Since the virus struck the nation with a vengeance, I have wondered if it eventually would increase or decrease President Trump’s chances for reelection. My curiosity led me to start compiling statistics on the virus’s daily changes from the CDC. My hypothesis is simple: as the number of new cases increases Trump’s popularity will decline.

Alternatively, as the President addresses the crisis, and is seen as a leader in a time of a crisis, his popularity would rise. A third possibility is that the crisis has no effect a all on his Presidency.

The CDC records several statistics regarding the virus: new daily cases, total number of cases, total deaths, and daily deaths. In examining the data, it was apparent the most important variable and where the chain begins is at the daily confirmed cases. New cases and daily deaths are reported each day by both print and television. And for the average person, the growing number of cases likely reflects the growing crisis all around all of us.

I have chosen the Presidential Job Approval rating (registered and likely voters) for a measure of Donald Trump’s popularity. The approval rating is the most accurate and important statistic in evaluating voters’ opinion of a president. (See Will the Economy Save Donald Trump?)

In the chart below, the gray line represents the daily job approval over the last 18 days. The orange bars denote the number of new cases for each of the same 18 days. During this period the number of daily new cases grew from 24 to 4,530, more than a 18,000% increase!

The average of all approval ratings during this 18 day period is 43.6%. At this point, we can’t determine whether the virus is having any effect on the President’s popularity. This is confirmed by correlation analysis, which although negative, is non-significant.

I will update this graph on a weekly basis and I encourage you to check back regularly. In the mean time, put in the comment section whether you believe or don’t believe the virus will help, hurt or will have no effect on Donald Trump’s election chances.

In his stunning 2016 Electoral College victory while losing the popular vote by three million votes, Donald Trump proved that individual parts can sometimes be greater than the whole. Obviously, Aristotle never studied the American election system.

The question is whether he can do it again. So I decided to take a peak at how Trump currently strands in each state he won in 2016. This is normally not an easy task, since key polling data is often not found in states like South Dakota. And in states where there have been surveys, the time differences often negates their usefulness.

But I recently discovered a survey site called Civiqs, which is an online opinion polling and data analytics company founded by Daily Kos founder Markos Moulitsas in March 2018. What is unique about this company is that it conducts daily tracking polls in all 50 states – in the same time frame.

Civiqs operates its own panel of Americans who have been recruited or volunteered to participate in political a surveys in all 50 states. These internet respondents complete a demographic profile that includes their residential location. Individuals are drawn randomly from voter based lists for political surveys and then randomly selected for an interview. Most importantly, they as a Trump Job Approval rating in every survey.

The Civiqs’ job approval surveys for this analysis began on January 20, 2017 and ended on March 3, 2020. During this period Civiqs interviewed 235,231 registered voters in all 50 states. The average job approval rating for this sample is 45.4%. The Real Clear Politics average for this same period was 45.3%.

As a political survey practitioner for over 30 years, I’m still skeptical of online surveys for political purposes. But for online surveys, their published methodology ( https://civiqs.com/methodology ) does address many of the concerns that researchers often have about internet surveys. Most importantly, their samples sizes are large and are conducted in each state within definitive time frames and they include the presidential job approval question. (For a review of the job approval importance, see the post “Will the Economy Save Donald Trump?”)

Why the Job Approval Rating?

The job approval rating is known as a global popularity variable, meaning it encompasses voters’ general impressions of Trump based on issues important to the voter. The correlation between vote choice and job approval is very high. Between 1972 and 2016, the correlation between the job approval rating and the presidential vote in my previous analysis was .840 (where 1 is a perfect match.) On a national basis, regressing the job approval rating on the percent of the two party vote from 1972 through 2016, the Rsq. was .699 (about 70% of the variance).

The importance of a positive job approval is highlighted by the fact that no incumbent president has won reelection since WW II with a job approval rating below 50%.

