Examining angler behavior using contingent behavior modeling: A case study of water quality change at a Wisconsin lake

Water Resources Research

Volumn 44, Issue 11, 2008, Pages

  Eiswerth, Mark E ^a   Kashian, Russell D ^a   Skidmore, Mark ^b  

^a UNIVERSITY OF WISCONSIN WHITEWATER   (United States)

^b MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ELECTRIC CIRCUIT BREAKERS; LAKES; MATHEMATICAL MODELS; WATER ANALYSIS; WATER POLLUTION; WATER QUALITY;

BEHAVIOR MODELING; CASE STUDIES; CONSUMER SURPLUSES; NEGATIVE BINOMIALS; POTENTIAL IMPACTS; QUALITY ATTRIBUTES; QUALITY CHANGES; REVEALED PREFERENCES; TRAVEL COSTS; WATER CLARITIES; WISCONSIN;

HYDRAULIC MODELS;

ANGLING; ANTHROPOGENIC EFFECT; CONTINGENT VALUATION; RECREATIONAL FACILITY; WATER QUALITY;

NORTH AMERICA; UNITED STATES; WISCONSIN; WISCONSIN LAKE;

EID: 58149096375     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2006WR005828     Document Type: Article

References (38)

1. Adamowicz, W., J. Louviere, and M. Williams (1994), Combining revealed and stated preference methods for valuing environmental amenities, J. Environ. Econ. Manage., 26, 271-292, doi:10.1006/jeem.1994.1017.
2. Bhat, M. G. (2003), Application of non-market valuation to the Florida Keys marine reserve management, J. Environ. Manage., 67, 315-325, doi:10.1016/S0301-4797(02)00207-4.
3. Bowker, J. M., and V. R. Leeworthy (1998), Accounting for ethnicity in recreation demand: A flexible count data approach, J. Leisure Res., 30, 64-78.
4. Cameron, T. A. (1992), Combining contingent valuation and travel cost data for the valuation of non-market goods, Land Econ., 68, 302-317, doi:10.2307/3146378.
5. Cameron, T. A., W. D. Shaw, S. Ragland, J. Callaway, and S. Keefe (1996), Using actual and contingent behavior data with differing levels of time aggregation to model recreation demand, J. Agric. Resour. Econ., 21, 130-149.
6. Cameron, T. A., W. D. Shaw, and S. Ragland (1999), Nonresponse bias in mail survey data: Salience vs. endogenous survey complexity, in Valuing Recreation and the Environment: Revealed Preference Methods in Theory and Practice, edited by J. A. Herriges and C. L. Kling, chap. 8, pp. 217-251, Edward Elgar, Cheltenham, U.K.
7. Cesario, F. J., and J. L. Knetsch (1970), Time bias in recreation benefits studies, Water Resour. Res., 6(3), 700-704, doi:10.1029/ WR006i003p00700.
8. Creel, M., and J. Loomis (1990), Theoretical and empirical advantages of truncated count data estimators for analysis of deer hunting in California, Am. J. Agric. Econ., 72(2), 434-441, doi:10.2307/1242345.
9. Creel, M., and J. Loomis (1992), Recreation value of water to wetlands in the San Joaquin Valley: Linked multinomial logit and count data trip frequency models, Water Resour. Res., 28(10), 2597-2606, doi:10.1029/92WR01514.
10. Delavan Lake Sanitary District (2004), Annual report of the Delavan Lake Sanitary District, Delavan, WI, 36 pp., Delavan, Wis., May.
11. Dobbs, I. M. (1993), Adjusting for sample selection bias in the individual travel cost method, J. Agric. Econ., 44, 335-342.
12. Earnhart, D., and V. Smith (2003), Countervailing effects of atrazine on water recreation: How do recreators evaluate them?, Water Resour. Res., 39(4), 1081, doi:10.1029/2002WR001598.
13. Eiswerth, M. E., J. Englin, E. Fadali, and W. D. Shaw (2000), The value of water levels in water-based recreation: A pooled revealed preference/contingent behavior model, Water Resour. Res., 36(4), 1079-1086, doi:10.1029/1999WR900332.
14. Eiswerth, M. E., R. Kashian, and M. Skidmore (2005), What is the value of a clean and healthy lake to a local community?, final report, 80 pp., Fiscal and Econ. Res. Cent., Univ. of Wis.-Whitewater, Whitewater, Wis., Feb.
15. Englin, J., and T. A. Cameron (1996), Augmenting travel cost models with contingent behavior data, Environ. Resour. Econ., 7, 133-147, doi:10.1007/BF00699288.
16. Englin, J., and J. S. Shonkwiler (1995), Estimating social welfare using count data models: An application to long-run recreation demand under conditions of endogenous stratification and truncation, Rev. Econ. Stat., 77, 104-112, doi:10.2307/2109996.
