Category Archives: Elections

Redrawing the Map: How Jowei Chen is Measuring Partisan Gerrymandering

post written by Solmaz Spence

“Gerrymandering”— when legislative maps are drawn to the advantage of one party over the other during redistricting—received its name in 1812, when Massachusetts Governor Elbridge Gerry signed off on a misshapen district that was said to resemble a salamander, which a newspaper dubbed a “gerrymander.”

But although the idea of gerrymandering has been around for a while, proving that a state’s legislature has deliberately skewed district lines to benefit one political party remains challenging.

The problem is that the mere presence of partisan bias in a district map tells us very little about the intentions of those drawing the districts. Factors such as racial segregation, housing and labor markets, and transportation infrastructure can lead to areas where one party’s supporters are more geographically clustered than those of the other party. When this happens, the party with a more concentrated support base achieves a smaller seat share because it racks up large numbers of “surplus” votes in the districts it wins, while falling just short of the winning threshold in many of the districts it loses.

Further, there are many benign reasons that legislatures may seek to redistrict voters—for example, to keep communities of interest together and facilitate the representation of minorities—that may have the unintended consequence of adding a partisan spin to the map.

The research of political scientists Jowei Chen and Jonathan Rodden is helping to differentiate cases of deliberate partisan gerrymandering from other redistricting efforts. Chen, Faculty Associate at the University of Michigan’s Center for Political Studies, and Rodden, Professor of Political Science at Stanford University, have devised a computer algorithm that ignores all partisan and racial considerations when drawing districts, and instead creates thousands of alternative district maps based on traditional districting goals, such as equalizing population, maximizing geographic compactness, and preserving county and municipal boundaries. These simulated maps are then compared against the district map that has been called into question to assess whether partisan goals motivated the legislature to deviate from traditional districting criteria.

We first wrote about Chen and Rodden’s work back in December 2016, detailing a 2015 paper in the Election Law Journal, which used the controversial 2012 Florida Congressional map to show how their approach can demonstrate and unconstitutional partisan gerrymander. Now, this work is back in the spotlight: Chen’s latest research has been cited in several cases of alleged gerrymandering that are currently working through the courts in Pennsylvania, North Carolina, Wisconsin and Maryland.

In January, Chen’s testimony as an expert witness was cited when the Pennsylvania Supreme Court threw out the state’s U.S. House of Representatives district map. In its opinion, the court said the Pennsylvania map unconstitutionally put partisan interests above other line-drawing criteria, such as eliminating municipal and county divisions.

The Pennsylvania districts in question were drawn by the Republican-controlled General Assembly in 2011. Immediately, the shape of the districts was an indicator that at least one traditional criterion of districting—compactness—had been overlooked.

Though few states define exactly what compactness means, it is generally taken to mean that all the voters within a district should live near one another, and that the boundaries of the district should be create a regular shape, rather than the sprawling polygon with donut holes or tentacles that characterized the Pennsylvania district map.

In particular, District 7—said to resemble Goofy kicking Donald Duck—had been called into question. “It is difficult to imagine how a district as roschachian and sprawling, which is contiguous in two locations only by virtue of a medical facility and a seafood/steakhouse, respectively, might plausibly be referred to as compact,” the court wrote.

Although there are more registered Democrats than Republicans in Pennsylvania, Democrats hold only five of the state’s 18 congressional districts. In the 2016 election, Democrats won each of their five House seats with an average of 75 percent of the vote while Republicans’ margin of victory was an average of 62 percent across their 13 districts. This is an indicator of “packing,” a gerrymandering practice that concentrates like-minded voters into as few districts as possible to deny them representation across districts.

Chen’s expert report assessed the district map and carried out simulations to generate alternative districting plans that strictly followed non-partisan, traditional districting criteria, and then measured the extent to which the current district map deviates from these simulated plans.

To measure the partisanship of the computer-simulated plans, Chen overlaid actual Pennsylvania election results from the past ten years onto the simulated districts, and calculated the number of districts that would have been won by Democrats and Republicans under each plan (see Figure 1).

The districting simulation process used precisely the same Census geographies and population data that the General Assembly used in creating congressional districts. In this way, the simulations were able to account for any geographical clustering of voters; if the population patterns of Pennsylvania voters naturally favor one party over the other, the simulated plans would capture that inherent bias.

Generally, the simulations created seven to ten Republican districts; not one of the 500 simulated districting plans created 13 Republican districts, as exists under the Republican-drawn district map. Thus, the map represented an extreme statistical outlier, a strong indication that the enacted plan was drawn with an overriding partisan intent to favor that political party. This led Chen to conclude “with overwhelmingly high statistical certainty that the enacted plan created a pro-Republican partisan outcome that would never have been possible under a districting process adhering to non-partisan traditional criteria.”

