Tsukuba Economics Working Papers
No. 2013-001	
 Learning from disaster: Community-based marine protected areas in Fijiby
Yoshito Takasaki	 January 2013

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

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Learning from disaster:Community-based marine protected areas in Fiji
Yoshito Takasaki*
University of Tsukuba
January 28, 2013

*Corresponding author. Faculty of Humanities and Social Sciences, University of Tsukuba, 1-11 Tennodai, Tsukuba, Ibaraki 305-8571 Japan, Tel./fax: +81 29 853 6280. E-mail address:
takasaki@sk.tsukuba.ac.jp.

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Learning from disaster:Community-based marine protected areas in Fiji
Abstract
This paper empirically examines whether and how experiencing climate-related disasters canimprove the rural poor’s adaptation to climate change through community-based resource
management. Original household survey data in Fiji capture the unique sequence of a tropical
cyclone and the establishment of community-based marine protected areas as a naturalexperiment. The analysis reveals that household disaster victimization increases its support forestablishing marine protected areas for future safety nets. Under Fijian traditional consensual
institutions, social learning from disaster experience among community members facilitates theircollective decision-making for conservation to enhance community resilience to climate shocks.
Keywords: community-based resource management; climate change adaptation; social learning;
natural disaster; marine protected area; Fiji
JEL classification: O13; Q22; Q54

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1. Introduction
Poor populations in rural developing areas are particularly vulnerable to increasingly
frequent and extreme weather events, as they rely heavily on weather-dependent livelihood
activities, such as agriculture and fishery (e.g., Adger et al., 2003; Pelling, 2003; Paavola andAdger, 2006; Eriksen and O'Brien, 2007). How the rural poor can cope with climate-related
disasters is a central issue in climate-adaptation policy. Although the rural poor’s limited coping
capacity and the adverse welfare consequences of disasters are commonly emphasized (e.g., Smit
and Pilifosova, 2001), some researchers pay significant attention to their potential adaptation forsystemic improvements. In particular, those working within a social-ecological resilience
framework emphasize the trigger role of disaster (e.g., Pelling and High, 2005; Westley et al.,2011): “In a resilient social-ecological system, disturbance has the potential to create opportunity
for doing new things, for innovation and for development” (Folke, 2006, p253). Agrawal (2010)stresses the critical role of local institutions in facilitating adaptation to climate change. Recent
empirical works provide supportive evidence: In an Amerindian community in Honduras,
Hurricane Mitch triggered institutional changes in land management, which led to sustainedsocial-ecological improvement (McSweeney and Coomes, 2011); in colonial Mexico, improved
adaptation through learning and innovation followed a series of extreme weather events andwealth-related events (Endfield, 2012). 1
1

Although most macroeconomic studies highlight adverse consequences of natural disasters,

Skidmore and Toya’s cross-country analysis (2002) shows that climate-related disasters canpositively influence long-run economic growth because of the substitution of physical-capital
accumulation with human-capital accumulation and triggered technological progress (see, forexample, Hallegatte and Dumas, 2009 for related theoretical work).

4

These findings on a positive side of disaster (Agrawal, 2011) suggest that “futureinterventions should foster local capacities for endogenous institutional change to enhancecommunity resilience to climate shocks” (McSweeney and Coomes, 2011, p5203). Although thisbottom-up approach is an important alternative to conventional targeted external assistance,
which policies can foster local capacities is unknown; the institutional change in Honduras, forexample, neither was facilitated by aid organizations nor resulted from the community’s explicit
collective decision, but occurred virally among households (McSweeney and Coomes, 2011).
Some researchers see such potential for fostering local capacities in the bottom-up nature ofcommunity-based resource management. In particular, using an example from Trinidad andTobago, Tompkins and Adger (2004) discuss the advantage of community-based resource
management in its inclusionary communication/deliberation, institutional mechanisms for social
learning, and networks connecting local and external actors (see also Adger et al., 2005;
Pomeroy et al., 2006). As community-based resource management is a mainstream conservation
policy in rural developing areas, this potential warrants systematic inquiry. Using original
household survey data in Fiji that capture a unique sequence of a tropical cyclone and theestablishment of community-based marine protected areas (MPAs) as a natural experiment, thispaper examines whether and how disaster experience can enhance local coping capacity throughcommunity-based conservation.
The paper serves as a benchmark case for studying the potential role of community-based
resource management in fostering community adaptation to climate change, because if it hassuch potential, we can expect that this would be so for Fiji’s MPAs for the following reasons.
First, Pacific island states have well adapted their traditional marine resource-management
practices to contemporary circumstances, and as a result, community-based marine resource

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management is more widespread in Oceania than in any other tropical region in the world(Johannes, 1978, 2002). In particular, Fiji recently expanded community-based MPAs across thecountry (Aalbersberg et al., 2005; Techera, 2008; Govan, 2009). Second, as customary marine
tenure is well established in Fiji, incomplete property rights that can preclude investments inlocal public goods (conservation) (Otsuka and Place, 2001) are unlikely to be a major concern.
Third, although tropical cyclones are among the most important climate-related disasters inisland states (Pelling and Uitto, 2001), some researchers criticize the deterioration of islanders’indigenous mechanisms in coping with cyclones because of their increasing dependency on
external aid (e.g., Campbell, 1984), and such concerns about local coping capacity underlie therecent expansion of MPAs in Fiji (e.g., Veitayaki, 2006).2
The paper explicitly addresses two lines of major critiques against extensive studies on
conditions facilitating successful community-based resource management, such as group size
and inequality (e.g., Wade, 1988; Ostrom, 1990; Baland and Platteau, 1996): 1) those studies
2

In the economics literature on the poverty-environment nexus, small-scale fisheries receive

relatively limited attention, as discussed by Béné (2003, though his review focuses on Asia andAfrica); indeed, systematic empirical studies on community-based marine resource management,such as MPAs, in the developing world are scarce. As exceptions, Gjertsen (2005) examine how
MPA characteristics influence welfare and environmental outcomes among MPAs in thePhilippines (see also Pollnac et al., 2001), and Silva (2006) investigates how alternative income
alters the choice of fishing gear among households in Tanzanian villages with MPAs.
Descriptive analyses are common in the broad literature on coastal management. For example,
Crawford et al. (2006) show various factors correlated with the establishment of communitybased MPAs in Indonesia without paying attention to natural disasters.

