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The Value of Changes in Deer Season Length: An Application of the Nested Multinomial Logit Model

Author

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  • Kurt Schwabe

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  • Peter Schuhmann
  • Roy Boyd
  • Khosrow Doroodian

Abstract

Increasing deer populations can be controlled through manipulatingharvest limits or season length. While such actions often result in benefitsto hunters, both motorists and the agricultural sector also benefit as alower deer population leads to fewer incidences of harmful human-deerencounters. Traditional recreation demand models are often employed toexamine the welfare implications of changes in daily hunting bag limits.Studies measuring the effects of changes in season length, however, arenoticeably absent from the literature. This study uses a nested randomutility model to examine hunter choice over site and season selection toderive the values of changes in season length. Copyright Kluwer Academic Publishers 2001

Suggested Citation

  • Kurt Schwabe & Peter Schuhmann & Roy Boyd & Khosrow Doroodian, 2001. "The Value of Changes in Deer Season Length: An Application of the Nested Multinomial Logit Model," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 19(2), pages 131-147, June.
  • Handle: RePEc:kap:enreec:v:19:y:2001:i:2:p:131-147
    DOI: 10.1023/A:1011121503549
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    References listed on IDEAS

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    1. Joseph A. Herriges & Catherine L. Kling, 1997. "The Performance of Nested Logit Models When Welfare Estimation Is the Goal," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(3), pages 792-802.
    2. Yoshiaki Kaoru & V. Kerry Smith & Jin Long Liu, 1995. "Using Random Utility Models to Estimate the Recreational Value of Estuarine Resources," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(1), pages 141-151.
    3. Hausman, Jerry A. & Leonard, Gregory K. & McFadden, Daniel, 1995. "A utility-consistent, combined discrete choice and count data model Assessing recreational use losses due to natural resource damage," Journal of Public Economics, Elsevier, vol. 56(1), pages 1-30, January.
    4. Herriges, Joseph A. & Kling, Catherine L., 1996. "Testing the consistency of nested logit models with utility maximization," Economics Letters, Elsevier, vol. 50(1), pages 33-39, January.
    5. Kling, Catherine L. & Bockstael, Nancy & Hanemann, W. Michael, 1987. "Estimating the Value of Water Quality Improvements in a Recreational Demand Framework," Staff General Research Papers Archive 1594, Iowa State University, Department of Economics.
    6. Parsons George R. & Kealy Mary Jo, 1995. "A Demand Theory for Number of Trips in a Random Utility Model of Recreation," Journal of Environmental Economics and Management, Elsevier, vol. 29(3), pages 357-367, November.
    7. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    8. Joseph Cooper, 1993. "A bioeconomic model for estimating the optimal level of deer and tag sales," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 3(6), pages 563-579, December.
    9. Herriges, Joseph A. & Kling, Catherine L., 1999. "Valuing Recreation and the Environment: Revealed Preference Methods in Theory and Practice, New Horizons in Environmental Economics," Staff General Research Papers Archive 12330, Iowa State University, Department of Economics.
    10. Catherine L. Kling & Cynthia J. Thomson, 1996. "The Implications of Model Specification for Welfare Estimation in Nested Logit Models," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(1), pages 103-114.
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    12. Borsch-Supan, Axel, 1990. "On the compatibility of nested logit models with utility maximization," Journal of Econometrics, Elsevier, vol. 43(3), pages 373-388, March.
    13. Herriges, Joseph A. & Kling, Catherine L., 2003. "Recreation Demand Models," Staff General Research Papers Archive 10211, Iowa State University, Department of Economics.
    14. Keith, John E. & Lyon, Kenneth S., 1985. "Valuing Wildlife Management: A Utah Deer Herd," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 10(02), December.
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    16. Herriges, Joseph A. & Kling, Catherine L. & Phaneuf, Daniel J., 1999. "Corner Solution Models of Recreation Demand: A Comparison of Competing Frameworks," Staff General Research Papers Archive 1513, Iowa State University, Department of Economics.
    17. Daniel McFadden, 1977. "Quantitative Methods for Analyzing Travel Behaviour of Individuals: Some Recent Developments," Cowles Foundation Discussion Papers 474, Cowles Foundation for Research in Economics, Yale University.
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    Citations

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    Cited by:

    1. Helen Scarborough & Jeff Bennett, 2012. "Cost–Benefit Analysis and Distributional Preferences," Books, Edward Elgar Publishing, number 14376.
    2. Batabyal, Amitrajeet A., 2002. "On temporal controls and the stochastic behaviour of renewable natural resources," Resources Policy, Elsevier, vol. 28(1-2), pages 7-12.
    3. repec:eee:foreco:v:28:y:2017:i:c:p:12-17 is not listed on IDEAS
    4. Mogas, Joan & Riera, Pere & Bennett, Jeff, 2006. "A comparison of contingent valuation and choice modelling with second-order interactions," Journal of Forest Economics, Elsevier, vol. 12(1), pages 5-30, March.
    5. Phaneuf, Daniel J. & Smith, V. Kerry, 2006. "Recreation Demand Models," Handbook of Environmental Economics,in: K. G. Mäler & J. R. Vincent (ed.), Handbook of Environmental Economics, edition 1, volume 2, chapter 15, pages 671-761 Elsevier.
    6. Knoche, Scott & Lupi, Frank, 2007. "Valuing deer hunting ecosystem services from farm landscapes," Ecological Economics, Elsevier, vol. 64(2), pages 313-320, December.

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