OF TIME AND CANDIDATES
A Forecast for 1996
HELMUT NORPOTH
State
University of New York, Stony Brook
To forecast presidential elections, I explore the dynamic of the vote ("time") and introduce a
measure of candidate support that covers both the incumbent and the challenger. Stochastic
models help identify the dynamic of the presidential vote as second-order autoregressive. The
strength of the candidates is gauged by an index of electoral success in presidential primaries—in
particular, whether the nominee won the first pnmary. Also included as a vote predictor is the
economy, as measured by gross national product (GNP) growth and inflation in the election year.
The forecasting equation predicts victory for Bill Clinton, with 57.1 % of the major party vote in
November 1996. Time is on his side, in the sense that the autoregressive dynamic favors election
of a presidential candidate whose party just captured the White House. But what predicts a
comfortable margin is Clinton’s edge in the candidate comparison, with the economy exerting
little electoral pull this year.
Let’s face it, we all indulge in election forecasting. Who would deny
having at least a hunch about the outcome of an upcoming election?
Many go further and bet money on electoral races-illegally, in most
cases, unless done through the services of the Iowa Electronic Market.
And some of us labor at distilling electoral wisdom into mathematical
formulae to produce precise predictions. No phenomenon in our
discipline comes in such a regular, precise, and verifiable form as an
election. Few research topics have consumed more funding support
than the study of electoral choice. We ought to have more to show for
that support than plausible postmortems.
Author’s Note: Although this is not a work of fiction, any resemblance between the forecasts
depicted in this article and the author’s own electoral preferences is purely coincidental. The
author also disavows liability for losses of any kind (financial, emotional, political) resulting
from decisions based on those forecasts. Earlier versions of this article were presented at
conferences or lectures in Alicante, Spain; Dusseldorf and Essen, Germany; London, England;
AMERICAN POLITICS QUARTERLY, Vol 24 No 4, October 1996 443-467
0 1996 Sage Publications, Inc
443
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444
Yet election forecasting is also one of those temptations, like
gambling, that rational people may find prudent to resist. For all the
research on electoral behavior, predicting the vote remains a high-risk
venture. Putting down a forecast on paper before the event is a sure
way to invite ridicule; there is no way to be right all the time, and there
is no way to hide from a wrong forecast when the outcome is a win-lose
case. More seriously, forecasting the choices of human beings, even
in the aggregate, is presumptuous. Many voters remain unsure about
their choices long after prognosticators have made their pronouncements. Are the latter so much smarter than the people whose behavior
they study?
If anything, forecasters may be more naive, given pitfalls akin to
Scylla and Charybdis facing them. On one hand, the universe of past
elections needed to fashion a forecasting model is pitifully small; on
the other hand, there is a forbiddingly long list of reasonable explanations of vote choices. Too few cases and too many variables make for
underspecified models, which are bound to lead to embarrassing
forecasts sooner or later. Not only that, but information on key predictors of the vote, such as the economy, is often not available in time
before election day. Hence the best we might do is offer vote forecasts
that are conditional on the guesses we have to make about our
predictors.
Wary, though undeterred by these warnings, the following is an
attempt to exploit a largely untapped treasure for electoral forecasting.
The venture is inspired by the indisputable premise that elections are
not decided in a manner of coin flips. Politics abhors chaos as much
as a vacuum. However unique each election may be, it bears some of
the markings of previous ones. But what is the balance between inertia
and change in electoral politics? In this article I use stochastic timeseries models to identify the dynamic process of presidential elections.
Although this approach is capable of generating predictions of the
presidential vote a full 4 years ahead of time, and although it can do
so without requiring any additional information, the main interest here
and Tucson, Ft. Lauderdale, and Chicago. Notes of thanks go to Antonio Alaminos, Mana Jose
Gonzales, Heiner Meulemann, Albert-Leo Norpoth, David Farrell, and Jerry Rusk.I am also
grateful to Jim Garand, Herb Weisberg, and an anonymous reviewer for comments on this article.
One section of this article is based on material that appeared in the June 1995 issue of PS: Political
Science and Politics.
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445
is to take advantage of the stochastic component of the vote in
fashioning multivariate models that are useful for short-run forecasting. Whatever the predictor variables may be, a forecaster can only
gain from the parameters of time.
Typically, vote models have paid close attention to the economy
and the incumbent’s approval as predictors of elections. This exercise
in forecasting introduces a measure of candidate strength that takes
account of both incumbent and challenger. The comparative gauge
relies on the electoral success of the nominees in presidential primaries. Nominees who have failed at that level of competition rarely
achieve victory in the general election. Turning to the election at hand,
what is the forecast for 1996? It is the consensus of all models
presented later in this article that Bill Clinton will defeat Bob Dole.
Time is on the side of the Democratic incumbent in 1996, but the
candidate comparison favors him to the point of predicting a comfortable margin of victory-57.1% of the major party vote.
