Following the success of the LEEPin2019 conference, and given the current situation, we have decided to move our seminar series online. The LEEPout series is designed as a platform for hosting a line-up of internationally recognised researchers to showcase their work at the cutting edge of environmental and resource economics. The LEEP Institute therefore invites you to the latest webinar in this series:
John Whitehead – Joint Estimation of Revealed Pref. Site Selection & Stated Pref. Choice Experiment Recreation Data Considering Attribute Non-Attendance (with Paul Hindsley, Craig E. Landry, Kurt Schnier)
Online event details
Date: Thursday 20 May 2021
Time: 16:00-17:00 BST (17:00-18:00 Central Europe,11:00-12:00 Eastern; 08:00-09:00 Pacific)
Location: This webinar series is hosted online, a link with joining instructions will be sent within the confirmation of registration email.
Biography
John C. Whitehead is professor in Environmental Economics at the Department of Economics at Appalachian State University. His research includes nonmarket valuation - in the context of recreation, water quality and sports – primarily combining revealed and stated preference data. He has published over 100 peer reviewed articles in journals including the American Journal of Agricultural Economics, Journal of Environmental Economics and Management, and Land Economics.
Abstract
We estimate recreation demand models with revealed preference (RP) site selection and stated preference (SP) discrete choice experiment marine fishing data. Catch rate information in the RP data are often thin, and use of SP scenarios for changes in catch can improve variation while minimizing multicollinearity. The SP data can suffer from hypothetical bias, which often manifests itself as bias in the cost coefficient. We combine data from the Marine Recreational Information Program (MRIP) creel survey with SP survey data from 2003/04. There are eight SP trip decisions and one RP trip decision for each of 1928 anglers who provided enough information to be included in the data. A joint RP-SP generalized multinomial logit model is estimated. We find that the SP travel cost coefficient is five times lower than the RP travel cost coefficient in absolute value, suggesting hypothetical bias in the SP data. This difference is reflected in the willingness to pay estimates, where the SP estimates for improved catch are five times larger than the RP estimates. Attribute non-attendance (ANA) arises when survey respondents ignore choice experiment attributes. We use inferred ANA methods to identify respondents who may be ignoring the SP cost variable. The generalized multinomial logit model accounting for ANA is statistically preferred. The SP cost coefficient accounting for ANA is 164% larger in absolute value than the SP coefficient from the model that does not account for ANA. The ANA model indicates much more consistency between the RP and SP data. The smaller difference in the travel cost coefficients is also reflected in the willingness to pay estimates.
Registration
Please register using the ‘register’ link on this page. If you wish to register without using Eventbrite, or if you have any questions regarding the event please contact LEEP@exeter.ac.uk
We may take photographs and/or record this event which may later be shared publicly.
Accessing the event
The Zoom link to access the event will be included on the ticket confirmation page and follow up email sent directly after registering.
Data Protection and Privacy
- To aid organisation of this event, attendee information may be shared with relevant staff in the University e.g. a list of attendees could be provided to the University host, guest speaker.
- We will store your information securely, so that we can communicate to you about the event (e.g. cancellation of event, changes to the itinerary, a satisfaction survey).
- We delete our access to personal information on Eventbrite after 6 months.
- More information on the University’s privacy policies can be found here.
- For more information on Eventbrite’s own privacy policy, please visit this page.
