“Estimating Recreation Benefits through Joint Estimation of Revealed and Stated Preference Discrete Choice Data”
John Whitehead, Professor, Department of Economics at Appalachian State University, will present “Estimating Recreation Benefits through Joint Estimation of Revealed and Stated Preference Discrete Choice Data” on Friday, October 20th from 10:30am-12:00pm in Room 250A, Agricultural Administration (2120 Fyffe Road, Columbus, OH 43210).
Abstract: We develop econometric models to jointly estimate revealed preference (RP) and stated preference (SP) models of recreational fishing behavior and preferences using survey data from the 2007 Alaska Saltwater Sportfishing Economic Survey. The RP data are from site choice survey questions, and the SP data are from a discrete choice experiment. Random utility models using only the RP data may be more likely to estimate the effect of cost on site selection well, but catch per day estimates may not reflect the benefits of the trip as perceived by anglers. The SP models may be more likely to estimate the effects of trip characteristics well, but less attention may be paid to the cost variable due to the hypothetical nature of the SP questions. The combination and joint estimation of RP and SP data seeks to exploit the contrasting strengths of both. We find that there are significant gains in econometric efficiency, and differences between RP and SP willingness to pay estimates are mitigated by joint estimation. We compare a number of models that have appeared in the environmental economics literature with the generalized multinomial logit model. The nested logit “trick” model fails to account for the panel nature of the data and is less preferred to the mixed logit error components model that accounts for panel data and scale differences. Naïve (1) scaled, (2) mixed logit, and (3) generalized multinomial logit models produced similar results to a generalized multinomial logit model that accounts for scale differences in RP and SP data. Willingness to pay estimates do not differ across these models but are greater than those in the mixed logit error components model. (Co-authored by Daniel K. Lew)