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Tsukuba Economics Working Papers
No. 2009-002

Forest and Sea as Insurance among Fijians
by
Yoshito Takasaki
March 2009

UNIVERSITY OF TSUKUBA
Department of Economics
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPANForest and Sea as Insurance among Fijians
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
January 8, 2009

Abstract
This paper examines how forest and marine resources serve as insurance against a
tropical cyclone using original household data gathered in rural Fiji. The fixed-effects
estimator for a censored dependent variable controls for unobservable household
heterogeneity that can cause bias. I propose a simple empirical strategy, which can be
widely applied, to test whether a household intensifies labor activity to earn extra income
to be shared under private risk-sharing arrangements. I find that while households
abandon forest product gathering right after the cyclone, value-added handicrafts made ofsome forest products by women serve as self-insurance against crop damage after the
emergency period and this is especially so among female-headed households. Fijians
intensify fishing to augment mutual insurance for the recovery from village facility
damage and housing damages experienced by others. I discuss how this distinct pattern
emerges as private adjustments to cyclone relief delivered to the region.

Acknowledgements
I wish to thank my field team – Jonati Torocake, Viliame Manavure, Viliame Lomaloma,
Maria Torocake, and Anaseini Savuiwasa – for their advice, enthusiasm, and exceptional
efforts on behalf of this project. Special thanks are owed also to the Fijians of the region
who so willingly participated in the survey. Xiaowen Mo undertook the laborious task ofdata entry and cleaning. This paper has benefited from suggestions from Oliver Coomes.
This research has been made possible through support provided by Sumitomo Foundation,
Japan Society for the Promotion of Science, and Ministry of Education, Culture, Sports,
Science and Technology in Japan. Any errors of interpretation are solely the
responsibility of the author.

1

Forest and Sea as Insurance among Fijians
1. Introduction
Do the poor use local natural resources to cope with adverse shocks? This question of‘natural insurance’ has received explicit attention among economists since 1980s (e.g.,
Jodha, 1986) and systematic works based on micro survey data recently started to provide
supportive evidence (Pattanayak and Sills, 2001; Takasaki!"#!$%&, 2004; Fisher andShively, 2005; McSweeney, 2005). Recent theoretical studies highlight advantages ofcommons as insurance: individual returns to resource use vary little across households incomparison to asset-dependent activities like farming and the former is often uncorrelatedwith shocks on the latter. For example, Baland and Francois (2005) demonstrate that even
if the resource is equitably privatized, commons can have better insurance properties thaninsurance markets in the presence of information and enforcement problems. Delacote(2007) shows how the insurance role of tropical forests is related to deforestation. The
natural insurance concept has led to increasing recognition that environmental
conservation is important for the poor not only for income earnings but also for safety net.
Using original survey data gathered in Fiji, this paper examines '() households
use natural insurance against a tropical cyclone. This question is of critical importance forsuccessful resource management and safety-net policy in small island states whoseenvironments are highly vulnerable and economies heavily rely on foreign aid (Bertram,1986). Some researchers have criticized the deterioration of islanders’ indigenous
mechanisms in coping with cyclones due to their increasing dependency on emergency
aid (e.g., Campbell, 1984). Contrary to extensive anthropological studies, economic
studies based on household survey data are almost nonexistent in the Pacific region.

2
The paper makes four contributions to the literature. First, distinct from previous
studies on natural insurance based on cross-section data or random-effects models, I
follow the theoretical framework of ex post labor supply (Kochar, 1999; Rose, 2001) andemploy fixed-effects estimators based on panel data. I show that to obtain unbiased
estimates of a coping response to idiosyncratic shocks which are partially insured byprivate risk sharing, unobservable household heterogeneity needs to be controlled for.
Second, I address the role of ex post labor supply not only as self-insurance but
also in mutual insurance (Rosenzweig, 2001 emphasizes the importance of the connectionof these two). I propose an empirical strategy to test whether a household intensifies its
labor activity against others’ shocks to augment resources to be shared. On the other hand,recent studies shed lights on risk-sharing networks (e.g., Fafchamps and Lund, 2003). I
examine how ex post labor supply is connected with risk sharing not only within a village
but also within a kin group in the village in the kin-based Fijian society.
Third, I compare the roles of natural insurance between forest and marine
resources. Most previous works focus on wood and non-timber forest products (NTFPs).
As an exception, Takasaki "#!$%& (2004) show that Amazonian peasants more commonly
resort to riverine fishing than NTFP gathering (and hunting) in response to crop loss
caused by floods. The paper examines both the collection of NTFPs and the sales ofhandicrafts made of NTFPs by women. While the importance of NTFPs among poor
women is frequently emphasized (e.g., Agarwal, 1990), the insurance role of value-added
NTFP products often produced by women has received limited attention in the literature.
Fourth, I examine how distinctively natural insurance works against different
shocks caused by the cyclone – housing, crop, and village facility damages. Distinct

3
availabilities of cyclone relief corresponding to these shocks in the region make therelative importance of risk sharing against them also distinct. When pre-disaster
information was collected retrospectively as in other post-disaster surveys (e.g., Morris!"#!
$%&, 2002), with the uniform and easily identified timing of the cyclone, respondents could
make a clear temporal separation between ex ante and ex post periods. I examine how
their coping behaviors evolve after the disaster, an unexplored question in the literature.
The major findings of the paper can be summarized as follows. Distinct fromprevious findings in forest-rich regions and despite rich marine resources in the Pacific,
Fijians abandon NTFP gathering in the emergency period but augment handicraft selling,
not fishing, as self-insurance against crop damage, not housing or village damage, after
the emergency period. Female-headed households do so more strongly than male-headed.
Fijians intensify fishing against village damage and housing damages experienced byothers in the same village and kin group. The way they do so depends on the relative
severity of own and others’ housing damages in the multilayered risk-sharing
arrangements among villagers and kin-group members.
The rest of the paper is organized as follows. Section 2 describes the study area,
labor activities, and the cyclone. Section 3 discusses the econometric specification
followed by the estimation results in Section 4. The last section concludes.
2. Study area, labor activities, and cyclone
On January 14, 2003, Cyclone Ami swept over the northern and eastern parts of
the Fiji Islands. My post-cyclone survey covered nine native Fijian villages on the coast.1

1

The nine villages were intentionally chosen to cover distinct environmental andeconomic conditions. Six and three villages, respectively, are located on Vanua Levu andTaveuni Islands, the second and third largest islands in the country which significantly

