Abstract
Dichotomous choice elicitation technique of contingent valuation method is broadly used in the research fields of environmental resource and recreational activity management. The binary choice type of questions are generally analyzed by using Logit or Probit probability distribution models in which a common analysis procedure is to apply MLE for estimating variable parameters before calculating the respondents’ willingness to pay. In this paper, a MCMC Gibbs sampling Probit model is adopted to maintain the three advantages it has in dealing with heteroscedasticity, high dimension numerical integral and sample size restriction problems. The results revealed that the MCMC model and MLE Probit model are strikingly consistent, which suggests that the former is much simple and reliable estimation method. At the same time, the empirically based existence value estimation of coastal beach quality improvement in Dalian, China is RMB?168 per person.
Highlights
Contingent valuation technique is commonly used in environmental resource management fields, and monetized benefit measurement for improvements of environmental goods
A Markov Chain Monte Carlo (MCMC) Gibbs sampling Probit model is adopted to maintain the three advantages it has in dealing with heteroscedasticity, high dimension numerical integral and sample size restriction problems
The results revealed that the MCMC model and MLE Probit model are strikingly consistent, which suggests that the former is much simple and reliable estimation method
Summary
Contingent valuation technique is commonly used in environmental resource management fields, and monetized benefit measurement for improvements of environmental goods. We are primarily concerned over the subject of coastal beach quality improvement with a particular emphasis on the relationship between tourism beach nourishment and tourists’ socioeconomic characteristics and the nature of their tourism activities as well. Beach conditions such as slope, width, mud, debris, congestion etc. Tourists’ responses to the improved situation of coastal tourism resource quality is depended on a sequence of bidding value data to let respondent decide “Yes” or “No” regarding acceptance or rejection in an empirical survey. The theoretical base of this method used in consumer welfare measurement is analyzed by Hanemann (1984) using Random Utility Maximization (RUM) [6]. In this paper, we try to examine the performance of the non-parametric kernel heteroscedastic MCMC method by comparing its results against those generated by MLE in order to reveal the robustness of MCMC Gibbs sampling method
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