Model Uncertainty in Characterizing Recreation Demand

Abstract

A Bayesian variable selection procedure is used to control for uncertainty in the specification of a recreational demand model. Specifically, we propose a model that draws on the Bayesian paradigm to integrate the variable selection process into model estimation and to reflect the accompanying uncertainty about which is the best specification in subsequent counterfactual predictions. The advantage of this procedure over previous non-Bayesian approaches is that it overcomes the problem of pre-testing in specification searches. In our application, evaluating demand for recreational lake usage in Iowa, we find clear evidence that site attributes, such as lakes size, handicap facilities and wake restrictions, do impact lake usage. There is also evidence that water quality matters in household recreation choices. Indeed, contrary to Abidoye et al. (Am J Agricult Econ, 2012), in which only a single functional form is considered, we find clear evidence that water quality matters, with posterior probability of less that 10 % associated with a model without any water quality variables. This suggests that the flexibility that the Bayesian variable selection model affords in capturing the linkage between recreation demand and site characteristics can be important.