1. The Economic Value of Ecosystem Conservation in Japan: Reduction of elicitation effect by Bid Effect Function Mitsuyasu YABE Kyushu University

2. Contents 1. Background and Purpose 2. Analytical Model 3. Survey Design and Explanatory Variables 4. Estimation Results 5. Conclusion

3. Background of the Study National Park Aso National Park Aso Over 18 million people visit and enjoy the view of Aso grassland. Over 18 million people visit and enjoy the view of Aso grassland. Many valuable flora and fauna were maintained by traditional human activities. Many valuable flora and fauna were maintained by traditional human activities. With decline of farmer and the change of farming pattern, the Aso grassland verge to crisis of maintaining With decline of farmer and the change of farming pattern, the Aso grassland verge to crisis of maintaining

4. Photo by Miura

11. Endangered Species in Aso Grassland

12. Photo by Miura

19. Purpose of the Study Estimating the conservation value of Aso Glass Land Estimating the conservation value of Aso Glass Land Improving the Contingent Valuation Method (CVM): Improving the Contingent Valuation Method (CVM): Deceasing Elicitation Effects Deceasing Elicitation Effects 1) Starting Point Effects 1) Starting Point Effects 2) Yea-saying 2) Yea-saying

20. Elicitation Effects in CV Dichotomous choice CV is most commonly applied since respondents only need to select “ accept ” or “ not accept ” Dichotomous choice CV is most commonly applied since respondents only need to select “ accept ” or “ not accept ” However, even when the bid is higher than the latent willingness to pay, the respondents tend to “ accept ” the bid. However, even when the bid is higher than the latent willingness to pay, the respondents tend to “ accept ” the bid.

21. Characteristics of the Model To decrease Elicitation Effects on the WTP To decrease Elicitation Effects on the WTP → Introducing Bid Effect Function → Introducing Bid Effect Function → Applying DC Approach for Five choice → Applying DC Approach for Five choice

22. Formulation of Bid Effects Latent WTP for the i respondent: Latent WTP for the i respondent: The gap between the bid t i and latent WTP: The gap between the bid t i and latent WTP: Bid effect function: Bid effect function:

23. Stated WTP the stated WTP can be expressed: the stated WTP can be expressed: The probability that the stated WTP y i is larger than the bid t i : The probability that the stated WTP y i is larger than the bid t i :

24. The Hypothetical Question （ 1 ） “ suppose that grassland could be converted to forest and grassland could be lost as grazing and open burning are discontinued in Aso region. “ suppose that grassland could be converted to forest and grassland could be lost as grazing and open burning are discontinued in Aso region. In order to prevent that happens, we set up the “ Aso Grassland World Heritage Fund ” to register and conserve the grassland In order to prevent that happens, we set up the “ Aso Grassland World Heritage Fund ” to register and conserve the grassland The activity cost of “ Aso Grassland World Heritage Fund ” is supported by the public contribution. The activity cost of “ Aso Grassland World Heritage Fund ” is supported by the public contribution.

25. The hypothetical question （ 2 ） If the fund costs (***) per household per annum, you may pay the amount of money? (Select only one) If the fund costs (***) per household per annum, you may pay the amount of money? (Select only one) 1. will pay > “ YES ” in Model 1 & 2 1. will pay > “ YES ” in Model 1 & 2 2. probably will pay > “ YES ” in Model 3 & 4 2. probably will pay > “ YES ” in Model 3 & 4 3. probably will not pay 3. probably will not pay 4. will not pay 4. will not pay 5. don ’ t know 5. don ’ t know

26. The Log-likelihood Function in DC-CVM Where d i 1 and d i 2 are coded 1 when respondent chose the option and otherwise 0.

