1. The Crystal Ball Forecasting Elections in the United States

2. I. Long-Term Forecasts: Can we do better than flipping a coin? A. Elections have patterns: Winning streaks B. Streaks tend to be 2-4 elections long

3. C. The weighted coin flip model 1. Best guess for Presidential elections years in advance

4. 2. Performance: Better than flipping a coin…

5. D. Congress 1. President’s party usually loses seats during midterms 2. More seats to defend = higher probability of losing seats (esp. in Senate -- outperform in 2010 and your seats in swing states will be difficult to hold in 2016). Dems outperformed in 2006: now have 23 seats to defend vs. only 10 for GOP.

6. 3. Otherwise, bet on the incumbents

7. II. Short-term forecasts A. Opinion polls: How well do they predict elections?

8. Accuracy Depends on Timing

9. B. The Wisdom of Crowds 1. How often is the public right? (early Oct)

10. 2. Electoral Stock Markets You can “buy” stock in a candidate (real money futures contracts)  essentially a form of gambling Theory: people who invest money have a huge stake in the outcome, so have incentives to weigh information carefully (invisible hand)

14. Congress 2006: Blue (DH/DS) comes from behind Black (DH/RS) and Red (RH/RS)

21. III. Statistical Models A. Presidential Elections: Models attempt to predict popular vote share. Incumbent party vote share depends on… 1. Presidential Popularity: Job Approval 2. Economy: Real GDP Growth, Unemployment Rate, Personal Disposable Income 3. Incumbency: 4 years helps, 8 years hurts

23. 2008 2012 Prediction

24. III. Statistical Models A. Presidential Elections: Models attempt to predict popular vote share. Incumbent party vote share depends on… 1. Presidential Popularity: Job Approval 2. Economy: Real GDP Growth, Unemployment Rate*, Personal Disposable Income 3. Incumbency: 4 years helps, 8 years hurts

25. 08 2012 Prediction

26. Gore 2000 Bush 2004 McCain 2008 Obama 2012 (projected from 1 st two quarters)

27. III. Statistical Models A. Presidential Election Variables: Models attempt to predict popular vote share. Incumbent party vote share depends on… 1. Presidential Popularity: Job Approval 2. Economy: Real GDP Growth, Unemployment Rate*, Personal Disposable Income 3. Incumbency: 4 years helps, 8 years hurts

28. 2012 Prediction

29. 4. Labor Day Polls: Good predictors of winner, poor predictors of vote share

30. 5. What about race? Lewis-Beck and Tien (2008) noted correlation between attitudes towards race and support for Obama in primaries If same relationship held for general election, prediction of McCain share shifted from 42.4 to 49.9 (Actual = 46.5) = some evidence of a racial effect (but should be reflected in polls)

31. Lewis-Beck and Tien (2009): “We argue that the expected landslide did not materialize, because a portion of the electorate could not bring itself to vote for a black candidate. What portion? In our paper, we estimated that number, on net, at 11.5%. If we apply that correction to this current Obama forecast, we get 58.7 × 0.885 = 51.9 %. This estimate is very close to the two-party popular vote share that candidate Obama won.”

32. Tien, Nadeau, and Lewis-Beck (2012) “Our evidence, drawn from an analysis comparable to that carried out for 2008, suggests Obama will pay a racial cost of three percentage points in popular vote share. In other words, his candidacy will experience a decrease in racial cost, if a small one. In 2008, this racial cost denied Obama a landslide victory. In the context of a closer election in 2012, this persistent racial cost, even smaller in size, could perhaps cost him his reelection.”

33. 6. Catastrophe and the Limits to the Predictions Most of the 2008 models performed poorly, consistently underestimating Obama support Some authors argue the Wall Street meltdown was unforeseeable But did it determine the race?

34. Campbell (2012) claims it did:

35. B. Presidential Election Models: Comparison ModelVariables 1992 error 1996 error 2000 error 2004 error 2008 error Ave error Abramowitz Approval, GDP, 3 rd Term 0.22.12.92.50.81.7 Campbell Polls, GDP, Incumbency 0.63.42.52.66.13.0 Holbrook Approval, Consumer Satisfaction, Incumbency --2.6104.92.45.0 Lockerbie Economic expectations, Terms --2.9106.44.96.0 Lewis-Beck and Tien Approval, GNP, Jobs, Incumbency, Race --0.15.11.03.4 (1.4) 2.4 (1.9) Norpoth Primary vote, Previous election share, electoral realignment --2.44.73.52.53.3 Wlezian and Erikson Polls, Economic indicators --1.34.90.51.32.0 Models overestimated incumbent % in ‘92, ‘96, ‘04, ‘08 and underestimated in ‘00

36. C. Congressional Elections: Key Variables 1. Timing: President’s party tends to lose seats in midterms (worse for Democrats) 2. Exposure: How many seats are exposed? a. House: Party has higher % of seats than historical average b. Senate: Number of each party’s seats up for grabs 3. The referendum model: Presidential approval helps/harms incumbent party

37. D. House seats: approval, growth, and timing

38. House seat model performance

39. E. Senate Seats: Exposure, Referendum, and Partisan Advantage

42. F. Recent models: Taking the Electoral College into Account National-level Congressional models use votes-to-seats curves Models are essentially poll-based, although some add state-level economic data to polls Examples: 538 and Princeton538Princeton Weaknesses: State polls are volatile, national economic data excluded, no control for incumbency effects

43. IV. Do Campaigns Matter? Yes…but we still don’t know exactly how