Explaining Midterm Loss:
The Tandem Effects of Withdrawn Coattails and Balancing
Robert S. Erikson
Department of Political Science
Columbia University
rse14@columbia.edu
Prepared for the 2010 Meeting of the American Political Science Association,
Washington DC, Sept. 2-5
Abstract
This paper tests coattail and balancing theories of midterm loss in congressional elections.
Neither theory can by itself account for the regularity of midterm loss. But together they can.
When, following a close presidential election, there are no coattails to withdraw, ideological
balancing at midterm generates a loss for the presidential party. When voters can anticipate a
presidential landslide allowing them to balance their congressional vote in the presidential year,
the winner’s coattails are withdrawn at midterm. Either way, the presidential party loses seats.
Testing the variables of each theory while controlling for those of the other, this paper finds
strong statistical evidence that both processes are at work to insure midterm loss.
One nearly universal “law” of American politics is that in midterm elections the
presidential party almost always loses seats in the House of Representatives. One
spectacular run occurred from 1938 through 1994. For fifteen consecutive midterm
elections, the “out” party gained seats at midterm. And even when the string was broken
in 1998, Clinton’s Democrats lost strength in terms of the national vote. The 2002
election presented one clear exception to the midterm rule as the Republicans gained both
seats and votes in the wake of 9/11. The 2006 election presented a return to normal, as
George W. Bush’s Republicans lost 30 House seats, based on a 5.5 percent decline in the
popular vote.
What accounts for the phenomenon of midterm loss? Familiar explanations
include included withdrawn presidential coattails (a.k.a. surge-and-decline), ideological
balancing; and a negative referendum on the president. Here, I show how none of these
explanations is sufficient in itself, yet they work together to account for a decline in
support for the president’s party as a near-certain outcome at midterm.
The challenge is not simply to explain why the presidential party loses seats in
most elections but why the phenomenon occurs with near-perfect regularity. One’s first
temptation might be to invoke “referendum theory,” or the idea that midterm loss
signifies a protest against unpopular presidents (Tufte, 1975). Certainly low presidential
approval can lead to a harsh midterm loss. But for referendum theory to account for the
regularity of midterm loss requires that presidents must almost always be embattled at the
midpoint of their election cycle, and this is decidedly untrue. For the 16 post-WWII
midterm years (1946-2006), one can consult the extensive Gallup Poll data bank on
presidential approval. In October of 16 midterm years 1946-2006, the average approval
1
of the president is 53 percent, with the president exceeding the 50 percent threshold in 10
of 16 instances. While unusually high presidential popularity can explain the 1998 and
2002 exceptions of midterm gain, a pattern of persistent presidential unpopularity cannot
account for the midterm loss in the remaining cases. Even excluding 1998 and 2002,
presidents averaged 51 percent approval in the October before the midterm election. To
understand the regularity of midterm loss, we must look elsewhere. However, as shown
below, referendum theory can account for the severity (including the rare revoking) of the
rule of midterm loss.
This brings the discussion to two other prominent theories, left to compete for the
explanation of the regularity of midterm loss: “coattail theory” and “balance theory.”
Coattail theory accounts for midterm loss in terms of a surge for the winning presidential
party in the presidential election year. Balance theory accounts for midterm loss in terms
of voters surging to the out party at midterm.
The coattail argument (e.g., J. Campbell, 1991), is that winning presidential
candidates sweep an exceptional number of ticket-mates into office on their presidential
“coattails,” only to see them swept out again at midterm when the coattails are
withdrawn. In the language of “surge and decline” (A. Campbell, 1966), support for the
presidential winner’s congressional ticket surges due to “strong short-term partisan
forces” which are aided by the participation of persuadable non-“core” peripheral
nonpartisan voters. At midterm these peripheral voters stay home, yielding a more partyline normal vote with minimal “short-term partisan forces” at the national level. In other
words, the winning president’s ticket-mates are boosted by the same short-term forces
2
that generate the presidential victory while the dissipation of these forces by midterm
account for the midterm decline.
How “coattails” work is an intriguing but challenging question. In part it could be
that voters lazily vote a straight-ticket with the presidential choice influencing downballot
choices. It can also be that presidential and congressional choices respond to identical
forces without either actually influencing the other (Ferejohn and Calvert, 1984). For
purposes here, this nuance is not important. The interest for this paper is on the simple
question of the degree to which the partisan vote for the House of Representatives rises
and falls with the presidential vote for whatever reason.
The balancing argument accounts for midterm loss as a surge toward the out-party
at midterm. Alesina and Rosenthal’s version of balance theory (see also Fiorina 1995)
holds that midterm voters shift toward the non-presidential party in order balance off the
ideological tendencies of the president. The theory starts with the fact that the president
is more conservative (if a Republican) or more liberal (if a Democrat) than most voters.
Thus, ideologically attuned voters will tend to support the opposition party to achieve
greater ideological balance between president and Congress. In this way, the midterm
vote provides a partial policy corrective to the presidential year verdict. Although
balance theory is generally framed in terms of liberal-conservatism, it is possible to
broaden the motivational possibilities to incorporate policy motivations generally without
the baggage of ideological language. For instance, some voters might try to balance the
influence of the two parties from the simple motivation of preventing any one group of
rascals to have too much influence over policy. For purposes here, this nuance is not
3
important. Interest is in the degree to which midterm congressional voters are
attracted—for whatever reason—to the party that does not hold the presidency.
It is tempting to depict the distinction between the coattail and balancing theories
as a clash between competing views of the American voter. Coattail voting is in
alignment with the traditional (“Michigan” school) model whereby electoral change is
largely the response of inattentive swing voters (e.g., Converse, 1962, Zaller, 1992,
2004). The idea of unenlightened coattail voters temporarily surging to the winning
president’s party fits this model. Balance theory is in alignment with models of rational
actors who vote based on ideological proximity and think strategically (e.g., Downs,
1957). In extreme one can model midterm balancing voters as solving a complex
problem of N-person game theory (e.g., Mebane, 2000). Of course the two types of voter
models could apply to different strata of the electorate. And it is also possible that some
voters share attributes of the voter as depicted in the separate models. In short, it is
possible for the two models to coexist and work together to explain midterm loss.
Evidence can be marshaled for both the coattail theory and balance theory.
Consistent with coattail theory, parties win a greater share of the congressional vote in
years when they win the presidency rather than lose. (As a postwar average, winning
rather than losing the presidency makes a 2.7 point differential in the presidential year
national vote for the House of Representatives, as a bonus for presidential victory.)
Consistent with balance theory, the parties win more votes at midterm when they do not
hold the presidency than when they do. (The differential here is an even large 4.1 point
differential, as a reward for not holding the presidency.) Still, these are mere tendencies
and not near-universal regularities. By itself, neither theory can fully account for the
4
near-universality of midterm loss. Moreover, at the theoretical level, each has a logical
flaw that makes it implausible as a universal explanation.
As a universal explanation for midterm loss, coattail theory has two problems.
First, coattails should arise only when the presidential winner achieves a decisive victory.
When presidential elections are close, they generate no presidential coattails to withdraw
at midterm. Second, the coattail explanation requires that short-term forces return to
normal at midterm. Only with a dampening of short-term forces at midterm (e.g., to
reflect the normal vote and nothing else) would a coattail-driven surge in the presidential
year guarantee a midterm loss. But the facts defy surge-and-decline theory. Historically
the variance of the two-party vote at midterm is almost twice as great at midterm than in
presidential years (13.7 vs. 7.0 over postwar elections).
Balancing theory also has a problem as a universal explanation for midterm loss.