17. Englin, J., P. Boxall, and D. Watson (1998), Modeling recreation demand in a Poisson system of equations: An analysis of the impact of international exchange rates, Am. J. Agric. Econ., 80, 255-263, doi:10.2307/1244498.
18. Feather, P., and W. D. Shaw (1999), Estimating the cost of leisure time for recreation demand models, J. Environ. Econ. Manage., 38, 49-65, doi:10.1006/jeem.1999.1076.
19. Fletcher, J. J., W. L. Adamowicz, and T. Graham-Tomasi (1990), The travel cost model of recreation demand: Theoretical and empirical issues, Leisure Sci., 12, 119-147.
20. Hausman, J., B. Hall, and Z. Griliches (1984), Econometric models for count data with an application to the patents-R&D relationship, Econometrica, 52, 909-938, doi:10.2307/1911191.
21. Hellerstein, D. (1991), Using count data models in travel cost analysis with aggregate data, Am. J. Agric. Econ., 73, 860-867, doi:10.2307/1242838.
22. Hellerstein, D., and R. Mendelsohn (1993), A theoretical foundation for count data models, Am. J. Agric. Econ., 75, 604-611, doi:10.2307/1243567.
23. Huszar, E., W. D. Shaw, J. Englin, and N. Netusil (1999), Recreational damages from reservoir storage level changes, Water Resour. Res., 35(11), 3489-3494, doi:10.1029/1999WR900235.
24. Kashian, R., M. E. Eiswerth, and M. Skidmore (2006), Lake rehabilitation and the value of shoreline real estate: Evidence from Delavan, Wisconsin, Rev. Reg. Stud., 36(2), 221-238.
25. Kling, C. L. (1997), The gains from combining travel cost and contingent valuation data to value nonmarket goods, Land Econ., 73(3), 428-439, doi:10.2307/3147177.
26. Loomis, J. (1993), An investigation into the reliability of intended visitation behavior, Environ. Resour. Econ., 3, 183-191, doi:10.1007/BF00338784.
27. Loomis, J. (2002), Quantifying recreation use values from removing dams and restoring free-flowing rivers: A contingent behavior travel cost demand model for the Lower Snake River, Water Resour. Res., 38(6), 1066, doi:10.1029/2000WR000136.
28. Mathews, L. G., F. R. Homans, and K. W. Easter (2002), Estimating the benefits of phosphorus pollution reductions: An application in the Minnesota River, J. Am. Water Resour. Assoc., 38, 1217-1223, doi:10.1111/j.1752- 1688.2002.tb04343.x.
29. Ovaskainen, V., J. Mikkola, and E. Pouta (2001), Estimating recreation demand with on-site data: An application of truncated and endogenously stratified count data models, J. For. Econ., 7, 125-144.
30. Pearce, D., D. Whittington, S. Georgiou, D. Moran, and N. Hadker (1998), Economic values and the environment in the developing world, Environ. Econ. Ser. Pap. 14, U.N. Environ. Programme, Nairobi.
31. Robertson, D. M., G. L. Goddard, D. R. Helsel, and K. L. MacKinnon (2000), Rehabilitation of Delavan Lake, Wisconsin, Lake Reservoir Manage., 16(3), 155-176.
32. Shaw, D. (1988), On site samples' regression: Problems of non-negative integers, truncation and endogenous stratification, J. Econometrics, 37, 211-223, doi:10.1016/0304-4076(88)90003-6.
33. Shonkwiler, J. S., and W. D. Shaw (1996), Hurdle count-data models in recreation demand analysis, J. Agric. Resour. Econ., 21, 210-219.
34. Smith, V. K., and W. H. Desvousges (1985), The generalized travel cost model and water quality benefits: A reconsideration, South. Econ. J., 52, 371-381, doi:10.2307/1059623.
35. Southeastern Wisconsin Regional Planning Commission (2002), A lake management plan for Delavan Lake: Walworth County, Wisconsin, Community Assistance Plann. Rep. 253, 267 pp., Waukesha, Wis., May.
36. Ward, F. (1987), Economics of water allocation to instream uses in a fully appropriated river basin: Evidence from a New Mexico wild river, Water Resour. Res., 23(3), 381-392, doi:10.1029/WR023i003p00381.
37. White, H. (1982), Maximum likelihood estimation of misspecified models, Econometrica, 50(1), 1-25, doi:10.2307/1912526.
38. Whitehead, J. C., T. C. Haab, and J. Huang (2000), Measuring recreation benefits of quality improvements with revealed and stated behavior data, Resour. Energy Econ., 22, 339-354, doi:10.1016/S0928-7655(00)00023-3.