Author Archives: Catherine Allen-West

Crime in Sweden: What the Data Tell Us

by Christopher Fariss and Kristine Eck

Christopher Fariss, University of Michigan and Kristine Eck, Uppsala University

Debate persists inside and outside of Sweden regarding the relationship between immigrants and crime in Sweden. But what can the data actually tell us? Shouldn’t it be able to identify the pattern between the number of crimes committed in Sweden and the proportion of those crimes committed by immigrants? The answer is complicated by the manner in which the information about crime is collected and catalogued. This is not just an issue for Sweden but any country interested in providing security to its citizens. Ultimately though, there is no information that supports the claim that Sweden is experiencing an “epidemic.”

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.

Who Commits the Most Crime?

Policymakers need accurate data and analytical strategies for using and understanding that data. This is because these tools form the basis for decision-making about crime and security.

When considering the reports about Swedish crime, certain demographic groups are unquestionably overrepresented. In Sweden, men, for example, are four times more likely than women to commit violent crimes. This statistical pattern however has not awoken the same type of media attention or political response as other demographic groups related to ethnicity or migrant status.

Secret Police Data: Conspiracy or Fact?

In the past, the Swedish government has collected data on ethnicity in its crime reports. The most recent of these data were analyzed by the Swedish National Council for Crime Prevention’s (BRÅ) for the period 1997-2001. The Swedish police no longer collect data on the ethnicity, religion, or race of either perpetrators or victims of crime. There are accusations that these data exist but are being withheld. Such ideas are not entirely unfounded: in the past, the Swedish police have kept secret—and illegal—registers, for example about abused women or individuals with Roma background. Accusations about a police conspiracy to suppress immigrant crime numbers tend to center around the existence of a supposedly secret criminal code used to track this data. This code is not secret and, when considered, reveals no evidence for a crime epidemic.

For the period  of November 11, 2015 through January 21, 2016 the Swedish police attempted to gauge the scope of newly arrived refugees involvement in crime, as victims, perpetrators, or witnesses. It did so by introducing a new criminal code—291—into its database. Using this code, police officers could add to reports in which an asylum seeker was involved in an interaction leading to a police report. Approximately 1% of police reports filed during this period contained this code. It is important to note here that only a fraction of these police incident reports actually lead to criminal charges being filed.

The data from these reports are problematic because there are over 400 criminal codes in the police’s STORM database, which leads to miscoding or inconsistent coding. Coding errors occur because the police officers themselves are responsible for determining which codes to enter in the system. The police note that there was variation in how the instructions for using this code were interpreted. The data show that 60% of the 3,287 police reports filed took place at asylum-seeker accommodation facilities, and that the majority of the incidents contained in these reports took place between asylum seekers. Are these numbers evidence of a crime epidemic?

Is there any Evidence for Crime Epidemic in Sweden?

If asylum-seekers are particularly crime-prone, then we would expect to see crime rates in which they are overrepresented relative to how many are living in Sweden. Sweden hosted approximately 180,000 asylum-seekers during this period and the population of Sweden is approximately 10 million. Therefore, asylum-seekers make up approximately 1.8% of the people living in Sweden, while 1% of the police reports filed in STORM were attributed to asylum-seekers.

While the Code 291 data are problematic because of issues discussed above, the data actually suggests that asylum seekers appear to be committing crime in lower numbers than the general population and does not provide support for claims of excessive criminal culpability. There were four rapes registered with code 291 for the 2.5 month period, which we find difficult to interpret as indicative of a “surge” in refugee rape. We in no way want to minimize the impact that these incidents had on the individual victims, but considering wider patterns, we consider a rate of four reports of rape over 76 days for a asylum-seeking population of 180,000 as not convincing evidence of an “epidemic” perpetrated by its members.

There is no doubt that crime occurs in Sweden. This is a problem for Swedish society and an important challenge for the government to address. It is a problem shared by all other countries. There is also no doubt that refugees and immigrants have committed crimes in Sweden, just as there is no doubt that Swedish-born citizens have committed crimes in Sweden as well. But if policy initiatives are to focus on particular demographic groups who are overrepresented in crime statistics, then it is essential that the analysis of the crimes committed by members of these groups be based on careful data analysis rather than anecdotes used for supporting political causes.