A map showing redistricting simulation in Pennsylvania

This table compares the simulated plans to the 2011 Pennsylvania district map with respect to these various districting criteria.

Following its ruling, on February 20 the Pennsylvania Supreme Court released a new congressional district map that has been described in a Washington Post analysis as “much more compact”. In response, the state’s Republican leadership announced plans to challenge the new map in court.

 

 

Understanding the Changing American Electorate

developed by Catherine Allen-West

The American National Election Studies (ANES) has surveyed American citizens before and after every presidential election since 1948.  The survey provides the public with a rigorous, non-partisan scientific basis for studying change over time in American politics.

The interactive graphs below illustrate the changing American electorate and some of the factors that may motivate voters’ choices at the ballot box. Mouse over the graphs for more detail.

Source: ANES Time Series Cumulative File and the 2016 ANES Time Series dataset. ANES offers 62 datasets, all free and available to the public, here.

 

Top 10 Most-Viewed CPS Blog Posts in 2017

post developed by Catherine Allen-West

Since its establishment in 2013, a total of 137 posts have appeared on the Center for Political Studies (CPS) Blog. As we approach the new year, we look back at 2017’s most-viewed posts. Listed below are the posts that you, our dear readers, found most interesting on the blog this year. 


What makes a political issue a moral issue? by Katie Brown and Timothy Ryan (2014)

There are political issues and then there are moral political issues. Often cited examples of the latter include abortion and same sex marriage. But what makes a political issue moral?An extensive literature already asserts a moral vs. not moral issue distinction. Yet, there is no consensus in how to distinguish between moral and non-moral political issues. Further, trying to sort issues into these categories proves challenging.

 


 

The Spread of Mass Surveillance, 1995 to Present by Nadiya Kostyuk and Muzammil M. Hussain (2017)

By closely investigating all known cases of state-backed cross-sector surveillance collaborations, our findings demonstrate that the deployment of mass surveillance systems by states has been globally increasing throughout the last twenty years. More importantly, from 2006-2010 to present, states have uniformly doubled their surveillance investments compared with the previous decade. 

 


 

Why do Black Americans overwhelmingly vote Democrat? by Vincent Hutchings, Hakeem Jefferson and Katie Brown (2014)

In 2012, Barack Obama received 93% of the African American vote but just 39% of the White vote. This 55% disparity is bigger than vote gaps by education level (4%), gender (10%), age (16%), income (16%), and religion (28%). And this wasn’t about just the 2012 or 2008 elections, notable for the first appearance of a major ticket African American candidate, Barack Obama. Democratic candidates typically receive 85-95% of the Black vote in the United States. Why the near unanimity among Black voters?

 


 

Measuring Political Polarization by Katie Brown and Shanto Iyengar (2014)

Both parties moving toward ideological poles has resulted in policy gridlock (see: government shutdowndebt ceiling negotiations). But does this polarization extend to the public in general? To answer this question, Iyengar measured individual resentment with both explicit and implicit measures.

 


 

Is policy driven by the rich, or does government respond to all? by Catherine Allen-West (2016)

The enthusiasm for both Trump and Sanders’ messages about the influence of money in politics brings up an important question: Is policy driven by the rich, or does government respond to all? Political scientists have long been interested in identifying to what degree wealth drives policy, but not all agree on it’s impact.

 

 


 

Exploring the Tone of the 2016 Election by U-M undergraduate students Megan Bayagich, Laura Cohen, Lauren Farfel, Andrew Krowitz, Emily Kuchman, Sarah Lindenberg, Natalie Sochacki, and Hannah Suh, and their professor Stuart Soroka (2017)

Political economists often theorize about relationships between politics and macroeconomics in the developing world; specifically, which political or social structures promote economic growth, or wealth, or economic openness, and conversely, how those economic outcomes affect politics. Answering these questions often requires some reference to macroeconomic statistics. However, recent work has questioned these data’s accuracy and objectivity. An under-explored aspect of these data’s limitations is their instability over time.

 


 

Crime in Sweden: What the Data Tell Us by Christopher Fariss and Kristine Eck (2017)

In a recent piece in the Washington Post, we addressed some common misconceptions about what the Swedish crime data can and cannot tell us. However, questions about the data persist. These questions are varied but are related to two core issues: (1) what kind of data policy makers need to inform their decisions and (2) what claims can be supported by the existing data.

 


 

Moral conviction stymies political compromise by Katie Brown and Timothy Ryan (2014)

Ryan’s overarching hypothesis boils non-compromise down to morals: a moral mindset orients citizens to oppose political compromises and punish compromising politicians. There are all kinds of issues for which some citizens seem resistant to compromises: tax reform, same-sex marriage, collective bargaining, etc. But who is resistant? Ryan shows that part of the answer has to do with who sees these issues through a moral lens.