6neither address the issue of identification of causality (omitted variable bias), 3 nor show why andhow such conditions matter (Agrawal, 2001); and 2) they treat the community as a small spatial
unit with a homogeneous social structure consisting of common interests and shared norms anddo not directly address political economy issues, such as multiple interests/actors within thecommunity, decision-making processes, and institutional arrangements (Agrawal and Gibson,1999). To illustrate the first critique, suppose that sufficient across-communities variations exist
in disaster shocks and the adoption of conservation measures following the disaster. Although acommunity-level regression analysis to estimate the disaster effects on conservation involves far
fewer identification problems than previous works on group characteristics, 4 such an inquiry has
not received attention in the literature because disaster experience has not been considered as a
facilitating condition.5 Even if the disaster's impacts on conservation are identified, such3

Ferraro and Pattanayak (2006) and Ferraro (2009) highlight the critical importance ofidentifying causality in evaluating biodiversity conservation programs and environmental
policies, respectively.
4

Identification may be still hampered by unobserved community factors correlated with disastershocks, such as post-disaster external support for conservation. The proposed analysis requires
community-level exogenous shock measures (e.g., deviation from the mean in rainfall). In
practice, however, such spatially rich data are rare – commons studies often rely on small-scalecommunity surveys (Agrawal, 2001), and objective community shock data are rarely available.
5

As an exception, the World Bank (1999) shows that past disaster experience positively

influences marine ecological outcomes (based on subjective assessment) across communitybased marine resource-management areas in five Pacific island states (including Fiji), thoughdisaster is not the study’s focus.

7community-level analysis still does not dissect mechanisms underlying the causality; forexample, disasters may damage habitats, thereby forcing people to alter their livelihood activities.
How the local political economy matters for community-based resource management
depends on specific local institutions. In Fiji’s customary marine resource management, building
consensus among community members is a primary decision-making process (Veitayaki, 1998).
Thus, the paper explores variations in household support for establishing MPA as a key
determinant of the collective decision. I show that household disaster victimization increases thehousehold's support. This disaster effect is identified only when the potential endogeneity ofhousehold-level housing damage is controlled for by using exogenous flood shock as an
instrument.
I argue that the positive disaster effect on conservation is a result of disaster victims
having a higher appreciation of local commons as safety nets than non-victims. This is because
in the same area as my study area in Fiji, Takasaki (2011b) finds that households with housing
not damaged by the cyclone intensify fishing to help other kin-group members with damaged
housing; that is, the rural poor with limited coping capacity rely on local commons not only for
self-insurance, as is commonly found in various locales (e.g., Jodha, 1986; Pattanayak and Sills,
2001; Takasaki et al., 2004; McSweeney, 2005), but also to augment mutual insurance even if
resource use (fishing) is risky (Coomes et al., 2010). 6 I provide evidence that strong support for
MPA among community members facilitates their consensual decision making for conservation,and then that community decision furthers overall support for conservation. As such, theinclusionary deliberation and decision making in community-based resource management can
6

Based on a case study in Tonga, Bender et al. (2002) even argue that informal insurance

arrangements per se can lead to sustainable marine resource use without management.

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facilitate community adaptation to climate change through social learning from disaster
experience, along the line of adaptive management (e.g., Holling, 1978; Walters, 1986; Lee,
1999; Plummer and Armitage, 2007).
The rest of the paper is organized as follows. Section 2 provides a description of themarine resource institution and management in Fiji. Section 3 describes the data, MPAs, and thedisaster. Section 4 presents the econometric specification to identify the disaster effects onconservation and the estimation results, including evidence for social learning, followed by arobustness check in Section 5. The last section concludes.
2. Marine resource institution and management in Fiji
Each native Fijian belongs to a lineage of the vanua-yavusa-mataqali-tokatoka hierarchy:
Vanua consists of several yavusa; yavusa consists of several mataqali; and mataqali consists ofseveral tokatoka (Ravuvu, 1983). Roughly matching an old district (tikina) in the administrative
unit, vanua ranges over several villages (koro); a village consists of one or a few yavusa, which
includes several lower-order units, mataqali, and then tokatoka. Fijian customary law guarantees
the collective ownership of land and marine resources. First, rural land is communally owned bymataqali (within a village) and is privately used, and by law it cannot be sold (communal landconsists of about 83% of the country’s total land). Second, the coastal zones across the country
are demarcated into over 400 customary fishing grounds (qoliqoli) to which vanua or several
yavusa (across several villages) hold customary rights to harvest marine resources; ownership ofthe foreshore rests with the state (Techera, 2008).
Ethnographic studies highlight Fijians’ traditional marine resource-management practices,such as ritual protocols, taboos (e.g., no-take areas), seasonal bans, and their effective
enforcement through social norms and sanctions (Siwatibau, 1984). The national government has

9been strengthening customary resource management by formalizing its arrangements andintegrating them into the contemporary system; examples include registration of customary
fishing grounds, boundary setting, licensing involving customary owners, and deterrence ofpoachers (Veitayaki, 1998). 7 As such, Fiji’s contemporary marine resource management is built
on its customary institution: “Community-based marine resource management is appropriate inFiji because, in most communities, the rights of individuals within their collectively owned
fishing area are related to group’s consensual position. Management decisions and policies are
based on consensus, which ensures the cooperation of everyone in the community” (Veitayaki,
1998, p51, emphasis added).
There has been an increasing interest across the country in the establishment of MPAs asa result of governments’ extension work and nongovernmental (NGO) activities under LocallyManaged Marine Area (LMMA) initiatives, which were officially launched in 2000 (Aalbersberg
et al., 2005; Techera, 2008; Govan, 2009). By 2005, 177 villages in Fiji had established LMMAs,
which cover 7,010km2 with 440km2 of MPAs (Locally-Managed Marine Area Network, 2006). 8
Establishing a village-level MPA involves consensual decisions among villagers in the village
and among villages collectively owning the customary fishing ground. “The development ofmarine reserves and protected areas in Fiji could be easier, as the ownership of the customary
fishing areas is already with the people who need only to agree as a group to have the7

By conducting economic experimental games among Colombian fishermen, Moreno-Sánchez

and Maldonado (2010) show the dominance of co-management – like one in Fiji – among
different management regimes of MPAs for resource sustainability.
8

Although examining effects of resource management is not the focus of this paper, Lawson-

Remer (2012) provides evidence that LMMAs improve household welfare.