4
In each village, all households were stratified by the smallest kin group unit defined
below and the combination of their leadership status and major asset holdings (like shop),and in each stratum, households were randomly sampled (n=374). Between late Augustand early November, 2003, one visit to each household in the sample was made and theinterview asked about its production, income, assets, demographics, cyclone damage, andrelief (neither consumption nor labor transfer data were collected). Pre-disaster
information was collected retrospectively. Respondents were asked about the productionof major crops and the catch of fish in the past one month and then monthly production ayear before – i.e., before the cyclone – in comparison to the latest figures. NTFP
gathering, handicraft production, and relief received in each month over the past one year
were also recorded. Using these two data sets, respectively, I constructed two-period
panel of pre-cyclone and post-cyclone periods and four-period panel of pre-cyclone
period 1 (October-December 2002) and post-cyclone periods 2-4 (January-March, AprilJune, and July-September 2003) whose descriptive statistics are shown in Tables 1 and 2.
Table 1 also reports household characteristics a year before the interview as well as
cyclone damages and both tables compare male- and female headed households. Potential
measurement errors are discussed in the next section. The paper analyzes 332 householdswith complete data (since kin-group means are used as explanatory variables, all kingroups consisting of one household in the sample are dropped).
The hierarchical kin structure in Fiji is well known. The bottom is extended
family, #(*$#(*$, followed by sub-clan, +$#$,$%-, clan, .$/01$, and tribe, /$20$. Alllag behind the first largest island Viti Levu where the state capital, two international
airports, and most tourism businesses are situated. Fiji is divided almost equally into
native Fijians and Indo-Fijians. This study focuses on the former.

5
native Fijians belong to one tokatoka which belongs to one mataqlai, and so forth(Ravuvu, 1983). While vanua extends over several villages, yavusa, mataqali, andtokatoka are often formed within a village. The sample includes 15 yavusa, 36 mataqali,
and 49 tokatoka and their average group sizes are 33, 15, and 10 households, respectively.
While all nine villages contain more than one mataqali, seven consist of only one yavusa,
i.e., clan and village coincide with each other. While two vanua range across two villages(there are seven vanua in the sample), there is no across-villages overlap of yavusa. Most
ritual activities like funeral, wedding, and traditional meetings are organized by mataqali
and yavusa and occasional large meetings are held by vanua. Land and sea tenures are
determined by kin groups. Rural land is communally owned by mataqali, privately used,
and is not allowed to be sold by law (communal land consists of about 83% of total landin the country). Customary rights for coastal fishing are held by vanua or several yavusa
often consisting of several villages. The sample includes seven customary fishing areas –they do not match with seven vanua – in some of which fishing regulations like the banof Sunday fishing and gill nets exist.
Almost all households in the sample employ traditional farming practices (using
no mechanized equipment or animal traction and limited purchased inputs) to mainly
produce taro, cassava, coconut, and kava plant (a pepper plant, locally known as .$,(2$,used to make a local beverage kava which is a dominant symbol in Fijian culture, Turner,1986). None of these crops are seasonal. Most households conduct subsistence fishingusing lines and hooks, simple spear guns, or rudimentary nets, and more commerciallyoriented fishermen use boats and engine along with more valuable nets. Fishing is less

6seasonal than forest resource use discussed next. 2 The cropping and fishing incomes ayear before the interview count for 54% and 29%, respectively, of total income.
Many dwellers, both men and women, gather forest products like wild fruits for
consumption and cash earnings. Only women make handicrafts from indigenous plants to
be used as ceremonial gifts and some craftswomen sell their produce in the local markets
and small resort hotels for tourists.3 These NTFPs on communal land are essentially open
access to all villagers and forest extraction is unregulated. In period 1 – before thecyclone – 58% and 12% households, respectively, in six out of nine villages (n=223)
gathered NTFPs and sold handicrafts but their contributions to total income were
negligible (in the remaining three villages these two activities were almost nonexistent).
12% households are headed by female. The female headed have a weaker farming
capacity – they are older, are less educated, and hold smaller land – than the male headed,earning much lower crop income before the cyclone; contrarily, the female headed earned
greater fishing income (the gendered division of labor is weak in farming and fishing andfemale fishers are known to be active in Fiji, Chapman, 1987) and the gender-disparity intotal income was not large. While the female headed more commonly sold handicrafts inperiod 1, there is no across-gender difference in sales and NTFP gathering.
Differing from other islands in the country, the study area is less prone to be hit
by cyclones and Ami was the only cyclone in the season – it was totally unpredicted by

2

In each village the seasonality of major forest and marine products were asked. 91%,
17%, and 77% of NTFPs (excluding firewood), finfish species, and other marine products,
respectively, are seasonal. The contribution of other marine products to income is muchsmaller than finfish.
3
The three most important handicrafts are famous Fijian mat /(-/(- made of screw pine,
3$24$201!#'051#(2--, finer mat *0#$ made of soft sedge, 6%"(7'$5-1!40%7-1, and bark cloth#$8$ made of paper mulberry, 95(0112"#-$!8$8.5-:"5$.

7
local residents. All nine sample villages were damaged and relief delivery by NGOs andthe government started only in March. 4 According to the subjective measure of
respondents, the cyclone damaged 53% residents’ houses – 8% completely destroyed and
45% partially damaged. 37% of households with housing damaged became refugees.
They stayed in others’ residence in the same village – permanent migration was
nonexistent – and about half and two thirds of them, respectively, lived with households
in the same tokatoka and mataqali. Clearly, kin groups served as a risk-sharing group.
Labor sharing for emergency housing repair was intensively undertaken among
males, and as more and more dwellings were repaired, refuges became uncommon.
Public support for housing rehabilitation was limited to primitive tarpaulins (to be used as
emergency shelter and for temporary housing repair) and their provision was scarce –only 12% households were recipients (by June). More than one year after the cyclone thegovernment started to provision construction materials. Hence, how victims could
rehabilitate their dwellings depends on how much construction materials and laborsupport they could secure in their own ways. At the time of interviews, refugees were
almost nonexistent, 16 victims built a new house, and 60% damaged dwellings (including
separate kitchen, shower, and toilet units) were repaired – 65% among the male headed
vs. 43% among the female headed. In the village-level public rehabilitation programs,
communal labor – mostly male – also played a main role and at the time of interviews
most village facilities were not fully rehabilitated due to lack of construction materials.

