Rousing the Sleeping Giant? Emotions and Latino Mobilization in an Anti-Immigration Era

Post developed by Nicholas Valentino, Ali Valenzuela, Omar Wasow, and Katherine Pearson 

ICYMI (In Case You Missed It), the following work was presented at the 2019 Annual Meeting of the American Political Science Association (APSA).  The presentation, titled “Rousing the Sleeping Giant? Emotions and Latino Mobilization in an Anti-Immigration Era” was a part of the session “The Rhetoric of Race” on Friday, August 30, 2019.

Since the 2016 presidential campaign anti-immigration policies have been very popular among President Trump’s strongest supporters, though they do not present obvious benefits to the economy or national security. Strategists suppose that the intent of the anti-immigration rhetoric and policies is to energize the president’s base. 

But what about people who identify with the targets of these policies, specifically Latinos? Are they mobilized against anti-immigration proposals, or are they further deterred from political participation? 

New research by Nicholas A. Valentino, Ali Valenzuela, and Omar Wasow finds that anger was associated with higher voter turnout among Latinos, but the Latinos who expressed more fear had lower voting rates.

voting rates by race and emotion

The role of emotions in politics is complex. The research team begins with the observation that negative emotions do not always have negative consequences for politics. Indeed, negative emotions may promote attention and interest, and drive people to vote. They draw a distinction between different negative emotions: while anger may spur political action, fear can suppress it. 

The research team fielded a nationally-representative panel survey of white and Latino registered voters before and after the 2018 midterm elections. Respondents were asked about their experience with Immigration and Customs Enforcement (ICE) officials and their exposure to campaign ads focused on immigration. Participants were also asked to rate their emotional reactions to the current direction of the country. 

The results showed that Latinos interacted with ICE more frequently than whites did, but both groups had the same level of exposure to campaign ads. Latinos reported more anger than whites, and also more fear. In fact, among the negative emotions in the survey, fear among Latinos was highest.  

In the sample the validated voting rate among Latinos was 39%; among whites in the sample it was 72%, demonstrating the under-mobilization of Latino voters. Whether Latinos vote in greater numbers in 2020 may depend on whether they are mobilized by anger against anti-immigration rhetoric, or whether they are deterred by fear stemming from policies like ICE detention and deportation. 

Accuracy in Reporting on Public Policy

Post developed by Katherine Pearson and Stuart Soroka

ICYMI (In Case You Missed It), the following work was presented at the 2019 Annual Meeting of the American Political Science Association (APSA).  The presentation, titled “Media (In)accuracy on Public Policy, 1980-2018” was a part of the session “Truth and/or Consequences” on Sunday, September 1, 2019.

Citizens can be well-informed about public policy only if the media accurately present information on the issues. Today’s media environment is faced with valid concerns about misinformation and biased reporting, but inaccurate reporting is nothing new. In their latest paper, Stuart Soroka and Christopher Wlezien analyze historical data on media coverage of defense spending to measure the accuracy of the reporting when compared to actual spending. 

In order to measure reporting on defense spending, Soroka and Wlezien compiled text of media reports between 1980 and 2018 from three corpuses: newspapers, television transcripts, and public affairs-focused Facebook posts. Using the Lexis-Nexis Web Services Kit, they developed a database of sentences focused on defense spending from the 17 newspapers with the highest circulation in the United States. Similar data were compiled with transcripts from the three major television broadcasters (ABC, CBS, NBC) and cable news networks (CNN, MSNBC, and Fox). Although more difficult to gather, data from the 500 top public affairs-oriented public pages on Facebook were compiled from the years 2010 through 2017. 

Soroka and Wlezien estimated the policy signal conveyed by the media sources by measuring the extent to which the text suggests that defense spending has increased, decreased, or stayed the same. Comparing this directly to actual defense spending over the same time period reveals the accuracy of year-to-year changes in the media coverage. For example, if media coverage were perfectly accurate, the signal would be exactly the same as actual changes in spending. 