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arrangement formalized declaring a portion of their fishing grounds a marine reserve or protectedarea” (Veitayaki, 1998, p57).
3. Data, marine protected areas, and disaster3.1. Data
In June-September 2005, I conducted a household survey among 906 households in 43
native Fijian villages in Cakaudrove Province in the northern region of the country. 9 There is a
total of 32 qoliqoli in the province. In each of 16 districts in the province, villages wereintentionally chosen to cover distinct environmental and economic conditions – 35 coastal and 8
inland villages. In each village, households were stratified by tokatoka and a combination ofindividual leadership status (e.g., kin leader, as defined below) and major asset holdings (e.g.,
shops) (all tokatoka were sampled); in each stratum, households were randomly sampled (50% of
the population in each stratum, on average). The 43 villages in the sample cover 29 qoliqoli, 20
vanua, 53 yavusa, 146 mataqali, and 234 tokatoka; 10 some vanua and yavusa (and thus villages)
own more than one qoliqoli.
9The province, consisting of part of Vanua Levu, all of Taveuni, and other small islands,

significantly lags behind the main 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. My study focuses on native Fijians who hold customary rights toharvest marine resources.
10

An average village consists of 3.4 mataqali and 5.6 tokatoka, and on average, each mataqali

and tokatoka consists of 14 and 8 households, respectively, in the population. Marriage acrossdifferent kin groups in the village or different villages is common, and this paper focuses on the
kin groups to which households currently belong.

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Virtually all households in the sample employ traditional farming practices, using no
mechanized equipment or animal traction to produce taro, cassava, coconut, and kava plants.
Most households in coastal villages engage in subsistence fishing using lines and hooks, simple
spear guns, or rudimentary nets, and more commercially oriented fishermen use boats andengines, along with more valuable nets. At the time of interviews, the mean monthly total
income earned by sample households is F$1,582, or F$288 per capita (F$1 = US$.60); farming
and fishing, respectively, account for 66% and 11% of income earned.
3.2. MPAs and marine resource management
Out of 35 coastal villages in the sample, 2 had established an MPA before the cyclone in
January 2003 (detailed below). The analysis focuses on the remaining 33 coastal villages. At thetime of interviews in 2005, 10 villages already had established MPAs, 1 was establishing an
MPA, and 9 were considering establishing MPAs (henceforth I call these 20 villages MPA
villages); the remaining 13 were not considering the establishment of MPA (henceforth nonMPA villages). Most MPA villages established or started to consider MPAs in 2004 or 2005, i.e.,
more than a year after the 2003 cyclone. The areas of MPAs established or being considered
range in size from 1 to 20 acres (5.2 acres, on average).
The village survey conducted with village leaders asked why the village was active about
establishing MPAs among the MPA villages (up to three reasons) and why it was not among
non-MPA villages (up to three reasons) (see Table 1). The most common reason among nonMPA villages is limited villagers’ interest (about 70%), and villagers’ good understanding is arelatively common reason among MPA villages, reflecting the village’s consensual decisionmaking process. The strength of leadership – at both the village and qoliqoli levels – is another12

important factor, 11 and declining marine resource stock is also a common concern. MPA villages
more commonly participated in MPA workshops than non-MPA villages, though the difference
is not statistically significant; only about one quarter of MPA villages received technical support.
Villages employ various resource-management practices: Almost all villages have banson Sunday fishing and poison fishing; about 50% and 85% of villages restrict gill net and scuba
diving, respectively, and there is no significant difference in these regulations between MPA andnon-MPA villages. Although many of these regulations are set at the qoliqoli level, some of them
are employed by individual villages. According to respondents’ subjective assessments using afive-point-scale measure for enforcement of the regulations (1: not at all, 2: a little, 3: somewhat,
4: well, 5: very well), compliance with gill-net restriction was lower than that of otherregulations, and compliance (for scuba diving, in particular) in MPA villages was higher thanthat in non-MPA villages. Overall, about three quarters of villages value overallmanagement/conservation – at both the qoliqoli and village levels – somewhat or well, and about
one quarter of villages, a little (according to respondents’ subjective assessments using the same
five-point-scale measure above); overall management/conservation is better in MPA villages
than in non-MPA villages, indicating their positive correlation with involvement in MPA as a
new conservation instrument.
3.3. Household support for MPAs
11

Each vanua has a paramount chief, and some yavusa and mataqali, but not tokatoka, have sub-chiefs; there are also traditional leaders other than chiefs/sub-chiefs. Village chiefs can be servedby chiefs, sub-chiefs, or non-chief leaders. Heads of qoliqoli are either vanua or yavusa chiefs.
These hereditary traditional leaders play a major role in local governance within and acrosscorresponding kin groups (Turner, 1992).

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The household-level analysis is based on 632 households in the 33 villages thatparticipated in fishing in the past one year at the time of interviews in 2005. The household
survey, which was mostly conducted with household heads, asked how strongly the householdsupports MPA at the time of interviews in 2005, using the five-point-scale measure (1: not at all,2: a little, 3: somewhat, 4: much, 5: very much); then, the survey asked the level of its support
one year before, using the former measure as an anchor to facilitate retrospection (measurement
errors in the latter measure are discussed later). The latter measure (henceforth prior support for
MPA) corresponds to the timing when many MPA villages established or started to consider
MPAs, thus capturing villagers’ support for MPAs that determined their collective decision; incontrast, the former measure (henceforth current support for MPA) could be altered by thecollective decision.
The results are shown in panel A of Table 2. Over three quarters of households show
strong or very strong prior support for MPA (4th or 5th category). Current support becomesstronger than prior support: Over one half of households show very strong support; in particular,
although the degree of support does not change among 83% of households, over 12% augment
their support and less than 5% decrease theirs. Qualitatively the same results hold regardingwhether or not the household supports MPAs very much (5th category) (panel B). These results
provide evidence that the village's collective decision for MPAs positively alters household
current support for MPA. Both prior and current support are stronger in MPA villages than nonMPA villages (the result for very strong current support is statistically weak) (see Table 3); thatis, consistent with consensual decision making, the stronger villagers’ prior support for MPA, thestronger is the community’s involvement in MPA.
3.4. Disaster14