 


 

Does the order of names on a ballot affect vote choice? by Katie Brown and Josh Pasek (2013)

Ballots list all candidates officially running for a given office so that voters can easily choose between them. But could the ordering of candidate names on a ballot change some voters’ choices? 

 

 

 


 

Inside the American Electorate: The 2016 ANES Time Series Study by Catherine Allen-West, Megan Bayagich and Ted Brader (2017)

Since 1948, the ANES- a collaborative project between the University of Michigan and Stanford University- has conducted benchmark election surveys on voting, public opinion, and political participation. This year’s polarizing election warranted especially interesting responses. 

 

Using Twitter to Observe Election Incidents in the United States

Post developed by Catherine Allen-West

Election forensics is the field devoted to using statistical methods to determine whether the results of an election accurately reflect the intentions of the electors. Problems in elections that are not due to fraud may stem from legal or administrative decisions. Some examples of concerns that may distort turnout or vote choice data are long wait times, crowded polling place conditions, bad ballot design and location of polling stations relative to population.

A key component of democratic elections is the actual, and perceived, legitimacy of the process. Individuals’ observations about how elections proceed can provide valuable, on-the-ground insight into any flaws in the administration of the election. In some countries there are robust systems for recording citizen complaints, but not in the United States. So, a team* of University of Michigan researchers led by Walter Mebane used Twitter to extract observations of election incidents by individuals across the United States throughout the 2016 election, including primaries, caucuses and the general election. Through their observations, the team shows how reported phenomena like waiting in long lines or having difficulties actually casting a vote are associated with state-level election procedures and demographic variables.

The information gathered is the beginnings of what Mebane is calling the “Twitter Election Observatory.”  The researchers collected tweets falling within a ten-day window around each primary/caucus election day and collected tweets continually during the October 1- November 9, 2016 lead up to the general election.

Mebane and his team then coded all of the tweets to extract the “incident observations” — tweets that mentioned an issue or complaint that an individual may have experienced when casting their vote. From the Twitter data, the researchers found that incidents occurred in every state during the general election period. Among the tweets that had recorded location information, the highest count of tweet observations occurred in California, Texas, Florida and New York and the smallest amount in Wyoming, North Dakota, South Dakota and Montana.

Additionally, the researchers calculated the rate of incidents relative to the population of the each state. On a per capita basis, the District of Columbia stands out with the highest rate of incident observation followed by Nevada and North Carolina with Wyoming as the lowest.

Every indication is that Twitter can be used to develop data about individuals’ observations of how American elections are conducted, data that cover the entire country with extensive and intensive local detail. Mebane notes that the frequency, and likely the diversity, of observations may vary depending on how many people care and want to participate in, observe and comment on an election. Ultimately, Mebane would like to dig further into the geolocation information of these tweets to try and pinpoint any incidents with exact polling locations.

*University of Michigan team includes: Walter R. Mebane, Jr., Alejandro Pineda, Logan Woods, Joseph Klaver, Patrick Wu and Blake Miller.

Link to full paper presented at 2017 meeting of the American Association for Political Science.

Donald Kinder Elected to the National Academy of Sciences

Post by Theresa Frasca

Earlier this year, the National Academy of Sciences (NAS) announced the election of the Institute for Social Research’s Donald Kinder, the only University of Michigan professor to be named in 2017 and the 28th professor to be named in U-M’s history. Established by Congress in 1863, the private, non-profit NAS promotes science through its consortium of more than 2,000 distinguished scholars, of which nearly 500 have won Nobel Prizes. NAS serves as  an independent advising entity to the government, and provides recommendations and guidance on matters of scientific or technological importance to the nation.

Photo of Donald Kinder

Donald Kinder

“It is a thrilling surprise to be elected to the National Academy of Sciences,” says Kinder. “I was very pleased when I received the call about my election, and I look forward to working with members on a variety of new projects.” As a member, Kinder will attend NAS membership meetings and help review papers for the multidisciplinary journal, Proceedings of the National Academy of Sciences, as well as provide his expertise on subject-related projects or efforts.

Kinder, a Research Professor at ISR’s Center for Political Studies, is notable for his research on prejudice and how it impacts contemporary American politics. “Most of my work over the last 20 years has focused on racial politics in the United States, as I’ve tried to understand the foundations of public opinion and the role that race plays in elections,” says Kinder. “This area of study has been a long-standing interest of mine that actually started in graduate school. I was in a specific time in a specific place at UCLA in the early 1970s and I became interested in how white suburban voters were affected by the racial identity of one of the mayoral candidates.”

