IDEAS home Printed from https://ideas.repec.org/p/ags/aaea05/19109.html
   My bibliography  Save this paper

A Pseudo-Sequential Choice Model for Valuing Multiple Environmental Policy or Program Components in Contingent Valuation Applications

Author

Listed:
  • Volinskiy, Dmitriy
  • Bergstrom, John C.
  • Cornwell, Christopher M.

Abstract

The study proposes a discrete-choice model for environmental policy/program valuation, to be used in cases when several policies are valued sequentially. The stochastic specification of the model is consistent with the transitivity and continuity axioms of utility analysis. An empirical methodology for the model is suggested. An application of this model to WTP estimation for Little Tennessee River watershed ecosystem restoration is provided. Findings from the application agree with the hypothesized agent's behavior.

Suggested Citation

  • Volinskiy, Dmitriy & Bergstrom, John C. & Cornwell, Christopher M., 2005. "A Pseudo-Sequential Choice Model for Valuing Multiple Environmental Policy or Program Components in Contingent Valuation Applications," 2005 Annual meeting, July 24-27, Providence, RI 19109, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  • Handle: RePEc:ags:aaea05:19109
    DOI: 10.22004/ag.econ.19109
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/19109/files/sp05vo01.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.19109?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Matzkin, Rosa L, 1992. "Nonparametric and Distribution-Free Estimation of the Binary Threshold Crossing and the Binary Choice Models," Econometrica, Econometric Society, vol. 60(2), pages 239-270, March.
    2. W. Michael Hanemann, 1984. "Welfare Evaluations in Contingent Valuation Experiments with Discrete Responses," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 66(3), pages 332-341.
    3. McClennen,Edward F., 1990. "Rationality and Dynamic Choice," Cambridge Books, Cambridge University Press, number 9780521360470, November.
    4. Hanemann, W Michael, 1984. "Discrete-Continuous Models of Consumer Demand," Econometrica, Econometric Society, vol. 52(3), pages 541-561, May.
    5. Matzkin, Rosa L., 1993. "Nonparametric identification and estimation of polychotomous choice models," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 137-168, July.
    6. John P. Hoehn, 1991. "Valuing the Multidimensional Impacts of Environmental Policy: Theory and Methods," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 73(2), pages 289-299.
    7. Read, Daniel & Antonides, Gerrit & van den Ouden, Laura & Trienekens, Harry, 2001. "Which Is Better: Simultaneous or Sequential Choice?," Organizational Behavior and Human Decision Processes, Elsevier, vol. 84(1), pages 54-70, January.
    8. John C. Bergstrom & John R. Stoll & Alan Randall, 1989. "Information Effects in Contingent Markets," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 71(3), pages 685-691.
    9. Holmes, Thomas P. & Bergstrom, John C. & Huszar, Eric & Kask, Susan B. & Orr, Fritz III, 2004. "Contingent valuation, net marginal benefits, and the scale of riparian ecosystem restoration," Ecological Economics, Elsevier, vol. 49(1), pages 19-30, May.
    10. Rosa L. Matzkin, 1988. "Nonparametric and Distribution-Free Estimation of the Binary Choice and the Threshold-Crossing Models," Cowles Foundation Discussion Papers 889, Cowles Foundation for Research in Economics, Yale University.
    Full references (including those not matched with items on IDEAS)