On January 13, 2003, Cyclone Ami swept over the northern and eastern regions of thecountry; Ami was the only cyclone in the northern region from 1991 through 2005 (McKenzie etal., 2005). According to respondents’ subjective assessments, over 60% of residents’ dwellings –a main house and/or free-standing units, such as the kitchen, shower, and toilet (not allhouseholds have such units, as these facilities are often located inside the main house) – weredamaged, and the mean value of total housing damage was about F$1,100 (Table 3); the cyclone
caused no casualties, and permanent migration was virtually nonexistent after the disaster.
The provision of emergency relief (see Takasaki, 2011c for details) was followed byhousing reconstruction programs. About one quarter of households received construction
materials, and the mean amount received was about F$700; although almost the same
proportions of households received aid more than a year before the interviews in 2005 and in thepast one year, the average amount received in the former period was larger than that in the latter
period. Although there was at least one disaster victim (with housing damage) in each village,
there was no recipient of housing aid in a few villages. Although almost all aid recipients weredisaster victims (i.e., virtually no leakage), about 40% of victims were recipients (i.e., large
under-coverage), and aid was strongly targeted on housing damage value (Takasaki, 2011a).
These descriptive statistics of damage/aid are very similar to those in the whole sample of 43
villages.
3.5. Disaster, fishing, and MPAs
Takasaki’s study (2011b), which shows fishing intensification to help disaster victims as
discussed above, captures fishing within a year after the 2003 cyclone based on his household
survey conducted in 2003 in 9 coastal villages, 7 of which are part of my 43 sample villages;
among his 9 villages, 2 villages, which do not overlap with my sample, and 1 overlapped village

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had established MPA before the cyclone; 4 other overlapped villages are MPA villages, and theremaining 2 are non-MPA villages. Thus, fishing served as local safety nets, especially invillages with strong involvement in MPAs.
If establishing/considering MPAs following the cyclone comes from an insurance motive
augmented by the disaster experience, we would expect to see a positive correlation between
disaster victimization and MPAs. There exists, however, no significant difference in householdlevel housing damage/aid between MPA and non-MPA villages (Table 3); this is so in terms ofboth the incidence of housing damage and damage value and both aid receipt and the amountreceived, regardless of the timing of receipt (flood measures also reported in Table 3 are
discussed later). 12 The village means of the household-level damage measures – proportion ofhouseholds with housing damaged and mean damage value – are qualitatively the same between
MPA and non-MPA villages; when a dummy for MPA villages is regressed on those villagelevel damage variables without or with other controls, such as population size and market access(n=33), none of their estimated coefficients are statistically significant (results not shown). These
descriptive and village-level regression analyses – along the same line as extant works oncommunity-based resource management – do not control for village heterogeneity in factors
determining the village's collective decision and vulnerability to disasters, which can becorrelated with each other. Identifying the impacts of household housing damage on its support
for MPAs within villages is the task carried out in the next two sections.
4. Empirical analysis
4.1. Econometric specification
12

Comparing housing damage/aid between households who support MPA very much and others

yields very similar results.

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I start with the following ordered probit specification:y1* = α1 + β1 y2 + δ1 x + V + u1 ,(1)where y1* is the latent indicator of household prior support for MPA; y2 is the observed 0-1
indicator for household housing damage; x is a vector of exogenous household controls (defined
below); V is a vector of village dummies; and u1 is an error term that is assumed to be normally
distributed. I consider disaster aid as an additional determinant in the next section. Village
dummies fully control for village-level factors affecting household support for MPA, such as
marine resource stock, local regulations, social norms, leadership, and heterogeneity in qoliqoli,
as well as village-level covariate disaster shocks and aid supply. If household disastervictimization increases its prior support for MPA, β1 > 0.
Social learning from disaster experience among community members suggests that thecollective decision in MPA villages (i.e., establishing MPAs) increases non-victims’ support for
MPAs, and as a result, strong support for MPAs becomes more prevalent over time (as found in
the descriptive statistics above). A simple way to test this is to estimate equation (1) for current
support for MPAs: With augmented support among non-victims, the damage effect on current
support should be smaller than that on prior support. Note that this altered equation (1) does not
capture the effects of the collective decision, because it is controlled for by village dummies.
The identification assumption in equation (1) that household-level housing damage isuncorrelated with unobserved household heterogeneity determining its support for MPAs, suchas preference for conservation, may not hold as follows. With village-level covariate shocks
controlled for, whether or not housing is damaged in the village depends on its unobserved
quality prior to the disaster, in particular, its durability against cyclone shocks (e.g., heavy wind,rain, flood), which is a function of cumulative investments in housing that the household had

17

made, as well as its location in the village. If housing quality is positively (negatively) correlatedwith say, preference for conservation, i.e., households with housing less (more) vulnerable tocyclones tend to support MPA more, the estimated damage effect is biased downward (upward).
My empirical strategy is two-fold. First, since housing location is fixed with mataqali (avillage subgroup owing land), it is effectively captured by mataqali fixed effects. Second, I
endogenize household housing damage by using flood shock as an excluded identifying variable.
The household survey asked the magnitude of flood, not damage caused by the flood, that thehousehold experienced on its land based on a five-point scale (0: none, 1: small, 2: some, 3: large,
4: very large); over 40% of households in the sample had experienced a flood (Table 3), andthere was at least one household with flood experience in each village. The identification
assumption is that household flood shock affects its housing damage within mataqali and isuncorrelated with its unobserved heterogeneity in support for conservation. In particular,household housing investments correlated with its vulnerability to flood on its micro location, if
any, are assumed to be uncorrelated with its unobserved heterogeneity. 13, 14
13

If flood shock augments housing damage, and the damage effect on household support for

MPA is positive, flood shock should increase the support. The descriptive statistics show mixed
evidence: Floods are more common and larger in magnitude among disaster victims (withhousing damage) than non-victims, as confirmed in the regression analyses below (descriptive
results not shown), but also in non-MPA villages than MPA villages (Table 3). Without
controlling for land conditions determining vulnerability to flood, however, the latter result isunlikely to reflect the true effect of flood; in particular, flood vulnerability may be negatively
correlated with MPA for any reason. Indeed, once land conditions are controlled for, flood shock
significantly increases household support for MPAs, as shown below.