With this new survey data I can calculate Trump’s potential electoral college vote as if the election were held today and compare it to 2016. Just to be clear, this is not a prediction model that estimates Trump’s electoral college vote. It does, however, portends a possible outcome. In Figure 1 below are all the 2016 states that Trump won and the Electoral College voter for each.

Figure 1

In 2016, Trump carried thirty states for a total of 304 electoral votes. Using the Civiqs’ Trump job approval ratings for each state, I classified a state likely won by Trump if the approval rating was six or more percent above the disapproval rating. If Trump’s rating is upside down by more than five percent, I categorized it as a potential win for the Democrats. And if the state approval rating was within five or less percent above the disapproval rating, it was classified as a toss-up if the election were held today.

In Figure 2, I have listed each state and applied the new state by state electoral vote to each state that Trump won in 2016 based on Trump’s current job approval. For example, if you look at Arizona you will see that it lists a change of 11 electoral votes from Trump to the Democratic candidate, based on the fact that Arizona’s current approval rating of Trump is 46% vs. a disapproval of 52%, a difference of a minus 6%.

State 2016 Electoral Vote Change

Alabama Trump Win

9

Alaska Toss Up

3

* Arizona to Democrat Change

11

-11

Arkansas Trump Win

6

California Democrat Win

Colorado Democrat Win

Connecticut Democrat Win

Delaware Democrat Win

* Florida Trump Win Now Toss Up

29

29

Georgia Trump Win Now Toss Up

16

16

Hawaii Democrat Win

Idaho Trump Win

4

Illinois Democrat Win

Indiana Trump Win

11

Iowa Toss Up

6

6

Kansas Trump Win

6

Kentucky Trump Win

8

Louisiana Trump Win

8

Maine

1

Maryland Democrat Win

Massachusetts Democrat Win

* Michigan to Democrat Change

16

-16

Minnesota Democrat Win

Mississippi Trump Win

6

Missouri Trump Win

10

Montana Toss Up

3

3

Nebraska Trump Win

Nevada Democrat Win

New Hampshire Democrat Win

New Jersey Democrat Win

New Mexico Democrat Win

New York Democrat Win

* North Carolina to Democrat Change

15

-15

North Dakota Trump Win

3

Ohio Toss Up

18

18

Oklahoma Trump Win

7

Oregon Democrat Win

* Pennsylvania Democrat Win

20

Rhode Island Democrat Win

South Carolina Trump Win

9

South Dakota Trump Win

3

Tennessee Trump Win

11

Texas Toss Up

36

Utah Trump

6

Vermont Democrat Win

Virginia Democrat Win

Washington Democrat Win

West Virginia Trump Win

5

* Wisconsin to Democrat Change

10

-10

Wyoming Trump Win

3

Figure 2

I have also put an asterisk* next to each “battleground” state, where 80% of campaign funds are usually spent. Traditionally, these are the states where each campaign focuses their efforts to win the electoral college. As of this date, four of these battleground states that Trump won in 2016 are now classified as Democratic wins.

Using this arbitrary division, the state by state analysis showed that seven states would move from Trump’s win column to the Democrats favor. This would reduce his electoral college vote by 98. Which would put his total electoral vote at 206, sixty-four vote shy of victory. In addition, seven states moved from his win column to the toss-up category, totaling an additional 111 electoral votes that are now up for grabs. That’s if the election were held today!

The job approval as a predictor has a limited life span and can change as we near the election day. For example, two months before the 2012 election, Barrack Obama’s rating was 48%. But just before the election he crossed the 50% line and was reelected. As we near the 2020, I will revisit the Civiqs data and the state by state results. Stay tuned…

Do you have to be tall to be president? For most political observers the answer is yes. A study by a Texas Tech Political Scientist, Gregg Murray, concluded that the taller candidate from 1789 to 2012 won 58% of presidential elections and received the majority of the popular vote in 67% of those elections.