1
A STOCHASTIC MODEL OF THE VOTE
Lacking the crystal ball of astrologers, forecasters often turn to past
phenomenon in search of prospective clues. History is
a great teacher, as the saying goes. In the case of elections, one would
scan past returns for hints of trends, cycles, or other dynamic regularities. For many countries with either a short history of elections, a
rapidly changing cast of partisan actors, or frequent changes of electoral rules, that search would not yield reliable hints. In the United
behavior of a
States, on the other hand, elections have been conducted with the same
cast of parties and much the same set of rules for more than a century.
With the 1860 election, the Republican Party established itself as a
competitor of equal standing to the Democratic Party, and these same
political parties have controlled U.S. elections ever since. Third-party
challenges, which occasionally disturb that control-most notably in
1912, 1924, 1968, and 1992-have invariably failed to turn one good
electoral showing into a permanent force alongside the established
parties. Hence it makes good sense to focus on the two-party division
in presidential elections. Figure 1 charts the Republican share of that
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446
---
Figure 1 : Republican Percentage of Major Party Vote for President, 1860-1992
vote from 1860 to 1992; readers more comfortable with the Democratic share should turn the chart upside down.
EQUILIBRIUM
The vote history depicted in Figure 1 forms a perfect example of
what is known as a stationary time series (Granger 1989, 66). A
stationary series possesses a fixed level to which it quickly reverts
after departing from it. In the case of the presidential vote, that level
is remarkably close to the 50% mark, given an average Republican
share of the presidential vote between 1860 and 1992 of 51.6%. More
important, the presidential vote travels within a highly restricted zone,
never dipping below 35% or topping 65%. Focusing on the latter
feature, Stokes and Iversen (1966) proved that such a restricted
movement was highly improbable without the existence of forces
restoring party competition. They did so not by identifying specific
forces and showing their influence but by constructing a model from
which such forces were absent-a random walk. Such a model, they
showed, was unable to account for the fact that the vote division stayed
within the narrow historical boundaries. A replication of this model
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447
with the vote series depicted in Figure 1 confirms that result. Hence,
regardless of the uneven distribution of party identification in the U.S.
electorate, neither party has an extended lease on the White House.
The presidential vote is characterized by equilibrium.
The existence of electoral equilibrium is an important foundation
for building a forecasting model. One should not mistake equilibrium
for unpredictability, with each election being decided by a coin flip.
In an established democracy, no election is an entirely new game. Even
short-term elements of electoral politics, such as candidates and issues,
have lives that transcend a single 4-year term. Almost every White
House occupant seeks election to a second term. All this leads us to
expect that the vote division will reflect some incumbency effect. It is
the force of this inertia, or restraint (as Midlarsky [1984] called it),
that the forces restoring competition have to overcome in bringing the
minority party back to power (Midlarsky 1984). What is certain is that
a reversal of electoral fortunes is lurking in the near future. What is
not certain is exactly when such a reversal will occur. This latter
uncertainty makes it imprudent to forecast the vote with any deterministic function of time that would fix the length of the period and the
amplitude.
CHOICE OF A STOCHASTIC MODEL
The more promising route is by way of stochastic models (Box and
Jenkins 1976; Granger 1989). To put it most generally, these models
depict the present as a combination of random events, past and present.
For the condition of equilibrium to hold, the influence of those random
events must be discounted with the passage of time. The most familiar
stochastic process is the Markov scheme of first-order autoregression:
VOTEt = <I>
VOTEt-1
+
Ut
-1
<0
<
1.
This process is well suited to capture the inertia of the vote movement, with VOTE,, encapsulating all past random events; the closer
the 0-parameter is to 1, the more sluggish the movement. There is no
hint in this model, however, of when random forces will succeed in
driving a winning party from power. Is that probability the same,
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448
of how long a party has occupied the White House? The
is no. There is a distinct pattern to partisan winning streaks in
presidential elections. On the average, parties have won the popular
vote in 2.5 successive presidential elections before losing it during the
1860 to 1992 period, the modal win streak being just two elections
(Norpoth 1995, Table 2). Win streaks longer than four elections are
extremely rare, as are &dquo;streaks&dquo; of just one.3 Hence a change of party
control in the White House is most likely to occur after two or three
terms (Abramowitz 1988). Such a process combining stability and
change can be captured by a second-order autoregressive process
(Midlarsky 1984).4 The model expresses the presidential vote in a
given election (VOTE) as a deviation from equilibrium; hence no
constant is included in the model. The estimated AR-parameters,
significant beyond the .01 level, given the t-ratios in parentheses, are
regardless
answer
as
follows:5
VOTEt
=
.52 VOTEt-i - .55 VOTEt-2 + Ut
(3.34)
(-3.55)
Error root mean square = 5.8
Number of cases = 34
the parameter for VOTE,, is positive, whereas the
for
VOTEt-2 is negative, and both are roughly equal in
parameter
absolute size.The positive sign for the VOTEt-1 parameter indicates
that a party winning a presidential election can expect to hold onto
much of its above-equilibrium vote portion in the immediately following election. At the same time, the winning party must reckon with a
reversal at elections after that, given the negative sign for VOTEt-2.
