Exploring the effect of stepwise-multiple-round bidding on willingness-to-pay in contingent valuation study–When can we trust respondents’ preferences

Wang, Yixin (2023). Exploring the effect of stepwise-multiple-round bidding on willingness-to-pay in contingent valuation study–When can we trust respondents’ preferences. University of Birmingham. Ph.D.

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The contingent valuation method (CVM) has been widely used by economists and statisticians to measure the benefits of non-market goods or services since the 1990s. The framework for CVM is derived from the utility function of welfare economics. CVM asks people directly about their willingness to pay (WTP) for the value of specific goods or services, or willingness to accept to give up the value of goods or services.

China has achieved rapid economic growth over the past three decades, during this period, however, it also faced serious environmental challenges and problems, for example, air pollution. China and the United States are jointly responsible for 40% of the world’s carbon emissions. This thesis analyses people's concerns about environmental issues using CVM. Specifically, we implement the classical maximum likelihood estimation (MLE) method and the Monte Carlo Markov Chain (MCMC) simulation-based method to evaluate people's willingness to pay (WTP) for the improvement of environmental quality via support of a "geo-engineering" project.

The data used in this thesis was collected through face-to-face interviewing in four cities in China, Harbin (northeast and inland), Zhengzhou (north and inland), Changsha (central-south and inland) and Zhuhai (southeast and coastal). We interviewed 1,044 participants, asked them to answer a CVM survey questionnaire and collected their responses. The CVM questionnaire included six aspects of information that could affect respondents’ WTP. In addition to the social-demographics related to the respondents’ preferences, e.g. gender, age, household income, we also asked about respondents’ health conditions, social connections and awareness of political issues, governmental support and risks of human activities on environment and etc. Using this sample, we initially employed the step-wise and logistic regression models to identify the significant factors, then we applied the classical MLE to model the single- and double-bounded CVM answers and the WTP values, and expanded the modelling procedures to multiple-rounds bidding processes. Further more we also used MCMC to analyse the mean WTP values through multiple rounds of bidding process.

MLE results suggested that the fitted mean WTP values from single-, double- and triple-bounded MLE models were CNY816.56, CNY565.79 and CNY539.27, respectively. The gap between the single- and the double-bounded estimates showed that the WTP estimates from commonly-used single-bounded approach could lead to unreliable results. We also discovered that the more respondents believed that they gained benefit from the ``geo-engineering'' project, the more they were willing to agree to the given bid, and were likely to pay a greater price; the more respondents were prepared to spend on pollution reduction products, the equated to their awareness of the harm of pollution, the more they would like to pay. Results also supported that being admitted to hospital was positively related to the value of WTP; being interested in news and public affairs had a negative effect on mean WTP. On the other hand, the estimated mean WTP values from the MCMC approach for the single, double and triple-bounded models were CNY810.82, CNY566.10 and CNY510.22, respectively, largely consistent with the results in MLE. MCMC improved WTP models because it produced more significant variables and narrower confidence intervals.

Type of Work: Thesis (Doctorates > Ph.D.)
Award Type: Doctorates > Ph.D.
Licence: All rights reserved
College/Faculty: Colleges (2008 onwards) > College of Engineering & Physical Sciences
School or Department: School of Mathematics
Funders: None/not applicable
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HA Statistics
URI: http://etheses.bham.ac.uk/id/eprint/13232


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