Cesar2000-Economics of Coral Reefs.pdf

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Table 2. Variables Used in the Econometric Analysis Variable Name Description Type Dependent Variables CpUE Trends Perception of change in catch per unit of effort over the past decade Ordinal Habitat Trends Perception of habitat trends over the past decade Ordinal Threat Trends Perception of threat trends over the past decade Ordinal Compliance Perception of level of compliance with management rules Ordinal Aggregated Success Factor scores from a principal component analysis used to weight the Continuous four indicators into an aggregated success variable Explanatory Variables Palau Country variable for Palau (‘1’ Palau Site; ‘0’ Other Sites) Dummy Samoa Country variable for Samoa (‘1’ Samoa Site; ‘0’ Other Sites) Dummy Solomon Islands Country variable for Solomons (‘1’ Solomons Site; ‘0’ Other Sites) Dummy Tonga Country variable for Tonga (‘1’ Tonga Site; ‘0’ Other Sites) Dummy Natural Disasters Variable reflecting the occurrence of natural disasters at the site over Dummy the past decade (‘1’ Event Occurred; ‘0’ No event) Quality of Leadership Study team’s assessment of the quality of local leadership (‘High’, Ordinal ‘Medium’, ‘Low’) Inability to Exclude Outsiders Ordered variable measuring the inability of local leaders to exclude Ordinal outsiders (higher value reflects higher inability to exclude) Presence of Pollution* Variable reflecting whether pollution threats were reported at the site Dummy (‘1’ Yes; ‘0’ No) Population Density Ratio of village population to the site size (persons per sq. km) Continuous Number of Ecosystems Number of distinct ecosystems identified by the study team Continuous Equal Sharing of Factor scores from a principal component analysis capturing the degree Continuous Costs/Benefits of equality and involvement of user groups in management of the site Destructive Fishing Variable reflecting the presence of destructive fishing practices Dummy (‘1’ Yes; ‘0’ No) Land-Based Threats Variable reflecting the presence of land-based threats (‘1’ Yes; ‘0’ No) Dummy Overfishing Threats Variable reflecting the presence of overfishing threats (‘1’ Yes; ‘0’ No) Dummy Type of Rule: National Rule type variable reflecting whether the rule is a national rule Dummy Rules Adopted Locally adopted locally (‘1’ Yes; ‘0’ No) Type of Rule: National Rules Rule type variable reflecting whether the rule is a purely national rule Dummy (‘1’ Yes; ‘0’ No) *This variable was used in all models except ‘Threat Trends’ as it is related to the ‘Land-Based Threats’ dummy used in that model. 172 JEFF MULLER, SOFIA BETTENCOURT & ROBERT GILLETT:
country-specific effects. For the ‘Threat Trends’ model, three variables were inserted to account for whether the perceived threat was a destructive fishing practice, a land-based threat (such as siltation, pollution, logging or mining), or overfishing. For the ‘Compliance’ model, two dummy variables were inserted to indicate whether the rule for which the compliance assessment was stated was a national rule or a national rule adopted locally by community leaders. Significance was then compared to the omitted variables, ‘all other threats’, and ‘indigenous rules’. Several potentially important variables were excluded from the analysis: community awareness and enforcement effectiveness were excluded because they are themselves dependent on site characteristics and processes (i.e., they are not intrinsic explanatory variables). However, both of these variables were found to be important in the qualitative analysis. Information on some variables judged to be important to management success — for example, traditional knowledge and attitudes towards coastal management — could not be collected accurately within the short time allotted to each site survey. 2.3 Key Limitations The analysis took into consideration the complex structure of the data, which were structured at four levels: countries (5 in number), sites (31), focus groups (133) and, for each focus group, perceptions of trends for different resources, habitats, threats and compliance with management rules (377 to 665 observations depending on the indicators). A methodological difficulty arose when comparing perceptions of success between different resources, habitats, or threats. Most non-parametric statistics assume that observations are independent, and the ratings given by an individual focus group to various resources violated this assumption. To minimise this problem, the study team created a semi-independent set of observations which eliminated any duplicate observation within a focus group: for example, if a group provided CpUE trends for two different reef fish, only the one ranked highest in importance was included in the dataset. In the descriptive statistics, where it was important to present the average results per site — for example, to display the most important threats — the number of observations in ‘focus’ sites was made equal to the ‘supplementary’ sites. To use all observations in the econometric analysis, a correction had to be made to account for the possibility that observations within each level (e.g., site or focus group) were correlated. Most estimators that assume independent observations would tend to result in biased standard errors. The analysis used a correction that allowed the standard errors to be calculated under the assumption of stratified, clustered, random sampling (with stratification by country and clustering by site). These estimators allow for correlation within the clustering units, and yield variance estimates which are approximately correct or biased towards more conservative estimates (Kendall et al. 1983; STATA 1999). It is important to recall that the sites were not chosen at random, as there was neither enough information nor funding to create a random stratified sample. Instead, the sites were selected to cover a range of factors that the study wanted to test. Expert advice was used to ensure that the selection was as representative as possible. Though not ideal, this still provides a justification for carrying out the econometric analysis as if the sites were chosen randomly. The limitations mean, however, that the study results need to be interpreted with care. This is especially the case for the CpUE indicator: first, community perceptions can differ from reality and stated perceptions can differ from actual perceptions. The study results should therefore be interpreted as perceived trends, rather than the actual status of coastal resources in the Pacific. Second, a negative or positive trend does not always reflect a failure or success of management. CpUE could be improving simply because of improved technology. For this reason, the causes reported by the community groups for a given trend were also analysed. A COMPARATIVE STUDY OF SOCIO-ECONOMIC AND MANAGEMENT ISSUES RELATED TO COASTAL RESOURCES IN THE PACIFIC ISLANDS 173
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Collected Essays on the Economics o
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Table of Contents Acknowledgements
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Foreword Corals and coral reefs hav
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● ● ● ● assessing the threa
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fishing is lucrative from the indiv
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Coral Reefs: Their Functions, Threa
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Table 1. Ecosystem functions and co
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Figure 1. Yield of Trochus Shells i
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Figure 2. Total Economic Value and
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Table 3. Total Net Benefits and Los
