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An Improved Method for Calibrating Purchase Intentions in Stated Preference Demand Models

Published online by Cambridge University Press:  26 January 2015

Stephen Davies  and
John Loomis
Stephen Davies
Affiliation:
Department of Agricultural and Resource Economics, Colorado State University, Fort Collins, CO
John Loomis
Affiliation:
Department of Agricultural and Resource Economics, Colorado State University, Fort Collins, CO
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Abstract

The Orbit demand model allows the magnitude of the calibration to stated purchase intentions to vary based on the magnitude of the stated quantities. Using an empirical example of stated trips, we find that the extent of calibration varies substantially with less correction needed at small stated trips (-25%) but larger corrections at higher quantities of stated visits (-48%). We extend the Orbit model to calculate consumer surplus per stated trip of $26. Combining the calibrations in stated trips and value per trip, the Orbit model provides estimates of annual benefits from 60% to 111% less than the count data model.

Keywords

hypothetical biasOrbitordered probit modeltravel cost modelrecreationstated preferenceD12H44Q26Q51

Information

Type
Research Article
Information
Journal of Agricultural and Applied Economics , Volume 42 , Issue 4 , November 2010 , pp. 679 - 693
Copyright
Copyright © Southern Agricultural Economics Association 2010

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References

Adamowicz, W., Louviere, J., and Williams, M. “Combining Stated and Revealed Preference Methods for Valuing Environmental Amenities.” Journal of Environmental Economics and Management 26(1994):271–92.Google Scholar
Archarya, R., Hatch, L.U., and Clonts, H.A. “The Role of On-Site Time in Recreation Demand for Wilderness.” Journal of Agricultural and Applied Economics 35(2003): 159–69.Google Scholar
Atker, S., Bennet, J., and Akhter, S. “Preference Uncertainty in Contingent Valuation.” Ecological Economics 67(2008): 345–51.Google Scholar
Azevedo, C., Herriges, J., and Kling, C. “Combining Revealed and Stated PReferences: Consistency Tests and Their Interpretations.” American Journal of Agricultural and Resource Economics 85(2003):525–37.CrossRefGoogle Scholar
Belsey, D., Kuh, E., and Welsch, R. Regression Diagnostics: Identifying Influence Data and Sources of Collinearity. New York: Wiley, 1980.Google Scholar
Bishop, R., Heberlein, T., and Kealy, M.J. “Contingent Valuation of Environmental Assets: Comparisons with a Simulated Market.” Natural Resources Journal 23(1983): 619–33.Google Scholar
Bowker, J.M., English, D., and Donovan, J. “Toward a Value for Guided Rafting on Southern Rivers.” Journal of Agricultural and Applied Economics 28(1996):423–32.Google Scholar
Carlsson, F., and Martinsson, P. “Do Hypothetical and Actual Marginal Willingness to Pay Differ in Choice Experiments?” Journal of Environmental Economics and Management 41(2001): 179–92.Google Scholar
Carson, R., Flores, N., Martin, K., and Wright, J. “Contingent Valuation and Revealed Preference Methodologies.” Land Economics 72(1996):80–99.Google Scholar
Casey, J., Vukina, T., and Danielson, L. “The Economie Value of Hiking.” Journal of Agricultural and Applied Economics 27(1995):658–68.CrossRefGoogle Scholar
Caudill, S.B., Ford, F.M., and Gropper, D.M. “Frontier Estimation and Firm-Specific Inefficiency Measures in the Presence of Hetero-scedasticity.” Journal of Business & Economic Statistics 13(1995):105–11.Google Scholar
Champ, P., Bishop, R., Brown, T., and McCollum, D. “Using Donation Mechanisms to Value Nonuse Benefits from Public Goods.” Journal of Environmental Economics and Management 33(1997):151–61.CrossRefGoogle Scholar
Creel, M., and Loomis, J. “Theoretical and Empirical of a Truncated Count Data Estimators for Analysis of Deer Hunting in California.” American Journal of Agricultural Economics 72(1990):434–41.CrossRefGoogle Scholar