That is not to say that a winning party is doomed to lose after 8 years
in office, only to return to power after two terms in opposition. Given
the parity of the two model parameters, the incumbent party may be
able to hold on for a third term, or even a fourth, depending on
short-term events affecting the vote. Sooner rather than later, however,
the negative force will claim its due. Although the presidential vote is
not decided in the manner of a coin flip, it does not run according to
the tide table either.
Most
telling,
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449
AN EXPLANATION
What might be able to account for the tendency of a newly elected
administration to hold onto its electoral majority at first but then see
it evaporate? One possibility is the electoral tradition, codified as law
in 1951 with the 22nd Amendment, of a sitting president not seeking
a third term. No doubt, the departure of a popular incumbent is more
likely to hurt than help the party’s electoral prospects. But the departure of an unpopular one may also help his party prolong control with
a fresh and more appealing candidate. Another possibility is a phenomenon known for cyclical behavior-that is, the economy. A recession is bound to occur within a span of 4 to 12 years, which is enough
to jeopardize the tenure of the incumbent party. In the analysis below,
I pursue this lead in connection with estimating the influence of the
economy on the presidential vote.
In terms of human behavior, a plausible explanation may be the
tendency to accord more importance to negative than positive factors.
It is commonplace that bad news is more newsy than good news;
mistakes attract more attention than do successes. Katona’s (1975)
Psychological Economics noted this phenomenon in the waxing and
waning of the public’s economic outlook (see also Haller and Norpoth
1994). Voters appear to behave similarly in their political choices.
Negativity is the engine of electoral change, according to a classic
account of voting behavior: &dquo;A party already in power is rewarded
much less for good times than it is punished for bad times&dquo; (Campbell
et al.
1960, 554-55).
Yet, without rewards for good times, an incumbent party would
invariably face bleak prospects. That is not what the stochastic model
suggests. If there is bias in the electorate’s response, there is also a
bias. Bad news is not uniformly more salient than good
When a newly elected administration seeks a second term, it is
favored to win rather than lose. It is as if it enjoyed a &dquo;honeymoon&dquo;
typical for a newly elected president early in his term (Mueller 1973;
Norpoth 1985). In judging a new administration after one term, the
electorate appears more inclined to give the new regime the benefit of
the doubt than seize on its failures. Consider the following example.
During the Reagan-Bush administration, the U.S. economy suffered
two recessions. Although most believe that the second recession
positivity
news.
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450
( 1990-1991 ) cost the GOP the White House, the first recession ( 19811982) did not prevent a Republican landslide victory in 1984. If
anything, the first recession was far more painful in economic terms,
and the second was over a year and a half before the 1992 election.
Good news was paramount after one term, bad news after three terms.
As infatuation gives way to fatigue in the electorate, incumbency turns
from boon to bane for the party in the White House.’
AN AUTOREGRESSIVE FORECAST
For the purpose of forecasting, a stochastic model such as the
autoregressive one derived earlier has one compelling advantage. It is
capable of delivering unconditional ex ante predictions. That is, the
forecast truly arrives before the event-with a long lead time, actually-and without requiring any additional information that might
have to be guessed. For the 1996 election, the model predicts a
Democratic victory with 52.3% of the major party vote (Norpoth
1995). Presumably, that means Bill Clinton, but it would also hold if
another Democrat were to run in his place, for the identity of the party’s
nominee does not matter for this model.
The autoregressive model described earlier could have issued the
forecast of a Democratic victory in 1996 as soon as the returns for the
1992 election were in. The model from which that forecast is derived
takes no account of the job that a president has been doing or of the
circumstances accompanying his term. For some observers of elections, that is more a liability than an asset. How presumptuous is it to
ignore not only the electoral campaign but also the full term of office
preceding the election? Without a doubt the early and cheap nature of
the forecast comes at a price. That catch, as the reader may have
suspected, is the uncomfortably large error of the model-5.8 percentage points. An error so large should make potential bettors think twice
before wagering the family fortune. The autoregressive model of the
presidential vote is not for the fainthearted. Risk-averse forecasters
had better shun it.
With an average error (root mean square) of nearly 6 percentage
points, the model is bound to get it wrong now and then. The worst
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451
miss was the 1980 election. According to the model, Jimmy Carter
should have easily defeated Ronald Reagan. A party wresting control
of the White House from the other one, as Carter had done in 1976, is
not supposed to lose it after one term. Such errors, however, are rare
because electoral outcomes of that sort are rare as well. Carter’s defeat
was the first case in 100 years of the incumbent party losing the vote
count after only one term of office. Although history was on Carter’s
side in 1980, the worst economic performance in a presidential year
since the Great Depression and a protracted foreign policy crisis took
their toll on him. There are limits to the willingness of the U.S.
electorate to give a fresh incumbent the benefit of the doubt. In
contrast, the model correctly picked the Republican defeat in 1992.
