Estimating consumer surplus for recreational sea fishing using individual travel cost method

Ocean and Polar Research

Volumn 30, Issue 2, 2008, Pages 141-148

  Pyo, Hee Dong ^a   Park, Cheol Hyung ^a   Chung, Jin Ho ^a  

^a Pukyong National University   (South Korea)

Author keywords

Consumer surplus for recreational sea fishing; Count data; Individual travel cost method; Over dispersion; Truncated Negative Binomial Model (TNBM)

Indexed keywords

DISPERSION; ESTIMATION METHOD; FISHING; POISSON RATIO;

EID: 47949107919     PISSN: 1598141X     EISSN: None     Source Type: Journal    
DOI: 10.4217/OPR.2008.30.2.141     Document Type: Article

References (27)

1. Korean source.
2. Korean source.
3. Korean source.
4. Korean source.
5. Korean source.
6. Korean source.
7. Korean source.
8. Korean source.
9. Korean source.
10. Korean source.
11. Korean source.
12. Korean source.
13. Bergstrom, J.C. and H.K. Cordell. 1991. An Analysis of the demand for and value of outdoor recreation in the United States. J. Leis. Res., 23, 67-86.
14. Cameron, A.C. and P.K. Trivedi. 1986. Econometric models based on count data: Comparisons and applications of some estimators and tests. J. Appl. Econom., 1(1), 29-53.
15. Cameron, A.C. and P.K. Trivedi. 1998. Regression analysis of count data. Cambridge University Press, Cambridge. 411 p.
16. Creel, M. and J. Loomis. 1993. Theoretical and empirical advantages of truncated count data estimators for analysis of deer hunting in California. Am. J. Agr. Econ., 72(2), 434-441.
17. Curtis, J.A. 2002. Estimating the demand for salmon angling in Ireland. Econ. Social Review, 33(3), 319-332.
18. Hagerty, D. and K. Moeltner. 2005. Specification of driving costs in models of recreation demand. Land Econ., 81(1), 127-143.
19. Hellerstein, D. and R. Mendelsohn. 1993. A theoretical foundation for count data models. Ame. J. Agr. Econ., 75, 604-611.
20. Loomis, J.B., R. Rosenberger, and R.K. Sjrestha. 1999. Updated estimates of recreation values for the RPA program by assessment region and use of meta-analysis for recreation benefit transfer. Colorado State University, Final Report for the USDA Forest Services. 66 p.
21. O'Neil, M.F. and M.J. Faddy. 2003. Use of binary and truncated negative binomial modelling in the analysis of recreational catch data. Fish. Res., 60, 471-477.
22. Pradhan, N.C. and P. Leung. 2006. A Poisson and negative binomial regression model of sea turtle interaction in Hawaii's longline fishery. Fish. Res., 78, 309-322.
23. Shaw, D. 1988. On-site sample's regression: Problems of non-negative integers, truncation, and endogeneous selection. J. Econom., 37(2), 211-223.
24. Shrestha, R.K., A.F. Seidl, and A.S. Moraes. 2002. Value of recreational fishing in the Brazilian Pantanal: A travel cost analysis using count data models. Ecol. Econ., 42, 289-299.
25. Ward, F.A. and D.J. Beal. 2000. Valuing nature with travel cost models: A manual. Edgar Elgar, Cheltenham. 255 p.
26. Ward, F.A. and J.B. Loomis. 1986. The travel cost demand model as an environmental policy assessment tool: A review of literature. J Agr. Resource Econ., 11(2), 164-178.
27. Yen, S.T. and W.L. Adamowicz. 1993. Statistical properties of welfare measures from count-data models of recreation demand. Rev. Agr. Econ., 15(2), 203-215.