The reasoning among some researchers is that taller candidates “look stronger” and are seen as having more leadership and communication skills. But do such perceptions still matter in the modern world since the age of television? Most studies that have looked at height and presidential races starting in 1900, where 20 of the 29 candidates were taller than their opponent (and two were the same height). Statistically, that’s 68% of all elections since 1900.

In a recent article, using these statistics the Independent had this to say about why tall men win presidential elections: “History suggests that beating Donald Trump in 2020 could be a tall order for Democrats – because voters tend to vote for presidential candidates with a height advantage over their opponent. While it may sound superficial, taller presidential candidates have fared better over the years, with the taller of the two candidates winning the popular vote in two-thirds of elections, and in the electoral college more than half of the time.”

All of these height statistics are mostly based on studies from 1900. Now you may ask yourself, how did voters prior to the age of television know which candidate was taller? The most common answer is stereographs, and of course, traditional black and white photographs. I find this explanation somewhat dubious, since the photograph would have to include both candidates side by side. Possible, but hardly the impact of a TV debate.

This height issue has now resurfaced since the entry and early exit of Michael Bloomberg into the race. Both Trump and Bloomberg have a history together in New York and its not a positive one. And true to Trump’s penchant to characterizing his opponents, he has renamed Bloomberg as “Mini Mike.”

Trump claimed that “Mini Mike” is only 5′ 4″, but his actual height is listed at 5′ 8,” still some seven inches short of Trump’s 6′ 3″ claim. In addition, if Bloomberg does become the Democratic nominee, he will have tied Michael Dukakis, as the shortest Presidential candidate since the television era.

So if “Mini Mike” had decided to stay in the race and became the Democratic nominee was he destined to lose the general election. So I finally decided to put this idea that bigger men usually win Presidential races to an empirical test. But this time, the starting date is at the beginning of the television age: 1952.

Since then there have been 16 presidential elections that had at least one televised debate and news coverage galore. Although there are no surveys that asked about height differences, the probability of voters noticing the taller candidate is certainly better than a black white stereograph.

Out of the 16 races (I heave excluded Hilary Clinton’s loss due to gender differences), five winners were the shorter candidate, giving up 4.6 centimeters (1.8 inches) to the taller losers. The average height for all winning candidates was 184 centimeters (72.4 inches); the average height for losers was 182 centimeters (71.6 inches), a difference of less than inch.

It’s worth noting that the average height for an American male during this period was 175 centimeters (69.1 inches). In other words, men who ran for president were on average taller (2.5 inches) than the general male population.

The question, of course, is there a significant statistical difference between the heights of winners and losers. Or, in the alternative, there is no statistical difference and taller men don’t have an electoral advantage when they run for president.

Using Analysis of the Variance (ANOVA) we can test the null hypothesis that there is no statistical difference between taller and shorter candidates when it comes to winning the White House.

ANOVA is a statistical technique that measures whether differences between two or more groups (tall vs. short candidates) and success in wining the election. Significance levels above .05 level, shows there is no difference and consequently, that heights do not contribute to election success.

When we measure the differences between tall and shorter candidates and victory at the ballot box from 1952, the significance level is far greater than the .05 level (.833), as shown under column “Significant” in Table 1 below. In other words, height had no impact on whether a candidate, tall or short, won or lost.

ANOVA

DIFFERENCE

Sum of
Squares

df

Mean
Square

F

Significant

Between Groups

2.286

1

2.286

.046

.833

Within Groups

691.714

14

49.408

Total

694.000

15

TABLE 1

On the surface, taller presidential candidates seem to win more elections (11 taller and 5 shorter), but this difference is random or the result of campaign effects and not based on the height differences.

So why do previous studies support the “height wins theory”? There are a couple of possibilities for these differences, foremost is the time difference. Previous studies used arbitrary starting dates for their data, predominantly the year 1900. Others have included even elections back to George Washington.

During the early Republic, partisanship was in its infancy and a candidate’s party label did not have the influence on election choice as it does today. For a lifelong Republican today, a tall Democrat against diminutive Republican would still not get his vote, just ask John Kerry.

