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Intertemporal data and travel cost analysis

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  • Daniel Hellerstein

Abstract

This paper considers the use of multi-year data in travel cost analysis. To exploit the information embedded within intertemporal data, two broad approaches are examined: multiple year cross sections and panel models. Multiple year cross sections can be used to detect trends, and to test for stability of behavior. Panel models can be used to control for unobservable factors that are individual specific. Unfortunately, the low intertemporal variability of travel cost data sets weakens the power of panel estimators. Using aggregate data from the Boundary Waters Canoe Area, the stability of demand processes over the 1980–1986 period is investigated, as well as the problems inherent in using panel estimators in travel cost analysis. Copyright Kluwer Academic Publishers 1993

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  • Daniel Hellerstein, 1993. "Intertemporal data and travel cost analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 3(2), pages 193-207, April.
    • Handle: RePEc:kap:enreec:v:3:y:1993:i:2:p:193-207
    DOI: 10.1007/BF00338785
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    3. Barrios, Salvador & Ibañez Rivas, J. Nicolás, 2014. "Climate Amenities and Adaptation to Climate Change: A Hedonic-Travel Cost Approach for Europe," Climate Change and Sustainable Development 165790, Fondazione Eni Enrico Mattei (FEEM).
    4. Sinclair, Michael & Ghermandi, Andrea & Signorello, Giovanni & Giuffrida, Laura & De Salvo, Maria, 2022. "Valuing Recreation in Italy's Protected Areas Using Spatial Big Data," Ecological Economics, Elsevier, vol. 200(C).
    5. Jones, Thomas E. & Yang, Yang & Yamamoto, Kiyotatsu, 2017. "Assessing the recreational value of world heritage site inscription: A longitudinal travel cost analysis of Mount Fuji climbers," Tourism Management, Elsevier, vol. 60(C), pages 67-78.
    6. J. Price & D. Dupont & W. Adamowicz, 2017. "As Time Goes By: Examination of Temporal Stability Across Stated Preference Question Formats," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 68(3), pages 643-662, November.
    7. Salvador Barrios & J. Ibañez, 2015. "Time is of the essence: adaptation of tourism demand to climate change in Europe," Climatic Change, Springer, vol. 132(4), pages 645-660, October.
    8. Starbuck, C.M.C. Meghan & Alexander, Susan J. & Berrens, Robert P. & Bohara, Alok K., 2004. "Valuing special forest products harvesting:: a two-step travel cost recreation demand analysis," Journal of Forest Economics, Elsevier, vol. 10(1), pages 37-53, May.
    9. Patrick Lloyd-Smith & Ewa Zawojska, 2024. "How stable and predictable are welfare estimates using recreation demand models?," Working Papers 2024-05, Faculty of Economic Sciences, University of Warsaw.
    10. Salvador Barrios & Juan Nicolas Ibañez Rivas, 2013. "Tourism demand, climatic conditions and transport costs: an integrated analysis for EU regions," JRC Research Reports JRC80898, Joint Research Centre.
    11. Malte Grossmann, 2011. "Impacts of boating trip limitations on the recreational value of the Spreewald wetland: a pooled revealed/contingent behaviour application of the travel cost method," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 54(2), pages 211-226.
    12. de Frutos, Pablo & Rodriguez-Prado, Beatriz & Latorre, Joaquín & Martinez-Peña, Fernando, 2019. "A Gravity Model to Explain Flows of Wild Edible Mushroom Picking. A Panel Data Analysis," Ecological Economics, Elsevier, vol. 156(C), pages 164-173.

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