    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. Steven T. Berry & Philip A. Haile, 2014. "Identification in Differentiated Products Markets Using Market Level Data," Econometrica, Econometric Society, vol. 82(5), pages 1749-1797, September.
    2. Steven T. Berry & Philip A. Haile, 2009. "Nonparametric Identification of Multinomial Choice Demand Models with Heterogeneous Consumers," NBER Working Papers 15276, National Bureau of Economic Research, Inc.
    3. Victor Aguirregabiria & Arvind Magesan, 2020. "Identification and Estimation of Dynamic Games When Players’ Beliefs Are Not in Equilibrium," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 87(2), pages 582-625.
    4. Joel L. Horowitz & N. E. Savin, 2001. "Binary Response Models: Logits, Probits and Semiparametrics," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 43-56, Fall.
    5. Daniel J. Henderson & Christopher F. Parmeter, 2009. "Imposing economic constraints in nonparametric regression: survey, implementation, and extension," Advances in Econometrics, in: Nonparametric Econometric Methods, pages 433-469, Emerald Group Publishing Limited.
    6. Bruneel-Zupanc, Christophe Alain, 2021. "Discrete-Continuous Dynamic Choice Models: Identification and Conditional Choice Probability Estimation," TSE Working Papers 21-1185, Toulouse School of Economics (TSE).
    7. Heckman, James J. & Urzúa, Sergio, 2010. "Comparing IV with structural models: What simple IV can and cannot identify," Journal of Econometrics, Elsevier, vol. 156(1), pages 27-37, May.
    8. Martin O'Connell & Pierre Dubois & Rachel Griffith, 2022. "The Use of Scanner Data for Economics Research," Annual Review of Economics, Annual Reviews, vol. 14(1), pages 723-745, August.
    9. Park, Byeong U. & Simar, Léopold & Zelenyuk, Valentin, 2017. "Nonparametric estimation of dynamic discrete choice models for time series data," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 97-120.
    10. Hoehn, John P. & Loomis, John B., 1992. "Substitution Effects in the Contingent Valuation of Multiple Environmental Programs: A Maximum Likelihood Estimator and Empirical Tests," Staff Paper Series 201147, Michigan State University, Department of Agricultural, Food, and Resource Economics.
    11. Peter Davis & Pasquale Schiraldi, 2014. "The flexible coefficient multinomial logit (FC-MNL) model of demand for differentiated products," RAND Journal of Economics, RAND Corporation, vol. 45(1), pages 32-63, March.
    12. Richard T. Carson & Miko_aj Czajkowski, 2014. "The discrete choice experiment approach to environmental contingent valuation," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 9, pages 202-235, Edward Elgar Publishing.
    13. Holmes, Thomas P. & Bergstrom, John C. & Huszar, Eric & Kask, Susan B. & Orr, Fritz, III, 2002. "Estimating The Local Economic Benefits Of Riparian Ecosystem Restoration Using Iterated Contingent Valuation," Faculty Series 16696, University of Georgia, Department of Agricultural and Applied Economics.
    14. Timothy Park & John Loomis, 1996. "Joint estimation of contingent valuation survey responses," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 7(2), pages 149-162, March.
    15. Fabio Boncinelli & Francesca Gerini & Benedetta Neri & Leonardo Casini, 2018. "Consumer willingness to pay for non‐mandatory indication of the fish catch zone," Agribusiness, John Wiley & Sons, Ltd., vol. 34(4), pages 728-741, October.
    16. Jaap Abbring & James Heckman, 2008. "Dynamic policy analysis," CeMMAP working papers CWP05/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    17. Johnston, Robert J., 2006. "Is hypothetical bias universal? Validating contingent valuation responses using a binding public referendum," Journal of Environmental Economics and Management, Elsevier, vol. 52(1), pages 469-481, July.
    18. Gaurab Aryal & Isabelle Perrigne & Quang Vuong, 2011. "Identification of Insurance Models with Multidimensional Screening," ANU Working Papers in Economics and Econometrics 2011-538, Australian National University, College of Business and Economics, School of Economics.
    19. Nikhil Agarwal & Paulo Somaini, 2018. "Demand Analysis Using Strategic Reports: An Application to a School Choice Mechanism," Econometrica, Econometric Society, vol. 86(2), pages 391-444, March.
    20. Aradillas-Lopez, Andres, 2010. "Semiparametric estimation of a simultaneous game with incomplete information," Journal of Econometrics, Elsevier, vol. 157(2), pages 409-431, August.

    More about this item

    Keywords

    Environmental Economics and Policy;

    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:ags:aaea05:19109. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.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.