This problem is the possibility of anticipatory balancing. Many presidential elections are
not close, with polls informing voters of the likely winner. In theory, this knowledge
would allow voters to balance in the presidential year without waiting for midterm, as
voters could balance in advance by casting their congressional votes for the party about to
lose the presidency. This sort of anticipatory balancing in presidential years would leave
no need for further corrective action at midterm that would generate midterm loss.
This leads to the central argument of this paper. Although neither coattails or
balancing can account for midterm loss by themselves, they do so by working together.
One or the other will apply, depending on the nature of the presidential race. Landslide
presidential elections provide coattails to be withdrawn at midterm. Close presidential
elections prevent advance balancing, allowing balancing behavior to newly arise at
5
midterm. Either way, the presidential party loses votes from the presidential to the
midterm year.
Together, coattails and balancing make a powerful engine that drives midterm
loss. Consider the prospects for midterm loss when the presidential outcome is a
landslide, complete with coattails, and anticipated in advance by voters who balance the
presidential winner by supporting the opposition for Congress. Although balancing in
advance negates the need for new balancing at midterm, the midterm withdrawal of
coattails still causes a decline in the presidential party’s seats. Consider the opposite case
of a close presidential election where the outcome is in doubt (no balancing) and no
coattails. Then, despite the lack of coattails, midterm loss occurs as a result of midtermyear balancing.
Note that the argument is more than an appeal to the axiom that two theories
should predict better than one. Rather, it is the two theories complement each other so
that the circumstances where one theory falters are those where the other theory prevails.
Whether the president wins big or small, one expects a subsequent pattern of midterm
loss. With a close presidential election, there are no coattails to withdraw; meanwhile the
electorate can balance at midterm. With a landslide presidential election, voters can
balance in advance; meanwhile coattails lift the congressional vote for the winning party
only to fall at midterm.
The greatest midterm loss would occur with a surprise presidential landslide (no
advance balancing, withdrawn coattails at midterm). The one combination of
circumstances where one would not expect midterm loss would be when the widely
6
anticipated presidential victor wins by a small margin. Then there would be full advance
balancing, no coattails to withdraw, and thus little or no midterm loss.
If the two theories complement each other in the manner described, why has this
not been evident from empirical studies? The problem is that the evidence for each
theory collides with that for the other. When landslide presidential victories are
accompanied by lackluster success downballot, the fault might not be a lack of coattails
but rather that the coattails are obscured by balancing voters offsetting coattail voters.
Similarly, the presence of balancing behavior in presidential years is obscured by the
offsetting vote by coattail voters.
This paper tests for the joint effects of coattails and balancing, with the key being
the measurement of both the actual presidential vote (representing coattail strength) and
expectations of who will win (representing the circumstance for balancing). The paper
finds that large presidential victory margins and a strong expectation of victory each
influence the congressional vote, with the expected opposite signs. Among coattail
voters, landslides generate coattails. Among balancers, the anticipation of presidential
victory generates voting for the opposite congressional party.
The crucial test is for the effect of anticipatory balancing. When anticipatory
balancing in the presidential year cab be estimated and controlled for, the evidence for
coattails is strengthened. Also, evidence for anticipatory balancing in the presidential
year helps to bolster ideological balancing as the source of the presidential party’s
electoral penalty at midterm. If voters are capable of punishing the winning presidential
party in anticipation of their holding office, it is easier to accept that they do so in the
midterm year when the presidential party is known with certainty.
7
The Model
The central task is to statistically model the congressional vote in the 32 postWWII congressional election 1946-2008, for the purpose of further accounting for the
phenomenon of midterm loss.1 The congressional vote is measured as the Democratic
percent of the national two-party vote for the House of Representatives, minus 50 percent
so that a 50-50 vote becomes the zero baseline. The congressional vote is modeled
separately for presidential and midterm years.2
The starting point is the classic “Michigan” model identified with the American
Voter authors. In the presidential year, the national vote for Congress is modeled as a
function of the normal vote, plus short-term forces. The normal vote is represented by
the national division of party identification in October of the election year. October party
identification is measured as the percent Democratic minus percent Republican, pooling
all available October polls.3 The short-term forces are represented by the presidential
vote. The presidential vote is measured as the Democratic percent of the national two
party vote minus 50 percent.
1
Votes are preferable to seats because votes comprise the most direct measure of the electoral
response. The partisan seat division varies over time in its responsiveness to partisan stimuli. As
the number of marginal districts vanish (Mayhew 1974; Jacobson, 1990), the swing-ration (ratio
of votes to seats) declines
This paper is a strictly “aggregate” level analysis. Some might insist that this analysis should be
conducted at the individual level as an examination of degrees of split-ticket voting. Balancing
behavior—even in presidential years—is not a matter of ticket splitting. Rational balancing for
one office is conditional on the voter’s expectations of the electorate’s verdict for the other office,
not the voter’s personal choice for the other office. (Alesina and Rosenthal, 1994). .
2
3
Alternatively, party identification could be measured as Erikson et al. (2002) do for
macropartisanship as the percent Democratic of combined Democrats and Republicans. The
measure here includes independents in the construction. The choice makes no difference,
however, since the alternative measures correlate at +.99. Election-year October partisanship has
an average N of 12,949, ranging from 1,500 to 35,488.
8
For the presidential year model, two additional variables are included to represent
possible balancing behavior. One is the expectation of the presidential vote winner in
terms of knowledgeable voters’ perception of the probability of a Democratic presidential
victory. This crucial variable is estimated from the gambling odds from betting markets
as reported on election eve. Details are presented below.
The second balancing variable is a dummy variable for the current presidential
party at the time of the presidential election. The reasoning is that the electoral demand
for balancing in the prior midterm year may not be entirely satisfied two years later at the
time of the presidential election. In the presidential year, voters may be still see value in
voting against the current presidential party for Congress in order to push policy in the
opposite direction from the persistent ideological tilt of the president’s party.
For midterm elections, the modeling is simpler. As with presidential years, the
normal vote is represented by party identification in October. Balancing is represented
by two variables. One is the dummy variable for the party of the president, which of
course is universally known at midterm. The other is a dummy variable for the party of
the president two years earlier. The idea for the latter variable is that the policy excesses
(from the median voter’s perspective) of the president 2 to 6 years earlier may still need
correction at midterm. The longer the administration party has been in office, the greater
the perceived need for ideological correction.
Measuring Electoral Expectations
The central challenge is to separate the effects of our two highly collinear
predictor variables of the presidential year congressional outcome—presidential coattails
9
and presidential election expectations. Presidential coattails are measured
straightforwardly as the Democratic percent of the two-party presidential vote. Needed is
a measure of the probable outcome as perceived by voters at the time they cast their
ballots. We can conceptualize this variable as the perceived probability (on election day)
that a Democrat rather than a Republican is about to be elected president. The goal is to
approximate the mean subjective probability of a Democratic presidential win among
voters who takes the probable outcome into account when casting a congressional vote.
To obtain an objective measure this subjective probability, I directly borrow
Snowberg,Wolfers, and Zitzewitz’s (2007) compilation of probabilities of a Democratic
presidential win based on actual election eve gambling odds. For the years through 1960,
Snowberg et al. use Rohde and Strumpf’s (2004) report of election-eve market prices
from the once flourishing Wall Street Curb markets on elections. For 1976-2004,
Snowberg et al. use election eve odds by London bookmakers and market prices from the
Iowa Political Stockmarket and Intrade. For the gap years 1964, 1968, and 1972, they
infer prices from public opinion polls. For 2008 I update using Intrade prices.4 Ideally,
these probabilities represent the collective beliefs of informed unbiased observers. At
the same time, oddsmakers—especially London bookmakers—do not necessarily set
prices to reflect what voters were thinking in the booth. (For instance, bookies might tilt
the late price in one direction to offset a flurry of earlier betting the other way.)
