A guest post by Oliver Strijbis, Sveinung Arnesen, Kjetil Thuen, and Lucas Rachow. Oliver Strijbis is a postdoctoral researcher at the University of Hamburg, Sveinung Arnesen is a postdoctoral researcher at the University of Bergen. Together with Kjetil Thuen and Lucas Rachow they are founders of politikprognosen.ch.
Who thinks in predictions of election and referenda outcomes thinks in surveys. However, an alternative method for forecasting has made its way into political science the last years. So called “prediction markets” are designed to aggregate information and produce predictions about future events. Prediction markets are markets for contracts that yield payments based on the outcome of an uncertain future event, such as an election or a referendum. A considerable bulk of literature has shown that prediction markets can easily compete with surveys in forecasting election outcomes. This has also been shown for the Swiss parliamentary elections in 2011 where the forecasts of the prediction markets were more accurate than those of the surveys (Tagesanzeiger online, 27th october 2011).
In an attempt to apply prediction markets in the context of Switzerland’s direct democracy we set up prediction markets for the ballots of March 11. We arrive at forecasts by setting up a market for each of the proposals. This means that on each of five markets the outcome of one proposal is treated as an asset. At voting day an asset pays the share of votes the proposal has received. For instance if the proposal “Schluss mit uferlosem Bau von Zweitwohnungen!” gets 45% of the votes, the final price of an asset of this proposal pays 45 units. Hence, a participant on the prediction market has an incentive to buy assets if the price is below 45 units and an incentive to sell if it is above. Consequently, rational players will buy assets if the current price is below the expected outcome and sell if it is above.
With the assistance of colleagues from various Swiss universities (special thanks to Laurent Bernhard) we were able to recruit 124 individuals of which 87 turned out to be active participants. In an attempt to recruit only the most talented players we were primarily approaching political scientists (students and professionals) and individuals trained in a related field. From the 87 participants 27% were political scientists, 21% economists, and 22% were trained social scientists from other disciplines. The participants are compensated with a small salary depending on their performance.
While theory tells us that our proceeding should yield accurate forecasts, only empirics can demonstrate it. So what do the prediction markets tell us for the ballots on March 11? Here are our predictions from March 3 (see Figure): 46.7% yes for the “Bauspar-Initiative”, 45.5% yes for the law on the “Buchpreisbindung”, 39.9% yes for the initiative “6 Wochen Ferien für alle!”, 70.5% yes for law on the “Neuregelung der Geldspiele”, and 45.5% yes for the initiative “Schluss mit uferlosem Bau von Zweitwohnungen!”. Hence, for all three proposals where a close race is expected we anticipate a narrow defeat.