IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v35y2019i1p1-10.html
   My bibliography  Save this article

Crowdsourcing the vote: New horizons in citizen forecasting

Author

Listed:
  • Temporão, Mickael
  • Dufresne, Yannick
  • Savoie, Justin
  • Linden, Clifton van der

Abstract

People do not know much about politics. This is one of the most robust findings in political science and is backed by decades of research. Most of this research has focused on people’s ability to know about political issues and party positions on these issues. But can people predict elections? Our research uses a very large dataset (n>2,000,000) collected during ten provincial and federal elections in Canada to test whether people can predict the electoral victor and the closeness of the race in their district throughout the campaign. The results show that they can. This paper also contributes to the emerging literature on citizen forecasting by developing a scaling method that allows us to compare the closeness of races and that can be applied to multiparty contexts with varying numbers of parties. Finally, we assess the accuracy of citizen forecasting in Canada when compared to voter expectations weighted by past votes and political competency.

Suggested Citation

  • Temporão, Mickael & Dufresne, Yannick & Savoie, Justin & Linden, Clifton van der, 2019. "Crowdsourcing the vote: New horizons in citizen forecasting," International Journal of Forecasting, Elsevier, vol. 35(1), pages 1-10.
    • Handle: RePEc:eee:intfor:v:35:y:2019:i:1:p:1-10
    DOI: 10.1016/j.ijforecast.2018.07.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207018301274
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2018.07.011?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Leiter, Debra & Murr, Andreas & Rascón Ramírez, Ericka & Stegmaier, Mary, 2018. "Social networks and citizen election forecasting: The more friends the better," International Journal of Forecasting, Elsevier, vol. 34(2), pages 235-248.
    2. Park, David K. & Gelman, Andrew & Bafumi, Joseph, 2004. "Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls," Political Analysis, Cambridge University Press, vol. 12(4), pages 375-385.
    3. Lewis-Beck, Michael S. & Skalaban, Andrew, 1989. "Citizen Forecasting: Can Voters See into the Future?," British Journal of Political Science, Cambridge University Press, vol. 19(1), pages 146-153, January.
    4. Murr, Andreas E., 2015. "The wisdom of crowds: Applying Condorcet’s jury theorem to forecasting US presidential elections," International Journal of Forecasting, Elsevier, vol. 31(3), pages 916-929.
    5. Gelman, Andrew & King, Gary, 1993. "Why Are American Presidential Election Campaign Polls So Variable When Votes Are So Predictable?," British Journal of Political Science, Cambridge University Press, vol. 23(4), pages 409-451, October.
    6. Lewis-Beck, Michael S. & Tien, Charles, 1999. "Voters as forecasters: a micromodel of election prediction," International Journal of Forecasting, Elsevier, vol. 15(2), pages 175-184, April.
    7. Wang, Wei & Rothschild, David & Goel, Sharad & Gelman, Andrew, 2015. "Forecasting elections with non-representative polls," International Journal of Forecasting, Elsevier, vol. 31(3), pages 980-991.
    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. Khan, Urmee & Lieli, Robert P., 2018. "Information flow between prediction markets, polls and media: Evidence from the 2008 presidential primaries," International Journal of Forecasting, Elsevier, vol. 34(4), pages 696-710.
    2. Leiter, Debra & Murr, Andreas & Rascón Ramírez, Ericka & Stegmaier, Mary, 2018. "Social networks and citizen election forecasting: The more friends the better," International Journal of Forecasting, Elsevier, vol. 34(2), pages 235-248.
    3. Montalvo, José G. & Papaspiliopoulos, Omiros & Stumpf-Fétizon, Timothée, 2019. "Bayesian forecasting of electoral outcomes with new parties’ competition," European Journal of Political Economy, Elsevier, vol. 59(C), pages 52-70.