More recently, Kinder’s work has revolved around ideology in the study of American politics and his newest book, Neither Liberal Nor Conservative, debuted in May. “This book is about American politics and how American elites seem highly ideological yet most American citizens are not,” says Kinder. “This is a condition that has been present over the past 50-60 years. In some ways, it’s a surprising argument to make because people who study politics and think about politics usually make the presumption that ordinary people think deeply about politics, too. But the reality is that regular citizens have better things to do with their lives and, as a consequence of that, their thinking is more casual and less organized and certainly less ideological.” The book, written with Louisiana State University professor, and U-M grad, Nathan Kalmoe has received several long-form journalism reviews including in VOX and Washington Monthly.

As Kinder reflects on both his current work and his new election to NAS, he says, “I’ve been at U-M for going on 40 years and what I love about the place is the endless parade of super smart graduate students who come through. I think of my election to the National Academy of Sciences as a reflection of this remarkable place, my great colleagues and wonderful students.”

The Politics of Latinidad

Post developed by Mara Ostfeld and Catherine Allen-West

The effectiveness of America’s system of democratic representation, in practice, turns on broad participation. Yet only about 60 percent of voting eligible Americans cast their vote in presidential elections. This number is nearly cut in half in off-year elections (about 36 percent), and participation in local elections is even lower. This lack of electoral engagement does not fall equally across racial and ethnic subgroups. Latinos, for one, are particularly underrepresented at polling booths across the country. In 2016, eligible Latino voters were about 20 percentage points less likely to vote than their White counterparts, and about 13 percentage points less likely to vote than their Black counterparts.

This fall, a group of 24 University of Michigan undergraduate students sought to explore this disparity and pinpoint what, if anything, works to increase Latino political participation. In the class, entitled The Politics of Latinidad, CPS Faculty Associate and U-M Political Science Professor Mara Ostfeld taught her students how to measure public opinion and challenged them to analyze the factors that affect Latino political participation.

Today, more than 50,000 Latinos live in Detroit and a majority of them reside in City Council District 6 in Southwest Detroit which is precisely where this course focused. The students began by studying the history of Latinos in Southeast Michigan and exploring how Latinos played critical roles in the city’s development dating back to before World War I. They analyzed broad trends in Latino public opinion, and considered how and why these patterns might be similar or different in Detroit. Students then designed their own pre-election polls to take into the field.

In order to understand what affects voter turnout, students surveyed over 300 residents of Southwest Detroit to measure the issues that were most important to them.

Photo of U-M students \

Students pictured here: Storm Boehlke, Mohamad Zawahra, Alex Tabet , Hannel So, Sion Lee.

The results illustrate some powerful patterns. Among the issues that the residents found most important, immigration and crime stood out. Forty-nine and 45 percent of Latinos listed immigration and crime, respectively, as issues of particular concern, with only 31 percent of residents saying that they felt safe in their own home.

Latinos in Southwest Detroit feel extremely high levels of discrimination.  Seventy percent of Latinos surveyed said they felt Latinos face “a great deal” of discrimination. This significantly exceeds the roughly half of Latinos nationwide who say they have experienced discrimination.

Student Alex Garcia visits residents in Detroit.

Local issues were also at the forefront of residents’ minds. Latinos had mixed views on the city’s use of blight tickets to combat housing code violations, with one third of respondents supporting them and one third opposing them.

As local organizations, like Michigan United, continue trying to get a paid sick leave initiative on the ballot in 2018, they can expect strong support among Latinos in Southwest Detroit. About two out of every three Latinos in the area indicated they would be more likely to support a candidate who supports the paid sick leave requirement.

The students then followed up with the residents a month later to see if they planned to vote in the upcoming city council election. At this point, the students implemented some interventions that have been used to increase political participation like, evoking emotions that have been shown to have a mobilizing effect, framing voting as an important social norm, and speaking with voters immediately before an election. With the election now over, students are back in the classroom analyzing the effectiveness of these interventions and will use their first-hand experience to better understand public opinion and political participation.

 

 

Inside the American Electorate: The 2016 ANES Time Series Study

Post developed by Catherine Allen-West, Megan Bayagich and Ted Brader

The initial release of the 2016 American National Election Studies (ANES) Time Series dataset is approaching. Since 1948, the ANES- a collaborative project between the University of Michigan and Stanford University- has conducted benchmark election surveys on voting, public opinion, and political participation. This year’s polarizing election warranted especially interesting responses. Shanto Iyengar, one of the project’s principal investigators and Stanford professor of political science, noted, “The data will tell us the extent to which Trump and Clinton voters inhabit distinct psychological worlds.”


To learn more about the study, we asked Ted Brader (University of Michigan professor of political science and one of the project’s principal investigators) a few questions about this year’s anticipated release.

