936 resultados para Contingent valuation


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In contingent valuation, the willingness to pay for hypothetical programs may be affected by the order in which programs are presented to respondents. With inclusive lists, economic theory suggests that sequence effects should be expected. However, when policy makers allocate public budgets to several environmental programs, they may be interested in assessing the value of the programs without the valuations being affected by the order in which the programs are presented. Using single-bounded dichotomous choice contingent valuation questions, we show that if respondents have the possibility to revise their willingness-to-pay answers, sequence effects are mitigated. (JEL Q51, Q54)

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Policymakers have largely replaced Single Bounded Discrete Choice (SBDC) valuation by the more statistically efficient repetitive methods; Double Bounded Discrete Choice (DBDC) and Discrete Choice Experiments (DCE) . Repetitive valuation permits classification into rational preferences: (i) a priori well-formed; (ii) consistent non-arbitrary values “discovered” through repetition and experience; (Plott, 1996; List 2003) and irrational preferences; (iii) consistent but arbitrary values as “shaped” by preceding bid level (Tufano, 2010; Ariely et al., 2003) and (iv) inconsistent and arbitrary values. Policy valuations should demonstrate behaviorally rational preferences. We outline novel methods for testing this in DBDC applied to renewable energy premiums in Chile.

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Much interest now focuses on the use of the contingent valuation method (CVM) to assess non-use value of environmental goods. The paper reviews recent literature and highlights particular problems of information provision and respondent knowledge, comprehension and cognition. These must be dealt with by economists in designing CVM surveys for eliciting non-use values. Cognitive questionnaire design methods are presented which invoke concepts from psychology and tools from cognitive survey design (focus groups and verbal reports) to reduce a complex environmnetal good into a meaningful commodity that can be valued by respondents in a contingent market. This process is illustrated with examples from the authors' own research valuing alternative afforestation programmes. -Authors

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In Contingent Valuation studies, researchers often base their definition of the environmental good on scientific/expert consensus. However, respondents may not hold this same commodity definition prior to the transaction. This raises questions as to the potential for staging a satisfactory transaction, based on Fischoff and Furby's (1988) criteria. Some unresolved issues regarding the provision of information to respondents to facilitate such a transaction are highlighted. In this paper, we apply content analysis to focus group discussions and develop a set of rules which take account of the non-independence of group data to explore whether researcher and respondents' prior definitions are in any way similar. We use the results to guide information provision in a subsequent questionnaire.

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Using the contingent valuation method, this study investigates the preferences of local people for a wind farm that is planned in the Province of Rome. We estimate the reductions in their bimonthly electrical bills over a period of time that respondents are willing to accept as compensation for the installation of the wind farm. Our results suggest that respondents who perceive that the wind farm generates substantial negative impacts on landscape beauty ask higher reductions than others, while respondents who believe that the wind farm produces economic benefits for local communities ask lower reductions than others. Finally, we find that the demand for compensative measures is influenced particularly by socio-economic factors such as age and education.

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A new approach is presented that simultaneously deals with Misreporting and Don't Know (DK) responses within a dichotomous-choice contingent valuation framework. Utilising a modification of the standard Bayesian Probit framework, a Gibbs with Metropolis-Hastings algorithm is used to estimate the posterior densities for the parameters of interest. Several model specifications are applied to two contingent valuation datasets: one on wolf management plans, and one on the US Fee Demonstration Program. We find that DKs are more likely to be from people who would be predicted to have positive utility for the bid. Therefore, a DK is more likely to be a YES than a NO. We also find evidence of misreporting, primarily in favour of the NO option. The inclusion of DK responses has an unpredictable impact on willingness-to-pay estimates, since it impacts differently on the results for the two datasets we examine. Copyright (C) 2009 John Wiley & Sons, Ltd.