4

The total cyclone damage across the country (mostly in Vanua Levu and Taveuni) is
estimated at F$104 million, of which dwelling damage is F$22 million and crop damage
is F$40 million (F$1 = US$.60) (National Disaster Management Office, 2003). Fourteen
people were killed. In the sample villages no casualties and very limited injuries andillnesses caused by the cyclone were reported.

8
88% households experienced crop damage. I calculated the value of damaged
crops based on the quantity damaged for each major crop reported by respondents. The
mean value of damaged crops was about 34 Fiji dollar per adult equivalent (F$1 =US$.60), or equivalently 57% mean monthly pre-cyclone crop income. Emergency foodaid was the largest relief in the region (the total cost of food ration was 20 times that of
tarpaulins in the country, National Disaster Management Office, 2003). By April almost
all households got some food aid and in each of the three post-cyclone periods theyreceived about 10 days of food per month on average (provisions continued until
September). 5 That is, an average household could rely on aid to cover about one third ofits food consumption. Compared to crop damage, this is a huge injection. Based on thegovernment estimate of the cost of food ration (F$1.73 per person per day, National
Disaster Management Office, 2003), the value of 90-days food ration for nine months,
F$156 per capita, is 4.5 times average crop damage per adult equivalent.
Distinct from housing and village rehabilitations, households individually
rehabilitated cropping by collecting harvestable damaged crops, cleaning fields, andplanting seeds. People planted fast-growing crops (like sweet potato) after their seeds
were provisioned as part of relief and the harvest already started at the time of interviews.
Mean crop income was still lower than the pre-cyclone level by 42%. While no genderdisparity exists in housing damages, the female headed experienced 85(8(5#-(2$%%.

5

Because the monetary value of food aid received was difficult for respondents to answer,
interviewers asked the quantity measured in days it would have taken to consume in2(5+$% periods (not actual duration of consumption). Four villages on Vanua Levu
interviewed from late August through mid-September, earlier than the other five,received (almost) no food aid in July or later, but they might receive some after theinterviews. Table 2 shows food aid in period 4 in the remaining five villages. All four
villages interviewed late are included in the six main handicraft villages.

9greater crop damage than the male headed – 54% vs. 101% of monthly pre-cyclone crop
income. Nevertheless, the relative gender-disparity in crop income did not increase overthe past one year, indicating that the female headed more rehabilitated cropping than themale headed.
In period 2 right after the cyclone, households abandoned NTFP gathering. Most
NTFPs are seasonal but seasonality only partly explains this pattern (see note 2). The
deteriorated access to gathering sites due to downed trees and debris precluded
households from doing this activity in some locations but not all. The most likely reason
for this abandonment is to free labor for other coping activities under emergency. Indeed,gathering recovered in periods 3 and 4 but participation was still much lower than the
pre-cyclone level. Participation in and revenues from handicraft selling also did notchange much in period 2 but greatly increased later. This pattern matches with therecovery of tourists’ demand. Main handicraft NTFPs are not seasonal.
The female headed more commonly participated in handicraft selling and soldmore than the male headed in period 4. Among the female headed, the sales amount was
16 times that in period 1 and the income share of handicrafts reached 19% in the wholefemale head sample. On the other hand, in comparison to the pre-cyclone level fishingincome significantly decreased especially among the female headed and as a result its
gender-disparity disappeared. Damages on fishing capital which could cause this patternwere not common. Households, especially the female headed, shifted labor from risky
fishing to less risky handicraft selling; they did not intensify NTFP gathering probably
because of its lower returns to labor (with lack of time allocation information, I cannot
compare returns to labor across activities).

10
These findings give initial evidence that handicraft selling in periods 3 and 4 –especially among the female headed in period 4 – served as self-insurance but NTFP
gathering and fishing did not. Other potential coping activities than private transfers andinformal loans must have played minor roles. Casual wage labor, a focus of previous
works on ex post labor supply, was very rare and its contribution to total income wasnegligible. Livestock selling contributed little to income earnings. The disposition offishing capital and the transfer of usufruct of land to smooth consumption after the
cyclone was nonexistent. Indeed, fishing capital and land holdings did not change in thepast one year almost at all. Formal credits and insurance were nonexistent.
3. Econometric specification
Numerous works have tested the full-risk sharing hypothesis using the equation

7-# " # 0 ! #1 <-# ! # 2;/# ! 0- ! /-# ,where -, /, and # stand for household, village, and time, respectively, 7-# is household
consumption, <-# and ;/#, respectively, are household- and village-level adverse shocks, 0is unobservable household heterogeneity, and "-# is a time-variant error term which isindividually and independently distributed (e.g., Townsend, 1994). The null hypothesis offull risk sharing among households is that individual consumption is unaltered byidiosyncratic shock as they efficiently share available resources which are only altered bycovariate shock, i.e., != = 0 and !> < 0. As unobservable welfare weights used in the risk
sharing are correlated with idiosyncratic shock, to eliminate 0- using fixed-effects models
is needed to obtain unbiased estimates. The full-risk sharing model has been extended to
examine risk sharing within other group than village such as cast (Morduch, 2005) andhousehold network (Fafchamps and Lund, 2003; De Weerdt and Dercon, 2006):

117-# " # 0 ! #1 <-# ! # 2;/# ! # 3 ) ?# ! 0- ! /-# ,where )?# is a covariate shock in group ?. If risk is shared at the village level not group,
the addition of the group shock is redundant as the village shock is already controlled for;its significant impact on consumption means that risk is shared among group members.
Rose (2001) offers a theoretical framework for household labor supply decisions
in response to a covariate shock on own farm production (regional rainfall). Adding an
idiosyncratic shock to her model gives rise to the ex post labor supply equation
C " C$ < A; A B A@ % ,where < and ;, respectively, are idiosyncratic and covariate shocks as before, and B and@, respectively, are household- and village-level factors which affect returns to labor like
productive assets and market prices. The standard income effect suggests that withgreater adverse shocks, income is lower and the household will increase labor supply tosmooth income (Rose, 2001): that is, "=, "D > 0 in the estimating equation

C-# " & 0 ! &1 <-# ! & 2;/# ! & 4 B -# ! & 5@/# ! 0- ! "-# .

(1)

As in the full-risk sharing model, the model (1) can be extended to
C-# " & 0 ! &1 <-# ! & 2;/# ! & 3 )?# ! & 4 B -# ! & 5@/# ! 0- ! "-# .