The Government of Sweden’s Facts about Migration and Crime in Sweden: http://www.government.se/articles/2017/02/facts-about-migration-and-crime-in-sweden/

Christopher Fariss is an Assistant Professor of Political Science and Faculty Associate at the Center for Political Studies at the University of Michigan.  Kristine Eck is Associate Professor at the Department of Peace and Conflict Research at Uppsala University.

 

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.

screen-shot-2016-12-01-at-12-15-45-pm

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.

screen-shot-2016-12-01-at-12-16-41-pm

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.

Another Reason Clinton Lost Michigan: Trump Was Listed First on the Ballot 

Post written by Josh Pasek, Faculty Associate, Center for Political Studies and Assistant Professor of Communication Studies, University of Michigan. 

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.

Yesterday, Donald Trump was declared the winner in Michigan by a mere 10,704 votes, out of nearly 5 million presidential votes cast. Although this is not the smallest state margin in recent history – President Bush won Florida and the election by 537 votes and Al Franken won his senate seat in Minnesota by 225 (after the result flipped in a recount) – it represented a margin of 0.22%. The best estimate of the effect of being listed first on the ballot in a presidential election is an improvement of the first-listed individual’s vote share of 0.31%. Thus, we would expect Hillary Clinton to have won Michigan by 0.4% if she were listed first and about 0.09% if neither candidate were consistently listed in the first position.

It may seem surprising to suggest that anyone’s presidential vote would hinge on the order of candidates’ names, but the evidence is strong. In a paper I published with colleagues in Public Opinion Quarterly in 2014, we looked at name order effects across 76 contests in California – one of the few states that rotates the order of candidates on the ballot – to estimate the size of this benefit. We later replicated the results in a study of North Dakota. Both times, we found that first-listed candidates received a benefit and that the effect was present, though smaller, at the presidential level.

There are many reasons that voters might choose the first name, even if they started their ballots without a predetermined presidential candidate. Some individuals might have been truly ambivalent and selected the first name they had heard of (in this case, “Trump”), others may have instead checked the first straight party box – listed in a similar order – without intending to select our new President-elect. Regardless of the cognitive mechanisms involved, the end result is clear – the “will” of the voters can be diverted by seemingly innocuous features of ballot design.

How broadly is this first-position benefit a problem? Across the country, only seven states vary the order of candidate names across precincts. Another nine choose a single random order for listing candidates in each contest, but use that same order across the entire state. And the rest generally use some combination of alphabetic ordering or a listing based on who won in prior elections at the state level. Michigan’s system – prioritizing the candidate who last won the Governor’s office – is among the most common methods.

states-ballots

Given the control that Republicans currently hold over governorships, this bias likely helps Republicans maintain their dominance over many state legislatures. And the effects of being listed first only grow as you move down the ballot. In our study of California, we found the average benefit for a Governor was also 0.31 percent*, Senators gained 0.37 percentage points of additional votes, and candidates for other statewide offices gained an average of 0.63 percentage points.

nameordereffect

Source: Prevalence and moderators of the candidate name-order effect evidence from statewide general elections in California Pasek J., Schneider D., Krosnick J.A., Tahk A., Ophir E., Milligan C. (2014) Public Opinion Quarterly, 78 (2) , pp. 416-439.

For a better answer, we might look to the strategy adopted by Michigan’s neighbor to the South. Ohio produces a unique ballot for each precinct where the ordering of candidates’ names is rotated. Although it is too late to prevent this effect from altering the 2016 election, it may have less of an impact if everyone were not filling out ballots with the same candidates listed first.


*this would likely be larger if California did not elect its Governors in non-presidential years.

 

Who Runs for Office? The Effects of Primary Election Reform in Ghana

This post was created by Catherine Allen-West, Nahomi Ichino and Noah Nathan.