However invincible George Bush appeared at the halfway point of his
term-basking in the glory of the Gulf War victory-the model shows
that his reelection in 1992 was clouded. When a party has been
occupying the White House for more than two terms, the odds are not
favorable for another win.
Although an average error close to 6 percentage points sounds
forbidding, it does not compare unfavorably with other forecasts made
so far in advance of the event. Consider the most familiar means of
election prediction-that is, polling the voters themselves. The Gallup
organization, which has the longest record of presidential election
polling, has picked the winner of all but 2 of the 15 elections since
1936, missing the notorious election of 1948 and the close one in 1976.
The final survey in each election year, according to Gallup’s own
report, deviated from the actual result by an average of 2.75 percentage
points (Gallup Poll Monthly 1992). But that, of course, is a short lead
time. Campbell and Wink (1990) compared the accuracy of Gallup
surveys conducted at various times in the election year. For surveys
taken in June, the average error came to 8 points; for late July surveys,
to 6 points. In other words, the autoregressive model does no worse
than polling voters barely 3 months before election day. Only in early
September does the survey error fall sharply below that level, to 4
points, inching below 3 percentage points only in the final survey in
November. By that time it may indeed be prudent to switch from a
long-term forecast to a more short-term one. Even so, polling the
voters themselves is not the only alternative.
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452
THE ECONOMY AS A VOTE PREDICTOR
and the approach introduced earlier, academic forecasters of elections rely on structural
models of the vote. Those models employ &dquo;leading indicators,&dquo;
that is, proven determinants of voting decisions. That might not
seem to be a difficult assignment. Few topics in social and political
research have been mined as thoroughly as the partisan choices of
voters at the polls. At the same time, that research offers almost too
much of a good thing for the forecaster of election outcomes. Studies
of voting behavior have named so many plausible suspects, covering
In contrast to both the
polling business
issues, candidate qualities, partisan sentiments, ideology, group loyalties, and more, that choosing a few for forecasting becomes an arduous
task.
For a number of reasons, the economy is the leading indicator of
choice among electoral forecasters (Lewis-Beck and Rice 1992,
ch. 6). Few would quibble with the notion that good economic times
spell reelection for the incumbent party, and bad times spell defeat. It
also helps that few variables are measured as regularly and provided
as easily as are economic performance indicators. Much survey research has documented the imprint of the economy on the decisions
of individual voters (e.g., Kiewiet 1983; Lewis-Beck 1988), and so
have aggregate studies of election outcomes and government popularity (Fair 1978; Kramer 1971; Norpoth, Lewis-Beck, and Lafay 1991;
Sanders 1996; Tufte 1978). Few election forecasters relying on structural vote models omit the economy as a predictor.
Yet few of those efforts have tracked the economy further back than
40 years ago, leaving barely a dozen elections for the estimation of the
forecasting equation.8 Funny things can happen on the way to forecasting with so few stops. Moreover, no forecasting model appears to
have exploited the stochastic regularities of the presidential vote.
However annoying and inexplicable serial correlations may be in one
sense, they should be happily embraced by anyone interested in
forecasting. The following is an effort to embed the autoregressive
regularities of the vote in economic voting models.
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453
TABLE 1
Economic Conditions, Past Elections, and
the Presidential Vote, 1872-1992
dependent variable is
presidential election, 1872-1992 (n
NOTE: The
Republican percentage of the two-party vote in a
31). Growth of gross national product (GNP) is the
the
=
rate of the GNP deflator in the election year.
Inflation was scored as zero in cases of negative deflator values. For elections with a Democratic
incumbent, the economic measures were inverted. Data were provided by Lynch and Munger
(1994) and Survey of Current Business, U.S. Department of Commerce (1996). t-l = vote in
previous election, t-2 = vote 2 elections prior.
percentage change of the GNP; inflation is the
*p<.10;**p<.05;***p<.01.
ECONOMIC CONDITIONS AND THE VOTE, 1872-1992
First, let us consider conditions of the real economy impinging on
electoral outcomes. It is now possible to trace data on the U.S.
economy as far back as 1872, the measures being gross national
product (GNP) growth and inflation.9 Note that the vote variable
continues to be the Republican share of the two-party vote, not the
share of the incumbent party at any given election.1O Because of that,
the economic variables were inverted (given the opposite sign) for
election years in which Democrats controlled the White House.&dquo;
The results in Table 1 confirm the sensitivity of the presidential vote
to economic performance, in this case all the way back to 1872. A rise
of the inflation rate of 1 %, according to those estimates, typically costs
the presidential party three quarters of a percentage point in the vote,
but a rise of the GNP by 1 % tends to raise its vote share by one third
of a point. It is worth noting that the economic effects leave the
autoregressive structure of the vote series largely undisturbed. For one
thing, that tells us that it is not the economic cycle generating the
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454
tendency of the presidential vote to alternate after two or three terms.
