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Addressing onsite sampling in recreation site choice models

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  • Hindsley, Paul
  • Landry, Craig E.
  • Gentner, Brad

Abstract

Independent experts and politicians have criticized statistical analyses of recreation behavior, which rely upon onsite samples due to their potential for biased inference. The use of onsite sampling usually reflects data or budgetary constraints, but can lead to two primary forms of bias in site choice models. First, the strategy entails sampling site choices rather than sampling individuals--a form of bias called endogenous stratification. Under these conditions, sample choices may not reflect the site choices of the true population. Second, exogenous attributes of the individuals sampled onsite may differ from the attributes of individuals in the population--the most common form in recreation demand is avidity bias. We propose addressing these biases by combining two the existing methods: Weighted Exogenous Stratification Maximum Likelihood estimation and propensity score estimation. We use the National Marine Fisheries Service's Marine Recreational Fishing Statistics Survey to illustrate methods of bias reduction, employing both simulated and empirical applications. We find that propensity score based weights can significantly reduce bias in estimation. Our results indicate that failure to account for these biases can overstate anglers' willingness to pay for improvements in fishing catch, but weighted models exhibit higher variance of parameter estimates and willingness to pay.

Suggested Citation

  • Hindsley, Paul & Landry, Craig E. & Gentner, Brad, 2011. "Addressing onsite sampling in recreation site choice models," Journal of Environmental Economics and Management, Elsevier, vol. 62(1), pages 95-110, July.
  • Handle: RePEc:eee:jeeman:v:62:y:2011:i:1:p:95-110
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    References listed on IDEAS

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    Cited by:

    1. Craig E. Landry & Alyson R. Lewis & Haiyong Liu & Hans Vogelsong, 2016. "Addressing Onsite Sampling in Analysis of Recreation Demand: Economic Value and Impact of Visitation to Cape Hatteras National Seashore," Marine Resource Economics, University of Chicago Press, vol. 31(3), pages 301-322.
    2. Koichi Kuriyama & James Hilger & Michael Hanemann, 2013. "A Random Parameter Model with Onsite Sampling for Recreation Site Choice: An Application to Southern California Shoreline Sportfishing," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 56(4), pages 481-497, December.
    3. Grilli, Gianluca & Curtis, John & Hynes, Stephen & Landgraf, Gavin, 2017. "The value of tourist angling: a travel cost method estimation of demand for two destination salmon rivers in Ireland," Papers WP570, Economic and Social Research Institute (ESRI).
    4. Ladenburg, Jacob & Lutzeyer, Sanja, 2012. "The economics of visual disamenity reductions of offshore wind farms—Review and suggestions from an emerging field," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(9), pages 6793-6802.
    5. Alvarez, Sergio & Larkin, Sherry L. & Whitehead, John C. & Haab, Timothy C., 2012. "Substitution, Damages, and Compensation for Anglers due to Oil Spills:The case of the Deepwater Horizon," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124779, Agricultural and Applied Economics Association.
    6. repec:eee:touman:v:60:y:2017:i:c:p:439-441 is not listed on IDEAS

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