Information about the candidates position on issues was often scarce. No CNN all day long, and voters had fewer heuristics to use to determine their choice. Consequently, voters might use height as an alternative for information. The stereotype that taller people are more successful and even, more intelligent, could make a difference absent any other information. Also possible, that the winning and losing differences between tall and short candidates are random.

So what about Mini Mike if had stood on that debate stage some seven inches lower than the Big Don? I have shown that being tall isn’t a requirement to become president in the modern age. That said, having Donald Trump labeling you continuously as “Mini Mike” could have a negative effect over time. Ask “Little Marco” how he feels about it. And he is 5’10” tall.

It is well established that the key element to a successful campaign for public office is the amount of money it raises. For example, between 2000 and 2016, 90% of Congressional House seats were won by the campaign who raised and spent the most money. Here money does matter.

But does money buy success in a Presidential race? It would seem logical, since so much money is poured into a national campaign. Between 1972 and 2016, the Republican and Democratic candidates spent a combined 4.8 billion dollars. That’s B as in Billions! If that kind of money had no effect on the outcome, somebody should be embarrassed.

Studies certainly agree that more spending in lower level races correlates with winning, particularly at the Congressional level. The impact of presidential spending on winning is significantly sparse, even among academic journals. Some look at resource allocation and strategic use of campaign funds but I found no actual studies of how much candidates spend in a presidential election and their success.

So to cure my curiosity, I compiled presidential election data for every election since 1972. In particular, how much money each campaign spent in pursuit of victory, derived FEC records. These expenditures, however, do not include the amount of money spent by independent groups (independent expenditures), which are not required by the candidate to report.

Over the past 12 presidential cycles, the winning candidate out spent their opponent by $618 million dollars. Which on the surface would suggest that more money leads to victory. However, this advantage is not evenly spread throughout each election cycle, as graphically shown in Figure 1.

The values above the zero horizontal line indicate the winner’s expenditures in millions. This chart shows that through most of these elections, the difference between winner and loser spending was relatively modest, until you reach 2008 when Barack Obama out spent John McCain by more than $520 million. This was followed by the $278 million spending advantage that Obama had over Mitt Romney in 2012. And in 2016 Hillary Clinton out spent Donald Trump by a whopping $211 million while losing the contest in the Electoral College.

Except for these three races, the spending deficit of losers does not appear to be overwhelming. For the years 1972 through 2004, the loser only gave up $30 million. But these aggregate figures don’t tell the whole story of whether out-spending your opponent usually leads to the White House.

First, lets see if there is a correlation between spending and winning or losing. We would expect, that if a candidate spends more money than his opponent, absent campaign effects, he would reign victorious. But in the last twelve presidential cycles, outspending the opponent is uncorrelated with winning!

Confirming this, is a scatter-dot graphic that compares the relationship between the incumbent’s (or his Party) percentage of the two-party vote and the difference between the winner and loser’s spending visually demonstrates that the vote is unrelated to winning or losing.

The scatter-dots in the graph are mostly horizontal along the zero spending difference level. We would expect that as the spending difference increases, the percentage of two-party vote would increase as well and that is not the case.

This is also confirmed by the R square of .064, which means there is almost zero relationship between the two variables. In other words, spending more or less than your opponent has no effect on the two-party share of the vote.

To corroborate that money has no effect on winning or losing, I regressed the winner-spending difference on whether the candidate won or loss (confirmed by logistic regression.) The significance level (.315) was far above the <.05 level needed to show a relationship, as shown in Table 1 regression coefficients. Again, it doesn’t matter that a candidate out spent his opponent or not, when it comes to winning.

Standardized Coefficients Beta

t

SIGNIFICANCE LEVEL

CONSTANT WINNER-SPENDING DIFFERENCE

-.317

4.823

-1.058

.001

.315

TABLE 1 REGRESSION COEFFICIENTS

So what’s going on here? The cardinal rule states that more money leads to winning in political campaigns, but this analysis says otherwise.