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Table 5. Valuation Techniques used
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people live from the Park, the high
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duction or logging) would have an e
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Hatcher, B. G., Johannes, R. E., &
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Thorhaug, A., (1992) “Oil Spills
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Authors Threats Mgt. Description Is
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Assessing the Benefits of Improving
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2.2 Coral Reef Quality Change and t
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Five groups were given and the resp
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The total sample mean was similar a
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one of the five categories were nex
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4.4 Protecting Rights and the Desir
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able for tourists versus locals was
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problem that where respondents are
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turists who regularly handle soil e
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expanding tourist industry based on
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ism and fisheries. Logging activiti
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Table 2. Fisheries gross revenue an
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an ‘Environmentally Critical Area
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25,000 20,000 15,000 10,000 5,000 0
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sized animals — even at the most
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greater willingness among environme
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stroyed was 7.5 fish times 0.19 ope
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were affected by cyanide squirts fo
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Russ, G. R., (1991) “Coral reef f
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eefs that are not too remote, can n
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Table 2. Estimated net income (US$/
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Table 4. Results of base case and t
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ating the destruction of its coral
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creased. Besides these direct effec
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3. ECONOMIC ANALYSIS: SOCIETAL COST
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3.2 Case Study 2: Sri Lanka The soc
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A first difference between the two
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Coral Bleaching in the Indian Ocean
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leaching occurred in the Maldives,
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months after the coral bleaching ev
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Level of importance 35 30 25 20 15
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trols. What is useful from the data
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most disappointing: food and bevera
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REFERENCES Anderson, R. C., Waheed,
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also seem capable of providing many
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Table 1. Summary of Findings of Eco
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Table 2-A. Summary Table of Bioecon
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lation for the spawning stock abund
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3.2.1 DENSITY-DEPENDENT MIGRATION M
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3.2.3 DENSITY-DEPENDENT AND UNI-DIR
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4.2 Habitat Protection A critical a
Page 120 and 121: REFERENCES Alcala, A. C., (1989)
Page 122 and 123: Russ, G. R., & Alcala, A. C., (1996
Page 124 and 125: poses of this review, the mechanism
Page 126 and 127: Supervision and training of labour
Page 128 and 129: Table 1. Comparison of restoration
Page 130 and 131: elating to the case studies. Recrea
Page 132 and 133: Value of reef products and services
Page 134 and 135: Fitzharding, R. C., & Bailey-Brock,
Page 136 and 137: 1995). Global annual retail value o
Page 138 and 139: Table 1. Market Prices in Hong Kong
Page 140 and 141: 2.3 Recent Trends The Asian financi
Page 142 and 143: destructive, since corals are often
Page 144 and 145: (input factors are indicated by blu
Page 146 and 147: Table 2. Challenges of grouper cult
Page 148 and 149: stages when natural mortality is hi
Page 150 and 151: STRENGTHEN ENFORCEMENT AND MONITORI
Page 152 and 153: 32 Several efforts along these line
Page 154 and 155: Jones, R., (1997) “Effects of Cya
Page 156 and 157: ▲ An Economic and Ecological Anal
Page 158 and 159: The Park was re-established and rev
Page 160 and 161: associated with dive tourism and th
Page 162 and 163: Apparent stress threshold B A S 1 P
Page 164 and 165: A Comparative Study of Socio-Econom
Page 166 and 167: Table 1. Key Site Characteristics C
Page 168 and 169: Level Target Respondent Information
Page 172 and 173: Table 3. Tied P-Values for Differen
Page 174 and 175: as, lagoons and coral reefs were pe
Page 176 and 177: Table 5. Results of the Econometric
Page 178 and 179: ties may not reveal their true perc
Page 180 and 181: Salafsky, N., Cordes, B., John Park
Page 182 and 183: ICZM guides jointly the activities
Page 184 and 185: such as alternative means of beach
Page 186 and 187: 2.3.2 CONTRIBUTIONS TO UTILITY —
Page 188 and 189: marginal benefit function, relating
Page 190 and 191: the type of use, are not linked to
Page 192 and 193: 4. FUTURE DIRECTIONS FOR DECISION S
Page 194 and 195: Huber, R. M., & Jameson, S. C., (19
Page 196 and 197: Appendix. Economic valuation studie
Page 198 and 199: Ecosystem and Approach ____________
Page 200 and 201: Ecosystem and Approach ____________
Page 202 and 203: Contemporary national development p
Page 204 and 205: The PBPA contains four prominent ex
Page 206 and 207: 5. INTERACTIONS BETWEEN ECOSYSTEM S
Page 208 and 209: Table 2. Categories of Ecosystems i
Page 210 and 211: MEY shows the enormous level of ove
Page 212 and 213: the ecosystem together. The total (
Page 214 and 215: ▲ tourism, coastal protection and
Page 216 and 217: has dramatically decreased from abo
Page 218 and 219: seaweed per year is generating abou
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Table 4. Current (1999) annual net
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The essential activities that can e
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their overall economic benefit to i
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Private Sector Management of Marine
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ety of important conservation and o
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50 40 # incidents 30 20 10 0 J A J
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● Investment security is reduced
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onmental practices, and therefore t
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7.4 NGOs Are Not Always Accountable
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Sterner, T, & Andersson, J., (1998)
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perfect but not completed and origi
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Jeff Muller, Economist, World Bank,
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