When was the data collected?

The study interviewed respondents in a pre-election survey between September 7 and November 7, 2016. Election day was November 8. The study re-interviewed as many as possible of the same respondents in a post-election survey between November 9 and January 8, 2017.

The ANES conducted face-to-face and internet interviews again for 2016. How are these samples different from 2012? What are the sample sizes and the response rates?

The study has two independently drawn probability samples that describe approximately the same population. The target population for the face-to-face mode was 222.6 million U.S. citizens age 18 or older living in the 48 contiguous states and the District of Columbia, and the target population for the Internet mode was 224.1 million U.S. citizens age 18 or older living in the 50 U.S. states or the District of Columbia. In both modes, the sampling frame was lists of residential addresses where mail is delivered, and to be eligible to participate, a respondent had to reside at the sampled address and be a U.S. citizen age 18 or older at the time of recruitment.

The response rate, using the American Association for Public Opinion Research (AAPOR) formula for the minimum response rate on the pre-election interview, was 50 percent for the face-to-face component and 44 percent for the Internet component. The response rate for the face-to-face component is weighted to account for subsampling during data collection; due to subsampling for the face-to-face mode, the unweighted response rate would not be meaningful.

Photo Credit: Mark Newman (University of Michigan)

The re-interview rate on the post-election survey was 90 percent for the face-to-face component and 84 percent for the Internet component.

Are there any other aspects of the design that you think are particularly important?

I’d emphasize the effort to collect high quality samples via both in-person and online interviews for the whole survey as obviously the most important design aspect of the 2016 study, helping us to learn more about the trade-offs between survey mode and potential benefits of mixed mode data collection.

Are there any new questions that you think users will be particularly interested in?

Along with many previous questions that allow researchers to look at short and long term trends, we have lots of new items related to trade, outsourcing, immigration, policing, political correctness, LGBT issues, gender issues, social mobility, economic inequality, campaign finance, and international affairs.

What do you think some of the biggest challenges were for the 2016 data collection?

With increasing levels of polarization and a highly negative campaign, some Americans were much more resistant to participating in the survey. Many seemed to feel alienated, distrustful, and sick of the election. Under these circumstances, we worked hard with our partners at Westat to overcome this reluctance and are pleased to have recruited such a high quality sample by Election Day.

What are you most excited about when you think of the 2016 ANES?

The 2016 contest was in many ways a particularly fascinating election, even for those of us who usually find elections interesting! The election ultimately centered on two highly polarizing candidates, and people of many different backgrounds felt a lot was at stake in the outcome. Thus, not surprisingly, there was energetic speculation throughout the year about what voters were thinking and why they supported Clinton or Trump. The 2016 ANES survey provides an incredibly rich and unparalleled set of data for examining and testing among these speculations. I expect it will take some time to arrive at definitive answers, but I’m excited to release this wealth of evidence so the search for the truth can begin in earnest.

Is there anything else you’d like to share?

I would note that future releases will include redacted open-ended comments by respondents, numerical codings of some of the open-ended answers, and administrative data (e.g., interviewer observations, timing, etc.).

For more information about ANES please visit electionstudies.org and follow ANES on Twitter @electionstudies

 

Exploring the Tone of the 2016 Campaign

By undergraduate students Megan Bayagich, Laura Cohen, Lauren Farfel, Andrew Krowitz, Emily Kuchman, Sarah Lindenberg, Natalie Sochacki, and Hannah Suh, and their professor Stuart Soroka, all from the University of Michigan.


The 2016 election campaign seems to many to have been one of the most negative campaigns in recent history. Our exploration of negativity in the campaign – focused on debate transcripts and Facebook-distributed news content – begins with the following observations.

Since the advent of the first radio-broadcasted debate in 1948, debates have become a staple in the presidential campaign process.  They are an opportunity for voters to see candidates’ debate policies and reply to attacks in real-time. Just as importantly, candidates use their time to establish a public persona to which viewers can feel attracted and connected.

Research has accordingly explored the effects of debates on voter preferences and behavior. Issue knowledge has been found to increase with debate viewership, as well as knowledge of candidates’ policy preferences. Debates also have an agenda-setting effect, as the issues discussed in debates then tend to be considered more important by viewers. Additionally, there is tentative support for debate influence on voter preferences, particularly for independent and nonpartisan viewers. While debate content might not alter the preferences of strong partisans, it may affect a significant percentage of the population who is unsure in its voting decision. (For a review of the literature on debates effects, see Benoit, Hansen, & Verser, 2003).