As the figure below shows, the signal is not perfect. While there are some years when the media coverage tracks very closely to actual spending, there are other years when there is a large gap between the signal that news reports send and the defense budget. The gap may not entirely represent misinformation, however. In some of these cases, the media may be reporting on anticipated future changes in spending. 

media signal

For most years, the gap representing misinformation is fairly small. Soroka and Wlezien note that this “serves as a warning against taking too seriously arguments focused entirely on the failure of mass media.” This analysis shows evidence that media coverage can inform citizens about policy change. 

The authors conclude that there are both optimistic and pessimistic interpretations of the results of this study. On one hand, for all of the contemporary concerns about fake news, it is still possible to get an accurate sense of changes in defense spending from the media, which is good news for democratic citizenship. However, they observed a wide variation in accuracy among individual news outlets, which is a cause for concern. Since long before the rise of social media, citizens have been at risk of consuming misinformation based on the sources they select. 

Toward a Typology of Populists

Post developed by Pauline Jones, Anil Menon, and Katherine Pearson 

ICYMI (In Case You Missed It), the following work was presented at the 2019 Annual Meeting of the American Political Science Association (APSA).  The presentation, titled “Putin’s Pivot to Populism” was a part of the session “Russia and Populism” on Sunday, September 1, 2019. 

The rise in populism around the world has received much attention, but not all populists are the same. In a new paper, Pauline Jones and Anil Menon present an original typology of populists that goes beyond typical left-wing versus right-wing classifications. 

To better understand the different types of populists and how they operate, Jones and Menon examine two key dimensions: position within the political landscape (outsider versus insider), and level of ideological commitment (true believer versus opportunist). 

Populists tend to frame their criticism of political elites differently depending on whether they are political outsiders or government insiders. While outsiders are free to criticize those in power broadly, populists who hold political power are more likely to tailor their criticisms to their political opponents. Insiders are also more careful not to attack members of the elite with whom they will need to build political coalitions. 

Many populists evoke the past, but outsiders and insiders tend to do so differently. Whereas outsiders focus on the near past as a critique of a corrupt elite, political insiders instead focus on the distant past to evoke better days of shared national values. 

Jones and Menon also draw distinctions between true believers in populism and those who embrace populism for purely strategic reasons. True believers will remain strongly committed to enacting their populist agenda once in office; opportunists will use populist rhetoric to gain power, but won’t support their platform strongly if elected.  

The intersection of these two dimensions leads to the classification of populists into four types, illustrated in the table below: Oppositional, Classical, Strategic, and Pivot. 

Classification of populists

The most common variety of populist is the oppositional populist, who are outsiders and true believers. Oppositional populists put their agenda before all else and distance themselves from the mainstream elite. 

Classical populists sometimes start out as outsiders who become insiders once they are elected to office. Like oppositional populists, they are strongly committed to enacting their agenda; unlike oppositional populists, classical populists can enact their agenda from a position of power. Because they are insiders, classical populists are more selective about criticizing elites. 

Pivot populists are a rare group of political insiders who adopt populist rhetoric with little or no commitment to the populist ideology. Jones and Menon point to Russia’s Vladimir Putin as an example of a pivot populist who has adopted populism to bolster support for his regime while deflecting blame for the country’s problems. 

The final category is strategic populists. Like Donald Trump in the United States, strategic populists are outsiders with a weak commitment to the populist agenda. Strategic populists are broadly anti-elite, and also use their rhetoric to create divisions among the people. Once in power, they are unlikely to alienate elites by pursuing populist policy goals. 

Incidental Exposure to Political News Increases Political Knowledge

Post developed by Brian Weeks and Katherine Pearson 

ICYMI (In Case You Missed It), the following work was presented at the 2019 Annual Meeting of the American Political Science Association (APSA). The presentation, titled “Can Incidental Exposure to News Close the Political Knowledge Gap?” was a part of the session “News in the Digital Age” on Friday, August 30, 2019.

We’re immersed in a media landscape full of choices. News, information, and entertainment are all at our fingertips. But does this mean that people are better informed about important issues? Is it is possible for people who aren’t interested in seeking out political news to learn about candidates and issues through the information they’re exposed to casually? Brian Weeks, Daniel S. Lane, Lauren B. Potts, and Nojin Kwak conducted two surveys to answer this question. 

