IDEAS home Printed from https://ideas.repec.org/p/hhs/umnees/0648.html
   My bibliography  Save this paper

Demand and Welfare Effects in Recreational Travel Models: A Bivariate Count Data Approach

Author

Listed:
  • Hellström, Jörgen

    (Department of Economics, Umeå University)

  • Nordström, Jonas

    (Department of Economics, Umeå University)

Abstract

In this paper we present a non-linear demand system for households' joint choice of number of trips and days to spend at a destination. The approach, which facilitates welfare analysis of exogenous policy and price changes, is used empirically to study the effects of an increased CO2 tax. In the empirical study, a bivariate zero-inflated Poisson lognormal regression model is introduced in order to accommodate the large number of zeroes in the sample. The welfare analysis reveals that the equivalent variation (EV) measure, for the count data demand system, can be seen as an upper bound for the households welfare loss. Approximating the welfare loss by the change in consumer surplus, accounting for the positive effect from longer stays, imposes a lower bound on the households welfare loss. From a distributional point of view, the results reveal that the CO2 tax reform is regressive, in the sense that low income households carry a larger part of the tax burden.

Suggested Citation

  • Hellström, Jörgen & Nordström, Jonas, 2005. "Demand and Welfare Effects in Recreational Travel Models: A Bivariate Count Data Approach," Umeå Economic Studies 648, Umeå University, Department of Economics.
  • Handle: RePEc:hhs:umnees:0648
    as