Of course, the impact of debates comes not just from watching them but also from the news that follows. The media’s power to determine the content that is seen and how it is presented can have significant consequences. The literatures on agenda setting, priming, and framing make clear the way in which media shape our political reality. And studies have found that media’s coverage of debates can alter the public’s perception of debate content and their attitudes toward candidates. (See, for instance, Hwang, Gotlieb, Nah & McLeod 2006, Fridkin, Kenney, Gershon & Woodall 2008.)

This is true not just for traditional media, but for social media as well. As noted by the Pew Research Center, “…44% of U.S. adults reported having learned about the 2016 presidential election in the past week from social media, outpacing both local and national print newspapers.” Social media has become a valuable tool for the public to gather news throughout election cycles, with 61% of millennials getting political news from Facebook in a given week versus 37% who receive it from local TV. The significance of news disseminated through Facebook continues to increase.

It is in this context that we explore the nature of the content and coverage of the presidential debates of 2016.  Over the course of a term-long seminar exploring media coverage surrounding the 2016 presidential election, we became interested in measuring fluctuations in negativity across the last 40 years of presidential debates, with a specific emphasis on the 2016 debates. We simultaneously were interested in the tone of media coverage over the election cycle, examined through media outlets’ Facebook posts.

To test these hypotheses, we compiled and coded debate transcripts from presidential debates between 1976 and 2016. We estimated “tone” using computer-automated analyses. Using the Lexicoder Sentiment Dictionary (LSD) we counted the number of positive and negative words across all debates. We then ran the same test over news articles posted on Facebook during the election cycle, taken news feeds of main media outlets including ABC, CBS, CNN, NBC, and FOX. (Facebook data are drawn from Martinchek 2016.)

We begin with a simple measure of the volume of tone, or “sentiment,” in debates.  Figure 1 shows the total amount of sentiment – the total number of positive and negative words combined, as a percentage of all words – in all statements made by each in candidate across all debates.  In contrast with what some may expect, the 2016 debates were not particularly emotion-laden when compared to past cycles. From 1976 through to 2016, roughy 6.9% of the words said during debates are included in our sentiment dictionary. Hillary Clinton and Donald Trump’s speeches were essentially on par with this average; neither reached the peak of 8% (like 2004) or the low of 6% (like 2012).

Figure 1: Total Sentiment in Debates, 1976-2016

Figure 2 shows the percent of all sentiment words that were negative (to be clear: negative words as a percent of all sentiment words), and here we see some interesting differences.  Negativity from Democratic candidates has not fluctuated very much over time. The average percent of negative sentiment words for Democrats is 33.6%.  Even so, Hillary Clinton’s debate speeches showed relatively high levels of negativity, at 40.2%. Indeed, Clinton was the only Democratic candidate other than Mondale to express sentiment that is more than 40% negative.

Figure 2: Negativity in Debate Speeches, By Political Party, 1976-2016

Clinton’s negativity pales in comparison with Trump’s, however.  Figure 2 makes clear the large jump in negativity for Donald Trump in comparison with past candidates. For the first time in 32 years, sentiment-laden words used by Trump are nearly 50% negative – a level similar to Reagan in 1980 and 1984. Indeed, when we look at negative words as a proportion of all words, not just words in the sentiment dictionary, it seems that nearly every one in ten words uttered by Trump during the debates was negative.

The 2016 debates thus appear to be markedly more negative than most past debates. To what extent is the tone of debate content reflected in news coverage? Does negative speech in debates produce news coverage reflecting similar degrees of negativity?  Figure 3 explores this question, illustrating negativity (again, negative words as a proportion of all sentiment words) in the text of all Facebook posts concerning either Trump or Clinton, as distributed by five major news networks.

What stands out most in Figure 3 are the differences across networks: ABC, CNN, and NBC show higher negativity for Trump-related posts, while Fox shows higher negativity for Clinton-related posts.  CBS posts reflect a more neutral position.

Figure 3: Negativity in Facebook News Postings by Major Broadcasters, By Candidate, 2016

Clearly, political news content varies greatly across news sources. Trump’s expressed negativity in debates (and perhaps in campaign communications more generally) does not necessarily translate to more negative news content, at least by these measures. For instance: even as Trump is expressing more negative sentiment than Clinton, coverage in Fox is more positive towards Trump.  Of course, news coverage isn’t (and shouldn’t be) just a reflection of what candidates say. But these make clear that the tone of coverage for candidates needn’t be in line with the sentiment expressed by those candidates.  Expressing negative sentiment can produce negative coverage, or positive coverage, or (as Figure 3 suggests), both.