(2)

In the case of sub-groups within the village, impacts of the covariate shock measured atthe village level in (1) are decomposed into those at the group and village levels in (2).
Ex post labor supply and risk sharing are related with each other. On one hand,risk sharing lowers the demand for income smoothing through labor activities. With fullrisk sharing (!= = 0), labor supply is unresponsive to idiosyncratic shock ("= = 0) while
uninsured covariate shock still matters. On the other hand, additional labor income
augments resources to be shared. As the full-risk sharing model captures consumption

12smoothing against idiosyncratic shock $:#"5 households earn ex post labor income, thereduced-form models (1) and (2) capture household labor response to idiosyncratic shock$:#"5 it is weakened by risk sharing. Hence, in the models (1) and (2), unobservablewelfare weights used in the risk sharing affect household labor supply decisions and any
partly insured idiosyncratic shocks (like illness) are endogenous. Unless researchers
focus on covariate shock, they need to control for household heterogeneity 0- using fixedeffects estimators. Random-effects estimates are unbiased on the assumption that ex post
labor supply is unrelated with risk sharing, which is too restrictive.
Although to develop a theory combining the household labor supply model andthe full risk-sharing model is beyond the scope of the paper, I empirically explore theresource-augmentation effect. Suppose that in the model (1) the idiosyncratic shock is adummy variable which perfectly distinguishes recipients and donors in the risk-sharing
arrangement among villagers, and the village-level covariate shock is measured by thevillage mean of this dummy (a standard procedure in the literature on risk sharing). I run
the reduced-form regressions among households with and without the idiosyncratic shock– recipients and donors – separately maintaining the village-level covariate shockmeasured in the whole sample. Then, the positive coefficient for the covariate shock inthe donor sample shows how much donors augment labor supply against the shocks
experienced by others in the same village. To extend this sub-sample analysis to themodel (2) with the group mean is analogous.
I examine two idiosyncratic shocks caused by the cyclone, housing damage
captured by two dummies, one for partial damage and another for complete damage, andcrop damage captured by the value of crop damage per adult equivalent (log). Apart from13
the aforementioned risk-sharing effect, household crop damage is endogenous because
unobservable household and village characteristics such as land quality, farming skills,
market conditions, environmental conditions (e.g., resource stock), and institutions (e.g.,
resource use regulations) affect pre-cyclone cropping decisions which determine crop
damage. Housing damage should be much more exogenous. Indeed, households with andwithout housing damaged do not significantly differ from each other in their pre-cyclone
income, asset holdings, and household characteristics (results are not shown), indicating
the randomness of housing damage. Housing damage can be related with unobservable
initial housing characteristics which might be somewhat correlated with unobservable
determinants of labor supply though. In the Fijian panel data, most of these unobservablefactors – including pre-cyclone cropping decisions and potential ex ante risk-management
behaviors – are fixed effects which can be controlled for by fixed-effects estimators.
Although all household-level factors determining production technology like productive
assets (B-#) are also fixed effects, they may influence household labor responses to theshocks as they alter returns to labor. This issue is examined in the next section.
In all models a time dummy captures common events or trend including
seasonality. I consider two specifications for village-level time-variant factors. The firstuses village-time dummies which capture all village-level factors including unobservable
ones like market prices of various products. The second specification focuses on
observable village-level factors – the village means of the three idiosyncratic shocks
discussed above, the village mean of food aid per month (log), the proportion ofhouseholds receiving tarpaulins in the village, and a dummy for damages on main village
facilities (school, health center, and church). The relief variables measured in periods 2

14
and 3 – period 4 is not included for the reason given in note 5 – capture cyclone relief as
positive shocks. Relief received by individual households is not included because it isendogenously determined as part of private risk sharing (Dercon and Krishnan, 2005).
With only nine villages in the sample, interpretations of estimation results of thesevillage-level variables need caution – better analysis requires a much larger number ofvillages with richer spatial coverage (ideally including villages with no cyclone damage).
In the model (2) I examine tokatoka and mataqali as a potential risk-sharing groupwithin the village by using the group means of the three idiosyncratic shocks as grouplevel covariate shocks (as discussed above, yavusa is almost the same as village and mostvanua and customary fishing areas are larger than village). With the relatively large
numbers of these groups, estimated standard errors are clustered by the groups. The
group and village mean estimates should be accurate because on average about 70%
households were sampled in each stratum defined by tokatoka.
Household labor response to the mean damage can be either for self-insurance
against uninsured covariate shock and for mutual insurance to help others, or both. I
conjecture that the sharp contrast between food aid and housing support in theiravailabilities makes informal risk sharing against housing damage play a much more
important role than that against crop damage, and as a result housing damage mostly
distinguishes net recipients and donors not only of labor transfer for repairing but also of
non-labor sharing to help victims purchase construction materials. Households are
divided almost equally into those with and without housing damaged, making such risk
sharing possible. Then, the sub-sample analysis for households without housing damaged
mainly captures the resource-augmentation effect of ex post labor supply. Importantly, as

15
housing damage is exogenous, potential selection bias is not a concern. The sign of theestimated coefficients for relief should be opposite to damages because public transfer
augments resources to be shared as a favorable covariate shock. A positive estimatedcoefficient for village facility damage indicates that the household augments labor supply
to make contributions to the village for rehabilitation.
Non-participation common in NTFP-based activities is an econometric challenge.
I employ the trimmed least squares estimator developed by Honoré (1992) to control forunobservable household heterogeneity 0-!with a censored dependent variable. Anotheradvantage of this fixed-effects model is that it is robust to heteroskedasticity and nonnormality which are other potential sources of bias in random-effects Tobit estimates.
This estimator has not been used in previous works on ex post labor supply. I use thefirst-difference estimator for fishing the participation of which is almost universal.
With lack of time allocation information, I use revenues as a proxy for labor input.
Market prices which determine revenues are controlled for by village-time dummies in
the first specification (the second specification is discussed below). The fixed-effects
estimator controls for any systematic difference between revenues and labor input caused
by unobservable skills, fishing capital (which changed little over time), and resource
stocks (labor is an effectively unique input for NTFP-based activities). Since the overallsignificance of estimation results for non-handicraft NTFP gathering is shown to be very
weak due to its uncommon participation and small revenues especially after the cyclone,
the analysis focuses on handicraft selling as a major form of forest resource use.
To examine the evolution of ex post handicraft responses, I conduct two-period
analysis for periods 1 and 2, periods 1 and 3, and periods 1 and 4 separately. In the latter16
two, labor input in period 3 or 4 is connected with the shocks experienced in period 2 andperiods 2 and 3 or periods 2-4 are treated as one post-cyclone period. This is a standardpractice in analyzing annual survey data lacking information over time within the year.
Two potential problems arise. First, coping behaviors in the previous post-cyclone
period(s) correlated with shocks may affect decisions in the subsequent period as an‘unobservable’ time-variant factor. Second, measurement errors in the timing ofproduction in the original monthly data may be systematically correlated with
idiosyncratic shocks. I ran the same regressions using monthly revenues calculated overperiods 2 and 3 and periods 2-4 as a dependent variable in the post-cyclone period.
Results were very similar to what are presented here. I also employ the 2-period model
for fishing, the ex post data of which is at the time of interviews.
Measurement errors in the retrospective data require particular attention. First ofall, measurement errors in the two housing damage dummies are minimal because thesame categories were used by relief officers for their damage assessments. All village
leaders could well recall the total number of damaged houses, that is, the damage statusof each dwelling was well known among villagers. On the other hand, measurement
errors in the value of crop damage can be considerable and systematic. I repeated all
analyses using a dummy for the incidence of crop damage, the errors of which should be
minimal. Qualitatively the same results were found (results are not shown).
The evolution of production found in the descriptive statistics may be caused byrespondents’ memory loss in early periods. Such memory loss should be small inparticipation because the very minor activity of NTFP gathering was much less common
in periods 3 and 4 than 1. While respondents could well recall the production of