Figure 1 lays out the scatterplot between the presidential vote two-party vote
division and the perceived probability of the outcome, 1936-2008. Outcomes and
expectations of the likely winner are highly correlated, as expected. The .81 correlation
10
allows little wiggle-room for testing their separate effects on the congressional vote.
With this high correlation among the key independent variables and only 16 cases, is it
possible to find convincing evidence that the Democratic vote in House elections
increases in response to the national Democratic presidential vote margin (coattails) and
decreases in response to the perceived chance of a Republican presidential victory?
(Figure 1 about here)
Coattails, Balancing, and the National Congressional Vote
We turn immediately to Table 1 for an accounting of the congressional vote in
presidential years to test for the separate effects of coattails and advance balancing.
Equation 1 predicts the vote from party identification alone. As can be seen, party
identification can explain 26 percent of the variance in the national vote for the House of
Representatives. Prediction improves by adding the presidential vote to capture the
impact of short-term forces at the presidential level (coattails). But as the explained
variance rises to 32 percent (equation 2), the presidential vote’s contribution is small and
not even statistically significant. For this preliminary equation, we can ask, where is the
evidence for coattails?
(Table 1 about here)
Equation 3 includes the balancing variables and party identification, while
ignoring presidential coattails. The coefficient for the current presidential party has the
expected negative sign, but is not statistically significant. The key coefficient for the
expected presidential outcome is positive, which of course is the “wrong” sign. Thus we
can ask, where is the evidence for balancing?
At the end of November 3, 2008, (the day before the election), Obama’s expected probability of
winning was .908, calculated from Obama’s share of the combined prices for Obama and McCain
4
11
The solution of course is to allow both coattails and balancing in the same
equation. Equation 4 does this. Now, with the presidential vote, current presidential
party, and expected presidential party in the same equation, all variables are statistically
significant with the “correct” sign, together explaining almost three-quarters of the
variance in the congressional vote. The coattail (presidential vote) effect now appears to
be strong with each percentage point of the presidential vote carrying with it almost half a
percentage point of the congressional vote (b=.0.46). For instance, a presidential win by
10 points (i.e., 55-45) rather than a close call (i.e., 50-50) makes a difference of 2.3 points
in the congressional vote. This suddenly strong coattail effect is offset by strong
balancing the opposite direction. Almost a 5 percentage point differential swings on the
difference between a certain Republican victory and a certain Democratic victory (b=4.73). For instance, a universal belief that the Democrat will win the presidency with
certainty (PrDEMPRES=1.00) rather than a tossup (PrDEMPRES=.50) means that it will
lose about 2.4 percentage points. Although the coattail effect is the stronger statistically,
the offsetting effect of expectations dampens the presidential party’s congressional yield
considerably.
Figure 2 illustrates the offsetting effects of coattails and anticipatory balancing by
means of a residual plot of the vote by first coattails and then balancing. In each
scatterplot, the two variables are residualized as the deviation from the prediction from all
other independent variables in equation 4. When the Democratic presidential vote is
greater than expected, the Democratic congressional vote also exceeds the model’s
expectations. When the Democratic presidential candidate’s perceived chance of victory
winning the presidential election.
12
is greater than the model’s projection, the Democratic congressional vote slumps relative
to expectations of the model.
(Figure 2 about here)
One crucial independent variable is yet to be discussed. Table 1 shows that not
only the anticipated presidential party matters but also the current presidential party, with
a coefficient of -3.61.5 If a party already holds the presidency and is expected to win
again, the balance penalty can be considerable. The difference for the congressional vote
between a party freshly taking over the presidency in an election perceived to be close
and then winning reelection as expected is a penalty shift of about 7 percentage points!6
Against this handicap, offsetting coattails may be at work but go undetected. Certain
landslide elections (1956, 1972, 1984, 1996) may have generated strong coattails that
were hidden from view because offsetting balancing voters were pushing in the opposite
direction. In each of these landslide reelections, the president’s congressional ticketmates
received less voter support than four years earlier when the presidential race was tighter.
The crucial finding from equation 4 is that strong coattails are offset (and
otherwise obscured) by anticipatory balancing the other direction. In fact the average
offset from anticipatory balancing truncates by half the average gain from coattails. Over
the 16 presidential year elections analyzed, the presidential winner averaged 54.4 percent
of the vote, or a 4.4 point surplus vote margin over 50-50. Calculating based on equation
5
The current party coefficient is smaller but more statistically significant than that for the
expected presidential winner. The reason for the difference in significance is that the current
presidential party is relatively uncorrelated with the presidential vote (r=+.34) unlike the case
with the expected winner (r=+.81)
6
The math is as follow. The net effect for winning a tossup presidential election from the
opposition is (+3.61 -.50 X 4.73) = 1.25. The net effect for reelection as expected is -3.61-
13
4, that was worth an additional 2.0 percentage point of the congressional vote beyond the
yield from a 50-50 presidential election (0.46  4.4).7 Meanwhile, on average over the
same election years, the winning presidential party was favored with a .71 probability of
winning in the betting markets. According to equation 4, that was worth an additional 1.0
percent of the vote to the losing presidential party beyond the yield from a .50-50
expectation (.21  4.73).
It should be emphasized that the statistical evaluation is not to choose one
explanation for the vote over another but rather to show that the coattail and balancing
explanations are complementary. To demonstrate the effect of balancing brings out the
evidence for coattails. And incorporating coattails in the model allows the statistical
evidence for anticipatory presidential-year balancing. In turn, evidence that voters are
capable of anticipatory balancing in presidential years strengthens the argument that what
looks like balancing at midterm is in fact ideological balancing.
Presidential years vs. Midterm years
Attention turns next to the evidence for voter balancing in midterm elections.
Table 2 compares the presidential year equation (equation 4 from Table 1, now repeated
as equation 5) with the comparable equation for midterm years. Equation 6 for midterm
years naturally does not include a coattail variable. Comparable to the presidential year’s
predicted presidential party, the midterm equation includes the actual presidential party.
4.73=8.34 The net change is 8.34-1.25 = 7.09 percentage points as the difference between the
expectation of reelection and taking the presidency from the other party in a close election.
7
This calculation ignores the winning presidential party’s congressional vote yield from its shortterm boost in party identification. Parties earn about 4 points on the party identification index
when they win rather than lose the presidency, which according to Table 1 translates into about
one percent of the House vote.
14
Comparable to the current party of the presidential year equation, the midterm equation
includes the previous presidential party (from two years earlier) as an independent
variable. The idea is that when the current administration party has held the presidency
for more than two years, existing policy at midterm will be farther from the median
voter’s preference than if a partisan transition had occurred two years earlier.
(Table 2 about here)
The presidential-year and midterm equations are remarkably similar. The party
identification coefficient is virtually identical in the two equations. The coefficient for
the current presidential party is highly significant and negative (-3.71), suggesting that
the price for winning the presidency includes a loss of almost four percentage points of
the vote at midterm two years later. The midterm vote is also sensitive to the lagged
presidential party, which has a coefficient of -2.37. Adding these two coefficients, one
sees that the differential in the midterm congressional vote between holding the
presidency for at least two terms versus being out of power for two terms is about six
percentage points, with the congressional advantage going to the “out” presidential
party.8
Note that the “balancing” coefficient for the presidential party at midterm is
slightly less negative than the balancing coefficient for the expectation of the presidential
8
Besides the vote in the presidential and midterm years, one can analyze vote intentions between
elections. In February of the midterm year, vote intentions monitored by generic ballot polls are
unrelated to both the presidential vote from the previous election (i.e., coattails have been
withdrawn) and the current presidential party (i.e., balancing is not yet on voters’ minds). As
recorded in the generic polls, voters gravitate to the “out” party over the course of the midterm
campaign. See Bafumi Erikson, and Wlezien, 2010.
