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Preference Heterogeneity in a Count Data Model of Demand for Off-Highway Vehicle Recreation

Published online by Cambridge University Press:  15 September 2016

Thomas P. Holmes
Affiliation:
Research Forest Economist with the Southern Research Station of the USDA Forest Service in Research Triangle Park, North Carolina
Jeffrey E. Englin
Affiliation:
Department of Resource Economics at the University of Nevada in Reno, Nevada
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Abstract

This paper examines heterogeneity in the preferences for OHV recreation by applying the random parameters Poisson model to a data set of off-highway vehicle (OHV) users at four National Forest sites in North Carolina. The analysis develops estimates of individual consumer surplus and finds that estimates are systematically affected by the random parameter specification. There is also substantial evidence that accounting for individual heterogeneity improves the statistical fit of the models and provides a more informative description of OHV riders.

Type
Contributed Papers
Copyright
Copyright © 2010 Northeastern Agricultural and Resource Economics Association 

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References

Bergstrom, J.C., and Cordell, H.K. 1991. “An Analysis of the Demand for and Value of Outdoor Recreation in the United States.” Journal of Leisure Research 23(1): 6786.CrossRefGoogle Scholar
Bowker, J.M., Miles, M.P., and Randall, E.J. 1997. “A Demand Analysis of Off-Road Motorized Recreation.” In Proceedings of Association of Marketing Theory and Practice, Expanding Marketing Horizons into the 21st Century, pp. 387391.Google Scholar
Cameron, A.C., and Trivedi, P. 1986. “Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators and Tests.” Journal of Applied Econometrics 1(1): 2953.CrossRefGoogle Scholar
Cordell, H.K., Betz, C.J., Green, G., and Owens, M. 2005. “Off-Highway Vehicle Recreation in the United States: A National Report from the National Survey on Recreation and the Environment.” USDA Forest Service, Southern Research Station, Asheville, NC.Google Scholar
Creel, M., and Loomis, J. 1990. “Theoretical and Empirical Advantages of Truncated Count Data Estimators for Analysis of Deer Hunting in California.” American Journal of Agricultural Economics 72(2): 434441.CrossRefGoogle Scholar
Deisenroth, D., Loomis, J., and Bond, C. 2009. “Non-Market Valuation of Off-Highway Vehicle Recreation in Larimer County, Colorado: Implications of Trail Closures.” Journal of Environmental Management 90(11): 34903497.CrossRefGoogle Scholar
Englin, J., Holmes, T., and Niell, R. 2006. “Alternative Models of Recreational Off-Highway Vehicle Site Demand.” Environmental and Resource Economics 35(4): 327338.CrossRefGoogle Scholar
Englin, J., Holmes, T., and Sills, E. 2003. “Estimating Forest Recreation Demand Using Count Data Models.” In Sills, E.O. and Abt, K.L., eds., Forests in a Market Economy. Dordrecht, The Netherlands: Kluwer Academic Publishers.Google Scholar
Foulke, T., Bastian, C.T., Taylor, D.T., Coupal, R.H., and Olson, D. 2008. “Off-Road Vehicle Recreation in the West: Implications of a Wyoming Analysis.” Western Economics Forum 7(2): 111.Google Scholar
Gouriéroux, C., and Monfort, A. 1996. Simulation-Based Econometric Methods. Oxford, UK: Oxford University Press.Google Scholar
Gouriéroux, C., and Monfort, A. 1991. “Simulation Based Inference in Models with Heterogeneity.” Annales D'Économie et de Statistique 20/21: 70107.Google Scholar
Havlik, D.G. 2002. No Place Distant: Roads and Motorized Recreation on America's Public Lands. Washington, DC: Island Press.Google Scholar
Jakus, P.M., Keith, J.E., and Liu, L. 2008. “Economic Impacts of Land Use Restrictions on OHV Recreation in Utah.” Report for the Utah Governor's Public Lands Policy Coordination Office. Department of Applied Economics, Utah State University, Logan, UT.Google Scholar
Loomis, J. 2006. “Estimating Recreation Use, Expenditures and Economic Benefits at Little Snake River Resource Area Using Visitor Data and Travel Cost Method.” Department of Agricultural Economics, Colorado State University, Fort Collins, CO. Google Scholar
Mullahy, J. 1997. “Heterogeneity, Excess Zeros, and the Structure of Count Data Models.” Journal of Applied Econometrics 12(3): 337350.3.0.CO;2-G>CrossRefGoogle Scholar
Priskin, J. 2003. “Physical Impacts of Four-Wheel Drive Related Tourism and Recreation in Semi-Arid, Natural Coastal Environment.” Ocean and Coastal Management 46(1-2): 127155.CrossRefGoogle Scholar
Silberman, J., and Andereck, K.L. 2006. “The Economic Value of Off-Highway Vehicle Recreation.” Journal of Leisure Research 38(2): 208223.CrossRefGoogle Scholar
Snyder, S.A., and Smail, R.A. 2009. “Are All-Terrain Vehicle Riders Willing to Pay Trail User Fees to Ride on Public Land in the USA?Tourism Economics 15(2): 437451.CrossRefGoogle Scholar
Train, K.E. 2003. Discrete Choice Methods with Simulation. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Wedel, M., Desarbo, W.S., Bult, J.R., and Ramaswamy, V. 1993. “A Latent Class Poisson Regression Model for Heterogeneous Count Data.” Journal of Applied Econometrics 8(4): 397411.CrossRefGoogle Scholar