)

25.2(15.8)-1.1(21.1)

Social groups

19.8(17.5)-44.4(30.5)

8.0(16.6)-19.0(21.8)All groups-84.5 *
(45.1)

90.0(61.9)-64.5(46.9)-0.8(40.8)

Village-10.2(10.9)-2.5(14.9)-6.6(10.7)

-12.4
(9.7)

Kin groups(tokatoka-time dummies)-22.7(19.6)

19.0(23.0)-23.1(27.4)-10.7(11.4)

Religious groups-33.3 **
(15.7)

34.1(21.0)-28.4 *
(16.7)-1.3(14.8)

Social groups-21.3(17.6)

48.1(30.3)-8.7(16.6)

20.6(21.7)

A2. Disaggregated across-household transfers

A3. Disaggregated household-group transfers

B. Gross transfers given to groups*10% significance, **5% significance. Robust standard errors are in parentheses. Village-time dummies areused to control for village-level covariate shocks unless otherwise noted.