Cummings, R., and Taylor, L. “Unbiased Value Estimates for Environmental Goods: A Cheap Talk Design for the Contingent Valuation Method.” The American Economic Review 89(1999):649–65.Google Scholar
Donovan, G. and Nicholls, D. “Consumer Preferences and Willingness to Pay for Character-Marketed Cabinets from Alaska Birch.” Forest Products Journal 53(2003):27–32.Google Scholar
Englin, J., and Cameron, T. “Augmenting Travel Cost Models with Contingent Behavior Data.” Environmental and Resource Economics 7(1996):133–47.Google Scholar
Fox, J., Shogren, J., Hayes, D., and Kliebenstein, J. “CVM-X: Calibrating Values with Experimental Auction Markets.” American Journal of Agricultural Economics 80(1998):455–65.Google Scholar
Grilvava, T. Berrens, R, Bohara, A., and Shaw, W.D. “Testing the Validity of the Contingent Behavior Trips Responses.” American Journal of Agricultural Economics 84(2002):401–14.Google Scholar
Hanemann, M. “Welfare Evaluations in Contingent Valuation Experiments with Discrete Responses.” American Journal of Agricultural Economics 66(1984):332–41.Google Scholar
Hellerstein, D. “Using Count Data Models in Travel Cost Analysis with Aggregate Data”. American Journal of Agricultural Economics 73(1991):860–66.Google Scholar
Klein, R., and Sherman, R. “Estimating New Product Demand From Biased Survey Data.” Journal of Econometrics 76(1997):53–76.Google Scholar
Layman, C, Boyce, J., and Criddle, K. “Economic Valuation of the Chinook Salmon Sport Fishery of the Gulkana River, Alaska, Under Current and Alternative Management Plans.” Land Economics 72(1996):113–28.Google Scholar
Loomis, J. “Panel Estimators to Combine Revealed and Stated Preference Dichotomous Choice Data.” Journal of Agricultural and Resource Economics 22(1997):233–45.Google Scholar
Loomis, J., Gonzalez-Caban, A., and Englin, J. “Testing for Differential Effects of Forest Fires on Hiking and Mountain Biking Demand and Benefits.” Journal of Agricultural and Resource Economics 26(2001):508–22.Google Scholar
Louviere, J. “Conjoint Analysis Modeling of Stated Preferences: A Review of Theory, Methods, Recent Developments and External Validity.” Journal of Transport Economics and Policy 10(1988):98–119.Google Scholar
Louviere, J., Hensher, D., and Swait, J. Stated Choice Methods—Analysis and Applications. Cambridge, UK: Cambridge University Press, 2000.Google Scholar
Loureiro, M.J., McClusky, , and Mittelhammer, R. “Are Stated Preferences Good Predictors of Market Behavior?” Land Economics 79(2003): 44–55.Google Scholar
Madalla, G.S. Limited-Dependent and Qualitative Variables in Econometrics. New York: Cambridge University Press, 1983.Google Scholar
Murphy, J., Allen, G., Stevens, T., and Weatherhead, D. “A Meta-Analysis of Hypothetical Bias in Stated Preference Valuation.” Environmental and Resource Economics 30(2005): 315–25.Google Scholar
Roe, B., Boyle, K., and Teisl, M. “Using Conjoint Analysis to Derive Estimates of Compensating Variation.” Journal of Environmental Economics and Management 31(1996): 145–59.Google Scholar
Stevens, T., Belkner, R., Dennis, D., Kittredge, D., and Willis, C. “Comparison of Contingent Valuation and Conjoint Analysis in Ecosystem Management.” Ecological Economics 32(2000):63–74.Google Scholar
Ward, F. “Economics of Water Allocation to Instream Uses in a Fully Appropriated River Basin: Evidence from a New Mexico Wild River.” Water Resources Research 23(1987):381–92.Google Scholar
Whitehead, J. “Environmental Risk and Averting Behavior: Predictive Validity of Jointly Estimated Revealed and Stated Preference Data.” Environmental and Resource Economics 32(2005): 301–16.Google Scholar
Whitehead, J., Dumas, C., Herstine, J., Hill, J., and Buerger, B. “Valuing Beach Access and Width with Revealed and Stated Preference Data.” Marine Resource Economics 23(2008): 119–35.Google Scholar
Whitehead, J.C., Haab, T.C., and Huang, J. “Measuring Recreation Benefits of Quality Improvements with Revealed and Stated Behavior Data.” Resource and Energy Economics 22(2000):339–54.Google Scholar