    4. José Garcia Montalvo & Omiros Papaspiliopoulos & Timothée Stumpf-Fétizon, 2018. "Bayesian forecasting of electoral outcomes with new parties' competition," Economics Working Papers 1624, Department of Economics and Business, Universitat Pompeu Fabra.
    5. José García-Montalvo & Omiros Papaspiliopoulos & Timothée Stumpf-Fétizon, 2018. "Bayesian Forecasting of Electoral Outcomes with new Parties' Competition," Working Papers 1065, Barcelona School of Economics.
    6. Liu, Yezheng & Ye, Chang & Sun, Jianshan & Jiang, Yuanchun & Wang, Hai, 2021. "Modeling undecided voters to forecast elections: From bandwagon behavior and the spiral of silence perspective," International Journal of Forecasting, Elsevier, vol. 37(2), pages 461-483.
    7. Wang, Wei & Rothschild, David & Goel, Sharad & Gelman, Andrew, 2015. "Forecasting elections with non-representative polls," International Journal of Forecasting, Elsevier, vol. 31(3), pages 980-991.
    8. A. Kamakura, Wagner & Afonso Mazzon, Jose & De Bruyn, Arnaud, 2006. "Modeling voter choice to predict the final outcome of two-stage elections," International Journal of Forecasting, Elsevier, vol. 22(4), pages 689-706.
    9. Stiers, Dieter & Dassonneville, Ruth, 2018. "Affect versus cognition: Wishful thinking on election day," International Journal of Forecasting, Elsevier, vol. 34(2), pages 199-215.
    10. Ron Johnston & Todd Hartman & Charles Pattie, 2019. "Predicting general election outcomes: campaigns and changing voter knowledge at the 2017 general election in England," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(3), pages 1369-1389, May.
    11. Lennart Sjöberg, 2009. "Are all crowds equally wise? a comparison of political election forecasts by experts and the public," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(1), pages 1-18.
    12. Cerina, Roberto & Duch, Raymond, 2020. "Measuring public opinion via digital footprints," International Journal of Forecasting, Elsevier, vol. 36(3), pages 987-1002.
    13. Franch, Fabio, 2021. "Political preferences nowcasting with factor analysis and internet data: The 2012 and 2016 US presidential elections," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    14. Murr, Andreas E., 2015. "The wisdom of crowds: Applying Condorcet’s jury theorem to forecasting US presidential elections," International Journal of Forecasting, Elsevier, vol. 31(3), pages 916-929.
    15. Bruch, Christian & Felderer, Barbara, 2024. "An Approximation of Joint Distributions of Weighting Variables Using a Pseudo Population Approach," OSF Preprints pg2wt, Center for Open Science.
    16. Mongrain, Philippe & Nadeau, Richard & Jérôme, Bruno, 2021. "Playing the synthesizer with Canadian data: Adding polls to a structural forecasting model," International Journal of Forecasting, Elsevier, vol. 37(1), pages 289-301.
    17. Munzert, Simon, 2017. "Forecasting elections at the constituency level: A correction–combination procedure," International Journal of Forecasting, Elsevier, vol. 33(2), pages 467-481.
    18. Sjöberg, Lennart, 2006. "Are all crowds equally wise? A comparison of political election forecasts by experts and the public," SSE/EFI Working Paper Series in Business Administration 2006:9, Stockholm School of Economics, revised 08 Sep 2008.
    19. Yonatan Ben-Shalom & Ignacio Martinez & Mariel Finucane, "undated". "Risk of Workforce Exit Due to Disability: State Differences in 2003–2016," Mathematica Policy Research Reports 8aed03744a06419dbda68be8c, Mathematica Policy Research.
    20. Laura C. Dawkins & Daniel B. Williamson & Stewart W. Barr & Sally R. Lampkin, 2020. "‘What drives commuter behaviour?': a Bayesian clustering approach for understanding opposing behaviours in social surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 251-280, January.

    More about this item

    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:eee:intfor:v:35:y:2019:i:1:p:1-10. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    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.