These 5 graphs explain who voted as in the mid-term elections of 2018


House Minority Leader Nancy Pelosi (D-Calif.) Speaks at a press conference on Wednesday in Washington, one day after the Democrats won control of the House. (Nicholas Kamm / AFP / Getty Images)

The mid-term elections of 2018 have drastically shifted the balance of power in the House of Representatives, from Republican control to democratic control. Many expected that, given President Trump's relatively low approval rating. Historically, this meant that the president's party would have lost many home games. The pre-election polls have largely confirmed the probable democratic acquisition.

But here's what we still have not known: which groups have supported the democrats in these elections? How do these models compare with previous elections?

Below, you can see five graphs that help explain what happened. For the most part, these graphs are based on data from the Cooperative Congressional Election Study (CCES), a large-scale academic survey conducted in each election year since 2008. For the CCES analysis of 2018, we used interviews pre-electoral with the weighted respondents being representative at the national level of the adult population. We then applied a probable electoral model trained on previous electoral cycles to create estimates for the 2018 electorate.

1. How did the different age groups vote?

First, let's look at the voting patterns between the different age groups in House competitions in the last decade. While this year all age groups voted more democratic than they had in 2016, those less than 50 years old have changed more. In particular, voters between the ages of 18 and 29 chose Democratic candidates over the Republican candidates by a 2 to 1 margin in 2018. And while we will not know for some time, some indications suggest that they may have constituted a larger share of 39, electorate that in the typical mid-term elections – or in other words, that the young have proved to be voting in particularly high numbers.

2. How did the suburbans vote?

Suburban districts were among the most heavily fought battlefields in this campaign. The data from the CCES show that the Democrats have done well in those districts. The chart below shows the House vote among the people living in the suburbs, broken down by the United States region. Suburban voters supported candidates in the Democratic Chamber with a healthy margin compared to Republican candidates in all regions except the South, where the party collapsed.

3. As expected, women and men have voted very differently

Another model that everyone observed was the gender gap – which, as the next graph shows, was the biggest we've seen in at least a decade. While nearly 60% of women who voted for one of the two major parties voted for democratic candidates, only 47% of men did. This is a gender gap of 13 points.

4. We bring down women and men by race and education

But of course, women and men are incredibly large groups, made up of every demographic in the United States. So what sub-groups of women and men were more distant? The following chart traces the two-part voting quota among white voters (since color voters have gone overwhelmingly to Democratic candidates, regardless of gender), by gender and education.

As you can see, white women without a degree have moved modestly towards Democrats, more than white without degree. But a far greater percentage of white women educated at universities passed to Democrats, even beyond their previous support for Democratic candidates in previous elections. How much did they swing? In 2018, white women educated in universities increased their support for Democratic candidates by eight percentage points compared to 2016. In previous cycles, this group represented around 15% of the electorate, thus the democratic margins of this sector demographic certainly helped the Democratic candidates to fuel success in 2018.

5. Why did white women educated at universities go so far towards the Democrats?

What explains the great change of white women educated in the university? The final graph comes from the analysis I conducted for Data for Progress. In this article, I compare the role of voters' attitudes to women in these elections to the role they played in 2018.

Among other factors, I examined what researchers call "hostile sexism", a series of antagonistic attitudes towards women that derive from the belief that women want to control men. While hostile sexism was a strong predictor of Trump's support, it did not affect the way people voted in their Home competitions in 2016. That changed in 2018.

The chart below shows how higher levels of sexism are related to the vote for the Republican candidate in both 2018 and 2016, controlling other factors such as ideology, partisanship, racial attitudes and demographic data. In 2016, the agreement of a voter or the disagreement with the sexist statements (which you can find on the axis of the abscissas) did not really matter because they voted for the Republican House candidate. In 2018, however, the people who were most likely to disagree with sexist statements (ie, had a less hostile sexism) were far less likely to vote Republican.

In essence, the less sexist voters have punished the candidates in the Republican House in a way they did not in 2016. In addition, Republicans have not earned more sexist voters to compensate for that loss.

Overall, these five graphs suggest that Republicans may wish to be worried about being tied to a president whose rhetoric is often divisive and offensive. Doing so is to turn off the younger voters at historical rates, while driving away women (especially those with degrees). If the Republican Party brand becomes more and more synonymous with Trump, these models could persist in 2020 and beyond.

Read more:

Brian F. Schaffner (@b_schaffner) is a professor of Newhouse of Civic Studies at Tisch College and the Department of Political Science at Tufts University.

Leave a comment

Send a Comment

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.