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.
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.
GDP Growth Rate
Per Capita GDP
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!