It also means that the same set of economic conditions leads to a
different electoral outcome, depending on how many terms the presidential party has occupied the White House. A recession or high
inflation during the first term hurts the electoral prospects less than is
true for later terms. Recall the comparison of Reagan in 1984 with
Bush in 1992. Likewise, the set of economic conditions that defeated
Bush in 1992 need not defeat his Democratic successor seeking a
second term in 1996.
PEASANTS OR BANKERS?
To be sure, measures of economic performance help us forecast the
vote more accurately than we could with the autoregressive model
alone, but the improvement is not breathtaking. We still have to
contend with an expected forecast error that is larger than 5 percentage
points. One could not advise anyone, in good conscience, to bet the
family savings on such a forecast either. Of course, the measures of
economic performance used here, as in most models of this sort, are
crude ones, rough approximations at best, of how the economy affects
individual voters. Moreover, the implied calculus of voter behavior is
purely retrospective and short-sighted: Voters make a judgment of how
the economy has done lately. In contrast to this view, there is a growing
school of thought claiming that expectations about the future economy
influence voter decisions (Chappell and Keech 1985; Fiorina 1981;
Lewis-Beck 1988). MacKuen, Erikson and Stimson (1992), in particular, have gone so far as to claim that the electorate behaves like bankers
rather than peasants, making political choices based on an intelligent
reading of the future economy. That claim points to a new and possibly
more secure road to election forecasting.
The Michigan Survey Research Center (SRC) has been surveying
U.S. consumers for nearly half a century about such items as current
financial situations and outlooks on business conditions in the future
(Appendix 1). These two measures match up almost perfectly with the
peasant-banker pair of metaphors. The results presented in Table 2,
however, do not favor the bankers over the peasants. Pitted head-tohead, as done in column 3 of that table, retrospective evaluations of
personal finances prove to be a highly significant predictor of the
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455
TABLE 2
Economic
Perceptions and the Presidential Vote, 1948-1992
NOTE: The dependent variable is the Republican percentage of the two-party vote in a
presidential election, 1948-1992 (n = 12). &dquo;Financial situation retrospective&dquo; refers to evaluations
of current personal finances compared to a year earlier; &dquo;business condition prospective&dquo; refers
to expectations about business conditions being better or worse a year ahead. For elections with
a Democratic incumbent, the economic measures were inverted. The data are displayed in
Appendix 1.
*p<.10;*~<.05;***~<.01.
presidential vote, whereas expectations about economic conditions in
the future have no effect on the vote whatsoever.’2 That refutes rather
starkly the &dquo;banker&dquo; model of MacKuen, Erikson, and Stimson (1992),
as far as electoral outcomes are concerned (see also Clarke and Stewart
1994; Norpoth 1996). We gain no explanatory or predictive value by
including expectations in vote equations. Retrospective measures do
well enough in predicting the vote.
CANDIDATE QUALITY AS A VOTE PREDICTOR
Aside from the economy, vote models have placed much weight on
presidential approval. There are good reasons, theoretical as well as
empirical ones, for doing so. In a rough way, presidential approval
encapsulates some key components of individual vote choice established in The American Voter (Campbell et al. 1960). It is also a key
variable in the &dquo;retrospective&dquo; model of voting behavior (Fiorina
1981). Paraphrasing Kramer’s (1971, 134) decision rule, one might
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456
say that whenever voters approve of the performance of the party in
the White House, they elect the incumbent to another term, and
whenever they disapprove, they vote for the opposing candidate.
That rule holds true for nearly all elections for which presidential
approval ratings are available (Brody and Sigelman 1983). Whenever
the incumbent president has had an approval rating above 50% in the
middle of the election year, he has gone on to win reelection in
November. By the same token, whenever the incumbent president fell
short of the 50-point mark in approval, he lost in his bid for reelection
all but once-in 1948. It is remarkable how well the relationship
between presidential approval and the vote holds up, even in instances
where the incumbent president is not running for reelection. In 1952,
1968, and 1988, the candidate of the party occupying the White House
won or lost just as the &dquo;rule&dquo; would have predicted for the incumbent
himself. The approval rating for a given president tells us something
about popular satisfaction with the incumbent party, not just its man
in the White House. The 1960 election, however, is a case in which
the high approval of the incumbent president failed to carry his party’s
presidential candidate to victory.
Even though presidential approval is arguably the single most
powerful predictor of the presidential vote, the exceptions are nonetheless quite instructive. They call our attention to a serious omission
in forecasting models: the candidate of the out-party. Those models
treat elections as one-man races. It is as if the identity and quality of
the nonincumbent candidate did not matter to the electorate. Just
imagine: Would Truman have won in 1948 had his opponent that year
been Eisenhower instead of Dewey? Would Nixon have lost in 1960
had his opponent been Stevenson instead of Kennedy?