Political parties across the developed and developing world increasingly rely on some form of primary to select nominees for legislative elections. The candidate selection process within political parties is crucial for shaping the extent to which voters can control elected representatives, as parties are important intermediaries between citizens and government.

The scientific literature has only scratched the surface in examining how primary elections operate in new democracies and what the implications of the system may be, both for the quality of candidates presented to the electorate and for general election outcomes. The process for selecting a candidate varies on several dimensions, including the restrictiveness of the rules for who may seek a nomination and who selects nominees.

In a new paper entitled, Democratizing the Party: The Effects of Primary Election Reforms in GhanaNahomi Ichino and Noah Nathan, collaborators in the Center for Political Studies at the University of Michigan, investigated the impact of recent reforms within one of the two major parties in Ghana that significantly expanded the size of the primary electorate.

Ghana has held regular, concurrent elections for president and a unicameral parliament since its transition to democracy in 1992. The two parties that dominate elections in Ghana’s parliament are the National Democratic Congress (NDC), the current ruling party, and the New Patriotic Party (NPP), the current opposition. The two parties pursue similar policy, but they differ in their ethnic bases of support. Voting is not exclusively along ethnic lines, but, in Ghana, ethnicity remains a strong determinant of vote choice overall.

One reason that Ichino and Nathan looked at primaries in a new democracy is that patronage is prevalent in these settings.  The primaries are a contest over who becomes the more important local patron in a given constituency rather than a contest over policy issues and ideology. In these scenarios, voting in primaries can involve extensive vote buying through the distribution of private goods.  In Ghana, aspirants (politicians seeking their party’s nomination) woo voters with gifts of flat screen TVs, new motorbikes, payment of school fees for their children and more. This system inherently benefits aspirants with immense personal wealth and the political connections to distribute these goods to the right people.  It also disadvantages aspirants who are outsiders who may better represent the interests of the party membership, like women or people from different ethnic groups.

So, given Ghana’s recent reforms that expanded the size of the electorate, Ichino and Nathan argue that this expansion will have positive effects on democratic representation through two changes:

  1. Vote buying becomes more difficult both logistically and financially due to the sheer size of new electorate.
  2. The expanded primary electorate will include new voters from groups that have been underrepresented in local party leadership positions.

These changes in turn will affect what types of politicians choose to compete in primary elections and the types of politicians who win nominations. In particular, the researchers hypothesize that more female politicians and politicians from groups that are usually excluded from power will compete for nominations and that nominees will be more likely to come from these marginalized groups. Ultimately, a larger pool of people- representing a more diverse distribution of preferences and interests- has a viable path to a nomination, and thus to elected office.

Their results show just that: With the new reforms, the total number of aspirants increased (including women and other ethnic groups) and more of these aspirants went on to become the party nominee. Also interesting to note: the number of aspirants with a private sector background (which indicates more personal wealth to put towards vote buying) decreased significantly.

This figure shows the effects on the number of aspirants (total, women aspirants only, and then aspirants from different sets of ethnic groups).

This figure shows the effects of reforms on the number of aspirants (total, women aspirants only, and then aspirants from different sets of ethnic groups).

Overall, the reforms increased the probability that the nominee will be from a non-plurality (local minority) ethnic group by 18 percentage points on average and reduced the probability that the nominee will be from the party’s core ethnic coalition by 12 percentage points on average.

This figure shows the effects on the characteristics of the selected nominee (whether that was the incumbent, someone who was a government official, etc.)

This figure shows the effects of reforms on the characteristics of the selected nominee (whether that was the incumbent, someone who was a government official, etc.)

The results suggest that, in Ghana, the reforms opened up important positions in the party to previously under-represented groups. This work is important because it advances our understanding of the nature and effects of primary elections in new democracies, contributes to research on institutional reforms that can improve the political incorporation of women, and also shows how internal political dynamics within parties shape the connections between parties and ethnic groups in setting where ethnic competition is prevalent.

Is policy driven by the rich, or does government respond to all?

