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Controlling for Observed and Unobserved Site Characteristics in Rum Models of Recreation Demand

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  • Abidoye, Babatunde
  • Herriges, Joseph A.
  • Tobias, Justin

Abstract

Random Utility Maximization (RUM) models of recreation demand are typically plagued by limited information on environmental and other attributes characterizing the available sites in the choice set. To the extent that these unobserved site attributes are correlated with the observed characteristics and/or the key travel cost variable, the resulting parameter estimates and subsequent welfare calculations are likely to be biased. In this paper we develop a Bayesian approach to estimating a RUM model that incorporates a full set of alternative specific constants, insulating the key travel cost parameter from the influence of the unobserved site attributes. In contrast to estimation procedures recently outlined in Murdock (2006), the posterior simulator we propose (combining data augmentation and Gibbs sampling techniques) can be used in the more general mixed logit framework in which some parameters of the conditional utility function are random. Following a series of generated data experiments to illustrate the performance of the simulator, we apply the estimation procedures to data from the Iowa Lakes Project. In contrast to an earlier study using the same data (Egan \textit{et al.} \cite{eganetal}), we find that, with the addition of a full set of alternative specific constants, water quality attributes no longer appear to influence the choice of where to recreate.

Suggested Citation

  • Abidoye, Babatunde & Herriges, Joseph A. & Tobias, Justin, 2010. "Controlling for Observed and Unobserved Site Characteristics in Rum Models of Recreation Demand," Staff General Research Papers Archive 31559, Iowa State University, Department of Economics.
  • Handle: RePEc:isu:genres:31559
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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, pages 83-92.
    2. Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2007. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9780521671736, December.
    3. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, November.
    4. Jiang, Renna & Manchanda, Puneet & Rossi, Peter E., 2009. "Bayesian analysis of random coefficient logit models using aggregate data," Journal of Econometrics, Elsevier, pages 136-148.
    5. 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.
    6. Sha Yang & Yuxin Chen & Greg Allenby, 2003. "Bayesian Analysis of Simultaneous Demand and Supply," Quantitative Marketing and Economics (QME), Springer, vol. 1(3), pages 251-275, September.
    7. Murdock, Jennifer, 2006. "Handling unobserved site characteristics in random utility models of recreation demand," Journal of Environmental Economics and Management, Elsevier, vol. 51(1), pages 1-25, January.
    8. David A. Hennessy, 1995. "Microeconomics of Agricultural Grading: Impacts on the Marketing Channel," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(4), pages 980-989.
    9. Steven T. Berry, 1994. "Estimating Discrete-Choice Models of Product Differentiation," RAND Journal of Economics, The RAND Corporation, pages 242-262.
    10. 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.
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    Citations

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    Cited by:

    1. Richard T. Melstrom & Deshamithra H. W. Jayasekera, 2017. "Two-Stage Estimation to Control for Unobservables in a Recreation Demand Model with Unvisited Sites," Land Economics, University of Wisconsin Press, pages 328-341.
    2. Longo, Alberto & Hutchinson, W. George & Hunter, Ruth F. & Tully, Mark A. & Kee, Frank, 2015. "Demand response to improved walking infrastructure: A study into the economics of walking and health behaviour change," Social Science & Medicine, Elsevier, pages 107-116.
    3. Yi, DongGyu, 2014. "Three studies on environmental valuation," ISU General Staff Papers 201401010800005065, Iowa State University, Department of Economics.
    4. María Pérez-Urdiales & María A. García-Valiñas & Roberto Martínez-Espiñeira, 2016. "Responses to Changes in Domestic Water Tariff Structures: A Latent Class Analysis on Household-Level Data from Granada, Spain," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 63(1), pages 167-191, January.
    5. Melstrom, Richard & Lupi, Frank, 2012. "Using a Control Function to Resolve the Travel Cost Endogeneity Problem in Recreation Demand Models," MPRA Paper 48036, University Library of Munich, Germany, revised May 2013.
    6. repec:kap:enreec:v:68:y:2017:i:4:d:10.1007_s10640-016-0060-0 is not listed on IDEAS
    7. Roy, Sunanda & Sabarwal, Tarun, 2012. "Characterizing stability properties in games with strategic substitutes," Games and Economic Behavior, Elsevier, pages 337-353.

    More about this item

    Keywords

    nonmarket valuation; water quality; discrete choice;

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • Q25 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Water
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects

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