Let’s first differentiate a presidential campaign from every other political contest. In presidential campaigns, every minute is covered by national media and debated twenty-four seven by TV pundits. This is not a race for small town mayor. Unlike even U.S. Senate campaigns, every move by the presidential candidates is recorded, printed and debated. There is no escaping the daily deluge of media coverage unless you move to Mars. Every baby kissed, every misspoken word, and every speech given in a day are recorded and played over and over again.

And then there are the televised debates, where millions of people around the world watch. In 2016, some 84 million viewers watched Hilary and Donald duke it out.

As pointed out in a previous post (“Will the economy save Donald Trump?”), presidential campaigns are often shaped by non-campaign forces such as the economy or a national crisis. Some contests are over before the campaigns reach their stride. If you doubt that, just ask Jimmy Carter. By time election day arrives, even first graders have an opinion who they would vote for (if only the country changed the age requirement.)

Consequently, the effect of paid media diminishes as it is replaced by outside forces, sometimes even outside the control of both campaigns. Spending more on TV commercials at this point is wasting money.

Do I think presidential campaigns will not stop fundraising and buying TV time? Absolutely not! Some traditions are hard to break. Ask President Trump, his presidential campaign has set a fundraising goal of one billion dollars! If he doesn’t win, somebody is going to look pretty stupid.

Since the Democratic presidential primary season is winding down to two male candidates (Tulsi Gabbard is still in the race at this writing), the subject that women candidates are discriminated against in elections has been raised again. This is not an easy subject for researchers to determine since the effects of such discrimination is impossible to divine at the voting booth. There is also the possibility that people who do have prejudices don’t even know they have them.

Most research focuses on stereotypes that both some voters have about women. The consensus of research indicates that stereotypes are usually activated in low visibility races. An example often occurs in school board elections, where women candidates often are elected. After all, who is better with the well being of our kids than a loving woman?

But this post is more about money than elect-ability, specifically in a Democratic presidential primary. Raising campaign money is often perceived by pundits as a demonstration of a candidate’s elect-ability. For both the media and campaign pundits, fundraising prowess often becomes a substitute for who’s winning and losing.

The FEC has campaign fundraising and spending for each candidate through January 31, 2020. For my purposes here, I’ve only included candidates who raised a million dollars or more and I have excluded both Bloomberg and Steyer, who mainly used their own funds.

That leaves us with 20 Democratic candidates: Sanders, Warren, Buttigieg, Biden, Harris, Yang, Klobushar,Delaney, Gillibrand, Gabbard, Williamson, Bennet, Hickenlooper, Patrick, Swalwell, Moulton, Ryan, and de Blasio.

Between all of them they raised a total of $594,823,936. During this same period, Donald Trump had raised $150,168,134.

The clear fundraiser of this group was Bernie Sanders with a remarkable $134,268,972. He is followed by Elizabeth Warren at $93,028,032. Joe Bidden came in at fourth with $69,947,288.

But our interest here is whether men have a fundraising advantage over women candidates. On the surface at least, Democratic men did raise more money than the Democratic woman.

The men raised a total of $388,628,507 compared to the Women’s $206,195,429, a $18,243,308 advantage. However, there were twenty male candidates versus six women. On average, the men raised $19,431,425 and the women $34,365,904. In other words, on average the woman out raised the men by almost 15 million dollars. But that still doesn’t tell us whether the women or men had a significant fundraising advantage.

Using Analysis of the Variance (ANOVA) we can test the null hypothesis that there is no statistical difference between men and women fundraising.

ANOVA is a statistical technique that measures whether differences between two groups (women vs. men candidates) and success in fundraising. Significance levels above .05 level, shows there is no difference and consequently, that there is no statistical differences. For those interested in statistics, I have included the statistical results for the ANOVA analysis in Table 1 below.

