IDEAS home Printed from https://ideas.repec.org/p/hhs/uunewp/2019_004.html
   My bibliography  Save this paper

Biased Forecasts to Affect Voting Decisions? The Brexit Case

Author

Listed:
  • Cipullo, Davide

    () (Department of Economics)

  • Reslow, André

    () (Department of Economics)

Abstract

This paper introduces macroeconomic forecasters as political agents and suggests that they use their forecasts to infuence voting outcomes. We develop a probabilistic voting model in which voters do not have complete information about the future states of the economy and have to rely on macroeconomic forecasters. The model predicts that it is optimal for forecasters with economic interest (stakes) and influence to publish biased forecasts prior to a referendum. We test our theory using high-frequency data at the forecaster level surrounding the Brexit referendum. The results show that forecasters with stakes and influence released much more pessimistic estimates for GDP growth in the following year than other forecasters. Actual GDP growth rate in 2017 shows that forecasters with stakes and influence were also more incorrect than other institutions and the propaganda bias explains up to 50 percent of their forecast error.

Suggested Citation

  • Cipullo, Davide & Reslow, André, 2019. "Biased Forecasts to Affect Voting Decisions? The Brexit Case," Working Paper Series 2019:4, Uppsala University, Department of Economics.
  • Handle: RePEc:hhs:uunewp:2019_004
    as

    Download full text from publisher

    File URL: http://uu.diva-portal.org/smash/get/diva2:1296155/FULLTEXT01.pdf
    File Function: Full text
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ottaviani, Marco & Sorensen, Peter Norman, 2006. "The strategy of professional forecasting," Journal of Financial Economics, Elsevier, vol. 81(2), pages 441-466, August.
    2. Assar Lindbeck & Jörgen Weibull, 1987. "Balanced-budget redistribution as the outcome of political competition," Public Choice, Springer, vol. 52(3), pages 273-297, January.
    3. repec:wly:jforec:v:36:y:2017:i:7:p:784-794 is not listed on IDEAS
    4. Alabrese, Eleonora & Becker, Sascha O. & Fetzer, Thiemo & Novy, Dennis, 2019. "Who voted for Brexit? Individual and regional data combined," European Journal of Political Economy, Elsevier, vol. 56(C), pages 132-150.
    5. Sascha O Becker & Thiemo Fetzer & Dennis Novy, 2017. "Who voted for Brexit? A comprehensive district-level analysis," Economic Policy, CEPR;CES;MSH, vol. 32(92), pages 601-650.
    6. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2011. "Robust Inference With Multiway Clustering," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 238-249, April.
    7. Stefano Della Vigna & Ruben Enikolopov & Vera Mironova & Maria Petrova & Ekaterina Zhuravskaya, 2014. "Cross-Border Media and Nationalism: Evidence from Serbian Radio in Croatia," American Economic Journal: Applied Economics, American Economic Association, vol. 6(3), pages 103-132, July.
    8. Matthew Gentzkow & Jesse M. Shapiro, 2006. "Media Bias and Reputation," Journal of Political Economy, University of Chicago Press, vol. 114(2), pages 280-316, April.
    9. Liberini, Federica & Oswald, Andrew J & Proto, Eugenio & Redoano, Michela, 2017. "Was Brexit Caused by the Unhappy and the Old?," CAGE Online Working Paper Series 342, Competitive Advantage in the Global Economy (CAGE).
    10. Davies, Anthony & Lahiri, Kajal, 1995. "A new framework for analyzing survey forecasts using three-dimensional panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 205-227, July.
    11. Anthony Downs, 1957. "An Economic Theory of Political Action in a Democracy," Journal of Political Economy, University of Chicago Press, vol. 65, pages 135-135.
    12. David Laster & Paul Bennett & In Sun Geoum, 1999. "Rational Bias in Macroeconomic Forecasts," The Quarterly Journal of Economics, Oxford University Press, vol. 114(1), pages 293-318.
    13. repec:cup:apsrev:v:88:y:1994:i:01:p:33-47_09 is not listed on IDEAS
    14. Nordhaus, William D, 1987. "Forecasting Efficiency: Concepts and Applications," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 667-674, November.
    15. Rahul Deb & Mallesh M. Pai & Maher Said, 2018. "Evaluating Strategic Forecasters," American Economic Review, American Economic Association, vol. 108(10), pages 3057-3103, October.
    16. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    17. Michael K Andersson & Ted Aranki & André Reslow, 2017. "Adjusting for information content when comparing forecast performance," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(7), pages 784-794, November.
    18. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
    19. repec:taf:applec:v:49:y:2017:i:26:p:2508-2514 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Brexit; Interest Groups; Forecasters Behavior; Voting;

    JEL classification:

    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • H30 - Public Economics - - Fiscal Policies and Behavior of Economic Agents - - - General

    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:uunewp:2019_004. 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: (Ulrika Öjdeby). General contact details of provider: http://edirc.repec.org/data/nekuuse.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.