17
handicrafts with significant cultural and social importance (they serve as major
ceremonial goods in kava ritual, Turner, 1987), revenues may contain significant
measurement errors. While common memory loss is controlled for by the time dummy,
the correlation of errors in revenues, especially before the cyclone, with idiosyncratic
shocks causes bias. There is no way to control for this potential bias. The positive(negative) correlation – households with larger idiosyncratic shocks tend to report higher(lower) pre-cyclone revenues than the real – causes upward (downward) bias. Thus,
unless the correlation is positive and large, estimated positive "= is qualitatively robust.
4. Estimation results
The first-difference estimates of determinants of log fishing revenues per adultequivalent per month are reported in Table 3. All results of the model (2) are based on
tokatoka. The first four columns show results for the whole sample. When time-variant
village-level factors are fully controlled for by village-time dummies (Column 1),
households intensify fishing against own crop damage not housing damage. Whenvillage-time dummies are replaced with observable village-level variables (Column 2),
the estimated coefficients for household-level shocks do not change almost at all,indicating that unobservable village-level time-variant factors do not cause bias.
Households strongly adjust fishing to village-level factors. While partial housing damage
at the village level exhibits no impacts, households in a village with main facilities
damaged and with more houses completely damaged in proportion fish more – the former
marginal effect is 25% and the latter is 6.8% for a 1% increase in the proportion of
victims in the village (equivalently about 12.6% for an additional victim in an average
village with 54 households). Households in a village with more tarpaulins in proportion

18
fish less and the marginal effect in magnitude is about half of that of the complete
housing damage. While crop damage at the village level exhibits no significant impact,
households in a village with more food aid fish less (8.8% reduction for a 10% increasein food aid). Although these estimates require caution as discussed above, they are quiteconsistent with the predictions.
When the three group-level shock variables are added to the model with villagetime dummies (Column 3), the estimated coefficients for two individual housing damage
variables increase in magnitude – neither of them is still statistically significant – andcrop damage loses its statistical significance, indicating that the omission of group-level
shock variables causes bias. Indeed, households in a kin group with more complete
housing damages intensify fishing – the marginal effect is 1% for a 1 % increase in the
proportion of victims in the same group (equivalently about 10% for an additional victim
in an average tokatoka with 10 member households). The partial housing damage andcrop damage at the group level exhibit no significant impacts.
When observable village-level variables are used (Column 4), the estimatedcoefficients for household- and group-level variables do not change almost at all andthose for the village-level variables are very similar to Column (2). The only exception isthat the estimated coefficient for the village-level complete housing damage decreases by
11% or equivalently by almost the same magnitude of the impact of the group-levelcomplete housing damage. This indicates that households more strongly respond to thevillage-level complete damage than the group-level damage.
Disaggregated results for households without and with housing damaged arereported in Columns (5)-(8). Further disaggregated results for those with housing19
partially damaged which consist of 85% victims (Columns 9 and 10) are almost the sameas those for all victims (to separately analyze the remaining 28 households with housing
completely damaged is infeasible). As results are almost the same between models withvillage-time dummies and with observable village-level variables, only the latter arereported. First of all, victims intensify fishing to earn extra income to help makecontributions to the village for rehabilitation; non-victims do not need to do so. Whileimpacts of village-level complete housing damage and food aid are similar between nonvictims and victims, group-level complete housing damage only influences non-victims
and partial damage positively and negatively affects non-victims and victims,
respectively. These findings suggest multilayered risk-sharing patterns: at the village
level, recipients are households with housing completely damaged and donors are thosewith housing undamaged and partially damaged; at the kin group level, recipients areboth of these two victims and donors are non-victims.
Households with housing partially damaged thus play dual roles: on one hand,
they decrease fishing against group-level partial housing damage as they rely on help
offered by non-victims in the same group; on the other hand, they intensify fishing tohelp others with severer damage in the same village as non-victims do. Since complete
housing damage is relatively uncommon and nonexistent in some kin groups and themagnitude of this shock is too large to be shared among kin members, the larger-scalerisk sharing at the village level plays a main role. Indeed, non-victims’ fishing response
to village-level complete housing damage is much stronger than that to group-level. Also
only victims are affected by tarpaulin, indicating the larger role played by non-victims as
donors than victims with partial damage. On the other hand, partial housing damage is20much more common and occurs in most kin groups and the moderate magnitude of thisshock can be shared within the group. Indeed, non-victims’ fishing response to villagelevel partial housing damage is even negative. The models with mataqali-level shock
variables generate very similar results. Hence, the resource-augmentation effect of ex
post fishing exists mostly within tokatoka (a sub-group of mataqali).
Are the resource-augmentation effects found a robust result? Regarding thevillage-level effect captured by observable village-level shocks, it depends on whether
they are correlated with unobservable time-variant covariate factors. Fishing competition
and fish prices are potentially important. Fishing competition in the customary fishingarea occurs among all villagers not among tokatoka or mataqali members and fish prices
mostly follow three local markets depending on village location. If victims lower fishingefforts to free labor for rehabilitation, the competition among fishers will decrease andthe reduced supply of fish may boost fish prices in comparison to other products, thereby
encouraging fishing among non-victims. That is, housing damages may be negatively andpositively correlated with unobserved competitions and relative prices, respectively. If
competition or price actually mattered, non-victims’ responses to village-level housing
damages should be stronger than victims’; however, the converse holds true for partial
damage and their responses to complete damage are very similar to each. Of course,
possibilities of other confounding effects cannot be ruled out. 6 On the other hand,
unobservable village-level factors do not cause bias in the estimates of household
responses to kin-group shocks, because the results are almost the same between the6

For example, village-level housing damage (especially complete damage) may becorrelated with damages on infrastructure that affected the transport of produce to local
markets, but most damaged roads were recovered at the time of interviews. Institutionalchange could matter, but no fishing regulations were altered after the cyclone.