What can be done, however, about including the out-party candidate
in forecasting models? One solution might be to rely on trial heats,
the standing of the presidential candidates in opinion polls. Without
much doubt, that is the best way to forecast the outcome close to the
event. But it neither helps us early in the year, as was shown earlier,
nor does its accuracy 3 months before the election exceed that of the
autoregressive model. Another way would be to use a summary
measure of candidate evaluations provided by the National Election
Studies since 1952 (Erikson 1989; Tufte 1978). For explanatory
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457
TABLE 3
Candidates, Economic Performance, and
the Presidential Vote, 1912-1992
------------------
NOTE: The dependent variable is the Republican percentage of the two-party vote in a
presidential election, 1912-1992 (n 21). &dquo;Candidates’ primary strength&dquo; is an index based on
whether the candidates won the first primary (Appendix 2). Growth of gross national product
(GNP) is the percentage change of the GNP; inflation is the rate of the GNP deflator in the
election year. Inflation was scored as zero in cases of negative deflator values. For elections with
=
a
Democratic
incumbent, the
econormc measures were
provided by Lynch and Munger (1994)
Commerce
and
inverted. The
economic
Survey of Current Busmess,
U.S.
data
were
Department
of
(1996).
*p < .10; **p < .05; ***p < .01.
purposes, those candidate evaluations
are quite suitable, but they are
available in time before the election to be useful for forecasting.
A gauge of candidate quality that is available fairly early in the year
and is traceable for elections before 1948, though not quite as far back
as 1860, takes its cue from the nomination process. In a nutshell, the
measure gauges the strength of a presidential candidate by determining whether the nominee won the first presidential primary (see
Appendix 2). Someone who did so is considered to have strong party
support both early on in the election year and at the national convention. In cases where someone other than the winner of the first primary
received the party’s nomination, the nominee often entered the general
election campaign with a divided party, foreshadowing large-scale
defections in November.
not
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458
Comparative candidate strength, as measured by success in the first
primary, proves to be a highly significant predictor of the general
election vote.13 The candidate factor adds considerable forecasting
value to the economic performance model, as shown by the results in
Table 3. It is worth noting that the measure of candidate strength, too,
leaves the autoregressive structure of the presidential vote largely
undisturbed. Although disappointing in the sense that we are no closer
to an explanation of that effect, the result is good for forecasting in
that it gives us an additional predictive factor besides the economy and
candidate strength.
The outcomes of those early nomination contests cast a long
shadow over the November election. Rare is the winner of the general
election who did not capture his party’s first primary. Three of four
presidential candidates who won the first primary went on to victory
in the general election. At the same time, roughly the same proportion
of candidates who did not win that first primary went on to defeat in
November. At the risk of pushing this point too far, note that this
pattern casts some light on the odd cases of 1948 and 1960. In 1948,
the challenger, Tom Dewey, failed to win the first Republican primary,
whereas in 1960, John Kennedy did win the first Democratic primary.
It should be noted that the first primary in some election years is
not literally the first primary held but the first one featuring the names
of presidential candidates on the ballot. New Hampshire is the state
that has held that spot since 1952. And New Hampshire could boast
until 1992 (when Bill Clinton ended the streak) that no candidate was
elected president who did not win the state’s primary. That was less a
tribute to New Hampshire as such than to the state’s starting position
in the primary season. Other states featuring the first primary have
pointed to the winner of the general election before, albeit with a lesser
degree of perfection.
FROM MODELS TO FORECASTS
To make a forecast, it is not enough to have a rule or equation
spelling out the effect of a predictor; one must also have a measure of
that predictor and have it in time before the event. A perfect model
would be of no use for forecasting if we have no clue about the values
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459
of the predictors in the model. Only one model introduced earlier
settles that information problem before it can be raised-the autoregressive model. The sole information required is the vote in the two
previous elections (1988 and 1992). That is a matter of record several
years before the event to be forecast.
In contrast, information on other predictors may only surface in the
middle of the election year, if at all before election day. In 1996, the
&dquo;primary strength&dquo; of the nominees was available at an unusually early
date. By April it was safe to score this predictor in favor of the
Democratic candidate, because Bill Clinton had won the first primary
(New Hampshire), whereas Bob Dole had not. On the other hand, the
economic performance predictors-GNP growth and inflation for
1996-will not be issued before January of 1997. By that time, the
1996 presidential election is history. What to do? The solution adopted
here is to use the latest available data on how those indicators have
changed during the past 12 months. Given the extreme degree of
inertia of those phenomena, that does not seem to be an unsafe course.
In Table 4,I present the forecasts derived from various specifications. These forecasts all agree in predicting a Democratic victory in
the presidential election of 1996. History is on Bill Clinton’s side
(Model 1 ). Beyond that, the economy appears to add little to his margin
(Model 2). It may surprise some observers, but the rate of economic
growth during the past 12 months has been barely 2.5 %.14 That does
not greatly exceed the growth rate exactly 4 years earlier (1.5%), when
George Bush faced grim reelection prospects. So why does the macroeconomic specification still forecast an incumbent victory in 1996?