ANOVA

RAISED

Sum of
Squares

df

Mean
Square

F

Significant

Between Groups

1.833E14

1

1.833E14

.125

.728

Within Groups

2.646E16

18

1.470E15

Total

2.664E16

19

Table 1

That there is no difference in fundraising between men and women does not mean that gender doesn’t influence voters’ election choice. But it should put to rest that women can’t compete financially in a presidential primary.

Under the significance column, the level is .728 and since it is well above the .05 level, we can conclude the null hypothesis is confirmed and the differences are likely random.

After you brewed you morning coffee, many of you went on-line to check the latest updates on Washington’s political news. I know this because that’s what I do every morning. (My wife says I need to get a life. And she’s right, but my addiction is too far gone.) In this morning ritual, I undoubtably check Real Clear Politics’ latest polls, but the one I’m most interested in is the daily Trump Job Approval numbers.

In modern American elections, there is no more ubiquitous statistic
than the President’s current job approval percent. Almost every day after a Presidential
election, at least one polling company releases their latest results. In 1937, George
Gallup created this simple question: “Do you approve or disapprove of the way (NAME)
is handling his job as president?”

The original concept of the approval question rested on the theory that the president was the CEO of the country and that a simple and easily understood question would best gauge the county’s “impression” of how well the president was running the business of government. Today, most academics refer to the approval rating as a popularity measure and not a specific indication of how voters think the President is running the business of running the country.

Since Donald Trump was elected in 2016, there have been 1,014 surveys that asked the Presidential Job Approval question. His rating has ranged from a low of 32 percent approve, to a high of 57 percent, with an average approval rating of 45.2 percent during his two years and 360 days in office. As of this writing, that means pollsters have completed almost one survey a day since Trump said, “I swear.”

His average Approval Rating of 45.2%, qualifies him as having the lowest average rating of any first term president since Gallup started asking this question, with the exception of Jimmy Carter who had an average first term rating of 45%.

Since World War II, not a single incumbent presidential candidate has won re-election with a job-approval rating below 50 percent.

The Trump campaign understands that his Job Approval rating is a problem for the upcoming 2020 election, but they are hanging their strategy on the economy. The major talking point for Trump and all his supporters is the economy and not the polls.

Conventional wisdom supports the theory that the president is reelected when the economy is good and loses if it’s bad. And some academic studies seem to support this theory in part, that the economy predicts who wins or loses. The important question is what part of the economy matters most in helping Trump offset his lagging Job Approval ratings, if any?

Will the economy make up for President Trump’s lackluster Job Approval ratings come November 2020?

To determine that, we need to find economic measures that have a significant effect on presidential elections. Most political analysts have focused a three important economic measures that effect political outcomes: GDP growth, inflation, and unemployment.

Using linear regression (OLS) we can measure the impact of each economic variable on each Presidential election since 1972, such as the GDP growth rate, on the incumbent’s (or his party) two-party share of the vote .

For those statically inclined, I have included the models coefficients in the Table below. Of the five economic variables only three are statistically significant (denoted by *): Job Approval, nominal GDP growth and per carpita GDP. (GDP growth rate is significant at the <.08 level). When a variable is not significant it means that it has no correlation (no impact) on the dependent variable, which in this case is the two-party share of the vote. A variable is considered significant at the <.05 level. Some researchers include levels <.10, when the sample is small. Consequently, I’ve included GDP growth rate which is <.075.

Model Coefficients

Unstandardized Coefficients
B

Standard Error

Beta

Significance

Constant

55.78

12.047

.004*

Job Approval

.327

.085

.783

.008*

GDP Growth Rate

.-1.3

.623

-.465

.075*

Per Capita GDP

-.003

.001

-.485

.025*

Unemployment

-.176

-.047

-..039

.816

Inflation

-.237

*.149

-.231

.499

In plain terms, unemployment and inflation have no effect (non-significant) on the President’s percent of the two-party vote, even as the he touts the “lowest unemployment figures ever.” No matter how good the unemployment numbers are (and they are), it won’t help him in November.