Motivation and opportunity play a big role in the amount of news we’re exposed to. People who are deeply interested in politics are motivated to seek out information, and as a result, they are better informed about candidates and policies. 

The nature of the media environment makes it hard to avoid news and political information; many people consume news without trying. As we have more access to all types of media, we are incidentally exposed to political information. Does increased accidental exposure make up for a lack of motivation to seek out news, or does all of that information rush past us without making us more knowledgeable? 

To test whether this incidental exposure to news translates into an increase in political knowledge, Weeks and his co-authors conducted a series of surveys. They collected panel survey data two waves during the 2012 presidential election and conducted another two waves of surveys during the 2016 presidential election. The surveys asked participants about whether they were exposed to political information they didn’t seek out, their level of political interest, and measured their knowledge of candidates’ policy positions. 

The surveys showed strong evidence that people who had incidental exposure to news about presidential candidates knew more about the candidates’ policy positions. 

Incidental exposure to media

The biggest benefit of incidental exposure was seen in the group of people who rated themselves least politically interested, which suggests that greater exposure can make up for a lack of motivation to seek out news. 

Knowledge of candidates and their policy positions is still essential for well-informed citizens, and the growth of opportunities to be exposed to news from many sources may reduce gaps in knowledge. 

Using Text and Images to Examine 2016 Election Tweets

Post developed by Dory Knight-Ingram 

ICYMI (In Case You Missed It), the following work was presented at the 2019 Annual Meeting of the American Political Science Association (APSA).  The presentation, titled “Using Neural Networks to Classify Based on Combined  Text and Image Content: An Application to Election Incident Observation” was a part of the session “Deep Learning in Political Science” on Friday, August 30, 2019.

A new election forensics process developed by Walter Mebane and Alejandro Pineda uses machine-learning to examine not just text, but images, too, for Twitter posts that are considered reports of “incidents” from the 2016 US Presidential Election. 

Mebane and Pineda show how to combine text and images into a single supervised learner for prediction in US politics using a multi-layer perceptron. The paper notes that in election forensics, polls are useful, but social media data may offer more extensive and granular coverage. 

The research team gathered individual observation data from Twitter in the months leading up to the 2016 US Presidential Election. Between Oct. 1-Nov. 8, 2016, the team used Twitter APIs to collect millions of tweets, arriving at more than 315,180 tweets that apparently reported one or more election “incidents” – an individual’s report of their personal experience with some aspect of the election process. 

At first, the research team used only text associated with tweets. But the researchers note that sometimes, images in a tweet are informative, while the text is not. It’s possible for the text alone to not make a tweet a report of an election incident, while the image may indeed show an incident. 

To solve this problem, the research team implemented some “deep neural network classifier methods that use both text and images associated with tweets. The network is constructed such that its text-focused parts learn from the image inputs, and its image-focused parts learn from the text inputs. Using such a dual-mode classifier ought to improve performance. In principle our architecture should improve performance classifying tweets that do not include images as well as tweets that do,” they wrote.

“Automating analysis for digital content proves difficult because the form of data takes so many different shapes. This paper offers a solution: a method for the automated classification of multi-modal content.” The research team’s model “takes image and text as input and outputs a single classification decision for each tweet – two inputs, one output.” 

The paper describes in detail how the research team processed and analyzed tweet-images, which included loading image files in batches, restricting image types to .jpeg or .png., and using small image sizes for better data processing results. 

The results were mixed.

The researchers trained two models using a sample of 1,278 tweets. One model combined text and images, the other focused only on text. In the text-only model, accuracy steadily increases until it achieves top accuracy at 99%. “Such high performance is testimony to the power of transfer learning,” the authors wrote. 

However, the team was surprised that including the images substantially worsened performance. “Our proof-of-concept combined classifier works. But the model structure and hyperparameter details need to be adjusted to enhance performance. And it’s time to mobilize hardware superior to what we’ve used for this paper. New issues will arise as we do that.” 

The Politicization of Policies to Address Climate Change

Post developed by Erin Cikanek, Nicholas Valentino, and Katherine Pearson 

ICYMI (In Case You Missed It), the following work was presented at the 2019 Annual Meeting of the American Political Science Association (APSA).  The presentation, titled “The Politicization of Policies to Address Climate Change” was a part of the session “The Dynamics of Climate Policy Support in the US” on Friday, August 30, 2019.

