Abstract
This study is to compare and evaluate the estimating method of WTP(willingness to pay) for the valuation of oceans and fisheries resources with non-market goods characteristics using contingent valuation method. In general, when estimating parameters of the WTP function, we should take into account the assumption of probability distribution, inclusion of covariates, method of inducement of payment, and the treatment of 0 payment intention and resistance responses. This study utilizes survey data that was used to estimate the value of fisheries resource protection zones, with a total of 1,200 samples. The main results of this study are summarized as follows: First, the final willness to pay amount is estimated at a statistical significance of less than 1 percent, and the distribution of the final willness to pay amount is from \6,926 of the double bounded dichotomous model to \10,721 of the spike model. Second, the willness to pay amount based on assumptions about the normal and logistic probability distributions are estimated to be \9,429 and \9,370 respectively, so there was no significant difference. Third, the willness to pay amount of the single bounded dichotomous model and the double bounded dichotomous model are estimated to be \8,951 and \6,926 respectively, making a relatively large difference. Fourth, the willness to pay amount of the model without covariates and the model with covariates are estimated to be \9,429 and \8,951, respectively, so the willness to pay amount is underestimated when the covariates are included. Fifth, the Spike model that considers zero payment intention and resistance response estimates \10,405 as the highest payment in this study. Finally, the CVM analysis guidelines proposed by the Korea Development Institute (KDI) are estimated to be \9,749 and \10,405 respectively, depending on including no covariates and with covariates. Compared to other models, the final willness to pay amount is not estimated underestimated. Therefore this study suggests the use of KDI's guidance under government public policy projects. In view of these results, the estimating model for willness to pay amount model will be selected by considering the sample size, the suitability of the model, the sign of the estimated coefficient, the statistical significance, the ratio of the zero payment intention and the payment rejection. And, for CVMs on government public policy projects, it is desirable to estimate by the method proposed by the KDI.
