Volume 14, Issue 4

Using election results as our point of departure, this article places the 2016 presidential election in historical perspective. Trump’s victory was an “expected” outcome, as races following two-terms of one party rule usually go to the opposition party. Trump also ran close to fellow Republicans competing for Congress despite his unusual relationship with many of those in the party he captured. Trump’s victory in the presidential contest proved remarkably economical. He lost most of his votes relative to Romney in 2012 in places where it did not matter, while his opponent ran up the score in the states she already had locked down. We further measure the increasing geographic concentration of each major party’s electoral support and consider how this relates to the controversy over the Electoral College.

In the days after the 2016 election, a variety of explanations has been offered to explain Donald Trump’s unique ascendancy in American politics. Scholars have discussed Trump’s appeal to rural voters, his hybrid media campaign strategy, shifts in voter turnout, Hillary Clinton’s campaign advertising strategy, economic anxiety, differences in sexist and racist attitudes among Trump voters and so forth. Here, we add another key factor to the conversation: Trump’s appeal to a smaller, often ignored, segment of the electorate: populist voters. Building upon our previous work – demonstrating that while American political elites compete across a single dimension of conflict, the American people organize their attitudes around two distinct dimensions, one economic and one social – we use 2008 American National Elections Study (ANES) data and 2016 ANES primary election data to show that populist support for Trump, and nationalist policies themselves, help us to understand how Trump captured the Republican nomination and the White House.

On the morning after, the aggregate story of the American elections of 2016 was one of surprise. Professional pundits – not to mention the pollsters, those masters of disaggregation – had been rather strikingly wrong. So, one of the major products of the elections of 2016 was a major research project for these professional disaggregators. But can those who treat elections as aggregate phenomena – as patterned wins and losses across time – fit these results into an ongoing pattern? Within this pattern, what looks familiar and what looks anomalous? Or, said the other way around, when does a recognizable “modern world” begin, and what are its contours? And in the end, even if 2016 proves to have more similarities than differences to some (surely not all) of its predecessors, what would have to happen in order for its anomalies to become the shape of a successor world, making 2016 look like the beginning of something seriously new? Those are the questions that motivate this paper.

Because of the particular candidates who ran, the 2016 presidential campaign was defined by gender to a remarkable degree. This led many observers to expect a historically large gender gap in voting. In contrast to these expectations, the gender gap between men and women’s votes in 2016 was only slightly larger than in other recent elections. We argue that an immense gender divide did not emerge because it was constrained by high levels of partisanship in the electorate, especially “negative partisanship” toward the opposing party that leaves little room for gender to matter. In addition, we challenge two common assumptions: that the gender gap helps Democratic candidates and that women were more persuadable than men over the course of the campaign. Both men and women vacillated in their views of Clinton’s honesty during the campaign, with men shifting away from her and toward Trump just before election day.

Partisanship is a stable trait but expressions of partisan preferences can vary according to social context. When particular preferences become socially undesirable, some individuals refrain from expressing them in public, even in relatively anonymous settings such as surveys and polls. In this study, we rely on the psychological trait of self-monitoring to show that Americans who are more likely to adjust their behaviors to comply with social norms (i.e. high self-monitors) were less likely to express support for Donald Trump during the 2016 Presidential Election. In turn, as self-monitoring decreases, we find that the tendency to express support for Trump increases. This study suggests that – at least for some individuals – there may have been a tendency in 2016 to repress expressed support for Donald Trump in order to mask socially undesirable attitudes.

The 2016 presidential campaign broke the mold when it comes to patterns of political advertising. Using data from the Wesleyan Media Project, we show the race featured far less advertising than the previous cycle, a huge imbalance in the number of ads across candidates and one candidate who almost ignored discussions of policy. This departure from past patterns, however, was not replicated at the congressional level. We draw some lessons about advertising from the 2016 campaign, suggesting that its seeming lack of effectiveness may owe to the unusual nature of the presidential campaign with one unconventional candidate and the other using an unconventional message strategy, among other non-advertising related factors.

Contemporary digital advertising operates through algorithms – a data-driven logic created by the constant loop between a voter’s voluntary choices and campaigns’ strategic feedback on these choices. By paying attention to the logic of digital advertising, I discuss how digital advertising algorithms adjudicate consequential decisions that broadly affect political involvement. I argue digital advertising limits algorithmic opportunity to access and acquire political information. Voters are strategically defined, and information inequality is created between the arbitrarily defined “strategically important” and “strategically unimportant.” Discriminately defined by campaigns, different voters receive different information, thereby engaging differently in politics. To illuminate main points, an exploratory analysis of empirical data that “reverse engineered” the algorithms of digital advertising is presented. I conclude that contemporary digital advertising perhaps propels inequality of political involvement and polarizes the electorate.

Gary Jacobson is one of the world’s leading experts studying US congressional elections. This essay examines his contributions to political science over the past 40 years.