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Augmenting travel cost models with contingent behavior data
Authors:Jeffrey Englin  Trudy Ann Cameron
Institution:(1) Department of Agricultural Economics, 204, University of Nevada, 89557-0105, NV, USA
Abstract:This paper proposes contingent behavior survey questions as a valuable supplement to observed data in travel cost models of non-market demand for recreational resources. A set of observed and contingent behavior results for each survey respondent allows the researcher to control for individual heterogeneity by taking advantage of panel data methods when exploring the nature of respondent demands. The contingent scenarios also provide opportunities to (a) test for differences between observed and contingent preferences and/or (b) assess likely demands under conditionsbeyond the domain of observed variation in costs or resource attributes. Most importantly, contingent scenarios allow the researcher to imposeexogenously varying travel costs. Exogenous imposition of travel costs together with panel methods reduces the omitted variables bias that plagues observed-data travel cost models of recreational demand. Using a convenience sample of data for illustrative purposes, we show how to estimate the demand for recreational angling by combining observed and contingent behavior data. We begin with simple naive pooled Poisson models and progress to more theoretically appropriate fixed effects panel Poisson specifications.The authors are at the University of Nevada and UCLA, respectively. We gratefully acknowledge the comments of both Scott Shonkwiler and participants in the W133 meetings in Santa Fe, New Mexico and for research material provided by Wayne Gray. The data were provided by Rang Narayanan. Research assistance was provided by Jerry McGraw and Natalie Tucker. Research partially supported by the Nevada Experiment Station. Any errors or omissions remain the authors' responsibility.
Keywords:Nonmarket valuation  recreational demand modelling  travel cost method  contingent behavior surveys  panel data methods  fixed effects models  Poisson regression
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