This much is clear: in line with our expectations, the 2016 presidential debates were among the most negative of all US presidential debates.  The same seems true of the campaigns, or at least the candidates’ stump speeches, more generally.  Although there was a good deal of negativity during debates, however, the tone of news coverage varied across sources.  Depending on citizens’ news source, even as candidates seem to have focused on negative themes, this may or may not have been a fundamentally negative campaign cycle. For those interested in the “tone” of political debate, our results highlight the importance of considering both politicians’ rhetoric, and the mass-mediated political debate that reaches citizens.

 


This article was co-authored by U-M capstone Communication Studies 463 class of 2016, which took place during the fall election campaign. Class readings and discussion focused on the campaign, and the class found themselves asking questions about the “tone” of the 2016 debates, and the campaign more generally. Using their professor Stuart Soroka as a data manager/research assistant, students looked for answers to some of their questions about the degree of negativity in the 2016 campaign.

 


Top 10 Most Viewed CPS Blog Posts in 2016

Post written by Catherine Allen-West.

Since it’s establishment in 2013, a total of 123 posts have appeared on the Center for Political Studies (CPS) Blog. As we approach the new year, we thought to take a look back at which of these 123 posts were most viewed across 2016.

 


 

01. Tracking the Themes of the 2016 Election by Lisa Singh, Stuart Soroka, Michael Traugott and Frank Newport (from the Election Dynamics blog)

“The results highlight a central aspect of the 2016 campaign: information about Trump has varied in theme, almost weekly, over the campaign – from Russia, to taxes, to women’s issues, etc; information about Clinton has in contrast been focused almost entirely on a single theme, email.”

 


 

02. Another Reason Clinton Lost Michigan: Trump Was Listed First on the Ballot by Josh Pasek

“If Rick Snyder weren’t the Governor of Michigan, Donald Trump would probably have 16 fewer electoral votes. I say this not because I think Governor Snyder did anything improper, but because Michigan law provides a small electoral benefit to the Governor’s party in all statewide elections; candidates from that party are listed first on the ballot.”

 


 

03. Motivated Reasoning in the Perceived Credibility of Public Opinion Polls by Catherine Allen-West and Ozan Kuru

“Our results showed that people frequently discredit polls that they disagree with. Moreover, in line with motivated reasoning theories, those who are more politically sophisticated actually discredit the polls more. That is, as political knowledge increases, the credibility drops substantially for those who disagree with the poll result.”

 

 


 

04. Why do Black Americans overwhelmingly vote Democrat? by Vincent Hutchings, Hakeem Jefferson, and Katie Brown, published in 2014.

“Democratic candidates typically receive 85-95% of the Black vote in the United States. Why the near unanimity among Black voters?”

 


 

05. Measuring Political Polarization by Katie Brown and Shanto Iyengar, published in 2014.

“Both parties moving toward ideological poles has resulted in policy gridlock (see: government shutdowndebt ceiling negotiations). But does this polarization extend to the public in general?”

 


 

06. What makes a political issue a moral issue? by Katie Brown and Timothy Ryan, published in 2014.

“There are political issues and then there are moral political issues. Often cited examples of the latter include abortion and same sex marriage. But what makes a political issue moral?”

 


 

07. Moral Conviction Stymies Political Compromise, by Katie Brown and Timothy Ryan, published in 2014.

Ryan’s overarching hypothesis boils non-compromise down to morals: a moral mindset orients citizens to oppose political compromises and punish compromising politicians. There are all kinds of issues for which some citizens seem resistant to compromises: tax reform, same-sex marriage, collective bargaining, etc. But who is resistant? Ryan shows that part of the answer has to do with who sees these issues through a moral lens.

 


 

08. Exploring the Effects of Skin Tone on Policy Preferences Among African Americans by Lauren Guggenheim and Vincent Hutchings, published in 2014.

In the United States, African Americans with darker skin tones have worse health outcomes, lower income, and face higher levels of discrimination in the work place and criminal justice system than lighter skinned Blacks. Could darker and lighter skinned African Americans in turn have different policy preferences that reflect their socio economic status-based outcomes and experiences?

 


 

09. What We Know About Race and the Gender Gap in the 2016 U.S. Election by Catherine Allen-West

As of October, the latest national polls, predicted that the 2016 Election results will reflect the largest gender gap in vote choice in modern U.S. history. If these polls had proven true, the 2016 results would indicate a much larger gender gap than what was observed in 2012, where women overwhelmingly supported Barack Obama over Mitt Romney. University of Texas at Austin Professor Tasha Philpot argues that what really may be driving this gap to even greater depths, is race.

 


 

10. How do the American people feel about gun control? by Katie Brown and Darrell Donakowski, published in 2014.

As we can see, the proportion of the public supporting tougher regulation is shrinking over the time period, while satisfaction with current regulations increased. Yet, support for tougher gun laws is the most popular choice in all included years. It is important to note that these data were collected before Aurora, Newtown, and the Navy Yard shootings. The 2016 ANES study will no doubt add more insight into this contentious, important issue.