A minor reason is that the other economic predictor, inflation, has
barely visible readings these days (2.0%) and that it counts for more,
electorally, than does economic growth. But the more compelling
reason is that Bill Clinton is a first-term president, whereas George
Bush in 1992 was part of a three-term administration elected in 1980.
The U.S. electorate appears to be more forgiving of imperfection when
a new president is struggling to get things right than when a party has
had several terms to do so.
The most complete specification, including candidate strength besides history and economy, predicts a rather comfortable margin for
Bill Clinton (Model 3). While Clinton sailed to renomination without
a mishap, the Republican nominee capsized in the first primary. Even
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460
TABLE 4
Summary
NOTE: GNP
parameters,
=
see
of 1996 Presidential Vote Forecasts
gross national product. Predictor values are shown
Tables 1, 2, and 3 or text preceding Table 4.
in
parentheses.
For model
though Bob Dole was able to right his campaign in record speed, the
New Hampshire defeat hinted at deep and numerous misgivings with
his candidacy. Clinton’s superiority in the candidate comparison-or,
to put it another way, Dole’s weakness-adds a tantalizing hint of
landslide victory to the forecast (57.1 %).
Although the outcome in November will have the final word about
this forecast, what can one say at this moment about its quality?
Judging by the near 20-point lead Clinton enjoyed in opinion polls
throughout May and June, some observers would consider this point
academic. Recall, though, that trial heats so early in the year have
proved quite unreliable in the end. A key criterion for judging a
forecast is the size of the confidence interval (Beck 1992). How much
confidence can we have that a forecast predicts victory rather than
defeat? The point forecast, based on the triple combination of history,
economy, and candidates (57.1 %), comes close to inspiring a 90%
degree of confidence. It is the highest that any of the models presented
in this article can offer. That is no guarantee to be right all the time,
especially for a phenomenon such as the vote, which rarely veers
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461
TABLE 5
Model Predictions of the Republican
Percentage of the Major Party Vote
NOTE: The predicted votes are in-sample predictions based on equation (2) in Table 3. Also note
that the prediction for 1968 before roundmg is 50.04.
outside the 40% to 60% range. As Table 5 shows, for elections since
1912, the model picked the wrong winner in only three casesY We
pay for the omission of important variables (scandal in 1976) and for
crude measurement of candidate strength (1960 and 1992), not to
mention the exclusion of the election campaign. Still, two of the misses
concern contests too close to forecast with any confidence (1960 and
1976).
CONCLUSION
This venture into election forecasting has originated from an indisputable though little used premise: Elections are not decided in the
manner of coin flips. The movement of the presidential vote from one
election to another is not without rhythm or reason. For nearly 150
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462
years, the &dquo;law&dquo; of electoral competition has been such that the party
taking control of the White House from the other has typically retained
control after one term but has lost the White House in elections after
two terms. In terms of stochastic models, this is a dynamic exhibiting
second-order autoregressive behavior. One can use this dynamic to
make long-range forecasts of the presidential vote as far as 4 years
ahead of the event and with no other information than previous vote
returns. No other forecasting model can deliver that.
Alternatively, the stochastic component of the vote may be used for
designing short-term forecasts based on complex models. Consider
models linking elections to the economy. Whatever the exact specification of the economy, forecasting can only gain from the parameters
of the electoral dynamic. It is by no means the case that the process
identified in this analysis is simply an offshoot of the business cycle.
To the contrary, the former modifies the latter’s impact on the vote in
significant ways.
It is the consensus forecast of all the vote models presented earlier
that Bill Clinton will win the 1996 presidential election. Time is on his
side, as the stochastic model has been predicting all along. Although
that was true for Jimmy Carter in 1980, his defeat that year marks a
rare exception in 100 years. And whatever Bob Dole may say, he is no
Ronald Reagan. By the measure of candidate strength introduced in
this article, Bill Clinton has the edge in the personal comparison over
his Republican challenger. Aside from history, it is not so much the
economy but the candidate factor that forecasts a comfortable victory
for Bill Clinton (57.1 % of the major party vote).
With all those advantages for the Democratic incumbent, how
might the forecast possibly go wrong? Some critics are bound to take
issue with the edge attributed to Clinton in the candidate comparison.
Doesn’t the president remain vulnerable to charges relating to character ? Doesn’t the American public favor Dole over Clinton in opinion
polls on matters of honesty and trustworthiness? Any new accusation
about influence peddling in Arkansas, abuse of power in the White
House, or actual convictions of former associates are bound to inflame
the character issue. A presidential campaign that revolves primarily
around the dimension of personal trust, I admit, could upset the
forecast of a Clinton victory. But what would it take to cast the
campaign in that mold? Would the choice of Colin Powell as Bob
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463
Dole’s vice-presidential candidate have accomplished that? That begs
the question of whether Dole could have persuaded the former general
to join the presidential ticket.