    Download full text from publisher

    File URL: http://www.econ.umu.se/DownloadAsset.action?contentId=53185&languageId=3&assetKey=ues648
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rainer Winkelmann, 2004. "Health care reform and the number of doctor visits-an econometric analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(4), pages 455-472.
    2. Murat K. Munkin & Pravin K. Trivedi, 1999. "Simulated maximum likelihood estimation of multivariate mixed-Poisson regression models, with application," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 29-48.
    3. Browning, Martin & Meghir, Costas, 1991. "The Effects of Male and Female Labor Supply on Commodity Demands," Econometrica, Econometric Society, vol. 59(4), pages 925-951, July.
    4. Jeffrey T. LaFrance, 1990. "Incomplete Demand Systems And Semilogarithmic Demand Models," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 34(2), pages 118-131, August.
    5. Jeffrey T. LaFrance & W. Michael Hanemann, 1989. "The Dual Structure of Incomplete Demand Systems," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 71(2), pages 262-274.
    6. Epstein, L, 1975. "A Disaggregate Analysis of Consumer Choice under Uncertainty," Econometrica, Econometric Society, vol. 43(5-6), pages 877-892, Sept.-Nov.
    7. W. Douglass Shaw & Peter Feather, 1999. "Possibilities for Including the Opportunity Cost of Time in Recreation Demand Systems," Land Economics, University of Wisconsin Press, vol. 75(4), pages 592-602.
    8. Kenneth E. Train, 1998. "Recreation Demand Models with Taste Differences over People," Land Economics, University of Wisconsin Press, vol. 74(2), pages 230-239.
    9. Brannlund, Runar & Nordstrom, Jonas, 2004. "Carbon tax simulations using a household demand model," European Economic Review, Elsevier, vol. 48(1), pages 211-233, February.
    10. Jeffrey Englin & Peter Boxall & David Watson, 1998. "Modeling Recreation Demand in a Poisson System of Equations: An Analysis of the Impact of International Exchange Rates," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(2), pages 255-263.
    11. Douglas M. Larson, 1993. "Joint Recreation Choices and Implied Values of Time," Land Economics, University of Wisconsin Press, vol. 69(3), pages 270-286.
    12. Morey, Edward R. & Shaw, W. Douglass & Rowe, Robert D., 1991. "A discrete-choice model of recreational participation, site choice, and activity valuation when complete trip data are not available," Journal of Environmental Economics and Management, Elsevier, vol. 20(2), pages 181-201, March.
    13. 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 104-112, February.
    14. Gurmu, Shiferaw & Trivedi, Pravin K, 1996. "Excess Zeros in Count Models for Recreational Trips," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 469-477, October.
    15. K. E. McConnell, 1992. "On-Site Time in the Demand for Recreation," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 74(4), pages 918-925.
    16. Larry G. Epstein, 1982. "Integrability of Incomplete Systems of Demand Functions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 49(3), pages 411-425.
    17. Chib, Siddhartha & Winkelmann, Rainer, 2001. "Markov Chain Monte Carlo Analysis of Correlated Count Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 428-435, October.
    18. Berman, Matthew D. & Kim, Hong Jin, 1999. "Endogenous On-Site Time In The Recreation Demand Model," 1999 Annual meeting, August 8-11, Nashville, TN 21616, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    19. J. R. Hicks, 1942. "Consumers' Surplus and Index-Numbers," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 9(2), pages 126-137.
    20. Rainer Winkelmann, 2002. "Health Care Reform and the Number of Doctor Visits � An Econometric Analysis," SOI - Working Papers 0210, Socioeconomic Institute - University of Zurich.
    21. Matthew D. Berman & Hong Jin Kim, 1999. "Endogenous On-Site Time in the Recreation Demand Model," Land Economics, University of Wisconsin Press, vol. 75(4), pages 603-619.
    22. Wang, Peiming, 2003. "A bivariate zero-inflated negative binomial regression model for count data with excess zeros," Economics Letters, Elsevier, vol. 78(3), pages 373-378, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jörgen Hellström & Jonas Nordström, 2008. "A count data model with endogenous household specific censoring: the number of nights to stay," Empirical Economics, Springer, vol. 35(1), pages 179-192, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hellström, Jörgen & Nordström, Jonas, 2012. "Demand and welfare effects in recreational travel models: Accounting for substitution between number of trips and days to stay," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(3), pages 446-456.
    2. Bowker, James Michael & Starbuck, C. Meghan & English, Donald B.K. & Bergstrom, John C. & Rosenberger, Randall S. & McCollum, Daniel W., 2009. "Estimating the Net Economic Value of National Forest Recreation: An Application of the National Visitor Use Monitoring Database," Faculty Series 59603, University of Georgia, Department of Agricultural and Applied Economics.
    3. Phaneuf, Daniel J. & Smith, V. Kerry, 2006. "Recreation Demand Models," Handbook of Environmental Economics, in: K. G. Mäler & J. R. Vincent (ed.), Handbook of Environmental Economics, edition 1, volume 2, chapter 15, pages 671-761, Elsevier.
    4. Jörgen Hellström & Jonas Nordström, 2008. "A count data model with endogenous household specific censoring: the number of nights to stay," Empirical Economics, Springer, vol. 35(1), pages 179-192, August.
    5. Herriges, Joseph A. & Phaneuf, Daniel J. & Tobias, Justin L., 2008. "Estimating demand systems when outcomes are correlated counts," Journal of Econometrics, Elsevier, vol. 147(2), pages 282-298, December.
    6. Isabel Mendes & Isabel Proença, 2009. "Measuring the Social Recreation Per-Day Net Benefit of Wildlife Amenities of a National Park: A Count-Data Travel Cost Approach," Working Papers Department of Economics 2009/35, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    7. Jörgen Hellström, 2006. "A bivariate count data model for household tourism demand," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 213-226, March.
    8. Hilger, James & Englin, Jeffrey, 2009. "Utility theoretic semi-logarithmic incomplete demand systems in a natural experiment: Forest fire impacts on recreational values and use," Resource and Energy Economics, Elsevier, vol. 31(4), pages 287-298, November.
    9. Danielle Hagerty & Klaus Moeltner, 2005. "Specification of Driving Costs in Models of Recreation Demand," Land Economics, University of Wisconsin Press, vol. 81(1).
    10. Moeltner, Klaus, 2003. "Addressing aggregation bias in zonal recreation models," Journal of Environmental Economics and Management, Elsevier, vol. 45(1), pages 128-144, January.
    11. Kono, Tatsuhito & Yoshida, Jun, 2020. "Travel Cost Method Considering Trip-day Counts as Integers," MPRA Paper 99244, University Library of Munich, Germany.
    12. Arwin Pang, 2022. "Investigating heteroscedasticity using the over-dispersion parameter in a travel cost model," Letters in Spatial and Resource Sciences, Springer, vol. 15(3), pages 507-516, December.
    13. Jeffrey Englin & Thomas Holmes & Rebecca Niell, 2006. "Alternative Models of Recreational Off-Highway Vehicle Site Demand," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 35(4), pages 327-338, December.
    14. LaFrance, Jeffrey T., 2004. "Integrability of the linear approximate almost ideal demand system," Economics Letters, Elsevier, vol. 84(3), pages 297-303, September.
    15. Borislava Mihaylova & Andrew Briggs & Anthony O'Hagan & Simon G. Thompson, 2011. "Review of statistical methods for analysing healthcare resources and costs," Health Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 897-916, August.
    16. Lew, Daniel K., 1998. "Some Implications Of The Two-Constraint Joint Recreational Choice Demand Model," 1998 Annual meeting, August 2-5, Salt Lake City, UT 20937, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    17. Jörgen Hellström, 2006. "A bivariate count data model for household tourism demand," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 213-226.
    18. Hynes, Stephen & Greene, William, 2012. "Panel Travel Cost Count Data Models for On-Site Samples that Incorporate Unobserved Heterogeneity with Respect to the Impact of the Explanatory Variables," Working Papers 148834, National University of Ireland, Galway, Socio-Economic Marine Research Unit.
    19. Stephen Hynes & William Greene, 2016. "Preference Heterogeneity in Contingent Behaviour Travel Cost Models with On-site Samples: A Random Parameter vs. a Latent Class Approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 67(2), pages 348-367, June.
    20. Baerenklau, Kenneth A. & González-Cabán, Armando & Paez, Catrina & Chavez, Edgar, 2010. "Spatial allocation of forest recreation value," Journal of Forest Economics, Elsevier, vol. 16(2), pages 113-126, April.

    More about this item

    Keywords

    demand analysis; welfare effects; count data; bivariate zero inflation;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hhs:umnees:0648. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: David Skog (email available below). General contact details of provider: https://edirc.repec.org/data/inumuse.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.