INFLUENCES ON PRESIDENTIAL ELECTION OUTCOMES

There are, no doubt, many potential variables we could include in the model. In the interest of parsimony and recognizing the small number of cases, we need to be selective. The literature on elections is replete with economic models. Because there are a small number of cases, we cannot include multiple variables representing economic conditions. One of the ongoing debates is whether voters look to the past or the future when casting a ballot. In one sense the decision is easy. The retrospective and prospective economic items are strongly correlated (.84).

Recognizing there are still debates on this topic, I follow the lead of Fiorina (Reference Fiorina1981), Lewis-Beck (Reference Lewis-Beck1988), Lockerbie (Reference Lockerbie2008, Reference Lockerbie2020), and Nadeau, Lewis-Beck, and Bélanger (Reference Nadeau, Lewis-Beck and Bélanger2013), whose models focus on the prospective. I and those who find themselves in disagreement with me can take some comfort in the strong relationship between the retrospective and prospective economic items at the aggregate level. As with earlier endeavors, I make use of the item from table 8 of the Survey of Consumer Attitudes and Behavior. Instead of averaging the scores from the second quarter, as done in the past, I make use of the percentage replying in the negative from June of the election year.Footnote ¹ This is because the volatility of the item and the desire to have the latest information consistent with an early forecast. The specific question is “Now looking ahead—do you think you (and your family living there) will be better off or worse off financially a year from now, or about the same?”Footnote ²

I should note here that this measure includes no sense of attribution to either party. One can think the economy will be worse off in the future and still vote for the incumbent party because one believes that the incumbent party will be better than the opposition. The retrospective item has one advantage. Even though it does not reference the incumbent party, there is no ambiguity about which party controls the White House during this time.

Aside from economics, there are other factors that are relevant for forecasting presidential elections. Specifically, Abramowitz (Reference Abramowitz, Campbell and Garand2000) has shown that the incumbent party’s share of the two-party vote is negatively related to how long it has controlled the White House. Specifically, having held the White House for two terms puts one in a worse position than does holding it for just one term. Nonetheless, we should note that in the last two elections, the incumbent party has lost after holding for just one term. Before that in 2012, the incumbent won with a lower percentage of the vote than was won in the original election. To account for the penalty for having held the White House for a long time, I make use of the log of the time the incumbent party has controlled the presidency.Footnote ³

In the interest of humility and transparency, this forecast model was off by quite a bit in 2020. Donald Trump was forecast to be the victor by a comfortable margin. The actual result was a comfortable victory for Joe Biden. There are several potential explanations for this. The unprecedented nature of the pandemic may have heightened anxiety about the overall state of the economy. Even with the modest pessimism about the economy in 2020, the pandemic may have given many people pause about voting to return Trump to the White House.

Similarly, the protests following the death of George Floyd and the rise of the Black Lives Matter movement might have made people more uneasy about the future. The noneconomic anxiety might have also made people less inclined to give Trump another four years. The 2020 election underscored the limitations of these models in the face of unprecedented national and international events. I still have confidence that the prospective model will perform well.

Consequently, I do not think there needs to be a fundamental change in the model. Even with the error of 2020, the model performs reasonably well over time. Nonetheless, forecasters and those who use forecasts should be aware of the role of events outside the model in influencing the accuracy of the forecast. Similarly, we ought not to be too quick to jump to current events to revise our models or explain mistaken forecasts. If we do so, we run the risk of engaging in ad hoc speculation that cannot be tested.

PRESIDENTIAL ELECTION FORECAST

Table 1 shows the results from the equations forecasting elections. The results of this effort are similar to those of earlier years. The more pessimistic people are, the more likely the incumbent party is to lose. As before, the longer a party has controlled the White House, the lower its share of the vote. The forecast for 2024 is that the incumbent party will receive 49.09% of the two-party vote. This and the popular press discussions of the election suggest a very close election.

Table 1 Forecasting Equations 1954–2024

Note: Significance levels in parentheses.

We can look at the out-of-sample equations to assess the utility of this model. To assess the model’s performance, we take one case out of the data set, reestimate the equation, and use the results to forecast the excluded election. We then repeat this process for each case. The results are shown in table 2. I have put the election years forecast correctly in bold. The average absolute error is 3.3 percentage points. Even with all the vagaries of presidential elections, the model does an excellent job of forecasting presidential elections.

Table 2 Out-of-Sample Presidential Forecasts and Errors

Influences on House Election Outcomes

House elections are a bit different from presidential elections. The House elections are more likely to be influenced by the incumbency. We know that if an incumbent seeks reelection, that incumbent is likely to be successful. We are confident in predicting that the overwhelming majority of House incumbents who seek reelection will win reelection (Alford and Hibbing Reference Alford and Hibbing1981; Collie Reference Collie1981; Ferejohn Reference Ferejohn1977). Open seats present the best opportunity for a party to pick off seats from the other party. To account for this, I include an interaction term between the number of open seats and whether it will be a good or bad year for the incumbent party. Midterms are, by definition, a bad year for the president’s party. On-year elections are a little more complicated. If the vast majority of the electorate believes one party is likely to be victorious, we should expect it to pick up more seats, if there are a large number of open seats. The number of open seats is multiplied by -1 if it looks like a bad year for the incumbent president’s party, by +1 if it looks like a good year for the incumbent president’s party, and by 0 if it is neither a good nor a bad year for the incumbent president’s party.Footnote ⁴

Looking at table 1, we can see that this equation does a reasonably good job of forecasting House elections. The more pessimistic people are, the more likely the incumbent party loses seats. The more open seats in a good (bad) year there are, the more seats the incumbent party is likely to gain (lose). The forecast for 2024 is that the incumbent party will lose 12 seats. We can, as before, look at the out-of-sample equations to assess the utility of this model. The results are shown in table 3. The average absolute error is 16.8 seats.

Table 3 Out-of-Sample House Forecasts and Errors