21
models with observable village-level shocks and with village-time dummies. Hence, theresource-augmentation effect at the kin-group level is likely to be robust.
I consider two fixed effects which can make household ex post labor responses
heterogeneous – fishing capital and gender. As fishing capital determines returns to labor,
the role of fishing as self-insurance may be asset-dependent. As in other developing
regions, share fishing is relatively common in the study area: households that are poor infishing capital work with others with large holdings in exchange for a share of the catch.
Then, the resource-augmentation effect may depend on group members’ capital holdings(if share fishing is done among them). In addition, although inequalities are often
considered to be an important determinant of the use of commons and mixed results on
their effects are found (e.g., Baland and Platteau, 1997; Alix-Garcia, 2008), how they
influence the insurance role of commons has not been examined in the literature. I add
individual shock variables interacted with individual capital holdings per adult equivalent(log) to the models (1) and (2) and group-level shock variables interacted with their groupmeans and coefficient variations (standard deviations divided by means) to the model (2).
No significant results on these interaction terms are found (results are not shown). As
discussed above, household characteristics are distinct in the gender sphere and the sharp
gendered division of labor exists in handicraft making (women) and labor sharing (men).
I add individual shock variables interacted with a dummy for female head except
complete housing damage which was experienced by a limited number of the female
headed. I find no significant results (results are not shown).
Trimmed least squared estimates for log handicraft sales per adult equivalent permonth – in six main participant villages – are reported in Table 4. The models with22
village-time dummies – with and without interaction terms with the female head dummy– are shown. As all results with tokatoka- and mataqali-level shocks are very similar toeach other, only the former are reported. While the overall fitness of the models is notstrong in periods 1 and 2, households with more crop damage sell more handicrafts inperiod 3 with no gendered difference (the interaction term is insignificant) and the female
headed also do so in period 4 (only the interaction term is significant). Without
interaction terms, crop damage is insignificant in period 4: that is, ignoring the genderedheterogeneity fails to capture the strong coping response of the female headed. With no apriori reason for the across-gender disparity in recall bias, this gendered difference is very
unlikely to be a biased result. No kin-group shocks exhibit significant results in a robust
manner. When observable village-level variables are used, almost none of them aresignificant and all other results are almost the same as what are presented here. 7
I argue that these distinct patterns between handicraft selling and fishing arecaused by households’ private adjustments to cyclone relief. With generous food aid, thedemand for self-insurance against crop damage is not high but households still need extra
cash. Although there are significant variations in crop damages among households,
information problems constrain risk sharing (crop rehabilitation involves no sharing).
Handicraft selling is attractive self-insurance because it is low-risk-low-return but morevalue-added than NTFP gathering and it can be done individually by women when males
are overwhelmed by labor-sharing tasks. However, handicrafts do not serve as insurance
under emergency; mutual insurance probably plays a central role as exemplified by co7

I ran the same regressions among households with housing damaged, finding similar
results to those for the whole sample. It was infeasible to analyze the non-victim sample
because of the small number of observations and the uncommon participation after the
cyclone in some villages.

23
residence for refugees. The demand for handicraft selling as insurance becomes highwhen they are waiting for the harvest of rehabilitated crops in the lean period 3. To
compensate for the proportionally greater crop damage than the male headed, the female
headed boost production in period 4 when their labor constraint becomes less tight.
With very limited public support for housing rehabilitation, victims seek large
extra cash to purchase construction materials (and tarpaulins) as well as labor support for
repair. Most victims rely on mutual insurance supported by non-victims as potential
donors. Lack of information problems and randomness in housing damage facilitate risk
sharing. Fishing is too risky for self-insurance even among large fishing capital holders.
Although cyclone relief – both tarpaulins and food aid – is of great help, it is not
sufficient for victims. To augment risk sharing donors exploit rich marine resources toearn extra cash at least at the time of interviews – whether they also did so earlier is
unknown. This augmentation is not affected by capital holdings among risk-sharing
members. Lastly, with limited public support for village rehabilitation, households makesignificant contributions and fishing helps those with housing damaged do so.
5. Conclusion
This paper examined how forest and marine resources serve as insurance against a
tropical cyclone using original household data gathered in rural Fiji. The fixed-effects
estimator for a censored dependent variable controlled for unobservable household
heterogeneity that could cause bias. I proposed a simple empirical strategy, which can be
widely applied, to test whether a household intensifies labor activity to earn extra income
to be shared under private risk-sharing arrangements. I found that while households
abandon forest product gathering right after the cyclone, value-added handicrafts made of24some forest products by women serve as self-insurance against crop damage after the
emergency period and this is especially so among female-headed households who
experienced proportionally greater damage than male-headed. Fijians intensify fishing toaugment mutual insurance for the recovery from village facility damage and housing
damages experienced by others (six to nine months after the cyclone). The risk-sharing
arrangements are multilayered: not only households without housing damaged fish more
to help others with severe damage in the same village and others with severe and lesssevere damage in the same kin group within the village, but also households with lesssevere damage do so to help others with severe damage in the same village. I discussed
how the distinct patterns of natural insurance across resources and shocks emerged as
private adjustments to cyclone relief delivered to the region over time.
These findings are context-specific but they still lead to the following generalpolicy implications. First, with value-adding process natural insurance may play asignificant role even in resource-poor environments. In some locales like Fijidevelopment of handicraft enterprises and markets is probably promising for both income
enhancement and better safety nets especially among poor women. Second, focusing on
the self-insurance role of nature may lead to incomplete or wrong understanding of its
broader role because it can be tightly linked with mutual insurance. This indicates theimportance of integrating community-based resource management and safety-net policy.
For example, such an approach helps avoid local overexploitation of commons underpressure, potential pitfall of natural insurance. In Fiji, however, kin groups which
determine land and sea tenures do not necessarily match with risk-sharing groups.
Coordinated managements of forest and marine resources are surely needed but in25practice they are quite separated as in other locales. Third, right after the disaster, people
do not resort to forest – with or without value-adding process – probably because mutual
insurance plays a greater role under emergency (the insurance role of sea at that time isunknown though). While emergency relief is of great help, policy makers need to notethat it may significantly alter informal mechanisms as recipients make adjustments.
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28
Table 1. Household income, characteristics, and cyclone damage.

