Exposure to violence and ethnic attitudes

During the last few years, research on the consequences of violence, including its effect on ethno-nationalism, has grown considerably. We know that exposure to violence increases hostility towards the outgroup and makes ethnic reconciliation more difficult (Bakke et al., Reference Bakke, O’Loughlin and Ward2009; Beber et al., Reference Beber, Roessler and Scacco2014). For example, exposure to wartime violence decreased political tolerance among Tamils in Sri Lanka (Rapp et al., Reference Rapp, Kijewski and Freitag2019) and increased ethnic identification and ethnopolitical preferences in Bosnia (Hadzic et al., Reference Hadzic, Carlson and Tavits2020). Research on civil war dynamics also suggests that violence against civilians increases grievances among the victims, which translates into group-specific processes of collective blaming (Wood, Reference Wood2003; Condra and Shapiro, Reference Condra and Shapiro2012; Lyall et al., Reference Lyall, Blair and Imai2013). This body of research is coherent with those works that focus on the legacies of political violence, which point out that individuals exposed to violence are more likely to reject the political identity of the perpetrator (e.g., Balcells, Reference Balcells2012; Lupu and Peisakhin, Reference Lupu and Peisakhin2017; Rozenas et al., Reference Rozenas, Schutte and Zhukov2017). Thus, an increase in ethnic identification can be a response to violence in contexts where ethnicity is highly politized.

I focus on the effect of violence on ethno-nationalist identification and ethnic-based grievances. While there are several works exploring these or related questions, most of the evidence comes from specific contexts (Davenport et al., Reference Davenport, Mokleiv Nygård, Fjelde and Armstrong2019). Thus, we do not know how this effect varies across contexts, and particularly whether it depends on the type of perpetrator or the previous history of group-level conflict.

I argue that the effect of violence on ethnic identification and grievances should be particularly strong when violence is committed by state authorities and when individuals have been collectively targeted because of their ethnic identity. First, state violence should have a larger effect because forms of victimization that are more likely to be perceived as morally wrong have a bigger impact on ethno-national identification and grievances. This idea is linked to the concept of ‘moral shock’ in social movement studies, when an unexpected event or information produces moral outrage and ‘helps a person think about her basic values and how the world diverges from them in some important way’ (Jasper, Reference Jasper1997, 106). Violence against civilians should have a similarly strong emotional reaction. Focusing on the attitudinal effects of criminal violence, García-Ponce, Young and Zeitzoff (Reference Garca-Ponce, Young and Zeitzoff2022, 6) argue that ‘violence that violates moral principles should be particularly likely to induce anger.’ Civilian victimization carried out by state authorities is likely to violate moral principles in a more profound way that violence by non-state actors, if anything because state forces are expected to protect citizens.

Previous works show that wartime state violence produces moral outrage and grievances (Goodwin, Reference Goodwin2001; Wood, Reference Wood2003). This effect can easily reinforce group identity because of group-based processes of blame formation and collective coping (Lyons et al., Reference Lyons, Mickelson, Sullivan and Coyne1998; Bar-Tal, Reference Bar-Tal2007), which should be more intense when facing violence by state authorities. As Khawaja (Reference Khawaja1993, 66) argues, ‘repression can strengthen collective identity, the sense of belonging to a group, by operating as a symbolic reminder of a group’s shared circumstance vis-a-vis authorities and their agents of control.’ In the context of ethnic politics, the effect of state violence on ethnic identification and grievances will be stronger because moral principles of ethnic inequality are usually defined on the basis of institutional relationships with the state. Previous research shows that state-led collective targeting against ethnic groups increases the risk of conflict escalation (Cederman et al., Reference Cederman, Hug, Schubiger and Villamil2020).

Second, the existence of a history of collective targeting bolsters moral reactions to violence. This group-based background also strengthens group divisions, which should be mirrored in the attitudinal effects of victimization. More specifically, we should expect civilian victimization to reinforce the cleavages along which it takes place, increasing social polarization (Wood, Reference Wood2008). The fact that an ethnic group has already suffered collective targeting strengthens the effect of violence along the ethnic cleavage. For instance, Mironova and Whitt (Reference Mironova and Whitt2018) show that individuals in war-torn societies show signs of in-group parochialism in contexts that are marked by those divisions. Moreover, where ethnic divisions have been polarized by previous rounds of violence, local social structures help to articulate an attitudinal response to violence. We could expect these structures to be particularly defined by group boundaries if there is a history of ethnic-based violence, contributing to the hardening effect of violence on ethnic divisions.

An important caveat in this argument is that ethnic attitudes are endogenous to the occurrence of violence against civilians. First, state violence can be a response to existing rebel activity or even a preventive strategy in the face of increasing mobilization (Moore, Reference Moore2000). Relatedly, previous research also highlights that actors can use violence against the civilian constituency of the opposite group for strategic reasons, with the goal of weakening the enemy’s base of support (Fjelde and Hultman, Reference Fjelde and Hultman2014). Second, rebels themselves can also use violence as a mobilization device. Laitin (Reference Laitin1995) argues that nationalist violence in the Basque Country was part of a strategy to increase support for a minority movement. Finally, the characteristics of civilians could also influence the use of rebel violence against their own constituencies, particularly when rebels rely on more heterogenous constituencies (Ottmann, Reference Ottmann2017).