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The two-equation system is

y1* = α1 + β1 y2 + δ1 x + G + u1 ,(2)y2* = α 2 + γ 2 w + δ2 x + G + u2 ,(3)

where y2* is the latent indicator of y2; w is flood shock; G is a vector of mataqali dummies; and u1
and u2 are error terms that are assumed to follow a bivariate normal distribution. The likelihood
function is the product of the probability that support for MPAs lies in interval j (= 1, …, 5) andhousing is damaged (y1 = j, y2 = 1) (y1 is the observable indicator of y1*) and the probability that
support for MPA lies in interval j and housing is not damaged (y1 = j, y2 = 0) across observations:[(L = ∏ ∏ P c j > y1*i > c j −1 , y2*i > 0
i)] ⋅ [P(c > y > c , y < 0)] ,
Z ij

j

*
1i

j −1

*
2i

Z ij

(4)

j

where i is observation (household); cj and cj-1 are the cutpoints of interval j to be estimated; andZij = 1 if y1i* falls in category j (these two probabilities are the same as equations 9 and 10 in
Adams et al., 2003). Equations (2) and (3) are jointly estimated by a maximum likelihood
estimator. The corresponding reduced-form equation is

y1* = α1 + γ 1w + δ1 x + G + u1 .

(5)

If β1 is positive and statistically significant in the two-equation system maximum likelihood
estimate, γ1 should be positive and significant in the ordered probit estimate of equation (5).

14

Errors in retrospectively measured prior support can be significant, and if they are correlatedwith housing damage in equation (1), the order-probit estimates are biased. In my empiricalstrategy, measurement errors in prior support, if any, are assumed to be uncorrelated withhousehold flood shock within mataqali.

19

I consider two sets of household controls: The first set includes social status (a dummy
for clan leader), 15 education (a dummy for secondary education of any adults), and demographic
factors (household size, the proportion of children/elderly, age of household head); the second
set adds asset holdings (fishing capital and land holdings) to the first set. Social status and adulteducation are fixed effects and demographic factors a year before and at the time of interviews,
respectively, are used for prior and current support; current asset holdings are used for both,although distinct from land holdings, fishing capital may change significantly over the course ofa year (information of fishing capital a year ago is lacking). Fishing capital can be endogenous,
because it is correlated with unobserved household skills and preference for fishing, which
determine its support for MPAs; MPAs may also influence household investment in fishingcapital. With the lack of a valid identifying variable for fishing capital, I estimate models withand without fishing capital to see how different the remaining estimation results are. 16
4.2. Estimation results
Estimation results are reported in Table 4 (results of household controls are discussed in
the next section).17 The ordered probit estimates of equation (2) – either without or withhousehold controls – show that housing damage has no significant effects on prior or current
15

I call households with leaders with chief, sub-chiefs, or non-chief leader status, as defined

above, clan leaders. In the sample, most clan leaders are sub-chiefs or non-chief leaders, as many
chiefs, including qoliqoli heads, live in cities.
16

Asset holdings and demographic factors capture household permanent income; controlling forincome is feasible only for current support for MPAs (information of past income is lacking).
17

Mataqali with no variation in support for MPAs across households are dropped because

mataqali dummies perfectly predict the support.

20household support for MPAs (panel A). (The results are very similar to each other without andwith asset holdings as household controls, and only the former results are shown; the ordered
probit estimates of equation 1 with village dummies are also very similar). In contrast, the twoequation maximum likelihood estimates show that the estimated coefficients of housing damage
are positive at a 1% significance level for prior support, but not current support, acrossspecifications (the estimated cross-equation correlations are about -.45 for prior support); floodshock – on a normalized five-point scale (with 1 = very large flood) – strongly determines
housing damage (panel B), and the ordered probit reduced-form results of equation (5)consistently show that flood shock significantly increases prior support, but not current support(panel A). 18 Estimated marginal effects of housing damage on prior support at the means are
positive only for very strong support (5th category) (the point estimates are about .22), and its
negative marginal effects on the other four categories are relatively similar to each other; theresults for current support are qualitatively the same with very limited statistical significance atconventional levels.
Housing damage is thus endogenous in equation (2); that assuming its exogeneity leads tostrong downward bias in its estimated effects suggests a significant positive correlation ofunobserved housing quality and household heterogeneity. The nonsignificant effects on the
dummy for MPA villages of village-level housing damage (constructed from household-level
damage measures) in the village-level regressions discussed above are consistent with theordered-probit results and also biased for the same reason; the descriptive results in Table 3 do
not reflect causality, either. Housing damage mostly affects whether or not the household shows18

The estimated cutpoints for prior support with household controls (column 2 of panel B), forexample, are -2.4, -2.0, -.96, and .07.

21

very strong prior support; consistent with social learning from disaster experience, this damage
effect diminishes following the collective decision made for MPAs.
5. Robustness check
The robustness check in this section focuses on the binary information about very strong
household support for MPAs in the following linear probability model:

y1 = α1 + β1 y2 + δ1 x + G + u1 ,(6)

y2 = α 2 + β 2 w + δ2 x + G + u2 ,(7)

where y1 is redefined as an observable 0-1 indicator for very strong prior (or current) support;
distributional assumptions for error terms are not made. The endogeneity of housing damage y2
is controlled for by estimating equation (6) using a two-stage least squares (2SLS) estimator withthe first-stage equation (7), where w is an excluded instrumental variable.
OLS and 2SLS estimation results, respectively, reported in Table 5 (where robust
standard errors are shown) are qualitatively the same as the ordered probit and two-equation
system maximum likelihood results in Table 4, suggesting that the latter results are notconditional on the specification assumptions (e.g., normality, homoscedasticity). Although OLS
estimates of the damage effects are not statistically different from zero (panel A), the 2SLS
estimates for prior support are over .30 at a 5% significance level, and those for current support
are positive but much smaller with no statistical significance (panel B). The first-stage results arestrong across specifications – a very large flood increases the probability that housing isdamaged by over .40 (the F value of this excluded instrument is over 50). In the corresponding
reduced-form models (equation 6 with y2 replaced with w), the results for prior support, but not
current support, are statistically significant – a very large flood increases the probability that thehousehold supports MPAs very much, by about .13.

