Table of Contents

Handbook on Contingent Valuation

Handbook on Contingent Valuation

Elgar original reference

Edited by Anna Alberini and James R. Kahn

The Handbook on Contingent Valuation is unique in that it focuses on contingent valuation as a method for evaluating environmental change. It examines econometric issues, conceptual underpinnings, implementation issues as well as alternatives to contingent valuation. Anna Alberini and James Kahn have compiled a comprehensive and original reference volume containing invaluable case studies that demonstrate the implementation of contingent valuation in a wide variety of applications. Chapters include those on the history of contingent valuation, a practical guide to its implementation, the use of experimental approaches, an ecological economics perspective on contingent valuation and approaches for developing nations.

Chapter 10: Modelling Behaviour in Dichotomous Choice with Bayesian Methods

Carmelo J. León and Roberto León

Subjects: economics and finance, environmental economics, valuation, environment, environmental economics, valuation


Carmelo J. León1 and Roberto León2 10.1 Introduction Contingent valuation (CV) aims at valuing public or environmental goods by relying on cross-section data from a sample of individuals. The essential variables to be elicited are the willingness to pay (WTP) for the commodity and the effect of covariates which may explain individual variation. In this chapter we discuss the use of Bayesian techniques in contingent valuation. The econometric analysis of contingent valuation data sets has evolved in parallel with the advance in elicitation techniques. For instance, mean willingness to pay from the dichotomous choice model may turn out to be a non-linear function of the parameters estimated from a survival distribution. The Bayesian approach to inference differs from the classical approach in that the likelihood function of observed data is combined with some prior information on the parameters of interest, in order to derive a posterior probability measure. Thus, the parameters to be estimated are always conditional on the observed data, and can be revised as new data comes out, whereas in classical methods the data are supposed to be the result of a probability measure determined by some population parameters. Bayesian methods can be introduced into the econometric analysis of any models for which the researcher is willing to specify a prior describing his/her beliefs about the parameters of interest before data are actually collected. In this sense, there are Bayesian models for the linear regression, logit, and probit models, which can be applied to...

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