IDEAS home Printed from https://ideas.repec.org/p/gue/guelph/2017-02.html
   My bibliography  Save this paper

Information Aggregation in a Prediction Market for Climate Outcomes

Author

Listed:
  • Elmira Aliakbari

    (Fraser Institute, Vancouver BC, Canada)

  • Ross McKitrick

    () (Department of Economics, University of Guelph, Guelph ON Canada)

Abstract

Two forms of uncertainty in climate policy are the wide range of estimated marginal costs and uncertainty over credibility of rival information sources. We show how a recently-proposed solution to the first problem also addresses the second. The policy is an emissions tax tied to average temperatures, coupled with permits that exempt the emitter from paying the tax in a future year. It has been shown that the resulting tax path will be correlated with future marginal damages. It has been conjectured that the permit prices will yield unbiased forecasts of the climate, which, if true, would address the second uncertainty. We confirm the conjecture by showing a trading mechanism that converges on unbiased forecasts if traders are risk-neutral. Risk aversion slows down but does not prevent convergence. We also show that the forecasts are more likely to be sufficient statistics the stronger the consensus on climate science.

Suggested Citation

  • Elmira Aliakbari & Ross McKitrick, 2017. "Information Aggregation in a Prediction Market for Climate Outcomes," Working Papers 1702, University of Guelph, Department of Economics and Finance.
    • Handle: RePEc:gue:guelph:2017-02
    as

    Download full text from publisher

    File URL: http://www.uoguelph.ca/economics/repec/workingpapers/2017/2017-02.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. repec:for:ijafaa:y:2017:i:45:p:38-42 is not listed on IDEAS
    2. Lemoine, Derek M. & Traeger, Christian P., 2010. "Tipping Points and Ambiguity in the Economics of Climate Change," CUDARE Working Papers 98127, University of California, Berkeley, Department of Agricultural and Resource Economics.
    3. Nordhaus, William D, 1991. "To Slow or Not to Slow: The Economics of the Greenhouse Effect," Economic Journal, Royal Economic Society, vol. 101(407), pages 920-937, July.
    4. McKitrick, Ross, 2011. "A simple state-contingent pricing rule for complex intertemporal externalities," Energy Economics, Elsevier, vol. 33(1), pages 111-120, January.
    5. Gerard Roe & Yoram Bauman, 2013. "Climate sensitivity: should the climate tail wag the policy dog?," Climatic Change, Springer, vol. 117(4), pages 647-662, April.
    6. Kelly, David L. & Kolstad, Charles D., 1999. "Bayesian learning, growth, and pollution," Journal of Economic Dynamics and Control, Elsevier, vol. 23(4), pages 491-518, February.
    7. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    8. Alp E. Atakan & Mehmet Ekmekci, 2014. "Auctions, Actions, and the Failure of Information Aggregation," American Economic Review, American Economic Association, vol. 104(7), pages 2014-2048, July.
    9. Leach, Andrew J., 2007. "The climate change learning curve," Journal of Economic Dynamics and Control, Elsevier, vol. 31(5), pages 1728-1752, May.
    10. Nordhaus, William D., 1993. "Rolling the 'DICE': an optimal transition path for controlling greenhouse gases," Resource and Energy Economics, Elsevier, vol. 15(1), pages 27-50, March.
    11. Martin L. Weitzman, 2009. "On Modeling and Interpreting the Economics of Catastrophic Climate Change," The Review of Economics and Statistics, MIT Press, vol. 91(1), pages 1-19, February.
    12. Nordhaus, William D, 1993. "Optimal Greenhouse-Gas Reductions and Tax Policy in the "Dice" Model," American Economic Review, American Economic Association, vol. 83(2), pages 313-317, May.
    13. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    14. Tol, Richard S.J., 2014. "Quantifying the consensus on anthropogenic global warming in the literature: A re-analysis," Energy Policy, Elsevier, vol. 73(C), pages 701-705.
    15. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    16. Bjorn Lomborg, 2016. "Impact of Current Climate Proposals," Global Policy, London School of Economics and Political Science, vol. 7(1), pages 109-118, February.
    17. Berg, Joyce E. & Nelson, Forrest D. & Rietz, Thomas A., 2008. "Prediction market accuracy in the long run," International Journal of Forecasting, Elsevier, vol. 24(2), pages 285-300.
    18. H. Henry Cao., 1995. "Imperfect Competition in Securities Markets with Diversely Informed Traders," Research Program in Finance Working Papers RPF-258, University of California at Berkeley.
    19. repec:hal:journl:dumas-00809694 is not listed on IDEAS
    20. Foster, F Douglas & Viswanathan, S, 1996. " Strategic Trading When Agents Forecast the Forecasts of Others," Journal of Finance, American Finance Association, vol. 51(4), pages 1437-1478, September.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Climate change; uncertainty; carbon tax; tradable permits; state-contingent pricing; prediction markets;

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy
    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

    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:gue:guelph:2017-02. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Stephen Kosempel). General contact details of provider: http://edirc.repec.org/data/degueca.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.