Cesar2000-Economics of Coral Reefs.pdf

Contrary to most other regions in the world, many
Pacific Island communities have adopted traditional marine
tenure systems that determine conditions for access
to their coastal waters. Local management rules for
coastal resources, such as taboo areas where harvesting is
prohibited, still exist in many villages. Combined, this
traditional tenure and the remoteness of many islands
make it virtually impossible for government-led management
to succeed on their own. Coastal managers
have correctly recognised that one of the key challenges
is to build upon existing traditional systems and identify
areas where assistance to coastal communities is most
needed.
In 1998–99, the World Bank sponsored a comparative
survey of coastal communities in the Pacific Islands
(World Bank 2000a). The study aimed to strengthen the
understanding, among coastal managers, of the factors
influencing the success of coastal resource management
from the perspective of local communities. The study
was based on a six-month survey of 31 coastal communities
in five countries: Fiji, Palau, Samoa, Solomon Islands
and Tonga. Site visits, conducted from July to December
1998, lasted from 2 to 5 days per community.
The sites were selected to cover a range of conditions
likely to influence management success: they included
both sites perceived by external stakeholders as being
successful, as well as sites perceived to be failures; conservation
and non-conservation sites; sites with external
partners, as well as sites without such partners; open
access sites as well as sites with strong customary marine
tenure; urban, peri-urban and rural areas; and low islands
and high islands. The key characteristics and location
of the study sites are shown in table 1 on next pages.
2. METHODS
2.1 Empirical Context
To understand factors influencing the success of coastal
resource management, one would ideally analyse a set of
data for different sites and different years for each site.
Such a set with a spatial dimension and a time dimension
is referred to as a panel dataset or longitudinal
dataset. However, no comprehensive baseline survey let
alone panel dataset exists in the Pacific at present. Most
anthropological and socio-economic studies have focused
on case studies of individual sites or countries (see
Adams 1997; Adams & Ledua 1997; Crocombe 1994;
Hviding & Ruddle 1991; Johannes 1994; 1978; Munro
& Fakahan 1993; Ram 1981; UNDP 1991; and Veitayaki
1990). A panel dataset allows for the separation of
country specific heterogeneity from the effects of time
varying variables. A pure cross-section dataset does not
enable one to determine the effect of policies that have
changed over time, and a pure time series of one country
does not lend itself to any generalisation beyond that
country (Hsiao 1986).
Unfortunately, panel datasets are rarely available to
coastal managers. To address this shortfall, Pomeroy,
Pollnac et al. (1996, 1997) developed a baselineindependent
method to achieve both a spatial and time
dimension and applied it to coastal villages in the Philippines.
The survey relied on respondents’ perceptions of
the impact of the Central Visayas Regional Project on
such variables as local income, community conflict and
control over resources. The advantage of this approach is
that a time series dimension — the perceptions of
changes prior to and after the project — can be added to
what is essentially a cross-section dataset. The Biodiversity
Conservation Network used a similar method to
determine whether or not micro-enterprises created to
provide alternative income to villagers helped achieve
conservation goals (BCN 1998; and Salafsky et al.
1999). One of the key indicators used by the BCN study
was a threat reduction index, or the program officers’
perception of the degree to which the threats to biodiversity
were reduced after project introduction.
This study used a similar approach to the two surveys
described above, where community perceptions of
trends in the condition of coastal resources were used to
mimic the time dimension. An additional advantage of
this approach is that the resulting data can be used in the
future to obtain a real panel dataset.
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