Somewhat less likely to upset the forecast is the reentry into
presidential politics by Ross Perot. At the moment, according to
opinion polls, he apparently would take rather evenhandedly from the
major party candidates’ electoral support, just as he did in the final
analysis in the 1992 election. That may change, however, if the Perot
campaign trained its guns primarily on Bill Clinton this time. Perhaps
the worst thing that could happen to the forecast of a Democratic
victory is for Bill Clinton to take it seriously and stop campaigning.
He won’t. That is a safe bet. What would life be for Bill Clinton without
campaigning?
APPENDIX 1
Economic
Perceptions:
Past and Future
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APPENDIX 2
Winners of First Presidential
Primaries, 1912-1992
NOTES
1. This section is based on material previously published in the June 1995 issue of PS:
Political Science and Politics (Norpoth, 1995).
2. Stokes and Iversen (1966, 188) reported a probability estimate of 0.042 for a symmetric
random walk, using data from 1868 to 1960. With data from 1860 to 1992, the probability drops
even further to 0.013. The result of a Dickey-Fuller test of unit roots also leads to a rejection of
-ratio is -3.95, which is significant at the .01 level (see
the random-walk model. The relevant t
Dickey, Bell, and Miller 1986).
3. This does not count the Republican victories in 1876 and 1888, which were achieved in
the electoral college, not in the popular tally.
4. The autocorrelations and partial autocorrelations of the presidential vote senes help to
identify this model. See Norpoth (1995, Table 1). Significant partial autocorrelations, at lags 1
and 2, make a compelling case for a second-order autoregressive process.
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465
5. The model easily passed the Ljung-Box test of white noise (3.3 based on the first eight
autocorrelations). Hence no further stochastic parameters need to be specified in the model.
6. The parameter estimates of the AR(2) model are such that the equilibrium (stationarity)
conditions
are
met:
1
(1)
2
<+
1; 0
0
(2)
2
1
<1; 0
0
(3) -1 < 0
2 < 1.
7. For an analogous process, see the "issue-attention cycle" proposed by Downs (1972).
8. To enhance the number of degrees of freedom, some forecasters have adopted the 50
states as their unit of analysis rather than the nation. Rosenstone (1983) pioneered this strategy,
followed more recently by Campbell (1992) and Gelman and King (1993). However, the
multiplication of cases resulting from using 50 state electorates pays only a limited dividend for
national-level forecasts. The focus on the states allows one to capture home state advantages for
presidential and vice-presidential candidates and take advantage of a state’s partisan history. But
the key predictor variables, be they social or racial issues, management of war, incumbency, or
trial heats, all pertain to the national electorate. Those variables are actually constants across all
states in a given election.
9. I am grateful to Patrick Lynch and Mike Munger for sharing these data. See Lynch and
Munger (1994, 13-6) for the listing of the economic growth and inflation data, as well as for
their sources.
10. The incumbent party vote does not form a series that exhibits this second-order
autoregressive process. Choosing the vote share of the incumbent party means that the time senes
has to switch from Republican to Democratic, or the other way around, whenever the party
controlling the White House changes. That "intervention" in the time series destroys the dynamic
found earlier, but it most likely creates another one instead, not white noise, as assumed in most
forecasting models using the incumbent party vote. That series shows a sizable autocorrelation
at lag 1 (-0.44) in the 1948 to 1992 period, something that is left unexploited by previous
forecasters.
11. To be precise, the overall mean is removed from each economic series (2.0 for gross
national product (GNP) growth and 2.9 for inflation), and the deviations from the mean are
inverted for the elections with Democratic control of the White House. Also note that no constant
is estimated, because the mean also was taken out of the vote variable.
12. With as few as 12 elections to examine these variables, the stochastic process is specified
as a moving average rather than as autoregressive. Preliminary tests suggest that only the MA(2)
term is necessary. On these model specifications, see Granger (1989, ch. 3).
13. The candidate variable comprises three categories: (a) The Republican candidate, not
the Democrat, won the first primary; (b) both or neither won the first primary; and (c) the
Democratic candidate, not the Republican, won the first primary. With the dependent variable
being the Republican vote, categories (a) and (b) are scored +1 and -1, respectively, for elections
with Republican incumbents and the reverse for elections with Democratic incumbents. Category
(b) is scored 0 throughout.
14. Because data on GNP are not available for the second quarter of 1996, the growth of
real gross domestic product (GDP) is used; the measure of inflation is the implicit GDP price
deflator. The final estimates for these variables, reported by the Commerce Department in early
August, are taken from Hershey (1996). For earlier data, I rely on the U.S. Department of
Commerce’s (1996) issue of the Survey of Current Business.
15. These are "in-sample" predictions&mdash;that is, best-fitting vote percentages based on the
model parameters, not forecasts of the ex ante king, that come from model estimates that exclude
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the case to be predicted. The time frame is too small to examine ex ante forecasts for more than
a handful of elections.
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Helmut Norpoth is a professor of political science at the State University of New York,
Stony Brook. His work dealmg with topics of American electoral politics has appeared
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Opinion Quarterly. He is coeditor of Economics and Politics: The Calculus of Support
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