Data & Resources Front Page General Theories women in politics

Aristotle said that “The whole is greater than the sum of its parts.” The Battle for the Electoral College.

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 ( ) 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…

Data & Resources Money and Elections Theories women in politics

Are Women Presidential Candidates Disadvantaged In Raising Money?

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.

  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.