Post created by Catherine Allen-West based on research presented by Benjamin Page and Christopher Wlezien at the Center for Political Studies Interdisciplinary Workshop on Politics and Policy.

With the U.S. Presidential Election just a day away, both campaigns have amped up their rhetoric to solidify support among their bases. Hillary Clinton is making her case for bringing America together and Donald Trump is using his platform to rally against a rigged system.

Trump’s claims of a rigged, or out-of-touch, political system seems to resonate with his base, a group of Americans that feel ignored and underrepresented by their current leaders. These sentiments are not just unique to Republicans. During the primaries, Bernie Sanders gained mass appeal with progressive Democrats as he trumpeted the idea that wealthy donors exert far too much influence on the U.S. political system.

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.

pagequote

Benjamin Page, Northwestern University Professor of Decision Making, and Christopher Wlezien, Professor of Government at the University of Texas at Austin, have both conducted illuminating research on the influence of affluent Americans on policy change. Recently, Page and Wlezien discussed their latest findings at the University of Michigan’s Center for Political Studies. The two scientists drew on results from their own work as well as analysis of data from Martin Gilens. The Gilens data is unique, in that, it documents public opinion on 1,800 issues from high, middle and low income groups over a long period of time (1964-2006).

Page and Wlezien looked at the same data but the results of their analyses produced two opposite viewpoints. Page contends that when it comes to policy change, average citizens are being thwarted by America’s “truly affluent”- multi-millionaires and billionaires- who are much more likely to see their preferences reflected in policy decisions. By comparison, Wlezien suggests that there isn’t pervasive disagreement or major inequality of representation between groups and that a large driver of policy change is the convergence of preferences between groups. In other words, when groups agree on an issue, policy change is most successful.

wlezienquote

Affluence and Influence

First, it’s important to know what the Gilens data says about how much influence an average American has on policy. According to Page, average citizens have little influence even when a policy is clearly supported by a majority of Americans. He illustrated his point with the graph below. Even when 80% of average Americans favor a policy change, they’re only getting it about 40% of the time.

Predicted probability of policy adoption (dark lines, left axes) by policy disposition; the distribution of preferences (gray columns, right axes)

Predicted probability of policy adoption (dark lines, left axes) by policy disposition; the distribution of preferences (gray columns, right axes). Source: Gilens, M. and Page, B.I. (2014) ‘Testing Theories of American Politics: Elites, Interest Groups, and Average Citizens’, Perspectives on Politics, 12(3), pp. 564–581. doi: 10.1017/S1537592714001595.

This is extremely important, Page says, because “in a changing world when policy change, in many people’s minds, is really needed on a whole bunch of areas” the public’s influence is being thwarted in a major way. Furthermore, Page argues that a small group of top income earners in America are more likely to see their preferences reflected in policy change, by a large margin.

screen-shot-2016-11-04-at-3-18-44-pm

Source: Gilens, M. and Page, B.I. (2014) ‘Testing Theories of American Politics: Elites, Interest Groups, and Average Citizens’, Perspectives on Politics, 12(3), pp. 564–581. doi: 10.1017/S1537592714001595.

Wlezien’s analysis paints a different picture:

 

Wlezien used Gilens’ data (in a forthcoming paper, When do the Rich Win?) to assess congruence between policy decisions and preferences for that policy across income groups. His results show that when the rich want something, the middle and the poor are more likely to want it as well, reiterating his claim that policy change does not favor one income group over another in a significant way. Additionally, even when the middle and high income groups disagree, Wlezien contends that “it’s still a coin flip as to whether the policy passes or not.”

Policy Support for High and Middle Income Groups

Policy Support for High and Middle Income Groups: Wlezien found, that across all of the issues from the Gilens data, there is little disagreement or inequality between group preferences reflected in policy change.

That’s not to say there’s total equality among all income groups.  According to Wlezien’s analysis, it’s the poor who are losing out most of the time when it comes to their preferences being reflected in policy change. In fact, when the poor by themselves favor a policy, it has the lowest rate of success at 18.6%. To put that in perspective, when NO ONE favors a policy, the policy still has a 23.8% passing success rate.

passage-rates-by-income-group-favor

This is a recurring theme. As Wlezien points in the clip above,  middle and high income group policy preferences are relatively similar most of the time, the real difference is between those two groups and the lower income group.

