How do economy and character affect elections

However, a nascent series of research papers in political science have suggested an intriguing new explanation: another factor, namely, genetic predisposition, is proposed to affect both personality and voting behavior Funk et al. How can we put these measures into context? Having a college degree increases the likelihood of voting for Clinton over Trump by 32 percentage points relative to no degree having a professional degree increases it by 42 points, not pictured , while being female increases the likelihood of voting for Clinton over Trump by 14 percentage points.

By comparison, Openness has the most predictive power out of all the other traits, since a 1 standard deviation increase in openness leads to a 9 percentage point increase in the likelihood of voting for Clinton over Trump. Keeping in mind that the personality traits are measured on a continuum while gender and college completion are not , this shows that while personality traits certainly do play a meaningful role in understanding voting behavior, they are not the main predictors of voting intentions.

More work is needed to answer the question of why people vote the way that they do. Agreeableness : Pro-social and communal orientation toward others. Conscientiousness : Socially prescribed impulse control that facilitates task and goal-oriented behavior, following rules and norms. Neuroticism : Negative emotionality, such as feeling anxious, nervous, sad or tense. Sometimes referred to by its inverse, Emotional Stability. Openness to experience : Describes breadth, depth, originality and complexity of thought, coming up with novel ways to do things.

How to interpret this figure? The figure shows the effect of increasing personality by 1 point on the percent likelihood of voting for Clinton, minus percent likelihood of voting for Trump. The estimates also control for background characteristics of each person.

In other words, the voters did reward the Democrats for reducing unemployment. Real personal income increased between and , so the negative regression coefficient shows that counties with a stronger rise in income were less likely to vote for Trump.

This effect also shows that voters were rewarding the Democrats for economic improvement. Manufacturing employment growth was negative, so the positive regression coefficient shows that counties with larger losses of manufacturing jobs were less likely to vote for Trump. In other words, voters did not think that he would bring back the vanished manufacturing jobs.

So we can conclude that the economic factors made counties less likely to vote for Trump. Trump lost the popular vote, but economic factors did not make counties more likely to support Trump. So we should look at other factors that motivated voters to support Trump and help him to a majority in the Electoral College. In fact, according to the economic model Hillary Clinton would have won the Electoral College if either unemployment had fallen further, real personal income had risen more, or manufacturing jobs loss had been more severe.

While economists were spinning their self-contained narrative in , other social scientists were pointing at demographic, sociological and even psychological variables to explain the Trump vote. To test this alternative narrative, several county-level variables were chosen to again fit a logit model of the Republican winning the county: median age, percent non-Hispanic white, percent male, percent without a college degree, number of drug deaths per ,, and percent of the population which is physically active in their leisure time [3].

That last variable is a little out of left field, but there has been recent interest in the so-called yoga vote. Basically, this is a lifestyle variable with strong implications for political preference that transcends the simple urban-rural divide. On the one hand, urbanites walk and cycle to work at a higher incidence, have more access to gyms and exercise classes, while also being more likely to vote Democrat. Because of their attachment to nature, they are frequently found in rural areas.

However, they tend to vote for the Democrats. Capturing this demographic group by the urban-rural divide would be misplaced, therefore we directly identify them through their level of physical activity. Here, average age, drug deaths, and no college degree are statistically insignificant. As the white population or male population increases, the probability of the Republican candidate being elected increases.

The insignificance of drug deaths is contrary to the narrative that communities in despair voted against the incumbent party. Additionally, the insignificance of not attaining a college degree is counter to the idea that the poorly-educated voted for Donald Trump. It seems that the significant variables already capture all of the voter behavior.

The most interesting variable is the percentage of the physically active population—the more a population exercises in their leisure time, the less likely they are to vote Republican. Using only demographic variables has a more statistically-significant regression than using only economic variables. The demographic model predicts more counties correctly than the economic model. Knowing the percentages of physically inactive white men living in a county is a better predictor of the Trump vote than changes in the unemployment rate, real personal income and manufacturing employment.

While comparing the fit of the two models reveals the explanatory power of economic versus demographic factors, it may be more insightful to compare the separate variables in a single encompassing model to see which variables are the most significant.

Demographics change slowly and linearly. Economic variables capture the relatively rapid changes experienced by electorate. What is the optimal mix of economic and demographic factors in explaining the Trump vote? Since both economic and demographic variables appear to explain the behavior of US voters, we combine them in an encompassing model.

To do this, we look at all first difference economic variables and demographic variables. For the demographic variables, the addition of economic variables does not change the conclusion: the percentages of white voters, male voters and physically active voters remain the only significant demographic variables.

Even the estimates do not change very much. In contrast, once we account for demographics, a slightly different set of economic variables is significant. Unemployment change and manufacturing employment growth remain significant, but real personal income is replaced by the change in the poverty rate. And that gap is clearly related to the rise of more partisan media sources. The media also perpetuate character-based scripts.

According to a recent Pew Research Center study , 62 percent of Americans get their news via social media platforms. What they might not realize is that the news they see is heavily filtered. On the other hand, social media gives users more direct access to candidates than ever before.

And candidates have unprecedented control over the images they present. The photos news organizations choose to publish and such factors as their size and layout can also influence voter perceptions — and reveal possible bias.

Some led with an image of her husband. And other newspapers led with an image of Donald Trump. Published images also become part of the permanent record preserved on the internet. Much of the data Silver crunches come from polls, one of the most common topics of election coverage. The media flock to the front-runners. And the more coverage those candidates get, the higher they tend to climb in the polls — a dynamic that can turn into a self-perpetuating cycle.

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