Abstract
In many instances, applying benefit transfer can be interpreted as an inherently Bayesian process. It typically requires the analyst to form beliefs (priors ) about the values of interest, using evidence from the literature, and then update these beliefs with specific information about the policy site of interest. The analyst’s benefit predictions are then based on this updated summary. Despite this methodological connection, relatively few benefit transfer studies have employed the Bayesian paradigm. In this chapter we describe a Bayesian approach using a structural benefit transfer model, meaning we use prior information and locally available data to estimate the parameters of a defined preference function. We demonstrate the approach through a recreation site choice application, which is based on (a) a prior distribution on marginal WTP for the recreation site attribute of interest (beach width ); (b) a small amount of policy site choice micro data ; and (c) an estimate of the aggregate proportion of times each alternative in the choice set is selected. Based on this experience, we conclude with observations regarding the advantages and challenges associated with the Bayesian approach.
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Notes
- 1.
More precisely, Eq. (23.9) is implied by the first order conditions that maximize the likelihood function when J − 1 unique alternative specific constants are included in the specification.
- 2.
In some applications it may make sense to use a qualitative prior for α that assigns zero probability to negative values (i.e., values that result in positive price effects) and uniform non-zero probability for all positive values. This approach is fairly easy to accommodate in our framework.
- 3.
This involves drawing candidate values \( \tilde{\alpha }^{t} \) and \( \tilde{\omega }_{z}^{t} \) and then evaluating the likelihood of the candidates relative to \( \alpha^{t - 1} \) and \( \omega_{z}^{t - 1} . \) For this comparison is also necessary to compute \( \tilde{\delta }_{1}^{t} , \ldots ,\tilde{\delta }_{J}^{t} \) at the candidate values \( \tilde{\alpha }^{t} \) and \( \tilde{\omega }_{z}^{t} \) and use them in the comparison step.
- 4.
Assuming $3 per gallon for gas, 20 miles per gallon average gas mileage, and $0.25 per mile for depreciation cost.
- 5.
This value cannot be interpreted as a maximum likelihood estimate with known properties, given the small sample. It does, however, provide a useful initial value that is based on the probability structure of the model.
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Acknowledgments
This work was funded by a grant from the U.S. Environmental Protection Agency (No. RD-83346101).
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Phaneuf, D.J., Van Houtven, G. (2015). Structural Benefit Transfer Using Bayesian Econometrics. In: Johnston, R., Rolfe, J., Rosenberger, R., Brouwer, R. (eds) Benefit Transfer of Environmental and Resource Values. The Economics of Non-Market Goods and Resources, vol 14. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9930-0_23
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