Democracy by Coincidence

If it’s the case the average Americans agree with affluent Americans a majority of the time, maybe their lack of influence doesn’t matter that much. If ordinary Americans are getting what they want a good amount of the time, should they care if the affluent truly do wield more influence?

Page says yes, they should absolutely care and here’s why:

 

Furthermore, Page argues that what “the truly wealthy”- multi-millionaires and billionaires -want from government policy is quite different from what average people do.

While Page and Wlezien clearly offer two different takeaways from this data, they both agree that the influence of money in politics deserves further research to parse out who, if anyone, is being thwarted by the current political process and to identify the ways all citizens can ensure that the government responds equally to their needs.

WATCH: Benjamin Page and Christopher Wlezien Discuss Research on Policy Responsiveness to Average Americans


Related Links:

Book: Degrees of Democracy by Stuart Soroka and Christopher Wlezien

Website: Degrees of Democracy

Book: Who Gets Represented? Peter K. Enns and Christopher Wlezien, editors, Russell Sage Foundation.

Critics argues with our analysis of U.S. political inequality. Here are 5 ways they’re wrong” by Martin Gilens and Benjamin Page, Washington Post, May 23, 2016.

Gilens, M. and Page, B.I. (2014) ‘Testing Theories of American Politics: Elites, Interest Groups, and Average Citizens’, Perspectives on Politics, 12(3), pp. 564–581. doi: 10.1017/S1537592714001595.

What We Know About Race and the Gender Gap in the 2016 US Election

This post was created 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. Today, according to NPR, “An average of three recent national polls shows that women prefer Clinton by roughly 13 points, while men prefer Trump by 12, totaling a 25-point gap.” If these polls prove 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.

2012 vote by gender based on exit polls.

2012 vote by gender based on national exit poll conducted by Edison Media Research.

University of Texas at Austin Professor Tasha Philpot argues that what really may be driving this gap to even greater depths, is race. For instance, here’s the same data from the 2012 Election, broken down by gender and race.

2012 vote by gender and race based on exit polls

2012 vote by gender and race based on national exit poll conducted by Edison Media Research.

Often overlooked in the discussion of the gender gap, race figures prominently into many American’s political identities.

2016 Gender Gap in Party Identification

2016 Gender Gap in Party Identification.

2016 Gender Gap in Party Identification.

Philpot recently participated in the panel “What We Know So Far About the 2016 Elections” at the University of Michigan’s Center for Political Studies. In her talk, “Race and the Gender Gap in the 2016 Election,” Philpot outlined the potential sources for the gender gap and emphasized the role that race is playing in widening the gap.

Using data from the ANES 2016 Pilot Study, Philpot compared opinions from white and black men and women on several issues such as government spending, inequality and discrimination, and evaluations of the economy. While there were noticeable differences strictly between men and women, the real story became clear when Philpot sorted the results by gender and race. Small gender gaps exist among both whites and blacks, but the most remarkable difference of opinions on all issues is between black women and white men.

SPENDING ON HEALTH CARE AND DEFENSE

2016 Gender Gap in Spending on Healthcare and Defense.

2016 Gender Gap in Spending on Healthcare and Defense.

Perceived Gender Discrimination

Gender Gap in Perceived Discrimination Based on Gender.

2016 Gender Gap in Perceived Discrimination Based on Gender

Evaluations of the Economy

2016 Gender Gap in Economic Evaluations.

2016 Gender Gap in Economic Evaluations.

On most issues, black women and white men fall on opposite sides of the political spectrum. Philpot concludes that it’s an oversimplification to consider the gender gap as merely a gap between men and women, when, in reality, the observed gender gap is largest between white men and black women.