15
party in the presidential year. While this differential is not statistically significant,9 it
suggests the possibility of more balancing behavior in those presidential elections where
the outcome is universally known in advance than at midterm. This possibility is not as
strange as it might seem. In the presidential year, the balancing electorate takes into
account the anticipated presidential policy position over the next four years. At midterm,
the clear horizon is only for the next two years.
We now have enough statistical evidence to roughly sketch how the coattails plus
balance model works to insure midterm loss. In the wake of a close presidential election
where the outcome had been uncertain and the winner has no coattails, midterm loss
occurs because the midterm electorate penalizes the president’s party, on average by
almost two percentage points—half the 3.61 midterm differential between holding and
not holding the presidency. If the presidential election is not close, and with the outcome
known in advance, then midterm loss occurs because the victorious president’s coattails
carry in almost one vote for Congress for every two votes the president wins. The
withdrawal of presidential coattails at midterm create a midterm loss.
Senate Elections
One useful robustness check is to repeat the analysis of the House vote on
elections for the US Senate. Each election year approximately 33 or 34 states hold
Senate elections. Because these races are held in states of uneven population, it is not
appropriate to use the net partisan vote in the year’s Senate elections as the dependent
variable. Instead, I use a measure of the mean percent Democratic in the year’s Senate
9
The significance test is from a pooled equation incorporating presidential and midterm years
together, where the party effect is allowed to vary by type of election year.
16
elections, adjusted for unopposed races.10 With this setup, the vote for each senate seat
counts equally.
Table 3 shows the results. Equation 7 models the vote in Senate elections in
presidential elections 1948-2008. Echoing the House analysis, the impact of coattails and
the balance variables—the expected presidential winner and the current presidential
party—are significant predictors, with opposite signs. Thus the crucial findings from
Table 1 are replicated. In fact, each effect is of considerably higher magnitude and
greater significance than for House elections. According to equation 7, the presidential
year vote in Senate elections follows the presidential margin virtually one to one—each
percent of the vote gained for a presidential candidate is also worth (on average) another
one percent for senatorial ticket-mates. A landslide 60-40 presidential win, for instance
would yield over ten points for the president’s senatorial ticket mates. But this effect is
tempered by a large effect of anticipatory balancing the opposite direction. If the
landslide winner is widely predicted to win at the time, the average cost would be about 6
percentage points for the president’s Senate ticketmates compared to the situation where
the election is seen as a 50-50 tossup. Figure 3 illustrates by means of a residual plot.
(Table 3, Figure 3 about here)
Equation 8 shows the companion senatorial equation for midterm years. In
midterms, the coefficient for the president’s party once again is statistically significantly
10
The estimate is the mean Democratic vote per year where the vote for unopposed seats is
interpolated. The interpolation is an average of the most recent past and previous contested vote
for the particular seat (at 6 year intervals) adjusted for the national vote trend for contested seats
between the base time period and the interpolated time periods. For instance, suppose for a
particular uncontested seat the state’s vote six years earlier had been 48 percent Democratic and
the national trend over the following 6 years had been +2 Democratic. The interpolated vote
17
and negatively related to the presidential party. The coefficient shrinks to about one-third
the size of the effect of the anticipated vote in presidential years but very similar to the
estimated effect for House elections. And at midterm the lagged presidential party seems
to have no Senate elections. These differences from equation 7 for presidential years
may be that the six-year horizon of senate elections. Looking forward well into the next
presidential term, the current and recent presidential parties are of lesser consequence.
In sum, the senatorial election analysis reinforces the key findings that the
electorate reacts against the candidates of the anticipated presidential winner in
presidential years and against the sitting president’s party at midterm. This is balancing
at work. At the same time, the more the electorate votes for the presidential winner, it
elects Senators of the winning president’s party. This is coattails at work.
While replication of key findings with Senate elections is a useful cross-check to
see further evidence of coattails and balancing—both in presidential and midterm years—
senatorial elections play little role in the larger story of the presidential midterm loss.
This is because change in Senate composition from the presidential to the midterm year is
a function of changing electoral behavior over a six-year rather than a two-year period.
When the in-party loses Senate seats at midterm (as it usually does, but with less
frequency than for House seats), the loss is due to support for the presidential party at
midterm being lower than it had been six years earlier when the seats that were up for
election were last contested. It is important to remember that results from the previous
presidential election (two years earlier) have nothing to do with senatorial midterm loss
would be 48+2=50 percent Democratic. If the race six years later was also contested, it would be
used similarly for a second interpolation. The average of the two estimates would be recorded.
18
of seats. For further discussion of this obvious but often neglected point, see Grofman,
Brunell adnd Koetzle (1998).11
Further Robustness Checks
This section presents some further robustness checks involving alternative
specifications of the statistical models for both presidential and midterm years, and both
House and Senate elections. The most crucial coefficients to watch for in these tables are
for coattails and anticipatory balancing in presidential years. Except for a somewhat
worrisome dependence on the critical 1948 election (see below), it holds up well under
alternative specifications. The relevant equations are presented in Table 4.
(Table 4 about here)
Since the equations involve time serial data, it is important to perform checks and
possible corrections for autocorrelated disturbances that could otherwise propel
overconfidence in the parameter estimates. One obvious specification is to include the
lagged dependent variable on the right hand side of the equations. When this is done
(equations 9a-9d), the lagged term is significant only for the presidential year House
election equation. The crucial coefficients for coattails and the president’s party (past,
present, or anticipated) are essentially unaffected.
An alternative specification is Prais-Winston regression. Here, the model
assumes a first-order autoregressive time series to the error structure, whereby the error in
one observation is a linear function of the error at the previous observation. A
11
While the two-year Senate seat change is a function of electoral change over six years, the
change in the mean vote for Senate seats over two years (for different seats in the two years)
approximates in magnitude the mean House of Representatives vote loss for presidential parties.
19
complication is that Prais-Winston regression requires one uninterrupted time-series,
whereas the presidential and midterm year observations are staggered. This requires one
pooled series with separate independent variables for each series, with each independent
variable set to zero when not operating. A midterm dummy variable is also added for
identification. The result is separate equations but with a pooled autoregressive structure.
The results are shown in equations 10a-10d. The coefficients and standard errors are
essentially unaffected by the Prais-Winston specification.
Still another specification is to exploit the procedure known as “seemingly
unrelated regression” (SUR). In SUR, two parallel time series equations with different
dependent variables share some of their error. This allows a more efficient estimation of
the standard errors. Here, we have a natural application—running SUR with the House
and Senate election equations, sharing the same unmeasured short-term forces or errors.
The results are shown in equations 11a-11d. With SUR, the coefficients are constrained
to be identical to the OLS coefficients, but the standard errors are not. The standard
errors shrink relative to their OLS counterparts, allowing each independent variable to
achieve a still greater level of statistical significance.12
Next, equations 12a-12d truncate the data by starting each time series one election
later—in 1950 (midterms) and 1952 (presidential years) rather than 1946 and 1948.
Starting the midterm series in 1950 makes little difference. But starting the presidential
year series in 1952 rather than 1948 does matter. The cropping out the 1948 observation
has little impact on the parameter estimates. But starting the series in 1952 rather than
1948 expands the standard errors for the expected presidential outcome, causing the
12
With SUR, the equations are identical to the OLS equations as long as the independent
variables in the two equations are identical, as here.