All

Male head

Mean/
Female head prop. test
(p-value)

Sectoral income per adult equivalent per month a year ago - pre-cyclone (F$):
Cropping
60.2 (90.8)
64.5 (95.2)
Fishing
32.1 (44.8)
30.4 (30.7)
1.2 (3.6)
1.2 (1.2)
NTFP gathering and handicraft sellinga
10.7 (41.8)
11.5 (11.4)
Permanent wage labora
0.5 (3.2)
0.4 (0.4)
Casula wage labora
6.9 (44.5)
7.1 (7.1)
OtherbTotal
112 (118)
115 (115)

28.5 (35.0)
44.9 (28.1)
1.0 (1.1)
4.4 (5.0)
1.1 (0.7)
5.6 (6.4)
85.5 (73.8)

0.019
0.054
0.744
0.311
0.201
0.841
0.136

Current sectoral income per adult equivalent per month - post-cyclone (F$):
Cropping
34.9 (59.6)
37.3 (37.1)
Fishing
21.6 (33.6)
21.9 (22.0)a3.2 (10.6)
2.2 (2.1)
NTFP gathering and handicraft sellinga10.1 (40.9)
10.9 (10.8)
Permanent wage labor
Casula wage labora
0.9 (5.4)
0.8 (0.8)
Otherb3.0 (12.3)
2.7 (2.8)
Total
73.7 (83.2)
75.9 (75.8)

17.3 (19.8)
18.7 (19.7)
10.7 (9.8)
4.5 (5.1)
1.2 (0.6)
5.0 (5.7)
57.5 (60.8)

0.047
0.571
0.000
0.351
0.665
0.282
0.189

Household characteristics a year ago - pre-cyclone:
Land per adult equivalent (acre)
1.1 (1.5)
Fishing capital per adult equivalent (F$)
107 (423)
Adults' secondary education dummy
0.83 (0.37)
Household size (adult equivalent)
4.9 (2.2)
Age of household head
48.6 (13.9)
Female head dummy
0.12 (0.33)

1.2 (5.0)
98 (444)0.85 (0.86)
4.9 (4.9)
47.0 (47.0)
0.00 (0.00)

0.6 (2.5)
174 (638)
0.70 (0.74)
4.4 (4.6)
59.5 (60.0)
1.00 (1.00)

0.013
0.290
0.015
0.126
0.000
n.a.

0.53 (0.50)
0.45 (0.50)
0.08 (0.28)

0.53 (0.53)
0.45 (0.44)
0.09 (0.09)

0.53 (0.51)
0.45 (0.49)
0.08 (0.03)

0.913
0.987
0.821

0.37 (0.48)
0.86 (0.35)
34.4 (44.7)

0.37 (0.37)
0.87 (0.87)
35.1 (35.0)

0.43 (0.39)
0.78 (0.89)
28.7 (32.8)

0.574
0.107
0.396

Housing rehabilitation (among households with housing damaged):
Complete dwelling repair dummy
0.59 (0.49)
0.65 (0.65)
New housing construction dummy
0.09 (0.29)
0.06 (0.06)

0.43 (0.46)
0.03 (0.03)

0.027
0.349

Cyclone damages:
Housing damaged dummy
Housing partially damaged dummy
Housing completely damaged dummy
Proportion of refugees right after the
cyclone (among households withhousing damaged)
Crop damaged dummy
Crop damage per adult equivalent (F$)

No. observations
a

332

292

40

Pre-cyclone and pos-cyclone sectoral incomes are for periods 1 and 4, respectively.
Other income consists of shop profit, livestock selling, and other self-employment activities like
middleman.
Note: Household means are shown along with standard deviations in parentheses. t-test and chi-squared
tests compare the means and proportion for continuous and dummy variables, respectively. Test results
are italicized and those with a 5% significance level are bolded.
b

29
Table 2. Household NTFP gathering, handicraft sales, and cyclone relief.
Pre-cyclone
Period 1
NTFP gaterhing (in six main participant villages, n=223):
Participation
All
0.58 (0.49)
Male head
0.59 (0.49)
Female head
0.55 (0.51)
Proportion test (p-value)
0.116
Revenues per adult equivalent per month (F$)
All
0.81 (2.72)
Male head
0.88 (2.89)
Female head
0.35 (1.11)
Mean test (p-value)
0.162
Handicraft sales (in six main participant villages, n=223):
Participation
All
0.12 (0.33)
Male head
0.11 (0.31)
Female head
0.19 (0.40)
Proportion test (p-value)
0.064
Revenues per adult equivalent per month (F$)
All
0.73 (3.29)
Male head
0.72 (3.47)
Female head
0.83 (1.80)
Mean test (p-value)
0.378
Cyclone relief (whole sample, n=332):
Tarpaulin receipt dummyaFood aid receipt dummy
Food aid per month (days)aa

Period 2

Post-cyclone
Period 3

Period 4

0.00 (0.07)
0.01 (0.07)
0.00 (0.00)
0.652

0.11 (0.32)
0.10 (0.30)
0.19 (0.40)
0.288

0.22 (0.41)
0.21 (0.41)
0.26 (0.44)
0.893

0.00 (0.00)
0.00 (0.00)
0.00 (0.00)
0.655

0.29 (1.20)
0.26 (1.23)
0.47 (1.03)
0.568

0.81 (3.48)
0.88 (3.72)
0.40 (1.15)
0.372

0.12 (0.32)
0.11 (0.31)
0.16 (0.37)
0.182

0.19 (0.39)
0.18 (0.39)
0.23 (0.43)
0.329

0.22 (0.42)
0.20 (0.40)
0.39 (0.50)
0.083

0.53 (2.34)
0.53 (2.47)
0.55 (1.35)
0.608

1.23 (3.87)
1.26 (3.97)
1.03 (3.26)
0.799

3.86 (12.2)
2.31 (7.39)
13.5 (25.5)
0.000

0.08 (0.27)
0.77 (0.42)
9.6 (8.5)

0.04 (0.19)
0.79 (0.41)
9.5 (8.1)

0.00 (0.00)
0.70 (0.46)
9.9 (10.1)

For period 4, the mean in five villages excluding other four in which interviews were conducted lateperiod 4 are shown.
Note - Household means are shown along with standard deviations in parentheses. t-test and chisquared tests compare the means and proportion for continuous and dummy variables, respectively. Test
results are italicized and those with a 5% significance level are bolded.

