GUEST BLOG / By Igor Geyn, New America Weekly--It’s
the rare election that doesn’t occur during a major geopolitical upheaval or
natural disaster. In November 2008, the U.S. unemployment rate had surpassed
6.7 percent—well on its way to a Great Recession peak of 10 percent in August
2009—and 29 years before that, 52 Americans were taken hostage in the U.S.
embassy in Tehran. But while some degree of tumult is baked into voters’
electoral decisions, an emergency on the scale of the COVID-19 pandemic can
upend many of the assumptions we typically make about presidential elections.
So, can historical precedent
and available data tell us anything about Trump’s prospects for reelection?
The history of presidential
polling doesn’t go back quite far enough for us to comment on the trajectory of
each president who failed to win reelection—but it does allow us to use Herbert
Hoover as an illustrative example. While 1932 found the nation several years into
the Great Depression, there is evidence that Hoover was at least equally
stymied by his support for Prohibition—a stance Franklin D. Roosevelt exploited
freely.
In fact, on economic issues,
voters were sometimes reluctant to assign blame to Hoover directly: While a
share of those polled in a study of Hoover’s reelection odds believed he was
ultimately responsible for the economic woes of the time, respondents were also
likely to blame Wall Street and the government as a whole.
In this context, Trump and
GOP leaders’ rebranding of COVID-19 as “Chinese virus” and "Wuhan
virus" reveals a potential strategy: Muddy the waters and hide the fact
that ours has been one of the least effective responses by nations impacted,
with well-documented failure on the part of administration leaders tapped to
handle the response. Republicans, in this case, are seeking to achieve a
dilution of public blame similar to what was seen during the Hoover
administration—but without the ultimate electoral loss.
What do polls show about the
effectiveness of these strategies? Using a FiveThirtyEight interactive, we can
compare the current segment of the presidency overseen by Trump (i.e., the
first 1,203 days, as of this writing) to the same segment for past presidents.
Of the first six post-World
War II presidents, all but Gerald Ford followed a fairly similar trajectory:
Except for a segment of Truman’s presidency, they enjoyed net approval ratings
well into the +40 percent to +60 percent range (Eisenhower never dipped below
+30 percent, for example, while John F. Kennedy only spent a few weeks below
+40 percent). Trump, on the other hand, has been underwater for basically the
entirety of his presidency, dipping below -20 percent.
Thus, the first conclusion is
a simple but necessary one: While Trump would like to call himself a wartime
president, measurements of his popularity don’t look like those of wartime
presidents—at least, not wars that glitter in the popular imagination. Instead,
it resembles public opinion associated with presidents who led the country’s
post-Vietnam foreign involvement—conflicts that have generally sapped public
support.
Since Ford left office in
1977, we’ve seen greater variation in the ratings by president, but the overall
trend has still been a noted decline—the average net approval in the first
1,203 days of the six most recent presidents appears to be around five to 15
percent. By comparison, the first six post-WWII presidents were sitting closer
to 20 to 30 percent (inclusive of Ford, who drags the average down
considerably).
Most commanders-in-chief in
the post-Ford 1977-2017 timeframe saw a similar trajectory: relatively high net
approval at election (an average of approximately +30 percent), followed by a
gradual decline toward zero and only minor vacillation above/below the net-zero
line through the end of the 1,203-day period.
In short, in the modern era,
polls indicate that there is no such thing as a broadly popular president—and
Trump is the starkest example we’ve had.
With Trump, you see the
effect of ‘Teflon Don’ working in reverse—while both George W. Bush and George
H.W. Bush saw relatively high flash points despite ultimately sinking into low
approval ratings, Trump has been in a trough since day one. This has basically
carried over throughout the duration of the administration’s COVID-19 response:
Although polling done in the middle of February and the beginning of April
shows a slight uptick in the president’s popularity, at no point during the
pandemic has Trump seen net positive ratings. (George W. Bush, meanwhile, saw a
60-point gain in his net approval rating following 9/11.)
Economic voting models
In March, Congress passed all
the necessary legislation on a $2.2 trillion stimulus package, which was signed
into law by Trump as the CARES Act. While the figure is subject to adjustment,
the record sum will almost certainly be at least three times the original value
of the 2008 bailout ($787 billion) and larger even than the “revised” estimate
($831 billion). At the same time, more than three million Americans filed for
unemployment in the third week of March (compared to 192,023 claims for the
same week in 2019), and more than six million initial claims were filed in each
of the two weeks after—largely resembling the kind of localized economic impact
we see in the wake of a Hurricane Katrina-grade natural disaster.
Readers familiar with
election predictions know that the models aren’t just based on public opinion
polling. Objective economic indicators—especially leading economic indicators
(LEI), such as those used by the Conference Board to predict economic
downturns—feature heavily in many election prediction models.
Political scientists
examining economic voting often focus on how voters interact with the economy
writ large, including the level at which voters associate the economy with the
presidency (i.e., is their vote based on their own economic situation, or the
economy as a whole?) and the timing (i.e., do voters mostly conduct retroactive
economic assessments and ‘assign grades’ at the ballot box, or do they project
forward and make educated guesses about which candidate will do the best job
with the economy?). As it happens, research suggests that voters mostly
evaluate the economy retroactively, and at the aggregate level—two potential
demerits for the incumbent president.
So, what are Trump’s chances under an
economically-derived presidential forecast model?
In March, Alan Abramowitz of
Emory University published a table of possible predictions based on his
economic model (often referred to as the ‘time-for-a-change’ model because it
takes into account the partisan baggage non-incumbent candidates can carry in
an election—for example, John McCain tanking after two consecutive Bush terms).