20
estimate for the House (but not the Senate) to fall below the standard .05 level. The 1948
observation is crucial to the analysis because guided by erroneous polls, the election-day
expectation was that Dewey would defeat Truman. Mistakenly expecting a Republican
president, 1948 voters elected a Democratic Congress—as if to block “President Dewey.”
Fortunately it is possible to apply some extra statistical firepower to help re-shrink
the standard error for the expected vote when the 1948 observation is excluded.
Equations 13a-13d maintain the election exclusions of Equations 12a-12d but apply
seemingly unrelated regression equations. Now, with SUR applied to the House equation
for presidential elections 1952 forward, the coefficient for Pr(DemPres) is .077 (twotailed test), at the cusp of the .05 significance level. Notably, applying SUR expands the
significance level of the Senate equation farther into the comfort zone--to .005. Thus,
while our degree of confidence in the precision of the estimated effect of presidential
expectation is heavily influenced by the inclusion or exclusion of the crucial 1948
observation, this is far from a knockout blow.13
In effect the 1948 observation is a test case that turns a strong statistical argument
into a more compelling one. If we were to ignore the 1948 observation, multicolinearity
among the independent variables would make the statistically analysis less conclusive
than one would like. The best way to overcome multicolinearity is to locate additional
cases where the correlated independent variables are in fact far apart. The 1948 election
fills this bill, as the one contest where electoral expectations and electoral reality severely
separate. The inclusion of the 1948 case provides an especially compelling case that
13
As a further test, the presidential year equation was run without 1948 but with the dependent
variable as the average of the House and the Senate vote. With this specification and OLS, the
expected presidential outcome is significant at the .01 level. And when the dependent variable is
21
beliefs about who will win the presidential election drive some voters toward the
opposition with its congressional votes.
The Dynamics of Midterm Loss: An Analysis in First-Differences
So far the analysis attempts to explain the congressional vote in terms of the level
of support for the Democratic party in different years. The question of midterm loss,
however, is dynamic—about change in the congressional outcome from the presidential
year to the next midterm. Accordingly, this section models the presidential-to-midterm
year vote shift. Attention centers on the role of withdrawn coattails and also the
balancing effect induced by the presidential surprise—the degree to which the
presidential election outcome had been correctly anticipated.
Table 5 models the change in the Democratic vote—presidential year to
midterm—as a function of four variables:

Lagged coattails: the Democratic vote for president in the prior election
(minus 50 percent). The more Democratic the presidential vote, the greater
the Democratic decline.

The presidential shock: the realization of the presidential winner (1 if
Democrat, 0 if Republican) minus the presidential year probability of a
Democratic win from the prediction markets. The larger the Democratic
shock, the greater the Democratic decline.

Party Identification Change: from October of the presidential year to
October of the midterm year. The larger the Democratic gain in
partisanship, the greater the Democratic vote gain.
House seats rather than votes for 1952-2008 using OLS, Pr(DemPres) achieves a .037 level of
22

Presidential approval: in October (Gallup), measured relative to the
1950-2006 October midterm mean (54.3 percent). For Democratic
administrations, the measure is the deviation of approval from 54.3. For
Republican administrations, the measure is the opposite—the degree to
which approval falls below the mean.
(Table 5 about here)
Table 5 offers two equations. Equation 14 models the vote shift as a function of
the first three variables. The coefficients approximate those from the static analysis in
levels for presidential and midterm years separately. All three variables—coattails,
change in the expected presidential party, and change in party identification, are
statistically significant.14 Equation 14 explains almost three-quarters of the variance in
the change in the Democratic vote from midterm to presidential years.15
To fully account for t the midterm vote shift, an important consideration is the
state of political play in the midterm year. Toward this end, equation 15 adds the
measure of presidential approval described above. The estimated effect of approval is a
significance.
14
A companion first-difference equation can be computed to explain the vote shift from midterms
to presidential years. The variables include the presidential vote, the midterm to presidential year
change in the expected presidential party, the change in the lagged presidential party, and the
change in party identification. All variables show statistically significant coefficients.
15
With modeling in first-differences, the 1948-1950 transition can be put under a microscope as a
crucial test case of the balancing hypothesis. Due to the public misperceiving the presidential
winner in 1948, any swing from 1948 to 1950 should be exceptionally large; following their
Democratic voting in 1948 to block “President Dewey,” balancing voters needed to reverse
directions in 1950 to blocking President Truman. Contrarily, if there had been no premature
advance balancing in 1948—if the evidence for it were an illusion—then we would see less of a
Republican swing in 1950 than the balancing model predicts. The swing in 1950 was 3.10
percentage points.. The model (equation 13) predicts a Republican swing of 3.04, virtually on the
mark. Moreover, if the 1948-1950 observation is deleted, the change in the presidential party
remains significant (at. .049).
23
coefficient of 0.14, or a 14 point increase for the presidential party’s vote for every 10
percentage point growth in approval. The incorporation of the approval effect cuts the
unexplained variance in the vote change (already reduced to only 24 percent, from
equation 13) in half (to 11 percent).16 Incorporating approval does not greatly affect the
coefficients for the other variables. In fact, the additional explained variance tightens the
standard errors for the original three variables, thus increasing their degree of statistical
significance.17
Midterm loss: The Accounting
Over the 15 midterm cycles from 1948-1950 to 2004-2006, whether a party won
or lost the presidency resulted in an average partisan swing of 6.2 percentage points of
the vote. From the perspective of the winning presidential party, the midterm loss is half
this amount, or a loss of 3.1 percentage points of the congressional vote from presidential
year to midterm. The present section shows how the twin effects of withdrawn coattails
and partisan balancing account for most of this midterm loss.
16
Besides presidential approval, another potential indicator of the electoral landscape in midterm
years is per capita income growth as an indicator of economic prosperity. Accordingly, I
considered annual per capita income growth, as a deviation from its midterm mean. This
variable contributed nothing (and was far from statistically significant) in all relevant
specifications.
17
Estimation of the effect of presidential approval on the Democratic vote (rather than the
presidential party vote) inevitably requires some awkwardness. In the analysis of first differences
in Table 5, any alternative to the 53.1 percent pivot point would present some shift in the
coefficient for approval and for other variables. Estimating the approval effect on the
presidential party vote yields a variety of estimates. One estimation procedure is to convert the
residuals from the equation 13 prediction to represent the residual presidential party vote and then
predict it from presidential approval. The resultant coefficient for approval is 0.12 (standard
error=0.03). Simply regressing the change in the presidential party vote change on approval
yields an 0.18 coefficient (standard error=0.05). Regressing the level of presidential party
support on the presidential party, identification with the president’s party and approval yields a
coefficient of 0.11 (standard deviation = 0.06).
24
For this exercise, it is necessary to analyze the change in the vote at midterm as
the net change for the presidential party rather than for the Democratic party. Doing this
truncates the variance by about 60 percent. Whereas the variance of the Democratic gain
(loss) at midterm is 16.5 percentage points, the variance of the presidential party’s gain
(loss) is a mere 6.3 percent.18
Table 6 shows the details. For each source of midterm loss (and the coattails plus
balance combination), the table shows the net average change (for the presidential party),
the estimated effect of a unit change from equation 15, and the product of the two as the
estimate of net mean impact on the vote. The final column presents the variance (across
the 15 midterm cycles) of the year-specific estimated effects.
(Table 6 about here)
The first thing to note is that the average net effects roughly add up to the average
midterm vote swing (3.1 percentage points against the presidential party) but also appear
surprisingly small. On average, withdrawn coattails and balancing from the presidential
shock account for vote declines of only 1.40 and 1.16 percentage points respectively.19
Additionally, an estimated 0.47 points of the congressional vote declines due to the
waning of identification with the presidential party between the presidential victory and
midterm.
