Statistically Measuring 2016 Presidential Candidate Electability: Evidence from Prediction Markets
AEI Economics Working Paper 2016-04
Posted: 10 May 2016
Date Written: February 3, 2016
We use prediction market data from Betfair, the world’s largest Internet betting exchange, to measure the electability of 2016 Presidential candidates using regressions that compare the general election contest and party nomination win probabilities for each candidate. A candidate who is more electable should see a higher response of the odds of becoming president to a given change in the odds of receiving a party’s nomination. Our regressions estimate this response for each major candidate, and these estimates constitute our measures of electability. The data indicate that there is a high degree of variability in the electability of candidates. We present a number of different model estimates, in order to explore the sensitivity of results to specific assumptions. Across specifications, we find that Jeb Bush and Marco Rubio are the most electable, while Chris Christie and John Kasich also have high electability scores. We also find that Hillary Clinton has the highest electability score in the Democratic field and that Bernie Sanders’ electability is sensitive to specification changes. We interpret Mrs. Clinton’s very high electability scores as suggesting that markets are pricing in a significant probability that Republicans will nominate a candidate who has little chance in the general election.
Keywords: Forecasting and Prediction Methods, Economic Models of Political Processes, Information and Market Efficiency
JEL Classification: C53, D72, G14
Suggested Citation: Suggested Citation