Climate change is a truly polarizing issue. Partisans on either side of the issue have such deeply entrenched beliefs that there is little that can change minds. But this wasn’t always the case. For example, in 1988 Democrats and Republicans were in close agreement about the amount of money the government should spend on environmental protection. More recently, partisans have become more polarized in their level of concern about climate change. 

How do scientific policy issues become so polarized, and how quickly does this happen? New research by Nicholas Valentino and Erin Cikanek measures public awareness of Carbon Dioxide Removal (CDR) polices, and explores whether attitudes toward these policies are as politicized as climate change overall. 

Valentino and Cikanek conducted two studies to examine political polarization of CDR. First, they surveyed a large, nationally representative sample of people to measure how much they knew about climate change. The questions covered a broad set of issues and strategies for dealing with the problem. This survey revealed that the public has a high level of knowledge about climate change. 

The study also demonstrates that partisanship is highly predictive of knowledge about climate change. Democrats responded with significantly more accuracy than Republicans did. When respondents were asked specifically about technologies to remove carbon dioxide from the atmosphere, overall knowledge was lower, but Democrats and Republicans answered questions with the same level of accuracy, as shown in the figure below.  Valentino and Cikanek note that “this pattern is consistent with the possibility that elite rhetoric has come to suppress accuracy on general climate change knowledge among Republicans, but this has not yet occurred for knowledge in this newer domain (CDR).” 

climate change

The second study experimentally tested whether CDR policies are sensitive to partisan cues. CDR policies have not been debated as much or as publicly as climate change in general. Are these policies as susceptible to political polarization?  

Survey respondents were randomly assigned to one of three groups. A control group was asked about current climate change policies, as well as carbon reduction policies. The first treatment group received information that applied partisan stereotypes to the CDR policies: Republican hesitation about CDR because it might hinder business, and Democratic encouragement to save the environment. The second treatment group received information that ran counter to those stereotypes: Republican support for a pro-business solution to climate change, and Democratic concern that the solution may encourage businesses to pollute. 

The partisan cues had very little effect on the response to CDR policies. Interestingly, the counter-intuitive partisan cues backfired: when Republican respondents read the treatment showing Republican support for CDR, they opposed it slightly more. The very weak effect of partisan cues on support for CDR may show that CDR policies may be more resistant to polarization. 

The more politicians discuss scientific policy issues, the more polarized the discussion tends to become. However, Valentino and Cikanek see reason to hope that compromises remain possible for issues like carbon removal, which have not yet been subjected to partisan rhetoric. 

Whites’ Responses to Police Violence Depend on the Race of the Victim

Post developed by Nicole Yadon, Kiela Crabtree, and Katherine Pearson

ICYMI (In Case You Missed It), the following work was presented at the 2019 Annual Meeting of the American Political Science Association (APSA).  The presentation, titled “Breeding Contempt: Whites’ Reactions to Police Violence against Men & Dogs” was a part of the session “Race and Politics: New Theoretical and Methodical Insights v. Old Paradigms” on Thursday, August 29, 2019.

Of 987 individuals killed by police officers’ use of fatal force in 2018, 209 were black, and, of those, 200 were black men. The targeting and killing of unarmed black men has become a point of interest for news cycles and social movement organizations alike and is indicative of a fraught relationship between communities of color and police. With increasing press coverage over the past decade, academics have also begun to focus on the intertwining relationship between police use of force and race, complementing a long-standing literature which links blacks to perceptions of criminality, violence, and hostility. One area that is not well-developed, however, is how news coverage of police shootings influences attitudes towards police and policies related to policing for white Americans.

Building from research on race, media coverage, and policing, new research by Nicole Yadon and Kiela Crabtree examines reactions to police and policing by white people after they read about a police officer shooting a white man, a black man, or a dog. They find that news reports about police shootings change attitudes about police, but the strength of the reaction varies depending on who the victim is. 