A particularly revealing aspect of Table 6 is the column of variances for the
components of midterm loss. Note that not only are the net effects due to coattails and
18
Except for 2002, each election result in effect is folded at the 50-50 mark.
19
By construction, the net effect of approval on midterm loss is zero. Also by construction, the
four components plus the trivial intercept must add up to the actual average loss when measured
as the change in the Democratic vote. When “folded” to represent the change in the presidential
25
balancing small; the variance around these expectations are also small. In other words,
the precise presidential party’s penalty from the combination of withdrawn coattails and
the presidential shock does not vary much across the midterm elections. Most
importantly, because loss from coattails and loss from the presidential shock are
negatively correlated, the variance for the net effect of the combination of coattails and
balancing effects is less than the sum of the variances for the two components considered
separately. With an average loss of about 2.8 percentage points from withdrawn coattails
plus the electoral shock, the variance of only 1.20 keeps this penalty at close to a constant
level across elections. On the one hand, withdrawn coattails and the presidential shock
create an average loss of 3.1 percentage points for the party winning the presidency. On
the other hand, there is little variation in their contribution, as together they account for
only 19 percent of the total variance in the presidential party vote.
Together, the combination of coattails and balancing account for the regularity of
midterm loss. To learn the size of this loss, we turn elsewhere—the change in party
identification and presidential approval. In determining the size of the loss (or the rare
gain), party identification is more important than the details of coattails and balancing.
Table 6 shows that the variance in the vote shift due to changing partisanship (1.77) is
slightly greater than that for coattails and balancing either separately or combined. A
similar amount of variance (1.83) is accounted for by presidential approval. Together,
the change in partisanship plus approval account for more than four times the variance in
the presidential party vote than the combination of coattails and presidential shockinduced balancing.
party vote, some jitter is entered due to unequal representation of Democratic and Republican
presidents during the time frame of the study.
26
The result of this exercise should now be evident. On the one hand, coattails and
balancing account for the existence of midterm loss. Call this the “structural” source of
midterm loss. One can anticipate the actuality of the structurally-induced midterm loss
immediately upon the determination of the presidential winner from the size of the
presidential victory and the degree of surprise in the outcome. On the other hand, the
degree of midterm loss is largely shaped by unanticipated political variables reflected in
the change in partisanship and the president’s approval at midterm. Call this the
referendum effect. Referendum theory cannot account for the presence of midterm loss
but it can account its magnitude.
Figure 4 illustrates, by showing the structural and referendum components
together as predictors of the presidential party’s midterm vote shift. The structural
component—due to withdrawn coattails and surprise-induced balancing varies little
around its average of -2.6. The referendum component shows a wide variation, but rarely
enough to predict midterm gain. The 2002 case is the notable exception, with George W.
Bush’s popularity plus post-9/11 Republican gains in party identification overriding the
structural pressure for a loss.20
(Figure 4 about here)
The argument of this section can be summarized as follows. First, withdrawn
coattails and balancing together explain most of the average magnitude of midterm loss.
20
An argument can be made for a more sophisticated but complex division into structural and
referendum components that goes as follows. Suppose we divide the change in party
identification into the expected and surprise portion based on an equation predicting partisan
change from lagged partisanship from the presidential year (b = 0.88). Then , include only the
residual with the referendum component, with the expected component assigned as structural.
The advantage would be to add slightly to the structural part predicted in advance at the moment
the presidential election is decided. As would be expected, the result is very similar to that
reported in the text.
27
(Midterm loss would even be larger except if there were no anticipatory balancing in
presidential elections.) This joint contribution of withdrawn coattails and balancing to
midterm loss is fairly constant over elections. The result is the regularity of midterm
loss, with an average of a few percentage points of the vote. But coattails and balancing
do not help much to predict the variation around the central tendency. Inter-election
shifts in party identification and the president’s popularity account for much of the
residual variation.
Discussion and Conclusion
Attempts to explain midterm loss typically focus on one aspect of the puzzle at
the expense of others. The withdrawal of presidential coattails is a strong contributor—
but not if the presidential election was too close for coattails to matter. Ideological
balancing also contributes—but not if the presidential election was so lopsided that voters
could balance in advance. Presidential approval contributes, along with changes in party
identification, contribute to the size of the loss—but cannot explain the presence of
balancing when presidents are popular. Individually, these explanations fail to account
for the near certainty of midterm loss as a “law” of politics. Collectively, however, they
work together to account for both the regularity of midterm loss, the magnitude of the
loss, and even the rare upset gain (2002).
The waxing and waning of coattails plus midterm balancing correction following
presidential election shocks work in tandem. Together they produce the near certainty of
presidential party loss at midterm. When the presidential election is close and coattails
are absent, the presidential election shock generally induces midterm voting for the
28
opposition. When the presidential election is lopsided and the outcome is widely
anticipated, the winner’s congressional ticketmates ride on the coattails, which are then
withdrawn at midterm.
A crucial part of this argument is the documentation of the evidence for
anticipatory balancing in presidential years. This serves two purposes. It strengthens the
case for ideological balancing. If the electorate can balance based on the anticipation of
the winner, the argument is strengthened that what looks like balancing at midterm is
exactly that. Also, the control for ideological balancing in presidential years strengthens
the evidence for coattails and their withdrawal.
Finally, while withdrawn coattails and balancing providing a structural
explanation for midterm loss, the variation in the loss can be clearly seen as a referendum
on the president and the president’s party. Political conditions at midterm determine the
extent of midterm loss. The near certainty that midterm loss will occur is established two
years earlier with the outcome of the presidential election.
References
Alesina, Albert and Howard Rosenthal. 1995. Partisan Politics, Divided Government,
and the Economy. New York: Cambridge University Press.
Calvert, Randall L. and John A. Ferejohn. 1983. “Coattail Voting in Recent Presidential
Elections.” American Political Science Review. 77: 407-19.
Campbell, Angus. 1966. “Surge and Decline: A Study of Electoral Change.” In Angus
Campbell, Philip E. Converse, Warren E. Miller, and Donald E. Stokes, Elections and the
Political Order. New York: Wiley.
Campbell, James A. 1991. “The Presidential Surge and its Midterm Decline in
Congressional Elections.” Journal of Politics, 53: 477-87.
Converse, Philip E. 1962. “Information Flow and the Stability of Partisan Attitudes.” Public
Opinion Quarterly 26 (Winter): 578–599.
29
Downs, Anthony. 1957. An Economic Theory of Democracy. New York: Harper and
Row.
Erikson, Robert S. 1988. “The Puzzle of Midterm Loss.” Journal of Politics, 50: 101229.
Ferejohn, John A. and Randall L. Calvert. 1984. “Presidential Coattails in Historical
Perspective.” American Journal of Political Science. 28: February, 1984.
Fiorina, Morris. 1995. Divided Government. New York: Allyn and Bacon.
Grofman, Bernard, Thomas L. Brunell, and William Koetzle. 1998. “Why Gain in the
Senate but Midterm Loss in the House? Evidence from a National Experiment.”
Legislative Studies Quarterly , 23 79-90.
Jacobson, Gary C. 1990. The Electoral Origins of Divided Government. Boulder:
Westview Press.
Mayhew, David R. 1974. “Congressional Elections: The Case of the Vanishing
Marginals.” Polity 6: 274-319.
Mebane, Walter. 2000. “Coordination, Moderation, and Institutional Balancing in
American Presidential and House Elections.” American Political Science Review. 94:
37-58.
Rohde, Paul W. and Koleman S. Strumpf. 2004. “Historic Presidential Betting Markets.”
Journal of Economic Perspectives 18 (Spring):127-142.