Abramowitz’s analysis presents a straightforward case: Assuming a net Trump
approval of -5 percent in June 2020—an entirely plausible scenario—Trump needs
the economy to grow by 1 percent GDP in order to be the probable winner in
November. Moving into more realistic economic scenarios (i.e., the economy
shrinking by 1 percent or more), Trump’s reelection becomes increasingly
unlikely. Not included in the table is the June 2020 net approval Trump would
need to win reelection if there’s even a 1 percent contraction in GDP—which can
be extrapolated to be at approval levels not seen at any point during his
presidency.
Should you bet the house on
Trump losing in November? Well, no. You’ve probably heard this adage a lot in
the past several weeks: All models are wrong, even if some are useful. Trump’s
2016 victory was, according to mainstream media consensus, not the likely
outcome. More pertinently, all of the prominent election models are
conditional: If the economy takes a dip, and/or if Trump’s net approval sees a
sharp drop, then his chances for re-election decline pretty quickly.
The issue for Trump, of
course, is that a lot of arrows point to precisely both of these trends
materializing. We’re already seeing macroeconomic records broken in both
unemployment claims and the scale of fiscal stimulus interventions. Assuming
leading forecasters’ Q3 and Q4 2020 estimates are correct, election prediction
models will turn away from Trump as the favored candidate. For the last two
months, we’ve seen Trump’s base wavering in their support; the ‘stickiness’ of
formerly Democrat voters who pulled the lever for Trump in 2016 is similarly
being tested.
The details of a recovery
from the looming recession will be important—while Democrats have an
opportunity to score policy victories over the roadblocks of Mitch McConnell
and the rest of the Senate Republicans, Trump and the GOP could also
effectively own the conversation and perceived benefits. Messaging, in addition
to the empirical economic reality, will play a key role: To the extent that
voters remember any of these details in November, who will they blame for lost
wages and rising premiums, and who will they praise for stimulus checks and
small business loans?
Nationally, what happens over
the next six months will likely hurt Trump’s chances for reelection. But, as we
were reminded in 2016, the U.S. presidential election is not a national
referendum.
What do economic models tell
us about decisive swing states?
The United States is in an
era of extremely close elections—as I previously observed, 50 congressional
races in 2018 were decided by five points or less. Trump won Michigan,
Pennsylvania, and Wisconsin by no more than 0.77 percent.
In this climate, the ultimate
outcome in November hinges on many factors influencing small margins in
elections around the country, including those in pivot counties—that is,
counties that voted for Obama twice and then swung for Trump in 2016.
Some of these counties are in
states that are fairly safe bets for Trump in November—the five in Georgia, for
example, as well as the two in Mississippi. However, the combined nine pivot
counties in Florida and Georgia went for Trump by an average of just 5 percent
in 2016—and the average across all pivot counties, which includes dozens in
purple states, was just 11.5 percent in Trump’s favor. Thus, while some races
may be relatively decided, many others remain in play for 2020.
So, what’s the economic
situation in these areas?
Even as some states relax
restrictions, the economy will still take time to bounce back—the millions who
filed unemployment claims since lockdown orders were initiated won't get their
jobs back overnight. Businesses are closing due to coronavirus-related
pressures, with retailers as large as J. Crew declaring bankruptcy. In March,
the national unemployment rate had already increased year-over-year (YoY) by
about 0.60 percentage points, and the rate in April (14.7 percent) was 11.1
percentage points higher than the rate in April 2019 (3.6 percent).
Importantly, the effect isn’t
homogenous across the country; many areas around the country had relatively
lower unemployment rates in March 2020 compared to March 2019. As it happens,
the map of these areas might seem somewhat familiar:
Across Minnesota, Wisconsin,
Indiana—Midwestern states that account for 47 out of 206 (22.8 percent) pivot
counties—unemployment remained relatively constant, rising no more than 0.1
percentage point and even declining in many areas. A similar picture appears in
New England, where unemployment shifted by -0.3 to 0.1 percentage points across
11 pivot counties. Michigan’s March 2020 numbers were even stronger, with
unemployment actually falling by 0.4 percentage points or more in every
metropolitan statistical area (MSA) besides Detroit-Warren-Dearborn (not a
pivot county).
This means that, according to
March 2020 BLS data, some parts of the country that swung for Trump after
voting twice for Obama are doing relatively well. And what does that mean?
Well, according to economic voting models, Trump has a pretty good chance of
hanging onto these counties—not a guarantee, of course, but a decent shot.
While this glimpse into the
Midwest and New England suggests relatively rosy prospects for a Trump victory,
the same can’t be said of two other states important for 2020: Florida, which is
red-leaning-purple, and Pennsylvania, which is purple-leaning-blue. The former
is showing some worrying signs for Trump: Across Florida, unemployment grew by
1.1 percentage point YoY in March 2020, with Homosassa Springs (+1.7 pp) and
Sebring (+1.6 pp) climbing closer to 2 points. None of Florida’s pivot
counties, which Trump won by an average of just 3.85 percent in 2016, are even
near areas with less than a one percentage point increase in unemployment.
The picture in Pennsylvania
is even bleaker. Across the state, March 2020 unemployment was at or above a
two percentage point increase relative to March 2019. Pivot county Luzerne is
located square in an MSA that’s seen a two point unemployment increase, and
fellow pivot Northampton comes in at 1.4 points. The biggest point of concern
is Erie—a county Trump won by just 1.56 percent in 2016—which has seen a 2.2
percentage point increase in unemployment.
All said, whether you’re
looking at public opinion polling or economic models or both, much remains uncertain.
We can make data-driven predictions about voting behavior in pivot counties,
but the fact remains that millions of people are making decisions based on any
number of factors—rational and emotional. Still, in looking at the conditions
in these areas, it doesn’t take a great leap of faith to envision Trump’s
ultimate loss this November.
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