22

Estimated coefficients of household controls (corresponding to columns 2, 3, 7, and 8 ofTable 5) are reported in Table 6, along with their descriptive statistics. The results are very
similar between OLS and 2SLS estimates, buttressing the exogeneity of these controls; whenasset holdings are added as additional controls, none of the remaining results change
significantly, suggesting that the potential endogeneity of fishing capital is not a major concern
for identifying the effects of housing damage. 19 The following statistically significant results arenoted. First, households with more children (in proportion) are more likely to show very strong
prior support for MPA, suggesting that parents prefer to protect marine resources for theirchildren; that is, they have long-term perspectives on conservation. Along with the diminisheddamage effect over time, this pattern becomes weaker for current support, suggesting thatthrough social learning, having long-term perspectives becomes more prevalent amonghouseholds regardless of their demographic composition. Second, in contrast, clan leaders tend
to show very strong current support, but not prior support, suggesting that although they are
initially neutral about MPAs, they adjust their support to the collective decision. Although thisdoes not tell anything about their leadership in the collective decision, which is controlled for bymataqali dummies, it provides evidence for social learning among local leaders, which could
lead to more establishment and better management of MPAs in the future.
Next, I estimate the damage effect at the margin by redefining y2 as housing damage
value (log). 20 Estimation results reported in Table 7 are qualitatively the same as those for the
19

Interpreting estimated coefficients of fishing capital, some of which are statisticallysignificant, requires caution because of its potential endogeneity discussed above.
20

Distinct from damage incidence, in which recall errors are minor, measurement errors indamage value can be significant, causing attenuation bias that also can be controlled for in the

23

damage dummy in Table 5 (the results without asset holdings as household controls are shown).
On one hand, OLS estimates are statistically nonsignificant across specifications (panel A). On
the other hand, according to the 2SLS estimates, a 10% increase in damage value augments theprobability that the household shows very strong prior support for MPAs by about .005 at a 5%
significance level; the estimated coefficients of damage value for current support are smallerwith no statistical significance (panel B). Flood shock is a strong instrument (a very large floodincreases housing damage value by about 300%), and the corresponding reduced-form results are
consistent with the 2SLS results.
So far, I have not considered disaster aid as a potential determinant of household support
for MPAs. As housing aid is positively correlated with housing damage (targeting), whether thisomitted variable causes bias in the estimated damage effects depends on whether it influences
household support. With a lack of valid instruments for housing aid, I estimate models withhousing aid as an additional exogenous control to see whether the remaining estimation resultschange significantly. Adding the dummy for aid receipt – more than a year before the interviews
in 2005 for prior support and by the time of interviews for current support – to the models withthe damage dummy does not significantly alter the remaining results (columns 4, 5, 9, and 10 ofTable 5), nor does adding the log amount of corresponding aid received to the models with thedamage value significantly change the remaining results (columns 3 and 6 of Table 7); and, both
aid receipt and amount are statistically nonsignficant across specifications. Hence, the potential
bias caused by omitted disaster aid in the early analyses is unlikely to be significant.

2SLS estimation. Jointly estimating the ordered probit and tobit models for the altered equations(2) and (3) with the damage dummy replaced with damage value (which is 0 for non-victims)
encounters convergence problems in the two-equation system maximum likelihood estimations.

24

6. Conclusion
Using original household survey data in Fiji on the establishment of community-based
MPAs following a tropical cyclone, this paper empirically examined whether and how
experiencing climate-related disasters can improve the rural poor’s adaptation to climate change
through community-based resource management. Controlling for the endogeneity of householdlevel housing damage revealed that household disaster victimization increases the household's
support for establishing MPAs for future safety nets. Under Fijian traditional consensual
institutions, social learning from disaster experience among community members facilitates theircollective decision for conservation to enhance community resilience to climate shocks.
In Fiji, the disaster played a trigger role for systemic improvement (McSweeney andCoomes, 2011), together with external (government/NGO) efforts to promote MPAs integrated
with customary resource institutions. Policy interventions in the form of community-based
resource management should explore the potential of its inclusionary mechanisms for facilitating
community adaptation to climate change through social learning (Tompkins and Adger, 2004).
At the same time, more research is needed to examine how large and common such potential isacross different local institutions (Agrawal, 2010). That treating disaster victimization
exogenously failed to reveal the potential in Fiji exemplifies the critical importance of theidentification of causality in natural resource management and conservation.
Acknowledgements
I wish to thank my field team – Jonati Torocake, Viliame Manavure, Viliame Lomaloma,
and 16 enumerators – for their advice, enthusiasm, and exceptional efforts on behalf of thisproject. Special thanks are owed to the Fijians of the region who so willingly participated in thesurvey. The Cakaudrove Provincial Office in Fiji offered valuable institutional support for this25

project. This paper has benefited significantly from the comments and suggestions of Oliver
Coomes. This research has been made possible through support provided by the Sumitomo
Foundation, the Japan Society for the Promotion of Science, and the Ministry of Education,
Culture, Sports, Science and Technology in Japan. Any errors of interpretation are solely theauthor’s responsibility.
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32

Table 1. Marine protected areas and marine resource management in Fiji.
All villages
for analysis
on MPA

MPA
villages

Non-MPAvillages

DifferenceStandard
error ofdifference

MPAs:
Reasons for establishing/considering MPAs among MPA villages (0/1)
Strong village leadership

0.50

Declined marine resource stock

0.40

Villagers' good understanding

0.20

Qoliqoli head's strong support

0.20

Reasons for not considering MPAs among non-MPA villages (0/1)
Limited villagers' interests

0.69

Qoliqoli head's limited support

0.23

Not good access to sea

0.23

Lack of technical support

0.15

Workshop attendance (0/1)

0.28Technical support received by MPA
villages (0/1)

0.35

0.17

0.18

0.17

0.26

Marine resource management:
Restricted fishing practices (0/1)
Gill net

0.48 (0.51)

