Daniel J. Phaneuf

Preferences for public lands management under competing uses: the case of Yellowstone National Park.

We examine snowmobile use conflict in Yellowstone National Park to assess the effect of different winter management policies on heterogeneous visitors’ welfare. Using a stated preference choice experiment we quantify welfare changes for snowmobile riders and non-riders under different snowmobile restrictions. A key determinant of welfare change is visitors’ willingness to trade-off reduced snowmobile access for improved ambient conditions in the park. Our findings support the notion that welfare losses to snowmobile riders could be offset by welfare gains to non-riders, but net benefits will depend on the number of riders and non-riders and the specifics of the policy. (JEL Q26, Q51)

Structural Benefit Transfer Using Bayesian Econometrics

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.

Chapter 12 Estimating the Demand for Quality with Discrete Choice Models

In this chapter, we describe how random utility maximization (RUM) discrete choice models are used to estimate the demand for commodity attributes in quality-differentiated goods. After presenting a conceptual overview, we focus specifically on the conditional logit model. We examine technical issues related to specification, interpretation, estimation, and policy use. We also discuss identification strategies for estimating the role of price and non-price attributes in preferences when product attributes are incompletely observed. We illustrate these concepts via a stylized application to new car purchases, in which our objective is to measure preferences for fuel economy.