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The Role of Water Quality Perceptions in Modeling Lake Recreation Demand

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  • Jeon, Yongsik
  • Herriges, Joseph A.
  • Kling, Catherine L.
  • Downing, John A.

Abstract

Recreation demand models typically incorporate measures of the physical attributes of recreational sites; e.g., Secchi depth or phosphorous levels in case of water quality. Moreover, most studies show that individuals do respond to these physical characteristics in choosing where to recreate. However, the question remains as to whether the available physical measures accurately capture individual perceptions of water quality and if there is a additional role to be played by elicited perception measures in modeling recreation demand. In this paper, we use data from the 2004 Iowa Lakes Survey to model recreation demand as a function of both the physical water quality at 131 lakes in the state and household perceptions of lake water quality. In general, water quality perceptions are correlated with the available physical measures, but not perfectly so, and both actual and perceived water quality are found to significantly impact recreational site choice.

Suggested Citation

  • Jeon, Yongsik & Herriges, Joseph A. & Kling, Catherine L. & Downing, John A., 2005. "The Role of Water Quality Perceptions in Modeling Lake Recreation Demand," Staff General Research Papers Archive 12474, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genres:12474
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    File URL: http://www2.econ.iastate.edu/papers/p3840-2005-11-14.pdf
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    References listed on IDEAS

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    1. Daniel J. Phaneuf & Catherine L. Kling & Joseph A. Herriges, 2000. "Estimation and Welfare Calculations in a Generalized Corner Solution Model with an Application to Recreation Demand," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 83-92, February.
    2. Adamowicz, Wiktor & Swait, Joffre & Boxall, Peter & Louviere, Jordan & Williams, Michael, 1997. "Perceptions versus Objective Measures of Environmental Quality in Combined Revealed and Stated Preference Models of Environmental Valuation," Journal of Environmental Economics and Management, Elsevier, vol. 32(1), pages 65-84, January.
    3. Joseph A. Herriges & Daniel J. Phaneuf, 2002. "Inducing Patterns of Correlation and Substitution in Repeated Logit Models of Recreation Demand," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(4), pages 1076-1090.
    4. Egan, Kevin J. & Herriges, Joseph A. & Kling, Catherine L. & Downing, John A., 2004. "Recreation Demand Using Physical Measures Of Water Quality," 2004 Annual meeting, August 1-4, Denver, CO 19958, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    5. Herriges, Joseph A. & Kling, Catherine L., 2003. "Recreation Demand Models," Staff General Research Papers Archive 10211, Iowa State University, Department of Economics.
    6. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
    7. Joseph A. Herriges & Catherine L. Kling (ed.), 1999. "Valuing Recreation and the Environment," Books, Edward Elgar Publishing, number 1315.
    8. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
    9. Kenneth E. Train, 1998. "Recreation Demand Models with Taste Differences over People," Land Economics, University of Wisconsin Press, vol. 74(2), pages 230-239.
    10. John A. Downing, 2009. "Valuing Water Quality as a Function of Water Quality Measures," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 91(1), pages 106-123.
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    Cited by:

    1. Christine Bertram & Heini Ahtiainen & Jürgen Meyerhoff & Kristine Pakalniete & Eija Pouta & Katrin Rehdanz, 2020. "Contingent Behavior and Asymmetric Preferences for Baltic Sea Coastal Recreation," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 75(1), pages 49-78, January.
    2. Walsh Patrick J. & Wheeler William J., 2013. "Water quality indices and benefit-cost analysis," Journal of Benefit-Cost Analysis, De Gruyter, vol. 4(1), pages 81-105, March.
    3. David A Keiser & Joseph S Shapiro, 2019. "Consequences of the Clean Water Act and the Demand for Water Quality," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(1), pages 349-396.
    4. Okumah, Murat & Yeboah, Ata Senior & Bonyah, Sylvester Kwaku, 2020. "What matters most? Stakeholders’ perceptions of river water quality," Land Use Policy, Elsevier, vol. 99(C).
    5. Sarah Nicholls & John Crompton, 2018. "A Comprehensive Review of the Evidence of the Impact of Surface Water Quality on Property Values," Sustainability, MDPI, vol. 10(2), pages 1-30, February.
    6. Deely, J. & Hynes, S. & Curtis, J., 2019. "Are objective data a suitable replacement for subjective data in site choice analysis?," Working Papers 309602, National University of Ireland, Galway, Socio-Economic Marine Research Unit.
    7. Marina Farr & Natalie Stoeckl & Michelle Esparon & Silva Larson & Diane Jarvis, 2016. "The Importance of Water Clarity to Great Barrier Reef Tourists and Their Willingness to Pay to Improve it," Tourism Economics, , vol. 22(2), pages 331-352, April.
    8. David A. Keiser & Joseph K. Shapiro, 2018. "Consequences of the Clean Water Act and the Demand for Water Quality," Center for Agricultural and Rural Development (CARD) Publications 17-wp571, Center for Agricultural and Rural Development (CARD) at Iowa State University.

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    JEL classification:

    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General

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