Two colleagues, Joe Stone and Steve Haynes, have been estimating economic models of voting in presidential elections for many years. Here's the latest iteration:
A disaggregate approach to economic models of voting in U.S. presidential elections: forecasts of the 2008 election, by Stephen Haynes and Joe Stone, Economics Bulletin: 1. Introduction Well before the 1982 William Clinton campaign phrase "it's the economy, stupid," Kramer 1971, Stigler 1973, and Fair 1978 proposed that voting in Presidential elections is largely determined by economic factors. These models emphasize economic growth, price stability, and the role of parties, and despite very limited degrees of freedom, have significant predictive power for the popular vote for the President. For some elections, however, the predictions of these models go awry, including two recent elections.
In the election of 1992, the models falsely predicted a landslide victory for the incumbent, President George H.W. Bush. Instead, he lost in a close election to Governor William Clinton. In response to this errant forecast, subsequent studies (e.g., Gleisner 1992, Haynes and Stone 1994, and Fair 1996) introduced additional determinants, e.g., the number of consecutive terms the incumbent party held the Presidency, the rate of change in the Dow-Jones stock market average, and changes in the proportion of the population in the military (a proxy for national security concerns). These determinants improved estimates and forecasts of voting models, yet each newly proposed variable raised the danger of "overfitting" given the small number of elections.
The 2004 election again appeared to pose a puzzle. As in 1992, the models (e.g., Fair 2004) predicted a landslide victory in the popular vote for the incumbent President ... George W. Bush,..., yet ... President Bush won by a small margin. One obvious omitted factor was the ongoing conflict in Iraq. To address this omission, in Haynes and Stone 2004 we introduced two factors, working in opposition, to account more fully for the potential role of armed conflicts and national security: the first, a "rally round the flag" proxy, which would increase support for an incumbent President, and the second, a proxy for the economic cost of national defense, which can draw support away from an incumbent. We showed that this second factor outweighed the first one in the 2004 election, reducing President Bush's predicted vote share and thereby narrowing the divergence between the model's prediction and both the pre-election polls and the final vote.
A major limitation with all previous tests of models of Presidential elections is the reliance on aggregate voting data, with very few Presidential elections (most estimates are based upon only 20 to 25 observations). Nevertheless, researchers have attempted to address perceived model limitations by introducing additional determinants of voting, e.g., for the 1992 and 2004 elections, which further increases the danger of "overfitting." In this note, we reexamine traditional economic voting models of U.S. Presidential elections by exploring disaggregate state-level data for the U.S. from 1916 through 2008. Our results reaffirm the general findings in previous aggregate estimates, but also reveal novel monotonic patterns in the disaggregate estimates, including that voters in high income states respond to inflation but voters in low income states respond to real growth. We also show that these income-contingent voting patterns have dramatic implications for forecasts of the upcoming 2008 Presidential election between Senator Barack Obama and Senator John McCain. ...
Our base equation follows Haynes and Stone 2004, modified for estimation with disaggregate state-level data. The model combines the primary components of Fair (1978, 1996, 2002), as extended by Gleisner 1992 to include a stock market variable and Haynes and Stone 2004 to add variables on the number of consecutive terms the incumbent party has been in power and on national security. Eq. (4) is the resulting estimation equation, where expected signs are listed [beside] the regressors [in parentheses].
VOTE = f [PARTY(+), DURATION*P(-), DOWJONES*P(+), GROWTH*P(+), INFLATION*P(-), ARMY*P(+), ARMYSPEND*P(-)]
where VOTE is the Democratic share of the two-party Presidential vote; PARTY (P) is 1 if the incumbent is a Democrat, and -1 if a Republican (all regressors are interacted with P to permit symmetric treatment of the two parties); DURATION is the number of consecutive terms the incumbent party has been in power; DOWJONES is the annual rate of change in the Dow-Jones stock market index, January to October of the election year; GROWTH is the annual growth rate of real per capita GNP (GDP) in 2nd and 3rd quarters of the election year; INFLATION is the absolute value of the annualized inflation rate (GNP/GDP deflator) in the two-year period prior to the election; ARMY is the annualized percentage change of the proportion of the population in the armed forces in the two-year period prior to the election; and ARMYSPEND is the annualized percentage change in the proportion of government spending devoted to national security in the two-year period prior to the election.
Our sample begins with the 1916 U.S. Presidential election...
4. Estimates ...
6. Concluding Comments
This note examines the reliability of aggregate estimates of U.S. voting in Presidential elections, typically based on only 20 or so observations, by re-estimation with state-level panel data, and reports forecasts of the 2008 U.S. Presidential election with these data. We present three conclusions. First, aggregate estimates using data from 1916 to 2004, with only 23 observations, are very similar to state-level panel estimates for the same years based on 1126 observations, suggesting surprising reliability of the findings in the aggregate Presidential voting literature despite limited degrees of freedom.
Second, after partitioning the U.S. states into quartiles based on income level, we find a novel and monotonically consistent income-contingent pattern... For the higher income states, increases in the number of consecutive terms a party has been in office reduces their probability of reelection. Also, for higher-income states, increases in the change in the Dow-Jones stock market index improves the incumbent’s probability of reelection, and increases in inflation reduces this probability. Conversely, only for lower-income states, increases in real growth improves the incumbent’s probability of reelection. Finally, national defense factors, both the positive dimension ("rally-round-the-flag" motive) and the negative dimension (the costs of sustaining a war), are significant for higher-income states but not lower-income states.
These income-contingent patterns in the regression coefficients represent an important yet neglected dimension of voting behavior for the U.S. President. The pattern of significance of the inflation and the Dow-Jones variables only for higher-income states, where presumably wealth preservation is a stronger motive than employment stability, in combination with significance of real growth only for lower-income states, where employment stability is likely a stronger motive than wealth preservation, is consistent with evidence in Joyadev (2006, p.71), who in a different context concludes that "the poor are more likely than the rich to prefer that unemployment be controlled rather than inflation (they are less relatively inflation averse)".
Our third general finding concerns forecasts for the 2008 U.S. Presidential election. Both aggregate and state-level panel data predict a statistical dead-heat. However, disaggregation shows that forecasts systematically differ by state income level. The lowest-income quartile of states clearly prefers Senator John McCain by a significant margin (55.4% to 44.6%); yet the middle-income quartile of states is almost evenly split between the two candidates; and finally the highest-income quartile of states prefers Senator Barack Obama by a substantial and significant margin (53.35% to 46.65%). This evidence suggests that predictions of 2008 voting in key "swing" states should include income-contingent differences in state-level voting preferences, especially in the context of the fall 2008 U.S. financial crisis. ... [complete paper]
I should be sure to note that the forecasts were made on Sept. 17, 2008, and that if they redid them today the aggregate estimates predict a 4 percentage point win for Obama. The change in the Dow-Jones, which can move rather quickly, explains why the predictions change so much in just in one month time. Note also that, as is apparent from the table, it is just voters in the higher income states (also the higher population states) that switch to Obama. Voters in the lower income states only care about real GDP growth.