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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.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Environmental Economics and Management.

Volume (Year): 62 (2011)
Issue (Month): 1 (July)
Pages: 95-110

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Handle: RePEc:eee:jeeman:v:62:y:2011:i:1:p:95-110

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Web page: http://www.elsevier.com/locate/inca/622870

Related research

Keywords: Onsite sampling Propensity score weighting Recreation demand Random utility models;

References

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  1. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity score matching methods for non-experimental causal studies," Discussion Papers 0102-14, Columbia University, Department of Economics.
  2. Englin, Jeffrey & Shonkwiler, J S, 1995. "Estimating Social Welfare Using Count Data Models: An Application to Long-Run Recreation Demand under Conditions of Endogenous Stratification and Truncation," The Review of Economics and Statistics, MIT Press, vol. 77(1), pages 104-12, February.
  3. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, 07.
  4. Aviv Nevo, 2001. "Using Weights to Adjust for Sample Selection When Auxiliary Information is Available," NBER Technical Working Papers 0275, National Bureau of Economic Research, Inc.
  5. Bierlaire, M. & Bolduc, D. & McFadden, D., 2008. "The estimation of generalized extreme value models from choice-based samples," Transportation Research Part B: Methodological, Elsevier, vol. 42(4), pages 381-394, May.
  6. Roderick J. Little & Sonya Vartivarian, 2005. "Does Weighting for Nonresponse Increase the Variance of Survey Means?," Mathematica Policy Research Reports 4780, Mathematica Policy Research.
  7. Basar, Gözen & Bhat, Chandra, 2004. "A parameterized consideration set model for airport choice: an application to the San Francisco Bay Area," Transportation Research Part B: Methodological, Elsevier, vol. 38(10), pages 889-904, December.
  8. Shaw, Daigee, 1988. "On-site samples' regression : Problems of non-negative integers, truncation, and endogenous stratification," Journal of Econometrics, Elsevier, vol. 37(2), pages 211-223, February.
  9. James Heckman & Salvador Navarro-Lozano, 2004. "Using Matching, Instrumental Variables, and Control Functions to Estimate Economic Choice Models," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 30-57, February.
  10. Jeffrey M. Wooldridge, 2002. "Inverse probability weighted M-estimators for sample selection, attrition and stratification," CeMMAP working papers CWP11/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  11. Krinsky, Itzhak & Robb, A Leslie, 1986. "On Approximating the Statistical Properties of Elasticities," The Review of Economics and Statistics, MIT Press, vol. 68(4), pages 715-19, November.
  12. George R. Parsons & A. Brett Hauber, 1998. "Spatial Boundaries and Choice Set Definition in a Random Utility Model of Recreation Demand," Land Economics, University of Wisconsin Press, vol. 74(1), pages 32-48.
  13. Cosslett, Stephen R, 1981. "Maximum Likelihood Estimator for Choice-Based Samples," Econometrica, Econometric Society, vol. 49(5), pages 1289-1316, September.
  14. M. K. Haener & P. C. Boxall & W. L. Adamowicz & D. H. Kuhnke, 2004. "Aggregation Bias in Recreation Site Choice Models: Resolving the Resolution Problem," Land Economics, University of Wisconsin Press, vol. 80(4).
  15. Manski, Charles F & Lerman, Steven R, 1977. "The Estimation of Choice Probabilities from Choice Based Samples," Econometrica, Econometric Society, vol. 45(8), pages 1977-88, November.
  16. Klaus Moeltner & J. Scott Shonkwiler, 2005. "Correcting for On-Site Sampling in Random Utility Models," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(2), pages 327-339.
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Citations

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Cited by:
  1. 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.
  2. 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.
  3. 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, European Association of Environmental and Resource Economists, vol. 56(4), pages 481-497, December.

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