Specifically, Yadon and Crabtree’s study examines white individuals’ feelings towards police following exposure to news of a fatal police shooting. They designed a survey that presents participants with a fictional but realistic news report about a fatal police shooting. In one version the shooting victim is a black man, another reports that the victim is a white man, and in the third version the victim is a dog. Key information about the shooting remains the same across all three versions. A control story, unrelated to race or police shootings, was given to a control group for purposes of comparison with the three treatment groups. This experiment was conducted via Amazon’s Mechanical Turk (MTurk) platform, with 802 white participants. After reading one of the news reports, participants were asked a series of questions about their perceptions of the events in the article and about their attitudes towards police more broadly.

When asked whether they agree or disagree that police officers rarely abuse their power, the control group who had not read about a police shooting had a neutral response — about 0.49 on the 0 to 1 scale. Respondents who read about a white man or a dog being shot by police had a markedly different reaction. Participants who read an article about a white man shot by police had a 7 percentage point decrease in belief that police rarely abuse their power while those who read about a dog shot by a police officer had an 8 percentage point decrease. This is equivalent to survey respondents moving from feeling neutral about whether police abuse their power to a slight disagreement that abuse of power is rare after reading about either a White victim or dog victim. 

Importantly, when white survey participants read about a black victim of a police shooting, it did not change their perception of abuse of power by police officers. Put differently, those who read about a black victim held views abuse police abuse of power that were indistinguishable from those who read the control story. The evidence suggests, then, that white respondents react more strongly to a police shooting if the victim is a dog than a black man. 

Belief that police rarely abuse power

 

A separate set of questions focused on interest in varying forms of political participation following exposure to the news story. Do white people feel moved toward political participation in response to a story about a police shooting? First, the survey asked whether respondents would support a civilian review board to oversee the police department in their community. Those who read about a police officer shooting a black man or a dog were no more likely to support a civilian review board than the control group. However, those who read about the shooting of a white man were more than 7 percentage points likely to support civilian review in their community.

Support for local civilian review board

A second question asked about interest in signing a petition urging Congress to take action towards reducing excessive use of force by police. In contrast to the civilian review board question, levels of support for signing a petition were very low across all groups. In fact, white participants do not appear increasingly motivated to urge Congress to take action against excessive police force regardless of the victim’s identity.

Taken together, Yadon and Crabtree’s results suggest that exposure to a news story about a police shooting draws strong reactions from white people. Of concern, however, is that such reactions are largely limited to viewing either a white man or a dog victim. Indeed, across most of the items which measure attitudes towards police, there are no statistically significant differences when comparing the control condition with the black victim treatment. Such connections are increasingly important to study as cities move toward tightening oversight of police forces and many such initiatives are presented to citizens at the ballot box. Thus, the attitudes citizens hold about police are not only their own. The public’s opinion has potentially lasting effects for the future of policing in local communities.

Presidents and/or Prime Ministers  

Post developed by Allen Hicken and Katherine Pearson 

ICYMI (In Case You Missed It), the following work was presented at the 2019 Annual Meeting of the American Political Science Association (APSA).  The presentation, titled “Presidents and/or Prime Ministers: A Historical Dataset” was a part of the session “Legislatures and Leaders: New Perspectives on Political Institutions” on Thursday, August 29, 2019. 

Classifying systems of government is a challenge for political scientists comparing regimes over time and across countries. A new historical dataset developed by Fabricio Vasselai, Samuel Baltz, and Allen Hicken addresses this challenge with a simplified classification scheme that presents data on four broad variables: whether there is an elected prime minister, whether there is an elected president, whether there is a non-elected prime minister, and whether there is a non-elected president. The dataset includes a yearly assessment for almost all sovereign countries since 1789, which amounts to 16,910 country-years. 

The simplicity of this classification system allows researchers to examine other characteristics separately, including the level of democracy or the powers of elected leaders. While the majority of country-years fit a neat definition, this dataset allows a clearer analysis of complex cases. 

The authors present France as an interesting test case. In the years included in this dataset, France had (1) only an unelected prime minister, (2) no elected or unelected prime minister or president, (3) only an elected prime minister, (4) an elected prime minister and an elected president, or (5) an elected prime minister and unelected president, with several of these states repeating multiple times throughout France’s history. The dataset presents this complicated historical narrative in the simplified manner below. 

