Transcript A Choice Experiment to Estiamte Non

Using the Choice Experiment Method
to Estimate Non-Use Values of Wetlands:
The Case of Cheimaditida, Greece
Ekin Birol, Katia Karousakis, Phoebe Koundouri
University College London
IWA Conference
Crete, July 2005
Presentation Outline
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Background
Objective of Study
Choice Experiment Method
Econometric Models
Results
Policy Implications
Background
• Wetlands are among the Earth’s most productive
ecosystems, providing valuable ecological functions and
services. These include water supply, improved water
quality, flood protection, food web support, and more.
• Wetlands are under increasing pressure from
anthropogenic activities:
– U.S. has lost 54% of its original wetlands.
– Between 1920-1991, Greece lost 63% of its wetlands.
– Similar figures in other European countries.
• International efforts to mitigate these trends include the
Ramsar Convention and the EU WFD 2000/60/EC.
Objective of Study
• The purpose is to use the choice experiment method to
estimate the relative values of non-use benefits from the
Cheimaditida wetland in NW Greece.
– Non-use (or passive) values refer to the values of the benefits
generated by environmental goods and services that are unrelated to
the value of their current or planned use. These are composed of
existence value, bequest value, and altruistic value.
– The Cheimaditida wetland contains one of the few remaining
freshwater lakes in Greece and is rich in flora, fauna, and habitat
diversity. Several species are listed under the EU Habitats Directive
and the CITES.
– Current environmental pressures include water demand for
agricultural irrigation, pollution run-off from industry, over-fishing.
Choice Experiment Method (CEM)
• CEM has distinct advantages over other stated preference
techniques (e.g., CVM). CEM is able to value multiple
attributes and impacts of environmental change.
• Theoretical basis in Lancaster’s characteristics theory of
demand (1966) and in random utility theory (McFadden,
1974).
Uii = Vij + ij
Utility of a choice is composed of a deterministic component
and an error component.
CEM continued
• Probability that individual i will choose option j:
Probij= expVij/hexpVij
• Conditional indirect utility function:
Vij    1Z1   2 Z2  ...  n Zn   a S1  b S2  ...  m Sk
• Estimates of utility parameters  are obtained with ML.
The CE Design
• Selection of the relevant attributes and their levels:
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Biodiversity: Low to High
Open Water Surface Area (OWSA): Low to High
Research and Education Extraction: Low to High
Employment Re-training: 30, 50, 75, 150
Monetary Payment: €3, €10, €40, €80
Status quo = Deterioration with €0 payment.
32 choice sets in total. Each respondent faced 8 choice
sets each with 3 options: A, B, or neither.
Information also obtained on the socio-economic
characteristics and environmental attitudes of the
respondents.
The CE Survey
• Conducted face-to-face interviews in 10 cities/towns in
Greece: Athens, Thessaloniki, Amyntaio, Ptolemaida,
Florina, Edessa, Kozani, Veroia, Naoussa, and Chalkithona
in Feb-March, 2005.
• Sample size n = 407
• Number of choices elicited = 3256 (407*8)
• Respondents who always selected neither management
scenario A nor B = 78
– Main reasons were that they did not believe funds would be used
correctly, that wetland management was the responsibility of the
government, and/or that they did not have the financial ability to
contribute to the management funds.
Conditional Logit Estimates
for Wetland Management Attributes
Attributes and interactions
ASC
Biodiversity
Open water surface area
Research and education
Re-training
Payment
Log likelihood
2
Sample size
Basic Conditional Logit Model
Coefficient
(s.e.)
0.784***
(0.064)
0.222***
(0.025)
0.140***
(0.027)
0.124***
(0.026)
0.002***
(0.001)
-0.014***
(0.001)
-3325.697
0.070
3256
Conditional Logit Model
• Test for IIA property was not rejected at 99%. Model is
thus appropriate.
• Coefficients are all significant at 1% level and intuitively
correct. Negative coefficient on payment indicates the
effect on utility of choosing a choice set with higher
payment level is negative.
• McFadden’s 2 is quite low.
Conditional Logit Model with Interactions
• To account for heterogeneity of preferences across
respondents need to use interaction terms.
• Selected 4 independent variables for interactions based on
Variance Inflation Factors (VIFs):
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Environmental Consciousness Index (ECI)
Education
Number of dependent children in household
Urban location dummy
Attributes and interactions
ASC
Biodiversity
Open water surface area
Research and education
Re-training
Payment
ASC*Dependent children
ASC*Urban
ASC* Education
ASC* ECI
Biodiversity *Dependent
children
OWSA* Urban
Research and education*Urban
Re-training* ECI
Payment* Education
Payment* ECI
Log likelihood
2
Sample size
Conditional Logit Model with Interactions
Coefficient
(s.e.)
-3.230***
(0.165)
0.273***
(0.040)
0.114**
(0.045)
0.103**
(0.043)
0.008***
(0.001)
-0.033***
(0.003)
5.285***
(1.091)
3.586***
(0.798)
4.439***
(0.782)
2.532***
(0.246)
0.055*
(0.032)
0.186***
(0.066)
0.163***
(0.063)
-0.001***
(0.0002)
0.007***
(0.002)
0.002***
(0.0004)
-1565.081
0.515
2935
Conditional Logit Model with Interactions
• Model has higher overall fit, McFadden’s 2 = 0.515
• Respondents with higher university education and higher
ECI’s are more likely to choose higher payment levels.
• Respondents in urban areas prefer higher levels of OWSA
and research and educational extraction.
• Respondents with higher number of dependent children are
more likely to choose higher levels of biodiversity.
Willingness to Pay Estimation
• W = -1 (attribute / monetary variable).
This part-worth (or implicit price) formula represents the marginal rate of
substitution between income and the attribute in question, i.e., the
marginal welfare measure (willingness to pay or willingness to accept)
for a change in any of the attributes.
Table 4. Estimates of WTP and confidence intervals, in € per respondent
Basic Conditional Logit Model
Conditional Logit Model with Interactions
Attributes
Mean WTP
Biodiversity
15.59
14.45
Open water surface area
9.85
9.07
Research and education
8.69
8.09
Re-training (per person)
0.12
0.123
Compensating Surplus Estimates
• Obtained CS estimates for a range of policy scenarios.
W = -1 (V0 - V1 ) / monetary variable
Table 5. Estimates of Compensation Surplus for each Scenario, in € per respondent
Scenario
Basic Conditional Logit Model
Conditional Logit Model with Interactions
1 – Low impact
16.86
29.74
2 – Low impact
19.86
23.26
3 – Medium impact
35.43
47.71
4 – High impact
56.14
85.88
Conclusions
• The Greek public derives significant and positive
benefits from the non-use values of the wetland.
• Can use this information to compare benefits from
non-use values with costs of managing the
wetland to improve the levels of the 4 selected
attributes.
• Important in wider Cost-Benefit Analysis.
Thank you.