Consequentially, I have dropped both inflation and unemployment from the model. Without these two non-significant variables included, the Rsq. for the model is .887, or almost 90% of the model variance is explained by the remaining variables. The Beta coefficient shows that the Job Approval rating has far more impact on Trump’s percentage of the two-party vote than any other variable.

Below is a graphic representation the model’s final estimate for each election since 1972.

Each circle above the red line represents an incumbent (or Party’s) victory and, of course, the circles below a defeat. The model’s estimate of all 12 election’s percent of the two party-vote is off by less than three-tenths of a percent.

Using this simple equation, we can estimate what President Trump’s percentage of the two party vote using his current Job Approval rating, GDP growth rate and a per capita GDP, if the election were today.

Can Donald Trump Win?

The equation is simple: Vote Percent = Constant + Job Approval + GDP percent growth. When we insert Trump’s current Job Approval (45%), and latest GDP growth rate (1.6%) and GDP per capita we get the final equation:

Simply put, if the election were today, with the President’s current Job Approval of 45% and a GDP growth rate of 1.6% and a per capita GDP of $5463,Trump’s share of the two party vote would be 48%. In other words, today’s economy improves Trump’s vote by about 3%.

If President Trump can raise his Job Approval rating to 48%, his percentage of the two-party vote would 49.3%, increasing by only 1.3%. As his approval rating rises, the economic impact on the popular vote decreases as well, until he reaches 50%, when good economic numbers no longer effects his percentage of the two-party vote. In other words, a 50% approval rating is his Holy Grail.

How Accurate is this Election Model?

We can’t, obviously, measure the actual accuracy of the model’s estimates until after the 2020 election (which I will). But we can test the model’s accuracy against the past 12 presidential elections. When we do, the model estimates the actual percentage of all 12 elections cycles within an average difference of only .19%, which is graphically shown in Chart 9.

The two lines represent the actual vote percent (Blue) and the Model’s estimate (Red). The two images are nearly a mirror image of each other. As visually shown in the chart, the model is a very good fit for the data.

Professor Ray Fair’s January presidential model predicts Trump will win with 54.4% of the two-party vote. Fair’s model relies only on economic data and does not include Trump’s job approval rating in his equation. It also incorporates data back to 1918. His economic models have a good track record, but in 2016 his estimate was off by 5.4%.

Its important to note that the Job Approval rating has short shelf life, and predictions based on it more than two months prior to the election are less likely to accurately measure the two-party vote, even when controlling for the economy.

In addition, this model only applies to the popular vote and not the Electoral College. In modern times, the Electoral College usually mirrors the popular vote. But in the last two decades the popular vote deviated from the Electoral College vote in two key elections: George W. Bush in 2000 and Donald J. Trump in 2016.

I will address the Electoral College vote in a future post. This model, however, does not predict the Electoral College vote.

This Model also predicts the outcome without the the challenging Democratic candidate or the campaign each wages. (There is some evidence that candidate quality does effect the outcome somewhat.)

Political scientists have been predicting presidential outcomes for years with mixed success. After teaching graduate level political science courses for many years, I believe campaigns still matter, but not as much as pundits would have you believe. Each campaign takes place in an environment that is predicated on the political and economic environment.

But there are fundamental elements that shape every election that campaigns and candidates cannot change. Some candidates and campaigns are doomed from the start, as Jimmy Carter can attest to. But many presidential election campaigns can and do have some impact on the outcome. But an incumbent facing a recession headwind, knows the odds of winning are small.

So the question we started with: can the economy put Trump over the finish line even with a Job Approval rating below 50%? The answer is probably not. There is a reason why no incumbent President since World War 2 has never won re-election without an approval rating 50% or higher.

But even with a lagging approval rating this race is likely to be very close. Close enough for a replay of 2016, where Donald Trump carried the Electoral College while losing the popular vote. I will address this possibility in a future post.

When election day arrives, do what mother taught me: vote early and often!