This classification system also allows researchers to explore the evolution of different systems of government over longer periods of time. The authors show that an explosion of elections took place in the 19th century. Beginning in the 20th century, the share of countries electing only a prime minister takes a slight lead; by 1945 almost twice as many countries elected only a prime minister compared to those electing only a president. The share of countries electing either leader climbs through the second half of the 20th century, with only about 10 percent of country-years lacking an elected leader by 2017. 

Evolution of types of system

By developing a simple, comprehensive dataset, Vasselai, Baltz, and Hicken have given researchers a resource that allows them to analyze regimes consistently and layer on additional information as needed. 

Angela Ocampo Examines the Importance of Belonging

Post developed by Katherine Pearson and Angela Ocampo

Feelings of belonging are powerfully important. A sense of inclusion in a group or society can motivate new attitudes and actions. The idea of belonging, or attaining inclusion, is the centerpiece of Angela Ocampo’s research. Her dissertation exploring the effect of inclusion on political participation among Latinos will receive the American Political Science Association’s (APSA) Race and Ethnic Politics Section’s award for the best dissertation in the field at the Fall 2019 APSA meetings.

Dissertation and Book Project

Dr. Ocampo’s dissertation grounds the theory of belonging and political participation within the literature. This research, which she is expanding into a book, finds that feelings of belonging in American society strongly predict higher levels of political engagement among Latinos. This concept represents the intersection of political science and political psychology. Dr. Ocampo draws from psychology research that belonging is a human need; people need to feel that they are a part of a group in order to succeed and have positive individual outcomes, as well as group outcomes. She builds on these psychological concepts to develop this theory of social belonging in the national community, and how this influences the perception of relationship to the polity. 

The book will explore the social inclusion of racial and ethnic minorities, and how that shapes the way they participate in politics. Dr. Ocampo argues that the idea of perceiving that you belong, and the extent to which others accept you, has an influence on your political engagement and opinion of policies. For the most part, Dr. Ocampo looks at Latinos in the US, but the framework is applicable to other racial and ethnic groups. She is also collecting data among Asian Americans, African Americans, and American Muslims to look at perceived belonging. 

Methodological Expertise

Before she began this research, there were no measures to capture data on belonging in existing surveys. Dr. Ocampo validated new measures and tested and replicated them in the 2016 collaborative multiracial postelection survey

While observational data is useful for finding correlations, it can’t identify causality. For this reason, experiments also inform Dr. Ocampo’s research. In one experiment, she randomly assigned people to a number of different conditions. Subjects assigned to the negative condition showed a significant decrease in their perceptions of belonging. However, among those assigned to the positive condition, there were no corresponding positive results. In both the observational data and experiments, Dr. Ocampo notes that experiences of discrimination are highly influential and highly determinant of feelings of belonging. That is, the more experiences of discrimination you’ve had in the past, the less likely you are to feel that you belong.

Doing qualitative research has taught Dr. Ocampo the importance of speaking with her research subjects. “It’s not until you get out and talk to people running for office and making things happen that you understand how politics works for everyday people. That’s why the qualitative data and survey work are really important,” she says. By leveraging both qualitative and quantitative methodologies, Dr. Ocampo is able to arrive at more robust conclusions. 

A Sense of Belonging in the Academic Community

Starting in the Fall of 2020, Dr. Ocampo will be an Assistant Professor of Political Science at the University of Michigan and a Faculty Associate of the Center for Political Studies. She says that the fact that her work is deeply personal to her is what keeps her engaged. As an immigrant herself, Dr. Ocampo says, “I’m doing this for my family. I’m in this for other young women and women of color, other first-generation scholars. When they see me give a class or a lecture, they know they can do it, too.” 

Dr. Ocampo is known as a supportive member of her academic community. She says it’s an important part of her work: “The reason it’s important is that I wouldn’t be here if it wouldn’t have been for others who opened doors, were supportive, were willing to believe in me. They were willing to amplify my voice in spaces where I couldn’t be, or where I wasn’t, or where I didn’t even know they were there.” She notes that in order to improve the profession and make it a more diverse and welcoming place where scholars thrive, academics have to take it upon themselves to be inclusive. 