Watch Tasha Philpot’s full presentation here: 

 


Related Links:

Tasha Philpot on NPR:  Reports of Lower Early Voting Turnout Among African-Americans, NPR, The Diane Rehm Show (November 4, 2016)

What We Know So Far About the 2016 Elections, was held on October 5, 2016 at the Center for Political Studies, University of Michigan. The panel also included the following talks:

Stuart Soroka: Read, Seen or Heard: A Text-Analytic Approach to Campaign Dynamics
Nicholas Valentino: The Underappreciated Role of Sexism in the 2016 Presidential Race
Michael Traugott: Pre-Election Polls in the 2016 Campaign

All videos from the event can be found here: https://www.youtube.com/playlist?list=PLAvEYYDf9x8XFzBWadaPcV6kFjZkBFuHP

 

 

Tracking the Dynamics of the 2016 Election

This post was developed by Catherine Allen-West, Stuart Soroka and Michael Traugott

It’s an election year in America, and with that comes an endless string of media coverage of the political campaigns. If you are like 70% to 80% of Americans over the past 12 weeks, you’ve read, seen or heard some information about the top two presidential candidates, Hillary Clinton and Donald Trump, on any given day.

These are the findings from an ongoing research collaboration between Gallup, the University of Michigan and Georgetown University. Since July 11, 2016 Gallup has asked 500 respondents per night what they have read, seen or heard about Clinton or Trump that day. The resulting data include open-ended responses from over 30,000 Americans thus far.

Content analyses of these open-ended responses offer a unique picture of campaign dynamics.  The responses capture whatever respondents remember hearing about the candidates over the previous few days from traditional media, social media, or friends and family. As Gallup points out in the article above, results from this project are noteworthy because while most survey research tracks Americans’ opinions on candidates leading up to an election, this study looks directly at the information the public absorbs, on a daily basis.


For up to date results from this project visit: www.electiondynamics.org


Tracking the ‘Tone’ of What Americans Have Read, Seen or Heard

In this blog post, we offer some supplementary analysis, focusing on the tone of responses to the “read, seen or heard” question.  Positive and negative tone (or sentiment) are captured using the Lexicoder Sentiment Dictionary, run in Lexicoder.  The Lexicoder Sentiment Dictionary includes roughly 6,000 positive or negative words.  We count the frequency of both, and produce a measure of tone that is the % positive words – % negative words, for every response, from every respondent.

Taking the average tone of responses daily provides insight into the content that American citizens are receiving (and remembering) during the campaign.  In this analysis, we focus on measures of “candidate advantage,” where “Clinton advantage” is the gap between the tone of responses to the “read, seen or heard” question about Clinton, and the tone of responses to the “read, seen or heard” question about Trump.  Positive values reflect a systematic advantage for Clinton; that is, a tendency for recalled information about Clinton to be more positive than recalled information about Trump.  Negative values reflect the opposite.

As would be expected, when we look at partisanship, Republicans have more a net positive assessment for Trump. This is particularly true in the first weeks of September.  Democrats show a similar tendency in that they have more net positive assessments for Clinton.  That said, the first few weeks of September show, at best, a very weak advantage for Clinton among Democrats.  During the early weeks of September, Democrats’ recalled news was not markedly more positive for Clinton than it was for Trump.  ‘Read, seen or heard’ comments from Democrats even turned to Trump’s advantage in the period from September 16th to 18th, before trending more positive towards Clinton again.  This shift from Democrats followed concerns about Clinton’s health, but it also (and relatedly) reduced mentions of emails. This trend continued after the recent bombings in New York and New Jersey became prominent. And then came her performance in the debate.  All of this coverage led to a steady increase in Clinton’s advantage among Democrats.

figure_cand_tone_daily_clinton_sept29

For Republicans, the picture is nearly the opposite.  The gap between recalled information about Trump and recalled information about Clinton was striking through the first few weeks of September.  While Democrats did not recall information favorable to Clinton, Republicans clearly recalled information favorable to Trump.  But responses started to shift in the middle of the month and the ‘Trump Advantage’ in the tone of recalled information from Republicans has continued to fall since the first debate.

figure_cand_tone_daily_trump_sept29-1

What do these findings suggest about the presidential campaign thus far?  While these results do not capture vote intentions, nor are they direct assessments of the candidates, these data do give us a unique sense for the information that voters remember.  Whether shifts in ‘read, seen or heard’ mentions are predictive of attitudes towards the candidates remains to be seen.  Exploring this possibility is one objective of the ongoing project.

