Using the Choice Experiment Methodto 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

• 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:

kmbannij SSSZZZV ...... 212211

• Estimates of utility parameters are obtained with ML.

The CE Design• Selection of the relevant attributes and their levels:

– 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

Basic Conditional Logit ModelAttributes and interactions Coefficient

(s.e.)ASC 0.784***

(0.064)Biodiversity 0.222***

(0.025)Open water surface area 0.140***

(0.027)Research and education 0.124***

(0.026)Re-training 0.002***

(0.001)Payment -0.014***

(0.001)Log likelihood2

Sample size

-3325.6970.0703256

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):

• Environmental Consciousness Index (ECI)

• Education

• Number of dependent children in household

• Urban location dummy

Conditional Logit Model with InteractionsAttributes and interactions Coefficient

(s.e.)ASC -3.230***

(0.165)Biodiversity 0.273***

(0.040)Open water surface area 0.114**

(0.045)Research and education 0.103**

(0.043)Re-training 0.008***

(0.001)Payment -0.033***

(0.003)ASC*Dependent children 5.285***

(1.091)ASC*Urban 3.586***

(0.798)ASC* Education 4.439***

(0.782)ASC* ECI 2.532***

(0.246)Biodiversity *Dependentchildren

0.055*(0.032)

OWSA* Urban 0.186***(0.066)

Research and education*Urban 0.163***(0.063)

Re-training* ECI -0.001***(0.0002)

Payment* Education 0.007***(0.002)

Payment* ECI 0.002***(0.0004)

Log likelihood2

Sample size

-1565.0810.5152935

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 InteractionsAttributes Mean WTPBiodiversity 15.59 14.45Open water surface area 9.85 9.07Research and education 8.69 8.09Re-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 respondentScenario Basic Conditional Logit Model Conditional Logit Model with Interactions1 – Low impact 16.86 29.742 – Low impact 19.86 23.263 – Medium impact 35.43 47.714 – 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.