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Employing a unique and rich data set of water quality attributes in conjunction with detailed household characteristics and trip information, we develop a mixed logit model of recreational lake usage. Our empirical analysis shows that individuals are responsive to the full set of water quality measures used by biologists to identify the impaired status of lakes. WTP estimates are reported based on improvements in these physical measures. This implies that cost benefit analysis based on water quality measures can be used as a direct policy tool. * The authors would like to thank participants in seminars at Resources for the Future, the University of Minnesota, and the Heartland Conference for helpful comments on earlier drafts of this paper. All remaining errors are, of course, our own. Funding for this project was provided by the Iowa Department of Natural Resources and the U.S. Environmental Protection Agency's Science to Achieve Results (STAR) program. Although the research described in the article has been funded in part by the U.S. Environmental Protection Agency's STAR program through grant R830818, it has not been subjected to any EPA review and therefore does not necessarily reflect the views of the Agency, and no official endorsement should be inferred. Valuing Water Quality as a Function of Water Quality Measures AbstractEmploying a unique and rich data set of water quality attributes in conjunction with detailed household characteristics and trip information, we develop a mixed logit model of recreational lake usage. Our empirical analysis shows that individuals are responsive to the full set of water quality measures used by biologists to identify the impaired status of lakes. WTP estimates are reported based on improvements in these physical measures. This implies that cost benefit analysis based on water quality measures can be used as a direct policy tool.

We offer an empirical test of a theoretical result in the contingent valuation literature. Specifically, it has been argued from a theoretical point of view that survey participants who perceive a survey to be "consequential" will respond to questions truthfully regardless of the degree of perceived consequentiality. Using survey data from the Iowa Lakes Project, we test this supposition. Specifically, we employ a Bayesian treatment effect model in which the degree of perceived consequentiality, measured as an ordinal response, is permitted to have a structural impact on willingness to pay (WTP) for a hypothetical environmental improvement. We test our theory by determining if the WTP distributions are the same for each value of the ordinal response.In our survey data, a subsample of individuals were randomly assigned supporting information suggesting that their responses to the questionnaires were important and will have an impact on policy decisions. In conjunction with a Bayesian posterior simulator, we use this source of exogenous variation to identify the structural impacts of consequentiality perceptions on willingness to pay, while controlling for the potential of confounding on unobservables. We find evidence consistent with the "knife-edge" theoretical results, namely that the willingness to pay distributions are equal among those believing the survey to be at least minimally consequential, and different for those believing that the survey is irrelevant for policy purposes.

The Kuhn-Tucker model of Wales and Woodland (1983) provides a utility theoretic framework for estimating preferences over commodities for which individuals choose not to consume one or more of the goods. Due to the complexity of the model, however, there have been few applications in the literature and little attention has been paid to the problems of welfare analysis within the Kuhn-Tucker framework. This paper provides an application of the model to the problem of recreation demand. In addition, we develop and apply a methodology for estimating compensating variation, relying on Monte Carlo integration to derive expected welfare changes. © 2000 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology

Random utility models (RUMs) are used in the literature to model consumer choices from among a discrete set of alternatives, and they typically impose a constant marginal utility of income on individual preferences. This assumption is driven partially by the difficulty of constructing welfare estimates in models with nonlinear income effects. Recently, McFadden (1995) developed an algorithm for computing these welfare impacts using a Monte Carlo Markov chain simulator for generalized extreme-value variates. This paper investigates the empirical consequences of nonlinear RUMs in the case of sportfishing modal choice, while refining and contrasting the available methods for welfare estimation. NONLINEAR INCOME EFFECTS IN RANDOM UTILITY MODELSJoseph A. Herriges and Catherine L. Kling* Abstract -Random utility models (RUMs) are used in the literature to model consumer choices from among a discrete set of alternatives, and they typically impose a constant marginal utility of income on individual preferences. This assumption is driven partially by the difficulty of constructing welfare estimates in models with nonlinear income effects. Recently, McFadden (1995) developed an algorithm for computing these welfare impacts using a Monte Carlo Markov chain simulator for generalized extreme-value variates. This paper investigates the empirical consequences of nonlinear RUMs in the case of sportfishing modal choice, while refining and contrasting the available methods for welfare estimation.

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A new approach is under way for the research agenda of environmental valuation. Rather than treating stated preference (SP) and revealed preference (RP) as competing valuation techniques, analysts have started to view them as complementary, where the strengths of each approach can be used to provide more precise and possibly more accurate benefit estimates. In this study, the authors reexamine the models and motives for combining SP and RP data. They also reconsider the interpretation of "consistent" or "inconsistent" findings of RP and SP data.

Repeated logit models are among the most commonly applied methods for modeling seasonal recreation demand. In this article we examine the capabilities of the repeated nested logit and repeated mixed logit models to capture patterns of error correlation and demand substitution. Particular attention is paid to the use of the mixed logit framework to generalize the strong assumptions on correlation patterns across sites and choice occasions imbedded in the nested logit model. We examine the implications for the range of price elasticities allowed in both models based on the implied correlation structures. Copyright 2002, Oxford University Press.