The direct implication is that any relationship of violence with ethnic attitudes could suffer from reverse causality. In other words, any increase in ethnic identification or grievances linked to state violence could actually be caused by rebel violence in the first place. Yet, even if the role of prior rebel activity is important, it is also difficult to say whether we would observe the same effect on ethnic attitudes without the use of violence by the state. Indeed, De la Calle (Reference De la Calle2015) offers a related argument where rebel violence in nationalist contexts is also influenced by the incentives of local political elites to prompt the central state into the use of repression, increasing the likelihood of conflict. Rather than identifying the effect of specific events of violence, the goal of this study is to analyze the contexts in which we should observe a stronger association between violence and ethno-nationalism. This implies acknowledging that violence, and individual events in particular, are influenced by the local conflict history. Taking this into account, my expectation is that we should observe a stronger link in those cases where the perpetrator is the state and where there is a history of group-based targeting.

Data and methods

I test these expectations using individual-level data that includes information on whether individuals were exposed to events of violence by either state authorities or the rebels, and whether they belong to a group with a history of collective targeting. To build such data, I combine three different datasets.

First, I use survey data from the first six rounds of Afrobarometer, fielded between 1999 and 2015, which also include georeferenced information (BenYishay et al., Reference BenYishay, Rotberg, Wells, Lv, Goodman, Kovacevic and Runfola2017). Using this survey, I create two binary outcomes measuring ethno-national attitudes: 1) ethnic identification, which indicates that the respondent identifies more by her ethnic groups than by her national identity, and 2) ethnic grievances, which is coded as 1 when the respondent feels her ethnic group is often or always treated unfairly by the government. I include these variables in binary form to maximize the number of compatible survey rounds. ^{Footnote 2} In the sample, 16% of respondents identify primarily with their ethnic group (varying from 33% in Nigeria to 4% in Burundi), and 21% express ethnic grievances (varying from 35% in Nigeria to 2% in Madagascar). These two variables are the two outcomes in the analyses.

Second, in order to build the variable on individual-level exposure to violence, I use the georeferenced information in Afrobarometer to match respondents to events of violence against civilians. I rely on the Georeferenced Event Dataset (Sundberg and Melander, Reference Sundberg and Melander2013), a data project run by the Uppsala Conflict Data Program (UCDP) which codes events of violence across the world since 1989 from both news and secondary sources, and includes information on the actor, location, and date, among other variables. Knowing the location and timing of each event of violence allows me to build a measure of exposure based on spatial and temporal distance between the Afrobarometer respondents and violence events. I focus on one-sided violence (OSV), or events when civilians were killed by an armed actor, distinguishing between violence by state authorities or by rebel groups. I match each respondent to OSV events that took place within the same country and before the survey was fielded, and calculate the spatial and temporal distance to each of them. I then code a binary measure of exposure based on distance thresholds, detailed below.

The full sample includes around 150,000 respondents across six survey rounds in 20 countries, although the number of observations varies depending on the model. ^{Footnote 3} Figure 1 maps these two data sources. The right panel, which shows the events of violence against civilians, includes both state and rebel violence, but the variables used in the model distinguish between these two actors. ^{Footnote 4}

Figure 1. Survey respondents and civilian victimization.

Finally, the third relevant variable is whether an individual belongs to an ethnic group that has a history of collective targeting. To code such variable, I rely on the Ethnic One-Sided Violence (EOSV) dataset (Fjelde et al., Reference Fjelde, Schubiger, Hultman, Cederman, Hug and Sollenberg2021). The EOSV dataset expands previous data by UCDP on violence against civilians by identifying each of the victims and linking them to an ethnic group from the ethnic power relations (EPR) dataset (Vogt et al., Reference Vogt, Bormann, Rüegger, Cederman, Hunziker and Girardin2015). Using this information, the EOSV then codes, for each perpetrator-ethnic group dyad and year, whether there was evidence of ethnic-based collective targeting, indicating that there is evidence that at least half the victims for a given year were killed because of their ethnic identity. This is different from just identifying the ethnicity of victims that could have been targeted for other reasons.

In order to match survey respondents to the EOSV data, I use the LEDA R package (Müller-Crepon et al., Reference Müller-Crepon, Pengl and Bormann2021), which uses information from different variables in Afrobarometer to match them to EPR groups. I then build a binary indicator for respondents who belong to a group that has suffered collective targeting at some point before the survey was fielded. When looking at exposure to events of state violence, I focus on collective targeting by state authorities, and when looking at exposure to rebel violence, I focus on collective targeting by rebels. ^{Footnote 5} I explain below the details of the victimization variables and how I combine them.

Individual and collective exposure to violence

Combining these datasets allows me to create a measure of individual-level exposure to events of civilian victimization and probe how its effects vary depending on whether there is prior collective targeting at the group level.

First, I code a binary measure of individual-level exposure when events of violence against civilians took place within 10km of a respondent’s location. I distinguish between two versions of this variable depending on when events of violence took place: 1) exposure to events that took place in the 5 years previous to the survey, and 2) exposure to events that took place before the 5 years previous to the survey, measuring longer-term effects. ^{Footnote 6} In all cases, I also distinguish between state and rebel violence. Figure 2 illustrates these different measures of exposure to violence. This measure of exposure to violence is independent from any form of ethnic-based collective targeting. The base models just include these individual-level exposure variables.

Figure 2. Defining exposure based on temporal distance to OSV events.

Second, in order to probe whether the effect of violence is different on individuals who belong to an ethnic group with a prior history of ethnic-based victimization than on those who do not, I interact the exposure variables with the indicator of group-level prior collective targeting. As I explain above, I use an indicator of collective targeting by the same actor as the events of violence against civilians (namely, state or rebels).