Improving Research on Subnational Violence with xSub

Post developed by Yuri M. Zhukov, Christian Davenport, Nadiya Kostyuk, and Katherine Pearson.

How can scholars of political conflict and violence apply research in different settings? Too often, data that are collected and analyzed in one setting cannot inform us about situations in other regions. The problem is not a lack of data. Instead, researchers are unable to make comparisons because variables, definitions, and units of analysis are inconsistent between sources.

xSub, a new freely available resource, solves these problems by building the infrastructure to compare data on political conflicts and violence at a subnational level (i.e., states, cities, and villages).

In a newly-published paper [1], Yuri M. Zhukov, Christian Davenport, and Nadiya Kostyuk introduce xSub, a database of databases that allows researchers to construct custom, analysis-ready datasets. xSub includes data on conflicts in 156 countries, from 21 sources. The project is also collecting data on countries with little or no publicly accessible information about what takes place within them. Additionally, scholars can contribute data they have collected for use by other researchers for future studies.

Why xSub?

Zhukov, Davenport, and Kostyuk describe five main problems with existing datasets:

  1. Most studies of political conflict aggregate data to the country level (i.e., the situation in Afghanistan or the United States writ large, rather than specific locations and contexts);
  2. Most micro-level studies focus on very few countries;
  3. Cross-dataset comparisons are rare;
  4. Operational definitions of variables (including event categories, actors, and spatial units) vary;
  5. There are no consistent units of analysis, which might otherwise enable direct comparisons.

To address these problems, xSub provides barrier-free access to data in an analysis-ready format, with consistent definitions, measures, and units. Without the effort to build this infrastructure, the field of study cannot move forward.

What’s in xSub?

xSub makes it easy to compare data across countries and sources because it organizes these data into consistent categories. Here’s what users will find:

  • Data sources: 25,112 datasets on the location, dynamics, and intensity of conflict events, in 156 countries (1969–2017), from 21 data sources, with consistent categories and customizable spatiotemporal units.

    Number of unique data sources per country

    Number of unique data sources per country

  • Actors: xSub organizes data on those involved in conflicts into four categories: government (Side A), opposition (Side B), civilian (Side C), and unaffiliated (Side D).
  • Actions: there are 4 general and 27 specific categories of actions, including any use of force, indirect force (e.g. shelling, air strikes, chemical weapons), direct force (e.g. firefights, arrests, assassinations), and protests, both violent and nonviolent.
  • Covariates: In addition to conflict, xSub includes multiple variables frequently used in subnational research: e.g., local demographics, geography, ethnicity, and weather.
  • Units of analysis: xSub provides event-level and spatial panel datasets. Researchers can choose the geographic units (e.g. countries, provinces, districts, PRIO-GRID cells, and electoral constituencies) and units of time (e.g. years, months, weeks, and days) to analyze. The units that a scholar chooses will affect the distribution of the data, allowing for a more precise description of findings.

How to access xSub

Anyone interested in analyzing xSub data is already able to access it because it is available in a user-friendly web interface and an R package. Removing barriers to access means that anyone from undergraduates to senior researchers will be able to work with these data, gauging whether or not patterns in one country apply to others in the same region or throughout the world.

The interactive web-based interface is available at cross-sub.org. Here scholars can select countries, data sources or units of analysis, preview the data, and download a zipped archive with the requested data and supporting documentation.

More advanced researchers can access the xSub R package at https://cran.r-project.org/package=xSub. This package provides additional functionality not supported by the website, including direct import of data into R and merging of datasets across countries.

By developing xSub, Zhukov, Davenport, and Kostyuk have created a public good that will advance a more meaningful understanding of political violence. With this new tool, researchers are now empowered to answer questions and share data in a way that was impossible until now. With numerous additions underway, the project looks to continue to advance the field into the future.

 

[1] Zhukov, Y. M., Davenport, C., & Kostyuk, N. (2019). Introducing xSub: A new portal for cross-national data on subnational violence. Journal of Peace Research. https://doi.org/10.1177/0022343319836697