The Gallup, Michigan, Georgetown Working Group consists of: Frank Newport, Lisa Singh, Stuart Soroka, Michael Traugott, and Andrew Dugan.

Related Article: After the Debate, Trump is still dominating news coverage. But Clinton is getting the good press. The Washington Post.

Identifying the Sources of Scientific Illiteracy

Post developed by Catherine Allen-West in coordination with Josh Pasek

ICYMI (In Case You Missed It), the following work was presented at the 2016 Annual Meeting of the American Political Science Association (APSA).  The presentation, titled “Motivated Reasoning and the Sources of Scientific Illiteracy” was a part of the session “Knowledge and Ideology in Environmental Politics” on Friday, September 2, 2016.

At APSA 2016, Josh Pasek, Assistant Professor of Communication Studies and Faculty Associate at the Center For Political Studies presented work that delves into the reasons that people do not believe in prevailing scientific consensus.

He argues that widespread scientific illiteracy in the general population is not simply a function of ignorance. In fact, there are several reasons why an individual may answer a question about science or a scientific topic incorrectly.

  1. They are ignorant of the correct answer
  2. They have misperceptions about the science
  3. They know what scientists say and disagree (rejectionism)
  4. They are trying to express some identity that they hold in their response

The typical approach to measuring knowledge involves asking individuals multiple-choice questions where they are presumed to know something when they answer the questions correctly and to lack information when they either answer the questions incorrectly or say that they don’t know.

Pasek Slide 2

Pasek suggests that this current model for measuring scientific knowledge is flawed, because individuals who have misperceptions can appear less knowledgeable than those who are ignorant. So he and his co-author Sedona Chinn, also from the University of Michigan, set out with a new approach to disentangle these cognitive states (knowledge, misperception, rejectionism and ignorance) and then determine which sorts of individuals fall into each of the camps.

Instead of posing multiple-choice questions, the researchers asked the participants what most scientists would say about a certain scientific topic (like, climate change or evolution) and then examined how those answers compared to the respondent’s personal beliefs.

Pasek Slide 4

Across two waves of data collection, respondent answers about scientific consensus could fall into four patterns. They could be consistently correct, change from correct to incorrect, change from incorrect to correct or be consistently correct.

Pasek Slide 5

This set of cognitive states lends itself to a set of equations producing each pattern of responses:

Consistently Correct = Knowledge + .5 x Learning + .25 x Ignorance
Correct then Incorrect = .25 x Ignorance
Incorrect -> Correct =.5 x Learning + .25 x Ignorance
Consistently Incorrect = Misperception + .25 x Ignorance

The researchers then reverse-engineered this estimation strategy for a survey aimed at measuring knowledge on various scientific topics. This yielded the following sort of translations:

Pasek Slide 6

In addition to classifying respondents as knowledgeable, ignorant, or misinformed, Pasek was especially interested in identifying a fourth category: rejectionist. These are individuals who assert that they know the scientific consensus but fail to hold corresponding personal beliefs. Significant rejectionism was apparent for most of the scientific knowledge items, but was particularly prevalent for questions about the big bang, whether humans evolved, and climate change.

Pasek Slide 3

Rejectionism surrounding these controversial scientific topics is closely linked to religious and political motivations. Pasek’s novel strategy of parsing out rejectionism from ignorance and knowledge provides evidence that religious individuals are not simply ignorant about the scientific consensus on evolution or that partisans are unaware of climate change research. Instead, respondents appear to have either systematically wrong beliefs about the state of the science or seem liberated to diverge in their views from a known scientific consensus.

Pasek’s results show a much more nuanced, yet at times predictable, relationship between scientific knowledge and belief in scientific consensus.