27. Form of Bid Effect Function If bid effect function is liner function : If bid effect function is liner function : We have a relation as follows: We have a relation as follows:

28. Bid Effect function based on logistic function Hypotheses: Hypotheses: >> >>

29. Summary of Survey Questionnaire Survey Period ： Survey Period ： December 1998 December 1998 Respondents ： Residents of Kumamoto Prefecture Respondents ： Residents of Kumamoto Prefecture Samples ： 1000 Samples ： 1000 Samples used for Analysis ： 418 Samples used for Analysis ： 418

30. Attributes of Survey Respondents Average Age ： 59 years old Average Age ： 59 years old Average Income ： 5,740,000 yen per ann. Average Income ： 5,740,000 yen per ann. (Approx. 52,000 US$) (Approx. 52,000 US$) Conservation Activities of Aso Glass land: Conservation Activities of Aso Glass land: Highly Appreciated Highly Appreciated

31. Explanatory Variables and Means Variables Description Description Mean Mean S.E. S.E. INCOME Income (million yen ） 5.7483.422 LAGE Logarithm of age 4.0702.773 BEAUTY Beauty of Aso grassland （ Log(1=not good,, 5=very good ） 1.5560.126 TRIP 1/0, 1=visit within 5 years 0.7654.124 ACT 1/0, 1=conservation should be expanded expanded0.3940.490 BEEF 1/0, 1=would buy meet of cows fed grass at more than 20 % fed grass at more than 20 % higher price higher price0.1960.397 POSSIBILITY 1/0, 1=possibility that grassland is conserved by fund is more than 70% 0.5390.499

32. Comparison of Estimated Results Model 1 Model 2 Model 3 Model 4 Constant0.7660.5125.391**5.701*** INCOME0.0440.081*0.0280.037 LAGE0.8121.013*0.3260.107 BEAUTY0.7390.3091.1361.412** TRIP0.652*0.624**0.0260.258*** ACT0.833***0.800***0.636**0.646*** BEEF1.279***0.747**0.806**0.922 POSSIBILITY0.662**0.652**0.022-0.058*** BID EFFECT 3.683***3.647*** ERROR1.682***0.494***1.079***0.252*** Log Likelihood -148.300-145.321-96.110-94.204

33. Comparison of Latent WTPs: Definitely Pay (Unite: Yen) Model 1 Model 1 Without Bid effect Func. Model 2 Model 2 With Bid effect Func. Mean3,9041,028 95% CI 2,055 to 8,884 799 to 1,374 Median948909 95% CI 714 to 1,252 715 to 1,163

34. Comparison of Latent WTPs: Probably Pay (Unite: Yen) Model 3 Model 3 Without Bid effect Func. Model 4 Model 4 With Bid effect Func. Mean15,8759,633 95% CI 10,144 to 27,461 7,274 to 12,415 Median8,8719,333 95% CI 6,867 to 11,345 7,096 to 12,415

35. Results of Bid Effect Function Bid effect coefficient was statistically significant Bid effect coefficient was statistically significant The error term was reduced by more than 70% The error term was reduced by more than 70% Difference between Mean and Median was also reduced Difference between Mean and Median was also reduced

36. Conservation Value of Aso Grassland Estimated Value who definitely pay ： Mean 1,028 Yen ( = US$ 9.3) Estimated Value who definitely pay ： Mean 1,028 Yen ( = US$ 9.3) Return rate of this survey: 41.8% Return rate of this survey: 41.8% Number of households of Kumamoto prefecture: 594,197 Number of households of Kumamoto prefecture: 594,197 Total Conservation Value per Year Total Conservation Value per Year = 1,028 x 0.418 x 594197 = 1,028 x 0.418 x 594197 = 255 Million Yen/Year = 255 Million Yen/Year = US$ 2.3 Million/Year = US$ 2.3 Million/Year

37. Conclusion Removal of influence from the bid effect bias enabled a more appropriate WTP estimation Removal of influence from the bid effect bias enabled a more appropriate WTP estimation Price Oriented Attribute affected the WTP more than income Price Oriented Attribute affected the WTP more than income The estimated total environmental value was more than the amount of the environmental gross investment at HTB The estimated total environmental value was more than the amount of the environmental gross investment at HTB

38. Thank you very much for your attention