 


 

Helping the Courts Detect Partisan Gerrymanders

Post written by Lauren Guggenheim and Catherine Allen-West.

In November, a federal court ruled that the Wisconsin Legislature’s 2011 redrawing of State Assembly districts unfairly favored Republicans deeming it an unconstitutional partisan gerrymander. This ruling is the first successful constitutional challenge to partisan gerrymandering since 1986.  The case will now head to the U.S. Supreme Court—which has yet to come up with a legal standard for distinguishing between acceptable redistricting efforts and unconstitutional gerrymandering.

While there have been successful challenges to gerrymandering based on racial grounds, most recently last week in North Carolina, proving partisan gerrymandering—where the plaintiffs must show that district lines were drawn with the intent to favor one political party over another—is more difficult. One reason is that research shows that even non-partisan commissions can produce unintentional gerrymandered redistricting plans solely on the basis of the geography of a party’s supporters. Also complicating matters are legislatures’ lawful efforts to keep communities of interest together and facilitate the representation of minorities. Because traditional efforts can produce results that appear biased, showing partisan asymmetries—the main form of evidence in previous trials—is not sufficient to challenge partisan gerrymandering in the courts.

However, in recent years, scientists have devised several standards that could be used to effectively measure partisan gerrymandering. In last month’s Wisconsin ruling, the court applied one such mathematical standard called the “efficiency gap“- a method that looks at statewide election results and calculates “wasted votes.” Using this method, the court found that Republicans had manipulated districts by packing Democrats into small districts or spreading them out across many districts, which ultimately led to Republican victories across the states larger districts.

Another method to determine partisan gerrymandering, developed by political scientists Jowei Chen and Jonathan Rodden, uses a straightforward redistricting algorithm to generate a benchmark against which to contrast a plan that has been called into constitutional question, thus laying bare any partisan advantage that cannot be attributed to legitimate legislative objectives. In a paper published last year in the Election Law Journal, Chen, a Faculty Associate at the University of Michigan’s Center for Political Studies and Rodden, Professor of Political Science at Stanford University, used the controversial 2012 Florida Congressional map to show how their approach can demonstrate and unconstitutional partisan gerrymander.

First, the algorithm simulates hundreds of valid districting plans, applying criteria traditionally used in redistricting decisions—compactness, geographic contiguity, population equality, the preservation of political communities, and the protection of voting rights for minorities—while disregarding partisanship. Then, the existing plan can be compared to the partisan distribution of the simulated plans to see where in the distribution it falls. If the partisanship of the existing plan lies in the extreme tail (or outside of the distribution) that was created by the simulations, it suggests the plan is likely to have been created with partisan intent. In other words, the asymmetry is less likely to be due to natural geography or a state’s interest in protecting minorities or keeping cohesive jurisdictions together (which is accounted for by the simulations). In this way, their approach distinguishes between unintentional and intentional asymmetries in partisanship.

Using data from the Florida case, Chen and Rodden simulated the results of 24 districts in 1,000 simulated plans. They kept three African-American districts intact because of Voting Rights Act protections. They also kept 46 counties and 384 cities together, giving the benefit of the doubt to the legislature that compelling reasons exist to keep these entities within the same simulated district. The algorithm uses a nearest distance criterion to keep districts geographically contiguous and highly compact, and it iteratively reassigns precincts to different districts until equally populated districts are achieved. The figure below shows how this looks in one of the 1,000 valid plans.

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Next, to measure partisanship, Chen and Rodden needed both the most recent data possible and precinct-level election results, which they found in the 2008 presidential election results. For both the existing plan and the simulated plans, they aggregated from the precinct to the district and calculated the number of districts where McCain voters outnumbered Obama voters. The figure below shows the partisan distribution of all of the plans. A majority of the plans created 14 Republican seats, and less than half of one percent of the plans produced 16 Republican seats. However, none of the simulations produced the 17 seats that were in the Florida Legislature’s plan, showing that the pro-Republican bias in the Legislature’s plan is an extreme outlier relative to the simulations.

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Because the simulations they created were a conservative test of redistricting (e.g., giving the benefit of the doubt to the Legislature by protecting three African-American districts), Chen and Rodden also tried the simulations by progressively dropping some of the districts they had previously kept intact. Results suggested the Legislature’s plan was even more atypical, as they had less pro-Republican bias than the simulations with the protected districts.

Chen and Rodden note that once a plaintiff can show that the partisanship of a redistricting plan is an extreme outlier, the burden of proof should shift to the state.  Ultimately in Florida, eight districts were found invalid and, and in December 2015, new maps were approved by the court and put into use for the 2016 Election.