한국지방행정연구원

Basic Report

Year
2023
Author
Ji-young Song · Lee, Min Gi · Jeong, Youn Baek

The Contingent Valuation Method in Feasibility Study: Focusing on WTP

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The Contingent Valuation Method (hereinafter referred to as CVM) is a methodology for measuring the economic value of non-market goods. It has been widely used to measure the economic benefits of public projects. LIMAC (Local Investment Management Center) widely uses CVM as a benefit estimation methodology for non-market goods such as Cultural-Sports-Tourist facilities, and Public Parks when conducting feasibility studies. However, in relation to the CVM estimation process, especially the model, research on the analytical methodological aspects of deriving and benefiting the appropriate willingness to pay (hereinafter referred to as WTP), which is the final purpose of the survey, is lacking. This study organizes various issues that have been raised while applying CVM in feasibility studies on local financial investment projects and analyzes through empirical analysis, how these issues affect the estimation of WTP through CVM.
   This study consists of three parts. The first is the construction of analysis data and CVM-related theoretical consideration. A historical and theoretical review was conducted on various prior research. So far all feasibility studies conducted by LIMAC & PIMAC (Public and Private Infrastructure Investment Management) using CVM were investigated and made into a database, and individual case analysis and comparative analysis were performed. Second, we addressed issues and important considerations that arise or may arise when applying CVM to local financial investment projects and estimating WTP using the CVM. In addition, for each stage of the issue, issues that require verification of their influence on WTP and issues that require improvement through the preparation of a guidelines were presented separately. We also identified factors that could affect the WTP of local financial investment projects and sought ways to reflect them in empirical analysis. In particular, an empirical analysis was conducted using survey raw data on the differences in WTP by model. The results showed that the WTP estimation significantly differed depending on the CVM estimation process and method. Therefore, the influencing factors of WTP were largely divided into four characteristics, including project-survey-models, and respondent characteristics, and a total of 27 factors were extracted.
   The third is a method to find an answer to the main research question: ‘Why do differences in willingness-to-pay estimated by CVM occur in individual studies? Influence factors on WTP were identified using 'meta-regression analysis'. The results from meta-regression analysis show that ① Project scale (site area or total floor area), ② Project region, ③ Project sector (parks, playgrounds, buildings, ecological river restoration, water pipes, etc.), ④ Maximum WTP value, ⑤ Season of conducting the survey, ⑥ Survey agency, ⑦ whether the payment target is an individual or a household, ⑧ whether the value to be estimated through CVM is total value or only non-use value have statistically significant impact on WTP.
   Despite these results, they need to be interpreted with caution due to the limitation of meta-regression analysis. Because meta-regression analysis is an analysis based on summary statistics from prior research, the quality of individual prior research and the extent of research accumulation have a great influence on the validity and reliability of the analysis. Although it is important to ensure both the quality and quantity of prior research, the data limitations were recognized.
   Our study suggests additional research is needed to establish LIMAC-CVM guidelines, which is the final goal of this study. Related research topics include establishing an additional sphere of influence for CVM, creating a sensual experience by setting up a hypothetical project and testing a questionnaire for each WTP influence factor that was contracted in this study targeting specific respondents, utilizing consumer raw data.