0.40 (0.50)

0.62 (0.51) -0.22

0.18

Scuba diving

0.85 (0.36)

0.85 (0.37)

0.85 (0.38)

0.13

0.00

Enforcement of restrictions among villages with restrictions (1: not at all, …, 5: very well)
Gill net

2.94 (1.06)

3.25 (1.16)

2.63 (0.92)

0.63

0.52

Scuba diving

3.43 (1.00)

3.71 (0.92)

3.00 (1.00)

0.71 *

0.37

Overall management and conservation (1: not at all, …, 5: very well)
Qoliqoli

3.36 (0.86)

3.60 (0.75)

3.00 (0.91)

0.60 **

0.29

Village

3.36 (0.86)

3.60 (0.75)

3.00 (0.91)

0.60 **

0.29

No. observations

33

20

13

Note: Standard deviations are shown in parentheses. *10% significance, **5% significance, ***1%
significance.

33

Table 2. Household support for marine protected areas.
A. Five-point scale - percentages (n=632)
Current support

Prior
supportNot at all

A little

Somewhat

Much

Very much

Total

Not at all (1)

2.2

1.0

0.0

0.0

0.0

3.2

A little (2)

0.0

2.4

0.5

0.2

0.2

3.2

Somewhat (3)

0.2

0.8

11.7

3.5

1.0

17.1

Much (4)

0.0

0.6

1.7

22.8

6.5

31.7

Very much (5)

0.0

0.0

0.0

1.3

43.7

44.9

Total

2.4

4.8

13.9

27.7

51.3

100.0

B. Very strong support (0/1) - percentages (n=632)
Current support
Prior
support

No

Yes

Total

No

47.5

7.6

55.1

Yes

1.3

43.7

44.9

Total

48.7

51.3

100.0

34

Table 3. Marine protected areas and disaster.
All villages foranalysis on
MPA

MPA
villages

Non-MPAvillages

DifferenceStandard
error ofdifference

Prior support (1: not at all, …, 5: very much)

4.12 (1.01)

4.30 (0.85)

3.87 (1.15)

0.43 **

0.20

Current support (1: not at all, …, 5: very much)

4.21 (1.01)

4.39 (0.80)

3.95 (1.19)

0.45 **

0.20

Very strong prior support (0/1)

0.45 (0.50)

0.51 (0.50)

0.36 (0.48)

0.15

0.09

Very strong current support (0/1)

0.51 (0.50)

0.56 (0.50)

0.45 (0.50)

0.11

0.09

All villages inwhole sample
Support for MPAs:

Disaster damage and aid:
Housing damage (0/1)

0.62 (0.49)

0.63 (0.48)

0.61 (0.49)

0.66 (0.47)

-0.05

0.07

Housing damage value (F$)

1071 (2136)

1126 (2181)

926 (1908)

1405 (2490)

-480

291

Aid receipt more than a year before (0/1)

0.12 (0.32)

0.13 (0.34)

0.12 (0.32)

0.16 (0.37)

-0.04

0.06

Aid receipt in the past one year (0/1)

0.13 (0.33)

0.13 (0.34)

0.11 (0.31)

0.16 (0.37)

-0.06

0.05

Aid received more than a year before (F$)

450 (1663)

516 (1762)

425 (1652)

643 (1901)

-218

255

Aid received in the past one year (F$)

235 (1168)

244 (1185)

156 (933)

367 (1459)

-211

174

Flood (0/1)

0.44 (0.50)

0.43 (0.50)

0.35 (0.48)

0.55 (0.50)-0.21 *

0.11

Flood magnitude (0: none, …., 4: very large)

1.23 (1.56)

1.16 (1.49)

0.86 (1.32)

1.59 (1.62)-0.73 **

0.32

906

632

368

264

No. observations

Notes: Standard deviations are shown in parentheses. Standard errors of differences are clustered by village. *10% significance, **5%
significance, ***1% significance.

35

Table 4. Damage effects on household support for marine protected areas (five-point scale).

Household controls
A. Ordered probit
Support for MPAs (1-5) (Equation 2):
Housing damage (0/1)
Log likelihood
Support for MPAs (1-5) (Equation 5):
Flood shock (0-1)
Log likelihood
B. Two-equation maximum likelihood
Support for MPAs (1-5) (Equation 2):
Housing damage (0/1)
Housing damage (0/1) (Equation 3):
Flood shock (0-1)

Prior support (n=588)
No
Yes
(1)
(2)

Current support (n=564)
No
Yes
(3)
(4)

0.022
(0.119)-631.50.021(0.119)-628.60.050(0.123)-600.80.064(0.123)-597.6

0.496 ***
(0.164)-626.90.535 ***
(0.165)-623.30.249(0.164)-599.70.266(0.164)-596.40.691 ***
(0.262)0.712 ***
(0.251)0.519(0.332)0.518(0.325)

1.668 ***
(0.244)-0.311(0.204)-850.3

1.717 ***
(0.248)-0.304(0.201)-843.6-0.028(0.021)-0.031(0.021)-0.067(0.041)-0.039 *(0.023)0.165(0.104)-0.027(0.020)-0.031(0.021)-0.067 *(0.041)-0.039 *(0.023)0.164(0.102)

1.685 ***
1.736 ***
(0.241)(0.245)
Cross-equation correlation-0.441-0.459(0.154)(0.147)Log likelihood-887.3-880.0Marginal effects of housing damage on each category at means:
Not all (1)-0.045 **-0.047 **(0.023)(0.022)A little (2)-0.030 **-0.031 **(0.014)(0.013)
Somewhat (3)-0.098 ***-0.100 ***(0.035)(0.033)
Much (4)-0.042 ***-0.043 ***(0.014)(0.014)
Very much (5)0.215 ***
0.221 ***
(0.080)(0.076)

Notes: Standard errors are shown in parentheses. Household controls are clan leader (0/1), adultsecondary education (0/1), household size (0/1), proportion of children (age 0-14), and proportion ofelderly (age 66 or above). Other controls not shown here are mataqali dummies and constant. *10%
significance, **5% significance, ***1% significance.

36

Table 5. Damage effects on very strong household support for marine protected areas.