(2)

(3)

0.000
0.150
664

Yes
0.000
0.148
664

No

-0.641(0.623)6.522 ***
(2.390)-0.019(0.204)-0.907 ***(0.301)-2.463 **(1.218)0.269 *
(0.140)

0.000
0.163
664

0.053
(0.048)

(5)

0.000
0.161
664

0.000
0.159
310

No

-0.875 ** -1.526(0.423)(0.998)5.782 *** 6.253 *(1.830)(3.277)-0.058-0.338(0.182)(0.326)-0.918 *** -1.069 **(0.238)(0.470)-2.481 ** -1.025(0.968)(2.060)0.273 **
0.010
(0.106)(0.205)-0.141(0.094)-0.276(0.191)
0.064(0.043)

(4)

0.269
0.267
(0.212)(0.221)
0.962 **
0.956 **
(0.457)(0.448)0.036
0.036(0.076)(0.078)Yes
No-0.087-0.088-0.141(0.093)(0.093)(0.094)-0.126-0.127-0.276(0.154)(0.155)(0.192)
0.066 **
0.066 **
0.064
(0.033)(0.033)(0.043)(1)-0.024(0.165)0.068 *
(0.040)

(7)

0.000
0.207
310

0.843 **
(0.360)1.467 ***
(0.478)0.052(0.131)
No

0.007
0.153
354

No

0.049
(0.045)

(9)

0.000
0.168
354

-0.389(0.252)0.147(0.661)
0.165(0.125)
No

0.025
0.129
298

No

0.000
0.146
298

-0.481 *(0.268)0.133(0.606)0.155(0.133)No

0.109(0.473)5.493 *
(3.033)0.024(0.212)-0.794 **(0.346)-2.787 *(1.456)0.480 **
(0.192)0.028(0.067)(10)

Housing partiallydamaged

0.325-0.461(0.375)(0.932)
6.569 **
6.209 *
(2.901)(3.630)0.041
0.122(0.196)(0.294)-0.863 *** -0.851 *(0.307)(0.449)-3.086 ** -2.671 *(1.426)(1.580)0.464 **
0.487 **
(0.173)(0.218)-0.083(0.170)0.050(0.059)(8)

H ousing
damaged-2.546 *** -0.134(0.860)(0.841)4.401 *
6.869 **
(2.262)(3.419)-0.4680.151(0.306)(0.266)-1.125 ** -0.904 **(0.419)(0.401)-0.225-2.965 *(1.790)(1.532)-0.004
0.466 **
(0.167)(0.199)0.045(0.058)(6)

Housingundamaged

Note: *10% significance, **5% significance, ***1% significance. Groups are tokatoka. Standard errors clustered by groups are in the parentheses of clumns (3),(4), (6), (8), and (10). Robust standard errors are in the parentheses of other columns. All models inlcude a time dummy.

F (p-value)
R squared
No. observations

Village-time dummies

Group-level shocks
Proportion of housing partiallydamaged in the kin group
Proportion of housing completely
damaged in the kin group
Kin-group mean of log crop damage

Proportion of tarpaulin received in
the village
Village facilities damaged dummy

Village mean of log food aid (days)

Village-level shocks
Proportion of housing partiallydamaged in the village
Proportion of housing completely
damaged in the village
Village mean of log crop damage

Housing completely damaged
dummy
Log crop damage (F$)

Household-level shocks
Housing partially damaged dummy

All

Table 3. Determinants of log fishing revenues per adult equivalent per month - First-difference.

30

0.063
20.2

Wald (p-value)
Loss function

0.033
21.0

0.001
35.3

-0.81
(1.60)
3.99 *(2.24)
0.76(0.65)-0.72(0.59)
0.31(1.08)
0.26 *(0.15)
0.39(0.94)-0.28(0.25)(4)

Periods 1 and 3
(6)
(7)
(8)

(9)

0.000
38.7

0.000
53.3

1.16
(1.30)
0.32(2.22)
0.65(0.46)
0.000
43.3

0.000
46.5

0.78
(1.84)
0.25(3.62)
1.51 *(0.88)
0.000
82.7

0.000
88.0

4.35 *(2.42)
1.72(3.66)
0.85 *(0.45)
0.000
103.4

-0.32
(0.55)
1.68(1.71)-0.03(0.17)-0.99(1.11)
0.52 *(0.27)

Periods 1 and 4
(10)
(11)

-0.43
-0.54
-0.45
-0.39
-0.44
-0.89
(0.55)(0.46)(0.45)(0.44)(0.56)(0.58)
0.81
0.98
0.77
1.12
3.25 **
1.90(0.69)(0.75)(0.67)(0.75)(1.34)(1.94)
0.39 *** 0.35 *** 0.46 *** 0.39 **
0.16
0.27
(0.13)(0.12)(0.15)(0.15)(0.16)(0.20)-0.05-0.23(1.10)(1.08)-0.22-0.30(0.25)(0.26)

(5)

0.000
134.3

2.40
(2.68)
0.94(5.87)
0.74(1.03)-0.51(0.54)
0.95(2.58)-0.03(0.20)-1.29(1.20)
0.58 **(0.26)(12)

Note: *10% significance, **5% significance, ***1% significance. Groups are tokatoka. R obust standard errors are in parentheses. All models inlcude a time dummy
and village-time dummies.

0.022
19.3

-0.74
(0.47)
0.78(0.86)
0.23 *(0.13)

1.20(2.77)-1.88(3.13)
0.15(0.54)-0.73(0.46)
0.31(0.46)
0.21(0.14)-0.72(0.60)
0.29(0.47)
0.25(0.22)
0.22(0.84)-0.10(0.26)

Periods 1 and 2(2)(3)(n=446)
Household-level shocks
Housing partially damaged
dummy
Housing completely damaged
dummy
Log crop damage per adultequivalent (F$)
Housing partially damaged
dummy*Female head dummy
Log crop damage per adultequivalent*Female head dummyGroup-level shocks
Proportion of housing partiallydamaged in the kin group
Proportion of housing completely
damaged in the kin group
Kin-group mean of log crop
damage per adult equivalent(1)

Table 4. Determinants of log handicraft revenues per adult equivalent per month - Trimmed least squares.

31