Snowberg, Erik, Justin Wolfers, and Eric Zitzewitz. 2007. “Partisan Impacts on the
Economy: Evidence from Prediction Markets and Close Elections.” Quarterly Journal of
Economics 122:807-829.
Tufte, Edward. 1975. “Determinants of the Outcomes of Congressional Elections.
American Political Science Review. 69: 812-826.
Zaller, John. 1992. The Nature and Origins of Mass Opinion. Cambridge, UK: Cambridge
University Press
30
1
1964
.5
1960
1952
19681976
2004
2000
.25
1956
1984
1980
1988
1948
1972
0
Market Price
.75
1996
1992
2008
35
40
55
50
45
% Democratic, Presidential Vote
60
65
Figure 1. Election-Eve Probability of a Democratic Victory in Election Markets by
Democratic Percent of the Two-Party Presidential Vote, 1946-2008.
31
4
Balancing
4
Coattails
1948
2
1948
1988
1976
1984
2008
1964
1976
2000
1972
2000
0
1984
1968
1980
1956
1968
1972
1992 1960
1956
1980
1996
1960
19921996
1952
-2
-2
0
2
Residual Democratic Vote for House
1988 1964
2008
2004
2004
-4
-4
1952
-6
-3
0
3
Residual Dem. Vote for Pres.
6
-.4
-.2
0
.2
.4
Residual Price of Dem. Win
Figure 2. Residual Plots Predicting the Vote for House of Representatives from Vote for
President (Coattails) and from Election Market Prices (Balancing). For the left panel
(coattails), the two variables are the residuals from regressing on market prices, the lagged
presidential party, and party identification. For the right panel (balancing), the two variables are
the residuals from regressing on the presidential vote, the lagged presidential party, and party
identification.
32
Coattails
Balancing
1948
1956
3
1964
1988
19601988
1956
1960
1968
1976
1964
1968
0
1976
1992
20082004
2000
2004
1980
1972
2000
1992
2008
1952
1984
-3
-3
0
3
Residual Mean Dem. Vote for Senate
6
6
1948
1980
1972
1984
1996
1996
-6
-6
1952
-5
0
5
Residual Dem. Vote for Pres.
10
-.4
-.2
0
.2
.4
Residual Price of Dem. Win
Figure 3. Residual Plots Predicting the MeanVote for Senate Races from Vote for
President (Coattails) and from Election Market Prices (Balancing). For the left panel
(coattails), the two variables are the residuals from regressing on market prices, the lagged
presidential party, and party identification. For the right panel (balancing), the two variables are
the residuals from regressing on the presidential vote, the lagged presidential party, and party
identification.
33
2
0
-2
1994
1962
1998
1970 1990
1978
1982
2006
-4
1958
1954
1986
1950
-6
1966
1974
2002
-4
-2
0
2
Party ID Change + Pres. Approval Prediction
4
Figure 4. Predicting the presidential party vote from structural variables (withdrawn
xoattails and balancing) versus Referendum variables (party identification change plus
presidential approval). Observations represent the predictions from equation 15. The diagonal
line (which incorporates the intercept) represents values at which the expected vote is 50 percent
Democratic.
34
Table 1. Predicting the Democratic Vote for the U.S. House of Representatives,
Presidential Years, 1948-2008
(1)
(2)
(3)
(4)
Party ID
Add
Add
Coattails
Only
Coattails
Balancing
and
Balancing
COATTAILS (Dem.
b
0.15
0.46
Presidential Vote minus
s.e.
(0.10)
(0.11)
50 % )
p
.15
.002
Pr(DEMPRES)
(Probability of
Democratic President)
b
s.e.
p
1.89
(1.66)
.278
-4.73
(2.00)
.038
CURRENT
PRESIDENTIAL PARTY
(1=Dem., 0=Rep.)
b
s.e.
p
-2.25
(1.06)
.055
-3.61
(0.79)
.001
OCTOBER PARTY
IDENTIFICATION,. (%
Dem. minus % Rep.)
b
s.e.
p
0.21
(0.08)
.026
0.19
(0.08)
,033
0.23
(0.08)
.012
0.25
(0.05)
.001
Constant
b
s.e.
p
-.0.09
(1.36)
.939
0.29
(1.21)
.813
-0.28
(1.28)
.828
3.66
(1.31)
.018
.257
.322
.404
.733
Adjusted R squared
Root MSE
2.27
2.17
2.04
1.36
N = 16. The congressional (House of Representatives) vote and the presidential vote are
each measured as the Democratic percent of the two-party vote, minus 50 percent. The
probability of a Democratic president is based on election-eve election market prices.
Reported p-values are based on two-tailed tests.
35
Table 2. Predicting the Democratic Vote for the U.S. House of Representatives,
Presidential Years and Midterm Years , 1946-2008
(5)
(6)
Presidential Years
Midterm Years
COATTAILS (Dem. Presidential
b
0.46
Vote minus 50 % )
s.e.
(0.11)
p
.002
Pr(DEMPRES) (Probability of
Democratic President)
b
s.e.
p
-4.73
(2.00)
.038
CURRENT PRESIDENTIAL PARTY
(1=Dem., 0=Rep.)
b
s.e.
p
-3.61
(0.79)
.001
LAGGED PRESIDENTIAL PARTY
(1=Dem., 0=Rep.)
b
s.e.
p
OCTOBER PARTY
IDENTIFICATION,. (% Dem. minus
% Rep.)
b
s.e.
p
0.25
(0.05)
.001
0.25
(0.05)
.001
Constant
b
s.e.
p
3.66
(1.31)
.018
1.74
(1.04)
.122
.733
.774
Adjusted R squared
-3.71
(0.89)
.001
-2.37
(0.89)
.021
RMSE
1.36
1.76
N
16
16
The congressional (House of Representatives) vote and the presidential vote are each
measured as the Democratic percent of the two-party vote, minus 50 percent. The
probability of a Democratic president is based on election-eve election market prices.
Reported p-values are based on two-tailed tests.
36
Table 3. Predicting the Democratic Vote for the U.S. Senate, Presidential Years
and Midterm Years , 1946-2008
(7)
(8)
Presidential Years
Midterm Years
COATTAILS (Dem. Presidential
b
1.04
Vote minus 50 % )
s.e.
(0.17)
p
.000
Pr(DEMPRES) (Probability of
Democratic President)
b
s.e.
p
-13.44
(2.92)
.001
CURRENT PRESIDENTIAL PARTY
(1=Dem., 0=Rep.)
b
s.e.
p
-3.30
(1.15)
.015
LAGGED PRESIDENTIAL PARTY
(1=Dem., 0=Rep.)
b
s.e.
p
OCTOBER PARTY
IDENTIFICATION,. (% Dem. minus
% Rep.)
b
s.e.
p
0.19
(0.08)
.031
0.30
(0.07)
.001
Constant
b
s.e.
p
8.64
(1.91)
..001
0.15
(1.41)
.916
.729
.615
Adjusted R squared
-3.54
(1.21)
.012
-0.13
(1.20)
.096
RMSE
1.99
2.37
N
16
16
The Senate vote and the presidential vote are each measured as the Democratic percent of
the two-party vote, minus 50 percent. The Senate vote is the mean state vote as
described in the text. The probability of a Democratic president is based on election-eve
election market prices. Reported p-values are based on two-tailed tests.
37
Table 4. Alternative Equations for House and Senate Election Equations, Presidential and Midterm
Years.
Lagged
PraisSeemingly
OLS,
SUR,
Dependent
Winston
Unrelated
Start with
Start with
Variable
Regression
Regressions
1950/1952
1950/1952
(9a)
(10a)
(11a)
(12a)
(13a)
House of Representative
Elections, Presidential Yrs.
