Journal of Forest Economics > Vol 20 > Issue 4

The effect of on-site forest experience on stated preferences for low-impact timber harvesting programs

Xiaoshu Li, Virginia Tech, Agricultural and Applied Economics, USA, xiaoshu@vt.edu Kevin J. Boyle, Virginia Tech, Agricultural and Applied Economics, USA, kjboyle@vt.edu Thomas P. Holmes, USDA Forest Service, Forestry Sciences Lab, Southern Research Station Research, USA, tholmes@fs.fed.us Genevieve Pullis LaRouche, U.S. Fish and Wildlife Service, Chesapeake Bay Field Office, USA, LaRouche@fws.gov
 
Suggested Citation
Xiaoshu Li, Kevin J. Boyle, Thomas P. Holmes and Genevieve Pullis LaRouche (2014), "The effect of on-site forest experience on stated preferences for low-impact timber harvesting programs", Journal of Forest Economics: Vol. 20: No. 4, pp 348-362. http://dx.doi.org/10.1016/j.jfe.2014.09.005

Published: 0/12/2014
© 0 2014 Xiaoshu Li, Kevin J. Boyle, Thomas P. Holmes, Genevieve Pullis LaRouche
 
Subjects
 
Keywords
Stated preferencesOn-site experienceLow impact forest managementBootstrap
 

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In this article:
Introduction
Previous literature
Study application and design
Model specification
Results
Conclusions and discussion

Abstract

An important issue in the design of stated-preference surveys is whether the information provided to respondents within a survey instrument is adequate to yield valid value estimates. Providing respondents with on-site experience about forest ecosystem management alternatives may influence their expectation of the effects from new policies and programs. In the research reported here, we investigate whether preference parameters for attributes of low-impact timber harvesting programs differ between respondents to a mail survey versus respondents provided with an on-site forest experience (walk through a research forest). The empirical analysis in our application shows that stated preferences for timber harvesting attributes are not statistically different between the mail and on-site applications of the survey, and this result is robust to pretest (before experience) and post-test (post experience) applications.

DOI:10.1016/j.jfe.2014.09.005