Household controls
Asset holdings controlled
A. OLS
Housing damage (0/1)

No
No
(1)

Very strong prior support (0/1) (n=538)
Yes
Yes
Yes
Yes
No
Yes
No
Yes
(2)
(3)
(4)
(5)

-0.001(0.049)-0.003(0.050)0.000(0.050)

0.182

0.195

0.204

0.303 **
(0.154)0.324 **
(0.153)0.315 **
(0.149)

Aid receipt (0/1)
R squared
B. 2SLS
Housing damage (0/1)
Aid receipt (0/1)
First stage:
Flood shock (0-1)
Reduced form:
Flood shock (0-1)

No
No
(6)

Very strong current support (0/1) (n=532)
Yes
Yes
Yes
Yes
No
Yes
No
Yes
(7)
(8)
(9)
(10)

-0.011(0.053)0.038(0.075)0.195-0.010(0.053)0.047(0.074)0.205-0.007(0.051)

0.002(0.051)

0.004(0.052)

0.181

0.193

0.197

0.367 **
(0.178)-0.112(0.097)0.352 **
(0.174)-0.097(0.095)0.140(0.150)0.162(0.148)0.162(0.148)-0.002(0.057)0.009(0.063)0.193-0.002(0.057)0.017(0.063)
0.197

0.195
(0.179)-0.079(0.095)0.191(0.178)-0.070(0.094)0.433 *** 0.435 *** 0.437 *** 0.380 *** 0.380 ***(0.058)(0.059)(0.060)(0.058)(0.058)0.422 *** 0.424 *** 0.424 *** 0.353 *** 0.353 ***(0.056)(0.057)(0.057)(0.053)(0.053)0.131 *
(0.069)0.059(0.068)0.141 **
(0.068)0.137 **
(0.067)0.139 **
(0.069)0.134 **
(0.068)0.069(0.067)0.069(0.067)0.069(0.068)0.067(0.068)Notes: Robust standard errors are shown in parentheses. Household controls are clan leader (0/1), adult secondary education (0/1), household size(0/1), proportion of children (age 0-14), and proportion of elderly (age 66 or above). Asset holdings are log of fishing capital (F$) and log of land (m2).
Other controls not shown here are mataqali dummies and constant. *10% significance, **5% significance, ***1% significance.

37

Table 6. Controls unreported in Table 5.
Very strong prior support (0/1)

Clan leader (0/1)
Secondary educationof adults (0/1)
Household size
Proportion of children(age 0-19)
Proportion of elderly(age 66 or above)
Age of householdhead
Log of fishing capital(F$)
Log of land (m2)Panel A (OLS)
(2)
(3)
0.092
0.097
(0.080)(0.080)0.018
0.021(0.061)(0.061)-0.007-0.009(0.010)(0.010)0.262 **
0.261 **
(0.118)(0.119)0.189
0.167(0.151)(0.152)0.001
0.001(0.002)(0.002)0.018 **
(0.009)-0.013(0.010)Panel B (2SLS)
(2)
(3)
0.115
0.120
(0.076)(0.075)0.047
0.049(0.059)(0.059)-0.007-0.010(0.009)(0.009)0.245 **
0.244 **
(0.114)(0.113)0.164
0.140(0.143)(0.142)0.001
0.001(0.002)(0.002)0.018 **
(0.009)-0.014(0.009)Very strong current support (0/1)
Panel A (OLS)(7)(8)
0.194 **
0.202 ***
(0.078)(0.078)-0.002-0.002(0.066)(0.066)-0.005-0.006(0.011)(0.011)0.116
0.109(0.125)(0.126)-0.160-0.183(0.133)(0.133)0.000
0.000(0.002)(0.002)0.007(0.009)-0.013(0.011)Panel B (2SLS)(7)(8)
0.208 ***
0.217 ***
(0.074)(0.073)0.012
0.012(0.061)(0.061)-0.006-0.007(0.010)(0.010)0.122
0.114(0.117)(0.117)-0.168-0.192(0.123)(0.123)0.000
0.000(0.002)(0.002)0.008(0.008)-0.014(0.010)Means(Standard
deviations)
0.09(0.29)
0.83(0.38)
5.7
(2.7)
0.33
(0.23)
0.08(0.19)
51.2(14.4)
392.8(2634.6)
3.0(5.4)

Notes: Panels and columns match those in Table 5. Robust standard errors are shown in parentheses below estimated coefficients. Means ofhousehold size, proportion of children and elderly, and age of household head are at the time of interviews; means of fishing capital (F$) and land(acres) are shown; standard deviations are in parentheses below means. *10% significance, **5% significance, ***1% significance.

38

Table 7. Effects of damage value on very strong household support for marine protected areas.

Household controls
A. OLSLog of housing damage value(F$)
Log of aid received (F$)
R squared
No. observations
B. 2SLS
Log of housing damage value(F$)
Log of aid received (F$)
No. observationsFirst stage:
Flood shock (0-1)
Reduced form:
Flood shock (0-1)

Very strong prior support (0/1)
No
Yes
Yes
(1)
(2)
(3)
0.005
(0.007)0.005(0.007)

0.367
538

0.375
538

0.042 **
(0.021)0.045 **
(0.021)

538

538

Very strong current support (0/1)
No
Yes
Yes
(4)
(5)
(6)

0.003
(0.008)0.004(0.010)
0.466
532

0.005
(0.007)0.005(0.007)

0.181
532

0.194
532

0.057 **
(0.026)-0.023(0.016)532

0.019(0.020)0.022(0.020)

532

532

0.007
(0.009)-0.001(0.009)
0.191
526
0.032
(0.025)-0.015(0.016)526

3.137 *** 3.171 *** 2.622 ***(0.416)(0.420)(0.400)3.137 *** 3.168 *** 2.565 ***(0.399)(0.406)(0.359)0.131 *
(0.069)0.059(0.068)0.141 **
(0.068)0.148 **
(0.070)0.069(0.067)0.081(0.069)Notes: Robust standard errors are shown in parentheses. Household controls are clan leader (0/1), adultsecondary education (0/1), household size (0/1), proportion of children (age 0-14), and proportion of elderly(age 66 or above). Other controls not shown here are mataqali dummies and constant. *10% significance,**5% significance, ***1% significance.