Lagged Dependent
b
0.33
Variable
s.e.
(0.14)*
.
Dem. Presidential Vote
b
0.50
0.46
0.46
0.45
0.45
minus 50 % )
s.e.
(0.10)***
(0.12)***
(0.10)***
(0.16)*
(0.13)**
Pr(DEMPRES) Prob. of
Dem. Pres.
b
s.e.
-5.14
(1.70)*
-4.27
(1.98)*
-4.73
(1.66)**
-4.57
(3.00)
Current Pres. Party
(1=Dem., 0=Rep.)
b
s.e.
-2.07
(0.94)
-3.54
(0.84)***
-3.61
(0.65)***
-3.62
(0.83)***
-3.62
(0.68)***
October Party
Identification (D-R )
b
s.e.
0.15
(0.06)*
0.24
(0.04)***
0.25
(0.04)**
0.25
(0.06)***
0.25
(0.05)***
Constant
b
s.e.
3.81
(1.11)*
3.58
(1.35)*
3.66
(1.09)***
3.56
(1.91)
3.56
(1.56)*
.810
1.15
16
.753
1.45
16
.804
1.13
16
.725
1.43
15
.803
1.43
15
(9b)
(10b)
(11b)
(12b)
(13b)
0.17
(0.28)
-4.22
(1.30)**
-2.17
(1.05)
0.24
(0.07)**
-3.58
(0.76)***
-2.38
(0.74)**
0.24
(0.04)***
-3.71
(0.77)***
-2.37
(0.77)**
0.25
(0.05)***
-3.74
(0.98)**
-2.40
(0.95)*
0.26
(0.06)**
-3.74
(0.94)***
-2.40
(0.82)**
0.26
(0.05)***
1.66
(1.18)
.670
1.89
15
1.63
(1.30)
.753
1.45
16
1.74
(0.90)
.819
1.53
16
1.70
(1.14)
.690
1.84
15
1.70
(0.98)
Adjusted R squared
RMSE
N
House of Representative
Elections, Midterm Years
Lagged Dependent
Variable
Current Pres. Party
(1=Dem., 0=Rep.)
Lagged Pres. Party
(1=Dem., 0=Rep.)
October Party
Identification (D –R)
Constant
Adjusted R squared
RMSE
N
-4.57
(2.45)
15
38
Table 4 continued. Alternative Equations for House and Senate Election Equations,
Presidential and Midterm Years.
Senate Elections, Presidential
Years
Lagged Dependent
b
Variable
s.e.
Lagged
Dependent
Variable
(9c)
PraisWinston
Regression
(10c)
Seemingly
Unrelated
Regressions
(11c)
OLS,
Start with
1950/1952
(12c)
SUR,
Start with
1950/1952
(13c)
0.26
(0.25)
Dem. Presidential Vote
minus 50 % )
b
s.e.
1.07
(0.17)***
1.11
(0.16)***
1.04
(0.14)***
0.91
(0.23)**
0.91
(0.19)**
Pr(DEMPRES) Prob. of
Dem. Pres.
b
s.e.
-13.34
(2.91)***
-14.16
(2.82)***
-13.43
(2.42)***
-10.92
(4.23)*
-10.92
(3.45)**
Current Pres. Party
(1=Dem., 0=Rep.)
b
s.e.
-2.17
(1.59)
-3.63
(1.20)**
-3.30
(0.95)
-3.38
(1.17)*
-3.38
(0.95)**
Oct. PartyID (D –R)
b
s.e.
0.09
(0.12)
0.23
(0.06)***
0.19
(0.06)**
0.20
(0.08)*
0.20
(0.06)**
Constant
b
s.e.
8.76
(1.91)***
8.68
(1.91)***
8.64
(1.58)***
7.08
(2.69)*
7.08
(2.20)**
Adjusted R squared
RMSE
N
.730
1.98
16
.732
2.06
16
.801
1.65
16
.654
2.02
15
.754
2.02
15
Senate Elections, Midterm
Years
Lagged Dependent
b
Variable
s.e.
(9d)
(10d)
(11d)
(12d)
(13d)
0.08
(0.30)
Current Pres. Party
(1=Dem., 0=Rep.)
b
s.e.
-4.36
(1.57)*
-3.31
(1.08)**
-3.54
(1.04)***
-4.02
(1.23)**
-4.02
(1.06)***
Lagged Pres. Party
(1=Dem., 0=Rep.)
b
s.e.
-0.46
(1.25)
-0.61
(1.06)
-0.13
(1.04)
-0.45
(1.20)
-0.45
(1.03)
Oct. Party ID (D –R)
b
s.e.
0.34
(0.08)**
0.23
(0.06)***
0.30
(0.06)***
0.35
(0.08)***
0.35
(0.07)***
Constant
b
s.e.
-0.34
(1.50)
1.22
(1.28)
0.15
(1.22)
-0.39)
(1.44)
-0.39)
(1.23)
Adjusted R squared
.591
.732
.692
.623
RMSE
2.41
2.06
2.05
2.31
N
15
16
16
15
15
Prais-Winston equations are pooled across all years, with effects estimated separately by presidential and
midterm year as indicated in the text. Separate midterm and presidential year dummies are estimated. (The
party identification effect is pooled.) The Prais-Winston Adjusted R Squareds and RMSEs are derived for
the full sample.of 32 cases. For both House and Senate election equations, rho=.38..
39
Table 5. Predicting Change in the Democratic Vote from the Presidential Year to
Midterm, 1944-1946—2004-2006.
(14)
(15)
Percent
Change
in
Democratic
Vote,

Presidential Year to Midterm
WITHDRAWN COATTAILS (50%
b
0.31
0.31
minus President’s Vote Two Year
s.e.
(0.12)
(0.08)
Earlier)
p
.026
.004
PRESIDENTIAL SHOCK (Change
from Pr(DEMPRES) to Actual
Presidential Party (1=Dem., 0 =Rep.)
b
s.e.
p
-4.48
(1.53)
.014
-3.88
(1.08)
.005
 OCTOBER PARTY
b
s.e.
p
0.33
(0.14)
.039
0.29
(0.10)
..017
IDENTIFICATION,. (% Dem. minus %
Rep.)
PRES. APPROVAL INDICATOR
(October deviation from mean, positive
if Dem. president., negative if Rep.
president
Constant
b
s.e.
p
b
s.e.
p
0.14
(0.04)
.005
-0.61
(0.53)
.279
-0.18
(0.39
..661
Adjusted R squared
.763
..886
Root MSE
N
1.98
15
1.38
15
40
Table 6. Estimating the Net Effects of Withdrawn Coattails, Balancing, and Party
ID Change on Midterm Loss for the Presidential Party
Component of Midterm Loss
Mean for
Coefficient
Mean Net
Variance
Presidential
(from
Effect
of Net
Party
equation
(Mean 
Effect
15)
Coefficient)
Withdrawn Coattails (50%
-4.52
0.31
-1.40
1.64
minus President’s Vote Two
Year Earlier)
Balancing (1 minus President’s.
Win Probability Two Years
Earlier)
0.30
-3.87
Coattails + Balancing
-1.16
1.00
-2.56
0.97
 Party Identification for Pres.
Party, Midterm minus
Presidential Year
-1.64
0.29
-0.47
1.77
Pres. Approval (October
deviation from mean, +if Dem.
president., - if Rep. president
1.23
0.14
0.00
1.83
-0.47
3.85
 Party + Approval
Constant
0.04
Total Prediction Equation
Actual Change in Presidential
Party’s Vote
-3.12
0.04
-2.99
5.72
-3.12
6.27
41